diff --git a/networks/ai85-autoencoder.yaml b/networks/ai85-autoencoder.yaml new file mode 100644 index 00000000..5a8c7dad --- /dev/null +++ b/networks/ai85-autoencoder.yaml @@ -0,0 +1,47 @@ +--- +arch: ai85autoencoder +dataset: SampleMotorDataLimerick +# Define layer parameters in order of the layer sequence +layers: + # Layer 0: 256 channels in --> 64 processors, 4 passes. Conv1D + - pad: 0 + activate: ReLU + out_offset: 0x2000 + processors: 0xffffffffffffffff + operation: Conv1d + kernel_size: 1 + data_format: HWC + # Layer 1: 128 channels in --> 64 processors, 2 passes. Conv1D + - pad: 0 + activate: ReLU + out_offset: 0x0000 + processors: 0xffffffffffffffff + kernel_size: 3 + operation: Conv1d + # Layer 2: 64 inputs --> 64 processors, 1 passes. MLP + - pad: 0 + activate: ReLU + out_offset: 0x2000 + processors: 0xffffffffffffffff + operation: MLP + # Layer 3: 32 inputs --> 32 processors, 1 pass. MLP + - activate: ReLU + out_offset: 0x0000 + processors: 0x00000000ffffffff + operation: MLP + # Layer 4: 16 inputs --> 16 processors, 1 pass. MLP + - activate: ReLU + out_offset: 0x2000 + processors: 0x000000000000000f + operation: MLP + # Layer 5: 32 inputs --> 32 processors, 1 pass. MLP + - activate: ReLU + out_offset: 0x4000 + processors: 0x00000000ffffffff + operation: MLP + # Layer 6: 96 inputs --> 48 processors, 2 passes. MLP + - pad: 0 + activate: None + out_offset: 0x0000 + processors: 0x0000ffffffffffff + operation: MLP diff --git a/trained/ai85-autoencoder-samplemotordatalimerick.log b/trained/ai85-autoencoder-samplemotordatalimerick.log new file mode 100644 index 00000000..c978f802 --- /dev/null +++ b/trained/ai85-autoencoder-samplemotordatalimerick.log @@ -0,0 +1,7618 @@ +2024-02-02 16:31:28,645 - Log file for this run: /home/asyaturhal/desktop/voyager/ai8x-training/logs/2024.02.02-163128/2024.02.02-163128.log +2024-02-02 16:31:30,774 - Optimizer Type: +2024-02-02 16:31:30,774 - Optimizer Args: {'lr': 0.001, 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0.0, 'amsgrad': False} +2024-02-02 16:31:30,782 - Dataset sizes: + training=230 + validation=60 + test=60 +2024-02-02 16:31:30,782 - + +2024-02-02 16:31:30,782 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:33,510 - Epoch: [0][ 1/ 8] Overall Loss 0.660247 Objective Loss 0.660247 MSE 0.660247 LR 0.001000 Time 2.727645 +2024-02-02 16:31:33,521 - Epoch: [0][ 2/ 8] Overall Loss 0.653253 Objective Loss 0.653253 MSE 0.653253 LR 0.001000 Time 1.369269 +2024-02-02 16:31:33,528 - Epoch: [0][ 3/ 8] Overall Loss 0.640441 Objective Loss 0.640441 MSE 0.640441 LR 0.001000 Time 0.915162 +2024-02-02 16:31:33,535 - Epoch: [0][ 4/ 8] Overall Loss 0.629159 Objective Loss 0.629159 MSE 0.629159 LR 0.001000 Time 0.687988 +2024-02-02 16:31:33,542 - Epoch: [0][ 5/ 8] Overall Loss 0.621007 Objective Loss 0.621007 MSE 0.621007 LR 0.001000 Time 0.551724 +2024-02-02 16:31:33,549 - Epoch: [0][ 6/ 8] Overall Loss 0.610622 Objective Loss 0.610622 MSE 0.610622 LR 0.001000 Time 0.460851 +2024-02-02 16:31:33,555 - Epoch: [0][ 7/ 8] Overall Loss 0.599406 Objective Loss 0.599406 MSE 0.599406 LR 0.001000 Time 0.395934 +2024-02-02 16:31:33,562 - Epoch: [0][ 8/ 8] Overall Loss 0.591719 Objective Loss 0.591719 MSE 0.597802 LR 0.001000 Time 0.347227 +2024-02-02 16:31:33,655 - --- validate (epoch=0)----------- +2024-02-02 16:31:33,655 - 60 samples (32 per mini-batch) +2024-02-02 16:31:34,023 - Epoch: [0][ 1/ 2] Loss 0.535350 MSE 0.535350 +2024-02-02 16:31:34,027 - Epoch: [0][ 2/ 2] Loss 0.536009 MSE 0.535965 +2024-02-02 16:31:34,168 - ==> MSE: 0.53596 Loss: 0.536 + +2024-02-02 16:31:34,170 - ==> Best [Top 1 (MSE): 0.53596 Sparsity:0.00 Params: 136448 on epoch: 0] +2024-02-02 16:31:34,170 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:34,176 - + +2024-02-02 16:31:34,176 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:34,533 - Epoch: [1][ 1/ 8] Overall Loss 0.477536 Objective Loss 0.477536 MSE 0.477536 LR 0.001000 Time 0.356998 +2024-02-02 16:31:34,540 - Epoch: [1][ 2/ 8] Overall Loss 0.462579 Objective Loss 0.462579 MSE 0.462579 LR 0.001000 Time 0.181869 +2024-02-02 16:31:34,547 - Epoch: [1][ 3/ 8] Overall Loss 0.452377 Objective Loss 0.452377 MSE 0.452377 LR 0.001000 Time 0.123319 +2024-02-02 16:31:34,553 - Epoch: [1][ 4/ 8] Overall Loss 0.442223 Objective Loss 0.442223 MSE 0.442223 LR 0.001000 Time 0.094022 +2024-02-02 16:31:34,559 - Epoch: [1][ 5/ 8] Overall Loss 0.429953 Objective Loss 0.429953 MSE 0.429953 LR 0.001000 Time 0.076461 +2024-02-02 16:31:34,566 - Epoch: [1][ 6/ 8] Overall Loss 0.411600 Objective Loss 0.411600 MSE 0.411600 LR 0.001000 Time 0.064743 +2024-02-02 16:31:34,572 - Epoch: [1][ 7/ 8] Overall Loss 0.397682 Objective Loss 0.397682 MSE 0.397682 LR 0.001000 Time 0.056388 +2024-02-02 16:31:34,578 - Epoch: [1][ 8/ 8] Overall Loss 0.382211 Objective Loss 0.382211 MSE 0.394453 LR 0.001000 Time 0.050080 +2024-02-02 16:31:34,723 - --- validate (epoch=1)----------- +2024-02-02 16:31:34,723 - 60 samples (32 per mini-batch) +2024-02-02 16:31:35,091 - Epoch: [1][ 1/ 2] Loss 0.387338 MSE 0.387338 +2024-02-02 16:31:35,095 - Epoch: [1][ 2/ 2] Loss 0.390635 MSE 0.390415 +2024-02-02 16:31:35,247 - ==> MSE: 0.39041 Loss: 0.391 + +2024-02-02 16:31:35,248 - ==> Best [Top 1 (MSE): 0.39041 Sparsity:0.00 Params: 136448 on epoch: 1] +2024-02-02 16:31:35,248 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:35,253 - + +2024-02-02 16:31:35,254 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:35,618 - Epoch: [2][ 1/ 8] Overall Loss 0.258295 Objective Loss 0.258295 MSE 0.258295 LR 0.001000 Time 0.363534 +2024-02-02 16:31:35,628 - Epoch: [2][ 2/ 8] Overall Loss 0.243514 Objective Loss 0.243514 MSE 0.243514 LR 0.001000 Time 0.186720 +2024-02-02 16:31:35,637 - Epoch: [2][ 3/ 8] Overall Loss 0.229650 Objective Loss 0.229650 MSE 0.229650 LR 0.001000 Time 0.127483 +2024-02-02 16:31:35,644 - Epoch: [2][ 4/ 8] Overall Loss 0.215624 Objective Loss 0.215624 MSE 0.215624 LR 0.001000 Time 0.097266 +2024-02-02 16:31:35,650 - Epoch: [2][ 5/ 8] Overall Loss 0.203646 Objective Loss 0.203646 MSE 0.203646 LR 0.001000 Time 0.079096 +2024-02-02 16:31:35,657 - Epoch: [2][ 6/ 8] Overall Loss 0.190991 Objective Loss 0.190991 MSE 0.190991 LR 0.001000 Time 0.066945 +2024-02-02 16:31:35,663 - Epoch: [2][ 7/ 8] Overall Loss 0.181743 Objective Loss 0.181743 MSE 0.181743 LR 0.001000 Time 0.058268 +2024-02-02 16:31:35,669 - Epoch: [2][ 8/ 8] Overall Loss 0.172635 Objective Loss 0.172635 MSE 0.179842 LR 0.001000 Time 0.051741 +2024-02-02 16:31:35,825 - --- validate (epoch=2)----------- +2024-02-02 16:31:35,825 - 60 samples (32 per mini-batch) +2024-02-02 16:31:36,179 - Epoch: [2][ 1/ 2] Loss 0.198465 MSE 0.198465 +2024-02-02 16:31:36,183 - Epoch: [2][ 2/ 2] Loss 0.198958 MSE 0.198925 +2024-02-02 16:31:36,321 - ==> MSE: 0.19892 Loss: 0.199 + +2024-02-02 16:31:36,323 - ==> Best [Top 1 (MSE): 0.19892 Sparsity:0.00 Params: 136448 on epoch: 2] +2024-02-02 16:31:36,323 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:36,336 - + +2024-02-02 16:31:36,336 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:36,697 - Epoch: [3][ 1/ 8] Overall Loss 0.106039 Objective Loss 0.106039 MSE 0.106039 LR 0.001000 Time 0.361120 +2024-02-02 16:31:36,704 - Epoch: [3][ 2/ 8] Overall Loss 0.102022 Objective Loss 0.102022 MSE 0.102022 LR 0.001000 Time 0.183871 +2024-02-02 16:31:36,710 - Epoch: [3][ 3/ 8] Overall Loss 0.095785 Objective Loss 0.095785 MSE 0.095785 LR 0.001000 Time 0.124632 +2024-02-02 16:31:36,717 - Epoch: [3][ 4/ 8] Overall Loss 0.091790 Objective Loss 0.091790 MSE 0.091790 LR 0.001000 Time 0.095032 +2024-02-02 16:31:36,724 - Epoch: [3][ 5/ 8] Overall Loss 0.088285 Objective Loss 0.088285 MSE 0.088285 LR 0.001000 Time 0.077345 +2024-02-02 16:31:36,730 - Epoch: [3][ 6/ 8] Overall Loss 0.085848 Objective Loss 0.085848 MSE 0.085848 LR 0.001000 Time 0.065503 +2024-02-02 16:31:36,737 - Epoch: [3][ 7/ 8] Overall Loss 0.083423 Objective Loss 0.083423 MSE 0.083423 LR 0.001000 Time 0.057061 +2024-02-02 16:31:36,743 - Epoch: [3][ 8/ 8] Overall Loss 0.085809 Objective Loss 0.085809 MSE 0.083921 LR 0.001000 Time 0.050709 +2024-02-02 16:31:36,894 - --- validate (epoch=3)----------- +2024-02-02 16:31:36,894 - 60 samples (32 per mini-batch) +2024-02-02 16:31:37,243 - Epoch: [3][ 1/ 2] Loss 0.088851 MSE 0.088851 +2024-02-02 16:31:37,249 - Epoch: [3][ 2/ 2] Loss 0.086587 MSE 0.086738 +2024-02-02 16:31:37,396 - ==> MSE: 0.08674 Loss: 0.087 + +2024-02-02 16:31:37,398 - ==> Best [Top 1 (MSE): 0.08674 Sparsity:0.00 Params: 136448 on epoch: 3] +2024-02-02 16:31:37,398 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:37,411 - + +2024-02-02 16:31:37,411 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:37,770 - Epoch: [4][ 1/ 8] Overall Loss 0.065407 Objective Loss 0.065407 MSE 0.065407 LR 0.001000 Time 0.358580 +2024-02-02 16:31:37,777 - Epoch: [4][ 2/ 8] Overall Loss 0.064548 Objective Loss 0.064548 MSE 0.064548 LR 0.001000 Time 0.182483 +2024-02-02 16:31:37,783 - Epoch: [4][ 3/ 8] Overall Loss 0.066270 Objective Loss 0.066270 MSE 0.066270 LR 0.001000 Time 0.123696 +2024-02-02 16:31:37,789 - Epoch: [4][ 4/ 8] Overall Loss 0.065814 Objective Loss 0.065814 MSE 0.065814 LR 0.001000 Time 0.094310 +2024-02-02 16:31:37,796 - Epoch: [4][ 5/ 8] Overall Loss 0.064199 Objective Loss 0.064199 MSE 0.064199 LR 0.001000 Time 0.076691 +2024-02-02 16:31:37,802 - Epoch: [4][ 6/ 8] Overall Loss 0.063047 Objective Loss 0.063047 MSE 0.063047 LR 0.001000 Time 0.064935 +2024-02-02 16:31:37,808 - Epoch: [4][ 7/ 8] Overall Loss 0.063432 Objective Loss 0.063432 MSE 0.063432 LR 0.001000 Time 0.056556 +2024-02-02 16:31:37,814 - Epoch: [4][ 8/ 8] Overall Loss 0.063604 Objective Loss 0.063604 MSE 0.063468 LR 0.001000 Time 0.050246 +2024-02-02 16:31:37,964 - --- validate (epoch=4)----------- +2024-02-02 16:31:37,964 - 60 samples (32 per mini-batch) +2024-02-02 16:31:38,322 - Epoch: [4][ 1/ 2] Loss 0.059248 MSE 0.059248 +2024-02-02 16:31:38,327 - Epoch: [4][ 2/ 2] Loss 0.061021 MSE 0.060902 +2024-02-02 16:31:38,476 - ==> MSE: 0.06090 Loss: 0.061 + +2024-02-02 16:31:38,478 - ==> Best [Top 1 (MSE): 0.06090 Sparsity:0.00 Params: 136448 on epoch: 4] +2024-02-02 16:31:38,478 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:38,491 - + +2024-02-02 16:31:38,491 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:38,835 - Epoch: [5][ 1/ 8] Overall Loss 0.054346 Objective Loss 0.054346 MSE 0.054346 LR 0.001000 Time 0.343796 +2024-02-02 16:31:38,842 - Epoch: [5][ 2/ 8] Overall Loss 0.056344 Objective Loss 0.056344 MSE 0.056344 LR 0.001000 Time 0.175198 +2024-02-02 16:31:38,848 - Epoch: [5][ 3/ 8] Overall Loss 0.056405 Objective Loss 0.056405 MSE 0.056405 LR 0.001000 Time 0.118907 +2024-02-02 16:31:38,854 - Epoch: [5][ 4/ 8] Overall Loss 0.056335 Objective Loss 0.056335 MSE 0.056335 LR 0.001000 Time 0.090691 +2024-02-02 16:31:38,861 - Epoch: [5][ 5/ 8] Overall Loss 0.055217 Objective Loss 0.055217 MSE 0.055217 LR 0.001000 Time 0.073768 +2024-02-02 16:31:38,867 - Epoch: [5][ 6/ 8] Overall Loss 0.054754 Objective Loss 0.054754 MSE 0.054754 LR 0.001000 Time 0.062490 +2024-02-02 16:31:38,873 - Epoch: [5][ 7/ 8] Overall Loss 0.053744 Objective Loss 0.053744 MSE 0.053744 LR 0.001000 Time 0.054441 +2024-02-02 16:31:38,879 - Epoch: [5][ 8/ 8] Overall Loss 0.054687 Objective Loss 0.054687 MSE 0.053941 LR 0.001000 Time 0.048397 +2024-02-02 16:31:39,021 - --- validate (epoch=5)----------- +2024-02-02 16:31:39,022 - 60 samples (32 per mini-batch) +2024-02-02 16:31:39,377 - Epoch: [5][ 1/ 2] Loss 0.055565 MSE 0.055565 +2024-02-02 16:31:39,382 - Epoch: [5][ 2/ 2] Loss 0.057929 MSE 0.057771 +2024-02-02 16:31:39,528 - ==> MSE: 0.05777 Loss: 0.058 + +2024-02-02 16:31:39,530 - ==> Best [Top 1 (MSE): 0.05777 Sparsity:0.00 Params: 136448 on epoch: 5] +2024-02-02 16:31:39,530 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:39,543 - + +2024-02-02 16:31:39,543 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:39,900 - Epoch: [6][ 1/ 8] Overall Loss 0.049503 Objective Loss 0.049503 MSE 0.049503 LR 0.001000 Time 0.356802 +2024-02-02 16:31:39,907 - Epoch: [6][ 2/ 8] Overall Loss 0.047394 Objective Loss 0.047394 MSE 0.047394 LR 0.001000 Time 0.181697 +2024-02-02 16:31:39,913 - Epoch: [6][ 3/ 8] Overall Loss 0.048527 Objective Loss 0.048527 MSE 0.048527 LR 0.001000 Time 0.123219 +2024-02-02 16:31:39,920 - Epoch: [6][ 4/ 8] Overall Loss 0.049259 Objective Loss 0.049259 MSE 0.049259 LR 0.001000 Time 0.093972 +2024-02-02 16:31:39,926 - Epoch: [6][ 5/ 8] Overall Loss 0.049929 Objective Loss 0.049929 MSE 0.049929 LR 0.001000 Time 0.076429 +2024-02-02 16:31:39,933 - Epoch: [6][ 6/ 8] Overall Loss 0.049870 Objective Loss 0.049870 MSE 0.049870 LR 0.001000 Time 0.064739 +2024-02-02 16:31:39,939 - Epoch: [6][ 7/ 8] Overall Loss 0.049723 Objective Loss 0.049723 MSE 0.049723 LR 0.001000 Time 0.056371 +2024-02-02 16:31:39,945 - Epoch: [6][ 8/ 8] Overall Loss 0.051464 Objective Loss 0.051464 MSE 0.050086 LR 0.001000 Time 0.050086 +2024-02-02 16:31:40,098 - --- validate (epoch=6)----------- +2024-02-02 16:31:40,098 - 60 samples (32 per mini-batch) +2024-02-02 16:31:40,455 - Epoch: [6][ 1/ 2] Loss 0.055308 MSE 0.055308 +2024-02-02 16:31:40,460 - Epoch: [6][ 2/ 2] Loss 0.054027 MSE 0.054112 +2024-02-02 16:31:40,611 - ==> MSE: 0.05411 Loss: 0.054 + +2024-02-02 16:31:40,613 - ==> Best [Top 1 (MSE): 0.05411 Sparsity:0.00 Params: 136448 on epoch: 6] +2024-02-02 16:31:40,613 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:40,626 - + +2024-02-02 16:31:40,627 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:40,984 - Epoch: [7][ 1/ 8] Overall Loss 0.042254 Objective Loss 0.042254 MSE 0.042254 LR 0.001000 Time 0.356777 +2024-02-02 16:31:40,991 - Epoch: [7][ 2/ 8] Overall Loss 0.043163 Objective Loss 0.043163 MSE 0.043163 LR 0.001000 Time 0.181692 +2024-02-02 16:31:40,997 - Epoch: [7][ 3/ 8] Overall Loss 0.044713 Objective Loss 0.044713 MSE 0.044713 LR 0.001000 Time 0.123237 +2024-02-02 16:31:41,004 - Epoch: [7][ 4/ 8] Overall Loss 0.046370 Objective Loss 0.046370 MSE 0.046370 LR 0.001000 Time 0.093995 +2024-02-02 16:31:41,010 - Epoch: [7][ 5/ 8] Overall Loss 0.047082 Objective Loss 0.047082 MSE 0.047082 LR 0.001000 Time 0.076426 +2024-02-02 16:31:41,016 - Epoch: [7][ 6/ 8] Overall Loss 0.046689 Objective Loss 0.046689 MSE 0.046689 LR 0.001000 Time 0.064713 +2024-02-02 16:31:41,022 - Epoch: [7][ 7/ 8] Overall Loss 0.047106 Objective Loss 0.047106 MSE 0.047106 LR 0.001000 Time 0.056348 +2024-02-02 16:31:41,029 - Epoch: [7][ 8/ 8] Overall Loss 0.046679 Objective Loss 0.046679 MSE 0.047017 LR 0.001000 Time 0.050060 +2024-02-02 16:31:41,181 - --- validate (epoch=7)----------- +2024-02-02 16:31:41,181 - 60 samples (32 per mini-batch) +2024-02-02 16:31:41,543 - Epoch: [7][ 1/ 2] Loss 0.055130 MSE 0.055130 +2024-02-02 16:31:41,548 - Epoch: [7][ 2/ 2] Loss 0.051365 MSE 0.051616 +2024-02-02 16:31:41,699 - ==> MSE: 0.05162 Loss: 0.051 + +2024-02-02 16:31:41,701 - ==> Best [Top 1 (MSE): 0.05162 Sparsity:0.00 Params: 136448 on epoch: 7] +2024-02-02 16:31:41,701 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:41,707 - + +2024-02-02 16:31:41,707 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:42,065 - Epoch: [8][ 1/ 8] Overall Loss 0.038300 Objective Loss 0.038300 MSE 0.038300 LR 0.001000 Time 0.357743 +2024-02-02 16:31:42,075 - Epoch: [8][ 2/ 8] Overall Loss 0.040925 Objective Loss 0.040925 MSE 0.040925 LR 0.001000 Time 0.183464 +2024-02-02 16:31:42,082 - Epoch: [8][ 3/ 8] Overall Loss 0.042706 Objective Loss 0.042706 MSE 0.042706 LR 0.001000 Time 0.124652 +2024-02-02 16:31:42,089 - Epoch: [8][ 4/ 8] Overall Loss 0.043273 Objective Loss 0.043273 MSE 0.043273 LR 0.001000 Time 0.095088 +2024-02-02 16:31:42,095 - Epoch: [8][ 5/ 8] Overall Loss 0.044312 Objective Loss 0.044312 MSE 0.044312 LR 0.001000 Time 0.077293 +2024-02-02 16:31:42,101 - Epoch: [8][ 6/ 8] Overall Loss 0.045078 Objective Loss 0.045078 MSE 0.045078 LR 0.001000 Time 0.065430 +2024-02-02 16:31:42,108 - Epoch: [8][ 7/ 8] Overall Loss 0.045149 Objective Loss 0.045149 MSE 0.045149 LR 0.001000 Time 0.056974 +2024-02-02 16:31:42,114 - Epoch: [8][ 8/ 8] Overall Loss 0.045631 Objective Loss 0.045631 MSE 0.045250 LR 0.001000 Time 0.050617 +2024-02-02 16:31:42,270 - --- validate (epoch=8)----------- +2024-02-02 16:31:42,270 - 60 samples (32 per mini-batch) +2024-02-02 16:31:42,631 - Epoch: [8][ 1/ 2] Loss 0.048542 MSE 0.048542 +2024-02-02 16:31:42,635 - Epoch: [8][ 2/ 2] Loss 0.049882 MSE 0.049793 +2024-02-02 16:31:42,789 - ==> MSE: 0.04979 Loss: 0.050 + +2024-02-02 16:31:42,790 - ==> Best [Top 1 (MSE): 0.04979 Sparsity:0.00 Params: 136448 on epoch: 8] +2024-02-02 16:31:42,790 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:42,805 - + +2024-02-02 16:31:42,805 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:43,168 - Epoch: [9][ 1/ 8] Overall Loss 0.045652 Objective Loss 0.045652 MSE 0.045652 LR 0.001000 Time 0.362216 +2024-02-02 16:31:43,177 - Epoch: [9][ 2/ 8] Overall Loss 0.041029 Objective Loss 0.041029 MSE 0.041029 LR 0.001000 Time 0.185503 +2024-02-02 16:31:43,185 - Epoch: [9][ 3/ 8] Overall Loss 0.041688 Objective Loss 0.041688 MSE 0.041688 LR 0.001000 Time 0.126217 +2024-02-02 16:31:43,191 - Epoch: [9][ 4/ 8] Overall Loss 0.041883 Objective Loss 0.041883 MSE 0.041883 LR 0.001000 Time 0.096234 +2024-02-02 16:31:43,197 - Epoch: [9][ 5/ 8] Overall Loss 0.043105 Objective Loss 0.043105 MSE 0.043105 LR 0.001000 Time 0.078230 +2024-02-02 16:31:43,204 - Epoch: [9][ 6/ 8] Overall Loss 0.043579 Objective Loss 0.043579 MSE 0.043579 LR 0.001000 Time 0.066222 +2024-02-02 16:31:43,210 - Epoch: [9][ 7/ 8] Overall Loss 0.043785 Objective Loss 0.043785 MSE 0.043785 LR 0.001000 Time 0.057638 +2024-02-02 16:31:43,216 - Epoch: [9][ 8/ 8] Overall Loss 0.043083 Objective Loss 0.043083 MSE 0.043638 LR 0.001000 Time 0.051197 +2024-02-02 16:31:43,369 - --- validate (epoch=9)----------- +2024-02-02 16:31:43,369 - 60 samples (32 per mini-batch) +2024-02-02 16:31:43,726 - Epoch: [9][ 1/ 2] Loss 0.046744 MSE 0.046744 +2024-02-02 16:31:43,730 - Epoch: [9][ 2/ 2] Loss 0.048081 MSE 0.047992 +2024-02-02 16:31:43,884 - ==> MSE: 0.04799 Loss: 0.048 + +2024-02-02 16:31:43,885 - ==> Best [Top 1 (MSE): 0.04799 Sparsity:0.00 Params: 136448 on epoch: 9] +2024-02-02 16:31:43,885 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:43,898 - + +2024-02-02 16:31:43,899 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:44,258 - Epoch: [10][ 1/ 8] Overall Loss 0.039286 Objective Loss 0.039286 MSE 0.039286 LR 0.001000 Time 0.359230 +2024-02-02 16:31:44,265 - Epoch: [10][ 2/ 8] Overall Loss 0.041218 Objective Loss 0.041218 MSE 0.041218 LR 0.001000 Time 0.183002 +2024-02-02 16:31:44,272 - Epoch: [10][ 3/ 8] Overall Loss 0.040428 Objective Loss 0.040428 MSE 0.040428 LR 0.001000 Time 0.124082 +2024-02-02 16:31:44,278 - Epoch: [10][ 4/ 8] Overall Loss 0.042057 Objective Loss 0.042057 MSE 0.042057 LR 0.001000 Time 0.094611 +2024-02-02 16:31:44,284 - Epoch: [10][ 5/ 8] Overall Loss 0.042010 Objective Loss 0.042010 MSE 0.042010 LR 0.001000 Time 0.076926 +2024-02-02 16:31:44,291 - Epoch: [10][ 6/ 8] Overall Loss 0.042226 Objective Loss 0.042226 MSE 0.042226 LR 0.001000 Time 0.065131 +2024-02-02 16:31:44,297 - Epoch: [10][ 7/ 8] Overall Loss 0.042627 Objective Loss 0.042627 MSE 0.042627 LR 0.001000 Time 0.056720 +2024-02-02 16:31:44,303 - Epoch: [10][ 8/ 8] Overall Loss 0.041750 Objective Loss 0.041750 MSE 0.042444 LR 0.001000 Time 0.050395 +2024-02-02 16:31:44,454 - --- validate (epoch=10)----------- +2024-02-02 16:31:44,454 - 60 samples (32 per mini-batch) +2024-02-02 16:31:44,808 - Epoch: [10][ 1/ 2] Loss 0.050042 MSE 0.050042 +2024-02-02 16:31:44,813 - Epoch: [10][ 2/ 2] Loss 0.046677 MSE 0.046902 +2024-02-02 16:31:44,964 - ==> MSE: 0.04690 Loss: 0.047 + +2024-02-02 16:31:44,966 - ==> Best [Top 1 (MSE): 0.04690 Sparsity:0.00 Params: 136448 on epoch: 10] +2024-02-02 16:31:44,966 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:44,979 - + +2024-02-02 16:31:44,979 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:45,340 - Epoch: [11][ 1/ 8] Overall Loss 0.040774 Objective Loss 0.040774 MSE 0.040774 LR 0.001000 Time 0.360962 +2024-02-02 16:31:45,347 - Epoch: [11][ 2/ 8] Overall Loss 0.041188 Objective Loss 0.041188 MSE 0.041188 LR 0.001000 Time 0.183787 +2024-02-02 16:31:45,354 - Epoch: [11][ 3/ 8] Overall Loss 0.040629 Objective Loss 0.040629 MSE 0.040629 LR 0.001000 Time 0.124646 +2024-02-02 16:31:45,360 - Epoch: [11][ 4/ 8] Overall Loss 0.040553 Objective Loss 0.040553 MSE 0.040553 LR 0.001000 Time 0.095047 +2024-02-02 16:31:45,367 - Epoch: [11][ 5/ 8] Overall Loss 0.041624 Objective Loss 0.041624 MSE 0.041624 LR 0.001000 Time 0.077291 +2024-02-02 16:31:45,373 - Epoch: [11][ 6/ 8] Overall Loss 0.041703 Objective Loss 0.041703 MSE 0.041703 LR 0.001000 Time 0.065448 +2024-02-02 16:31:45,379 - Epoch: [11][ 7/ 8] Overall Loss 0.041320 Objective Loss 0.041320 MSE 0.041320 LR 0.001000 Time 0.056968 +2024-02-02 16:31:45,385 - Epoch: [11][ 8/ 8] Overall Loss 0.042371 Objective Loss 0.042371 MSE 0.041539 LR 0.001000 Time 0.050609 +2024-02-02 16:31:45,538 - --- validate (epoch=11)----------- +2024-02-02 16:31:45,538 - 60 samples (32 per mini-batch) +2024-02-02 16:31:45,890 - Epoch: [11][ 1/ 2] Loss 0.048373 MSE 0.048373 +2024-02-02 16:31:45,895 - Epoch: [11][ 2/ 2] Loss 0.046019 MSE 0.046176 +2024-02-02 16:31:46,046 - ==> MSE: 0.04618 Loss: 0.046 + +2024-02-02 16:31:46,047 - ==> Best [Top 1 (MSE): 0.04618 Sparsity:0.00 Params: 136448 on epoch: 11] +2024-02-02 16:31:46,047 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:46,059 - + +2024-02-02 16:31:46,059 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:46,424 - Epoch: [12][ 1/ 8] Overall Loss 0.043008 Objective Loss 0.043008 MSE 0.043008 LR 0.001000 Time 0.363779 +2024-02-02 16:31:46,432 - Epoch: [12][ 2/ 8] Overall Loss 0.043139 Objective Loss 0.043139 MSE 0.043139 LR 0.001000 Time 0.185900 +2024-02-02 16:31:46,438 - Epoch: [12][ 3/ 8] Overall Loss 0.042566 Objective Loss 0.042566 MSE 0.042566 LR 0.001000 Time 0.126036 +2024-02-02 16:31:46,445 - Epoch: [12][ 4/ 8] Overall Loss 0.042266 Objective Loss 0.042266 MSE 0.042266 LR 0.001000 Time 0.096094 +2024-02-02 16:31:46,451 - Epoch: [12][ 5/ 8] Overall Loss 0.042481 Objective Loss 0.042481 MSE 0.042481 LR 0.001000 Time 0.078120 +2024-02-02 16:31:46,458 - Epoch: [12][ 6/ 8] Overall Loss 0.041749 Objective Loss 0.041749 MSE 0.041749 LR 0.001000 Time 0.066132 +2024-02-02 16:31:46,464 - Epoch: [12][ 7/ 8] Overall Loss 0.041090 Objective Loss 0.041090 MSE 0.041090 LR 0.001000 Time 0.057579 +2024-02-02 16:31:46,470 - Epoch: [12][ 8/ 8] Overall Loss 0.040890 Objective Loss 0.040890 MSE 0.041048 LR 0.001000 Time 0.051170 +2024-02-02 16:31:46,622 - --- validate (epoch=12)----------- +2024-02-02 16:31:46,622 - 60 samples (32 per mini-batch) +2024-02-02 16:31:46,982 - Epoch: [12][ 1/ 2] Loss 0.044146 MSE 0.044146 +2024-02-02 16:31:46,986 - Epoch: [12][ 2/ 2] Loss 0.045730 MSE 0.045624 +2024-02-02 16:31:47,141 - ==> MSE: 0.04562 Loss: 0.046 + +2024-02-02 16:31:47,143 - ==> Best [Top 1 (MSE): 0.04562 Sparsity:0.00 Params: 136448 on epoch: 12] +2024-02-02 16:31:47,143 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:47,149 - + +2024-02-02 16:31:47,149 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:47,507 - Epoch: [13][ 1/ 8] Overall Loss 0.044416 Objective Loss 0.044416 MSE 0.044416 LR 0.001000 Time 0.357774 +2024-02-02 16:31:47,514 - Epoch: [13][ 2/ 8] Overall Loss 0.042003 Objective Loss 0.042003 MSE 0.042003 LR 0.001000 Time 0.182168 +2024-02-02 16:31:47,520 - Epoch: [13][ 3/ 8] Overall Loss 0.041470 Objective Loss 0.041470 MSE 0.041470 LR 0.001000 Time 0.123541 +2024-02-02 16:31:47,527 - Epoch: [13][ 4/ 8] Overall Loss 0.041249 Objective Loss 0.041249 MSE 0.041249 LR 0.001000 Time 0.094224 +2024-02-02 16:31:47,533 - Epoch: [13][ 5/ 8] Overall Loss 0.040594 Objective Loss 0.040594 MSE 0.040594 LR 0.001000 Time 0.076621 +2024-02-02 16:31:47,539 - Epoch: [13][ 6/ 8] Overall Loss 0.041253 Objective Loss 0.041253 MSE 0.041253 LR 0.001000 Time 0.064867 +2024-02-02 16:31:47,546 - Epoch: [13][ 7/ 8] Overall Loss 0.039869 Objective Loss 0.039869 MSE 0.039869 LR 0.001000 Time 0.056487 +2024-02-02 16:31:47,552 - Epoch: [13][ 8/ 8] Overall Loss 0.041810 Objective Loss 0.041810 MSE 0.040274 LR 0.001000 Time 0.050189 +2024-02-02 16:31:47,707 - --- validate (epoch=13)----------- +2024-02-02 16:31:47,707 - 60 samples (32 per mini-batch) +2024-02-02 16:31:48,064 - Epoch: [13][ 1/ 2] Loss 0.049041 MSE 0.049041 +2024-02-02 16:31:48,068 - Epoch: [13][ 2/ 2] Loss 0.045044 MSE 0.045310 +2024-02-02 16:31:48,211 - ==> MSE: 0.04531 Loss: 0.045 + +2024-02-02 16:31:48,213 - ==> Best [Top 1 (MSE): 0.04531 Sparsity:0.00 Params: 136448 on epoch: 13] +2024-02-02 16:31:48,213 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:48,223 - + +2024-02-02 16:31:48,223 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:48,582 - Epoch: [14][ 1/ 8] Overall Loss 0.036056 Objective Loss 0.036056 MSE 0.036056 LR 0.001000 Time 0.358770 +2024-02-02 16:31:48,589 - Epoch: [14][ 2/ 8] Overall Loss 0.039388 Objective Loss 0.039388 MSE 0.039388 LR 0.001000 Time 0.182709 +2024-02-02 16:31:48,596 - Epoch: [14][ 3/ 8] Overall Loss 0.040599 Objective Loss 0.040599 MSE 0.040599 LR 0.001000 Time 0.123922 +2024-02-02 16:31:48,602 - Epoch: [14][ 4/ 8] Overall Loss 0.040544 Objective Loss 0.040544 MSE 0.040544 LR 0.001000 Time 0.094485 +2024-02-02 16:31:48,608 - Epoch: [14][ 5/ 8] Overall Loss 0.040532 Objective Loss 0.040532 MSE 0.040532 LR 0.001000 Time 0.076820 +2024-02-02 16:31:48,615 - Epoch: [14][ 6/ 8] Overall Loss 0.040151 Objective Loss 0.040151 MSE 0.040151 LR 0.001000 Time 0.065051 +2024-02-02 16:31:48,621 - Epoch: [14][ 7/ 8] Overall Loss 0.039844 Objective Loss 0.039844 MSE 0.039844 LR 0.001000 Time 0.056645 +2024-02-02 16:31:48,627 - Epoch: [14][ 8/ 8] Overall Loss 0.038863 Objective Loss 0.038863 MSE 0.039640 LR 0.001000 Time 0.050331 +2024-02-02 16:31:48,778 - --- validate (epoch=14)----------- +2024-02-02 16:31:48,779 - 60 samples (32 per mini-batch) +2024-02-02 16:31:49,132 - Epoch: [14][ 1/ 2] Loss 0.045361 MSE 0.045361 +2024-02-02 16:31:49,136 - Epoch: [14][ 2/ 2] Loss 0.045645 MSE 0.045626 +2024-02-02 16:31:49,282 - ==> MSE: 0.04563 Loss: 0.046 + +2024-02-02 16:31:49,284 - ==> Best [Top 1 (MSE): 0.04531 Sparsity:0.00 Params: 136448 on epoch: 13] +2024-02-02 16:31:49,284 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:49,289 - + +2024-02-02 16:31:49,289 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:49,646 - Epoch: [15][ 1/ 8] Overall Loss 0.040593 Objective Loss 0.040593 MSE 0.040593 LR 0.001000 Time 0.356669 +2024-02-02 16:31:49,653 - Epoch: [15][ 2/ 8] Overall Loss 0.040064 Objective Loss 0.040064 MSE 0.040064 LR 0.001000 Time 0.181690 +2024-02-02 16:31:49,659 - Epoch: [15][ 3/ 8] Overall Loss 0.038888 Objective Loss 0.038888 MSE 0.038888 LR 0.001000 Time 0.123251 +2024-02-02 16:31:49,666 - Epoch: [15][ 4/ 8] Overall Loss 0.038624 Objective Loss 0.038624 MSE 0.038624 LR 0.001000 Time 0.094026 +2024-02-02 16:31:49,672 - Epoch: [15][ 5/ 8] Overall Loss 0.038846 Objective Loss 0.038846 MSE 0.038846 LR 0.001000 Time 0.076493 +2024-02-02 16:31:49,679 - Epoch: [15][ 6/ 8] Overall Loss 0.038720 Objective Loss 0.038720 MSE 0.038720 LR 0.001000 Time 0.064816 +2024-02-02 16:31:49,685 - Epoch: [15][ 7/ 8] Overall Loss 0.039298 Objective Loss 0.039298 MSE 0.039298 LR 0.001000 Time 0.056454 +2024-02-02 16:31:49,692 - Epoch: [15][ 8/ 8] Overall Loss 0.040938 Objective Loss 0.040938 MSE 0.039640 LR 0.001000 Time 0.050173 +2024-02-02 16:31:49,842 - --- validate (epoch=15)----------- +2024-02-02 16:31:49,842 - 60 samples (32 per mini-batch) +2024-02-02 16:31:50,202 - Epoch: [15][ 1/ 2] Loss 0.045282 MSE 0.045282 +2024-02-02 16:31:50,206 - Epoch: [15][ 2/ 2] Loss 0.045632 MSE 0.045608 +2024-02-02 16:31:50,359 - ==> MSE: 0.04561 Loss: 0.046 + +2024-02-02 16:31:50,362 - ==> Best [Top 1 (MSE): 0.04531 Sparsity:0.00 Params: 136448 on epoch: 13] +2024-02-02 16:31:50,362 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:50,370 - + +2024-02-02 16:31:50,370 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:50,735 - Epoch: [16][ 1/ 8] Overall Loss 0.043091 Objective Loss 0.043091 MSE 0.043091 LR 0.001000 Time 0.363753 +2024-02-02 16:31:50,743 - Epoch: [16][ 2/ 8] Overall Loss 0.038992 Objective Loss 0.038992 MSE 0.038992 LR 0.001000 Time 0.185754 +2024-02-02 16:31:50,750 - Epoch: [16][ 3/ 8] Overall Loss 0.038216 Objective Loss 0.038216 MSE 0.038216 LR 0.001000 Time 0.126313 +2024-02-02 16:31:50,757 - Epoch: [16][ 4/ 8] Overall Loss 0.038194 Objective Loss 0.038194 MSE 0.038194 LR 0.001000 Time 0.096312 +2024-02-02 16:31:50,763 - Epoch: [16][ 5/ 8] Overall Loss 0.038256 Objective Loss 0.038256 MSE 0.038256 LR 0.001000 Time 0.078317 +2024-02-02 16:31:50,769 - Epoch: [16][ 6/ 8] Overall Loss 0.037779 Objective Loss 0.037779 MSE 0.037779 LR 0.001000 Time 0.066292 +2024-02-02 16:31:50,776 - Epoch: [16][ 7/ 8] Overall Loss 0.038623 Objective Loss 0.038623 MSE 0.038623 LR 0.001000 Time 0.057720 +2024-02-02 16:31:50,782 - Epoch: [16][ 8/ 8] Overall Loss 0.039716 Objective Loss 0.039716 MSE 0.038851 LR 0.001000 Time 0.051288 +2024-02-02 16:31:50,934 - --- validate (epoch=16)----------- +2024-02-02 16:31:50,935 - 60 samples (32 per mini-batch) +2024-02-02 16:31:51,293 - Epoch: [16][ 1/ 2] Loss 0.049186 MSE 0.049186 +2024-02-02 16:31:51,298 - Epoch: [16][ 2/ 2] Loss 0.045825 MSE 0.046050 +2024-02-02 16:31:51,445 - ==> MSE: 0.04605 Loss: 0.046 + +2024-02-02 16:31:51,447 - ==> Best [Top 1 (MSE): 0.04531 Sparsity:0.00 Params: 136448 on epoch: 13] +2024-02-02 16:31:51,447 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:51,452 - + +2024-02-02 16:31:51,452 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:51,810 - Epoch: [17][ 1/ 8] Overall Loss 0.036561 Objective Loss 0.036561 MSE 0.036561 LR 0.001000 Time 0.358142 +2024-02-02 16:31:51,817 - Epoch: [17][ 2/ 8] Overall Loss 0.039297 Objective Loss 0.039297 MSE 0.039297 LR 0.001000 Time 0.182414 +2024-02-02 16:31:51,824 - Epoch: [17][ 3/ 8] Overall Loss 0.038925 Objective Loss 0.038925 MSE 0.038925 LR 0.001000 Time 0.123733 +2024-02-02 16:31:51,830 - Epoch: [17][ 4/ 8] Overall Loss 0.038896 Objective Loss 0.038896 MSE 0.038896 LR 0.001000 Time 0.094365 +2024-02-02 16:31:51,837 - Epoch: [17][ 5/ 8] Overall Loss 0.039472 Objective Loss 0.039472 MSE 0.039472 LR 0.001000 Time 0.076742 +2024-02-02 16:31:51,843 - Epoch: [17][ 6/ 8] Overall Loss 0.039332 Objective Loss 0.039332 MSE 0.039332 LR 0.001000 Time 0.064963 +2024-02-02 16:31:51,849 - Epoch: [17][ 7/ 8] Overall Loss 0.038875 Objective Loss 0.038875 MSE 0.038875 LR 0.001000 Time 0.056577 +2024-02-02 16:31:51,856 - Epoch: [17][ 8/ 8] Overall Loss 0.037798 Objective Loss 0.037798 MSE 0.038650 LR 0.001000 Time 0.050380 +2024-02-02 16:31:52,009 - --- validate (epoch=17)----------- +2024-02-02 16:31:52,009 - 60 samples (32 per mini-batch) +2024-02-02 16:31:52,540 - Epoch: [17][ 1/ 2] Loss 0.043941 MSE 0.043941 +2024-02-02 16:31:52,544 - Epoch: [17][ 2/ 2] Loss 0.044747 MSE 0.044694 +2024-02-02 16:31:52,692 - ==> MSE: 0.04469 Loss: 0.045 + +2024-02-02 16:31:52,695 - ==> Best [Top 1 (MSE): 0.04469 Sparsity:0.00 Params: 136448 on epoch: 17] +2024-02-02 16:31:52,695 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:52,707 - + +2024-02-02 16:31:52,707 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:53,041 - Epoch: [18][ 1/ 8] Overall Loss 0.043026 Objective Loss 0.043026 MSE 0.043026 LR 0.001000 Time 0.333762 +2024-02-02 16:31:53,048 - Epoch: [18][ 2/ 8] Overall Loss 0.041307 Objective Loss 0.041307 MSE 0.041307 LR 0.001000 Time 0.170235 +2024-02-02 16:31:53,055 - Epoch: [18][ 3/ 8] Overall Loss 0.040192 Objective Loss 0.040192 MSE 0.040192 LR 0.001000 Time 0.115605 +2024-02-02 16:31:53,061 - Epoch: [18][ 4/ 8] Overall Loss 0.037949 Objective Loss 0.037949 MSE 0.037949 LR 0.001000 Time 0.088257 +2024-02-02 16:31:53,068 - Epoch: [18][ 5/ 8] Overall Loss 0.037486 Objective Loss 0.037486 MSE 0.037486 LR 0.001000 Time 0.071878 +2024-02-02 16:31:53,074 - Epoch: [18][ 6/ 8] Overall Loss 0.037707 Objective Loss 0.037707 MSE 0.037707 LR 0.001000 Time 0.060939 +2024-02-02 16:31:53,081 - Epoch: [18][ 7/ 8] Overall Loss 0.038226 Objective Loss 0.038226 MSE 0.038226 LR 0.001000 Time 0.053140 +2024-02-02 16:31:53,087 - Epoch: [18][ 8/ 8] Overall Loss 0.038377 Objective Loss 0.038377 MSE 0.038258 LR 0.001000 Time 0.047285 +2024-02-02 16:31:53,239 - --- validate (epoch=18)----------- +2024-02-02 16:31:53,239 - 60 samples (32 per mini-batch) +2024-02-02 16:31:53,595 - Epoch: [18][ 1/ 2] Loss 0.038812 MSE 0.038812 +2024-02-02 16:31:53,600 - Epoch: [18][ 2/ 2] Loss 0.043690 MSE 0.043365 +2024-02-02 16:31:53,750 - ==> MSE: 0.04336 Loss: 0.044 + +2024-02-02 16:31:53,751 - ==> Best [Top 1 (MSE): 0.04336 Sparsity:0.00 Params: 136448 on epoch: 18] +2024-02-02 16:31:53,752 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:53,764 - + +2024-02-02 16:31:53,764 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:54,123 - Epoch: [19][ 1/ 8] Overall Loss 0.041935 Objective Loss 0.041935 MSE 0.041935 LR 0.001000 Time 0.358272 +2024-02-02 16:31:54,130 - Epoch: [19][ 2/ 8] Overall Loss 0.036545 Objective Loss 0.036545 MSE 0.036545 LR 0.001000 Time 0.182532 +2024-02-02 16:31:54,136 - Epoch: [19][ 3/ 8] Overall Loss 0.036209 Objective Loss 0.036209 MSE 0.036209 LR 0.001000 Time 0.123830 +2024-02-02 16:31:54,143 - Epoch: [19][ 4/ 8] Overall Loss 0.036775 Objective Loss 0.036775 MSE 0.036775 LR 0.001000 Time 0.094452 +2024-02-02 16:31:54,149 - Epoch: [19][ 5/ 8] Overall Loss 0.036893 Objective Loss 0.036893 MSE 0.036893 LR 0.001000 Time 0.076844 +2024-02-02 16:31:54,156 - Epoch: [19][ 6/ 8] Overall Loss 0.037794 Objective Loss 0.037794 MSE 0.037794 LR 0.001000 Time 0.065085 +2024-02-02 16:31:54,162 - Epoch: [19][ 7/ 8] Overall Loss 0.038092 Objective Loss 0.038092 MSE 0.038092 LR 0.001000 Time 0.056694 +2024-02-02 16:31:54,169 - Epoch: [19][ 8/ 8] Overall Loss 0.037349 Objective Loss 0.037349 MSE 0.037937 LR 0.001000 Time 0.050388 +2024-02-02 16:31:54,321 - --- validate (epoch=19)----------- +2024-02-02 16:31:54,322 - 60 samples (32 per mini-batch) +2024-02-02 16:31:54,682 - Epoch: [19][ 1/ 2] Loss 0.043315 MSE 0.043315 +2024-02-02 16:31:54,686 - Epoch: [19][ 2/ 2] Loss 0.043038 MSE 0.043056 +2024-02-02 16:31:54,839 - ==> MSE: 0.04306 Loss: 0.043 + +2024-02-02 16:31:54,841 - ==> Best [Top 1 (MSE): 0.04306 Sparsity:0.00 Params: 136448 on epoch: 19] +2024-02-02 16:31:54,841 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:54,853 - + +2024-02-02 16:31:54,853 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:55,202 - Epoch: [20][ 1/ 8] Overall Loss 0.040904 Objective Loss 0.040904 MSE 0.040904 LR 0.001000 Time 0.348502 +2024-02-02 16:31:55,209 - Epoch: [20][ 2/ 8] Overall Loss 0.037556 Objective Loss 0.037556 MSE 0.037556 LR 0.001000 Time 0.177658 +2024-02-02 16:31:55,216 - Epoch: [20][ 3/ 8] Overall Loss 0.037029 Objective Loss 0.037029 MSE 0.037029 LR 0.001000 Time 0.120627 +2024-02-02 16:31:55,223 - Epoch: [20][ 4/ 8] Overall Loss 0.037580 Objective Loss 0.037580 MSE 0.037580 LR 0.001000 Time 0.092123 +2024-02-02 16:31:55,229 - Epoch: [20][ 5/ 8] Overall Loss 0.037598 Objective Loss 0.037598 MSE 0.037598 LR 0.001000 Time 0.074975 +2024-02-02 16:31:55,236 - Epoch: [20][ 6/ 8] Overall Loss 0.037227 Objective Loss 0.037227 MSE 0.037227 LR 0.001000 Time 0.063548 +2024-02-02 16:31:55,242 - Epoch: [20][ 7/ 8] Overall Loss 0.036891 Objective Loss 0.036891 MSE 0.036891 LR 0.001000 Time 0.055391 +2024-02-02 16:31:55,249 - Epoch: [20][ 8/ 8] Overall Loss 0.038433 Objective Loss 0.038433 MSE 0.037213 LR 0.001000 Time 0.049257 +2024-02-02 16:31:55,403 - --- validate (epoch=20)----------- +2024-02-02 16:31:55,403 - 60 samples (32 per mini-batch) +2024-02-02 16:31:55,756 - Epoch: [20][ 1/ 2] Loss 0.039887 MSE 0.039887 +2024-02-02 16:31:55,760 - Epoch: [20][ 2/ 2] Loss 0.042399 MSE 0.042231 +2024-02-02 16:31:55,904 - ==> MSE: 0.04223 Loss: 0.042 + +2024-02-02 16:31:55,905 - ==> Best [Top 1 (MSE): 0.04223 Sparsity:0.00 Params: 136448 on epoch: 20] +2024-02-02 16:31:55,906 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:55,918 - + +2024-02-02 16:31:55,918 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:56,277 - Epoch: [21][ 1/ 8] Overall Loss 0.032503 Objective Loss 0.032503 MSE 0.032503 LR 0.001000 Time 0.358384 +2024-02-02 16:31:56,284 - Epoch: [21][ 2/ 8] Overall Loss 0.034833 Objective Loss 0.034833 MSE 0.034833 LR 0.001000 Time 0.182641 +2024-02-02 16:31:56,291 - Epoch: [21][ 3/ 8] Overall Loss 0.033797 Objective Loss 0.033797 MSE 0.033797 LR 0.001000 Time 0.123947 +2024-02-02 16:31:56,297 - Epoch: [21][ 4/ 8] Overall Loss 0.034589 Objective Loss 0.034589 MSE 0.034589 LR 0.001000 Time 0.094564 +2024-02-02 16:31:56,304 - Epoch: [21][ 5/ 8] Overall Loss 0.035160 Objective Loss 0.035160 MSE 0.035160 LR 0.001000 Time 0.076958 +2024-02-02 16:31:56,311 - Epoch: [21][ 6/ 8] Overall Loss 0.035841 Objective Loss 0.035841 MSE 0.035841 LR 0.001000 Time 0.065196 +2024-02-02 16:31:56,317 - Epoch: [21][ 7/ 8] Overall Loss 0.036922 Objective Loss 0.036922 MSE 0.036922 LR 0.001000 Time 0.056788 +2024-02-02 16:31:56,324 - Epoch: [21][ 8/ 8] Overall Loss 0.039351 Objective Loss 0.039351 MSE 0.037429 LR 0.001000 Time 0.050476 +2024-02-02 16:31:56,476 - --- validate (epoch=21)----------- +2024-02-02 16:31:56,477 - 60 samples (32 per mini-batch) +2024-02-02 16:31:56,830 - Epoch: [21][ 1/ 2] Loss 0.047301 MSE 0.047301 +2024-02-02 16:31:56,834 - Epoch: [21][ 2/ 2] Loss 0.047493 MSE 0.047481 +2024-02-02 16:31:56,976 - ==> MSE: 0.04748 Loss: 0.047 + +2024-02-02 16:31:56,979 - ==> Best [Top 1 (MSE): 0.04223 Sparsity:0.00 Params: 136448 on epoch: 20] +2024-02-02 16:31:56,979 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:56,984 - + +2024-02-02 16:31:56,984 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:57,350 - Epoch: [22][ 1/ 8] Overall Loss 0.037154 Objective Loss 0.037154 MSE 0.037154 LR 0.001000 Time 0.365627 +2024-02-02 16:31:57,357 - Epoch: [22][ 2/ 8] Overall Loss 0.036821 Objective Loss 0.036821 MSE 0.036821 LR 0.001000 Time 0.186259 +2024-02-02 16:31:57,364 - Epoch: [22][ 3/ 8] Overall Loss 0.035085 Objective Loss 0.035085 MSE 0.035085 LR 0.001000 Time 0.126304 +2024-02-02 16:31:57,370 - Epoch: [22][ 4/ 8] Overall Loss 0.036083 Objective Loss 0.036083 MSE 0.036083 LR 0.001000 Time 0.096302 +2024-02-02 16:31:57,377 - Epoch: [22][ 5/ 8] Overall Loss 0.035432 Objective Loss 0.035432 MSE 0.035432 LR 0.001000 Time 0.078316 +2024-02-02 16:31:57,383 - Epoch: [22][ 6/ 8] Overall Loss 0.036409 Objective Loss 0.036409 MSE 0.036409 LR 0.001000 Time 0.066319 +2024-02-02 16:31:57,390 - Epoch: [22][ 7/ 8] Overall Loss 0.036110 Objective Loss 0.036110 MSE 0.036110 LR 0.001000 Time 0.057747 +2024-02-02 16:31:57,396 - Epoch: [22][ 8/ 8] Overall Loss 0.036943 Objective Loss 0.036943 MSE 0.036284 LR 0.001000 Time 0.051311 +2024-02-02 16:31:57,543 - --- validate (epoch=22)----------- +2024-02-02 16:31:57,543 - 60 samples (32 per mini-batch) +2024-02-02 16:31:57,908 - Epoch: [22][ 1/ 2] Loss 0.048732 MSE 0.048732 +2024-02-02 16:31:57,913 - Epoch: [22][ 2/ 2] Loss 0.049070 MSE 0.049048 +2024-02-02 16:31:58,065 - ==> MSE: 0.04905 Loss: 0.049 + +2024-02-02 16:31:58,068 - ==> Best [Top 1 (MSE): 0.04223 Sparsity:0.00 Params: 136448 on epoch: 20] +2024-02-02 16:31:58,068 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:58,073 - + +2024-02-02 16:31:58,073 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:58,433 - Epoch: [23][ 1/ 8] Overall Loss 0.034101 Objective Loss 0.034101 MSE 0.034101 LR 0.001000 Time 0.359791 +2024-02-02 16:31:58,440 - Epoch: [23][ 2/ 8] Overall Loss 0.036243 Objective Loss 0.036243 MSE 0.036243 LR 0.001000 Time 0.183312 +2024-02-02 16:31:58,447 - Epoch: [23][ 3/ 8] Overall Loss 0.037325 Objective Loss 0.037325 MSE 0.037325 LR 0.001000 Time 0.124370 +2024-02-02 16:31:58,453 - Epoch: [23][ 4/ 8] Overall Loss 0.038095 Objective Loss 0.038095 MSE 0.038095 LR 0.001000 Time 0.094862 +2024-02-02 16:31:58,460 - Epoch: [23][ 5/ 8] Overall Loss 0.037974 Objective Loss 0.037974 MSE 0.037974 LR 0.001000 Time 0.077171 +2024-02-02 16:31:58,466 - Epoch: [23][ 6/ 8] Overall Loss 0.036919 Objective Loss 0.036919 MSE 0.036919 LR 0.001000 Time 0.065387 +2024-02-02 16:31:58,473 - Epoch: [23][ 7/ 8] Overall Loss 0.035718 Objective Loss 0.035718 MSE 0.035718 LR 0.001000 Time 0.056962 +2024-02-02 16:31:58,479 - Epoch: [23][ 8/ 8] Overall Loss 0.038614 Objective Loss 0.038614 MSE 0.036323 LR 0.001000 Time 0.050655 +2024-02-02 16:31:58,631 - --- validate (epoch=23)----------- +2024-02-02 16:31:58,631 - 60 samples (32 per mini-batch) +2024-02-02 16:31:58,990 - Epoch: [23][ 1/ 2] Loss 0.044011 MSE 0.044011 +2024-02-02 16:31:58,994 - Epoch: [23][ 2/ 2] Loss 0.047644 MSE 0.047402 +2024-02-02 16:31:59,141 - ==> MSE: 0.04740 Loss: 0.048 + +2024-02-02 16:31:59,143 - ==> Best [Top 1 (MSE): 0.04223 Sparsity:0.00 Params: 136448 on epoch: 20] +2024-02-02 16:31:59,143 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:31:59,148 - + +2024-02-02 16:31:59,148 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:31:59,493 - Epoch: [24][ 1/ 8] Overall Loss 0.036456 Objective Loss 0.036456 MSE 0.036456 LR 0.001000 Time 0.344185 +2024-02-02 16:31:59,500 - Epoch: [24][ 2/ 8] Overall Loss 0.036784 Objective Loss 0.036784 MSE 0.036784 LR 0.001000 Time 0.175562 +2024-02-02 16:31:59,507 - Epoch: [24][ 3/ 8] Overall Loss 0.034669 Objective Loss 0.034669 MSE 0.034669 LR 0.001000 Time 0.119224 +2024-02-02 16:31:59,513 - Epoch: [24][ 4/ 8] Overall Loss 0.035194 Objective Loss 0.035194 MSE 0.035194 LR 0.001000 Time 0.091007 +2024-02-02 16:31:59,520 - Epoch: [24][ 5/ 8] Overall Loss 0.035332 Objective Loss 0.035332 MSE 0.035332 LR 0.001000 Time 0.074091 +2024-02-02 16:31:59,526 - Epoch: [24][ 6/ 8] Overall Loss 0.035375 Objective Loss 0.035375 MSE 0.035375 LR 0.001000 Time 0.062788 +2024-02-02 16:31:59,533 - Epoch: [24][ 7/ 8] Overall Loss 0.035485 Objective Loss 0.035485 MSE 0.035485 LR 0.001000 Time 0.054722 +2024-02-02 16:31:59,539 - Epoch: [24][ 8/ 8] Overall Loss 0.036227 Objective Loss 0.036227 MSE 0.035640 LR 0.001000 Time 0.048662 +2024-02-02 16:31:59,683 - --- validate (epoch=24)----------- +2024-02-02 16:31:59,683 - 60 samples (32 per mini-batch) +2024-02-02 16:32:00,027 - Epoch: [24][ 1/ 2] Loss 0.067677 MSE 0.067677 +2024-02-02 16:32:00,032 - Epoch: [24][ 2/ 2] Loss 0.063758 MSE 0.064019 +2024-02-02 16:32:00,182 - ==> MSE: 0.06402 Loss: 0.064 + +2024-02-02 16:32:00,184 - ==> Best [Top 1 (MSE): 0.04223 Sparsity:0.00 Params: 136448 on epoch: 20] +2024-02-02 16:32:00,184 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:00,189 - + +2024-02-02 16:32:00,189 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:00,552 - Epoch: [25][ 1/ 8] Overall Loss 0.035936 Objective Loss 0.035936 MSE 0.035936 LR 0.001000 Time 0.362528 +2024-02-02 16:32:00,559 - Epoch: [25][ 2/ 8] Overall Loss 0.034954 Objective Loss 0.034954 MSE 0.034954 LR 0.001000 Time 0.184720 +2024-02-02 16:32:00,565 - Epoch: [25][ 3/ 8] Overall Loss 0.035107 Objective Loss 0.035107 MSE 0.035107 LR 0.001000 Time 0.125359 +2024-02-02 16:32:00,572 - Epoch: [25][ 4/ 8] Overall Loss 0.035067 Objective Loss 0.035067 MSE 0.035067 LR 0.001000 Time 0.095658 +2024-02-02 16:32:00,579 - Epoch: [25][ 5/ 8] Overall Loss 0.034992 Objective Loss 0.034992 MSE 0.034992 LR 0.001000 Time 0.077819 +2024-02-02 16:32:00,586 - Epoch: [25][ 6/ 8] Overall Loss 0.035184 Objective Loss 0.035184 MSE 0.035184 LR 0.001000 Time 0.065949 +2024-02-02 16:32:00,592 - Epoch: [25][ 7/ 8] Overall Loss 0.035184 Objective Loss 0.035184 MSE 0.035184 LR 0.001000 Time 0.057487 +2024-02-02 16:32:00,599 - Epoch: [25][ 8/ 8] Overall Loss 0.036062 Objective Loss 0.036062 MSE 0.035367 LR 0.001000 Time 0.051097 +2024-02-02 16:32:00,748 - --- validate (epoch=25)----------- +2024-02-02 16:32:00,749 - 60 samples (32 per mini-batch) +2024-02-02 16:32:01,092 - Epoch: [25][ 1/ 2] Loss 0.063933 MSE 0.063933 +2024-02-02 16:32:01,096 - Epoch: [25][ 2/ 2] Loss 0.060125 MSE 0.060378 +2024-02-02 16:32:01,242 - ==> MSE: 0.06038 Loss: 0.060 + +2024-02-02 16:32:01,244 - ==> Best [Top 1 (MSE): 0.04223 Sparsity:0.00 Params: 136448 on epoch: 20] +2024-02-02 16:32:01,244 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:01,249 - + +2024-02-02 16:32:01,249 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:01,609 - Epoch: [26][ 1/ 8] Overall Loss 0.040520 Objective Loss 0.040520 MSE 0.040520 LR 0.001000 Time 0.359251 +2024-02-02 16:32:01,617 - Epoch: [26][ 2/ 8] Overall Loss 0.036169 Objective Loss 0.036169 MSE 0.036169 LR 0.001000 Time 0.183740 +2024-02-02 16:32:01,627 - Epoch: [26][ 3/ 8] Overall Loss 0.035477 Objective Loss 0.035477 MSE 0.035477 LR 0.001000 Time 0.125539 +2024-02-02 16:32:01,634 - Epoch: [26][ 4/ 8] Overall Loss 0.034845 Objective Loss 0.034845 MSE 0.034845 LR 0.001000 Time 0.095809 +2024-02-02 16:32:01,640 - Epoch: [26][ 5/ 8] Overall Loss 0.036251 Objective Loss 0.036251 MSE 0.036251 LR 0.001000 Time 0.077978 +2024-02-02 16:32:01,647 - Epoch: [26][ 6/ 8] Overall Loss 0.035340 Objective Loss 0.035340 MSE 0.035340 LR 0.001000 Time 0.066066 +2024-02-02 16:32:01,654 - Epoch: [26][ 7/ 8] Overall Loss 0.035370 Objective Loss 0.035370 MSE 0.035370 LR 0.001000 Time 0.057577 +2024-02-02 16:32:01,660 - Epoch: [26][ 8/ 8] Overall Loss 0.036565 Objective Loss 0.036565 MSE 0.035619 LR 0.001000 Time 0.051183 +2024-02-02 16:32:01,810 - --- validate (epoch=26)----------- +2024-02-02 16:32:01,810 - 60 samples (32 per mini-batch) +2024-02-02 16:32:02,163 - Epoch: [26][ 1/ 2] Loss 0.046234 MSE 0.046234 +2024-02-02 16:32:02,168 - Epoch: [26][ 2/ 2] Loss 0.043711 MSE 0.043879 +2024-02-02 16:32:02,321 - ==> MSE: 0.04388 Loss: 0.044 + +2024-02-02 16:32:02,324 - ==> Best [Top 1 (MSE): 0.04223 Sparsity:0.00 Params: 136448 on epoch: 20] +2024-02-02 16:32:02,324 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:02,328 - + +2024-02-02 16:32:02,329 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:02,694 - Epoch: [27][ 1/ 8] Overall Loss 0.031477 Objective Loss 0.031477 MSE 0.031477 LR 0.001000 Time 0.364916 +2024-02-02 16:32:02,705 - Epoch: [27][ 2/ 8] Overall Loss 0.034584 Objective Loss 0.034584 MSE 0.034584 LR 0.001000 Time 0.187603 +2024-02-02 16:32:02,715 - Epoch: [27][ 3/ 8] Overall Loss 0.033252 Objective Loss 0.033252 MSE 0.033252 LR 0.001000 Time 0.128501 +2024-02-02 16:32:02,722 - Epoch: [27][ 4/ 8] Overall Loss 0.033686 Objective Loss 0.033686 MSE 0.033686 LR 0.001000 Time 0.098124 +2024-02-02 16:32:02,729 - Epoch: [27][ 5/ 8] Overall Loss 0.034446 Objective Loss 0.034446 MSE 0.034446 LR 0.001000 Time 0.079864 +2024-02-02 16:32:02,736 - Epoch: [27][ 6/ 8] Overall Loss 0.033958 Objective Loss 0.033958 MSE 0.033958 LR 0.001000 Time 0.067679 +2024-02-02 16:32:02,743 - Epoch: [27][ 7/ 8] Overall Loss 0.034531 Objective Loss 0.034531 MSE 0.034531 LR 0.001000 Time 0.058951 +2024-02-02 16:32:02,750 - Epoch: [27][ 8/ 8] Overall Loss 0.034359 Objective Loss 0.034359 MSE 0.034495 LR 0.001000 Time 0.052393 +2024-02-02 16:32:02,899 - --- validate (epoch=27)----------- +2024-02-02 16:32:02,899 - 60 samples (32 per mini-batch) +2024-02-02 16:32:03,244 - Epoch: [27][ 1/ 2] Loss 0.039841 MSE 0.039841 +2024-02-02 16:32:03,248 - Epoch: [27][ 2/ 2] Loss 0.040125 MSE 0.040106 +2024-02-02 16:32:03,390 - ==> MSE: 0.04011 Loss: 0.040 + +2024-02-02 16:32:03,392 - ==> Best [Top 1 (MSE): 0.04011 Sparsity:0.00 Params: 136448 on epoch: 27] +2024-02-02 16:32:03,392 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:03,405 - + +2024-02-02 16:32:03,405 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:03,769 - Epoch: [28][ 1/ 8] Overall Loss 0.036602 Objective Loss 0.036602 MSE 0.036602 LR 0.001000 Time 0.363237 +2024-02-02 16:32:03,776 - Epoch: [28][ 2/ 8] Overall Loss 0.038356 Objective Loss 0.038356 MSE 0.038356 LR 0.001000 Time 0.185048 +2024-02-02 16:32:03,783 - Epoch: [28][ 3/ 8] Overall Loss 0.035449 Objective Loss 0.035449 MSE 0.035449 LR 0.001000 Time 0.125590 +2024-02-02 16:32:03,789 - Epoch: [28][ 4/ 8] Overall Loss 0.034616 Objective Loss 0.034616 MSE 0.034616 LR 0.001000 Time 0.095808 +2024-02-02 16:32:03,796 - Epoch: [28][ 5/ 8] Overall Loss 0.034788 Objective Loss 0.034788 MSE 0.034788 LR 0.001000 Time 0.077951 +2024-02-02 16:32:03,803 - Epoch: [28][ 6/ 8] Overall Loss 0.034182 Objective Loss 0.034182 MSE 0.034182 LR 0.001000 Time 0.066059 +2024-02-02 16:32:03,809 - Epoch: [28][ 7/ 8] Overall Loss 0.034527 Objective Loss 0.034527 MSE 0.034527 LR 0.001000 Time 0.057572 +2024-02-02 16:32:03,816 - Epoch: [28][ 8/ 8] Overall Loss 0.033471 Objective Loss 0.033471 MSE 0.034307 LR 0.001000 Time 0.051202 +2024-02-02 16:32:03,964 - --- validate (epoch=28)----------- +2024-02-02 16:32:03,965 - 60 samples (32 per mini-batch) +2024-02-02 16:32:04,320 - Epoch: [28][ 1/ 2] Loss 0.041065 MSE 0.041065 +2024-02-02 16:32:04,324 - Epoch: [28][ 2/ 2] Loss 0.039882 MSE 0.039961 +2024-02-02 16:32:04,470 - ==> MSE: 0.03996 Loss: 0.040 + +2024-02-02 16:32:04,472 - ==> Best [Top 1 (MSE): 0.03996 Sparsity:0.00 Params: 136448 on epoch: 28] +2024-02-02 16:32:04,472 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:04,485 - + +2024-02-02 16:32:04,485 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:04,839 - Epoch: [29][ 1/ 8] Overall Loss 0.033466 Objective Loss 0.033466 MSE 0.033466 LR 0.001000 Time 0.353635 +2024-02-02 16:32:04,846 - Epoch: [29][ 2/ 8] Overall Loss 0.034094 Objective Loss 0.034094 MSE 0.034094 LR 0.001000 Time 0.180235 +2024-02-02 16:32:04,853 - Epoch: [29][ 3/ 8] Overall Loss 0.034507 Objective Loss 0.034507 MSE 0.034507 LR 0.001000 Time 0.122349 +2024-02-02 16:32:04,859 - Epoch: [29][ 4/ 8] Overall Loss 0.033814 Objective Loss 0.033814 MSE 0.033814 LR 0.001000 Time 0.093369 +2024-02-02 16:32:04,866 - Epoch: [29][ 5/ 8] Overall Loss 0.033269 Objective Loss 0.033269 MSE 0.033269 LR 0.001000 Time 0.075987 +2024-02-02 16:32:04,873 - Epoch: [29][ 6/ 8] Overall Loss 0.033368 Objective Loss 0.033368 MSE 0.033368 LR 0.001000 Time 0.064392 +2024-02-02 16:32:04,879 - Epoch: [29][ 7/ 8] Overall Loss 0.033785 Objective Loss 0.033785 MSE 0.033785 LR 0.001000 Time 0.056104 +2024-02-02 16:32:04,886 - Epoch: [29][ 8/ 8] Overall Loss 0.034584 Objective Loss 0.034584 MSE 0.033952 LR 0.001000 Time 0.049878 +2024-02-02 16:32:05,031 - --- validate (epoch=29)----------- +2024-02-02 16:32:05,031 - 60 samples (32 per mini-batch) +2024-02-02 16:32:05,369 - Epoch: [29][ 1/ 2] Loss 0.036738 MSE 0.036738 +2024-02-02 16:32:05,374 - Epoch: [29][ 2/ 2] Loss 0.039923 MSE 0.039710 +2024-02-02 16:32:05,510 - ==> MSE: 0.03971 Loss: 0.040 + +2024-02-02 16:32:05,512 - ==> Best [Top 1 (MSE): 0.03971 Sparsity:0.00 Params: 136448 on epoch: 29] +2024-02-02 16:32:05,513 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:05,525 - + +2024-02-02 16:32:05,525 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:05,873 - Epoch: [30][ 1/ 8] Overall Loss 0.033145 Objective Loss 0.033145 MSE 0.033145 LR 0.001000 Time 0.347430 +2024-02-02 16:32:05,880 - Epoch: [30][ 2/ 8] Overall Loss 0.031544 Objective Loss 0.031544 MSE 0.031544 LR 0.001000 Time 0.177239 +2024-02-02 16:32:05,887 - Epoch: [30][ 3/ 8] Overall Loss 0.033018 Objective Loss 0.033018 MSE 0.033018 LR 0.001000 Time 0.120359 +2024-02-02 16:32:05,894 - Epoch: [30][ 4/ 8] Overall Loss 0.032720 Objective Loss 0.032720 MSE 0.032720 LR 0.001000 Time 0.091950 +2024-02-02 16:32:05,901 - Epoch: [30][ 5/ 8] Overall Loss 0.033517 Objective Loss 0.033517 MSE 0.033517 LR 0.001000 Time 0.074892 +2024-02-02 16:32:05,908 - Epoch: [30][ 6/ 8] Overall Loss 0.033510 Objective Loss 0.033510 MSE 0.033510 LR 0.001000 Time 0.063505 +2024-02-02 16:32:05,914 - Epoch: [30][ 7/ 8] Overall Loss 0.033659 Objective Loss 0.033659 MSE 0.033659 LR 0.001000 Time 0.055365 +2024-02-02 16:32:05,921 - Epoch: [30][ 8/ 8] Overall Loss 0.035427 Objective Loss 0.035427 MSE 0.034028 LR 0.001000 Time 0.049241 +2024-02-02 16:32:06,069 - --- validate (epoch=30)----------- +2024-02-02 16:32:06,069 - 60 samples (32 per mini-batch) +2024-02-02 16:32:06,407 - Epoch: [30][ 1/ 2] Loss 0.037349 MSE 0.037349 +2024-02-02 16:32:06,412 - Epoch: [30][ 2/ 2] Loss 0.039551 MSE 0.039405 +2024-02-02 16:32:06,560 - ==> MSE: 0.03940 Loss: 0.040 + +2024-02-02 16:32:06,563 - ==> Best [Top 1 (MSE): 0.03940 Sparsity:0.00 Params: 136448 on epoch: 30] +2024-02-02 16:32:06,563 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:06,569 - + +2024-02-02 16:32:06,569 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:06,918 - Epoch: [31][ 1/ 8] Overall Loss 0.031894 Objective Loss 0.031894 MSE 0.031894 LR 0.001000 Time 0.348112 +2024-02-02 16:32:06,925 - Epoch: [31][ 2/ 8] Overall Loss 0.029957 Objective Loss 0.029957 MSE 0.029957 LR 0.001000 Time 0.177549 +2024-02-02 16:32:06,932 - Epoch: [31][ 3/ 8] Overall Loss 0.031716 Objective Loss 0.031716 MSE 0.031716 LR 0.001000 Time 0.120582 +2024-02-02 16:32:06,938 - Epoch: [31][ 4/ 8] Overall Loss 0.032121 Objective Loss 0.032121 MSE 0.032121 LR 0.001000 Time 0.092084 +2024-02-02 16:32:06,945 - Epoch: [31][ 5/ 8] Overall Loss 0.032401 Objective Loss 0.032401 MSE 0.032401 LR 0.001000 Time 0.074988 +2024-02-02 16:32:06,952 - Epoch: [31][ 6/ 8] Overall Loss 0.033090 Objective Loss 0.033090 MSE 0.033090 LR 0.001000 Time 0.063597 +2024-02-02 16:32:06,959 - Epoch: [31][ 7/ 8] Overall Loss 0.033862 Objective Loss 0.033862 MSE 0.033862 LR 0.001000 Time 0.055440 +2024-02-02 16:32:06,965 - Epoch: [31][ 8/ 8] Overall Loss 0.036140 Objective Loss 0.036140 MSE 0.034337 LR 0.001000 Time 0.049332 +2024-02-02 16:32:07,113 - --- validate (epoch=31)----------- +2024-02-02 16:32:07,114 - 60 samples (32 per mini-batch) +2024-02-02 16:32:07,468 - Epoch: [31][ 1/ 2] Loss 0.038477 MSE 0.038477 +2024-02-02 16:32:07,473 - Epoch: [31][ 2/ 2] Loss 0.039799 MSE 0.039711 +2024-02-02 16:32:07,620 - ==> MSE: 0.03971 Loss: 0.040 + +2024-02-02 16:32:07,623 - ==> Best [Top 1 (MSE): 0.03940 Sparsity:0.00 Params: 136448 on epoch: 30] +2024-02-02 16:32:07,623 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:07,628 - + +2024-02-02 16:32:07,628 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:07,990 - Epoch: [32][ 1/ 8] Overall Loss 0.035634 Objective Loss 0.035634 MSE 0.035634 LR 0.001000 Time 0.361647 +2024-02-02 16:32:07,997 - Epoch: [32][ 2/ 8] Overall Loss 0.033522 Objective Loss 0.033522 MSE 0.033522 LR 0.001000 Time 0.184272 +2024-02-02 16:32:08,004 - Epoch: [32][ 3/ 8] Overall Loss 0.032897 Objective Loss 0.032897 MSE 0.032897 LR 0.001000 Time 0.125074 +2024-02-02 16:32:08,011 - Epoch: [32][ 4/ 8] Overall Loss 0.034695 Objective Loss 0.034695 MSE 0.034695 LR 0.001000 Time 0.095442 +2024-02-02 16:32:08,018 - Epoch: [32][ 5/ 8] Overall Loss 0.035283 Objective Loss 0.035283 MSE 0.035283 LR 0.001000 Time 0.077671 +2024-02-02 16:32:08,024 - Epoch: [32][ 6/ 8] Overall Loss 0.035040 Objective Loss 0.035040 MSE 0.035040 LR 0.001000 Time 0.065829 +2024-02-02 16:32:08,031 - Epoch: [32][ 7/ 8] Overall Loss 0.034242 Objective Loss 0.034242 MSE 0.034242 LR 0.001000 Time 0.057350 +2024-02-02 16:32:08,038 - Epoch: [32][ 8/ 8] Overall Loss 0.035601 Objective Loss 0.035601 MSE 0.034525 LR 0.001000 Time 0.050999 +2024-02-02 16:32:08,185 - --- validate (epoch=32)----------- +2024-02-02 16:32:08,185 - 60 samples (32 per mini-batch) +2024-02-02 16:32:08,537 - Epoch: [32][ 1/ 2] Loss 0.036457 MSE 0.036457 +2024-02-02 16:32:08,541 - Epoch: [32][ 2/ 2] Loss 0.039298 MSE 0.039109 +2024-02-02 16:32:08,690 - ==> MSE: 0.03911 Loss: 0.039 + +2024-02-02 16:32:08,692 - ==> Best [Top 1 (MSE): 0.03911 Sparsity:0.00 Params: 136448 on epoch: 32] +2024-02-02 16:32:08,693 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:08,698 - + +2024-02-02 16:32:08,699 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:09,062 - Epoch: [33][ 1/ 8] Overall Loss 0.035388 Objective Loss 0.035388 MSE 0.035388 LR 0.001000 Time 0.362678 +2024-02-02 16:32:09,072 - Epoch: [33][ 2/ 8] Overall Loss 0.033094 Objective Loss 0.033094 MSE 0.033094 LR 0.001000 Time 0.186490 +2024-02-02 16:32:09,080 - Epoch: [33][ 3/ 8] Overall Loss 0.032595 Objective Loss 0.032595 MSE 0.032595 LR 0.001000 Time 0.126917 +2024-02-02 16:32:09,087 - Epoch: [33][ 4/ 8] Overall Loss 0.032682 Objective Loss 0.032682 MSE 0.032682 LR 0.001000 Time 0.096870 +2024-02-02 16:32:09,094 - Epoch: [33][ 5/ 8] Overall Loss 0.032952 Objective Loss 0.032952 MSE 0.032952 LR 0.001000 Time 0.078852 +2024-02-02 16:32:09,101 - Epoch: [33][ 6/ 8] Overall Loss 0.033698 Objective Loss 0.033698 MSE 0.033698 LR 0.001000 Time 0.066807 +2024-02-02 16:32:09,108 - Epoch: [33][ 7/ 8] Overall Loss 0.033594 Objective Loss 0.033594 MSE 0.033594 LR 0.001000 Time 0.058201 +2024-02-02 16:32:09,114 - Epoch: [33][ 8/ 8] Overall Loss 0.033844 Objective Loss 0.033844 MSE 0.033647 LR 0.001000 Time 0.051735 +2024-02-02 16:32:09,260 - --- validate (epoch=33)----------- +2024-02-02 16:32:09,261 - 60 samples (32 per mini-batch) +2024-02-02 16:32:09,620 - Epoch: [33][ 1/ 2] Loss 0.041548 MSE 0.041548 +2024-02-02 16:32:09,624 - Epoch: [33][ 2/ 2] Loss 0.038917 MSE 0.039093 +2024-02-02 16:32:09,767 - ==> MSE: 0.03909 Loss: 0.039 + +2024-02-02 16:32:09,769 - ==> Best [Top 1 (MSE): 0.03909 Sparsity:0.00 Params: 136448 on epoch: 33] +2024-02-02 16:32:09,769 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:09,775 - + +2024-02-02 16:32:09,775 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:10,137 - Epoch: [34][ 1/ 8] Overall Loss 0.037531 Objective Loss 0.037531 MSE 0.037531 LR 0.001000 Time 0.361495 +2024-02-02 16:32:10,144 - Epoch: [34][ 2/ 8] Overall Loss 0.035930 Objective Loss 0.035930 MSE 0.035930 LR 0.001000 Time 0.184270 +2024-02-02 16:32:10,151 - Epoch: [34][ 3/ 8] Overall Loss 0.034638 Objective Loss 0.034638 MSE 0.034638 LR 0.001000 Time 0.125088 +2024-02-02 16:32:10,157 - Epoch: [34][ 4/ 8] Overall Loss 0.033139 Objective Loss 0.033139 MSE 0.033139 LR 0.001000 Time 0.095470 +2024-02-02 16:32:10,164 - Epoch: [34][ 5/ 8] Overall Loss 0.032638 Objective Loss 0.032638 MSE 0.032638 LR 0.001000 Time 0.077703 +2024-02-02 16:32:10,171 - Epoch: [34][ 6/ 8] Overall Loss 0.033587 Objective Loss 0.033587 MSE 0.033587 LR 0.001000 Time 0.065841 +2024-02-02 16:32:10,178 - Epoch: [34][ 7/ 8] Overall Loss 0.033447 Objective Loss 0.033447 MSE 0.033447 LR 0.001000 Time 0.057386 +2024-02-02 16:32:10,184 - Epoch: [34][ 8/ 8] Overall Loss 0.034910 Objective Loss 0.034910 MSE 0.033752 LR 0.001000 Time 0.051021 +2024-02-02 16:32:10,329 - --- validate (epoch=34)----------- +2024-02-02 16:32:10,329 - 60 samples (32 per mini-batch) +2024-02-02 16:32:10,685 - Epoch: [34][ 1/ 2] Loss 0.037160 MSE 0.037160 +2024-02-02 16:32:10,689 - Epoch: [34][ 2/ 2] Loss 0.039318 MSE 0.039174 +2024-02-02 16:32:10,831 - ==> MSE: 0.03917 Loss: 0.039 + +2024-02-02 16:32:10,835 - ==> Best [Top 1 (MSE): 0.03909 Sparsity:0.00 Params: 136448 on epoch: 33] +2024-02-02 16:32:10,835 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:10,844 - + +2024-02-02 16:32:10,844 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:11,204 - Epoch: [35][ 1/ 8] Overall Loss 0.034355 Objective Loss 0.034355 MSE 0.034355 LR 0.001000 Time 0.359135 +2024-02-02 16:32:11,212 - Epoch: [35][ 2/ 8] Overall Loss 0.032831 Objective Loss 0.032831 MSE 0.032831 LR 0.001000 Time 0.183646 +2024-02-02 16:32:11,220 - Epoch: [35][ 3/ 8] Overall Loss 0.032786 Objective Loss 0.032786 MSE 0.032786 LR 0.001000 Time 0.125072 +2024-02-02 16:32:11,227 - Epoch: [35][ 4/ 8] Overall Loss 0.033227 Objective Loss 0.033227 MSE 0.033227 LR 0.001000 Time 0.095465 +2024-02-02 16:32:11,234 - Epoch: [35][ 5/ 8] Overall Loss 0.033126 Objective Loss 0.033126 MSE 0.033126 LR 0.001000 Time 0.077715 +2024-02-02 16:32:11,241 - Epoch: [35][ 6/ 8] Overall Loss 0.033595 Objective Loss 0.033595 MSE 0.033595 LR 0.001000 Time 0.065863 +2024-02-02 16:32:11,247 - Epoch: [35][ 7/ 8] Overall Loss 0.033364 Objective Loss 0.033364 MSE 0.033364 LR 0.001000 Time 0.057381 +2024-02-02 16:32:11,254 - Epoch: [35][ 8/ 8] Overall Loss 0.034389 Objective Loss 0.034389 MSE 0.033578 LR 0.001000 Time 0.051007 +2024-02-02 16:32:11,398 - --- validate (epoch=35)----------- +2024-02-02 16:32:11,398 - 60 samples (32 per mini-batch) +2024-02-02 16:32:11,757 - Epoch: [35][ 1/ 2] Loss 0.039709 MSE 0.039709 +2024-02-02 16:32:11,762 - Epoch: [35][ 2/ 2] Loss 0.038679 MSE 0.038748 +2024-02-02 16:32:11,911 - ==> MSE: 0.03875 Loss: 0.039 + +2024-02-02 16:32:11,913 - ==> Best [Top 1 (MSE): 0.03875 Sparsity:0.00 Params: 136448 on epoch: 35] +2024-02-02 16:32:11,913 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:11,919 - + +2024-02-02 16:32:11,919 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:12,285 - Epoch: [36][ 1/ 8] Overall Loss 0.031386 Objective Loss 0.031386 MSE 0.031386 LR 0.001000 Time 0.365880 +2024-02-02 16:32:12,292 - Epoch: [36][ 2/ 8] Overall Loss 0.032082 Objective Loss 0.032082 MSE 0.032082 LR 0.001000 Time 0.186411 +2024-02-02 16:32:12,299 - Epoch: [36][ 3/ 8] Overall Loss 0.032705 Objective Loss 0.032705 MSE 0.032705 LR 0.001000 Time 0.126423 +2024-02-02 16:32:12,305 - Epoch: [36][ 4/ 8] Overall Loss 0.032733 Objective Loss 0.032733 MSE 0.032733 LR 0.001000 Time 0.096436 +2024-02-02 16:32:12,312 - Epoch: [36][ 5/ 8] Overall Loss 0.033314 Objective Loss 0.033314 MSE 0.033314 LR 0.001000 Time 0.078482 +2024-02-02 16:32:12,319 - Epoch: [36][ 6/ 8] Overall Loss 0.033398 Objective Loss 0.033398 MSE 0.033398 LR 0.001000 Time 0.066483 +2024-02-02 16:32:12,326 - Epoch: [36][ 7/ 8] Overall Loss 0.033111 Objective Loss 0.033111 MSE 0.033111 LR 0.001000 Time 0.057917 +2024-02-02 16:32:12,332 - Epoch: [36][ 8/ 8] Overall Loss 0.033218 Objective Loss 0.033218 MSE 0.033133 LR 0.001000 Time 0.051485 +2024-02-02 16:32:12,482 - --- validate (epoch=36)----------- +2024-02-02 16:32:12,482 - 60 samples (32 per mini-batch) +2024-02-02 16:32:12,838 - Epoch: [36][ 1/ 2] Loss 0.042238 MSE 0.042238 +2024-02-02 16:32:12,843 - Epoch: [36][ 2/ 2] Loss 0.037952 MSE 0.038238 +2024-02-02 16:32:12,982 - ==> MSE: 0.03824 Loss: 0.038 + +2024-02-02 16:32:12,984 - ==> Best [Top 1 (MSE): 0.03824 Sparsity:0.00 Params: 136448 on epoch: 36] +2024-02-02 16:32:12,985 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:12,990 - + +2024-02-02 16:32:12,990 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:13,356 - Epoch: [37][ 1/ 8] Overall Loss 0.035542 Objective Loss 0.035542 MSE 0.035542 LR 0.001000 Time 0.364744 +2024-02-02 16:32:13,363 - Epoch: [37][ 2/ 8] Overall Loss 0.034022 Objective Loss 0.034022 MSE 0.034022 LR 0.001000 Time 0.185826 +2024-02-02 16:32:13,370 - Epoch: [37][ 3/ 8] Overall Loss 0.034753 Objective Loss 0.034753 MSE 0.034753 LR 0.001000 Time 0.126102 +2024-02-02 16:32:13,376 - Epoch: [37][ 4/ 8] Overall Loss 0.033262 Objective Loss 0.033262 MSE 0.033262 LR 0.001000 Time 0.096215 +2024-02-02 16:32:13,383 - Epoch: [37][ 5/ 8] Overall Loss 0.032699 Objective Loss 0.032699 MSE 0.032699 LR 0.001000 Time 0.078280 +2024-02-02 16:32:13,390 - Epoch: [37][ 6/ 8] Overall Loss 0.032757 Objective Loss 0.032757 MSE 0.032757 LR 0.001000 Time 0.066317 +2024-02-02 16:32:13,396 - Epoch: [37][ 7/ 8] Overall Loss 0.032546 Objective Loss 0.032546 MSE 0.032546 LR 0.001000 Time 0.057773 +2024-02-02 16:32:13,403 - Epoch: [37][ 8/ 8] Overall Loss 0.034261 Objective Loss 0.034261 MSE 0.032904 LR 0.001000 Time 0.051347 +2024-02-02 16:32:13,549 - --- validate (epoch=37)----------- +2024-02-02 16:32:13,549 - 60 samples (32 per mini-batch) +2024-02-02 16:32:13,907 - Epoch: [37][ 1/ 2] Loss 0.042742 MSE 0.042742 +2024-02-02 16:32:13,912 - Epoch: [37][ 2/ 2] Loss 0.037644 MSE 0.037984 +2024-02-02 16:32:14,059 - ==> MSE: 0.03798 Loss: 0.038 + +2024-02-02 16:32:14,064 - ==> Best [Top 1 (MSE): 0.03798 Sparsity:0.00 Params: 136448 on epoch: 37] +2024-02-02 16:32:14,064 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:14,074 - + +2024-02-02 16:32:14,074 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:14,435 - Epoch: [38][ 1/ 8] Overall Loss 0.029152 Objective Loss 0.029152 MSE 0.029152 LR 0.001000 Time 0.359729 +2024-02-02 16:32:14,443 - Epoch: [38][ 2/ 8] Overall Loss 0.030639 Objective Loss 0.030639 MSE 0.030639 LR 0.001000 Time 0.183972 +2024-02-02 16:32:14,450 - Epoch: [38][ 3/ 8] Overall Loss 0.032741 Objective Loss 0.032741 MSE 0.032741 LR 0.001000 Time 0.124870 +2024-02-02 16:32:14,457 - Epoch: [38][ 4/ 8] Overall Loss 0.032949 Objective Loss 0.032949 MSE 0.032949 LR 0.001000 Time 0.095310 +2024-02-02 16:32:14,464 - Epoch: [38][ 5/ 8] Overall Loss 0.032931 Objective Loss 0.032931 MSE 0.032931 LR 0.001000 Time 0.077586 +2024-02-02 16:32:14,470 - Epoch: [38][ 6/ 8] Overall Loss 0.032779 Objective Loss 0.032779 MSE 0.032779 LR 0.001000 Time 0.065746 +2024-02-02 16:32:14,477 - Epoch: [38][ 7/ 8] Overall Loss 0.032574 Objective Loss 0.032574 MSE 0.032574 LR 0.001000 Time 0.057293 +2024-02-02 16:32:14,484 - Epoch: [38][ 8/ 8] Overall Loss 0.032403 Objective Loss 0.032403 MSE 0.032538 LR 0.001000 Time 0.050936 +2024-02-02 16:32:14,628 - --- validate (epoch=38)----------- +2024-02-02 16:32:14,629 - 60 samples (32 per mini-batch) +2024-02-02 16:32:14,973 - Epoch: [38][ 1/ 2] Loss 0.039153 MSE 0.039153 +2024-02-02 16:32:14,978 - Epoch: [38][ 2/ 2] Loss 0.037691 MSE 0.037789 +2024-02-02 16:32:15,125 - ==> MSE: 0.03779 Loss: 0.038 + +2024-02-02 16:32:15,128 - ==> Best [Top 1 (MSE): 0.03779 Sparsity:0.00 Params: 136448 on epoch: 38] +2024-02-02 16:32:15,128 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:15,134 - + +2024-02-02 16:32:15,134 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:15,498 - Epoch: [39][ 1/ 8] Overall Loss 0.031962 Objective Loss 0.031962 MSE 0.031962 LR 0.001000 Time 0.363481 +2024-02-02 16:32:15,505 - Epoch: [39][ 2/ 8] Overall Loss 0.031592 Objective Loss 0.031592 MSE 0.031592 LR 0.001000 Time 0.185197 +2024-02-02 16:32:15,512 - Epoch: [39][ 3/ 8] Overall Loss 0.030920 Objective Loss 0.030920 MSE 0.030920 LR 0.001000 Time 0.125692 +2024-02-02 16:32:15,519 - Epoch: [39][ 4/ 8] Overall Loss 0.032204 Objective Loss 0.032204 MSE 0.032204 LR 0.001000 Time 0.095915 +2024-02-02 16:32:15,526 - Epoch: [39][ 5/ 8] Overall Loss 0.032796 Objective Loss 0.032796 MSE 0.032796 LR 0.001000 Time 0.078072 +2024-02-02 16:32:15,532 - Epoch: [39][ 6/ 8] Overall Loss 0.033000 Objective Loss 0.033000 MSE 0.033000 LR 0.001000 Time 0.066154 +2024-02-02 16:32:15,539 - Epoch: [39][ 7/ 8] Overall Loss 0.032316 Objective Loss 0.032316 MSE 0.032316 LR 0.001000 Time 0.057636 +2024-02-02 16:32:15,546 - Epoch: [39][ 8/ 8] Overall Loss 0.033167 Objective Loss 0.033167 MSE 0.032493 LR 0.001000 Time 0.051256 +2024-02-02 16:32:15,693 - --- validate (epoch=39)----------- +2024-02-02 16:32:15,694 - 60 samples (32 per mini-batch) +2024-02-02 16:32:16,044 - Epoch: [39][ 1/ 2] Loss 0.038239 MSE 0.038239 +2024-02-02 16:32:16,049 - Epoch: [39][ 2/ 2] Loss 0.037535 MSE 0.037582 +2024-02-02 16:32:16,193 - ==> MSE: 0.03758 Loss: 0.038 + +2024-02-02 16:32:16,196 - ==> Best [Top 1 (MSE): 0.03758 Sparsity:0.00 Params: 136448 on epoch: 39] +2024-02-02 16:32:16,196 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:16,202 - + +2024-02-02 16:32:16,202 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:16,570 - Epoch: [40][ 1/ 8] Overall Loss 0.037418 Objective Loss 0.037418 MSE 0.037418 LR 0.001000 Time 0.367273 +2024-02-02 16:32:16,581 - Epoch: [40][ 2/ 8] Overall Loss 0.035167 Objective Loss 0.035167 MSE 0.035167 LR 0.001000 Time 0.188804 +2024-02-02 16:32:16,590 - Epoch: [40][ 3/ 8] Overall Loss 0.034055 Objective Loss 0.034055 MSE 0.034055 LR 0.001000 Time 0.128846 +2024-02-02 16:32:16,597 - Epoch: [40][ 4/ 8] Overall Loss 0.032906 Objective Loss 0.032906 MSE 0.032906 LR 0.001000 Time 0.098321 +2024-02-02 16:32:16,603 - Epoch: [40][ 5/ 8] Overall Loss 0.031912 Objective Loss 0.031912 MSE 0.031912 LR 0.001000 Time 0.079994 +2024-02-02 16:32:16,610 - Epoch: [40][ 6/ 8] Overall Loss 0.032078 Objective Loss 0.032078 MSE 0.032078 LR 0.001000 Time 0.067758 +2024-02-02 16:32:16,617 - Epoch: [40][ 7/ 8] Overall Loss 0.032300 Objective Loss 0.032300 MSE 0.032300 LR 0.001000 Time 0.059019 +2024-02-02 16:32:16,624 - Epoch: [40][ 8/ 8] Overall Loss 0.032274 Objective Loss 0.032274 MSE 0.032295 LR 0.001000 Time 0.052460 +2024-02-02 16:32:16,772 - --- validate (epoch=40)----------- +2024-02-02 16:32:16,772 - 60 samples (32 per mini-batch) +2024-02-02 16:32:17,116 - Epoch: [40][ 1/ 2] Loss 0.037427 MSE 0.037427 +2024-02-02 16:32:17,121 - Epoch: [40][ 2/ 2] Loss 0.037332 MSE 0.037338 +2024-02-02 16:32:17,270 - ==> MSE: 0.03734 Loss: 0.037 + +2024-02-02 16:32:17,272 - ==> Best [Top 1 (MSE): 0.03734 Sparsity:0.00 Params: 136448 on epoch: 40] +2024-02-02 16:32:17,273 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:17,278 - + +2024-02-02 16:32:17,278 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:17,643 - Epoch: [41][ 1/ 8] Overall Loss 0.031554 Objective Loss 0.031554 MSE 0.031554 LR 0.001000 Time 0.363842 +2024-02-02 16:32:17,650 - Epoch: [41][ 2/ 8] Overall Loss 0.032362 Objective Loss 0.032362 MSE 0.032362 LR 0.001000 Time 0.185397 +2024-02-02 16:32:17,657 - Epoch: [41][ 3/ 8] Overall Loss 0.032268 Objective Loss 0.032268 MSE 0.032268 LR 0.001000 Time 0.125822 +2024-02-02 16:32:17,663 - Epoch: [41][ 4/ 8] Overall Loss 0.031285 Objective Loss 0.031285 MSE 0.031285 LR 0.001000 Time 0.096001 +2024-02-02 16:32:17,670 - Epoch: [41][ 5/ 8] Overall Loss 0.031646 Objective Loss 0.031646 MSE 0.031646 LR 0.001000 Time 0.078109 +2024-02-02 16:32:17,677 - Epoch: [41][ 6/ 8] Overall Loss 0.031576 Objective Loss 0.031576 MSE 0.031576 LR 0.001000 Time 0.066177 +2024-02-02 16:32:17,684 - Epoch: [41][ 7/ 8] Overall Loss 0.031866 Objective Loss 0.031866 MSE 0.031866 LR 0.001000 Time 0.057679 +2024-02-02 16:32:17,690 - Epoch: [41][ 8/ 8] Overall Loss 0.033672 Objective Loss 0.033672 MSE 0.032243 LR 0.001000 Time 0.051286 +2024-02-02 16:32:17,834 - --- validate (epoch=41)----------- +2024-02-02 16:32:17,835 - 60 samples (32 per mini-batch) +2024-02-02 16:32:18,186 - Epoch: [41][ 1/ 2] Loss 0.034832 MSE 0.034832 +2024-02-02 16:32:18,190 - Epoch: [41][ 2/ 2] Loss 0.037362 MSE 0.037193 +2024-02-02 16:32:18,338 - ==> MSE: 0.03719 Loss: 0.037 + +2024-02-02 16:32:18,340 - ==> Best [Top 1 (MSE): 0.03719 Sparsity:0.00 Params: 136448 on epoch: 41] +2024-02-02 16:32:18,340 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:18,346 - + +2024-02-02 16:32:18,346 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:18,698 - Epoch: [42][ 1/ 8] Overall Loss 0.026557 Objective Loss 0.026557 MSE 0.026557 LR 0.001000 Time 0.351482 +2024-02-02 16:32:18,705 - Epoch: [42][ 2/ 8] Overall Loss 0.028598 Objective Loss 0.028598 MSE 0.028598 LR 0.001000 Time 0.179204 +2024-02-02 16:32:18,712 - Epoch: [42][ 3/ 8] Overall Loss 0.029476 Objective Loss 0.029476 MSE 0.029476 LR 0.001000 Time 0.121682 +2024-02-02 16:32:18,719 - Epoch: [42][ 4/ 8] Overall Loss 0.031260 Objective Loss 0.031260 MSE 0.031260 LR 0.001000 Time 0.092915 +2024-02-02 16:32:18,726 - Epoch: [42][ 5/ 8] Overall Loss 0.031132 Objective Loss 0.031132 MSE 0.031132 LR 0.001000 Time 0.075661 +2024-02-02 16:32:18,732 - Epoch: [42][ 6/ 8] Overall Loss 0.032458 Objective Loss 0.032458 MSE 0.032458 LR 0.001000 Time 0.064158 +2024-02-02 16:32:18,739 - Epoch: [42][ 7/ 8] Overall Loss 0.031863 Objective Loss 0.031863 MSE 0.031863 LR 0.001000 Time 0.055922 +2024-02-02 16:32:18,746 - Epoch: [42][ 8/ 8] Overall Loss 0.033198 Objective Loss 0.033198 MSE 0.032142 LR 0.001000 Time 0.049742 +2024-02-02 16:32:18,894 - --- validate (epoch=42)----------- +2024-02-02 16:32:18,895 - 60 samples (32 per mini-batch) +2024-02-02 16:32:19,249 - Epoch: [42][ 1/ 2] Loss 0.038602 MSE 0.038602 +2024-02-02 16:32:19,254 - Epoch: [42][ 2/ 2] Loss 0.037015 MSE 0.037120 +2024-02-02 16:32:19,401 - ==> MSE: 0.03712 Loss: 0.037 + +2024-02-02 16:32:19,404 - ==> Best [Top 1 (MSE): 0.03712 Sparsity:0.00 Params: 136448 on epoch: 42] +2024-02-02 16:32:19,404 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:19,410 - + +2024-02-02 16:32:19,410 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:19,771 - Epoch: [43][ 1/ 8] Overall Loss 0.034053 Objective Loss 0.034053 MSE 0.034053 LR 0.001000 Time 0.360617 +2024-02-02 16:32:19,778 - Epoch: [43][ 2/ 8] Overall Loss 0.031604 Objective Loss 0.031604 MSE 0.031604 LR 0.001000 Time 0.183755 +2024-02-02 16:32:19,785 - Epoch: [43][ 3/ 8] Overall Loss 0.033206 Objective Loss 0.033206 MSE 0.033206 LR 0.001000 Time 0.124696 +2024-02-02 16:32:19,791 - Epoch: [43][ 4/ 8] Overall Loss 0.032496 Objective Loss 0.032496 MSE 0.032496 LR 0.001000 Time 0.095122 +2024-02-02 16:32:19,798 - Epoch: [43][ 5/ 8] Overall Loss 0.031789 Objective Loss 0.031789 MSE 0.031789 LR 0.001000 Time 0.077391 +2024-02-02 16:32:19,804 - Epoch: [43][ 6/ 8] Overall Loss 0.032489 Objective Loss 0.032489 MSE 0.032489 LR 0.001000 Time 0.065571 +2024-02-02 16:32:19,811 - Epoch: [43][ 7/ 8] Overall Loss 0.032152 Objective Loss 0.032152 MSE 0.032152 LR 0.001000 Time 0.057137 +2024-02-02 16:32:19,818 - Epoch: [43][ 8/ 8] Overall Loss 0.030965 Objective Loss 0.030965 MSE 0.031904 LR 0.001000 Time 0.050813 +2024-02-02 16:32:19,967 - --- validate (epoch=43)----------- +2024-02-02 16:32:19,968 - 60 samples (32 per mini-batch) +2024-02-02 16:32:20,324 - Epoch: [43][ 1/ 2] Loss 0.036156 MSE 0.036156 +2024-02-02 16:32:20,328 - Epoch: [43][ 2/ 2] Loss 0.037363 MSE 0.037283 +2024-02-02 16:32:20,475 - ==> MSE: 0.03728 Loss: 0.037 + +2024-02-02 16:32:20,478 - ==> Best [Top 1 (MSE): 0.03712 Sparsity:0.00 Params: 136448 on epoch: 42] +2024-02-02 16:32:20,478 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:20,483 - + +2024-02-02 16:32:20,483 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:20,839 - Epoch: [44][ 1/ 8] Overall Loss 0.031284 Objective Loss 0.031284 MSE 0.031284 LR 0.001000 Time 0.355533 +2024-02-02 16:32:20,846 - Epoch: [44][ 2/ 8] Overall Loss 0.030649 Objective Loss 0.030649 MSE 0.030649 LR 0.001000 Time 0.181228 +2024-02-02 16:32:20,853 - Epoch: [44][ 3/ 8] Overall Loss 0.031932 Objective Loss 0.031932 MSE 0.031932 LR 0.001000 Time 0.123026 +2024-02-02 16:32:20,859 - Epoch: [44][ 4/ 8] Overall Loss 0.031815 Objective Loss 0.031815 MSE 0.031815 LR 0.001000 Time 0.093910 +2024-02-02 16:32:20,866 - Epoch: [44][ 5/ 8] Overall Loss 0.032884 Objective Loss 0.032884 MSE 0.032884 LR 0.001000 Time 0.076470 +2024-02-02 16:32:20,873 - Epoch: [44][ 6/ 8] Overall Loss 0.032249 Objective Loss 0.032249 MSE 0.032249 LR 0.001000 Time 0.064823 +2024-02-02 16:32:20,880 - Epoch: [44][ 7/ 8] Overall Loss 0.032001 Objective Loss 0.032001 MSE 0.032001 LR 0.001000 Time 0.056492 +2024-02-02 16:32:20,886 - Epoch: [44][ 8/ 8] Overall Loss 0.032162 Objective Loss 0.032162 MSE 0.032035 LR 0.001000 Time 0.050253 +2024-02-02 16:32:21,038 - --- validate (epoch=44)----------- +2024-02-02 16:32:21,039 - 60 samples (32 per mini-batch) +2024-02-02 16:32:21,390 - Epoch: [44][ 1/ 2] Loss 0.037967 MSE 0.037967 +2024-02-02 16:32:21,394 - Epoch: [44][ 2/ 2] Loss 0.037364 MSE 0.037405 +2024-02-02 16:32:21,528 - ==> MSE: 0.03740 Loss: 0.037 + +2024-02-02 16:32:21,531 - ==> Best [Top 1 (MSE): 0.03712 Sparsity:0.00 Params: 136448 on epoch: 42] +2024-02-02 16:32:21,531 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:21,535 - + +2024-02-02 16:32:21,536 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:21,880 - Epoch: [45][ 1/ 8] Overall Loss 0.032798 Objective Loss 0.032798 MSE 0.032798 LR 0.001000 Time 0.344546 +2024-02-02 16:32:21,888 - Epoch: [45][ 2/ 8] Overall Loss 0.032993 Objective Loss 0.032993 MSE 0.032993 LR 0.001000 Time 0.175757 +2024-02-02 16:32:21,894 - Epoch: [45][ 3/ 8] Overall Loss 0.033561 Objective Loss 0.033561 MSE 0.033561 LR 0.001000 Time 0.119404 +2024-02-02 16:32:21,901 - Epoch: [45][ 4/ 8] Overall Loss 0.032988 Objective Loss 0.032988 MSE 0.032988 LR 0.001000 Time 0.091186 +2024-02-02 16:32:21,908 - Epoch: [45][ 5/ 8] Overall Loss 0.031620 Objective Loss 0.031620 MSE 0.031620 LR 0.001000 Time 0.074326 +2024-02-02 16:32:21,915 - Epoch: [45][ 6/ 8] Overall Loss 0.031565 Objective Loss 0.031565 MSE 0.031565 LR 0.001000 Time 0.063064 +2024-02-02 16:32:21,922 - Epoch: [45][ 7/ 8] Overall Loss 0.031500 Objective Loss 0.031500 MSE 0.031500 LR 0.001000 Time 0.055011 +2024-02-02 16:32:21,929 - Epoch: [45][ 8/ 8] Overall Loss 0.031946 Objective Loss 0.031946 MSE 0.031593 LR 0.001000 Time 0.048966 +2024-02-02 16:32:22,069 - --- validate (epoch=45)----------- +2024-02-02 16:32:22,069 - 60 samples (32 per mini-batch) +2024-02-02 16:32:22,407 - Epoch: [45][ 1/ 2] Loss 0.039020 MSE 0.039020 +2024-02-02 16:32:22,412 - Epoch: [45][ 2/ 2] Loss 0.036658 MSE 0.036815 +2024-02-02 16:32:22,552 - ==> MSE: 0.03682 Loss: 0.037 + +2024-02-02 16:32:22,556 - ==> Best [Top 1 (MSE): 0.03682 Sparsity:0.00 Params: 136448 on epoch: 45] +2024-02-02 16:32:22,556 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:22,562 - + +2024-02-02 16:32:22,562 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:22,907 - Epoch: [46][ 1/ 8] Overall Loss 0.031692 Objective Loss 0.031692 MSE 0.031692 LR 0.001000 Time 0.344126 +2024-02-02 16:32:22,914 - Epoch: [46][ 2/ 8] Overall Loss 0.030724 Objective Loss 0.030724 MSE 0.030724 LR 0.001000 Time 0.175547 +2024-02-02 16:32:22,920 - Epoch: [46][ 3/ 8] Overall Loss 0.031488 Objective Loss 0.031488 MSE 0.031488 LR 0.001000 Time 0.119218 +2024-02-02 16:32:22,927 - Epoch: [46][ 4/ 8] Overall Loss 0.031077 Objective Loss 0.031077 MSE 0.031077 LR 0.001000 Time 0.091028 +2024-02-02 16:32:22,934 - Epoch: [46][ 5/ 8] Overall Loss 0.030726 Objective Loss 0.030726 MSE 0.030726 LR 0.001000 Time 0.074130 +2024-02-02 16:32:22,940 - Epoch: [46][ 6/ 8] Overall Loss 0.031589 Objective Loss 0.031589 MSE 0.031589 LR 0.001000 Time 0.062860 +2024-02-02 16:32:22,947 - Epoch: [46][ 7/ 8] Overall Loss 0.031205 Objective Loss 0.031205 MSE 0.031205 LR 0.001000 Time 0.054791 +2024-02-02 16:32:22,953 - Epoch: [46][ 8/ 8] Overall Loss 0.032068 Objective Loss 0.032068 MSE 0.031385 LR 0.001000 Time 0.048730 +2024-02-02 16:32:23,098 - --- validate (epoch=46)----------- +2024-02-02 16:32:23,098 - 60 samples (32 per mini-batch) +2024-02-02 16:32:23,438 - Epoch: [46][ 1/ 2] Loss 0.035990 MSE 0.035990 +2024-02-02 16:32:23,443 - Epoch: [46][ 2/ 2] Loss 0.037065 MSE 0.036994 +2024-02-02 16:32:23,580 - ==> MSE: 0.03699 Loss: 0.037 + +2024-02-02 16:32:23,584 - ==> Best [Top 1 (MSE): 0.03682 Sparsity:0.00 Params: 136448 on epoch: 45] +2024-02-02 16:32:23,584 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:23,589 - + +2024-02-02 16:32:23,589 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:23,943 - Epoch: [47][ 1/ 8] Overall Loss 0.032087 Objective Loss 0.032087 MSE 0.032087 LR 0.001000 Time 0.354213 +2024-02-02 16:32:23,952 - Epoch: [47][ 2/ 8] Overall Loss 0.031607 Objective Loss 0.031607 MSE 0.031607 LR 0.001000 Time 0.181213 +2024-02-02 16:32:23,959 - Epoch: [47][ 3/ 8] Overall Loss 0.030610 Objective Loss 0.030610 MSE 0.030610 LR 0.001000 Time 0.123044 +2024-02-02 16:32:23,965 - Epoch: [47][ 4/ 8] Overall Loss 0.031027 Objective Loss 0.031027 MSE 0.031027 LR 0.001000 Time 0.093906 +2024-02-02 16:32:23,972 - Epoch: [47][ 5/ 8] Overall Loss 0.030563 Objective Loss 0.030563 MSE 0.030563 LR 0.001000 Time 0.076435 +2024-02-02 16:32:23,979 - Epoch: [47][ 6/ 8] Overall Loss 0.030236 Objective Loss 0.030236 MSE 0.030236 LR 0.001000 Time 0.064769 +2024-02-02 16:32:23,985 - Epoch: [47][ 7/ 8] Overall Loss 0.030530 Objective Loss 0.030530 MSE 0.030530 LR 0.001000 Time 0.056433 +2024-02-02 16:32:23,992 - Epoch: [47][ 8/ 8] Overall Loss 0.032696 Objective Loss 0.032696 MSE 0.030982 LR 0.001000 Time 0.050194 +2024-02-02 16:32:24,146 - --- validate (epoch=47)----------- +2024-02-02 16:32:24,146 - 60 samples (32 per mini-batch) +2024-02-02 16:32:24,495 - Epoch: [47][ 1/ 2] Loss 0.035153 MSE 0.035153 +2024-02-02 16:32:24,499 - Epoch: [47][ 2/ 2] Loss 0.036313 MSE 0.036236 +2024-02-02 16:32:24,645 - ==> MSE: 0.03624 Loss: 0.036 + +2024-02-02 16:32:24,648 - ==> Best [Top 1 (MSE): 0.03624 Sparsity:0.00 Params: 136448 on epoch: 47] +2024-02-02 16:32:24,649 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:24,654 - + +2024-02-02 16:32:24,654 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:25,016 - Epoch: [48][ 1/ 8] Overall Loss 0.031728 Objective Loss 0.031728 MSE 0.031728 LR 0.001000 Time 0.360804 +2024-02-02 16:32:25,023 - Epoch: [48][ 2/ 8] Overall Loss 0.029442 Objective Loss 0.029442 MSE 0.029442 LR 0.001000 Time 0.183806 +2024-02-02 16:32:25,029 - Epoch: [48][ 3/ 8] Overall Loss 0.030718 Objective Loss 0.030718 MSE 0.030718 LR 0.001000 Time 0.124710 +2024-02-02 16:32:25,036 - Epoch: [48][ 4/ 8] Overall Loss 0.030862 Objective Loss 0.030862 MSE 0.030862 LR 0.001000 Time 0.095202 +2024-02-02 16:32:25,043 - Epoch: [48][ 5/ 8] Overall Loss 0.030981 Objective Loss 0.030981 MSE 0.030981 LR 0.001000 Time 0.077488 +2024-02-02 16:32:25,051 - Epoch: [48][ 6/ 8] Overall Loss 0.030908 Objective Loss 0.030908 MSE 0.030908 LR 0.001000 Time 0.065941 +2024-02-02 16:32:25,062 - Epoch: [48][ 7/ 8] Overall Loss 0.030615 Objective Loss 0.030615 MSE 0.030615 LR 0.001000 Time 0.058023 +2024-02-02 16:32:25,073 - Epoch: [48][ 8/ 8] Overall Loss 0.031154 Objective Loss 0.031154 MSE 0.030728 LR 0.001000 Time 0.052052 +2024-02-02 16:32:25,225 - --- validate (epoch=48)----------- +2024-02-02 16:32:25,225 - 60 samples (32 per mini-batch) +2024-02-02 16:32:25,576 - Epoch: [48][ 1/ 2] Loss 0.034201 MSE 0.034201 +2024-02-02 16:32:25,581 - Epoch: [48][ 2/ 2] Loss 0.035899 MSE 0.035786 +2024-02-02 16:32:25,728 - ==> MSE: 0.03579 Loss: 0.036 + +2024-02-02 16:32:25,732 - ==> Best [Top 1 (MSE): 0.03579 Sparsity:0.00 