FastRCNNPredictor doesn't return prediction in evaluation #1952
Description
🐛 Bug
Dear all,
I am doing object detection in an image with one class. After training, FastRCNNPredictor does not return anything in validation mode. I have followed this official tutorial https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html.
Thanks.
To Reproduce
Steps to reproduce the behavior:
I have created a custom dataset, this is one of the output:
tensor([[[0.0549, 0.0549, 0.0549, ..., 0.1647, 0.1569, 0.1569],
[0.0549, 0.0549, 0.0549, ..., 0.1686, 0.1569, 0.1569],
[0.0549, 0.0549, 0.0549, ..., 0.1647, 0.1569, 0.1529],
...,
[0.0471, 0.0471, 0.0471, ..., 0.1490, 0.1490, 0.1490],
[0.0471, 0.0471, 0.0471, ..., 0.1490, 0.1490, 0.1490],
[0.0471, 0.0471, 0.0471, ..., 0.1490, 0.1490, 0.1490]],
[[0.0471, 0.0471, 0.0471, ..., 0.1255, 0.1176, 0.1176],
[0.0471, 0.0471, 0.0471, ..., 0.1294, 0.1176, 0.1176],
[0.0471, 0.0471, 0.0471, ..., 0.1255, 0.1176, 0.1137],
...,
[0.0235, 0.0235, 0.0235, ..., 0.1098, 0.1098, 0.1098],
[0.0235, 0.0235, 0.0235, ..., 0.1098, 0.1098, 0.1098],
[0.0235, 0.0235, 0.0235, ..., 0.1098, 0.1098, 0.1098]],
[[0.0510, 0.0510, 0.0510, ..., 0.1176, 0.1098, 0.1098],
[0.0510, 0.0510, 0.0510, ..., 0.1216, 0.1098, 0.1098],
[0.0510, 0.0510, 0.0510, ..., 0.1176, 0.1098, 0.1059],
...,
[0.0314, 0.0314, 0.0314, ..., 0.1059, 0.1059, 0.1059],
[0.0314, 0.0314, 0.0314, ..., 0.1059, 0.1059, 0.1059],
[0.0314, 0.0314, 0.0314, ..., 0.1059, 0.1059, 0.1059]]]),
{'boxes': tensor([[315.0003, 213.5002, 626.0004, 329.5002]]),
'labels': tensor([0]),
'image_id': tensor([1]),
'area': tensor([36503.9961]),
'iscrowd': tensor([0])})
To prove its correctness I have also visualized the bbox on the image:
Then I create a Dataloader:
dl = DataLoader(ds, batch_size=8, num_workers=4, collate_fn=lambda x: tuple(zip(*x)))
model = fasterrcnn_resnet50_fpn(num_classes=1).to(device)
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005,
momentum=0.9, weight_decay=0.0005)Training works:
model.train()
for i in range(5):
for images, targets in dl:
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k,v in t.items()} for t in targets]
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
optimizer.zero_grad()
losses.backward()
optimizer.step()
print(losses)Output:
tensor(0.6391, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.6329, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.6139, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.5965, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.5814, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.5468, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.5049, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.4502, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.3787, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.2502, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.1605, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.0940, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.0558, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.0507, device='cuda:0', grad_fn=<AddBackward0>)
tensor(0.0413, device='cuda:0', grad_fn=<AddBackward0>)
But, when I try to get a prediction I have no output:
model = model.eval()
with torch.no_grad():
model = model.cuda()
pred = model([ds[2][0].cuda()])pred is
[{'boxes': tensor([], size=(0, 4)),
'labels': tensor([], dtype=torch.int64),
'scores': tensor([])}]
Thank you in advance
Expected behavior
The model should return a valid prediction.
Environment
PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1
OS: Ubuntu 18.04.4 LTS
GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
CMake version: Could not collect
Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.1.243
GPU models and configuration: GPU 0: GeForce GTX 1080 Ti
Nvidia driver version: 430.50
cuDNN version: Could not collect
Versions of relevant libraries:
[pip] efficientnet-pytorch==0.5.1
[pip] msgpack-numpy==0.4.3.2
[pip] numpy==1.17.4
[pip] PytorchStorage==0.0.0
[pip] torch==1.4.0
[pip] torchbearer==0.5.3
[pip] torchlego==0.0.0
[pip] torchsummary==1.5.1
[pip] torchvision==0.5.0
[conda] _pytorch_select 0.2 gpu_0
[conda] blas 1.0 mkl
[conda] efficientnet-pytorch 0.5.1 pypi_0 pypi
[conda] libblas 3.8.0 14_mkl conda-forge
[conda] libcblas 3.8.0 14_mkl conda-forge
[conda] liblapack 3.8.0 14_mkl conda-forge
[conda] liblapacke 3.8.0 14_mkl conda-forge
[conda] mkl 2019.4 243
[conda] mkl-service 2.3.0 py37he904b0f_0
[conda] mkl_fft 1.0.15 py37ha843d7b_0
[conda] mkl_random 1.1.0 py37hd6b4f25_0
[conda] pytorchstorage 0.0.0 pypi_0 pypi
[conda] torch 1.4.0 pypi_0 pypi
[conda] torchbearer 0.5.3 pypi_0 pypi
[conda] torchlego 0.0.0 pypi_0 pypi
[conda] torchsummary 1.5.1 pypi_0 pypi
[conda] torchvision 0.5.0 pypi_0 pypi