Skip to content

Fix prelu bug in pytorch frontend #8192

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

Merged
masahi merged 6 commits into from
Jun 4, 2021
Merged

Fix prelu bug in pytorch frontend #8192

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
0e474b3
Fix prelu bug in pytorch frontend
YuhengHuang42 Jun 4, 2021
7c2f4e8
Fix lint error
YuhengHuang42 Jun 4, 2021
89ae95c
fix lint error
YuhengHuang42 Jun 4, 2021
2998c15
Fix lint error
YuhengHuang42 Jun 4, 2021
a27931c
Try to fix lint error
YuhengHuang42 Jun 4, 2021
31193ca
Fix lint error
YuhengHuang42 Jun 4, 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: 6 additions & 2 deletions python/tvm/relay/frontend/pytorch.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -754,9 +754,13 @@ def relu(self, inputs, input_types):
return _op.nn.relu(data)

def prelu(self, inputs, input_types):
# Reference: https://pytorch.org/docs/stable/generated/torch.nn.PReLU.html#torch.nn.PReLU
data = inputs[0]
alpha = inputs[1]
return _op.nn.prelu(data, alpha)
dim = self.get_dims(data)
ndims = len(dim)
axis = 0 if ndims == 1 else 1
alpha = _op.broadcast_to(inputs[1], (dim[axis]))
return _op.nn.prelu(data, alpha, axis)

def leaky_relu(self, inputs, input_types):
data = inputs[0]
Expand Down
4 changes: 4 additions & 0 deletions tests/python/frontend/pytorch/test_forward.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -643,6 +643,10 @@ def test_forward_prelu():
input_shape = [1, 3, 10, 10]
input_data = torch.rand(input_shape).float()
verify_model(torch.nn.PReLU(num_parameters=3).eval(), input_data=input_data)
# Test when input channel > 1 and num parameters = 1
verify_model(torch.nn.PReLU(num_parameters=1).eval(), input_data=input_data)
# Test when input dims < 2
verify_model(torch.nn.PReLU(num_parameters=1).eval(), input_data=torch.randn(2))


@tvm.testing.uses_gpu
Expand Down