iter : 0
out : tensor([[-0.1432, 0.0819, 0.0342, ..., -0.0377, -0.0745, 0.1312],
[ 0.1110, -0.0650, 0.2410, ..., -0.0765, 0.3328, 0.1908]],
device='cuda:0', dtype=torch.float16, grad_fn=<AddmmBackward>)
loss : tensor(1., device='cuda:0', dtype=torch.float16, grad_fn=<ClampBackward>)
loss : data tensor(1., device='cuda:0', dtype=torch.float16)
test.py:94: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the gradient for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations.
print(' loss : grad ',loss.grad , '\n')
loss : grad None
darknet.0.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.0.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.0.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.2.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.2.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.2.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.4.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.4.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.4.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.5.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.5.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.5.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.6.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.6.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.6.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.7.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.7.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.7.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.9.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.9.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.9.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.10.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.10.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.10.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.11.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.11.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.11.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.12.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.12.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.12.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.13.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.13.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.13.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.14.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.14.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.14.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.15.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.15.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.15.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.16.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.16.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.16.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.17.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.17.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.17.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.18.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.18.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.18.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.20.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.20.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.20.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.21.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.21.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.21.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.22.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.22.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.22.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.23.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.23.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.23.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.24.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.24.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.24.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.25.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.25.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.25.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.26.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.26.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.26.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.27.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.27.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.27.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
fcs.1.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
fcs.1.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
fcs.4.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
fcs.4.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
iter : 1
out : tensor([[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]], device='cuda:0',
dtype=torch.float16, grad_fn=<AddmmBackward>)
[W python_anomaly_mode.cpp:104] Warning: Error detected in MseLossBackward. Traceback of forward call that caused the error:
File "test.py", line 86, in <module>
loss = loss_func(out,y)
File "/home/buckaroo/miniconda3/envs/dev/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/mnt/e/workspace/@training/@datasets/cnns/yolo/yolo-v1-pytorch/loss.py", line 120, in forward
torch.flatten(exists_box * target[..., :20], end_dim=-2,),
File "/home/buckaroo/miniconda3/envs/dev/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/buckaroo/miniconda3/envs/dev/lib/python3.7/site-packages/torch/nn/modules/loss.py", line 528, in forward
return F.mse_loss(input, target, reduction=self.reduction)
File "/home/buckaroo/miniconda3/envs/dev/lib/python3.7/site-packages/torch/nn/functional.py", line 2929, in mse_loss
return torch._C._nn.mse_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))
(function _print_stack)
Traceback (most recent call last):
File "test.py", line 89, in <module>
loss.backward()
File "/home/buckaroo/miniconda3/envs/dev/lib/python3.7/site-packages/torch/tensor.py", line 245, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File "/home/buckaroo/miniconda3/envs/dev/lib/python3.7/site-packages/torch/autograd/__init__.py", line 147, in backward
allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag
RuntimeError: Function 'MseLossBackward' returned nan values in its 0th output.
(dev) buckaroo@hansolo:/mnt/e/workspace/@training/@datasets/cnns/yolo/yolo-v1-pytorch$ python3 test.py
iter : 0
out : tensor([[-0.1044, -0.3135, -0.4897, ..., -0.1079, -0.0055, -0.0380],
[ 0.1190, -0.3154, -0.0910, ..., -0.0995, -0.1595, -0.0576]],
device='cuda:0', dtype=torch.float16, grad_fn=<AddmmBackward>)
loss : tensor(1.0010, device='cuda:0', dtype=torch.float16, grad_fn=<AddBackward0>)
loss : data tensor(1.0010, device='cuda:0', dtype=torch.float16)
test.py:94: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the gradient for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations.
print(' loss : grad ',loss.grad , '\n')
loss : grad None
darknet.0.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.0.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.0.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.2.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.2.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.2.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.4.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.4.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.4.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.5.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.5.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.5.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.6.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.6.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.6.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.7.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.7.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.7.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.9.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.9.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.9.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.10.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.10.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.10.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.11.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.11.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.11.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.12.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.12.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.12.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.13.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.13.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.13.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.14.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.14.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.14.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.15.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.15.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.15.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.16.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.16.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.16.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.17.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.17.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.17.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.18.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.18.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.18.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.20.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.20.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.20.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.21.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.21.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.21.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.22.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.22.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.22.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.23.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.23.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.23.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.24.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.24.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.24.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.25.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.25.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.25.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.26.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.26.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.26.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.27.conv.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.27.batchnorm.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
darknet.27.batchnorm.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
fcs.1.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
fcs.1.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
fcs.4.weight tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
fcs.4.bias tensor(True, device='cuda:0') tensor(0., device='cuda:0', dtype=torch.float16)
iter : 1
out : tensor([[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]], device='cuda:0',
dtype=torch.float16, grad_fn=<AddmmBackward>)
But this didnot fix my issue.