pytorch-video-recognition
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jfzhang95
PyTorch implemented C3D, R3D, R2Plus1D models for video activity recognition.
https://github.com/jfzhang95/pytorch-video-recognition/blob/ca37de9f69a961f22a821c157e9ccf47a601904d/dataloaders/dataset.py#L81
I was checking your Pytorch implementation of the R2Plus1D model against the implementation in Caffe2 in the repository of the original paper (https://github.com/facebookresearch/VMZ), and I was wondering why you chose...
your pretrained model work very badly in inference.py in any av. why ? I init models as follows: model = C3D_model.C3D(num_classes=101, pretrained=True) # checkpoint = torch.load('./models/C3D_ucf101_epoch-39.pth', map_location=lambda storage, loc: storage)...
optimizer.step() File "/home/z/anaconda3/envs/py3/lib/python3.6/site-packages/torch/optim/sgd.py", line 101, in step buf.mul_(momentum).add_(1 - dampening, d_p) RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #4 'other' something wrong?
`frames = sorted([os.path.join(file_dir, img) for img in os.listdir(file_dir)])` this operation will get something like 0001 00010 00011 00012 ... 00019 0002 00020 00021 ... the correct code should be `frames...
In R3D_model and R2+1D model. bn and relu already included in the SpatioTemporalConv but in SpatioTemporalResBlock you add bn and relu after SpatioTemporalConv
I got some loss like this: ``` 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 424/424 [04:10
I want to check the testing accuracy for each individual action. This way i can identify the weakest action and train it more. So how can I do this?
In your README, the tensorboardx output for Test Acc is steadily decreasing over all 100 epochs. Is that just a 'typo' or were those your actual results?  PS Thanks...