Params: 136448 on epoch: 48] +2024-02-02 16:32:25,732 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:25,738 - + +2024-02-02 16:32:25,738 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:26,092 - Epoch: [49][ 1/ 8] Overall Loss 0.030195 Objective Loss 0.030195 MSE 0.030195 LR 0.001000 Time 0.353412 +2024-02-02 16:32:26,103 - Epoch: [49][ 2/ 8] Overall Loss 0.028739 Objective Loss 0.028739 MSE 0.028739 LR 0.001000 Time 0.181756 +2024-02-02 16:32:26,113 - Epoch: [49][ 3/ 8] Overall Loss 0.028137 Objective Loss 0.028137 MSE 0.028137 LR 0.001000 Time 0.124532 +2024-02-02 16:32:26,121 - Epoch: [49][ 4/ 8] Overall Loss 0.029855 Objective Loss 0.029855 MSE 0.029855 LR 0.001000 Time 0.095317 +2024-02-02 16:32:26,128 - Epoch: [49][ 5/ 8] Overall Loss 0.030236 Objective Loss 0.030236 MSE 0.030236 LR 0.001000 Time 0.077634 +2024-02-02 16:32:26,135 - Epoch: [49][ 6/ 8] Overall Loss 0.030078 Objective Loss 0.030078 MSE 0.030078 LR 0.001000 Time 0.065788 +2024-02-02 16:32:26,141 - Epoch: [49][ 7/ 8] Overall Loss 0.030178 Objective Loss 0.030178 MSE 0.030178 LR 0.001000 Time 0.057319 +2024-02-02 16:32:26,148 - Epoch: [49][ 8/ 8] Overall Loss 0.031980 Objective Loss 0.031980 MSE 0.030554 LR 0.001000 Time 0.050965 +2024-02-02 16:32:26,299 - --- validate (epoch=49)----------- +2024-02-02 16:32:26,299 - 60 samples (32 per mini-batch) +2024-02-02 16:32:26,654 - Epoch: [49][ 1/ 2] Loss 0.033783 MSE 0.033783 +2024-02-02 16:32:26,661 - Epoch: [49][ 2/ 2] Loss 0.035879 MSE 0.035739 +2024-02-02 16:32:26,807 - ==> MSE: 0.03574 Loss: 0.036 + +2024-02-02 16:32:26,809 - ==> Best [Top 1 (MSE): 0.03574 Sparsity:0.00 Params: 136448 on epoch: 49] +2024-02-02 16:32:26,809 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:26,815 - + +2024-02-02 16:32:26,815 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:27,166 - Epoch: [50][ 1/ 8] Overall Loss 0.028630 Objective Loss 0.028630 MSE 0.028630 LR 0.001000 Time 0.349982 +2024-02-02 16:32:27,174 - Epoch: [50][ 2/ 8] Overall Loss 0.030282 Objective Loss 0.030282 MSE 0.030282 LR 0.001000 Time 0.179079 +2024-02-02 16:32:27,182 - Epoch: [50][ 3/ 8] Overall Loss 0.030269 Objective Loss 0.030269 MSE 0.030269 LR 0.001000 Time 0.122008 +2024-02-02 16:32:27,189 - Epoch: [50][ 4/ 8] Overall Loss 0.029800 Objective Loss 0.029800 MSE 0.029800 LR 0.001000 Time 0.093141 +2024-02-02 16:32:27,196 - Epoch: [50][ 5/ 8] Overall Loss 0.030224 Objective Loss 0.030224 MSE 0.030224 LR 0.001000 Time 0.075804 +2024-02-02 16:32:27,202 - Epoch: [50][ 6/ 8] Overall Loss 0.030382 Objective Loss 0.030382 MSE 0.030382 LR 0.001000 Time 0.064254 +2024-02-02 16:32:27,209 - Epoch: [50][ 7/ 8] Overall Loss 0.029985 Objective Loss 0.029985 MSE 0.029985 LR 0.001000 Time 0.055999 +2024-02-02 16:32:27,215 - Epoch: [50][ 8/ 8] Overall Loss 0.030458 Objective Loss 0.030458 MSE 0.030084 LR 0.001000 Time 0.049799 +2024-02-02 16:32:27,366 - --- validate (epoch=50)----------- +2024-02-02 16:32:27,366 - 60 samples (32 per mini-batch) +2024-02-02 16:32:27,718 - Epoch: [50][ 1/ 2] Loss 0.031958 MSE 0.031958 +2024-02-02 16:32:27,722 - Epoch: [50][ 2/ 2] Loss 0.035393 MSE 0.035164 +2024-02-02 16:32:27,873 - ==> MSE: 0.03516 Loss: 0.035 + +2024-02-02 16:32:27,877 - ==> Best [Top 1 (MSE): 0.03516 Sparsity:0.00 Params: 136448 on epoch: 50] +2024-02-02 16:32:27,877 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:27,883 - + +2024-02-02 16:32:27,883 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:28,238 - Epoch: [51][ 1/ 8] Overall Loss 0.028884 Objective Loss 0.028884 MSE 0.028884 LR 0.001000 Time 0.354704 +2024-02-02 16:32:28,245 - Epoch: [51][ 2/ 8] Overall Loss 0.028322 Objective Loss 0.028322 MSE 0.028322 LR 0.001000 Time 0.180728 +2024-02-02 16:32:28,251 - Epoch: [51][ 3/ 8] Overall Loss 0.027389 Objective Loss 0.027389 MSE 0.027389 LR 0.001000 Time 0.122649 +2024-02-02 16:32:28,258 - Epoch: [51][ 4/ 8] Overall Loss 0.027874 Objective Loss 0.027874 MSE 0.027874 LR 0.001000 Time 0.093613 +2024-02-02 16:32:28,265 - Epoch: [51][ 5/ 8] Overall Loss 0.027955 Objective Loss 0.027955 MSE 0.027955 LR 0.001000 Time 0.076225 +2024-02-02 16:32:28,273 - Epoch: [51][ 6/ 8] Overall Loss 0.029208 Objective Loss 0.029208 MSE 0.029208 LR 0.001000 Time 0.064789 +2024-02-02 16:32:28,283 - Epoch: [51][ 7/ 8] Overall Loss 0.029720 Objective Loss 0.029720 MSE 0.029720 LR 0.001000 Time 0.056985 +2024-02-02 16:32:28,294 - Epoch: [51][ 8/ 8] Overall Loss 0.031558 Objective Loss 0.031558 MSE 0.030104 LR 0.001000 Time 0.051156 +2024-02-02 16:32:28,446 - --- validate (epoch=51)----------- +2024-02-02 16:32:28,446 - 60 samples (32 per mini-batch) +2024-02-02 16:32:28,805 - Epoch: [51][ 1/ 2] Loss 0.034608 MSE 0.034608 +2024-02-02 16:32:28,810 - Epoch: [51][ 2/ 2] Loss 0.034029 MSE 0.034067 +2024-02-02 16:32:28,953 - ==> MSE: 0.03407 Loss: 0.034 + +2024-02-02 16:32:28,957 - ==> Best [Top 1 (MSE): 0.03407 Sparsity:0.00 Params: 136448 on epoch: 51] +2024-02-02 16:32:28,957 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:28,962 - + +2024-02-02 16:32:28,962 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:29,325 - Epoch: [52][ 1/ 8] Overall Loss 0.031774 Objective Loss 0.031774 MSE 0.031774 LR 0.001000 Time 0.362411 +2024-02-02 16:32:29,336 - Epoch: [52][ 2/ 8] Overall Loss 0.030642 Objective Loss 0.030642 MSE 0.030642 LR 0.001000 Time 0.186224 +2024-02-02 16:32:29,345 - Epoch: [52][ 3/ 8] Overall Loss 0.029523 Objective Loss 0.029523 MSE 0.029523 LR 0.001000 Time 0.127103 +2024-02-02 16:32:29,352 - Epoch: [52][ 4/ 8] Overall Loss 0.029534 Objective Loss 0.029534 MSE 0.029534 LR 0.001000 Time 0.097173 +2024-02-02 16:32:29,359 - Epoch: [52][ 5/ 8] Overall Loss 0.030389 Objective Loss 0.030389 MSE 0.030389 LR 0.001000 Time 0.079035 +2024-02-02 16:32:29,366 - Epoch: [52][ 6/ 8] Overall Loss 0.029235 Objective Loss 0.029235 MSE 0.029235 LR 0.001000 Time 0.067035 +2024-02-02 16:32:29,373 - Epoch: [52][ 7/ 8] Overall Loss 0.028952 Objective Loss 0.028952 MSE 0.028952 LR 0.001000 Time 0.058412 +2024-02-02 16:32:29,381 - Epoch: [52][ 8/ 8] Overall Loss 0.029241 Objective Loss 0.029241 MSE 0.029012 LR 0.001000 Time 0.052053 +2024-02-02 16:32:29,534 - --- validate (epoch=52)----------- +2024-02-02 16:32:29,534 - 60 samples (32 per mini-batch) +2024-02-02 16:32:29,896 - Epoch: [52][ 1/ 2] Loss 0.031948 MSE 0.031948 +2024-02-02 16:32:29,900 - Epoch: [52][ 2/ 2] Loss 0.033654 MSE 0.033540 +2024-02-02 16:32:30,047 - ==> MSE: 0.03354 Loss: 0.034 + +2024-02-02 16:32:30,050 - ==> Best [Top 1 (MSE): 0.03354 Sparsity:0.00 Params: 136448 on epoch: 52] +2024-02-02 16:32:30,050 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:30,056 - + +2024-02-02 16:32:30,056 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:30,419 - Epoch: [53][ 1/ 8] Overall Loss 0.028894 Objective Loss 0.028894 MSE 0.028894 LR 0.001000 Time 0.362440 +2024-02-02 16:32:30,426 - Epoch: [53][ 2/ 8] Overall Loss 0.029588 Objective Loss 0.029588 MSE 0.029588 LR 0.001000 Time 0.184577 +2024-02-02 16:32:30,433 - Epoch: [53][ 3/ 8] Overall Loss 0.030976 Objective Loss 0.030976 MSE 0.030976 LR 0.001000 Time 0.125226 +2024-02-02 16:32:30,439 - Epoch: [53][ 4/ 8] Overall Loss 0.030462 Objective Loss 0.030462 MSE 0.030462 LR 0.001000 Time 0.095526 +2024-02-02 16:32:30,446 - Epoch: [53][ 5/ 8] Overall Loss 0.029493 Objective Loss 0.029493 MSE 0.029493 LR 0.001000 Time 0.077723 +2024-02-02 16:32:30,452 - Epoch: [53][ 6/ 8] Overall Loss 0.029091 Objective Loss 0.029091 MSE 0.029091 LR 0.001000 Time 0.065849 +2024-02-02 16:32:30,459 - Epoch: [53][ 7/ 8] Overall Loss 0.028841 Objective Loss 0.028841 MSE 0.028841 LR 0.001000 Time 0.057378 +2024-02-02 16:32:30,466 - Epoch: [53][ 8/ 8] Overall Loss 0.030369 Objective Loss 0.030369 MSE 0.029160 LR 0.001000 Time 0.051021 +2024-02-02 16:32:30,613 - --- validate (epoch=53)----------- +2024-02-02 16:32:30,614 - 60 samples (32 per mini-batch) +2024-02-02 16:32:30,964 - Epoch: [53][ 1/ 2] Loss 0.033244 MSE 0.033244 +2024-02-02 16:32:30,969 - Epoch: [53][ 2/ 2] Loss 0.032583 MSE 0.032627 +2024-02-02 16:32:31,126 - ==> MSE: 0.03263 Loss: 0.033 + +2024-02-02 16:32:31,131 - ==> Best [Top 1 (MSE): 0.03263 Sparsity:0.00 Params: 136448 on epoch: 53] +2024-02-02 16:32:31,131 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:31,137 - + +2024-02-02 16:32:31,137 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:31,497 - Epoch: [54][ 1/ 8] Overall Loss 0.026007 Objective Loss 0.026007 MSE 0.026007 LR 0.001000 Time 0.359367 +2024-02-02 16:32:31,505 - Epoch: [54][ 2/ 8] Overall Loss 0.025499 Objective Loss 0.025499 MSE 0.025499 LR 0.001000 Time 0.183673 +2024-02-02 16:32:31,512 - Epoch: [54][ 3/ 8] Overall Loss 0.026964 Objective Loss 0.026964 MSE 0.026964 LR 0.001000 Time 0.124669 +2024-02-02 16:32:31,519 - Epoch: [54][ 4/ 8] Overall Loss 0.027752 Objective Loss 0.027752 MSE 0.027752 LR 0.001000 Time 0.095159 +2024-02-02 16:32:31,526 - Epoch: [54][ 5/ 8] Overall Loss 0.027976 Objective Loss 0.027976 MSE 0.027976 LR 0.001000 Time 0.077478 +2024-02-02 16:32:31,533 - Epoch: [54][ 6/ 8] Overall Loss 0.028367 Objective Loss 0.028367 MSE 0.028367 LR 0.001000 Time 0.065664 +2024-02-02 16:32:31,539 - Epoch: [54][ 7/ 8] Overall Loss 0.028180 Objective Loss 0.028180 MSE 0.028180 LR 0.001000 Time 0.057206 +2024-02-02 16:32:31,546 - Epoch: [54][ 8/ 8] Overall Loss 0.028541 Objective Loss 0.028541 MSE 0.028256 LR 0.001000 Time 0.050853 +2024-02-02 16:32:31,698 - --- validate (epoch=54)----------- +2024-02-02 16:32:31,699 - 60 samples (32 per mini-batch) +2024-02-02 16:32:32,052 - Epoch: [54][ 1/ 2] Loss 0.030141 MSE 0.030141 +2024-02-02 16:32:32,057 - Epoch: [54][ 2/ 2] Loss 0.032078 MSE 0.031949 +2024-02-02 16:32:32,209 - ==> MSE: 0.03195 Loss: 0.032 + +2024-02-02 16:32:32,212 - ==> Best [Top 1 (MSE): 0.03195 Sparsity:0.00 Params: 136448 on epoch: 54] +2024-02-02 16:32:32,213 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:32,219 - + +2024-02-02 16:32:32,219 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:32,579 - Epoch: [55][ 1/ 8] Overall Loss 0.028116 Objective Loss 0.028116 MSE 0.028116 LR 0.001000 Time 0.360165 +2024-02-02 16:32:32,586 - Epoch: [55][ 2/ 8] Overall Loss 0.027729 Objective Loss 0.027729 MSE 0.027729 LR 0.001000 Time 0.183545 +2024-02-02 16:32:32,593 - Epoch: [55][ 3/ 8] Overall Loss 0.028122 Objective Loss 0.028122 MSE 0.028122 LR 0.001000 Time 0.124594 +2024-02-02 16:32:32,600 - Epoch: [55][ 4/ 8] Overall Loss 0.028198 Objective Loss 0.028198 MSE 0.028198 LR 0.001000 Time 0.095082 +2024-02-02 16:32:32,607 - Epoch: [55][ 5/ 8] Overall Loss 0.028108 Objective Loss 0.028108 MSE 0.028108 LR 0.001000 Time 0.077369 +2024-02-02 16:32:32,613 - Epoch: [55][ 6/ 8] Overall Loss 0.028478 Objective Loss 0.028478 MSE 0.028478 LR 0.001000 Time 0.065564 +2024-02-02 16:32:32,620 - Epoch: [55][ 7/ 8] Overall Loss 0.027792 Objective Loss 0.027792 MSE 0.027792 LR 0.001000 Time 0.057172 +2024-02-02 16:32:32,627 - Epoch: [55][ 8/ 8] Overall Loss 0.028291 Objective Loss 0.028291 MSE 0.027896 LR 0.001000 Time 0.050844 +2024-02-02 16:32:32,769 - --- validate (epoch=55)----------- +2024-02-02 16:32:32,769 - 60 samples (32 per mini-batch) +2024-02-02 16:32:33,103 - Epoch: [55][ 1/ 2] Loss 0.030216 MSE 0.030216 +2024-02-02 16:32:33,107 - Epoch: [55][ 2/ 2] Loss 0.031454 MSE 0.031371 +2024-02-02 16:32:33,251 - ==> MSE: 0.03137 Loss: 0.031 + +2024-02-02 16:32:33,253 - ==> Best [Top 1 (MSE): 0.03137 Sparsity:0.00 Params: 136448 on epoch: 55] +2024-02-02 16:32:33,254 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:33,259 - + +2024-02-02 16:32:33,259 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:33,601 - Epoch: [56][ 1/ 8] Overall Loss 0.032104 Objective Loss 0.032104 MSE 0.032104 LR 0.001000 Time 0.341538 +2024-02-02 16:32:33,608 - Epoch: [56][ 2/ 8] Overall Loss 0.028133 Objective Loss 0.028133 MSE 0.028133 LR 0.001000 Time 0.174195 +2024-02-02 16:32:33,615 - Epoch: [56][ 3/ 8] Overall Loss 0.029222 Objective Loss 0.029222 MSE 0.029222 LR 0.001000 Time 0.118337 +2024-02-02 16:32:33,622 - Epoch: [56][ 4/ 8] Overall Loss 0.029423 Objective Loss 0.029423 MSE 0.029423 LR 0.001000 Time 0.090486 +2024-02-02 16:32:33,629 - Epoch: [56][ 5/ 8] Overall Loss 0.028386 Objective Loss 0.028386 MSE 0.028386 LR 0.001000 Time 0.073692 +2024-02-02 16:32:33,636 - Epoch: [56][ 6/ 8] Overall Loss 0.028153 Objective Loss 0.028153 MSE 0.028153 LR 0.001000 Time 0.062502 +2024-02-02 16:32:33,643 - Epoch: [56][ 7/ 8] Overall Loss 0.027658 Objective Loss 0.027658 MSE 0.027658 LR 0.001000 Time 0.054525 +2024-02-02 16:32:33,649 - Epoch: [56][ 8/ 8] Overall Loss 0.027024 Objective Loss 0.027024 MSE 0.027526 LR 0.001000 Time 0.048504 +2024-02-02 16:32:33,798 - --- validate (epoch=56)----------- +2024-02-02 16:32:33,798 - 60 samples (32 per mini-batch) +2024-02-02 16:32:34,159 - Epoch: [56][ 1/ 2] Loss 0.035433 MSE 0.035433 +2024-02-02 16:32:34,164 - Epoch: [56][ 2/ 2] Loss 0.031032 MSE 0.031326 +2024-02-02 16:32:34,310 - ==> MSE: 0.03133 Loss: 0.031 + +2024-02-02 16:32:34,313 - ==> Best [Top 1 (MSE): 0.03133 Sparsity:0.00 Params: 136448 on epoch: 56] +2024-02-02 16:32:34,313 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:34,319 - + +2024-02-02 16:32:34,319 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:34,687 - Epoch: [57][ 1/ 8] Overall Loss 0.028594 Objective Loss 0.028594 MSE 0.028594 LR 0.001000 Time 0.366964 +2024-02-02 16:32:34,694 - Epoch: [57][ 2/ 8] Overall Loss 0.029231 Objective Loss 0.029231 MSE 0.029231 LR 0.001000 Time 0.187030 +2024-02-02 16:32:34,701 - Epoch: [57][ 3/ 8] Overall Loss 0.028465 Objective Loss 0.028465 MSE 0.028465 LR 0.001000 Time 0.126891 +2024-02-02 16:32:34,708 - Epoch: [57][ 4/ 8] Overall Loss 0.027973 Objective Loss 0.027973 MSE 0.027973 LR 0.001000 Time 0.096821 +2024-02-02 16:32:34,715 - Epoch: [57][ 5/ 8] Overall Loss 0.027577 Objective Loss 0.027577 MSE 0.027577 LR 0.001000 Time 0.078799 +2024-02-02 16:32:34,721 - Epoch: [57][ 6/ 8] Overall Loss 0.026875 Objective Loss 0.026875 MSE 0.026875 LR 0.001000 Time 0.066750 +2024-02-02 16:32:34,728 - Epoch: [57][ 7/ 8] Overall Loss 0.026769 Objective Loss 0.026769 MSE 0.026769 LR 0.001000 Time 0.058145 +2024-02-02 16:32:34,735 - Epoch: [57][ 8/ 8] Overall Loss 0.027036 Objective Loss 0.027036 MSE 0.026825 LR 0.001000 Time 0.051688 +2024-02-02 16:32:34,888 - --- validate (epoch=57)----------- +2024-02-02 16:32:34,888 - 60 samples (32 per mini-batch) +2024-02-02 16:32:35,244 - Epoch: [57][ 1/ 2] Loss 0.034785 MSE 0.034785 +2024-02-02 16:32:35,248 - Epoch: [57][ 2/ 2] Loss 0.030667 MSE 0.030941 +2024-02-02 16:32:35,390 - ==> MSE: 0.03094 Loss: 0.031 + +2024-02-02 16:32:35,393 - ==> Best [Top 1 (MSE): 0.03094 Sparsity:0.00 Params: 136448 on epoch: 57] +2024-02-02 16:32:35,393 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:35,399 - + +2024-02-02 16:32:35,399 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:35,763 - Epoch: [58][ 1/ 8] Overall Loss 0.024011 Objective Loss 0.024011 MSE 0.024011 LR 0.001000 Time 0.363402 +2024-02-02 16:32:35,773 - Epoch: [58][ 2/ 8] Overall Loss 0.025585 Objective Loss 0.025585 MSE 0.025585 LR 0.001000 Time 0.186360 +2024-02-02 16:32:35,780 - Epoch: [58][ 3/ 8] Overall Loss 0.026662 Objective Loss 0.026662 MSE 0.026662 LR 0.001000 Time 0.126409 +2024-02-02 16:32:35,786 - Epoch: [58][ 4/ 8] Overall Loss 0.026383 Objective Loss 0.026383 MSE 0.026383 LR 0.001000 Time 0.096457 +2024-02-02 16:32:35,793 - Epoch: [58][ 5/ 8] Overall Loss 0.026950 Objective Loss 0.026950 MSE 0.026950 LR 0.001000 Time 0.078489 +2024-02-02 16:32:35,800 - Epoch: [58][ 6/ 8] Overall Loss 0.026317 Objective Loss 0.026317 MSE 0.026317 LR 0.001000 Time 0.066507 +2024-02-02 16:32:35,807 - Epoch: [58][ 7/ 8] Overall Loss 0.026396 Objective Loss 0.026396 MSE 0.026396 LR 0.001000 Time 0.057966 +2024-02-02 16:32:35,813 - Epoch: [58][ 8/ 8] Overall Loss 0.026539 Objective Loss 0.026539 MSE 0.026426 LR 0.001000 Time 0.051524 +2024-02-02 16:32:35,964 - --- validate (epoch=58)----------- +2024-02-02 16:32:35,964 - 60 samples (32 per mini-batch) +2024-02-02 16:32:36,321 - Epoch: [58][ 1/ 2] Loss 0.033284 MSE 0.033284 +2024-02-02 16:32:36,325 - Epoch: [58][ 2/ 2] Loss 0.030323 MSE 0.030521 +2024-02-02 16:32:36,463 - ==> MSE: 0.03052 Loss: 0.030 + +2024-02-02 16:32:36,468 - ==> Best [Top 1 (MSE): 0.03052 Sparsity:0.00 Params: 136448 on epoch: 58] +2024-02-02 16:32:36,468 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:36,474 - + +2024-02-02 16:32:36,474 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:36,832 - Epoch: [59][ 1/ 8] Overall Loss 0.024298 Objective Loss 0.024298 MSE 0.024298 LR 0.001000 Time 0.357310 +2024-02-02 16:32:36,841 - Epoch: [59][ 2/ 8] Overall Loss 0.022654 Objective Loss 0.022654 MSE 0.022654 LR 0.001000 Time 0.183072 +2024-02-02 16:32:36,848 - Epoch: [59][ 3/ 8] Overall Loss 0.023768 Objective Loss 0.023768 MSE 0.023768 LR 0.001000 Time 0.124232 +2024-02-02 16:32:36,854 - Epoch: [59][ 4/ 8] Overall Loss 0.024687 Objective Loss 0.024687 MSE 0.024687 LR 0.001000 Time 0.094793 +2024-02-02 16:32:36,861 - Epoch: [59][ 5/ 8] Overall Loss 0.025287 Objective Loss 0.025287 MSE 0.025287 LR 0.001000 Time 0.077133 +2024-02-02 16:32:36,868 - Epoch: [59][ 6/ 8] Overall Loss 0.026077 Objective Loss 0.026077 MSE 0.026077 LR 0.001000 Time 0.065367 +2024-02-02 16:32:36,874 - Epoch: [59][ 7/ 8] Overall Loss 0.026294 Objective Loss 0.026294 MSE 0.026294 LR 0.001000 Time 0.056985 +2024-02-02 16:32:36,881 - Epoch: [59][ 8/ 8] Overall Loss 0.029618 Objective Loss 0.029618 MSE 0.026988 LR 0.001000 Time 0.050674 +2024-02-02 16:32:37,023 - --- validate (epoch=59)----------- +2024-02-02 16:32:37,023 - 60 samples (32 per mini-batch) +2024-02-02 16:32:37,365 - Epoch: [59][ 1/ 2] Loss 0.032059 MSE 0.032059 +2024-02-02 16:32:37,370 - Epoch: [59][ 2/ 2] Loss 0.030268 MSE 0.030388 +2024-02-02 16:32:37,504 - ==> MSE: 0.03039 Loss: 0.030 + +2024-02-02 16:32:37,508 - ==> Best [Top 1 (MSE): 0.03039 Sparsity:0.00 Params: 136448 on epoch: 59] +2024-02-02 16:32:37,508 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:37,514 - + +2024-02-02 16:32:37,514 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:37,849 - Epoch: [60][ 1/ 8] Overall Loss 0.024562 Objective Loss 0.024562 MSE 0.024562 LR 0.001000 Time 0.334092 +2024-02-02 16:32:37,859 - Epoch: [60][ 2/ 8] Overall Loss 0.025896 Objective Loss 0.025896 MSE 0.025896 LR 0.001000 Time 0.172125 +2024-02-02 16:32:37,869 - Epoch: [60][ 3/ 8] Overall Loss 0.025865 Objective Loss 0.025865 MSE 0.025865 LR 0.001000 Time 0.118044 +2024-02-02 16:32:37,879 - Epoch: [60][ 4/ 8] Overall Loss 0.025242 Objective Loss 0.025242 MSE 0.025242 LR 0.001000 Time 0.090905 +2024-02-02 16:32:37,886 - Epoch: [60][ 5/ 8] Overall Loss 0.025749 Objective Loss 0.025749 MSE 0.025749 LR 0.001000 Time 0.074061 +2024-02-02 16:32:37,893 - Epoch: [60][ 6/ 8] Overall Loss 0.025563 Objective Loss 0.025563 MSE 0.025563 LR 0.001000 Time 0.062837 +2024-02-02 16:32:37,899 - Epoch: [60][ 7/ 8] Overall Loss 0.026054 Objective Loss 0.026054 MSE 0.026054 LR 0.001000 Time 0.054808 +2024-02-02 16:32:37,906 - Epoch: [60][ 8/ 8] Overall Loss 0.027019 Objective Loss 0.027019 MSE 0.026256 LR 0.001000 Time 0.048773 +2024-02-02 16:32:38,055 - --- validate (epoch=60)----------- +2024-02-02 16:32:38,055 - 60 samples (32 per mini-batch) +2024-02-02 16:32:38,421 - Epoch: [60][ 1/ 2] Loss 0.029475 MSE 0.029475 +2024-02-02 16:32:38,426 - Epoch: [60][ 2/ 2] Loss 0.030832 MSE 0.030741 +2024-02-02 16:32:38,573 - ==> MSE: 0.03074 Loss: 0.031 + +2024-02-02 16:32:38,577 - ==> Best [Top 1 (MSE): 0.03039 Sparsity:0.00 Params: 136448 on epoch: 59] +2024-02-02 16:32:38,577 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:38,582 - + +2024-02-02 16:32:38,582 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:38,947 - Epoch: [61][ 1/ 8] Overall Loss 0.026162 Objective Loss 0.026162 MSE 0.026162 LR 0.001000 Time 0.365068 +2024-02-02 16:32:38,958 - Epoch: [61][ 2/ 8] Overall Loss 0.026545 Objective Loss 0.026545 MSE 0.026545 LR 0.001000 Time 0.187722 +2024-02-02 16:32:38,968 - Epoch: [61][ 3/ 8] Overall Loss 0.026250 Objective Loss 0.026250 MSE 0.026250 LR 0.001000 Time 0.128549 +2024-02-02 16:32:38,979 - Epoch: [61][ 4/ 8] Overall Loss 0.026250 Objective Loss 0.026250 MSE 0.026250 LR 0.001000 Time 0.099094 +2024-02-02 16:32:38,990 - Epoch: [61][ 5/ 8] Overall Loss 0.026431 Objective Loss 0.026431 MSE 0.026431 LR 0.001000 Time 0.081437 +2024-02-02 16:32:39,001 - Epoch: [61][ 6/ 8] Overall Loss 0.026399 Objective Loss 0.026399 MSE 0.026399 LR 0.001000 Time 0.069640 +2024-02-02 16:32:39,013 - Epoch: [61][ 7/ 8] Overall Loss 0.025905 Objective Loss 0.025905 MSE 0.025905 LR 0.001000 Time 0.061250 +2024-02-02 16:32:39,024 - Epoch: [61][ 8/ 8] Overall Loss 0.026634 Objective Loss 0.026634 MSE 0.026057 LR 0.001000 Time 0.054936 +2024-02-02 16:32:39,172 - --- validate (epoch=61)----------- +2024-02-02 16:32:39,173 - 60 samples (32 per mini-batch) +2024-02-02 16:32:39,538 - Epoch: [61][ 1/ 2] Loss 0.031504 MSE 0.031504 +2024-02-02 16:32:39,543 - Epoch: [61][ 2/ 2] Loss 0.029921 MSE 0.030027 +2024-02-02 16:32:39,686 - ==> MSE: 0.03003 Loss: 0.030 + +2024-02-02 16:32:39,689 - ==> Best [Top 1 (MSE): 0.03003 Sparsity:0.00 Params: 136448 on epoch: 61] +2024-02-02 16:32:39,690 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:39,695 - + +2024-02-02 16:32:39,695 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:40,055 - Epoch: [62][ 1/ 8] Overall Loss 0.027297 Objective Loss 0.027297 MSE 0.027297 LR 0.001000 Time 0.359011 +2024-02-02 16:32:40,064 - Epoch: [62][ 2/ 8] Overall Loss 0.026022 Objective Loss 0.026022 MSE 0.026022 LR 0.001000 Time 0.184202 +2024-02-02 16:32:40,073 - Epoch: [62][ 3/ 8] Overall Loss 0.025984 Objective Loss 0.025984 MSE 0.025984 LR 0.001000 Time 0.125726 +2024-02-02 16:32:40,083 - Epoch: [62][ 4/ 8] Overall Loss 0.025733 Objective Loss 0.025733 MSE 0.025733 LR 0.001000 Time 0.096691 +2024-02-02 16:32:40,090 - Epoch: [62][ 5/ 8] Overall Loss 0.024983 Objective Loss 0.024983 MSE 0.024983 LR 0.001000 Time 0.078701 +2024-02-02 16:32:40,097 - Epoch: [62][ 6/ 8] Overall Loss 0.025620 Objective Loss 0.025620 MSE 0.025620 LR 0.001000 Time 0.066724 +2024-02-02 16:32:40,104 - Epoch: [62][ 7/ 8] Overall Loss 0.025997 Objective Loss 0.025997 MSE 0.025997 LR 0.001000 Time 0.058169 +2024-02-02 16:32:40,111 - Epoch: [62][ 8/ 8] Overall Loss 0.027332 Objective Loss 0.027332 MSE 0.026275 LR 0.001000 Time 0.051726 +2024-02-02 16:32:40,263 - --- validate (epoch=62)----------- +2024-02-02 16:32:40,263 - 60 samples (32 per mini-batch) +2024-02-02 16:32:40,628 - Epoch: [62][ 1/ 2] Loss 0.029915 MSE 0.029915 +2024-02-02 16:32:40,632 - Epoch: [62][ 2/ 2] Loss 0.030276 MSE 0.030252 +2024-02-02 16:32:40,771 - ==> MSE: 0.03025 Loss: 0.030 + +2024-02-02 16:32:40,775 - ==> Best [Top 1 (MSE): 0.03003 Sparsity:0.00 Params: 136448 on epoch: 61] +2024-02-02 16:32:40,775 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:40,780 - + +2024-02-02 16:32:40,780 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:41,123 - Epoch: [63][ 1/ 8] Overall Loss 0.026761 Objective Loss 0.026761 MSE 0.026761 LR 0.001000 Time 0.342825 +2024-02-02 16:32:41,130 - Epoch: [63][ 2/ 8] Overall Loss 0.026154 Objective Loss 0.026154 MSE 0.026154 LR 0.001000 Time 0.174853 +2024-02-02 16:32:41,137 - Epoch: [63][ 3/ 8] Overall Loss 0.025374 Objective Loss 0.025374 MSE 0.025374 LR 0.001000 Time 0.118748 +2024-02-02 16:32:41,143 - Epoch: [63][ 4/ 8] Overall Loss 0.025119 Objective Loss 0.025119 MSE 0.025119 LR 0.001000 Time 0.090690 +2024-02-02 16:32:41,150 - Epoch: [63][ 5/ 8] Overall Loss 0.024733 Objective Loss 0.024733 MSE 0.024733 LR 0.001000 Time 0.073870 +2024-02-02 16:32:41,157 - Epoch: [63][ 6/ 8] Overall Loss 0.025648 Objective Loss 0.025648 MSE 0.025648 LR 0.001000 Time 0.062658 +2024-02-02 16:32:41,164 - Epoch: [63][ 7/ 8] Overall Loss 0.025797 Objective Loss 0.025797 MSE 0.025797 LR 0.001000 Time 0.054633 +2024-02-02 16:32:41,170 - Epoch: [63][ 8/ 8] Overall Loss 0.027428 Objective Loss 0.027428 MSE 0.026137 LR 0.001000 Time 0.048613 +2024-02-02 16:32:41,314 - --- validate (epoch=63)----------- +2024-02-02 16:32:41,314 - 60 samples (32 per mini-batch) +2024-02-02 16:32:41,661 - Epoch: [63][ 1/ 2] Loss 0.027154 MSE 0.027154 +2024-02-02 16:32:41,666 - Epoch: [63][ 2/ 2] Loss 0.031457 MSE 0.031170 +2024-02-02 16:32:41,810 - ==> MSE: 0.03117 Loss: 0.031 + +2024-02-02 16:32:41,813 - ==> Best [Top 1 (MSE): 0.03003 Sparsity:0.00 Params: 136448 on epoch: 61] +2024-02-02 16:32:41,813 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:41,818 - + +2024-02-02 16:32:41,818 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:42,182 - Epoch: [64][ 1/ 8] Overall Loss 0.025650 Objective Loss 0.025650 MSE 0.025650 LR 0.001000 Time 0.363190 +2024-02-02 16:32:42,189 - Epoch: [64][ 2/ 8] Overall Loss 0.024731 Objective Loss 0.024731 MSE 0.024731 LR 0.001000 Time 0.185012 +2024-02-02 16:32:42,195 - Epoch: [64][ 3/ 8] Overall Loss 0.024923 Objective Loss 0.024923 MSE 0.024923 LR 0.001000 Time 0.125488 +2024-02-02 16:32:42,202 - Epoch: [64][ 4/ 8] Overall Loss 0.025524 Objective Loss 0.025524 MSE 0.025524 LR 0.001000 Time 0.095755 +2024-02-02 16:32:42,209 - Epoch: [64][ 5/ 8] Overall Loss 0.025444 Objective Loss 0.025444 MSE 0.025444 LR 0.001000 Time 0.077904 +2024-02-02 16:32:42,215 - Epoch: [64][ 6/ 8] Overall Loss 0.025959 Objective Loss 0.025959 MSE 0.025959 LR 0.001000 Time 0.066001 +2024-02-02 16:32:42,222 - Epoch: [64][ 7/ 8] Overall Loss 0.025880 Objective Loss 0.025880 MSE 0.025880 LR 0.001000 Time 0.057508 +2024-02-02 16:32:42,228 - Epoch: [64][ 8/ 8] Overall Loss 0.025598 Objective Loss 0.025598 MSE 0.025821 LR 0.001000 Time 0.051124 +2024-02-02 16:32:42,379 - --- validate (epoch=64)----------- +2024-02-02 16:32:42,379 - 60 samples (32 per mini-batch) +2024-02-02 16:32:42,733 - Epoch: [64][ 1/ 2] Loss 0.032119 MSE 0.032119 +2024-02-02 16:32:42,737 - Epoch: [64][ 2/ 2] Loss 0.030581 MSE 0.030684 +2024-02-02 16:32:42,886 - ==> MSE: 0.03068 Loss: 0.031 + +2024-02-02 16:32:42,889 - ==> Best [Top 1 (MSE): 0.03003 Sparsity:0.00 Params: 136448 on epoch: 61] +2024-02-02 16:32:42,889 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:42,894 - + +2024-02-02 16:32:42,894 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:43,256 - Epoch: [65][ 1/ 8] Overall Loss 0.024025 Objective Loss 0.024025 MSE 0.024025 LR 0.001000 Time 0.361639 +2024-02-02 16:32:43,263 - Epoch: [65][ 2/ 8] Overall Loss 0.025776 Objective Loss 0.025776 MSE 0.025776 LR 0.001000 Time 0.184178 +2024-02-02 16:32:43,270 - Epoch: [65][ 3/ 8] Overall Loss 0.024521 Objective Loss 0.024521 MSE 0.024521 LR 0.001000 Time 0.125013 +2024-02-02 16:32:43,277 - Epoch: [65][ 4/ 8] Overall Loss 0.025226 Objective Loss 0.025226 MSE 0.025226 LR 0.001000 Time 0.095433 +2024-02-02 16:32:43,285 - Epoch: [65][ 5/ 8] Overall Loss 0.026235 Objective Loss 0.026235 MSE 0.026235 LR 0.001000 Time 0.077883 +2024-02-02 16:32:43,293 - Epoch: [65][ 6/ 8] Overall Loss 0.026020 Objective Loss 0.026020 MSE 0.026020 LR 0.001000 Time 0.066213 +2024-02-02 16:32:43,300 - Epoch: [65][ 7/ 8] Overall Loss 0.025724 Objective Loss 0.025724 MSE 0.025724 LR 0.001000 Time 0.057697 +2024-02-02 16:32:43,306 - Epoch: [65][ 8/ 8] Overall Loss 0.025872 Objective Loss 0.025872 MSE 0.025755 LR 0.001000 Time 0.051278 +2024-02-02 16:32:43,458 - --- validate (epoch=65)----------- +2024-02-02 16:32:43,465 - 60 samples (32 per mini-batch) +2024-02-02 16:32:43,817 - Epoch: [65][ 1/ 2] Loss 0.029558 MSE 0.029558 +2024-02-02 16:32:43,823 - Epoch: [65][ 2/ 2] Loss 0.031320 MSE 0.031203 +2024-02-02 16:32:43,977 - ==> MSE: 0.03120 Loss: 0.031 + +2024-02-02 16:32:43,980 - ==> Best [Top 1 (MSE): 0.03003 Sparsity:0.00 Params: 136448 on epoch: 61] +2024-02-02 16:32:43,981 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:43,985 - + +2024-02-02 16:32:43,985 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:44,351 - Epoch: [66][ 1/ 8] Overall Loss 0.026400 Objective Loss 0.026400 MSE 0.026400 LR 0.001000 Time 0.365469 +2024-02-02 16:32:44,358 - Epoch: [66][ 2/ 8] Overall Loss 0.026633 Objective Loss 0.026633 MSE 0.026633 LR 0.001000 Time 0.186143 +2024-02-02 16:32:44,365 - Epoch: [66][ 3/ 8] Overall Loss 0.025873 Objective Loss 0.025873 MSE 0.025873 LR 0.001000 Time 0.126258 +2024-02-02 16:32:44,372 - Epoch: [66][ 4/ 8] Overall Loss 0.025508 Objective Loss 0.025508 MSE 0.025508 LR 0.001000 Time 0.096322 +2024-02-02 16:32:44,379 - Epoch: [66][ 5/ 8] Overall Loss 0.025553 Objective Loss 0.025553 MSE 0.025553 LR 0.001000 Time 0.078406 +2024-02-02 16:32:44,385 - Epoch: [66][ 6/ 8] Overall Loss 0.025897 Objective Loss 0.025897 MSE 0.025897 LR 0.001000 Time 0.066436 +2024-02-02 16:32:44,392 - Epoch: [66][ 7/ 8] Overall Loss 0.025688 Objective Loss 0.025688 MSE 0.025688 LR 0.001000 Time 0.057910 +2024-02-02 16:32:44,399 - Epoch: [66][ 8/ 8] Overall Loss 0.024850 Objective Loss 0.024850 MSE 0.025513 LR 0.001000 Time 0.051476 +2024-02-02 16:32:44,549 - --- validate (epoch=66)----------- +2024-02-02 16:32:44,549 - 60 samples (32 per mini-batch) +2024-02-02 16:32:44,900 - Epoch: [66][ 1/ 2] Loss 0.029258 MSE 0.029258 +2024-02-02 16:32:44,905 - Epoch: [66][ 2/ 2] Loss 0.029878 MSE 0.029837 +2024-02-02 16:32:45,050 - ==> MSE: 0.02984 Loss: 0.030 + +2024-02-02 16:32:45,053 - ==> Best [Top 1 (MSE): 0.02984 Sparsity:0.00 Params: 136448 on epoch: 66] +2024-02-02 16:32:45,053 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:45,059 - + +2024-02-02 16:32:45,059 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:45,425 - Epoch: [67][ 1/ 8] Overall Loss 0.022619 Objective Loss 0.022619 MSE 0.022619 LR 0.001000 Time 0.365772 +2024-02-02 16:32:45,435 - Epoch: [67][ 2/ 8] Overall Loss 0.023331 Objective Loss 0.023331 MSE 0.023331 LR 0.001000 Time 0.187603 +2024-02-02 16:32:45,444 - Epoch: [67][ 3/ 8] Overall Loss 0.024211 Objective Loss 0.024211 MSE 0.024211 LR 0.001000 Time 0.127976 +2024-02-02 16:32:45,451 - Epoch: [67][ 4/ 8] Overall Loss 0.024245 Objective Loss 0.024245 MSE 0.024245 LR 0.001000 Time 0.097733 +2024-02-02 16:32:45,458 - Epoch: [67][ 5/ 8] Overall Loss 0.024734 Objective Loss 0.024734 MSE 0.024734 LR 0.001000 Time 0.079516 +2024-02-02 16:32:45,465 - Epoch: [67][ 6/ 8] Overall Loss 0.024765 Objective Loss 0.024765 MSE 0.024765 LR 0.001000 Time 0.067380 +2024-02-02 16:32:45,471 - Epoch: [67][ 7/ 8] Overall Loss 0.025082 Objective Loss 0.025082 MSE 0.025082 LR 0.001000 Time 0.058698 +2024-02-02 16:32:45,478 - Epoch: [67][ 8/ 8] Overall Loss 0.026732 Objective Loss 0.026732 MSE 0.025427 LR 0.001000 Time 0.052184 +2024-02-02 16:32:45,623 - --- validate (epoch=67)----------- +2024-02-02 16:32:45,623 - 60 samples (32 per mini-batch) +2024-02-02 16:32:45,975 - Epoch: [67][ 1/ 2] Loss 0.030348 MSE 0.030348 +2024-02-02 16:32:45,979 - Epoch: [67][ 2/ 2] Loss 0.029903 MSE 0.029933 +2024-02-02 16:32:46,126 - ==> MSE: 0.02993 Loss: 0.030 + +2024-02-02 16:32:46,130 - ==> Best [Top 1 (MSE): 0.02984 Sparsity:0.00 Params: 136448 on epoch: 66] +2024-02-02 16:32:46,130 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:46,134 - + +2024-02-02 16:32:46,135 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:46,498 - Epoch: [68][ 1/ 8] Overall Loss 0.023995 Objective Loss 0.023995 MSE 0.023995 LR 0.001000 Time 0.362656 +2024-02-02 16:32:46,505 - Epoch: [68][ 2/ 8] Overall Loss 0.024726 Objective Loss 0.024726 MSE 0.024726 LR 0.001000 Time 0.184820 +2024-02-02 16:32:46,512 - Epoch: [68][ 3/ 8] Overall Loss 0.024775 Objective Loss 0.024775 MSE 0.024775 LR 0.001000 Time 0.125438 +2024-02-02 16:32:46,519 - Epoch: [68][ 4/ 8] Overall Loss 0.024827 Objective Loss 0.024827 MSE 0.024827 LR 0.001000 Time 0.095755 +2024-02-02 16:32:46,527 - Epoch: [68][ 5/ 8] Overall Loss 0.024390 Objective Loss 0.024390 MSE 0.024390 LR 0.001000 Time 0.078332 +2024-02-02 16:32:46,534 - Epoch: [68][ 6/ 8] Overall Loss 0.024816 Objective Loss 0.024816 MSE 0.024816 LR 0.001000 Time 0.066400 +2024-02-02 16:32:46,541 - Epoch: [68][ 7/ 8] Overall Loss 0.024883 Objective Loss 0.024883 MSE 0.024883 LR 0.001000 Time 0.057859 +2024-02-02 16:32:46,548 - Epoch: [68][ 8/ 8] Overall Loss 0.027203 Objective Loss 0.027203 MSE 0.025367 LR 0.001000 Time 0.051438 +2024-02-02 16:32:46,702 - --- validate (epoch=68)----------- +2024-02-02 16:32:46,702 - 60 samples (32 per mini-batch) +2024-02-02 16:32:47,061 - Epoch: [68][ 1/ 2] Loss 0.031795 MSE 0.031795 +2024-02-02 16:32:47,065 - Epoch: [68][ 2/ 2] Loss 0.029319 MSE 0.029484 +2024-02-02 16:32:47,217 - ==> MSE: 0.02948 Loss: 0.029 + +2024-02-02 16:32:47,221 - ==> Best [Top 1 (MSE): 0.02948 Sparsity:0.00 Params: 136448 on epoch: 68] +2024-02-02 16:32:47,221 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:47,227 - + +2024-02-02 16:32:47,227 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:47,592 - Epoch: [69][ 1/ 8] Overall Loss 0.023698 Objective Loss 0.023698 MSE 0.023698 LR 0.001000 Time 0.364392 +2024-02-02 16:32:47,603 - Epoch: [69][ 2/ 8] Overall Loss 0.027105 Objective Loss 0.027105 MSE 0.027105 LR 0.001000 Time 0.187477 +2024-02-02 16:32:47,609 - Epoch: [69][ 3/ 8] Overall Loss 0.025716 Objective Loss 0.025716 MSE 0.025716 LR 0.001000 Time 0.127218 +2024-02-02 16:32:47,616 - Epoch: [69][ 4/ 8] Overall Loss 0.024380 Objective Loss 0.024380 MSE 0.024380 LR 0.001000 Time 0.097107 +2024-02-02 16:32:47,623 - Epoch: [69][ 5/ 8] Overall Loss 0.024344 Objective Loss 0.024344 MSE 0.024344 LR 0.001000 Time 0.079018 +2024-02-02 16:32:47,630 - Epoch: [69][ 6/ 8] Overall Loss 0.025371 Objective Loss 0.025371 MSE 0.025371 LR 0.001000 Time 0.066959 +2024-02-02 16:32:47,637 - Epoch: [69][ 7/ 8] Overall Loss 0.025225 Objective Loss 0.025225 MSE 0.025225 LR 0.001000 Time 0.058351 +2024-02-02 16:32:47,644 - Epoch: [69][ 8/ 8] Overall Loss 0.025543 Objective Loss 0.025543 MSE 0.025292 LR 0.001000 Time 0.051917 +2024-02-02 16:32:47,791 - --- validate (epoch=69)----------- +2024-02-02 16:32:47,791 - 60 samples (32 per mini-batch) +2024-02-02 16:32:48,151 - Epoch: [69][ 1/ 2] Loss 0.030951 MSE 0.030951 +2024-02-02 16:32:48,155 - Epoch: [69][ 2/ 2] Loss 0.029125 MSE 0.029247 +2024-02-02 16:32:48,311 - ==> MSE: 0.02925 Loss: 0.029 + +2024-02-02 16:32:48,318 - ==> Best [Top 1 (MSE): 0.02925 Sparsity:0.00 Params: 136448 on epoch: 69] +2024-02-02 16:32:48,318 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:48,328 - + +2024-02-02 16:32:48,328 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:48,705 - Epoch: [70][ 1/ 8] Overall Loss 0.025288 Objective Loss 0.025288 MSE 0.025288 LR 0.001000 Time 0.375815 +2024-02-02 16:32:48,713 - Epoch: [70][ 2/ 8] Overall Loss 0.024927 Objective Loss 0.024927 MSE 0.024927 LR 0.001000 Time 0.191814 +2024-02-02 16:32:48,720 - Epoch: [70][ 3/ 8] Overall Loss 0.024238 Objective Loss 0.024238 MSE 0.024238 LR 0.001000 Time 0.130134 +2024-02-02 16:32:48,726 - Epoch: [70][ 4/ 8] Overall Loss 0.024617 Objective Loss 0.024617 MSE 0.024617 LR 0.001000 Time 0.099240 +2024-02-02 16:32:48,733 - Epoch: [70][ 5/ 8] Overall Loss 0.024944 Objective Loss 0.024944 MSE 0.024944 LR 0.001000 Time 0.080725 +2024-02-02 16:32:48,740 - Epoch: [70][ 6/ 8] Overall Loss 0.024851 Objective Loss 0.024851 MSE 0.024851 LR 0.001000 Time 0.068376 +2024-02-02 16:32:48,747 - Epoch: [70][ 7/ 8] Overall Loss 0.024535 Objective Loss 0.024535 MSE 0.024535 LR 0.001000 Time 0.059546 +2024-02-02 16:32:48,753 - Epoch: [70][ 8/ 8] Overall Loss 0.026513 Objective Loss 0.026513 MSE 0.024948 LR 0.001000 Time 0.052913 +2024-02-02 16:32:48,904 - --- validate (epoch=70)----------- +2024-02-02 16:32:48,904 - 60 samples (32 per mini-batch) +2024-02-02 16:32:49,259 - Epoch: [70][ 1/ 2] Loss 0.029622 MSE 0.029622 +2024-02-02 16:32:49,264 - Epoch: [70][ 2/ 2] Loss 0.028838 MSE 0.028891 +2024-02-02 16:32:49,413 - ==> MSE: 0.02889 Loss: 0.029 + +2024-02-02 16:32:49,417 - ==> Best [Top 1 (MSE): 0.02889 Sparsity:0.00 Params: 136448 on epoch: 70] +2024-02-02 16:32:49,417 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:49,423 - + +2024-02-02 16:32:49,423 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:49,773 - Epoch: [71][ 1/ 8] Overall Loss 0.024936 Objective Loss 0.024936 MSE 0.024936 LR 0.001000 Time 0.349621 +2024-02-02 16:32:49,783 - Epoch: [71][ 2/ 8] Overall Loss 0.023551 Objective Loss 0.023551 MSE 0.023551 LR 0.001000 Time 0.179480 +2024-02-02 16:32:49,790 - Epoch: [71][ 3/ 8] Overall Loss 0.023916 Objective Loss 0.023916 MSE 0.023916 LR 0.001000 Time 0.121944 +2024-02-02 16:32:49,797 - Epoch: [71][ 4/ 8] Overall Loss 0.024319 Objective Loss 0.024319 MSE 0.024319 LR 0.001000 Time 0.093119 +2024-02-02 16:32:49,804 - Epoch: [71][ 5/ 8] Overall Loss 0.024944 Objective Loss 0.024944 MSE 0.024944 LR 0.001000 Time 0.075817 +2024-02-02 16:32:49,810 - Epoch: [71][ 6/ 8] Overall Loss 0.024724 Objective Loss 0.024724 MSE 0.024724 LR 0.001000 Time 0.064292 +2024-02-02 16:32:49,817 - Epoch: [71][ 7/ 8] Overall Loss 0.024678 Objective Loss 0.024678 MSE 0.024678 LR 0.001000 Time 0.056047 +2024-02-02 16:32:49,824 - Epoch: [71][ 8/ 8] Overall Loss 0.025556 Objective Loss 0.025556 MSE 0.024861 LR 0.001000 Time 0.049859 +2024-02-02 16:32:49,977 - --- validate (epoch=71)----------- +2024-02-02 16:32:49,977 - 60 samples (32 per mini-batch) +2024-02-02 16:32:50,337 - Epoch: [71][ 1/ 2] Loss 0.025410 MSE 0.025410 +2024-02-02 16:32:50,341 - Epoch: [71][ 2/ 2] Loss 0.028871 MSE 0.028640 +2024-02-02 16:32:50,493 - ==> MSE: 0.02864 Loss: 0.029 + +2024-02-02 16:32:50,497 - ==> Best [Top 1 (MSE): 0.02864 Sparsity:0.00 Params: 136448 on epoch: 71] +2024-02-02 16:32:50,497 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:50,503 - + +2024-02-02 16:32:50,503 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:50,856 - Epoch: [72][ 1/ 8] Overall Loss 0.024444 Objective Loss 0.024444 MSE 0.024444 LR 0.001000 Time 0.352791 +2024-02-02 16:32:50,863 - Epoch: [72][ 2/ 8] Overall Loss 0.026249 Objective Loss 0.026249 MSE 0.026249 LR 0.001000 Time 0.179786 +2024-02-02 16:32:50,870 - Epoch: [72][ 3/ 8] Overall Loss 0.026624 Objective Loss 0.026624 MSE 0.026624 LR 0.001000 Time 0.122027 +2024-02-02 16:32:50,877 - Epoch: [72][ 4/ 8] Overall Loss 0.026671 Objective Loss 0.026671 MSE 0.026671 LR 0.001000 Time 0.093179 +2024-02-02 16:32:50,883 - Epoch: [72][ 5/ 8] Overall Loss 0.025649 Objective Loss 0.025649 MSE 0.025649 LR 0.001000 Time 0.075868 +2024-02-02 16:32:50,890 - Epoch: [72][ 6/ 8] Overall Loss 0.024700 Objective Loss 0.024700 MSE 0.024700 LR 0.001000 Time 0.064321 +2024-02-02 16:32:50,897 - Epoch: [72][ 7/ 8] Overall Loss 0.024384 Objective Loss 0.024384 MSE 0.024384 LR 0.001000 Time 0.056084 +2024-02-02 16:32:50,903 - Epoch: [72][ 8/ 8] Overall Loss 0.024154 Objective Loss 0.024154 MSE 0.024336 LR 0.001000 Time 0.049873 +2024-02-02 16:32:51,054 - --- validate (epoch=72)----------- +2024-02-02 16:32:51,054 - 60 samples (32 per mini-batch) +2024-02-02 16:32:51,404 - Epoch: [72][ 1/ 2] Loss 0.028757 MSE 0.028757 +2024-02-02 16:32:51,409 - Epoch: [72][ 2/ 2] Loss 0.028601 MSE 0.028611 +2024-02-02 16:32:51,554 - ==> MSE: 0.02861 Loss: 0.029 + +2024-02-02 16:32:51,561 - ==> Best [Top 1 (MSE): 0.02861 Sparsity:0.00 Params: 136448 on epoch: 72] +2024-02-02 16:32:51,561 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:51,571 - + +2024-02-02 16:32:51,572 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:51,934 - Epoch: [73][ 1/ 8] Overall Loss 0.028428 Objective Loss 0.028428 MSE 0.028428 LR 0.001000 Time 0.362064 +2024-02-02 16:32:51,941 - Epoch: [73][ 2/ 8] Overall Loss 0.025499 Objective Loss 0.025499 MSE 0.025499 LR 0.001000 Time 0.184447 +2024-02-02 16:32:51,948 - Epoch: [73][ 3/ 8] Overall Loss 0.026477 Objective Loss 0.026477 MSE 0.026477 LR 0.001000 Time 0.125189 +2024-02-02 16:32:51,955 - Epoch: [73][ 4/ 8] Overall Loss 0.025727 Objective Loss 0.025727 MSE 0.025727 LR 0.001000 Time 0.095517 +2024-02-02 16:32:51,962 - Epoch: [73][ 5/ 8] Overall Loss 0.024613 Objective Loss 0.024613 MSE 0.024613 LR 0.001000 Time 0.077709 +2024-02-02 16:32:51,968 - Epoch: [73][ 6/ 8] Overall Loss 0.024480 Objective Loss 0.024480 MSE 0.024480 LR 0.001000 Time 0.065839 +2024-02-02 16:32:51,975 - Epoch: [73][ 7/ 8] Overall Loss 0.024670 Objective Loss 0.024670 MSE 0.024670 LR 0.001000 Time 0.057366 +2024-02-02 16:32:51,981 - Epoch: [73][ 8/ 8] Overall Loss 0.024565 Objective Loss 0.024565 MSE 0.024648 LR 0.001000 Time 0.051001 +2024-02-02 16:32:52,133 - --- validate (epoch=73)----------- +2024-02-02 16:32:52,134 - 60 samples (32 per mini-batch) +2024-02-02 16:32:52,492 - Epoch: [73][ 1/ 2] Loss 0.026711 MSE 0.026711 +2024-02-02 16:32:52,496 - Epoch: [73][ 2/ 2] Loss 0.028652 MSE 0.028522 +2024-02-02 16:32:52,645 - ==> MSE: 0.02852 Loss: 0.029 + +2024-02-02 16:32:52,649 - ==> Best [Top 1 (MSE): 0.02852 Sparsity:0.00 Params: 136448 on epoch: 73] +2024-02-02 16:32:52,649 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:52,655 - + +2024-02-02 16:32:52,655 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:53,014 - Epoch: [74][ 1/ 8] Overall Loss 0.024819 Objective Loss 0.024819 MSE 0.024819 LR 0.001000 Time 0.358279 +2024-02-02 16:32:53,021 - Epoch: [74][ 2/ 8] Overall Loss 0.025332 Objective Loss 0.025332 MSE 0.025332 LR 0.001000 Time 0.182673 +2024-02-02 16:32:53,028 - Epoch: [74][ 3/ 8] Overall Loss 0.023945 Objective Loss 0.023945 MSE 0.023945 LR 0.001000 Time 0.124031 +2024-02-02 16:32:53,035 - Epoch: [74][ 4/ 8] Overall Loss 0.024617 Objective Loss 0.024617 MSE 0.024617 LR 0.001000 Time 0.094665 +2024-02-02 16:32:53,041 - Epoch: [74][ 5/ 8] Overall Loss 0.024156 Objective Loss 0.024156 MSE 0.024156 LR 0.001000 Time 0.077042 +2024-02-02 16:32:53,048 - Epoch: [74][ 6/ 8] Overall Loss 0.024067 Objective Loss 0.024067 MSE 0.024067 LR 0.001000 Time 0.065298 +2024-02-02 16:32:53,055 - Epoch: [74][ 7/ 8] Overall Loss 0.023989 Objective Loss 0.023989 MSE 0.023989 LR 0.001000 Time 0.056912 +2024-02-02 16:32:53,062 - Epoch: [74][ 8/ 8] Overall Loss 0.024728 Objective Loss 0.024728 MSE 0.024143 LR 0.001000 Time 0.050607 +2024-02-02 16:32:53,213 - --- validate (epoch=74)----------- +2024-02-02 16:32:53,214 - 60 samples (32 per mini-batch) +2024-02-02 16:32:53,571 - Epoch: [74][ 1/ 2] Loss 0.029373 MSE 0.029373 +2024-02-02 16:32:53,575 - Epoch: [74][ 2/ 2] Loss 0.028491 MSE 0.028550 +2024-02-02 16:32:53,719 - ==> MSE: 0.02855 Loss: 0.028 + +2024-02-02 16:32:53,722 - ==> Best [Top 1 (MSE): 0.02852 Sparsity:0.00 Params: 136448 on epoch: 73] +2024-02-02 16:32:53,723 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:53,727 - + +2024-02-02 16:32:53,728 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:54,088 - Epoch: [75][ 1/ 8] Overall Loss 0.024387 Objective Loss 0.024387 MSE 0.024387 LR 0.001000 Time 0.359770 +2024-02-02 16:32:54,095 - Epoch: [75][ 2/ 8] Overall Loss 0.023340 Objective Loss 0.023340 MSE 0.023340 LR 0.001000 Time 0.183325 +2024-02-02 16:32:54,102 - Epoch: [75][ 3/ 8] Overall Loss 0.024045 Objective Loss 0.024045 MSE 0.024045 LR 0.001000 Time 0.124429 +2024-02-02 16:32:54,108 - Epoch: [75][ 4/ 8] Overall Loss 0.023839 Objective Loss 0.023839 MSE 0.023839 LR 0.001000 Time 0.094948 +2024-02-02 16:32:54,115 - Epoch: [75][ 5/ 8] Overall Loss 0.023847 Objective Loss 0.023847 MSE 0.023847 LR 0.001000 Time 0.077289 +2024-02-02 16:32:54,122 - Epoch: [75][ 6/ 8] Overall Loss 0.023437 Objective Loss 0.023437 MSE 0.023437 LR 0.001000 Time 0.065496 +2024-02-02 16:32:54,129 - Epoch: [75][ 7/ 8] Overall Loss 0.023951 Objective Loss 0.023951 MSE 0.023951 LR 0.001000 Time 0.057088 +2024-02-02 16:32:54,135 - Epoch: [75][ 8/ 8] Overall Loss 0.024879 Objective Loss 0.024879 MSE 0.024145 LR 0.001000 Time 0.050755 +2024-02-02 16:32:54,286 - --- validate (epoch=75)----------- +2024-02-02 16:32:54,286 - 60 samples (32 per mini-batch) +2024-02-02 16:32:54,644 - Epoch: [75][ 1/ 2] Loss 0.028158 MSE 0.028158 +2024-02-02 16:32:54,648 - Epoch: [75][ 2/ 2] Loss 0.028370 MSE 0.028356 +2024-02-02 16:32:54,780 - ==> MSE: 0.02836 Loss: 0.028 + +2024-02-02 16:32:54,783 - ==> Best [Top 1 (MSE): 0.02836 Sparsity:0.00 Params: 136448 on epoch: 75] +2024-02-02 16:32:54,783 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:54,789 - + +2024-02-02 16:32:54,789 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:55,292 - Epoch: [76][ 1/ 8] Overall Loss 0.026065 Objective Loss 0.026065 MSE 0.026065 LR 0.001000 Time 0.502903 +2024-02-02 16:32:55,300 - Epoch: [76][ 2/ 8] Overall Loss 0.024017 Objective Loss 0.024017 MSE 0.024017 LR 0.001000 Time 0.255029 +2024-02-02 16:32:55,307 - Epoch: [76][ 3/ 8] Overall Loss 0.024964 Objective Loss 0.024964 MSE 0.024964 LR 0.001000 Time 0.172246 +2024-02-02 16:32:55,313 - Epoch: [76][ 4/ 8] Overall Loss 0.024928 Objective Loss 0.024928 MSE 0.024928 LR 0.001000 Time 0.130849 +2024-02-02 16:32:55,320 - Epoch: [76][ 5/ 8] Overall Loss 0.024346 Objective Loss 0.024346 MSE 0.024346 LR 0.001000 Time 0.106019 +2024-02-02 16:32:55,327 - Epoch: [76][ 6/ 8] Overall Loss 0.024860 Objective Loss 0.024860 MSE 0.024860 LR 0.001000 Time 0.089454 +2024-02-02 16:32:55,334 - Epoch: [76][ 7/ 8] Overall Loss 0.024450 Objective Loss 0.024450 MSE 0.024450 LR 0.001000 Time 0.077629 +2024-02-02 16:32:55,341 - Epoch: [76][ 8/ 8] Overall Loss 0.024205 Objective Loss 0.024205 MSE 0.024399 LR 0.001000 Time 0.068761 +2024-02-02 16:32:55,493 - --- validate (epoch=76)----------- +2024-02-02 16:32:55,494 - 60 samples (32 per mini-batch) +2024-02-02 16:32:55,848 - Epoch: [76][ 1/ 2] Loss 0.027578 MSE 0.027578 +2024-02-02 16:32:55,852 - Epoch: [76][ 2/ 2] Loss 0.028558 MSE 0.028492 +2024-02-02 16:32:55,999 - ==> MSE: 0.02849 Loss: 0.029 + +2024-02-02 16:32:56,002 - ==> Best [Top 1 (MSE): 0.02836 Sparsity:0.00 Params: 136448 on epoch: 75] +2024-02-02 16:32:56,002 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:56,007 - + +2024-02-02 16:32:56,007 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:56,372 - Epoch: [77][ 1/ 8] Overall Loss 0.022779 Objective Loss 0.022779 MSE 0.022779 LR 0.001000 Time 0.363792 +2024-02-02 16:32:56,382 - Epoch: [77][ 2/ 8] Overall Loss 0.024070 Objective Loss 0.024070 MSE 0.024070 LR 0.001000 Time 0.187019 +2024-02-02 16:32:56,389 - Epoch: [77][ 3/ 8] Overall Loss 0.023715 Objective Loss 0.023715 MSE 0.023715 LR 0.001000 Time 0.126961 +2024-02-02 16:32:56,396 - Epoch: [77][ 4/ 8] Overall Loss 0.023972 Objective Loss 0.023972 MSE 0.023972 LR 0.001000 Time 0.096865 +2024-02-02 16:32:56,403 - Epoch: [77][ 5/ 8] Overall Loss 0.023419 Objective Loss 0.023419 MSE 0.023419 LR 0.001000 Time 0.078805 +2024-02-02 16:32:56,409 - Epoch: [77][ 6/ 8] Overall Loss 0.023521 Objective Loss 0.023521 MSE 0.023521 LR 0.001000 Time 0.066756 +2024-02-02 16:32:56,416 - Epoch: [77][ 7/ 8] Overall Loss 0.023641 Objective Loss 0.023641 MSE 0.023641 LR 0.001000 Time 0.058148 +2024-02-02 16:32:56,423 - Epoch: [77][ 8/ 8] Overall Loss 0.025683 Objective Loss 0.025683 MSE 0.024067 LR 0.001000 Time 0.051688 +2024-02-02 16:32:56,568 - --- validate (epoch=77)----------- +2024-02-02 16:32:56,568 - 60 samples (32 per mini-batch) +2024-02-02 16:32:56,917 - Epoch: [77][ 1/ 2] Loss 0.029967 MSE 0.029967 +2024-02-02 16:32:56,921 - Epoch: [77][ 2/ 2] Loss 0.028080 MSE 0.028206 +2024-02-02 16:32:57,067 - ==> MSE: 0.02821 Loss: 0.028 + +2024-02-02 16:32:57,071 - ==> Best [Top 1 (MSE): 0.02821 Sparsity:0.00 Params: 136448 on epoch: 77] +2024-02-02 16:32:57,071 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:57,076 - + +2024-02-02 16:32:57,077 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:57,441 - Epoch: [78][ 1/ 8] Overall Loss 0.024208 Objective Loss 0.024208 MSE 0.024208 LR 0.001000 Time 0.363801 +2024-02-02 16:32:57,449 - Epoch: [78][ 2/ 8] Overall Loss 0.023538 Objective Loss 0.023538 MSE 0.023538 LR 0.001000 Time 0.186017 +2024-02-02 16:32:57,457 - Epoch: [78][ 3/ 8] Overall Loss 0.023542 Objective Loss 0.023542 MSE 0.023542 LR 0.001000 Time 0.126613 +2024-02-02 16:32:57,464 - Epoch: [78][ 4/ 8] Overall Loss 0.023900 Objective Loss 0.023900 MSE 0.023900 LR 0.001000 Time 0.096623 +2024-02-02 16:32:57,472 - Epoch: [78][ 5/ 8] Overall Loss 0.023789 Objective Loss 0.023789 MSE 0.023789 LR 0.001000 Time 0.078877 +2024-02-02 16:32:57,479 - Epoch: [78][ 6/ 8] Overall Loss 0.023603 Objective Loss 0.023603 MSE 0.023603 LR 0.001000 Time 0.066847 +2024-02-02 16:32:57,485 - Epoch: [78][ 7/ 8] Overall Loss 0.023697 Objective Loss 0.023697 MSE 0.023697 LR 0.001000 Time 0.058219 +2024-02-02 16:32:57,492 - Epoch: [78][ 8/ 8] Overall Loss 0.024460 Objective Loss 0.024460 MSE 0.023857 LR 0.001000 Time 0.051765 +2024-02-02 16:32:57,643 - --- validate (epoch=78)----------- +2024-02-02 16:32:57,643 - 60 samples (32 per mini-batch) +2024-02-02 16:32:58,016 - Epoch: [78][ 1/ 2] Loss 0.029106 MSE 0.029106 +2024-02-02 16:32:58,021 - Epoch: [78][ 2/ 2] Loss 0.028302 MSE 0.028356 +2024-02-02 16:32:58,163 - ==> MSE: 0.02836 Loss: 0.028 + +2024-02-02 16:32:58,170 - ==> Best [Top 1 (MSE): 0.02821 Sparsity:0.00 Params: 136448 on epoch: 77] +2024-02-02 16:32:58,171 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:58,179 - + +2024-02-02 16:32:58,179 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:58,540 - Epoch: [79][ 1/ 8] Overall Loss 0.020904 Objective Loss 0.020904 MSE 0.020904 LR 0.001000 Time 0.360282 +2024-02-02 16:32:58,549 - Epoch: [79][ 2/ 8] Overall Loss 0.023038 Objective Loss 0.023038 MSE 0.023038 LR 0.001000 Time 0.184335 +2024-02-02 16:32:58,556 - Epoch: [79][ 3/ 8] Overall Loss 0.022804 Objective Loss 0.022804 MSE 0.022804 LR 0.001000 Time 0.125123 +2024-02-02 16:32:58,569 - Epoch: [79][ 4/ 8] Overall Loss 0.023169 Objective Loss 0.023169 MSE 0.023169 LR 0.001000 Time 0.095594 +2024-02-02 16:32:58,575 - Epoch: [79][ 5/ 8] Overall Loss 0.023716 Objective Loss 0.023716 MSE 0.023716 LR 0.001000 Time 0.077822 +2024-02-02 16:32:58,582 - Epoch: [79][ 6/ 8] Overall Loss 0.023840 Objective Loss 0.023840 MSE 0.023840 LR 0.001000 Time 0.065974 +2024-02-02 16:32:58,589 - Epoch: [79][ 7/ 8] Overall Loss 0.023633 Objective Loss 0.023633 MSE 0.023633 LR 0.001000 Time 0.057506 +2024-02-02 16:32:58,596 - Epoch: [79][ 8/ 8] Overall Loss 0.026366 Objective Loss 0.026366 MSE 0.024204 LR 0.001000 Time 0.051111 +2024-02-02 16:32:58,744 - --- validate (epoch=79)----------- +2024-02-02 16:32:58,745 - 60 samples (32 per mini-batch) +2024-02-02 16:32:59,103 - Epoch: [79][ 1/ 2] Loss 0.028935 MSE 0.028935 +2024-02-02 16:32:59,108 - Epoch: [79][ 2/ 2] Loss 0.028254 MSE 0.028299 +2024-02-02 16:32:59,255 - ==> MSE: 0.02830 Loss: 0.028 + +2024-02-02 16:32:59,259 - ==> Best [Top 1 (MSE): 0.02821 Sparsity:0.00 Params: 136448 on epoch: 77] +2024-02-02 16:32:59,259 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:32:59,264 - + +2024-02-02 16:32:59,264 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:32:59,629 - Epoch: [80][ 1/ 8] Overall Loss 0.022457 Objective Loss 0.022457 MSE 0.022457 LR 0.001000 Time 0.364964 +2024-02-02 16:32:59,636 - Epoch: [80][ 2/ 8] Overall Loss 0.024464 Objective Loss 0.024464 MSE 0.024464 LR 0.001000 Time 0.185983 +2024-02-02 16:32:59,643 - Epoch: [80][ 3/ 8] Overall Loss 0.025034 Objective Loss 0.025034 MSE 0.025034 LR 0.001000 Time 0.126213 +2024-02-02 16:32:59,650 - Epoch: [80][ 4/ 8] Overall Loss 0.024488 Objective Loss 0.024488 MSE 0.024488 LR 0.001000 Time 0.096297 +2024-02-02 16:32:59,657 - Epoch: [80][ 5/ 8] Overall Loss 0.024426 Objective Loss 0.024426 MSE 0.024426 LR 0.001000 Time 0.078369 +2024-02-02 16:32:59,663 - Epoch: [80][ 6/ 8] Overall Loss 0.024444 Objective Loss 0.024444 MSE 0.024444 LR 0.001000 Time 0.066388 +2024-02-02 16:32:59,670 - Epoch: [80][ 7/ 8] Overall Loss 0.023930 Objective Loss 0.023930 MSE 0.023930 LR 0.001000 Time 0.057844 +2024-02-02 16:32:59,677 - Epoch: [80][ 8/ 8] Overall Loss 0.023761 Objective Loss 0.023761 MSE 0.023895 LR 0.001000 Time 0.051425 +2024-02-02 16:32:59,825 - --- validate (epoch=80)----------- +2024-02-02 16:32:59,825 - 60 samples (32 per mini-batch) +2024-02-02 16:33:00,176 - Epoch: [80][ 1/ 2] Loss 0.034418 MSE 0.034418 +2024-02-02 16:33:00,181 - Epoch: [80][ 2/ 2] Loss 0.034890 MSE 0.034858 +2024-02-02 16:33:00,330 - ==> MSE: 0.03486 Loss: 0.035 + +2024-02-02 16:33:00,334 - ==> Best [Top 1 (MSE): 0.02821 Sparsity:0.00 Params: 136448 on epoch: 77] +2024-02-02 16:33:00,334 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:00,339 - + +2024-02-02 16:33:00,339 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:00,702 - Epoch: [81][ 1/ 8] Overall Loss 0.022811 Objective Loss 0.022811 MSE 0.022811 LR 0.001000 Time 0.362734 +2024-02-02 16:33:00,709 - Epoch: [81][ 2/ 8] Overall Loss 0.022110 Objective Loss 0.022110 MSE 0.022110 LR 0.001000 Time 0.184700 +2024-02-02 16:33:00,715 - Epoch: [81][ 3/ 8] Overall Loss 0.022871 Objective Loss 0.022871 MSE 0.022871 LR 0.001000 Time 0.125226 +2024-02-02 16:33:00,722 - Epoch: [81][ 4/ 8] Overall Loss 0.023271 Objective Loss 0.023271 MSE 0.023271 LR 0.001000 Time 0.095492 +2024-02-02 16:33:00,729 - Epoch: [81][ 5/ 8] Overall Loss 0.023482 Objective Loss 0.023482 MSE 0.023482 LR 0.001000 Time 0.077699 +2024-02-02 16:33:00,735 - Epoch: [81][ 6/ 8] Overall Loss 0.023652 Objective Loss 0.023652 MSE 0.023652 LR 0.001000 Time 0.065805 +2024-02-02 16:33:00,742 - Epoch: [81][ 7/ 8] Overall Loss 0.023534 Objective Loss 0.023534 MSE 0.023534 LR 0.001000 Time 0.057340 +2024-02-02 16:33:00,748 - Epoch: [81][ 8/ 8] Overall Loss 0.025224 Objective Loss 0.025224 MSE 0.023887 LR 0.001000 Time 0.050971 +2024-02-02 16:33:00,891 - --- validate (epoch=81)----------- +2024-02-02 16:33:00,892 - 60 samples (32 per mini-batch) +2024-02-02 16:33:01,222 - Epoch: [81][ 1/ 2] Loss 0.032237 MSE 0.032237 +2024-02-02 16:33:01,226 - Epoch: [81][ 2/ 2] Loss 0.032623 MSE 0.032597 +2024-02-02 16:33:01,365 - ==> MSE: 0.03260 Loss: 0.033 + +2024-02-02 16:33:01,369 - ==> Best [Top 1 (MSE): 0.02821 Sparsity:0.00 Params: 136448 on epoch: 77] +2024-02-02 16:33:01,370 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:01,374 - + +2024-02-02 16:33:01,374 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:01,731 - Epoch: [82][ 1/ 8] Overall Loss 0.024341 Objective Loss 0.024341 MSE 0.024341 LR 0.001000 Time 0.355995 +2024-02-02 16:33:01,738 - Epoch: [82][ 2/ 8] Overall Loss 0.024617 Objective Loss 0.024617 MSE 0.024617 LR 0.001000 Time 0.181331 +2024-02-02 16:33:01,744 - Epoch: [82][ 3/ 8] Overall Loss 0.024230 Objective Loss 0.024230 MSE 0.024230 LR 0.001000 Time 0.123070 +2024-02-02 16:33:01,751 - Epoch: [82][ 4/ 8] Overall Loss 0.023755 Objective Loss 0.023755 MSE 0.023755 LR 0.001000 Time 0.093909 +2024-02-02 16:33:01,757 - Epoch: [82][ 5/ 8] Overall Loss 0.022944 Objective Loss 0.022944 MSE 0.022944 LR 0.001000 Time 0.076403 +2024-02-02 16:33:01,764 - Epoch: [82][ 6/ 8] Overall Loss 0.023300 Objective Loss 0.023300 MSE 0.023300 LR 0.001000 Time 0.064751 +2024-02-02 16:33:01,771 - Epoch: [82][ 7/ 8] Overall Loss 0.023718 Objective Loss 0.023718 MSE 0.023718 LR 0.001000 Time 0.056416 +2024-02-02 16:33:01,777 - Epoch: [82][ 8/ 8] Overall Loss 0.024453 Objective Loss 0.024453 MSE 0.023872 LR 0.001000 Time 0.050177 +2024-02-02 16:33:01,925 - --- validate (epoch=82)----------- +2024-02-02 16:33:01,925 - 60 samples (32 per mini-batch) +2024-02-02 16:33:02,287 - Epoch: [82][ 1/ 2] Loss 0.029021 MSE 0.029021 +2024-02-02 16:33:02,292 - Epoch: [82][ 2/ 2] Loss 0.028570 MSE 0.028600 +2024-02-02 16:33:02,436 - ==> MSE: 0.02860 Loss: 0.029 + +2024-02-02 16:33:02,441 - ==> Best [Top 1 (MSE): 0.02821 Sparsity:0.00 Params: 136448 on epoch: 77] +2024-02-02 16:33:02,441 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:02,446 - + +2024-02-02 16:33:02,446 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:02,807 - Epoch: [83][ 1/ 8] Overall Loss 0.024365 Objective Loss 0.024365 MSE 0.024365 LR 0.001000 Time 0.360588 +2024-02-02 16:33:02,818 - Epoch: [83][ 2/ 8] Overall Loss 0.023457 Objective Loss 0.023457 MSE 0.023457 LR 0.001000 Time 0.185347 +2024-02-02 16:33:02,827 - Epoch: [83][ 3/ 8] Overall Loss 0.024406 Objective Loss 0.024406 MSE 0.024406 LR 0.001000 Time 0.126651 +2024-02-02 16:33:02,834 - Epoch: [83][ 4/ 8] Overall Loss 0.024850 Objective Loss 0.024850 MSE 0.024850 LR 0.001000 Time 0.096643 +2024-02-02 16:33:02,841 - Epoch: [83][ 5/ 8] Overall Loss 0.024689 Objective Loss 0.024689 MSE 0.024689 LR 0.001000 Time 0.078631 +2024-02-02 16:33:02,847 - Epoch: [83][ 6/ 8] Overall Loss 0.024140 Objective Loss 0.024140 MSE 0.024140 LR 0.001000 Time 0.066633 +2024-02-02 16:33:02,854 - Epoch: [83][ 7/ 8] Overall Loss 0.024006 Objective Loss 0.024006 MSE 0.024006 LR 0.001000 Time 0.058060 +2024-02-02 16:33:02,861 - Epoch: [83][ 8/ 8] Overall Loss 0.025587 Objective Loss 0.025587 MSE 0.024336 LR 0.001000 Time 0.051616 +2024-02-02 16:33:03,008 - --- validate (epoch=83)----------- +2024-02-02 16:33:03,008 - 60 samples (32 per mini-batch) +2024-02-02 16:33:03,363 - Epoch: [83][ 1/ 2] Loss 0.026844 MSE 0.026844 +2024-02-02 16:33:03,367 - Epoch: [83][ 2/ 2] Loss 0.028761 MSE 0.028633 +2024-02-02 16:33:03,516 - ==> MSE: 0.02863 Loss: 0.029 + +2024-02-02 16:33:03,521 - ==> Best [Top 1 (MSE): 0.02821 Sparsity:0.00 Params: 136448 on epoch: 77] +2024-02-02 16:33:03,521 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:03,526 - + +2024-02-02 16:33:03,526 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:03,886 - Epoch: [84][ 1/ 8] Overall Loss 0.025886 Objective Loss 0.025886 MSE 0.025886 LR 0.001000 Time 0.359285 +2024-02-02 16:33:03,893 - Epoch: [84][ 2/ 8] Overall Loss 0.024670 Objective Loss 0.024670 MSE 0.024670 LR 0.001000 Time 0.183118 +2024-02-02 16:33:03,900 - Epoch: [84][ 3/ 8] Overall Loss 0.023210 Objective Loss 0.023210 MSE 0.023210 LR 0.001000 Time 0.124316 +2024-02-02 16:33:03,907 - Epoch: [84][ 4/ 8] Overall Loss 0.023293 Objective Loss 0.023293 MSE 0.023293 LR 0.001000 Time 0.094889 +2024-02-02 16:33:03,913 - Epoch: [84][ 5/ 8] Overall Loss 0.023947 Objective Loss 0.023947 MSE 0.023947 LR 0.001000 Time 0.077219 +2024-02-02 16:33:03,920 - Epoch: [84][ 6/ 8] Overall Loss 0.023647 Objective Loss 0.023647 MSE 0.023647 LR 0.001000 Time 0.065445 +2024-02-02 16:33:03,927 - Epoch: [84][ 7/ 8] Overall Loss 0.023102 Objective Loss 0.023102 MSE 0.023102 LR 0.001000 Time 0.057017 +2024-02-02 16:33:03,933 - Epoch: [84][ 8/ 8] Overall Loss 0.025142 Objective Loss 0.025142 MSE 0.023528 LR 0.001000 Time 0.050689 +2024-02-02 16:33:04,085 - --- validate (epoch=84)----------- +2024-02-02 16:33:04,086 - 60 samples (32 per mini-batch) +2024-02-02 16:33:04,439 - Epoch: [84][ 1/ 2] Loss 0.026830 MSE 0.026830 +2024-02-02 16:33:04,444 - Epoch: [84][ 2/ 2] Loss 0.028100 MSE 0.028015 +2024-02-02 16:33:04,592 - ==> MSE: 0.02802 Loss: 0.028 + +2024-02-02 16:33:04,597 - ==> Best [Top 1 (MSE): 0.02802 Sparsity:0.00 Params: 136448 on epoch: 84] +2024-02-02 16:33:04,597 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:04,603 - + +2024-02-02 16:33:04,603 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:04,970 - Epoch: [85][ 1/ 8] Overall Loss 0.022904 Objective Loss 0.022904 MSE 0.022904 LR 0.001000 Time 0.366631 +2024-02-02 16:33:04,981 - Epoch: [85][ 2/ 8] Overall Loss 0.023547 Objective Loss 0.023547 MSE 0.023547 LR 0.001000 Time 0.188460 +2024-02-02 16:33:04,991 - Epoch: [85][ 3/ 8] Overall Loss 0.022322 Objective Loss 0.022322 MSE 0.022322 LR 0.001000 Time 0.128916 +2024-02-02 16:33:04,998 - Epoch: [85][ 4/ 8] Overall Loss 0.022345 Objective Loss 0.022345 MSE 0.022345 LR 0.001000 Time 0.098412 +2024-02-02 16:33:05,005 - Epoch: [85][ 5/ 8] Overall Loss 0.023296 Objective Loss 0.023296 MSE 0.023296 LR 0.001000 Time 0.080067 +2024-02-02 16:33:05,012 - Epoch: [85][ 6/ 8] Overall Loss 0.023181 Objective Loss 0.023181 MSE 0.023181 LR 0.001000 Time 0.067839 +2024-02-02 16:33:05,018 - Epoch: [85][ 7/ 8] Overall Loss 0.023404 Objective Loss 0.023404 MSE 0.023404 LR 0.001000 Time 0.059094 +2024-02-02 16:33:05,025 - Epoch: [85][ 8/ 8] Overall Loss 0.023635 Objective Loss 0.023635 MSE 0.023452 LR 0.001000 Time 0.052531 +2024-02-02 16:33:05,173 - --- validate (epoch=85)----------- +2024-02-02 16:33:05,173 - 60 samples (32 per mini-batch) +2024-02-02 16:33:05,533 - Epoch: [85][ 1/ 2] Loss 0.027486 MSE 0.027486 +2024-02-02 16:33:05,537 - Epoch: [85][ 2/ 2] Loss 0.028254 MSE 0.028203 +2024-02-02 16:33:05,680 - ==> MSE: 0.02820 Loss: 0.028 + +2024-02-02 16:33:05,685 - ==> Best [Top 1 (MSE): 0.02802 Sparsity:0.00 Params: 136448 on epoch: 84] +2024-02-02 16:33:05,686 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:05,690 - + +2024-02-02 16:33:05,691 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:06,042 - Epoch: [86][ 1/ 8] Overall Loss 0.024687 Objective Loss 0.024687 MSE 0.024687 LR 0.001000 Time 0.351167 +2024-02-02 16:33:06,049 - Epoch: [86][ 2/ 8] Overall Loss 0.025217 Objective Loss 0.025217 MSE 0.025217 LR 0.001000 Time 0.179145 +2024-02-02 16:33:06,056 - Epoch: [86][ 3/ 8] Overall Loss 0.023046 Objective Loss 0.023046 MSE 0.023046 LR 0.001000 Time 0.121711 +2024-02-02 16:33:06,063 - Epoch: [86][ 4/ 8] Overall Loss 0.023235 Objective Loss 0.023235 MSE 0.023235 LR 0.001000 Time 0.092938 +2024-02-02 16:33:06,070 - Epoch: [86][ 5/ 8] Overall Loss 0.023057 Objective Loss 0.023057 MSE 0.023057 LR 0.001000 Time 0.075684 +2024-02-02 16:33:06,077 - Epoch: [86][ 6/ 8] Overall Loss 0.023024 Objective Loss 0.023024 MSE 0.023024 LR 0.001000 Time 0.064180 +2024-02-02 16:33:06,083 - Epoch: [86][ 7/ 8] Overall Loss 0.023295 Objective Loss 0.023295 MSE 0.023295 LR 0.001000 Time 0.055943 +2024-02-02 16:33:06,090 - Epoch: [86][ 8/ 8] Overall Loss 0.023895 Objective Loss 0.023895 MSE 0.023421 LR 0.001000 Time 0.049771 +2024-02-02 16:33:06,237 - --- validate (epoch=86)----------- +2024-02-02 16:33:06,237 - 60 samples (32 per mini-batch) +2024-02-02 16:33:06,596 - Epoch: [86][ 1/ 2] Loss 0.029519 MSE 0.029519 +2024-02-02 16:33:06,601 - Epoch: [86][ 2/ 2] Loss 0.027662 MSE 0.027786 +2024-02-02 16:33:06,750 - ==> MSE: 0.02779 Loss: 0.028 + +2024-02-02 16:33:06,754 - ==> Best [Top 1 (MSE): 0.02779 Sparsity:0.00 Params: 136448 on epoch: 86] +2024-02-02 16:33:06,754 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:06,760 - + +2024-02-02 16:33:06,760 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:07,130 - Epoch: [87][ 1/ 8] Overall Loss 0.022590 Objective Loss 0.022590 MSE 0.022590 LR 0.001000 Time 0.369112 +2024-02-02 16:33:07,137 - Epoch: [87][ 2/ 8] Overall Loss 0.021850 Objective Loss 0.021850 MSE 0.021850 LR 0.001000 Time 0.188026 +2024-02-02 16:33:07,144 - Epoch: [87][ 3/ 8] Overall Loss 0.021094 Objective Loss 0.021094 MSE 0.021094 LR 0.001000 Time 0.127548 +2024-02-02 16:33:07,150 - Epoch: [87][ 4/ 8] Overall Loss 0.021421 Objective Loss 0.021421 MSE 0.021421 LR 0.001000 Time 0.097314 +2024-02-02 16:33:07,157 - Epoch: [87][ 5/ 8] Overall Loss 0.022349 Objective Loss 0.022349 MSE 0.022349 LR 0.001000 Time 0.079181 +2024-02-02 16:33:07,164 - Epoch: [87][ 6/ 8] Overall Loss 0.023042 Objective Loss 0.023042 MSE 0.023042 LR 0.001000 Time 0.067090 +2024-02-02 16:33:07,171 - Epoch: [87][ 7/ 8] Overall Loss 0.023204 Objective Loss 0.023204 MSE 0.023204 LR 0.001000 Time 0.058439 +2024-02-02 16:33:07,177 - Epoch: [87][ 8/ 8] Overall Loss 0.024595 Objective Loss 0.024595 MSE 0.023495 LR 0.001000 Time 0.051948 +2024-02-02 16:33:07,328 - --- validate (epoch=87)----------- +2024-02-02 16:33:07,328 - 60 samples (32 per mini-batch) +2024-02-02 16:33:07,686 - Epoch: [87][ 1/ 2] Loss 0.027775 MSE 0.027775 +2024-02-02 16:33:07,691 - Epoch: [87][ 2/ 2] Loss 0.027836 MSE 0.027832 +2024-02-02 16:33:07,829 - ==> MSE: 0.02783 Loss: 0.028 + +2024-02-02 16:33:07,834 - ==> Best [Top 1 (MSE): 0.02779 Sparsity:0.00 Params: 136448 on epoch: 86] +2024-02-02 16:33:07,834 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:07,839 - + +2024-02-02 16:33:07,839 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:08,198 - Epoch: [88][ 1/ 8] Overall Loss 0.024186 Objective Loss 0.024186 MSE 0.024186 LR 0.001000 Time 0.358618 +2024-02-02 16:33:08,207 - Epoch: [88][ 2/ 8] Overall Loss 0.022901 Objective Loss 0.022901 MSE 0.022901 LR 0.001000 Time 0.183763 +2024-02-02 16:33:08,214 - Epoch: [88][ 3/ 8] Overall Loss 0.022952 Objective Loss 0.022952 MSE 0.022952 LR 0.001000 Time 0.124738 +2024-02-02 16:33:08,221 - Epoch: [88][ 4/ 8] Overall Loss 0.023260 Objective Loss 0.023260 MSE 0.023260 LR 0.001000 Time 0.095211 +2024-02-02 16:33:08,227 - Epoch: [88][ 5/ 8] Overall Loss 0.023463 Objective Loss 0.023463 MSE 0.023463 LR 0.001000 Time 0.077473 +2024-02-02 16:33:08,234 - Epoch: [88][ 6/ 8] Overall Loss 0.023471 Objective Loss 0.023471 MSE 0.023471 LR 0.001000 Time 0.065679 +2024-02-02 16:33:08,241 - Epoch: [88][ 7/ 8] Overall Loss 0.023197 Objective Loss 0.023197 MSE 0.023197 LR 0.001000 Time 0.057228 +2024-02-02 16:33:08,247 - Epoch: [88][ 8/ 8] Overall Loss 0.023277 Objective Loss 0.023277 MSE 0.023214 LR 0.001000 Time 0.050878 +2024-02-02 16:33:08,397 - --- validate (epoch=88)----------- +2024-02-02 16:33:08,397 - 60 samples (32 per mini-batch) +2024-02-02 16:33:08,749 - Epoch: [88][ 1/ 2] Loss 0.024896 MSE 0.024896 +2024-02-02 16:33:08,754 - Epoch: [88][ 2/ 2] Loss 0.027642 MSE 0.027458 +2024-02-02 16:33:08,891 - ==> MSE: 0.02746 Loss: 0.028 + +2024-02-02 16:33:08,895 - ==> Best [Top 1 (MSE): 0.02746 Sparsity:0.00 Params: 136448 on epoch: 88] +2024-02-02 16:33:08,895 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:08,901 - + +2024-02-02 16:33:08,901 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:09,250 - Epoch: [89][ 1/ 8] Overall Loss 0.025643 Objective Loss 0.025643 MSE 0.025643 LR 0.001000 Time 0.348586 +2024-02-02 16:33:09,257 - Epoch: [89][ 2/ 8] Overall Loss 0.026431 Objective Loss 0.026431 MSE 0.026431 LR 0.001000 Time 0.177755 +2024-02-02 16:33:09,264 - Epoch: [89][ 3/ 8] Overall Loss 0.024826 Objective Loss 0.024826 MSE 0.024826 LR 0.001000 Time 0.120782 +2024-02-02 16:33:09,271 - Epoch: [89][ 4/ 8] Overall Loss 0.024289 Objective Loss 0.024289 MSE 0.024289 LR 0.001000 Time 0.092251 +2024-02-02 16:33:09,278 - Epoch: [89][ 5/ 8] Overall Loss 0.023869 Objective Loss 0.023869 MSE 0.023869 LR 0.001000 Time 0.075127 +2024-02-02 16:33:09,284 - Epoch: [89][ 6/ 8] Overall Loss 0.023443 Objective Loss 0.023443 MSE 0.023443 LR 0.001000 Time 0.063694 +2024-02-02 16:33:09,291 - Epoch: [89][ 7/ 8] Overall Loss 0.023230 Objective Loss 0.023230 MSE 0.023230 LR 0.001000 Time 0.055540 +2024-02-02 16:33:09,298 - Epoch: [89][ 8/ 8] Overall Loss 0.024017 Objective Loss 0.024017 MSE 0.023394 LR 0.001000 Time 0.049410 +2024-02-02 16:33:09,441 - --- validate (epoch=89)----------- +2024-02-02 16:33:09,442 - 60 samples (32 per mini-batch) +2024-02-02 16:33:09,790 - Epoch: [89][ 1/ 2] Loss 0.028077 MSE 0.028077 +2024-02-02 16:33:09,794 - Epoch: [89][ 2/ 2] Loss 0.027456 MSE 0.027497 +2024-02-02 16:33:09,940 - ==> MSE: 0.02750 Loss: 0.027 + +2024-02-02 16:33:09,944 - ==> Best [Top 1 (MSE): 0.02746 Sparsity:0.00 Params: 136448 on epoch: 88] +2024-02-02 16:33:09,944 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:09,949 - + +2024-02-02 16:33:09,949 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:10,307 - Epoch: [90][ 1/ 8] Overall Loss 0.023357 Objective Loss 0.023357 MSE 0.023357 LR 0.001000 Time 0.358161 +2024-02-02 16:33:10,316 - Epoch: [90][ 2/ 8] Overall Loss 0.023279 Objective Loss 0.023279 MSE 0.023279 LR 0.001000 Time 0.183090 +2024-02-02 16:33:10,322 - Epoch: [90][ 3/ 8] Overall Loss 0.022871 Objective Loss 0.022871 MSE 0.022871 LR 0.001000 Time 0.124275 +2024-02-02 16:33:10,329 - Epoch: [90][ 4/ 8] Overall Loss 0.023265 Objective Loss 0.023265 MSE 0.023265 LR 0.001000 Time 0.094862 +2024-02-02 16:33:10,336 - Epoch: [90][ 5/ 8] Overall Loss 0.023308 Objective Loss 0.023308 MSE 0.023308 LR 0.001000 Time 0.077234 +2024-02-02 16:33:10,343 - Epoch: [90][ 6/ 8] Overall Loss 0.022776 Objective Loss 0.022776 MSE 0.022776 LR 0.001000 Time 0.065450 +2024-02-02 16:33:10,349 - Epoch: [90][ 7/ 8] Overall Loss 0.022937 Objective Loss 0.022937 MSE 0.022937 LR 0.001000 Time 0.057038 +2024-02-02 16:33:10,356 - Epoch: [90][ 8/ 8] Overall Loss 0.023317 Objective Loss 0.023317 MSE 0.023016 LR 0.001000 Time 0.050736 +2024-02-02 16:33:10,504 - --- validate (epoch=90)----------- +2024-02-02 16:33:10,504 - 60 samples (32 per mini-batch) +2024-02-02 16:33:10,848 - Epoch: [90][ 1/ 2] Loss 0.028935 MSE 0.028935 +2024-02-02 16:33:10,852 - Epoch: [90][ 2/ 2] Loss 0.027363 MSE 0.027468 +2024-02-02 16:33:11,000 - ==> MSE: 0.02747 Loss: 0.027 + +2024-02-02 16:33:11,005 - ==> Best [Top 1 (MSE): 0.02746 Sparsity:0.00 Params: 136448 on epoch: 88] +2024-02-02 16:33:11,005 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:11,010 - + +2024-02-02 16:33:11,010 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:11,363 - Epoch: [91][ 1/ 8] Overall Loss 0.026181 Objective Loss 0.026181 MSE 0.026181 LR 0.001000 Time 0.352162 +2024-02-02 16:33:11,370 - Epoch: [91][ 2/ 8] Overall Loss 0.026134 Objective Loss 0.026134 MSE 0.026134 LR 0.001000 Time 0.179468 +2024-02-02 16:33:11,377 - Epoch: [91][ 3/ 8] Overall Loss 0.024668 Objective Loss 0.024668 MSE 0.024668 LR 0.001000 Time 0.121840 +2024-02-02 16:33:11,383 - Epoch: [91][ 4/ 8] Overall Loss 0.023366 Objective Loss 0.023366 MSE 0.023366 LR 0.001000 Time 0.092981 +2024-02-02 16:33:11,390 - Epoch: [91][ 5/ 8] Overall Loss 0.022872 Objective Loss 0.022872 MSE 0.022872 LR 0.001000 Time 0.075673 +2024-02-02 16:33:11,396 - Epoch: [91][ 6/ 8] Overall Loss 0.023015 Objective Loss 0.023015 MSE 0.023015 LR 0.001000 Time 0.064146 +2024-02-02 16:33:11,403 - Epoch: [91][ 7/ 8] Overall Loss 0.022939 Objective Loss 0.022939 MSE 0.022939 LR 0.001000 Time 0.055910 +2024-02-02 16:33:11,410 - Epoch: [91][ 8/ 8] Overall Loss 0.026109 Objective Loss 0.026109 MSE 0.023600 LR 0.001000 Time 0.049721 +2024-02-02 16:33:11,560 - --- validate (epoch=91)----------- +2024-02-02 16:33:11,561 - 60 samples (32 per mini-batch) +2024-02-02 16:33:11,906 - Epoch: [91][ 1/ 2] Loss 0.026759 MSE 0.026759 +2024-02-02 16:33:11,910 - Epoch: [91][ 2/ 2] Loss 0.027488 MSE 0.027439 +2024-02-02 16:33:12,057 - ==> MSE: 0.02744 Loss: 0.027 + +2024-02-02 16:33:12,062 - ==> Best [Top 1 (MSE): 0.02744 Sparsity:0.00 Params: 136448 on epoch: 91] +2024-02-02 16:33:12,062 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:12,067 - + +2024-02-02 16:33:12,067 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:12,430 - Epoch: [92][ 1/ 8] Overall Loss 0.023184 Objective Loss 0.023184 MSE 0.023184 LR 0.001000 Time 0.362671 +2024-02-02 16:33:12,438 - Epoch: [92][ 2/ 8] Overall Loss 0.024693 Objective Loss 0.024693 MSE 0.024693 LR 0.001000 Time 0.184867 +2024-02-02 16:33:12,445 - Epoch: [92][ 3/ 8] Overall Loss 0.024117 Objective Loss 0.024117 MSE 0.024117 LR 0.001000 Time 0.125492 +2024-02-02 16:33:12,451 - Epoch: [92][ 4/ 8] Overall Loss 0.023968 Objective Loss 0.023968 MSE 0.023968 LR 0.001000 Time 0.095775 +2024-02-02 16:33:12,458 - Epoch: [92][ 5/ 8] Overall Loss 0.023437 Objective Loss 0.023437 MSE 0.023437 LR 0.001000 Time 0.077955 +2024-02-02 16:33:12,465 - Epoch: [92][ 6/ 8] Overall Loss 0.023504 Objective Loss 0.023504 MSE 0.023504 LR 0.001000 Time 0.066050 +2024-02-02 16:33:12,472 - Epoch: [92][ 7/ 8] Overall Loss 0.022932 Objective Loss 0.022932 MSE 0.022932 LR 0.001000 Time 0.057561 +2024-02-02 16:33:12,478 - Epoch: [92][ 8/ 8] Overall Loss 0.023573 Objective Loss 0.023573 MSE 0.023066 LR 0.001000 Time 0.051184 +2024-02-02 16:33:12,634 - --- validate (epoch=92)----------- +2024-02-02 16:33:12,634 - 60 samples (32 per mini-batch) +2024-02-02 16:33:12,987 - Epoch: [92][ 1/ 2] Loss 0.027133 MSE 0.027133 +2024-02-02 16:33:12,992 - Epoch: [92][ 2/ 2] Loss 0.027411 MSE 0.027392 +2024-02-02 16:33:13,134 - ==> MSE: 0.02739 Loss: 0.027 + +2024-02-02 16:33:13,141 - ==> Best [Top 1 (MSE): 0.02739 Sparsity:0.00 Params: 136448 on epoch: 92] +2024-02-02 16:33:13,141 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:13,147 - + +2024-02-02 16:33:13,147 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:13,502 - Epoch: [93][ 1/ 8] Overall Loss 0.022752 Objective Loss 0.022752 MSE 0.022752 LR 0.001000 Time 0.354812 +2024-02-02 16:33:13,509 - Epoch: [93][ 2/ 8] Overall Loss 0.023500 Objective Loss 0.023500 MSE 0.023500 LR 0.001000 Time 0.180851 +2024-02-02 16:33:13,516 - Epoch: [93][ 3/ 8] Overall Loss 0.022851 Objective Loss 0.022851 MSE 0.022851 LR 0.001000 Time 0.122766 +2024-02-02 16:33:13,523 - Epoch: [93][ 4/ 8] Overall Loss 0.022777 Objective Loss 0.022777 MSE 0.022777 LR 0.001000 Time 0.093713 +2024-02-02 16:33:13,529 - Epoch: [93][ 5/ 8] Overall Loss 0.022364 Objective Loss 0.022364 MSE 0.022364 LR 0.001000 Time 0.076290 +2024-02-02 16:33:13,536 - Epoch: [93][ 6/ 8] Overall Loss 0.022839 Objective Loss 0.022839 MSE 0.022839 LR 0.001000 Time 0.064672 +2024-02-02 16:33:13,543 - Epoch: [93][ 7/ 8] Overall Loss 0.022438 Objective Loss 0.022438 MSE 0.022438 LR 0.001000 Time 0.056367 +2024-02-02 16:33:13,549 - Epoch: [93][ 8/ 8] Overall Loss 0.025733 Objective Loss 0.025733 MSE 0.023126 LR 0.001000 Time 0.050129 +2024-02-02 16:33:13,699 - --- validate (epoch=93)----------- +2024-02-02 16:33:13,699 - 60 samples (32 per mini-batch) +2024-02-02 16:33:14,052 - Epoch: [93][ 1/ 2] Loss 0.030090 MSE 0.030090 +2024-02-02 16:33:14,057 - Epoch: [93][ 2/ 2] Loss 0.027221 MSE 0.027412 +2024-02-02 16:33:14,202 - ==> MSE: 0.02741 Loss: 0.027 + +2024-02-02 16:33:14,206 - ==> Best [Top 1 (MSE): 0.02739 Sparsity:0.00 Params: 136448 on epoch: 92] +2024-02-02 16:33:14,207 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:14,211 - + +2024-02-02 16:33:14,211 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:14,560 - Epoch: [94][ 1/ 8] Overall Loss 0.021223 Objective Loss 0.021223 MSE 0.021223 LR 0.001000 Time 0.348318 +2024-02-02 16:33:14,567 - Epoch: [94][ 2/ 8] Overall Loss 0.021895 Objective Loss 0.021895 MSE 0.021895 LR 0.001000 Time 0.177621 +2024-02-02 16:33:14,574 - Epoch: [94][ 3/ 8] Overall Loss 0.022884 Objective Loss 0.022884 MSE 0.022884 LR 0.001000 Time 0.120621 +2024-02-02 16:33:14,581 - Epoch: [94][ 4/ 8] Overall Loss 0.021720 Objective Loss 0.021720 MSE 0.021720 LR 0.001000 Time 0.092156 +2024-02-02 16:33:14,588 - Epoch: [94][ 5/ 8] Overall Loss 0.022138 Objective Loss 0.022138 MSE 0.022138 LR 0.001000 Time 0.075054 +2024-02-02 16:33:14,595 - Epoch: [94][ 6/ 8] Overall Loss 0.022411 Objective Loss 0.022411 MSE 0.022411 LR 0.001000 Time 0.063649 +2024-02-02 16:33:14,601 - Epoch: [94][ 7/ 8] Overall Loss 0.022591 Objective Loss 0.022591 MSE 0.022591 LR 0.001000 Time 0.055492 +2024-02-02 16:33:14,608 - Epoch: [94][ 8/ 8] Overall Loss 0.022842 Objective Loss 0.022842 MSE 0.022643 LR 0.001000 Time 0.049374 +2024-02-02 16:33:14,750 - --- validate (epoch=94)----------- +2024-02-02 16:33:14,751 - 60 samples (32 per mini-batch) +2024-02-02 16:33:15,109 - Epoch: [94][ 1/ 2] Loss 0.026542 MSE 0.026542 +2024-02-02 16:33:15,113 - Epoch: [94][ 2/ 2] Loss 0.026987 MSE 0.026958 +2024-02-02 16:33:15,256 - ==> MSE: 0.02696 Loss: 0.027 + +2024-02-02 16:33:15,262 - ==> Best [Top 1 (MSE): 0.02696 Sparsity:0.00 Params: 136448 on epoch: 94] +2024-02-02 16:33:15,262 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:15,268 - + +2024-02-02 16:33:15,268 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:15,620 - Epoch: [95][ 1/ 8] Overall Loss 0.021915 Objective Loss 0.021915 MSE 0.021915 LR 0.001000 Time 0.351239 +2024-02-02 16:33:15,630 - Epoch: [95][ 2/ 8] Overall Loss 0.022429 Objective Loss 0.022429 MSE 0.022429 LR 0.001000 Time 0.180751 +2024-02-02 16:33:15,639 - Epoch: [95][ 3/ 8] Overall Loss 0.021980 Objective Loss 0.021980 MSE 0.021980 LR 0.001000 Time 0.123416 +2024-02-02 16:33:15,646 - Epoch: [95][ 4/ 8] Overall Loss 0.021708 Objective Loss 0.021708 MSE 0.021708 LR 0.001000 Time 0.094243 +2024-02-02 16:33:15,653 - Epoch: [95][ 5/ 8] Overall Loss 0.022360 Objective Loss 0.022360 MSE 0.022360 LR 0.001000 Time 0.076733 +2024-02-02 16:33:15,660 - Epoch: [95][ 6/ 8] Overall Loss 0.022427 Objective Loss 0.022427 MSE 0.022427 LR 0.001000 Time 0.065038 +2024-02-02 16:33:15,666 - Epoch: [95][ 7/ 8] Overall Loss 0.022474 Objective Loss 0.022474 MSE 0.022474 LR 0.001000 Time 0.056686 +2024-02-02 16:33:15,673 - Epoch: [95][ 8/ 8] Overall Loss 0.024625 Objective Loss 0.024625 MSE 0.022923 LR 0.001000 Time 0.050408 +2024-02-02 16:33:15,819 - --- validate (epoch=95)----------- +2024-02-02 16:33:15,819 - 60 samples (32 per mini-batch) +2024-02-02 16:33:16,174 - Epoch: [95][ 1/ 2] Loss 0.027667 MSE 0.027667 +2024-02-02 16:33:16,179 - Epoch: [95][ 2/ 2] Loss 0.026847 MSE 0.026902 +2024-02-02 16:33:16,327 - ==> MSE: 0.02690 Loss: 0.027 + +2024-02-02 16:33:16,332 - ==> Best [Top 1 (MSE): 0.02690 Sparsity:0.00 Params: 136448 on epoch: 95] +2024-02-02 16:33:16,332 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:16,337 - + +2024-02-02 16:33:16,337 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:16,701 - Epoch: [96][ 1/ 8] Overall Loss 0.025990 Objective Loss 0.025990 MSE 0.025990 LR 0.001000 Time 0.363332 +2024-02-02 16:33:16,708 - Epoch: [96][ 2/ 8] Overall Loss 0.023643 Objective Loss 0.023643 MSE 0.023643 LR 0.001000 Time 0.185052 +2024-02-02 16:33:16,715 - Epoch: [96][ 3/ 8] Overall Loss 0.023602 Objective Loss 0.023602 MSE 0.023602 LR 0.001000 Time 0.125551 +2024-02-02 16:33:16,722 - Epoch: [96][ 4/ 8] Overall Loss 0.023016 Objective Loss 0.023016 MSE 0.023016 LR 0.001000 Time 0.095781 +2024-02-02 16:33:16,728 - Epoch: [96][ 5/ 8] Overall Loss 0.023410 Objective Loss 0.023410 MSE 0.023410 LR 0.001000 Time 0.077952 +2024-02-02 16:33:16,735 - Epoch: [96][ 6/ 8] Overall Loss 0.022845 Objective Loss 0.022845 MSE 0.022845 LR 0.001000 Time 0.066040 +2024-02-02 16:33:16,742 - Epoch: [96][ 7/ 8] Overall Loss 0.022591 Objective Loss 0.022591 MSE 0.022591 LR 0.001000 Time 0.057538 +2024-02-02 16:33:16,748 - Epoch: [96][ 8/ 8] Overall Loss 0.022516 Objective Loss 0.022516 MSE 0.022575 LR 0.001000 Time 0.051158 +2024-02-02 16:33:16,894 - --- validate (epoch=96)----------- +2024-02-02 16:33:16,894 - 60 samples (32 per mini-batch) +2024-02-02 16:33:17,244 - Epoch: [96][ 1/ 2] Loss 0.027170 MSE 0.027170 +2024-02-02 16:33:17,248 - Epoch: [96][ 2/ 2] Loss 0.026774 MSE 0.026800 +2024-02-02 16:33:17,396 - ==> MSE: 0.02680 Loss: 0.027 + +2024-02-02 16:33:17,401 - ==> Best [Top 1 (MSE): 0.02680 Sparsity:0.00 Params: 136448 on epoch: 96] +2024-02-02 16:33:17,401 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:17,407 - + +2024-02-02 16:33:17,407 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:17,771 - Epoch: [97][ 1/ 8] Overall Loss 0.022789 Objective Loss 0.022789 MSE 0.022789 LR 0.001000 Time 0.363698 +2024-02-02 16:33:17,778 - Epoch: [97][ 2/ 8] Overall Loss 0.022630 Objective Loss 0.022630 MSE 0.022630 LR 0.001000 Time 0.185311 +2024-02-02 16:33:17,785 - Epoch: [97][ 3/ 8] Overall Loss 0.021841 Objective Loss 0.021841 MSE 0.021841 LR 0.001000 Time 0.125745 +2024-02-02 16:33:17,792 - Epoch: [97][ 4/ 8] Overall Loss 0.021665 Objective Loss 0.021665 MSE 0.021665 LR 0.001000 Time 0.095951 +2024-02-02 16:33:17,799 - Epoch: [97][ 5/ 8] Overall Loss 0.022350 Objective Loss 0.022350 MSE 0.022350 LR 0.001000 Time 0.078088 +2024-02-02 16:33:17,805 - Epoch: [97][ 6/ 8] Overall Loss 0.022126 Objective Loss 0.022126 MSE 0.022126 LR 0.001000 Time 0.066156 +2024-02-02 16:33:17,812 - Epoch: [97][ 7/ 8] Overall Loss 0.022209 Objective Loss 0.022209 MSE 0.022209 LR 0.001000 Time 0.057639 +2024-02-02 16:33:17,819 - Epoch: [97][ 8/ 8] Overall Loss 0.025325 Objective Loss 0.025325 MSE 0.022859 LR 0.001000 Time 0.051255 +2024-02-02 16:33:17,969 - --- validate (epoch=97)----------- +2024-02-02 16:33:17,969 - 60 samples (32 per mini-batch) +2024-02-02 16:33:18,315 - Epoch: [97][ 1/ 2] Loss 0.025831 MSE 0.025831 +2024-02-02 16:33:18,321 - Epoch: [97][ 2/ 2] Loss 0.026770 MSE 0.026708 +2024-02-02 16:33:18,470 - ==> MSE: 0.02671 Loss: 0.027 + +2024-02-02 16:33:18,475 - ==> Best [Top 1 (MSE): 0.02671 Sparsity:0.00 Params: 136448 on epoch: 97] +2024-02-02 16:33:18,475 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:18,481 - + +2024-02-02 16:33:18,481 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:18,830 - Epoch: [98][ 1/ 8] Overall Loss 0.020202 Objective Loss 0.020202 MSE 0.020202 LR 0.001000 Time 0.349180 +2024-02-02 16:33:18,841 - Epoch: [98][ 2/ 8] Overall Loss 0.022235 Objective Loss 0.022235 MSE 0.022235 LR 0.001000 Time 0.179695 +2024-02-02 16:33:18,851 - Epoch: [98][ 3/ 8] Overall Loss 0.021835 Objective Loss 0.021835 MSE 0.021835 LR 0.001000 Time 0.123108 +2024-02-02 16:33:18,858 - Epoch: [98][ 4/ 8] Overall Loss 0.021093 Objective Loss 0.021093 MSE 0.021093 LR 0.001000 Time 0.094044 +2024-02-02 16:33:18,865 - Epoch: [98][ 5/ 8] Overall Loss 0.022001 Objective Loss 0.022001 MSE 0.022001 LR 0.001000 Time 0.076575 +2024-02-02 16:33:18,872 - Epoch: [98][ 6/ 8] Overall Loss 0.021978 Objective Loss 0.021978 MSE 0.021978 LR 0.001000 Time 0.064926 +2024-02-02 16:33:18,879 - Epoch: [98][ 7/ 8] Overall Loss 0.022343 Objective Loss 0.022343 MSE 0.022343 LR 0.001000 Time 0.056590 +2024-02-02 16:33:18,885 - Epoch: [98][ 8/ 8] Overall Loss 0.024199 Objective Loss 0.024199 MSE 0.022730 LR 0.001000 Time 0.050336 +2024-02-02 16:33:19,033 - --- validate (epoch=98)----------- +2024-02-02 16:33:19,034 - 60 samples (32 per mini-batch) +2024-02-02 16:33:19,384 - Epoch: [98][ 1/ 2] Loss 0.026531 MSE 0.026531 +2024-02-02 16:33:19,388 - Epoch: [98][ 2/ 2] Loss 0.026898 MSE 0.026873 +2024-02-02 16:33:19,536 - ==> MSE: 0.02687 Loss: 0.027 + +2024-02-02 16:33:19,540 - ==> Best [Top 1 (MSE): 0.02671 Sparsity:0.00 Params: 136448 on epoch: 97] +2024-02-02 16:33:19,540 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:19,545 - + +2024-02-02 16:33:19,545 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:19,900 - Epoch: [99][ 1/ 8] Overall Loss 0.026306 Objective Loss 