RuntimeError: offset output dims: (160, 160) - computed output dims: (80, 80)
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 17.9k
- Forks
- 7.3k
- Avg merge
- 1d 15h
- Merged PRs (30d)
- 13
Description
🐛 Describe the bug
I run source build pytorch(torch==1.10.0) and torchvision(torchvision==0.12.0).
train code snippet:
from torchvision.ops import DeformConv2d as ModulatedDeformConv
conv_op = ModulatedDeformConv
offset_channels = 27
self.conv2_offset = nn.Conv2d(
planes, deformable_groups * offset_channels,
kernel_size=3,
padding=1)
self.conv2 = conv_op(
planes, planes, kernel_size=3, padding=1, stride=stride,
groups=deformable_groups, bias=False)
offset_mask = self.conv2_offset(out)
offset = offset_mask[:, :18, :, :]
mask = offset_mask[:, -9:, :, :].sigmoid()
out = self.conv2(out, offset, mask)
tensor shape below:
(Pdb) offset_mask.shape
torch.Size([16, 27, 160, 160])
(Pdb) offset.shape
torch.Size([16, 18, 160, 160])
(Pdb) mask.shape
torch.Size([16, 9, 160, 160])
(Pdb) self.conv2.__doc__
'\n See :func:`deform_conv2d`.\n '
(Pdb) out.shape
torch.Size([16, 128, 160, 160])
When training(run out = self.conv2(out, offset, mask) this line), issue the error below, pls help me, tks.
Traceback (most recent call last):
File "/DB/train.py", line 89, in <module>
main()
File "/DB/train.py", line 86, in main
trainer.train()
File "/DB/trainer.py", line 115, in train
epoch=epoch, step=self.steps)
File "/DB/trainer.py", line 138, in train_step
results = model.forward(batch, training=True)
File "/DB/structure/model.py", line 56, in forward
pred = self.model(data, training=self.training)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward
return self.module(*inputs, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/DB/structure/model.py", line 19, in forward
return self.decoder(self.backbone(data), *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/DB/backbones/resnet.py", line 242, in forward
x3 = self.layer2(x2)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py", line 141, in forward
input = module(input)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/DB/backbones/resnet.py", line 161, in forward
out = self.conv2(out, offset, mask)
File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/torchvision-0.12.0a0+031e129-py3.7-linux-x86_64.egg/torchvision/ops/deform_conv.py", line 177, in forward
mask=mask,
File "/opt/conda/lib/python3.7/site-packages/torchvision-0.12.0a0+031e129-py3.7-linux-x86_64.egg/torchvision/ops/deform_conv.py", line 106, in deform_conv2d
use_mask,
RuntimeError: offset output dims: (160, 160) - computed output dims: (80, 80)
tks~
Versions
Collecting environment information...
PyTorch version: 1.10.0a0+git36449ea
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.21.4
Libc version: glibc-2.10
Python version: 3.7.6 (default, Jan 8 2020, 19:59:22) [GCC 7.3.0] (64-bit runtime)
Python platform: Linux-5.4.0-86-generic-x86_64-with-debian-buster-sid
Is CUDA available: False
CUDA runtime version: No CUDA
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] intel-extension-for-pytorch==1.10.0+cpu
[pip3] numpy==1.21.2
[pip3] torch==1.10.0a0+git36449ea
[pip3] torchvision==0.12.0a0+031e129
[conda] blas 1.0 mkl
[conda] intel-extension-for-pytorch 1.10.0+cpu pypi_0 pypi
[conda] mkl 2021.4.0 h06a4308_640
[conda] mkl-include 2021.4.0 h06a4308_640
[conda] mkl-service 2.4.0 py37h7f8727e_0
[conda] mkl_fft 1.3.1 py37hd3c417c_0
[conda] mkl_random 1.2.2 py37h51133e4_0
[conda] numpy 1.21.2 py37h20f2e39_0
[conda] numpy-base 1.21.2 py37h79a1101_0
[conda] torch 1.10.0a0+git36449ea pypi_0 pypi
[conda] torchvision 0.12.0a0+031e129 pypi_0 pypi
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the shown call at self.conv2(out, offset, mask) with the reported tensor shapes and versions. Read torchvision/ops/deform_conv.py around deform_conv2d and DeformConv2d.forward, especially the lines reported in the traceback. Done means the dimension mismatch is explained and the operation either runs with matching dimensions or has a verified regression test.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100