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lint fixes

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Summary:
Ran the linter.
TODO: need to update the linter as per D21353065.

Reviewed By: bottler

Differential Revision: D21362270

fbshipit-source-id: ad0e781de0a29f565ad25c43bc94a19b1828c020
This commit is contained in:
Nikhila Ravi
2020-05-04 09:55:03 -07:00
committed by Facebook GitHub Bot
parent 0c595dcf5b
commit 0eca74fa5f
15 changed files with 73 additions and 57 deletions
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8
tests/test_point_mesh_distance.py
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@@ -431,7 +431,7 @@ class TestPointMeshDistance(TestCaseMixin, unittest.TestCase):
# Naive implementation: forward & backward
edges_packed = meshes.edges_packed()
edges_list = packed_to_list(edges_packed, meshes.num_edges_per_mesh().tolist())
loss_naive = torch.zeros((N), dtype=torch.float32, device=device)
loss_naive = torch.zeros(N, dtype=torch.float32, device=device)
for i in range(N):
points = pcls.points_list()[i]
verts = meshes.verts_list()[i]
@@ -461,7 +461,7 @@ class TestPointMeshDistance(TestCaseMixin, unittest.TestCase):
self.assertClose(loss_op, loss_naive)
# Compare backward pass
rand_val = torch.rand((1)).item()
rand_val = torch.rand(1).item()
grad_dist = torch.tensor(rand_val, dtype=torch.float32, device=device)
loss_naive.backward(grad_dist)
@@ -707,7 +707,7 @@ class TestPointMeshDistance(TestCaseMixin, unittest.TestCase):
pcls_op = Pointclouds(points_op)
# naive implementation
loss_naive = torch.zeros((N), dtype=torch.float32, device=device)
loss_naive = torch.zeros(N, dtype=torch.float32, device=device)
for i in range(N):
points = pcls.points_list()[i]
verts = meshes.verts_list()[i]
@@ -735,7 +735,7 @@ class TestPointMeshDistance(TestCaseMixin, unittest.TestCase):
self.assertClose(loss_op, loss_naive)
# Compare backward pass
rand_val = torch.rand((1)).item()
rand_val = torch.rand(1).item()
grad_dist = torch.tensor(rand_val, dtype=torch.float32, device=device)
loss_naive.backward(grad_dist)