0.026306 MSE 0.026306 LR 0.001000 Time 0.355053 +2024-02-02 16:33:19,911 - Epoch: [99][ 2/ 8] Overall Loss 0.023108 Objective Loss 0.023108 MSE 0.023108 LR 0.001000 Time 0.182508 +2024-02-02 16:33:19,921 - Epoch: [99][ 3/ 8] Overall Loss 0.022416 Objective Loss 0.022416 MSE 0.022416 LR 0.001000 Time 0.124864 +2024-02-02 16:33:19,928 - Epoch: [99][ 4/ 8] Overall Loss 0.022014 Objective Loss 0.022014 MSE 0.022014 LR 0.001000 Time 0.095532 +2024-02-02 16:33:19,935 - Epoch: [99][ 5/ 8] Overall Loss 0.021984 Objective Loss 0.021984 MSE 0.021984 LR 0.001000 Time 0.077783 +2024-02-02 16:33:19,942 - Epoch: [99][ 6/ 8] Overall Loss 0.022066 Objective Loss 0.022066 MSE 0.022066 LR 0.001000 Time 0.065934 +2024-02-02 16:33:19,949 - Epoch: [99][ 7/ 8] Overall Loss 0.022234 Objective Loss 0.022234 MSE 0.022234 LR 0.001000 Time 0.057479 +2024-02-02 16:33:19,956 - Epoch: [99][ 8/ 8] Overall Loss 0.023132 Objective Loss 0.023132 MSE 0.022422 LR 0.001000 Time 0.051117 +2024-02-02 16:33:20,104 - --- validate (epoch=99)----------- +2024-02-02 16:33:20,104 - 60 samples (32 per mini-batch) +2024-02-02 16:33:20,458 - Epoch: [99][ 1/ 2] Loss 0.025915 MSE 0.025915 +2024-02-02 16:33:20,462 - Epoch: [99][ 2/ 2] Loss 0.026925 MSE 0.026858 +2024-02-02 16:33:20,610 - ==> MSE: 0.02686 Loss: 0.027 + +2024-02-02 16:33:20,615 - ==> Best [Top 1 (MSE): 0.02671 Sparsity:0.00 Params: 136448 on epoch: 97] +2024-02-02 16:33:20,615 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:20,620 - + +2024-02-02 16:33:20,620 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:20,982 - Epoch: [100][ 1/ 8] Overall Loss 0.021514 Objective Loss 0.021514 MSE 0.021514 LR 0.001000 Time 0.361945 +2024-02-02 16:33:20,989 - Epoch: [100][ 2/ 8] Overall Loss 0.020776 Objective Loss 0.020776 MSE 0.020776 LR 0.001000 Time 0.184393 +2024-02-02 16:33:20,996 - Epoch: [100][ 3/ 8] Overall Loss 0.021031 Objective Loss 0.021031 MSE 0.021031 LR 0.001000 Time 0.125132 +2024-02-02 16:33:21,003 - Epoch: [100][ 4/ 8] Overall Loss 0.021527 Objective Loss 0.021527 MSE 0.021527 LR 0.001000 Time 0.095498 +2024-02-02 16:33:21,010 - Epoch: [100][ 5/ 8] Overall Loss 0.021694 Objective Loss 0.021694 MSE 0.021694 LR 0.001000 Time 0.077703 +2024-02-02 16:33:21,016 - Epoch: [100][ 6/ 8] Overall Loss 0.022171 Objective Loss 0.022171 MSE 0.022171 LR 0.001000 Time 0.065845 +2024-02-02 16:33:21,023 - Epoch: [100][ 7/ 8] Overall Loss 0.022164 Objective Loss 0.022164 MSE 0.022164 LR 0.001000 Time 0.057377 +2024-02-02 16:33:21,030 - Epoch: [100][ 8/ 8] Overall Loss 0.022633 Objective Loss 0.022633 MSE 0.022262 LR 0.001000 Time 0.051017 +2024-02-02 16:33:21,181 - --- validate (epoch=100)----------- +2024-02-02 16:33:21,181 - 60 samples (32 per mini-batch) +2024-02-02 16:33:21,534 - Epoch: [100][ 1/ 2] Loss 0.024358 MSE 0.024358 +2024-02-02 16:33:21,538 - Epoch: [100][ 2/ 2] Loss 0.026623 MSE 0.026472 +2024-02-02 16:33:21,683 - ==> MSE: 0.02647 Loss: 0.027 + +2024-02-02 16:33:21,688 - ==> Best [Top 1 (MSE): 0.02647 Sparsity:0.00 Params: 136448 on epoch: 100] +2024-02-02 16:33:21,688 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:21,694 - + +2024-02-02 16:33:21,694 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:22,046 - Epoch: [101][ 1/ 8] Overall Loss 0.018898 Objective Loss 0.018898 MSE 0.018898 LR 0.001000 Time 0.352096 +2024-02-02 16:33:22,055 - Epoch: [101][ 2/ 8] Overall Loss 0.020015 Objective Loss 0.020015 MSE 0.020015 LR 0.001000 Time 0.180082 +2024-02-02 16:33:22,063 - Epoch: [101][ 3/ 8] Overall Loss 0.020414 Objective Loss 0.020414 MSE 0.020414 LR 0.001000 Time 0.122697 +2024-02-02 16:33:22,070 - Epoch: [101][ 4/ 8] Overall Loss 0.021164 Objective Loss 0.021164 MSE 0.021164 LR 0.001000 Time 0.093666 +2024-02-02 16:33:22,076 - Epoch: [101][ 5/ 8] Overall Loss 0.021388 Objective Loss 0.021388 MSE 0.021388 LR 0.001000 Time 0.076239 +2024-02-02 16:33:22,083 - Epoch: [101][ 6/ 8] Overall Loss 0.021979 Objective Loss 0.021979 MSE 0.021979 LR 0.001000 Time 0.064622 +2024-02-02 16:33:22,089 - Epoch: [101][ 7/ 8] Overall Loss 0.022219 Objective Loss 0.022219 MSE 0.022219 LR 0.001000 Time 0.056311 +2024-02-02 16:33:22,096 - Epoch: [101][ 8/ 8] Overall Loss 0.023194 Objective Loss 0.023194 MSE 0.022422 LR 0.001000 Time 0.050084 +2024-02-02 16:33:22,249 - --- validate (epoch=101)----------- +2024-02-02 16:33:22,249 - 60 samples (32 per mini-batch) +2024-02-02 16:33:22,605 - Epoch: [101][ 1/ 2] Loss 0.026057 MSE 0.026057 +2024-02-02 16:33:22,609 - Epoch: [101][ 2/ 2] Loss 0.026568 MSE 0.026534 +2024-02-02 16:33:22,749 - ==> MSE: 0.02653 Loss: 0.027 + +2024-02-02 16:33:22,754 - ==> Best [Top 1 (MSE): 0.02647 Sparsity:0.00 Params: 136448 on epoch: 100] +2024-02-02 16:33:22,755 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:22,759 - + +2024-02-02 16:33:22,759 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:23,106 - Epoch: [102][ 1/ 8] Overall Loss 0.019711 Objective Loss 0.019711 MSE 0.019711 LR 0.001000 Time 0.346212 +2024-02-02 16:33:23,117 - Epoch: [102][ 2/ 8] Overall Loss 0.020812 Objective Loss 0.020812 MSE 0.020812 LR 0.001000 Time 0.178212 +2024-02-02 16:33:23,127 - Epoch: [102][ 3/ 8] Overall Loss 0.022168 Objective Loss 0.022168 MSE 0.022168 LR 0.001000 Time 0.122145 +2024-02-02 16:33:23,134 - Epoch: [102][ 4/ 8] Overall Loss 0.022641 Objective Loss 0.022641 MSE 0.022641 LR 0.001000 Time 0.093337 +2024-02-02 16:33:23,141 - Epoch: [102][ 5/ 8] Overall Loss 0.022056 Objective Loss 0.022056 MSE 0.022056 LR 0.001000 Time 0.076027 +2024-02-02 16:33:23,148 - Epoch: [102][ 6/ 8] Overall Loss 0.022053 Objective Loss 0.022053 MSE 0.022053 LR 0.001000 Time 0.064437 +2024-02-02 16:33:23,154 - Epoch: [102][ 7/ 8] Overall Loss 0.022091 Objective Loss 0.022091 MSE 0.022091 LR 0.001000 Time 0.056163 +2024-02-02 16:33:23,161 - Epoch: [102][ 8/ 8] Overall Loss 0.023495 Objective Loss 0.023495 MSE 0.022384 LR 0.001000 Time 0.049940 +2024-02-02 16:33:23,310 - --- validate (epoch=102)----------- +2024-02-02 16:33:23,311 - 60 samples (32 per mini-batch) +2024-02-02 16:33:23,661 - Epoch: [102][ 1/ 2] Loss 0.027501 MSE 0.027501 +2024-02-02 16:33:23,665 - Epoch: [102][ 2/ 2] Loss 0.027024 MSE 0.027056 +2024-02-02 16:33:23,813 - ==> MSE: 0.02706 Loss: 0.027 + +2024-02-02 16:33:23,818 - ==> Best [Top 1 (MSE): 0.02647 Sparsity:0.00 Params: 136448 on epoch: 100] +2024-02-02 16:33:23,819 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:23,823 - + +2024-02-02 16:33:23,823 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:24,186 - Epoch: [103][ 1/ 8] Overall Loss 0.022406 Objective Loss 0.022406 MSE 0.022406 LR 0.001000 Time 0.362626 +2024-02-02 16:33:24,197 - Epoch: [103][ 2/ 8] Overall Loss 0.023763 Objective Loss 0.023763 MSE 0.023763 LR 0.001000 Time 0.186301 +2024-02-02 16:33:24,207 - Epoch: [103][ 3/ 8] Overall Loss 0.022556 Objective Loss 0.022556 MSE 0.022556 LR 0.001000 Time 0.127479 +2024-02-02 16:33:24,214 - Epoch: [103][ 4/ 8] Overall Loss 0.022605 Objective Loss 0.022605 MSE 0.022605 LR 0.001000 Time 0.097284 +2024-02-02 16:33:24,220 - Epoch: [103][ 5/ 8] Overall Loss 0.022655 Objective Loss 0.022655 MSE 0.022655 LR 0.001000 Time 0.079163 +2024-02-02 16:33:24,227 - Epoch: [103][ 6/ 8] Overall Loss 0.022139 Objective Loss 0.022139 MSE 0.022139 LR 0.001000 Time 0.067076 +2024-02-02 16:33:24,234 - Epoch: [103][ 7/ 8] Overall Loss 0.022067 Objective Loss 0.022067 MSE 0.022067 LR 0.001000 Time 0.058437 +2024-02-02 16:33:24,241 - Epoch: [103][ 8/ 8] Overall Loss 0.022830 Objective Loss 0.022830 MSE 0.022227 LR 0.001000 Time 0.051938 +2024-02-02 16:33:24,392 - --- validate (epoch=103)----------- +2024-02-02 16:33:24,392 - 60 samples (32 per mini-batch) +2024-02-02 16:33:24,751 - Epoch: [103][ 1/ 2] Loss 0.028976 MSE 0.028976 +2024-02-02 16:33:24,756 - Epoch: [103][ 2/ 2] Loss 0.027061 MSE 0.027189 +2024-02-02 16:33:24,902 - ==> MSE: 0.02719 Loss: 0.027 + +2024-02-02 16:33:24,907 - ==> Best [Top 1 (MSE): 0.02647 Sparsity:0.00 Params: 136448 on epoch: 100] +2024-02-02 16:33:24,907 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:24,912 - + +2024-02-02 16:33:24,912 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:25,268 - Epoch: [104][ 1/ 8] Overall Loss 0.024215 Objective Loss 0.024215 MSE 0.024215 LR 0.001000 Time 0.356029 +2024-02-02 16:33:25,279 - Epoch: [104][ 2/ 8] Overall Loss 0.022780 Objective Loss 0.022780 MSE 0.022780 LR 0.001000 Time 0.183296 +2024-02-02 16:33:25,290 - Epoch: [104][ 3/ 8] Overall Loss 0.022781 Objective Loss 0.022781 MSE 0.022781 LR 0.001000 Time 0.125612 +2024-02-02 16:33:25,300 - Epoch: [104][ 4/ 8] Overall Loss 0.022905 Objective Loss 0.022905 MSE 0.022905 LR 0.001000 Time 0.096790 +2024-02-02 16:33:25,311 - Epoch: [104][ 5/ 8] Overall Loss 0.022888 Objective Loss 0.022888 MSE 0.022888 LR 0.001000 Time 0.079581 +2024-02-02 16:33:25,322 - Epoch: [104][ 6/ 8] Overall Loss 0.022814 Objective Loss 0.022814 MSE 0.022814 LR 0.001000 Time 0.068066 +2024-02-02 16:33:25,331 - Epoch: [104][ 7/ 8] Overall Loss 0.022322 Objective Loss 0.022322 MSE 0.022322 LR 0.001000 Time 0.059650 +2024-02-02 16:33:25,339 - Epoch: [104][ 8/ 8] Overall Loss 0.023304 Objective Loss 0.023304 MSE 0.022527 LR 0.001000 Time 0.053060 +2024-02-02 16:33:25,490 - --- validate (epoch=104)----------- +2024-02-02 16:33:25,490 - 60 samples (32 per mini-batch) +2024-02-02 16:33:25,847 - Epoch: [104][ 1/ 2] Loss 0.026523 MSE 0.026523 +2024-02-02 16:33:25,851 - Epoch: [104][ 2/ 2] Loss 0.026468 MSE 0.026471 +2024-02-02 16:33:26,000 - ==> MSE: 0.02647 Loss: 0.026 + +2024-02-02 16:33:26,006 - ==> Best [Top 1 (MSE): 0.02647 Sparsity:0.00 Params: 136448 on epoch: 104] +2024-02-02 16:33:26,006 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:26,012 - + +2024-02-02 16:33:26,012 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:26,366 - Epoch: [105][ 1/ 8] Overall Loss 0.023740 Objective Loss 0.023740 MSE 0.023740 LR 0.001000 Time 0.353945 +2024-02-02 16:33:26,375 - Epoch: [105][ 2/ 8] Overall Loss 0.021267 Objective Loss 0.021267 MSE 0.021267 LR 0.001000 Time 0.181134 +2024-02-02 16:33:26,385 - Epoch: [105][ 3/ 8] Overall Loss 0.020621 Objective Loss 0.020621 MSE 0.020621 LR 0.001000 Time 0.124086 +2024-02-02 16:33:26,393 - Epoch: [105][ 4/ 8] Overall Loss 0.021143 Objective Loss 0.021143 MSE 0.021143 LR 0.001000 Time 0.094892 +2024-02-02 16:33:26,400 - Epoch: [105][ 5/ 8] Overall Loss 0.021840 Objective Loss 0.021840 MSE 0.021840 LR 0.001000 Time 0.077256 +2024-02-02 16:33:26,406 - Epoch: [105][ 6/ 8] Overall Loss 0.022143 Objective Loss 0.022143 MSE 0.022143 LR 0.001000 Time 0.065475 +2024-02-02 16:33:26,413 - Epoch: [105][ 7/ 8] Overall Loss 0.022075 Objective Loss 0.022075 MSE 0.022075 LR 0.001000 Time 0.057050 +2024-02-02 16:33:26,420 - Epoch: [105][ 8/ 8] Overall Loss 0.021801 Objective Loss 0.021801 MSE 0.022018 LR 0.001000 Time 0.050733 +2024-02-02 16:33:26,573 - --- validate (epoch=105)----------- +2024-02-02 16:33:26,573 - 60 samples (32 per mini-batch) +2024-02-02 16:33:26,927 - Epoch: [105][ 1/ 2] Loss 0.027106 MSE 0.027106 +2024-02-02 16:33:26,931 - Epoch: [105][ 2/ 2] Loss 0.026423 MSE 0.026468 +2024-02-02 16:33:27,078 - ==> MSE: 0.02647 Loss: 0.026 + +2024-02-02 16:33:27,083 - ==> Best [Top 1 (MSE): 0.02647 Sparsity:0.00 Params: 136448 on epoch: 105] +2024-02-02 16:33:27,083 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:27,089 - + +2024-02-02 16:33:27,089 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:27,445 - Epoch: [106][ 1/ 8] Overall Loss 0.020156 Objective Loss 0.020156 MSE 0.020156 LR 0.001000 Time 0.355325 +2024-02-02 16:33:27,455 - Epoch: [106][ 2/ 8] Overall Loss 0.022139 Objective Loss 0.022139 MSE 0.022139 LR 0.001000 Time 0.182748 +2024-02-02 16:33:27,465 - Epoch: [106][ 3/ 8] Overall Loss 0.022202 Objective Loss 0.022202 MSE 0.022202 LR 0.001000 Time 0.125194 +2024-02-02 16:33:27,473 - Epoch: [106][ 4/ 8] Overall Loss 0.021782 Objective Loss 0.021782 MSE 0.021782 LR 0.001000 Time 0.095711 +2024-02-02 16:33:27,480 - Epoch: [106][ 5/ 8] Overall Loss 0.021861 Objective Loss 0.021861 MSE 0.021861 LR 0.001000 Time 0.077888 +2024-02-02 16:33:27,486 - Epoch: [106][ 6/ 8] Overall Loss 0.021999 Objective Loss 0.021999 MSE 0.021999 LR 0.001000 Time 0.066013 +2024-02-02 16:33:27,493 - Epoch: [106][ 7/ 8] Overall Loss 0.021909 Objective Loss 0.021909 MSE 0.021909 LR 0.001000 Time 0.057521 +2024-02-02 16:33:27,500 - Epoch: [106][ 8/ 8] Overall Loss 0.023250 Objective Loss 0.023250 MSE 0.022189 LR 0.001000 Time 0.051141 +2024-02-02 16:33:27,646 - --- validate (epoch=106)----------- +2024-02-02 16:33:27,646 - 60 samples (32 per mini-batch) +2024-02-02 16:33:27,988 - Epoch: [106][ 1/ 2] Loss 0.028215 MSE 0.028215 +2024-02-02 16:33:27,993 - Epoch: [106][ 2/ 2] Loss 0.026311 MSE 0.026438 +2024-02-02 16:33:28,137 - ==> MSE: 0.02644 Loss: 0.026 + +2024-02-02 16:33:28,142 - ==> Best [Top 1 (MSE): 0.02644 Sparsity:0.00 Params: 136448 on epoch: 106] +2024-02-02 16:33:28,143 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:28,148 - + +2024-02-02 16:33:28,148 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:28,508 - Epoch: [107][ 1/ 8] Overall Loss 0.024073 Objective Loss 0.024073 MSE 0.024073 LR 0.001000 Time 0.359787 +2024-02-02 16:33:28,516 - Epoch: [107][ 2/ 8] Overall Loss 0.023013 Objective Loss 0.023013 MSE 0.023013 LR 0.001000 Time 0.183445 +2024-02-02 16:33:28,523 - Epoch: [107][ 3/ 8] Overall Loss 0.022314 Objective Loss 0.022314 MSE 0.022314 LR 0.001000 Time 0.124556 +2024-02-02 16:33:28,530 - Epoch: [107][ 4/ 8] Overall Loss 0.021939 Objective Loss 0.021939 MSE 0.021939 LR 0.001000 Time 0.095114 +2024-02-02 16:33:28,537 - Epoch: [107][ 5/ 8] Overall Loss 0.022276 Objective Loss 0.022276 MSE 0.022276 LR 0.001000 Time 0.077436 +2024-02-02 16:33:28,543 - Epoch: [107][ 6/ 8] Overall Loss 0.022244 Objective Loss 0.022244 MSE 0.022244 LR 0.001000 Time 0.065623 +2024-02-02 16:33:28,550 - Epoch: [107][ 7/ 8] Overall Loss 0.021685 Objective Loss 0.021685 MSE 0.021685 LR 0.001000 Time 0.057198 +2024-02-02 16:33:28,557 - Epoch: [107][ 8/ 8] Overall Loss 0.023419 Objective Loss 0.023419 MSE 0.022047 LR 0.001000 Time 0.050870 +2024-02-02 16:33:28,703 - --- validate (epoch=107)----------- +2024-02-02 16:33:28,703 - 60 samples (32 per mini-batch) +2024-02-02 16:33:29,041 - Epoch: [107][ 1/ 2] Loss 0.026784 MSE 0.026784 +2024-02-02 16:33:29,046 - Epoch: [107][ 2/ 2] Loss 0.026321 MSE 0.026352 +2024-02-02 16:33:29,185 - ==> MSE: 0.02635 Loss: 0.026 + +2024-02-02 16:33:29,190 - ==> Best [Top 1 (MSE): 0.02635 Sparsity:0.00 Params: 136448 on epoch: 107] +2024-02-02 16:33:29,190 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:29,196 - + +2024-02-02 16:33:29,196 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:29,546 - Epoch: [108][ 1/ 8] Overall Loss 0.021715 Objective Loss 0.021715 MSE 0.021715 LR 0.001000 Time 0.349134 +2024-02-02 16:33:29,554 - Epoch: [108][ 2/ 8] Overall Loss 0.021490 Objective Loss 0.021490 MSE 0.021490 LR 0.001000 Time 0.178609 +2024-02-02 16:33:29,561 - Epoch: [108][ 3/ 8] Overall Loss 0.021014 Objective Loss 0.021014 MSE 0.021014 LR 0.001000 Time 0.121300 +2024-02-02 16:33:29,568 - Epoch: [108][ 4/ 8] Overall Loss 0.021795 Objective Loss 0.021795 MSE 0.021795 LR 0.001000 Time 0.092607 +2024-02-02 16:33:29,575 - Epoch: [108][ 5/ 8] Overall Loss 0.022128 Objective Loss 0.022128 MSE 0.022128 LR 0.001000 Time 0.075429 +2024-02-02 16:33:29,581 - Epoch: [108][ 6/ 8] Overall Loss 0.021715 Objective Loss 0.021715 MSE 0.021715 LR 0.001000 Time 0.063959 +2024-02-02 16:33:29,588 - Epoch: [108][ 7/ 8] Overall Loss 0.021789 Objective Loss 0.021789 MSE 0.021789 LR 0.001000 Time 0.055778 +2024-02-02 16:33:29,595 - Epoch: [108][ 8/ 8] Overall Loss 0.023432 Objective Loss 0.023432 MSE 0.022132 LR 0.001000 Time 0.049624 +2024-02-02 16:33:29,742 - --- validate (epoch=108)----------- +2024-02-02 16:33:29,743 - 60 samples (32 per mini-batch) +2024-02-02 16:33:30,102 - Epoch: [108][ 1/ 2] Loss 0.028693 MSE 0.028693 +2024-02-02 16:33:30,106 - Epoch: [108][ 2/ 2] Loss 0.026511 MSE 0.026656 +2024-02-02 16:33:30,256 - ==> MSE: 0.02666 Loss: 0.027 + +2024-02-02 16:33:30,262 - ==> Best [Top 1 (MSE): 0.02635 Sparsity:0.00 Params: 136448 on epoch: 107] +2024-02-02 16:33:30,262 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:30,267 - + +2024-02-02 16:33:30,267 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:30,633 - Epoch: [109][ 1/ 8] Overall Loss 0.020712 Objective Loss 0.020712 MSE 0.020712 LR 0.001000 Time 0.365087 +2024-02-02 16:33:30,643 - Epoch: [109][ 2/ 8] Overall Loss 0.021288 Objective Loss 0.021288 MSE 0.021288 LR 0.001000 Time 0.187565 +2024-02-02 16:33:30,650 - Epoch: [109][ 3/ 8] Overall Loss 0.021351 Objective Loss 0.021351 MSE 0.021351 LR 0.001000 Time 0.127501 +2024-02-02 16:33:30,659 - Epoch: [109][ 4/ 8] Overall Loss 0.022370 Objective Loss 0.022370 MSE 0.022370 LR 0.001000 Time 0.097670 +2024-02-02 16:33:30,666 - Epoch: [109][ 5/ 8] Overall Loss 0.022362 Objective Loss 0.022362 MSE 0.022362 LR 0.001000 Time 0.079487 +2024-02-02 16:33:30,673 - Epoch: [109][ 6/ 8] Overall Loss 0.022171 Objective Loss 0.022171 MSE 0.022171 LR 0.001000 Time 0.067351 +2024-02-02 16:33:30,679 - Epoch: [109][ 7/ 8] Overall Loss 0.021968 Objective Loss 0.021968 MSE 0.021968 LR 0.001000 Time 0.058671 +2024-02-02 16:33:30,686 - Epoch: [109][ 8/ 8] Overall Loss 0.022700 Objective Loss 0.022700 MSE 0.022121 LR 0.001000 Time 0.052149 +2024-02-02 16:33:30,837 - --- validate (epoch=109)----------- +2024-02-02 16:33:30,838 - 60 samples (32 per mini-batch) +2024-02-02 16:33:31,192 - Epoch: [109][ 1/ 2] Loss 0.028794 MSE 0.028794 +2024-02-02 16:33:31,197 - Epoch: [109][ 2/ 2] Loss 0.026787 MSE 0.026921 +2024-02-02 16:33:31,341 - ==> MSE: 0.02692 Loss: 0.027 + +2024-02-02 16:33:31,347 - ==> Best [Top 1 (MSE): 0.02635 Sparsity:0.00 Params: 136448 on epoch: 107] +2024-02-02 16:33:31,347 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:31,352 - + +2024-02-02 16:33:31,352 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:31,706 - Epoch: [110][ 1/ 8] Overall Loss 0.020609 Objective Loss 0.020609 MSE 0.020609 LR 0.001000 Time 0.353492 +2024-02-02 16:33:31,716 - Epoch: [110][ 2/ 8] Overall Loss 0.020439 Objective Loss 0.020439 MSE 0.020439 LR 0.001000 Time 0.181865 +2024-02-02 16:33:31,723 - Epoch: [110][ 3/ 8] Overall Loss 0.020210 Objective Loss 0.020210 MSE 0.020210 LR 0.001000 Time 0.123442 +2024-02-02 16:33:31,730 - Epoch: [110][ 4/ 8] Overall Loss 0.020471 Objective Loss 0.020471 MSE 0.020471 LR 0.001000 Time 0.094201 +2024-02-02 16:33:31,736 - Epoch: [110][ 5/ 8] Overall Loss 0.020790 Objective Loss 0.020790 MSE 0.020790 LR 0.001000 Time 0.076682 +2024-02-02 16:33:31,743 - Epoch: [110][ 6/ 8] Overall Loss 0.021058 Objective Loss 0.021058 MSE 0.021058 LR 0.001000 Time 0.064979 +2024-02-02 16:33:31,750 - Epoch: [110][ 7/ 8] Overall Loss 0.021655 Objective Loss 0.021655 MSE 0.021655 LR 0.001000 Time 0.056619 +2024-02-02 16:33:31,756 - Epoch: [110][ 8/ 8] Overall Loss 0.023689 Objective Loss 0.023689 MSE 0.022079 LR 0.001000 Time 0.050350 +2024-02-02 16:33:31,913 - --- validate (epoch=110)----------- +2024-02-02 16:33:31,913 - 60 samples (32 per mini-batch) +2024-02-02 16:33:32,271 - Epoch: [110][ 1/ 2] Loss 0.028406 MSE 0.028406 +2024-02-02 16:33:32,275 - Epoch: [110][ 2/ 2] Loss 0.026522 MSE 0.026648 +2024-02-02 16:33:32,424 - ==> MSE: 0.02665 Loss: 0.027 + +2024-02-02 16:33:32,430 - ==> Best [Top 1 (MSE): 0.02635 Sparsity:0.00 Params: 136448 on epoch: 107] +2024-02-02 16:33:32,430 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:32,435 - + +2024-02-02 16:33:32,435 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:32,792 - Epoch: [111][ 1/ 8] Overall Loss 0.019666 Objective Loss 0.019666 MSE 0.019666 LR 0.001000 Time 0.356996 +2024-02-02 16:33:32,799 - Epoch: [111][ 2/ 8] Overall Loss 0.019987 Objective Loss 0.019987 MSE 0.019987 LR 0.001000 Time 0.181860 +2024-02-02 16:33:32,806 - Epoch: [111][ 3/ 8] Overall Loss 0.020657 Objective Loss 0.020657 MSE 0.020657 LR 0.001000 Time 0.123456 +2024-02-02 16:33:32,813 - Epoch: [111][ 4/ 8] Overall Loss 0.021189 Objective Loss 0.021189 MSE 0.021189 LR 0.001000 Time 0.094264 +2024-02-02 16:33:32,820 - Epoch: [111][ 5/ 8] Overall Loss 0.021818 Objective Loss 0.021818 MSE 0.021818 LR 0.001000 Time 0.076747 +2024-02-02 16:33:32,826 - Epoch: [111][ 6/ 8] Overall Loss 0.021443 Objective Loss 0.021443 MSE 0.021443 LR 0.001000 Time 0.065046 +2024-02-02 16:33:32,833 - Epoch: [111][ 7/ 8] Overall Loss 0.021765 Objective Loss 0.021765 MSE 0.021765 LR 0.001000 Time 0.056686 +2024-02-02 16:33:32,840 - Epoch: [111][ 8/ 8] Overall Loss 0.023425 Objective Loss 0.023425 MSE 0.022112 LR 0.001000 Time 0.050411 +2024-02-02 16:33:32,984 - --- validate (epoch=111)----------- +2024-02-02 16:33:32,984 - 60 samples (32 per mini-batch) +2024-02-02 16:33:33,330 - Epoch: [111][ 1/ 2] Loss 0.027453 MSE 0.027453 +2024-02-02 16:33:33,335 - Epoch: [111][ 2/ 2] Loss 0.026141 MSE 0.026229 +2024-02-02 16:33:33,479 - ==> MSE: 0.02623 Loss: 0.026 + +2024-02-02 16:33:33,484 - ==> Best [Top 1 (MSE): 0.02623 Sparsity:0.00 Params: 136448 on epoch: 111] +2024-02-02 16:33:33,484 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:33,489 - + +2024-02-02 16:33:33,489 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:33,852 - Epoch: [112][ 1/ 8] Overall Loss 0.022263 Objective Loss 0.022263 MSE 0.022263 LR 0.001000 Time 0.361886 +2024-02-02 16:33:33,859 - Epoch: [112][ 2/ 8] Overall Loss 0.022671 Objective Loss 0.022671 MSE 0.022671 LR 0.001000 Time 0.184374 +2024-02-02 16:33:33,865 - Epoch: [112][ 3/ 8] Overall Loss 0.021873 Objective Loss 0.021873 MSE 0.021873 LR 0.001000 Time 0.125080 +2024-02-02 16:33:33,872 - Epoch: [112][ 4/ 8] Overall Loss 0.022479 Objective Loss 0.022479 MSE 0.022479 LR 0.001000 Time 0.095448 +2024-02-02 16:33:33,879 - Epoch: [112][ 5/ 8] Overall Loss 0.021968 Objective Loss 0.021968 MSE 0.021968 LR 0.001000 Time 0.077665 +2024-02-02 16:33:33,885 - Epoch: [112][ 6/ 8] Overall Loss 0.021936 Objective Loss 0.021936 MSE 0.021936 LR 0.001000 Time 0.065785 +2024-02-02 16:33:33,892 - Epoch: [112][ 7/ 8] Overall Loss 0.021570 Objective Loss 0.021570 MSE 0.021570 LR 0.001000 Time 0.057323 +2024-02-02 16:33:33,899 - Epoch: [112][ 8/ 8] Overall Loss 0.022355 Objective Loss 0.022355 MSE 0.021734 LR 0.001000 Time 0.050948 +2024-02-02 16:33:34,052 - --- validate (epoch=112)----------- +2024-02-02 16:33:34,053 - 60 samples (32 per mini-batch) +2024-02-02 16:33:34,408 - Epoch: [112][ 1/ 2] Loss 0.023205 MSE 0.023205 +2024-02-02 16:33:34,412 - Epoch: [112][ 2/ 2] Loss 0.026477 MSE 0.026259 +2024-02-02 16:33:34,554 - ==> MSE: 0.02626 Loss: 0.026 + +2024-02-02 16:33:34,559 - ==> Best [Top 1 (MSE): 0.02623 Sparsity:0.00 Params: 136448 on epoch: 111] +2024-02-02 16:33:34,559 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:34,564 - + +2024-02-02 16:33:34,564 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:34,927 - Epoch: [113][ 1/ 8] Overall Loss 0.024538 Objective Loss 0.024538 MSE 0.024538 LR 0.001000 Time 0.362170 +2024-02-02 16:33:34,934 - Epoch: [113][ 2/ 8] Overall Loss 0.023668 Objective Loss 0.023668 MSE 0.023668 LR 0.001000 Time 0.184599 +2024-02-02 16:33:34,941 - Epoch: [113][ 3/ 8] Overall Loss 0.022170 Objective Loss 0.022170 MSE 0.022170 LR 0.001000 Time 0.125280 +2024-02-02 16:33:34,948 - Epoch: [113][ 4/ 8] Overall Loss 0.022040 Objective Loss 0.022040 MSE 0.022040 LR 0.001000 Time 0.095606 +2024-02-02 16:33:34,955 - Epoch: [113][ 5/ 8] Overall Loss 0.022149 Objective Loss 0.022149 MSE 0.022149 LR 0.001000 Time 0.077807 +2024-02-02 16:33:34,961 - Epoch: [113][ 6/ 8] Overall Loss 0.022122 Objective Loss 0.022122 MSE 0.022122 LR 0.001000 Time 0.065912 +2024-02-02 16:33:34,968 - Epoch: [113][ 7/ 8] Overall Loss 0.021874 Objective Loss 0.021874 MSE 0.021874 LR 0.001000 Time 0.057444 +2024-02-02 16:33:34,975 - Epoch: [113][ 8/ 8] Overall Loss 0.021730 Objective Loss 0.021730 MSE 0.021844 LR 0.001000 Time 0.051077 +2024-02-02 16:33:35,126 - --- validate (epoch=113)----------- +2024-02-02 16:33:35,127 - 60 samples (32 per mini-batch) +2024-02-02 16:33:35,485 - Epoch: [113][ 1/ 2] Loss 0.027074 MSE 0.027074 +2024-02-02 16:33:35,489 - Epoch: [113][ 2/ 2] Loss 0.027040 MSE 0.027043 +2024-02-02 16:33:35,627 - ==> MSE: 0.02704 Loss: 0.027 + +2024-02-02 16:33:35,633 - ==> Best [Top 1 (MSE): 0.02623 Sparsity:0.00 Params: 136448 on epoch: 111] +2024-02-02 16:33:35,633 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:35,637 - + +2024-02-02 16:33:35,637 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:35,986 - Epoch: [114][ 1/ 8] Overall Loss 0.020107 Objective Loss 0.020107 MSE 0.020107 LR 0.001000 Time 0.348552 +2024-02-02 16:33:35,993 - Epoch: [114][ 2/ 8] Overall Loss 0.020472 Objective Loss 0.020472 MSE 0.020472 LR 0.001000 Time 0.177691 +2024-02-02 16:33:36,000 - Epoch: [114][ 3/ 8] Overall Loss 0.021304 Objective Loss 0.021304 MSE 0.021304 LR 0.001000 Time 0.120669 +2024-02-02 16:33:36,007 - Epoch: [114][ 4/ 8] Overall Loss 0.021555 Objective Loss 0.021555 MSE 0.021555 LR 0.001000 Time 0.092106 +2024-02-02 16:33:36,014 - Epoch: [114][ 5/ 8] Overall Loss 0.021651 Objective Loss 0.021651 MSE 0.021651 LR 0.001000 Time 0.075009 +2024-02-02 16:33:36,020 - Epoch: [114][ 6/ 8] Overall Loss 0.021526 Objective Loss 0.021526 MSE 0.021526 LR 0.001000 Time 0.063608 +2024-02-02 16:33:36,027 - Epoch: [114][ 7/ 8] Overall Loss 0.021584 Objective Loss 0.021584 MSE 0.021584 LR 0.001000 Time 0.055473 +2024-02-02 16:33:36,034 - Epoch: [114][ 8/ 8] Overall Loss 0.021935 Objective Loss 0.021935 MSE 0.021657 LR 0.001000 Time 0.049369 +2024-02-02 16:33:36,186 - --- validate (epoch=114)----------- +2024-02-02 16:33:36,186 - 60 samples (32 per mini-batch) +2024-02-02 16:33:36,534 - Epoch: [114][ 1/ 2] Loss 0.024994 MSE 0.024994 +2024-02-02 16:33:36,539 - Epoch: [114][ 2/ 2] Loss 0.026904 MSE 0.026777 +2024-02-02 16:33:36,678 - ==> MSE: 0.02678 Loss: 0.027 + +2024-02-02 16:33:36,683 - ==> Best [Top 1 (MSE): 0.02623 Sparsity:0.00 Params: 136448 on epoch: 111] +2024-02-02 16:33:36,683 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:36,695 - + +2024-02-02 16:33:36,695 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:37,041 - Epoch: [115][ 1/ 8] Overall Loss 0.023357 Objective Loss 0.023357 MSE 0.023357 LR 0.001000 Time 0.344867 +2024-02-02 16:33:37,048 - Epoch: [115][ 2/ 8] Overall Loss 0.021639 Objective Loss 0.021639 MSE 0.021639 LR 0.001000 Time 0.175856 +2024-02-02 16:33:37,054 - Epoch: [115][ 3/ 8] Overall Loss 0.021364 Objective Loss 0.021364 MSE 0.021364 LR 0.001000 Time 0.119443 +2024-02-02 16:33:37,061 - Epoch: [115][ 4/ 8] Overall Loss 0.021024 Objective Loss 0.021024 MSE 0.021024 LR 0.001000 Time 0.091208 +2024-02-02 16:33:37,068 - Epoch: [115][ 5/ 8] Overall Loss 0.021442 Objective Loss 0.021442 MSE 0.021442 LR 0.001000 Time 0.074275 +2024-02-02 16:33:37,074 - Epoch: [115][ 6/ 8] Overall Loss 0.021658 Objective Loss 0.021658 MSE 0.021658 LR 0.001000 Time 0.062970 +2024-02-02 16:33:37,081 - Epoch: [115][ 7/ 8] Overall Loss 0.021485 Objective Loss 0.021485 MSE 0.021485 LR 0.001000 Time 0.054921 +2024-02-02 16:33:37,088 - Epoch: [115][ 8/ 8] Overall Loss 0.023942 Objective Loss 0.023942 MSE 0.021998 LR 0.001000 Time 0.048860 +2024-02-02 16:33:37,239 - --- validate (epoch=115)----------- +2024-02-02 16:33:37,240 - 60 samples (32 per mini-batch) +2024-02-02 16:33:37,594 - Epoch: [115][ 1/ 2] Loss 0.025405 MSE 0.025405 +2024-02-02 16:33:37,599 - Epoch: [115][ 2/ 2] Loss 0.025991 MSE 0.025952 +2024-02-02 16:33:37,746 - ==> MSE: 0.02595 Loss: 0.026 + +2024-02-02 16:33:37,752 - ==> Best [Top 1 (MSE): 0.02595 Sparsity:0.00 Params: 136448 on epoch: 115] +2024-02-02 16:33:37,752 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:37,758 - + +2024-02-02 16:33:37,758 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:38,121 - Epoch: [116][ 1/ 8] Overall Loss 0.022162 Objective Loss 0.022162 MSE 0.022162 LR 0.001000 Time 0.362390 +2024-02-02 16:33:38,131 - Epoch: [116][ 2/ 8] Overall Loss 0.022074 Objective Loss 0.022074 MSE 0.022074 LR 0.001000 Time 0.186310 +2024-02-02 16:33:38,142 - Epoch: [116][ 3/ 8] Overall Loss 0.021304 Objective Loss 0.021304 MSE 0.021304 LR 0.001000 Time 0.127602 +2024-02-02 16:33:38,152 - Epoch: [116][ 4/ 8] Overall Loss 0.020886 Objective Loss 0.020886 MSE 0.020886 LR 0.001000 Time 0.098207 +2024-02-02 16:33:38,162 - Epoch: [116][ 5/ 8] Overall Loss 0.021159 Objective Loss 0.021159 MSE 0.021159 LR 0.001000 Time 0.080563 +2024-02-02 16:33:38,171 - Epoch: [116][ 6/ 8] Overall Loss 0.021248 Objective Loss 0.021248 MSE 0.021248 LR 0.001000 Time 0.068583 +2024-02-02 16:33:38,178 - Epoch: [116][ 7/ 8] Overall Loss 0.021443 Objective Loss 0.021443 MSE 0.021443 LR 0.001000 Time 0.059747 +2024-02-02 16:33:38,185 - Epoch: [116][ 8/ 8] Overall Loss 0.022251 Objective Loss 0.022251 MSE 0.021611 LR 0.001000 Time 0.053087 +2024-02-02 16:33:38,332 - --- validate (epoch=116)----------- +2024-02-02 16:33:38,332 - 60 samples (32 per mini-batch) +2024-02-02 16:33:38,683 - Epoch: [116][ 1/ 2] Loss 0.022464 MSE 0.022464 +2024-02-02 16:33:38,688 - Epoch: [116][ 2/ 2] Loss 0.025923 MSE 0.025693 +2024-02-02 16:33:38,833 - ==> MSE: 0.02569 Loss: 0.026 + +2024-02-02 16:33:38,839 - ==> Best [Top 1 (MSE): 0.02569 Sparsity:0.00 Params: 136448 on epoch: 116] +2024-02-02 16:33:38,839 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:38,845 - + +2024-02-02 16:33:38,845 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:39,211 - Epoch: [117][ 1/ 8] Overall Loss 0.022043 Objective Loss 0.022043 MSE 0.022043 LR 0.001000 Time 0.365904 +2024-02-02 16:33:39,222 - Epoch: [117][ 2/ 8] Overall Loss 0.021617 Objective Loss 0.021617 MSE 0.021617 LR 0.001000 Time 0.188124 +2024-02-02 16:33:39,232 - Epoch: [117][ 3/ 8] Overall Loss 0.021991 Objective Loss 0.021991 MSE 0.021991 LR 0.001000 Time 0.128882 +2024-02-02 16:33:39,243 - Epoch: [117][ 4/ 8] Overall Loss 0.022142 Objective Loss 0.022142 MSE 0.022142 LR 0.001000 Time 0.099288 +2024-02-02 16:33:39,254 - Epoch: [117][ 5/ 8] Overall Loss 0.021512 Objective Loss 0.021512 MSE 0.021512 LR 0.001000 Time 0.081549 +2024-02-02 16:33:39,263 - Epoch: [117][ 6/ 8] Overall Loss 0.021418 Objective Loss 0.021418 MSE 0.021418 LR 0.001000 Time 0.069424 +2024-02-02 16:33:39,270 - Epoch: [117][ 7/ 8] Overall Loss 0.021633 Objective Loss 0.021633 MSE 0.021633 LR 0.001000 Time 0.060450 +2024-02-02 16:33:39,277 - Epoch: [117][ 8/ 8] Overall Loss 0.021638 Objective Loss 0.021638 MSE 0.021634 LR 0.001000 Time 0.053710 +2024-02-02 16:33:39,421 - --- validate (epoch=117)----------- +2024-02-02 16:33:39,422 - 60 samples (32 per mini-batch) +2024-02-02 16:33:39,761 - Epoch: [117][ 1/ 2] Loss 0.025833 MSE 0.025833 +2024-02-02 16:33:39,765 - Epoch: [117][ 2/ 2] Loss 0.025506 MSE 0.025527 +2024-02-02 16:33:39,898 - ==> MSE: 0.02553 Loss: 0.026 + +2024-02-02 16:33:39,903 - ==> Best [Top 1 (MSE): 0.02553 Sparsity:0.00 Params: 136448 on epoch: 117] +2024-02-02 16:33:39,903 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:39,909 - + +2024-02-02 16:33:39,909 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:40,261 - Epoch: [118][ 1/ 8] Overall Loss 0.022650 Objective Loss 0.022650 MSE 0.022650 LR 0.001000 Time 0.351615 +2024-02-02 16:33:40,268 - Epoch: [118][ 2/ 8] Overall Loss 0.021883 Objective Loss 0.021883 MSE 0.021883 LR 0.001000 Time 0.179269 +2024-02-02 16:33:40,275 - Epoch: [118][ 3/ 8] Overall Loss 0.021386 Objective Loss 0.021386 MSE 0.021386 LR 0.001000 Time 0.121690 +2024-02-02 16:33:40,282 - Epoch: [118][ 4/ 8] Overall Loss 0.022388 Objective Loss 0.022388 MSE 0.022388 LR 0.001000 Time 0.092922 +2024-02-02 16:33:40,289 - Epoch: [118][ 5/ 8] Overall Loss 0.022102 Objective Loss 0.022102 MSE 0.022102 LR 0.001000 Time 0.075683 +2024-02-02 16:33:40,296 - Epoch: [118][ 6/ 8] Overall Loss 0.022077 Objective Loss 0.022077 MSE 0.022077 LR 0.001000 Time 0.064177 +2024-02-02 16:33:40,302 - Epoch: [118][ 7/ 8] Overall Loss 0.021491 Objective Loss 0.021491 MSE 0.021491 LR 0.001000 Time 0.055966 +2024-02-02 16:33:40,309 - Epoch: [118][ 8/ 8] Overall Loss 0.021227 Objective Loss 0.021227 MSE 0.021436 LR 0.001000 Time 0.049786 +2024-02-02 16:33:40,459 - --- validate (epoch=118)----------- +2024-02-02 16:33:40,459 - 60 samples (32 per mini-batch) +2024-02-02 16:33:40,806 - Epoch: [118][ 1/ 2] Loss 0.025058 MSE 0.025058 +2024-02-02 16:33:40,811 - Epoch: [118][ 2/ 2] Loss 0.025528 MSE 0.025497 +2024-02-02 16:33:40,959 - ==> MSE: 0.02550 Loss: 0.026 + +2024-02-02 16:33:40,964 - ==> Best [Top 1 (MSE): 0.02550 Sparsity:0.00 Params: 136448 on epoch: 118] +2024-02-02 16:33:40,964 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:40,970 - + +2024-02-02 16:33:40,970 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:41,331 - Epoch: [119][ 1/ 8] Overall Loss 0.021273 Objective Loss 0.021273 MSE 0.021273 LR 0.001000 Time 0.360980 +2024-02-02 16:33:41,339 - Epoch: [119][ 2/ 8] Overall Loss 0.020992 Objective Loss 0.020992 MSE 0.020992 LR 0.001000 Time 0.183927 +2024-02-02 16:33:41,345 - Epoch: [119][ 3/ 8] Overall Loss 0.021584 Objective Loss 0.021584 MSE 0.021584 LR 0.001000 Time 0.124785 +2024-02-02 16:33:41,352 - Epoch: [119][ 4/ 8] Overall Loss 0.021550 Objective Loss 0.021550 MSE 0.021550 LR 0.001000 Time 0.095208 +2024-02-02 16:33:41,358 - Epoch: [119][ 5/ 8] Overall Loss 0.021354 Objective Loss 0.021354 MSE 0.021354 LR 0.001000 Time 0.077476 +2024-02-02 16:33:41,365 - Epoch: [119][ 6/ 8] Overall Loss 0.021045 Objective Loss 0.021045 MSE 0.021045 LR 0.001000 Time 0.065623 +2024-02-02 16:33:41,372 - Epoch: [119][ 7/ 8] Overall Loss 0.020987 Objective Loss 0.020987 MSE 0.020987 LR 0.001000 Time 0.057182 +2024-02-02 16:33:41,378 - Epoch: [119][ 8/ 8] Overall Loss 0.022583 Objective Loss 0.022583 MSE 0.021320 LR 0.001000 Time 0.050838 +2024-02-02 16:33:41,528 - --- validate (epoch=119)----------- +2024-02-02 16:33:41,529 - 60 samples (32 per mini-batch) +2024-02-02 16:33:41,877 - Epoch: [119][ 1/ 2] Loss 0.025776 MSE 0.025776 +2024-02-02 16:33:41,882 - Epoch: [119][ 2/ 2] Loss 0.025471 MSE 0.025491 +2024-02-02 16:33:42,030 - ==> MSE: 0.02549 Loss: 0.025 + +2024-02-02 16:33:42,042 - ==> Best [Top 1 (MSE): 0.02549 Sparsity:0.00 Params: 136448 on epoch: 119] +2024-02-02 16:33:42,042 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:42,052 - + +2024-02-02 16:33:42,052 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:42,421 - Epoch: [120][ 1/ 8] Overall Loss 0.021590 Objective Loss 0.021590 MSE 0.021590 LR 0.001000 Time 0.367575 +2024-02-02 16:33:42,431 - Epoch: [120][ 2/ 8] Overall Loss 0.021620 Objective Loss 0.021620 MSE 0.021620 LR 0.001000 Time 0.188909 +2024-02-02 16:33:42,440 - Epoch: [120][ 3/ 8] Overall Loss 0.021895 Objective Loss 0.021895 MSE 0.021895 LR 0.001000 Time 0.128700 +2024-02-02 16:33:42,446 - Epoch: [120][ 4/ 8] Overall Loss 0.022250 Objective Loss 0.022250 MSE 0.022250 LR 0.001000 Time 0.098181 +2024-02-02 16:33:42,453 - Epoch: [120][ 5/ 8] Overall Loss 0.021816 Objective Loss 0.021816 MSE 0.021816 LR 0.001000 Time 0.079900 +2024-02-02 16:33:42,460 - Epoch: [120][ 6/ 8] Overall Loss 0.021719 Objective Loss 0.021719 MSE 0.021719 LR 0.001000 Time 0.067700 +2024-02-02 16:33:42,467 - Epoch: [120][ 7/ 8] Overall Loss 0.021819 Objective Loss 0.021819 MSE 0.021819 LR 0.001000 Time 0.058978 +2024-02-02 16:33:42,474 - Epoch: [120][ 8/ 8] Overall Loss 0.023130 Objective Loss 0.023130 MSE 0.022092 LR 0.001000 Time 0.052423 +2024-02-02 16:33:42,625 - --- validate (epoch=120)----------- +2024-02-02 16:33:42,625 - 60 samples (32 per mini-batch) +2024-02-02 16:33:42,977 - Epoch: [120][ 1/ 2] Loss 0.025671 MSE 0.025671 +2024-02-02 16:33:42,981 - Epoch: [120][ 2/ 2] Loss 0.025593 MSE 0.025599 +2024-02-02 16:33:43,126 - ==> MSE: 0.02560 Loss: 0.026 + +2024-02-02 16:33:43,132 - ==> Best [Top 1 (MSE): 0.02549 Sparsity:0.00 Params: 136448 on epoch: 119] +2024-02-02 16:33:43,132 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:43,137 - + +2024-02-02 16:33:43,137 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:43,493 - Epoch: [121][ 1/ 8] Overall Loss 0.022141 Objective Loss 0.022141 MSE 0.022141 LR 0.001000 Time 0.355791 +2024-02-02 16:33:43,504 - Epoch: [121][ 2/ 8] Overall Loss 0.022643 Objective Loss 0.022643 MSE 0.022643 LR 0.001000 Time 0.183033 +2024-02-02 16:33:43,513 - Epoch: [121][ 3/ 8] Overall Loss 0.022578 Objective Loss 0.022578 MSE 0.022578 LR 0.001000 Time 0.124972 +2024-02-02 16:33:43,520 - Epoch: [121][ 4/ 8] Overall Loss 0.022257 Objective Loss 0.022257 MSE 0.022257 LR 0.001000 Time 0.095405 +2024-02-02 16:33:43,527 - Epoch: [121][ 5/ 8] Overall Loss 0.021663 Objective Loss 0.021663 MSE 0.021663 LR 0.001000 Time 0.077654 +2024-02-02 16:33:43,533 - Epoch: [121][ 6/ 8] Overall Loss 0.021242 Objective Loss 0.021242 MSE 0.021242 LR 0.001000 Time 0.065810 +2024-02-02 16:33:43,540 - Epoch: [121][ 7/ 8] Overall Loss 0.021183 Objective Loss 0.021183 MSE 0.021183 LR 0.001000 Time 0.057358 +2024-02-02 16:33:43,547 - Epoch: [121][ 8/ 8] Overall Loss 0.021186 Objective Loss 0.021186 MSE 0.021184 LR 0.001000 Time 0.050988 +2024-02-02 16:33:43,697 - --- validate (epoch=121)----------- +2024-02-02 16:33:43,697 - 60 samples (32 per mini-batch) +2024-02-02 16:33:44,063 - Epoch: [121][ 1/ 2] Loss 0.025335 MSE 0.025335 +2024-02-02 16:33:44,068 - Epoch: [121][ 2/ 2] Loss 0.025626 MSE 0.025606 +2024-02-02 16:33:44,210 - ==> MSE: 0.02561 Loss: 0.026 + +2024-02-02 16:33:44,216 - ==> Best [Top 1 (MSE): 0.02549 Sparsity:0.00 Params: 136448 on epoch: 119] +2024-02-02 16:33:44,216 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:44,221 - + +2024-02-02 16:33:44,221 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:44,577 - Epoch: [122][ 1/ 8] Overall Loss 0.019297 Objective Loss 0.019297 MSE 0.019297 LR 0.001000 Time 0.355821 +2024-02-02 16:33:44,585 - Epoch: [122][ 2/ 8] Overall Loss 0.018983 Objective Loss 0.018983 MSE 0.018983 LR 0.001000 Time 0.181369 +2024-02-02 16:33:44,591 - Epoch: [122][ 3/ 8] Overall Loss 0.020095 Objective Loss 0.020095 MSE 0.020095 LR 0.001000 Time 0.123057 +2024-02-02 16:33:44,598 - Epoch: [122][ 4/ 8] Overall Loss 0.020527 Objective Loss 0.020527 MSE 0.020527 LR 0.001000 Time 0.093903 +2024-02-02 16:33:44,604 - Epoch: [122][ 5/ 8] Overall Loss 0.020389 Objective Loss 0.020389 MSE 0.020389 LR 0.001000 Time 0.076414 +2024-02-02 16:33:44,611 - Epoch: [122][ 6/ 8] Overall Loss 0.020507 Objective Loss 0.020507 MSE 0.020507 LR 0.001000 Time 0.064743 +2024-02-02 16:33:44,617 - Epoch: [122][ 7/ 8] Overall Loss 0.020863 Objective Loss 0.020863 MSE 0.020863 LR 0.001000 Time 0.056408 +2024-02-02 16:33:44,624 - Epoch: [122][ 8/ 8] Overall Loss 0.022657 Objective Loss 0.022657 MSE 0.021237 LR 0.001000 Time 0.050153 +2024-02-02 16:33:44,775 - --- validate (epoch=122)----------- +2024-02-02 16:33:44,775 - 60 samples (32 per mini-batch) +2024-02-02 16:33:45,132 - Epoch: [122][ 1/ 2] Loss 0.027689 MSE 0.027689 +2024-02-02 16:33:45,136 - Epoch: [122][ 2/ 2] Loss 0.025600 MSE 0.025740 +2024-02-02 16:33:45,286 - ==> MSE: 0.02574 Loss: 0.026 + +2024-02-02 16:33:45,292 - ==> Best [Top 1 (MSE): 0.02549 Sparsity:0.00 Params: 136448 on epoch: 119] +2024-02-02 16:33:45,292 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:45,297 - + +2024-02-02 16:33:45,297 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:45,657 - Epoch: [123][ 1/ 8] Overall Loss 0.021993 Objective Loss 0.021993 MSE 0.021993 LR 0.001000 Time 0.358926 +2024-02-02 16:33:45,664 - Epoch: [123][ 2/ 8] Overall Loss 0.021089 Objective Loss 0.021089 MSE 0.021089 LR 0.001000 Time 0.182995 +2024-02-02 16:33:45,671 - Epoch: [123][ 3/ 8] Overall Loss 0.021216 Objective Loss 0.021216 MSE 0.021216 LR 0.001000 Time 0.124219 +2024-02-02 16:33:45,677 - Epoch: [123][ 4/ 8] Overall Loss 0.021648 Objective Loss 0.021648 MSE 0.021648 LR 0.001000 Time 0.094808 +2024-02-02 16:33:45,684 - Epoch: [123][ 5/ 8] Overall Loss 0.021282 Objective Loss 0.021282 MSE 0.021282 LR 0.001000 Time 0.077173 +2024-02-02 16:33:45,691 - Epoch: [123][ 6/ 8] Overall Loss 0.021276 Objective Loss 0.021276 MSE 0.021276 LR 0.001000 Time 0.065377 +2024-02-02 16:33:45,697 - Epoch: [123][ 7/ 8] Overall Loss 0.021090 Objective Loss 0.021090 MSE 0.021090 LR 0.001000 Time 0.056950 +2024-02-02 16:33:45,704 - Epoch: [123][ 8/ 8] Overall Loss 0.021508 Objective Loss 0.021508 MSE 0.021177 LR 0.001000 Time 0.050632 +2024-02-02 16:33:45,859 - --- validate (epoch=123)----------- +2024-02-02 16:33:45,859 - 60 samples (32 per mini-batch) +2024-02-02 16:33:46,214 - Epoch: [123][ 1/ 2] Loss 0.024488 MSE 0.024488 +2024-02-02 16:33:46,218 - Epoch: [123][ 2/ 2] Loss 0.025526 MSE 0.025456 +2024-02-02 16:33:46,365 - ==> MSE: 0.02546 Loss: 0.026 + +2024-02-02 16:33:46,371 - ==> Best [Top 1 (MSE): 0.02546 Sparsity:0.00 Params: 136448 on epoch: 123] +2024-02-02 16:33:46,371 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:46,377 - + +2024-02-02 16:33:46,377 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:46,741 - Epoch: [124][ 1/ 8] Overall Loss 0.022406 Objective Loss 0.022406 MSE 0.022406 LR 0.001000 Time 0.363181 +2024-02-02 16:33:46,748 - Epoch: [124][ 2/ 8] Overall Loss 0.020440 Objective Loss 0.020440 MSE 0.020440 LR 0.001000 Time 0.184992 +2024-02-02 16:33:46,754 - Epoch: [124][ 3/ 8] Overall Loss 0.020461 Objective Loss 0.020461 MSE 0.020461 LR 0.001000 Time 0.125479 +2024-02-02 16:33:46,761 - Epoch: [124][ 4/ 8] Overall Loss 0.020416 Objective Loss 0.020416 MSE 0.020416 LR 0.001000 Time 0.095718 +2024-02-02 16:33:46,768 - Epoch: [124][ 5/ 8] Overall Loss 0.020412 Objective Loss 0.020412 MSE 0.020412 LR 0.001000 Time 0.077886 +2024-02-02 16:33:46,774 - Epoch: [124][ 6/ 8] Overall Loss 0.020658 Objective Loss 0.020658 MSE 0.020658 LR 0.001000 Time 0.065984 +2024-02-02 16:33:46,781 - Epoch: [124][ 7/ 8] Overall Loss 0.020893 Objective Loss 0.020893 MSE 0.020893 LR 0.001000 Time 0.057491 +2024-02-02 16:33:46,787 - Epoch: [124][ 8/ 8] Overall Loss 0.021789 Objective Loss 0.021789 MSE 0.021080 LR 0.001000 Time 0.051119 +2024-02-02 16:33:46,938 - --- validate (epoch=124)----------- +2024-02-02 16:33:46,938 - 60 samples (32 per mini-batch) +2024-02-02 16:33:47,286 - Epoch: [124][ 1/ 2] Loss 0.024580 MSE 0.024580 +2024-02-02 16:33:47,292 - Epoch: [124][ 2/ 2] Loss 0.025225 MSE 0.025182 +2024-02-02 16:33:47,439 - ==> MSE: 0.02518 Loss: 0.025 + +2024-02-02 16:33:47,445 - ==> Best [Top 1 (MSE): 0.02518 Sparsity:0.00 Params: 136448 on epoch: 124] +2024-02-02 16:33:47,445 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:47,451 - + +2024-02-02 16:33:47,451 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:47,808 - Epoch: [125][ 1/ 8] Overall Loss 0.020270 Objective Loss 0.020270 MSE 0.020270 LR 0.001000 Time 0.356546 +2024-02-02 16:33:47,819 - Epoch: [125][ 2/ 8] Overall Loss 0.020055 Objective Loss 0.020055 MSE 0.020055 LR 0.001000 Time 0.183465 +2024-02-02 16:33:47,829 - Epoch: [125][ 3/ 8] Overall Loss 0.020407 Objective Loss 0.020407 MSE 0.020407 LR 0.001000 Time 0.125659 +2024-02-02 16:33:47,837 - Epoch: [125][ 4/ 8] Overall Loss 0.022084 Objective Loss 0.022084 MSE 0.022084 LR 0.001000 Time 0.096248 +2024-02-02 16:33:47,844 - Epoch: [125][ 5/ 8] Overall Loss 0.022180 Objective Loss 0.022180 MSE 0.022180 LR 0.001000 Time 0.078368 +2024-02-02 16:33:47,851 - Epoch: [125][ 6/ 8] Overall Loss 0.021596 Objective Loss 0.021596 MSE 0.021596 LR 0.001000 Time 0.066438 +2024-02-02 16:33:47,858 - Epoch: [125][ 7/ 8] Overall Loss 0.021348 Objective Loss 0.021348 MSE 0.021348 LR 0.001000 Time 0.057871 +2024-02-02 16:33:47,864 - Epoch: [125][ 8/ 8] Overall Loss 0.021495 Objective Loss 0.021495 MSE 0.021379 LR 0.001000 Time 0.051426 +2024-02-02 16:33:48,014 - --- validate (epoch=125)----------- +2024-02-02 16:33:48,014 - 60 samples (32 per mini-batch) +2024-02-02 16:33:48,369 - Epoch: [125][ 1/ 2] Loss 0.026970 MSE 0.026970 +2024-02-02 16:33:48,374 - Epoch: [125][ 2/ 2] Loss 0.025121 MSE 0.025244 +2024-02-02 16:33:48,516 - ==> MSE: 0.02524 Loss: 0.025 + +2024-02-02 16:33:48,529 - ==> Best [Top 1 (MSE): 0.02518 Sparsity:0.00 Params: 136448 on epoch: 124] +2024-02-02 16:33:48,529 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:48,538 - + +2024-02-02 16:33:48,538 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:48,902 - Epoch: [126][ 1/ 8] Overall Loss 0.020659 Objective Loss 0.020659 MSE 0.020659 LR 0.001000 Time 0.362946 +2024-02-02 16:33:48,909 - Epoch: [126][ 2/ 8] Overall Loss 0.020379 Objective Loss 0.020379 MSE 0.020379 LR 0.001000 Time 0.184930 +2024-02-02 16:33:48,916 - Epoch: [126][ 3/ 8] Overall Loss 0.019630 Objective Loss 0.019630 MSE 0.019630 LR 0.001000 Time 0.125498 +2024-02-02 16:33:48,922 - Epoch: [126][ 4/ 8] Overall Loss 0.019960 Objective Loss 0.019960 MSE 0.019960 LR 0.001000 Time 0.095787 +2024-02-02 16:33:48,929 - Epoch: [126][ 5/ 8] Overall Loss 0.020159 Objective Loss 0.020159 MSE 0.020159 LR 0.001000 Time 0.077938 +2024-02-02 16:33:48,936 - Epoch: [126][ 6/ 8] Overall Loss 0.021018 Objective Loss 0.021018 MSE 0.021018 LR 0.001000 Time 0.066040 +2024-02-02 16:33:48,942 - Epoch: [126][ 7/ 8] Overall Loss 0.020849 Objective Loss 0.020849 MSE 0.020849 LR 0.001000 Time 0.057533 +2024-02-02 16:33:48,949 - Epoch: [126][ 8/ 8] Overall Loss 0.022009 Objective Loss 0.022009 MSE 0.021091 LR 0.001000 Time 0.051147 +2024-02-02 16:33:49,105 - --- validate (epoch=126)----------- +2024-02-02 16:33:49,105 - 60 samples (32 per mini-batch) +2024-02-02 16:33:49,475 - Epoch: [126][ 1/ 2] Loss 0.025233 MSE 0.025233 +2024-02-02 16:33:49,480 - Epoch: [126][ 2/ 2] Loss 0.025225 MSE 0.025225 +2024-02-02 16:33:49,627 - ==> MSE: 0.02523 Loss: 0.025 + +2024-02-02 16:33:49,633 - ==> Best [Top 1 (MSE): 0.02518 Sparsity:0.00 Params: 136448 on epoch: 124] +2024-02-02 16:33:49,633 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:49,638 - + +2024-02-02 16:33:49,638 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:49,999 - Epoch: [127][ 1/ 8] Overall Loss 0.019695 Objective Loss 0.019695 MSE 0.019695 LR 0.001000 Time 0.360675 +2024-02-02 16:33:50,006 - Epoch: [127][ 2/ 8] Overall Loss 0.020705 Objective Loss 0.020705 MSE 0.020705 LR 0.001000 Time 0.183732 +2024-02-02 16:33:50,013 - Epoch: [127][ 3/ 8] Overall Loss 0.021085 Objective Loss 0.021085 MSE 0.021085 LR 0.001000 Time 0.124635 +2024-02-02 16:33:50,020 - Epoch: [127][ 4/ 8] Overall Loss 0.020210 Objective Loss 0.020210 MSE 0.020210 LR 0.001000 Time 0.095097 +2024-02-02 16:33:50,026 - Epoch: [127][ 5/ 8] Overall Loss 0.020928 Objective Loss 0.020928 MSE 0.020928 LR 0.001000 Time 0.077373 +2024-02-02 16:33:50,033 - Epoch: [127][ 6/ 8] Overall Loss 0.021102 Objective Loss 0.021102 MSE 0.021102 LR 0.001000 Time 0.065559 +2024-02-02 16:33:50,040 - Epoch: [127][ 7/ 8] Overall Loss 0.020998 Objective Loss 0.020998 MSE 0.020998 LR 0.001000 Time 0.057122 +2024-02-02 16:33:50,046 - Epoch: [127][ 8/ 8] Overall Loss 0.021988 Objective Loss 0.021988 MSE 0.021205 LR 0.001000 Time 0.050780 +2024-02-02 16:33:50,198 - --- validate (epoch=127)----------- +2024-02-02 16:33:50,198 - 60 samples (32 per mini-batch) +2024-02-02 16:33:50,554 - Epoch: [127][ 1/ 2] Loss 0.023811 MSE 0.023811 +2024-02-02 16:33:50,559 - Epoch: [127][ 2/ 2] Loss 0.025513 MSE 0.025400 +2024-02-02 16:33:50,696 - ==> MSE: 0.02540 Loss: 0.026 + +2024-02-02 16:33:50,703 - ==> Best [Top 1 (MSE): 0.02518 Sparsity:0.00 Params: 136448 on epoch: 124] +2024-02-02 16:33:50,703 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:50,708 - + +2024-02-02 16:33:50,708 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:51,065 - Epoch: [128][ 1/ 8] Overall Loss 0.018020 Objective Loss 0.018020 MSE 0.018020 LR 0.001000 Time 0.357147 +2024-02-02 16:33:51,073 - Epoch: [128][ 2/ 8] Overall Loss 0.018751 Objective Loss 0.018751 MSE 0.018751 LR 0.001000 Time 0.182503 +2024-02-02 16:33:51,082 - Epoch: [128][ 3/ 8] Overall Loss 0.018824 Objective Loss 0.018824 MSE 0.018824 LR 0.001000 Time 0.124418 +2024-02-02 16:33:51,090 - Epoch: [128][ 4/ 8] Overall Loss 0.019682 Objective Loss 0.019682 MSE 0.019682 LR 0.001000 Time 0.095257 +2024-02-02 16:33:51,097 - Epoch: [128][ 5/ 8] Overall Loss 0.020250 Objective Loss 0.020250 MSE 0.020250 LR 0.001000 Time 0.077559 +2024-02-02 16:33:51,104 - Epoch: [128][ 6/ 8] Overall Loss 0.020551 Objective Loss 0.020551 MSE 0.020551 LR 0.001000 Time 0.065745 +2024-02-02 16:33:51,110 - Epoch: [128][ 7/ 8] Overall Loss 0.020684 Objective Loss 0.020684 MSE 0.020684 LR 0.001000 Time 0.057315 +2024-02-02 16:33:51,117 - Epoch: [128][ 8/ 8] Overall Loss 0.022050 Objective Loss 0.022050 MSE 0.020969 LR 0.001000 Time 0.050968 +2024-02-02 16:33:51,266 - --- validate (epoch=128)----------- +2024-02-02 16:33:51,266 - 60 samples (32 per mini-batch) +2024-02-02 16:33:51,620 - Epoch: [128][ 1/ 2] Loss 0.023613 MSE 0.023613 +2024-02-02 16:33:51,624 - Epoch: [128][ 2/ 2] Loss 0.025375 MSE 0.025258 +2024-02-02 16:33:51,774 - ==> MSE: 0.02526 Loss: 0.025 + +2024-02-02 16:33:51,779 - ==> Best [Top 1 (MSE): 0.02518 Sparsity:0.00 Params: 136448 on epoch: 124] +2024-02-02 16:33:51,780 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:51,784 - + +2024-02-02 16:33:51,784 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:52,156 - Epoch: [129][ 1/ 8] Overall Loss 0.022421 Objective Loss 0.022421 MSE 0.022421 LR 0.001000 Time 0.371197 +2024-02-02 16:33:52,167 - Epoch: [129][ 2/ 8] Overall Loss 0.021885 Objective Loss 0.021885 MSE 0.021885 LR 0.001000 Time 0.190686 +2024-02-02 16:33:52,175 - Epoch: [129][ 3/ 8] Overall Loss 0.021151 Objective Loss 0.021151 MSE 0.021151 LR 0.001000 Time 0.129924 +2024-02-02 16:33:52,182 - Epoch: [129][ 4/ 8] Overall Loss 0.021192 Objective Loss 0.021192 MSE 0.021192 LR 0.001000 Time 0.099083 +2024-02-02 16:33:52,189 - Epoch: [129][ 5/ 8] Overall Loss 0.020631 Objective Loss 0.020631 MSE 0.020631 LR 0.001000 Time 0.080601 +2024-02-02 16:33:52,195 - Epoch: [129][ 6/ 8] Overall Loss 0.020268 Objective Loss 0.020268 MSE 0.020268 LR 0.001000 Time 0.068244 +2024-02-02 16:33:52,202 - Epoch: [129][ 7/ 8] Overall Loss 0.020972 Objective Loss 0.020972 MSE 0.020972 LR 0.001000 Time 0.059420 +2024-02-02 16:33:52,209 - Epoch: [129][ 8/ 8] Overall Loss 0.021368 Objective Loss 0.021368 MSE 0.021055 LR 0.001000 Time 0.052804 +2024-02-02 16:33:52,350 - --- validate (epoch=129)----------- +2024-02-02 16:33:52,350 - 60 samples (32 per mini-batch) +2024-02-02 16:33:52,709 - Epoch: [129][ 1/ 2] Loss 0.026329 MSE 0.026329 +2024-02-02 16:33:52,713 - Epoch: [129][ 2/ 2] Loss 0.025289 MSE 0.025359 +2024-02-02 16:33:52,860 - ==> MSE: 0.02536 Loss: 0.025 + +2024-02-02 16:33:52,865 - ==> Best [Top 1 (MSE): 0.02518 Sparsity:0.00 Params: 136448 on epoch: 124] +2024-02-02 16:33:52,865 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:52,870 - + +2024-02-02 16:33:52,870 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:53,221 - Epoch: [130][ 1/ 8] Overall Loss 0.020669 Objective Loss 0.020669 MSE 0.020669 LR 0.001000 Time 0.350792 +2024-02-02 16:33:53,228 - Epoch: [130][ 2/ 8] Overall Loss 0.021292 Objective Loss 0.021292 MSE 0.021292 LR 0.001000 Time 0.178879 +2024-02-02 16:33:53,235 - Epoch: [130][ 3/ 8] Overall Loss 0.021989 Objective Loss 0.021989 MSE 0.021989 LR 0.001000 Time 0.121423 +2024-02-02 16:33:53,241 - Epoch: [130][ 4/ 8] Overall Loss 0.021066 Objective Loss 0.021066 MSE 0.021066 LR 0.001000 Time 0.092715 +2024-02-02 16:33:53,248 - Epoch: [130][ 5/ 8] Overall Loss 0.020901 Objective Loss 0.020901 MSE 0.020901 LR 0.001000 Time 0.075504 +2024-02-02 16:33:53,255 - Epoch: [130][ 6/ 8] Overall Loss 0.020735 Objective Loss 0.020735 MSE 0.020735 LR 0.001000 Time 0.064010 +2024-02-02 16:33:53,262 - Epoch: [130][ 7/ 8] Overall Loss 0.020850 Objective Loss 0.020850 MSE 0.020850 LR 0.001000 Time 0.055799 +2024-02-02 16:33:53,268 - Epoch: [130][ 8/ 8] Overall Loss 0.022096 Objective Loss 0.022096 MSE 0.021110 LR 0.001000 Time 0.049627 +2024-02-02 16:33:53,415 - --- validate (epoch=130)----------- +2024-02-02 16:33:53,416 - 60 samples (32 per mini-batch) +2024-02-02 16:33:53,762 - Epoch: [130][ 1/ 2] Loss 0.026002 MSE 0.026002 +2024-02-02 16:33:53,766 - Epoch: [130][ 2/ 2] Loss 0.024929 MSE 0.025001 +2024-02-02 16:33:53,913 - ==> MSE: 0.02500 Loss: 0.025 + +2024-02-02 16:33:53,919 - ==> Best [Top 1 (MSE): 0.02500 Sparsity:0.00 Params: 136448 on epoch: 130] +2024-02-02 16:33:53,919 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:53,925 - + +2024-02-02 16:33:53,925 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:54,285 - Epoch: [131][ 1/ 8] Overall Loss 0.021076 Objective Loss 0.021076 MSE 0.021076 LR 0.001000 Time 0.359218 +2024-02-02 16:33:54,292 - Epoch: [131][ 2/ 8] Overall Loss 0.020885 Objective Loss 0.020885 MSE 0.020885 LR 0.001000 Time 0.183123 +2024-02-02 16:33:54,299 - Epoch: [131][ 3/ 8] Overall Loss 0.020745 Objective Loss 0.020745 MSE 0.020745 LR 0.001000 Time 0.124312 +2024-02-02 16:33:54,306 - Epoch: [131][ 4/ 8] Overall Loss 0.020673 Objective Loss 0.020673 MSE 0.020673 LR 0.001000 Time 0.094906 +2024-02-02 16:33:54,312 - Epoch: [131][ 5/ 8] Overall Loss 0.020618 Objective Loss 0.020618 MSE 0.020618 LR 0.001000 Time 0.077255 +2024-02-02 16:33:54,319 - Epoch: [131][ 6/ 8] Overall Loss 0.020873 Objective Loss 0.020873 MSE 0.020873 LR 0.001000 Time 0.065484 +2024-02-02 16:33:54,326 - Epoch: [131][ 7/ 8] Overall Loss 0.020744 Objective Loss 0.020744 MSE 0.020744 LR 0.001000 Time 0.057068 +2024-02-02 16:33:54,333 - Epoch: [131][ 8/ 8] Overall Loss 0.021516 Objective Loss 0.021516 MSE 0.020905 LR 0.001000 Time 0.050738 +2024-02-02 16:33:54,481 - --- validate (epoch=131)----------- +2024-02-02 16:33:54,481 - 60 samples (32 per mini-batch) +2024-02-02 16:33:54,832 - Epoch: [131][ 1/ 2] Loss 0.023437 MSE 0.023437 +2024-02-02 16:33:54,837 - Epoch: [131][ 2/ 2] Loss 0.025296 MSE 0.025172 +2024-02-02 16:33:54,979 - ==> MSE: 0.02517 Loss: 0.025 + +2024-02-02 16:33:54,984 - ==> Best [Top 1 (MSE): 0.02500 Sparsity:0.00 Params: 136448 on epoch: 130] +2024-02-02 16:33:54,984 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:54,989 - + +2024-02-02 16:33:54,989 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:55,350 - Epoch: [132][ 1/ 8] Overall Loss 0.022516 Objective Loss 0.022516 MSE 0.022516 LR 0.001000 Time 0.360693 +2024-02-02 16:33:55,357 - Epoch: [132][ 2/ 8] Overall Loss 0.021932 Objective Loss 0.021932 MSE 0.021932 LR 0.001000 Time 0.183691 +2024-02-02 16:33:55,364 - Epoch: [132][ 3/ 8] Overall Loss 0.021705 Objective Loss 0.021705 MSE 0.021705 LR 0.001000 Time 0.124642 +2024-02-02 16:33:55,371 - Epoch: [132][ 4/ 8] Overall Loss 0.020987 Objective Loss 0.020987 MSE 0.020987 LR 0.001000 Time 0.095079 +2024-02-02 16:33:55,377 - Epoch: [132][ 5/ 8] Overall Loss 0.020920 Objective Loss 0.020920 MSE 0.020920 LR 0.001000 Time 0.077376 +2024-02-02 16:33:55,384 - Epoch: [132][ 6/ 8] Overall Loss 0.020842 Objective Loss 0.020842 MSE 0.020842 LR 0.001000 Time 0.065569 +2024-02-02 16:33:55,391 - Epoch: [132][ 7/ 8] Overall Loss 0.020714 Objective Loss 0.020714 MSE 0.020714 LR 0.001000 Time 0.057149 +2024-02-02 16:33:55,397 - Epoch: [132][ 8/ 8] Overall Loss 0.022667 Objective Loss 0.022667 MSE 0.021121 LR 0.001000 Time 0.050805 +2024-02-02 16:33:55,549 - --- validate (epoch=132)----------- +2024-02-02 16:33:55,549 - 60 samples (32 per mini-batch) +2024-02-02 16:33:55,910 - Epoch: [132][ 1/ 2] Loss 0.025241 MSE 0.025241 +2024-02-02 16:33:55,914 - Epoch: [132][ 2/ 2] Loss 0.024949 MSE 0.024969 +2024-02-02 16:33:56,062 - ==> MSE: 0.02497 Loss: 0.025 + +2024-02-02 16:33:56,068 - ==> Best [Top 1 (MSE): 0.02497 Sparsity:0.00 Params: 136448 on epoch: 132] +2024-02-02 16:33:56,068 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:56,074 - + +2024-02-02 16:33:56,074 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:56,434 - Epoch: [133][ 1/ 8] Overall Loss 0.022953 Objective Loss 0.022953 MSE 0.022953 LR 0.001000 Time 0.360313 +2024-02-02 16:33:56,441 - Epoch: [133][ 2/ 8] Overall Loss 0.022216 Objective Loss 0.022216 MSE 0.022216 LR 0.001000 Time 0.183552 +2024-02-02 16:33:56,448 - Epoch: [133][ 3/ 8] Overall Loss 0.021906 Objective Loss 0.021906 MSE 0.021906 LR 0.001000 Time 0.124573 +2024-02-02 16:33:56,455 - Epoch: [133][ 4/ 8] Overall Loss 0.021288 Objective Loss 0.021288 MSE 0.021288 LR 0.001000 Time 0.095064 +2024-02-02 16:33:56,462 - Epoch: [133][ 5/ 8] Overall Loss 0.020679 Objective Loss 0.020679 MSE 0.020679 LR 0.001000 Time 0.077351 +2024-02-02 16:33:56,468 - Epoch: [133][ 6/ 8] Overall Loss 0.020875 Objective Loss 0.020875 MSE 0.020875 LR 0.001000 Time 0.065540 +2024-02-02 16:33:56,475 - Epoch: [133][ 7/ 8] Overall Loss 0.020736 Objective Loss 0.020736 MSE 0.020736 LR 0.001000 Time 0.057091 +2024-02-02 16:33:56,481 - Epoch: [133][ 8/ 8] Overall Loss 0.022001 Objective Loss 0.022001 MSE 0.021000 LR 0.001000 Time 0.050759 +2024-02-02 16:33:56,631 - --- validate (epoch=133)----------- +2024-02-02 16:33:56,631 - 60 samples (32 per mini-batch) +2024-02-02 16:33:56,981 - Epoch: [133][ 1/ 2] Loss 0.024360 MSE 0.024360 +2024-02-02 16:33:56,986 - Epoch: [133][ 2/ 2] Loss 0.024959 MSE 0.024919 +2024-02-02 16:33:57,124 - ==> MSE: 0.02492 Loss: 0.025 + +2024-02-02 16:33:57,130 - ==> Best [Top 1 (MSE): 0.02492 Sparsity:0.00 Params: 136448 on epoch: 133] +2024-02-02 16:33:57,130 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:57,136 - + +2024-02-02 16:33:57,136 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:57,493 - Epoch: [134][ 1/ 8] Overall Loss 0.017782 Objective Loss 0.017782 MSE 0.017782 LR 0.001000 Time 0.357115 +2024-02-02 16:33:57,501 - Epoch: [134][ 2/ 8] Overall Loss 0.020269 Objective Loss 0.020269 MSE 0.020269 LR 0.001000 Time 0.182070 +2024-02-02 16:33:57,661 - Epoch: [134][ 3/ 8] Overall Loss 0.021246 Objective Loss 0.021246 MSE 0.021246 LR 0.001000 Time 0.174933 +2024-02-02 16:33:57,669 - Epoch: [134][ 4/ 8] Overall Loss 0.020869 Objective Loss 0.020869 MSE 0.020869 LR 0.001000 Time 0.132965 +2024-02-02 16:33:57,675 - Epoch: [134][ 5/ 8] Overall Loss 0.020543 Objective Loss 0.020543 MSE 0.020543 LR 0.001000 Time 0.107664 +2024-02-02 16:33:57,682 - Epoch: [134][ 6/ 8] Overall Loss 0.020622 Objective Loss 0.020622 MSE 0.020622 LR 0.001000 Time 0.090804 +2024-02-02 16:33:57,689 - Epoch: [134][ 7/ 8] Overall Loss 0.020648 Objective Loss 0.020648 MSE 0.020648 LR 0.001000 Time 0.078781 +2024-02-02 16:33:57,696 - Epoch: [134][ 8/ 8] Overall Loss 0.020799 Objective Loss 0.020799 MSE 0.020679 LR 0.001000 Time 0.069751 +2024-02-02 16:33:57,845 - --- validate (epoch=134)----------- +2024-02-02 16:33:57,845 - 60 samples (32 per mini-batch) +2024-02-02 16:33:58,190 - Epoch: [134][ 1/ 2] Loss 0.026039 MSE 0.026039 +2024-02-02 16:33:58,194 - Epoch: [134][ 2/ 2] Loss 0.025110 MSE 0.025172 +2024-02-02 16:33:58,344 - ==> MSE: 0.02517 Loss: 0.025 + +2024-02-02 16:33:58,349 - ==> Best [Top 1 (MSE): 0.02492 Sparsity:0.00 Params: 136448 on epoch: 133] +2024-02-02 16:33:58,349 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:58,354 - + +2024-02-02 16:33:58,354 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:58,722 - Epoch: [135][ 1/ 8] Overall Loss 0.021000 Objective Loss 0.021000 MSE 0.021000 LR 0.001000 Time 0.367056 +2024-02-02 16:33:58,729 - Epoch: [135][ 2/ 8] Overall Loss 0.021169 Objective Loss 0.021169 MSE 0.021169 LR 0.001000 Time 0.187102 +2024-02-02 16:33:58,736 - Epoch: [135][ 3/ 8] Overall Loss 0.021136 Objective Loss 0.021136 MSE 0.021136 LR 0.001000 Time 0.126901 +2024-02-02 16:33:58,742 - Epoch: [135][ 4/ 8] Overall Loss 0.020841 Objective Loss 0.020841 MSE 0.020841 LR 0.001000 Time 0.096784 +2024-02-02 16:33:58,749 - Epoch: [135][ 5/ 8] Overall Loss 0.021492 Objective Loss 0.021492 MSE 0.021492 LR 0.001000 Time 0.078717 +2024-02-02 16:33:58,756 - Epoch: [135][ 6/ 8] Overall Loss 0.020914 Objective Loss 0.020914 MSE 0.020914 LR 0.001000 Time 0.066679 +2024-02-02 16:33:58,762 - Epoch: [135][ 7/ 8] Overall Loss 0.020856 Objective Loss 0.020856 MSE 0.020856 LR 0.001000 Time 0.058073 +2024-02-02 16:33:58,769 - Epoch: [135][ 8/ 8] Overall Loss 0.023684 Objective Loss 0.023684 MSE 0.021446 LR 0.001000 Time 0.051620 +2024-02-02 16:33:58,916 - --- validate (epoch=135)----------- +2024-02-02 16:33:58,916 - 60 samples (32 per mini-batch) +2024-02-02 16:33:59,273 - Epoch: [135][ 1/ 2] Loss 0.026983 MSE 0.026983 +2024-02-02 16:33:59,277 - Epoch: [135][ 2/ 2] Loss 0.025064 MSE 0.025192 +2024-02-02 16:33:59,420 - ==> MSE: 0.02519 Loss: 0.025 + +2024-02-02 16:33:59,426 - ==> Best [Top 1 (MSE): 0.02492 Sparsity:0.00 Params: 136448 on epoch: 133] +2024-02-02 16:33:59,426 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:33:59,431 - + +2024-02-02 16:33:59,431 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:33:59,783 - Epoch: [136][ 1/ 8] Overall Loss 0.020508 Objective Loss 0.020508 MSE 0.020508 LR 0.001000 Time 0.351836 +2024-02-02 16:33:59,790 - Epoch: [136][ 2/ 8] Overall Loss 0.023144 Objective Loss 0.023144 MSE 0.023144 LR 0.001000 Time 0.179420 +2024-02-02 16:33:59,797 - Epoch: [136][ 3/ 8] Overall Loss 0.022031 Objective Loss 0.022031 MSE 0.022031 LR 0.001000 Time 0.121797 +2024-02-02 16:33:59,804 - Epoch: [136][ 4/ 8] Overall Loss 0.021513 Objective Loss 0.021513 MSE 0.021513 LR 0.001000 Time 0.093006 +2024-02-02 16:33:59,811 - Epoch: [136][ 5/ 8] Overall Loss 0.021513 Objective Loss 0.021513 MSE 0.021513 LR 0.001000 Time 0.075717 +2024-02-02 16:33:59,817 - Epoch: [136][ 6/ 8] Overall Loss 0.021157 Objective Loss 0.021157 MSE 0.021157 LR 0.001000 Time 0.064198 +2024-02-02 16:33:59,824 - Epoch: [136][ 7/ 8] Overall Loss 0.020794 Objective Loss 0.020794 MSE 0.020794 LR 0.001000 Time 0.055974 +2024-02-02 16:33:59,831 - Epoch: [136][ 8/ 8] Overall Loss 0.021876 Objective Loss 0.021876 MSE 0.021020 LR 0.001000 Time 0.049796 +2024-02-02 16:33:59,979 - --- validate (epoch=136)----------- +2024-02-02 16:33:59,979 - 60 samples (32 per mini-batch) +2024-02-02 16:34:00,336 - Epoch: [136][ 1/ 2] Loss 0.027227 MSE 0.027227 +2024-02-02 16:34:00,341 - Epoch: [136][ 2/ 2] Loss 0.025681 MSE 0.025784 +2024-02-02 16:34:00,490 - ==> MSE: 0.02578 Loss: 0.026 + +2024-02-02 16:34:00,496 - ==> Best [Top 1 (MSE): 0.02492 Sparsity:0.00 Params: 136448 on epoch: 133] +2024-02-02 16:34:00,496 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:00,501 - + +2024-02-02 16:34:00,501 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:00,863 - Epoch: [137][ 1/ 8] Overall Loss 0.018744 Objective Loss 0.018744 MSE 0.018744 LR 0.001000 Time 0.361047 +2024-02-02 16:34:00,870 - Epoch: [137][ 2/ 8] Overall Loss 0.019360 Objective Loss 0.019360 MSE 0.019360 LR 0.001000 Time 0.183981 +2024-02-02 16:34:00,877 - Epoch: [137][ 3/ 8] Overall Loss 0.019226 Objective Loss 0.019226 MSE 0.019226 LR 0.001000 Time 0.124888 +2024-02-02 16:34:00,884 - Epoch: [137][ 4/ 8] Overall Loss 0.019821 Objective Loss 0.019821 MSE 0.019821 LR 0.001000 Time 0.095330 +2024-02-02 16:34:00,890 - Epoch: [137][ 5/ 8] Overall Loss 0.020230 Objective Loss 0.020230 MSE 0.020230 LR 0.001000 Time 0.077590 +2024-02-02 16:34:00,897 - Epoch: [137][ 6/ 8] Overall Loss 0.020511 Objective Loss 0.020511 MSE 0.020511 LR 0.001000 Time 0.065735 +2024-02-02 16:34:00,904 - Epoch: [137][ 7/ 8] Overall Loss 0.020578 Objective Loss 0.020578 MSE 0.020578 LR 0.001000 Time 0.057265 +2024-02-02 16:34:00,910 - Epoch: [137][ 8/ 8] Overall Loss 0.021730 Objective Loss 0.021730 MSE 0.020819 LR 0.001000 Time 0.050900 +2024-02-02 16:34:01,061 - --- validate (epoch=137)----------- +2024-02-02 16:34:01,061 - 60 samples (32 per mini-batch) +2024-02-02 16:34:01,424 - Epoch: [137][ 1/ 2] Loss 0.024402 MSE 0.024402 +2024-02-02 16:34:01,428 - Epoch: [137][ 2/ 2] Loss 0.025462 MSE 0.025392 +2024-02-02 16:34:01,575 - ==> MSE: 0.02539 Loss: 0.025 + +2024-02-02 16:34:01,589 - ==> Best [Top 1 (MSE): 0.02492 Sparsity:0.00 Params: 136448 on epoch: 133] +2024-02-02 16:34:01,589 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:01,597 - + +2024-02-02 16:34:01,597 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:01,961 - Epoch: [138][ 1/ 8] Overall Loss 0.019338 Objective Loss 0.019338 MSE 0.019338 LR 0.001000 Time 0.363047 +2024-02-02 16:34:01,968 - Epoch: [138][ 2/ 8] Overall Loss 0.019508 Objective Loss 0.019508 MSE 0.019508 LR 0.001000 Time 0.184939 +2024-02-02 16:34:01,975 - Epoch: [138][ 3/ 8] Overall Loss 0.019424 Objective Loss 0.019424 MSE 0.019424 LR 0.001000 Time 0.125470 +2024-02-02 16:34:01,981 - Epoch: [138][ 4/ 8] Overall Loss 0.020000 Objective Loss 0.020000 MSE 0.020000 LR 0.001000 Time 0.095744 +2024-02-02 16:34:01,988 - Epoch: [138][ 5/ 8] Overall Loss 0.020420 Objective Loss 0.020420 MSE 0.020420 LR 0.001000 Time 0.077926 +2024-02-02 16:34:01,995 - Epoch: [138][ 6/ 8] Overall Loss 0.020443 Objective Loss 0.020443 MSE 0.020443 LR 0.001000 Time 0.066021 +2024-02-02 16:34:02,002 - Epoch: [138][ 7/ 8] Overall Loss 0.020614 Objective Loss 0.020614 MSE 0.020614 LR 0.001000 Time 0.057514 +2024-02-02 16:34:02,008 - Epoch: [138][ 8/ 8] Overall Loss 0.020786 Objective Loss 0.020786 MSE 0.020650 LR 0.001000 Time 0.051134 +2024-02-02 16:34:02,150 - --- validate (epoch=138)----------- +2024-02-02 16:34:02,150 - 60 samples (32 per mini-batch) +2024-02-02 16:34:02,508 - Epoch: [138][ 1/ 2] Loss 0.024498 MSE 0.024498 +2024-02-02 16:34:02,512 - Epoch: [138][ 2/ 2] Loss 0.025320 MSE 0.025266 +2024-02-02 16:34:02,659 - ==> MSE: 0.02527 Loss: 0.025 + +2024-02-02 16:34:02,664 - ==> Best [Top 1 (MSE): 0.02492 Sparsity:0.00 Params: 136448 on epoch: 133] +2024-02-02 16:34:02,665 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:02,669 - + +2024-02-02 16:34:02,669 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:03,032 - Epoch: [139][ 1/ 8] Overall Loss 0.020767 Objective Loss 0.020767 MSE 0.020767 LR 0.001000 Time 0.362631 +2024-02-02 16:34:03,040 - Epoch: [139][ 2/ 8] Overall Loss 0.020467 Objective Loss 0.020467 MSE 0.020467 LR 0.001000 Time 0.184724 +2024-02-02 16:34:03,046 - Epoch: [139][ 3/ 8] Overall Loss 0.020707 Objective Loss 0.020707 MSE 0.020707 LR 0.001000 Time 0.125331 +2024-02-02 16:34:03,053 - Epoch: [139][ 4/ 8] Overall Loss 0.020268 Objective Loss 0.020268 MSE 0.020268 LR 0.001000 Time 0.095639 +2024-02-02 16:34:03,060 - Epoch: [139][ 5/ 8] Overall Loss 0.019873 Objective Loss 0.019873 MSE 0.019873 LR 0.001000 Time 0.077816 +2024-02-02 16:34:03,066 - Epoch: [139][ 6/ 8] Overall Loss 0.020102 Objective Loss 0.020102 MSE 0.020102 LR 0.001000 Time 0.065924 +2024-02-02 16:34:03,073 - Epoch: [139][ 7/ 8] Overall Loss 0.020310 Objective Loss 0.020310 MSE 0.020310 LR 0.001000 Time 0.057438 +2024-02-02 16:34:03,080 - Epoch: [139][ 8/ 8] Overall Loss 0.023248 Objective Loss 0.023248 MSE 0.020923 LR 0.001000 Time 0.051073 +2024-02-02 16:34:03,229 - --- validate (epoch=139)----------- +2024-02-02 16:34:03,229 - 60 samples (32 per mini-batch) +2024-02-02 16:34:03,590 - Epoch: [139][ 1/ 2] Loss 0.023058 MSE 0.023058 +2024-02-02 16:34:03,594 - Epoch: [139][ 2/ 2] Loss 0.024978 MSE 0.024850 +2024-02-02 16:34:03,742 - ==> MSE: 0.02485 Loss: 0.025 + +2024-02-02 16:34:03,748 - ==> Best [Top 1 (MSE): 0.02485 Sparsity:0.00 Params: 136448 on epoch: 139] +2024-02-02 16:34:03,748 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:03,754 - + +2024-02-02 16:34:03,754 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:04,113 - Epoch: [140][ 1/ 8] Overall Loss 0.018909 Objective Loss 0.018909 MSE 0.018909 LR 0.001000 Time 0.359006 +2024-02-02 16:34:04,121 - Epoch: [140][ 2/ 8] Overall Loss 0.020343 Objective Loss 0.020343 MSE 0.020343 LR 0.001000 Time 0.182996 +2024-02-02 16:34:04,127 - Epoch: [140][ 3/ 8] Overall Loss 0.020019 Objective Loss 0.020019 MSE 0.020019 LR 0.001000 Time 0.124211 +2024-02-02 16:34:04,134 - Epoch: [140][ 4/ 8] Overall Loss 0.020087 Objective Loss 0.020087 MSE 0.020087 LR 0.001000 Time 0.094836 +2024-02-02 16:34:04,141 - Epoch: [140][ 5/ 8] Overall Loss 0.021045 Objective Loss 0.021045 MSE 0.021045 LR 0.001000 Time 0.077198 +2024-02-02 16:34:04,148 - Epoch: [140][ 6/ 8] Overall Loss 0.021126 Objective Loss 0.021126 MSE 0.021126 LR 0.001000 Time 0.065428 +2024-02-02 16:34:04,155 - Epoch: [140][ 7/ 8] Overall Loss 0.020752 Objective Loss 0.020752 MSE 0.020752 LR 0.001000 Time 0.057024 +2024-02-02 16:34:04,161 - Epoch: [140][ 8/ 8] Overall Loss 0.020871 Objective Loss 0.020871 MSE 0.020777 LR 0.001000 Time 0.050709 +2024-02-02 16:34:04,314 - --- validate (epoch=140)----------- +2024-02-02 16:34:04,314 - 60 samples (32 per mini-batch) +2024-02-02 16:34:04,675 - Epoch: [140][ 1/ 2] Loss 0.027431 MSE 0.027431 +2024-02-02 16:34:04,679 - Epoch: [140][ 2/ 2] Loss 0.024725 MSE 0.024905 +2024-02-02 16:34:04,830 - ==> MSE: 0.02491 Loss: 0.025 + +2024-02-02 16:34:04,836 - ==> Best [Top 1 (MSE): 0.02485 Sparsity:0.00 Params: 136448 on epoch: 139] +2024-02-02 16:34:04,836 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:04,841 - + +2024-02-02 16:34:04,841 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:05,205 - Epoch: [141][ 1/ 8] Overall Loss 0.020145 Objective Loss 0.020145 MSE 0.020145 LR 0.001000 Time 0.364094 +2024-02-02 16:34:05,212 - Epoch: [141][ 2/ 8] Overall Loss 0.020792 Objective Loss 0.020792 MSE 0.020792 LR 0.001000 Time 0.185439 +2024-02-02 16:34:05,219 - Epoch: [141][ 3/ 8] Overall Loss 0.020050 Objective Loss 0.020050 MSE 0.020050 LR 0.001000 Time 0.125811 +2024-02-02 16:34:05,226 - Epoch: [141][ 4/ 8] Overall Loss 0.020906 Objective Loss 0.020906 MSE 0.020906 LR 0.001000 Time 0.096000 +2024-02-02 16:34:05,233 - Epoch: [141][ 5/ 8] Overall Loss 0.021045 Objective Loss 0.021045 MSE 0.021045 LR 0.001000 Time 0.078106 +2024-02-02 16:34:05,239 - Epoch: [141][ 6/ 8] Overall Loss 0.021079 Objective Loss 0.021079 MSE 0.021079 LR 0.001000 Time 0.066172 +2024-02-02 16:34:05,246 - Epoch: [141][ 7/ 8] Overall Loss 0.020689 Objective Loss 0.020689 MSE 0.020689 LR 0.001000 Time 0.057653 +2024-02-02 16:34:05,252 - Epoch: [141][ 8/ 8] Overall Loss 0.022041 Objective Loss 0.022041 MSE 0.020971 LR 0.001000 Time 0.051248 +2024-02-02 16:34:05,403 - --- validate (epoch=141)----------- +2024-02-02 16:34:05,403 - 60 samples (32 per mini-batch) +2024-02-02 16:34:05,754 - Epoch: [141][ 1/ 2] Loss 0.024335 MSE 0.024335 +2024-02-02 16:34:05,758 - Epoch: [141][ 2/ 2] Loss 0.024971 MSE 0.024929 +2024-02-02 16:34:05,910 - ==> MSE: 0.02493 Loss: 0.025 + +2024-02-02 16:34:05,916 - ==> Best [Top 1 (MSE): 0.02485 Sparsity:0.00 Params: 136448 on epoch: 139] +2024-02-02 16:34:05,916 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:05,921 - + +2024-02-02 16:34:05,921 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:06,283 - Epoch: [142][ 1/ 8] Overall Loss 0.018790 Objective Loss 0.018790 MSE 0.018790 LR 0.001000 Time 0.361849 +2024-02-02 16:34:06,294 - Epoch: [142][ 2/ 8] Overall Loss 0.019434 Objective Loss 0.019434 MSE 0.019434 LR 0.001000 Time 0.186019 +2024-02-02 16:34:06,302 - Epoch: [142][ 3/ 8] Overall Loss 0.019931 Objective Loss 0.019931 MSE 0.019931 LR 0.001000 Time 0.126624 +2024-02-02 16:34:06,310 - Epoch: [142][ 4/ 8] Overall Loss 0.020053 Objective Loss 0.020053 MSE 0.020053 LR 0.001000 Time 0.096928 +2024-02-02 16:34:06,317 - Epoch: [142][ 5/ 8] Overall Loss 0.020490 Objective Loss 0.020490 MSE 0.020490 LR 0.001000 Time 0.078882 +2024-02-02 16:34:06,323 - Epoch: [142][ 6/ 8] Overall Loss 0.020618 Objective Loss 0.020618 MSE 0.020618 LR 0.001000 Time 0.066844 +2024-02-02 16:34:06,330 - Epoch: [142][ 7/ 8] Overall Loss 0.020484 Objective Loss 0.020484 MSE 0.020484 LR 0.001000 Time 0.058250 +2024-02-02 16:34:06,337 - Epoch: [142][ 8/ 8] Overall Loss 0.021868 Objective Loss 0.021868 MSE 0.020773 LR 0.001000 Time 0.051793 +2024-02-02 16:34:06,490 - --- validate (epoch=142)----------- +2024-02-02 16:34:06,491 - 60 samples (32 per mini-batch) +2024-02-02 16:34:06,843 - Epoch: [142][ 1/ 2] Loss 0.025424 MSE 0.025424 +2024-02-02 16:34:06,847 - Epoch: [142][ 2/ 2] Loss 0.024954 MSE 0.024985 +2024-02-02 16:34:06,997 - ==> MSE: 0.02499 Loss: 0.025 + +2024-02-02 16:34:07,003 - ==> Best [Top 1 (MSE): 0.02485 Sparsity:0.00 Params: 136448 on epoch: 139] +2024-02-02 16:34:07,003 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:07,008 - + +2024-02-02 16:34:07,008 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:07,374 - Epoch: [143][ 1/ 8] Overall Loss 0.019802 Objective Loss 0.019802 MSE 0.019802 LR 0.001000 Time 0.365018 +2024-02-02 16:34:07,384 - Epoch: [143][ 2/ 8] Overall Loss 0.019339 Objective Loss 0.019339 MSE 0.019339 LR 0.001000 Time 0.187623 +2024-02-02 16:34:07,393 - Epoch: [143][ 3/ 8] Overall Loss 0.020045 Objective Loss 0.020045 MSE 0.020045 LR 0.001000 Time 0.127868 +2024-02-02 16:34:07,400 - Epoch: [143][ 4/ 8] Overall Loss 0.020365 Objective Loss 0.020365 MSE 0.020365 LR 0.001000 Time 0.097587 +2024-02-02 16:34:07,407 - Epoch: [143][ 5/ 8] Overall Loss 0.020565 Objective Loss 0.020565 MSE 0.020565 LR 0.001000 Time 0.079422 +2024-02-02 16:34:07,414 - Epoch: [143][ 6/ 8] Overall Loss 0.020813 Objective Loss 0.020813 MSE 0.020813 LR 0.001000 Time 0.067289 +2024-02-02 16:34:07,420 - Epoch: [143][ 7/ 8] Overall Loss 0.020505 Objective Loss 0.020505 MSE 0.020505 LR 0.001000 Time 0.058626 +2024-02-02 16:34:07,427 - Epoch: [143][ 8/ 8] Overall Loss 0.020964 Objective Loss 0.020964 MSE 0.020600 LR 0.001000 Time 0.052114 +2024-02-02 16:34:07,575 - --- validate (epoch=143)----------- +2024-02-02 16:34:07,575 - 60 samples (32 per mini-batch) +2024-02-02 16:34:07,919 - Epoch: [143][ 1/ 2] Loss 0.023098 MSE 0.023098 +2024-02-02 16:34:07,925 - Epoch: [143][ 2/ 2] Loss 0.024994 MSE 0.024868 +2024-02-02 16:34:08,072 - ==> MSE: 0.02487 Loss: 0.025 + +2024-02-02 16:34:08,078 - ==> Best [Top 1 (MSE): 0.02485 Sparsity:0.00 Params: 136448 on epoch: 139] +2024-02-02 16:34:08,078 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:08,083 - + +2024-02-02 16:34:08,083 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:08,442 - Epoch: [144][ 1/ 8] Overall Loss 0.021230 Objective Loss 0.021230 MSE 0.021230 LR 0.001000 Time 0.358813 +2024-02-02 16:34:08,453 - Epoch: [144][ 2/ 8] Overall Loss 0.021017 Objective Loss 0.021017 MSE 0.021017 LR 0.001000 Time 0.184497 +2024-02-02 16:34:08,463 - Epoch: [144][ 3/ 8] Overall Loss 0.020019 Objective Loss 0.020019 MSE 0.020019 LR 0.001000 Time 0.126212 +2024-02-02 16:34:08,470 - Epoch: [144][ 4/ 8] Overall Loss 0.020258 Objective Loss 0.020258 MSE 0.020258 LR 0.001000 Time 0.096334 +2024-02-02 16:34:08,477 - Epoch: [144][ 5/ 8] Overall Loss 0.020182 Objective Loss 0.020182 MSE 0.020182 LR 0.001000 Time 0.078415 +2024-02-02 16:34:08,483 - Epoch: [144][ 6/ 8] Overall Loss 0.020290 Objective Loss 0.020290 MSE 0.020290 LR 0.001000 Time 0.066432 +2024-02-02 16:34:08,490 - Epoch: [144][ 7/ 8] Overall Loss 0.020650 Objective Loss 0.020650 MSE 0.020650 LR 0.001000 Time 0.057891 +2024-02-02 16:34:08,497 - Epoch: [144][ 8/ 8] Overall Loss 0.020962 Objective Loss 0.020962 MSE 0.020715 LR 0.001000 Time 0.051476 +2024-02-02 16:34:08,651 - --- validate (epoch=144)----------- +2024-02-02 16:34:08,651 - 60 samples (32 per mini-batch) +2024-02-02 16:34:09,009 - Epoch: [144][ 1/ 2] Loss 0.023558 MSE 0.023558 +2024-02-02 16:34:09,014 - Epoch: [144][ 2/ 2] Loss 0.024838 MSE 0.024752 +2024-02-02 16:34:09,157 - ==> MSE: 0.02475 Loss: 0.025 + +2024-02-02 16:34:09,164 - ==> Best [Top 1 (MSE): 0.02475 Sparsity:0.00 Params: 136448 on epoch: 144] +2024-02-02 16:34:09,164 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:09,170 - + +2024-02-02 16:34:09,170 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:09,536 - Epoch: [145][ 1/ 8] Overall Loss 0.020552 Objective Loss 0.020552 MSE 0.020552 LR 0.001000 Time 0.365951 +2024-02-02 16:34:09,543 - Epoch: [145][ 2/ 8] Overall Loss 0.020204 Objective Loss 0.020204 MSE 0.020204 LR 0.001000 Time 0.186430 +2024-02-02 16:34:09,550 - Epoch: [145][ 3/ 8] Overall Loss 0.020517 Objective Loss 0.020517 MSE 0.020517 LR 0.001000 Time 0.126515 +2024-02-02 16:34:09,557 - Epoch: [145][ 4/ 8] Overall Loss 0.020105 Objective Loss 0.020105 MSE 0.020105 LR 0.001000 Time 0.096555 +2024-02-02 16:34:09,563 - Epoch: [145][ 5/ 8] Overall Loss 0.019829 Objective Loss 0.019829 MSE 0.019829 LR 0.001000 Time 0.078535 +2024-02-02 16:34:09,570 - Epoch: [145][ 6/ 8] Overall Loss 0.020095 Objective Loss 0.020095 MSE 0.020095 LR 0.001000 Time 0.066540 +2024-02-02 16:34:09,577 - Epoch: [145][ 7/ 8] Overall Loss 0.020371 Objective Loss 0.020371 MSE 0.020371 LR 0.001000 Time 0.057980 +2024-02-02 16:34:09,584 - Epoch: [145][ 8/ 8] Overall Loss 0.021167 Objective Loss 0.021167 MSE 0.020537 LR 0.001000 Time 0.051551 +2024-02-02 16:34:09,734 - --- validate (epoch=145)----------- +2024-02-02 16:34:09,734 - 60 samples (32 per mini-batch) +2024-02-02 16:34:10,094 - Epoch: [145][ 1/ 2] Loss 0.025515 MSE 0.025515 +2024-02-02 16:34:10,098 - Epoch: [145][ 2/ 2] Loss 0.024862 MSE 0.024906 +2024-02-02 16:34:10,246 - ==> MSE: 0.02491 Loss: 0.025 + +2024-02-02 16:34:10,252 - ==> Best [Top 1 (MSE): 0.02475 Sparsity:0.00 Params: 136448 on epoch: 144] +2024-02-02 16:34:10,252 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:10,257 - + +2024-02-02 16:34:10,257 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:10,619 - Epoch: [146][ 1/ 8] Overall Loss 0.021470 Objective Loss 0.021470 MSE 0.021470 LR 0.001000 Time 0.361116 +2024-02-02 16:34:10,626 - Epoch: [146][ 2/ 8] Overall Loss 0.022521 Objective Loss 0.022521 MSE 0.022521 LR 0.001000 Time 0.183998 +2024-02-02 16:34:10,633 - Epoch: [146][ 3/ 8] Overall Loss 0.021035 Objective Loss 0.021035 MSE 0.021035 LR 0.001000 Time 0.124907 +2024-02-02 16:34:10,639 - Epoch: [146][ 4/ 8] Overall Loss 0.020186 Objective Loss 0.020186 MSE 0.020186 LR 0.001000 Time 0.095309 +2024-02-02 16:34:10,646 - Epoch: [146][ 5/ 8] Overall Loss 0.020366 Objective Loss 0.020366 MSE 0.020366 LR 0.001000 Time 0.077545 +2024-02-02 16:34:10,653 - Epoch: [146][ 6/ 8] Overall Loss 0.020524 Objective Loss 0.020524 MSE 0.020524 LR 0.001000 Time 0.065720 +2024-02-02 16:34:10,659 - Epoch: [146][ 7/ 8] Overall Loss 0.020602 Objective Loss 0.020602 MSE 0.020602 LR 0.001000 Time 0.057255 +2024-02-02 16:34:10,666 - Epoch: [146][ 8/ 8] Overall Loss 0.020764 Objective Loss 0.020764 MSE 0.020636 LR 0.001000 Time 0.050898 +2024-02-02 16:34:10,815 - --- validate (epoch=146)----------- +2024-02-02 16:34:10,815 - 60 samples (32 per mini-batch) +2024-02-02 16:34:11,179 - Epoch: [146][ 1/ 2] Loss 0.024874 MSE 0.024874 +2024-02-02 16:34:11,184 - Epoch: [146][ 2/ 2] Loss 0.024969 MSE 0.024963 +2024-02-02 16:34:11,331 - ==> MSE: 0.02496 Loss: 0.025 + +2024-02-02 16:34:11,337 - ==> Best [Top 1 (MSE): 0.02475 Sparsity:0.00 Params: 136448 on epoch: 144] +2024-02-02 16:34:11,337 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:11,342 - + +2024-02-02 16:34:11,342 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:11,693 - Epoch: [147][ 1/ 8] Overall Loss 0.020204 Objective Loss 0.020204 MSE 0.020204 LR 0.001000 Time 0.351068 +2024-02-02 16:34:11,701 - Epoch: [147][ 2/ 8] Overall Loss 0.019690 Objective Loss 0.019690 MSE 0.019690 LR 0.001000 Time 0.178993 +2024-02-02 16:34:11,707 - Epoch: [147][ 3/ 8] Overall Loss 0.019019 Objective Loss 0.019019 MSE 0.019019 LR 0.001000 Time 0.121518 +2024-02-02 16:34:11,714 - Epoch: [147][ 4/ 8] Overall Loss 0.019024 Objective Loss 0.019024 MSE 0.019024 LR 0.001000 Time 0.092821 +2024-02-02 16:34:11,721 - Epoch: [147][ 5/ 8] Overall Loss 0.019545 Objective Loss 0.019545 MSE 0.019545 LR 0.001000 Time 0.075594 +2024-02-02 16:34:11,728 - Epoch: [147][ 6/ 8] Overall Loss 0.019732 Objective Loss 0.019732 MSE 0.019732 LR 0.001000 Time 0.064090 +2024-02-02 16:34:11,735 - Epoch: [147][ 7/ 8] Overall Loss 0.020270 Objective Loss 0.020270 MSE 0.020270 LR 0.001000 Time 0.055884 +2024-02-02 16:34:11,741 - Epoch: [147][ 8/ 8] Overall Loss 0.020690 Objective Loss 0.020690 MSE 0.020358 LR 0.001000 Time 0.049706 +2024-02-02 16:34:11,893 - --- validate (epoch=147)----------- +2024-02-02 16:34:11,893 - 60 samples (32 per mini-batch) +2024-02-02 16:34:12,251 - Epoch: [147][ 1/ 2] Loss 0.024097 MSE 0.024097 +2024-02-02 16:34:12,256 - Epoch: [147][ 2/ 2] Loss 0.024816 MSE 0.024768 +2024-02-02 16:34:12,403 - ==> MSE: 0.02477 Loss: 0.025 + +2024-02-02 16:34:12,409 - ==> Best [Top 1 (MSE): 0.02475 Sparsity:0.00 Params: 136448 on epoch: 144] +2024-02-02 16:34:12,409 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:12,414 - + +2024-02-02 16:34:12,414 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:12,775 - Epoch: [148][ 1/ 8] Overall Loss 0.021296 Objective Loss 0.021296 MSE 0.021296 LR 0.001000 Time 0.360360 +2024-02-02 16:34:12,782 - Epoch: [148][ 2/ 8] Overall Loss 0.020215 Objective Loss 0.020215 MSE 0.020215 LR 0.001000 Time 0.183463 +2024-02-02 16:34:12,788 - Epoch: [148][ 3/ 8] Overall Loss 0.020515 Objective Loss 0.020515 MSE 0.020515 LR 0.001000 Time 0.124431 +2024-02-02 16:34:12,795 - Epoch: [148][ 4/ 8] Overall Loss 0.020149 Objective Loss 0.020149 MSE 0.020149 LR 0.001000 Time 0.094921 +2024-02-02 16:34:12,801 - Epoch: [148][ 5/ 8] Overall Loss 0.020334 Objective Loss 0.020334 MSE 0.020334 LR 0.001000 Time 0.077202 +2024-02-02 16:34:12,808 - Epoch: [148][ 6/ 8] Overall Loss 0.020521 Objective Loss 0.020521 MSE 0.020521 LR 0.001000 Time 0.065404 +2024-02-02 16:34:12,814 - Epoch: [148][ 7/ 8] Overall Loss 0.020235 Objective Loss 0.020235 MSE 0.020235 LR 0.001000 Time 0.056979 +2024-02-02 16:34:12,821 - Epoch: [148][ 8/ 8] Overall Loss 0.020773 Objective Loss 0.020773 MSE 0.020347 LR 0.001000 Time 0.050648 +2024-02-02 16:34:12,963 - --- validate (epoch=148)----------- +2024-02-02 16:34:12,964 - 60 samples (32 per mini-batch) +2024-02-02 16:34:13,310 - Epoch: [148][ 1/ 2] Loss 0.027663 MSE 0.027663 +2024-02-02 16:34:13,314 - Epoch: [148][ 2/ 2] Loss 0.024490 MSE 0.024702 +2024-02-02 16:34:13,462 - ==> MSE: 0.02470 Loss: 0.024 + +2024-02-02 16:34:13,468 - ==> Best [Top 1 (MSE): 0.02470 Sparsity:0.00 Params: 136448 on epoch: 148] +2024-02-02 16:34:13,468 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:13,474 - + +2024-02-02 16:34:13,474 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:13,838 - Epoch: [149][ 1/ 8] Overall Loss 0.018273 Objective Loss 0.018273 MSE 0.018273 LR 0.001000 Time 0.363304 +2024-02-02 16:34:13,849 - Epoch: [149][ 2/ 8] Overall Loss 0.019217 Objective Loss 0.019217 MSE 0.019217 LR 0.001000 Time 0.186764 +2024-02-02 16:34:13,859 - Epoch: [149][ 3/ 8] Overall Loss 0.019895 Objective Loss 0.019895 MSE 0.019895 LR 0.001000 Time 0.127771 +2024-02-02 16:34:13,866 - Epoch: [149][ 4/ 8] Overall Loss 0.019943 Objective Loss 0.019943 MSE 0.019943 LR 0.001000 Time 0.097491 +2024-02-02 16:34:13,872 - Epoch: [149][ 5/ 8] Overall Loss 0.019890 Objective Loss 0.019890 MSE 0.019890 LR 0.001000 Time 0.079296 +2024-02-02 16:34:13,879 - Epoch: [149][ 6/ 8] Overall Loss 0.019802 Objective Loss 0.019802 MSE 0.019802 LR 0.001000 Time 0.067166 +2024-02-02 16:34:13,886 - Epoch: [149][ 7/ 8] Overall Loss 0.020201 Objective Loss 0.020201 MSE 0.020201 LR 0.001000 Time 0.058503 +2024-02-02 16:34:13,892 - Epoch: [149][ 8/ 8] Overall Loss 0.023496 Objective Loss 0.023496 MSE 0.020889 LR 0.001000 Time 0.051997 +2024-02-02 16:34:14,043 - --- validate (epoch=149)----------- +2024-02-02 16:34:14,043 - 60 samples (32 per mini-batch) +2024-02-02 16:34:14,401 - Epoch: [149][ 1/ 2] Loss 0.025450 MSE 0.025450 +2024-02-02 16:34:14,406 - Epoch: [149][ 2/ 2] Loss 0.024735 MSE 0.024783 +2024-02-02 16:34:14,552 - ==> MSE: 0.02478 Loss: 0.025 + +2024-02-02 16:34:14,559 - ==> Best [Top 1 (MSE): 0.02470 Sparsity:0.00 Params: 136448 on epoch: 148] +2024-02-02 16:34:14,559 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:14,564 - + +2024-02-02 16:34:14,564 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:14,917 - Epoch: [150][ 1/ 8] Overall Loss 0.019253 Objective Loss 0.019253 MSE 0.019253 LR 0.001000 Time 0.352176 +2024-02-02 16:34:14,924 - Epoch: [150][ 2/ 8] Overall Loss 0.019163 Objective Loss 0.019163 MSE 0.019163 LR 0.001000 Time 0.179553 +2024-02-02 16:34:14,931 - Epoch: [150][ 3/ 8] Overall Loss 0.020040 Objective Loss 0.020040 MSE 0.020040 LR 0.001000 Time 0.121906 +2024-02-02 16:34:14,938 - Epoch: [150][ 4/ 8] Overall Loss 0.019747 Objective Loss 0.019747 MSE 0.019747 LR 0.001000 Time 0.093075 +2024-02-02 16:34:14,944 - Epoch: [150][ 5/ 8] Overall Loss 0.020562 Objective Loss 0.020562 MSE 0.020562 LR 0.001000 Time 0.075795 +2024-02-02 16:34:14,951 - Epoch: [150][ 6/ 8] Overall Loss 0.020353 Objective Loss 0.020353 MSE 0.020353 LR 0.001000 Time 0.064253 +2024-02-02 16:34:14,958 - Epoch: [150][ 7/ 8] Overall Loss 0.020361 Objective Loss 0.020361 MSE 0.020361 LR 0.001000 Time 0.056016 +2024-02-02 16:34:14,965 - Epoch: [150][ 8/ 8] Overall Loss 0.021843 Objective Loss 0.021843 MSE 0.020670 LR 0.001000 Time 0.049837 +2024-02-02 16:34:15,117 - --- validate (epoch=150)----------- +2024-02-02 16:34:15,118 - 60 samples (32 per mini-batch) +2024-02-02 16:34:15,470 - Epoch: [150][ 1/ 2] Loss 0.023478 MSE 0.023478 +2024-02-02 16:34:15,474 - Epoch: [150][ 2/ 2] Loss 0.025334 MSE 0.025210 +2024-02-02 16:34:15,611 - ==> MSE: 0.02521 Loss: 0.025 + +2024-02-02 16:34:15,618 - ==> Best [Top 1 (MSE): 0.02470 Sparsity:0.00 Params: 136448 on epoch: 148] +2024-02-02 16:34:15,618 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:15,622 - + +2024-02-02 16:34:15,623 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:15,982 - Epoch: [151][ 1/ 8] Overall Loss 0.021036 Objective Loss 0.021036 MSE 0.021036 LR 0.001000 Time 0.359168 +2024-02-02 16:34:15,989 - Epoch: [151][ 2/ 8] Overall Loss 0.020742 Objective Loss 0.020742 MSE 0.020742 LR 0.001000 Time 0.183075 +2024-02-02 16:34:15,996 - Epoch: [151][ 3/ 8] Overall Loss 0.020078 Objective Loss 0.020078 MSE 0.020078 LR 0.001000 Time 0.124287 +2024-02-02 16:34:16,003 - Epoch: [151][ 4/ 8] Overall Loss 0.020379 Objective Loss 0.020379 MSE 0.020379 LR 0.001000 Time 0.094863 +2024-02-02 16:34:16,010 - Epoch: [151][ 5/ 8] Overall Loss 0.020275 Objective Loss 0.020275 MSE 0.020275 LR 0.001000 Time 0.077197 +2024-02-02 16:34:16,016 - Epoch: [151][ 6/ 8] Overall Loss 0.020474 Objective Loss 0.020474 MSE 0.020474 LR 0.001000 Time 0.065434 +2024-02-02 16:34:16,023 - Epoch: [151][ 7/ 8] Overall Loss 0.020637 Objective Loss 0.020637 MSE 0.020637 LR 0.001000 Time 0.057023 +2024-02-02 16:34:16,030 - Epoch: [151][ 8/ 8] Overall Loss 0.020471 Objective Loss 0.020471 MSE 0.020602 LR 0.001000 Time 0.050716 +2024-02-02 16:34:16,180 - --- validate (epoch=151)----------- +2024-02-02 16:34:16,180 - 60 samples (32 per mini-batch) +2024-02-02 16:34:16,544 - Epoch: [151][ 1/ 2] Loss 0.025835 MSE 0.025835 +2024-02-02 16:34:16,549 - Epoch: [151][ 2/ 2] Loss 0.025468 MSE 0.025493 +2024-02-02 16:34:16,696 - ==> MSE: 0.02549 Loss: 0.025 + +2024-02-02 16:34:16,703 - ==> Best [Top 1 (MSE): 0.02470 Sparsity:0.00 Params: 136448 on epoch: 148] +2024-02-02 16:34:16,703 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:16,708 - + +2024-02-02 16:34:16,708 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:17,084 - Epoch: [152][ 1/ 8] Overall Loss 0.017156 Objective Loss 0.017156 MSE 0.017156 LR 0.001000 Time 0.374978 +2024-02-02 16:34:17,095 - Epoch: [152][ 2/ 8] Overall Loss 0.018791 Objective Loss 0.018791 MSE 0.018791 LR 0.001000 Time 0.193092 +2024-02-02 16:34:17,106 - Epoch: [152][ 3/ 8] Overall Loss 0.019154 Objective Loss 0.019154 MSE 0.019154 LR 0.001000 Time 0.132279 +2024-02-02 16:34:17,115 - Epoch: [152][ 4/ 8] Overall Loss 0.019670 Objective Loss 0.019670 MSE 0.019670 LR 0.001000 Time 0.101478 +2024-02-02 16:34:17,125 - Epoch: [152][ 5/ 8] Overall Loss 0.020111 Objective Loss 0.020111 MSE 0.020111 LR 0.001000 Time 0.083076 +2024-02-02 16:34:17,137 - Epoch: [152][ 6/ 8] Overall Loss 0.020292 Objective Loss 0.020292 MSE 0.020292 LR 0.001000 Time 0.071199 +2024-02-02 16:34:17,149 - Epoch: [152][ 7/ 8] Overall Loss 0.020318 Objective Loss 0.020318 MSE 0.020318 LR 0.001000 Time 0.062733 +2024-02-02 16:34:17,160 - Epoch: [152][ 8/ 8] Overall Loss 0.020620 Objective Loss 0.020620 MSE 0.020381 LR 0.001000 Time 0.056164 +2024-02-02 16:34:17,311 - --- validate (epoch=152)----------- +2024-02-02 16:34:17,311 - 60 samples (32 per mini-batch) +2024-02-02 16:34:17,669 - Epoch: [152][ 1/ 2] Loss 0.024831 MSE 0.024831 +2024-02-02 16:34:17,673 - Epoch: [152][ 2/ 2] Loss 0.024774 MSE 0.024778 +2024-02-02 16:34:17,823 - ==> MSE: 0.02478 Loss: 0.025 + +2024-02-02 16:34:17,830 - ==> Best [Top 1 (MSE): 0.02470 Sparsity:0.00 Params: 136448 on epoch: 148] +2024-02-02 16:34:17,830 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:17,835 - + +2024-02-02 16:34:17,835 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:18,203 - Epoch: [153][ 1/ 8] Overall Loss 0.019102 Objective Loss 0.019102 MSE 0.019102 LR 0.001000 Time 0.368100 +2024-02-02 16:34:18,212 - Epoch: [153][ 2/ 8] Overall Loss 0.019142 Objective Loss 0.019142 MSE 0.019142 LR 0.001000 Time 0.188219 +2024-02-02 16:34:18,219 - Epoch: [153][ 3/ 8] Overall Loss 0.019983 Objective Loss 0.019983 MSE 0.019983 LR 0.001000 Time 0.127707 +2024-02-02 16:34:18,225 - Epoch: [153][ 4/ 8] Overall Loss 0.019691 Objective Loss 0.019691 MSE 0.019691 LR 0.001000 Time 0.097418 +2024-02-02 16:34:18,232 - Epoch: [153][ 5/ 8] Overall Loss 0.019616 Objective Loss 0.019616 MSE 0.019616 LR 0.001000 Time 0.079273 +2024-02-02 16:34:18,239 - Epoch: [153][ 6/ 8] Overall Loss 0.019805 Objective Loss 0.019805 MSE 0.019805 LR 0.001000 Time 0.067167 +2024-02-02 16:34:18,246 - Epoch: [153][ 7/ 8] Overall Loss 0.020047 Objective Loss 0.020047 MSE 0.020047 LR 0.001000 Time 0.058521 +2024-02-02 16:34:18,252 - Epoch: [153][ 8/ 8] Overall Loss 0.021859 Objective Loss 0.021859 MSE 0.020425 LR 0.001000 Time 0.052020 +2024-02-02 16:34:18,403 - --- validate (epoch=153)----------- +2024-02-02 16:34:18,403 - 60 samples (32 per mini-batch) +2024-02-02 16:34:18,763 - Epoch: [153][ 1/ 2] Loss 0.025010 MSE 0.025010 +2024-02-02 16:34:18,769 - Epoch: [153][ 2/ 2] Loss 0.024568 MSE 0.024598 +2024-02-02 16:34:18,914 - ==> MSE: 0.02460 Loss: 0.025 + +2024-02-02 16:34:18,921 - ==> Best [Top 1 (MSE): 0.02460 Sparsity:0.00 Params: 136448 on epoch: 153] +2024-02-02 16:34:18,921 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:18,927 - + +2024-02-02 16:34:18,927 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:19,291 - Epoch: [154][ 1/ 8] Overall Loss 0.019320 Objective Loss 0.019320 MSE 0.019320 LR 0.001000 Time 0.364379 +2024-02-02 16:34:19,299 - Epoch: [154][ 2/ 8] Overall Loss 0.020012 Objective Loss 0.020012 MSE 0.020012 LR 0.001000 Time 0.185632 +2024-02-02 16:34:19,305 - Epoch: [154][ 3/ 8] Overall Loss 0.019719 Objective Loss 0.019719 MSE 0.019719 LR 0.001000 Time 0.125936 +2024-02-02 16:34:19,312 - Epoch: [154][ 4/ 8] Overall Loss 0.019666 Objective Loss 0.019666 MSE 0.019666 LR 0.001000 Time 0.096098 +2024-02-02 16:34:19,319 - Epoch: [154][ 5/ 8] Overall Loss 0.019946 Objective Loss 0.019946 MSE 0.019946 LR 0.001000 Time 0.078169 +2024-02-02 16:34:19,325 - Epoch: [154][ 6/ 8] Overall Loss 0.020070 Objective Loss 0.020070 MSE 0.020070 LR 0.001000 Time 0.066241 +2024-02-02 16:34:19,332 - Epoch: [154][ 7/ 8] Overall Loss 0.020204 Objective Loss 0.020204 MSE 0.020204 LR 0.001000 Time 0.057729 +2024-02-02 16:34:19,339 - Epoch: [154][ 8/ 8] Overall Loss 0.021715 Objective Loss 0.021715 MSE 0.020519 LR 0.001000 Time 0.051336 +2024-02-02 16:34:19,491 - --- validate (epoch=154)----------- +2024-02-02 16:34:19,491 - 60 samples (32 per mini-batch) +2024-02-02 16:34:19,851 - Epoch: [154][ 1/ 2] Loss 0.026528 MSE 0.026528 +2024-02-02 16:34:19,855 - Epoch: [154][ 2/ 2] Loss 0.024524 MSE 0.024658 +2024-02-02 16:34:20,000 - ==> MSE: 0.02466 Loss: 0.025 + +2024-02-02 16:34:20,007 - ==> Best [Top 1 (MSE): 0.02460 Sparsity:0.00 Params: 136448 on epoch: 153] +2024-02-02 16:34:20,008 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:20,012 - + +2024-02-02 16:34:20,012 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:20,369 - Epoch: [155][ 1/ 8] Overall Loss 0.020775 Objective Loss 0.020775 MSE 0.020775 LR 0.001000 Time 0.356867 +2024-02-02 16:34:20,378 - Epoch: [155][ 2/ 8] Overall Loss 0.020095 Objective Loss 0.020095 MSE 0.020095 LR 0.001000 Time 0.182464 +2024-02-02 16:34:20,385 - Epoch: [155][ 3/ 8] Overall Loss 0.020116 Objective Loss 0.020116 MSE 0.020116 LR 0.001000 Time 0.123904 +2024-02-02 16:34:20,391 - Epoch: [155][ 4/ 8] Overall Loss 0.020153 Objective Loss 0.020153 MSE 0.020153 LR 0.001000 Time 0.094575 +2024-02-02 16:34:20,398 - Epoch: [155][ 5/ 8] Overall Loss 0.019934 Objective Loss 0.019934 MSE 0.019934 LR 0.001000 Time 0.076970 +2024-02-02 16:34:20,405 - Epoch: [155][ 6/ 8] Overall Loss 0.020301 Objective Loss 0.020301 MSE 0.020301 LR 0.001000 Time 0.065227 +2024-02-02 16:34:20,411 - Epoch: [155][ 7/ 8] Overall Loss 0.020074 Objective Loss 0.020074 MSE 0.020074 LR 0.001000 Time 0.056827 +2024-02-02 16:34:20,418 - Epoch: [155][ 8/ 8] Overall Loss 0.021210 Objective Loss 0.021210 MSE 0.020311 LR 0.001000 Time 0.050515 +2024-02-02 16:34:20,566 - --- validate (epoch=155)----------- +2024-02-02 16:34:20,566 - 60 samples (32 per mini-batch) +2024-02-02 16:34:20,922 - Epoch: [155][ 1/ 2] Loss 0.023545 MSE 0.023545 +2024-02-02 16:34:20,927 - Epoch: [155][ 2/ 2] Loss 0.024898 MSE 0.024808 +2024-02-02 16:34:21,065 - ==> MSE: 0.02481 Loss: 0.025 + +2024-02-02 16:34:21,071 - ==> Best [Top 1 (MSE): 0.02460 Sparsity:0.00 Params: 136448 on epoch: 153] +2024-02-02 16:34:21,072 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:21,076 - + +2024-02-02 16:34:21,076 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:21,438 - Epoch: [156][ 1/ 8] Overall Loss 0.020607 Objective Loss 0.020607 MSE 0.020607 LR 0.001000 Time 0.360955 +2024-02-02 16:34:21,445 - Epoch: [156][ 2/ 8] Overall Loss 0.019886 Objective Loss 0.019886 MSE 0.019886 LR 0.001000 Time 0.183888 +2024-02-02 16:34:21,451 - Epoch: [156][ 3/ 8] Overall Loss 0.020363 Objective Loss 0.020363 MSE 0.020363 LR 0.001000 Time 0.124786 +2024-02-02 16:34:21,458 - Epoch: [156][ 4/ 8] Overall Loss 0.021107 Objective Loss 0.021107 MSE 0.021107 LR 0.001000 Time 0.095227 +2024-02-02 16:34:21,465 - Epoch: [156][ 5/ 8] Overall Loss 0.020506 Objective Loss 0.020506 MSE 0.020506 LR 0.001000 Time 0.077538 +2024-02-02 16:34:21,472 - Epoch: [156][ 6/ 8] Overall Loss 0.020603 Objective Loss 0.020603 MSE 0.020603 LR 0.001000 Time 0.065712 +2024-02-02 16:34:21,479 - Epoch: [156][ 7/ 8] Overall Loss 0.020184 Objective Loss 0.020184 MSE 0.020184 LR 0.001000 Time 0.057273 +2024-02-02 16:34:21,485 - Epoch: [156][ 8/ 8] Overall Loss 0.021931 Objective Loss 0.021931 MSE 0.020549 LR 0.001000 Time 0.050927 +2024-02-02 16:34:21,637 - --- validate (epoch=156)----------- +2024-02-02 16:34:21,637 - 60 samples (32 per mini-batch) +2024-02-02 16:34:21,990 - Epoch: [156][ 1/ 2] Loss 0.024538 MSE 0.024538 +2024-02-02 16:34:21,995 - Epoch: [156][ 2/ 2] Loss 0.024636 MSE 0.024630 +2024-02-02 16:34:22,143 - ==> MSE: 0.02463 Loss: 0.025 + +2024-02-02 16:34:22,149 - ==> Best [Top 1 (MSE): 0.02460 Sparsity:0.00 Params: 136448 on epoch: 153] +2024-02-02 16:34:22,149 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:22,154 - + +2024-02-02 16:34:22,154 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:22,513 - Epoch: [157][ 1/ 8] Overall Loss 0.022608 Objective Loss 0.022608 MSE 0.022608 LR 0.001000 Time 0.358241 +2024-02-02 16:34:22,520 - Epoch: [157][ 2/ 8] Overall Loss 0.020591 Objective Loss 0.020591 MSE 0.020591 LR 0.001000 Time 0.182620 +2024-02-02 16:34:22,527 - Epoch: [157][ 3/ 8] Overall Loss 0.020182 Objective Loss 0.020182 MSE 0.020182 LR 0.001000 Time 0.123966 +2024-02-02 16:34:22,534 - Epoch: [157][ 4/ 8] Overall Loss 0.019846 Objective Loss 0.019846 MSE 0.019846 LR 0.001000 Time 0.094631 +2024-02-02 16:34:22,540 - Epoch: [157][ 5/ 8] Overall Loss 0.019964 Objective Loss 0.019964 MSE 0.019964 LR 0.001000 Time 0.077014 +2024-02-02 16:34:22,547 - Epoch: [157][ 6/ 8] Overall Loss 0.020250 Objective Loss 0.020250 MSE 0.020250 LR 0.001000 Time 0.065267 +2024-02-02 16:34:22,554 - Epoch: [157][ 7/ 8] Overall Loss 0.020117 Objective Loss 0.020117 MSE 0.020117 LR 0.001000 Time 0.056872 +2024-02-02 16:34:22,560 - Epoch: [157][ 8/ 8] Overall Loss 0.020299 Objective Loss 0.020299 MSE 0.020155 LR 0.001000 Time 0.050569 +2024-02-02 16:34:22,707 - --- validate (epoch=157)----------- +2024-02-02 16:34:22,708 - 60 samples (32 per mini-batch) +2024-02-02 16:34:23,064 - Epoch: [157][ 1/ 2] Loss 0.023456 MSE 0.023456 +2024-02-02 16:34:23,068 - Epoch: [157][ 2/ 2] Loss 0.024728 MSE 0.024643 +2024-02-02 16:34:23,209 - ==> MSE: 0.02464 Loss: 0.025 + +2024-02-02 16:34:23,216 - ==> Best [Top 1 (MSE): 0.02460 Sparsity:0.00 Params: 136448 on epoch: 153] +2024-02-02 16:34:23,217 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:23,221 - + +2024-02-02 16:34:23,221 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:23,581 - Epoch: [158][ 1/ 8] Overall Loss 0.020678 Objective Loss 0.020678 MSE 0.020678 LR 0.001000 Time 0.359184 +2024-02-02 16:34:23,589 - Epoch: [158][ 2/ 8] Overall Loss 0.020959 Objective Loss 0.020959 MSE 0.020959 LR 0.001000 Time 0.183469 +2024-02-02 16:34:23,596 - Epoch: [158][ 3/ 8] Overall Loss 0.020930 Objective Loss 0.020930 MSE 0.020930 LR 0.001000 Time 0.124614 +2024-02-02 16:34:23,603 - Epoch: [158][ 4/ 8] Overall Loss 0.020496 Objective Loss 0.020496 MSE 0.020496 LR 0.001000 Time 0.095132 +2024-02-02 16:34:23,610 - Epoch: [158][ 5/ 8] Overall Loss 0.019905 Objective Loss 0.019905 MSE 0.019905 LR 0.001000 Time 0.077434 +2024-02-02 16:34:23,616 - Epoch: [158][ 6/ 8] Overall Loss 0.019747 Objective Loss 0.019747 MSE 0.019747 LR 0.001000 Time 0.065638 +2024-02-02 16:34:23,623 - Epoch: [158][ 7/ 8] Overall Loss 0.020038 Objective Loss 0.020038 MSE 0.020038 LR 0.001000 Time 0.057203 +2024-02-02 16:34:23,630 - Epoch: [158][ 8/ 8] Overall Loss 0.020812 Objective Loss 0.020812 MSE 0.020199 LR 0.001000 Time 0.050865 +2024-02-02 16:34:23,777 - --- validate (epoch=158)----------- +2024-02-02 16:34:23,777 - 60 samples (32 per mini-batch) +2024-02-02 16:34:24,132 - Epoch: [158][ 1/ 2] Loss 0.024880 MSE 0.024880 +2024-02-02 16:34:24,137 - Epoch: [158][ 2/ 2] Loss 0.024628 MSE 0.024645 +2024-02-02 16:34:24,279 - ==> MSE: 0.02464 Loss: 0.025 + +2024-02-02 16:34:24,285 - ==> Best [Top 1 (MSE): 0.02460 Sparsity:0.00 Params: 136448 on epoch: 153] +2024-02-02 16:34:24,285 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:24,290 - + +2024-02-02 16:34:24,290 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:24,655 - Epoch: [159][ 1/ 8] Overall Loss 0.019697 Objective Loss 0.019697 MSE 0.019697 LR 0.001000 Time 0.364153 +2024-02-02 16:34:24,666 - Epoch: [159][ 2/ 8] Overall Loss 0.019814 Objective Loss 0.019814 MSE 0.019814 LR 0.001000 Time 0.187478 +2024-02-02 16:34:24,673 - Epoch: [159][ 3/ 8] Overall Loss 0.019753 Objective Loss 0.019753 MSE 0.019753 LR 0.001000 Time 0.127238 +2024-02-02 16:34:24,680 - Epoch: [159][ 4/ 8] Overall Loss 0.019642 Objective Loss 0.019642 MSE 0.019642 LR 0.001000 Time 0.097198 +2024-02-02 16:34:24,687 - Epoch: [159][ 5/ 8] Overall Loss 0.020052 Objective Loss 0.020052 MSE 0.020052 LR 0.001000 Time 0.079085 +2024-02-02 16:34:24,694 - Epoch: [159][ 6/ 8] Overall Loss 0.019978 Objective Loss 0.019978 MSE 0.019978 LR 0.001000 Time 0.067005 +2024-02-02 16:34:24,700 - Epoch: [159][ 7/ 8] Overall Loss 0.019987 Objective Loss 0.019987 MSE 0.019987 LR 0.001000 Time 0.058367 +2024-02-02 16:34:24,707 - Epoch: [159][ 8/ 8] Overall Loss 0.020480 Objective Loss 0.020480 MSE 0.020090 LR 0.001000 Time 0.051887 +2024-02-02 16:34:24,850 - --- validate (epoch=159)----------- +2024-02-02 16:34:24,851 - 60 samples (32 per mini-batch) +2024-02-02 16:34:25,207 - Epoch: [159][ 1/ 2] Loss 0.024624 MSE 0.024624 +2024-02-02 16:34:25,212 - Epoch: [159][ 2/ 2] Loss 0.024377 MSE 0.024393 +2024-02-02 16:34:25,359 - ==> MSE: 0.02439 Loss: 0.024 + +2024-02-02 16:34:25,365 - ==> Best [Top 1 (MSE): 0.02439 Sparsity:0.00 Params: 136448 on epoch: 159] +2024-02-02 16:34:25,365 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:25,371 - + +2024-02-02 16:34:25,371 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:25,737 - Epoch: [160][ 1/ 8] Overall Loss 0.021902 Objective Loss 0.021902 MSE 0.021902 LR 0.001000 Time 0.365716 +2024-02-02 16:34:25,744 - Epoch: [160][ 2/ 8] Overall Loss 0.021141 Objective Loss 0.021141 MSE 0.021141 LR 0.001000 Time 0.186349 +2024-02-02 16:34:25,751 - Epoch: [160][ 3/ 8] Overall Loss 0.020594 Objective Loss 0.020594 MSE 0.020594 LR 0.001000 Time 0.126398 +2024-02-02 16:34:25,758 - Epoch: [160][ 4/ 8] Overall Loss 0.020347 Objective Loss 0.020347 MSE 0.020347 LR 0.001000 Time 0.096471 +2024-02-02 16:34:25,765 - Epoch: [160][ 5/ 8] Overall Loss 0.020439 Objective Loss 0.020439 MSE 0.020439 LR 0.001000 Time 0.078491 +2024-02-02 16:34:25,771 - Epoch: [160][ 6/ 8] Overall Loss 0.020098 Objective Loss 0.020098 MSE 0.020098 LR 0.001000 Time 0.066503 +2024-02-02 16:34:25,778 - Epoch: [160][ 7/ 8] Overall Loss 0.019988 Objective Loss 0.019988 MSE 0.019988 LR 0.001000 Time 0.057947 +2024-02-02 16:34:25,785 - Epoch: [160][ 8/ 8] Overall Loss 0.020596 Objective Loss 0.020596 MSE 0.020115 LR 0.001000 Time 0.051525 +2024-02-02 16:34:25,936 - --- validate (epoch=160)----------- +2024-02-02 16:34:25,936 - 60 samples (32 per mini-batch) +2024-02-02 16:34:26,292 - Epoch: [160][ 1/ 2] Loss 0.024485 MSE 0.024485 +2024-02-02 16:34:26,297 - Epoch: [160][ 2/ 2] Loss 0.024537 MSE 0.024533 +2024-02-02 16:34:26,441 - ==> MSE: 0.02453 Loss: 0.025 + +2024-02-02 16:34:26,448 - ==> Best [Top 1 (MSE): 0.02439 Sparsity:0.00 Params: 136448 on epoch: 159] +2024-02-02 16:34:26,448 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:26,453 - + +2024-02-02 16:34:26,453 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:26,811 - Epoch: [161][ 1/ 8] Overall Loss 0.019574 Objective Loss 0.019574 MSE 0.019574 LR 0.001000 Time 0.356977 +2024-02-02 16:34:26,819 - Epoch: [161][ 2/ 8] Overall Loss 0.019185 Objective Loss 0.019185 MSE 0.019185 LR 0.001000 Time 0.182518 +2024-02-02 16:34:26,826 - Epoch: [161][ 3/ 8] Overall Loss 0.019870 Objective Loss 0.019870 MSE 0.019870 LR 0.001000 Time 0.123968 +2024-02-02 16:34:26,833 - Epoch: [161][ 4/ 8] Overall Loss 0.019824 Objective Loss 0.019824 MSE 0.019824 LR 0.001000 Time 0.094830 +2024-02-02 16:34:26,840 - Epoch: [161][ 5/ 8] Overall Loss 0.020238 Objective Loss 0.020238 MSE 0.020238 LR 0.001000 Time 0.077175 +2024-02-02 16:34:26,847 - Epoch: [161][ 6/ 8] Overall Loss 0.020102 Objective Loss 0.020102 MSE 0.020102 LR 0.001000 Time 0.065402 +2024-02-02 16:34:26,854 - Epoch: [161][ 7/ 8] Overall Loss 0.019983 Objective Loss 0.019983 MSE 0.019983 LR 0.001000 Time 0.057003 +2024-02-02 16:34:26,860 - Epoch: [161][ 8/ 8] Overall Loss 0.019717 Objective Loss 0.019717 MSE 0.019927 LR 0.001000 Time 0.050704 +2024-02-02 16:34:27,008 - --- validate (epoch=161)----------- +2024-02-02 16:34:27,008 - 60 samples (32 per mini-batch) +2024-02-02 16:34:27,368 - Epoch: [161][ 1/ 2] Loss 0.024081 MSE 0.024081 +2024-02-02 16:34:27,372 - Epoch: [161][ 2/ 2] Loss 0.024506 MSE 0.024477 +2024-02-02 16:34:27,517 - ==> MSE: 0.02448 Loss: 0.025 + +2024-02-02 16:34:27,524 - ==> Best [Top 1 (MSE): 0.02439 Sparsity:0.00 Params: 136448 on epoch: 159] +2024-02-02 16:34:27,524 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:27,529 - + +2024-02-02 16:34:27,529 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:27,890 - Epoch: [162][ 1/ 8] Overall Loss 0.019340 Objective Loss 0.019340 MSE 0.019340 LR 0.001000 Time 0.360160 +2024-02-02 16:34:27,901 - Epoch: [162][ 2/ 8] Overall Loss 0.019178 Objective Loss 0.019178 MSE 0.019178 LR 0.001000 Time 0.185338 +2024-02-02 16:34:27,909 - Epoch: [162][ 3/ 8] Overall Loss 0.019620 Objective Loss 0.019620 MSE 0.019620 LR 0.001000 Time 0.126150 +2024-02-02 16:34:27,916 - Epoch: [162][ 4/ 8] Overall Loss 0.019880 Objective Loss 0.019880 MSE 0.019880 LR 0.001000 Time 0.096316 +2024-02-02 16:34:27,923 - Epoch: [162][ 5/ 8] Overall Loss 0.020284 Objective Loss 0.020284 MSE 0.020284 LR 0.001000 Time 0.078407 +2024-02-02 16:34:27,930 - Epoch: [162][ 6/ 8] Overall Loss 0.020228 Objective Loss 0.020228 MSE 0.020228 LR 0.001000 Time 0.066453 +2024-02-02 16:34:27,936 - Epoch: [162][ 7/ 8] Overall Loss 0.020105 Objective Loss 0.020105 MSE 0.020105 LR 0.001000 Time 0.057911 +2024-02-02 16:34:27,943 - Epoch: [162][ 8/ 8] Overall Loss 0.019617 Objective Loss 0.019617 MSE 0.020003 LR 0.001000 Time 0.051485 +2024-02-02 16:34:28,090 - --- validate (epoch=162)----------- +2024-02-02 16:34:28,091 - 60 samples (32 per mini-batch) +2024-02-02 16:34:28,448 - Epoch: [162][ 1/ 2] Loss 0.023855 MSE 0.023855 +2024-02-02 16:34:28,452 - Epoch: [162][ 2/ 2] Loss 0.024474 MSE 0.024433 +2024-02-02 16:34:28,598 - ==> MSE: 0.02443 Loss: 0.024 + +2024-02-02 16:34:28,605 - ==> Best [Top 1 (MSE): 0.02439 Sparsity:0.00 Params: 136448 on epoch: 159] +2024-02-02 16:34:28,605 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:28,610 - + +2024-02-02 16:34:28,610 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:28,975 - Epoch: [163][ 1/ 8] Overall Loss 0.019246 Objective Loss 0.019246 MSE 0.019246 LR 0.001000 Time 0.364548 +2024-02-02 16:34:28,984 - Epoch: [163][ 2/ 8] Overall Loss 0.018996 Objective Loss 0.018996 MSE 0.018996 LR 0.001000 Time 0.186506 +2024-02-02 16:34:28,991 - Epoch: [163][ 3/ 8] Overall Loss 0.019548 Objective Loss 0.019548 MSE 0.019548 LR 0.001000 Time 0.126563 +2024-02-02 16:34:28,997 - Epoch: [163][ 4/ 8] Overall Loss 0.020039 Objective Loss 0.020039 MSE 0.020039 LR 0.001000 Time 0.096554 +2024-02-02 16:34:29,004 - Epoch: [163][ 5/ 8] Overall Loss 0.020262 Objective Loss 0.020262 MSE 0.020262 LR 0.001000 Time 0.078559 +2024-02-02 16:34:29,011 - Epoch: [163][ 6/ 8] Overall Loss 0.020281 Objective Loss 0.020281 MSE 0.020281 LR 0.001000 Time 0.066550 +2024-02-02 16:34:29,018 - Epoch: [163][ 7/ 8] Overall Loss 0.020167 Objective Loss 0.020167 MSE 0.020167 LR 0.001000 Time 0.057983 +2024-02-02 16:34:29,024 - Epoch: [163][ 8/ 8] Overall Loss 0.022771 Objective Loss 0.022771 MSE 0.020710 LR 0.001000 Time 0.051555 +2024-02-02 16:34:29,177 - --- validate (epoch=163)----------- +2024-02-02 16:34:29,177 - 60 samples (32 per mini-batch) +2024-02-02 16:34:29,529 - Epoch: [163][ 1/ 2] Loss 0.023814 MSE 0.023814 +2024-02-02 16:34:29,533 - Epoch: [163][ 2/ 2] Loss 0.024659 MSE 0.024603 +2024-02-02 16:34:29,675 - ==> MSE: 0.02460 Loss: 0.025 + +2024-02-02 16:34:29,682 - ==> Best [Top 1 (MSE): 0.02439 Sparsity:0.00 Params: 136448 on epoch: 159] +2024-02-02 16:34:29,682 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:29,687 - + +2024-02-02 16:34:29,687 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:30,044 - Epoch: [164][ 1/ 8] Overall Loss 0.021825 Objective Loss 0.021825 MSE 0.021825 LR 0.001000 Time 0.356950 +2024-02-02 16:34:30,051 - Epoch: [164][ 2/ 8] Overall Loss 0.020122 Objective Loss 0.020122 MSE 0.020122 LR 0.001000 Time 0.181874 +2024-02-02 16:34:30,058 - Epoch: [164][ 3/ 8] Overall Loss 0.019731 Objective Loss 0.019731 MSE 0.019731 LR 0.001000 Time 0.123482 +2024-02-02 16:34:30,065 - Epoch: [164][ 4/ 8] Overall Loss 0.020254 Objective Loss 0.020254 MSE 0.020254 LR 0.001000 Time 0.094253 +2024-02-02 16:34:30,071 - Epoch: [164][ 5/ 8] Overall Loss 0.020224 Objective Loss 0.020224 MSE 0.020224 LR 0.001000 Time 0.076684 +2024-02-02 16:34:30,078 - Epoch: [164][ 6/ 8] Overall Loss 0.020321 Objective Loss 0.020321 MSE 0.020321 LR 0.001000 Time 0.064982 +2024-02-02 16:34:30,085 - Epoch: [164][ 7/ 8] Overall Loss 0.020108 Objective Loss 0.020108 MSE 0.020108 LR 0.001000 Time 0.056628 +2024-02-02 16:34:30,091 - Epoch: [164][ 8/ 8] Overall Loss 0.021133 Objective Loss 0.021133 MSE 0.020322 LR 0.001000 Time 0.050342 +2024-02-02 16:34:30,243 - --- validate (epoch=164)----------- +2024-02-02 16:34:30,243 - 60 samples (32 per mini-batch) +2024-02-02 16:34:30,597 - Epoch: [164][ 1/ 2] Loss 0.025697 MSE 0.025697 +2024-02-02 16:34:30,601 - Epoch: [164][ 2/ 2] Loss 0.025191 MSE 0.025224 +2024-02-02 16:34:30,754 - ==> MSE: 0.02522 Loss: 0.025 + +2024-02-02 16:34:30,761 - ==> Best [Top 1 (MSE): 0.02439 Sparsity:0.00 Params: 136448 on epoch: 159] +2024-02-02 16:34:30,761 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:30,766 - + +2024-02-02 16:34:30,766 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:31,117 - Epoch: [165][ 1/ 8] Overall Loss 0.019265 Objective Loss 0.019265 MSE 0.019265 LR 0.001000 Time 0.350516 +2024-02-02 16:34:31,124 - Epoch: [165][ 2/ 8] Overall Loss 0.019627 Objective Loss 0.019627 MSE 0.019627 LR 0.001000 Time 0.178705 +2024-02-02 16:34:31,131 - Epoch: [165][ 3/ 8] Overall Loss 0.020404 Objective Loss 0.020404 MSE 0.020404 LR 0.001000 Time 0.121341 +2024-02-02 16:34:31,137 - Epoch: [165][ 4/ 8] Overall Loss 0.019989 Objective Loss 0.019989 MSE 0.019989 LR 0.001000 Time 0.092608 +2024-02-02 16:34:31,144 - Epoch: [165][ 5/ 8] Overall Loss 0.019835 Objective Loss 0.019835 MSE 0.019835 LR 0.001000 Time 0.075411 +2024-02-02 16:34:31,151 - Epoch: [165][ 6/ 8] Overall Loss 0.019988 Objective Loss 0.019988 MSE 0.019988 LR 0.001000 Time 0.063933 +2024-02-02 16:34:31,157 - Epoch: [165][ 7/ 8] Overall Loss 0.020150 Objective Loss 0.020150 MSE 0.020150 LR 0.001000 Time 0.055732 +2024-02-02 16:34:31,164 - Epoch: [165][ 8/ 8] Overall Loss 0.021010 Objective Loss 0.021010 MSE 0.020329 LR 0.001000 Time 0.049592 +2024-02-02 16:34:31,315 - --- validate (epoch=165)----------- +2024-02-02 16:34:31,316 - 60 samples (32 per mini-batch) +2024-02-02 16:34:31,678 - Epoch: [165][ 1/ 2] Loss 0.024893 MSE 0.024893 +2024-02-02 16:34:31,682 - Epoch: [165][ 2/ 2] Loss 0.025152 MSE 0.025135 +2024-02-02 16:34:31,823 - ==> MSE: 0.02513 Loss: 0.025 + +2024-02-02 16:34:31,830 - ==> Best [Top 1 (MSE): 0.02439 Sparsity:0.00 Params: 136448 on epoch: 159] +2024-02-02 16:34:31,830 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:31,835 - + +2024-02-02 16:34:31,835 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:32,193 - Epoch: [166][ 1/ 8] Overall Loss 0.018287 Objective Loss 0.018287 MSE 0.018287 LR 0.001000 Time 0.357998 +2024-02-02 16:34:32,200 - Epoch: [166][ 2/ 8] Overall Loss 0.019606 Objective Loss 0.019606 MSE 0.019606 LR 0.001000 Time 0.182399 +2024-02-02 16:34:32,207 - Epoch: [166][ 3/ 8] Overall Loss 0.019470 Objective Loss 0.019470 MSE 0.019470 LR 0.001000 Time 0.123754 +2024-02-02 16:34:32,214 - Epoch: [166][ 4/ 8] Overall Loss 0.019951 Objective Loss 0.019951 MSE 0.019951 LR 0.001000 Time 0.094443 +2024-02-02 16:34:32,220 - Epoch: [166][ 5/ 8] Overall Loss 0.019906 Objective Loss 0.019906 MSE 0.019906 LR 0.001000 Time 0.076855 +2024-02-02 16:34:32,227 - Epoch: [166][ 6/ 8] Overall Loss 0.019750 Objective Loss 0.019750 MSE 0.019750 LR 0.001000 Time 0.065106 +2024-02-02 16:34:32,233 - Epoch: [166][ 7/ 8] Overall Loss 0.020009 Objective Loss 0.020009 MSE 0.020009 LR 0.001000 Time 0.056721 +2024-02-02 16:34:32,240 - Epoch: [166][ 8/ 8] Overall Loss 0.022807 Objective Loss 0.022807 MSE 0.020593 LR 0.001000 Time 0.050434 +2024-02-02 16:34:32,385 - --- validate (epoch=166)----------- +2024-02-02 16:34:32,385 - 60 samples (32 per mini-batch) +2024-02-02 16:34:32,742 - Epoch: [166][ 1/ 2] Loss 0.022526 MSE 0.022526 +2024-02-02 16:34:32,747 - Epoch: [166][ 2/ 2] Loss 0.024469 MSE 0.024340 +2024-02-02 16:34:32,883 - ==> MSE: 0.02434 Loss: 0.024 + +2024-02-02 16:34:32,889 - ==> Best [Top 1 (MSE): 0.02434 Sparsity:0.00 Params: 136448 on epoch: 166] +2024-02-02 16:34:32,889 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:32,895 - + +2024-02-02 16:34:32,895 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:33,228 - Epoch: [167][ 1/ 8] Overall Loss 0.023761 Objective Loss 0.023761 MSE 0.023761 LR 0.001000 Time 0.331871 +2024-02-02 16:34:33,235 - Epoch: [167][ 2/ 8] Overall Loss 0.021834 Objective Loss 0.021834 MSE 0.021834 LR 0.001000 Time 0.169377 +2024-02-02 16:34:33,241 - Epoch: [167][ 3/ 8] Overall Loss 0.021270 Objective Loss 0.021270 MSE 0.021270 LR 0.001000 Time 0.115094 +2024-02-02 16:34:33,248 - Epoch: [167][ 4/ 8] Overall Loss 0.020776 Objective Loss 0.020776 MSE 0.020776 LR 0.001000 Time 0.087942 +2024-02-02 16:34:33,255 - Epoch: [167][ 5/ 8] Overall Loss 0.020856 Objective Loss 0.020856 MSE 0.020856 LR 0.001000 Time 0.071620 +2024-02-02 16:34:33,261 - Epoch: [167][ 6/ 8] Overall Loss 0.020430 Objective Loss 0.020430 MSE 0.020430 LR 0.001000 Time 0.060741 +2024-02-02 16:34:33,268 - Epoch: [167][ 7/ 8] Overall Loss 0.020347 Objective Loss 0.020347 MSE 0.020347 LR 0.001000 Time 0.052975 +2024-02-02 16:34:33,274 - Epoch: [167][ 8/ 8] Overall Loss 0.019894 Objective Loss 0.019894 MSE 0.020253 LR 0.001000 Time 0.047145 +2024-02-02 16:34:33,415 - --- validate (epoch=167)----------- +2024-02-02 16:34:33,416 - 60 samples (32 per mini-batch) +2024-02-02 16:34:33,766 - Epoch: [167][ 1/ 2] Loss 0.024658 MSE 0.024658 +2024-02-02 16:34:33,771 - Epoch: [167][ 2/ 2] Loss 0.025036 MSE 0.025011 +2024-02-02 16:34:33,923 - ==> MSE: 0.02501 Loss: 0.025 + +2024-02-02 16:34:33,930 - ==> Best [Top 1 (MSE): 0.02434 Sparsity:0.00 Params: 136448 on epoch: 166] +2024-02-02 16:34:33,930 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:33,935 - + +2024-02-02 16:34:33,935 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:34,300 - Epoch: [168][ 1/ 8] Overall Loss 0.020345 Objective Loss 0.020345 MSE 0.020345 LR 0.001000 Time 0.364133 +2024-02-02 16:34:34,307 - Epoch: [168][ 2/ 8] Overall Loss 0.020437 Objective Loss 0.020437 MSE 0.020437 LR 0.001000 Time 0.185546 +2024-02-02 16:34:34,313 - Epoch: [168][ 3/ 8] Overall Loss 0.020633 Objective Loss 0.020633 MSE 0.020633 LR 0.001000 Time 0.125877 +2024-02-02 16:34:34,320 - Epoch: [168][ 4/ 8] Overall Loss 0.021019 Objective Loss 0.021019 MSE 0.021019 LR 0.001000 Time 0.096040 +2024-02-02 16:34:34,327 - Epoch: [168][ 5/ 8] Overall Loss 0.020775 Objective Loss 0.020775 MSE 0.020775 LR 0.001000 Time 0.078148 +2024-02-02 16:34:34,334 - Epoch: [168][ 6/ 8] Overall Loss 0.020400 Objective Loss 0.020400 MSE 0.020400 LR 0.001000 Time 0.066228 +2024-02-02 16:34:34,340 - Epoch: [168][ 7/ 8] Overall Loss 0.020196 Objective Loss 0.020196 MSE 0.020196 LR 0.001000 Time 0.057692 +2024-02-02 16:34:34,347 - Epoch: [168][ 8/ 8] Overall Loss 0.019601 Objective Loss 0.019601 MSE 0.020072 LR 0.001000 Time 0.051325 +2024-02-02 16:34:34,498 - --- validate (epoch=168)----------- +2024-02-02 16:34:34,498 - 60 samples (32 per mini-batch) +2024-02-02 16:34:34,847 - Epoch: [168][ 1/ 2] Loss 0.023567 MSE 0.023567 +2024-02-02 16:34:34,852 - Epoch: [168][ 2/ 2] Loss 0.024579 MSE 0.024512 +2024-02-02 16:34:34,998 - ==> MSE: 0.02451 Loss: 0.025 + +2024-02-02 16:34:35,005 - ==> Best [Top 1 (MSE): 0.02434 Sparsity:0.00 Params: 136448 on epoch: 166] +2024-02-02 16:34:35,005 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:35,010 - + +2024-02-02 16:34:35,010 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:35,373 - Epoch: [169][ 1/ 8] Overall Loss 0.021153 Objective Loss 0.021153 MSE 0.021153 LR 0.001000 Time 0.362248 +2024-02-02 16:34:35,380 - Epoch: [169][ 2/ 8] Overall Loss 0.021127 Objective Loss 0.021127 MSE 0.021127 LR 0.001000 Time 0.184611 +2024-02-02 16:34:35,387 - Epoch: [169][ 3/ 8] Overall Loss 0.020313 Objective Loss 0.020313 MSE 0.020313 LR 0.001000 Time 0.125279 +2024-02-02 16:34:35,394 - Epoch: [169][ 4/ 8] Overall Loss 0.020767 Objective Loss 0.020767 MSE 0.020767 LR 0.001000 Time 0.095605 +2024-02-02 16:34:35,400 - Epoch: [169][ 5/ 8] Overall Loss 0.020636 Objective Loss 0.020636 MSE 0.020636 LR 0.001000 Time 0.077788 +2024-02-02 16:34:35,407 - Epoch: [169][ 6/ 8] Overall Loss 0.020389 Objective Loss 0.020389 MSE 0.020389 LR 0.001000 Time 0.065906 +2024-02-02 16:34:35,414 - Epoch: [169][ 7/ 8] Overall Loss 0.020225 Objective Loss 0.020225 MSE 0.020225 LR 0.001000 Time 0.057425 +2024-02-02 16:34:35,420 - Epoch: [169][ 8/ 8] Overall Loss 0.020480 Objective Loss 0.020480 MSE 0.020278 LR 0.001000 Time 0.051056 +2024-02-02 16:34:35,567 - --- validate (epoch=169)----------- +2024-02-02 16:34:35,568 - 60 samples (32 per mini-batch) +2024-02-02 16:34:35,929 - Epoch: [169][ 1/ 2] Loss 0.024086 MSE 0.024086 +2024-02-02 16:34:35,934 - Epoch: [169][ 2/ 2] Loss 0.024564 MSE 0.024532 +2024-02-02 16:34:36,085 - ==> MSE: 0.02453 Loss: 0.025 + +2024-02-02 16:34:36,093 - ==> Best [Top 1 (MSE): 0.02434 Sparsity:0.00 Params: 136448 on epoch: 166] +2024-02-02 16:34:36,093 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:36,098 - + +2024-02-02 16:34:36,098 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:36,460 - Epoch: [170][ 1/ 8] Overall Loss 0.021386 Objective Loss 0.021386 MSE 0.021386 LR 0.001000 Time 0.361630 +2024-02-02 16:34:36,467 - Epoch: [170][ 2/ 8] Overall Loss 0.020015 Objective Loss 0.020015 MSE 0.020015 LR 0.001000 Time 0.184240 +2024-02-02 16:34:36,474 - Epoch: [170][ 3/ 8] Overall Loss 0.019622 Objective Loss 0.019622 MSE 0.019622 LR 0.001000 Time 0.125049 +2024-02-02 16:34:36,480 - Epoch: [170][ 4/ 8] Overall Loss 0.019574 Objective Loss 0.019574 MSE 0.019574 LR 0.001000 Time 0.095441 +2024-02-02 16:34:36,487 - Epoch: [170][ 5/ 8] Overall Loss 0.019939 Objective Loss 0.019939 MSE 0.019939 LR 0.001000 Time 0.077644 +2024-02-02 16:34:36,494 - Epoch: [170][ 6/ 8] Overall Loss 0.019714 Objective Loss 0.019714 MSE 0.019714 LR 0.001000 Time 0.065785 +2024-02-02 16:34:36,500 - Epoch: [170][ 7/ 8] Overall Loss 0.020158 Objective Loss 0.020158 MSE 0.020158 LR 0.001000 Time 0.057332 +2024-02-02 16:34:36,507 - Epoch: [170][ 8/ 8] Overall Loss 0.020639 Objective Loss 0.020639 MSE 0.020259 LR 0.001000 Time 0.050968 +2024-02-02 16:34:36,653 - --- validate (epoch=170)----------- +2024-02-02 16:34:36,654 - 60 samples (32 per mini-batch) +2024-02-02 16:34:36,994 - Epoch: [170][ 1/ 2] Loss 0.024953 MSE 0.024953 +2024-02-02 16:34:36,998 - Epoch: [170][ 2/ 2] Loss 0.024206 MSE 0.024256 +2024-02-02 16:34:37,145 - ==> MSE: 0.02426 Loss: 0.024 + +2024-02-02 16:34:37,153 - ==> Best [Top 1 (MSE): 0.02426 Sparsity:0.00 Params: 136448 on epoch: 170] +2024-02-02 16:34:37,153 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:37,159 - + +2024-02-02 16:34:37,159 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:37,522 - Epoch: [171][ 1/ 8] Overall Loss 0.020039 Objective Loss 0.020039 MSE 0.020039 LR 0.001000 Time 0.362153 +2024-02-02 16:34:37,529 - Epoch: [171][ 2/ 8] Overall Loss 0.019152 Objective Loss 0.019152 MSE 0.019152 LR 0.001000 Time 0.184593 +2024-02-02 16:34:37,536 - Epoch: [171][ 3/ 8] Overall Loss 0.019592 Objective Loss 0.019592 MSE 0.019592 LR 0.001000 Time 0.125306 +2024-02-02 16:34:37,542 - Epoch: [171][ 4/ 8] Overall Loss 0.020230 Objective Loss 0.020230 MSE 0.020230 LR 0.001000 Time 0.095644 +2024-02-02 16:34:37,549 - Epoch: [171][ 5/ 8] Overall Loss 0.019714 Objective Loss 0.019714 MSE 0.019714 LR 0.001000 Time 0.077856 +2024-02-02 16:34:37,556 - Epoch: [171][ 6/ 8] Overall Loss 0.019690 Objective Loss 0.019690 MSE 0.019690 LR 0.001000 Time 0.065977 +2024-02-02 16:34:37,563 - Epoch: [171][ 7/ 8] Overall Loss 0.019796 Objective Loss 0.019796 MSE 0.019796 LR 0.001000 Time 0.057499 +2024-02-02 16:34:37,570 - Epoch: [171][ 8/ 8] Overall Loss 0.024731 Objective Loss 0.024731 MSE 0.020826 LR 0.001000 Time 0.051131 +2024-02-02 16:34:37,716 - --- validate (epoch=171)----------- +2024-02-02 16:34:37,716 - 60 samples (32 per mini-batch) +2024-02-02 16:34:38,073 - Epoch: [171][ 1/ 2] Loss 0.027122 MSE 0.027122 +2024-02-02 16:34:38,077 - Epoch: [171][ 2/ 2] Loss 0.024660 MSE 0.024824 +2024-02-02 16:34:38,219 - ==> MSE: 0.02482 Loss: 0.025 + +2024-02-02 16:34:38,226 - ==> Best [Top 1 (MSE): 0.02426 Sparsity:0.00 Params: 136448 on epoch: 170] +2024-02-02 16:34:38,226 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:38,231 - + +2024-02-02 16:34:38,231 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:38,590 - Epoch: [172][ 1/ 8] Overall Loss 0.018778 Objective Loss 0.018778 MSE 0.018778 LR 0.001000 Time 0.358424 +2024-02-02 16:34:38,600 - Epoch: [172][ 2/ 8] Overall Loss 0.019846 Objective Loss 0.019846 MSE 0.019846 LR 0.001000 Time 0.184371 +2024-02-02 16:34:38,611 - Epoch: [172][ 3/ 8] Overall Loss 0.020076 Objective Loss 0.020076 MSE 0.020076 LR 0.001000 Time 0.126299 +2024-02-02 16:34:38,618 - Epoch: [172][ 4/ 8] Overall Loss 0.019947 Objective Loss 0.019947 MSE 0.019947 LR 0.001000 Time 0.096444 +2024-02-02 16:34:38,625 - Epoch: [172][ 5/ 8] Overall Loss 0.020128 Objective Loss 0.020128 MSE 0.020128 LR 0.001000 Time 0.078512 +2024-02-02 16:34:38,631 - Epoch: [172][ 6/ 8] Overall Loss 0.020232 Objective Loss 0.020232 MSE 0.020232 LR 0.001000 Time 0.066527 +2024-02-02 16:34:38,638 - Epoch: [172][ 7/ 8] Overall Loss 0.020516 Objective Loss 0.020516 MSE 0.020516 LR 0.001000 Time 0.057974 +2024-02-02 16:34:38,645 - Epoch: [172][ 8/ 8] Overall Loss 0.021466 Objective Loss 0.021466 MSE 0.020714 LR 0.001000 Time 0.051540 +2024-02-02 16:34:38,798 - --- validate (epoch=172)----------- +2024-02-02 16:34:38,798 - 60 samples (32 per mini-batch) +2024-02-02 16:34:39,160 - Epoch: [172][ 1/ 2] Loss 0.025809 MSE 0.025809 +2024-02-02 16:34:39,165 - Epoch: [172][ 2/ 2] Loss 0.025015 MSE 0.025068 +2024-02-02 16:34:39,311 - ==> MSE: 0.02507 Loss: 0.025 + +2024-02-02 16:34:39,318 - ==> Best [Top 1 (MSE): 0.02426 Sparsity:0.00 Params: 136448 on epoch: 170] +2024-02-02 16:34:39,318 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:39,322 - + +2024-02-02 16:34:39,323 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:39,679 - Epoch: [173][ 1/ 8] Overall Loss 0.018284 Objective Loss 0.018284 MSE 0.018284 LR 0.001000 Time 0.356359 +2024-02-02 16:34:39,687 - Epoch: [173][ 2/ 8] Overall Loss 0.019038 Objective Loss 0.019038 MSE 0.019038 LR 0.001000 Time 0.181696 +2024-02-02 16:34:39,693 - Epoch: [173][ 3/ 8] Overall Loss 0.019799 Objective Loss 0.019799 MSE 0.019799 LR 0.001000 Time 0.123336 +2024-02-02 16:34:39,700 - Epoch: [173][ 4/ 8] Overall Loss 0.020118 Objective Loss 0.020118 MSE 0.020118 LR 0.001000 Time 0.094151 +2024-02-02 16:34:39,707 - Epoch: [173][ 5/ 8] Overall Loss 0.019835 Objective Loss 0.019835 MSE 0.019835 LR 0.001000 Time 0.076620 +2024-02-02 16:34:39,713 - Epoch: [173][ 6/ 8] Overall Loss 0.019874 Objective Loss 0.019874 MSE 0.019874 LR 0.001000 Time 0.064936 +2024-02-02 16:34:39,720 - Epoch: [173][ 7/ 8] Overall Loss 0.020207 Objective Loss 0.020207 MSE 0.020207 LR 0.001000 Time 0.056596 +2024-02-02 16:34:39,727 - Epoch: [173][ 8/ 8] Overall Loss 0.020631 Objective Loss 0.020631 MSE 0.020296 LR 0.001000 Time 0.050321 +2024-02-02 16:34:39,876 - --- validate (epoch=173)----------- +2024-02-02 16:34:39,876 - 60 samples (32 per mini-batch) +2024-02-02 16:34:40,232 - Epoch: [173][ 1/ 2] Loss 0.025871 MSE 0.025871 +2024-02-02 16:34:40,238 - Epoch: [173][ 2/ 2] Loss 0.024264 MSE 0.024371 +2024-02-02 16:34:40,385 - ==> MSE: 0.02437 Loss: 0.024 + +2024-02-02 16:34:40,393 - ==> Best [Top 1 (MSE): 0.02426 Sparsity:0.00 Params: 136448 on epoch: 170] +2024-02-02 16:34:40,393 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:40,398 - + +2024-02-02 16:34:40,398 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:40,761 - Epoch: [174][ 1/ 8] Overall Loss 0.021251 Objective Loss 0.021251 MSE 0.021251 LR 0.001000 Time 0.362563 +2024-02-02 16:34:40,768 - Epoch: [174][ 2/ 8] Overall Loss 0.020148 Objective Loss 0.020148 MSE 0.020148 LR 0.001000 Time 0.184735 +2024-02-02 16:34:40,775 - Epoch: [174][ 3/ 8] Overall Loss 0.019909 Objective Loss 0.019909 MSE 0.019909 LR 0.001000 Time 0.125362 +2024-02-02 16:34:40,782 - Epoch: [174][ 4/ 8] Overall Loss 0.019333 Objective Loss 0.019333 MSE 0.019333 LR 0.001000 Time 0.095658 +2024-02-02 16:34:40,789 - Epoch: [174][ 5/ 8] Overall Loss 0.019849 Objective Loss 0.019849 MSE 0.019849 LR 0.001000 Time 0.077858 +2024-02-02 16:34:40,795 - Epoch: [174][ 6/ 8] Overall Loss 0.020090 Objective Loss 0.020090 MSE 0.020090 LR 0.001000 Time 0.065960 +2024-02-02 16:34:40,802 - Epoch: [174][ 7/ 8] Overall Loss 0.019989 Objective Loss 0.019989 MSE 0.019989 LR 0.001000 Time 0.057473 +2024-02-02 16:34:40,809 - Epoch: [174][ 8/ 8] Overall Loss 0.022015 Objective Loss 0.022015 MSE 0.020412 LR 0.001000 Time 0.051140 +2024-02-02 16:34:40,954 - --- validate (epoch=174)----------- +2024-02-02 16:34:40,954 - 60 samples (32 per mini-batch) +2024-02-02 16:34:41,310 - Epoch: [174][ 1/ 2] Loss 0.023777 MSE 0.023777 +2024-02-02 16:34:41,314 - Epoch: [174][ 2/ 2] Loss 0.024153 MSE 0.024128 +2024-02-02 16:34:41,452 - ==> MSE: 0.02413 Loss: 0.024 + +2024-02-02 16:34:41,459 - ==> Best [Top 1 (MSE): 0.02413 Sparsity:0.00 Params: 136448 on epoch: 174] +2024-02-02 16:34:41,459 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:41,465 - + +2024-02-02 16:34:41,465 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:41,824 - Epoch: [175][ 1/ 8] Overall Loss 0.018137 Objective Loss 0.018137 MSE 0.018137 LR 0.001000 Time 0.358585 +2024-02-02 16:34:41,831 - Epoch: [175][ 2/ 8] Overall Loss 0.018351 Objective Loss 0.018351 MSE 0.018351 LR 0.001000 Time 0.182755 +2024-02-02 16:34:41,838 - Epoch: [175][ 3/ 8] Overall Loss 0.019032 Objective Loss 0.019032 MSE 0.019032 LR 0.001000 Time 0.124045 +2024-02-02 16:34:41,844 - Epoch: [175][ 4/ 8] Overall Loss 0.019258 Objective Loss 0.019258 MSE 0.019258 LR 0.001000 Time 0.094703 +2024-02-02 16:34:41,851 - Epoch: [175][ 5/ 8] Overall Loss 0.019670 Objective Loss 0.019670 MSE 0.019670 LR 0.001000 Time 0.077081 +2024-02-02 16:34:41,858 - Epoch: [175][ 6/ 8] Overall Loss 0.019417 Objective Loss 0.019417 MSE 0.019417 LR 0.001000 Time 0.065334 +2024-02-02 16:34:41,865 - Epoch: [175][ 7/ 8] Overall Loss 0.019877 Objective Loss 0.019877 MSE 0.019877 LR 0.001000 Time 0.056938 +2024-02-02 16:34:41,871 - Epoch: [175][ 8/ 8] Overall Loss 0.020769 Objective Loss 0.020769 MSE 0.020064 LR 0.001000 Time 0.050625 +2024-02-02 16:34:42,021 - --- validate (epoch=175)----------- +2024-02-02 16:34:42,021 - 60 samples (32 per mini-batch) +2024-02-02 16:34:42,382 - Epoch: [175][ 1/ 2] Loss 0.024871 MSE 0.024871 +2024-02-02 16:34:42,386 - Epoch: [175][ 2/ 2] Loss 0.024271 MSE 0.024311 +2024-02-02 16:34:42,527 - ==> MSE: 0.02431 Loss: 0.024 + +2024-02-02 16:34:42,534 - ==> Best [Top 1 (MSE): 0.02413 Sparsity:0.00 Params: 136448 on epoch: 174] +2024-02-02 16:34:42,534 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:42,539 - + +2024-02-02 16:34:42,539 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:42,894 - Epoch: [176][ 1/ 8] Overall Loss 0.019859 Objective Loss 0.019859 MSE 0.019859 LR 0.001000 Time 0.353881 +2024-02-02 16:34:42,904 - Epoch: [176][ 2/ 8] Overall Loss 0.020302 Objective Loss 0.020302 MSE 0.020302 LR 0.001000 Time 0.182119 +2024-02-02 16:34:42,915 - Epoch: [176][ 3/ 8] Overall Loss 0.020226 Objective Loss 0.020226 MSE 0.020226 LR 0.001000 Time 0.124778 +2024-02-02 16:34:42,925 - Epoch: [176][ 4/ 8] Overall Loss 0.020259 Objective Loss 0.020259 MSE 0.020259 LR 0.001000 Time 0.096111 +2024-02-02 16:34:42,935 - Epoch: [176][ 5/ 8] Overall Loss 0.020144 Objective Loss 0.020144 MSE 0.020144 LR 0.001000 Time 0.078867 +2024-02-02 16:34:42,946 - Epoch: [176][ 6/ 8] Overall Loss 0.020047 Objective Loss 0.020047 MSE 0.020047 LR 0.001000 Time 0.067454 +2024-02-02 16:34:42,953 - Epoch: [176][ 7/ 8] Overall Loss 0.019865 Objective Loss 0.019865 MSE 0.019865 LR 0.001000 Time 0.058831 +2024-02-02 16:34:42,960 - Epoch: [176][ 8/ 8] Overall Loss 0.020199 Objective Loss 0.020199 MSE 0.019935 LR 0.001000 Time 0.052295 +2024-02-02 16:34:43,108 - --- validate (epoch=176)----------- +2024-02-02 16:34:43,108 - 60 samples (32 per mini-batch) +2024-02-02 16:34:43,470 - Epoch: [176][ 1/ 2] Loss 0.024732 MSE 0.024732 +2024-02-02 16:34:43,477 - Epoch: [176][ 2/ 2] Loss 0.024089 MSE 0.024132 +2024-02-02 16:34:43,625 - ==> MSE: 0.02413 Loss: 0.024 + +2024-02-02 16:34:43,633 - ==> Best [Top 1 (MSE): 0.02413 Sparsity:0.00 Params: 136448 on epoch: 174] +2024-02-02 16:34:43,633 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:43,638 - + +2024-02-02 16:34:43,638 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:44,003 - Epoch: [177][ 1/ 8] Overall Loss 0.019475 Objective Loss 0.019475 MSE 0.019475 LR 0.001000 Time 0.363728 +2024-02-02 16:34:44,010 - Epoch: [177][ 2/ 8] Overall Loss 0.018453 Objective Loss 0.018453 MSE 0.018453 LR 0.001000 Time 0.185346 +2024-02-02 16:34:44,017 - Epoch: [177][ 3/ 8] Overall Loss 0.019144 Objective Loss 0.019144 MSE 0.019144 LR 0.001000 Time 0.125815 +2024-02-02 16:34:44,023 - Epoch: [177][ 4/ 8] Overall Loss 0.019643 Objective Loss 0.019643 MSE 0.019643 LR 0.001000 Time 0.095990 +2024-02-02 16:34:44,030 - Epoch: [177][ 5/ 8] Overall Loss 0.019839 Objective Loss 0.019839 MSE 0.019839 LR 0.001000 Time 0.078140 +2024-02-02 16:34:44,037 - Epoch: [177][ 6/ 8] Overall Loss 0.019664 Objective Loss 0.019664 MSE 0.019664 LR 0.001000 Time 0.066223 +2024-02-02 16:34:44,044 - Epoch: [177][ 7/ 8] Overall Loss 0.019897 Objective Loss 0.019897 MSE 0.019897 LR 0.001000 Time 0.057712 +2024-02-02 16:34:44,051 - Epoch: [177][ 8/ 8] Overall Loss 0.020597 Objective Loss 0.020597 MSE 0.020043 LR 0.001000 Time 0.051332 +2024-02-02 16:34:44,201 - --- validate (epoch=177)----------- +2024-02-02 16:34:44,202 - 60 samples (32 per mini-batch) +2024-02-02 16:34:44,562 - Epoch: [177][ 1/ 2] Loss 0.023238 MSE 0.023238 +2024-02-02 16:34:44,566 - Epoch: [177][ 2/ 2] Loss 0.024139 MSE 0.024078 +2024-02-02 16:34:44,712 - ==> MSE: 0.02408 Loss: 0.024 + +2024-02-02 16:34:44,720 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:44,721 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:44,726 - + +2024-02-02 16:34:44,727 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:45,086 - Epoch: [178][ 1/ 8] Overall Loss 0.020070 Objective Loss 0.020070 MSE 0.020070 LR 0.001000 Time 0.359390 +2024-02-02 16:34:45,094 - Epoch: [178][ 2/ 8] Overall Loss 0.019603 Objective Loss 0.019603 MSE 0.019603 LR 0.001000 Time 0.183217 +2024-02-02 16:34:45,100 - Epoch: [178][ 3/ 8] Overall Loss 0.019532 Objective Loss 0.019532 MSE 0.019532 LR 0.001000 Time 0.124346 +2024-02-02 16:34:45,107 - Epoch: [178][ 4/ 8] Overall Loss 0.020063 Objective Loss 0.020063 MSE 0.020063 LR 0.001000 Time 0.094902 +2024-02-02 16:34:45,114 - Epoch: [178][ 5/ 8] Overall Loss 0.019857 Objective Loss 0.019857 MSE 0.019857 LR 0.001000 Time 0.077253 +2024-02-02 16:34:45,121 - Epoch: [178][ 6/ 8] Overall Loss 0.019885 Objective Loss 0.019885 MSE 0.019885 LR 0.001000 Time 0.065482 +2024-02-02 16:34:45,127 - Epoch: [178][ 7/ 8] Overall Loss 0.019831 Objective Loss 0.019831 MSE 0.019831 LR 0.001000 Time 0.057073 +2024-02-02 16:34:45,134 - Epoch: [178][ 8/ 8] Overall Loss 0.020471 Objective Loss 0.020471 MSE 0.019965 LR 0.001000 Time 0.050744 +2024-02-02 16:34:45,279 - --- validate (epoch=178)----------- +2024-02-02 16:34:45,279 - 60 samples (32 per mini-batch) +2024-02-02 16:34:45,644 - Epoch: [178][ 1/ 2] Loss 0.024960 MSE 0.024960 +2024-02-02 16:34:45,648 - Epoch: [178][ 2/ 2] Loss 0.024042 MSE 0.024104 +2024-02-02 16:34:45,794 - ==> MSE: 0.02410 Loss: 0.024 + +2024-02-02 16:34:45,802 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:45,802 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:45,807 - + +2024-02-02 16:34:45,807 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:46,165 - Epoch: [179][ 1/ 8] Overall Loss 0.018843 Objective Loss 0.018843 MSE 0.018843 LR 0.001000 Time 0.357503 +2024-02-02 16:34:46,172 - Epoch: [179][ 2/ 8] Overall Loss 0.018451 Objective Loss 0.018451 MSE 0.018451 LR 0.001000 Time 0.182280 +2024-02-02 16:34:46,179 - Epoch: [179][ 3/ 8] Overall Loss 0.020147 Objective Loss 0.020147 MSE 0.020147 LR 0.001000 Time 0.123770 +2024-02-02 16:34:46,186 - Epoch: [179][ 4/ 8] Overall Loss 0.020083 Objective Loss 0.020083 MSE 0.020083 LR 0.001000 Time 0.094500 +2024-02-02 16:34:46,193 - Epoch: [179][ 5/ 8] Overall Loss 0.020271 Objective Loss 0.020271 MSE 0.020271 LR 0.001000 Time 0.076926 +2024-02-02 16:34:46,199 - Epoch: [179][ 6/ 8] Overall Loss 0.019990 Objective Loss 0.019990 MSE 0.019990 LR 0.001000 Time 0.065191 +2024-02-02 16:34:46,206 - Epoch: [179][ 7/ 8] Overall Loss 0.019661 Objective Loss 0.019661 MSE 0.019661 LR 0.001000 Time 0.056823 +2024-02-02 16:34:46,213 - Epoch: [179][ 8/ 8] Overall Loss 0.022798 Objective Loss 0.022798 MSE 0.020316 LR 0.001000 Time 0.050532 +2024-02-02 16:34:46,361 - --- validate (epoch=179)----------- +2024-02-02 16:34:46,362 - 60 samples (32 per mini-batch) +2024-02-02 16:34:46,717 - Epoch: [179][ 1/ 2] Loss 0.025803 MSE 0.025803 +2024-02-02 16:34:46,722 - Epoch: [179][ 2/ 2] Loss 0.024343 MSE 0.024440 +2024-02-02 16:34:46,865 - ==> MSE: 0.02444 Loss: 0.024 + +2024-02-02 16:34:46,873 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:46,873 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:46,878 - + +2024-02-02 16:34:46,878 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:47,227 - Epoch: [180][ 1/ 8] Overall Loss 0.019354 Objective Loss 0.019354 MSE 0.019354 LR 0.001000 Time 0.348709 +2024-02-02 16:34:47,238 - Epoch: [180][ 2/ 8] Overall Loss 0.019443 Objective Loss 0.019443 MSE 0.019443 LR 0.001000 Time 0.179427 +2024-02-02 16:34:47,245 - Epoch: [180][ 3/ 8] Overall Loss 0.019544 Objective Loss 0.019544 MSE 0.019544 LR 0.001000 Time 0.122006 +2024-02-02 16:34:47,252 - Epoch: [180][ 4/ 8] Overall Loss 0.019917 Objective Loss 0.019917 MSE 0.019917 LR 0.001000 Time 0.093138 +2024-02-02 16:34:47,258 - Epoch: [180][ 5/ 8] Overall Loss 0.019956 Objective Loss 0.019956 MSE 0.019956 LR 0.001000 Time 0.075819 +2024-02-02 16:34:47,265 - Epoch: [180][ 6/ 8] Overall Loss 0.019535 Objective Loss 0.019535 MSE 0.019535 LR 0.001000 Time 0.064266 +2024-02-02 16:34:47,272 - Epoch: [180][ 7/ 8] Overall Loss 0.020061 Objective Loss 0.020061 MSE 0.020061 LR 0.001000 Time 0.056018 +2024-02-02 16:34:47,278 - Epoch: [180][ 8/ 8] Overall Loss 0.021228 Objective Loss 0.021228 MSE 0.020304 LR 0.001000 Time 0.049826 +2024-02-02 16:34:47,417 - --- validate (epoch=180)----------- +2024-02-02 16:34:47,418 - 60 samples (32 per mini-batch) +2024-02-02 16:34:47,776 - Epoch: [180][ 1/ 2] Loss 0.024398 MSE 0.024398 +2024-02-02 16:34:47,780 - Epoch: [180][ 2/ 2] Loss 0.024065 MSE 0.024087 +2024-02-02 16:34:47,926 - ==> MSE: 0.02409 Loss: 0.024 + +2024-02-02 16:34:47,934 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:47,934 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:47,939 - + +2024-02-02 16:34:47,939 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:48,297 - Epoch: [181][ 1/ 8] Overall Loss 0.020292 Objective Loss 0.020292 MSE 0.020292 LR 0.001000 Time 0.357715 +2024-02-02 16:34:48,305 - Epoch: [181][ 2/ 8] Overall Loss 0.019651 Objective Loss 0.019651 MSE 0.019651 LR 0.001000 Time 0.182918 +2024-02-02 16:34:48,313 - Epoch: [181][ 3/ 8] Overall Loss 0.019495 Objective Loss 0.019495 MSE 0.019495 LR 0.001000 Time 0.124490 +2024-02-02 16:34:48,321 - Epoch: [181][ 4/ 8] Overall Loss 0.019458 Objective Loss 0.019458 MSE 0.019458 LR 0.001000 Time 0.095299 +2024-02-02 16:34:48,328 - Epoch: [181][ 5/ 8] Overall Loss 0.019287 Objective Loss 0.019287 MSE 0.019287 LR 0.001000 Time 0.077627 +2024-02-02 16:34:48,335 - Epoch: [181][ 6/ 8] Overall Loss 0.019950 Objective Loss 0.019950 MSE 0.019950 LR 0.001000 Time 0.065817 +2024-02-02 16:34:48,342 - Epoch: [181][ 7/ 8] Overall Loss 0.019851 Objective Loss 0.019851 MSE 0.019851 LR 0.001000 Time 0.057375 +2024-02-02 16:34:48,349 - Epoch: [181][ 8/ 8] Overall Loss 0.022129 Objective Loss 0.022129 MSE 0.020326 LR 0.001000 Time 0.051029 +2024-02-02 16:34:48,500 - --- validate (epoch=181)----------- +2024-02-02 16:34:48,500 - 60 samples (32 per mini-batch) +2024-02-02 16:34:48,859 - Epoch: [181][ 1/ 2] Loss 0.024585 MSE 0.024585 +2024-02-02 16:34:48,863 - Epoch: [181][ 2/ 2] Loss 0.024894 MSE 0.024874 +2024-02-02 16:34:49,012 - ==> MSE: 0.02487 Loss: 0.025 + +2024-02-02 16:34:49,029 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:49,029 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:49,038 - + +2024-02-02 16:34:49,038 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:49,410 - Epoch: [182][ 1/ 8] Overall Loss 0.021384 Objective Loss 0.021384 MSE 0.021384 LR 0.001000 Time 0.370964 +2024-02-02 16:34:49,420 - Epoch: [182][ 2/ 8] Overall Loss 0.020183 Objective Loss 0.020183 MSE 0.020183 LR 0.001000 Time 0.190577 +2024-02-02 16:34:49,431 - Epoch: [182][ 3/ 8] Overall Loss 0.019392 Objective Loss 0.019392 MSE 0.019392 LR 0.001000 Time 0.130389 +2024-02-02 16:34:49,441 - Epoch: [182][ 4/ 8] Overall Loss 0.019255 Objective Loss 0.019255 MSE 0.019255 LR 0.001000 Time 0.100258 +2024-02-02 16:34:49,449 - Epoch: [182][ 5/ 8] Overall Loss 0.019778 Objective Loss 0.019778 MSE 0.019778 LR 0.001000 Time 0.081725 +2024-02-02 16:34:49,455 - Epoch: [182][ 6/ 8] Overall Loss 0.019931 Objective Loss 0.019931 MSE 0.019931 LR 0.001000 Time 0.069198 +2024-02-02 16:34:49,462 - Epoch: [182][ 7/ 8] Overall Loss 0.020216 Objective Loss 0.020216 MSE 0.020216 LR 0.001000 Time 0.060253 +2024-02-02 16:34:49,469 - Epoch: [182][ 8/ 8] Overall Loss 0.020696 Objective Loss 0.020696 MSE 0.020317 LR 0.001000 Time 0.053526 +2024-02-02 16:34:49,621 - --- validate (epoch=182)----------- +2024-02-02 16:34:49,621 - 60 samples (32 per mini-batch) +2024-02-02 16:34:49,979 - Epoch: [182][ 1/ 2] Loss 0.027746 MSE 0.027746 +2024-02-02 16:34:49,984 - Epoch: [182][ 2/ 2] Loss 0.030432 MSE 0.030253 +2024-02-02 16:34:50,131 - ==> MSE: 0.03025 Loss: 0.030 + +2024-02-02 16:34:50,148 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:50,148 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:50,157 - + +2024-02-02 16:34:50,157 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:50,523 - Epoch: [183][ 1/ 8] Overall Loss 0.019427 Objective Loss 0.019427 MSE 0.019427 LR 0.001000 Time 0.365307 +2024-02-02 16:34:50,530 - Epoch: [183][ 2/ 8] Overall Loss 0.019916 Objective Loss 0.019916 MSE 0.019916 LR 0.001000 Time 0.186167 +2024-02-02 16:34:50,537 - Epoch: [183][ 3/ 8] Overall Loss 0.019726 Objective Loss 0.019726 MSE 0.019726 LR 0.001000 Time 0.126320 +2024-02-02 16:34:50,544 - Epoch: [183][ 4/ 8] Overall Loss 0.019955 Objective Loss 0.019955 MSE 0.019955 LR 0.001000 Time 0.096388 +2024-02-02 16:34:50,550 - Epoch: [183][ 5/ 8] Overall Loss 0.020170 Objective Loss 0.020170 MSE 0.020170 LR 0.001000 Time 0.078439 +2024-02-02 16:34:50,557 - Epoch: [183][ 6/ 8] Overall Loss 0.019876 Objective Loss 0.019876 MSE 0.019876 LR 0.001000 Time 0.066445 +2024-02-02 16:34:50,564 - Epoch: [183][ 7/ 8] Overall Loss 0.019936 Objective Loss 0.019936 MSE 0.019936 LR 0.001000 Time 0.057935 +2024-02-02 16:34:50,571 - Epoch: [183][ 8/ 8] Overall Loss 0.020892 Objective Loss 0.020892 MSE 0.020135 LR 0.001000 Time 0.051506 +2024-02-02 16:34:50,711 - --- validate (epoch=183)----------- +2024-02-02 16:34:50,712 - 60 samples (32 per mini-batch) +2024-02-02 16:34:51,072 - Epoch: [183][ 1/ 2] Loss 0.029472 MSE 0.029472 +2024-02-02 16:34:51,076 - Epoch: [183][ 2/ 2] Loss 0.027837 MSE 0.027946 +2024-02-02 16:34:51,226 - ==> MSE: 0.02795 Loss: 0.028 + +2024-02-02 16:34:51,234 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:51,234 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:51,239 - + +2024-02-02 16:34:51,239 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:51,600 - Epoch: [184][ 1/ 8] Overall Loss 0.020085 Objective Loss 0.020085 MSE 0.020085 LR 0.001000 Time 0.360367 +2024-02-02 16:34:51,607 - Epoch: [184][ 2/ 8] Overall Loss 0.020260 Objective Loss 0.020260 MSE 0.020260 LR 0.001000 Time 0.183620 +2024-02-02 16:34:51,613 - Epoch: [184][ 3/ 8] Overall Loss 0.020542 Objective Loss 0.020542 MSE 0.020542 LR 0.001000 Time 0.124568 +2024-02-02 16:34:51,620 - Epoch: [184][ 4/ 8] Overall Loss 0.020250 Objective Loss 0.020250 MSE 0.020250 LR 0.001000 Time 0.095082 +2024-02-02 16:34:51,627 - Epoch: [184][ 5/ 8] Overall Loss 0.020536 Objective Loss 0.020536 MSE 0.020536 LR 0.001000 Time 0.077406 +2024-02-02 16:34:51,634 - Epoch: [184][ 6/ 8] Overall Loss 0.020183 Objective Loss 0.020183 MSE 0.020183 LR 0.001000 Time 0.065632 +2024-02-02 16:34:51,641 - Epoch: [184][ 7/ 8] Overall Loss 0.020115 Objective Loss 0.020115 MSE 0.020115 LR 0.001000 Time 0.057208 +2024-02-02 16:34:51,647 - Epoch: [184][ 8/ 8] Overall Loss 0.021347 Objective Loss 0.021347 MSE 0.020372 LR 0.001000 Time 0.050869 +2024-02-02 16:34:51,794 - --- validate (epoch=184)----------- +2024-02-02 16:34:51,794 - 60 samples (32 per mini-batch) +2024-02-02 16:34:52,144 - Epoch: [184][ 1/ 2] Loss 0.023950 MSE 0.023950 +2024-02-02 16:34:52,148 - Epoch: [184][ 2/ 2] Loss 0.027183 MSE 0.026967 +2024-02-02 16:34:52,297 - ==> MSE: 0.02697 Loss: 0.027 + +2024-02-02 16:34:52,305 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:52,306 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:52,310 - + +2024-02-02 16:34:52,310 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:52,666 - Epoch: [185][ 1/ 8] Overall Loss 0.021431 Objective Loss 0.021431 MSE 0.021431 LR 0.001000 Time 0.354842 +2024-02-02 16:34:52,673 - Epoch: [185][ 2/ 8] Overall Loss 0.021235 Objective Loss 0.021235 MSE 0.021235 LR 0.001000 Time 0.180865 +2024-02-02 16:34:52,680 - Epoch: [185][ 3/ 8] Overall Loss 0.020431 Objective Loss 0.020431 MSE 0.020431 LR 0.001000 Time 0.122826 +2024-02-02 16:34:52,687 - Epoch: [185][ 4/ 8] Overall Loss 0.020349 Objective Loss 0.020349 MSE 0.020349 LR 0.001000 Time 0.093790 +2024-02-02 16:34:52,693 - Epoch: [185][ 5/ 8] Overall Loss 0.020405 Objective Loss 0.020405 MSE 0.020405 LR 0.001000 Time 0.076348 +2024-02-02 16:34:52,700 - Epoch: [185][ 6/ 8] Overall Loss 0.020008 Objective Loss 0.020008 MSE 0.020008 LR 0.001000 Time 0.064713 +2024-02-02 16:34:52,707 - Epoch: [185][ 7/ 8] Overall Loss 0.019721 Objective Loss 0.019721 MSE 0.019721 LR 0.001000 Time 0.056399 +2024-02-02 16:34:52,713 - Epoch: [185][ 8/ 8] Overall Loss 0.019785 Objective Loss 0.019785 MSE 0.019734 LR 0.001000 Time 0.050166 +2024-02-02 16:34:52,865 - --- validate (epoch=185)----------- +2024-02-02 16:34:52,865 - 60 samples (32 per mini-batch) +2024-02-02 16:34:53,223 - Epoch: [185][ 1/ 2] Loss 0.025664 MSE 0.025664 +2024-02-02 16:34:53,228 - Epoch: [185][ 2/ 2] Loss 0.026358 MSE 0.026312 +2024-02-02 16:34:53,374 - ==> MSE: 0.02631 Loss: 0.026 + +2024-02-02 16:34:53,382 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:53,382 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:53,387 - + +2024-02-02 16:34:53,387 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:53,751 - Epoch: [186][ 1/ 8] Overall Loss 0.018400 Objective Loss 0.018400 MSE 0.018400 LR 0.001000 Time 0.363065 +2024-02-02 16:34:53,761 - Epoch: [186][ 2/ 8] Overall Loss 0.019303 Objective Loss 0.019303 MSE 0.019303 LR 0.001000 Time 0.186575 +2024-02-02 16:34:53,768 - Epoch: [186][ 3/ 8] Overall Loss 0.020723 Objective Loss 0.020723 MSE 0.020723 LR 0.001000 Time 0.126604 +2024-02-02 16:34:53,775 - Epoch: [186][ 4/ 8] Overall Loss 0.020245 Objective Loss 0.020245 MSE 0.020245 LR 0.001000 Time 0.096690 +2024-02-02 16:34:53,782 - Epoch: [186][ 5/ 8] Overall Loss 0.019930 Objective Loss 0.019930 MSE 0.019930 LR 0.001000 Time 0.078706 +2024-02-02 16:34:53,789 - Epoch: [186][ 6/ 8] Overall Loss 0.020301 Objective Loss 0.020301 MSE 0.020301 LR 0.001000 Time 0.066672 +2024-02-02 16:34:53,795 - Epoch: [186][ 7/ 8] Overall Loss 0.019957 Objective Loss 0.019957 MSE 0.019957 LR 0.001000 Time 0.058084 +2024-02-02 16:34:53,802 - Epoch: [186][ 8/ 8] Overall Loss 0.021778 Objective Loss 0.021778 MSE 0.020337 LR 0.001000 Time 0.051631 +2024-02-02 16:34:53,947 - --- validate (epoch=186)----------- +2024-02-02 16:34:53,947 - 60 samples (32 per mini-batch) +2024-02-02 16:34:54,298 - Epoch: [186][ 1/ 2] Loss 0.025508 MSE 0.025508 +2024-02-02 16:34:54,303 - Epoch: [186][ 2/ 2] Loss 0.025296 MSE 0.025310 +2024-02-02 16:34:54,460 - ==> MSE: 0.02531 Loss: 0.025 + +2024-02-02 16:34:54,479 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:54,479 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:54,487 - + +2024-02-02 16:34:54,488 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:54,850 - Epoch: [187][ 1/ 8] Overall Loss 0.019053 Objective Loss 0.019053 MSE 0.019053 LR 0.001000 Time 0.362059 +2024-02-02 16:34:54,859 - Epoch: [187][ 2/ 8] Overall Loss 0.018815 Objective Loss 0.018815 MSE 0.018815 LR 0.001000 Time 0.185382 +2024-02-02 16:34:54,866 - Epoch: [187][ 3/ 8] Overall Loss 0.018516 Objective Loss 0.018516 MSE 0.018516 LR 0.001000 Time 0.125856 +2024-02-02 16:34:54,873 - Epoch: [187][ 4/ 8] Overall Loss 0.018814 Objective Loss 0.018814 MSE 0.018814 LR 0.001000 Time 0.096073 +2024-02-02 16:34:54,880 - Epoch: [187][ 5/ 8] Overall Loss 0.018926 Objective Loss 0.018926 MSE 0.018926 LR 0.001000 Time 0.078232 +2024-02-02 16:34:54,887 - Epoch: [187][ 6/ 8] Overall Loss 0.019366 Objective Loss 0.019366 MSE 0.019366 LR 0.001000 Time 0.066314 +2024-02-02 16:34:54,894 - Epoch: [187][ 7/ 8] Overall Loss 0.019544 Objective Loss 0.019544 MSE 0.019544 LR 0.001000 Time 0.057799 +2024-02-02 16:34:54,901 - Epoch: [187][ 8/ 8] Overall Loss 0.020359 Objective Loss 0.020359 MSE 0.019714 LR 0.001000 Time 0.051405 +2024-02-02 16:34:55,051 - --- validate (epoch=187)----------- +2024-02-02 16:34:55,051 - 60 samples (32 per mini-batch) +2024-02-02 16:34:55,394 - Epoch: [187][ 1/ 2] Loss 0.025282 MSE 0.025282 +2024-02-02 16:34:55,399 - Epoch: [187][ 2/ 2] Loss 0.024902 MSE 0.024927 +2024-02-02 16:34:55,543 - ==> MSE: 0.02493 Loss: 0.025 + +2024-02-02 16:34:55,553 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:55,553 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:55,558 - + +2024-02-02 16:34:55,558 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:55,922 - Epoch: [188][ 1/ 8] Overall Loss 0.020065 Objective Loss 0.020065 MSE 0.020065 LR 0.001000 Time 0.363789 +2024-02-02 16:34:55,932 - Epoch: [188][ 2/ 8] Overall Loss 0.020508 Objective Loss 0.020508 MSE 0.020508 LR 0.001000 Time 0.186937 +2024-02-02 16:34:55,940 - Epoch: [188][ 3/ 8] Overall Loss 0.020493 Objective Loss 0.020493 MSE 0.020493 LR 0.001000 Time 0.127251 +2024-02-02 16:34:55,947 - Epoch: [188][ 4/ 8] Overall Loss 0.020123 Objective Loss 0.020123 MSE 0.020123 LR 0.001000 Time 0.097070 +2024-02-02 16:34:55,954 - Epoch: [188][ 5/ 8] Overall Loss 0.019553 Objective Loss 0.019553 MSE 0.019553 LR 0.001000 Time 0.078957 +2024-02-02 16:34:55,960 - Epoch: [188][ 6/ 8] Overall Loss 0.019515 Objective Loss 0.019515 MSE 0.019515 LR 0.001000 Time 0.066889 +2024-02-02 16:34:55,967 - Epoch: [188][ 7/ 8] Overall Loss 0.019656 Objective Loss 0.019656 MSE 0.019656 LR 0.001000 Time 0.058256 +2024-02-02 16:34:55,974 - Epoch: [188][ 8/ 8] Overall Loss 0.021975 Objective Loss 0.021975 MSE 0.020140 LR 0.001000 Time 0.051771 +2024-02-02 16:34:56,128 - --- validate (epoch=188)----------- +2024-02-02 16:34:56,128 - 60 samples (32 per mini-batch) +2024-02-02 16:34:56,485 - Epoch: [188][ 1/ 2] Loss 0.024289 MSE 0.024289 +2024-02-02 16:34:56,489 - Epoch: [188][ 2/ 2] Loss 0.024671 MSE 0.024645 +2024-02-02 16:34:56,636 - ==> MSE: 0.02465 Loss: 0.025 + +2024-02-02 16:34:56,646 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:56,646 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:56,651 - + +2024-02-02 16:34:56,651 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:57,014 - Epoch: [189][ 1/ 8] Overall Loss 0.019356 Objective Loss 0.019356 MSE 0.019356 LR 0.001000 Time 0.362957 +2024-02-02 16:34:57,025 - Epoch: [189][ 2/ 8] Overall Loss 0.020123 Objective Loss 0.020123 MSE 0.020123 LR 0.001000 Time 0.186718 +2024-02-02 16:34:57,035 - Epoch: [189][ 3/ 8] Overall Loss 0.020012 Objective Loss 0.020012 MSE 0.020012 LR 0.001000 Time 0.127758 +2024-02-02 16:34:57,042 - Epoch: [189][ 4/ 8] Overall Loss 0.019706 Objective Loss 0.019706 MSE 0.019706 LR 0.001000 Time 0.097532 +2024-02-02 16:34:57,049 - Epoch: [189][ 5/ 8] Overall Loss 0.019586 Objective Loss 0.019586 MSE 0.019586 LR 0.001000 Time 0.079380 +2024-02-02 16:34:57,056 - Epoch: [189][ 6/ 8] Overall Loss 0.019593 Objective Loss 0.019593 MSE 0.019593 LR 0.001000 Time 0.067241 +2024-02-02 16:34:57,062 - Epoch: [189][ 7/ 8] Overall Loss 0.019630 Objective Loss 0.019630 MSE 0.019630 LR 0.001000 Time 0.058581 +2024-02-02 16:34:57,069 - Epoch: [189][ 8/ 8] Overall Loss 0.020429 Objective Loss 0.020429 MSE 0.019797 LR 0.001000 Time 0.052074 +2024-02-02 16:34:57,217 - --- validate (epoch=189)----------- +2024-02-02 16:34:57,217 - 60 samples (32 per mini-batch) +2024-02-02 16:34:57,577 - Epoch: [189][ 1/ 2] Loss 0.025154 MSE 0.025154 +2024-02-02 16:34:57,582 - Epoch: [189][ 2/ 2] Loss 0.024449 MSE 0.024496 +2024-02-02 16:34:57,724 - ==> MSE: 0.02450 Loss: 0.024 + +2024-02-02 16:34:57,743 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:57,743 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:57,752 - + +2024-02-02 16:34:57,752 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:58,113 - Epoch: [190][ 1/ 8] Overall Loss 0.021233 Objective Loss 0.021233 MSE 0.021233 LR 0.001000 Time 0.360977 +2024-02-02 16:34:58,124 - Epoch: [190][ 2/ 8] Overall Loss 0.020435 Objective Loss 0.020435 MSE 0.020435 LR 0.001000 Time 0.185640 +2024-02-02 16:34:58,133 - Epoch: [190][ 3/ 8] Overall Loss 0.020322 Objective Loss 0.020322 MSE 0.020322 LR 0.001000 Time 0.126761 +2024-02-02 16:34:58,140 - Epoch: [190][ 4/ 8] Overall Loss 0.019705 Objective Loss 0.019705 MSE 0.019705 LR 0.001000 Time 0.096812 +2024-02-02 16:34:58,147 - Epoch: [190][ 5/ 8] Overall Loss 0.019591 Objective Loss 0.019591 MSE 0.019591 LR 0.001000 Time 0.078781 +2024-02-02 16:34:58,154 - Epoch: [190][ 6/ 8] Overall Loss 0.019655 Objective Loss 0.019655 MSE 0.019655 LR 0.001000 Time 0.066762 +2024-02-02 16:34:58,161 - Epoch: [190][ 7/ 8] Overall Loss 0.019627 Objective Loss 0.019627 MSE 0.019627 LR 0.001000 Time 0.058184 +2024-02-02 16:34:58,167 - Epoch: [190][ 8/ 8] Overall Loss 0.019463 Objective Loss 0.019463 MSE 0.019593 LR 0.001000 Time 0.051727 +2024-02-02 16:34:58,319 - --- validate (epoch=190)----------- +2024-02-02 16:34:58,319 - 60 samples (32 per mini-batch) +2024-02-02 16:34:58,679 - Epoch: [190][ 1/ 2] Loss 0.024409 MSE 0.024409 +2024-02-02 16:34:58,684 - Epoch: [190][ 2/ 2] Loss 0.024246 MSE 0.024257 +2024-02-02 16:34:58,831 - ==> MSE: 0.02426 Loss: 0.024 + +2024-02-02 16:34:58,839 - ==> Best [Top 1 (MSE): 0.02408 Sparsity:0.00 Params: 136448 on epoch: 177] +2024-02-02 16:34:58,839 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:34:58,843 - + +2024-02-02 16:34:58,844 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:34:59,207 - Epoch: [191][ 1/ 8] Overall Loss 0.021462 Objective Loss 0.021462 MSE 0.021462 LR 0.001000 Time 0.363432 +2024-02-02 16:34:59,215 - Epoch: [191][ 2/ 8] Overall Loss 0.021419 Objective Loss 0.021419 MSE 0.021419 LR 0.001000 Time 0.185233 +2024-02-02 16:34:59,222 - Epoch: [191][ 3/ 8] Overall Loss 0.020825 Objective Loss 0.020825 MSE 0.020825 LR 0.001000 Time 0.125705 +2024-02-02 16:34:59,228 - Epoch: [191][ 4/ 8] Overall Loss 0.020060 Objective Loss 0.020060 MSE 0.020060 LR 0.001000 Time 0.095933 +2024-02-02 16:34:59,235 - Epoch: [191][ 5/ 8] Overall Loss 0.019914 Objective Loss 0.019914 MSE 0.019914 LR 0.001000 Time 0.078067 +2024-02-02 16:34:59,242 - Epoch: [191][ 6/ 8] Overall Loss 0.019797 Objective Loss 0.019797 MSE 0.019797 LR 0.001000 Time 0.066114 +2024-02-02 16:34:59,248 - Epoch: [191][ 7/ 8] Overall Loss 0.019453 Objective Loss 0.019453 MSE 0.019453 LR 0.001000 Time 0.057597 +2024-02-02 16:34:59,255 - Epoch: [191][ 8/ 8] Overall Loss 0.019508 Objective Loss 0.019508 MSE 0.019464 LR 0.001000 Time 0.051191 +2024-02-02 16:34:59,404 - --- validate (epoch=191)----------- +2024-02-02 16:34:59,405 - 60 samples (32 per mini-batch) +2024-02-02 16:34:59,919 - Epoch: [191][ 1/ 2] Loss 0.022028 MSE 0.022028 +2024-02-02 16:34:59,924 - Epoch: [191][ 2/ 2] Loss 0.024162 MSE 0.024019 +2024-02-02 16:35:00,077 - ==> MSE: 0.02402 Loss: 0.024 + +2024-02-02 16:35:00,086 - ==> Best [Top 1 (MSE): 0.02402 Sparsity:0.00 Params: 136448 on epoch: 191] +2024-02-02 16:35:00,086 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:35:00,092 - + +2024-02-02 16:35:00,092 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:00,450 - Epoch: [192][ 1/ 8] Overall Loss 0.021255 Objective Loss 0.021255 MSE 0.021255 LR 0.001000 Time 0.357928 +2024-02-02 16:35:00,461 - Epoch: [192][ 2/ 8] Overall Loss 0.021021 Objective Loss 0.021021 MSE 0.021021 LR 0.001000 Time 0.184151 +2024-02-02 16:35:00,469 - Epoch: [192][ 3/ 8] Overall Loss 0.019690 Objective Loss 0.019690 MSE 0.019690 LR 0.001000 Time 0.125217 +2024-02-02 16:35:00,475 - Epoch: [192][ 4/ 8] Overall Loss 0.019646 Objective Loss 0.019646 MSE 0.019646 LR 0.001000 Time 0.095557 +2024-02-02 16:35:00,482 - Epoch: [192][ 5/ 8] Overall Loss 0.019793 Objective Loss 0.019793 MSE 0.019793 LR 0.001000 Time 0.077784 +2024-02-02 16:35:00,489 - Epoch: [192][ 6/ 8] Overall Loss 0.019677 Objective Loss 0.019677 MSE 0.019677 LR 0.001000 Time 0.065937 +2024-02-02 16:35:00,496 - Epoch: [192][ 7/ 8] Overall Loss 0.019739 Objective Loss 0.019739 MSE 0.019739 LR 0.001000 Time 0.057458 +2024-02-02 16:35:00,502 - Epoch: [192][ 8/ 8] Overall Loss 0.019462 Objective Loss 0.019462 MSE 0.019681 LR 0.001000 Time 0.051085 +2024-02-02 16:35:00,651 - --- validate (epoch=192)----------- +2024-02-02 16:35:00,651 - 60 samples (32 per mini-batch) +2024-02-02 16:35:00,990 - Epoch: [192][ 1/ 2] Loss 0.022640 MSE 0.022640 +2024-02-02 16:35:00,995 - Epoch: [192][ 2/ 2] Loss 0.024126 MSE 0.024027 +2024-02-02 16:35:01,136 - ==> MSE: 0.02403 Loss: 0.024 + +2024-02-02 16:35:01,145 - ==> Best [Top 1 (MSE): 0.02402 Sparsity:0.00 Params: 136448 on epoch: 191] +2024-02-02 16:35:01,146 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:35:01,150 - + +2024-02-02 16:35:01,150 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:01,507 - Epoch: [193][ 1/ 8] Overall Loss 0.018435 Objective Loss 0.018435 MSE 0.018435 LR 0.001000 Time 0.356229 +2024-02-02 16:35:01,515 - Epoch: [193][ 2/ 8] Overall Loss 0.018729 Objective Loss 0.018729 MSE 0.018729 LR 0.001000 Time 0.182130 +2024-02-02 16:35:01,523 - Epoch: [193][ 3/ 8] Overall Loss 0.018844 Objective Loss 0.018844 MSE 0.018844 LR 0.001000 Time 0.124041 +2024-02-02 16:35:01,530 - Epoch: [193][ 4/ 8] Overall Loss 0.018743 Objective Loss 0.018743 MSE 0.018743 LR 0.001000 Time 0.094724 +2024-02-02 16:35:01,537 - Epoch: [193][ 5/ 8] Overall Loss 0.018741 Objective Loss 0.018741 MSE 0.018741 LR 0.001000 Time 0.077138 +2024-02-02 16:35:01,544 - Epoch: [193][ 6/ 8] Overall Loss 0.018835 Objective Loss 0.018835 MSE 0.018835 LR 0.001000 Time 0.065404 +2024-02-02 16:35:01,551 - Epoch: [193][ 7/ 8] Overall Loss 0.019336 Objective Loss 0.019336 MSE 0.019336 LR 0.001000 Time 0.057012 +2024-02-02 16:35:01,558 - Epoch: [193][ 8/ 8] Overall Loss 0.020533 Objective Loss 0.020533 MSE 0.019586 LR 0.001000 Time 0.050714 +2024-02-02 16:35:01,706 - --- validate (epoch=193)----------- +2024-02-02 16:35:01,707 - 60 samples (32 per mini-batch) +2024-02-02 16:35:02,054 - Epoch: [193][ 1/ 2] Loss 0.024564 MSE 0.024564 +2024-02-02 16:35:02,058 - Epoch: [193][ 2/ 2] Loss 0.023666 MSE 0.023726 +2024-02-02 16:35:02,211 - ==> MSE: 0.02373 Loss: 0.024 + +2024-02-02 16:35:02,219 - ==> Best [Top 1 (MSE): 0.02373 Sparsity:0.00 Params: 136448 on epoch: 193] +2024-02-02 16:35:02,220 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:35:02,225 - + +2024-02-02 16:35:02,226 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:02,585 - Epoch: [194][ 1/ 8] Overall Loss 0.017099 Objective Loss 0.017099 MSE 0.017099 LR 0.001000 Time 0.358605 +2024-02-02 16:35:02,592 - Epoch: [194][ 2/ 8] Overall Loss 0.017421 Objective Loss 0.017421 MSE 0.017421 LR 0.001000 Time 0.182832 +2024-02-02 16:35:02,599 - Epoch: [194][ 3/ 8] Overall Loss 0.017530 Objective Loss 0.017530 MSE 0.017530 LR 0.001000 Time 0.124139 +2024-02-02 16:35:02,605 - Epoch: [194][ 4/ 8] Overall Loss 0.017833 Objective Loss 0.017833 MSE 0.017833 LR 0.001000 Time 0.094743 +2024-02-02 16:35:02,612 - Epoch: [194][ 5/ 8] Overall Loss 0.018539 Objective Loss 0.018539 MSE 0.018539 LR 0.001000 Time 0.077120 +2024-02-02 16:35:02,619 - Epoch: [194][ 6/ 8] Overall Loss 0.019106 Objective Loss 0.019106 MSE 0.019106 LR 0.001000 Time 0.065360 +2024-02-02 16:35:02,626 - Epoch: [194][ 7/ 8] Overall Loss 0.019477 Objective Loss 0.019477 MSE 0.019477 LR 0.001000 Time 0.056979 +2024-02-02 16:35:02,633 - Epoch: [194][ 8/ 8] Overall Loss 0.019379 Objective Loss 0.019379 MSE 0.019456 LR 0.001000 Time 0.050685 +2024-02-02 16:35:02,777 - --- validate (epoch=194)----------- +2024-02-02 16:35:02,777 - 60 samples (32 per mini-batch) +2024-02-02 16:35:03,125 - Epoch: [194][ 1/ 2] Loss 0.024104 MSE 0.024104 +2024-02-02 16:35:03,129 - Epoch: [194][ 2/ 2] Loss 0.024047 MSE 0.024051 +2024-02-02 16:35:03,279 - ==> MSE: 0.02405 Loss: 0.024 + +2024-02-02 16:35:03,287 - ==> Best [Top 1 (MSE): 0.02373 Sparsity:0.00 Params: 136448 on epoch: 193] +2024-02-02 16:35:03,287 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:35:03,292 - + +2024-02-02 16:35:03,292 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:03,653 - Epoch: [195][ 1/ 8] Overall Loss 0.019386 Objective Loss 0.019386 MSE 0.019386 LR 0.001000 Time 0.360586 +2024-02-02 16:35:03,660 - Epoch: [195][ 2/ 8] Overall Loss 0.019661 Objective Loss 0.019661 MSE 0.019661 LR 0.001000 Time 0.183758 +2024-02-02 16:35:03,667 - Epoch: [195][ 3/ 8] Overall Loss 0.019754 Objective Loss 0.019754 MSE 0.019754 LR 0.001000 Time 0.124718 +2024-02-02 16:35:03,674 - Epoch: [195][ 4/ 8] Overall Loss 0.020342 Objective Loss 0.020342 MSE 0.020342 LR 0.001000 Time 0.095144 +2024-02-02 16:35:03,680 - Epoch: [195][ 5/ 8] Overall Loss 0.019813 Objective Loss 0.019813 MSE 0.019813 LR 0.001000 Time 0.077444 +2024-02-02 16:35:03,687 - Epoch: [195][ 6/ 8] Overall Loss 0.020092 Objective Loss 0.020092 MSE 0.020092 LR 0.001000 Time 0.065620 +2024-02-02 16:35:03,694 - Epoch: [195][ 7/ 8] Overall Loss 0.019743 Objective Loss 0.019743 MSE 0.019743 LR 0.001000 Time 0.057163 +2024-02-02 16:35:03,700 - Epoch: [195][ 8/ 8] Overall Loss 0.020296 Objective Loss 0.020296 MSE 0.019859 LR 0.001000 Time 0.050825 +2024-02-02 16:35:03,846 - --- validate (epoch=195)----------- +2024-02-02 16:35:03,847 - 60 samples (32 per mini-batch) +2024-02-02 16:35:04,197 - Epoch: [195][ 1/ 2] Loss 0.024376 MSE 0.024376 +2024-02-02 16:35:04,201 - Epoch: [195][ 2/ 2] Loss 0.023911 MSE 0.023942 +2024-02-02 16:35:04,348 - ==> MSE: 0.02394 Loss: 0.024 + +2024-02-02 16:35:04,358 - ==> Best [Top 1 (MSE): 0.02373 Sparsity:0.00 Params: 136448 on epoch: 193] +2024-02-02 16:35:04,358 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:35:04,363 - + +2024-02-02 16:35:04,363 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:04,729 - Epoch: [196][ 1/ 8] Overall Loss 0.016628 Objective Loss 0.016628 MSE 0.016628 LR 0.001000 Time 0.365641 +2024-02-02 16:35:04,736 - Epoch: [196][ 2/ 8] Overall Loss 0.019424 Objective Loss 0.019424 MSE 0.019424 LR 0.001000 Time 0.186302 +2024-02-02 16:35:04,743 - Epoch: [196][ 3/ 8] Overall Loss 0.019227 Objective Loss 0.019227 MSE 0.019227 LR 0.001000 Time 0.126364 +2024-02-02 16:35:04,749 - Epoch: [196][ 4/ 8] Overall Loss 0.018711 Objective Loss 0.018711 MSE 0.018711 LR 0.001000 Time 0.096406 +2024-02-02 16:35:04,756 - Epoch: [196][ 5/ 8] Overall Loss 0.019552 Objective Loss 0.019552 MSE 0.019552 LR 0.001000 Time 0.078447 +2024-02-02 16:35:04,763 - Epoch: [196][ 6/ 8] Overall Loss 0.019728 Objective Loss 0.019728 MSE 0.019728 LR 0.001000 Time 0.066445 +2024-02-02 16:35:04,769 - Epoch: [196][ 7/ 8] Overall Loss 0.019515 Objective Loss 0.019515 MSE 0.019515 LR 0.001000 Time 0.057900 +2024-02-02 16:35:04,776 - Epoch: [196][ 8/ 8] Overall Loss 0.019915 Objective Loss 0.019915 MSE 0.019599 LR 0.001000 Time 0.051491 +2024-02-02 16:35:04,929 - --- validate (epoch=196)----------- +2024-02-02 16:35:04,929 - 60 samples (32 per mini-batch) +2024-02-02 16:35:05,286 - Epoch: [196][ 1/ 2] Loss 0.022707 MSE 0.022707 +2024-02-02 16:35:05,293 - Epoch: [196][ 2/ 2] Loss 0.023970 MSE 0.023886 +2024-02-02 16:35:05,440 - ==> MSE: 0.02389 Loss: 0.024 + +2024-02-02 16:35:05,450 - ==> Best [Top 1 (MSE): 0.02373 Sparsity:0.00 Params: 136448 on epoch: 193] +2024-02-02 16:35:05,450 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:35:05,454 - + +2024-02-02 16:35:05,455 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:05,814 - Epoch: [197][ 1/ 8] Overall Loss 0.019970 Objective Loss 0.019970 MSE 0.019970 LR 0.001000 Time 0.359309 +2024-02-02 16:35:05,821 - Epoch: [197][ 2/ 8] Overall Loss 0.019961 Objective Loss 0.019961 MSE 0.019961 LR 0.001000 Time 0.183105 +2024-02-02 16:35:05,828 - Epoch: [197][ 3/ 8] Overall Loss 0.019139 Objective Loss 0.019139 MSE 0.019139 LR 0.001000 Time 0.124293 +2024-02-02 16:35:05,835 - Epoch: [197][ 4/ 8] Overall Loss 0.019281 Objective Loss 0.019281 MSE 0.019281 LR 0.001000 Time 0.094853 +2024-02-02 16:35:05,842 - Epoch: [197][ 5/ 8] Overall Loss 0.019125 Objective Loss 0.019125 MSE 0.019125 LR 0.001000 Time 0.077214 +2024-02-02 16:35:05,848 - Epoch: [197][ 6/ 8] Overall Loss 0.019221 Objective Loss 0.019221 MSE 0.019221 LR 0.001000 Time 0.065445 +2024-02-02 16:35:05,855 - Epoch: [197][ 7/ 8] Overall Loss 0.019441 Objective Loss 0.019441 MSE 0.019441 LR 0.001000 Time 0.057033 +2024-02-02 16:35:05,862 - Epoch: [197][ 8/ 8] Overall Loss 0.020265 Objective Loss 0.020265 MSE 0.019613 LR 0.001000 Time 0.050720 +2024-02-02 16:35:06,010 - --- validate (epoch=197)----------- +2024-02-02 16:35:06,010 - 60 samples (32 per mini-batch) +2024-02-02 16:35:06,355 - Epoch: [197][ 1/ 2] Loss 0.022712 MSE 0.022712 +2024-02-02 16:35:06,360 - Epoch: [197][ 2/ 2] Loss 0.023740 MSE 0.023671 +2024-02-02 16:35:06,507 - ==> MSE: 0.02367 Loss: 0.024 + +2024-02-02 16:35:06,515 - ==> Best [Top 1 (MSE): 0.02367 Sparsity:0.00 Params: 136448 on epoch: 197] +2024-02-02 16:35:06,515 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:35:06,521 - + +2024-02-02 16:35:06,521 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:06,877 - Epoch: [198][ 1/ 8] Overall Loss 0.020485 Objective Loss 0.020485 MSE 0.020485 LR 0.001000 Time 0.355005 +2024-02-02 16:35:06,886 - Epoch: [198][ 2/ 8] Overall Loss 0.019172 Objective Loss 0.019172 MSE 0.019172 LR 0.001000 Time 0.181785 +2024-02-02 16:35:06,892 - Epoch: [198][ 3/ 8] Overall Loss 0.019772 Objective Loss 0.019772 MSE 0.019772 LR 0.001000 Time 0.123377 +2024-02-02 16:35:06,899 - Epoch: [198][ 4/ 8] Overall Loss 0.019365 Objective Loss 0.019365 MSE 0.019365 LR 0.001000 Time 0.094136 +2024-02-02 16:35:06,906 - Epoch: [198][ 5/ 8] Overall Loss 0.019431 Objective Loss 0.019431 MSE 0.019431 LR 0.001000 Time 0.076611 +2024-02-02 16:35:06,915 - Epoch: [198][ 6/ 8] Overall Loss 0.019303 Objective Loss 0.019303 MSE 0.019303 LR 0.001000 Time 0.065328 +2024-02-02 16:35:06,924 - Epoch: [198][ 7/ 8] Overall Loss 0.019270 Objective Loss 0.019270 MSE 0.019270 LR 0.001000 Time 0.057284 +2024-02-02 16:35:06,933 - Epoch: [198][ 8/ 8] Overall Loss 0.020985 Objective Loss 0.020985 MSE 0.019628 LR 0.001000 Time 0.051248 +2024-02-02 16:35:07,084 - --- validate (epoch=198)----------- +2024-02-02 16:35:07,085 - 60 samples (32 per mini-batch) +2024-02-02 16:35:07,435 - Epoch: [198][ 1/ 2] Loss 0.023175 MSE 0.023175 +2024-02-02 16:35:07,440 - Epoch: [198][ 2/ 2] Loss 0.023665 MSE 0.023632 +2024-02-02 16:35:07,580 - ==> MSE: 0.02363 Loss: 0.024 + +2024-02-02 16:35:07,590 - ==> Best [Top 1 (MSE): 0.02363 Sparsity:0.00 Params: 136448 on epoch: 198] +2024-02-02 16:35:07,590 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:35:07,596 - + +2024-02-02 