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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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42
tests/test_compositing.py
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@@ -1,8 +1,8 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import unittest
import torch
import torch
from pytorch3d.renderer.compositing import (
alpha_composite,
norm_weighted_sum,
@@ -37,9 +37,7 @@ class TestAccumulatePoints(unittest.TestCase):
continue
alpha = alphas[b, k, j, i]
output[b, c, j, i] += (
features[c, n_idx] * alpha * t_alpha
)
output[b, c, j, i] += features[c, n_idx] * alpha * t_alpha
t_alpha = (1 - alpha) * t_alpha
return output
@@ -105,17 +103,13 @@ class TestAccumulatePoints(unittest.TestCase):
continue
alpha = alphas[b, k, j, i]
output[b, c, j, i] += (
features[c, n_idx] * alpha / t_alpha
)
output[b, c, j, i] += features[c, n_idx] * alpha / t_alpha
return output
def test_python(self):
device = torch.device("cpu")
self._simple_alphacomposite(
self.accumulate_alphacomposite_python, device
)
self._simple_alphacomposite(self.accumulate_alphacomposite_python, device)
self._simple_wsum(self.accumulate_weightedsum_python, device)
self._simple_wsumnorm(self.accumulate_weightedsumnorm_python, device)
@@ -138,9 +132,7 @@ class TestAccumulatePoints(unittest.TestCase):
self._python_vs_cpu_vs_cuda(
self.accumulate_weightedsumnorm_python, norm_weighted_sum
)
self._python_vs_cpu_vs_cuda(
self.accumulate_weightedsum_python, weighted_sum
)
self._python_vs_cpu_vs_cuda(self.accumulate_weightedsum_python, weighted_sum)
def _python_vs_cpu_vs_cuda(self, accumulate_func_python, accumulate_func):
torch.manual_seed(231)
@@ -208,15 +200,11 @@ class TestAccumulatePoints(unittest.TestCase):
grads2 = [gradsi.grad.data.clone().cpu() for gradsi in grads2]
for i in range(0, len(grads1)):
self.assertTrue(
torch.allclose(grads1[i].cpu(), grads2[i].cpu(), atol=1e-6)
)
self.assertTrue(torch.allclose(grads1[i].cpu(), grads2[i].cpu(), atol=1e-6))
def _simple_wsum(self, accum_func, device):
# Initialise variables
features = torch.Tensor(
[[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]
).to(device)
features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
alphas = torch.Tensor(
[
@@ -285,15 +273,11 @@ class TestAccumulatePoints(unittest.TestCase):
]
).to(device)
self.assertTrue(
torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3)
)
self.assertTrue(torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3))
def _simple_wsumnorm(self, accum_func, device):
# Initialise variables
features = torch.Tensor(
[[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]
).to(device)
features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
alphas = torch.Tensor(
[
@@ -362,15 +346,11 @@ class TestAccumulatePoints(unittest.TestCase):
]
).to(device)
self.assertTrue(
torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3)
)
self.assertTrue(torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3))
def _simple_alphacomposite(self, accum_func, device):
# Initialise variables
features = torch.Tensor(
[[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]
).to(device)
features = torch.Tensor([[0.1, 0.4, 0.6, 0.9], [0.1, 0.4, 0.6, 0.9]]).to(device)
alphas = torch.Tensor(
[