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Address black + isort fbsource linter warnings

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Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff)

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8
This commit is contained in:
Patrick Labatut
2020-03-29 14:46:33 -07:00
committed by Facebook GitHub Bot
parent eb512ffde3
commit d57daa6f85
110 changed files with 705 additions and 1850 deletions
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48
tests/test_lighting.py
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@@ -1,14 +1,13 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import numpy as np
import unittest
import torch
import numpy as np
import torch
from common_testing import TestCaseMixin
from pytorch3d.renderer.lighting import DirectionalLights, PointLights
from pytorch3d.transforms import RotateAxisAngle
from common_testing import TestCaseMixin
class TestLights(TestCaseMixin, unittest.TestCase):
def test_init_lights(self):
@@ -56,9 +55,7 @@ class TestLights(TestCaseMixin, unittest.TestCase):
self.assertSeparate(new_prop, prop)
def test_lights_accessor(self):
d_light = DirectionalLights(
ambient_color=((0.0, 0.0, 0.0), (1.0, 1.0, 1.0))
)
d_light = DirectionalLights(ambient_color=((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)))
p_light = PointLights(ambient_color=((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)))
for light in [d_light, p_light]:
# Update element
@@ -96,14 +93,12 @@ class TestLights(TestCaseMixin, unittest.TestCase):
"""
with self.assertRaises(ValueError):
DirectionalLights(
ambient_color=torch.randn(10, 3),
diffuse_color=torch.randn(15, 3),
ambient_color=torch.randn(10, 3), diffuse_color=torch.randn(15, 3)
)
with self.assertRaises(ValueError):
PointLights(
ambient_color=torch.randn(10, 3),
diffuse_color=torch.randn(15, 3),
ambient_color=torch.randn(10, 3), diffuse_color=torch.randn(15, 3)
)
def test_initialize_lights_dimensions_fail(self):
@@ -138,8 +133,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
normals = torch.tensor([0, 0, 1], dtype=torch.float32)
normals = normals[None, None, :]
expected_output = torch.tensor(
[1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
dtype=torch.float32,
[1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
)
expected_output = expected_output.view(1, 1, 3).repeat(3, 1, 1)
light = DirectionalLights(diffuse_color=color, direction=direction)
@@ -169,13 +163,10 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
points = torch.tensor([0, 0, 0], dtype=torch.float32)
normals = torch.tensor([0, 0, 1], dtype=torch.float32)
expected_output = torch.tensor(
[1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
dtype=torch.float32,
[1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
)
expected_output = expected_output.view(-1, 1, 3)
light = PointLights(
diffuse_color=color[None, :], location=location[None, :]
)
light = PointLights(diffuse_color=color[None, :], location=location[None, :])
output_light = light.diffuse(
points=points[None, None, :], normals=normals[None, None, :]
)
@@ -184,9 +175,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
# Change light direction to be 90 degrees apart from normal direction.
location = torch.tensor([0, 1, 0], dtype=torch.float32)
expected_output = torch.zeros_like(expected_output)
light = PointLights(
diffuse_color=color[None, :], location=location[None, :]
)
light = PointLights(diffuse_color=color[None, :], location=location[None, :])
output_light = light.diffuse(
points=points[None, None, :], normals=normals[None, None, :]
)
@@ -204,8 +193,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
)
normals = torch.tensor([0, 0, 1], dtype=torch.float32)
expected_out = torch.tensor(
[1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
dtype=torch.float32,
[1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
)
# Reshape
@@ -231,8 +219,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
)
normals = torch.tensor([0, 0, 1], dtype=torch.float32)
expected_out = torch.tensor(
[1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
dtype=torch.float32,
[1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
)
# Reshape
@@ -258,9 +245,7 @@ class TestDiffuseLighting(TestCaseMixin, unittest.TestCase):
device = torch.device("cuda:0")
color = torch.tensor([1, 1, 1], dtype=torch.float32, device=device)
direction = torch.tensor(
[0, 1 / np.sqrt(2), 1 / np.sqrt(2)],
dtype=torch.float32,
device=device,
[0, 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32, device=device
)
normals = torch.tensor([0, 0, 1], dtype=torch.float32, device=device)
normals = normals.view(1, 1, 1, 1, 3).expand(N, H, W, K, -1)
@@ -373,9 +358,7 @@ class TestSpecularLighting(TestCaseMixin, unittest.TestCase):
normals = torch.tensor([0, 1, 0], dtype=torch.float32)
expected_output = torch.tensor([1.0, 0.0, 1.0], dtype=torch.float32)
expected_output = expected_output.view(-1, 1, 3)
lights = PointLights(
specular_color=color[None, :], location=location[None, :]
)
lights = PointLights(specular_color=color[None, :], location=location[None, :])
output_light = lights.specular(
points=points[None, None, :],
normals=normals[None, None, :],
@@ -528,8 +511,7 @@ class TestSpecularLighting(TestCaseMixin, unittest.TestCase):
mesh_to_vert_idx = torch.tensor(mesh_to_vert_idx, dtype=torch.int64)
color = torch.tensor([[1, 1, 1], [1, 0, 1]], dtype=torch.float32)
direction = torch.tensor(
[[-1 / np.sqrt(2), 1 / np.sqrt(2), 0], [-1, 1, 0]],
dtype=torch.float32,
[[-1 / np.sqrt(2), 1 / np.sqrt(2), 0], [-1, 1, 0]], dtype=torch.float32
)
camera_position = torch.tensor(
[