16:35:07,596 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:07,957 - Epoch: [199][ 1/ 8] Overall Loss 0.021251 Objective Loss 0.021251 MSE 0.021251 LR 0.001000 Time 0.360702 +2024-02-02 16:35:07,964 - Epoch: [199][ 2/ 8] Overall Loss 0.020564 Objective Loss 0.020564 MSE 0.020564 LR 0.001000 Time 0.183912 +2024-02-02 16:35:07,971 - Epoch: [199][ 3/ 8] Overall Loss 0.020029 Objective Loss 0.020029 MSE 0.020029 LR 0.001000 Time 0.124826 +2024-02-02 16:35:07,978 - Epoch: [199][ 4/ 8] Overall Loss 0.020030 Objective Loss 0.020030 MSE 0.020030 LR 0.001000 Time 0.095240 +2024-02-02 16:35:07,985 - Epoch: [199][ 5/ 8] Overall Loss 0.019802 Objective Loss 0.019802 MSE 0.019802 LR 0.001000 Time 0.077498 +2024-02-02 16:35:07,991 - Epoch: [199][ 6/ 8] Overall Loss 0.019695 Objective Loss 0.019695 MSE 0.019695 LR 0.001000 Time 0.065667 +2024-02-02 16:35:07,998 - Epoch: [199][ 7/ 8] Overall Loss 0.019467 Objective Loss 0.019467 MSE 0.019467 LR 0.001000 Time 0.057217 +2024-02-02 16:35:08,004 - Epoch: [199][ 8/ 8] Overall Loss 0.019800 Objective Loss 0.019800 MSE 0.019537 LR 0.001000 Time 0.050862 +2024-02-02 16:35:08,154 - --- validate (epoch=199)----------- +2024-02-02 16:35:08,154 - 60 samples (32 per mini-batch) +2024-02-02 16:35:08,508 - Epoch: [199][ 1/ 2] Loss 0.023829 MSE 0.023829 +2024-02-02 16:35:08,512 - Epoch: [199][ 2/ 2] Loss 0.023980 MSE 0.023970 +2024-02-02 16:35:08,661 - ==> MSE: 0.02397 Loss: 0.024 + +2024-02-02 16:35:08,671 - ==> Best [Top 1 (MSE): 0.02363 Sparsity:0.00 Params: 136448 on epoch: 198] +2024-02-02 16:35:08,671 - Saving checkpoint to: logs/2024.02.02-163128/checkpoint.pth.tar +2024-02-02 16:35:08,676 - + +2024-02-02 16:35:08,676 - Initiating quantization aware training (QAT)... +2024-02-02 16:35:08,687 - + +2024-02-02 16:35:08,687 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:09,049 - Epoch: [200][ 1/ 8] Overall Loss 0.050455 Objective Loss 0.050455 MSE 0.050455 LR 0.001000 Time 0.361927 +2024-02-02 16:35:09,058 - Epoch: [200][ 2/ 8] Overall Loss 0.046025 Objective Loss 0.046025 MSE 0.046025 LR 0.001000 Time 0.185266 +2024-02-02 16:35:09,067 - Epoch: [200][ 3/ 8] Overall Loss 0.041041 Objective Loss 0.041041 MSE 0.041041 LR 0.001000 Time 0.126421 +2024-02-02 16:35:09,075 - Epoch: [200][ 4/ 8] Overall Loss 0.039314 Objective Loss 0.039314 MSE 0.039314 LR 0.001000 Time 0.096891 +2024-02-02 16:35:09,084 - Epoch: [200][ 5/ 8] Overall Loss 0.037946 Objective Loss 0.037946 MSE 0.037946 LR 0.001000 Time 0.079166 +2024-02-02 16:35:09,092 - Epoch: [200][ 6/ 8] Overall Loss 0.037122 Objective Loss 0.037122 MSE 0.037122 LR 0.001000 Time 0.067357 +2024-02-02 16:35:09,101 - Epoch: [200][ 7/ 8] Overall Loss 0.037376 Objective Loss 0.037376 MSE 0.037376 LR 0.001000 Time 0.058915 +2024-02-02 16:35:09,109 - Epoch: [200][ 8/ 8] Overall Loss 0.037116 Objective Loss 0.037116 MSE 0.037322 LR 0.001000 Time 0.052572 +2024-02-02 16:35:09,260 - --- validate (epoch=200)----------- +2024-02-02 16:35:09,260 - 60 samples (32 per mini-batch) +2024-02-02 16:35:09,616 - Epoch: [200][ 1/ 2] Loss 0.031896 MSE 0.031896 +2024-02-02 16:35:09,622 - Epoch: [200][ 2/ 2] Loss 0.030843 MSE 0.030913 +2024-02-02 16:35:09,772 - ==> MSE: 0.03091 Loss: 0.031 + +2024-02-02 16:35:09,773 - ==> Best [Top 1 (MSE): 0.03091 Sparsity:0.00 Params: 136448 on epoch: 200] +2024-02-02 16:35:09,774 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:09,779 - + +2024-02-02 16:35:09,779 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:10,136 - Epoch: [201][ 1/ 8] Overall Loss 0.030566 Objective Loss 0.030566 MSE 0.030566 LR 0.001000 Time 0.356345 +2024-02-02 16:35:10,145 - Epoch: [201][ 2/ 8] Overall Loss 0.028439 Objective Loss 0.028439 MSE 0.028439 LR 0.001000 Time 0.182413 +2024-02-02 16:35:10,153 - Epoch: [201][ 3/ 8] Overall Loss 0.027510 Objective Loss 0.027510 MSE 0.027510 LR 0.001000 Time 0.124348 +2024-02-02 16:35:10,162 - Epoch: [201][ 4/ 8] Overall Loss 0.028272 Objective Loss 0.028272 MSE 0.028272 LR 0.001000 Time 0.095312 +2024-02-02 16:35:10,170 - Epoch: [201][ 5/ 8] Overall Loss 0.027662 Objective Loss 0.027662 MSE 0.027662 LR 0.001000 Time 0.077931 +2024-02-02 16:35:10,179 - Epoch: [201][ 6/ 8] Overall Loss 0.026792 Objective Loss 0.026792 MSE 0.026792 LR 0.001000 Time 0.066330 +2024-02-02 16:35:10,187 - Epoch: [201][ 7/ 8] Overall Loss 0.026606 Objective Loss 0.026606 MSE 0.026606 LR 0.001000 Time 0.058043 +2024-02-02 16:35:10,195 - Epoch: [201][ 8/ 8] Overall Loss 0.026196 Objective Loss 0.026196 MSE 0.026520 LR 0.001000 Time 0.051808 +2024-02-02 16:35:10,345 - --- validate (epoch=201)----------- +2024-02-02 16:35:10,345 - 60 samples (32 per mini-batch) +2024-02-02 16:35:10,709 - Epoch: [201][ 1/ 2] Loss 0.029319 MSE 0.029319 +2024-02-02 16:35:10,715 - Epoch: [201][ 2/ 2] Loss 0.029256 MSE 0.029260 +2024-02-02 16:35:10,863 - ==> MSE: 0.02926 Loss: 0.029 + +2024-02-02 16:35:10,865 - ==> Best [Top 1 (MSE): 0.02926 Sparsity:0.00 Params: 136448 on epoch: 201] +2024-02-02 16:35:10,865 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:10,874 - + +2024-02-02 16:35:10,874 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:11,256 - Epoch: [202][ 1/ 8] Overall Loss 0.027996 Objective Loss 0.027996 MSE 0.027996 LR 0.001000 Time 0.381822 +2024-02-02 16:35:11,267 - Epoch: [202][ 2/ 8] Overall Loss 0.025774 Objective Loss 0.025774 MSE 0.025774 LR 0.001000 Time 0.196184 +2024-02-02 16:35:11,278 - Epoch: [202][ 3/ 8] Overall Loss 0.024903 Objective Loss 0.024903 MSE 0.024903 LR 0.001000 Time 0.134370 +2024-02-02 16:35:11,289 - Epoch: [202][ 4/ 8] Overall Loss 0.024931 Objective Loss 0.024931 MSE 0.024931 LR 0.001000 Time 0.103509 +2024-02-02 16:35:11,300 - Epoch: [202][ 5/ 8] Overall Loss 0.024701 Objective Loss 0.024701 MSE 0.024701 LR 0.001000 Time 0.084981 +2024-02-02 16:35:11,311 - Epoch: [202][ 6/ 8] Overall Loss 0.024401 Objective Loss 0.024401 MSE 0.024401 LR 0.001000 Time 0.072612 +2024-02-02 16:35:11,320 - Epoch: [202][ 7/ 8] Overall Loss 0.024496 Objective Loss 0.024496 MSE 0.024496 LR 0.001000 Time 0.063532 +2024-02-02 16:35:11,330 - Epoch: [202][ 8/ 8] Overall Loss 0.024762 Objective Loss 0.024762 MSE 0.024551 LR 0.001000 Time 0.056719 +2024-02-02 16:35:11,479 - --- validate (epoch=202)----------- +2024-02-02 16:35:11,479 - 60 samples (32 per mini-batch) +2024-02-02 16:35:11,830 - Epoch: [202][ 1/ 2] Loss 0.027156 MSE 0.027156 +2024-02-02 16:35:11,836 - Epoch: [202][ 2/ 2] Loss 0.027965 MSE 0.027911 +2024-02-02 16:35:11,977 - ==> MSE: 0.02791 Loss: 0.028 + +2024-02-02 16:35:11,979 - ==> Best [Top 1 (MSE): 0.02791 Sparsity:0.00 Params: 136448 on epoch: 202] +2024-02-02 16:35:11,979 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:11,985 - + +2024-02-02 16:35:11,985 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:12,346 - Epoch: [203][ 1/ 8] Overall Loss 0.022087 Objective Loss 0.022087 MSE 0.022087 LR 0.001000 Time 0.360931 +2024-02-02 16:35:12,359 - Epoch: [203][ 2/ 8] Overall Loss 0.022342 Objective Loss 0.022342 MSE 0.022342 LR 0.001000 Time 0.186745 +2024-02-02 16:35:12,368 - Epoch: [203][ 3/ 8] Overall Loss 0.022432 Objective Loss 0.022432 MSE 0.022432 LR 0.001000 Time 0.127482 +2024-02-02 16:35:12,377 - Epoch: [203][ 4/ 8] Overall Loss 0.022485 Objective Loss 0.022485 MSE 0.022485 LR 0.001000 Time 0.097652 +2024-02-02 16:35:12,385 - Epoch: [203][ 5/ 8] Overall Loss 0.022268 Objective Loss 0.022268 MSE 0.022268 LR 0.001000 Time 0.079755 +2024-02-02 16:35:12,393 - Epoch: [203][ 6/ 8] Overall Loss 0.022042 Objective Loss 0.022042 MSE 0.022042 LR 0.001000 Time 0.067827 +2024-02-02 16:35:12,402 - Epoch: [203][ 7/ 8] Overall Loss 0.021942 Objective Loss 0.021942 MSE 0.021942 LR 0.001000 Time 0.059335 +2024-02-02 16:35:12,410 - Epoch: [203][ 8/ 8] Overall Loss 0.022360 Objective Loss 0.022360 MSE 0.022029 LR 0.001000 Time 0.052956 +2024-02-02 16:35:12,563 - --- validate (epoch=203)----------- +2024-02-02 16:35:12,563 - 60 samples (32 per mini-batch) +2024-02-02 16:35:12,919 - Epoch: [203][ 1/ 2] Loss 0.025470 MSE 0.025470 +2024-02-02 16:35:12,926 - Epoch: [203][ 2/ 2] Loss 0.025626 MSE 0.025615 +2024-02-02 16:35:13,074 - ==> MSE: 0.02562 Loss: 0.026 + +2024-02-02 16:35:13,076 - ==> Best [Top 1 (MSE): 0.02562 Sparsity:0.00 Params: 136448 on epoch: 203] +2024-02-02 16:35:13,076 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:13,081 - + +2024-02-02 16:35:13,081 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:13,454 - Epoch: [204][ 1/ 8] Overall Loss 0.021793 Objective Loss 0.021793 MSE 0.021793 LR 0.001000 Time 0.371942 +2024-02-02 16:35:13,463 - Epoch: [204][ 2/ 8] Overall Loss 0.021606 Objective Loss 0.021606 MSE 0.021606 LR 0.001000 Time 0.190500 +2024-02-02 16:35:13,471 - Epoch: [204][ 3/ 8] Overall Loss 0.021061 Objective Loss 0.021061 MSE 0.021061 LR 0.001000 Time 0.129781 +2024-02-02 16:35:13,480 - Epoch: [204][ 4/ 8] Overall Loss 0.021919 Objective Loss 0.021919 MSE 0.021919 LR 0.001000 Time 0.099405 +2024-02-02 16:35:13,488 - Epoch: [204][ 5/ 8] Overall Loss 0.021415 Objective Loss 0.021415 MSE 0.021415 LR 0.001000 Time 0.081203 +2024-02-02 16:35:13,497 - Epoch: [204][ 6/ 8] Overall Loss 0.021373 Objective Loss 0.021373 MSE 0.021373 LR 0.001000 Time 0.069070 +2024-02-02 16:35:13,506 - Epoch: [204][ 7/ 8] Overall Loss 0.021112 Objective Loss 0.021112 MSE 0.021112 LR 0.001000 Time 0.060404 +2024-02-02 16:35:13,514 - Epoch: [204][ 8/ 8] Overall Loss 0.020738 Objective Loss 0.020738 MSE 0.021034 LR 0.001000 Time 0.053904 +2024-02-02 16:35:13,667 - --- validate (epoch=204)----------- +2024-02-02 16:35:13,667 - 60 samples (32 per mini-batch) +2024-02-02 16:35:14,027 - Epoch: [204][ 1/ 2] Loss 0.026006 MSE 0.026006 +2024-02-02 16:35:14,033 - Epoch: [204][ 2/ 2] Loss 0.025891 MSE 0.025899 +2024-02-02 16:35:14,177 - ==> MSE: 0.02590 Loss: 0.026 + +2024-02-02 16:35:14,178 - ==> Best [Top 1 (MSE): 0.02562 Sparsity:0.00 Params: 136448 on epoch: 203] +2024-02-02 16:35:14,179 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:14,183 - + +2024-02-02 16:35:14,183 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:14,542 - Epoch: [205][ 1/ 8] Overall Loss 0.022270 Objective Loss 0.022270 MSE 0.022270 LR 0.001000 Time 0.358884 +2024-02-02 16:35:14,551 - Epoch: [205][ 2/ 8] Overall Loss 0.021533 Objective Loss 0.021533 MSE 0.021533 LR 0.001000 Time 0.183690 +2024-02-02 16:35:14,560 - Epoch: [205][ 3/ 8] Overall Loss 0.021213 Objective Loss 0.021213 MSE 0.021213 LR 0.001000 Time 0.125246 +2024-02-02 16:35:14,568 - Epoch: [205][ 4/ 8] Overall Loss 0.020706 Objective Loss 0.020706 MSE 0.020706 LR 0.001000 Time 0.095972 +2024-02-02 16:35:14,576 - Epoch: [205][ 5/ 8] Overall Loss 0.020438 Objective Loss 0.020438 MSE 0.020438 LR 0.001000 Time 0.078448 +2024-02-02 16:35:14,585 - Epoch: [205][ 6/ 8] Overall Loss 0.020527 Objective Loss 0.020527 MSE 0.020527 LR 0.001000 Time 0.066770 +2024-02-02 16:35:14,594 - Epoch: [205][ 7/ 8] Overall Loss 0.020763 Objective Loss 0.020763 MSE 0.020763 LR 0.001000 Time 0.058436 +2024-02-02 16:35:14,602 - Epoch: [205][ 8/ 8] Overall Loss 0.020651 Objective Loss 0.020651 MSE 0.020740 LR 0.001000 Time 0.052178 +2024-02-02 16:35:14,752 - --- validate (epoch=205)----------- +2024-02-02 16:35:14,752 - 60 samples (32 per mini-batch) +2024-02-02 16:35:15,113 - Epoch: [205][ 1/ 2] Loss 0.022349 MSE 0.022349 +2024-02-02 16:35:15,119 - Epoch: [205][ 2/ 2] Loss 0.025153 MSE 0.024966 +2024-02-02 16:35:15,259 - ==> MSE: 0.02497 Loss: 0.025 + +2024-02-02 16:35:15,260 - ==> Best [Top 1 (MSE): 0.02497 Sparsity:0.00 Params: 136448 on epoch: 205] +2024-02-02 16:35:15,260 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:15,265 - + +2024-02-02 16:35:15,265 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:15,627 - Epoch: [206][ 1/ 8] Overall Loss 0.022338 Objective Loss 0.022338 MSE 0.022338 LR 0.001000 Time 0.361302 +2024-02-02 16:35:15,639 - Epoch: [206][ 2/ 8] Overall Loss 0.021978 Objective Loss 0.021978 MSE 0.021978 LR 0.001000 Time 0.186425 +2024-02-02 16:35:15,648 - Epoch: [206][ 3/ 8] Overall Loss 0.021543 Objective Loss 0.021543 MSE 0.021543 LR 0.001000 Time 0.127120 +2024-02-02 16:35:15,656 - Epoch: [206][ 4/ 8] Overall Loss 0.020382 Objective Loss 0.020382 MSE 0.020382 LR 0.001000 Time 0.097457 +2024-02-02 16:35:15,665 - Epoch: [206][ 5/ 8] Overall Loss 0.020831 Objective Loss 0.020831 MSE 0.020831 LR 0.001000 Time 0.079655 +2024-02-02 16:35:15,674 - Epoch: [206][ 6/ 8] Overall Loss 0.020992 Objective Loss 0.020992 MSE 0.020992 LR 0.001000 Time 0.067812 +2024-02-02 16:35:15,682 - Epoch: [206][ 7/ 8] Overall Loss 0.020267 Objective Loss 0.020267 MSE 0.020267 LR 0.001000 Time 0.059330 +2024-02-02 16:35:15,691 - Epoch: [206][ 8/ 8] Overall Loss 0.020066 Objective Loss 0.020066 MSE 0.020225 LR 0.001000 Time 0.052958 +2024-02-02 16:35:15,842 - --- validate (epoch=206)----------- +2024-02-02 16:35:15,842 - 60 samples (32 per mini-batch) +2024-02-02 16:35:16,207 - Epoch: [206][ 1/ 2] Loss 0.023886 MSE 0.023886 +2024-02-02 16:35:16,214 - Epoch: [206][ 2/ 2] Loss 0.024453 MSE 0.024415 +2024-02-02 16:35:16,361 - ==> MSE: 0.02442 Loss: 0.024 + +2024-02-02 16:35:16,363 - ==> Best [Top 1 (MSE): 0.02442 Sparsity:0.00 Params: 136448 on epoch: 206] +2024-02-02 16:35:16,363 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:16,369 - + +2024-02-02 16:35:16,369 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:16,735 - Epoch: [207][ 1/ 8] Overall Loss 0.019875 Objective Loss 0.019875 MSE 0.019875 LR 0.001000 Time 0.365098 +2024-02-02 16:35:16,743 - Epoch: [207][ 2/ 8] Overall Loss 0.020383 Objective Loss 0.020383 MSE 0.020383 LR 0.001000 Time 0.186874 +2024-02-02 16:35:16,752 - Epoch: [207][ 3/ 8] Overall Loss 0.020267 Objective Loss 0.020267 MSE 0.020267 LR 0.001000 Time 0.127342 +2024-02-02 16:35:16,760 - Epoch: [207][ 4/ 8] Overall Loss 0.020363 Objective Loss 0.020363 MSE 0.020363 LR 0.001000 Time 0.097589 +2024-02-02 16:35:16,768 - Epoch: [207][ 5/ 8] Overall Loss 0.020096 Objective Loss 0.020096 MSE 0.020096 LR 0.001000 Time 0.079662 +2024-02-02 16:35:16,777 - Epoch: [207][ 6/ 8] Overall Loss 0.020034 Objective Loss 0.020034 MSE 0.020034 LR 0.001000 Time 0.067774 +2024-02-02 16:35:16,785 - Epoch: [207][ 7/ 8] Overall Loss 0.020214 Objective Loss 0.020214 MSE 0.020214 LR 0.001000 Time 0.059277 +2024-02-02 16:35:16,794 - Epoch: [207][ 8/ 8] Overall Loss 0.019464 Objective Loss 0.019464 MSE 0.020058 LR 0.001000 Time 0.052909 +2024-02-02 16:35:16,943 - --- validate (epoch=207)----------- +2024-02-02 16:35:16,943 - 60 samples (32 per mini-batch) +2024-02-02 16:35:17,302 - Epoch: [207][ 1/ 2] Loss 0.024584 MSE 0.024584 +2024-02-02 16:35:17,308 - Epoch: [207][ 2/ 2] Loss 0.024652 MSE 0.024648 +2024-02-02 16:35:17,449 - ==> MSE: 0.02465 Loss: 0.025 + +2024-02-02 16:35:17,451 - ==> Best [Top 1 (MSE): 0.02442 Sparsity:0.00 Params: 136448 on epoch: 206] +2024-02-02 16:35:17,451 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:17,456 - + +2024-02-02 16:35:17,456 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:17,822 - Epoch: [208][ 1/ 8] Overall Loss 0.018910 Objective Loss 0.018910 MSE 0.018910 LR 0.001000 Time 0.365605 +2024-02-02 16:35:17,830 - Epoch: [208][ 2/ 8] Overall Loss 0.019615 Objective Loss 0.019615 MSE 0.019615 LR 0.001000 Time 0.187070 +2024-02-02 16:35:17,839 - Epoch: [208][ 3/ 8] Overall Loss 0.019830 Objective Loss 0.019830 MSE 0.019830 LR 0.001000 Time 0.127504 +2024-02-02 16:35:17,847 - Epoch: [208][ 4/ 8] Overall Loss 0.020128 Objective Loss 0.020128 MSE 0.020128 LR 0.001000 Time 0.097675 +2024-02-02 16:35:17,856 - Epoch: [208][ 5/ 8] Overall Loss 0.019876 Objective Loss 0.019876 MSE 0.019876 LR 0.001000 Time 0.079817 +2024-02-02 16:35:17,865 - Epoch: [208][ 6/ 8] Overall Loss 0.019895 Objective Loss 0.019895 MSE 0.019895 LR 0.001000 Time 0.067931 +2024-02-02 16:35:17,873 - Epoch: [208][ 7/ 8] Overall Loss 0.019890 Objective Loss 0.019890 MSE 0.019890 LR 0.001000 Time 0.059442 +2024-02-02 16:35:17,882 - Epoch: [208][ 8/ 8] Overall Loss 0.019769 Objective Loss 0.019769 MSE 0.019864 LR 0.001000 Time 0.053041 +2024-02-02 16:35:18,031 - --- validate (epoch=208)----------- +2024-02-02 16:35:18,031 - 60 samples (32 per mini-batch) +2024-02-02 16:35:18,385 - Epoch: [208][ 1/ 2] Loss 0.024531 MSE 0.024531 +2024-02-02 16:35:18,392 - Epoch: [208][ 2/ 2] Loss 0.024427 MSE 0.024433 +2024-02-02 16:35:18,538 - ==> MSE: 0.02443 Loss: 0.024 + +2024-02-02 16:35:18,540 - ==> Best [Top 1 (MSE): 0.02442 Sparsity:0.00 Params: 136448 on epoch: 206] +2024-02-02 16:35:18,540 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:18,544 - + +2024-02-02 16:35:18,544 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:18,911 - Epoch: [209][ 1/ 8] Overall Loss 0.022835 Objective Loss 0.022835 MSE 0.022835 LR 0.001000 Time 0.365773 +2024-02-02 16:35:18,919 - Epoch: [209][ 2/ 8] Overall Loss 0.021326 Objective Loss 0.021326 MSE 0.021326 LR 0.001000 Time 0.187138 +2024-02-02 16:35:18,928 - Epoch: [209][ 3/ 8] Overall Loss 0.020323 Objective Loss 0.020323 MSE 0.020323 LR 0.001000 Time 0.127539 +2024-02-02 16:35:18,936 - Epoch: [209][ 4/ 8] Overall Loss 0.019821 Objective Loss 0.019821 MSE 0.019821 LR 0.001000 Time 0.097727 +2024-02-02 16:35:18,945 - Epoch: [209][ 5/ 8] Overall Loss 0.019518 Objective Loss 0.019518 MSE 0.019518 LR 0.001000 Time 0.079848 +2024-02-02 16:35:18,953 - Epoch: [209][ 6/ 8] Overall Loss 0.019548 Objective Loss 0.019548 MSE 0.019548 LR 0.001000 Time 0.067912 +2024-02-02 16:35:18,962 - Epoch: [209][ 7/ 8] Overall Loss 0.019737 Objective Loss 0.019737 MSE 0.019737 LR 0.001000 Time 0.059405 +2024-02-02 16:35:18,970 - Epoch: [209][ 8/ 8] Overall Loss 0.019953 Objective Loss 0.019953 MSE 0.019782 LR 0.001000 Time 0.053013 +2024-02-02 16:35:19,121 - --- validate (epoch=209)----------- +2024-02-02 16:35:19,121 - 60 samples (32 per mini-batch) +2024-02-02 16:35:19,484 - Epoch: [209][ 1/ 2] Loss 0.024553 MSE 0.024553 +2024-02-02 16:35:19,490 - Epoch: [209][ 2/ 2] Loss 0.024381 MSE 0.024392 +2024-02-02 16:35:19,641 - ==> MSE: 0.02439 Loss: 0.024 + +2024-02-02 16:35:19,643 - ==> Best [Top 1 (MSE): 0.02439 Sparsity:0.00 Params: 136448 on epoch: 209] +2024-02-02 16:35:19,643 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:19,649 - + +2024-02-02 16:35:19,649 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:20,014 - Epoch: [210][ 1/ 8] Overall Loss 0.017447 Objective Loss 0.017447 MSE 0.017447 LR 0.001000 Time 0.364979 +2024-02-02 16:35:20,023 - Epoch: [210][ 2/ 8] Overall Loss 0.018797 Objective Loss 0.018797 MSE 0.018797 LR 0.001000 Time 0.186779 +2024-02-02 16:35:20,032 - Epoch: [210][ 3/ 8] Overall Loss 0.018809 Objective Loss 0.018809 MSE 0.018809 LR 0.001000 Time 0.127280 +2024-02-02 16:35:20,040 - Epoch: [210][ 4/ 8] Overall Loss 0.019428 Objective Loss 0.019428 MSE 0.019428 LR 0.001000 Time 0.097571 +2024-02-02 16:35:20,049 - Epoch: [210][ 5/ 8] Overall Loss 0.019455 Objective Loss 0.019455 MSE 0.019455 LR 0.001000 Time 0.079727 +2024-02-02 16:35:20,057 - Epoch: [210][ 6/ 8] Overall Loss 0.019768 Objective Loss 0.019768 MSE 0.019768 LR 0.001000 Time 0.067812 +2024-02-02 16:35:20,066 - Epoch: [210][ 7/ 8] Overall Loss 0.019736 Objective Loss 0.019736 MSE 0.019736 LR 0.001000 Time 0.059316 +2024-02-02 16:35:20,074 - Epoch: [210][ 8/ 8] Overall Loss 0.019543 Objective Loss 0.019543 MSE 0.019696 LR 0.001000 Time 0.052946 +2024-02-02 16:35:20,222 - --- validate (epoch=210)----------- +2024-02-02 16:35:20,222 - 60 samples (32 per mini-batch) +2024-02-02 16:35:20,581 - Epoch: [210][ 1/ 2] Loss 0.024079 MSE 0.024079 +2024-02-02 16:35:20,587 - Epoch: [210][ 2/ 2] Loss 0.024222 MSE 0.024213 +2024-02-02 16:35:20,730 - ==> MSE: 0.02421 Loss: 0.024 + +2024-02-02 16:35:20,732 - ==> Best [Top 1 (MSE): 0.02421 Sparsity:0.00 Params: 136448 on epoch: 210] +2024-02-02 16:35:20,732 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:20,737 - + +2024-02-02 16:35:20,737 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:21,087 - Epoch: [211][ 1/ 8] Overall Loss 0.019254 Objective Loss 0.019254 MSE 0.019254 LR 0.001000 Time 0.349033 +2024-02-02 16:35:21,099 - Epoch: [211][ 2/ 8] Overall Loss 0.019289 Objective Loss 0.019289 MSE 0.019289 LR 0.001000 Time 0.180306 +2024-02-02 16:35:21,107 - Epoch: [211][ 3/ 8] Overall Loss 0.019138 Objective Loss 0.019138 MSE 0.019138 LR 0.001000 Time 0.122989 +2024-02-02 16:35:21,116 - Epoch: [211][ 4/ 8] Overall Loss 0.019490 Objective Loss 0.019490 MSE 0.019490 LR 0.001000 Time 0.094338 +2024-02-02 16:35:21,124 - Epoch: [211][ 5/ 8] Overall Loss 0.019103 Objective Loss 0.019103 MSE 0.019103 LR 0.001000 Time 0.077162 +2024-02-02 16:35:21,133 - Epoch: [211][ 6/ 8] Overall Loss 0.019509 Objective Loss 0.019509 MSE 0.019509 LR 0.001000 Time 0.065735 +2024-02-02 16:35:21,142 - Epoch: [211][ 7/ 8] Overall Loss 0.019577 Objective Loss 0.019577 MSE 0.019577 LR 0.001000 Time 0.057545 +2024-02-02 16:35:21,150 - Epoch: [211][ 8/ 8] Overall Loss 0.019880 Objective Loss 0.019880 MSE 0.019641 LR 0.001000 Time 0.051397 +2024-02-02 16:35:21,297 - --- validate (epoch=211)----------- +2024-02-02 16:35:21,297 - 60 samples (32 per mini-batch) +2024-02-02 16:35:21,656 - Epoch: [211][ 1/ 2] Loss 0.024112 MSE 0.024112 +2024-02-02 16:35:21,662 - Epoch: [211][ 2/ 2] Loss 0.024293 MSE 0.024281 +2024-02-02 16:35:21,808 - ==> MSE: 0.02428 Loss: 0.024 + +2024-02-02 16:35:21,810 - ==> Best [Top 1 (MSE): 0.02421 Sparsity:0.00 Params: 136448 on epoch: 210] +2024-02-02 16:35:21,810 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:21,815 - + +2024-02-02 16:35:21,815 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:22,178 - Epoch: [212][ 1/ 8] Overall Loss 0.018215 Objective Loss 0.018215 MSE 0.018215 LR 0.001000 Time 0.362591 +2024-02-02 16:35:22,187 - Epoch: [212][ 2/ 8] Overall Loss 0.018737 Objective Loss 0.018737 MSE 0.018737 LR 0.001000 Time 0.185526 +2024-02-02 16:35:22,195 - Epoch: [212][ 3/ 8] Overall Loss 0.019424 Objective Loss 0.019424 MSE 0.019424 LR 0.001000 Time 0.126458 +2024-02-02 16:35:22,203 - Epoch: [212][ 4/ 8] Overall Loss 0.019360 Objective Loss 0.019360 MSE 0.019360 LR 0.001000 Time 0.096885 +2024-02-02 16:35:22,212 - Epoch: [212][ 5/ 8] Overall Loss 0.018918 Objective Loss 0.018918 MSE 0.018918 LR 0.001000 Time 0.079156 +2024-02-02 16:35:22,220 - Epoch: [212][ 6/ 8] Overall Loss 0.019310 Objective Loss 0.019310 MSE 0.019310 LR 0.001000 Time 0.067356 +2024-02-02 16:35:22,229 - Epoch: [212][ 7/ 8] Overall Loss 0.019637 Objective Loss 0.019637 MSE 0.019637 LR 0.001000 Time 0.058925 +2024-02-02 16:35:22,237 - Epoch: [212][ 8/ 8] Overall Loss 0.019305 Objective Loss 0.019305 MSE 0.019568 LR 0.001000 Time 0.052595 +2024-02-02 16:35:22,392 - --- validate (epoch=212)----------- +2024-02-02 16:35:22,392 - 60 samples (32 per mini-batch) +2024-02-02 16:35:22,752 - Epoch: [212][ 1/ 2] Loss 0.023687 MSE 0.023687 +2024-02-02 16:35:22,758 - Epoch: [212][ 2/ 2] Loss 0.024214 MSE 0.024179 +2024-02-02 16:35:22,904 - ==> MSE: 0.02418 Loss: 0.024 + +2024-02-02 16:35:22,907 - ==> Best [Top 1 (MSE): 0.02418 Sparsity:0.00 Params: 136448 on epoch: 212] +2024-02-02 16:35:22,907 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:22,912 - + +2024-02-02 16:35:22,912 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:23,275 - Epoch: [213][ 1/ 8] Overall Loss 0.018991 Objective Loss 0.018991 MSE 0.018991 LR 0.001000 Time 0.362389 +2024-02-02 16:35:23,284 - Epoch: [213][ 2/ 8] Overall Loss 0.018939 Objective Loss 0.018939 MSE 0.018939 LR 0.001000 Time 0.185421 +2024-02-02 16:35:23,292 - Epoch: [213][ 3/ 8] Overall Loss 0.018876 Objective Loss 0.018876 MSE 0.018876 LR 0.001000 Time 0.126414 +2024-02-02 16:35:23,301 - Epoch: [213][ 4/ 8] Overall Loss 0.019499 Objective Loss 0.019499 MSE 0.019499 LR 0.001000 Time 0.096876 +2024-02-02 16:35:23,309 - Epoch: [213][ 5/ 8] Overall Loss 0.019020 Objective Loss 0.019020 MSE 0.019020 LR 0.001000 Time 0.079194 +2024-02-02 16:35:23,318 - Epoch: [213][ 6/ 8] Overall Loss 0.019306 Objective Loss 0.019306 MSE 0.019306 LR 0.001000 Time 0.067381 +2024-02-02 16:35:23,326 - Epoch: [213][ 7/ 8] Overall Loss 0.019411 Objective Loss 0.019411 MSE 0.019411 LR 0.001000 Time 0.058960 +2024-02-02 16:35:23,335 - Epoch: [213][ 8/ 8] Overall Loss 0.019907 Objective Loss 0.019907 MSE 0.019514 LR 0.001000 Time 0.052623 +2024-02-02 16:35:23,488 - --- validate (epoch=213)----------- +2024-02-02 16:35:23,489 - 60 samples (32 per mini-batch) +2024-02-02 16:35:23,846 - Epoch: [213][ 1/ 2] Loss 0.024539 MSE 0.024539 +2024-02-02 16:35:23,852 - Epoch: [213][ 2/ 2] Loss 0.024140 MSE 0.024166 +2024-02-02 16:35:23,998 - ==> MSE: 0.02417 Loss: 0.024 + +2024-02-02 16:35:24,000 - ==> Best [Top 1 (MSE): 0.02417 Sparsity:0.00 Params: 136448 on epoch: 213] +2024-02-02 16:35:24,001 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:24,007 - + +2024-02-02 16:35:24,007 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:24,370 - Epoch: [214][ 1/ 8] Overall Loss 0.018662 Objective Loss 0.018662 MSE 0.018662 LR 0.001000 Time 0.362377 +2024-02-02 16:35:24,379 - Epoch: [214][ 2/ 8] Overall Loss 0.019806 Objective Loss 0.019806 MSE 0.019806 LR 0.001000 Time 0.185500 +2024-02-02 16:35:24,387 - Epoch: [214][ 3/ 8] Overall Loss 0.020075 Objective Loss 0.020075 MSE 0.020075 LR 0.001000 Time 0.126422 +2024-02-02 16:35:24,396 - Epoch: [214][ 4/ 8] Overall Loss 0.019595 Objective Loss 0.019595 MSE 0.019595 LR 0.001000 Time 0.096938 +2024-02-02 16:35:24,405 - Epoch: [214][ 5/ 8] Overall Loss 0.019684 Objective Loss 0.019684 MSE 0.019684 LR 0.001000 Time 0.079230 +2024-02-02 16:35:24,413 - Epoch: [214][ 6/ 8] Overall Loss 0.019614 Objective Loss 0.019614 MSE 0.019614 LR 0.001000 Time 0.067409 +2024-02-02 16:35:24,422 - Epoch: [214][ 7/ 8] Overall Loss 0.019560 Objective Loss 0.019560 MSE 0.019560 LR 0.001000 Time 0.058986 +2024-02-02 16:35:24,430 - Epoch: [214][ 8/ 8] Overall Loss 0.019182 Objective Loss 0.019182 MSE 0.019481 LR 0.001000 Time 0.052661 +2024-02-02 16:35:24,583 - --- validate (epoch=214)----------- +2024-02-02 16:35:24,583 - 60 samples (32 per mini-batch) +2024-02-02 16:35:24,933 - Epoch: [214][ 1/ 2] Loss 0.024235 MSE 0.024235 +2024-02-02 16:35:24,940 - Epoch: [214][ 2/ 2] Loss 0.024032 MSE 0.024046 +2024-02-02 16:35:25,090 - ==> MSE: 0.02405 Loss: 0.024 + +2024-02-02 16:35:25,091 - ==> Best [Top 1 (MSE): 0.02405 Sparsity:0.00 Params: 136448 on epoch: 214] +2024-02-02 16:35:25,092 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:25,097 - + +2024-02-02 16:35:25,098 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:25,454 - Epoch: [215][ 1/ 8] Overall Loss 0.019382 Objective Loss 0.019382 MSE 0.019382 LR 0.001000 Time 0.356223 +2024-02-02 16:35:25,465 - Epoch: [215][ 2/ 8] Overall Loss 0.019461 Objective Loss 0.019461 MSE 0.019461 LR 0.001000 Time 0.183203 +2024-02-02 16:35:25,473 - Epoch: [215][ 3/ 8] Overall Loss 0.019737 Objective Loss 0.019737 MSE 0.019737 LR 0.001000 Time 0.124836 +2024-02-02 16:35:25,482 - Epoch: [215][ 4/ 8] Overall Loss 0.019484 Objective Loss 0.019484 MSE 0.019484 LR 0.001000 Time 0.095734 +2024-02-02 16:35:25,490 - Epoch: [215][ 5/ 8] Overall Loss 0.019576 Objective Loss 0.019576 MSE 0.019576 LR 0.001000 Time 0.078230 +2024-02-02 16:35:25,498 - Epoch: [215][ 6/ 8] Overall Loss 0.019627 Objective Loss 0.019627 MSE 0.019627 LR 0.001000 Time 0.066553 +2024-02-02 16:35:25,507 - Epoch: [215][ 7/ 8] Overall Loss 0.019450 Objective Loss 0.019450 MSE 0.019450 LR 0.001000 Time 0.058235 +2024-02-02 16:35:25,517 - Epoch: [215][ 8/ 8] Overall Loss 0.019429 Objective Loss 0.019429 MSE 0.019445 LR 0.001000 Time 0.052152 +2024-02-02 16:35:25,667 - --- validate (epoch=215)----------- +2024-02-02 16:35:25,667 - 60 samples (32 per mini-batch) +2024-02-02 16:35:26,017 - Epoch: [215][ 1/ 2] Loss 0.025478 MSE 0.025478 +2024-02-02 16:35:26,022 - Epoch: [215][ 2/ 2] Loss 0.023940 MSE 0.024042 +2024-02-02 16:35:26,168 - ==> MSE: 0.02404 Loss: 0.024 + +2024-02-02 16:35:26,171 - ==> Best [Top 1 (MSE): 0.02404 Sparsity:0.00 Params: 136448 on epoch: 215] +2024-02-02 16:35:26,171 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:26,176 - + +2024-02-02 16:35:26,177 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:26,533 - Epoch: [216][ 1/ 8] Overall Loss 0.019250 Objective Loss 0.019250 MSE 0.019250 LR 0.001000 Time 0.355577 +2024-02-02 16:35:26,545 - Epoch: [216][ 2/ 8] Overall Loss 0.019684 Objective Loss 0.019684 MSE 0.019684 LR 0.001000 Time 0.183612 +2024-02-02 16:35:26,556 - Epoch: [216][ 3/ 8] Overall Loss 0.019399 Objective Loss 0.019399 MSE 0.019399 LR 0.001000 Time 0.126239 +2024-02-02 16:35:26,568 - Epoch: [216][ 4/ 8] Overall Loss 0.019725 Objective Loss 0.019725 MSE 0.019725 LR 0.001000 Time 0.097632 +2024-02-02 16:35:26,581 - Epoch: [216][ 5/ 8] Overall Loss 0.019902 Objective Loss 0.019902 MSE 0.019902 LR 0.001000 Time 0.080475 +2024-02-02 16:35:26,593 - Epoch: [216][ 6/ 8] Overall Loss 0.019500 Objective Loss 0.019500 MSE 0.019500 LR 0.001000 Time 0.069063 +2024-02-02 16:35:26,603 - Epoch: [216][ 7/ 8] Overall Loss 0.019455 Objective Loss 0.019455 MSE 0.019455 LR 0.001000 Time 0.060622 +2024-02-02 16:35:26,612 - Epoch: [216][ 8/ 8] Overall Loss 0.019525 Objective Loss 0.019525 MSE 0.019469 LR 0.001000 Time 0.054121 +2024-02-02 16:35:26,763 - --- validate (epoch=216)----------- +2024-02-02 16:35:26,763 - 60 samples (32 per mini-batch) +2024-02-02 16:35:27,110 - Epoch: [216][ 1/ 2] Loss 0.021968 MSE 0.021968 +2024-02-02 16:35:27,116 - Epoch: [216][ 2/ 2] Loss 0.024156 MSE 0.024010 +2024-02-02 16:35:27,269 - ==> MSE: 0.02401 Loss: 0.024 + +2024-02-02 16:35:27,271 - ==> Best [Top 1 (MSE): 0.02401 Sparsity:0.00 Params: 136448 on epoch: 216] +2024-02-02 16:35:27,272 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:27,277 - + +2024-02-02 16:35:27,277 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:27,640 - Epoch: [217][ 1/ 8] Overall Loss 0.018624 Objective Loss 0.018624 MSE 0.018624 LR 0.001000 Time 0.362648 +2024-02-02 16:35:27,652 - Epoch: [217][ 2/ 8] Overall Loss 0.018546 Objective Loss 0.018546 MSE 0.018546 LR 0.001000 Time 0.187164 +2024-02-02 16:35:27,665 - Epoch: [217][ 3/ 8] Overall Loss 0.018317 Objective Loss 0.018317 MSE 0.018317 LR 0.001000 Time 0.128774 +2024-02-02 16:35:27,677 - Epoch: [217][ 4/ 8] Overall Loss 0.018425 Objective Loss 0.018425 MSE 0.018425 LR 0.001000 Time 0.099541 +2024-02-02 16:35:27,689 - Epoch: [217][ 5/ 8] Overall Loss 0.019106 Objective Loss 0.019106 MSE 0.019106 LR 0.001000 Time 0.081998 +2024-02-02 16:35:27,699 - Epoch: [217][ 6/ 8] Overall Loss 0.019472 Objective Loss 0.019472 MSE 0.019472 LR 0.001000 Time 0.069915 +2024-02-02 16:35:27,707 - Epoch: [217][ 7/ 8] Overall Loss 0.019346 Objective Loss 0.019346 MSE 0.019346 LR 0.001000 Time 0.061081 +2024-02-02 16:35:27,715 - Epoch: [217][ 8/ 8] Overall Loss 0.019506 Objective Loss 0.019506 MSE 0.019379 LR 0.001000 Time 0.054476 +2024-02-02 16:35:27,865 - --- validate (epoch=217)----------- +2024-02-02 16:35:27,865 - 60 samples (32 per mini-batch) +2024-02-02 16:35:28,224 - Epoch: [217][ 1/ 2] Loss 0.024793 MSE 0.024793 +2024-02-02 16:35:28,230 - Epoch: [217][ 2/ 2] Loss 0.023919 MSE 0.023978 +2024-02-02 16:35:28,372 - ==> MSE: 0.02398 Loss: 0.024 + +2024-02-02 16:35:28,375 - ==> Best [Top 1 (MSE): 0.02398 Sparsity:0.00 Params: 136448 on epoch: 217] +2024-02-02 16:35:28,375 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:28,380 - + +2024-02-02 16:35:28,380 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:28,729 - Epoch: [218][ 1/ 8] Overall Loss 0.019927 Objective Loss 0.019927 MSE 0.019927 LR 0.001000 Time 0.348465 +2024-02-02 16:35:28,741 - Epoch: [218][ 2/ 8] Overall Loss 0.020101 Objective Loss 0.020101 MSE 0.020101 LR 0.001000 Time 0.179840 +2024-02-02 16:35:28,752 - Epoch: [218][ 3/ 8] Overall Loss 0.019374 Objective Loss 0.019374 MSE 0.019374 LR 0.001000 Time 0.123592 +2024-02-02 16:35:28,764 - Epoch: [218][ 4/ 8] Overall Loss 0.019222 Objective Loss 0.019222 MSE 0.019222 LR 0.001000 Time 0.095604 +2024-02-02 16:35:28,776 - Epoch: [218][ 5/ 8] Overall Loss 0.019342 Objective Loss 0.019342 MSE 0.019342 LR 0.001000 Time 0.078874 +2024-02-02 16:35:28,789 - Epoch: [218][ 6/ 8] Overall Loss 0.019483 Objective Loss 0.019483 MSE 0.019483 LR 0.001000 Time 0.067722 +2024-02-02 16:35:28,801 - Epoch: [218][ 7/ 8] Overall Loss 0.019447 Objective Loss 0.019447 MSE 0.019447 LR 0.001000 Time 0.059743 +2024-02-02 16:35:28,813 - Epoch: [218][ 8/ 8] Overall Loss 0.019102 Objective Loss 0.019102 MSE 0.019375 LR 0.001000 Time 0.053755 +2024-02-02 16:35:28,959 - --- validate (epoch=218)----------- +2024-02-02 16:35:28,960 - 60 samples (32 per mini-batch) +2024-02-02 16:35:29,310 - Epoch: [218][ 1/ 2] Loss 0.025375 MSE 0.025375 +2024-02-02 16:35:29,316 - Epoch: [218][ 2/ 2] Loss 0.024082 MSE 0.024168 +2024-02-02 16:35:29,462 - ==> MSE: 0.02417 Loss: 0.024 + +2024-02-02 16:35:29,464 - ==> Best [Top 1 (MSE): 0.02398 Sparsity:0.00 Params: 136448 on epoch: 217] +2024-02-02 16:35:29,464 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:29,468 - + +2024-02-02 16:35:29,469 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:29,831 - Epoch: [219][ 1/ 8] Overall Loss 0.019629 Objective Loss 0.019629 MSE 0.019629 LR 0.001000 Time 0.361562 +2024-02-02 16:35:29,839 - Epoch: [219][ 2/ 8] Overall Loss 0.019160 Objective Loss 0.019160 MSE 0.019160 LR 0.001000 Time 0.185069 +2024-02-02 16:35:29,848 - Epoch: [219][ 3/ 8] Overall Loss 0.019643 Objective Loss 0.019643 MSE 0.019643 LR 0.001000 Time 0.126182 +2024-02-02 16:35:29,857 - Epoch: [219][ 4/ 8] Overall Loss 0.019310 Objective Loss 0.019310 MSE 0.019310 LR 0.001000 Time 0.096737 +2024-02-02 16:35:29,865 - Epoch: [219][ 5/ 8] Overall Loss 0.019052 Objective Loss 0.019052 MSE 0.019052 LR 0.001000 Time 0.079056 +2024-02-02 16:35:29,874 - Epoch: [219][ 6/ 8] Overall Loss 0.019147 Objective Loss 0.019147 MSE 0.019147 LR 0.001000 Time 0.067269 +2024-02-02 16:35:29,882 - Epoch: [219][ 7/ 8] Overall Loss 0.019361 Objective Loss 0.019361 MSE 0.019361 LR 0.001000 Time 0.058848 +2024-02-02 16:35:29,891 - Epoch: [219][ 8/ 8] Overall Loss 0.019543 Objective Loss 0.019543 MSE 0.019399 LR 0.001000 Time 0.052533 +2024-02-02 16:35:30,041 - --- validate (epoch=219)----------- +2024-02-02 16:35:30,041 - 60 samples (32 per mini-batch) +2024-02-02 16:35:30,404 - Epoch: [219][ 1/ 2] Loss 0.023871 MSE 0.023871 +2024-02-02 16:35:30,410 - Epoch: [219][ 2/ 2] Loss 0.023994 MSE 0.023985 +2024-02-02 16:35:30,554 - ==> MSE: 0.02399 Loss: 0.024 + +2024-02-02 16:35:30,556 - ==> Best [Top 1 (MSE): 0.02398 Sparsity:0.00 Params: 136448 on epoch: 217] +2024-02-02 16:35:30,556 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:30,560 - + +2024-02-02 16:35:30,560 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:30,914 - Epoch: [220][ 1/ 8] Overall Loss 0.019155 Objective Loss 0.019155 MSE 0.019155 LR 0.001000 Time 0.352960 +2024-02-02 16:35:30,923 - Epoch: [220][ 2/ 8] Overall Loss 0.019875 Objective Loss 0.019875 MSE 0.019875 LR 0.001000 Time 0.180878 +2024-02-02 16:35:30,931 - Epoch: [220][ 3/ 8] Overall Loss 0.019105 Objective Loss 0.019105 MSE 0.019105 LR 0.001000 Time 0.123387 +2024-02-02 16:35:30,940 - Epoch: [220][ 4/ 8] Overall Loss 0.019293 Objective Loss 0.019293 MSE 0.019293 LR 0.001000 Time 0.094612 +2024-02-02 16:35:30,948 - Epoch: [220][ 5/ 8] Overall Loss 0.019274 Objective Loss 0.019274 MSE 0.019274 LR 0.001000 Time 0.077365 +2024-02-02 16:35:30,957 - Epoch: [220][ 6/ 8] Overall Loss 0.019483 Objective Loss 0.019483 MSE 0.019483 LR 0.001000 Time 0.065877 +2024-02-02 16:35:30,965 - Epoch: [220][ 7/ 8] Overall Loss 0.019296 Objective Loss 0.019296 MSE 0.019296 LR 0.001000 Time 0.057676 +2024-02-02 16:35:30,974 - Epoch: [220][ 8/ 8] Overall Loss 0.019284 Objective Loss 0.019284 MSE 0.019293 LR 0.001000 Time 0.051520 +2024-02-02 16:35:31,124 - --- validate (epoch=220)----------- +2024-02-02 16:35:31,124 - 60 samples (32 per mini-batch) +2024-02-02 16:35:31,477 - Epoch: [220][ 1/ 2] Loss 0.021646 MSE 0.021646 +2024-02-02 16:35:31,483 - Epoch: [220][ 2/ 2] Loss 0.024214 MSE 0.024043 +2024-02-02 16:35:31,635 - ==> MSE: 0.02404 Loss: 0.024 + +2024-02-02 16:35:31,638 - ==> Best [Top 1 (MSE): 0.02398 Sparsity:0.00 Params: 136448 on epoch: 217] +2024-02-02 16:35:31,638 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:31,646 - + +2024-02-02 16:35:31,646 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:32,019 - Epoch: [221][ 1/ 8] Overall Loss 0.021559 Objective Loss 0.021559 MSE 0.021559 LR 0.001000 Time 0.372577 +2024-02-02 16:35:32,031 - Epoch: [221][ 2/ 8] Overall Loss 0.019006 Objective Loss 0.019006 MSE 0.019006 LR 0.001000 Time 0.191868 +2024-02-02 16:35:32,039 - Epoch: [221][ 3/ 8] Overall Loss 0.018583 Objective Loss 0.018583 MSE 0.018583 LR 0.001000 Time 0.130693 +2024-02-02 16:35:32,048 - Epoch: [221][ 4/ 8] Overall Loss 0.018817 Objective Loss 0.018817 MSE 0.018817 LR 0.001000 Time 0.100101 +2024-02-02 16:35:32,056 - Epoch: [221][ 5/ 8] Overall Loss 0.019028 Objective Loss 0.019028 MSE 0.019028 LR 0.001000 Time 0.081739 +2024-02-02 16:35:32,065 - Epoch: [221][ 6/ 8] Overall Loss 0.019375 Objective Loss 0.019375 MSE 0.019375 LR 0.001000 Time 0.069494 +2024-02-02 16:35:32,073 - Epoch: [221][ 7/ 8] Overall Loss 0.019196 Objective Loss 0.019196 MSE 0.019196 LR 0.001000 Time 0.060735 +2024-02-02 16:35:32,081 - Epoch: [221][ 8/ 8] Overall Loss 0.019534 Objective Loss 0.019534 MSE 0.019266 LR 0.001000 Time 0.054148 +2024-02-02 16:35:32,229 - --- validate (epoch=221)----------- +2024-02-02 16:35:32,229 - 60 samples (32 per mini-batch) +2024-02-02 16:35:32,588 - Epoch: [221][ 1/ 2] Loss 0.024052 MSE 0.024052 +2024-02-02 16:35:32,594 - Epoch: [221][ 2/ 2] Loss 0.024047 MSE 0.024047 +2024-02-02 16:35:32,742 - ==> MSE: 0.02405 Loss: 0.024 + +2024-02-02 16:35:32,744 - ==> Best [Top 1 (MSE): 0.02398 Sparsity:0.00 Params: 136448 on epoch: 217] +2024-02-02 16:35:32,744 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:32,748 - + +2024-02-02 16:35:32,748 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:33,115 - Epoch: [222][ 1/ 8] Overall Loss 0.020263 Objective Loss 0.020263 MSE 0.020263 LR 0.001000 Time 0.366690 +2024-02-02 16:35:33,126 - Epoch: [222][ 2/ 8] Overall Loss 0.018524 Objective Loss 0.018524 MSE 0.018524 LR 0.001000 Time 0.188646 +2024-02-02 16:35:33,135 - Epoch: [222][ 3/ 8] Overall Loss 0.019189 Objective Loss 0.019189 MSE 0.019189 LR 0.001000 Time 0.128586 +2024-02-02 16:35:33,143 - Epoch: [222][ 4/ 8] Overall Loss 0.019214 Objective Loss 0.019214 MSE 0.019214 LR 0.001000 Time 0.098517 +2024-02-02 16:35:33,152 - Epoch: [222][ 5/ 8] Overall Loss 0.019185 Objective Loss 0.019185 MSE 0.019185 LR 0.001000 Time 0.080472 +2024-02-02 16:35:33,160 - Epoch: [222][ 6/ 8] Overall Loss 0.019104 Objective Loss 0.019104 MSE 0.019104 LR 0.001000 Time 0.068443 +2024-02-02 16:35:33,168 - Epoch: [222][ 7/ 8] Overall Loss 0.019195 Objective Loss 0.019195 MSE 0.019195 LR 0.001000 Time 0.059796 +2024-02-02 16:35:33,177 - Epoch: [222][ 8/ 8] Overall Loss 0.019373 Objective Loss 0.019373 MSE 0.019232 LR 0.001000 Time 0.053330 +2024-02-02 16:35:33,330 - --- validate (epoch=222)----------- +2024-02-02 16:35:33,330 - 60 samples (32 per mini-batch) +2024-02-02 16:35:33,674 - Epoch: [222][ 1/ 2] Loss 0.023716 MSE 0.023716 +2024-02-02 16:35:33,680 - Epoch: [222][ 2/ 2] Loss 0.023955 MSE 0.023939 +2024-02-02 16:35:33,825 - ==> MSE: 0.02394 Loss: 0.024 + +2024-02-02 16:35:33,827 - ==> Best [Top 1 (MSE): 0.02394 Sparsity:0.00 Params: 136448 on epoch: 222] +2024-02-02 16:35:33,828 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:33,833 - + +2024-02-02 16:35:33,833 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:34,194 - Epoch: [223][ 1/ 8] Overall Loss 0.016361 Objective Loss 0.016361 MSE 0.016361 LR 0.001000 Time 0.360557 +2024-02-02 16:35:34,206 - Epoch: [223][ 2/ 8] Overall Loss 0.018617 Objective Loss 0.018617 MSE 0.018617 LR 0.001000 Time 0.185965 +2024-02-02 16:35:34,217 - Epoch: [223][ 3/ 8] Overall Loss 0.018975 Objective Loss 0.018975 MSE 0.018975 LR 0.001000 Time 0.127769 +2024-02-02 16:35:34,229 - Epoch: [223][ 4/ 8] Overall Loss 0.019351 Objective Loss 0.019351 MSE 0.019351 LR 0.001000 Time 0.098716 +2024-02-02 16:35:34,241 - Epoch: [223][ 5/ 8] Overall Loss 0.019051 Objective Loss 0.019051 MSE 0.019051 LR 0.001000 Time 0.081304 +2024-02-02 16:35:34,251 - Epoch: [223][ 6/ 8] Overall Loss 0.019273 Objective Loss 0.019273 MSE 0.019273 LR 0.001000 Time 0.069270 +2024-02-02 16:35:34,259 - Epoch: [223][ 7/ 8] Overall Loss 0.019209 Objective Loss 0.019209 MSE 0.019209 LR 0.001000 Time 0.060569 +2024-02-02 16:35:34,268 - Epoch: [223][ 8/ 8] Overall Loss 0.019112 Objective Loss 0.019112 MSE 0.019189 LR 0.001000 Time 0.054027 +2024-02-02 16:35:34,418 - --- validate (epoch=223)----------- +2024-02-02 16:35:34,419 - 60 samples (32 per mini-batch) +2024-02-02 16:35:34,776 - Epoch: [223][ 1/ 2] Loss 0.023553 MSE 0.023553 +2024-02-02 16:35:34,783 - Epoch: [223][ 2/ 2] Loss 0.023964 MSE 0.023937 +2024-02-02 16:35:34,923 - ==> MSE: 0.02394 Loss: 0.024 + +2024-02-02 16:35:34,924 - ==> Best [Top 1 (MSE): 0.02394 Sparsity:0.00 Params: 136448 on epoch: 223] +2024-02-02 16:35:34,924 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:34,930 - + +2024-02-02 16:35:34,930 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:35,303 - Epoch: [224][ 1/ 8] Overall Loss 0.019261 Objective Loss 0.019261 MSE 0.019261 LR 0.001000 Time 0.372226 +2024-02-02 16:35:35,311 - Epoch: [224][ 2/ 8] Overall Loss 0.018466 Objective Loss 0.018466 MSE 0.018466 LR 0.001000 Time 0.190409 +2024-02-02 16:35:35,320 - Epoch: [224][ 3/ 8] Overall Loss 0.017931 Objective Loss 0.017931 MSE 0.017931 LR 0.001000 Time 0.129732 +2024-02-02 16:35:35,328 - Epoch: [224][ 4/ 8] Overall Loss 0.018605 Objective Loss 0.018605 MSE 0.018605 LR 0.001000 Time 0.099364 +2024-02-02 16:35:35,337 - Epoch: [224][ 5/ 8] Overall Loss 0.019172 Objective Loss 0.019172 MSE 0.019172 LR 0.001000 Time 0.081142 +2024-02-02 16:35:35,345 - Epoch: [224][ 6/ 8] Overall Loss 0.018840 Objective Loss 0.018840 MSE 0.018840 LR 0.001000 Time 0.069001 +2024-02-02 16:35:35,353 - Epoch: [224][ 7/ 8] Overall Loss 0.019124 Objective Loss 0.019124 MSE 0.019124 LR 0.001000 Time 0.060275 +2024-02-02 16:35:35,362 - Epoch: [224][ 8/ 8] Overall Loss 0.019338 Objective Loss 0.019338 MSE 0.019169 LR 0.001000 Time 0.053766 +2024-02-02 16:35:35,513 - --- validate (epoch=224)----------- +2024-02-02 16:35:35,513 - 60 samples (32 per mini-batch) +2024-02-02 16:35:35,871 - Epoch: [224][ 1/ 2] Loss 0.022644 MSE 0.022644 +2024-02-02 16:35:35,877 - Epoch: [224][ 2/ 2] Loss 0.024172 MSE 0.024071 +2024-02-02 16:35:36,029 - ==> MSE: 0.02407 Loss: 0.024 + +2024-02-02 16:35:36,032 - ==> Best [Top 1 (MSE): 0.02394 Sparsity:0.00 Params: 136448 on epoch: 223] +2024-02-02 16:35:36,032 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:36,036 - + +2024-02-02 16:35:36,036 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:36,402 - Epoch: [225][ 1/ 8] Overall Loss 0.020010 Objective Loss 0.020010 MSE 0.020010 LR 0.001000 Time 0.365614 +2024-02-02 16:35:36,411 - Epoch: [225][ 2/ 8] Overall Loss 0.020237 Objective Loss 0.020237 MSE 0.020237 LR 0.001000 Time 0.187071 +2024-02-02 16:35:36,420 - Epoch: [225][ 3/ 8] Overall Loss 0.019776 Objective Loss 0.019776 MSE 0.019776 LR 0.001000 Time 0.127520 +2024-02-02 16:35:36,428 - Epoch: [225][ 4/ 8] Overall Loss 0.019292 Objective Loss 0.019292 MSE 0.019292 LR 0.001000 Time 0.097717 +2024-02-02 16:35:36,437 - Epoch: [225][ 5/ 8] Overall Loss 0.019193 Objective Loss 0.019193 MSE 0.019193 LR 0.001000 Time 0.079838 +2024-02-02 16:35:36,445 - Epoch: [225][ 6/ 8] Overall Loss 0.019213 Objective Loss 0.019213 MSE 0.019213 LR 0.001000 Time 0.067912 +2024-02-02 16:35:36,453 - Epoch: [225][ 7/ 8] Overall Loss 0.019344 Objective Loss 0.019344 MSE 0.019344 LR 0.001000 Time 0.059385 +2024-02-02 16:35:36,462 - Epoch: [225][ 8/ 8] Overall Loss 0.018817 Objective Loss 0.018817 MSE 0.019234 LR 0.001000 Time 0.053011 +2024-02-02 16:35:36,606 - --- validate (epoch=225)----------- +2024-02-02 16:35:36,606 - 60 samples (32 per mini-batch) +2024-02-02 16:35:36,953 - Epoch: [225][ 1/ 2] Loss 0.024083 MSE 0.024083 +2024-02-02 16:35:36,959 - Epoch: [225][ 2/ 2] Loss 0.024169 MSE 0.024164 +2024-02-02 16:35:37,105 - ==> MSE: 0.02416 Loss: 0.024 + +2024-02-02 16:35:37,108 - ==> Best [Top 1 (MSE): 0.02394 Sparsity:0.00 Params: 136448 on epoch: 223] +2024-02-02 16:35:37,108 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:37,112 - + +2024-02-02 16:35:37,113 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:37,478 - Epoch: [226][ 1/ 8] Overall Loss 0.019450 Objective Loss 0.019450 MSE 0.019450 LR 0.001000 Time 0.364754 +2024-02-02 16:35:37,487 - Epoch: [226][ 2/ 8] Overall Loss 0.018511 Objective Loss 0.018511 MSE 0.018511 LR 0.001000 Time 0.186732 +2024-02-02 16:35:37,495 - Epoch: [226][ 3/ 8] Overall Loss 0.018836 Objective Loss 0.018836 MSE 0.018836 LR 0.001000 Time 0.127284 +2024-02-02 16:35:37,504 - Epoch: [226][ 4/ 8] Overall Loss 0.018762 Objective Loss 0.018762 MSE 0.018762 LR 0.001000 Time 0.097536 +2024-02-02 16:35:37,512 - Epoch: [226][ 5/ 8] Overall Loss 0.018852 Objective Loss 0.018852 MSE 0.018852 LR 0.001000 Time 0.079703 +2024-02-02 16:35:37,521 - Epoch: [226][ 6/ 8] Overall Loss 0.018871 Objective Loss 0.018871 MSE 0.018871 LR 0.001000 Time 0.067796 +2024-02-02 16:35:37,529 - Epoch: [226][ 7/ 8] Overall Loss 0.019102 Objective Loss 0.019102 MSE 0.019102 LR 0.001000 Time 0.059292 +2024-02-02 16:35:37,538 - Epoch: [226][ 8/ 8] Overall Loss 0.019461 Objective Loss 0.019461 MSE 0.019177 LR 0.001000 Time 0.052938 +2024-02-02 16:35:37,690 - --- validate (epoch=226)----------- +2024-02-02 16:35:37,690 - 60 samples (32 per mini-batch) +2024-02-02 16:35:38,051 - Epoch: [226][ 1/ 2] Loss 0.024605 MSE 0.024605 +2024-02-02 16:35:38,058 - Epoch: [226][ 2/ 2] Loss 0.023858 MSE 0.023908 +2024-02-02 16:35:38,202 - ==> MSE: 0.02391 Loss: 0.024 + +2024-02-02 16:35:38,204 - ==> Best [Top 1 (MSE): 0.02391 Sparsity:0.00 Params: 136448 on epoch: 226] +2024-02-02 16:35:38,204 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:38,210 - + +2024-02-02 16:35:38,210 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:38,565 - Epoch: [227][ 1/ 8] Overall Loss 0.018974 Objective Loss 0.018974 MSE 0.018974 LR 0.001000 Time 0.354495 +2024-02-02 16:35:38,574 - Epoch: [227][ 2/ 8] Overall Loss 0.019545 Objective Loss 0.019545 MSE 0.019545 LR 0.001000 Time 0.181512 +2024-02-02 16:35:38,582 - Epoch: [227][ 3/ 8] Overall Loss 0.020275 Objective Loss 0.020275 MSE 0.020275 LR 0.001000 Time 0.123818 +2024-02-02 16:35:38,591 - Epoch: [227][ 4/ 8] Overall Loss 0.019302 Objective Loss 0.019302 MSE 0.019302 LR 0.001000 Time 0.094946 +2024-02-02 16:35:38,599 - Epoch: [227][ 5/ 8] Overall Loss 0.019335 Objective Loss 0.019335 MSE 0.019335 LR 0.001000 Time 0.077614 +2024-02-02 16:35:38,608 - Epoch: [227][ 6/ 8] Overall Loss 0.019250 Objective Loss 0.019250 MSE 0.019250 LR 0.001000 Time 0.066080 +2024-02-02 16:35:38,616 - Epoch: [227][ 7/ 8] Overall Loss 0.019280 Objective Loss 0.019280 MSE 0.019280 LR 0.001000 Time 0.057845 +2024-02-02 16:35:38,625 - Epoch: [227][ 8/ 8] Overall Loss 0.018773 Objective Loss 0.018773 MSE 0.019174 LR 0.001000 Time 0.051658 +2024-02-02 16:35:38,778 - --- validate (epoch=227)----------- +2024-02-02 16:35:38,779 - 60 samples (32 per mini-batch) +2024-02-02 16:35:39,124 - Epoch: [227][ 1/ 2] Loss 0.023737 MSE 0.023737 +2024-02-02 16:35:39,130 - Epoch: [227][ 2/ 2] Loss 0.024096 MSE 0.024072 +2024-02-02 16:35:39,277 - ==> MSE: 0.02407 Loss: 0.024 + +2024-02-02 16:35:39,279 - ==> Best [Top 1 (MSE): 0.02391 Sparsity:0.00 Params: 136448 on epoch: 226] +2024-02-02 16:35:39,279 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:39,284 - + +2024-02-02 16:35:39,284 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:39,645 - Epoch: [228][ 1/ 8] Overall Loss 0.019995 Objective Loss 0.019995 MSE 0.019995 LR 0.001000 Time 0.361035 +2024-02-02 16:35:39,654 - Epoch: [228][ 2/ 8] Overall Loss 0.019302 Objective Loss 0.019302 MSE 0.019302 LR 0.001000 Time 0.184796 +2024-02-02 16:35:39,663 - Epoch: [228][ 3/ 8] Overall Loss 0.018557 Objective Loss 0.018557 MSE 0.018557 LR 0.001000 Time 0.126246 +2024-02-02 16:35:39,671 - Epoch: [228][ 4/ 8] Overall Loss 0.018789 Objective Loss 0.018789 MSE 0.018789 LR 0.001000 Time 0.096642 +2024-02-02 16:35:39,680 - Epoch: [228][ 5/ 8] Overall Loss 0.018868 Objective Loss 0.018868 MSE 0.018868 LR 0.001000 Time 0.078954 +2024-02-02 16:35:39,688 - Epoch: [228][ 6/ 8] Overall Loss 0.019007 Objective Loss 0.019007 MSE 0.019007 LR 0.001000 Time 0.067173 +2024-02-02 16:35:39,697 - Epoch: [228][ 7/ 8] Overall Loss 0.019083 Objective Loss 0.019083 MSE 0.019083 LR 0.001000 Time 0.058774 +2024-02-02 16:35:39,705 - Epoch: [228][ 8/ 8] Overall Loss 0.019055 Objective Loss 0.019055 MSE 0.019077 LR 0.001000 Time 0.052464 +2024-02-02 16:35:39,855 - --- validate (epoch=228)----------- +2024-02-02 16:35:39,856 - 60 samples (32 per mini-batch) +2024-02-02 16:35:40,212 - Epoch: [228][ 1/ 2] Loss 0.025345 MSE 0.025345 +2024-02-02 16:35:40,218 - Epoch: [228][ 2/ 2] Loss 0.023702 MSE 0.023812 +2024-02-02 16:35:40,368 - ==> MSE: 0.02381 Loss: 0.024 + +2024-02-02 16:35:40,371 - ==> Best [Top 1 (MSE): 0.02381 Sparsity:0.00 Params: 136448 on epoch: 228] +2024-02-02 16:35:40,371 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:40,377 - + +2024-02-02 16:35:40,377 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:40,738 - Epoch: [229][ 1/ 8] Overall Loss 0.019181 Objective Loss 0.019181 MSE 0.019181 LR 0.001000 Time 0.360613 +2024-02-02 16:35:40,747 - Epoch: [229][ 2/ 8] Overall Loss 0.019698 Objective Loss 0.019698 MSE 0.019698 LR 0.001000 Time 0.184533 +2024-02-02 16:35:40,755 - Epoch: [229][ 3/ 8] Overall Loss 0.019498 Objective Loss 0.019498 MSE 0.019498 LR 0.001000 Time 0.125785 +2024-02-02 16:35:40,764 - Epoch: [229][ 4/ 8] Overall Loss 0.019317 Objective Loss 0.019317 MSE 0.019317 LR 0.001000 Time 0.096392 +2024-02-02 16:35:40,772 - Epoch: [229][ 5/ 8] Overall Loss 0.019394 Objective Loss 0.019394 MSE 0.019394 LR 0.001000 Time 0.078801 +2024-02-02 16:35:40,781 - Epoch: [229][ 6/ 8] Overall Loss 0.019127 Objective Loss 0.019127 MSE 0.019127 LR 0.001000 Time 0.067063 +2024-02-02 16:35:40,790 - Epoch: [229][ 7/ 8] Overall Loss 0.019221 Objective Loss 0.019221 MSE 0.019221 LR 0.001000 Time 0.058681 +2024-02-02 16:35:40,798 - Epoch: [229][ 8/ 8] Overall Loss 0.018570 Objective Loss 0.018570 MSE 0.019085 LR 0.001000 Time 0.052374 +2024-02-02 16:35:40,945 - --- validate (epoch=229)----------- +2024-02-02 16:35:40,945 - 60 samples (32 per mini-batch) +2024-02-02 16:35:41,288 - Epoch: [229][ 1/ 2] Loss 0.022471 MSE 0.022471 +2024-02-02 16:35:41,294 - Epoch: [229][ 2/ 2] Loss 0.024113 MSE 0.024004 +2024-02-02 16:35:41,440 - ==> MSE: 0.02400 Loss: 0.024 + +2024-02-02 16:35:41,441 - ==> Best [Top 1 (MSE): 0.02381 Sparsity:0.00 Params: 136448 on epoch: 228] +2024-02-02 16:35:41,441 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:41,446 - + +2024-02-02 16:35:41,446 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:41,815 - Epoch: [230][ 1/ 8] Overall Loss 0.022281 Objective Loss 0.022281 MSE 0.022281 LR 0.001000 Time 0.368446 +2024-02-02 16:35:41,823 - Epoch: [230][ 2/ 8] Overall Loss 0.019876 Objective Loss 0.019876 MSE 0.019876 LR 0.001000 Time 0.188497 +2024-02-02 16:35:41,832 - Epoch: [230][ 3/ 8] Overall Loss 0.019632 Objective Loss 0.019632 MSE 0.019632 LR 0.001000 Time 0.128449 +2024-02-02 16:35:41,841 - Epoch: [230][ 4/ 8] Overall Loss 0.019414 Objective Loss 0.019414 MSE 0.019414 LR 0.001000 Time 0.098443 +2024-02-02 16:35:41,849 - Epoch: [230][ 5/ 8] Overall Loss 0.019397 Objective Loss 0.019397 MSE 0.019397 LR 0.001000 Time 0.080400 +2024-02-02 16:35:41,857 - Epoch: [230][ 6/ 8] Overall Loss 0.019324 Objective Loss 0.019324 MSE 0.019324 LR 0.001000 Time 0.068395 +2024-02-02 16:35:41,866 - Epoch: [230][ 7/ 8] Overall Loss 0.019219 Objective Loss 0.019219 MSE 0.019219 LR 0.001000 Time 0.059831 +2024-02-02 16:35:41,874 - Epoch: [230][ 8/ 8] Overall Loss 0.018735 Objective Loss 0.018735 MSE 0.019118 LR 0.001000 Time 0.053382 +2024-02-02 16:35:42,016 - --- validate (epoch=230)----------- +2024-02-02 16:35:42,017 - 60 samples (32 per mini-batch) +2024-02-02 16:35:42,362 - Epoch: [230][ 1/ 2] Loss 0.021994 MSE 0.021994 +2024-02-02 16:35:42,368 - Epoch: [230][ 2/ 2] Loss 0.024038 MSE 0.023901 +2024-02-02 16:35:42,509 - ==> MSE: 0.02390 Loss: 0.024 + +2024-02-02 16:35:42,512 - ==> Best [Top 1 (MSE): 0.02381 Sparsity:0.00 Params: 136448 on epoch: 228] +2024-02-02 16:35:42,512 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:42,516 - + +2024-02-02 16:35:42,516 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:42,889 - Epoch: [231][ 1/ 8] Overall Loss 0.019334 Objective Loss 0.019334 MSE 0.019334 LR 0.001000 Time 0.372003 +2024-02-02 16:35:42,898 - Epoch: [231][ 2/ 8] Overall Loss 0.018767 Objective Loss 0.018767 MSE 0.018767 LR 0.001000 Time 0.190270 +2024-02-02 16:35:42,906 - Epoch: [231][ 3/ 8] Overall Loss 0.019068 Objective Loss 0.019068 MSE 0.019068 LR 0.001000 Time 0.129602 +2024-02-02 16:35:42,914 - Epoch: [231][ 4/ 8] Overall Loss 0.019439 Objective Loss 0.019439 MSE 0.019439 LR 0.001000 Time 0.099254 +2024-02-02 16:35:42,923 - Epoch: [231][ 5/ 8] Overall Loss 0.019057 Objective Loss 0.019057 MSE 0.019057 LR 0.001000 Time 0.081042 +2024-02-02 16:35:42,931 - Epoch: [231][ 6/ 8] Overall Loss 0.019150 Objective Loss 0.019150 MSE 0.019150 LR 0.001000 Time 0.068906 +2024-02-02 16:35:42,940 - Epoch: [231][ 7/ 8] Overall Loss 0.019131 Objective Loss 0.019131 MSE 0.019131 LR 0.001000 Time 0.060258 +2024-02-02 16:35:42,948 - Epoch: [231][ 8/ 8] Overall Loss 0.019542 Objective Loss 0.019542 MSE 0.019217 LR 0.001000 Time 0.053777 +2024-02-02 16:35:43,095 - --- validate (epoch=231)----------- +2024-02-02 16:35:43,095 - 60 samples (32 per mini-batch) +2024-02-02 16:35:43,451 - Epoch: [231][ 1/ 2] Loss 0.023340 MSE 0.023340 +2024-02-02 16:35:43,457 - Epoch: [231][ 2/ 2] Loss 0.023995 MSE 0.023951 +2024-02-02 16:35:43,600 - ==> MSE: 0.02395 Loss: 0.024 + +2024-02-02 16:35:43,603 - ==> Best [Top 1 (MSE): 0.02381 Sparsity:0.00 Params: 136448 on epoch: 228] +2024-02-02 16:35:43,603 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:43,607 - + +2024-02-02 16:35:43,608 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:43,969 - Epoch: [232][ 1/ 8] Overall Loss 0.018275 Objective Loss 0.018275 MSE 0.018275 LR 0.001000 Time 0.361205 +2024-02-02 16:35:43,978 - Epoch: [232][ 2/ 8] Overall Loss 0.018259 Objective Loss 0.018259 MSE 0.018259 LR 0.001000 Time 0.184841 +2024-02-02 16:35:43,986 - Epoch: [232][ 3/ 8] Overall Loss 0.018075 Objective Loss 0.018075 MSE 0.018075 LR 0.001000 Time 0.125998 +2024-02-02 16:35:43,995 - Epoch: [232][ 4/ 8] Overall Loss 0.018248 Objective Loss 0.018248 MSE 0.018248 LR 0.001000 Time 0.096553 +2024-02-02 16:35:44,003 - Epoch: [232][ 5/ 8] Overall Loss 0.018466 Objective Loss 0.018466 MSE 0.018466 LR 0.001000 Time 0.078912 +2024-02-02 16:35:44,012 - Epoch: [232][ 6/ 8] Overall Loss 0.018874 Objective Loss 0.018874 MSE 0.018874 LR 0.001000 Time 0.067144 +2024-02-02 16:35:44,020 - Epoch: [232][ 7/ 8] Overall Loss 0.019086 Objective Loss 0.019086 MSE 0.019086 LR 0.001000 Time 0.058729 +2024-02-02 16:35:44,028 - Epoch: [232][ 8/ 8] Overall Loss 0.019852 Objective Loss 0.019852 MSE 0.019246 LR 0.001000 Time 0.052391 +2024-02-02 16:35:44,182 - --- validate (epoch=232)----------- +2024-02-02 16:35:44,182 - 60 samples (32 per mini-batch) +2024-02-02 16:35:44,544 - Epoch: [232][ 1/ 2] Loss 0.023604 MSE 0.023604 +2024-02-02 16:35:44,550 - Epoch: [232][ 2/ 2] Loss 0.023718 MSE 0.023710 +2024-02-02 16:35:44,695 - ==> MSE: 0.02371 Loss: 0.024 + +2024-02-02 16:35:44,697 - ==> Best [Top 1 (MSE): 0.02371 Sparsity:0.00 Params: 136448 on epoch: 232] +2024-02-02 16:35:44,697 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:44,702 - + +2024-02-02 16:35:44,703 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:45,051 - Epoch: [233][ 1/ 8] Overall Loss 0.018671 Objective Loss 0.018671 MSE 0.018671 LR 0.001000 Time 0.348331 +2024-02-02 16:35:45,060 - Epoch: [233][ 2/ 8] Overall Loss 0.019922 Objective Loss 0.019922 MSE 0.019922 LR 0.001000 Time 0.178453 +2024-02-02 16:35:45,069 - Epoch: [233][ 3/ 8] Overall Loss 0.019208 Objective Loss 0.019208 MSE 0.019208 LR 0.001000 Time 0.121763 +2024-02-02 16:35:45,077 - Epoch: [233][ 4/ 8] Overall Loss 0.019502 Objective Loss 0.019502 MSE 0.019502 LR 0.001000 Time 0.093413 +2024-02-02 16:35:45,086 - Epoch: [233][ 5/ 8] Overall Loss 0.019058 Objective Loss 0.019058 MSE 0.019058 LR 0.001000 Time 0.076400 +2024-02-02 16:35:45,094 - Epoch: [233][ 6/ 8] Overall Loss 0.019282 Objective Loss 0.019282 MSE 0.019282 LR 0.001000 Time 0.065090 +2024-02-02 16:35:45,103 - Epoch: [233][ 7/ 8] Overall Loss 0.019234 Objective Loss 0.019234 MSE 0.019234 LR 0.001000 Time 0.057022 +2024-02-02 16:35:45,112 - Epoch: [233][ 8/ 8] Overall Loss 0.019149 Objective Loss 0.019149 MSE 0.019217 LR 0.001000 Time 0.050939 +2024-02-02 16:35:45,261 - --- validate (epoch=233)----------- +2024-02-02 16:35:45,261 - 60 samples (32 per mini-batch) +2024-02-02 16:35:45,622 - Epoch: [233][ 1/ 2] Loss 0.022206 MSE 0.022206 +2024-02-02 16:35:45,629 - Epoch: [233][ 2/ 2] Loss 0.024098 MSE 0.023972 +2024-02-02 16:35:45,778 - ==> MSE: 0.02397 Loss: 0.024 + +2024-02-02 16:35:45,780 - ==> Best [Top 1 (MSE): 0.02371 Sparsity:0.00 Params: 136448 on epoch: 232] +2024-02-02 16:35:45,781 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:45,785 - + +2024-02-02 16:35:45,785 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:46,148 - Epoch: [234][ 1/ 8] Overall Loss 0.020042 Objective Loss 0.020042 MSE 0.020042 LR 0.001000 Time 0.362553 +2024-02-02 16:35:46,157 - Epoch: [234][ 2/ 8] Overall Loss 0.018956 Objective Loss 0.018956 MSE 0.018956 LR 0.001000 Time 0.185574 +2024-02-02 16:35:46,166 - Epoch: [234][ 3/ 8] Overall Loss 0.019478 Objective Loss 0.019478 MSE 0.019478 LR 0.001000 Time 0.126522 +2024-02-02 16:35:46,174 - Epoch: [234][ 4/ 8] Overall Loss 0.018893 Objective Loss 0.018893 MSE 0.018893 LR 0.001000 Time 0.096981 +2024-02-02 16:35:46,183 - Epoch: [234][ 5/ 8] Overall Loss 0.018415 Objective Loss 0.018415 MSE 0.018415 LR 0.001000 Time 0.079230 +2024-02-02 16:35:46,191 - Epoch: [234][ 6/ 8] Overall Loss 0.018743 Objective Loss 0.018743 MSE 0.018743 LR 0.001000 Time 0.067411 +2024-02-02 16:35:46,200 - Epoch: [234][ 7/ 8] Overall Loss 0.019051 Objective Loss 0.019051 MSE 0.019051 LR 0.001000 Time 0.058993 +2024-02-02 16:35:46,208 - Epoch: [234][ 8/ 8] Overall Loss 0.019263 Objective Loss 0.019263 MSE 0.019095 LR 0.001000 Time 0.052667 +2024-02-02 16:35:46,357 - --- validate (epoch=234)----------- +2024-02-02 16:35:46,357 - 60 samples (32 per mini-batch) +2024-02-02 16:35:46,715 - Epoch: [234][ 1/ 2] Loss 0.024005 MSE 0.024005 +2024-02-02 16:35:46,722 - Epoch: [234][ 2/ 2] Loss 0.023633 MSE 0.023658 +2024-02-02 16:35:46,862 - ==> MSE: 0.02366 Loss: 0.024 + +2024-02-02 16:35:46,866 - ==> Best [Top 1 (MSE): 0.02366 Sparsity:0.00 Params: 136448 on epoch: 234] +2024-02-02 16:35:46,866 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:46,874 - + +2024-02-02 16:35:46,874 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:47,198 - Epoch: [235][ 1/ 8] Overall Loss 0.022049 Objective Loss 0.022049 MSE 0.022049 LR 0.001000 Time 0.323771 +2024-02-02 16:35:47,207 - Epoch: [235][ 2/ 8] Overall Loss 0.019943 Objective Loss 0.019943 MSE 0.019943 LR 0.001000 Time 0.165904 +2024-02-02 16:35:47,215 - Epoch: [235][ 3/ 8] Overall Loss 0.019181 Objective Loss 0.019181 MSE 0.019181 LR 0.001000 Time 0.113186 +2024-02-02 16:35:47,225 - Epoch: [235][ 4/ 8] Overall Loss 0.018820 Objective Loss 0.018820 MSE 0.018820 LR 0.001000 Time 0.087490 +2024-02-02 16:35:47,233 - Epoch: [235][ 5/ 8] Overall Loss 0.018746 Objective Loss 0.018746 MSE 0.018746 LR 0.001000 Time 0.071514 +2024-02-02 16:35:47,241 - Epoch: [235][ 6/ 8] Overall Loss 0.018775 Objective Loss 0.018775 MSE 0.018775 LR 0.001000 Time 0.060917 +2024-02-02 16:35:47,249 - Epoch: [235][ 7/ 8] Overall Loss 0.019054 Objective Loss 0.019054 MSE 0.019054 LR 0.001000 Time 0.053336 +2024-02-02 16:35:47,257 - Epoch: [235][ 8/ 8] Overall Loss 0.018605 Objective Loss 0.018605 MSE 0.018961 LR 0.001000 Time 0.047661 +2024-02-02 16:35:47,392 - --- validate (epoch=235)----------- +2024-02-02 16:35:47,393 - 60 samples (32 per mini-batch) +2024-02-02 16:35:47,713 - Epoch: [235][ 1/ 2] Loss 0.024160 MSE 0.024160 +2024-02-02 16:35:47,719 - Epoch: [235][ 2/ 2] Loss 0.023805 MSE 0.023829 +2024-02-02 16:35:47,853 - ==> MSE: 0.02383 Loss: 0.024 + +2024-02-02 16:35:47,856 - ==> Best [Top 1 (MSE): 0.02366 Sparsity:0.00 Params: 136448 on epoch: 234] +2024-02-02 16:35:47,856 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:47,860 - + +2024-02-02 16:35:47,860 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:48,217 - Epoch: [236][ 1/ 8] Overall Loss 0.018912 Objective Loss 0.018912 MSE 0.018912 LR 0.001000 Time 0.356070 +2024-02-02 16:35:48,228 - Epoch: [236][ 2/ 8] Overall Loss 0.018754 Objective Loss 0.018754 MSE 0.018754 LR 0.001000 Time 0.183737 +2024-02-02 16:35:48,240 - Epoch: [236][ 3/ 8] Overall Loss 0.019581 Objective Loss 0.019581 MSE 0.019581 LR 0.001000 Time 0.126443 +2024-02-02 16:35:48,250 - Epoch: [236][ 4/ 8] Overall Loss 0.019362 Objective Loss 0.019362 MSE 0.019362 LR 0.001000 Time 0.097273 +2024-02-02 16:35:48,259 - Epoch: [236][ 5/ 8] Overall Loss 0.019098 Objective Loss 0.019098 MSE 0.019098 LR 0.001000 Time 0.079554 +2024-02-02 16:35:48,267 - Epoch: [236][ 6/ 8] Overall Loss 0.018951 Objective Loss 0.018951 MSE 0.018951 LR 0.001000 Time 0.067614 +2024-02-02 16:35:48,276 - Epoch: [236][ 7/ 8] Overall Loss 0.018848 Objective Loss 0.018848 MSE 0.018848 LR 0.001000 Time 0.059148 +2024-02-02 16:35:48,284 - Epoch: [236][ 8/ 8] Overall Loss 0.019277 Objective Loss 0.019277 MSE 0.018938 LR 0.001000 Time 0.052781 +2024-02-02 16:35:48,427 - --- validate (epoch=236)----------- +2024-02-02 16:35:48,427 - 60 samples (32 per mini-batch) +2024-02-02 16:35:48,788 - Epoch: [236][ 1/ 2] Loss 0.022065 MSE 0.022065 +2024-02-02 16:35:48,794 - Epoch: [236][ 2/ 2] Loss 0.023776 MSE 0.023662 +2024-02-02 16:35:48,944 - ==> MSE: 0.02366 Loss: 0.024 + +2024-02-02 16:35:48,948 - ==> Best [Top 1 (MSE): 0.02366 Sparsity:0.00 Params: 136448 on epoch: 234] +2024-02-02 16:35:48,948 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:48,956 - + +2024-02-02 16:35:48,957 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:49,329 - Epoch: [237][ 1/ 8] Overall Loss 0.020144 Objective Loss 0.020144 MSE 0.020144 LR 0.001000 Time 0.371434 +2024-02-02 16:35:49,337 - Epoch: [237][ 2/ 8] Overall Loss 0.019105 Objective Loss 0.019105 MSE 0.019105 LR 0.001000 Time 0.189945 +2024-02-02 16:35:49,346 - Epoch: [237][ 3/ 8] Overall Loss 0.018936 Objective Loss 0.018936 MSE 0.018936 LR 0.001000 Time 0.129392 +2024-02-02 16:35:49,354 - Epoch: [237][ 4/ 8] Overall Loss 0.018937 Objective Loss 0.018937 MSE 0.018937 LR 0.001000 Time 0.099104 +2024-02-02 16:35:49,363 - Epoch: [237][ 5/ 8] Overall Loss 0.018763 Objective Loss 0.018763 MSE 0.018763 LR 0.001000 Time 0.080989 +2024-02-02 16:35:49,527 - Epoch: [237][ 6/ 8] Overall Loss 0.019020 Objective Loss 0.019020 MSE 0.019020 LR 0.001000 Time 0.094878 +2024-02-02 16:35:49,536 - Epoch: [237][ 7/ 8] Overall Loss 0.019007 Objective Loss 0.019007 MSE 0.019007 LR 0.001000 Time 0.082591 +2024-02-02 16:35:49,545 - Epoch: [237][ 8/ 8] Overall Loss 0.018808 Objective Loss 0.018808 MSE 0.018966 LR 0.001000 Time 0.073334 +2024-02-02 16:35:49,698 - --- validate (epoch=237)----------- +2024-02-02 16:35:49,698 - 60 samples (32 per mini-batch) +2024-02-02 16:35:50,050 - Epoch: [237][ 1/ 2] Loss 0.024458 MSE 0.024458 +2024-02-02 16:35:50,059 - Epoch: [237][ 2/ 2] Loss 0.023635 MSE 0.023690 +2024-02-02 16:35:50,205 - ==> MSE: 0.02369 Loss: 0.024 + +2024-02-02 16:35:50,208 - ==> Best [Top 1 (MSE): 0.02366 Sparsity:0.00 Params: 136448 on epoch: 234] +2024-02-02 16:35:50,209 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:50,213 - + +2024-02-02 16:35:50,213 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:50,581 - Epoch: [238][ 1/ 8] Overall Loss 0.019503 Objective Loss 0.019503 MSE 0.019503 LR 0.001000 Time 0.367346 +2024-02-02 16:35:50,590 - Epoch: [238][ 2/ 8] Overall Loss 0.019197 Objective Loss 0.019197 MSE 0.019197 LR 0.001000 Time 0.187955 +2024-02-02 16:35:50,598 - Epoch: [238][ 3/ 8] Overall Loss 0.018800 Objective Loss 0.018800 MSE 0.018800 LR 0.001000 Time 0.128079 +2024-02-02 16:35:50,606 - Epoch: [238][ 4/ 8] Overall Loss 0.018897 Objective Loss 0.018897 MSE 0.018897 LR 0.001000 Time 0.098036 +2024-02-02 16:35:50,615 - Epoch: [238][ 5/ 8] Overall Loss 0.018939 Objective Loss 0.018939 MSE 0.018939 LR 0.001000 Time 0.080102 +2024-02-02 16:35:50,623 - Epoch: [238][ 6/ 8] Overall Loss 0.019122 Objective Loss 0.019122 MSE 0.019122 LR 0.001000 Time 0.068130 +2024-02-02 16:35:50,632 - Epoch: [238][ 7/ 8] Overall Loss 0.018821 Objective Loss 0.018821 MSE 0.018821 LR 0.001000 Time 0.059579 +2024-02-02 16:35:50,640 - Epoch: [238][ 8/ 8] Overall Loss 0.019366 Objective Loss 0.019366 MSE 0.018934 LR 0.001000 Time 0.053159 +2024-02-02 16:35:50,788 - --- validate (epoch=238)----------- +2024-02-02 16:35:50,789 - 60 samples (32 per mini-batch) +2024-02-02 16:35:51,152 - Epoch: [238][ 1/ 2] Loss 0.023703 MSE 0.023703 +2024-02-02 16:35:51,158 - Epoch: [238][ 2/ 2] Loss 0.023560 MSE 0.023570 +2024-02-02 16:35:51,304 - ==> MSE: 0.02357 Loss: 0.024 + +2024-02-02 16:35:51,308 - ==> Best [Top 1 (MSE): 0.02357 Sparsity:0.00 Params: 136448 on epoch: 238] +2024-02-02 16:35:51,308 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:51,313 - + +2024-02-02 16:35:51,313 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:51,676 - Epoch: [239][ 1/ 8] Overall Loss 0.019764 Objective Loss 0.019764 MSE 0.019764 LR 0.001000 Time 0.362441 +2024-02-02 16:35:51,685 - Epoch: [239][ 2/ 8] Overall Loss 0.019708 Objective Loss 0.019708 MSE 0.019708 LR 0.001000 Time 0.185492 +2024-02-02 16:35:51,694 - Epoch: [239][ 3/ 8] Overall Loss 0.018781 Objective Loss 0.018781 MSE 0.018781 LR 0.001000 Time 0.126459 +2024-02-02 16:35:51,702 - Epoch: [239][ 4/ 8] Overall Loss 0.018946 Objective Loss 0.018946 MSE 0.018946 LR 0.001000 Time 0.096961 +2024-02-02 16:35:51,710 - Epoch: [239][ 5/ 8] Overall Loss 0.018871 Objective Loss 0.018871 MSE 0.018871 LR 0.001000 Time 0.079138 +2024-02-02 16:35:51,719 - Epoch: [239][ 6/ 8] Overall Loss 0.018617 Objective Loss 0.018617 MSE 0.018617 LR 0.001000 Time 0.067325 +2024-02-02 16:35:51,727 - Epoch: [239][ 7/ 8] Overall Loss 0.018750 Objective Loss 0.018750 MSE 0.018750 LR 0.001000 Time 0.058878 +2024-02-02 16:35:51,735 - Epoch: [239][ 8/ 8] Overall Loss 0.019357 Objective Loss 0.019357 MSE 0.018876 LR 0.001000 Time 0.052532 +2024-02-02 16:35:51,885 - --- validate (epoch=239)----------- +2024-02-02 16:35:51,886 - 60 samples (32 per mini-batch) +2024-02-02 16:35:52,241 - Epoch: [239][ 1/ 2] Loss 0.022834 MSE 0.022834 +2024-02-02 16:35:52,248 - Epoch: [239][ 2/ 2] Loss 0.023605 MSE 0.023553 +2024-02-02 16:35:52,394 - ==> MSE: 0.02355 Loss: 0.024 + +2024-02-02 16:35:52,396 - ==> Best [Top 1 (MSE): 0.02355 Sparsity:0.00 Params: 136448 on epoch: 239] +2024-02-02 16:35:52,397 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:52,402 - + +2024-02-02 16:35:52,402 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:52,762 - Epoch: [240][ 1/ 8] Overall Loss 0.018522 Objective Loss 0.018522 MSE 0.018522 LR 0.001000 Time 0.359494 +2024-02-02 16:35:52,773 - Epoch: [240][ 2/ 8] Overall Loss 0.019219 Objective Loss 0.019219 MSE 0.019219 LR 0.001000 Time 0.184914 +2024-02-02 16:35:52,781 - Epoch: [240][ 3/ 8] Overall Loss 0.019283 Objective Loss 0.019283 MSE 0.019283 LR 0.001000 Time 0.125991 +2024-02-02 16:35:52,789 - Epoch: [240][ 4/ 8] Overall Loss 0.019461 Objective Loss 0.019461 MSE 0.019461 LR 0.001000 Time 0.096536 +2024-02-02 16:35:52,798 - Epoch: [240][ 5/ 8] Overall Loss 0.019184 Objective Loss 0.019184 MSE 0.019184 LR 0.001000 Time 0.078900 +2024-02-02 16:35:52,807 - Epoch: [240][ 6/ 8] Overall Loss 0.019042 Objective Loss 0.019042 MSE 0.019042 LR 0.001000 Time 0.067143 +2024-02-02 16:35:52,815 - Epoch: [240][ 7/ 8] Overall Loss 0.018897 Objective Loss 0.018897 MSE 0.018897 LR 0.001000 Time 0.058734 +2024-02-02 16:35:52,823 - Epoch: [240][ 8/ 8] Overall Loss 0.018628 Objective Loss 0.018628 MSE 0.018841 LR 0.001000 Time 0.052416 +2024-02-02 16:35:52,967 - --- validate (epoch=240)----------- +2024-02-02 16:35:52,967 - 60 samples (32 per mini-batch) +2024-02-02 16:35:53,303 - Epoch: [240][ 1/ 2] Loss 0.022526 MSE 0.022526 +2024-02-02 16:35:53,309 - Epoch: [240][ 2/ 2] Loss 0.023814 MSE 0.023729 +2024-02-02 16:35:53,454 - ==> MSE: 0.02373 Loss: 0.024 + +2024-02-02 16:35:53,457 - ==> Best [Top 1 (MSE): 0.02355 Sparsity:0.00 Params: 136448 on epoch: 239] +2024-02-02 16:35:53,457 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:53,461 - + +2024-02-02 16:35:53,461 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:53,816 - Epoch: [241][ 1/ 8] Overall Loss 0.021279 Objective Loss 0.021279 MSE 0.021279 LR 0.001000 Time 0.354133 +2024-02-02 16:35:53,825 - Epoch: [241][ 2/ 8] Overall Loss 0.018653 Objective Loss 0.018653 MSE 0.018653 LR 0.001000 Time 0.181384 +2024-02-02 16:35:53,833 - Epoch: [241][ 3/ 8] Overall Loss 0.018374 Objective Loss 0.018374 MSE 0.018374 LR 0.001000 Time 0.123706 +2024-02-02 16:35:53,841 - Epoch: [241][ 4/ 8] Overall Loss 0.018661 Objective Loss 0.018661 MSE 0.018661 LR 0.001000 Time 0.094843 +2024-02-02 16:35:53,850 - Epoch: [241][ 5/ 8] Overall Loss 0.018609 Objective Loss 0.018609 MSE 0.018609 LR 0.001000 Time 0.077529 +2024-02-02 16:35:53,858 - Epoch: [241][ 6/ 8] Overall Loss 0.019001 Objective Loss 0.019001 MSE 0.019001 LR 0.001000 Time 0.065997 +2024-02-02 16:35:53,866 - Epoch: [241][ 7/ 8] Overall Loss 0.018838 Objective Loss 0.018838 MSE 0.018838 LR 0.001000 Time 0.057708 +2024-02-02 16:35:53,875 - Epoch: [241][ 8/ 8] Overall Loss 0.018774 Objective Loss 0.018774 MSE 0.018825 LR 0.001000 Time 0.051521 +2024-02-02 16:35:54,023 - --- validate (epoch=241)----------- +2024-02-02 16:35:54,023 - 60 samples (32 per mini-batch) +2024-02-02 16:35:54,377 - Epoch: [241][ 1/ 2] Loss 0.022563 MSE 0.022563 +2024-02-02 16:35:54,383 - Epoch: [241][ 2/ 2] Loss 0.023509 MSE 0.023446 +2024-02-02 16:35:54,529 - ==> MSE: 0.02345 Loss: 0.024 + +2024-02-02 16:35:54,532 - ==> Best [Top 1 (MSE): 0.02345 Sparsity:0.00 Params: 136448 on epoch: 241] +2024-02-02 16:35:54,533 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:54,538 - + +2024-02-02 16:35:54,538 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:54,896 - Epoch: [242][ 1/ 8] Overall Loss 0.018705 Objective Loss 0.018705 MSE 0.018705 LR 0.001000 Time 0.357610 +2024-02-02 16:35:54,905 - Epoch: [242][ 2/ 8] Overall Loss 0.017598 Objective Loss 0.017598 MSE 0.017598 LR 0.001000 Time 0.182963 +2024-02-02 16:35:54,913 - Epoch: [242][ 3/ 8] Overall Loss 0.018766 Objective Loss 0.018766 MSE 0.018766 LR 0.001000 Time 0.124685 +2024-02-02 16:35:54,921 - Epoch: [242][ 4/ 8] Overall Loss 0.018833 Objective Loss 0.018833 MSE 0.018833 LR 0.001000 Time 0.095564 +2024-02-02 16:35:54,930 - Epoch: [242][ 5/ 8] Overall Loss 0.018919 Objective Loss 0.018919 MSE 0.018919 LR 0.001000 Time 0.078060 +2024-02-02 16:35:54,938 - Epoch: [242][ 6/ 8] Overall Loss 0.018717 Objective Loss 0.018717 MSE 0.018717 LR 0.001000 Time 0.066432 +2024-02-02 16:35:54,946 - Epoch: [242][ 7/ 8] Overall Loss 0.018837 Objective Loss 0.018837 MSE 0.018837 LR 0.001000 Time 0.058127 +2024-02-02 16:35:54,955 - Epoch: [242][ 8/ 8] Overall Loss 0.018685 Objective Loss 0.018685 MSE 0.018805 LR 0.001000 Time 0.051883 +2024-02-02 16:35:55,107 - --- validate (epoch=242)----------- +2024-02-02 16:35:55,107 - 60 samples (32 per mini-batch) +2024-02-02 16:35:55,474 - Epoch: [242][ 1/ 2] Loss 0.023459 MSE 0.023459 +2024-02-02 16:35:55,480 - Epoch: [242][ 2/ 2] Loss 0.023731 MSE 0.023713 +2024-02-02 16:35:55,629 - ==> MSE: 0.02371 Loss: 0.024 + +2024-02-02 16:35:55,632 - ==> Best [Top 1 (MSE): 0.02345 Sparsity:0.00 Params: 136448 on epoch: 241] +2024-02-02 16:35:55,632 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:55,637 - + +2024-02-02 16:35:55,637 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:56,006 - Epoch: [243][ 1/ 8] Overall Loss 0.017792 Objective Loss 0.017792 MSE 0.017792 LR 0.001000 Time 0.369084 +2024-02-02 16:35:56,018 - Epoch: [243][ 2/ 8] Overall Loss 0.017547 Objective Loss 0.017547 MSE 0.017547 LR 0.001000 Time 0.190280 +2024-02-02 16:35:56,027 - Epoch: [243][ 3/ 8] Overall Loss 0.018176 Objective Loss 0.018176 MSE 0.018176 LR 0.001000 Time 0.129700 +2024-02-02 16:35:56,035 - Epoch: [243][ 4/ 8] Overall Loss 0.018369 Objective Loss 0.018369 MSE 0.018369 LR 0.001000 Time 0.099378 +2024-02-02 16:35:56,044 - Epoch: [243][ 5/ 8] Overall Loss 0.018396 Objective Loss 0.018396 MSE 0.018396 LR 0.001000 Time 0.081172 +2024-02-02 16:35:56,053 - Epoch: [243][ 6/ 8] Overall Loss 0.018712 Objective Loss 0.018712 MSE 0.018712 LR 0.001000 Time 0.069189 +2024-02-02 16:35:56,062 - Epoch: [243][ 7/ 8] Overall Loss 0.018939 Objective Loss 0.018939 MSE 0.018939 LR 0.001000 Time 0.060597 +2024-02-02 16:35:56,071 - Epoch: [243][ 8/ 8] Overall Loss 0.018426 Objective Loss 0.018426 MSE 0.018832 LR 0.001000 Time 0.054060 +2024-02-02 16:35:56,221 - --- validate (epoch=243)----------- +2024-02-02 16:35:56,222 - 60 samples (32 per mini-batch) +2024-02-02 16:35:56,569 - Epoch: [243][ 1/ 2] Loss 0.022969 MSE 0.022969 +2024-02-02 16:35:56,578 - Epoch: [243][ 2/ 2] Loss 0.023548 MSE 0.023509 +2024-02-02 16:35:56,724 - ==> MSE: 0.02351 Loss: 0.024 + +2024-02-02 16:35:56,728 - ==> Best [Top 1 (MSE): 0.02345 Sparsity:0.00 Params: 136448 on epoch: 241] +2024-02-02 16:35:56,728 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:56,732 - + +2024-02-02 16:35:56,733 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:57,094 - Epoch: [244][ 1/ 8] Overall Loss 0.018911 Objective Loss 0.018911 MSE 0.018911 LR 0.001000 Time 0.360737 +2024-02-02 16:35:57,102 - Epoch: [244][ 2/ 8] Overall Loss 0.019224 Objective Loss 0.019224 MSE 0.019224 LR 0.001000 Time 0.184516 +2024-02-02 16:35:57,111 - Epoch: [244][ 3/ 8] Overall Loss 0.019130 Objective Loss 0.019130 MSE 0.019130 LR 0.001000 Time 0.125755 +2024-02-02 16:35:57,119 - Epoch: [244][ 4/ 8] Overall Loss 0.018505 Objective Loss 0.018505 MSE 0.018505 LR 0.001000 Time 0.096374 +2024-02-02 16:35:57,127 - Epoch: [244][ 5/ 8] Overall Loss 0.018128 Objective Loss 0.018128 MSE 0.018128 LR 0.001000 Time 0.078748 +2024-02-02 16:35:57,136 - Epoch: [244][ 6/ 8] Overall Loss 0.018326 Objective Loss 0.018326 MSE 0.018326 LR 0.001000 Time 0.067034 +2024-02-02 16:35:57,145 - Epoch: [244][ 7/ 8] Overall Loss 0.018722 Objective Loss 0.018722 MSE 0.018722 LR 0.001000 Time 0.058769 +2024-02-02 16:35:57,157 - Epoch: [244][ 8/ 8] Overall Loss 0.018760 Objective Loss 0.018760 MSE 0.018730 LR 0.001000 Time 0.052893 +2024-02-02 16:35:57,309 - --- validate (epoch=244)----------- +2024-02-02 16:35:57,309 - 60 samples (32 per mini-batch) +2024-02-02 16:35:57,658 - Epoch: [244][ 1/ 2] Loss 0.023144 MSE 0.023144 +2024-02-02 16:35:57,665 - Epoch: [244][ 2/ 2] Loss 0.023640 MSE 0.023607 +2024-02-02 16:35:57,811 - ==> MSE: 0.02361 Loss: 0.024 + +2024-02-02 16:35:57,813 - ==> Best [Top 1 (MSE): 0.02345 Sparsity:0.00 Params: 136448 on epoch: 241] +2024-02-02 16:35:57,813 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:57,818 - + +2024-02-02 16:35:57,818 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:58,171 - Epoch: [245][ 1/ 8] Overall Loss 0.019905 Objective Loss 0.019905 MSE 0.019905 LR 0.001000 Time 0.352881 +2024-02-02 16:35:58,182 - Epoch: [245][ 2/ 8] Overall Loss 0.018937 Objective Loss 0.018937 MSE 0.018937 LR 0.001000 Time 0.181725 +2024-02-02 16:35:58,192 - Epoch: [245][ 3/ 8] Overall Loss 0.019431 Objective Loss 0.019431 MSE 0.019431 LR 0.001000 Time 0.124355 +2024-02-02 16:35:58,200 - Epoch: [245][ 4/ 8] Overall Loss 0.019038 Objective Loss 0.019038 MSE 0.019038 LR 0.001000 Time 0.095385 +2024-02-02 16:35:58,209 - Epoch: [245][ 5/ 8] Overall Loss 0.018840 Objective Loss 0.018840 MSE 0.018840 LR 0.001000 Time 0.077974 +2024-02-02 16:35:58,219 - Epoch: [245][ 6/ 8] Overall Loss 0.018992 Objective Loss 0.018992 MSE 0.018992 LR 0.001000 Time 0.066577 +2024-02-02 16:35:58,231 - Epoch: [245][ 7/ 8] Overall Loss 0.018715 Objective Loss 0.018715 MSE 0.018715 LR 0.001000 Time 0.058748 +2024-02-02 16:35:58,241 - Epoch: [245][ 8/ 8] Overall Loss 0.018734 Objective Loss 0.018734 MSE 0.018719 LR 0.001000 Time 0.052682 +2024-02-02 16:35:58,389 - --- validate (epoch=245)----------- +2024-02-02 16:35:58,389 - 60 samples (32 per mini-batch) +2024-02-02 16:35:58,741 - Epoch: [245][ 1/ 2] Loss 0.022041 MSE 0.022041 +2024-02-02 16:35:58,747 - Epoch: [245][ 2/ 2] Loss 0.023852 MSE 0.023732 +2024-02-02 16:35:58,894 - ==> MSE: 0.02373 Loss: 0.024 + +2024-02-02 16:35:58,899 - ==> Best [Top 1 (MSE): 0.02345 Sparsity:0.00 Params: 136448 on epoch: 241] +2024-02-02 16:35:58,899 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:35:58,903 - + +2024-02-02 16:35:58,903 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:35:59,270 - Epoch: [246][ 1/ 8] Overall Loss 0.018147 Objective Loss 0.018147 MSE 0.018147 LR 0.001000 Time 0.366091 +2024-02-02 16:35:59,279 - Epoch: [246][ 2/ 8] Overall Loss 0.019259 Objective Loss 0.019259 MSE 0.019259 LR 0.001000 Time 0.187349 +2024-02-02 16:35:59,287 - Epoch: [246][ 3/ 8] Overall Loss 0.018868 Objective Loss 0.018868 MSE 0.018868 LR 0.001000 Time 0.127687 +2024-02-02 16:35:59,295 - Epoch: [246][ 4/ 8] Overall Loss 0.019328 Objective Loss 0.019328 MSE 0.019328 LR 0.001000 Time 0.097740 +2024-02-02 16:35:59,305 - Epoch: [246][ 5/ 8] Overall Loss 0.019280 Objective Loss 0.019280 MSE 0.019280 LR 0.001000 Time 0.080113 +2024-02-02 16:35:59,318 - Epoch: [246][ 6/ 8] Overall Loss 0.018885 Objective Loss 0.018885 MSE 0.018885 LR 0.001000 Time 0.068799 +2024-02-02 16:35:59,327 - Epoch: [246][ 7/ 8] Overall Loss 0.018662 Objective Loss 0.018662 MSE 0.018662 LR 0.001000 Time 0.060298 +2024-02-02 16:35:59,336 - Epoch: [246][ 8/ 8] Overall Loss 0.019002 Objective Loss 0.019002 MSE 0.018733 LR 0.001000 Time 0.053817 +2024-02-02 16:35:59,485 - --- validate (epoch=246)----------- +2024-02-02 16:35:59,486 - 60 samples (32 per mini-batch) +2024-02-02 16:35:59,844 - Epoch: [246][ 1/ 2] Loss 0.022707 MSE 0.022707 +2024-02-02 16:35:59,850 - Epoch: [246][ 2/ 2] Loss 0.023455 MSE 0.023405 +2024-02-02 16:36:00,001 - ==> MSE: 0.02341 Loss: 0.023 + +2024-02-02 16:36:00,004 - ==> Best [Top 1 (MSE): 0.02341 Sparsity:0.00 Params: 136448 on epoch: 246] +2024-02-02 16:36:00,004 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:00,010 - + +2024-02-02 16:36:00,010 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:00,374 - Epoch: [247][ 1/ 8] Overall Loss 0.019589 Objective Loss 0.019589 MSE 0.019589 LR 0.001000 Time 0.363419 +2024-02-02 16:36:00,383 - Epoch: [247][ 2/ 8] Overall Loss 0.019467 Objective Loss 0.019467 MSE 0.019467 LR 0.001000 Time 0.185948 +2024-02-02 16:36:00,391 - Epoch: [247][ 3/ 8] Overall Loss 0.019056 Objective Loss 0.019056 MSE 0.019056 LR 0.001000 Time 0.126702 +2024-02-02 16:36:00,400 - Epoch: [247][ 4/ 8] Overall Loss 0.018899 Objective Loss 0.018899 MSE 0.018899 LR 0.001000 Time 0.097082 +2024-02-02 16:36:00,408 - Epoch: [247][ 5/ 8] Overall Loss 0.018651 Objective Loss 0.018651 MSE 0.018651 LR 0.001000 Time 0.079352 +2024-02-02 16:36:00,417 - Epoch: [247][ 6/ 8] Overall Loss 0.018619 Objective Loss 0.018619 MSE 0.018619 LR 0.001000 Time 0.067541 +2024-02-02 16:36:00,425 - Epoch: [247][ 7/ 8] Overall Loss 0.018618 Objective Loss 0.018618 MSE 0.018618 LR 0.001000 Time 0.059071 +2024-02-02 16:36:00,434 - Epoch: [247][ 8/ 8] Overall Loss 0.018744 Objective Loss 0.018744 MSE 0.018645 LR 0.001000 Time 0.052728 +2024-02-02 16:36:00,577 - --- validate (epoch=247)----------- +2024-02-02 16:36:00,577 - 60 samples (32 per mini-batch) +2024-02-02 16:36:00,935 - Epoch: [247][ 1/ 2] Loss 0.023907 MSE 0.023907 +2024-02-02 16:36:00,942 - Epoch: [247][ 2/ 2] Loss 0.023477 MSE 0.023506 +2024-02-02 16:36:01,089 - ==> MSE: 0.02351 Loss: 0.023 + +2024-02-02 16:36:01,092 - ==> Best [Top 1 (MSE): 0.02341 Sparsity:0.00 Params: 136448 on epoch: 246] +2024-02-02 16:36:01,092 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:01,097 - + +2024-02-02 16:36:01,097 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:01,447 - Epoch: [248][ 1/ 8] Overall Loss 0.017954 Objective Loss 0.017954 MSE 0.017954 LR 0.001000 Time 0.350049 +2024-02-02 16:36:01,456 - Epoch: [248][ 2/ 8] Overall Loss 0.018050 Objective Loss 0.018050 MSE 0.018050 LR 0.001000 Time 0.179315 +2024-02-02 16:36:01,465 - Epoch: [248][ 3/ 8] Overall Loss 0.018580 Objective Loss 0.018580 MSE 0.018580 LR 0.001000 Time 0.122377 +2024-02-02 16:36:01,474 - Epoch: [248][ 4/ 8] Overall Loss 0.018526 Objective Loss 0.018526 MSE 0.018526 LR 0.001000 Time 0.093929 +2024-02-02 16:36:01,482 - Epoch: [248][ 5/ 8] Overall Loss 0.018292 Objective Loss 0.018292 MSE 0.018292 LR 0.001000 Time 0.076865 +2024-02-02 16:36:01,491 - Epoch: [248][ 6/ 8] Overall Loss 0.018644 Objective Loss 0.018644 MSE 0.018644 LR 0.001000 Time 0.065490 +2024-02-02 16:36:01,500 - Epoch: [248][ 7/ 8] Overall Loss 0.018829 Objective Loss 0.018829 MSE 0.018829 LR 0.001000 Time 0.057366 +2024-02-02 16:36:01,509 - Epoch: [248][ 8/ 8] Overall Loss 0.017800 Objective Loss 0.017800 MSE 0.018615 LR 0.001000 Time 0.051264 +2024-02-02 16:36:01,657 - --- validate (epoch=248)----------- +2024-02-02 