RuntimeError: The size of tensor a (13) must match the size of tensor b (70135) at non-singleton dimension 3

[bahulkark]

I am trying to implement the code in Google Colab. I am getting this error, I had a similar issue in cgf.py but I solved it.
Below is the output and error that I am getting after running train_meta.py
!python train_meta.py cfg/metayolo.data cfg/darknet_dynamic.cfg cfg/reweighting_net.cfg darknet19_448.conv.23

/content/Fewshot_Detection/data/coco.names
('save_interval', 10)
['bird', 'bus', 'cow', 'motorbike', 'sofa']
('base_ids', [0, 1, 3, 4, 6, 7, 8, 10, 11, 12, 14, 15, 16, 18, 19])
logging to backup/metayolofix_novel0_neg1
('class_scale', 1)
layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32
1 max 2 x 2 / 2 416 x 416 x 32 -> 208 x 208 x 32
2 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64
3 max 2 x 2 / 2 208 x 208 x 64 -> 104 x 104 x 64
4 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
5 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64
6 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
7 max 2 x 2 / 2 104 x 104 x 128 -> 52 x 52 x 128
8 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
9 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
10 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
11 max 2 x 2 / 2 52 x 52 x 256 -> 26 x 26 x 256
12 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
13 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
14 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
15 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
16 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
17 max 2 x 2 / 2 26 x 26 x 512 -> 13 x 13 x 512
18 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
19 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
20 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
21 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
22 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
23 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
24 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
25 route 16
26 conv 64 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 64
27 reorg / 2 26 x 26 x 64 -> 13 x 13 x 256
28 route 27 24
29 conv 1024 3 x 3 / 1 13 x 13 x1280 -> 13 x 13 x1024
30 dconv 1024 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x1024
31 conv 30 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 30
32 detection

layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 4 -> 416 x 416 x 32
1 max 2 x 2 / 2 416 x 416 x 32 -> 208 x 208 x 32
2 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64
3 max 2 x 2 / 2 208 x 208 x 64 -> 104 x 104 x 64
4 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
5 max 2 x 2 / 2 104 x 104 x 128 -> 52 x 52 x 128
6 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
7 max 2 x 2 / 2 52 x 52 x 256 -> 26 x 26 x 256
8 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
9 max 2 x 2 / 2 26 x 26 x 512 -> 13 x 13 x 512
10 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
11 max 2 x 2 / 2 13 x 13 x1024 -> 6 x 6 x1024
12 conv 1024 3 x 3 / 1 6 x 6 x1024 -> 6 x 6 x1024
13 glomax 6 x 6 / 1 6 x 6 x1024 -> 1 x 1 x1024
1 14554 80200 32
10
===> Number of samples (before filtring): 4952
===> Number of samples (after filtring): 4952
('num classes: ', 15)
factor: 3.0
===> Number of samples (before filtring): 14554
===> Number of samples (after filtring): 14554
('num classes: ', 15)
2020-07-03 08:55:33 epoch 0/177, processed 0 samples, lr 0.000033
/usr/local/lib/python2.7/dist-packages/torch/nn/functional.py:1351: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.
warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.")
Traceback (most recent call last):
File "train_meta.py", line 328, in
train(epoch)
File "train_meta.py", line 223, in train
loss = region_loss(output, target)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/content/Fewshot_Detection/region_loss.py", line 294, in forward
pred_boxes[0] = x.data + grid_x
RuntimeError: The size of tensor a (13) must match the size of tensor b (70135) at non-singleton dimension 3

[risemeup]

@bahulkark hello,I met the same mistake with you. Have you fixed it ?

[bahulkark]

Nope.

…

On Mon, 7 Sep 2020, 17:37 risemeup, ***@***.***> wrote: @bahulkark <https://github.com/bahulkark> hello,I met the same mistake with you. Have you fixed it ? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#42 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ALYEWBGGKQS2DIG2AWPV2D3SETEINANCNFSM4OPT4E5A> .

[tailororrr]

I met the same mistake with you

[azharshaikh06]

Anyone have a fix for this?

[cgg99]

Is the pytorch version wrong?

[azharshaikh06]

I am using torch 1.4 and can perform the forward pass through the model error is in the regionloss.py pred_boxes[0] = x.data + grid_x here x.data is a 4D tensor while grid_x is a 2D tensor

[zhanghao5]

I am using pytorch 1.7. With the view() method I can reshape and fit in the addition
pred_boxes[0] = x.data.view(nBnAnHnW) + grid_x
pred_boxes[1] = y.data.view(nBnAnHnW)+ grid_y
pred_boxes[2] = torch.exp(w.data).view(nBnAnHnW) * anchor_w
pred_boxes[3] = torch.exp(h.data).view(nBnAnHnW) * anchor_h

[SoonFa]

pred_boxes[0] = x.data.view(nB*nA*nH*nW) + grid_x
pred_boxes[1] = y.data.view(nB*nA*nH*nW) + grid_y
pred_boxes[2] = torch.exp(w.data).view(nB*nA*nH*nW) * anchor_w
pred_boxes[3] = torch.exp(h.data).view(nB*nA*nH*nW) * anchor_h

[HeuristicLU]

change region_loss.py
294-297:
pred_boxes[0] = x.data.view(nBnAnHnW) + grid_x
pred_boxes[1] = y.data.view(nBnAnHnW) + grid_y
pred_boxes[2] = torch.exp(w.data).view(nBnAnHnW) * anchor_w
pred_boxes[3] = torch.exp(h.data).view(nBnAnHnW) * anchor_h

and this problem can be resolved