r3d_18(3D_resnet) do not work!! (When I import it in my torch.nn.Module)

Hi, I just loaded pre-trained 3D-resnets (https://pytorch.org/vision/stable/models.html#video-classification) What I loaded is ResNet 3D 18(r3d_18) and my input shape is [1 x 3 x 192 x 112 x 112] where 1 is batch size, 3 is [R,G,B] and 192 is video_length and 112 and 112 is W and H.

I got this error, at forward()

  File "train_total.py", line 497, in forward
    timed_CNN_out_front = self.CNNlayers_front(pixel.cuda())
  File "/home/ai/anaconda3/envs/mos/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ai/anaconda3/envs/mos/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
    input = module(input)
  File "/home/ai/anaconda3/envs/mos/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ai/anaconda3/envs/mos/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 87, in forward
    return F.linear(input, self.weight, self.bias)
  File "/home/ai/anaconda3/envs/mos/lib/python3.6/site-packages/torch/nn/functional.py", line 1372, in linear
    output = input.matmul(weight.t())
RuntimeError: size mismatch, m1: [512 x 1], m2: [512 x 400] at /opt/anaconda/conda-bld/pytorch-base_1600153523661/work/aten/src/THC/generic/THCTensorMathBlas.cu:290

Why this error come out?? I just read that resnet3D requries the above shape what I mentioned…

[NicolasHug]

Thanks for the feedback @SungmanHong , you’re right that ideally model ablation / tweaking could be a bit easier. As @fmassa mentioned, hopefully #3597 will make this easier and will be available in the next release. We’ll make sure to write good docs to explain how to perform that.

Since the original problem is resolved here, I’ll close the issue if you don’t mind

[NeighborhoodCoding]

Thank you for helping me. I didn’t know that the flatten layer was missing because I lacked detailed debugging skills, so I asked foolishly, but thank you for your kind reply.

I think, PyTorch was good because we are able to selectively import the layers by nn.Sequential(*list(model.children()). For example, When we are doing transfer learning on images or videos, choosing What layers to import from pre-trained ResNets(Not all layers) would be great for very flexible and easy way coding for newbie researchers.

Of course, it may be difficult to ensure full compatibility for all models to nn.Sequential(*list(model.children()), but if compatibility is up-leveled, I think it would help researchers in many areas (in my case, 3d-resnet for video classification or regression).