16:36:01,658 - 60 samples (32 per mini-batch) +2024-02-02 16:36:02,017 - Epoch: [248][ 1/ 2] Loss 0.023062 MSE 0.023062 +2024-02-02 16:36:02,023 - Epoch: [248][ 2/ 2] Loss 0.023515 MSE 0.023485 +2024-02-02 16:36:02,168 - ==> MSE: 0.02348 Loss: 0.024 + +2024-02-02 16:36:02,172 - ==> Best [Top 1 (MSE): 0.02341 Sparsity:0.00 Params: 136448 on epoch: 246] +2024-02-02 16:36:02,172 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:02,176 - + +2024-02-02 16:36:02,177 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:02,542 - Epoch: [249][ 1/ 8] Overall Loss 0.017260 Objective Loss 0.017260 MSE 0.017260 LR 0.001000 Time 0.365226 +2024-02-02 16:36:02,551 - Epoch: [249][ 2/ 8] Overall Loss 0.018025 Objective Loss 0.018025 MSE 0.018025 LR 0.001000 Time 0.186900 +2024-02-02 16:36:02,559 - Epoch: [249][ 3/ 8] Overall Loss 0.017424 Objective Loss 0.017424 MSE 0.017424 LR 0.001000 Time 0.127343 +2024-02-02 16:36:02,568 - Epoch: [249][ 4/ 8] Overall Loss 0.018306 Objective Loss 0.018306 MSE 0.018306 LR 0.001000 Time 0.097573 +2024-02-02 16:36:02,576 - Epoch: [249][ 5/ 8] Overall Loss 0.018833 Objective Loss 0.018833 MSE 0.018833 LR 0.001000 Time 0.079756 +2024-02-02 16:36:02,585 - Epoch: [249][ 6/ 8] Overall Loss 0.018252 Objective Loss 0.018252 MSE 0.018252 LR 0.001000 Time 0.067840 +2024-02-02 16:36:02,593 - Epoch: [249][ 7/ 8] Overall Loss 0.018591 Objective Loss 0.018591 MSE 0.018591 LR 0.001000 Time 0.059340 +2024-02-02 16:36:02,602 - Epoch: [249][ 8/ 8] Overall Loss 0.018389 Objective Loss 0.018389 MSE 0.018549 LR 0.001000 Time 0.052964 +2024-02-02 16:36:02,753 - --- validate (epoch=249)----------- +2024-02-02 16:36:02,753 - 60 samples (32 per mini-batch) +2024-02-02 16:36:03,118 - Epoch: [249][ 1/ 2] Loss 0.024397 MSE 0.024397 +2024-02-02 16:36:03,124 - Epoch: [249][ 2/ 2] Loss 0.023210 MSE 0.023289 +2024-02-02 16:36:03,273 - ==> MSE: 0.02329 Loss: 0.023 + +2024-02-02 16:36:03,276 - ==> Best [Top 1 (MSE): 0.02329 Sparsity:0.00 Params: 136448 on epoch: 249] +2024-02-02 16:36:03,276 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:03,282 - + +2024-02-02 16:36:03,282 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:03,648 - Epoch: [250][ 1/ 8] Overall Loss 0.018829 Objective Loss 0.018829 MSE 0.018829 LR 0.001000 Time 0.365683 +2024-02-02 16:36:03,660 - Epoch: [250][ 2/ 8] Overall Loss 0.018672 Objective Loss 0.018672 MSE 0.018672 LR 0.001000 Time 0.188829 +2024-02-02 16:36:03,672 - Epoch: [250][ 3/ 8] Overall Loss 0.018300 Objective Loss 0.018300 MSE 0.018300 LR 0.001000 Time 0.129657 +2024-02-02 16:36:03,682 - Epoch: [250][ 4/ 8] Overall Loss 0.017917 Objective Loss 0.017917 MSE 0.017917 LR 0.001000 Time 0.099733 +2024-02-02 16:36:03,691 - Epoch: [250][ 5/ 8] Overall Loss 0.018751 Objective Loss 0.018751 MSE 0.018751 LR 0.001000 Time 0.081510 +2024-02-02 16:36:03,700 - Epoch: [250][ 6/ 8] Overall Loss 0.018622 Objective Loss 0.018622 MSE 0.018622 LR 0.001000 Time 0.069336 +2024-02-02 16:36:03,708 - Epoch: [250][ 7/ 8] Overall Loss 0.018514 Objective Loss 0.018514 MSE 0.018514 LR 0.001000 Time 0.060642 +2024-02-02 16:36:03,717 - Epoch: [250][ 8/ 8] Overall Loss 0.018482 Objective Loss 0.018482 MSE 0.018507 LR 0.001000 Time 0.054114 +2024-02-02 16:36:03,865 - --- validate (epoch=250)----------- +2024-02-02 16:36:03,865 - 60 samples (32 per mini-batch) +2024-02-02 16:36:04,224 - Epoch: [250][ 1/ 2] Loss 0.023391 MSE 0.023391 +2024-02-02 16:36:04,230 - Epoch: [250][ 2/ 2] Loss 0.023277 MSE 0.023284 +2024-02-02 16:36:04,371 - ==> MSE: 0.02328 Loss: 0.023 + +2024-02-02 16:36:04,374 - ==> Best [Top 1 (MSE): 0.02328 Sparsity:0.00 Params: 136448 on epoch: 250] +2024-02-02 16:36:04,374 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:04,379 - + +2024-02-02 16:36:04,379 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:04,740 - Epoch: [251][ 1/ 8] Overall Loss 0.018764 Objective Loss 0.018764 MSE 0.018764 LR 0.001000 Time 0.360492 +2024-02-02 16:36:04,749 - Epoch: [251][ 2/ 8] Overall Loss 0.018645 Objective Loss 0.018645 MSE 0.018645 LR 0.001000 Time 0.184406 +2024-02-02 16:36:04,757 - Epoch: [251][ 3/ 8] Overall Loss 0.018208 Objective Loss 0.018208 MSE 0.018208 LR 0.001000 Time 0.125642 +2024-02-02 16:36:04,766 - Epoch: [251][ 4/ 8] Overall Loss 0.018287 Objective Loss 0.018287 MSE 0.018287 LR 0.001000 Time 0.096322 +2024-02-02 16:36:04,774 - Epoch: [251][ 5/ 8] Overall Loss 0.018111 Objective Loss 0.018111 MSE 0.018111 LR 0.001000 Time 0.078711 +2024-02-02 16:36:04,782 - Epoch: [251][ 6/ 8] Overall Loss 0.018284 Objective Loss 0.018284 MSE 0.018284 LR 0.001000 Time 0.066984 +2024-02-02 16:36:04,791 - Epoch: [251][ 7/ 8] Overall Loss 0.018461 Objective Loss 0.018461 MSE 0.018461 LR 0.001000 Time 0.058614 +2024-02-02 16:36:04,799 - Epoch: [251][ 8/ 8] Overall Loss 0.018651 Objective Loss 0.018651 MSE 0.018501 LR 0.001000 Time 0.052322 +2024-02-02 16:36:04,941 - --- validate (epoch=251)----------- +2024-02-02 16:36:04,942 - 60 samples (32 per mini-batch) +2024-02-02 16:36:05,281 - Epoch: [251][ 1/ 2] Loss 0.023982 MSE 0.023982 +2024-02-02 16:36:05,288 - Epoch: [251][ 2/ 2] Loss 0.023353 MSE 0.023395 +2024-02-02 16:36:05,434 - ==> MSE: 0.02340 Loss: 0.023 + +2024-02-02 16:36:05,437 - ==> Best [Top 1 (MSE): 0.02328 Sparsity:0.00 Params: 136448 on epoch: 250] +2024-02-02 16:36:05,437 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:05,442 - + +2024-02-02 16:36:05,442 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:05,808 - Epoch: [252][ 1/ 8] Overall Loss 0.017731 Objective Loss 0.017731 MSE 0.017731 LR 0.001000 Time 0.366170 +2024-02-02 16:36:05,817 - Epoch: [252][ 2/ 8] Overall Loss 0.017839 Objective Loss 0.017839 MSE 0.017839 LR 0.001000 Time 0.187358 +2024-02-02 16:36:05,826 - Epoch: [252][ 3/ 8] Overall Loss 0.018548 Objective Loss 0.018548 MSE 0.018548 LR 0.001000 Time 0.127721 +2024-02-02 16:36:05,834 - Epoch: [252][ 4/ 8] Overall Loss 0.018430 Objective Loss 0.018430 MSE 0.018430 LR 0.001000 Time 0.097775 +2024-02-02 16:36:05,842 - Epoch: [252][ 5/ 8] Overall Loss 0.018420 Objective Loss 0.018420 MSE 0.018420 LR 0.001000 Time 0.079861 +2024-02-02 16:36:05,851 - Epoch: [252][ 6/ 8] Overall Loss 0.018343 Objective Loss 0.018343 MSE 0.018343 LR 0.001000 Time 0.067939 +2024-02-02 16:36:05,859 - Epoch: [252][ 7/ 8] Overall Loss 0.018566 Objective Loss 0.018566 MSE 0.018566 LR 0.001000 Time 0.059426 +2024-02-02 16:36:05,868 - Epoch: [252][ 8/ 8] Overall Loss 0.018177 Objective Loss 0.018177 MSE 0.018484 LR 0.001000 Time 0.053035 +2024-02-02 16:36:06,010 - --- validate (epoch=252)----------- +2024-02-02 16:36:06,010 - 60 samples (32 per mini-batch) +2024-02-02 16:36:06,360 - Epoch: [252][ 1/ 2] Loss 0.022531 MSE 0.022531 +2024-02-02 16:36:06,366 - Epoch: [252][ 2/ 2] Loss 0.023247 MSE 0.023200 +2024-02-02 16:36:06,515 - ==> MSE: 0.02320 Loss: 0.023 + +2024-02-02 16:36:06,518 - ==> Best [Top 1 (MSE): 0.02320 Sparsity:0.00 Params: 136448 on epoch: 252] +2024-02-02 16:36:06,518 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:06,524 - + +2024-02-02 16:36:06,524 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:06,886 - Epoch: [253][ 1/ 8] Overall Loss 0.018119 Objective Loss 0.018119 MSE 0.018119 LR 0.001000 Time 0.361554 +2024-02-02 16:36:06,895 - Epoch: [253][ 2/ 8] Overall Loss 0.017520 Objective Loss 0.017520 MSE 0.017520 LR 0.001000 Time 0.185105 +2024-02-02 16:36:06,903 - Epoch: [253][ 3/ 8] Overall Loss 0.018086 Objective Loss 0.018086 MSE 0.018086 LR 0.001000 Time 0.126235 +2024-02-02 16:36:06,912 - Epoch: [253][ 4/ 8] Overall Loss 0.018385 Objective Loss 0.018385 MSE 0.018385 LR 0.001000 Time 0.096716 +2024-02-02 16:36:06,920 - Epoch: [253][ 5/ 8] Overall Loss 0.018361 Objective Loss 0.018361 MSE 0.018361 LR 0.001000 Time 0.079033 +2024-02-02 16:36:06,929 - Epoch: [253][ 6/ 8] Overall Loss 0.018476 Objective Loss 0.018476 MSE 0.018476 LR 0.001000 Time 0.067262 +2024-02-02 16:36:06,937 - Epoch: [253][ 7/ 8] Overall Loss 0.018424 Objective Loss 0.018424 MSE 0.018424 LR 0.001000 Time 0.058783 +2024-02-02 16:36:06,945 - Epoch: [253][ 8/ 8] Overall Loss 0.018610 Objective Loss 0.018610 MSE 0.018463 LR 0.001000 Time 0.052457 +2024-02-02 16:36:07,097 - --- validate (epoch=253)----------- +2024-02-02 16:36:07,097 - 60 samples (32 per mini-batch) +2024-02-02 16:36:07,460 - Epoch: [253][ 1/ 2] Loss 0.023144 MSE 0.023144 +2024-02-02 16:36:07,466 - Epoch: [253][ 2/ 2] Loss 0.023371 MSE 0.023356 +2024-02-02 16:36:07,613 - ==> MSE: 0.02336 Loss: 0.023 + +2024-02-02 16:36:07,616 - ==> Best [Top 1 (MSE): 0.02320 Sparsity:0.00 Params: 136448 on epoch: 252] +2024-02-02 16:36:07,617 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:07,621 - + +2024-02-02 16:36:07,621 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:07,986 - Epoch: [254][ 1/ 8] Overall Loss 0.017391 Objective Loss 0.017391 MSE 0.017391 LR 0.001000 Time 0.364356 +2024-02-02 16:36:07,994 - Epoch: [254][ 2/ 8] Overall Loss 0.017010 Objective Loss 0.017010 MSE 0.017010 LR 0.001000 Time 0.186375 +2024-02-02 16:36:08,003 - Epoch: [254][ 3/ 8] Overall Loss 0.017519 Objective Loss 0.017519 MSE 0.017519 LR 0.001000 Time 0.127021 +2024-02-02 16:36:08,011 - Epoch: [254][ 4/ 8] Overall Loss 0.018006 Objective Loss 0.018006 MSE 0.018006 LR 0.001000 Time 0.097327 +2024-02-02 16:36:08,020 - Epoch: [254][ 5/ 8] Overall Loss 0.018314 Objective Loss 0.018314 MSE 0.018314 LR 0.001000 Time 0.079519 +2024-02-02 16:36:08,028 - Epoch: [254][ 6/ 8] Overall Loss 0.018629 Objective Loss 0.018629 MSE 0.018629 LR 0.001000 Time 0.067669 +2024-02-02 16:36:08,037 - Epoch: [254][ 7/ 8] Overall Loss 0.018462 Objective Loss 0.018462 MSE 0.018462 LR 0.001000 Time 0.059196 +2024-02-02 16:36:08,045 - Epoch: [254][ 8/ 8] Overall Loss 0.018195 Objective Loss 0.018195 MSE 0.018406 LR 0.001000 Time 0.052838 +2024-02-02 16:36:08,197 - --- validate (epoch=254)----------- +2024-02-02 16:36:08,198 - 60 samples (32 per mini-batch) +2024-02-02 16:36:08,555 - Epoch: [254][ 1/ 2] Loss 0.023721 MSE 0.023721 +2024-02-02 16:36:08,561 - Epoch: [254][ 2/ 2] Loss 0.023124 MSE 0.023164 +2024-02-02 16:36:08,707 - ==> MSE: 0.02316 Loss: 0.023 + +2024-02-02 16:36:08,710 - ==> Best [Top 1 (MSE): 0.02316 Sparsity:0.00 Params: 136448 on epoch: 254] +2024-02-02 16:36:08,710 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:08,715 - + +2024-02-02 16:36:08,716 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:09,062 - Epoch: [255][ 1/ 8] Overall Loss 0.019543 Objective Loss 0.019543 MSE 0.019543 LR 0.001000 Time 0.346216 +2024-02-02 16:36:09,071 - Epoch: [255][ 2/ 8] Overall Loss 0.019215 Objective Loss 0.019215 MSE 0.019215 LR 0.001000 Time 0.177393 +2024-02-02 16:36:09,079 - Epoch: [255][ 3/ 8] Overall Loss 0.018686 Objective Loss 0.018686 MSE 0.018686 LR 0.001000 Time 0.121027 +2024-02-02 16:36:09,088 - Epoch: [255][ 4/ 8] Overall Loss 0.018427 Objective Loss 0.018427 MSE 0.018427 LR 0.001000 Time 0.092827 +2024-02-02 16:36:09,096 - Epoch: [255][ 5/ 8] Overall Loss 0.018624 Objective Loss 0.018624 MSE 0.018624 LR 0.001000 Time 0.075919 +2024-02-02 16:36:09,105 - Epoch: [255][ 6/ 8] Overall Loss 0.018710 Objective Loss 0.018710 MSE 0.018710 LR 0.001000 Time 0.064651 +2024-02-02 16:36:09,113 - Epoch: [255][ 7/ 8] Overall Loss 0.018550 Objective Loss 0.018550 MSE 0.018550 LR 0.001000 Time 0.056634 +2024-02-02 16:36:09,122 - Epoch: [255][ 8/ 8] Overall Loss 0.017839 Objective Loss 0.017839 MSE 0.018402 LR 0.001000 Time 0.050613 +2024-02-02 16:36:09,264 - --- validate (epoch=255)----------- +2024-02-02 16:36:09,264 - 60 samples (32 per mini-batch) +2024-02-02 16:36:09,620 - Epoch: [255][ 1/ 2] Loss 0.023503 MSE 0.023503 +2024-02-02 16:36:09,626 - Epoch: [255][ 2/ 2] Loss 0.023194 MSE 0.023214 +2024-02-02 16:36:09,769 - ==> MSE: 0.02321 Loss: 0.023 + +2024-02-02 16:36:09,773 - ==> Best [Top 1 (MSE): 0.02316 Sparsity:0.00 Params: 136448 on epoch: 254] +2024-02-02 16:36:09,773 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:09,777 - + +2024-02-02 16:36:09,777 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:10,140 - Epoch: [256][ 1/ 8] Overall Loss 0.018249 Objective Loss 0.018249 MSE 0.018249 LR 0.001000 Time 0.362128 +2024-02-02 16:36:10,149 - Epoch: [256][ 2/ 8] Overall Loss 0.018255 Objective Loss 0.018255 MSE 0.018255 LR 0.001000 Time 0.185337 +2024-02-02 16:36:10,157 - Epoch: [256][ 3/ 8] Overall Loss 0.018269 Objective Loss 0.018269 MSE 0.018269 LR 0.001000 Time 0.126347 +2024-02-02 16:36:10,166 - Epoch: [256][ 4/ 8] Overall Loss 0.018527 Objective Loss 0.018527 MSE 0.018527 LR 0.001000 Time 0.096832 +2024-02-02 16:36:10,174 - Epoch: [256][ 5/ 8] Overall Loss 0.018449 Objective Loss 0.018449 MSE 0.018449 LR 0.001000 Time 0.079151 +2024-02-02 16:36:10,183 - Epoch: [256][ 6/ 8] Overall Loss 0.018462 Objective Loss 0.018462 MSE 0.018462 LR 0.001000 Time 0.067347 +2024-02-02 16:36:10,191 - Epoch: [256][ 7/ 8] Overall Loss 0.018393 Objective Loss 0.018393 MSE 0.018393 LR 0.001000 Time 0.058920 +2024-02-02 16:36:10,200 - Epoch: [256][ 8/ 8] Overall Loss 0.018368 Objective Loss 0.018368 MSE 0.018388 LR 0.001000 Time 0.052577 +2024-02-02 16:36:10,348 - --- validate (epoch=256)----------- +2024-02-02 16:36:10,348 - 60 samples (32 per mini-batch) +2024-02-02 16:36:10,707 - Epoch: [256][ 1/ 2] Loss 0.022638 MSE 0.022638 +2024-02-02 16:36:10,713 - Epoch: [256][ 2/ 2] Loss 0.023269 MSE 0.023227 +2024-02-02 16:36:10,859 - ==> MSE: 0.02323 Loss: 0.023 + +2024-02-02 16:36:10,862 - ==> Best [Top 1 (MSE): 0.02316 Sparsity:0.00 Params: 136448 on epoch: 254] +2024-02-02 16:36:10,862 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:10,867 - + +2024-02-02 16:36:10,867 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:11,236 - Epoch: [257][ 1/ 8] Overall Loss 0.019587 Objective Loss 0.019587 MSE 0.019587 LR 0.001000 Time 0.368791 +2024-02-02 16:36:11,248 - Epoch: [257][ 2/ 8] Overall Loss 0.019354 Objective Loss 0.019354 MSE 0.019354 LR 0.001000 Time 0.190177 +2024-02-02 16:36:11,257 - Epoch: [257][ 3/ 8] Overall Loss 0.018345 Objective Loss 0.018345 MSE 0.018345 LR 0.001000 Time 0.129908 +2024-02-02 16:36:11,266 - Epoch: [257][ 4/ 8] Overall Loss 0.018229 Objective Loss 0.018229 MSE 0.018229 LR 0.001000 Time 0.099541 +2024-02-02 16:36:11,275 - Epoch: [257][ 5/ 8] Overall Loss 0.017790 Objective Loss 0.017790 MSE 0.017790 LR 0.001000 Time 0.081328 +2024-02-02 16:36:11,283 - Epoch: [257][ 6/ 8] Overall Loss 0.018064 Objective Loss 0.018064 MSE 0.018064 LR 0.001000 Time 0.069165 +2024-02-02 16:36:11,292 - Epoch: [257][ 7/ 8] Overall Loss 0.018216 Objective Loss 0.018216 MSE 0.018216 LR 0.001000 Time 0.060480 +2024-02-02 16:36:11,300 - Epoch: [257][ 8/ 8] Overall Loss 0.018719 Objective Loss 0.018719 MSE 0.018321 LR 0.001000 Time 0.053963 +2024-02-02 16:36:11,449 - --- validate (epoch=257)----------- +2024-02-02 16:36:11,449 - 60 samples (32 per mini-batch) +2024-02-02 16:36:11,805 - Epoch: [257][ 1/ 2] Loss 0.022922 MSE 0.022922 +2024-02-02 16:36:11,812 - Epoch: [257][ 2/ 2] Loss 0.023260 MSE 0.023238 +2024-02-02 16:36:11,953 - ==> MSE: 0.02324 Loss: 0.023 + +2024-02-02 16:36:11,957 - ==> Best [Top 1 (MSE): 0.02316 Sparsity:0.00 Params: 136448 on epoch: 254] +2024-02-02 16:36:11,957 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:11,961 - + +2024-02-02 16:36:11,962 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:12,329 - Epoch: [258][ 1/ 8] Overall Loss 0.018801 Objective Loss 0.018801 MSE 0.018801 LR 0.001000 Time 0.366862 +2024-02-02 16:36:12,341 - Epoch: [258][ 2/ 8] Overall Loss 0.018979 Objective Loss 0.018979 MSE 0.018979 LR 0.001000 Time 0.189276 +2024-02-02 16:36:12,350 - Epoch: [258][ 3/ 8] Overall Loss 0.018650 Objective Loss 0.018650 MSE 0.018650 LR 0.001000 Time 0.129154 +2024-02-02 16:36:12,359 - Epoch: [258][ 4/ 8] Overall Loss 0.018340 Objective Loss 0.018340 MSE 0.018340 LR 0.001000 Time 0.098982 +2024-02-02 16:36:12,367 - Epoch: [258][ 5/ 8] Overall Loss 0.018294 Objective Loss 0.018294 MSE 0.018294 LR 0.001000 Time 0.080857 +2024-02-02 16:36:12,376 - Epoch: [258][ 6/ 8] Overall Loss 0.018139 Objective Loss 0.018139 MSE 0.018139 LR 0.001000 Time 0.068777 +2024-02-02 16:36:12,384 - Epoch: [258][ 7/ 8] Overall Loss 0.018233 Objective Loss 0.018233 MSE 0.018233 LR 0.001000 Time 0.060154 +2024-02-02 16:36:12,393 - Epoch: [258][ 8/ 8] Overall Loss 0.018497 Objective Loss 0.018497 MSE 0.018289 LR 0.001000 Time 0.053668 +2024-02-02 16:36:12,540 - --- validate (epoch=258)----------- +2024-02-02 16:36:12,540 - 60 samples (32 per mini-batch) +2024-02-02 16:36:12,900 - Epoch: [258][ 1/ 2] Loss 0.024389 MSE 0.024389 +2024-02-02 16:36:12,906 - Epoch: [258][ 2/ 2] Loss 0.023208 MSE 0.023287 +2024-02-02 16:36:13,054 - ==> MSE: 0.02329 Loss: 0.023 + +2024-02-02 16:36:13,058 - ==> Best [Top 1 (MSE): 0.02316 Sparsity:0.00 Params: 136448 on epoch: 254] +2024-02-02 16:36:13,058 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:13,062 - + +2024-02-02 16:36:13,062 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:13,429 - Epoch: [259][ 1/ 8] Overall Loss 0.018965 Objective Loss 0.018965 MSE 0.018965 LR 0.001000 Time 0.366177 +2024-02-02 16:36:13,438 - Epoch: [259][ 2/ 8] Overall Loss 0.019553 Objective Loss 0.019553 MSE 0.019553 LR 0.001000 Time 0.187247 +2024-02-02 16:36:13,446 - Epoch: [259][ 3/ 8] Overall Loss 0.019669 Objective Loss 0.019669 MSE 0.019669 LR 0.001000 Time 0.127622 +2024-02-02 16:36:13,455 - Epoch: [259][ 4/ 8] Overall Loss 0.019478 Objective Loss 0.019478 MSE 0.019478 LR 0.001000 Time 0.097803 +2024-02-02 16:36:13,463 - Epoch: [259][ 5/ 8] Overall Loss 0.018840 Objective Loss 0.018840 MSE 0.018840 LR 0.001000 Time 0.079909 +2024-02-02 16:36:13,472 - Epoch: [259][ 6/ 8] Overall Loss 0.018567 Objective Loss 0.018567 MSE 0.018567 LR 0.001000 Time 0.068005 +2024-02-02 16:36:13,480 - Epoch: [259][ 7/ 8] Overall Loss 0.018407 Objective Loss 0.018407 MSE 0.018407 LR 0.001000 Time 0.059471 +2024-02-02 16:36:13,489 - Epoch: [259][ 8/ 8] Overall Loss 0.017865 Objective Loss 0.017865 MSE 0.018294 LR 0.001000 Time 0.053073 +2024-02-02 16:36:13,632 - --- validate (epoch=259)----------- +2024-02-02 16:36:13,632 - 60 samples (32 per mini-batch) +2024-02-02 16:36:13,993 - Epoch: [259][ 1/ 2] Loss 0.023402 MSE 0.023402 +2024-02-02 16:36:13,999 - Epoch: [259][ 2/ 2] Loss 0.023349 MSE 0.023352 +2024-02-02 16:36:14,145 - ==> MSE: 0.02335 Loss: 0.023 + +2024-02-02 16:36:14,149 - ==> Best [Top 1 (MSE): 0.02316 Sparsity:0.00 Params: 136448 on epoch: 254] +2024-02-02 16:36:14,149 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:14,154 - + +2024-02-02 16:36:14,154 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:14,519 - Epoch: [260][ 1/ 8] Overall Loss 0.018601 Objective Loss 0.018601 MSE 0.018601 LR 0.001000 Time 0.364577 +2024-02-02 16:36:14,530 - Epoch: [260][ 2/ 8] Overall Loss 0.019062 Objective Loss 0.019062 MSE 0.019062 LR 0.001000 Time 0.187967 +2024-02-02 16:36:14,541 - Epoch: [260][ 3/ 8] Overall Loss 0.019207 Objective Loss 0.019207 MSE 0.019207 LR 0.001000 Time 0.128729 +2024-02-02 16:36:14,549 - Epoch: [260][ 4/ 8] Overall Loss 0.018617 Objective Loss 0.018617 MSE 0.018617 LR 0.001000 Time 0.098588 +2024-02-02 16:36:14,558 - Epoch: [260][ 5/ 8] Overall Loss 0.018951 Objective Loss 0.018951 MSE 0.018951 LR 0.001000 Time 0.080502 +2024-02-02 16:36:14,566 - Epoch: [260][ 6/ 8] Overall Loss 0.018602 Objective Loss 0.018602 MSE 0.018602 LR 0.001000 Time 0.068455 +2024-02-02 16:36:14,574 - Epoch: [260][ 7/ 8] Overall Loss 0.018416 Objective Loss 0.018416 MSE 0.018416 LR 0.001000 Time 0.059863 +2024-02-02 16:36:14,583 - Epoch: [260][ 8/ 8] Overall Loss 0.018063 Objective Loss 0.018063 MSE 0.018343 LR 0.001000 Time 0.053404 +2024-02-02 16:36:14,730 - --- validate (epoch=260)----------- +2024-02-02 16:36:14,730 - 60 samples (32 per mini-batch) +2024-02-02 16:36:15,084 - Epoch: [260][ 1/ 2] Loss 0.023766 MSE 0.023766 +2024-02-02 16:36:15,092 - Epoch: [260][ 2/ 2] Loss 0.023275 MSE 0.023308 +2024-02-02 16:36:15,242 - ==> MSE: 0.02331 Loss: 0.023 + +2024-02-02 16:36:15,245 - ==> Best [Top 1 (MSE): 0.02316 Sparsity:0.00 Params: 136448 on epoch: 254] +2024-02-02 16:36:15,245 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:15,250 - + +2024-02-02 16:36:15,250 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:15,615 - Epoch: [261][ 1/ 8] Overall Loss 0.017597 Objective Loss 0.017597 MSE 0.017597 LR 0.001000 Time 0.365206 +2024-02-02 16:36:15,624 - Epoch: [261][ 2/ 8] Overall Loss 0.018845 Objective Loss 0.018845 MSE 0.018845 LR 0.001000 Time 0.186885 +2024-02-02 16:36:15,633 - Epoch: [261][ 3/ 8] Overall Loss 0.018621 Objective Loss 0.018621 MSE 0.018621 LR 0.001000 Time 0.127364 +2024-02-02 16:36:15,641 - Epoch: [261][ 4/ 8] Overall Loss 0.018245 Objective Loss 0.018245 MSE 0.018245 LR 0.001000 Time 0.097600 +2024-02-02 16:36:15,650 - Epoch: [261][ 5/ 8] Overall Loss 0.018553 Objective Loss 0.018553 MSE 0.018553 LR 0.001000 Time 0.079774 +2024-02-02 16:36:15,658 - Epoch: [261][ 6/ 8] Overall Loss 0.018012 Objective Loss 0.018012 MSE 0.018012 LR 0.001000 Time 0.067870 +2024-02-02 16:36:15,667 - Epoch: [261][ 7/ 8] Overall Loss 0.018221 Objective Loss 0.018221 MSE 0.018221 LR 0.001000 Time 0.059376 +2024-02-02 16:36:15,675 - Epoch: [261][ 8/ 8] Overall Loss 0.018541 Objective Loss 0.018541 MSE 0.018288 LR 0.001000 Time 0.052984 +2024-02-02 16:36:15,835 - --- validate (epoch=261)----------- +2024-02-02 16:36:15,835 - 60 samples (32 per mini-batch) +2024-02-02 16:36:16,194 - Epoch: [261][ 1/ 2] Loss 0.021826 MSE 0.021826 +2024-02-02 16:36:16,200 - Epoch: [261][ 2/ 2] Loss 0.023223 MSE 0.023130 +2024-02-02 16:36:16,347 - ==> MSE: 0.02313 Loss: 0.023 + +2024-02-02 16:36:16,350 - ==> Best [Top 1 (MSE): 0.02313 Sparsity:0.00 Params: 136448 on epoch: 261] +2024-02-02 16:36:16,350 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:16,355 - + +2024-02-02 16:36:16,356 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:16,717 - Epoch: [262][ 1/ 8] Overall Loss 0.018612 Objective Loss 0.018612 MSE 0.018612 LR 0.001000 Time 0.360832 +2024-02-02 16:36:16,726 - Epoch: [262][ 2/ 8] Overall Loss 0.018079 Objective Loss 0.018079 MSE 0.018079 LR 0.001000 Time 0.185029 +2024-02-02 16:36:16,735 - Epoch: [262][ 3/ 8] Overall Loss 0.018032 Objective Loss 0.018032 MSE 0.018032 LR 0.001000 Time 0.125997 +2024-02-02 16:36:16,743 - Epoch: [262][ 4/ 8] Overall Loss 0.018390 Objective Loss 0.018390 MSE 0.018390 LR 0.001000 Time 0.096548 +2024-02-02 16:36:16,751 - Epoch: [262][ 5/ 8] Overall Loss 0.018342 Objective Loss 0.018342 MSE 0.018342 LR 0.001000 Time 0.078872 +2024-02-02 16:36:16,760 - Epoch: [262][ 6/ 8] Overall Loss 0.018010 Objective Loss 0.018010 MSE 0.018010 LR 0.001000 Time 0.067132 +2024-02-02 16:36:16,768 - Epoch: [262][ 7/ 8] Overall Loss 0.018265 Objective Loss 0.018265 MSE 0.018265 LR 0.001000 Time 0.058750 +2024-02-02 16:36:16,777 - Epoch: [262][ 8/ 8] Overall Loss 0.018098 Objective Loss 0.018098 MSE 0.018230 LR 0.001000 Time 0.052447 +2024-02-02 16:36:16,924 - --- validate (epoch=262)----------- +2024-02-02 16:36:16,924 - 60 samples (32 per mini-batch) +2024-02-02 16:36:17,283 - Epoch: [262][ 1/ 2] Loss 0.023491 MSE 0.023491 +2024-02-02 16:36:17,293 - Epoch: [262][ 2/ 2] Loss 0.023248 MSE 0.023265 +2024-02-02 16:36:17,441 - ==> MSE: 0.02326 Loss: 0.023 + +2024-02-02 16:36:17,445 - ==> Best [Top 1 (MSE): 0.02313 Sparsity:0.00 Params: 136448 on epoch: 261] +2024-02-02 16:36:17,445 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:17,449 - + +2024-02-02 16:36:17,449 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:17,810 - Epoch: [263][ 1/ 8] Overall Loss 0.017786 Objective Loss 0.017786 MSE 0.017786 LR 0.001000 Time 0.360653 +2024-02-02 16:36:17,819 - Epoch: [263][ 2/ 8] Overall Loss 0.018354 Objective Loss 0.018354 MSE 0.018354 LR 0.001000 Time 0.184636 +2024-02-02 16:36:17,828 - Epoch: [263][ 3/ 8] Overall Loss 0.018308 Objective Loss 0.018308 MSE 0.018308 LR 0.001000 Time 0.125902 +2024-02-02 16:36:17,837 - Epoch: [263][ 4/ 8] Overall Loss 0.018612 Objective Loss 0.018612 MSE 0.018612 LR 0.001000 Time 0.096547 +2024-02-02 16:36:17,845 - Epoch: [263][ 5/ 8] Overall Loss 0.018306 Objective Loss 0.018306 MSE 0.018306 LR 0.001000 Time 0.078894 +2024-02-02 16:36:17,853 - Epoch: [263][ 6/ 8] Overall Loss 0.018438 Objective Loss 0.018438 MSE 0.018438 LR 0.001000 Time 0.067120 +2024-02-02 16:36:17,862 - Epoch: [263][ 7/ 8] Overall Loss 0.018407 Objective Loss 0.018407 MSE 0.018407 LR 0.001000 Time 0.058720 +2024-02-02 16:36:17,870 - Epoch: [263][ 8/ 8] Overall Loss 0.018510 Objective Loss 0.018510 MSE 0.018429 LR 0.001000 Time 0.052421 +2024-02-02 16:36:18,027 - --- validate (epoch=263)----------- +2024-02-02 16:36:18,027 - 60 samples (32 per mini-batch) +2024-02-02 16:36:18,374 - Epoch: [263][ 1/ 2] Loss 0.025112 MSE 0.025112 +2024-02-02 16:36:18,380 - Epoch: [263][ 2/ 2] Loss 0.023001 MSE 0.023142 +2024-02-02 16:36:18,529 - ==> MSE: 0.02314 Loss: 0.023 + +2024-02-02 16:36:18,533 - ==> Best [Top 1 (MSE): 0.02313 Sparsity:0.00 Params: 136448 on epoch: 261] +2024-02-02 16:36:18,533 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:18,537 - + +2024-02-02 16:36:18,537 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:18,898 - Epoch: [264][ 1/ 8] Overall Loss 0.020568 Objective Loss 0.020568 MSE 0.020568 LR 0.001000 Time 0.360348 +2024-02-02 16:36:18,907 - Epoch: [264][ 2/ 8] Overall Loss 0.020269 Objective Loss 0.020269 MSE 0.020269 LR 0.001000 Time 0.184454 +2024-02-02 16:36:18,915 - Epoch: [264][ 3/ 8] Overall Loss 0.019418 Objective Loss 0.019418 MSE 0.019418 LR 0.001000 Time 0.125756 +2024-02-02 16:36:18,924 - Epoch: [264][ 4/ 8] Overall Loss 0.019091 Objective Loss 0.019091 MSE 0.019091 LR 0.001000 Time 0.096411 +2024-02-02 16:36:18,932 - Epoch: [264][ 5/ 8] Overall Loss 0.018799 Objective Loss 0.018799 MSE 0.018799 LR 0.001000 Time 0.078819 +2024-02-02 16:36:18,941 - Epoch: [264][ 6/ 8] Overall Loss 0.018710 Objective Loss 0.018710 MSE 0.018710 LR 0.001000 Time 0.067094 +2024-02-02 16:36:18,951 - Epoch: [264][ 7/ 8] Overall Loss 0.018493 Objective Loss 0.018493 MSE 0.018493 LR 0.001000 Time 0.058931 +2024-02-02 16:36:18,964 - Epoch: [264][ 8/ 8] Overall Loss 0.017488 Objective Loss 0.017488 MSE 0.018283 LR 0.001000 Time 0.053082 +2024-02-02 16:36:19,115 - --- validate (epoch=264)----------- +2024-02-02 16:36:19,116 - 60 samples (32 per mini-batch) +2024-02-02 16:36:19,474 - Epoch: [264][ 1/ 2] Loss 0.023892 MSE 0.023892 +2024-02-02 16:36:19,480 - Epoch: [264][ 2/ 2] Loss 0.023251 MSE 0.023294 +2024-02-02 16:36:19,629 - ==> MSE: 0.02329 Loss: 0.023 + +2024-02-02 16:36:19,632 - ==> Best [Top 1 (MSE): 0.02313 Sparsity:0.00 Params: 136448 on epoch: 261] +2024-02-02 16:36:19,633 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:19,637 - + +2024-02-02 16:36:19,637 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:20,002 - Epoch: [265][ 1/ 8] Overall Loss 0.017927 Objective Loss 0.017927 MSE 0.017927 LR 0.001000 Time 0.364380 +2024-02-02 16:36:20,014 - Epoch: [265][ 2/ 8] Overall Loss 0.017739 Objective Loss 0.017739 MSE 0.017739 LR 0.001000 Time 0.188009 +2024-02-02 16:36:20,022 - Epoch: [265][ 3/ 8] Overall Loss 0.018165 Objective Loss 0.018165 MSE 0.018165 LR 0.001000 Time 0.128101 +2024-02-02 16:36:20,031 - Epoch: [265][ 4/ 8] Overall Loss 0.017747 Objective Loss 0.017747 MSE 0.017747 LR 0.001000 Time 0.098128 +2024-02-02 16:36:20,039 - Epoch: [265][ 5/ 8] Overall Loss 0.018068 Objective Loss 0.018068 MSE 0.018068 LR 0.001000 Time 0.080152 +2024-02-02 16:36:20,047 - Epoch: [265][ 6/ 8] Overall Loss 0.018154 Objective Loss 0.018154 MSE 0.018154 LR 0.001000 Time 0.068165 +2024-02-02 16:36:20,056 - Epoch: [265][ 7/ 8] Overall Loss 0.018242 Objective Loss 0.018242 MSE 0.018242 LR 0.001000 Time 0.059573 +2024-02-02 16:36:20,064 - Epoch: [265][ 8/ 8] Overall Loss 0.018540 Objective Loss 0.018540 MSE 0.018304 LR 0.001000 Time 0.053159 +2024-02-02 16:36:20,210 - --- validate (epoch=265)----------- +2024-02-02 16:36:20,211 - 60 samples (32 per mini-batch) +2024-02-02 16:36:20,560 - Epoch: [265][ 1/ 2] Loss 0.022322 MSE 0.022322 +2024-02-02 16:36:20,571 - Epoch: [265][ 2/ 2] Loss 0.023138 MSE 0.023084 +2024-02-02 16:36:20,722 - ==> MSE: 0.02308 Loss: 0.023 + +2024-02-02 16:36:20,725 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:20,726 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:20,731 - + +2024-02-02 16:36:20,731 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:21,096 - Epoch: [266][ 1/ 8] Overall Loss 0.016563 Objective Loss 0.016563 MSE 0.016563 LR 0.001000 Time 0.364815 +2024-02-02 16:36:21,108 - Epoch: [266][ 2/ 8] Overall Loss 0.017083 Objective Loss 0.017083 MSE 0.017083 LR 0.001000 Time 0.187927 +2024-02-02 16:36:21,118 - Epoch: [266][ 3/ 8] Overall Loss 0.017607 Objective Loss 0.017607 MSE 0.017607 LR 0.001000 Time 0.128548 +2024-02-02 16:36:21,126 - Epoch: [266][ 4/ 8] Overall Loss 0.017981 Objective Loss 0.017981 MSE 0.017981 LR 0.001000 Time 0.098495 +2024-02-02 16:36:21,135 - Epoch: [266][ 5/ 8] Overall Loss 0.018494 Objective Loss 0.018494 MSE 0.018494 LR 0.001000 Time 0.080463 +2024-02-02 16:36:21,143 - Epoch: [266][ 6/ 8] Overall Loss 0.018253 Objective Loss 0.018253 MSE 0.018253 LR 0.001000 Time 0.068421 +2024-02-02 16:36:21,152 - Epoch: [266][ 7/ 8] Overall Loss 0.018300 Objective Loss 0.018300 MSE 0.018300 LR 0.001000 Time 0.059816 +2024-02-02 16:36:21,160 - Epoch: [266][ 8/ 8] Overall Loss 0.017799 Objective Loss 0.017799 MSE 0.018195 LR 0.001000 Time 0.053359 +2024-02-02 16:36:21,309 - --- validate (epoch=266)----------- +2024-02-02 16:36:21,309 - 60 samples (32 per mini-batch) +2024-02-02 16:36:21,671 - Epoch: [266][ 1/ 2] Loss 0.022636 MSE 0.022636 +2024-02-02 16:36:21,676 - Epoch: [266][ 2/ 2] Loss 0.023126 MSE 0.023093 +2024-02-02 16:36:21,821 - ==> MSE: 0.02309 Loss: 0.023 + +2024-02-02 16:36:21,825 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:21,825 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:21,829 - + +2024-02-02 16:36:21,830 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:22,192 - Epoch: [267][ 1/ 8] Overall Loss 0.015997 Objective Loss 0.015997 MSE 0.015997 LR 0.001000 Time 0.362105 +2024-02-02 16:36:22,201 - Epoch: [267][ 2/ 8] Overall Loss 0.017287 Objective Loss 0.017287 MSE 0.017287 LR 0.001000 Time 0.185395 +2024-02-02 16:36:22,209 - Epoch: [267][ 3/ 8] Overall Loss 0.018112 Objective Loss 0.018112 MSE 0.018112 LR 0.001000 Time 0.126347 +2024-02-02 16:36:22,218 - Epoch: [267][ 4/ 8] Overall Loss 0.017938 Objective Loss 0.017938 MSE 0.017938 LR 0.001000 Time 0.096839 +2024-02-02 16:36:22,226 - Epoch: [267][ 5/ 8] Overall Loss 0.018339 Objective Loss 0.018339 MSE 0.018339 LR 0.001000 Time 0.079140 +2024-02-02 16:36:22,235 - Epoch: [267][ 6/ 8] Overall Loss 0.018175 Objective Loss 0.018175 MSE 0.018175 LR 0.001000 Time 0.067328 +2024-02-02 16:36:22,243 - Epoch: [267][ 7/ 8] Overall Loss 0.018236 Objective Loss 0.018236 MSE 0.018236 LR 0.001000 Time 0.058897 +2024-02-02 16:36:22,252 - Epoch: [267][ 8/ 8] Overall Loss 0.018601 Objective Loss 0.018601 MSE 0.018312 LR 0.001000 Time 0.052539 +2024-02-02 16:36:22,401 - --- validate (epoch=267)----------- +2024-02-02 16:36:22,401 - 60 samples (32 per mini-batch) +2024-02-02 16:36:22,754 - Epoch: [267][ 1/ 2] Loss 0.024795 MSE 0.024795 +2024-02-02 16:36:22,761 - Epoch: [267][ 2/ 2] Loss 0.023717 MSE 0.023789 +2024-02-02 16:36:22,905 - ==> MSE: 0.02379 Loss: 0.024 + +2024-02-02 16:36:22,909 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:22,909 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:22,914 - + +2024-02-02 16:36:22,914 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:23,265 - Epoch: [268][ 1/ 8] Overall Loss 0.018756 Objective Loss 0.018756 MSE 0.018756 LR 0.001000 Time 0.350971 +2024-02-02 16:36:23,274 - Epoch: [268][ 2/ 8] Overall Loss 0.018282 Objective Loss 0.018282 MSE 0.018282 LR 0.001000 Time 0.179878 +2024-02-02 16:36:23,283 - Epoch: [268][ 3/ 8] Overall Loss 0.019340 Objective Loss 0.019340 MSE 0.019340 LR 0.001000 Time 0.122671 +2024-02-02 16:36:23,291 - Epoch: [268][ 4/ 8] Overall Loss 0.018937 Objective Loss 0.018937 MSE 0.018937 LR 0.001000 Time 0.094041 +2024-02-02 16:36:23,299 - Epoch: [268][ 5/ 8] Overall Loss 0.018776 Objective Loss 0.018776 MSE 0.018776 LR 0.001000 Time 0.076882 +2024-02-02 16:36:23,308 - Epoch: [268][ 6/ 8] Overall Loss 0.018707 Objective Loss 0.018707 MSE 0.018707 LR 0.001000 Time 0.065434 +2024-02-02 16:36:23,316 - Epoch: [268][ 7/ 8] Overall Loss 0.018591 Objective Loss 0.018591 MSE 0.018591 LR 0.001000 Time 0.057270 +2024-02-02 16:36:23,324 - Epoch: [268][ 8/ 8] Overall Loss 0.018446 Objective Loss 0.018446 MSE 0.018561 LR 0.001000 Time 0.051134 +2024-02-02 16:36:23,468 - --- validate (epoch=268)----------- +2024-02-02 16:36:23,469 - 60 samples (32 per mini-batch) +2024-02-02 16:36:23,827 - Epoch: [268][ 1/ 2] Loss 0.023523 MSE 0.023523 +2024-02-02 16:36:23,833 - Epoch: [268][ 2/ 2] Loss 0.023359 MSE 0.023369 +2024-02-02 16:36:23,979 - ==> MSE: 0.02337 Loss: 0.023 + +2024-02-02 16:36:23,986 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:23,986 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:23,994 - + +2024-02-02 16:36:23,994 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:24,364 - Epoch: [269][ 1/ 8] Overall Loss 0.019214 Objective Loss 0.019214 MSE 0.019214 LR 0.001000 Time 0.369040 +2024-02-02 16:36:24,374 - Epoch: [269][ 2/ 8] Overall Loss 0.019804 Objective Loss 0.019804 MSE 0.019804 LR 0.001000 Time 0.189341 +2024-02-02 16:36:24,382 - Epoch: [269][ 3/ 8] Overall Loss 0.019102 Objective Loss 0.019102 MSE 0.019102 LR 0.001000 Time 0.129007 +2024-02-02 16:36:24,391 - Epoch: [269][ 4/ 8] Overall Loss 0.019185 Objective Loss 0.019185 MSE 0.019185 LR 0.001000 Time 0.098898 +2024-02-02 16:36:24,400 - Epoch: [269][ 5/ 8] Overall Loss 0.019174 Objective Loss 0.019174 MSE 0.019174 LR 0.001000 Time 0.080775 +2024-02-02 16:36:24,408 - Epoch: [269][ 6/ 8] Overall Loss 0.018982 Objective Loss 0.018982 MSE 0.018982 LR 0.001000 Time 0.068708 +2024-02-02 16:36:24,417 - Epoch: [269][ 7/ 8] Overall Loss 0.018825 Objective Loss 0.018825 MSE 0.018825 LR 0.001000 Time 0.060086 +2024-02-02 16:36:24,425 - Epoch: [269][ 8/ 8] Overall Loss 0.018265 Objective Loss 0.018265 MSE 0.018708 LR 0.001000 Time 0.053604 +2024-02-02 16:36:24,579 - --- validate (epoch=269)----------- +2024-02-02 16:36:24,580 - 60 samples (32 per mini-batch) +2024-02-02 16:36:24,937 - Epoch: [269][ 1/ 2] Loss 0.022257 MSE 0.022257 +2024-02-02 16:36:24,943 - Epoch: [269][ 2/ 2] Loss 0.023706 MSE 0.023610 +2024-02-02 16:36:25,091 - ==> MSE: 0.02361 Loss: 0.024 + +2024-02-02 16:36:25,095 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:25,095 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:25,099 - + +2024-02-02 16:36:25,099 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:25,467 - Epoch: [270][ 1/ 8] Overall Loss 0.018311 Objective Loss 0.018311 MSE 0.018311 LR 0.001000 Time 0.367378 +2024-02-02 16:36:25,479 - Epoch: [270][ 2/ 8] Overall Loss 0.019255 Objective Loss 0.019255 MSE 0.019255 LR 0.001000 Time 0.189395 +2024-02-02 16:36:25,487 - Epoch: [270][ 3/ 8] Overall Loss 0.018890 Objective Loss 0.018890 MSE 0.018890 LR 0.001000 Time 0.129081 +2024-02-02 16:36:25,496 - Epoch: [270][ 4/ 8] Overall Loss 0.018837 Objective Loss 0.018837 MSE 0.018837 LR 0.001000 Time 0.098934 +2024-02-02 16:36:25,505 - Epoch: [270][ 5/ 8] Overall Loss 0.018671 Objective Loss 0.018671 MSE 0.018671 LR 0.001000 Time 0.080822 +2024-02-02 16:36:25,513 - Epoch: [270][ 6/ 8] Overall Loss 0.019056 Objective Loss 0.019056 MSE 0.019056 LR 0.001000 Time 0.068739 +2024-02-02 16:36:25,522 - Epoch: [270][ 7/ 8] Overall Loss 0.018767 Objective Loss 0.018767 MSE 0.018767 LR 0.001000 Time 0.060114 +2024-02-02 16:36:25,530 - Epoch: [270][ 8/ 8] Overall Loss 0.017913 Objective Loss 0.017913 MSE 0.018589 LR 0.001000 Time 0.053646 +2024-02-02 16:36:25,676 - --- validate (epoch=270)----------- +2024-02-02 16:36:25,676 - 60 samples (32 per mini-batch) +2024-02-02 16:36:26,036 - Epoch: [270][ 1/ 2] Loss 0.023313 MSE 0.023313 +2024-02-02 16:36:26,042 - Epoch: [270][ 2/ 2] Loss 0.023642 MSE 0.023620 +2024-02-02 16:36:26,188 - ==> MSE: 0.02362 Loss: 0.024 + +2024-02-02 16:36:26,193 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:26,193 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:26,197 - + +2024-02-02 16:36:26,197 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:26,555 - Epoch: [271][ 1/ 8] Overall Loss 0.017288 Objective Loss 0.017288 MSE 0.017288 LR 0.001000 Time 0.357114 +2024-02-02 16:36:26,564 - Epoch: [271][ 2/ 8] Overall Loss 0.017910 Objective Loss 0.017910 MSE 0.017910 LR 0.001000 Time 0.182848 +2024-02-02 16:36:26,572 - Epoch: [271][ 3/ 8] Overall Loss 0.017413 Objective Loss 0.017413 MSE 0.017413 LR 0.001000 Time 0.124676 +2024-02-02 16:36:26,581 - Epoch: [271][ 4/ 8] Overall Loss 0.017589 Objective Loss 0.017589 MSE 0.017589 LR 0.001000 Time 0.095555 +2024-02-02 16:36:26,589 - Epoch: [271][ 5/ 8] Overall Loss 0.017968 Objective Loss 0.017968 MSE 0.017968 LR 0.001000 Time 0.078104 +2024-02-02 16:36:26,597 - Epoch: [271][ 6/ 8] Overall Loss 0.018444 Objective Loss 0.018444 MSE 0.018444 LR 0.001000 Time 0.066432 +2024-02-02 16:36:26,606 - Epoch: [271][ 7/ 8] Overall Loss 0.018551 Objective Loss 0.018551 MSE 0.018551 LR 0.001000 Time 0.058119 +2024-02-02 16:36:26,614 - Epoch: [271][ 8/ 8] Overall Loss 0.018698 Objective Loss 0.018698 MSE 0.018582 LR 0.001000 Time 0.051896 +2024-02-02 16:36:26,764 - --- validate (epoch=271)----------- +2024-02-02 16:36:26,764 - 60 samples (32 per mini-batch) +2024-02-02 16:36:27,120 - Epoch: [271][ 1/ 2] Loss 0.022908 MSE 0.022908 +2024-02-02 16:36:27,129 - Epoch: [271][ 2/ 2] Loss 0.023292 MSE 0.023266 +2024-02-02 16:36:27,280 - ==> MSE: 0.02327 Loss: 0.023 + +2024-02-02 16:36:27,285 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:27,285 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:27,289 - + +2024-02-02 16:36:27,290 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:27,647 - Epoch: [272][ 1/ 8] Overall Loss 0.018905 Objective Loss 0.018905 MSE 0.018905 LR 0.001000 Time 0.356803 +2024-02-02 16:36:27,661 - Epoch: [272][ 2/ 8] Overall Loss 0.019841 Objective Loss 0.019841 MSE 0.019841 LR 0.001000 Time 0.185338 +2024-02-02 16:36:27,669 - Epoch: [272][ 3/ 8] Overall Loss 0.018893 Objective Loss 0.018893 MSE 0.018893 LR 0.001000 Time 0.126272 +2024-02-02 16:36:27,678 - Epoch: [272][ 4/ 8] Overall Loss 0.018871 Objective Loss 0.018871 MSE 0.018871 LR 0.001000 Time 0.096761 +2024-02-02 16:36:27,686 - Epoch: [272][ 5/ 8] Overall Loss 0.018163 Objective Loss 0.018163 MSE 0.018163 LR 0.001000 Time 0.079073 +2024-02-02 16:36:27,695 - Epoch: [272][ 6/ 8] Overall Loss 0.018187 Objective Loss 0.018187 MSE 0.018187 LR 0.001000 Time 0.067274 +2024-02-02 16:36:27,703 - Epoch: [272][ 7/ 8] Overall Loss 0.018233 Objective Loss 0.018233 MSE 0.018233 LR 0.001000 Time 0.058843 +2024-02-02 16:36:27,711 - Epoch: [272][ 8/ 8] Overall Loss 0.018779 Objective Loss 0.018779 MSE 0.018347 LR 0.001000 Time 0.052472 +2024-02-02 16:36:27,860 - --- validate (epoch=272)----------- +2024-02-02 16:36:27,860 - 60 samples (32 per mini-batch) +2024-02-02 16:36:28,220 - Epoch: [272][ 1/ 2] Loss 0.022289 MSE 0.022289 +2024-02-02 16:36:28,226 - Epoch: [272][ 2/ 2] Loss 0.023485 MSE 0.023405 +2024-02-02 16:36:28,365 - ==> MSE: 0.02340 Loss: 0.023 + +2024-02-02 16:36:28,369 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:28,370 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:28,374 - + +2024-02-02 16:36:28,374 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:28,719 - Epoch: [273][ 1/ 8] Overall Loss 0.017788 Objective Loss 0.017788 MSE 0.017788 LR 0.001000 Time 0.344376 +2024-02-02 16:36:28,728 - Epoch: [273][ 2/ 8] Overall Loss 0.017031 Objective Loss 0.017031 MSE 0.017031 LR 0.001000 Time 0.176485 +2024-02-02 16:36:28,736 - Epoch: [273][ 3/ 8] Overall Loss 0.017733 Objective Loss 0.017733 MSE 0.017733 LR 0.001000 Time 0.120456 +2024-02-02 16:36:28,745 - Epoch: [273][ 4/ 8] Overall Loss 0.017983 Objective Loss 0.017983 MSE 0.017983 LR 0.001000 Time 0.092391 +2024-02-02 16:36:28,753 - Epoch: [273][ 5/ 8] Overall Loss 0.018458 Objective Loss 0.018458 MSE 0.018458 LR 0.001000 Time 0.075595 +2024-02-02 16:36:28,762 - Epoch: [273][ 6/ 8] Overall Loss 0.018350 Objective Loss 0.018350 MSE 0.018350 LR 0.001000 Time 0.064363 +2024-02-02 16:36:28,770 - Epoch: [273][ 7/ 8] Overall Loss 0.018386 Objective Loss 0.018386 MSE 0.018386 LR 0.001000 Time 0.056343 +2024-02-02 16:36:28,778 - Epoch: [273][ 8/ 8] Overall Loss 0.018528 Objective Loss 0.018528 MSE 0.018416 LR 0.001000 Time 0.050277 +2024-02-02 16:36:28,926 - --- validate (epoch=273)----------- +2024-02-02 16:36:28,926 - 60 samples (32 per mini-batch) +2024-02-02 16:36:29,280 - Epoch: [273][ 1/ 2] Loss 0.023333 MSE 0.023333 +2024-02-02 16:36:29,286 - Epoch: [273][ 2/ 2] Loss 0.023591 MSE 0.023573 +2024-02-02 16:36:29,431 - ==> MSE: 0.02357 Loss: 0.024 + +2024-02-02 16:36:29,435 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:29,435 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:29,439 - + +2024-02-02 16:36:29,439 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:29,806 - Epoch: [274][ 1/ 8] Overall Loss 0.017983 Objective Loss 0.017983 MSE 0.017983 LR 0.001000 Time 0.366286 +2024-02-02 16:36:29,820 - Epoch: [274][ 2/ 8] Overall Loss 0.018458 Objective Loss 0.018458 MSE 0.018458 LR 0.001000 Time 0.189908 +2024-02-02 16:36:29,832 - Epoch: [274][ 3/ 8] Overall Loss 0.018236 Objective Loss 0.018236 MSE 0.018236 LR 0.001000 Time 0.130457 +2024-02-02 16:36:29,840 - Epoch: [274][ 4/ 8] Overall Loss 0.018107 Objective Loss 0.018107 MSE 0.018107 LR 0.001000 Time 0.099932 +2024-02-02 16:36:29,848 - Epoch: [274][ 5/ 8] Overall Loss 0.018612 Objective Loss 0.018612 MSE 0.018612 LR 0.001000 Time 0.081552 +2024-02-02 16:36:29,857 - Epoch: [274][ 6/ 8] Overall Loss 0.018628 Objective Loss 0.018628 MSE 0.018628 LR 0.001000 Time 0.069367 +2024-02-02 16:36:29,866 - Epoch: [274][ 7/ 8] Overall Loss 0.018579 Objective Loss 0.018579 MSE 0.018579 LR 0.001000 Time 0.060658 +2024-02-02 16:36:29,874 - Epoch: [274][ 8/ 8] Overall Loss 0.018573 Objective Loss 0.018573 MSE 0.018578 LR 0.001000 Time 0.054122 +2024-02-02 16:36:30,024 - --- validate (epoch=274)----------- +2024-02-02 16:36:30,025 - 60 samples (32 per mini-batch) +2024-02-02 16:36:30,372 - Epoch: [274][ 1/ 2] Loss 0.023913 MSE 0.023913 +2024-02-02 16:36:30,378 - Epoch: [274][ 2/ 2] Loss 0.023188 MSE 0.023236 +2024-02-02 16:36:30,524 - ==> MSE: 0.02324 Loss: 0.023 + +2024-02-02 16:36:30,527 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:30,527 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:30,531 - + +2024-02-02 16:36:30,532 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:30,883 - Epoch: [275][ 1/ 8] Overall Loss 0.021216 Objective Loss 0.021216 MSE 0.021216 LR 0.001000 Time 0.351012 +2024-02-02 16:36:30,892 - Epoch: [275][ 2/ 8] Overall Loss 0.019473 Objective Loss 0.019473 MSE 0.019473 LR 0.001000 Time 0.179770 +2024-02-02 16:36:30,900 - Epoch: [275][ 3/ 8] Overall Loss 0.018831 Objective Loss 0.018831 MSE 0.018831 LR 0.001000 Time 0.122643 +2024-02-02 16:36:30,909 - Epoch: [275][ 4/ 8] Overall Loss 0.018874 Objective Loss 0.018874 MSE 0.018874 LR 0.001000 Time 0.094088 +2024-02-02 16:36:30,917 - Epoch: [275][ 5/ 8] Overall Loss 0.018514 Objective Loss 0.018514 MSE 0.018514 LR 0.001000 Time 0.076939 +2024-02-02 16:36:30,926 - Epoch: [275][ 6/ 8] Overall Loss 0.018542 Objective Loss 0.018542 MSE 0.018542 LR 0.001000 Time 0.065509 +2024-02-02 16:36:30,934 - Epoch: [275][ 7/ 8] Overall Loss 0.018510 Objective Loss 0.018510 MSE 0.018510 LR 0.001000 Time 0.057352 +2024-02-02 16:36:30,943 - Epoch: [275][ 8/ 8] Overall Loss 0.017924 Objective Loss 0.017924 MSE 0.018388 LR 0.001000 Time 0.051217 +2024-02-02 16:36:31,091 - --- validate (epoch=275)----------- +2024-02-02 16:36:31,091 - 60 samples (32 per mini-batch) +2024-02-02 16:36:31,452 - Epoch: [275][ 1/ 2] Loss 0.023122 MSE 0.023122 +2024-02-02 16:36:31,458 - Epoch: [275][ 2/ 2] Loss 0.023797 MSE 0.023752 +2024-02-02 16:36:31,608 - ==> MSE: 0.02375 Loss: 0.024 + +2024-02-02 16:36:31,613 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:31,613 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:31,617 - + +2024-02-02 16:36:31,617 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:31,985 - Epoch: [276][ 1/ 8] Overall Loss 0.018029 Objective Loss 0.018029 MSE 0.018029 LR 0.001000 Time 0.367387 +2024-02-02 16:36:31,997 - Epoch: [276][ 2/ 8] Overall Loss 0.017653 Objective Loss 0.017653 MSE 0.017653 LR 0.001000 Time 0.189370 +2024-02-02 16:36:32,008 - Epoch: [276][ 3/ 8] Overall Loss 0.017974 Objective Loss 0.017974 MSE 0.017974 LR 0.001000 Time 0.129932 +2024-02-02 16:36:32,018 - Epoch: [276][ 4/ 8] Overall Loss 0.018567 Objective Loss 0.018567 MSE 0.018567 LR 0.001000 Time 0.099851 +2024-02-02 16:36:32,027 - Epoch: [276][ 5/ 8] Overall Loss 0.018783 Objective Loss 0.018783 MSE 0.018783 LR 0.001000 Time 0.081610 +2024-02-02 16:36:32,035 - Epoch: [276][ 6/ 8] Overall Loss 0.018696 Objective Loss 0.018696 MSE 0.018696 LR 0.001000 Time 0.069418 +2024-02-02 16:36:32,044 - Epoch: [276][ 7/ 8] Overall Loss 0.018535 Objective Loss 0.018535 MSE 0.018535 LR 0.001000 Time 0.060716 +2024-02-02 16:36:32,053 - Epoch: [276][ 8/ 8] Overall Loss 0.018710 Objective Loss 0.018710 MSE 0.018571 LR 0.001000 Time 0.054161 +2024-02-02 16:36:32,201 - --- validate (epoch=276)----------- +2024-02-02 16:36:32,201 - 60 samples (32 per mini-batch) +2024-02-02 16:36:32,549 - Epoch: [276][ 1/ 2] Loss 0.023102 MSE 0.023102 +2024-02-02 16:36:32,555 - Epoch: [276][ 2/ 2] Loss 0.023272 MSE 0.023261 +2024-02-02 16:36:32,700 - ==> MSE: 0.02326 Loss: 0.023 + +2024-02-02 16:36:32,705 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:32,705 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:32,709 - + +2024-02-02 16:36:32,709 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:33,059 - Epoch: [277][ 1/ 8] Overall Loss 0.018639 Objective Loss 0.018639 MSE 0.018639 LR 0.001000 Time 0.349446 +2024-02-02 16:36:33,068 - Epoch: [277][ 2/ 8] Overall Loss 0.017823 Objective Loss 0.017823 MSE 0.017823 LR 0.001000 Time 0.178950 +2024-02-02 16:36:33,076 - Epoch: [277][ 3/ 8] Overall Loss 0.018817 Objective Loss 0.018817 MSE 0.018817 LR 0.001000 Time 0.122103 +2024-02-02 16:36:33,085 - Epoch: [277][ 4/ 8] Overall Loss 0.018914 Objective Loss 0.018914 MSE 0.018914 LR 0.001000 Time 0.093638 +2024-02-02 16:36:33,093 - Epoch: [277][ 5/ 8] Overall Loss 0.018711 Objective Loss 0.018711 MSE 0.018711 LR 0.001000 Time 0.076577 +2024-02-02 16:36:33,102 - Epoch: [277][ 6/ 8] Overall Loss 0.018305 Objective Loss 0.018305 MSE 0.018305 LR 0.001000 Time 0.065192 +2024-02-02 16:36:33,110 - Epoch: [277][ 7/ 8] Overall Loss 0.018298 Objective Loss 0.018298 MSE 0.018298 LR 0.001000 Time 0.057071 +2024-02-02 16:36:33,119 - Epoch: [277][ 8/ 8] Overall Loss 0.018418 Objective Loss 0.018418 MSE 0.018323 LR 0.001000 Time 0.050975 +2024-02-02 16:36:33,264 - --- validate (epoch=277)----------- +2024-02-02 16:36:33,265 - 60 samples (32 per mini-batch) +2024-02-02 16:36:33,612 - Epoch: [277][ 1/ 2] Loss 0.024833 MSE 0.024833 +2024-02-02 16:36:33,620 - Epoch: [277][ 2/ 2] Loss 0.022975 MSE 0.023099 +2024-02-02 16:36:33,763 - ==> MSE: 0.02310 Loss: 0.023 + +2024-02-02 16:36:33,771 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:33,771 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:33,779 - + +2024-02-02 16:36:33,779 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:34,150 - Epoch: [278][ 1/ 8] Overall Loss 0.016877 Objective Loss 0.016877 MSE 0.016877 LR 0.001000 Time 0.370582 +2024-02-02 16:36:34,159 - Epoch: [278][ 2/ 8] Overall Loss 0.018615 Objective Loss 0.018615 MSE 0.018615 LR 0.001000 Time 0.189526 +2024-02-02 16:36:34,168 - Epoch: [278][ 3/ 8] Overall Loss 0.018707 Objective Loss 0.018707 MSE 0.018707 LR 0.001000 Time 0.129115 +2024-02-02 16:36:34,176 - Epoch: [278][ 4/ 8] Overall Loss 0.018128 Objective Loss 0.018128 MSE 0.018128 LR 0.001000 Time 0.098937 +2024-02-02 16:36:34,184 - Epoch: [278][ 5/ 8] Overall Loss 0.018516 Objective Loss 0.018516 MSE 0.018516 LR 0.001000 Time 0.080722 +2024-02-02 16:36:34,192 - Epoch: [278][ 6/ 8] Overall Loss 0.018410 Objective Loss 0.018410 MSE 0.018410 LR 0.001000 Time 0.068630 +2024-02-02 16:36:34,201 - Epoch: [278][ 7/ 8] Overall Loss 0.018250 Objective Loss 0.018250 MSE 0.018250 LR 0.001000 Time 0.059996 +2024-02-02 16:36:34,209 - Epoch: [278][ 8/ 8] Overall Loss 0.018090 Objective Loss 0.018090 MSE 0.018217 LR 0.001000 Time 0.053534 +2024-02-02 16:36:34,361 - --- validate (epoch=278)----------- +2024-02-02 16:36:34,361 - 60 samples (32 per mini-batch) +2024-02-02 16:36:34,723 - Epoch: [278][ 1/ 2] Loss 0.022394 MSE 0.022394 +2024-02-02 16:36:34,729 - Epoch: [278][ 2/ 2] Loss 0.023395 MSE 0.023328 +2024-02-02 16:36:34,879 - ==> MSE: 0.02333 Loss: 0.023 + +2024-02-02 16:36:34,882 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:34,882 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:34,887 - + +2024-02-02 16:36:34,887 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:35,251 - Epoch: [279][ 1/ 8] Overall Loss 0.018656 Objective Loss 0.018656 MSE 0.018656 LR 0.001000 Time 0.363436 +2024-02-02 16:36:35,260 - Epoch: [279][ 2/ 8] Overall Loss 0.017767 Objective Loss 0.017767 MSE 0.017767 LR 0.001000 Time 0.186142 +2024-02-02 16:36:35,268 - Epoch: [279][ 3/ 8] Overall Loss 0.018211 Objective Loss 0.018211 MSE 0.018211 LR 0.001000 Time 0.126879 +2024-02-02 16:36:35,277 - Epoch: [279][ 4/ 8] Overall Loss 0.017945 Objective Loss 0.017945 MSE 0.017945 LR 0.001000 Time 0.097194 +2024-02-02 16:36:35,285 - Epoch: [279][ 5/ 8] Overall Loss 0.018177 Objective Loss 0.018177 MSE 0.018177 LR 0.001000 Time 0.079388 +2024-02-02 16:36:35,293 - Epoch: [279][ 6/ 8] Overall Loss 0.017922 Objective Loss 0.017922 MSE 0.017922 LR 0.001000 Time 0.067536 +2024-02-02 16:36:35,302 - Epoch: [279][ 7/ 8] Overall Loss 0.018291 Objective Loss 0.018291 MSE 0.018291 LR 0.001000 Time 0.059089 +2024-02-02 16:36:35,311 - Epoch: [279][ 8/ 8] Overall Loss 0.018115 Objective Loss 0.018115 MSE 0.018255 LR 0.001000 Time 0.052747 +2024-02-02 16:36:35,461 - --- validate (epoch=279)----------- +2024-02-02 16:36:35,462 - 60 samples (32 per mini-batch) +2024-02-02 16:36:35,821 - Epoch: [279][ 1/ 2] Loss 0.021752 MSE 0.021752 +2024-02-02 16:36:35,827 - Epoch: [279][ 2/ 2] Loss 0.023395 MSE 0.023285 +2024-02-02 16:36:35,967 - ==> MSE: 0.02329 Loss: 0.023 + +2024-02-02 16:36:35,973 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:35,973 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:35,978 - + +2024-02-02 16:36:35,978 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:36,328 - Epoch: [280][ 1/ 8] Overall Loss 0.018687 Objective Loss 0.018687 MSE 0.018687 LR 0.001000 Time 0.349658 +2024-02-02 16:36:36,340 - Epoch: [280][ 2/ 8] Overall Loss 0.018905 Objective Loss 0.018905 MSE 0.018905 LR 0.001000 Time 0.180571 +2024-02-02 16:36:36,351 - Epoch: [280][ 3/ 8] Overall Loss 0.019634 Objective Loss 0.019634 MSE 0.019634 LR 0.001000 Time 0.124190 +2024-02-02 16:36:36,362 - Epoch: [280][ 4/ 8] Overall Loss 0.019400 Objective Loss 0.019400 MSE 0.019400 LR 0.001000 Time 0.095628 +2024-02-02 16:36:36,370 - Epoch: [280][ 5/ 8] Overall Loss 0.018888 Objective Loss 0.018888 MSE 0.018888 LR 0.001000 Time 0.078207 +2024-02-02 16:36:36,379 - Epoch: [280][ 6/ 8] Overall Loss 0.018853 Objective Loss 0.018853 MSE 0.018853 LR 0.001000 Time 0.066564 +2024-02-02 16:36:36,387 - Epoch: [280][ 7/ 8] Overall Loss 0.018580 Objective Loss 0.018580 MSE 0.018580 LR 0.001000 Time 0.058241 +2024-02-02 16:36:36,396 - Epoch: [280][ 8/ 8] Overall Loss 0.018230 Objective Loss 0.018230 MSE 0.018507 LR 0.001000 Time 0.051989 +2024-02-02 16:36:36,546 - --- validate (epoch=280)----------- +2024-02-02 16:36:36,547 - 60 samples (32 per mini-batch) +2024-02-02 16:36:36,915 - Epoch: [280][ 1/ 2] Loss 0.021525 MSE 0.021525 +2024-02-02 16:36:36,921 - Epoch: [280][ 2/ 2] Loss 0.023529 MSE 0.023395 +2024-02-02 16:36:37,062 - ==> MSE: 0.02340 Loss: 0.024 + +2024-02-02 16:36:37,068 - ==> Best [Top 1 (MSE): 0.02308 Sparsity:0.00 Params: 136448 on epoch: 265] +2024-02-02 16:36:37,068 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:37,073 - + +2024-02-02 16:36:37,073 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:37,439 - Epoch: [281][ 1/ 8] Overall Loss 0.017653 Objective Loss 0.017653 MSE 0.017653 LR 0.001000 Time 0.366088 +2024-02-02 16:36:37,448 - Epoch: [281][ 2/ 8] Overall Loss 0.018412 Objective Loss 0.018412 MSE 0.018412 LR 0.001000 Time 0.187347 +2024-02-02 16:36:37,457 - Epoch: [281][ 3/ 8] Overall Loss 0.018934 Objective Loss 0.018934 MSE 0.018934 LR 0.001000 Time 0.127688 +2024-02-02 16:36:37,465 - Epoch: [281][ 4/ 8] Overall Loss 0.018668 Objective Loss 0.018668 MSE 0.018668 LR 0.001000 Time 0.097815 +2024-02-02 16:36:37,473 - Epoch: [281][ 5/ 8] Overall Loss 0.018550 Objective Loss 0.018550 MSE 0.018550 LR 0.001000 Time 0.079898 +2024-02-02 16:36:37,482 - Epoch: [281][ 6/ 8] Overall Loss 0.018294 Objective Loss 0.018294 MSE 0.018294 LR 0.001000 Time 0.067967 +2024-02-02 16:36:37,491 - Epoch: [281][ 7/ 8] Overall Loss 0.018241 Objective Loss 0.018241 MSE 0.018241 LR 0.001000 Time 0.059458 +2024-02-02 16:36:37,656 - Epoch: [281][ 8/ 8] Overall Loss 0.018499 Objective Loss 0.018499 MSE 0.018295 LR 0.001000 Time 0.072674 +2024-02-02 16:36:37,800 - --- validate (epoch=281)----------- +2024-02-02 16:36:37,801 - 60 samples (32 per mini-batch) +2024-02-02 16:36:38,163 - Epoch: [281][ 1/ 2] Loss 0.024857 MSE 0.024857 +2024-02-02 16:36:38,169 - Epoch: [281][ 2/ 2] Loss 0.022924 MSE 0.023053 +2024-02-02 16:36:38,312 - ==> MSE: 0.02305 Loss: 0.023 + +2024-02-02 16:36:38,317 - ==> Best [Top 1 (MSE): 0.02305 Sparsity:0.00 Params: 136448 on epoch: 281] +2024-02-02 16:36:38,317 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:38,323 - + +2024-02-02 16:36:38,323 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:38,682 - Epoch: [282][ 1/ 8] Overall Loss 0.017764 Objective Loss 0.017764 MSE 0.017764 LR 0.001000 Time 0.358915 +2024-02-02 16:36:38,691 - Epoch: [282][ 2/ 8] Overall Loss 0.018600 Objective Loss 0.018600 MSE 0.018600 LR 0.001000 Time 0.183752 +2024-02-02 16:36:38,699 - Epoch: [282][ 3/ 8] Overall Loss 0.017822 Objective Loss 0.017822 MSE 0.017822 LR 0.001000 Time 0.125257 +2024-02-02 16:36:38,708 - Epoch: [282][ 4/ 8] Overall Loss 0.018143 Objective Loss 0.018143 MSE 0.018143 LR 0.001000 Time 0.096028 +2024-02-02 16:36:38,716 - Epoch: [282][ 5/ 8] Overall Loss 0.018782 Objective Loss 0.018782 MSE 0.018782 LR 0.001000 Time 0.078497 +2024-02-02 16:36:38,725 - Epoch: [282][ 6/ 8] Overall Loss 0.018515 Objective Loss 0.018515 MSE 0.018515 LR 0.001000 Time 0.066807 +2024-02-02 16:36:38,733 - Epoch: [282][ 7/ 8] Overall Loss 0.018172 Objective Loss 0.018172 MSE 0.018172 LR 0.001000 Time 0.058470 +2024-02-02 16:36:38,742 - Epoch: [282][ 8/ 8] Overall Loss 0.018209 Objective Loss 0.018209 MSE 0.018180 LR 0.001000 Time 0.052202 +2024-02-02 16:36:38,886 - --- validate (epoch=282)----------- +2024-02-02 16:36:38,886 - 60 samples (32 per mini-batch) +2024-02-02 16:36:39,245 - Epoch: [282][ 1/ 2] Loss 0.023246 MSE 0.023246 +2024-02-02 16:36:39,252 - Epoch: [282][ 2/ 2] Loss 0.022913 MSE 0.022935 +2024-02-02 16:36:39,402 - ==> MSE: 0.02294 Loss: 0.023 + +2024-02-02 16:36:39,406 - ==> Best [Top 1 (MSE): 0.02294 Sparsity:0.00 Params: 136448 on epoch: 282] +2024-02-02 16:36:39,407 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:39,412 - + +2024-02-02 16:36:39,412 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:39,768 - Epoch: [283][ 1/ 8] Overall Loss 0.018787 Objective Loss 0.018787 MSE 0.018787 LR 0.001000 Time 0.355471 +2024-02-02 16:36:39,777 - Epoch: [283][ 2/ 8] Overall Loss 0.019422 Objective Loss 0.019422 MSE 0.019422 LR 0.001000 Time 0.182123 +2024-02-02 16:36:39,786 - Epoch: [283][ 3/ 8] Overall Loss 0.018895 Objective Loss 0.018895 MSE 0.018895 LR 0.001000 Time 0.124220 +2024-02-02 16:36:39,794 - Epoch: [283][ 4/ 8] Overall Loss 0.018185 Objective Loss 0.018185 MSE 0.018185 LR 0.001000 Time 0.095237 +2024-02-02 16:36:39,803 - Epoch: [283][ 5/ 8] Overall Loss 0.018023 Objective Loss 0.018023 MSE 0.018023 LR 0.001000 Time 0.077842 +2024-02-02 16:36:39,811 - Epoch: [283][ 6/ 8] Overall Loss 0.018368 Objective Loss 0.018368 MSE 0.018368 LR 0.001000 Time 0.066290 +2024-02-02 16:36:39,820 - Epoch: [283][ 7/ 8] Overall Loss 0.018085 Objective Loss 0.018085 MSE 0.018085 LR 0.001000 Time 0.058016 +2024-02-02 16:36:39,828 - Epoch: [283][ 8/ 8] Overall Loss 0.017942 Objective Loss 0.017942 MSE 0.018055 LR 0.001000 Time 0.051806 +2024-02-02 16:36:39,979 - --- validate (epoch=283)----------- +2024-02-02 16:36:39,979 - 60 samples (32 per mini-batch) +2024-02-02 16:36:40,328 - Epoch: [283][ 1/ 2] Loss 0.023267 MSE 0.023267 +2024-02-02 16:36:40,334 - Epoch: [283][ 2/ 2] Loss 0.022909 MSE 0.022933 +2024-02-02 16:36:40,474 - ==> MSE: 0.02293 Loss: 0.023 + +2024-02-02 16:36:40,479 - ==> Best [Top 1 (MSE): 0.02293 Sparsity:0.00 Params: 136448 on epoch: 283] +2024-02-02 16:36:40,479 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:40,484 - + +2024-02-02 16:36:40,484 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:40,843 - Epoch: [284][ 1/ 8] Overall Loss 0.016622 Objective Loss 0.016622 MSE 0.016622 LR 0.001000 Time 0.358884 +2024-02-02 16:36:40,852 - Epoch: [284][ 2/ 8] Overall Loss 0.016981 Objective Loss 0.016981 MSE 0.016981 LR 0.001000 Time 0.183767 +2024-02-02 16:36:40,861 - Epoch: [284][ 3/ 8] Overall Loss 0.017187 Objective Loss 0.017187 MSE 0.017188 LR 0.001000 Time 0.125289 +2024-02-02 16:36:40,869 - Epoch: [284][ 4/ 8] Overall Loss 0.017571 Objective Loss 0.017571 MSE 0.017571 LR 0.001000 Time 0.096030 +2024-02-02 16:36:40,878 - Epoch: [284][ 5/ 8] Overall Loss 0.018016 Objective Loss 0.018016 MSE 0.018016 LR 0.001000 Time 0.078468 +2024-02-02 16:36:40,886 - Epoch: [284][ 6/ 8] Overall Loss 0.018040 Objective Loss 0.018040 MSE 0.018040 LR 0.001000 Time 0.066797 +2024-02-02 16:36:40,895 - Epoch: [284][ 7/ 8] Overall Loss 0.018073 Objective Loss 0.018073 MSE 0.018073 LR 0.001000 Time 0.058447 +2024-02-02 16:36:40,903 - Epoch: [284][ 8/ 8] Overall Loss 0.018108 Objective Loss 0.018108 MSE 0.018081 LR 0.001000 Time 0.052179 +2024-02-02 16:36:41,044 - --- validate (epoch=284)----------- +2024-02-02 16:36:41,044 - 60 samples (32 per mini-batch) +2024-02-02 16:36:41,394 - Epoch: [284][ 1/ 2] Loss 0.022136 MSE 0.022136 +2024-02-02 16:36:41,400 - Epoch: [284][ 2/ 2] Loss 0.022894 MSE 0.022843 +2024-02-02 16:36:41,547 - ==> MSE: 0.02284 Loss: 0.023 + +2024-02-02 16:36:41,551 - ==> Best [Top 1 (MSE): 0.02284 Sparsity:0.00 Params: 136448 on epoch: 284] +2024-02-02 16:36:41,551 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:41,556 - + +2024-02-02 16:36:41,556 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:41,926 - Epoch: [285][ 1/ 8] Overall Loss 0.017652 Objective Loss 0.017652 MSE 0.017652 LR 0.001000 Time 0.369225 +2024-02-02 16:36:41,935 - Epoch: [285][ 2/ 8] Overall Loss 0.018558 Objective Loss 0.018558 MSE 0.018558 LR 0.001000 Time 0.188897 +2024-02-02 16:36:41,943 - Epoch: [285][ 3/ 8] Overall Loss 0.018950 Objective Loss 0.018950 MSE 0.018950 LR 0.001000 Time 0.128693 +2024-02-02 16:36:41,951 - Epoch: [285][ 4/ 8] Overall Loss 0.018553 Objective Loss 0.018553 MSE 0.018553 LR 0.001000 Time 0.098567 +2024-02-02 16:36:41,960 - Epoch: [285][ 5/ 8] Overall Loss 0.018416 Objective Loss 0.018416 MSE 0.018416 LR 0.001000 Time 0.080560 +2024-02-02 16:36:41,969 - Epoch: [285][ 6/ 8] Overall Loss 0.018232 Objective Loss 0.018232 MSE 0.018232 LR 0.001000 Time 0.068537 +2024-02-02 16:36:41,977 - Epoch: [285][ 7/ 8] Overall Loss 0.017973 Objective Loss 0.017973 MSE 0.017973 LR 0.001000 Time 0.059939 +2024-02-02 16:36:41,985 - Epoch: [285][ 8/ 8] Overall Loss 0.017530 Objective Loss 0.017530 MSE 0.017881 LR 0.001000 Time 0.053470 +2024-02-02 16:36:42,134 - --- validate (epoch=285)----------- +2024-02-02 16:36:42,134 - 60 samples (32 per mini-batch) +2024-02-02 16:36:42,492 - Epoch: [285][ 1/ 2] Loss 0.021883 MSE 0.021883 +2024-02-02 16:36:42,498 - Epoch: [285][ 2/ 2] Loss 0.023088 MSE 0.023007 +2024-02-02 16:36:42,644 - ==> MSE: 0.02301 Loss: 0.023 + +2024-02-02 16:36:42,649 - ==> Best [Top 1 (MSE): 0.02284 Sparsity:0.00 Params: 136448 on epoch: 284] +2024-02-02 16:36:42,650 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:42,654 - + +2024-02-02 16:36:42,654 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:43,022 - Epoch: [286][ 1/ 8] Overall Loss 0.015816 Objective Loss 0.015816 MSE 0.015816 LR 0.001000 Time 0.367881 +2024-02-02 16:36:43,035 - Epoch: [286][ 2/ 8] Overall Loss 0.016698 Objective Loss 0.016698 MSE 0.016698 LR 0.001000 Time 0.189778 +2024-02-02 16:36:43,043 - Epoch: [286][ 3/ 8] Overall Loss 0.017490 Objective Loss 0.017490 MSE 0.017490 LR 0.001000 Time 0.129352 +2024-02-02 16:36:43,052 - Epoch: [286][ 4/ 8] Overall Loss 0.017395 Objective Loss 0.017395 MSE 0.017395 LR 0.001000 Time 0.099098 +2024-02-02 16:36:43,060 - Epoch: [286][ 5/ 8] Overall Loss 0.017379 Objective Loss 0.017379 MSE 0.017379 LR 0.001000 Time 0.080956 +2024-02-02 16:36:43,069 - Epoch: [286][ 6/ 8] Overall Loss 0.017626 Objective Loss 0.017626 MSE 0.017626 LR 0.001000 Time 0.068852 +2024-02-02 16:36:43,077 - Epoch: [286][ 7/ 8] Overall Loss 0.017892 Objective Loss 0.017892 MSE 0.017892 LR 0.001000 Time 0.060194 +2024-02-02 16:36:43,086 - Epoch: [286][ 8/ 8] Overall Loss 0.017798 Objective Loss 0.017798 MSE 0.017872 LR 0.001000 Time 0.053698 +2024-02-02 16:36:43,234 - --- validate (epoch=286)----------- +2024-02-02 16:36:43,234 - 60 samples (32 per mini-batch) +2024-02-02 16:36:43,583 - Epoch: [286][ 1/ 2] Loss 0.021415 MSE 0.021415 +2024-02-02 16:36:43,589 - Epoch: [286][ 2/ 2] Loss 0.022938 MSE 0.022836 +2024-02-02 16:36:43,741 - ==> MSE: 0.02284 Loss: 0.023 + +2024-02-02 16:36:43,746 - ==> Best [Top 1 (MSE): 0.02284 Sparsity:0.00 Params: 136448 on epoch: 286] +2024-02-02 16:36:43,746 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:43,752 - + +2024-02-02 16:36:43,752 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:44,109 - Epoch: [287][ 1/ 8] Overall Loss 0.020658 Objective Loss 0.020658 MSE 0.020658 LR 0.001000 Time 0.357299 +2024-02-02 16:36:44,118 - Epoch: [287][ 2/ 8] Overall Loss 0.018967 Objective Loss 0.018967 MSE 0.018967 LR 0.001000 Time 0.182977 +2024-02-02 16:36:44,127 - Epoch: [287][ 3/ 8] Overall Loss 0.019575 Objective Loss 0.019575 MSE 0.019575 LR 0.001000 Time 0.124825 +2024-02-02 16:36:44,135 - Epoch: [287][ 4/ 8] Overall Loss 0.018662 Objective Loss 0.018662 MSE 0.018662 LR 0.001000 Time 0.095685 +2024-02-02 16:36:44,144 - Epoch: [287][ 5/ 8] Overall Loss 0.018022 Objective Loss 0.018022 MSE 0.018022 LR 0.001000 Time 0.078230 +2024-02-02 16:36:44,153 - Epoch: [287][ 6/ 8] Overall Loss 0.018049 Objective Loss 0.018049 MSE 0.018049 LR 0.001000 Time 0.066604 +2024-02-02 16:36:44,161 - Epoch: [287][ 7/ 8] Overall Loss 0.017892 Objective Loss 0.017892 MSE 0.017892 LR 0.001000 Time 0.058268 +2024-02-02 16:36:44,170 - Epoch: [287][ 8/ 8] Overall Loss 0.017610 Objective Loss 0.017610 MSE 0.017833 LR 0.001000 Time 0.052029 +2024-02-02 16:36:44,320 - --- validate (epoch=287)----------- +2024-02-02 16:36:44,320 - 60 samples (32 per mini-batch) +2024-02-02 16:36:44,680 - Epoch: [287][ 1/ 2] Loss 0.021876 MSE 0.021876 +2024-02-02 16:36:44,688 - Epoch: [287][ 2/ 2] Loss 0.022862 MSE 0.022796 +2024-02-02 16:36:44,839 - ==> MSE: 0.02280 Loss: 0.023 + +2024-02-02 16:36:44,843 - ==> Best [Top 1 (MSE): 0.02280 Sparsity:0.00 Params: 136448 on epoch: 287] +2024-02-02 16:36:44,843 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:44,849 - + +2024-02-02 16:36:44,849 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:45,213 - Epoch: [288][ 1/ 8] Overall Loss 0.018137 Objective Loss 0.018137 MSE 0.018137 LR 0.001000 Time 0.363770 +2024-02-02 16:36:45,222 - Epoch: [288][ 2/ 8] Overall Loss 0.017254 Objective Loss 0.017254 MSE 0.017254 LR 0.001000 Time 0.186137 +2024-02-02 16:36:45,231 - Epoch: [288][ 3/ 8] Overall Loss 0.018385 Objective Loss 0.018385 MSE 0.018385 LR 0.001000 Time 0.126931 +2024-02-02 16:36:45,239 - Epoch: [288][ 4/ 8] Overall Loss 0.017934 Objective Loss 0.017934 MSE 0.017934 LR 0.001000 Time 0.097235 +2024-02-02 16:36:45,247 - Epoch: [288][ 5/ 8] Overall Loss 0.017914 Objective Loss 0.017914 MSE 0.017914 LR 0.001000 Time 0.079439 +2024-02-02 16:36:45,256 - Epoch: [288][ 6/ 8] Overall Loss 0.017972 Objective Loss 0.017972 MSE 0.017972 LR 0.001000 Time 0.067600 +2024-02-02 16:36:45,264 - Epoch: [288][ 7/ 8] Overall Loss 0.017964 Objective Loss 0.017964 MSE 0.017964 LR 0.001000 Time 0.059149 +2024-02-02 16:36:45,273 - Epoch: [288][ 8/ 8] Overall Loss 0.017187 Objective Loss 0.017187 MSE 0.017802 LR 0.001000 Time 0.052783 +2024-02-02 16:36:45,423 - --- validate (epoch=288)----------- +2024-02-02 16:36:45,423 - 60 samples (32 per mini-batch) +2024-02-02 16:36:45,779 - Epoch: [288][ 1/ 2] Loss 0.023741 MSE 0.023741 +2024-02-02 16:36:45,785 - Epoch: [288][ 2/ 2] Loss 0.022806 MSE 0.022868 +2024-02-02 16:36:45,935 - ==> MSE: 0.02287 Loss: 0.023 + +2024-02-02 16:36:45,939 - ==> Best [Top 1 (MSE): 0.02280 Sparsity:0.00 Params: 136448 on epoch: 287] +2024-02-02 16:36:45,939 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:45,943 - + +2024-02-02 16:36:45,944 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:46,300 - Epoch: [289][ 1/ 8] Overall Loss 0.016187 Objective Loss 0.016187 MSE 0.016187 LR 0.001000 Time 0.355900 +2024-02-02 16:36:46,309 - Epoch: [289][ 2/ 8] Overall Loss 0.016300 Objective Loss 0.016300 MSE 0.016300 LR 0.001000 Time 0.182260 +2024-02-02 16:36:46,317 - Epoch: [289][ 3/ 8] Overall Loss 0.017985 Objective Loss 0.017985 MSE 0.017985 LR 0.001000 Time 0.124355 +2024-02-02 16:36:46,326 - Epoch: [289][ 4/ 8] Overall Loss 0.017759 Objective Loss 0.017759 MSE 0.017759 LR 0.001000 Time 0.095362 +2024-02-02 16:36:46,334 - Epoch: [289][ 5/ 8] Overall Loss 0.018011 Objective Loss 0.018011 MSE 0.018011 LR 0.001000 Time 0.077957 +2024-02-02 16:36:46,343 - Epoch: [289][ 6/ 8] Overall Loss 0.017743 Objective Loss 0.017743 MSE 0.017743 LR 0.001000 Time 0.066364 +2024-02-02 16:36:46,352 - Epoch: [289][ 7/ 8] Overall Loss 0.017806 Objective Loss 0.017806 MSE 0.017806 LR 0.001000 Time 0.058083 +2024-02-02 16:36:46,360 - Epoch: [289][ 8/ 8] Overall Loss 0.017805 Objective Loss 0.017805 MSE 0.017806 LR 0.001000 Time 0.051870 +2024-02-02 16:36:46,508 - --- validate (epoch=289)----------- +2024-02-02 16:36:46,508 - 60 samples (32 per mini-batch) +2024-02-02 16:36:46,858 - Epoch: [289][ 1/ 2] Loss 0.022630 MSE 0.022630 +2024-02-02 16:36:46,864 - Epoch: [289][ 2/ 2] Loss 0.022763 MSE 0.022754 +2024-02-02 16:36:47,002 - ==> MSE: 0.02275 Loss: 0.023 + +2024-02-02 16:36:47,006 - ==> Best [Top 1 (MSE): 0.02275 Sparsity:0.00 Params: 136448 on epoch: 289] +2024-02-02 16:36:47,006 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:47,011 - + +2024-02-02 16:36:47,011 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:47,361 - Epoch: [290][ 1/ 8] Overall Loss 0.017618 Objective Loss 0.017618 MSE 0.017618 LR 0.001000 Time 0.349276 +2024-02-02 16:36:47,370 - Epoch: [290][ 2/ 8] Overall Loss 0.018262 Objective Loss 0.018262 MSE 0.018262 LR 0.001000 Time 0.178893 +2024-02-02 16:36:47,378 - Epoch: [290][ 3/ 8] Overall Loss 0.017946 Objective Loss 0.017946 MSE 0.017946 LR 0.001000 Time 0.122113 +2024-02-02 16:36:47,387 - Epoch: [290][ 4/ 8] Overall Loss 0.017222 Objective Loss 0.017222 MSE 0.017222 LR 0.001000 Time 0.093668 +2024-02-02 16:36:47,395 - Epoch: [290][ 5/ 8] Overall Loss 0.017509 Objective Loss 0.017509 MSE 0.017509 LR 0.001000 Time 0.076591 +2024-02-02 16:36:47,404 - Epoch: [290][ 6/ 8] Overall Loss 0.017316 Objective Loss 0.017316 MSE 0.017316 LR 0.001000 Time 0.065222 +2024-02-02 16:36:47,412 - Epoch: [290][ 7/ 8] Overall Loss 0.017676 Objective Loss 0.017676 MSE 0.017676 LR 0.001000 Time 0.057105 +2024-02-02 16:36:47,421 - Epoch: [290][ 8/ 8] Overall Loss 0.018368 Objective Loss 0.018368 MSE 0.017820 LR 0.001000 Time 0.050996 +2024-02-02 16:36:47,563 - --- validate (epoch=290)----------- +2024-02-02 16:36:47,563 - 60 samples (32 per mini-batch) +2024-02-02 16:36:47,901 - Epoch: [290][ 1/ 2] Loss 0.024044 MSE 0.024044 +2024-02-02 16:36:47,907 - Epoch: [290][ 2/ 2] Loss 0.022707 MSE 0.022796 +2024-02-02 16:36:48,045 - ==> MSE: 0.02280 Loss: 0.023 + +2024-02-02 16:36:48,050 - ==> Best [Top 1 (MSE): 0.02275 Sparsity:0.00 Params: 136448 on epoch: 289] +2024-02-02 16:36:48,050 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:48,054 - + +2024-02-02 16:36:48,055 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:48,419 - Epoch: [291][ 1/ 8] Overall Loss 0.019083 Objective Loss 0.019083 MSE 0.019083 LR 0.001000 Time 0.364246 +2024-02-02 16:36:48,429 - Epoch: [291][ 2/ 8] Overall Loss 0.018388 Objective Loss 0.018388 MSE 0.018388 LR 0.001000 Time 0.187079 +2024-02-02 16:36:48,441 - Epoch: [291][ 3/ 8] Overall Loss 0.018311 Objective Loss 0.018311 MSE 0.018311 LR 0.001000 Time 0.128642 +2024-02-02 16:36:48,451 - Epoch: [291][ 4/ 8] Overall Loss 0.018690 Objective Loss 0.018690 MSE 0.018690 LR 0.001000 Time 0.098722 +2024-02-02 16:36:48,459 - Epoch: [291][ 5/ 8] Overall Loss 0.018297 Objective Loss 0.018297 MSE 0.018297 LR 0.001000 Time 0.080659 +2024-02-02 16:36:48,468 - Epoch: [291][ 6/ 8] Overall Loss 0.018116 Objective Loss 0.018116 MSE 0.018116 LR 0.001000 Time 0.068588 +2024-02-02 16:36:48,476 - Epoch: [291][ 7/ 8] Overall Loss 0.017854 Objective Loss 0.017854 MSE 0.017854 LR 0.001000 Time 0.059970 +2024-02-02 16:36:48,484 - Epoch: [291][ 8/ 8] Overall Loss 0.017864 Objective Loss 0.017864 MSE 0.017856 LR 0.001000 Time 0.053497 +2024-02-02 16:36:48,636 - --- validate (epoch=291)----------- +2024-02-02 16:36:48,636 - 60 samples (32 per mini-batch) +2024-02-02 16:36:48,985 - Epoch: [291][ 1/ 2] Loss 0.023750 MSE 0.023750 +2024-02-02 16:36:48,992 - Epoch: [291][ 2/ 2] Loss 0.023029 MSE 0.023077 +2024-02-02 16:36:49,144 - ==> MSE: 0.02308 Loss: 0.023 + +2024-02-02 16:36:49,149 - ==> Best [Top 1 (MSE): 0.02275 Sparsity:0.00 Params: 136448 on epoch: 289] +2024-02-02 16:36:49,150 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:49,154 - + +2024-02-02 16:36:49,154 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:49,501 - Epoch: [292][ 1/ 8] Overall Loss 0.017194 Objective Loss 0.017194 MSE 0.017194 LR 0.001000 Time 0.346676 +2024-02-02 16:36:49,510 - Epoch: [292][ 2/ 8] Overall Loss 0.017264 Objective Loss 0.017264 MSE 0.017264 LR 0.001000 Time 0.177587 +2024-02-02 16:36:49,519 - Epoch: [292][ 3/ 8] Overall Loss 0.017424 Objective Loss 0.017424 MSE 0.017424 LR 0.001000 Time 0.121162 +2024-02-02 16:36:49,527 - Epoch: [292][ 4/ 8] Overall Loss 0.017577 Objective Loss 0.017577 MSE 0.017577 LR 0.001000 Time 0.092924 +2024-02-02 16:36:49,535 - Epoch: [292][ 5/ 8] Overall Loss 0.017720 Objective Loss 0.017720 MSE 0.017720 LR 0.001000 Time 0.075995 +2024-02-02 16:36:49,544 - Epoch: [292][ 6/ 8] Overall Loss 0.017941 Objective Loss 0.017941 MSE 0.017941 LR 0.001000 Time 0.064725 +2024-02-02 16:36:49,553 - Epoch: [292][ 7/ 8] Overall Loss 0.017770 Objective Loss 0.017770 MSE 0.017770 LR 0.001000 Time 0.056694 +2024-02-02 16:36:49,561 - Epoch: [292][ 8/ 8] Overall Loss 0.018243 Objective Loss 0.018243 MSE 0.017869 LR 0.001000 Time 0.050643 +2024-02-02 16:36:49,707 - --- validate (epoch=292)----------- +2024-02-02 16:36:49,707 - 60 samples (32 per mini-batch) +2024-02-02 16:36:50,067 - Epoch: [292][ 1/ 2] Loss 0.024399 MSE 0.024399 +2024-02-02 16:36:50,073 - Epoch: [292][ 2/ 2] Loss 0.022759 MSE 0.022869 +2024-02-02 16:36:50,220 - ==> MSE: 0.02287 Loss: 0.023 + +2024-02-02 16:36:50,226 - ==> Best [Top 1 (MSE): 0.02275 Sparsity:0.00 Params: 136448 on epoch: 289] +2024-02-02 16:36:50,226 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:50,230 - + +2024-02-02 16:36:50,230 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:50,600 - Epoch: [293][ 1/ 8] Overall Loss 0.017383 Objective Loss 0.017383 MSE 0.017383 LR 0.001000 Time 0.369587 +2024-02-02 16:36:50,609 - Epoch: [293][ 2/ 8] Overall Loss 0.017477 Objective Loss 0.017477 MSE 0.017477 LR 0.001000 Time 0.189076 +2024-02-02 16:36:50,617 - Epoch: [293][ 3/ 8] Overall Loss 0.018015 Objective Loss 0.018015 MSE 0.018015 LR 0.001000 Time 0.128776 +2024-02-02 16:36:50,626 - Epoch: [293][ 4/ 8] Overall Loss 0.017462 Objective Loss 0.017462 MSE 0.017462 LR 0.001000 Time 0.098694 +2024-02-02 16:36:50,635 - Epoch: [293][ 5/ 8] Overall Loss 0.017751 Objective Loss 0.017751 MSE 0.017751 LR 0.001000 Time 0.080634 +2024-02-02 16:36:50,643 - Epoch: [293][ 6/ 8] Overall Loss 0.017970 Objective Loss 0.017970 MSE 0.017970 LR 0.001000 Time 0.068589 +2024-02-02 16:36:50,652 - Epoch: [293][ 7/ 8] Overall Loss 0.017848 Objective Loss 0.017848 MSE 0.017848 LR 0.001000 Time 0.059990 +2024-02-02 16:36:50,661 - Epoch: [293][ 8/ 8] Overall Loss 0.017601 Objective Loss 0.017601 MSE 0.017797 LR 0.001000 Time 0.053630 +2024-02-02 16:36:50,804 - --- validate (epoch=293)----------- +2024-02-02 16:36:50,804 - 60 samples (32 per mini-batch) +2024-02-02 16:36:51,131 - Epoch: [293][ 1/ 2] Loss 0.023355 MSE 0.023355 +2024-02-02 16:36:51,140 - Epoch: [293][ 2/ 2] Loss 0.022889 MSE 0.022920 +2024-02-02 16:36:51,282 - ==> MSE: 0.02292 Loss: 0.023 + +2024-02-02 16:36:51,287 - ==> Best [Top 1 (MSE): 0.02275 Sparsity:0.00 Params: 136448 on epoch: 289] +2024-02-02 16:36:51,287 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:51,291 - + +2024-02-02 16:36:51,291 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:51,633 - Epoch: [294][ 1/ 8] Overall Loss 0.018529 Objective Loss 0.018529 MSE 0.018529 LR 0.001000 Time 0.341483 +2024-02-02 16:36:51,644 - Epoch: [294][ 2/ 8] Overall Loss 0.018055 Objective Loss 0.018055 MSE 0.018055 LR 0.001000 Time 0.175917 +2024-02-02 16:36:51,652 - Epoch: [294][ 3/ 8] Overall Loss 0.017980 Objective Loss 0.017980 MSE 0.017980 LR 0.001000 Time 0.120088 +2024-02-02 16:36:51,661 - Epoch: [294][ 4/ 8] Overall Loss 0.018483 Objective Loss 0.018483 MSE 0.018483 LR 0.001000 Time 0.092144 +2024-02-02 16:36:51,669 - Epoch: [294][ 5/ 8] Overall Loss 0.018610 Objective Loss 0.018610 MSE 0.018610 LR 0.001000 Time 0.075394 +2024-02-02 16:36:51,678 - Epoch: [294][ 6/ 8] Overall Loss 0.018508 Objective Loss 0.018508 MSE 0.018508 LR 0.001000 Time 0.064221 +2024-02-02 16:36:51,687 - Epoch: [294][ 7/ 8] Overall Loss 0.017926 Objective Loss 0.017926 MSE 0.017926 LR 0.001000 Time 0.056247 +2024-02-02 16:36:51,695 - Epoch: [294][ 8/ 8] Overall Loss 0.017237 Objective Loss 0.017237 MSE 0.017782 LR 0.001000 Time 0.050270 +2024-02-02 16:36:51,848 - --- validate (epoch=294)----------- +2024-02-02 16:36:51,848 - 60 samples (32 per mini-batch) +2024-02-02 16:36:52,208 - Epoch: [294][ 1/ 2] Loss 0.024415 MSE 0.024415 +2024-02-02 16:36:52,214 - Epoch: [294][ 2/ 2] Loss 0.022560 MSE 0.022684 +2024-02-02 16:36:52,354 - ==> MSE: 0.02268 Loss: 0.023 + +2024-02-02 16:36:52,358 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:36:52,358 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:52,364 - + +2024-02-02 16:36:52,364 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:52,727 - Epoch: [295][ 1/ 8] Overall Loss 0.018469 Objective Loss 0.018469 MSE 0.018469 LR 0.001000 Time 0.363054 +2024-02-02 16:36:52,737 - Epoch: [295][ 2/ 8] Overall Loss 0.018359 Objective Loss 0.018359 MSE 0.018359 LR 0.001000 Time 0.186447 +2024-02-02 16:36:52,746 - Epoch: [295][ 3/ 8] Overall Loss 0.017815 Objective Loss 0.017815 MSE 0.017815 LR 0.001000 Time 0.127116 +2024-02-02 16:36:52,755 - Epoch: [295][ 4/ 8] Overall Loss 0.017425 Objective Loss 0.017425 MSE 0.017425 LR 0.001000 Time 0.097440 +2024-02-02 16:36:52,763 - Epoch: [295][ 5/ 8] Overall Loss 0.017733 Objective Loss 0.017733 MSE 0.017733 LR 0.001000 Time 0.079631 +2024-02-02 16:36:52,772 - Epoch: [295][ 6/ 8] Overall Loss 0.017843 Objective Loss 0.017843 MSE 0.017843 LR 0.001000 Time 0.067772 +2024-02-02 16:36:52,780 - Epoch: [295][ 7/ 8] Overall Loss 0.017774 Objective Loss 0.017774 MSE 0.017774 LR 0.001000 Time 0.059291 +2024-02-02 16:36:52,789 - Epoch: [295][ 8/ 8] Overall Loss 0.017579 Objective Loss 0.017579 MSE 0.017733 LR 0.001000 Time 0.052932 +2024-02-02 16:36:52,938 - --- validate (epoch=295)----------- +2024-02-02 16:36:52,938 - 60 samples (32 per mini-batch) +2024-02-02 16:36:53,282 - Epoch: [295][ 1/ 2] Loss 0.022627 MSE 0.022627 +2024-02-02 16:36:53,289 - Epoch: [295][ 2/ 2] Loss 0.022714 MSE 0.022708 +2024-02-02 16:36:53,438 - ==> MSE: 0.02271 Loss: 0.023 + +2024-02-02 16:36:53,443 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:36:53,443 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:53,447 - + +2024-02-02 16:36:53,448 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:53,811 - Epoch: [296][ 1/ 8] Overall Loss 0.017379 Objective Loss 0.017379 MSE 0.017379 LR 0.001000 Time 0.363231 +2024-02-02 16:36:53,820 - Epoch: [296][ 2/ 8] Overall Loss 0.018405 Objective Loss 0.018405 MSE 0.018405 LR 0.001000 Time 0.185950 +2024-02-02 16:36:53,829 - Epoch: [296][ 3/ 8] Overall Loss 0.018049 Objective Loss 0.018049 MSE 0.018049 LR 0.001000 Time 0.126773 +2024-02-02 16:36:53,837 - Epoch: [296][ 4/ 8] Overall Loss 0.017688 Objective Loss 0.017688 MSE 0.017688 LR 0.001000 Time 0.097159 +2024-02-02 16:36:53,846 - Epoch: [296][ 5/ 8] Overall Loss 0.017431 Objective Loss 0.017431 MSE 0.017431 LR 0.001000 Time 0.079405 +2024-02-02 16:36:53,854 - Epoch: [296][ 6/ 8] Overall Loss 0.017501 Objective Loss 0.017501 MSE 0.017501 LR 0.001000 Time 0.067576 +2024-02-02 16:36:53,863 - Epoch: [296][ 7/ 8] Overall Loss 0.017621 Objective Loss 0.017621 MSE 0.017621 LR 0.001000 Time 0.059121 +2024-02-02 16:36:53,871 - Epoch: [296][ 8/ 8] Overall Loss 0.017930 Objective Loss 0.017930 MSE 0.017686 LR 0.001000 Time 0.052772 +2024-02-02 16:36:54,022 - --- validate (epoch=296)----------- +2024-02-02 16:36:54,022 - 60 samples (32 per mini-batch) +2024-02-02 16:36:54,380 - Epoch: [296][ 1/ 2] Loss 0.022914 MSE 0.022914 +2024-02-02 16:36:54,386 - Epoch: [296][ 2/ 2] Loss 0.022913 MSE 0.022913 +2024-02-02 16:36:54,528 - ==> MSE: 0.02291 Loss: 0.023 + +2024-02-02 16:36:54,534 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:36:54,534 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:54,539 - + +2024-02-02 16:36:54,539 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:54,890 - Epoch: [297][ 1/ 8] Overall Loss 0.018391 Objective Loss 0.018391 MSE 0.018391 LR 0.001000 Time 0.351160 +2024-02-02 16:36:54,902 - Epoch: [297][ 2/ 8] Overall Loss 0.017839 Objective Loss 0.017839 MSE 0.017839 LR 0.001000 Time 0.181496 +2024-02-02 16:36:54,914 - Epoch: [297][ 3/ 8] Overall Loss 0.017691 Objective Loss 0.017691 MSE 0.017691 LR 0.001000 Time 0.124883 +2024-02-02 16:36:54,923 - Epoch: [297][ 4/ 8] Overall Loss 0.017564 Objective Loss 0.017564 MSE 0.017564 LR 0.001000 Time 0.095672 +2024-02-02 16:36:54,931 - Epoch: [297][ 5/ 8] Overall Loss 0.017422 Objective Loss 0.017422 MSE 0.017422 LR 0.001000 Time 0.078252 +2024-02-02 16:36:54,940 - Epoch: [297][ 6/ 8] Overall Loss 0.017749 Objective Loss 0.017749 MSE 0.017749 LR 0.001000 Time 0.066602 +2024-02-02 16:36:54,948 - Epoch: [297][ 7/ 8] Overall Loss 0.017662 Objective Loss 0.017662 MSE 0.017662 LR 0.001000 Time 0.058281 +2024-02-02 16:36:54,956 - Epoch: [297][ 8/ 8] Overall Loss 0.017935 Objective Loss 0.017935 MSE 0.017719 LR 0.001000 Time 0.051983 +2024-02-02 16:36:55,105 - --- validate (epoch=297)----------- +2024-02-02 16:36:55,105 - 60 samples (32 per mini-batch) +2024-02-02 16:36:55,466 - Epoch: [297][ 1/ 2] Loss 0.023389 MSE 0.023389 +2024-02-02 16:36:55,472 - Epoch: [297][ 2/ 2] Loss 0.022669 MSE 0.022717 +2024-02-02 16:36:55,622 - ==> MSE: 0.02272 Loss: 0.023 + +2024-02-02 16:36:55,627 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:36:55,627 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:55,631 - + +2024-02-02 16:36:55,631 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:55,987 - Epoch: [298][ 1/ 8] Overall Loss 0.017099 Objective Loss 0.017099 MSE 0.017099 LR 0.001000 Time 0.355907 +2024-02-02 16:36:56,000 - Epoch: [298][ 2/ 8] Overall Loss 0.017300 Objective Loss 0.017300 MSE 0.017300 LR 0.001000 Time 0.184104 +2024-02-02 16:36:56,010 - Epoch: [298][ 3/ 8] Overall Loss 0.017520 Objective Loss 0.017520 MSE 0.017520 LR 0.001000 Time 0.125924 +2024-02-02 16:36:56,019 - Epoch: [298][ 4/ 8] Overall Loss 0.018176 Objective Loss 0.018176 MSE 0.018176 LR 0.001000 Time 0.096550 +2024-02-02 16:36:56,027 - Epoch: [298][ 5/ 8] Overall Loss 0.018154 Objective Loss 0.018154 MSE 0.018154 LR 0.001000 Time 0.078906 +2024-02-02 16:36:56,036 - Epoch: [298][ 6/ 8] Overall Loss 0.017834 Objective Loss 0.017834 MSE 0.017834 LR 0.001000 Time 0.067132 +2024-02-02 16:36:56,044 - Epoch: [298][ 7/ 8] Overall Loss 0.017728 Objective Loss 0.017728 MSE 0.017728 LR 0.001000 Time 0.058726 +2024-02-02 16:36:56,054 - Epoch: [298][ 8/ 8] Overall Loss 0.017768 Objective Loss 0.017768 MSE 0.017736 LR 0.001000 Time 0.052553 +2024-02-02 16:36:56,203 - --- validate (epoch=298)----------- +2024-02-02 16:36:56,203 - 60 samples (32 per mini-batch) +2024-02-02 16:36:56,563 - Epoch: [298][ 1/ 2] Loss 0.022246 MSE 0.022246 +2024-02-02 16:36:56,569 - Epoch: [298][ 2/ 2] Loss 0.022828 MSE 0.022789 +2024-02-02 16:36:56,715 - ==> MSE: 0.02279 Loss: 0.023 + +2024-02-02 16:36:56,720 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:36:56,720 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:56,724 - + +2024-02-02 16:36:56,725 