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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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56
tests/test_rasterize_meshes.py
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@@ -2,8 +2,9 @@
import functools
import unittest
import torch
import torch
from common_testing import TestCaseMixin
from pytorch3d import _C
from pytorch3d.renderer.mesh.rasterize_meshes import (
rasterize_meshes,
@@ -12,20 +13,14 @@ from pytorch3d.renderer.mesh.rasterize_meshes import (
from pytorch3d.structures import Meshes
from pytorch3d.utils import ico_sphere
from common_testing import TestCaseMixin
class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
def test_simple_python(self):
device = torch.device("cpu")
self._simple_triangle_raster(
rasterize_meshes_python, device, bin_size=-1
)
self._simple_triangle_raster(rasterize_meshes_python, device, bin_size=-1)
self._simple_blurry_raster(rasterize_meshes_python, device, bin_size=-1)
self._test_behind_camera(rasterize_meshes_python, device, bin_size=-1)
self._test_perspective_correct(
rasterize_meshes_python, device, bin_size=-1
)
self._test_perspective_correct(rasterize_meshes_python, device, bin_size=-1)
def test_simple_cpu_naive(self):
device = torch.device("cpu")
@@ -350,9 +345,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
fn1 = functools.partial(rasterize_meshes, meshes1, **kwargs)
fn2 = functools.partial(rasterize_meshes_python, meshes2, **kwargs)
args = ()
self._compare_impls(
fn1, fn2, args, args, verts1, verts2, compare_grads=True
)
self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
def test_cpp_vs_cuda_perspective_correct(self):
meshes = ico_sphere(2, device=torch.device("cpu"))
@@ -367,9 +360,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
fn1 = functools.partial(rasterize_meshes, meshes1, **kwargs)
fn2 = functools.partial(rasterize_meshes, meshes2, bin_size=0, **kwargs)
args = ()
self._compare_impls(
fn1, fn2, args, args, verts1, verts2, compare_grads=True
)
self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
def test_cuda_naive_vs_binned_perspective_correct(self):
meshes = ico_sphere(2, device=torch.device("cuda"))
@@ -384,9 +375,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
fn1 = functools.partial(rasterize_meshes, meshes1, bin_size=0, **kwargs)
fn2 = functools.partial(rasterize_meshes, meshes2, bin_size=8, **kwargs)
args = ()
self._compare_impls(
fn1, fn2, args, args, verts1, verts2, compare_grads=True
)
self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
def _compare_impls(
self,
@@ -433,9 +422,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
grad_verts2 = grad_var2.grad.data.clone().cpu()
self.assertClose(grad_verts1, grad_verts2, rtol=1e-3)
def _test_perspective_correct(
self, rasterize_meshes_fn, device, bin_size=None
):
def _test_perspective_correct(self, rasterize_meshes_fn, device, bin_size=None):
# fmt: off
verts = torch.tensor([
[-0.4, -0.4, 10], # noqa: E241, E201
@@ -542,12 +529,8 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
zbuf_f_bary = w0_f * z0 + w1_f * z1 + w2_f * z2
zbuf_t_bary = w0_t * z0 + w1_t * z1 + w2_t * z2
mask = idx_expected != -1
zbuf_f_bary_diff = (
(zbuf_f_bary[mask] - zbuf_f_expected[mask]).abs().max()
)
zbuf_t_bary_diff = (
(zbuf_t_bary[mask] - zbuf_t_expected[mask]).abs().max()
)
zbuf_f_bary_diff = (zbuf_f_bary[mask] - zbuf_f_expected[mask]).abs().max()
zbuf_t_bary_diff = (zbuf_t_bary[mask] - zbuf_t_expected[mask]).abs().max()
self.assertLess(zbuf_f_bary_diff, 1e-4)
self.assertLess(zbuf_t_bary_diff, 1e-4)
@@ -719,9 +702,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
# k = 1, second closest point.
expected_p2face_k1 = expected_p2face_k0.clone()
expected_p2face_k1[0, :] = (
torch.ones_like(expected_p2face_k1[0, :]) * -1
)
expected_p2face_k1[0, :] = torch.ones_like(expected_p2face_k1[0, :]) * -1
# fmt: off
expected_p2face_k1[1, :] = torch.tensor(
@@ -763,9 +744,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
# Coordinate conventions +Y up, +Z in, +X left
if bin_size == -1:
# simple python, no bin_size
p2face, zbuf, bary, pix_dists = raster_fn(
meshes, image_size, 0.0, 2
)
p2face, zbuf, bary, pix_dists = raster_fn(meshes, image_size, 0.0, 2)
else:
p2face, zbuf, bary, pix_dists = raster_fn(
meshes, image_size, 0.0, 2, bin_size
@@ -914,9 +893,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
# Expected faces using axes convention +Y down, + X right, + Z in
bin_faces_expected = (
torch.ones(
(1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device
)
torch.ones((1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device)
* -1
)
bin_faces_expected[0, 0, 0, 0] = torch.tensor([1])
@@ -979,12 +956,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
def rasterize():
rasterize_meshes(
meshes_batch,
image_size,
blur_radius,
8,
bin_size,
max_faces_per_bin,
meshes_batch, image_size, blur_radius, 8, bin_size, max_faces_per_bin
)
torch.cuda.synchronize()