I have set up the dataset etc. When I start training in one of the models' directory (eg. "/1 SConvLSTM/yolov5/", I get the following error after the image scanning is done:

Scanning labels ../../../../datasets//all_in_one/train/labels.cache (20942 found, 0 missing, 3847 empty, 0 duplicate, for 24789 images): 24789it [00:00, 30033.60it/s] Traceback (most recent call last): File "train.py", line 544, in <module> train(hyp, opt, device, tb_writer) File "train.py", line 196, in train testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, File "/home/local/xxxx/TYolov5/1 SConvLSTM/yolov5/utils/datasets.py", line 54, in create_dataloader dataset = LoadImagesAndLabels(path, imgsz, batch_size, File "/home/local/xxxx/TYolov5/1 SConvLSTM/yolov5/utils/datasets.py", line 385, in __init__ cache = torch.load(cache_path) # load File "/home/local/xxxx/miniconda3/envs/tyolo/lib/python3.8/site-packages/torch/serialization.py", line 585, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "/home/local/xxxx/miniconda3/envs/tyolo/lib/python3.8/site-packages/torch/serialization.py", line 755, in _legacy_load magic_number = pickle_module.load(f, **pickle_load_args) _pickle.UnpicklingError: STACK_GLOBAL requires str

I looked around and it seems that the issue might be caused by loading the cached labels. What do you think would fix the issue?

Hi I am trying to run detect.py file using image as well as camera as input source, but in both cases I get this error:

Using CPU

Fusing layers...
Model Summary: 152 layers, 3.20299e+07 parameters, 0 gradients
image 1/1 /Users/user/Downloads/Temporal_YOLOv5/dataset/HGP/images/val2017/V00000118.png: Traceback (most recent call last):
  File "detect.py", line 170, in <module>
    detect()
  File "detect.py", line 73, in detect
    pred = model(img, augment=opt.augment)[0]
  File "/Users/user/miniconda3/envs/TYOLO/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Users/user/Downloads/Temporal_YOLOv5/TYolov5/1 SConvLSTM/yolov5/models/yolo.py", line 186, in forward
    return self.forward_once(x, masksOP, profile)  # single-scale inference, train
  File "/Users/user/Downloads/Temporal_YOLOv5/TYolov5/1 SConvLSTM/yolov5/models/yolo.py", line 207, in forward_once
    x = m(x, masksOP)
  File "/Users/user/miniconda3/envs/TYOLO/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Users/user/Downloads/Temporal_YOLOv5/TYolov5/1 SConvLSTM/yolov5/models/yolo.py", line 98, in forward
    cx = x[i].view(-1, self.sl, x[i].shape[1], x[i].shape[2], x[i].shape[3])
RuntimeError: shape '[-1, 2, 128, 80, 80]' is invalid for input of size 819200

I got this error running detect.py from both SConvLSTM as well as SQRNN folders. I tried running this code with both PyTorch=1.6.0 / 1.7.1, but still the same issue. Thanks.

I am working on Yolov5. I have checked this repo. All the models of Yolov5 is the same as it is. Didn't see any chnages.

I wished to know how exactly or where in the code have you incorporated the LSTM, QRNN portion?

Waiting for your response in anticipation. Thanks !

I try this code， but in one min-batch，the images is shuffled. The images are not arranged in the order they appear in the video.How to ensure that what the network extracts is timing information, not the accuracy improvement caused by the participation of model parameters. thanks！

index 65 is out of bounds for dimension 0 with size 64 File "/home/wch/TYolov5/4 SQRNN/yolov5/utils/general.py", line 507, in compute_loss ps = pi[b, a, gj, gi] # prediction subset corresponding to targets File "/home/wch/TYolov5/4 SQRNN/yolov5/train.py", line 342, in train model, model.model[-1].maps_outputs, att_criterion, masks) File "/home/wch/TYolov5/4 SQRNN/yolov5/train.py", line 548, in train(hyp, opt, device, tb_writer)

