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Hi, I am new to machine learning and has little experience with coding related to this field. I am stuck at this problem for a long time. Can anyone tell me what change should I make to solve this problem. I have provided my parameters and error log below. I am currently working on this for my university thesis. It will be very much helpful if anyone help me in this matter.
/content/Efficient-3DCNNs/jester/20-bn-jester/82241
/content/Efficient-3DCNNs/jester/20-bn-jester/18224
/content/Efficient-3DCNNs/jester/20-bn-jester/99358
/content/Efficient-3DCNNs/jester/20-bn-jester/75507
/content/Efficient-3DCNNs/jester/20-bn-jester/48433
/content/Efficient-3DCNNs/jester/20-bn-jester/146421
/content/Efficient-3DCNNs/jester/20-bn-jester/49514
/content/Efficient-3DCNNs/jester/20-bn-jester/4502
Traceback (most recent call last):
File "/content/Efficient-3DCNNs/main.py", line 90, in
train_loader = torch.utils.data.DataLoader(
File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 351, in init
sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type]
File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/sampler.py", line 144, in init
raise ValueError(f"num_samples should be a positive integer value, but got num_samples={self.num_samples}")
ValueError: num_samples should be a positive integer value, but got num_samples=0
The dataset that I used are from kaggle repository which contains a subset of 20bn-jester dataset
!kaggle datasets download -d toxicmender/20bn-jester
and I have unzipped the file before use
The text was updated successfully, but these errors were encountered:
Hi, I am new to machine learning and has little experience with coding related to this field. I am stuck at this problem for a long time. Can anyone tell me what change should I make to solve this problem. I have provided my parameters and error log below. I am currently working on this for my university thesis. It will be very much helpful if anyone help me in this matter.
##parameters
!python /content/Efficient-3DCNNs/main.py --root_path /content/Efficient-3DCNNs
--video_path /content/Efficient-3DCNNs/jester/
--annotation_path /content/Efficient-3DCNNs/annotation_Jester/jester.json
--result_path /content/Efficient-3DCNNs/results
--pretrain_path Efficient-3DCNNs/results/kinetics_shufflenet_0.5x_G3_RGB_16_best.pth
--dataset ucf101
--n_classes 600
--n_finetune_classes 101
--ft_portion last_layer
--model shufflenet
--groups 3
--width_mult 0.5
--train_crop random
--learning_rate 0.1
--sample_duration 16
--downsample 1
--batch_size 64
--n_threads 16
--checkpoint 1
--n_val_samples 1 \
error log
/content/Efficient-3DCNNs/jester/20-bn-jester/82241
/content/Efficient-3DCNNs/jester/20-bn-jester/18224
/content/Efficient-3DCNNs/jester/20-bn-jester/99358
/content/Efficient-3DCNNs/jester/20-bn-jester/75507
/content/Efficient-3DCNNs/jester/20-bn-jester/48433
/content/Efficient-3DCNNs/jester/20-bn-jester/146421
/content/Efficient-3DCNNs/jester/20-bn-jester/49514
/content/Efficient-3DCNNs/jester/20-bn-jester/4502
Traceback (most recent call last):
File "/content/Efficient-3DCNNs/main.py", line 90, in
train_loader = torch.utils.data.DataLoader(
File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 351, in init
sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type]
File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/sampler.py", line 144, in init
raise ValueError(f"num_samples should be a positive integer value, but got num_samples={self.num_samples}")
ValueError: num_samples should be a positive integer value, but got num_samples=0
The dataset that I used are from kaggle repository which contains a subset of 20bn-jester dataset
!kaggle datasets download -d toxicmender/20bn-jester
and I have unzipped the file before use
The text was updated successfully, but these errors were encountered: