diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6eced82 --- /dev/null +++ b/.gitignore @@ -0,0 +1,8 @@ +__pycache__ +/MA_SNN/DVSGait/data +/MA_SNN/DVSGestures/data/DvsGesture +/MA_SNN/DVSGestures/data/train +/MA_SNN/DVSGestures/data/test +MA_SNN/DVSGestures/data/DvsGesture.tar.gz +info.txt +Attention-SNN.code-workspace diff --git a/MA_SNN/DVSGestures/Att_SNN_CNN.py b/MA_SNN/DVSGestures/Att_SNN_CNN.py index b78d86a..5e613d6 100644 --- a/MA_SNN/DVSGestures/Att_SNN_CNN.py +++ b/MA_SNN/DVSGestures/Att_SNN_CNN.py @@ -1,41 +1,42 @@ -import os - -import sys - -sys.path.append(os.path.dirname("__file__")) -from DVSGestures.CNN import Att_SNN - -rootPath = os.path.abspath(os.path.dirname(__file__)) -rootPath = os.path.split(rootPath)[0] -sys.path.append(rootPath) - -from DVSGestures.CNN import Config - -os.environ["CUDA_VISIBLE_DEVICES"] = "4," - - -class Logger(object): - def __init__(self, fileN="Default.log"): - self.terminal = sys.stdout - self.log = open(fileN, "w") - - def write(self, message): - self.terminal.write(message) - self.log.write(message) - - def flush(self): - pass - - -logPath = Config.configs().recordPath -if not os.path.exists(logPath): - os.makedirs(logPath) -sys.stdout = Logger(logPath + os.sep + "log_DVS_Gesture_SNN.txt") - - -def main(): - Att_SNN.main() - - -if __name__ == "__main__": - main() +import os + +import sys + +# sys.path.append(os.path.dirname("__file__")) +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from DVSGestures.CNN import Att_SNN + +rootPath = os.path.abspath(os.path.dirname(__file__)) +rootPath = os.path.split(rootPath)[0] +sys.path.append(rootPath) + +from DVSGestures.CNN import Config + +# os.environ["CUDA_VISIBLE_DEVICES"] = "4," + + +class Logger(object): + def __init__(self, fileN="Default.log"): + self.terminal = sys.stdout + self.log = open(fileN, "w") + + def write(self, message): + self.terminal.write(message) + self.log.write(message) + + def flush(self): + pass + + +logPath = Config.configs().recordPath +if not os.path.exists(logPath): + os.makedirs(logPath) +sys.stdout = Logger(logPath + os.sep + "log_DVS_Gesture_SNN.txt") + + +def main(): + Att_SNN.main() + + +if __name__ == "__main__": + main() diff --git a/MA_SNN/DVSGestures/Att_SNN_CNN_SpikingJelly.py b/MA_SNN/DVSGestures/Att_SNN_CNN_SpikingJelly.py new file mode 100644 index 0000000..ba1d999 --- /dev/null +++ b/MA_SNN/DVSGestures/Att_SNN_CNN_SpikingJelly.py @@ -0,0 +1,42 @@ +import os + +import sys + +# sys.path.append(os.path.dirname("__file__")) +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from DVSGestures.CNN import Att_SNN_SpikingJelly as Att_SNN + +rootPath = os.path.abspath(os.path.dirname(__file__)) +rootPath = os.path.split(rootPath)[0] +sys.path.append(rootPath) + +from DVSGestures.CNN import Config + +# os.environ["CUDA_VISIBLE_DEVICES"] = "0" + + +class Logger(object): + def __init__(self, fileN="Default.log"): + self.terminal = sys.stdout + self.log = open(fileN, "w") + + def write(self, message): + self.terminal.write(message) + self.log.write(message) + + def flush(self): + pass + + +logPath = Config.configs().recordPath +if not os.path.exists(logPath): + os.makedirs(logPath) +sys.stdout = Logger(logPath + os.sep + "log_DVS_Gesture_SNN.txt") + + +def main(): + Att_SNN.main() + + +if __name__ == "__main__": + main() diff --git a/MA_SNN/DVSGestures/CNN/Att_SNN_SpikingJelly.py b/MA_SNN/DVSGestures/CNN/Att_SNN_SpikingJelly.py new file mode 100644 index 0000000..7791b04 --- /dev/null +++ b/MA_SNN/DVSGestures/CNN/Att_SNN_SpikingJelly.py @@ -0,0 +1,44 @@ +import torch +from utils import util +from DVSGestures.DVS_Gesture_utils.dataset import create_data +from DVSGestures.CNN.Networks.Att_SNN_SpikingJelly import create_net +from DVSGestures.CNN.Config import configs +from DVSGestures.DVS_Gesture_utils.process import process +from DVSGestures.DVS_Gesture_utils.save import save_csv + + +def main(): + + config = configs() + config.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(config.device) + + config.device_ids = range(torch.cuda.device_count()) + print(config.device_ids) + + config.name = ( + config.attention + + "_SNN(CNN)-DVS-Gesture_dt=" + + str(config.dt) + + "ms" + + "_T=" + + str(config.T) + ) + config.modelNames = config.name + ".t7" + config.recordNames = config.name + ".csv" + + print(config) + + create_data(config=config) + + create_net(config=config) + + print(config.model) + + print(util.get_parameter_number(config.model)) + + process(config=config) + + print("best acc:", config.best_acc, "best_epoch:", config.best_epoch) + + save_csv(config=config) diff --git a/MA_SNN/DVSGestures/CNN/Config.py b/MA_SNN/DVSGestures/CNN/Config.py index 75a8bf1..2490e9c 100644 --- a/MA_SNN/DVSGestures/CNN/Config.py +++ b/MA_SNN/DVSGestures/CNN/Config.py @@ -1,13 +1,14 @@ import os, torch import torch.nn as nn +from spikingjelly.activation_based.neuron import surrogate class configs(object): def __init__(self): - self.dt = 25 + self.dt = 15 self.T = 60 - self.attention = "no" + self.attention = "TCSA" self.c_ratio=8 self.t_ratio=5 self.epoch = 0 @@ -16,7 +17,7 @@ def __init__(self): self.pretrained_path = None self.batch_size = 128 - self.batch_size_test = 128 + self.batch_size_test = 28 # None 'kaiming' 'xavier' self.init_method = None @@ -37,18 +38,24 @@ def __init__(self): self.interval_scaling = False # network - self.beta = 0 + self.beta = 0. self.alpha = 0.3 - self.Vreset = 0 + self.Vreset = 0. self.Vthres = 0.3 self.reduction = 16 self.T_extend_Conv = False self.T_extend_BN = False self.h_conv = False + self.step_mode = "m" + # self.surrogate_function = surrogate.Sigmoid() + self.surrogate_function = surrogate.LeakyKReLU() + self.backend = "cupy" + # Old parameters self.mem_act = torch.relu self.mode_select = "spike" self.TR_model = "NTR" + # BatchNorm self.track_running_stats = True diff --git a/MA_SNN/DVSGestures/CNN/Networks/Att_SNN.py b/MA_SNN/DVSGestures/CNN/Networks/Att_SNN.py index 1b6205a..a926913 100644 --- a/MA_SNN/DVSGestures/CNN/Networks/Att_SNN.py +++ b/MA_SNN/DVSGestures/CNN/Networks/Att_SNN.py @@ -195,18 +195,24 @@ def __init__( ) def forward(self, input): + # print("input: ",input.shape) b, t, _, _, _ = input.size() outputs = input outputs = self.convAttLIF0(outputs) + # print("convAttLIF0: ",outputs.shape) outputs = self.convAttLIF1(outputs) + # print("convAttLIF1: ",outputs.shape) outputs = self.convAttLIF2(outputs) + # print("convAttLIF1: ",outputs.shape) outputs = outputs.reshape(b, t, -1) - + # print("fc_input: ",outputs.shape) outputs = self.FC0(outputs) + # print("FC0: ",outputs.shape) outputs = self.FC1(outputs) + # print("FC1: ",outputs.shape) outputs = torch.sum(outputs, dim=1) outputs = outputs / t diff --git a/MA_SNN/DVSGestures/CNN/Networks/Att_SNN_SpikingJelly.py b/MA_SNN/DVSGestures/CNN/Networks/Att_SNN_SpikingJelly.py new file mode 100644 index 0000000..987c6b7 --- /dev/null +++ b/MA_SNN/DVSGestures/CNN/Networks/Att_SNN_SpikingJelly.py @@ -0,0 +1,210 @@ +import torch +import torch.nn as nn +from module.LIF_Module_SpikingJelly import AttLIF, ConvAttLIF +from torch import optim + + +def create_net(config): + # Net + # define approximate firing function + + class ActFun(torch.autograd.Function): + def __init__(self): + super(ActFun, self).__init__() + + @staticmethod + def forward(ctx, input): + ctx.save_for_backward(input) + return input.ge(0.0).float() + + @staticmethod + def backward(ctx, grad_output): + (input,) = ctx.saved_tensors + temp = abs(input) < config.lens + return grad_output * temp.float() / (2 * config.lens) + + # cnn_layer(in_planes, out_planes, stride, padding, kernel_size) + cfg_cnn = [ + ( + 2, + 64, + 1, + 1, + 3, + ), + ( + 64, + 128, + 1, + 1, + 3, + ), + ( + 128, + 128, + 1, + 1, + 3, + ), + ] + # pooling kernel_size + cfg_pool = [1, 2, 2] + # fc layer + cfg_fc = [cfg_cnn[2][1] * 8 * 8, 256, config.target_size] + + class Net(nn.Module): + def __init__( + self, + ): + super(Net, self).__init__() + h, w = config.im_height, config.im_width + in_planes, out_planes, stride, padding, kernel_size = cfg_cnn[0] + pooling_kernel_size = cfg_pool[0] + h, w = h // cfg_pool[0], w // cfg_pool[0] + self.convAttLIF0 = ConvAttLIF( + attention=config.attention, + inputSize=in_planes, + hiddenSize=out_planes, + kernel_size=(kernel_size, kernel_size), + init_method=config.init_method, + useBatchNorm=True, + pooling_kernel_size=pooling_kernel_size, + T=config.T, + pa_dict={ + "Vreset": config.Vreset, + "Vthres": config.Vthres, + }, + step_mode=config.step_mode, + surrogate_function=config.surrogate_function, + backend=config.backend, + track_running_stats=config.track_running_stats, + c_ratio=config.c_ratio, + t_ratio=config.t_ratio + ) + + in_planes, out_planes, stride, padding, kernel_size = cfg_cnn[1] + pooling_kernel_size = cfg_pool[1] + h, w = h // cfg_pool[1], w // cfg_pool[1] + self.convAttLIF1 = ConvAttLIF( + attention=config.attention, + inputSize=in_planes, + hiddenSize=out_planes, + kernel_size=(kernel_size, kernel_size), + init_method=config.init_method, + useBatchNorm=True, + pooling_kernel_size=pooling_kernel_size, + T=config.T, + pa_dict={ + "Vreset": config.Vreset, + "Vthres": config.Vthres, + }, + step_mode=config.step_mode, + surrogate_function=config.surrogate_function, + backend=config.backend, + track_running_stats=config.track_running_stats, + c_ratio=config.c_ratio, + t_ratio=config.t_ratio + ) + + in_planes, out_planes, stride, padding, kernel_size = cfg_cnn[2] + pooling_kernel_size = cfg_pool[2] + h, w = h // cfg_pool[2], w // cfg_pool[2] + self.convAttLIF2 = ConvAttLIF( + attention=config.attention, + inputSize=in_planes, + hiddenSize=out_planes, + kernel_size=(kernel_size, kernel_size), + init_method=config.init_method, + useBatchNorm=True, + pooling_kernel_size=pooling_kernel_size, + T=config.T, + pa_dict={ + "Vreset": config.Vreset, + "Vthres": config.Vthres, + }, + step_mode=config.step_mode, + surrogate_function=config.surrogate_function, + backend=config.backend, + track_running_stats=config.track_running_stats, + c_ratio=config.c_ratio, + t_ratio=config.t_ratio + ) + + self.FC0 = AttLIF( + attention="no" + if config.attention in ["no", "CA", "SA", "CSA"] + else "TA", + inputSize=cfg_fc[0], + hiddenSize=cfg_fc[1], + useBatchNorm=True, + T=config.T, + pa_dict={ + "Vreset": config.Vreset, + "Vthres": config.Vthres, + }, + step_mode=config.step_mode, + surrogate_function=config.surrogate_function, + backend=config.backend, + track_running_stats=config.track_running_stats, + t_ratio=config.t_ratio + ) + + self.FC1 = AttLIF( + attention="no" + if config.attention in ["no", "CA", "SA", "CSA"] + else "TA", + inputSize=cfg_fc[1], + hiddenSize=cfg_fc[2], + useBatchNorm=True, + T=config.T, + pa_dict={ + "Vreset": config.Vreset, + "Vthres": config.Vthres, + }, + step_mode=config.step_mode, + surrogate_function=config.surrogate_function, + backend=config.backend, + track_running_stats=config.track_running_stats, + t_ratio=config.t_ratio + ) + + def forward(self, input): + # print("input: ",input.shape) + b, t, _, _, _ = input.size() + outputs = input + + outputs = self.convAttLIF0(outputs) + # print("convAttLIF0: ",outputs.shape) + outputs = self.convAttLIF1(outputs) + # print("convAttLIF1: ",outputs.shape) + outputs = self.convAttLIF2(outputs) + # print("convAttLIF2: ",outputs.shape) + + outputs = outputs.reshape(b, t, -1) + # print("fc_input: ",outputs.shape) + outputs = self.FC0(outputs) + # print("FC0: ",outputs.shape) + + outputs = self.FC1(outputs) + # print("FC1: ",outputs.shape) + outputs = torch.sum(outputs, dim=1) + outputs = outputs / t + + return outputs + + config.model = Net().to(config.device) + + # optimizer + config.optimizer = optim.Adam( + config.model.parameters(), + lr=config.lr, + betas=config.betas, + eps=config.eps, + weight_decay=config.weight_decay, + ) + + config.scheduler = optim.lr_scheduler.ReduceLROnPlateau( + optimizer=config.optimizer, mode="min", factor=0.1, patience=5, verbose=True + ) + + config.model = nn.DataParallel(config.model, device_ids=config.device_ids) diff --git a/MA_SNN/DVSGestures/CNN/Result/log_DVS_Gesture_SNN.txt b/MA_SNN/DVSGestures/CNN/Result/log_DVS_Gesture_SNN.txt new file mode 100644 index 0000000..ae0cdc8 --- /dev/null +++ b/MA_SNN/DVSGestures/CNN/Result/log_DVS_Gesture_SNN.txt @@ -0,0 +1,252 @@ +cuda +range(0, 4) +dt==15 +T==60 +attention==TCSA +c_ratio==8 +t_ratio==5 +epoch==0 +num_epochs==300 +onlyTest==False +pretrained_path==None +batch_size==128 +batch_size_test==28 +init_method==None +ds==4 +in_channels==2 +im_width==32 +im_height==32 +target_size==11 +clip==10 +is_train_Enhanced==True +is_spike==False +interval_scaling==False +beta==0.0 +alpha==0.3 +Vreset==0.0 +Vthres==0.3 +reduction==16 +T_extend_Conv==False +T_extend_BN==False +h_conv==False +step_mode==m +surrogate_function==LeakyKReLU() +backend==cupy +mem_act== +mode_select==spike +TR_model==NTR +track_running_stats==True +a==0.5 +lens==0.25 +lr==0.0001 +betas==[0.9, 0.999] +eps==1e-08 +weight_decay==0 +lr_scheduler==True +lr_scheduler_epoch==25 +name==TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60 +modelPath==/home/zbx/Attention-SNN/MA_SNN/DVSGestures/CNN/Result +modelNames==TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.t7 +recordPath==/home/zbx/Attention-SNN/MA_SNN/DVSGestures/CNN/Result +recordNames==TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.csv +savePath==/home/zbx/Attention-SNN/MA_SNN/DVSGestures/data +train_dataset==None +test_dataset==None +train_loader==None +test_loader==None +drop_last==False +pip_memory==False +num_work==8 +model==None +criterion==MSELoss() +optimizer==None +device==cuda +device_ids==range(0, 4) +best_acc==0 +best_epoch==0 +epoch_list==[] +loss_train_list==[] +loss_test_list==[] +acc_train_list==[] +acc_test_list==[] +train_loss==0 +train_correct==0 +train_acc==0 +test_loss==0 +test_correct==0 +test_acc==0 +state==None + +DataParallel( + (module): Net( + (convAttLIF0): ConvAttLIF( + (surrogate_function): LeakyKReLU() + (conv2d): Conv2d(2, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (BNLayer): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (attention): TCSA( + (relu): ReLU(inplace=True) + (ca): ChannelAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(64, 8, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(8, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(60, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(12, 60, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (sa): SpatialAttention( + (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (sigmoid): Sigmoid() + ) + ) + (network): Sequential( + (ConvIF): IFNode( + v_threshold=0.3, v_reset=0.0, detach_reset=False, step_mode=m, backend=cupy + (surrogate_function): LeakyKReLU() + ) + ) + ) + (convAttLIF1): ConvAttLIF( + (surrogate_function): LeakyKReLU() + (conv2d): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (BNLayer): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pooling): AvgPool2d(kernel_size=2, stride=2, padding=0) + (attention): TCSA( + (relu): ReLU(inplace=True) + (ca): ChannelAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(128, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(16, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(60, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(12, 60, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (sa): SpatialAttention( + (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (sigmoid): Sigmoid() + ) + ) + (network): Sequential( + (ConvIF): IFNode( + v_threshold=0.3, v_reset=0.0, detach_reset=False, step_mode=m, backend=cupy + (surrogate_function): LeakyKReLU() + ) + ) + ) + (convAttLIF2): ConvAttLIF( + (surrogate_function): LeakyKReLU() + (conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (BNLayer): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pooling): AvgPool2d(kernel_size=2, stride=2, padding=0) + (attention): TCSA( + (relu): ReLU(inplace=True) + (ca): ChannelAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(128, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(16, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(60, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(12, 60, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (sa): SpatialAttention( + (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (sigmoid): Sigmoid() + ) + ) + (network): Sequential( + (ConvIF): IFNode( + v_threshold=0.3, v_reset=0.0, detach_reset=False, step_mode=m, backend=cupy + (surrogate_function): LeakyKReLU() + ) + ) + ) + (FC0): AttLIF( + (surrogate_function): LeakyKReLU() + (network): Sequential( + (IFNode): IFNode( + v_threshold=0.3, v_reset=0.0, detach_reset=False, step_mode=m, backend=cupy + (surrogate_function): LeakyKReLU() + ) + ) + (linear): Linear(in_features=8192, out_features=256, bias=True) + (BNLayer): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (attention): TA( + (relu): ReLU(inplace=True) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool1d(output_size=1) + (max_pool): AdaptiveMaxPool1d(output_size=1) + (sharedMLP): Sequential( + (0): Conv1d(60, 12, kernel_size=(1,), stride=(1,), bias=False) + (1): ReLU() + (2): Conv1d(12, 60, kernel_size=(1,), stride=(1,), bias=False) + ) + (sigmoid): Sigmoid() + ) + ) + ) + (FC1): AttLIF( + (surrogate_function): LeakyKReLU() + (network): Sequential( + (IFNode): IFNode( + v_threshold=0.3, v_reset=0.0, detach_reset=False, step_mode=m, backend=cupy + (surrogate_function): LeakyKReLU() + ) + ) + (linear): Linear(in_features=256, out_features=11, bias=True) + (BNLayer): BatchNorm1d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (attention): TA( + (relu): ReLU(inplace=True) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool1d(output_size=1) + (max_pool): AdaptiveMaxPool1d(output_size=1) + (sharedMLP): Sequential( + (0): Conv1d(60, 12, kernel_size=(1,), stride=(1,), bias=False) + (1): ReLU() + (2): Conv1d(12, 60, kernel_size=(1,), stride=(1,), bias=False) + ) + (sigmoid): Sigmoid() + ) + ) + ) + ) +) +{'Total': 2340535, 'Trainable': 2340535} +epoch: 1 +dt: 15 +T: 60 +Tarin loss:0.12404 diff --git a/MA_SNN/DVSGestures/DVS_Gesture_utils/process.py b/MA_SNN/DVSGestures/DVS_Gesture_utils/process.py index 719fb3d..9f20c32 100644 --- a/MA_SNN/DVSGestures/DVS_Gesture_utils/process.py +++ b/MA_SNN/DVSGestures/DVS_Gesture_utils/process.py @@ -1,6 +1,8 @@ from DVSGestures.DVS_Gesture_utils.train import train from DVSGestures.DVS_Gesture_utils.test import test -import torch +import torch,gc +import time +from torch import nn import os @@ -49,10 +51,19 @@ def process(config): config.loss_train_list.append(config.train_loss) config.acc_train_list.append(config.train_acc) + # config.model=config.model.module.cpu() + # device_saved=config.device + # config.device='cpu' + gc.collect() + torch.cuda.empty_cache() + #time.sleep(3) + #config.model=config.model.to(torch.device('cuda:0')) + #config.model = nn.DataParallel(config.model, device_ids=config.device_ids) + + # test with torch.no_grad(): - config.model.eval() test(config=config) @@ -87,3 +98,9 @@ def process(config): ) print('beat acc:',config.best_acc) + gc.collect() + torch.cuda.empty_cache() + # if config.onlyTest == False: + # config.device=device_saved + # config.model=config.model.to(torch.device('cuda:0')) + # config.model = nn.DataParallel(config.model, device_ids=config.device_ids) diff --git a/MA_SNN/DVSGestures/DVS_Gesture_utils/train.py b/MA_SNN/DVSGestures/DVS_Gesture_utils/train.py index ae55308..c244f3f 100644 --- a/MA_SNN/DVSGestures/DVS_Gesture_utils/train.py +++ b/MA_SNN/DVSGestures/DVS_Gesture_utils/train.py @@ -15,7 +15,9 @@ def train(config): input = input.float().to(config.device) labels = labels[:, 1, :].float().to(config.device) - + # print(input) + # print(input.shape) + # exit() outputs = config.model(input) @@ -36,4 +38,7 @@ def train(config): bar_train.set_description("Train:Epoch[%d/%d]" % (config.epoch + 1, config.num_epochs)) bar_train.set_postfix(Loss=loss.item()) + del loss,outputs,input,labels + torch.cuda.empty_cache() + bar_train.close() diff --git a/MA_SNN/DVSGestures/DVS_gesture_data_process/events_timeslices.py b/MA_SNN/DVSGestures/DVS_gesture_data_process/events_timeslices.py index 49ac832..21b9fc1 100644 --- a/MA_SNN/DVSGestures/DVS_gesture_data_process/events_timeslices.py +++ b/MA_SNN/DVSGestures/DVS_gesture_data_process/events_timeslices.py @@ -34,9 +34,11 @@ def cast_evs(evs): def get_subsampled_coordinates(evs, ds_h, ds_w): x_coords = evs[:, 1] // ds_w y_coords = evs[:, 2] // ds_h - if x_coords.dtype != np.int: + # if x_coords.dtype != np.int: + if x_coords.dtype != np.int64: x_coords = x_coords.astype(int) - if y_coords.dtype != np.int: + # if y_coords.dtype != np.int: + if x_coords.dtype != np.int64: y_coords = y_coords.astype(int) return x_coords, y_coords @@ -114,7 +116,8 @@ def frame_evs(times, addrs, deltat=1000, duration=500, size=[240], downsample=[1 idx_end += find_first(times[idx_end:], t) if idx_end > idx_start: ee = addrs[idx_start:idx_end] - ev = [(ee[:, i] // d).astype(np.int) for i, d in enumerate(downsample)] + # ev = [(ee[:, i] // d).astype(np.int) for i, d in enumerate(downsample)] + ev = [(ee[:, i] // d).astype(np.int64) for i, d in enumerate(downsample)] np.add.at(chunks, tuple([i] + ev), 1) idx_start = idx_end return chunks @@ -130,7 +133,8 @@ def chunk_evs_pol_dvs(times, addrs, deltat=1000, chunk_size=500, size=[2, 304, 2 idx_end += find_first(times[idx_end:], t) if idx_end > idx_start: ee = addrs[idx_start:idx_end] - pol, x, y = ee[:, 2], (ee[:, 0] // ds_w).astype(np.int), (ee[:, 1] // ds_h).astype(np.int) + # pol, x, y = ee[:, 2], (ee[:, 0] // ds_w).astype(np.int), (ee[:, 1] // ds_h).astype(np.int) + pol, x, y = ee[:, 2], (ee[:, 0] // ds_w).astype(np.int64), (ee[:, 1] // ds_h).astype(np.int64) np.add.at(chunks, (i, pol, x, y), 1) idx_start = idx_end return chunks @@ -149,7 +153,8 @@ def my_chunk_evs_pol_dvs(data, dt=1000, T=500, size=[2, 304, 240], ds=[4, 4]): idx_end += find_first(data[idx_end:, 0], t + dt) if idx_end > idx_start: ee = data[idx_start:idx_end, 1:] - pol, x, y = ee[:, 0], (ee[:, 1] // ds[0]).astype(np.int), (ee[:, 2] // ds[1]).astype(np.int) + # pol, x, y = ee[:, 0], (ee[:, 1] // ds[0]).astype(np.int), (ee[:, 2] // ds[1]).astype(np.int) + pol, x, y = ee[:, 0], (ee[:, 1] // ds[0]).astype(np.int64), (ee[:, 2] // ds[1]).astype(np.int64) np.add.at(chunks, (i, pol, x, y), 1) idx_start = idx_end return chunks \ No newline at end of file diff --git a/MA_SNN/module/LIF.py b/MA_SNN/module/LIF.py index cf8ed9e..97f4d70 100644 --- a/MA_SNN/module/LIF.py +++ b/MA_SNN/module/LIF.py @@ -6,15 +6,15 @@ class IFCell(nn.Module): def __init__(self, inputSize, hiddenSize, - spikeActFun, - scale=0.3, + spikeActFun,#放电函数 + scale=0.3,# pa_dict=None, pa_train_self=False, bias=True, p=0, - mode_select='spike', + mode_select='spike',#模式设置，spike：脉冲放电，mem：膜电位放电 mem_act=torch.relu, - TR_model='NTR', + TR_model='NTR',#膜电位是否使用时间常数规则，NTR：不使用，TR：使用 ): super().