Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

empty tensor inference backward compatible #1131

Merged
merged 3 commits into from
Jun 24, 2021
Merged

empty tensor inference backward compatible #1131

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
a023ef6
empty tensor inference backward continity
dreamerlin Jun 23, 2021
6b95a32
update
dreamerlin Jun 24, 2021
6ddfa29
add 3d
dreamerlin Jun 24, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 4 additions & 4 deletions mmcv/cnn/bricks/wrappers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -128,8 +128,8 @@ def forward(self, x):
class MaxPool2d(nn.MaxPool2d):

def forward(self, x):
# PyTorch 1.7 does not support empty tensor inference yet
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 7)):
# PyTorch 1.9 does not support empty tensor inference yet
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
out_shape = list(x.shape[:2])
for i, k, p, s, d in zip(x.shape[-2:], _pair(self.kernel_size),
_pair(self.padding), _pair(self.stride),
Expand All @@ -146,8 +146,8 @@ def forward(self, x):
class MaxPool3d(nn.MaxPool3d):

def forward(self, x):
# PyTorch 1.7 does not support empty tensor inference yet
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 7)):
# PyTorch 1.9 does not support empty tensor inference yet
if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
out_shape = list(x.shape[:2])
for i, k, p, s, d in zip(x.shape[-3:], _triple(self.kernel_size),
_triple(self.padding),
Expand Down
16 changes: 15 additions & 1 deletion tests/test_cnn/test_wrappers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -330,7 +330,7 @@ def test_linear(in_w, in_h, in_feature, out_feature):
wrapper(x_empty)


@patch('mmcv.cnn.bricks.wrappers.TORCH_VERSION', (1, 8))
@patch('mmcv.cnn.bricks.wrappers.TORCH_VERSION', (1, 10))
def test_nn_op_forward_called():

for m in ['Conv2d', 'ConvTranspose2d', 'MaxPool2d']:
Expand All @@ -347,6 +347,20 @@ def test_nn_op_forward_called():
wrapper(x_normal)
nn_module_forward.assert_called_with(x_normal)

for m in ['Conv3d', 'ConvTranspose3d', 'MaxPool3d']:
with patch(f'torch.nn.{m}.forward') as nn_module_forward:
# randn input
x_empty = torch.randn(0, 3, 10, 10, 10)
wrapper = eval(m)(3, 2, 1)
wrapper(x_empty)
nn_module_forward.assert_called_with(x_empty)

# non-randn input
x_normal = torch.randn(1, 3, 10, 10, 10)
wrapper = eval(m)(3, 2, 1)
wrapper(x_normal)
nn_module_forward.assert_called_with(x_normal)

with patch('torch.nn.Linear.forward') as nn_module_forward:
# randn input
x_empty = torch.randn(0, 3)
Expand Down