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:57,094 - Epoch: [299][ 1/ 8] Overall Loss 0.018353 Objective Loss 0.018353 MSE 0.018353 LR 0.001000 Time 0.368625 +2024-02-02 16:36:57,107 - Epoch: [299][ 2/ 8] Overall Loss 0.016910 Objective Loss 0.016910 MSE 0.016910 LR 0.001000 Time 0.190725 +2024-02-02 16:36:57,120 - Epoch: [299][ 3/ 8] Overall Loss 0.017164 Objective Loss 0.017164 MSE 0.017164 LR 0.001000 Time 0.131351 +2024-02-02 16:36:57,133 - Epoch: [299][ 4/ 8] Overall Loss 0.017935 Objective Loss 0.017935 MSE 0.017935 LR 0.001000 Time 0.101817 +2024-02-02 16:36:57,144 - Epoch: [299][ 5/ 8] Overall Loss 0.017623 Objective Loss 0.017623 MSE 0.017623 LR 0.001000 Time 0.083427 +2024-02-02 16:36:57,153 - Epoch: [299][ 6/ 8] Overall Loss 0.017715 Objective Loss 0.017715 MSE 0.017715 LR 0.001000 Time 0.071131 +2024-02-02 16:36:57,163 - Epoch: [299][ 7/ 8] Overall Loss 0.017680 Objective Loss 0.017680 MSE 0.017680 LR 0.001000 Time 0.062344 +2024-02-02 16:36:57,173 - Epoch: [299][ 8/ 8] Overall Loss 0.017893 Objective Loss 0.017893 MSE 0.017724 LR 0.001000 Time 0.055723 +2024-02-02 16:36:57,325 - --- validate (epoch=299)----------- +2024-02-02 16:36:57,325 - 60 samples (32 per mini-batch) +2024-02-02 16:36:57,684 - Epoch: [299][ 1/ 2] Loss 0.023238 MSE 0.023238 +2024-02-02 16:36:57,690 - Epoch: [299][ 2/ 2] Loss 0.022750 MSE 0.022782 +2024-02-02 16:36:57,833 - ==> MSE: 0.02278 Loss: 0.023 + +2024-02-02 16:36:57,839 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:36:57,839 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:57,843 - + +2024-02-02 16:36:57,843 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:58,205 - Epoch: [300][ 1/ 8] Overall Loss 0.017180 Objective Loss 0.017180 MSE 0.017180 LR 0.001000 Time 0.361818 +2024-02-02 16:36:58,215 - Epoch: [300][ 2/ 8] Overall Loss 0.018108 Objective Loss 0.018108 MSE 0.018108 LR 0.001000 Time 0.185676 +2024-02-02 16:36:58,225 - Epoch: [300][ 3/ 8] Overall Loss 0.018299 Objective Loss 0.018299 MSE 0.018299 LR 0.001000 Time 0.126917 +2024-02-02 16:36:58,234 - Epoch: [300][ 4/ 8] Overall Loss 0.017294 Objective Loss 0.017294 MSE 0.017294 LR 0.001000 Time 0.097523 +2024-02-02 16:36:58,244 - Epoch: [300][ 5/ 8] Overall Loss 0.017429 Objective Loss 0.017429 MSE 0.017429 LR 0.001000 Time 0.079886 +2024-02-02 16:36:58,253 - Epoch: [300][ 6/ 8] Overall Loss 0.017669 Objective Loss 0.017669 MSE 0.017669 LR 0.001000 Time 0.068127 +2024-02-02 16:36:58,263 - Epoch: [300][ 7/ 8] Overall Loss 0.017666 Objective Loss 0.017666 MSE 0.017666 LR 0.001000 Time 0.059730 +2024-02-02 16:36:58,272 - Epoch: [300][ 8/ 8] Overall Loss 0.017561 Objective Loss 0.017561 MSE 0.017644 LR 0.001000 Time 0.053350 +2024-02-02 16:36:58,422 - --- validate (epoch=300)----------- +2024-02-02 16:36:58,422 - 60 samples (32 per mini-batch) +2024-02-02 16:36:58,774 - Epoch: [300][ 1/ 2] Loss 0.022657 MSE 0.022657 +2024-02-02 16:36:58,780 - Epoch: [300][ 2/ 2] Loss 0.023049 MSE 0.023023 +2024-02-02 16:36:58,927 - ==> MSE: 0.02302 Loss: 0.023 + +2024-02-02 16:36:58,932 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:36:58,932 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:36:58,936 - + +2024-02-02 16:36:58,936 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:36:59,293 - Epoch: [301][ 1/ 8] Overall Loss 0.017512 Objective Loss 0.017512 MSE 0.017512 LR 0.001000 Time 0.355863 +2024-02-02 16:36:59,305 - Epoch: [301][ 2/ 8] Overall Loss 0.017463 Objective Loss 0.017463 MSE 0.017463 LR 0.001000 Time 0.184007 +2024-02-02 16:36:59,317 - Epoch: [301][ 3/ 8] Overall Loss 0.017606 Objective Loss 0.017606 MSE 0.017606 LR 0.001000 Time 0.126494 +2024-02-02 16:36:59,326 - Epoch: [301][ 4/ 8] Overall Loss 0.017550 Objective Loss 0.017550 MSE 0.017550 LR 0.001000 Time 0.097160 +2024-02-02 16:36:59,335 - Epoch: [301][ 5/ 8] Overall Loss 0.017559 Objective Loss 0.017559 MSE 0.017559 LR 0.001000 Time 0.079438 +2024-02-02 16:36:59,343 - Epoch: [301][ 6/ 8] Overall Loss 0.017558 Objective Loss 0.017558 MSE 0.017558 LR 0.001000 Time 0.067608 +2024-02-02 16:36:59,352 - Epoch: [301][ 7/ 8] Overall Loss 0.017784 Objective Loss 0.017784 MSE 0.017784 LR 0.001000 Time 0.059167 +2024-02-02 16:36:59,361 - Epoch: [301][ 8/ 8] Overall Loss 0.017245 Objective Loss 0.017245 MSE 0.017671 LR 0.001000 Time 0.052824 +2024-02-02 16:36:59,510 - --- validate (epoch=301)----------- +2024-02-02 16:36:59,510 - 60 samples (32 per mini-batch) +2024-02-02 16:36:59,860 - Epoch: [301][ 1/ 2] Loss 0.022795 MSE 0.022795 +2024-02-02 16:36:59,866 - Epoch: [301][ 2/ 2] Loss 0.022890 MSE 0.022884 +2024-02-02 16:37:00,013 - ==> MSE: 0.02288 Loss: 0.023 + +2024-02-02 16:37:00,019 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:00,019 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:00,023 - + +2024-02-02 16:37:00,023 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:00,388 - Epoch: [302][ 1/ 8] Overall Loss 0.018162 Objective Loss 0.018162 MSE 0.018162 LR 0.001000 Time 0.364311 +2024-02-02 16:37:00,398 - Epoch: [302][ 2/ 8] Overall Loss 0.018205 Objective Loss 0.018205 MSE 0.018205 LR 0.001000 Time 0.187171 +2024-02-02 16:37:00,408 - Epoch: [302][ 3/ 8] Overall Loss 0.017577 Objective Loss 0.017577 MSE 0.017577 LR 0.001000 Time 0.127908 +2024-02-02 16:37:00,416 - Epoch: [302][ 4/ 8] Overall Loss 0.017601 Objective Loss 0.017601 MSE 0.017601 LR 0.001000 Time 0.098024 +2024-02-02 16:37:00,425 - Epoch: [302][ 5/ 8] Overall Loss 0.017585 Objective Loss 0.017585 MSE 0.017585 LR 0.001000 Time 0.080092 +2024-02-02 16:37:00,433 - Epoch: [302][ 6/ 8] Overall Loss 0.017638 Objective Loss 0.017638 MSE 0.017638 LR 0.001000 Time 0.068138 +2024-02-02 16:37:00,442 - Epoch: [302][ 7/ 8] Overall Loss 0.017645 Objective Loss 0.017645 MSE 0.017645 LR 0.001000 Time 0.059543 +2024-02-02 16:37:00,450 - Epoch: [302][ 8/ 8] Overall Loss 0.017766 Objective Loss 0.017766 MSE 0.017670 LR 0.001000 Time 0.053126 +2024-02-02 16:37:00,595 - --- validate (epoch=302)----------- +2024-02-02 16:37:00,596 - 60 samples (32 per mini-batch) +2024-02-02 16:37:00,955 - Epoch: [302][ 1/ 2] Loss 0.023500 MSE 0.023500 +2024-02-02 16:37:00,961 - Epoch: [302][ 2/ 2] Loss 0.022922 MSE 0.022961 +2024-02-02 16:37:01,111 - ==> MSE: 0.02296 Loss: 0.023 + +2024-02-02 16:37:01,116 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:01,116 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:01,121 - + +2024-02-02 16:37:01,121 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:01,488 - Epoch: [303][ 1/ 8] Overall Loss 0.018479 Objective Loss 0.018479 MSE 0.018479 LR 0.001000 Time 0.366576 +2024-02-02 16:37:01,500 - Epoch: [303][ 2/ 8] Overall Loss 0.017501 Objective Loss 0.017501 MSE 0.017501 LR 0.001000 Time 0.189150 +2024-02-02 16:37:01,510 - Epoch: [303][ 3/ 8] Overall Loss 0.017914 Objective Loss 0.017914 MSE 0.017914 LR 0.001000 Time 0.129451 +2024-02-02 16:37:01,519 - Epoch: [303][ 4/ 8] Overall Loss 0.017110 Objective Loss 0.017110 MSE 0.017110 LR 0.001000 Time 0.099209 +2024-02-02 16:37:01,527 - Epoch: [303][ 5/ 8] Overall Loss 0.017129 Objective Loss 0.017129 MSE 0.017129 LR 0.001000 Time 0.081041 +2024-02-02 16:37:01,536 - Epoch: [303][ 6/ 8] Overall Loss 0.017363 Objective Loss 0.017363 MSE 0.017363 LR 0.001000 Time 0.068929 +2024-02-02 16:37:01,544 - Epoch: [303][ 7/ 8] Overall Loss 0.017687 Objective Loss 0.017687 MSE 0.017687 LR 0.001000 Time 0.060286 +2024-02-02 16:37:01,553 - Epoch: [303][ 8/ 8] Overall Loss 0.017537 Objective Loss 0.017537 MSE 0.017655 LR 0.001000 Time 0.053803 +2024-02-02 16:37:01,706 - --- validate (epoch=303)----------- +2024-02-02 16:37:01,706 - 60 samples (32 per mini-batch) +2024-02-02 16:37:02,067 - Epoch: [303][ 1/ 2] Loss 0.024318 MSE 0.024318 +2024-02-02 16:37:02,074 - Epoch: [303][ 2/ 2] Loss 0.022678 MSE 0.022787 +2024-02-02 16:37:02,223 - ==> MSE: 0.02279 Loss: 0.023 + +2024-02-02 16:37:02,228 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:02,228 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:02,233 - + +2024-02-02 16:37:02,233 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:02,590 - Epoch: [304][ 1/ 8] Overall Loss 0.018330 Objective Loss 0.018330 MSE 0.018330 LR 0.001000 Time 0.357332 +2024-02-02 16:37:02,603 - Epoch: [304][ 2/ 8] Overall Loss 0.017857 Objective Loss 0.017857 MSE 0.017857 LR 0.001000 Time 0.184563 +2024-02-02 16:37:02,611 - Epoch: [304][ 3/ 8] Overall Loss 0.017306 Objective Loss 0.017306 MSE 0.017306 LR 0.001000 Time 0.125903 +2024-02-02 16:37:02,620 - Epoch: [304][ 4/ 8] Overall Loss 0.017284 Objective Loss 0.017284 MSE 0.017284 LR 0.001000 Time 0.096496 +2024-02-02 16:37:02,628 - Epoch: [304][ 5/ 8] Overall Loss 0.017290 Objective Loss 0.017290 MSE 0.017290 LR 0.001000 Time 0.078899 +2024-02-02 16:37:02,637 - Epoch: [304][ 6/ 8] Overall Loss 0.017652 Objective Loss 0.017652 MSE 0.017652 LR 0.001000 Time 0.067150 +2024-02-02 16:37:02,646 - Epoch: [304][ 7/ 8] Overall Loss 0.017736 Objective Loss 0.017736 MSE 0.017736 LR 0.001000 Time 0.058762 +2024-02-02 16:37:02,654 - Epoch: [304][ 8/ 8] Overall Loss 0.017013 Objective Loss 0.017013 MSE 0.017585 LR 0.001000 Time 0.052504 +2024-02-02 16:37:02,804 - --- validate (epoch=304)----------- +2024-02-02 16:37:02,805 - 60 samples (32 per mini-batch) +2024-02-02 16:37:03,156 - Epoch: [304][ 1/ 2] Loss 0.023383 MSE 0.023383 +2024-02-02 16:37:03,163 - Epoch: [304][ 2/ 2] Loss 0.022802 MSE 0.022841 +2024-02-02 16:37:03,310 - ==> MSE: 0.02284 Loss: 0.023 + +2024-02-02 16:37:03,316 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:03,316 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:03,320 - + +2024-02-02 16:37:03,320 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:03,679 - Epoch: [305][ 1/ 8] Overall Loss 0.019339 Objective Loss 0.019339 MSE 0.019339 LR 0.001000 Time 0.358008 +2024-02-02 16:37:03,687 - Epoch: [305][ 2/ 8] Overall Loss 0.017848 Objective Loss 0.017848 MSE 0.017848 LR 0.001000 Time 0.183264 +2024-02-02 16:37:03,696 - Epoch: [305][ 3/ 8] Overall Loss 0.017294 Objective Loss 0.017294 MSE 0.017294 LR 0.001000 Time 0.124974 +2024-02-02 16:37:03,704 - Epoch: [305][ 4/ 8] Overall Loss 0.017491 Objective Loss 0.017491 MSE 0.017491 LR 0.001000 Time 0.095799 +2024-02-02 16:37:03,713 - Epoch: [305][ 5/ 8] Overall Loss 0.017762 Objective Loss 0.017762 MSE 0.017762 LR 0.001000 Time 0.078319 +2024-02-02 16:37:03,722 - Epoch: [305][ 6/ 8] Overall Loss 0.017473 Objective Loss 0.017473 MSE 0.017473 LR 0.001000 Time 0.066679 +2024-02-02 16:37:03,730 - Epoch: [305][ 7/ 8] Overall Loss 0.017457 Objective Loss 0.017457 MSE 0.017457 LR 0.001000 Time 0.058375 +2024-02-02 16:37:03,739 - Epoch: [305][ 8/ 8] Overall Loss 0.018023 Objective Loss 0.018023 MSE 0.017575 LR 0.001000 Time 0.052133 +2024-02-02 16:37:03,895 - --- validate (epoch=305)----------- +2024-02-02 16:37:03,895 - 60 samples (32 per mini-batch) +2024-02-02 16:37:04,266 - Epoch: [305][ 1/ 2] Loss 0.022355 MSE 0.022355 +2024-02-02 16:37:04,273 - Epoch: [305][ 2/ 2] Loss 0.022908 MSE 0.022871 +2024-02-02 16:37:04,423 - ==> MSE: 0.02287 Loss: 0.023 + +2024-02-02 16:37:04,428 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:04,428 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:04,432 - + +2024-02-02 16:37:04,432 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:04,796 - Epoch: [306][ 1/ 8] Overall Loss 0.018742 Objective Loss 0.018742 MSE 0.018742 LR 0.001000 Time 0.363682 +2024-02-02 16:37:04,808 - Epoch: [306][ 2/ 8] Overall Loss 0.017245 Objective Loss 0.017245 MSE 0.017245 LR 0.001000 Time 0.187523 +2024-02-02 16:37:04,818 - Epoch: [306][ 3/ 8] Overall Loss 0.017317 Objective Loss 0.017317 MSE 0.017317 LR 0.001000 Time 0.128385 +2024-02-02 16:37:04,827 - Epoch: [306][ 4/ 8] Overall Loss 0.017084 Objective Loss 0.017084 MSE 0.017084 LR 0.001000 Time 0.098357 +2024-02-02 16:37:04,835 - Epoch: [306][ 5/ 8] Overall Loss 0.017559 Objective Loss 0.017559 MSE 0.017559 LR 0.001000 Time 0.080362 +2024-02-02 16:37:04,844 - Epoch: [306][ 6/ 8] Overall Loss 0.017755 Objective Loss 0.017755 MSE 0.017755 LR 0.001000 Time 0.068336 +2024-02-02 16:37:04,852 - Epoch: [306][ 7/ 8] Overall Loss 0.017680 Objective Loss 0.017680 MSE 0.017680 LR 0.001000 Time 0.059755 +2024-02-02 16:37:04,860 - Epoch: [306][ 8/ 8] Overall Loss 0.017099 Objective Loss 0.017099 MSE 0.017559 LR 0.001000 Time 0.053314 +2024-02-02 16:37:05,012 - --- validate (epoch=306)----------- +2024-02-02 16:37:05,012 - 60 samples (32 per mini-batch) +2024-02-02 16:37:05,372 - Epoch: [306][ 1/ 2] Loss 0.023127 MSE 0.023127 +2024-02-02 16:37:05,378 - Epoch: [306][ 2/ 2] Loss 0.022935 MSE 0.022948 +2024-02-02 16:37:05,524 - ==> MSE: 0.02295 Loss: 0.023 + +2024-02-02 16:37:05,530 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:05,531 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:05,535 - + +2024-02-02 16:37:05,535 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:05,884 - Epoch: [307][ 1/ 8] Overall Loss 0.017394 Objective Loss 0.017394 MSE 0.017394 LR 0.001000 Time 0.348543 +2024-02-02 16:37:05,893 - Epoch: [307][ 2/ 8] Overall Loss 0.017729 Objective Loss 0.017729 MSE 0.017729 LR 0.001000 Time 0.178505 +2024-02-02 16:37:05,901 - Epoch: [307][ 3/ 8] Overall Loss 0.017928 Objective Loss 0.017928 MSE 0.017928 LR 0.001000 Time 0.121754 +2024-02-02 16:37:05,910 - Epoch: [307][ 4/ 8] Overall Loss 0.017938 Objective Loss 0.017938 MSE 0.017938 LR 0.001000 Time 0.093373 +2024-02-02 16:37:05,918 - Epoch: [307][ 5/ 8] Overall Loss 0.017864 Objective Loss 0.017864 MSE 0.017864 LR 0.001000 Time 0.076372 +2024-02-02 16:37:05,927 - Epoch: [307][ 6/ 8] Overall Loss 0.017860 Objective Loss 0.017860 MSE 0.017860 LR 0.001000 Time 0.065067 +2024-02-02 16:37:05,935 - Epoch: [307][ 7/ 8] Overall Loss 0.017672 Objective Loss 0.017672 MSE 0.017672 LR 0.001000 Time 0.056964 +2024-02-02 16:37:05,944 - Epoch: [307][ 8/ 8] Overall Loss 0.018309 Objective Loss 0.018309 MSE 0.017805 LR 0.001000 Time 0.050877 +2024-02-02 16:37:06,098 - --- validate (epoch=307)----------- +2024-02-02 16:37:06,098 - 60 samples (32 per mini-batch) +2024-02-02 16:37:06,457 - Epoch: [307][ 1/ 2] Loss 0.021446 MSE 0.021446 +2024-02-02 16:37:06,465 - Epoch: [307][ 2/ 2] Loss 0.022885 MSE 0.022789 +2024-02-02 16:37:06,614 - ==> MSE: 0.02279 Loss: 0.023 + +2024-02-02 16:37:06,620 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:06,620 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:06,624 - + +2024-02-02 16:37:06,624 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:06,983 - Epoch: [308][ 1/ 8] Overall Loss 0.017471 Objective Loss 0.017471 MSE 0.017471 LR 0.001000 Time 0.358206 +2024-02-02 16:37:06,995 - Epoch: [308][ 2/ 8] Overall Loss 0.017853 Objective Loss 0.017853 MSE 0.017853 LR 0.001000 Time 0.184987 +2024-02-02 16:37:07,007 - Epoch: [308][ 3/ 8] Overall Loss 0.017752 Objective Loss 0.017752 MSE 0.017752 LR 0.001000 Time 0.127178 +2024-02-02 16:37:07,015 - Epoch: [308][ 4/ 8] Overall Loss 0.018460 Objective Loss 0.018460 MSE 0.018460 LR 0.001000 Time 0.097385 +2024-02-02 16:37:07,024 - Epoch: [308][ 5/ 8] Overall Loss 0.018517 Objective Loss 0.018517 MSE 0.018517 LR 0.001000 Time 0.079569 +2024-02-02 16:37:07,032 - Epoch: [308][ 6/ 8] Overall Loss 0.018142 Objective Loss 0.018142 MSE 0.018142 LR 0.001000 Time 0.067685 +2024-02-02 16:37:07,041 - Epoch: [308][ 7/ 8] Overall Loss 0.017948 Objective Loss 0.017948 MSE 0.017948 LR 0.001000 Time 0.059196 +2024-02-02 16:37:07,049 - Epoch: [308][ 8/ 8] Overall Loss 0.017645 Objective Loss 0.017645 MSE 0.017885 LR 0.001000 Time 0.052811 +2024-02-02 16:37:07,202 - --- validate (epoch=308)----------- +2024-02-02 16:37:07,202 - 60 samples (32 per mini-batch) +2024-02-02 16:37:07,550 - Epoch: [308][ 1/ 2] Loss 0.022239 MSE 0.022239 +2024-02-02 16:37:07,556 - Epoch: [308][ 2/ 2] Loss 0.023062 MSE 0.023007 +2024-02-02 16:37:07,704 - ==> MSE: 0.02301 Loss: 0.023 + +2024-02-02 16:37:07,710 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:07,710 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:07,714 - + +2024-02-02 16:37:07,714 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:08,084 - Epoch: [309][ 1/ 8] Overall Loss 0.019788 Objective Loss 0.019788 MSE 0.019788 LR 0.001000 Time 0.369633 +2024-02-02 16:37:08,093 - Epoch: [309][ 2/ 8] Overall Loss 0.018309 Objective Loss 0.018309 MSE 0.018309 LR 0.001000 Time 0.189094 +2024-02-02 16:37:08,102 - Epoch: [309][ 3/ 8] Overall Loss 0.017872 Objective Loss 0.017872 MSE 0.017872 LR 0.001000 Time 0.128815 +2024-02-02 16:37:08,110 - Epoch: [309][ 4/ 8] Overall Loss 0.017547 Objective Loss 0.017547 MSE 0.017547 LR 0.001000 Time 0.098717 +2024-02-02 16:37:08,119 - Epoch: [309][ 5/ 8] Overall Loss 0.017882 Objective Loss 0.017882 MSE 0.017882 LR 0.001000 Time 0.080666 +2024-02-02 16:37:08,127 - Epoch: [309][ 6/ 8] Overall Loss 0.017947 Objective Loss 0.017947 MSE 0.017947 LR 0.001000 Time 0.068608 +2024-02-02 16:37:08,136 - Epoch: [309][ 7/ 8] Overall Loss 0.018055 Objective Loss 0.018055 MSE 0.018055 LR 0.001000 Time 0.059993 +2024-02-02 16:37:08,144 - Epoch: [309][ 8/ 8] Overall Loss 0.017532 Objective Loss 0.017532 MSE 0.017946 LR 0.001000 Time 0.053544 +2024-02-02 16:37:08,296 - --- validate (epoch=309)----------- +2024-02-02 16:37:08,296 - 60 samples (32 per mini-batch) +2024-02-02 16:37:08,644 - Epoch: [309][ 1/ 2] Loss 0.022235 MSE 0.022235 +2024-02-02 16:37:08,650 - Epoch: [309][ 2/ 2] Loss 0.022904 MSE 0.022859 +2024-02-02 16:37:08,794 - ==> MSE: 0.02286 Loss: 0.023 + +2024-02-02 16:37:08,799 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:08,799 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:08,804 - + +2024-02-02 16:37:08,804 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:09,169 - Epoch: [310][ 1/ 8] Overall Loss 0.016175 Objective Loss 0.016175 MSE 0.016175 LR 0.001000 Time 0.364530 +2024-02-02 16:37:09,178 - Epoch: [310][ 2/ 8] Overall Loss 0.016762 Objective Loss 0.016762 MSE 0.016762 LR 0.001000 Time 0.186640 +2024-02-02 16:37:09,187 - Epoch: [310][ 3/ 8] Overall Loss 0.017434 Objective Loss 0.017434 MSE 0.017434 LR 0.001000 Time 0.127256 +2024-02-02 16:37:09,195 - Epoch: [310][ 4/ 8] Overall Loss 0.017807 Objective Loss 0.017807 MSE 0.017807 LR 0.001000 Time 0.097521 +2024-02-02 16:37:09,203 - Epoch: [310][ 5/ 8] Overall Loss 0.017617 Objective Loss 0.017617 MSE 0.017617 LR 0.001000 Time 0.079637 +2024-02-02 16:37:09,212 - Epoch: [310][ 6/ 8] Overall Loss 0.017843 Objective Loss 0.017843 MSE 0.017843 LR 0.001000 Time 0.067753 +2024-02-02 16:37:09,220 - Epoch: [310][ 7/ 8] Overall Loss 0.017781 Objective Loss 0.017781 MSE 0.017781 LR 0.001000 Time 0.059261 +2024-02-02 16:37:09,229 - Epoch: [310][ 8/ 8] Overall Loss 0.017953 Objective Loss 0.017953 MSE 0.017817 LR 0.001000 Time 0.052888 +2024-02-02 16:37:09,378 - --- validate (epoch=310)----------- +2024-02-02 16:37:09,378 - 60 samples (32 per mini-batch) +2024-02-02 16:37:09,731 - Epoch: [310][ 1/ 2] Loss 0.022709 MSE 0.022709 +2024-02-02 16:37:09,737 - Epoch: [310][ 2/ 2] Loss 0.023209 MSE 0.023176 +2024-02-02 16:37:09,884 - ==> MSE: 0.02318 Loss: 0.023 + +2024-02-02 16:37:09,891 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:09,891 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:09,895 - + +2024-02-02 16:37:09,895 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:10,257 - Epoch: [311][ 1/ 8] Overall Loss 0.017906 Objective Loss 0.017906 MSE 0.017906 LR 0.001000 Time 0.361062 +2024-02-02 16:37:10,265 - Epoch: [311][ 2/ 8] Overall Loss 0.017698 Objective Loss 0.017698 MSE 0.017698 LR 0.001000 Time 0.184756 +2024-02-02 16:37:10,274 - Epoch: [311][ 3/ 8] Overall Loss 0.018271 Objective Loss 0.018271 MSE 0.018271 LR 0.001000 Time 0.125944 +2024-02-02 16:37:10,283 - Epoch: [311][ 4/ 8] Overall Loss 0.018306 Objective Loss 0.018306 MSE 0.018306 LR 0.001000 Time 0.096609 +2024-02-02 16:37:10,291 - Epoch: [311][ 5/ 8] Overall Loss 0.017948 Objective Loss 0.017948 MSE 0.017948 LR 0.001000 Time 0.078946 +2024-02-02 16:37:10,300 - Epoch: [311][ 6/ 8] Overall Loss 0.018010 Objective Loss 0.018010 MSE 0.018010 LR 0.001000 Time 0.067169 +2024-02-02 16:37:10,308 - Epoch: [311][ 7/ 8] Overall Loss 0.017888 Objective Loss 0.017888 MSE 0.017888 LR 0.001000 Time 0.058763 +2024-02-02 16:37:10,317 - Epoch: [311][ 8/ 8] Overall Loss 0.017442 Objective Loss 0.017442 MSE 0.017795 LR 0.001000 Time 0.052470 +2024-02-02 16:37:10,468 - --- validate (epoch=311)----------- +2024-02-02 16:37:10,468 - 60 samples (32 per mini-batch) +2024-02-02 16:37:10,828 - Epoch: [311][ 1/ 2] Loss 0.022886 MSE 0.022886 +2024-02-02 16:37:10,834 - Epoch: [311][ 2/ 2] Loss 0.023020 MSE 0.023011 +2024-02-02 16:37:10,983 - ==> MSE: 0.02301 Loss: 0.023 + +2024-02-02 16:37:10,990 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:10,990 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:10,995 - + +2024-02-02 16:37:10,995 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:11,356 - Epoch: [312][ 1/ 8] Overall Loss 0.017168 Objective Loss 0.017168 MSE 0.017168 LR 0.001000 Time 0.361233 +2024-02-02 16:37:11,365 - Epoch: [312][ 2/ 8] Overall Loss 0.017486 Objective Loss 0.017486 MSE 0.017486 LR 0.001000 Time 0.184912 +2024-02-02 16:37:11,374 - Epoch: [312][ 3/ 8] Overall Loss 0.017638 Objective Loss 0.017638 MSE 0.017638 LR 0.001000 Time 0.126096 +2024-02-02 16:37:11,382 - Epoch: [312][ 4/ 8] Overall Loss 0.017893 Objective Loss 0.017893 MSE 0.017893 LR 0.001000 Time 0.096663 +2024-02-02 16:37:11,391 - Epoch: [312][ 5/ 8] Overall Loss 0.017891 Objective Loss 0.017891 MSE 0.017891 LR 0.001000 Time 0.079016 +2024-02-02 16:37:11,399 - Epoch: [312][ 6/ 8] Overall Loss 0.017501 Objective Loss 0.017501 MSE 0.017501 LR 0.001000 Time 0.067242 +2024-02-02 16:37:11,408 - Epoch: [312][ 7/ 8] Overall Loss 0.017748 Objective Loss 0.017748 MSE 0.017748 LR 0.001000 Time 0.058829 +2024-02-02 16:37:11,417 - Epoch: [312][ 8/ 8] Overall Loss 0.018046 Objective Loss 0.018046 MSE 0.017810 LR 0.001000 Time 0.052519 +2024-02-02 16:37:11,567 - --- validate (epoch=312)----------- +2024-02-02 16:37:11,567 - 60 samples (32 per mini-batch) +2024-02-02 16:37:11,926 - Epoch: [312][ 1/ 2] Loss 0.023224 MSE 0.023224 +2024-02-02 16:37:11,932 - Epoch: [312][ 2/ 2] Loss 0.022700 MSE 0.022735 +2024-02-02 16:37:12,078 - ==> MSE: 0.02273 Loss: 0.023 + +2024-02-02 16:37:12,084 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:12,084 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:12,088 - + +2024-02-02 16:37:12,088 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:12,451 - Epoch: [313][ 1/ 8] Overall Loss 0.017184 Objective Loss 0.017184 MSE 0.017184 LR 0.001000 Time 0.362417 +2024-02-02 16:37:12,460 - Epoch: [313][ 2/ 8] Overall Loss 0.016610 Objective Loss 0.016610 MSE 0.016610 LR 0.001000 Time 0.185410 +2024-02-02 16:37:12,468 - Epoch: [313][ 3/ 8] Overall Loss 0.017598 Objective Loss 0.017598 MSE 0.017598 LR 0.001000 Time 0.126406 +2024-02-02 16:37:12,477 - Epoch: [313][ 4/ 8] Overall Loss 0.017715 Objective Loss 0.017715 MSE 0.017715 LR 0.001000 Time 0.096855 +2024-02-02 16:37:12,485 - Epoch: [313][ 5/ 8] Overall Loss 0.017706 Objective Loss 0.017706 MSE 0.017706 LR 0.001000 Time 0.079137 +2024-02-02 16:37:12,494 - Epoch: [313][ 6/ 8] Overall Loss 0.017684 Objective Loss 0.017684 MSE 0.017684 LR 0.001000 Time 0.067333 +2024-02-02 16:37:12,502 - Epoch: [313][ 7/ 8] Overall Loss 0.017697 Objective Loss 0.017697 MSE 0.017697 LR 0.001000 Time 0.058916 +2024-02-02 16:37:12,511 - Epoch: [313][ 8/ 8] Overall Loss 0.017532 Objective Loss 0.017532 MSE 0.017663 LR 0.001000 Time 0.052581 +2024-02-02 16:37:12,662 - --- validate (epoch=313)----------- +2024-02-02 16:37:12,663 - 60 samples (32 per mini-batch) +2024-02-02 16:37:13,022 - Epoch: [313][ 1/ 2] Loss 0.021822 MSE 0.021822 +2024-02-02 16:37:13,028 - Epoch: [313][ 2/ 2] Loss 0.022995 MSE 0.022916 +2024-02-02 16:37:13,174 - ==> MSE: 0.02292 Loss: 0.023 + +2024-02-02 16:37:13,180 - ==> Best [Top 1 (MSE): 0.02268 Sparsity:0.00 Params: 136448 on epoch: 294] +2024-02-02 16:37:13,180 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:13,185 - + +2024-02-02 16:37:13,185 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:13,552 - Epoch: [314][ 1/ 8] Overall Loss 0.015864 Objective Loss 0.015864 MSE 0.015864 LR 0.001000 Time 0.366866 +2024-02-02 16:37:13,563 - Epoch: [314][ 2/ 8] Overall Loss 0.016858 Objective Loss 0.016858 MSE 0.016858 LR 0.001000 Time 0.188731 +2024-02-02 16:37:13,571 - Epoch: [314][ 3/ 8] Overall Loss 0.017257 Objective Loss 0.017257 MSE 0.017257 LR 0.001000 Time 0.128539 +2024-02-02 16:37:13,580 - Epoch: [314][ 4/ 8] Overall Loss 0.017473 Objective Loss 0.017473 MSE 0.017473 LR 0.001000 Time 0.098457 +2024-02-02 16:37:13,588 - Epoch: [314][ 5/ 8] Overall Loss 0.017406 Objective Loss 0.017406 MSE 0.017406 LR 0.001000 Time 0.080416 +2024-02-02 16:37:13,597 - Epoch: [314][ 6/ 8] Overall Loss 0.017483 Objective Loss 0.017483 MSE 0.017483 LR 0.001000 Time 0.068389 +2024-02-02 16:37:13,605 - Epoch: [314][ 7/ 8] Overall Loss 0.017674 Objective Loss 0.017674 MSE 0.017674 LR 0.001000 Time 0.059767 +2024-02-02 16:37:13,613 - Epoch: [314][ 8/ 8] Overall Loss 0.017485 Objective Loss 0.017485 MSE 0.017634 LR 0.001000 Time 0.053318 +2024-02-02 16:37:13,757 - --- validate (epoch=314)----------- +2024-02-02 16:37:13,757 - 60 samples (32 per mini-batch) +2024-02-02 16:37:14,111 - Epoch: [314][ 1/ 2] Loss 0.021822 MSE 0.021822 +2024-02-02 16:37:14,117 - Epoch: [314][ 2/ 2] Loss 0.022664 MSE 0.022608 +2024-02-02 16:37:14,260 - ==> MSE: 0.02261 Loss: 0.023 + +2024-02-02 16:37:14,279 - ==> Best [Top 1 (MSE): 0.02261 Sparsity:0.00 Params: 136448 on epoch: 314] +2024-02-02 16:37:14,279 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:14,289 - + +2024-02-02 16:37:14,289 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:14,663 - Epoch: [315][ 1/ 8] Overall Loss 0.016167 Objective Loss 0.016167 MSE 0.016167 LR 0.001000 Time 0.373928 +2024-02-02 16:37:14,675 - Epoch: [315][ 2/ 8] Overall Loss 0.016220 Objective Loss 0.016220 MSE 0.016220 LR 0.001000 Time 0.192743 +2024-02-02 16:37:14,687 - Epoch: [315][ 3/ 8] Overall Loss 0.016616 Objective Loss 0.016616 MSE 0.016616 LR 0.001000 Time 0.132262 +2024-02-02 16:37:14,695 - Epoch: [315][ 4/ 8] Overall Loss 0.017163 Objective Loss 0.017163 MSE 0.017163 LR 0.001000 Time 0.101204 +2024-02-02 16:37:14,703 - Epoch: [315][ 5/ 8] Overall Loss 0.017811 Objective Loss 0.017811 MSE 0.017811 LR 0.001000 Time 0.082566 +2024-02-02 16:37:14,712 - Epoch: [315][ 6/ 8] Overall Loss 0.017505 Objective Loss 0.017505 MSE 0.017505 LR 0.001000 Time 0.070175 +2024-02-02 16:37:14,720 - Epoch: [315][ 7/ 8] Overall Loss 0.017784 Objective Loss 0.017784 MSE 0.017784 LR 0.001000 Time 0.061341 +2024-02-02 16:37:14,729 - Epoch: [315][ 8/ 8] Overall Loss 0.016988 Objective Loss 0.016988 MSE 0.017618 LR 0.001000 Time 0.054703 +2024-02-02 16:37:14,877 - --- validate (epoch=315)----------- +2024-02-02 16:37:14,878 - 60 samples (32 per mini-batch) +2024-02-02 16:37:15,228 - Epoch: [315][ 1/ 2] Loss 0.023463 MSE 0.023463 +2024-02-02 16:37:15,234 - Epoch: [315][ 2/ 2] Loss 0.022362 MSE 0.022435 +2024-02-02 16:37:15,377 - ==> MSE: 0.02244 Loss: 0.022 + +2024-02-02 16:37:15,383 - ==> Best [Top 1 (MSE): 0.02244 Sparsity:0.00 Params: 136448 on epoch: 315] +2024-02-02 16:37:15,384 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:15,389 - + +2024-02-02 16:37:15,389 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:15,753 - Epoch: [316][ 1/ 8] Overall Loss 0.016542 Objective Loss 0.016542 MSE 0.016542 LR 0.001000 Time 0.363318 +2024-02-02 16:37:15,762 - Epoch: [316][ 2/ 8] Overall Loss 0.017014 Objective Loss 0.017014 MSE 0.017014 LR 0.001000 Time 0.185961 +2024-02-02 16:37:15,770 - Epoch: [316][ 3/ 8] Overall Loss 0.016898 Objective Loss 0.016898 MSE 0.016898 LR 0.001000 Time 0.126764 +2024-02-02 16:37:15,779 - Epoch: [316][ 4/ 8] Overall Loss 0.017176 Objective Loss 0.017176 MSE 0.017176 LR 0.001000 Time 0.097159 +2024-02-02 16:37:15,787 - Epoch: [316][ 5/ 8] Overall Loss 0.017386 Objective Loss 0.017386 MSE 0.017386 LR 0.001000 Time 0.079400 +2024-02-02 16:37:15,796 - Epoch: [316][ 6/ 8] Overall Loss 0.017496 Objective Loss 0.017496 MSE 0.017496 LR 0.001000 Time 0.067551 +2024-02-02 16:37:15,804 - Epoch: [316][ 7/ 8] Overall Loss 0.017473 Objective Loss 0.017473 MSE 0.017473 LR 0.001000 Time 0.059104 +2024-02-02 16:37:15,812 - Epoch: [316][ 8/ 8] Overall Loss 0.017856 Objective Loss 0.017856 MSE 0.017553 LR 0.001000 Time 0.052707 +2024-02-02 16:37:15,965 - --- validate (epoch=316)----------- +2024-02-02 16:37:15,965 - 60 samples (32 per mini-batch) +2024-02-02 16:37:16,324 - Epoch: [316][ 1/ 2] Loss 0.022272 MSE 0.022272 +2024-02-02 16:37:16,330 - Epoch: [316][ 2/ 2] Loss 0.022526 MSE 0.022509 +2024-02-02 16:37:16,474 - ==> MSE: 0.02251 Loss: 0.023 + +2024-02-02 16:37:16,480 - ==> Best [Top 1 (MSE): 0.02244 Sparsity:0.00 Params: 136448 on epoch: 315] +2024-02-02 16:37:16,480 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:16,485 - + +2024-02-02 16:37:16,485 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:16,855 - Epoch: [317][ 1/ 8] Overall Loss 0.018481 Objective Loss 0.018481 MSE 0.018481 LR 0.001000 Time 0.369762 +2024-02-02 16:37:16,864 - Epoch: [317][ 2/ 8] Overall Loss 0.017313 Objective Loss 0.017313 MSE 0.017313 LR 0.001000 Time 0.189210 +2024-02-02 16:37:16,872 - Epoch: [317][ 3/ 8] Overall Loss 0.017186 Objective Loss 0.017186 MSE 0.017186 LR 0.001000 Time 0.128938 +2024-02-02 16:37:16,881 - Epoch: [317][ 4/ 8] Overall Loss 0.016891 Objective Loss 0.016891 MSE 0.016891 LR 0.001000 Time 0.098770 +2024-02-02 16:37:16,889 - Epoch: [317][ 5/ 8] Overall Loss 0.017299 Objective Loss 0.017299 MSE 0.017299 LR 0.001000 Time 0.080687 +2024-02-02 16:37:16,898 - Epoch: [317][ 6/ 8] Overall Loss 0.017265 Objective Loss 0.017265 MSE 0.017265 LR 0.001000 Time 0.068665 +2024-02-02 16:37:16,907 - Epoch: [317][ 7/ 8] Overall Loss 0.017497 Objective Loss 0.017497 MSE 0.017497 LR 0.001000 Time 0.060072 +2024-02-02 16:37:16,915 - Epoch: [317][ 8/ 8] Overall Loss 0.017613 Objective Loss 0.017613 MSE 0.017521 LR 0.001000 Time 0.053609 +2024-02-02 16:37:17,062 - --- validate (epoch=317)----------- +2024-02-02 16:37:17,062 - 60 samples (32 per mini-batch) +2024-02-02 16:37:17,421 - Epoch: [317][ 1/ 2] Loss 0.021779 MSE 0.021779 +2024-02-02 16:37:17,427 - Epoch: [317][ 2/ 2] Loss 0.022777 MSE 0.022710 +2024-02-02 16:37:17,576 - ==> MSE: 0.02271 Loss: 0.023 + +2024-02-02 16:37:17,583 - ==> Best [Top 1 (MSE): 0.02244 Sparsity:0.00 Params: 136448 on epoch: 315] +2024-02-02 16:37:17,583 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:17,588 - + +2024-02-02 16:37:17,588 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:17,950 - Epoch: [318][ 1/ 8] Overall Loss 0.017383 Objective Loss 0.017383 MSE 0.017383 LR 0.001000 Time 0.361875 +2024-02-02 16:37:17,961 - Epoch: [318][ 2/ 8] Overall Loss 0.018293 Objective Loss 0.018293 MSE 0.018293 LR 0.001000 Time 0.186281 +2024-02-02 16:37:17,970 - Epoch: [318][ 3/ 8] Overall Loss 0.017536 Objective Loss 0.017536 MSE 0.017536 LR 0.001000 Time 0.127043 +2024-02-02 16:37:17,979 - Epoch: [318][ 4/ 8] Overall Loss 0.016977 Objective Loss 0.016977 MSE 0.016977 LR 0.001000 Time 0.097418 +2024-02-02 16:37:17,987 - Epoch: [318][ 5/ 8] Overall Loss 0.017324 Objective Loss 0.017324 MSE 0.017324 LR 0.001000 Time 0.079616 +2024-02-02 16:37:17,996 - Epoch: [318][ 6/ 8] Overall Loss 0.017576 Objective Loss 0.017576 MSE 0.017576 LR 0.001000 Time 0.067729 +2024-02-02 16:37:18,004 - Epoch: [318][ 7/ 8] Overall Loss 0.017485 Objective Loss 0.017485 MSE 0.017485 LR 0.001000 Time 0.059247 +2024-02-02 16:37:18,012 - Epoch: [318][ 8/ 8] Overall Loss 0.017606 Objective Loss 0.017606 MSE 0.017510 LR 0.001000 Time 0.052818 +2024-02-02 16:37:18,162 - --- validate (epoch=318)----------- +2024-02-02 16:37:18,162 - 60 samples (32 per mini-batch) +2024-02-02 16:37:18,519 - Epoch: [318][ 1/ 2] Loss 0.022704 MSE 0.022704 +2024-02-02 16:37:18,528 - Epoch: [318][ 2/ 2] Loss 0.022563 MSE 0.022572 +2024-02-02 16:37:18,679 - ==> MSE: 0.02257 Loss: 0.023 + +2024-02-02 16:37:18,684 - ==> Best [Top 1 (MSE): 0.02244 Sparsity:0.00 Params: 136448 on epoch: 315] +2024-02-02 16:37:18,685 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:18,689 - + +2024-02-02 16:37:18,689 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:19,052 - Epoch: [319][ 1/ 8] Overall Loss 0.017371 Objective Loss 0.017371 MSE 0.017371 LR 0.001000 Time 0.362478 +2024-02-02 16:37:19,061 - Epoch: [319][ 2/ 8] Overall Loss 0.016887 Objective Loss 0.016887 MSE 0.016887 LR 0.001000 Time 0.185506 +2024-02-02 16:37:19,069 - Epoch: [319][ 3/ 8] Overall Loss 0.017080 Objective Loss 0.017080 MSE 0.017080 LR 0.001000 Time 0.126469 +2024-02-02 16:37:19,078 - Epoch: [319][ 4/ 8] Overall Loss 0.016770 Objective Loss 0.016770 MSE 0.016770 LR 0.001000 Time 0.096957 +2024-02-02 16:37:19,087 - Epoch: [319][ 5/ 8] Overall Loss 0.016954 Objective Loss 0.016954 MSE 0.016954 LR 0.001000 Time 0.079234 +2024-02-02 16:37:19,095 - Epoch: [319][ 6/ 8] Overall Loss 0.017344 Objective Loss 0.017344 MSE 0.017344 LR 0.001000 Time 0.067443 +2024-02-02 16:37:19,103 - Epoch: [319][ 7/ 8] Overall Loss 0.017418 Objective Loss 0.017418 MSE 0.017418 LR 0.001000 Time 0.058930 +2024-02-02 16:37:19,112 - Epoch: [319][ 8/ 8] Overall Loss 0.017568 Objective Loss 0.017568 MSE 0.017449 LR 0.001000 Time 0.052590 +2024-02-02 16:37:19,268 - --- validate (epoch=319)----------- +2024-02-02 16:37:19,269 - 60 samples (32 per mini-batch) +2024-02-02 16:37:19,647 - Epoch: [319][ 1/ 2] Loss 0.022490 MSE 0.022490 +2024-02-02 16:37:19,653 - Epoch: [319][ 2/ 2] Loss 0.022589 MSE 0.022583 +2024-02-02 16:37:19,793 - ==> MSE: 0.02258 Loss: 0.023 + +2024-02-02 16:37:19,800 - ==> Best [Top 1 (MSE): 0.02244 Sparsity:0.00 Params: 136448 on epoch: 315] +2024-02-02 16:37:19,800 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:19,804 - + +2024-02-02 16:37:19,804 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:20,168 - Epoch: [320][ 1/ 8] Overall Loss 0.018633 Objective Loss 0.018633 MSE 0.018633 LR 0.001000 Time 0.363362 +2024-02-02 16:37:20,180 - Epoch: [320][ 2/ 8] Overall Loss 0.017969 Objective Loss 0.017969 MSE 0.017969 LR 0.001000 Time 0.187439 +2024-02-02 16:37:20,190 - Epoch: [320][ 3/ 8] Overall Loss 0.017648 Objective Loss 0.017648 MSE 0.017648 LR 0.001000 Time 0.128149 +2024-02-02 16:37:20,198 - Epoch: [320][ 4/ 8] Overall Loss 0.017599 Objective Loss 0.017599 MSE 0.017599 LR 0.001000 Time 0.098216 +2024-02-02 16:37:20,207 - Epoch: [320][ 5/ 8] Overall Loss 0.017498 Objective Loss 0.017498 MSE 0.017498 LR 0.001000 Time 0.080246 +2024-02-02 16:37:20,215 - Epoch: [320][ 6/ 8] Overall Loss 0.017390 Objective Loss 0.017390 MSE 0.017390 LR 0.001000 Time 0.068253 +2024-02-02 16:37:20,224 - Epoch: [320][ 7/ 8] Overall Loss 0.017595 Objective Loss 0.017595 MSE 0.017595 LR 0.001000 Time 0.059710 +2024-02-02 16:37:20,232 - Epoch: [320][ 8/ 8] Overall Loss 0.016980 Objective Loss 0.016980 MSE 0.017466 LR 0.001000 Time 0.053279 +2024-02-02 16:37:20,386 - --- validate (epoch=320)----------- +2024-02-02 16:37:20,386 - 60 samples (32 per mini-batch) +2024-02-02 16:37:20,747 - Epoch: [320][ 1/ 2] Loss 0.023099 MSE 0.023099 +2024-02-02 16:37:20,753 - Epoch: [320][ 2/ 2] Loss 0.022563 MSE 0.022599 +2024-02-02 16:37:20,906 - ==> MSE: 0.02260 Loss: 0.023 + +2024-02-02 16:37:20,912 - ==> Best [Top 1 (MSE): 0.02244 Sparsity:0.00 Params: 136448 on epoch: 315] +2024-02-02 16:37:20,912 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:20,917 - + +2024-02-02 16:37:20,917 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:21,274 - Epoch: [321][ 1/ 8] Overall Loss 0.015184 Objective Loss 0.015184 MSE 0.015184 LR 0.001000 Time 0.356640 +2024-02-02 16:37:21,283 - Epoch: [321][ 2/ 8] Overall Loss 0.016215 Objective Loss 0.016215 MSE 0.016215 LR 0.001000 Time 0.182748 +2024-02-02 16:37:21,291 - Epoch: [321][ 3/ 8] Overall Loss 0.016564 Objective Loss 0.016564 MSE 0.016564 LR 0.001000 Time 0.124620 +2024-02-02 16:37:21,301 - Epoch: [321][ 4/ 8] Overall Loss 0.016613 Objective Loss 0.016613 MSE 0.016613 LR 0.001000 Time 0.095782 +2024-02-02 16:37:21,310 - Epoch: [321][ 5/ 8] Overall Loss 0.016845 Objective Loss 0.016845 MSE 0.016845 LR 0.001000 Time 0.078508 +2024-02-02 16:37:21,320 - Epoch: [321][ 6/ 8] Overall Loss 0.017214 Objective Loss 0.017214 MSE 0.017214 LR 0.001000 Time 0.066960 +2024-02-02 16:37:21,329 - Epoch: [321][ 7/ 8] Overall Loss 0.017338 Objective Loss 0.017338 MSE 0.017338 LR 0.001000 Time 0.058720 +2024-02-02 16:37:21,338 - Epoch: [321][ 8/ 8] Overall Loss 0.017701 Objective Loss 0.017701 MSE 0.017414 LR 0.001000 Time 0.052505 +2024-02-02 16:37:21,490 - --- validate (epoch=321)----------- +2024-02-02 16:37:21,490 - 60 samples (32 per mini-batch) +2024-02-02 16:37:21,848 - Epoch: [321][ 1/ 2] Loss 0.022943 MSE 0.022943 +2024-02-02 16:37:21,854 - Epoch: [321][ 2/ 2] Loss 0.022531 MSE 0.022558 +2024-02-02 16:37:22,005 - ==> MSE: 0.02256 Loss: 0.023 + +2024-02-02 16:37:22,011 - ==> Best [Top 1 (MSE): 0.02244 Sparsity:0.00 Params: 136448 on epoch: 315] +2024-02-02 16:37:22,012 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:22,016 - + +2024-02-02 16:37:22,016 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:22,383 - Epoch: [322][ 1/ 8] Overall Loss 0.017816 Objective Loss 0.017816 MSE 0.017816 LR 0.001000 Time 0.366077 +2024-02-02 16:37:22,393 - Epoch: [322][ 2/ 8] Overall Loss 0.018571 Objective Loss 0.018571 MSE 0.018571 LR 0.001000 Time 0.187873 +2024-02-02 16:37:22,402 - Epoch: [322][ 3/ 8] Overall Loss 0.017323 Objective Loss 0.017323 MSE 0.017323 LR 0.001000 Time 0.128404 +2024-02-02 16:37:22,412 - Epoch: [322][ 4/ 8] Overall Loss 0.016899 Objective Loss 0.016899 MSE 0.016899 LR 0.001000 Time 0.098686 +2024-02-02 16:37:22,422 - Epoch: [322][ 5/ 8] Overall Loss 0.017575 Objective Loss 0.017575 MSE 0.017575 LR 0.001000 Time 0.080839 +2024-02-02 16:37:22,431 - Epoch: [322][ 6/ 8] Overall Loss 0.017550 Objective Loss 0.017550 MSE 0.017550 LR 0.001000 Time 0.068967 +2024-02-02 16:37:22,441 - Epoch: [322][ 7/ 8] Overall Loss 0.017463 Objective Loss 0.017463 MSE 0.017463 LR 0.001000 Time 0.060478 +2024-02-02 16:37:22,450 - Epoch: [322][ 8/ 8] Overall Loss 0.017451 Objective Loss 0.017451 MSE 0.017461 LR 0.001000 Time 0.054072 +2024-02-02 16:37:22,600 - --- validate (epoch=322)----------- +2024-02-02 16:37:22,600 - 60 samples (32 per mini-batch) +2024-02-02 16:37:22,947 - Epoch: [322][ 1/ 2] Loss 0.022322 MSE 0.022322 +2024-02-02 16:37:22,956 - Epoch: [322][ 2/ 2] Loss 0.022422 MSE 0.022415 +2024-02-02 16:37:23,099 - ==> MSE: 0.02242 Loss: 0.022 + +2024-02-02 16:37:23,112 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:23,113 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:23,122 - + +2024-02-02 16:37:23,122 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:23,497 - Epoch: [323][ 1/ 8] Overall Loss 0.016094 Objective Loss 0.016094 MSE 0.016094 LR 0.001000 Time 0.374854 +2024-02-02 16:37:23,507 - Epoch: [323][ 2/ 8] Overall Loss 0.018069 Objective Loss 0.018069 MSE 0.018069 LR 0.001000 Time 0.192130 +2024-02-02 16:37:23,516 - Epoch: [323][ 3/ 8] Overall Loss 0.017277 Objective Loss 0.017277 MSE 0.017277 LR 0.001000 Time 0.131168 +2024-02-02 16:37:23,526 - Epoch: [323][ 4/ 8] Overall Loss 0.017699 Objective Loss 0.017699 MSE 0.017699 LR 0.001000 Time 0.100728 +2024-02-02 16:37:23,536 - Epoch: [323][ 5/ 8] Overall Loss 0.017486 Objective Loss 0.017486 MSE 0.017486 LR 0.001000 Time 0.082470 +2024-02-02 16:37:23,545 - Epoch: [323][ 6/ 8] Overall Loss 0.017439 Objective Loss 0.017439 MSE 0.017439 LR 0.001000 Time 0.070292 +2024-02-02 16:37:23,555 - Epoch: [323][ 7/ 8] Overall Loss 0.017308 Objective Loss 0.017308 MSE 0.017308 LR 0.001000 Time 0.061602 +2024-02-02 16:37:23,564 - Epoch: [323][ 8/ 8] Overall Loss 0.018013 Objective Loss 0.018013 MSE 0.017456 LR 0.001000 Time 0.055056 +2024-02-02 16:37:23,714 - --- validate (epoch=323)----------- +2024-02-02 16:37:23,714 - 60 samples (32 per mini-batch) +2024-02-02 16:37:24,075 - Epoch: [323][ 1/ 2] Loss 0.022885 MSE 0.022885 +2024-02-02 16:37:24,081 - Epoch: [323][ 2/ 2] Loss 0.022645 MSE 0.022661 +2024-02-02 16:37:24,226 - ==> MSE: 0.02266 Loss: 0.023 + +2024-02-02 16:37:24,232 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:24,232 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:24,237 - + +2024-02-02 16:37:24,237 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:24,611 - Epoch: [324][ 1/ 8] Overall Loss 0.016029 Objective Loss 0.016029 MSE 0.016029 LR 0.001000 Time 0.374116 +2024-02-02 16:37:24,625 - Epoch: [324][ 2/ 8] Overall Loss 0.016438 Objective Loss 0.016438 MSE 0.016438 LR 0.001000 Time 0.193407 +2024-02-02 16:37:24,635 - Epoch: [324][ 3/ 8] Overall Loss 0.016852 Objective Loss 0.016852 MSE 0.016852 LR 0.001000 Time 0.132320 +2024-02-02 16:37:24,644 - Epoch: [324][ 4/ 8] Overall Loss 0.017146 Objective Loss 0.017146 MSE 0.017146 LR 0.001000 Time 0.101604 +2024-02-02 16:37:24,654 - Epoch: [324][ 5/ 8] Overall Loss 0.017448 Objective Loss 0.017448 MSE 0.017448 LR 0.001000 Time 0.083212 +2024-02-02 16:37:24,664 - Epoch: [324][ 6/ 8] Overall Loss 0.017539 Objective Loss 0.017539 MSE 0.017539 LR 0.001000 Time 0.070948 +2024-02-02 16:37:24,674 - Epoch: [324][ 7/ 8] Overall Loss 0.017558 Objective Loss 0.017558 MSE 0.017558 LR 0.001000 Time 0.062178 +2024-02-02 16:37:24,683 - Epoch: [324][ 8/ 8] Overall Loss 0.017156 Objective Loss 0.017156 MSE 0.017474 LR 0.001000 Time 0.055575 +2024-02-02 16:37:24,836 - --- validate (epoch=324)----------- +2024-02-02 16:37:24,836 - 60 samples (32 per mini-batch) +2024-02-02 16:37:25,203 - Epoch: [324][ 1/ 2] Loss 0.023703 MSE 0.023703 +2024-02-02 16:37:25,209 - Epoch: [324][ 2/ 2] Loss 0.022445 MSE 0.022528 +2024-02-02 16:37:25,359 - ==> MSE: 0.02253 Loss: 0.022 + +2024-02-02 16:37:25,365 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:25,365 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:25,369 - + +2024-02-02 16:37:25,369 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:25,723 - Epoch: [325][ 1/ 8] Overall Loss 0.016350 Objective Loss 0.016350 MSE 0.016350 LR 0.001000 Time 0.353466 +2024-02-02 16:37:25,733 - Epoch: [325][ 2/ 8] Overall Loss 0.018081 Objective Loss 0.018081 MSE 0.018081 LR 0.001000 Time 0.181540 +2024-02-02 16:37:25,742 - Epoch: [325][ 3/ 8] Overall Loss 0.017341 Objective Loss 0.017341 MSE 0.017341 LR 0.001000 Time 0.124155 +2024-02-02 16:37:25,752 - Epoch: [325][ 4/ 8] Overall Loss 0.017324 Objective Loss 0.017324 MSE 0.017324 LR 0.001000 Time 0.095458 +2024-02-02 16:37:25,760 - Epoch: [325][ 5/ 8] Overall Loss 0.017477 Objective Loss 0.017477 MSE 0.017477 LR 0.001000 Time 0.078046 +2024-02-02 16:37:25,769 - Epoch: [325][ 6/ 8] Overall Loss 0.017646 Objective Loss 0.017646 MSE 0.017646 LR 0.001000 Time 0.066433 +2024-02-02 16:37:25,778 - Epoch: [325][ 7/ 8] Overall Loss 0.017617 Objective Loss 0.017617 MSE 0.017617 LR 0.001000 Time 0.058150 +2024-02-02 16:37:25,786 - Epoch: [325][ 8/ 8] Overall Loss 0.017631 Objective Loss 0.017631 MSE 0.017620 LR 0.001000 Time 0.051874 +2024-02-02 16:37:25,935 - --- validate (epoch=325)----------- +2024-02-02 16:37:25,935 - 60 samples (32 per mini-batch) +2024-02-02 16:37:26,292 - Epoch: [325][ 1/ 2] Loss 0.021548 MSE 0.021548 +2024-02-02 16:37:26,454 - Epoch: [325][ 2/ 2] Loss 0.022856 MSE 0.022768 +2024-02-02 16:37:26,603 - ==> MSE: 0.02277 Loss: 0.023 + +2024-02-02 16:37:26,610 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:26,610 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:26,614 - + +2024-02-02 16:37:26,614 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:26,966 - Epoch: [326][ 1/ 8] Overall Loss 0.018429 Objective Loss 0.018429 MSE 0.018429 LR 0.001000 Time 0.351804 +2024-02-02 16:37:26,975 - Epoch: [326][ 2/ 8] Overall Loss 0.018382 Objective Loss 0.018382 MSE 0.018382 LR 0.001000 Time 0.180145 +2024-02-02 16:37:26,984 - Epoch: [326][ 3/ 8] Overall Loss 0.018004 Objective Loss 0.018004 MSE 0.018004 LR 0.001000 Time 0.122900 +2024-02-02 16:37:26,992 - Epoch: [326][ 4/ 8] Overall Loss 0.017981 Objective Loss 0.017981 MSE 0.017981 LR 0.001000 Time 0.094256 +2024-02-02 16:37:27,001 - Epoch: [326][ 5/ 8] Overall Loss 0.018002 Objective Loss 0.018002 MSE 0.018002 LR 0.001000 Time 0.077112 +2024-02-02 16:37:27,009 - Epoch: [326][ 6/ 8] Overall Loss 0.017846 Objective Loss 0.017846 MSE 0.017846 LR 0.001000 Time 0.065657 +2024-02-02 16:37:27,018 - Epoch: [326][ 7/ 8] Overall Loss 0.017705 Objective Loss 0.017705 MSE 0.017705 LR 0.001000 Time 0.057490 +2024-02-02 16:37:27,027 - Epoch: [326][ 8/ 8] Overall Loss 0.017193 Objective Loss 0.017193 MSE 0.017598 LR 0.001000 Time 0.051351 +2024-02-02 16:37:27,175 - --- validate (epoch=326)----------- +2024-02-02 16:37:27,175 - 60 samples (32 per mini-batch) +2024-02-02 16:37:27,523 - Epoch: [326][ 1/ 2] Loss 0.024026 MSE 0.024026 +2024-02-02 16:37:27,529 - Epoch: [326][ 2/ 2] Loss 0.022456 MSE 0.022561 +2024-02-02 16:37:27,677 - ==> MSE: 0.02256 Loss: 0.022 + +2024-02-02 16:37:27,684 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:27,684 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:27,688 - + +2024-02-02 16:37:27,689 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:28,063 - Epoch: [327][ 1/ 8] Overall Loss 0.015898 Objective Loss 0.015898 MSE 0.015898 LR 0.001000 Time 0.374465 +2024-02-02 16:37:28,073 - Epoch: [327][ 2/ 8] Overall Loss 0.017190 Objective Loss 0.017190 MSE 0.017190 LR 0.001000 Time 0.191674 +2024-02-02 16:37:28,081 - Epoch: [327][ 3/ 8] Overall Loss 0.016960 Objective Loss 0.016960 MSE 0.016960 LR 0.001000 Time 0.130559 +2024-02-02 16:37:28,090 - Epoch: [327][ 4/ 8] Overall Loss 0.017479 Objective Loss 0.017479 MSE 0.017479 LR 0.001000 Time 0.099994 +2024-02-02 16:37:28,098 - Epoch: [327][ 5/ 8] Overall Loss 0.017316 Objective Loss 0.017316 MSE 0.017316 LR 0.001000 Time 0.081667 +2024-02-02 16:37:28,107 - Epoch: [327][ 6/ 8] Overall Loss 0.017498 Objective Loss 0.017498 MSE 0.017498 LR 0.001000 Time 0.069435 +2024-02-02 16:37:28,115 - Epoch: [327][ 7/ 8] Overall Loss 0.017537 Objective Loss 0.017537 MSE 0.017537 LR 0.001000 Time 0.060694 +2024-02-02 16:37:28,123 - Epoch: [327][ 8/ 8] Overall Loss 0.017449 Objective Loss 0.017449 MSE 0.017519 LR 0.001000 Time 0.054127 +2024-02-02 16:37:28,277 - --- validate (epoch=327)----------- +2024-02-02 16:37:28,277 - 60 samples (32 per mini-batch) +2024-02-02 16:37:28,630 - Epoch: [327][ 1/ 2] Loss 0.022447 MSE 0.022447 +2024-02-02 16:37:28,636 - Epoch: [327][ 2/ 2] Loss 0.022553 MSE 0.022546 +2024-02-02 16:37:28,783 - ==> MSE: 0.02255 Loss: 0.023 + +2024-02-02 16:37:28,789 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:28,789 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:28,793 - + +2024-02-02 16:37:28,794 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:29,157 - Epoch: [328][ 1/ 8] Overall Loss 0.017893 Objective Loss 0.017893 MSE 0.017893 LR 0.001000 Time 0.363341 +2024-02-02 16:37:29,169 - Epoch: [328][ 2/ 8] Overall Loss 0.018257 Objective Loss 0.018257 MSE 0.018257 LR 0.001000 Time 0.187409 +2024-02-02 16:37:29,178 - Epoch: [328][ 3/ 8] Overall Loss 0.017634 Objective Loss 0.017634 MSE 0.017634 LR 0.001000 Time 0.127932 +2024-02-02 16:37:29,187 - Epoch: [328][ 4/ 8] Overall Loss 0.017561 Objective Loss 0.017561 MSE 0.017561 LR 0.001000 Time 0.098046 +2024-02-02 16:37:29,195 - Epoch: [328][ 5/ 8] Overall Loss 0.017500 Objective Loss 0.017500 MSE 0.017500 LR 0.001000 Time 0.080082 +2024-02-02 16:37:29,203 - Epoch: [328][ 6/ 8] Overall Loss 0.017615 Objective Loss 0.017615 MSE 0.017615 LR 0.001000 Time 0.068044 +2024-02-02 16:37:29,212 - Epoch: [328][ 7/ 8] Overall Loss 0.017565 Objective Loss 0.017565 MSE 0.017565 LR 0.001000 Time 0.059495 +2024-02-02 16:37:29,220 - Epoch: [328][ 8/ 8] Overall Loss 0.017354 Objective Loss 0.017354 MSE 0.017521 LR 0.001000 Time 0.053086 +2024-02-02 16:37:29,364 - --- validate (epoch=328)----------- +2024-02-02 16:37:29,364 - 60 samples (32 per mini-batch) +2024-02-02 16:37:29,729 - Epoch: [328][ 1/ 2] Loss 0.023151 MSE 0.023151 +2024-02-02 16:37:29,735 - Epoch: [328][ 2/ 2] Loss 0.022573 MSE 0.022611 +2024-02-02 16:37:29,882 - ==> MSE: 0.02261 Loss: 0.023 + +2024-02-02 16:37:29,896 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:29,896 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:29,904 - + +2024-02-02 16:37:29,904 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:30,270 - Epoch: [329][ 1/ 8] Overall Loss 0.016982 Objective Loss 0.016982 MSE 0.016982 LR 0.001000 Time 0.364859 +2024-02-02 16:37:30,278 - Epoch: [329][ 2/ 8] Overall Loss 0.016905 Objective Loss 0.016905 MSE 0.016905 LR 0.001000 Time 0.186685 +2024-02-02 16:37:30,287 - Epoch: [329][ 3/ 8] Overall Loss 0.016672 Objective Loss 0.016672 MSE 0.016672 LR 0.001000 Time 0.127210 +2024-02-02 16:37:30,295 - Epoch: [329][ 4/ 8] Overall Loss 0.016641 Objective Loss 0.016641 MSE 0.016641 LR 0.001000 Time 0.097454 +2024-02-02 16:37:30,304 - Epoch: [329][ 5/ 8] Overall Loss 0.016917 Objective Loss 0.016917 MSE 0.016917 LR 0.001000 Time 0.079632 +2024-02-02 16:37:30,312 - Epoch: [329][ 6/ 8] Overall Loss 0.017308 Objective Loss 0.017308 MSE 0.017308 LR 0.001000 Time 0.067749 +2024-02-02 16:37:30,321 - Epoch: [329][ 7/ 8] Overall Loss 0.017560 Objective Loss 0.017560 MSE 0.017560 LR 0.001000 Time 0.059256 +2024-02-02 16:37:30,329 - Epoch: [329][ 8/ 8] Overall Loss 0.017115 Objective Loss 0.017115 MSE 0.017467 LR 0.001000 Time 0.052869 +2024-02-02 16:37:30,482 - --- validate (epoch=329)----------- +2024-02-02 16:37:30,482 - 60 samples (32 per mini-batch) +2024-02-02 16:37:30,841 - Epoch: [329][ 1/ 2] Loss 0.021530 MSE 0.021530 +2024-02-02 16:37:30,847 - Epoch: [329][ 2/ 2] Loss 0.022648 MSE 0.022574 +2024-02-02 16:37:30,997 - ==> MSE: 0.02257 Loss: 0.023 + +2024-02-02 16:37:31,003 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:31,003 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:31,008 - + +2024-02-02 16:37:31,008 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:31,367 - Epoch: [330][ 1/ 8] Overall Loss 0.015469 Objective Loss 0.015469 MSE 0.015469 LR 0.001000 Time 0.358041 +2024-02-02 16:37:31,375 - Epoch: [330][ 2/ 8] Overall Loss 0.017026 Objective Loss 0.017026 MSE 0.017026 LR 0.001000 Time 0.183256 +2024-02-02 16:37:31,384 - Epoch: [330][ 3/ 8] Overall Loss 0.017004 Objective Loss 0.017004 MSE 0.017004 LR 0.001000 Time 0.124958 +2024-02-02 16:37:31,392 - Epoch: [330][ 4/ 8] Overall Loss 0.017192 Objective Loss 0.017192 MSE 0.017192 LR 0.001000 Time 0.095782 +2024-02-02 16:37:31,401 - Epoch: [330][ 5/ 8] Overall Loss 0.017143 Objective Loss 0.017143 MSE 0.017143 LR 0.001000 Time 0.078298 +2024-02-02 16:37:31,409 - Epoch: [330][ 6/ 8] Overall Loss 0.017508 Objective Loss 0.017508 MSE 0.017508 LR 0.001000 Time 0.066642 +2024-02-02 16:37:31,418 - Epoch: [330][ 7/ 8] Overall Loss 0.017464 Objective Loss 0.017464 MSE 0.017464 LR 0.001000 Time 0.058318 +2024-02-02 16:37:31,427 - Epoch: [330][ 8/ 8] Overall Loss 0.016886 Objective Loss 0.016886 MSE 0.017343 LR 0.001000 Time 0.052082 +2024-02-02 16:37:31,577 - --- validate (epoch=330)----------- +2024-02-02 16:37:31,578 - 60 samples (32 per mini-batch) +2024-02-02 16:37:31,934 - Epoch: [330][ 1/ 2] Loss 0.022178 MSE 0.022178 +2024-02-02 16:37:31,940 - Epoch: [330][ 2/ 2] Loss 0.022553 MSE 0.022528 +2024-02-02 16:37:32,089 - ==> MSE: 0.02253 Loss: 0.023 + +2024-02-02 16:37:32,095 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:32,096 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:32,100 - + +2024-02-02 16:37:32,100 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:32,463 - Epoch: [331][ 1/ 8] Overall Loss 0.018146 Objective Loss 0.018146 MSE 0.018146 LR 0.001000 Time 0.362109 +2024-02-02 16:37:32,475 - Epoch: [331][ 2/ 8] Overall Loss 0.017526 Objective Loss 0.017526 MSE 0.017526 LR 0.001000 Time 0.186901 +2024-02-02 16:37:32,484 - Epoch: [331][ 3/ 8] Overall Loss 0.017438 Objective Loss 0.017438 MSE 0.017438 LR 0.001000 Time 0.127600 +2024-02-02 16:37:32,493 - Epoch: [331][ 4/ 8] Overall Loss 0.017953 Objective Loss 0.017953 MSE 0.017953 LR 0.001000 Time 0.097794 +2024-02-02 16:37:32,501 - Epoch: [331][ 5/ 8] Overall Loss 0.018171 Objective Loss 0.018171 MSE 0.018171 LR 0.001000 Time 0.079907 +2024-02-02 16:37:32,510 - Epoch: [331][ 6/ 8] Overall Loss 0.017775 Objective Loss 0.017775 MSE 0.017775 LR 0.001000 Time 0.067991 +2024-02-02 16:37:32,518 - Epoch: [331][ 7/ 8] Overall Loss 0.017282 Objective Loss 0.017282 MSE 0.017282 LR 0.001000 Time 0.059472 +2024-02-02 16:37:32,527 - Epoch: [331][ 8/ 8] Overall Loss 0.017570 Objective Loss 0.017570 MSE 0.017342 LR 0.001000 Time 0.053061 +2024-02-02 16:37:32,667 - --- validate (epoch=331)----------- +2024-02-02 16:37:32,668 - 60 samples (32 per mini-batch) +2024-02-02 16:37:33,011 - Epoch: [331][ 1/ 2] Loss 0.022419 MSE 0.022419 +2024-02-02 16:37:33,017 - Epoch: [331][ 2/ 2] Loss 0.022621 MSE 0.022607 +2024-02-02 16:37:33,161 - ==> MSE: 0.02261 Loss: 0.023 + +2024-02-02 16:37:33,167 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:33,168 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:33,172 - + +2024-02-02 16:37:33,172 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:33,521 - Epoch: [332][ 1/ 8] Overall Loss 0.017039 Objective Loss 0.017039 MSE 0.017039 LR 0.001000 Time 0.348901 +2024-02-02 16:37:33,530 - Epoch: [332][ 2/ 8] Overall Loss 0.016695 Objective Loss 0.016695 MSE 0.016695 LR 0.001000 Time 0.178756 +2024-02-02 16:37:33,539 - Epoch: [332][ 3/ 8] Overall Loss 0.016369 Objective Loss 0.016369 MSE 0.016369 LR 0.001000 Time 0.121914 +2024-02-02 16:37:33,547 - Epoch: [332][ 4/ 8] Overall Loss 0.016385 Objective Loss 0.016385 MSE 0.016385 LR 0.001000 Time 0.093496 +2024-02-02 16:37:33,556 - Epoch: [332][ 5/ 8] Overall Loss 0.016869 Objective Loss 0.016869 MSE 0.016869 LR 0.001000 Time 0.076454 +2024-02-02 16:37:33,564 - Epoch: [332][ 6/ 8] Overall Loss 0.017012 Objective Loss 0.017012 MSE 0.017012 LR 0.001000 Time 0.065088 +2024-02-02 16:37:33,572 - Epoch: [332][ 7/ 8] Overall Loss 0.017195 Objective Loss 0.017195 MSE 0.017195 LR 0.001000 Time 0.056976 +2024-02-02 16:37:33,581 - Epoch: [332][ 8/ 8] Overall Loss 0.017780 Objective Loss 0.017780 MSE 0.017317 LR 0.001000 Time 0.050884 +2024-02-02 16:37:33,725 - --- validate (epoch=332)----------- +2024-02-02 16:37:33,725 - 60 samples (32 per mini-batch) +2024-02-02 16:37:34,065 - Epoch: [332][ 1/ 2] Loss 0.023056 MSE 0.023056 +2024-02-02 16:37:34,071 - Epoch: [332][ 2/ 2] Loss 0.022370 MSE 0.022416 +2024-02-02 16:37:34,216 - ==> MSE: 0.02242 Loss: 0.022 + +2024-02-02 16:37:34,222 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:34,222 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:34,226 - + +2024-02-02 16:37:34,226 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:34,592 - Epoch: [333][ 1/ 8] Overall Loss 0.016476 Objective Loss 0.016476 MSE 0.016476 LR 0.001000 Time 0.365424 +2024-02-02 16:37:34,601 - Epoch: [333][ 2/ 8] Overall Loss 0.016952 Objective Loss 0.016952 MSE 0.016952 LR 0.001000 Time 0.186864 +2024-02-02 16:37:34,609 - Epoch: [333][ 3/ 8] Overall Loss 0.017057 Objective Loss 0.017057 MSE 0.017057 LR 0.001000 Time 0.127263 +2024-02-02 16:37:34,619 - Epoch: [333][ 4/ 8] Overall Loss 0.017737 Objective Loss 0.017737 MSE 0.017737 LR 0.001000 Time 0.097884 +2024-02-02 16:37:34,631 - Epoch: [333][ 5/ 8] Overall Loss 0.017191 Objective Loss 0.017191 MSE 0.017191 LR 0.001000 Time 0.080702 +2024-02-02 16:37:34,642 - Epoch: [333][ 6/ 8] Overall Loss 0.016973 Objective Loss 0.016973 MSE 0.016973 LR 0.001000 Time 0.069020 +2024-02-02 16:37:34,651 - Epoch: [333][ 7/ 8] Overall Loss 0.017215 Objective Loss 0.017215 MSE 0.017215 LR 0.001000 Time 0.060420 +2024-02-02 16:37:34,660 - Epoch: [333][ 8/ 8] Overall Loss 0.017974 Objective Loss 0.017974 MSE 0.017374 LR 0.001000 Time 0.053924 +2024-02-02 16:37:34,812 - --- validate (epoch=333)----------- +2024-02-02 16:37:34,812 - 60 samples (32 per mini-batch) +2024-02-02 16:37:35,168 - Epoch: [333][ 1/ 2] Loss 0.024321 MSE 0.024321 +2024-02-02 16:37:35,176 - Epoch: [333][ 2/ 2] Loss 0.022445 MSE 0.022570 +2024-02-02 16:37:35,325 - ==> MSE: 0.02257 Loss: 0.022 + +2024-02-02 16:37:35,332 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:35,332 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:35,336 - + +2024-02-02 16:37:35,336 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:35,695 - Epoch: [334][ 1/ 8] Overall Loss 0.018243 Objective Loss 0.018243 MSE 0.018243 LR 0.001000 Time 0.358082 +2024-02-02 16:37:35,707 - Epoch: [334][ 2/ 8] Overall Loss 0.018161 Objective Loss 0.018161 MSE 0.018161 LR 0.001000 Time 0.184816 +2024-02-02 16:37:35,717 - Epoch: [334][ 3/ 8] Overall Loss 0.017785 Objective Loss 0.017785 MSE 0.017785 LR 0.001000 Time 0.126666 +2024-02-02 16:37:35,726 - Epoch: [334][ 4/ 8] Overall Loss 0.017465 Objective Loss 0.017465 MSE 0.017465 LR 0.001000 Time 0.097209 +2024-02-02 16:37:35,735 - Epoch: [334][ 5/ 8] Overall Loss 0.017271 Objective Loss 0.017271 MSE 0.017271 LR 0.001000 Time 0.079482 +2024-02-02 16:37:35,744 - Epoch: [334][ 6/ 8] Overall Loss 0.017176 Objective Loss 0.017176 MSE 0.017176 LR 0.001000 Time 0.067641 +2024-02-02 16:37:35,753 - Epoch: [334][ 7/ 8] Overall Loss 0.017302 Objective Loss 0.017302 MSE 0.017302 LR 0.001000 Time 0.059191 +2024-02-02 16:37:35,761 - Epoch: [334][ 8/ 8] Overall Loss 0.017544 Objective Loss 0.017544 MSE 0.017352 LR 0.001000 Time 0.052774 +2024-02-02 16:37:35,911 - --- validate (epoch=334)----------- +2024-02-02 16:37:35,911 - 60 samples (32 per mini-batch) +2024-02-02 16:37:36,269 - Epoch: [334][ 1/ 2] Loss 0.021822 MSE 0.021822 +2024-02-02 16:37:36,275 - Epoch: [334][ 2/ 2] Loss 0.022564 MSE 0.022515 +2024-02-02 16:37:36,424 - ==> MSE: 0.02251 Loss: 0.023 + +2024-02-02 16:37:36,431 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:36,431 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:36,436 - + +2024-02-02 16:37:36,436 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:36,799 - Epoch: [335][ 1/ 8] Overall Loss 0.018696 Objective Loss 0.018696 MSE 0.018696 LR 0.001000 Time 0.362949 +2024-02-02 16:37:36,808 - Epoch: [335][ 2/ 8] Overall Loss 0.017591 Objective Loss 0.017591 MSE 0.017591 LR 0.001000 Time 0.185733 +2024-02-02 16:37:36,817 - Epoch: [335][ 3/ 8] Overall Loss 0.017205 Objective Loss 0.017205 MSE 0.017205 LR 0.001000 Time 0.126625 +2024-02-02 16:37:36,825 - Epoch: [335][ 4/ 8] Overall Loss 0.017381 Objective Loss 0.017381 MSE 0.017381 LR 0.001000 Time 0.097061 +2024-02-02 16:37:36,834 - Epoch: [335][ 5/ 8] Overall Loss 0.017235 Objective Loss 0.017235 MSE 0.017235 LR 0.001000 Time 0.079315 +2024-02-02 16:37:36,842 - Epoch: [335][ 6/ 8] Overall Loss 0.017134 Objective Loss 0.017134 MSE 0.017134 LR 0.001000 Time 0.067483 +2024-02-02 16:37:36,851 - Epoch: [335][ 7/ 8] Overall Loss 0.017273 Objective Loss 0.017273 MSE 0.017273 LR 0.001000 Time 0.059030 +2024-02-02 16:37:36,859 - Epoch: [335][ 8/ 8] Overall Loss 0.017428 Objective Loss 0.017428 MSE 0.017305 LR 0.001000 Time 0.052652 +2024-02-02 16:37:37,009 - --- validate (epoch=335)----------- +2024-02-02 16:37:37,009 - 60 samples (32 per mini-batch) +2024-02-02 16:37:37,371 - Epoch: [335][ 1/ 2] Loss 0.023557 MSE 0.023557 +2024-02-02 16:37:37,377 - Epoch: [335][ 2/ 2] Loss 0.022335 MSE 0.022416 +2024-02-02 16:37:37,521 - ==> MSE: 0.02242 Loss: 0.022 + +2024-02-02 16:37:37,528 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:37,528 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:37,532 - + +2024-02-02 16:37:37,532 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:37,898 - Epoch: [336][ 1/ 8] Overall Loss 0.017265 Objective Loss 0.017265 MSE 0.017265 LR 0.001000 Time 0.365002 +2024-02-02 16:37:37,907 - Epoch: [336][ 2/ 8] Overall Loss 0.017861 Objective Loss 0.017861 MSE 0.017861 LR 0.001000 Time 0.186822 +2024-02-02 16:37:37,915 - Epoch: [336][ 3/ 8] Overall Loss 0.016968 Objective Loss 0.016968 MSE 0.016968 LR 0.001000 Time 0.127319 +2024-02-02 16:37:37,925 - Epoch: [336][ 4/ 8] Overall Loss 0.016853 Objective Loss 0.016853 MSE 0.016853 LR 0.001000 Time 0.097762 +2024-02-02 16:37:37,934 - Epoch: [336][ 5/ 8] Overall Loss 0.016919 Objective Loss 0.016919 MSE 0.016919 LR 0.001000 Time 0.080077 +2024-02-02 16:37:37,943 - Epoch: [336][ 6/ 8] Overall Loss 0.017169 Objective Loss 0.017169 MSE 0.017169 LR 0.001000 Time 0.068151 +2024-02-02 16:37:37,951 - Epoch: [336][ 7/ 8] Overall Loss 0.017290 Objective Loss 0.017290 MSE 0.017290 LR 0.001000 Time 0.059591 +2024-02-02 16:37:37,960 - Epoch: [336][ 8/ 8] Overall Loss 0.017349 Objective Loss 0.017349 MSE 0.017302 LR 0.001000 Time 0.053164 +2024-02-02 16:37:38,110 - --- validate (epoch=336)----------- +2024-02-02 16:37:38,110 - 60 samples (32 per mini-batch) +2024-02-02 16:37:38,460 - Epoch: [336][ 1/ 2] Loss 0.022394 MSE 0.022394 +2024-02-02 16:37:38,468 - Epoch: [336][ 2/ 2] Loss 0.022433 MSE 0.022430 +2024-02-02 16:37:38,619 - ==> MSE: 0.02243 Loss: 0.022 + +2024-02-02 16:37:38,625 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:38,625 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:38,629 - + +2024-02-02 16:37:38,629 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:38,989 - Epoch: [337][ 1/ 8] Overall Loss 0.017525 Objective Loss 0.017525 MSE 0.017525 LR 0.001000 Time 0.358852 +2024-02-02 16:37:38,997 - Epoch: [337][ 2/ 8] Overall Loss 0.017960 Objective Loss 0.017960 MSE 0.017960 LR 0.001000 Time 0.183619 +2024-02-02 16:37:39,006 - Epoch: [337][ 3/ 8] Overall Loss 0.017811 Objective Loss 0.017811 MSE 0.017811 LR 0.001000 Time 0.125148 +2024-02-02 16:37:39,014 - Epoch: [337][ 4/ 8] Overall Loss 0.017732 Objective Loss 0.017732 MSE 0.017732 LR 0.001000 Time 0.095904 +2024-02-02 16:37:39,023 - Epoch: [337][ 5/ 8] Overall Loss 0.017905 Objective Loss 0.017905 MSE 0.017905 LR 0.001000 Time 0.078420 +2024-02-02 16:37:39,031 - Epoch: [337][ 6/ 8] Overall Loss 0.017716 Objective Loss 0.017716 MSE 0.017716 LR 0.001000 Time 0.066727 +2024-02-02 16:37:39,040 - Epoch: [337][ 7/ 8] Overall Loss 0.017414 Objective Loss 0.017414 MSE 0.017414 LR 0.001000 Time 0.058397 +2024-02-02 16:37:39,048 - Epoch: [337][ 8/ 8] Overall Loss 0.017081 Objective Loss 0.017081 MSE 0.017345 LR 0.001000 Time 0.052148 +2024-02-02 16:37:39,199 - --- validate (epoch=337)----------- +2024-02-02 16:37:39,199 - 60 samples (32 per mini-batch) +2024-02-02 16:37:39,554 - Epoch: [337][ 1/ 2] Loss 0.022771 MSE 0.022771 +2024-02-02 16:37:39,561 - Epoch: [337][ 2/ 2] Loss 0.022510 MSE 0.022528 +2024-02-02 16:37:39,710 - ==> MSE: 0.02253 Loss: 0.023 + +2024-02-02 16:37:39,716 - ==> Best [Top 1 (MSE): 0.02242 Sparsity:0.00 Params: 136448 on epoch: 322] +2024-02-02 16:37:39,716 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:39,721 - + +2024-02-02 16:37:39,721 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:40,086 - Epoch: [338][ 1/ 8] Overall Loss 0.017380 Objective Loss 0.017380 MSE 0.017380 LR 0.001000 Time 0.364674 +2024-02-02 16:37:40,097 - Epoch: [338][ 2/ 8] Overall Loss 0.016962 Objective Loss 0.016962 MSE 0.016962 LR 0.001000 Time 0.187730 +2024-02-02 16:37:40,108 - Epoch: [338][ 3/ 8] Overall Loss 0.017119 Objective Loss 0.017119 MSE 0.017119 LR 0.001000 Time 0.128702 +2024-02-02 16:37:40,119 - Epoch: [338][ 4/ 8] Overall Loss 0.017344 Objective Loss 0.017344 MSE 0.017344 LR 0.001000 Time 0.099224 +2024-02-02 16:37:40,130 - Epoch: [338][ 5/ 8] Overall Loss 0.017285 Objective Loss 0.017285 MSE 0.017285 LR 0.001000 Time 0.081581 +2024-02-02 16:37:40,140 - Epoch: [338][ 6/ 8] Overall Loss 0.017019 Objective Loss 0.017019 MSE 0.017019 LR 0.001000 Time 0.069615 +2024-02-02 16:37:40,149 - Epoch: [338][ 7/ 8] Overall Loss 0.017138 Objective Loss 0.017138 MSE 0.017138 LR 0.001000 Time 0.060890 +2024-02-02 16:37:40,157 - Epoch: [338][ 8/ 8] Overall Loss 0.017757 Objective Loss 0.017757 MSE 0.017268 LR 0.001000 Time 0.054310 +2024-02-02 16:37:40,307 - --- validate (epoch=338)----------- +2024-02-02 16:37:40,307 - 60 samples (32 per mini-batch) +2024-02-02 16:37:40,663 - Epoch: [338][ 1/ 2] Loss 0.024120 MSE 0.024120 +2024-02-02 16:37:40,669 - Epoch: [338][ 2/ 2] Loss 0.022221 MSE 0.022348 +2024-02-02 16:37:40,812 - ==> MSE: 0.02235 Loss: 0.022 + +2024-02-02 16:37:40,820 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:40,820 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:40,825 - + +2024-02-02 16:37:40,825 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:41,180 - Epoch: [339][ 1/ 8] Overall Loss 0.017390 Objective Loss 0.017390 MSE 0.017390 LR 0.001000 Time 0.354030 +2024-02-02 16:37:41,189 - Epoch: [339][ 2/ 8] Overall Loss 0.016644 Objective Loss 0.016644 MSE 0.016644 LR 0.001000 Time 0.181324 +2024-02-02 16:37:41,197 - Epoch: [339][ 3/ 8] Overall Loss 0.016509 Objective Loss 0.016509 MSE 0.016509 LR 0.001000 Time 0.123619 +2024-02-02 16:37:41,205 - Epoch: [339][ 4/ 8] Overall Loss 0.016507 Objective Loss 0.016507 MSE 0.016507 LR 0.001000 Time 0.094738 +2024-02-02 16:37:41,214 - Epoch: [339][ 5/ 8] Overall Loss 0.016443 Objective Loss 0.016443 MSE 0.016443 LR 0.001000 Time 0.077415 +2024-02-02 16:37:41,222 - Epoch: [339][ 6/ 8] Overall Loss 0.016682 Objective Loss 0.016682 MSE 0.016682 LR 0.001000 Time 0.065928 +2024-02-02 16:37:41,231 - Epoch: [339][ 7/ 8] Overall Loss 0.017361 Objective Loss 0.017361 MSE 0.017361 LR 0.001000 Time 0.057709 +2024-02-02 16:37:41,239 - Epoch: [339][ 8/ 8] Overall Loss 0.017134 Objective Loss 0.017134 MSE 0.017313 LR 0.001000 Time 0.051521 +2024-02-02 16:37:41,391 - --- validate (epoch=339)----------- +2024-02-02 16:37:41,391 - 60 samples (32 per mini-batch) +2024-02-02 16:37:41,750 - Epoch: [339][ 1/ 2] Loss 0.021373 MSE 0.021373 +2024-02-02 16:37:41,756 - Epoch: [339][ 2/ 2] Loss 0.022672 MSE 0.022586 +2024-02-02 16:37:41,903 - ==> MSE: 0.02259 Loss: 0.023 + +2024-02-02 16:37:41,910 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:41,910 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:41,914 - + +2024-02-02 16:37:41,914 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:42,273 - Epoch: [340][ 1/ 8] Overall Loss 0.017364 Objective Loss 0.017364 MSE 0.017364 LR 0.001000 Time 0.358305 +2024-02-02 16:37:42,285 - Epoch: [340][ 2/ 8] Overall Loss 0.017713 Objective Loss 0.017713 MSE 0.017713 LR 0.001000 Time 0.184972 +2024-02-02 16:37:42,297 - Epoch: [340][ 3/ 8] Overall Loss 0.017308 Objective Loss 0.017308 MSE 0.017308 LR 0.001000 Time 0.127287 +2024-02-02 16:37:42,309 - Epoch: [340][ 4/ 8] Overall Loss 0.017565 Objective Loss 0.017565 MSE 0.017565 LR 0.001000 Time 0.098442 +2024-02-02 16:37:42,321 - Epoch: [340][ 5/ 8] Overall Loss 0.017501 Objective Loss 0.017501 MSE 0.017501 LR 0.001000 Time 0.080988 +2024-02-02 16:37:42,332 - Epoch: [340][ 6/ 8] Overall Loss 0.017376 Objective Loss 0.017376 MSE 0.017376 LR 0.001000 Time 0.069321 +2024-02-02 16:37:42,343 - Epoch: [340][ 7/ 8] Overall Loss 0.017271 Objective Loss 0.017271 MSE 0.017271 LR 0.001000 Time 0.060982 +2024-02-02 16:37:42,354 - Epoch: [340][ 8/ 8] Overall Loss 0.017167 Objective Loss 0.017167 MSE 0.017249 LR 0.001000 Time 0.054704 +2024-02-02 16:37:42,504 - --- validate (epoch=340)----------- +2024-02-02 16:37:42,505 - 60 samples (32 per mini-batch) +2024-02-02 16:37:42,854 - Epoch: [340][ 1/ 2] Loss 0.022798 MSE 0.022798 +2024-02-02 16:37:42,860 - Epoch: [340][ 2/ 2] Loss 0.022522 MSE 0.022541 +2024-02-02 16:37:43,003 - ==> MSE: 0.02254 Loss: 0.023 + +2024-02-02 16:37:43,009 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:43,010 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:43,014 - + +2024-02-02 16:37:43,014 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:43,367 - Epoch: [341][ 1/ 8] Overall Loss 0.016386 Objective Loss 0.016386 MSE 0.016386 LR 0.001000 Time 0.352839 +2024-02-02 16:37:43,379 - Epoch: [341][ 2/ 8] Overall Loss 0.016819 Objective Loss 0.016819 MSE 0.016819 LR 0.001000 Time 0.181975 +2024-02-02 16:37:43,388 - Epoch: [341][ 3/ 8] Overall Loss 0.016999 Objective Loss 0.016999 MSE 0.016999 LR 0.001000 Time 0.124425 +2024-02-02 16:37:43,397 - Epoch: [341][ 4/ 8] Overall Loss 0.017142 Objective Loss 0.017142 MSE 0.017142 LR 0.001000 Time 0.095427 +2024-02-02 16:37:43,405 - Epoch: [341][ 5/ 8] Overall Loss 0.017208 Objective Loss 0.017208 MSE 0.017208 LR 0.001000 Time 0.078032 +2024-02-02 16:37:43,414 - Epoch: [341][ 6/ 8] Overall Loss 0.017209 Objective Loss 0.017209 MSE 0.017209 LR 0.001000 Time 0.066430 +2024-02-02 16:37:43,423 - Epoch: [341][ 7/ 8] Overall Loss 0.017291 Objective Loss 0.017291 MSE 0.017291 LR 0.001000 Time 0.058147 +2024-02-02 16:37:43,431 - Epoch: [341][ 8/ 8] Overall Loss 0.017106 Objective Loss 0.017106 MSE 0.017253 LR 0.001000 Time 0.051915 +2024-02-02 16:37:43,584 - --- validate (epoch=341)----------- +2024-02-02 16:37:43,585 - 60 samples (32 per mini-batch) +2024-02-02 16:37:43,949 - Epoch: [341][ 1/ 2] Loss 0.023967 MSE 0.023967 +2024-02-02 16:37:43,954 - Epoch: [341][ 2/ 2] Loss 0.022558 MSE 0.022652 +2024-02-02 16:37:44,101 - ==> MSE: 0.02265 Loss: 0.023 + +2024-02-02 16:37:44,108 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:44,108 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:44,113 - + +2024-02-02 16:37:44,113 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:44,465 - Epoch: [342][ 1/ 8] Overall Loss 0.017335 Objective Loss 0.017335 MSE 0.017335 LR 0.001000 Time 0.351957 +2024-02-02 16:37:44,474 - Epoch: [342][ 2/ 8] Overall Loss 0.017431 Objective Loss 0.017431 MSE 0.017431 LR 0.001000 Time 0.180314 +2024-02-02 16:37:44,482 - Epoch: [342][ 3/ 8] Overall Loss 0.016967 Objective Loss 0.016967 MSE 0.016967 LR 0.001000 Time 0.122921 +2024-02-02 16:37:44,491 - Epoch: [342][ 4/ 8] Overall Loss 0.017628 Objective Loss 0.017628 MSE 0.017628 LR 0.001000 Time 0.094253 +2024-02-02 16:37:44,499 - Epoch: [342][ 5/ 8] Overall Loss 0.017470 Objective Loss 0.017470 MSE 0.017470 LR 0.001000 Time 0.077079 +2024-02-02 16:37:44,508 - Epoch: [342][ 6/ 8] Overall Loss 0.017164 Objective Loss 0.017164 MSE 0.017164 LR 0.001000 Time 0.065633 +2024-02-02 16:37:44,516 - Epoch: [342][ 7/ 8] Overall Loss 0.017327 Objective Loss 0.017327 MSE 0.017327 LR 0.001000 Time 0.057443 +2024-02-02 16:37:44,525 - Epoch: [342][ 8/ 8] Overall Loss 0.017241 Objective Loss 0.017241 MSE 0.017309 LR 0.001000 Time 0.051299 +2024-02-02 16:37:44,675 - --- validate (epoch=342)----------- +2024-02-02 16:37:44,675 - 60 samples (32 per mini-batch) +2024-02-02 16:37:45,040 - Epoch: [342][ 1/ 2] Loss 0.021758 MSE 0.021758 +2024-02-02 16:37:45,046 - Epoch: [342][ 2/ 2] Loss 0.022533 MSE 0.022482 +2024-02-02 16:37:45,195 - ==> MSE: 0.02248 Loss: 0.023 + +2024-02-02 16:37:45,201 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:45,201 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:45,206 - + +2024-02-02 16:37:45,206 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:45,568 - Epoch: [343][ 1/ 8] Overall Loss 0.017384 Objective Loss 0.017384 MSE 0.017384 LR 0.001000 Time 0.362391 +2024-02-02 16:37:45,581 - Epoch: [343][ 2/ 8] Overall Loss 0.017104 Objective Loss 0.017104 MSE 0.017104 LR 0.001000 Time 0.187061 +2024-02-02 16:37:45,590 - Epoch: [343][ 3/ 8] Overall Loss 0.016986 Objective Loss 0.016986 MSE 0.016986 LR 0.001000 Time 0.127736 +2024-02-02 16:37:45,598 - Epoch: [343][ 4/ 8] Overall Loss 0.016918 Objective Loss 0.016918 MSE 0.016918 LR 0.001000 Time 0.097890 +2024-02-02 16:37:45,607 - Epoch: [343][ 5/ 8] Overall Loss 0.017161 Objective Loss 0.017161 MSE 0.017161 LR 0.001000 Time 0.079983 +2024-02-02 16:37:45,615 - Epoch: [343][ 6/ 8] Overall Loss 0.017075 Objective Loss 0.017075 MSE 0.017075 LR 0.001000 Time 0.068058 +2024-02-02 16:37:45,624 - Epoch: [343][ 7/ 8] Overall Loss 0.017298 Objective Loss 0.017298 MSE 0.017298 LR 0.001000 Time 0.059527 +2024-02-02 16:37:45,633 - Epoch: [343][ 8/ 8] Overall Loss 0.017374 Objective Loss 0.017374 MSE 0.017314 LR 0.001000 Time 0.053127 +2024-02-02 16:37:45,782 - --- validate (epoch=343)----------- +2024-02-02 16:37:45,782 - 60 samples (32 per mini-batch) +2024-02-02 16:37:46,136 - Epoch: [343][ 1/ 2] Loss 0.023672 MSE 0.023672 +2024-02-02 16:37:46,142 - Epoch: [343][ 2/ 2] Loss 0.022538 MSE 0.022614 +2024-02-02 16:37:46,288 - ==> MSE: 0.02261 Loss: 0.023 + +2024-02-02 16:37:46,296 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:46,296 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:46,301 - + +2024-02-02 16:37:46,301 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:46,657 - Epoch: [344][ 1/ 8] Overall Loss 0.018856 Objective Loss 0.018856 MSE 0.018856 LR 0.001000 Time 0.355879 +2024-02-02 16:37:46,670 - Epoch: [344][ 2/ 8] Overall Loss 0.017951 Objective Loss 0.017951 MSE 0.017951 LR 0.001000 Time 0.184191 +2024-02-02 16:37:46,679 - Epoch: [344][ 3/ 8] Overall Loss 0.017758 Objective Loss 0.017758 MSE 0.017758 LR 0.001000 Time 0.125722 +2024-02-02 16:37:46,687 - Epoch: [344][ 4/ 8] Overall Loss 0.017469 Objective Loss 0.017469 MSE 0.017469 LR 0.001000 Time 0.096428 +2024-02-02 16:37:46,696 - Epoch: [344][ 5/ 8] Overall Loss 0.017276 Objective Loss 0.017276 MSE 0.017276 LR 0.001000 Time 0.078840 +2024-02-02 16:37:46,705 - Epoch: [344][ 6/ 8] Overall Loss 0.017265 Objective Loss 0.017265 MSE 0.017265 LR 0.001000 Time 0.067119 +2024-02-02 16:37:46,713 - Epoch: [344][ 7/ 8] Overall Loss 0.017164 Objective Loss 0.017164 MSE 0.017164 LR 0.001000 Time 0.058732 +2024-02-02 16:37:46,722 - Epoch: [344][ 8/ 8] Overall Loss 0.017530 Objective Loss 0.017530 MSE 0.017241 LR 0.001000 Time 0.052442 +2024-02-02 16:37:46,871 - --- validate (epoch=344)----------- +2024-02-02 16:37:46,871 - 60 samples (32 per mini-batch) +2024-02-02 16:37:47,227 - Epoch: [344][ 1/ 2] Loss 0.021854 MSE 0.021854 +2024-02-02 16:37:47,233 - Epoch: [344][ 2/ 2] Loss 0.022634 MSE 0.022582 +2024-02-02 16:37:47,393 - ==> MSE: 0.02258 Loss: 0.023 + +2024-02-02 16:37:47,400 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:47,401 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:47,405 - + +2024-02-02 16:37:47,405 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:47,756 - Epoch: [345][ 1/ 8] Overall Loss 0.016449 Objective Loss 0.016449 MSE 0.016449 LR 0.001000 Time 0.350756 +2024-02-02 16:37:47,765 - Epoch: [345][ 2/ 8] Overall Loss 0.016535 Objective Loss 0.016535 MSE 0.016535 LR 0.001000 Time 0.179634 +2024-02-02 16:37:47,773 - Epoch: [345][ 3/ 8] Overall Loss 0.017478 Objective Loss 0.017478 MSE 0.017478 LR 0.001000 Time 0.122506 +2024-02-02 16:37:47,782 - Epoch: [345][ 4/ 8] Overall Loss 0.017007 Objective Loss 0.017007 MSE 0.017007 LR 0.001000 Time 0.093967 +2024-02-02 16:37:47,791 - Epoch: [345][ 5/ 8] Overall Loss 0.016870 Objective Loss 0.016870 MSE 0.016870 LR 0.001000 Time 0.076868 +2024-02-02 16:37:47,799 - Epoch: [345][ 6/ 8] Overall Loss 0.017050 Objective Loss 0.017050 MSE 0.017050 LR 0.001000 Time 0.065440 +2024-02-02 16:37:47,807 - Epoch: [345][ 7/ 8] Overall Loss 0.017205 Objective Loss 0.017205 MSE 0.017205 LR 0.001000 Time 0.057270 +2024-02-02 16:37:47,816 - Epoch: [345][ 8/ 8] Overall Loss 0.017527 Objective Loss 0.017527 MSE 0.017272 LR 0.001000 Time 0.051155 +2024-02-02 16:37:47,964 - --- validate (epoch=345)----------- +2024-02-02 16:37:47,964 - 60 samples (32 per mini-batch) +2024-02-02 16:37:48,315 - Epoch: [345][ 1/ 2] Loss 0.022894 MSE 0.022894 +2024-02-02 16:37:48,321 - Epoch: [345][ 2/ 2] Loss 0.022462 MSE 0.022491 +2024-02-02 16:37:48,467 - ==> MSE: 0.02249 Loss: 0.022 + +2024-02-02 16:37:48,474 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:48,474 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:48,479 - + +2024-02-02 16:37:48,479 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:48,845 - Epoch: [346][ 1/ 8] Overall Loss 0.019366 Objective Loss 0.019366 MSE 0.019366 LR 0.001000 Time 0.365462 +2024-02-02 16:37:48,856 - Epoch: [346][ 2/ 8] Overall Loss 0.018546 Objective Loss 0.018546 MSE 0.018546 LR 0.001000 Time 0.188409 +2024-02-02 16:37:48,866 - Epoch: [346][ 3/ 8] Overall Loss 0.018458 Objective Loss 0.018458 MSE 0.018458 LR 0.001000 Time 0.128612 +2024-02-02 16:37:48,875 - Epoch: [346][ 4/ 8] Overall Loss 0.018284 Objective Loss 0.018284 MSE 0.018284 LR 0.001000 Time 0.098791 +2024-02-02 16:37:48,883 - Epoch: [346][ 5/ 8] Overall Loss 0.017809 Objective Loss 0.017809 MSE 0.017809 LR 0.001000 Time 0.080688 +2024-02-02 16:37:48,892 - Epoch: [346][ 6/ 8] Overall Loss 0.017545 Objective Loss 0.017545 MSE 0.017545 LR 0.001000 Time 0.068649 +2024-02-02 16:37:48,901 - Epoch: [346][ 7/ 8] Overall Loss 0.017244 Objective Loss 0.017244 MSE 0.017244 LR 0.001000 Time 0.060063 +2024-02-02 16:37:48,909 - Epoch: [346][ 8/ 8] Overall Loss 0.017140 Objective Loss 0.017140 MSE 0.017222 LR 0.001000 Time 0.053594 +2024-02-02 16:37:49,056 - --- validate (epoch=346)----------- +2024-02-02 16:37:49,056 - 60 samples (32 per mini-batch) +2024-02-02 16:37:49,416 - Epoch: [346][ 1/ 2] Loss 0.022482 MSE 0.022482 +2024-02-02 16:37:49,422 - Epoch: [346][ 2/ 2] Loss 0.022472 MSE 0.022473 +2024-02-02 16:37:49,568 - ==> MSE: 0.02247 Loss: 0.022 + +2024-02-02 16:37:49,576 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:49,576 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:49,580 - + +2024-02-02 16:37:49,580 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:49,946 - Epoch: [347][ 1/ 8] Overall Loss 0.018287 Objective Loss 0.018287 MSE 0.018287 LR 0.001000 Time 0.364748 +2024-02-02 16:37:49,957 - Epoch: [347][ 2/ 8] Overall Loss 0.017887 Objective Loss 0.017887 MSE 0.017887 LR 0.001000 Time 0.187871 +2024-02-02 16:37:49,966 - Epoch: [347][ 3/ 8] Overall Loss 0.017488 Objective Loss 0.017488 MSE 0.017488 LR 0.001000 Time 0.128067 +2024-02-02 16:37:49,974 - Epoch: [347][ 4/ 8] Overall Loss 0.017271 Objective Loss 0.017271 MSE 0.017271 LR 0.001000 Time 0.098177 +2024-02-02 16:37:49,983 - Epoch: [347][ 5/ 8] Overall Loss 0.017301 Objective Loss 0.017301 MSE 0.017301 LR 0.001000 Time 0.080236 +2024-02-02 16:37:49,991 - Epoch: [347][ 6/ 8] Overall Loss 0.017535 Objective Loss 0.017535 MSE 0.017535 LR 0.001000 Time 0.068254 +2024-02-02 16:37:50,000 - Epoch: [347][ 7/ 8] Overall Loss 0.017283 Objective Loss 0.017283 MSE 0.017283 LR 0.001000 Time 0.059702 +2024-02-02 16:37:50,009 - Epoch: [347][ 8/ 8] Overall Loss 0.017308 Objective Loss 0.017308 MSE 0.017288 LR 0.001000 Time 0.053286 +2024-02-02 16:37:50,158 - --- validate (epoch=347)----------- +2024-02-02 16:37:50,158 - 60 samples (32 per mini-batch) +2024-02-02 16:37:50,506 - Epoch: [347][ 1/ 2] Loss 0.023278 MSE 0.023278 +2024-02-02 16:37:50,513 - Epoch: [347][ 2/ 2] Loss 0.022409 MSE 0.022467 +2024-02-02 16:37:50,659 - ==> MSE: 0.02247 Loss: 0.022 + +2024-02-02 16:37:50,665 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:50,665 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:50,670 - + +2024-02-02 16:37:50,670 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:51,016 - Epoch: [348][ 1/ 8] Overall Loss 0.017418 Objective Loss 0.017418 MSE 0.017418 LR 0.001000 Time 0.345473 +2024-02-02 16:37:51,024 - Epoch: [348][ 2/ 8] Overall Loss 0.017212 Objective Loss 0.017212 MSE 0.017212 LR 0.001000 Time 0.176937 +2024-02-02 16:37:51,033 - Epoch: [348][ 3/ 8] Overall Loss 0.017750 Objective Loss 0.017750 MSE 0.017750 LR 0.001000 Time 0.120714 +2024-02-02 16:37:51,041 - Epoch: [348][ 4/ 8] Overall Loss 0.017667 Objective Loss 0.017667 MSE 0.017667 LR 0.001000 Time 0.092606 +2024-02-02 16:37:51,050 - Epoch: [348][ 5/ 8] Overall Loss 0.017320 Objective Loss 0.017320 MSE 0.017320 LR 0.001000 Time 0.075757 +2024-02-02 16:37:51,058 - Epoch: [348][ 6/ 8] Overall Loss 0.017345 Objective Loss 0.017345 MSE 0.017345 LR 0.001000 Time 0.064548 +2024-02-02 16:37:51,067 - Epoch: [348][ 7/ 8] Overall Loss 0.017346 Objective Loss 0.017346 MSE 0.017346 LR 0.001000 Time 0.056527 +2024-02-02 16:37:51,075 - Epoch: [348][ 8/ 8] Overall Loss 0.016931 Objective Loss 0.016931 MSE 0.017259 LR 0.001000 Time 0.050489 +2024-02-02 16:37:51,221 - --- validate (epoch=348)----------- +2024-02-02 16:37:51,221 - 60 samples (32 per mini-batch) +2024-02-02 16:37:51,575 - Epoch: [348][ 1/ 2] Loss 0.024192 MSE 0.024192 +2024-02-02 16:37:51,581 - Epoch: [348][ 2/ 2] Loss 0.022356 MSE 0.022479 +2024-02-02 16:37:51,731 - ==> MSE: 0.02248 Loss: 0.022 + +2024-02-02 16:37:51,737 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:51,737 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:51,742 - + +2024-02-02 16:37:51,742 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:52,098 - Epoch: [349][ 1/ 8] Overall Loss 0.016905 Objective Loss 0.016905 MSE 0.016905 LR 0.001000 Time 0.356123 +2024-02-02 16:37:52,111 - Epoch: [349][ 2/ 8] Overall Loss 0.017208 Objective Loss 0.017208 MSE 0.017208 LR 0.001000 Time 0.184067 +2024-02-02 16:37:52,121 - Epoch: [349][ 3/ 8] Overall Loss 0.017527 Objective Loss 0.017527 MSE 0.017527 LR 0.001000 Time 0.126015 +2024-02-02 16:37:52,130 - Epoch: [349][ 4/ 8] Overall Loss 0.017682 Objective Loss 0.017682 MSE 0.017682 LR 0.001000 Time 0.096632 +2024-02-02 16:37:52,138 - Epoch: [349][ 5/ 8] Overall Loss 0.017481 Objective Loss 0.017481 MSE 0.017481 LR 0.001000 Time 0.078991 +2024-02-02 16:37:52,147 - Epoch: [349][ 6/ 8] Overall Loss 0.017003 Objective Loss 0.017003 MSE 0.017003 LR 0.001000 Time 0.067230 +2024-02-02 16:37:52,155 - Epoch: [349][ 7/ 8] Overall Loss 0.017342 Objective Loss 0.017342 MSE 0.017342 LR 0.001000 Time 0.058833 +2024-02-02 16:37:52,164 - Epoch: [349][ 8/ 8] Overall Loss 0.017392 Objective Loss 0.017392 MSE 0.017353 LR 0.001000 Time 0.052524 +2024-02-02 16:37:52,310 - --- validate (epoch=349)----------- +2024-02-02 16:37:52,310 - 60 samples (32 per mini-batch) +2024-02-02 16:37:52,670 - Epoch: [349][ 1/ 2] Loss 0.022793 MSE 0.022793 +2024-02-02 