__init__() self.inputSize = inputSize @@ -84,6 +84,7 @@ def forward(self, input, init_v=None): if input.device != self.h.device: input = input.to(self.h.device) + # 整合放电过程 # Step 1: accumulate and reset,spike used as forgetting gate u = self.h + input @@ -95,7 +96,7 @@ def forward(self, input, init_v=None): # step 4: if self.mode_select == 'spike': x = x - elif self.mode_select == 'mem': + elif self.mode_select == 'mem':# 膜电位模式 # TR if self.TR_model == 'TR': diff --git a/MA_SNN/module/LIF_Module_SpikingJelly.py b/MA_SNN/module/LIF_Module_SpikingJelly.py new file mode 100644 index 0000000..77bc2fc --- /dev/null +++ b/MA_SNN/module/LIF_Module_SpikingJelly.py @@ -0,0 +1,219 @@ +import torch +from torch import nn +from spikingjelly.activation_based.neuron import IFNode,surrogate +from module.Attention import * +from typing import Callable + +# 模型权重初始化 +def paramInit(model, method='xavier'): + scale = 0.05 + for name, w in model.named_parameters(): + if 'weight' in name: + if method == 'xavier': + nn.init.xavier_normal_(w) + elif method == 'kaiming': + nn.init.kaiming_normal_(w) + else: + nn.init.normal_(w) + w *= scale + elif 'bias' in name: + nn.init.constant_(w, 0) + else: + pass + +class AttLIF(nn.Module): + def __init__( + self, + inputSize:int, + hiddenSize:int, + attention:str="TA",# 是否应用注意力 + useBatchNorm:bool=False, + init_method:str=None,# 模型权重初始化方式 + pa_dict:dict=None,# 电压参数字典 + bias:bool=True, + track_running_stats:bool=False,# batchnorm参数 + step_mode:str='m',# multi_step or single_step + surrogate_function:Callable=surrogate.Sigmoid(), + onlyLast:bool=False,# 是否选择仅保留最后时刻的电压值 + backend:str='cupy',# IFNode是否使用cupy进行后端 + T:int=60, + t_ratio:int=16 + ) -> None: + super().__init__() + self.store_v_req = not onlyLast + self.step_mode = step_mode + self.useBatchNorm = useBatchNorm + self.surrogate_function = surrogate_function + self.backend = backend + + self.network = nn.Sequential() + self.attention_flag = attention + self.linear = nn.Linear( + in_features=inputSize, + out_features=hiddenSize, + bias=bias, + ) + + if self.useBatchNorm: + self.BNLayer = nn.BatchNorm1d( + num_features=hiddenSize, track_running_stats=track_running_stats + ) + + if init_method is not None: + paramInit(model=self.linear, method=init_method) + if self.attention_flag == "TA": + self.attention = TA(T, hiddenSize, t_ratio=t_ratio, fc=True) + elif self.attention_flag == "no": + pass + + if pa_dict is None: + pa_dict={'Vreset': 0., 'Vthres': 0.6} + self.v_threshold = float(pa_dict["Vthres"]) if pa_dict["Vthres"] is not None else 0.6 + self.v_reset = float(pa_dict["Vreset"]) if pa_dict["Vreset"] is not None else None + + self.network.add_module( + "IFNode", + IFNode(v_threshold=self.v_threshold,v_reset=self.v_reset,surrogate_function=self.surrogate_function, + step_mode=self.step_mode,backend=self.backend,store_v_seq=self.store_v_req) + ) + + def forward(self,data:torch.Tensor) -> torch.Tensor: + #*对data shape的操作应该视具体问题进行调整* + for layer in self.network: + layer.reset() + + b, t, _ = data.size() + output = self.linear(data.reshape(b * t, -1)) + + if self.useBatchNorm: + output = self.BNLayer(output) + + outputsum = output.reshape(b, t, -1) + + if self.attention_flag == "no": + data=outputsum + else: + data=self.attention(outputsum) + + #*单独使用此神经元时建议取消本行代码的注释* + # data.reshape(b, t, c, h, w) + + output=self.network(data.transpose(0,1)) + + return output.transpose(0,1) + +class ConvAttLIF(nn.Module): + def __init__( + self, + inputSize:int, + hiddenSize:int, + kernel_size:tuple, + attention:str="TA", + onlyLast:bool=False, + padding:int=1, + useBatchNorm:bool=False, + init_method:str=None, + pa_dict:dict=None, + step_mode:str="m", + surrogate_function:Callable=surrogate.Sigmoid(), + backend:str="cupy", + T:int=60, + stride:int=1, + pooling_kernel_size:int=1, + p:float=0,#dropout几率 + track_running_stats:int=False, + c_ratio=16, + t_ratio=16 + ) -> None: + super().__init__() + self.store_v_req = not onlyLast + self.step_mode = step_mode + self.useBatchNorm = useBatchNorm + self.surrogate_function = surrogate_function + self.backend = backend + self.attention_flag = attention + self.p=p + + self.conv2d = nn.Conv2d( + in_channels=inputSize, + out_channels=hiddenSize, + kernel_size=kernel_size, + bias=True, + padding=padding, + stride=stride, + ) + + if init_method is not None: + paramInit(model=self.conv2d, method=init_method) + + self.useBatchNorm = useBatchNorm + + if self.useBatchNorm: + self.BNLayer = nn.BatchNorm2d( + hiddenSize, track_running_stats=track_running_stats + ) + + self.pooling_kernel_size = pooling_kernel_size + if self.pooling_kernel_size > 1: + self.pooling = nn.AvgPool2d(kernel_size=pooling_kernel_size) + + if self.attention_flag == "TCSA": + self.attention = TCSA(T, hiddenSize, c_ratio=c_ratio, t_ratio=t_ratio) + elif self.attention_flag == "TSA": + self.attention = TSA(T, hiddenSize, t_ratio=t_ratio) + elif self.attention_flag == "TCA": + self.attention = TCA(T, hiddenSize, c_ratio=c_ratio, t_ratio=t_ratio) + elif self.attention_flag == "CSA": + self.attention = CSA(T, hiddenSize, c_ratio=c_ratio) + elif self.attention_flag == "TA": + self.attention = TA(T, hiddenSize, t_ratio=t_ratio) + elif self.attention_flag == "CA": + self.attention = CA(T, hiddenSize, c_ratio=c_ratio) + elif self.attention_flag == "SA": + self.attention = SA(T, hiddenSize) + elif self.attention_flag == "no": + pass + + if pa_dict is None: + pa_dict={'alpha': 0.3, 'beta': 0., 'Vreset': 0., 'Vthres': 0.6} + self.v_threshold = pa_dict["Vthres"] + self.v_reset = pa_dict["Vreset"] + + self.network = nn.Sequential() + self.network.add_module( + "ConvIF", + IFNode(v_threshold=self.v_threshold,v_reset=self.v_reset,surrogate_function=self.surrogate_function, + step_mode=self.step_mode,backend=self.backend,store_v_seq=self.store_v_req) + ) + if 0 < self.p < 1: + self.network.add_module( + "ConvIF_Dropout", + nn.Dropout2d(p=self.p) + ) + + def forward(self,data:torch.Tensor) -> torch.Tensor: + + for layer in self.network: + layer.reset() + + b, t, c, h, w = data.size() + out = data.reshape(b * t, c, h, w) + output = self.conv2d(out) + + if self.useBatchNorm: + output = self.BNLayer(output) + + if self.pooling_kernel_size > 1: + output = self.pooling(output) + + _, c, h, w = output.size() + outputsum = output.reshape(b, t, c, h, w) + + if self.attention_flag == "no": + data = outputsum + else: + data = self.attention(outputsum) + + output=self.network(data.transpose(0,1)) + + return output.transpose(0,1) \ No newline at end of file diff --git a/Results/Result/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.csv b/Results/Result/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.csv new file mode 100644 index 0000000..42b5963 --- /dev/null +++ b/Results/Result/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.csv @@ -0,0 +1,5 @@ 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diff --git a/Results/Result/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.t7 b/Results/Result/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.t7 new file mode 100644 index 0000000..86998ee Binary files /dev/null and b/Results/Result/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.t7 differ diff --git a/Results/Result/log_DVS_Gesture_SNN.txt b/Results/Result/log_DVS_Gesture_SNN.txt new file mode 100644 index 0000000..42dc672 --- /dev/null +++ b/Results/Result/log_DVS_Gesture_SNN.txt @@ -0,0 +1,2653 @@ +cuda +range(0, 4) +dt==15 +T==60 +attention==TCSA +c_ratio==8 +t_ratio==5 +epoch==0 +num_epochs==300 +onlyTest==False +pretrained_path==None +batch_size==128 +batch_size_test==32 +init_method==None +ds==4 +in_channels==2 +im_width==32 +im_height==32 +target_size==11 +clip==10 +is_train_Enhanced==True +is_spike==False +interval_scaling==False +beta==0 +alpha==0.3 +Vreset==0 +Vthres==0.3 +reduction==16 +T_extend_Conv==False +T_extend_BN==False +h_conv==False +mem_act== +mode_select==spike +TR_model==NTR +track_running_stats==True +a==0.5 +lens==0.25 +lr==0.0001 +betas==[0.9, 0.999] +eps==1e-08 +weight_decay==0 +lr_scheduler==True +lr_scheduler_epoch==25 +name==TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60 +modelPath==/home/noitom-server/PycharmProjects/pythonProject/Attention-SNN/MA_SNN/DVSGestures/CNN/Result +modelNames==TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.t7 +recordPath==/home/noitom-server/PycharmProjects/pythonProject/Attention-SNN/MA_SNN/DVSGestures/CNN/Result +recordNames==TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.csv +savePath==/home/noitom-server/PycharmProjects/pythonProject/Attention-SNN/MA_SNN/DVSGestures/data +train_dataset==None +test_dataset==None +train_loader==None +test_loader==None +drop_last==False +pip_memory==False +num_work==8 +model==None +criterion==MSELoss() +optimizer==None +device==cuda +device_ids==range(0, 4) +best_acc==0 +best_epoch==0 +epoch_list==[] +loss_train_list==[] +loss_test_list==[] +acc_train_list==[] +acc_test_list==[] +train_loss==0 +train_correct==0 +train_acc==0 +test_loss==0 +test_correct==0 +test_acc==0 +state==None + +DataParallel( + (module): Net( + (convAttLIF0): ConvAttLIF( + (conv2d): Conv2d(2, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (BNLayer): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (attention): TCSA( + (relu): ReLU(inplace=True) + (ca): ChannelAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(64, 8, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(8, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(60, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(12, 60, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (sa): SpatialAttention( + (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (sigmoid): Sigmoid() + ) + ) + (network): Sequential( + (ConvIF): ConvIFCell() + ) + ) + (convAttLIF1): ConvAttLIF( + (conv2d): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (BNLayer): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pooling): AvgPool2d(kernel_size=2, stride=2, padding=0) + (attention): TCSA( + (relu): ReLU(inplace=True) + (ca): ChannelAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(128, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(16, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(60, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(12, 60, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (sa): SpatialAttention( + (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (sigmoid): Sigmoid() + ) + ) + (network): Sequential( + (ConvIF): ConvIFCell() + ) + ) + (convAttLIF2): ConvAttLIF( + (conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (BNLayer): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pooling): AvgPool2d(kernel_size=2, stride=2, padding=0) + (attention): TCSA( + (relu): ReLU(inplace=True) + (ca): ChannelAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(128, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(16, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(60, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(12, 60, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (sa): SpatialAttention( + (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (sigmoid): Sigmoid() + ) + ) + (network): Sequential( + (ConvIF): ConvIFCell() + ) + ) + (FC0): AttLIF( + (network): Sequential( + (IF): IFCell() + ) + (linear): Linear(in_features=8192, out_features=256, bias=True) + (BNLayer): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (attention): TA( + (relu): ReLU(inplace=True) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool1d(output_size=1) + (max_pool): AdaptiveMaxPool1d(output_size=1) + (sharedMLP): Sequential( + (0): Conv1d(60, 12, kernel_size=(1,), stride=(1,), bias=False) + (1): ReLU() + (2): Conv1d(12, 60, kernel_size=(1,), stride=(1,), bias=False) + ) + (sigmoid): Sigmoid() + ) + ) + ) + (FC1): AttLIF( + (network): Sequential( + (IF): IFCell() + ) + (linear): Linear(in_features=256, out_features=11, bias=True) + (BNLayer): BatchNorm1d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (attention): TA( + (relu): ReLU(inplace=True) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool1d(output_size=1) + (max_pool): AdaptiveMaxPool1d(output_size=1) + (sharedMLP): Sequential( + (0): Conv1d(60, 12, kernel_size=(1,), stride=(1,), bias=False) + (1): ReLU() + (2): Conv1d(12, 60, kernel_size=(1,), stride=(1,), bias=False) + ) + (sigmoid): Sigmoid() + ) + ) + ) + ) +) +{'Total': 2340535, 'Trainable': 2340535} +epoch: 1 +dt: 15 +T: 60 +Tarin loss:0.09031 +Train acc: 45.362 +Test loss:0.00909 +Test acc: 8.333 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acc: 95.13888888888889 +epoch: 62 +dt: 15 +T: 60 +Tarin loss:0.00895 +Train acc: 98.128 +Test loss:0.00140 +Test acc: 95.139 +beat acc: 95.13888888888889 +epoch: 63 +dt: 15 +T: 60 +Tarin loss:0.00895 +Train acc: 98.043 +Test loss:0.00169 +Test acc: 92.708 +beat acc: 95.13888888888889 +epoch: 64 +dt: 15 +T: 60 +Tarin loss:0.01044 +Train acc: 97.787 +Test loss:0.00143 +Test acc: 92.361 +beat acc: 95.13888888888889 +epoch: 65 +dt: 15 +T: 60 +Tarin loss:0.00881 +Train acc: 98.383 +Test loss:0.00155 +Test acc: 93.056 +beat acc: 95.13888888888889 +epoch: 66 +dt: 15 +T: 60 +Tarin loss:0.00936 +Train acc: 97.787 +Test loss:0.00150 +Test acc: 93.750 +beat acc: 95.13888888888889 +epoch: 67 +dt: 15 +T: 60 +Tarin loss:0.00860 +Train acc: 97.447 +Test loss:0.00136 +Test acc: 95.139 +beat acc: 95.13888888888889 +epoch: 68 +dt: 15 +T: 60 +Tarin loss:0.00800 +Train acc: 98.383 +Test loss:0.00131 +Test acc: 94.792 +beat acc: 95.13888888888889 +epoch: 69 +dt: 15 +T: 60 +Tarin loss:0.00779 +Train acc: 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+dt: 15 +T: 60 +Tarin loss:0.00703 +Train acc: 98.979 +Test loss:0.00131 +Test acc: 93.750 +beat acc: 95.13888888888889 +epoch: 78 +dt: 15 +T: 60 +Tarin loss:0.00639 +Train acc: 98.723 +Test loss:0.00132 +Test acc: 94.444 +beat acc: 95.13888888888889 +epoch: 79 +dt: 15 +T: 60 +Tarin loss:0.00642 +Train acc: 98.979 +Test loss:0.00124 +Test acc: 94.097 +beat acc: 95.13888888888889 +epoch: 80 +dt: 15 +T: 60 +Tarin loss:0.00679 +Train acc: 98.979 +Test loss:0.00128 +Test acc: 93.403 +beat acc: 95.13888888888889 +epoch: 81 +dt: 15 +T: 60 +Tarin loss:0.00680 +Train acc: 98.809 +Test loss:0.00122 +Test acc: 94.792 +beat acc: 95.13888888888889 +epoch: 82 +dt: 15 +T: 60 +Tarin loss:0.00631 +Train acc: 98.553 +Epoch 82: reducing learning rate of group 0 to 1.0000e-05. +Test loss:0.00120 +Test acc: 94.792 +beat acc: 95.13888888888889 +epoch: 83 +dt: 15 +T: 60 +Tarin loss:0.00623 +Train acc: 99.064 +Test loss:0.00118 +Test acc: 94.444 +beat acc: 95.13888888888889 +epoch: 84 +dt: 15 +T: 60 +Tarin 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acc: 98.809 +Test loss:0.00120 +Test acc: 94.444 +beat acc: 95.13888888888889 +epoch: 92 +dt: 15 +T: 60 +Tarin loss:0.00600 +Train acc: 98.894 +Test loss:0.00119 +Test acc: 94.097 +beat acc: 95.13888888888889 +epoch: 93 +dt: 15 +T: 60 +Tarin loss:0.00586 +Train acc: 98.979 +Test loss:0.00121 +Test acc: 94.097 +beat acc: 95.13888888888889 +epoch: 94 +dt: 15 +T: 60 +Tarin loss:0.00583 +Train acc: 99.064 +Test loss:0.00121 +Test acc: 94.444 +beat acc: 95.13888888888889 +epoch: 95 +dt: 15 +T: 60 +Tarin loss:0.00526 +Train acc: 99.234 +Test loss:0.00120 +Test acc: 93.403 +beat acc: 95.13888888888889 +epoch: 96 +dt: 15 +T: 60 +Tarin loss:0.00635 +Train acc: 98.809 +Epoch 96: reducing learning rate of group 0 to 1.0000e-07. +Test loss:0.00121 +Test acc: 94.792 +beat acc: 95.13888888888889 +epoch: 97 +dt: 15 +T: 60 +Tarin loss:0.00505 +Train acc: 99.149 +Test loss:0.00120 +Test acc: 94.444 +beat acc: 95.13888888888889 +epoch: 98 +dt: 15 +T: 60 +Tarin loss:0.00581 +Train acc: 99.234 +Test 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b/Results/Result_43/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.csv new file mode 100644 index 0000000..68291d8 --- /dev/null +++ b/Results/Result_43/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.csv @@ -0,0 +1,5 @@ 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diff --git a/Results/Result_43/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.t7 b/Results/Result_43/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.t7 new file mode 100644 index 0000000..32d0010 Binary files /dev/null and