When I start training, the following error appears: RuntimeError: Error(s) in loading state_dict for Model: Missing key(s) in state_dict: "model.0.conv.conv.weight", "model.0.conv.bn.weight", "model.0.conv.bn.bias", "model.0.conv.bn.running_mean", "model.0.conv.bn.running_var", "model.1.conv.weight", "model.1.bn.weight", "model.1.bn.bias", "model.1.bn.running_mean", "model.1.bn.running_var", "model.2.cv1.conv.weight", "model.2.cv1.bn.weight", "model.2.cv1.bn.bias", "model.2.cv1.bn.running_mean", "model.2.cv1.bn.running_var", "model.2.cv2.weight", "model.2.cv3.weight", "model.2.cv4.conv.weight", "model.2.cv4.bn.weight", "model.2.cv4.bn.bias", "model.2.cv4.bn.running_mean", "model.2.cv4.bn.running_var", "model.2.bn.weight", "model.2.bn.bias", "model.2.bn.running_mean", "model.2.bn.running_var", "model.2.m.0.cv1.conv.weight", "model.2.m.0.cv1.bn.weight", "model.2.m.0.cv1.bn.bias", "model.2.m.0.cv1.bn.running_mean", "model.2.m.0.cv1.bn.running_var", "model.2.m.0.cv2.conv.weight", 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"model.24.bn.1.bias", "model.24.bn.1.running_mean", "model.24.bn.1.running_var", "model.24.bn.2.weight", "model.24.bn.2.bias", "model.24.bn.2.running_mean", "model.24.bn.2.running_var", "model.24.convlstm1.cell_list.0.conv.weight", "model.24.convlstm1.cell_list.0.conv.bias", "model.24.convlstm2.cell_list.0.conv.weight", "model.24.convlstm2.cell_list.0.conv.bias", "model.24.convlstm3.cell_list.0.conv.weight", "model.24.convlstm3.cell_list.0.conv.bias". Unexpected key(s) in state_dict: "module.model.0.conv.conv.weight", "module.model.0.conv.bn.weight", "module.model.0.conv.bn.bias", "module.model.0.conv.bn.running_mean", "module.model.0.conv.bn.running_var", "module.model.0.conv.bn.num_batches_tracked", "module.model.1.conv.weight", "module.model.1.bn.weight", "module.model.1.bn.bias", "module.model.1.bn.running_mean", "module.model.1.bn.running_var", "module.model.1.bn.num_batches_tracked", "module.model.2.cv1.conv.weight", "module.model.2.cv1.bn.weight", "module.model.2.cv1.bn.bias", "module.model.2.cv1.bn.running_mean", "module.model.2.cv1.bn.running_var", "module.model.2.cv1.bn.num_batches_tracked", "module.model.2.cv2.weight", "module.model.2.cv3.weight", "module.model.2.cv4.conv.weight", "module.model.2.cv4.bn.weight", "module.model.2.cv4.bn.bias", "module.model.2.cv4.bn.running_mean", "module.model.2.cv4.bn.running_var", "module.model.2.cv4.bn.num_batches_tracked", "module.model.2.bn.weight", "module.model.2.bn.bias", 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"module.model.4.cv2.weight", "module.model.4.cv3.weight", "module.model.4.cv4.conv.weight", "module.model.4.cv4.bn.weight", "module.model.4.cv4.bn.bias", "module.model.4.cv4.bn.running_mean", "module.model.4.cv4.bn.running_var", "module.model.4.cv4.bn.num_batches_tracked", "module.model.4.bn.weight", "module.model.4.bn.bias", "module.model.4.bn.running_mean", "module.model.4.bn.running_var", "module.model.4.bn.num_batches_tracked", "module.model.4.m.0.cv1.conv.weight", "module.model.4.m.0.cv1.bn.weight", "module.model.4.m.0.cv1.bn.bias", "module.model.4.m.0.cv1.bn.running_mean", "module.model.4.m.0.cv1.bn.running_var", "module.model.4.m.0.cv1.bn.num_batches_tracked", "module.model.4.m.0.cv2.conv.weight", "module.model.4.m.0.cv2.bn.weight", "module.model.4.m.0.cv2.bn.bias", "module.model.4.m.0.cv2.bn.running_mean", "module.model.4.m.0.cv2.bn.running_var", "module.model.4.m.0.cv2.bn.num_batches_tracked", "module.model.4.m.1.cv1.conv.weight", "module.model.4.m.1.cv1.bn.weight", 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