16:37:52,676 - Epoch: [349][ 2/ 2] Loss 0.022460 MSE 0.022482 +2024-02-02 16:37:52,818 - ==> MSE: 0.02248 Loss: 0.022 + +2024-02-02 16:37:52,824 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:52,824 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:52,828 - + +2024-02-02 16:37:52,828 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:53,171 - Epoch: [350][ 1/ 8] Overall Loss 0.016541 Objective Loss 0.016541 MSE 0.016541 LR 0.001000 Time 0.342406 +2024-02-02 16:37:53,180 - Epoch: [350][ 2/ 8] Overall Loss 0.016762 Objective Loss 0.016762 MSE 0.016762 LR 0.001000 Time 0.175505 +2024-02-02 16:37:53,189 - Epoch: [350][ 3/ 8] Overall Loss 0.017514 Objective Loss 0.017514 MSE 0.017514 LR 0.001000 Time 0.119840 +2024-02-02 16:37:53,197 - Epoch: [350][ 4/ 8] Overall Loss 0.016894 Objective Loss 0.016894 MSE 0.016894 LR 0.001000 Time 0.091983 +2024-02-02 16:37:53,206 - Epoch: [350][ 5/ 8] Overall Loss 0.016806 Objective Loss 0.016806 MSE 0.016806 LR 0.001000 Time 0.075294 +2024-02-02 16:37:53,214 - Epoch: [350][ 6/ 8] Overall Loss 0.017157 Objective Loss 0.017157 MSE 0.017157 LR 0.001000 Time 0.064135 +2024-02-02 16:37:53,223 - Epoch: [350][ 7/ 8] Overall Loss 0.017207 Objective Loss 0.017207 MSE 0.017207 LR 0.001000 Time 0.056170 +2024-02-02 16:37:53,231 - Epoch: [350][ 8/ 8] Overall Loss 0.017538 Objective Loss 0.017538 MSE 0.017276 LR 0.001000 Time 0.050178 +2024-02-02 16:37:53,383 - --- validate (epoch=350)----------- +2024-02-02 16:37:53,383 - 60 samples (32 per mini-batch) +2024-02-02 16:37:53,737 - Epoch: [350][ 1/ 2] Loss 0.021924 MSE 0.021924 +2024-02-02 16:37:53,742 - Epoch: [350][ 2/ 2] Loss 0.022534 MSE 0.022493 +2024-02-02 16:37:53,880 - ==> MSE: 0.02249 Loss: 0.023 + +2024-02-02 16:37:53,888 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:53,888 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:53,892 - + +2024-02-02 16:37:53,892 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:54,260 - Epoch: [351][ 1/ 8] Overall Loss 0.018619 Objective Loss 0.018619 MSE 0.018619 LR 0.001000 Time 0.367496 +2024-02-02 16:37:54,271 - Epoch: [351][ 2/ 8] Overall Loss 0.017965 Objective Loss 0.017965 MSE 0.017965 LR 0.001000 Time 0.188964 +2024-02-02 16:37:54,279 - Epoch: [351][ 3/ 8] Overall Loss 0.017296 Objective Loss 0.017296 MSE 0.017296 LR 0.001000 Time 0.128824 +2024-02-02 16:37:54,288 - Epoch: [351][ 4/ 8] Overall Loss 0.017515 Objective Loss 0.017515 MSE 0.017515 LR 0.001000 Time 0.098763 +2024-02-02 16:37:54,297 - Epoch: [351][ 5/ 8] Overall Loss 0.017542 Objective Loss 0.017542 MSE 0.017542 LR 0.001000 Time 0.080714 +2024-02-02 16:37:54,305 - Epoch: [351][ 6/ 8] Overall Loss 0.017335 Objective Loss 0.017335 MSE 0.017335 LR 0.001000 Time 0.068601 +2024-02-02 16:37:54,314 - Epoch: [351][ 7/ 8] Overall Loss 0.017249 Objective Loss 0.017249 MSE 0.017249 LR 0.001000 Time 0.060014 +2024-02-02 16:37:54,322 - Epoch: [351][ 8/ 8] Overall Loss 0.016879 Objective Loss 0.016879 MSE 0.017172 LR 0.001000 Time 0.053577 +2024-02-02 16:37:54,471 - --- validate (epoch=351)----------- +2024-02-02 16:37:54,472 - 60 samples (32 per mini-batch) +2024-02-02 16:37:54,827 - Epoch: [351][ 1/ 2] Loss 0.023217 MSE 0.023217 +2024-02-02 16:37:54,833 - Epoch: [351][ 2/ 2] Loss 0.022461 MSE 0.022511 +2024-02-02 16:37:54,982 - ==> MSE: 0.02251 Loss: 0.022 + +2024-02-02 16:37:54,989 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:54,989 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:54,993 - + +2024-02-02 16:37:54,993 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:55,356 - Epoch: [352][ 1/ 8] Overall Loss 0.017032 Objective Loss 0.017032 MSE 0.017032 LR 0.001000 Time 0.362141 +2024-02-02 16:37:55,365 - Epoch: [352][ 2/ 8] Overall Loss 0.016568 Objective Loss 0.016568 MSE 0.016568 LR 0.001000 Time 0.185436 +2024-02-02 16:37:55,373 - Epoch: [352][ 3/ 8] Overall Loss 0.016528 Objective Loss 0.016528 MSE 0.016528 LR 0.001000 Time 0.126442 +2024-02-02 16:37:55,382 - Epoch: [352][ 4/ 8] Overall Loss 0.016450 Objective Loss 0.016450 MSE 0.016450 LR 0.001000 Time 0.096961 +2024-02-02 16:37:55,390 - Epoch: [352][ 5/ 8] Overall Loss 0.017263 Objective Loss 0.017263 MSE 0.017263 LR 0.001000 Time 0.079256 +2024-02-02 16:37:55,399 - Epoch: [352][ 6/ 8] Overall Loss 0.017148 Objective Loss 0.017148 MSE 0.017148 LR 0.001000 Time 0.067447 +2024-02-02 16:37:55,407 - Epoch: [352][ 7/ 8] Overall Loss 0.017114 Objective Loss 0.017114 MSE 0.017114 LR 0.001000 Time 0.058998 +2024-02-02 16:37:55,416 - Epoch: [352][ 8/ 8] Overall Loss 0.017449 Objective Loss 0.017449 MSE 0.017184 LR 0.001000 Time 0.052655 +2024-02-02 16:37:55,567 - --- validate (epoch=352)----------- +2024-02-02 16:37:55,568 - 60 samples (32 per mini-batch) +2024-02-02 16:37:55,923 - Epoch: [352][ 1/ 2] Loss 0.023618 MSE 0.023618 +2024-02-02 16:37:55,930 - Epoch: [352][ 2/ 2] Loss 0.022699 MSE 0.022760 +2024-02-02 16:37:56,082 - ==> MSE: 0.02276 Loss: 0.023 + +2024-02-02 16:37:56,089 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:56,089 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:56,093 - + +2024-02-02 16:37:56,094 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:56,462 - Epoch: [353][ 1/ 8] Overall Loss 0.017649 Objective Loss 0.017649 MSE 0.017649 LR 0.001000 Time 0.368471 +2024-02-02 16:37:56,472 - Epoch: [353][ 2/ 8] Overall Loss 0.018804 Objective Loss 0.018804 MSE 0.018804 LR 0.001000 Time 0.188748 +2024-02-02 16:37:56,480 - Epoch: [353][ 3/ 8] Overall Loss 0.018082 Objective Loss 0.018082 MSE 0.018082 LR 0.001000 Time 0.128617 +2024-02-02 16:37:56,489 - Epoch: [353][ 4/ 8] Overall Loss 0.017969 Objective Loss 0.017969 MSE 0.017969 LR 0.001000 Time 0.098566 +2024-02-02 16:37:56,497 - Epoch: [353][ 5/ 8] Overall Loss 0.018000 Objective Loss 0.018000 MSE 0.018000 LR 0.001000 Time 0.080534 +2024-02-02 16:37:56,506 - Epoch: [353][ 6/ 8] Overall Loss 0.017981 Objective Loss 0.017981 MSE 0.017981 LR 0.001000 Time 0.068514 +2024-02-02 16:37:56,515 - Epoch: [353][ 7/ 8] Overall Loss 0.017923 Objective Loss 0.017923 MSE 0.017923 LR 0.001000 Time 0.059920 +2024-02-02 16:37:56,523 - Epoch: [353][ 8/ 8] Overall Loss 0.017584 Objective Loss 0.017584 MSE 0.017852 LR 0.001000 Time 0.053480 +2024-02-02 16:37:56,675 - --- validate (epoch=353)----------- +2024-02-02 16:37:56,675 - 60 samples (32 per mini-batch) +2024-02-02 16:37:57,036 - Epoch: [353][ 1/ 2] Loss 0.024837 MSE 0.024837 +2024-02-02 16:37:57,042 - Epoch: [353][ 2/ 2] Loss 0.022759 MSE 0.022897 +2024-02-02 16:37:57,199 - ==> MSE: 0.02290 Loss: 0.023 + +2024-02-02 16:37:57,207 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:57,208 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:57,212 - + +2024-02-02 16:37:57,212 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:57,573 - Epoch: [354][ 1/ 8] Overall Loss 0.017745 Objective Loss 0.017745 MSE 0.017745 LR 0.001000 Time 0.361041 +2024-02-02 16:37:57,585 - Epoch: [354][ 2/ 8] Overall Loss 0.016879 Objective Loss 0.016879 MSE 0.016879 LR 0.001000 Time 0.186338 +2024-02-02 16:37:57,594 - Epoch: [354][ 3/ 8] Overall Loss 0.017119 Objective Loss 0.017119 MSE 0.017119 LR 0.001000 Time 0.127097 +2024-02-02 16:37:57,603 - Epoch: [354][ 4/ 8] Overall Loss 0.017379 Objective Loss 0.017379 MSE 0.017379 LR 0.001000 Time 0.097416 +2024-02-02 16:37:57,611 - Epoch: [354][ 5/ 8] Overall Loss 0.017414 Objective Loss 0.017414 MSE 0.017414 LR 0.001000 Time 0.079590 +2024-02-02 16:37:57,620 - Epoch: [354][ 6/ 8] Overall Loss 0.017652 Objective Loss 0.017652 MSE 0.017652 LR 0.001000 Time 0.067692 +2024-02-02 16:37:57,628 - Epoch: [354][ 7/ 8] Overall Loss 0.017667 Objective Loss 0.017667 MSE 0.017667 LR 0.001000 Time 0.059215 +2024-02-02 16:37:57,636 - Epoch: [354][ 8/ 8] Overall Loss 0.018507 Objective Loss 0.018507 MSE 0.017843 LR 0.001000 Time 0.052836 +2024-02-02 16:37:57,788 - --- validate (epoch=354)----------- +2024-02-02 16:37:57,788 - 60 samples (32 per mini-batch) +2024-02-02 16:37:58,145 - Epoch: [354][ 1/ 2] Loss 0.023288 MSE 0.023288 +2024-02-02 16:37:58,151 - Epoch: [354][ 2/ 2] Loss 0.022363 MSE 0.022425 +2024-02-02 16:37:58,301 - ==> MSE: 0.02242 Loss: 0.022 + +2024-02-02 16:37:58,309 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:58,309 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:58,313 - + +2024-02-02 16:37:58,313 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:58,677 - Epoch: [355][ 1/ 8] Overall Loss 0.019803 Objective Loss 0.019803 MSE 0.019803 LR 0.001000 Time 0.362948 +2024-02-02 16:37:58,688 - Epoch: [355][ 2/ 8] Overall Loss 0.018607 Objective Loss 0.018607 MSE 0.018607 LR 0.001000 Time 0.187141 +2024-02-02 16:37:58,700 - Epoch: [355][ 3/ 8] Overall Loss 0.018585 Objective Loss 0.018585 MSE 0.018585 LR 0.001000 Time 0.128494 +2024-02-02 16:37:58,710 - Epoch: [355][ 4/ 8] Overall Loss 0.018254 Objective Loss 0.018254 MSE 0.018254 LR 0.001000 Time 0.098903 +2024-02-02 16:37:58,719 - Epoch: [355][ 5/ 8] Overall Loss 0.018259 Objective Loss 0.018259 MSE 0.018259 LR 0.001000 Time 0.080877 +2024-02-02 16:37:58,728 - Epoch: [355][ 6/ 8] Overall Loss 0.017711 Objective Loss 0.017711 MSE 0.017711 LR 0.001000 Time 0.068855 +2024-02-02 16:37:58,737 - Epoch: [355][ 7/ 8] Overall Loss 0.017586 Objective Loss 0.017586 MSE 0.017586 LR 0.001000 Time 0.060257 +2024-02-02 16:37:58,745 - Epoch: [355][ 8/ 8] Overall Loss 0.017589 Objective Loss 0.017589 MSE 0.017587 LR 0.001000 Time 0.053741 +2024-02-02 16:37:58,894 - --- validate (epoch=355)----------- +2024-02-02 16:37:58,895 - 60 samples (32 per mini-batch) +2024-02-02 16:37:59,248 - Epoch: [355][ 1/ 2] Loss 0.022715 MSE 0.022715 +2024-02-02 16:37:59,254 - Epoch: [355][ 2/ 2] Loss 0.023028 MSE 0.023007 +2024-02-02 16:37:59,401 - ==> MSE: 0.02301 Loss: 0.023 + +2024-02-02 16:37:59,409 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:37:59,409 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:37:59,413 - + +2024-02-02 16:37:59,413 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:37:59,776 - Epoch: [356][ 1/ 8] Overall Loss 0.016820 Objective Loss 0.016820 MSE 0.016820 LR 0.001000 Time 0.362067 +2024-02-02 16:37:59,785 - Epoch: [356][ 2/ 8] Overall Loss 0.018671 Objective Loss 0.018671 MSE 0.018671 LR 0.001000 Time 0.185268 +2024-02-02 16:37:59,793 - Epoch: [356][ 3/ 8] Overall Loss 0.018072 Objective Loss 0.018072 MSE 0.018072 LR 0.001000 Time 0.126262 +2024-02-02 16:37:59,801 - Epoch: [356][ 4/ 8] Overall Loss 0.018055 Objective Loss 0.018055 MSE 0.018055 LR 0.001000 Time 0.096741 +2024-02-02 16:37:59,810 - Epoch: [356][ 5/ 8] Overall Loss 0.018119 Objective Loss 0.018119 MSE 0.018119 LR 0.001000 Time 0.079033 +2024-02-02 16:37:59,818 - Epoch: [356][ 6/ 8] Overall Loss 0.018055 Objective Loss 0.018055 MSE 0.018055 LR 0.001000 Time 0.067236 +2024-02-02 16:37:59,827 - Epoch: [356][ 7/ 8] Overall Loss 0.017561 Objective Loss 0.017561 MSE 0.017561 LR 0.001000 Time 0.058823 +2024-02-02 16:37:59,835 - Epoch: [356][ 8/ 8] Overall Loss 0.017973 Objective Loss 0.017973 MSE 0.017647 LR 0.001000 Time 0.052527 +2024-02-02 16:37:59,986 - --- validate (epoch=356)----------- +2024-02-02 16:37:59,986 - 60 samples (32 per mini-batch) +2024-02-02 16:38:00,335 - Epoch: [356][ 1/ 2] Loss 0.020768 MSE 0.020768 +2024-02-02 16:38:00,341 - Epoch: [356][ 2/ 2] Loss 0.022981 MSE 0.022833 +2024-02-02 16:38:00,490 - ==> MSE: 0.02283 Loss: 0.023 + +2024-02-02 16:38:00,497 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:00,497 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:00,502 - + +2024-02-02 16:38:00,502 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:00,864 - Epoch: [357][ 1/ 8] Overall Loss 0.016495 Objective Loss 0.016495 MSE 0.016495 LR 0.001000 Time 0.362180 +2024-02-02 16:38:00,877 - Epoch: [357][ 2/ 8] Overall Loss 0.016744 Objective Loss 0.016744 MSE 0.016744 LR 0.001000 Time 0.187014 +2024-02-02 16:38:00,889 - Epoch: [357][ 3/ 8] Overall Loss 0.016895 Objective Loss 0.016895 MSE 0.016895 LR 0.001000 Time 0.128619 +2024-02-02 16:38:00,901 - Epoch: [357][ 4/ 8] Overall Loss 0.016954 Objective Loss 0.016954 MSE 0.016954 LR 0.001000 Time 0.099441 +2024-02-02 16:38:00,913 - Epoch: [357][ 5/ 8] Overall Loss 0.016919 Objective Loss 0.016919 MSE 0.016919 LR 0.001000 Time 0.081994 +2024-02-02 16:38:00,922 - Epoch: [357][ 6/ 8] Overall Loss 0.017240 Objective Loss 0.017240 MSE 0.017240 LR 0.001000 Time 0.069792 +2024-02-02 16:38:00,931 - Epoch: [357][ 7/ 8] Overall Loss 0.017412 Objective Loss 0.017412 MSE 0.017412 LR 0.001000 Time 0.061044 +2024-02-02 16:38:00,940 - Epoch: [357][ 8/ 8] Overall Loss 0.018262 Objective Loss 0.018262 MSE 0.017590 LR 0.001000 Time 0.054448 +2024-02-02 16:38:01,091 - --- validate (epoch=357)----------- +2024-02-02 16:38:01,092 - 60 samples (32 per mini-batch) +2024-02-02 16:38:01,450 - Epoch: [357][ 1/ 2] Loss 0.022719 MSE 0.022719 +2024-02-02 16:38:01,456 - Epoch: [357][ 2/ 2] Loss 0.022642 MSE 0.022647 +2024-02-02 16:38:01,605 - ==> MSE: 0.02265 Loss: 0.023 + +2024-02-02 16:38:01,613 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:01,613 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:01,617 - + +2024-02-02 16:38:01,617 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:01,982 - Epoch: [358][ 1/ 8] Overall Loss 0.018718 Objective Loss 0.018718 MSE 0.018718 LR 0.001000 Time 0.364371 +2024-02-02 16:38:01,997 - Epoch: [358][ 2/ 8] Overall Loss 0.017766 Objective Loss 0.017766 MSE 0.017766 LR 0.001000 Time 0.189517 +2024-02-02 16:38:02,007 - Epoch: [358][ 3/ 8] Overall Loss 0.018044 Objective Loss 0.018044 MSE 0.018044 LR 0.001000 Time 0.129459 +2024-02-02 16:38:02,016 - Epoch: [358][ 4/ 8] Overall Loss 0.017715 Objective Loss 0.017715 MSE 0.017715 LR 0.001000 Time 0.099250 +2024-02-02 16:38:02,024 - Epoch: [358][ 5/ 8] Overall Loss 0.017379 Objective Loss 0.017379 MSE 0.017379 LR 0.001000 Time 0.081104 +2024-02-02 16:38:02,033 - Epoch: [358][ 6/ 8] Overall Loss 0.017413 Objective Loss 0.017413 MSE 0.017413 LR 0.001000 Time 0.068937 +2024-02-02 16:38:02,041 - Epoch: [358][ 7/ 8] Overall Loss 0.017467 Objective Loss 0.017467 MSE 0.017467 LR 0.001000 Time 0.060287 +2024-02-02 16:38:02,050 - Epoch: [358][ 8/ 8] Overall Loss 0.017687 Objective Loss 0.017687 MSE 0.017513 LR 0.001000 Time 0.053800 +2024-02-02 16:38:02,194 - --- validate (epoch=358)----------- +2024-02-02 16:38:02,194 - 60 samples (32 per mini-batch) +2024-02-02 16:38:02,555 - Epoch: [358][ 1/ 2] Loss 0.023468 MSE 0.023468 +2024-02-02 16:38:02,561 - Epoch: [358][ 2/ 2] Loss 0.022711 MSE 0.022762 +2024-02-02 16:38:02,706 - ==> MSE: 0.02276 Loss: 0.023 + +2024-02-02 16:38:02,713 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:02,713 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:02,718 - + +2024-02-02 16:38:02,718 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:03,083 - Epoch: [359][ 1/ 8] Overall Loss 0.017229 Objective Loss 0.017229 MSE 0.017229 LR 0.001000 Time 0.364959 +2024-02-02 16:38:03,092 - Epoch: [359][ 2/ 8] Overall Loss 0.016571 Objective Loss 0.016571 MSE 0.016571 LR 0.001000 Time 0.186703 +2024-02-02 16:38:03,100 - Epoch: [359][ 3/ 8] Overall Loss 0.017101 Objective Loss 0.017101 MSE 0.017101 LR 0.001000 Time 0.127226 +2024-02-02 16:38:03,109 - Epoch: [359][ 4/ 8] Overall Loss 0.017302 Objective Loss 0.017302 MSE 0.017302 LR 0.001000 Time 0.097526 +2024-02-02 16:38:03,117 - Epoch: [359][ 5/ 8] Overall Loss 0.017358 Objective Loss 0.017358 MSE 0.017358 LR 0.001000 Time 0.079656 +2024-02-02 16:38:03,126 - Epoch: [359][ 6/ 8] Overall Loss 0.017464 Objective Loss 0.017464 MSE 0.017464 LR 0.001000 Time 0.067775 +2024-02-02 16:38:03,134 - Epoch: [359][ 7/ 8] Overall Loss 0.017389 Objective Loss 0.017389 MSE 0.017389 LR 0.001000 Time 0.059310 +2024-02-02 16:38:03,143 - Epoch: [359][ 8/ 8] Overall Loss 0.017595 Objective Loss 0.017595 MSE 0.017432 LR 0.001000 Time 0.052946 +2024-02-02 16:38:03,291 - --- validate (epoch=359)----------- +2024-02-02 16:38:03,291 - 60 samples (32 per mini-batch) +2024-02-02 16:38:03,641 - Epoch: [359][ 1/ 2] Loss 0.023327 MSE 0.023327 +2024-02-02 16:38:03,647 - Epoch: [359][ 2/ 2] Loss 0.022646 MSE 0.022692 +2024-02-02 16:38:03,794 - ==> MSE: 0.02269 Loss: 0.023 + +2024-02-02 16:38:03,801 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:03,801 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:03,806 - + +2024-02-02 16:38:03,806 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:04,171 - Epoch: [360][ 1/ 8] Overall Loss 0.017280 Objective Loss 0.017280 MSE 0.017280 LR 0.001000 Time 0.364660 +2024-02-02 16:38:04,180 - Epoch: [360][ 2/ 8] Overall Loss 0.017737 Objective Loss 0.017737 MSE 0.017737 LR 0.001000 Time 0.186678 +2024-02-02 16:38:04,188 - Epoch: [360][ 3/ 8] Overall Loss 0.016294 Objective Loss 0.016294 MSE 0.016294 LR 0.001000 Time 0.127241 +2024-02-02 16:38:04,197 - Epoch: [360][ 4/ 8] Overall Loss 0.016817 Objective Loss 0.016817 MSE 0.016817 LR 0.001000 Time 0.097512 +2024-02-02 16:38:04,205 - Epoch: [360][ 5/ 8] Overall Loss 0.017153 Objective Loss 0.017153 MSE 0.017153 LR 0.001000 Time 0.079686 +2024-02-02 16:38:04,214 - Epoch: [360][ 6/ 8] Overall Loss 0.017220 Objective Loss 0.017220 MSE 0.017220 LR 0.001000 Time 0.067829 +2024-02-02 16:38:04,223 - Epoch: [360][ 7/ 8] Overall Loss 0.017348 Objective Loss 0.017348 MSE 0.017348 LR 0.001000 Time 0.059334 +2024-02-02 16:38:04,231 - Epoch: [360][ 8/ 8] Overall Loss 0.017263 Objective Loss 0.017263 MSE 0.017330 LR 0.001000 Time 0.052902 +2024-02-02 16:38:04,380 - --- validate (epoch=360)----------- +2024-02-02 16:38:04,380 - 60 samples (32 per mini-batch) +2024-02-02 16:38:04,739 - Epoch: [360][ 1/ 2] Loss 0.023092 MSE 0.023092 +2024-02-02 16:38:04,745 - Epoch: [360][ 2/ 2] Loss 0.022521 MSE 0.022559 +2024-02-02 16:38:04,893 - ==> MSE: 0.02256 Loss: 0.023 + +2024-02-02 16:38:04,901 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:04,901 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:04,906 - + +2024-02-02 16:38:04,906 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:05,267 - Epoch: [361][ 1/ 8] Overall Loss 0.018205 Objective Loss 0.018205 MSE 0.018205 LR 0.001000 Time 0.360680 +2024-02-02 16:38:05,277 - Epoch: [361][ 2/ 8] Overall Loss 0.017427 Objective Loss 0.017427 MSE 0.017427 LR 0.001000 Time 0.185316 +2024-02-02 16:38:05,289 - Epoch: [361][ 3/ 8] Overall Loss 0.017079 Objective Loss 0.017079 MSE 0.017079 LR 0.001000 Time 0.127554 +2024-02-02 16:38:05,301 - Epoch: [361][ 4/ 8] Overall Loss 0.017049 Objective Loss 0.017049 MSE 0.017049 LR 0.001000 Time 0.098633 +2024-02-02 16:38:05,313 - Epoch: [361][ 5/ 8] Overall Loss 0.017000 Objective Loss 0.017000 MSE 0.017000 LR 0.001000 Time 0.081158 +2024-02-02 16:38:05,321 - Epoch: [361][ 6/ 8] Overall Loss 0.016999 Objective Loss 0.016999 MSE 0.016999 LR 0.001000 Time 0.069014 +2024-02-02 16:38:05,330 - Epoch: [361][ 7/ 8] Overall Loss 0.017192 Objective Loss 0.017192 MSE 0.017192 LR 0.001000 Time 0.060363 +2024-02-02 16:38:05,338 - Epoch: [361][ 8/ 8] Overall Loss 0.017182 Objective Loss 0.017182 MSE 0.017190 LR 0.001000 Time 0.053854 +2024-02-02 16:38:05,491 - --- validate (epoch=361)----------- +2024-02-02 16:38:05,492 - 60 samples (32 per mini-batch) +2024-02-02 16:38:05,843 - Epoch: [361][ 1/ 2] Loss 0.023145 MSE 0.023145 +2024-02-02 16:38:05,849 - Epoch: [361][ 2/ 2] Loss 0.022542 MSE 0.022582 +2024-02-02 16:38:05,997 - ==> MSE: 0.02258 Loss: 0.023 + +2024-02-02 16:38:06,014 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:06,014 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:06,022 - + +2024-02-02 16:38:06,022 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:06,399 - Epoch: [362][ 1/ 8] Overall Loss 0.017430 Objective Loss 0.017430 MSE 0.017430 LR 0.001000 Time 0.377055 +2024-02-02 16:38:06,411 - Epoch: [362][ 2/ 8] Overall Loss 0.018198 Objective Loss 0.018198 MSE 0.018198 LR 0.001000 Time 0.194085 +2024-02-02 16:38:06,419 - Epoch: [362][ 3/ 8] Overall Loss 0.017645 Objective Loss 0.017645 MSE 0.017645 LR 0.001000 Time 0.132156 +2024-02-02 16:38:06,428 - Epoch: [362][ 4/ 8] Overall Loss 0.017762 Objective Loss 0.017762 MSE 0.017762 LR 0.001000 Time 0.101184 +2024-02-02 16:38:06,444 - Epoch: [362][ 5/ 8] Overall Loss 0.017568 Objective Loss 0.017568 MSE 0.017568 LR 0.001000 Time 0.082776 +2024-02-02 16:38:06,453 - Epoch: [362][ 6/ 8] Overall Loss 0.017417 Objective Loss 0.017417 MSE 0.017417 LR 0.001000 Time 0.070396 +2024-02-02 16:38:06,462 - Epoch: [362][ 7/ 8] Overall Loss 0.017169 Objective Loss 0.017169 MSE 0.017169 LR 0.001000 Time 0.061559 +2024-02-02 16:38:06,470 - Epoch: [362][ 8/ 8] Overall Loss 0.017204 Objective Loss 0.017204 MSE 0.017176 LR 0.001000 Time 0.054922 +2024-02-02 16:38:06,623 - --- validate (epoch=362)----------- +2024-02-02 16:38:06,623 - 60 samples (32 per mini-batch) +2024-02-02 16:38:06,979 - Epoch: [362][ 1/ 2] Loss 0.021033 MSE 0.021033 +2024-02-02 16:38:06,985 - Epoch: [362][ 2/ 2] Loss 0.022627 MSE 0.022521 +2024-02-02 16:38:07,134 - ==> MSE: 0.02252 Loss: 0.023 + +2024-02-02 16:38:07,141 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:07,142 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:07,146 - + +2024-02-02 16:38:07,146 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:07,510 - Epoch: [363][ 1/ 8] Overall Loss 0.015821 Objective Loss 0.015821 MSE 0.015821 LR 0.001000 Time 0.363538 +2024-02-02 16:38:07,522 - Epoch: [363][ 2/ 8] Overall Loss 0.016483 Objective Loss 0.016483 MSE 0.016483 LR 0.001000 Time 0.187367 +2024-02-02 16:38:07,530 - Epoch: [363][ 3/ 8] Overall Loss 0.016727 Objective Loss 0.016727 MSE 0.016727 LR 0.001000 Time 0.127797 +2024-02-02 16:38:07,539 - Epoch: [363][ 4/ 8] Overall Loss 0.016234 Objective Loss 0.016234 MSE 0.016234 LR 0.001000 Time 0.097960 +2024-02-02 16:38:07,548 - Epoch: [363][ 5/ 8] Overall Loss 0.016552 Objective Loss 0.016552 MSE 0.016552 LR 0.001000 Time 0.080054 +2024-02-02 16:38:07,556 - Epoch: [363][ 6/ 8] Overall Loss 0.016852 Objective Loss 0.016852 MSE 0.016852 LR 0.001000 Time 0.068134 +2024-02-02 16:38:07,565 - Epoch: [363][ 7/ 8] Overall Loss 0.017154 Objective Loss 0.017154 MSE 0.017154 LR 0.001000 Time 0.059603 +2024-02-02 16:38:07,573 - Epoch: [363][ 8/ 8] Overall Loss 0.017038 Objective Loss 0.017038 MSE 0.017130 LR 0.001000 Time 0.053192 +2024-02-02 16:38:07,724 - --- validate (epoch=363)----------- +2024-02-02 16:38:07,724 - 60 samples (32 per mini-batch) +2024-02-02 16:38:08,084 - Epoch: [363][ 1/ 2] Loss 0.023409 MSE 0.023409 +2024-02-02 16:38:08,089 - Epoch: [363][ 2/ 2] Loss 0.022312 MSE 0.022386 +2024-02-02 16:38:08,243 - ==> MSE: 0.02239 Loss: 0.022 + +2024-02-02 16:38:08,250 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:08,251 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:08,255 - + +2024-02-02 16:38:08,255 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:08,630 - Epoch: [364][ 1/ 8] Overall Loss 0.016827 Objective Loss 0.016827 MSE 0.016827 LR 0.001000 Time 0.374381 +2024-02-02 16:38:08,639 - Epoch: [364][ 2/ 8] Overall Loss 0.016890 Objective Loss 0.016890 MSE 0.016890 LR 0.001000 Time 0.191424 +2024-02-02 16:38:08,649 - Epoch: [364][ 3/ 8] Overall Loss 0.017417 Objective Loss 0.017417 MSE 0.017417 LR 0.001000 Time 0.130962 +2024-02-02 16:38:08,661 - Epoch: [364][ 4/ 8] Overall Loss 0.017391 Objective Loss 0.017391 MSE 0.017391 LR 0.001000 Time 0.101217 +2024-02-02 16:38:08,674 - Epoch: [364][ 5/ 8] Overall Loss 0.017158 Objective Loss 0.017158 MSE 0.017158 LR 0.001000 Time 0.083356 +2024-02-02 16:38:08,683 - Epoch: [364][ 6/ 8] Overall Loss 0.017303 Objective Loss 0.017303 MSE 0.017303 LR 0.001000 Time 0.071029 +2024-02-02 16:38:08,692 - Epoch: [364][ 7/ 8] Overall Loss 0.017048 Objective Loss 0.017048 MSE 0.017048 LR 0.001000 Time 0.062106 +2024-02-02 16:38:08,700 - Epoch: [364][ 8/ 8] Overall Loss 0.017290 Objective Loss 0.017290 MSE 0.017099 LR 0.001000 Time 0.055348 +2024-02-02 16:38:08,847 - --- validate (epoch=364)----------- +2024-02-02 16:38:08,847 - 60 samples (32 per mini-batch) +2024-02-02 16:38:09,198 - Epoch: [364][ 1/ 2] Loss 0.024173 MSE 0.024173 +2024-02-02 16:38:09,204 - Epoch: [364][ 2/ 2] Loss 0.022536 MSE 0.022645 +2024-02-02 16:38:09,349 - ==> MSE: 0.02264 Loss: 0.023 + +2024-02-02 16:38:09,357 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:09,357 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:09,361 - + +2024-02-02 16:38:09,361 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:09,717 - Epoch: [365][ 1/ 8] Overall Loss 0.015693 Objective Loss 0.015693 MSE 0.015693 LR 0.001000 Time 0.355461 +2024-02-02 16:38:09,729 - Epoch: [365][ 2/ 8] Overall Loss 0.016564 Objective Loss 0.016564 MSE 0.016564 LR 0.001000 Time 0.183536 +2024-02-02 16:38:09,740 - Epoch: [365][ 3/ 8] Overall Loss 0.016938 Objective Loss 0.016938 MSE 0.016938 LR 0.001000 Time 0.126088 +2024-02-02 16:38:09,750 - Epoch: [365][ 4/ 8] Overall Loss 0.017027 Objective Loss 0.017027 MSE 0.017027 LR 0.001000 Time 0.096821 +2024-02-02 16:38:09,758 - Epoch: [365][ 5/ 8] Overall Loss 0.017341 Objective Loss 0.017341 MSE 0.017341 LR 0.001000 Time 0.079153 +2024-02-02 16:38:09,767 - Epoch: [365][ 6/ 8] Overall Loss 0.016978 Objective Loss 0.016978 MSE 0.016978 LR 0.001000 Time 0.067397 +2024-02-02 16:38:09,776 - Epoch: [365][ 7/ 8] Overall Loss 0.017138 Objective Loss 0.017138 MSE 0.017138 LR 0.001000 Time 0.058988 +2024-02-02 16:38:09,785 - Epoch: [365][ 8/ 8] Overall Loss 0.017078 Objective Loss 0.017078 MSE 0.017126 LR 0.001000 Time 0.052676 +2024-02-02 16:38:09,936 - --- validate (epoch=365)----------- +2024-02-02 16:38:09,936 - 60 samples (32 per mini-batch) +2024-02-02 16:38:10,294 - Epoch: [365][ 1/ 2] Loss 0.021402 MSE 0.021402 +2024-02-02 16:38:10,300 - Epoch: [365][ 2/ 2] Loss 0.022522 MSE 0.022447 +2024-02-02 16:38:10,445 - ==> MSE: 0.02245 Loss: 0.023 + +2024-02-02 16:38:10,453 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:10,454 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:10,458 - + +2024-02-02 16:38:10,458 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:10,824 - Epoch: [366][ 1/ 8] Overall Loss 0.017174 Objective Loss 0.017174 MSE 0.017174 LR 0.001000 Time 0.365265 +2024-02-02 16:38:10,832 - Epoch: [366][ 2/ 8] Overall Loss 0.017711 Objective Loss 0.017711 MSE 0.017711 LR 0.001000 Time 0.186838 +2024-02-02 16:38:10,841 - Epoch: [366][ 3/ 8] Overall Loss 0.016782 Objective Loss 0.016782 MSE 0.016782 LR 0.001000 Time 0.127361 +2024-02-02 16:38:10,849 - Epoch: [366][ 4/ 8] Overall Loss 0.016993 Objective Loss 0.016993 MSE 0.016993 LR 0.001000 Time 0.097588 +2024-02-02 16:38:10,858 - Epoch: [366][ 5/ 8] Overall Loss 0.016673 Objective Loss 0.016673 MSE 0.016673 LR 0.001000 Time 0.079746 +2024-02-02 16:38:10,867 - Epoch: [366][ 6/ 8] Overall Loss 0.017013 Objective Loss 0.017013 MSE 0.017013 LR 0.001000 Time 0.067849 +2024-02-02 16:38:10,875 - Epoch: [366][ 7/ 8] Overall Loss 0.017194 Objective Loss 0.017194 MSE 0.017194 LR 0.001000 Time 0.059347 +2024-02-02 16:38:10,883 - Epoch: [366][ 8/ 8] Overall Loss 0.017201 Objective Loss 0.017201 MSE 0.017195 LR 0.001000 Time 0.052957 +2024-02-02 16:38:11,032 - --- validate (epoch=366)----------- +2024-02-02 16:38:11,032 - 60 samples (32 per mini-batch) +2024-02-02 16:38:11,394 - Epoch: [366][ 1/ 2] Loss 0.024384 MSE 0.024384 +2024-02-02 16:38:11,399 - Epoch: [366][ 2/ 2] Loss 0.022429 MSE 0.022560 +2024-02-02 16:38:11,548 - ==> MSE: 0.02256 Loss: 0.022 + +2024-02-02 16:38:11,557 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:11,557 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:11,561 - + +2024-02-02 16:38:11,562 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:11,934 - Epoch: [367][ 1/ 8] Overall Loss 0.017324 Objective Loss 0.017324 MSE 0.017324 LR 0.001000 Time 0.372207 +2024-02-02 16:38:11,943 - Epoch: [367][ 2/ 8] Overall Loss 0.018492 Objective Loss 0.018492 MSE 0.018492 LR 0.001000 Time 0.190501 +2024-02-02 16:38:11,952 - Epoch: [367][ 3/ 8] Overall Loss 0.018768 Objective Loss 0.018768 MSE 0.018768 LR 0.001000 Time 0.129795 +2024-02-02 16:38:11,960 - Epoch: [367][ 4/ 8] Overall Loss 0.018429 Objective Loss 0.018429 MSE 0.018429 LR 0.001000 Time 0.099438 +2024-02-02 16:38:11,969 - Epoch: [367][ 5/ 8] Overall Loss 0.017902 Objective Loss 0.017902 MSE 0.017902 LR 0.001000 Time 0.081193 +2024-02-02 16:38:11,977 - Epoch: [367][ 6/ 8] Overall Loss 0.017686 Objective Loss 0.017686 MSE 0.017686 LR 0.001000 Time 0.069057 +2024-02-02 16:38:11,986 - Epoch: [367][ 7/ 8] Overall Loss 0.017282 Objective Loss 0.017282 MSE 0.017282 LR 0.001000 Time 0.060388 +2024-02-02 16:38:11,994 - Epoch: [367][ 8/ 8] Overall Loss 0.017482 Objective Loss 0.017482 MSE 0.017324 LR 0.001000 Time 0.053870 +2024-02-02 16:38:12,145 - --- validate (epoch=367)----------- +2024-02-02 16:38:12,146 - 60 samples (32 per mini-batch) +2024-02-02 16:38:12,508 - Epoch: [367][ 1/ 2] Loss 0.021671 MSE 0.021671 +2024-02-02 16:38:12,514 - Epoch: [367][ 2/ 2] Loss 0.022604 MSE 0.022542 +2024-02-02 16:38:12,663 - ==> MSE: 0.02254 Loss: 0.023 + +2024-02-02 16:38:12,670 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:12,670 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:12,675 - + +2024-02-02 16:38:12,675 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:13,036 - Epoch: [368][ 1/ 8] Overall Loss 0.017370 Objective Loss 0.017370 MSE 0.017370 LR 0.001000 Time 0.360367 +2024-02-02 16:38:13,045 - Epoch: [368][ 2/ 8] Overall Loss 0.016717 Objective Loss 0.016717 MSE 0.016717 LR 0.001000 Time 0.184637 +2024-02-02 16:38:13,053 - Epoch: [368][ 3/ 8] Overall Loss 0.016919 Objective Loss 0.016919 MSE 0.016919 LR 0.001000 Time 0.125896 +2024-02-02 16:38:13,062 - Epoch: [368][ 4/ 8] Overall Loss 0.017415 Objective Loss 0.017415 MSE 0.017415 LR 0.001000 Time 0.096490 +2024-02-02 16:38:13,070 - Epoch: [368][ 5/ 8] Overall Loss 0.017496 Objective Loss 0.017496 MSE 0.017496 LR 0.001000 Time 0.078863 +2024-02-02 16:38:13,079 - Epoch: [368][ 6/ 8] Overall Loss 0.017344 Objective Loss 0.017344 MSE 0.017344 LR 0.001000 Time 0.067120 +2024-02-02 16:38:13,087 - Epoch: [368][ 7/ 8] Overall Loss 0.017381 Objective Loss 0.017381 MSE 0.017381 LR 0.001000 Time 0.058726 +2024-02-02 16:38:13,095 - Epoch: [368][ 8/ 8] Overall Loss 0.017097 Objective Loss 0.017097 MSE 0.017321 LR 0.001000 Time 0.052373 +2024-02-02 16:38:13,248 - --- validate (epoch=368)----------- +2024-02-02 16:38:13,248 - 60 samples (32 per mini-batch) +2024-02-02 16:38:13,611 - Epoch: [368][ 1/ 2] Loss 0.022055 MSE 0.022055 +2024-02-02 16:38:13,617 - Epoch: [368][ 2/ 2] Loss 0.022587 MSE 0.022552 +2024-02-02 16:38:13,764 - ==> MSE: 0.02255 Loss: 0.023 + +2024-02-02 16:38:13,773 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:13,773 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:13,777 - + +2024-02-02 16:38:13,777 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:14,136 - Epoch: [369][ 1/ 8] Overall Loss 0.017332 Objective Loss 0.017332 MSE 0.017332 LR 0.001000 Time 0.358589 +2024-02-02 16:38:14,145 - Epoch: [369][ 2/ 8] Overall Loss 0.017349 Objective Loss 0.017349 MSE 0.017349 LR 0.001000 Time 0.183635 +2024-02-02 16:38:14,154 - Epoch: [369][ 3/ 8] Overall Loss 0.016604 Objective Loss 0.016604 MSE 0.016604 LR 0.001000 Time 0.125245 +2024-02-02 16:38:14,163 - Epoch: [369][ 4/ 8] Overall Loss 0.017191 Objective Loss 0.017191 MSE 0.017191 LR 0.001000 Time 0.096087 +2024-02-02 16:38:14,171 - Epoch: [369][ 5/ 8] Overall Loss 0.017189 Objective Loss 0.017189 MSE 0.017189 LR 0.001000 Time 0.078567 +2024-02-02 16:38:14,180 - Epoch: [369][ 6/ 8] Overall Loss 0.017100 Objective Loss 0.017100 MSE 0.017100 LR 0.001000 Time 0.066890 +2024-02-02 16:38:14,189 - Epoch: [369][ 7/ 8] Overall Loss 0.017120 Objective Loss 0.017120 MSE 0.017120 LR 0.001000 Time 0.058529 +2024-02-02 16:38:14,197 - Epoch: [369][ 8/ 8] Overall Loss 0.017449 Objective Loss 0.017449 MSE 0.017189 LR 0.001000 Time 0.052261 +2024-02-02 16:38:14,347 - --- validate (epoch=369)----------- +2024-02-02 16:38:14,347 - 60 samples (32 per mini-batch) +2024-02-02 16:38:14,709 - Epoch: [369][ 1/ 2] Loss 0.022382 MSE 0.022382 +2024-02-02 16:38:14,715 - Epoch: [369][ 2/ 2] Loss 0.022431 MSE 0.022428 +2024-02-02 16:38:14,868 - ==> MSE: 0.02243 Loss: 0.022 + +2024-02-02 16:38:14,877 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:14,877 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:14,881 - + +2024-02-02 16:38:14,882 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:15,246 - Epoch: [370][ 1/ 8] Overall Loss 0.017024 Objective Loss 0.017024 MSE 0.017024 LR 0.001000 Time 0.364222 +2024-02-02 16:38:15,411 - Epoch: [370][ 2/ 8] Overall Loss 0.016722 Objective Loss 0.016722 MSE 0.016722 LR 0.001000 Time 0.264246 +2024-02-02 16:38:15,420 - Epoch: [370][ 3/ 8] Overall Loss 0.017292 Objective Loss 0.017292 MSE 0.017292 LR 0.001000 Time 0.179121 +2024-02-02 16:38:15,429 - Epoch: [370][ 4/ 8] Overall Loss 0.017811 Objective Loss 0.017811 MSE 0.017811 LR 0.001000 Time 0.136508 +2024-02-02 16:38:15,437 - Epoch: [370][ 5/ 8] Overall Loss 0.017303 Objective Loss 0.017303 MSE 0.017303 LR 0.001000 Time 0.110818 +2024-02-02 16:38:15,445 - Epoch: [370][ 6/ 8] Overall Loss 0.017223 Objective Loss 0.017223 MSE 0.017223 LR 0.001000 Time 0.093749 +2024-02-02 16:38:15,454 - Epoch: [370][ 7/ 8] Overall Loss 0.017162 Objective Loss 0.017162 MSE 0.017162 LR 0.001000 Time 0.081560 +2024-02-02 16:38:15,463 - Epoch: [370][ 8/ 8] Overall Loss 0.017088 Objective Loss 0.017088 MSE 0.017147 LR 0.001000 Time 0.072408 +2024-02-02 16:38:15,611 - --- validate (epoch=370)----------- +2024-02-02 16:38:15,611 - 60 samples (32 per mini-batch) +2024-02-02 16:38:15,954 - Epoch: [370][ 1/ 2] Loss 0.022059 MSE 0.022059 +2024-02-02 16:38:15,961 - Epoch: [370][ 2/ 2] Loss 0.022661 MSE 0.022621 +2024-02-02 16:38:16,112 - ==> MSE: 0.02262 Loss: 0.023 + +2024-02-02 16:38:16,121 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:16,121 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:16,125 - + +2024-02-02 16:38:16,125 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:16,491 - Epoch: [371][ 1/ 8] Overall Loss 0.017099 Objective Loss 0.017099 MSE 0.017099 LR 0.001000 Time 0.365100 +2024-02-02 16:38:16,500 - Epoch: [371][ 2/ 8] Overall Loss 0.017150 Objective Loss 0.017150 MSE 0.017150 LR 0.001000 Time 0.186862 +2024-02-02 16:38:16,508 - Epoch: [371][ 3/ 8] Overall Loss 0.017530 Objective Loss 0.017530 MSE 0.017530 LR 0.001000 Time 0.127384 +2024-02-02 16:38:16,517 - Epoch: [371][ 4/ 8] Overall Loss 0.017084 Objective Loss 0.017084 MSE 0.017084 LR 0.001000 Time 0.097637 +2024-02-02 16:38:16,526 - Epoch: [371][ 5/ 8] Overall Loss 0.016996 Objective Loss 0.016996 MSE 0.016996 LR 0.001000 Time 0.079792 +2024-02-02 16:38:16,534 - Epoch: [371][ 6/ 8] Overall Loss 0.017079 Objective Loss 0.017079 MSE 0.017079 LR 0.001000 Time 0.067892 +2024-02-02 16:38:16,543 - Epoch: [371][ 7/ 8] Overall Loss 0.017134 Objective Loss 0.017134 MSE 0.017134 LR 0.001000 Time 0.059382 +2024-02-02 16:38:16,551 - Epoch: [371][ 8/ 8] Overall Loss 0.017254 Objective Loss 0.017254 MSE 0.017159 LR 0.001000 Time 0.053006 +2024-02-02 16:38:16,705 - --- validate (epoch=371)----------- +2024-02-02 16:38:16,705 - 60 samples (32 per mini-batch) +2024-02-02 16:38:17,066 - Epoch: [371][ 1/ 2] Loss 0.023228 MSE 0.023228 +2024-02-02 16:38:17,073 - Epoch: [371][ 2/ 2] Loss 0.022466 MSE 0.022517 +2024-02-02 16:38:17,219 - ==> MSE: 0.02252 Loss: 0.022 + +2024-02-02 16:38:17,227 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:17,227 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:17,231 - + +2024-02-02 16:38:17,232 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:17,597 - Epoch: [372][ 1/ 8] Overall Loss 0.017241 Objective Loss 0.017241 MSE 0.017241 LR 0.001000 Time 0.365485 +2024-02-02 16:38:17,606 - Epoch: [372][ 2/ 8] Overall Loss 0.017961 Objective Loss 0.017961 MSE 0.017961 LR 0.001000 Time 0.187039 +2024-02-02 16:38:17,615 - Epoch: [372][ 3/ 8] Overall Loss 0.017438 Objective Loss 0.017438 MSE 0.017438 LR 0.001000 Time 0.127538 +2024-02-02 16:38:17,623 - Epoch: [372][ 4/ 8] Overall Loss 0.017318 Objective Loss 0.017318 MSE 0.017318 LR 0.001000 Time 0.097715 +2024-02-02 16:38:17,632 - Epoch: [372][ 5/ 8] Overall Loss 0.016963 Objective Loss 0.016963 MSE 0.016963 LR 0.001000 Time 0.079864 +2024-02-02 16:38:17,641 - Epoch: [372][ 6/ 8] Overall Loss 0.017210 Objective Loss 0.017210 MSE 0.017210 LR 0.001000 Time 0.067994 +2024-02-02 16:38:17,649 - Epoch: [372][ 7/ 8] Overall Loss 0.017141 Objective Loss 0.017141 MSE 0.017141 LR 0.001000 Time 0.059478 +2024-02-02 16:38:17,658 - Epoch: [372][ 8/ 8] Overall Loss 0.017257 Objective Loss 0.017257 MSE 0.017165 LR 0.001000 Time 0.053087 +2024-02-02 16:38:17,811 - --- validate (epoch=372)----------- +2024-02-02 16:38:17,811 - 60 samples (32 per mini-batch) +2024-02-02 16:38:18,163 - Epoch: [372][ 1/ 2] Loss 0.023091 MSE 0.023091 +2024-02-02 16:38:18,170 - Epoch: [372][ 2/ 2] Loss 0.022561 MSE 0.022596 +2024-02-02 16:38:18,310 - ==> MSE: 0.02260 Loss: 0.023 + +2024-02-02 16:38:18,318 - ==> Best [Top 1 (MSE): 0.02235 Sparsity:0.00 Params: 136448 on epoch: 338] +2024-02-02 16:38:18,319 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:18,323 - + +2024-02-02 16:38:18,323 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:18,681 - Epoch: [373][ 1/ 8] Overall Loss 0.016551 Objective Loss 0.016551 MSE 0.016551 LR 0.001000 Time 0.357627 +2024-02-02 16:38:18,691 - Epoch: [373][ 2/ 8] Overall Loss 0.017127 Objective Loss 0.017127 MSE 0.017127 LR 0.001000 Time 0.183372 +2024-02-02 16:38:18,699 - Epoch: [373][ 3/ 8] Overall Loss 0.017443 Objective Loss 0.017443 MSE 0.017443 LR 0.001000 Time 0.125066 +2024-02-02 16:38:18,708 - Epoch: [373][ 4/ 8] Overall Loss 0.017663 Objective Loss 0.017663 MSE 0.017663 LR 0.001000 Time 0.095861 +2024-02-02 16:38:18,716 - Epoch: [373][ 5/ 8] Overall Loss 0.017313 Objective Loss 0.017313 MSE 0.017313 LR 0.001000 Time 0.078348 +2024-02-02 16:38:18,725 - Epoch: [373][ 6/ 8] Overall Loss 0.017079 Objective Loss 0.017079 MSE 0.017079 LR 0.001000 Time 0.066684 +2024-02-02 16:38:18,733 - Epoch: [373][ 7/ 8] Overall Loss 0.017173 Objective Loss 0.017173 MSE 0.017173 LR 0.001000 Time 0.058366 +2024-02-02 16:38:18,742 - Epoch: [373][ 8/ 8] Overall Loss 0.016802 Objective Loss 0.016802 MSE 0.017096 LR 0.001000 Time 0.052133 +2024-02-02 16:38:18,886 - --- validate (epoch=373)----------- +2024-02-02 16:38:18,886 - 60 samples (32 per mini-batch) +2024-02-02 16:38:19,231 - Epoch: [373][ 1/ 2] Loss 0.022493 MSE 0.022493 +2024-02-02 16:38:19,237 - Epoch: [373][ 2/ 2] Loss 0.022312 MSE 0.022324 +2024-02-02 16:38:19,387 - ==> MSE: 0.02232 Loss: 0.022 + +2024-02-02 16:38:19,406 - ==> Best [Top 1 (MSE): 0.02232 Sparsity:0.00 Params: 136448 on epoch: 373] +2024-02-02 16:38:19,406 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:19,415 - + +2024-02-02 16:38:19,415 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:19,788 - Epoch: [374][ 1/ 8] Overall Loss 0.018193 Objective Loss 0.018193 MSE 0.018193 LR 0.001000 Time 0.372145 +2024-02-02 16:38:19,800 - Epoch: [374][ 2/ 8] Overall Loss 0.017765 Objective Loss 0.017765 MSE 0.017765 LR 0.001000 Time 0.191861 +2024-02-02 16:38:19,812 - Epoch: [374][ 3/ 8] Overall Loss 0.016881 Objective Loss 0.016881 MSE 0.016881 LR 0.001000 Time 0.131699 +2024-02-02 16:38:19,823 - Epoch: [374][ 4/ 8] Overall Loss 0.017233 Objective Loss 0.017233 MSE 0.017233 LR 0.001000 Time 0.101596 +2024-02-02 16:38:19,833 - Epoch: [374][ 5/ 8] Overall Loss 0.017177 Objective Loss 0.017177 MSE 0.017177 LR 0.001000 Time 0.083228 +2024-02-02 16:38:19,842 - Epoch: [374][ 6/ 8] Overall Loss 0.017183 Objective Loss 0.017183 MSE 0.017183 LR 0.001000 Time 0.070765 +2024-02-02 16:38:19,850 - Epoch: [374][ 7/ 8] Overall Loss 0.016975 Objective Loss 0.016975 MSE 0.016975 LR 0.001000 Time 0.061851 +2024-02-02 16:38:19,859 - Epoch: [374][ 8/ 8] Overall Loss 0.017093 Objective Loss 0.017093 MSE 0.017000 LR 0.001000 Time 0.055155 +2024-02-02 16:38:20,009 - --- validate (epoch=374)----------- +2024-02-02 16:38:20,009 - 60 samples (32 per mini-batch) +2024-02-02 16:38:20,363 - Epoch: [374][ 1/ 2] Loss 0.022154 MSE 0.022154 +2024-02-02 16:38:20,369 - Epoch: [374][ 2/ 2] Loss 0.022392 MSE 0.022376 +2024-02-02 16:38:20,515 - ==> MSE: 0.02238 Loss: 0.022 + +2024-02-02 16:38:20,522 - ==> Best [Top 1 (MSE): 0.02232 Sparsity:0.00 Params: 136448 on epoch: 373] +2024-02-02 16:38:20,523 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:20,527 - + +2024-02-02 16:38:20,527 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:20,892 - Epoch: [375][ 1/ 8] Overall Loss 0.017653 Objective Loss 0.017653 MSE 0.017653 LR 0.001000 Time 0.364603 +2024-02-02 16:38:20,904 - Epoch: [375][ 2/ 8] Overall Loss 0.017459 Objective Loss 0.017459 MSE 0.017459 LR 0.001000 Time 0.188083 +2024-02-02 16:38:20,914 - Epoch: [375][ 3/ 8] Overall Loss 0.017399 Objective Loss 0.017399 MSE 0.017399 LR 0.001000 Time 0.128465 +2024-02-02 16:38:20,922 - Epoch: [375][ 4/ 8] Overall Loss 0.017250 Objective Loss 0.017250 MSE 0.017250 LR 0.001000 Time 0.098436 +2024-02-02 16:38:20,930 - Epoch: [375][ 5/ 8] Overall Loss 0.017277 Objective Loss 0.017277 MSE 0.017277 LR 0.001000 Time 0.080339 +2024-02-02 16:38:20,939 - Epoch: [375][ 6/ 8] Overall Loss 0.017154 Objective Loss 0.017154 MSE 0.017154 LR 0.001000 Time 0.068339 +2024-02-02 16:38:20,947 - Epoch: [375][ 7/ 8] Overall Loss 0.017036 Objective Loss 0.017036 MSE 0.017036 LR 0.001000 Time 0.059772 +2024-02-02 16:38:20,956 - Epoch: [375][ 8/ 8] Overall Loss 0.016793 Objective Loss 0.016793 MSE 0.016985 LR 0.001000 Time 0.053336 +2024-02-02 16:38:21,106 - --- validate (epoch=375)----------- +2024-02-02 16:38:21,106 - 60 samples (32 per mini-batch) +2024-02-02 16:38:21,472 - Epoch: [375][ 1/ 2] Loss 0.021707 MSE 0.021707 +2024-02-02 16:38:21,480 - Epoch: [375][ 2/ 2] Loss 0.022341 MSE 0.022298 +2024-02-02 16:38:21,627 - ==> MSE: 0.02230 Loss: 0.022 + +2024-02-02 16:38:21,645 - ==> Best [Top 1 (MSE): 0.02230 Sparsity:0.00 Params: 136448 on epoch: 375] +2024-02-02 16:38:21,645 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:21,654 - + +2024-02-02 16:38:21,655 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:22,025 - Epoch: [376][ 1/ 8] Overall Loss 0.015710 Objective Loss 0.015710 MSE 0.015710 LR 0.001000 Time 0.369423 +2024-02-02 16:38:22,033 - Epoch: [376][ 2/ 8] Overall Loss 0.017023 Objective Loss 0.017023 MSE 0.017023 LR 0.001000 Time 0.188957 +2024-02-02 16:38:22,042 - Epoch: [376][ 3/ 8] Overall Loss 0.016822 Objective Loss 0.016822 MSE 0.016822 LR 0.001000 Time 0.128745 +2024-02-02 16:38:22,050 - Epoch: [376][ 4/ 8] Overall Loss 0.016932 Objective Loss 0.016932 MSE 0.016932 LR 0.001000 Time 0.098653 +2024-02-02 16:38:22,059 - Epoch: [376][ 5/ 8] Overall Loss 0.016829 Objective Loss 0.016829 MSE 0.016829 LR 0.001000 Time 0.080595 +2024-02-02 16:38:22,068 - Epoch: [376][ 6/ 8] Overall Loss 0.016743 Objective Loss 0.016743 MSE 0.016743 LR 0.001000 Time 0.068550 +2024-02-02 16:38:22,076 - Epoch: [376][ 7/ 8] Overall Loss 0.016836 Objective Loss 0.016836 MSE 0.016836 LR 0.001000 Time 0.059943 +2024-02-02 16:38:22,085 - Epoch: [376][ 8/ 8] Overall Loss 0.017416 Objective Loss 0.017416 MSE 0.016957 LR 0.001000 Time 0.053498 +2024-02-02 16:38:22,231 - --- validate (epoch=376)----------- +2024-02-02 16:38:22,231 - 60 samples (32 per mini-batch) +2024-02-02 16:38:22,594 - Epoch: [376][ 1/ 2] Loss 0.022504 MSE 0.022504 +2024-02-02 16:38:22,600 - Epoch: [376][ 2/ 2] Loss 0.022252 MSE 0.022269 +2024-02-02 16:38:22,750 - ==> MSE: 0.02227 Loss: 0.022 + +2024-02-02 16:38:22,758 - ==> Best [Top 1 (MSE): 0.02227 Sparsity:0.00 Params: 136448 on epoch: 376] +2024-02-02 16:38:22,759 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:22,764 - + +2024-02-02 16:38:22,764 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:23,129 - Epoch: [377][ 1/ 8] Overall Loss 0.016977 Objective Loss 0.016977 MSE 0.016977 LR 0.001000 Time 0.364086 +2024-02-02 16:38:23,138 - Epoch: [377][ 2/ 8] Overall Loss 0.016839 Objective Loss 0.016839 MSE 0.016839 LR 0.001000 Time 0.186321 +2024-02-02 16:38:23,146 - Epoch: [377][ 3/ 8] Overall Loss 0.017148 Objective Loss 0.017148 MSE 0.017148 LR 0.001000 Time 0.127039 +2024-02-02 16:38:23,155 - Epoch: [377][ 4/ 8] Overall Loss 0.017119 Objective Loss 0.017119 MSE 0.017119 LR 0.001000 Time 0.097390 +2024-02-02 16:38:23,163 - Epoch: [377][ 5/ 8] Overall Loss 0.016922 Objective Loss 0.016922 MSE 0.016922 LR 0.001000 Time 0.079577 +2024-02-02 16:38:23,172 - Epoch: [377][ 6/ 8] Overall Loss 0.016981 Objective Loss 0.016981 MSE 0.016981 LR 0.001000 Time 0.067701 +2024-02-02 16:38:23,180 - Epoch: [377][ 7/ 8] Overall Loss 0.016984 Objective Loss 0.016984 MSE 0.016984 LR 0.001000 Time 0.059235 +2024-02-02 16:38:23,189 - Epoch: [377][ 8/ 8] Overall Loss 0.016802 Objective Loss 0.016802 MSE 0.016946 LR 0.001000 Time 0.052869 +2024-02-02 16:38:23,339 - --- validate (epoch=377)----------- +2024-02-02 16:38:23,340 - 60 samples (32 per mini-batch) +2024-02-02 16:38:23,703 - Epoch: [377][ 1/ 2] Loss 0.022179 MSE 0.022179 +2024-02-02 16:38:23,709 - Epoch: [377][ 2/ 2] Loss 0.022478 MSE 0.022458 +2024-02-02 16:38:23,856 - ==> MSE: 0.02246 Loss: 0.022 + +2024-02-02 16:38:23,865 - ==> Best [Top 1 (MSE): 0.02227 Sparsity:0.00 Params: 136448 on epoch: 376] +2024-02-02 16:38:23,865 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:23,870 - + +2024-02-02 16:38:23,870 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:24,237 - Epoch: [378][ 1/ 8] Overall Loss 0.017384 Objective Loss 0.017384 MSE 0.017384 LR 0.001000 Time 0.367016 +2024-02-02 16:38:24,246 - Epoch: [378][ 2/ 8] Overall Loss 0.017375 Objective Loss 0.017375 MSE 0.017375 LR 0.001000 Time 0.187825 +2024-02-02 16:38:24,255 - Epoch: [378][ 3/ 8] Overall Loss 0.017770 Objective Loss 0.017770 MSE 0.017770 LR 0.001000 Time 0.128044 +2024-02-02 16:38:24,263 - Epoch: [378][ 4/ 8] Overall Loss 0.017338 Objective Loss 0.017338 MSE 0.017338 LR 0.001000 Time 0.098065 +2024-02-02 16:38:24,272 - Epoch: [378][ 5/ 8] Overall Loss 0.017447 Objective Loss 0.017447 MSE 0.017447 LR 0.001000 Time 0.080133 +2024-02-02 16:38:24,280 - Epoch: [378][ 6/ 8] Overall Loss 0.017409 Objective Loss 0.017409 MSE 0.017409 LR 0.001000 Time 0.068173 +2024-02-02 16:38:24,289 - Epoch: [378][ 7/ 8] Overall Loss 0.017113 Objective Loss 0.017113 MSE 0.017113 LR 0.001000 Time 0.059639 +2024-02-02 16:38:24,297 - Epoch: [378][ 8/ 8] Overall Loss 0.016609 Objective Loss 0.016609 MSE 0.017008 LR 0.001000 Time 0.053232 +2024-02-02 16:38:24,450 - --- validate (epoch=378)----------- +2024-02-02 16:38:24,451 - 60 samples (32 per mini-batch) +2024-02-02 16:38:24,804 - Epoch: [378][ 1/ 2] Loss 0.023162 MSE 0.023162 +2024-02-02 16:38:24,810 - Epoch: [378][ 2/ 2] Loss 0.022136 MSE 0.022205 +2024-02-02 16:38:24,957 - ==> MSE: 0.02220 Loss: 0.022 + +2024-02-02 16:38:24,966 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 378] +2024-02-02 16:38:24,966 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:24,972 - + +2024-02-02 16:38:24,972 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:25,332 - Epoch: [379][ 1/ 8] Overall Loss 0.017494 Objective Loss 0.017494 MSE 0.017494 LR 0.001000 Time 0.359427 +2024-02-02 16:38:25,344 - Epoch: [379][ 2/ 8] Overall Loss 0.017279 Objective Loss 0.017279 MSE 0.017279 LR 0.001000 Time 0.185570 +2024-02-02 16:38:25,355 - Epoch: [379][ 3/ 8] Overall Loss 0.017400 Objective Loss 0.017400 MSE 0.017400 LR 0.001000 Time 0.127364 +2024-02-02 16:38:25,364 - Epoch: [379][ 4/ 8] Overall Loss 0.017076 Objective Loss 0.017076 MSE 0.017076 LR 0.001000 Time 0.097637 +2024-02-02 16:38:25,372 - Epoch: [379][ 5/ 8] Overall Loss 0.016947 Objective Loss 0.016947 MSE 0.016947 LR 0.001000 Time 0.079792 +2024-02-02 16:38:25,381 - Epoch: [379][ 6/ 8] Overall Loss 0.017112 Objective Loss 0.017112 MSE 0.017112 LR 0.001000 Time 0.067885 +2024-02-02 16:38:25,389 - Epoch: [379][ 7/ 8] Overall Loss 0.016917 Objective Loss 0.016917 MSE 0.016917 LR 0.001000 Time 0.059323 +2024-02-02 16:38:25,397 - Epoch: [379][ 8/ 8] Overall Loss 0.017660 Objective Loss 0.017660 MSE 0.017072 LR 0.001000 Time 0.052929 +2024-02-02 16:38:25,550 - --- validate (epoch=379)----------- +2024-02-02 16:38:25,550 - 60 samples (32 per mini-batch) +2024-02-02 16:38:25,907 - Epoch: [379][ 1/ 2] Loss 0.021512 MSE 0.021512 +2024-02-02 16:38:25,914 - Epoch: [379][ 2/ 2] Loss 0.022409 MSE 0.022349 +2024-02-02 16:38:26,067 - ==> MSE: 0.02235 Loss: 0.022 + +2024-02-02 16:38:26,075 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 378] +2024-02-02 16:38:26,075 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:26,080 - + +2024-02-02 16:38:26,080 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:26,452 - Epoch: [380][ 1/ 8] Overall Loss 0.017746 Objective Loss 0.017746 MSE 0.017746 LR 0.001000 Time 0.371656 +2024-02-02 16:38:26,464 - Epoch: [380][ 2/ 8] Overall Loss 0.017682 Objective Loss 0.017682 MSE 0.017682 LR 0.001000 Time 0.191667 +2024-02-02 16:38:26,475 - Epoch: [380][ 3/ 8] Overall Loss 0.016893 Objective Loss 0.016893 MSE 0.016893 LR 0.001000 Time 0.131540 +2024-02-02 16:38:26,487 - Epoch: [380][ 4/ 8] Overall Loss 0.016484 Objective Loss 0.016484 MSE 0.016484 LR 0.001000 Time 0.101597 +2024-02-02 16:38:26,499 - Epoch: [380][ 5/ 8] Overall Loss 0.016463 Objective Loss 0.016463 MSE 0.016463 LR 0.001000 Time 0.083591 +2024-02-02 16:38:26,511 - Epoch: [380][ 6/ 8] Overall Loss 0.016621 Objective Loss 0.016621 MSE 0.016621 LR 0.001000 Time 0.071573 +2024-02-02 16:38:26,520 - Epoch: [380][ 7/ 8] Overall Loss 0.016922 Objective Loss 0.016922 MSE 0.016922 LR 0.001000 Time 0.062562 +2024-02-02 16:38:26,528 - Epoch: [380][ 8/ 8] Overall Loss 0.017068 Objective Loss 0.017068 MSE 0.016952 LR 0.001000 Time 0.055803 +2024-02-02 16:38:26,681 - --- validate (epoch=380)----------- +2024-02-02 16:38:26,681 - 60 samples (32 per mini-batch) +2024-02-02 16:38:27,043 - Epoch: [380][ 1/ 2] Loss 0.021241 MSE 0.021241 +2024-02-02 16:38:27,050 - Epoch: [380][ 2/ 2] Loss 0.022368 MSE 0.022293 +2024-02-02 16:38:27,197 - ==> MSE: 0.02229 Loss: 0.022 + +2024-02-02 16:38:27,206 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 378] +2024-02-02 16:38:27,206 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:27,210 - + +2024-02-02 16:38:27,211 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:27,576 - Epoch: [381][ 1/ 8] Overall Loss 0.016575 Objective Loss 0.016575 MSE 0.016575 LR 0.001000 Time 0.364882 +2024-02-02 16:38:27,585 - Epoch: [381][ 2/ 8] Overall Loss 0.016916 Objective Loss 0.016916 MSE 0.016916 LR 0.001000 Time 0.186674 +2024-02-02 16:38:27,593 - Epoch: [381][ 3/ 8] Overall Loss 0.016437 Objective Loss 0.016437 MSE 0.016437 LR 0.001000 Time 0.127176 +2024-02-02 16:38:27,601 - Epoch: [381][ 4/ 8] Overall Loss 0.016585 Objective Loss 0.016585 MSE 0.016585 LR 0.001000 Time 0.097432 +2024-02-02 16:38:27,610 - Epoch: [381][ 5/ 8] Overall Loss 0.016872 Objective Loss 0.016872 MSE 0.016872 LR 0.001000 Time 0.079623 +2024-02-02 16:38:27,618 - Epoch: [381][ 6/ 8] Overall Loss 0.017057 Objective Loss 0.017057 MSE 0.017057 LR 0.001000 Time 0.067745 +2024-02-02 16:38:27,627 - Epoch: [381][ 7/ 8] Overall Loss 0.017013 Objective Loss 0.017013 MSE 0.017013 LR 0.001000 Time 0.059270 +2024-02-02 16:38:27,635 - Epoch: [381][ 8/ 8] Overall Loss 0.016809 Objective Loss 0.016809 MSE 0.016971 LR 0.001000 Time 0.052894 +2024-02-02 16:38:27,787 - --- validate (epoch=381)----------- +2024-02-02 16:38:27,787 - 60 samples (32 per mini-batch) +2024-02-02 16:38:28,147 - Epoch: [381][ 1/ 2] Loss 0.023096 MSE 0.023096 +2024-02-02 16:38:28,153 - Epoch: [381][ 2/ 2] Loss 0.022497 MSE 0.022536 +2024-02-02 16:38:28,300 - ==> MSE: 0.02254 Loss: 0.022 + +2024-02-02 16:38:28,309 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 378] +2024-02-02 16:38:28,310 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:28,314 - + +2024-02-02 16:38:28,314 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:28,674 - Epoch: [382][ 1/ 8] Overall Loss 0.017825 Objective Loss 0.017825 MSE 0.017825 LR 0.001000 Time 0.359741 +2024-02-02 16:38:28,685 - Epoch: [382][ 2/ 8] Overall Loss 0.018507 Objective Loss 0.018507 MSE 0.018507 LR 0.001000 Time 0.184918 +2024-02-02 16:38:28,693 - Epoch: [382][ 3/ 8] Overall Loss 0.017423 Objective Loss 0.017423 MSE 0.017423 LR 0.001000 Time 0.125914 +2024-02-02 16:38:28,701 - Epoch: [382][ 4/ 8] Overall Loss 0.017364 Objective Loss 0.017364 MSE 0.017364 LR 0.001000 Time 0.096483 +2024-02-02 16:38:28,710 - Epoch: [382][ 5/ 8] Overall Loss 0.016968 Objective Loss 0.016968 MSE 0.016968 LR 0.001000 Time 0.078841 +2024-02-02 16:38:28,718 - Epoch: [382][ 6/ 8] Overall Loss 0.016801 Objective Loss 0.016801 MSE 0.016801 LR 0.001000 Time 0.067084 +2024-02-02 16:38:28,727 - Epoch: [382][ 7/ 8] Overall Loss 0.017128 Objective Loss 0.017128 MSE 0.017128 LR 0.001000 Time 0.058703 +2024-02-02 16:38:28,735 - Epoch: [382][ 8/ 8] Overall Loss 0.016302 Objective Loss 0.016302 MSE 0.016955 LR 0.001000 Time 0.052385 +2024-02-02 16:38:28,884 - --- validate (epoch=382)----------- +2024-02-02 16:38:28,884 - 60 samples (32 per mini-batch) +2024-02-02 16:38:29,243 - Epoch: [382][ 1/ 2] Loss 0.021763 MSE 0.021763 +2024-02-02 16:38:29,249 - Epoch: [382][ 2/ 2] Loss 0.022337 MSE 0.022299 +2024-02-02 16:38:29,402 - ==> MSE: 0.02230 Loss: 0.022 + +2024-02-02 16:38:29,411 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 378] +2024-02-02 16:38:29,411 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:29,415 - + +2024-02-02 16:38:29,415 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:29,782 - Epoch: [383][ 1/ 8] Overall Loss 0.017150 Objective Loss 0.017150 MSE 0.017150 LR 0.001000 Time 0.365912 +2024-02-02 16:38:29,790 - Epoch: [383][ 2/ 8] Overall Loss 0.016508 Objective Loss 0.016508 MSE 0.016508 LR 0.001000 Time 0.187208 +2024-02-02 16:38:29,799 - Epoch: [383][ 3/ 8] Overall Loss 0.016726 Objective Loss 0.016726 MSE 0.016726 LR 0.001000 Time 0.127585 +2024-02-02 16:38:29,807 - Epoch: [383][ 4/ 8] Overall Loss 0.016568 Objective Loss 0.016568 MSE 0.016568 LR 0.001000 Time 0.097769 +2024-02-02 16:38:29,816 - Epoch: [383][ 5/ 8] Overall Loss 0.017148 Objective Loss 0.017148 MSE 0.017148 LR 0.001000 Time 0.079891 +2024-02-02 16:38:29,824 - Epoch: [383][ 6/ 8] Overall Loss 0.016721 Objective Loss 0.016721 MSE 0.016721 LR 0.001000 Time 0.067956 +2024-02-02 16:38:29,833 - Epoch: [383][ 7/ 8] Overall Loss 0.016918 Objective Loss 0.016918 MSE 0.016918 LR 0.001000 Time 0.059452 +2024-02-02 16:38:29,842 - Epoch: [383][ 8/ 8] Overall Loss 0.016735 Objective Loss 0.016735 MSE 0.016880 LR 0.001000 Time 0.053074 +2024-02-02 16:38:29,992 - --- validate (epoch=383)----------- +2024-02-02 16:38:29,993 - 60 samples (32 per mini-batch) +2024-02-02 16:38:30,355 - Epoch: [383][ 1/ 2] Loss 0.021373 MSE 0.021373 +2024-02-02 16:38:30,362 - Epoch: [383][ 2/ 2] Loss 0.022385 MSE 0.022318 +2024-02-02 16:38:30,508 - ==> MSE: 0.02232 Loss: 0.022 + +2024-02-02 16:38:30,517 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 378] +2024-02-02 16:38:30,518 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:30,522 - + +2024-02-02 16:38:30,522 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:30,888 - Epoch: [384][ 1/ 8] Overall Loss 0.015434 Objective Loss 0.015434 MSE 0.015434 LR 0.001000 Time 0.365750 +2024-02-02 16:38:30,897 - Epoch: [384][ 2/ 8] Overall Loss 0.016491 Objective Loss 0.016491 MSE 0.016491 LR 0.001000 Time 0.187223 +2024-02-02 16:38:30,906 - Epoch: [384][ 3/ 8] Overall Loss 0.016864 Objective Loss 0.016864 MSE 0.016864 LR 0.001000 Time 0.127667 +2024-02-02 16:38:30,915 - Epoch: [384][ 4/ 8] Overall Loss 0.016716 Objective Loss 0.016716 MSE 0.016716 LR 0.001000 Time 0.097849 +2024-02-02 16:38:30,923 - Epoch: [384][ 5/ 8] Overall Loss 0.016964 Objective Loss 0.016964 MSE 0.016964 LR 0.001000 Time 0.079958 +2024-02-02 16:38:30,932 - Epoch: [384][ 6/ 8] Overall Loss 0.017062 Objective Loss 0.017062 MSE 0.017062 LR 0.001000 Time 0.068063 +2024-02-02 16:38:30,941 - Epoch: [384][ 7/ 8] Overall Loss 0.016918 Objective Loss 0.016918 MSE 0.016918 LR 0.001000 Time 0.059541 +2024-02-02 16:38:30,949 - Epoch: [384][ 8/ 8] Overall Loss 0.016678 Objective Loss 0.016678 MSE 0.016868 LR 0.001000 Time 0.053133 +2024-02-02 16:38:31,096 - --- validate (epoch=384)----------- +2024-02-02 16:38:31,096 - 60 samples (32 per mini-batch) +2024-02-02 16:38:31,457 - Epoch: [384][ 1/ 2] Loss 0.020934 MSE 0.020934 +2024-02-02 16:38:31,463 - Epoch: [384][ 2/ 2] Loss 0.022370 MSE 0.022274 +2024-02-02 16:38:31,614 - ==> MSE: 0.02227 Loss: 0.022 + +2024-02-02 16:38:31,633 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 378] +2024-02-02 16:38:31,634 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:31,641 - + +2024-02-02 16:38:31,642 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:32,009 - Epoch: [385][ 1/ 8] Overall Loss 0.016153 Objective Loss 0.016153 MSE 0.016153 LR 0.001000 Time 0.366792 +2024-02-02 16:38:32,018 - Epoch: [385][ 2/ 8] Overall Loss 0.016310 Objective Loss 0.016310 MSE 0.016310 LR 0.001000 Time 0.187830 +2024-02-02 16:38:32,027 - Epoch: [385][ 3/ 8] Overall Loss 0.017006 Objective Loss 0.017006 MSE 0.017006 LR 0.001000 Time 0.128090 +2024-02-02 16:38:32,035 - Epoch: [385][ 4/ 8] Overall Loss 0.017019 Objective Loss 0.017019 MSE 0.017019 LR 0.001000 Time 0.098133 +2024-02-02 16:38:32,044 - Epoch: [385][ 5/ 8] Overall Loss 0.017217 Objective Loss 0.017217 MSE 0.017217 LR 0.001000 Time 0.080198 +2024-02-02 16:38:32,053 - Epoch: [385][ 6/ 8] Overall Loss 0.016732 Objective Loss 0.016732 MSE 0.016732 LR 0.001000 Time 0.068233 +2024-02-02 16:38:32,061 - Epoch: [385][ 7/ 8] Overall Loss 0.016945 Objective Loss 0.016945 MSE 0.016945 LR 0.001000 Time 0.059674 +2024-02-02 16:38:32,069 - Epoch: [385][ 8/ 8] Overall Loss 0.016638 Objective Loss 0.016638 MSE 0.016880 LR 0.001000 Time 0.053250 +2024-02-02 16:38:32,221 - --- validate (epoch=385)----------- +2024-02-02 16:38:32,221 - 60 samples (32 per mini-batch) +2024-02-02 16:38:32,580 - Epoch: [385][ 1/ 2] Loss 0.023092 MSE 0.023092 +2024-02-02 16:38:32,586 - Epoch: [385][ 2/ 2] Loss 0.022137 MSE 0.022200 +2024-02-02 16:38:32,734 - ==> MSE: 0.02220 Loss: 0.022 + +2024-02-02 16:38:32,743 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 385] +2024-02-02 16:38:32,743 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:32,748 - + +2024-02-02 16:38:32,748 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:33,112 - Epoch: [386][ 1/ 8] Overall Loss 0.015668 Objective Loss 0.015668 MSE 0.015668 LR 0.001000 Time 0.363447 +2024-02-02 16:38:33,121 - Epoch: [386][ 2/ 8] Overall Loss 0.015975 Objective Loss 0.015975 MSE 0.015975 LR 0.001000 Time 0.186071 +2024-02-02 16:38:33,130 - Epoch: [386][ 3/ 8] Overall Loss 0.016845 Objective Loss 0.016845 MSE 0.016845 LR 0.001000 Time 0.126850 +2024-02-02 16:38:33,138 - Epoch: [386][ 4/ 8] Overall Loss 0.017244 Objective Loss 0.017244 MSE 0.017244 LR 0.001000 Time 0.097257 +2024-02-02 16:38:33,147 - Epoch: [386][ 5/ 8] Overall Loss 0.016711 Objective Loss 0.016711 MSE 0.016711 LR 0.001000 Time 0.079514 +2024-02-02 16:38:33,155 - Epoch: [386][ 6/ 8] Overall Loss 0.016836 Objective Loss 0.016836 MSE 0.016836 LR 0.001000 Time 0.067647 +2024-02-02 16:38:33,164 - Epoch: [386][ 7/ 8] Overall Loss 0.016933 Objective Loss 0.016933 MSE 0.016933 LR 0.001000 Time 0.059200 +2024-02-02 16:38:33,173 - Epoch: [386][ 8/ 8] Overall Loss 0.016591 Objective Loss 0.016591 MSE 0.016861 LR 0.001000 Time 0.052837 +2024-02-02 16:38:33,323 - --- validate (epoch=386)----------- +2024-02-02 16:38:33,324 - 60 samples (32 per mini-batch) +2024-02-02 16:38:33,683 - Epoch: [386][ 1/ 2] Loss 0.022754 MSE 0.022754 +2024-02-02 16:38:33,689 - Epoch: [386][ 2/ 2] Loss 0.022324 MSE 0.022352 +2024-02-02 16:38:33,839 - ==> MSE: 0.02235 Loss: 0.022 + +2024-02-02 16:38:33,847 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 385] +2024-02-02 16:38:33,847 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:33,851 - + +2024-02-02 16:38:33,852 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:34,219 - Epoch: [387][ 1/ 8] Overall Loss 0.016876 Objective Loss 0.016876 MSE 0.016876 LR 0.001000 Time 0.367209 +2024-02-02 16:38:34,232 - Epoch: [387][ 2/ 8] Overall Loss 0.018446 Objective Loss 0.018446 MSE 0.018446 LR 0.001000 Time 0.189743 +2024-02-02 16:38:34,244 - Epoch: [387][ 3/ 8] Overall Loss 0.017022 Objective Loss 0.017022 MSE 0.017022 LR 0.001000 Time 0.130466 +2024-02-02 16:38:34,256 - Epoch: [387][ 4/ 8] Overall Loss 0.017109 Objective Loss 0.017109 MSE 0.017109 LR 0.001000 Time 0.100857 +2024-02-02 16:38:34,266 - Epoch: [387][ 5/ 8] Overall Loss 0.016865 Objective Loss 0.016865 MSE 0.016865 LR 0.001000 Time 0.082583 +2024-02-02 16:38:34,275 - Epoch: [387][ 6/ 8] Overall Loss 0.016934 Objective Loss 0.016934 MSE 0.016934 LR 0.001000 Time 0.070262 +2024-02-02 16:38:34,284 - Epoch: [387][ 7/ 8] Overall Loss 0.017005 Objective Loss 0.017005 MSE 0.017005 LR 0.001000 Time 0.061413 +2024-02-02 16:38:34,292 - Epoch: [387][ 8/ 8] Overall Loss 0.016581 Objective Loss 0.016581 MSE 0.016917 LR 0.001000 Time 0.054770 +2024-02-02 16:38:34,439 - --- validate (epoch=387)----------- +2024-02-02 16:38:34,439 - 60 samples (32 per mini-batch) +2024-02-02 16:38:34,802 - Epoch: [387][ 1/ 2] Loss 0.023237 MSE 0.023237 +2024-02-02 16:38:34,808 - Epoch: [387][ 2/ 2] Loss 0.022374 MSE 0.022432 +2024-02-02 16:38:34,957 - ==> MSE: 0.02243 Loss: 0.022 + +2024-02-02 16:38:34,966 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 385] +2024-02-02 16:38:34,967 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:34,971 - + +2024-02-02 16:38:34,971 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:35,334 - Epoch: [388][ 1/ 8] Overall Loss 0.015825 Objective Loss 0.015825 MSE 0.015825 LR 0.001000 Time 0.362467 +2024-02-02 16:38:35,346 - Epoch: [388][ 2/ 8] Overall Loss 0.016595 Objective Loss 0.016595 MSE 0.016595 LR 0.001000 Time 0.187129 +2024-02-02 16:38:35,355 - Epoch: [388][ 3/ 8] Overall Loss 0.016703 Objective Loss 0.016703 MSE 0.016703 LR 0.001000 Time 0.127586 +2024-02-02 16:38:35,363 - Epoch: [388][ 4/ 8] Overall Loss 0.016792 Objective Loss 0.016792 MSE 0.016792 LR 0.001000 Time 0.097826 +2024-02-02 16:38:35,372 - Epoch: [388][ 5/ 8] Overall Loss 0.017001 Objective Loss 0.017001 MSE 0.017001 LR 0.001000 Time 0.079924 +2024-02-02 16:38:35,380 - Epoch: [388][ 6/ 8] Overall Loss 0.017114 Objective Loss 0.017114 MSE 0.017114 LR 0.001000 Time 0.068006 +2024-02-02 16:38:35,389 - Epoch: [388][ 7/ 8] Overall Loss 0.016897 Objective Loss 0.016897 MSE 0.016897 LR 0.001000 Time 0.059487 +2024-02-02 16:38:35,397 - Epoch: [388][ 8/ 8] Overall Loss 0.016829 Objective Loss 0.016829 MSE 0.016883 LR 0.001000 Time 0.053082 +2024-02-02 16:38:35,551 - --- validate (epoch=388)----------- +2024-02-02 16:38:35,551 - 60 samples (32 per mini-batch) +2024-02-02 16:38:35,910 - Epoch: [388][ 1/ 2] Loss 0.023701 MSE 0.023701 +2024-02-02 16:38:35,916 - Epoch: [388][ 2/ 2] Loss 0.022143 MSE 0.022247 +2024-02-02 16:38:36,065 - ==> MSE: 0.02225 Loss: 0.022 + +2024-02-02 16:38:36,074 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 385] +2024-02-02 16:38:36,075 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:36,079 - + +2024-02-02 16:38:36,079 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:36,446 - Epoch: [389][ 1/ 8] Overall Loss 0.014282 Objective Loss 0.014282 MSE 0.014282 LR 0.001000 Time 0.366627 +2024-02-02 16:38:36,457 - Epoch: [389][ 2/ 8] Overall Loss 0.015111 Objective Loss 0.015111 MSE 0.015111 LR 0.001000 Time 0.188510 +2024-02-02 16:38:36,466 - Epoch: [389][ 3/ 8] Overall Loss 0.016241 Objective Loss 0.016241 MSE 0.016241 LR 0.001000 Time 0.128514 +2024-02-02 16:38:36,474 - Epoch: [389][ 4/ 8] Overall Loss 0.016581 Objective Loss 0.016581 MSE 0.016581 LR 0.001000 Time 0.098481 +2024-02-02 16:38:36,483 - Epoch: [389][ 5/ 8] Overall Loss 0.016633 Objective Loss 0.016633 MSE 0.016633 LR 0.001000 Time 0.080465 +2024-02-02 16:38:36,491 - Epoch: [389][ 6/ 8] Overall Loss 0.016714 Objective Loss 0.016714 MSE 0.016714 LR 0.001000 Time 0.068443 +2024-02-02 16:38:36,500 - Epoch: [389][ 7/ 8] Overall Loss 0.016820 Objective Loss 0.016820 MSE 0.016820 LR 0.001000 Time 0.059857 +2024-02-02 16:38:36,508 - Epoch: [389][ 8/ 8] Overall Loss 0.016709 Objective Loss 0.016709 MSE 0.016797 LR 0.001000 Time 0.053424 +2024-02-02 16:38:36,661 - --- validate (epoch=389)----------- +2024-02-02 16:38:36,662 - 60 samples (32 per mini-batch) +2024-02-02 16:38:37,022 - Epoch: [389][ 1/ 2] Loss 0.023761 MSE 0.023761 +2024-02-02 16:38:37,028 - Epoch: [389][ 2/ 2] Loss 0.022147 MSE 0.022255 +2024-02-02 16:38:37,177 - ==> MSE: 0.02225 Loss: 0.022 + +2024-02-02 16:38:37,186 - ==> Best [Top 1 (MSE): 0.02220 Sparsity:0.00 Params: 136448 on epoch: 385] +2024-02-02 16:38:37,186 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:37,191 - + +2024-02-02 16:38:37,191 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:37,553 - Epoch: [390][ 1/ 8] Overall Loss 0.018127 Objective Loss 0.018127 MSE 0.018127 LR 0.001000 Time 0.362108 +2024-02-02 16:38:37,562 - Epoch: [390][ 2/ 8] Overall Loss 0.016394 Objective Loss 0.016394 MSE 0.016394 LR 0.001000 Time 0.185346 +2024-02-02 16:38:37,571 - Epoch: [390][ 3/ 8] Overall Loss 0.016162 Objective Loss 0.016162 MSE 0.016162 LR 0.001000 Time 0.126408 +2024-02-02 16:38:37,579 - Epoch: [390][ 4/ 8] Overall Loss 0.016536 Objective Loss 0.016536 MSE 0.016536 LR 0.001000 Time 0.096856 +2024-02-02 16:38:37,588 - Epoch: [390][ 5/ 8] Overall Loss 0.016980 Objective Loss 0.016980 MSE 0.016980 LR 0.001000 Time 0.079146 +2024-02-02 16:38:37,596 - Epoch: [390][ 6/ 8] Overall Loss 0.016789 Objective Loss 0.016789 MSE 0.016789 LR 0.001000 Time 0.067333 +2024-02-02 16:38:37,605 - Epoch: [390][ 7/ 8] Overall Loss 0.016869 Objective Loss 0.016869 MSE 0.016869 LR 0.001000 Time 0.058902 +2024-02-02 16:38:37,613 - Epoch: [390][ 8/ 8] Overall Loss 0.016505 Objective Loss 0.016505 MSE 0.016793 LR 0.001000 Time 0.052568 +2024-02-02 16:38:37,764 - --- validate (epoch=390)----------- +2024-02-02 16:38:37,765 - 60 samples (32 per mini-batch) +2024-02-02 16:38:38,117 - Epoch: [390][ 1/ 2] Loss 0.021914 MSE 0.021914 +2024-02-02 16:38:38,123 - Epoch: [390][ 2/ 2] Loss 0.022167 MSE 0.022150 +2024-02-02 16:38:38,267 - ==> MSE: 0.02215 Loss: 0.022 + +2024-02-02 16:38:38,276 - ==> Best [Top 1 (MSE): 0.02215 Sparsity:0.00 Params: 136448 on epoch: 390] +2024-02-02 16:38:38,276 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:38,282 - + +2024-02-02 16:38:38,282 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:38,642 - Epoch: [391][ 1/ 8] Overall Loss 0.016591 Objective Loss 0.016591 MSE 0.016591 LR 0.001000 Time 0.360163 +2024-02-02 16:38:38,651 - Epoch: [391][ 2/ 8] Overall Loss 0.016112 Objective Loss 0.016112 MSE 0.016112 LR 0.001000 Time 0.184327 +2024-02-02 16:38:38,660 - Epoch: [391][ 3/ 8] Overall Loss 0.017233 Objective Loss 0.017233 MSE 0.017233 LR 0.001000 Time 0.125682 +2024-02-02 16:38:38,668 - Epoch: [391][ 4/ 8] Overall Loss 0.016760 Objective Loss 0.016760 MSE 0.016760 LR 0.001000 Time 0.096352 +2024-02-02 16:38:38,677 - Epoch: [391][ 5/ 8] Overall Loss 0.016573 Objective Loss 0.016573 MSE 0.016573 LR 0.001000 Time 0.078750 +2024-02-02 16:38:38,685 - Epoch: [391][ 6/ 8] Overall Loss 0.016808 Objective Loss 0.016808 MSE 0.016808 LR 0.001000 Time 0.067028 +2024-02-02 16:38:38,694 - Epoch: [391][ 7/ 8] Overall Loss 0.016894 Objective Loss 0.016894 MSE 0.016894 LR 0.001000 Time 0.058660 +2024-02-02 16:38:38,702 - Epoch: [391][ 8/ 8] Overall Loss 0.016345 Objective Loss 0.016345 MSE 0.016779 LR 0.001000 Time 0.052376 +2024-02-02 16:38:38,853 - --- validate (epoch=391)----------- +2024-02-02 16:38:38,853 - 60 samples (32 per mini-batch) +2024-02-02 16:38:39,201 - Epoch: [391][ 1/ 2] Loss 0.022245 MSE 0.022245 +2024-02-02 16:38:39,207 - Epoch: [391][ 2/ 2] Loss 0.022221 MSE 0.022223 +2024-02-02 16:38:39,351 - ==> MSE: 0.02222 Loss: 0.022 + +2024-02-02 16:38:39,360 - ==> Best [Top 1 (MSE): 0.02215 Sparsity:0.00 Params: 136448 on epoch: 390] +2024-02-02 16:38:39,360 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:39,365 - + +2024-02-02 16:38:39,365 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:39,732 - Epoch: [392][ 1/ 8] Overall Loss 0.017633 Objective Loss 0.017633 MSE 0.017633 LR 0.001000 Time 0.366106 +2024-02-02 16:38:39,741 - Epoch: [392][ 2/ 8] Overall Loss 0.017018 Objective Loss 0.017018 MSE 0.017018 LR 0.001000 Time 0.187457 +2024-02-02 16:38:39,749 - Epoch: [392][ 3/ 8] Overall Loss 0.017026 Objective Loss 0.017026 MSE 0.017026 LR 0.001000 Time 0.127780 +2024-02-02 16:38:39,758 - Epoch: [392][ 4/ 8] Overall Loss 0.016764 Objective Loss 0.016764 MSE 0.016764 LR 0.001000 Time 0.097948 +2024-02-02 16:38:39,766 - Epoch: [392][ 5/ 8] Overall Loss 0.016862 Objective Loss 0.016862 MSE 0.016862 LR 0.001000 Time 0.080022 +2024-02-02 16:38:39,775 - Epoch: [392][ 6/ 8] Overall Loss 0.016866 Objective Loss 0.016866 MSE 0.016866 LR 0.001000 Time 0.068091 +2024-02-02 16:38:39,783 - Epoch: [392][ 7/ 8] Overall Loss 0.016824 Objective Loss 0.016824 MSE 0.016824 LR 0.001000 Time 0.059559 +2024-02-02 16:38:39,792 - Epoch: [392][ 8/ 8] Overall Loss 0.016601 Objective Loss 0.016601 MSE 0.016777 LR 0.001000 Time 0.053162 +2024-02-02 16:38:39,944 - --- validate (epoch=392)----------- +2024-02-02 16:38:39,944 - 60 samples (32 per mini-batch) +2024-02-02 16:38:40,315 - Epoch: [392][ 1/ 2] Loss 0.022989 MSE 0.022989 +2024-02-02 16:38:40,322 - Epoch: [392][ 2/ 2] Loss 0.022299 MSE 0.022345 +2024-02-02 16:38:40,482 - ==> MSE: 0.02234 Loss: 0.022 + +2024-02-02 16:38:40,491 - ==> Best [Top 1 (MSE): 0.02215 Sparsity:0.00 Params: 136448 on epoch: 390] +2024-02-02 16:38:40,491 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:40,496 - + +2024-02-02 16:38:40,496 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:40,866 - Epoch: [393][ 1/ 8] Overall Loss 0.016781 Objective Loss 0.016781 MSE 0.016781 LR 0.001000 Time 0.369655 +2024-02-02 16:38:40,878 - Epoch: [393][ 2/ 8] Overall Loss 0.016660 Objective Loss 0.016660 MSE 0.016660 LR 0.001000 Time 0.190769 +2024-02-02 16:38:40,889 - Epoch: [393][ 3/ 8] Overall Loss 0.016923 Objective Loss 0.016923 MSE 0.016923 LR 0.001000 Time 0.130698 +2024-02-02 16:38:40,898 - Epoch: [393][ 4/ 8] Overall Loss 0.016777 Objective Loss 0.016777 MSE 0.016777 LR 0.001000 Time 0.100147 +2024-02-02 16:38:40,906 - Epoch: [393][ 5/ 8] Overall Loss 0.016754 Objective Loss 0.016754 MSE 0.016754 LR 0.001000 Time 0.081817 +2024-02-02 16:38:40,915 - Epoch: [393][ 6/ 8] Overall Loss 0.016806 Objective Loss 0.016806 MSE 0.016806 LR 0.001000 Time 0.069595 +2024-02-02 16:38:40,924 - Epoch: [393][ 7/ 8] Overall Loss 0.016904 Objective Loss 0.016904 MSE 0.016904 LR 0.001000 Time 0.060861 +2024-02-02 16:38:40,932 - Epoch: [393][ 8/ 8] Overall Loss 0.016204 Objective Loss 0.016204 MSE 0.016758 LR 0.001000 Time 0.054301 +2024-02-02 16:38:41,076 - --- validate (epoch=393)----------- +2024-02-02 16:38:41,076 - 60 samples (32 per mini-batch) +2024-02-02 16:38:41,425 - Epoch: [393][ 1/ 2] Loss 0.022269 MSE 0.022269 +2024-02-02 16:38:41,432 - Epoch: [393][ 2/ 2] Loss 0.022313 MSE 0.022310 +2024-02-02 16:38:41,580 - ==> MSE: 0.02231 Loss: 0.022 + +2024-02-02 16:38:41,590 - ==> Best [Top 1 (MSE): 0.02215 Sparsity:0.00 Params: 136448 on epoch: 390] +2024-02-02 16:38:41,590 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:41,595 - + +2024-02-02 16:38:41,595 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:41,966 - Epoch: [394][ 1/ 8] Overall Loss 0.015822 Objective Loss 0.015822 MSE 0.015822 LR 0.001000 Time 0.371201 +2024-02-02 16:38:41,978 - Epoch: [394][ 2/ 8] Overall Loss 0.016466 Objective Loss 0.016466 MSE 0.016466 LR 0.001000 Time 0.191156 +2024-02-02 16:38:41,986 - Epoch: [394][ 3/ 8] Overall Loss 0.016520 Objective Loss 0.016520 MSE 0.016520 LR 0.001000 Time 0.130265 +2024-02-02 16:38:41,995 - Epoch: [394][ 4/ 8] Overall Loss 0.016385 Objective Loss 0.016385 MSE 0.016385 LR 0.001000 Time 0.099796 +2024-02-02 16:38:42,004 - Epoch: [394][ 5/ 8] Overall Loss 0.016406 Objective Loss 0.016406 MSE 0.016406 LR 0.001000 Time 0.081517 +2024-02-02 16:38:42,012 - Epoch: [394][ 6/ 8] Overall Loss 0.016526 Objective Loss 0.016526 MSE 0.016526 LR 0.001000 Time 0.069330 +2024-02-02 16:38:42,021 - Epoch: [394][ 7/ 8] Overall Loss 0.016793 Objective Loss 0.016793 MSE 0.016793 LR 0.001000 Time 0.060627 +2024-02-02 16:38:42,029 - Epoch: [394][ 8/ 8] Overall Loss 0.016656 Objective Loss 0.016656 MSE 0.016764 LR 0.001000 Time 0.054083 +2024-02-02 16:38:42,181 - --- validate (epoch=394)----------- +2024-02-02 16:38:42,181 - 60 samples (32 per mini-batch) +2024-02-02 16:38:42,541 - Epoch: [394][ 1/ 2] Loss 0.022546 MSE 0.022546 +2024-02-02 16:38:42,548 - Epoch: [394][ 2/ 2] Loss 0.022012 MSE 0.022047 +2024-02-02 16:38:42,691 - ==> MSE: 0.02205 Loss: 0.022 + +2024-02-02 16:38:42,702 - ==> Best [Top 1 (MSE): 0.02205 Sparsity:0.00 Params: 136448 on epoch: 394] +2024-02-02 16:38:42,702 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:42,707 - + +2024-02-02 16:38:42,707 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:43,068 - Epoch: [395][ 1/ 8] Overall Loss 0.016571 Objective Loss 0.016571 MSE 0.016571 LR 0.001000 Time 0.359842 +2024-02-02 16:38:43,077 - Epoch: [395][ 2/ 8] Overall Loss 0.017262 Objective Loss 0.017262 MSE 0.017262 LR 0.001000 Time 0.184259 +2024-02-02 16:38:43,085 - Epoch: [395][ 3/ 8] Overall Loss 0.016929 Objective Loss 0.016929 MSE 0.016929 LR 0.001000 Time 0.125627 +2024-02-02 16:38:43,094 - Epoch: [395][ 4/ 8] Overall Loss 0.016881 Objective Loss 0.016881 MSE 0.016881 LR 0.001000 Time 0.096303 +2024-02-02 16:38:43,102 - Epoch: [395][ 5/ 8] Overall Loss 0.016886 Objective Loss 0.016886 MSE 0.016886 LR 0.001000 Time 0.078724 +2024-02-02 16:38:43,111 - Epoch: [395][ 6/ 8] Overall Loss 0.016865 Objective Loss 0.016865 MSE 0.016865 LR 0.001000 Time 0.067010 +2024-02-02 16:38:43,119 - Epoch: [395][ 7/ 8] Overall Loss 0.016725 Objective Loss 0.016725 MSE 0.016725 LR 0.001000 Time 0.058639 +2024-02-02 16:38:43,128 - Epoch: [395][ 8/ 8] Overall Loss 0.016875 Objective Loss 0.016875 MSE 0.016756 LR 0.001000 Time 0.052338 +2024-02-02 16:38:43,277 - --- validate (epoch=395)----------- +2024-02-02 16:38:43,277 - 60 samples (32 per mini-batch) +2024-02-02 16:38:43,647 - Epoch: [395][ 1/ 2] Loss 0.021871 MSE 0.021871 +2024-02-02 16:38:43,654 - Epoch: [395][ 2/ 2] Loss 0.022363 MSE 0.022330 +2024-02-02 16:38:43,801 - ==> MSE: 0.02233 Loss: 0.022 + +2024-02-02 16:38:43,810 - ==> Best [Top 1 (MSE): 0.02205 Sparsity:0.00 Params: 136448 on epoch: 394] +2024-02-02 16:38:43,810 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:43,814 - + +2024-02-02 16:38:43,815 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:44,177 - Epoch: [396][ 1/ 8] Overall Loss 0.014241 Objective Loss 0.014241 MSE 0.014241 LR 0.001000 Time 0.361601 +2024-02-02 16:38:44,185 - Epoch: [396][ 2/ 8] Overall Loss 0.015391 Objective Loss 0.015391 MSE 0.015391 LR 0.001000 Time 0.185058 +2024-02-02 16:38:44,194 - Epoch: [396][ 3/ 8] Overall Loss 0.015928 Objective Loss 0.015928 MSE 0.015928 LR 0.001000 Time 0.126161 +2024-02-02 16:38:44,202 - Epoch: [396][ 4/ 8] Overall Loss 0.015979 Objective Loss 0.015979 MSE 0.015979 LR 0.001000 Time 0.096718 +2024-02-02 16:38:44,211 - Epoch: [396][ 5/ 8] Overall Loss 0.016193 Objective Loss 0.016193 MSE 0.016193 LR 0.001000 Time 0.079035 +2024-02-02 16:38:44,220 - Epoch: [396][ 6/ 8] Overall Loss 0.016725 Objective Loss 0.016725 MSE 0.016725 LR 0.001000 Time 0.067268 +2024-02-02 16:38:44,228 - Epoch: [396][ 7/ 8] Overall Loss 0.016917 Objective Loss 0.016917 MSE 0.016917 LR 0.001000 Time 0.058836 +2024-02-02 16:38:44,236 - Epoch: [396][ 8/ 8] Overall Loss 0.016483 Objective Loss 0.016483 MSE 0.016826 LR 0.001000 Time 0.052519 +2024-02-02 16:38:44,386 - --- validate (epoch=396)----------- +2024-02-02 16:38:44,387 - 60 samples (32 per mini-batch) +2024-02-02 16:38:44,746 - Epoch: [396][ 1/ 2] Loss 0.022345 MSE 0.022345 +2024-02-02 16:38:44,753 - Epoch: [396][ 2/ 2] Loss 0.022319 MSE 0.022321 +2024-02-02 16:38:44,898 - ==> MSE: 0.02232 Loss: 0.022 + +2024-02-02 16:38:44,907 - ==> Best [Top 1 (MSE): 0.02205 Sparsity:0.00 Params: 136448 on epoch: 394] +2024-02-02 16:38:44,908 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:44,912 - + +2024-02-02 16:38:44,912 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:45,277 - Epoch: [397][ 1/ 8] Overall Loss 0.017559 Objective Loss 0.017559 MSE 0.017559 LR 0.001000 Time 0.364822 +2024-02-02 16:38:45,287 - Epoch: [397][ 2/ 8] Overall Loss 0.017396 Objective Loss 0.017396 MSE 0.017396 LR 0.001000 Time 0.187049 +2024-02-02 16:38:45,295 - Epoch: [397][ 3/ 8] Overall Loss 0.017768 Objective Loss 0.017768 MSE 0.017768 LR 0.001000 Time 0.127471 +2024-02-02 16:38:45,304 - Epoch: [397][ 4/ 8] Overall Loss 0.017307 Objective Loss 0.017307 MSE 0.017307 LR 0.001000 Time 0.097697 +2024-02-02 16:38:45,312 - Epoch: [397][ 5/ 8] Overall Loss 0.016727 Objective Loss 0.016727 MSE 0.016727 LR 0.001000 Time 0.079741 +2024-02-02 16:38:45,320 - Epoch: [397][ 6/ 8] Overall Loss 0.016821 Objective Loss 0.016821 MSE 0.016821 LR 0.001000 Time 0.067837 +2024-02-02 16:38:45,329 - Epoch: [397][ 7/ 8] Overall Loss 0.016874 Objective Loss 0.016874 MSE 0.016874 LR 0.001000 Time 0.059336 +2024-02-02 16:38:45,337 - Epoch: [397][ 8/ 8] Overall Loss 0.016604 Objective Loss 0.016604 MSE 0.016818 LR 0.001000 Time 0.052945 +2024-02-02 16:38:45,491 - --- validate (epoch=397)----------- +2024-02-02 16:38:45,491 - 60 samples (32 per mini-batch) +2024-02-02 16:38:45,852 - Epoch: [397][ 1/ 2] Loss 0.023587 MSE 0.023587 +2024-02-02 16:38:45,858 - Epoch: [397][ 2/ 2] Loss 0.022185 MSE 0.022278 +2024-02-02 16:38:46,007 - ==> MSE: 0.02228 Loss: 0.022 + +2024-02-02 16:38:46,029 - ==> Best [Top 1 (MSE): 0.02205 Sparsity:0.00 Params: 136448 on epoch: 394] +2024-02-02 16:38:46,029 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:46,037 - + +2024-02-02 16:38:46,037 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:46,427 - Epoch: [398][ 1/ 8] Overall Loss 0.016187 Objective Loss 0.016187 MSE 0.016187 LR 0.001000 Time 0.389081 +2024-02-02 16:38:46,438 - Epoch: [398][ 2/ 8] Overall Loss 0.016401 Objective Loss 0.016401 MSE 0.016401 LR 0.001000 Time 0.200247 +2024-02-02 16:38:46,447 - Epoch: [398][ 3/ 8] Overall Loss 0.017047 Objective Loss 0.017047 MSE 0.017047 LR 0.001000 Time 0.136408 +2024-02-02 16:38:46,456 - Epoch: [398][ 4/ 8] Overall Loss 0.016870 Objective Loss 0.016870 MSE 0.016870 LR 0.001000 Time 0.104351 +2024-02-02 16:38:46,464 - Epoch: [398][ 5/ 8] Overall Loss 0.016819 Objective Loss 0.016819 MSE 0.016819 LR 0.001000 Time 0.085158 +2024-02-02 16:38:46,473 - Epoch: [398][ 6/ 8] Overall Loss 0.017152 Objective Loss 0.017152 MSE 0.017152 LR 0.001000 Time 0.072331 +2024-02-02 16:38:46,481 - Epoch: [398][ 7/ 8] Overall Loss 0.016942 Objective Loss 0.016942 MSE 0.016942 LR 0.001000 Time 0.063182 +2024-02-02 16:38:46,489 - Epoch: [398][ 8/ 8] Overall Loss 0.016408 Objective Loss 0.016408 MSE 0.016830 LR 0.001000 Time 0.056310 +2024-02-02 16:38:46,645 - --- validate (epoch=398)----------- +2024-02-02 16:38:46,645 - 60 samples (32 per mini-batch) +2024-02-02 16:38:46,995 - Epoch: [398][ 1/ 2] Loss 0.021874 MSE 0.021874 +2024-02-02 16:38:47,001 - Epoch: [398][ 2/ 2] Loss 0.022326 MSE 0.022296 +2024-02-02 16:38:47,141 - ==> MSE: 0.02230 Loss: 0.022 + +2024-02-02 16:38:47,151 - ==> Best [Top 1 (MSE): 0.02205 Sparsity:0.00 Params: 136448 on epoch: 394] +2024-02-02 16:38:47,151 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:47,155 - + +2024-02-02 16:38:47,155 - Training epoch: 230 samples (32 per mini-batch) +2024-02-02 16:38:47,522 - Epoch: [399][ 1/ 8] Overall Loss 0.017984 Objective Loss 0.017984 MSE 0.017984 LR 0.001000 Time 0.366146 +2024-02-02 16:38:47,534 - Epoch: [399][ 2/ 8] Overall Loss 0.016862 Objective Loss 0.016862 MSE 0.016862 LR 0.001000 Time 0.189055 +2024-02-02 16:38:47,545 - Epoch: [399][ 3/ 8] Overall Loss 0.017421 Objective Loss 0.017421 MSE 0.017421 LR 0.001000 Time 0.129522 +2024-02-02 16:38:47,554 - Epoch: [399][ 4/ 8] Overall Loss 0.017464 Objective Loss 0.017464 MSE 0.017464 LR 0.001000 Time 0.099279 +2024-02-02 16:38:47,562 - Epoch: [399][ 5/ 8] Overall Loss 0.017510 Objective Loss 0.017510 MSE 0.017510 LR 0.001000 Time 0.081152 +2024-02-02 16:38:47,571 - Epoch: [399][ 6/ 8] Overall Loss 0.017173 Objective Loss 0.017173 MSE 0.017173 LR 0.001000 Time 0.069036 +2024-02-02 16:38:47,580 - Epoch: [399][ 7/ 8] Overall Loss 0.016739 Objective Loss 0.016739 MSE 0.016739 LR 0.001000 Time 0.060367 +2024-02-02 16:38:47,588 - Epoch: [399][ 8/ 8] Overall Loss 0.017030 Objective Loss 0.017030 MSE 0.016800 LR 0.001000 Time 0.053874 +2024-02-02 16:38:47,734 - --- validate (epoch=399)----------- +2024-02-02 16:38:47,734 - 60 samples (32 per mini-batch) +2024-02-02 16:38:48,082 - Epoch: [399][ 1/ 2] Loss 0.022167 MSE 0.022167 +2024-02-02 16:38:48,088 - Epoch: [399][ 2/ 2] Loss 0.022484 MSE 0.022463 +2024-02-02 16:38:48,235 - ==> MSE: 0.02246 Loss: 0.022 + +2024-02-02 16:38:48,244 - ==> Best [Top 1 (MSE): 0.02205 Sparsity:0.00 Params: 136448 on epoch: 394] +2024-02-02 16:38:48,244 - Saving checkpoint to: logs/2024.02.02-163128/qat_checkpoint.pth.tar +2024-02-02 16:38:48,248 - --- test --------------------- +2024-02-02 16:38:48,248 - 60 samples (32 per mini-batch) +2024-02-02 16:38:48,604 - Test: [ 1/ 2] Loss 0.021445 MSE 0.021445 +2024-02-02 16:38:48,610 - Test: [ 2/ 2] Loss 0.022536 MSE 0.022463 +2024-02-02 16:38:48,751 - ==> MSE: 0.02246 Loss: 0.023 + +2024-02-02 16:38:48,753 - +2024-02-02 16:38:48,753 - Log file for this run: /home/asyaturhal/desktop/voyager/ai8x-training/logs/2024.02.02-163128/2024.02.02-163128.log