b/Results/Result_43/TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.t7 differ diff --git a/Results/Result_43/log_DVS_Gesture_SNN.txt b/Results/Result_43/log_DVS_Gesture_SNN.txt new file mode 100644 index 0000000..07f9081 --- /dev/null +++ b/Results/Result_43/log_DVS_Gesture_SNN.txt @@ -0,0 +1,2657 @@ +cuda +range(0, 4) +dt==15 +T==60 +attention==TCSA +c_ratio==8 +t_ratio==5 +epoch==0 +num_epochs==300 +onlyTest==False +pretrained_path==None +batch_size==128 +batch_size_test==32 +init_method==None +ds==4 +in_channels==2 +im_width==32 +im_height==32 +target_size==11 +clip==10 +is_train_Enhanced==True +is_spike==False +interval_scaling==False +beta==0 +alpha==0.3 +Vreset==0 +Vthres==0.3 +reduction==16 +T_extend_Conv==False +T_extend_BN==False +h_conv==False +mem_act== +mode_select==spike +TR_model==NTR +track_running_stats==True +a==0.5 +lens==0.25 +lr==0.0001 +betas==[0.9, 0.999] +eps==1e-08 +weight_decay==0 +lr_scheduler==True +lr_scheduler_epoch==25 +name==TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60 +modelPath==/home/noitom-server/PycharmProjects/pythonProject/Attention-SNN/MA_SNN/DVSGestures/CNN/Result +modelNames==TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.t7 +recordPath==/home/noitom-server/PycharmProjects/pythonProject/Attention-SNN/MA_SNN/DVSGestures/CNN/Result +recordNames==TCSA_SNN(CNN)-DVS-Gesture_dt=15ms_T=60.csv +savePath==/home/noitom-server/PycharmProjects/pythonProject/Attention-SNN/MA_SNN/DVSGestures/data +train_dataset==None +test_dataset==None +train_loader==None +test_loader==None +drop_last==False +pip_memory==False +num_work==8 +model==None +criterion==MSELoss() +optimizer==None +device==cuda +device_ids==range(0, 4) +best_acc==0 +best_epoch==0 +epoch_list==[] +loss_train_list==[] +loss_test_list==[] +acc_train_list==[] +acc_test_list==[] +train_loss==0 +train_correct==0 +train_acc==0 +test_loss==0 +test_correct==0 +test_acc==0 +state==None + +DataParallel( + (module): Net( + (convAttLIF0): ConvAttLIF( + (conv2d): Conv2d(2, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (BNLayer): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (attention): TCSA( + (relu): ReLU(inplace=True) + (ca): ChannelAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(64, 8, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(8, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(60, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(12, 60, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (sa): SpatialAttention( + (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (sigmoid): Sigmoid() + ) + ) + (network): Sequential( + (ConvIF): ConvIFCell() + ) + ) + (convAttLIF1): ConvAttLIF( + (conv2d): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (BNLayer): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pooling): AvgPool2d(kernel_size=2, stride=2, padding=0) + (attention): TCSA( + (relu): ReLU(inplace=True) + (ca): ChannelAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(128, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(16, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(60, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(12, 60, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (sa): SpatialAttention( + (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (sigmoid): Sigmoid() + ) + ) + (network): Sequential( + (ConvIF): ConvIFCell() + ) + ) + (convAttLIF2): ConvAttLIF( + (conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (BNLayer): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (pooling): AvgPool2d(kernel_size=2, stride=2, padding=0) + (attention): TCSA( + (relu): ReLU(inplace=True) + (ca): ChannelAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(128, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(16, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool3d(output_size=1) + (max_pool): AdaptiveMaxPool3d(output_size=1) + (sharedMLP): Sequential( + (0): Conv3d(60, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + (1): ReLU() + (2): Conv3d(12, 60, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) + ) + (sigmoid): Sigmoid() + ) + (sa): SpatialAttention( + (conv): Conv2d(2, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (sigmoid): Sigmoid() + ) + ) + (network): Sequential( + (ConvIF): ConvIFCell() + ) + ) + (FC0): AttLIF( + (network): Sequential( + (IF): IFCell() + ) + (linear): Linear(in_features=8192, out_features=256, bias=True) + (BNLayer): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (attention): TA( + (relu): ReLU(inplace=True) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool1d(output_size=1) + (max_pool): AdaptiveMaxPool1d(output_size=1) + (sharedMLP): Sequential( + (0): Conv1d(60, 12, kernel_size=(1,), stride=(1,), bias=False) + (1): ReLU() + (2): Conv1d(12, 60, kernel_size=(1,), stride=(1,), bias=False) + ) + (sigmoid): Sigmoid() + ) + ) + ) + (FC1): AttLIF( + (network): Sequential( + (IF): IFCell() + ) + (linear): Linear(in_features=256, out_features=11, bias=True) + (BNLayer): BatchNorm1d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (attention): TA( + (relu): ReLU(inplace=True) + (ta): TimeAttention( + (avg_pool): AdaptiveAvgPool1d(output_size=1) + (max_pool): AdaptiveMaxPool1d(output_size=1) + (sharedMLP): Sequential( + (0): Conv1d(60, 12, kernel_size=(1,), stride=(1,), bias=False) + (1): ReLU() + (2): Conv1d(12, 60, kernel_size=(1,), stride=(1,), bias=False) + ) + (sigmoid): Sigmoid() + ) + ) + ) + ) +) +{'Total': 2340535, 'Trainable': 2340535} +epoch: 1 +dt: 15 +T: 60 +Tarin loss:0.08176 +Train acc: 26.723 +Test loss:0.00909 +Test acc: 8.333 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