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Commit d57daa6f authored by Patrick Labatut's avatar Patrick Labatut Committed by Facebook GitHub Bot
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Address black + isort fbsource linter warnings 

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

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8
parent eb512ffd
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Showing with 118 additions and 305 deletions
tests/test_rendering_meshes.py
View file @ d57daa6f
......@@ -4,23 +4,17 @@
"""
Sanity checks for output images from the renderer.
"""
import numpy as np
import unittest
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from pytorch3d.io import load_objs_as_meshes
from pytorch3d.renderer.cameras import (
OpenGLPerspectiveCameras,
look_at_view_transform,
)
from pytorch3d.renderer.cameras import OpenGLPerspectiveCameras, look_at_view_transform
from pytorch3d.renderer.lighting import PointLights
from pytorch3d.renderer.materials import Materials
from pytorch3d.renderer.mesh.rasterizer import (
MeshRasterizer,
RasterizationSettings,
)
from pytorch3d.renderer.mesh.rasterizer import MeshRasterizer, RasterizationSettings
from pytorch3d.renderer.mesh.renderer import MeshRenderer
from pytorch3d.renderer.mesh.shader import (
BlendParams,
......@@ -34,6 +28,7 @@ from pytorch3d.renderer.mesh.texturing import Textures
from pytorch3d.structures.meshes import Meshes
from pytorch3d.utils.ico_sphere import ico_sphere
# If DEBUG=True, save out images generated in the tests for debugging.
# All saved images have prefix DEBUG_
DEBUG = False
......@@ -65,9 +60,7 @@ class TestRenderingMeshes(unittest.TestCase):
verts_padded = sphere_mesh.verts_padded()
faces_padded = sphere_mesh.faces_padded()
textures = Textures(verts_rgb=torch.ones_like(verts_padded))
sphere_mesh = Meshes(
verts=verts_padded, faces=faces_padded, textures=textures
)
sphere_mesh = Meshes(verts=verts_padded, faces=faces_padded, textures=textures)
# Init rasterizer settings
if elevated_camera:
......@@ -90,9 +83,7 @@ class TestRenderingMeshes(unittest.TestCase):
raster_settings = RasterizationSettings(
image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0
)
rasterizer = MeshRasterizer(
cameras=cameras, raster_settings=raster_settings
)
rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings)
# Test several shaders
shaders = {
......@@ -101,9 +92,7 @@ class TestRenderingMeshes(unittest.TestCase):
"flat": HardFlatShader,
}
for (name, shader_init) in shaders.items():
shader = shader_init(
lights=lights, cameras=cameras, materials=materials
)
shader = shader_init(lights=lights, cameras=cameras, materials=materials)
renderer = MeshRenderer(rasterizer=rasterizer, shader=shader)
images = renderer(sphere_mesh)
filename = "simple_sphere_light_%s%s.png" % (name, postfix)
......@@ -125,9 +114,7 @@ class TestRenderingMeshes(unittest.TestCase):
phong_shader = HardPhongShader(
lights=lights, cameras=cameras, materials=materials
)
phong_renderer = MeshRenderer(
rasterizer=rasterizer, shader=phong_shader
)
phong_renderer = MeshRenderer(rasterizer=rasterizer, shader=phong_shader)
images = phong_renderer(sphere_mesh, lights=lights)
rgb = images[0, ..., :3].squeeze().cpu()
if DEBUG:
......@@ -137,9 +124,7 @@ class TestRenderingMeshes(unittest.TestCase):
)
# Load reference image
image_ref_phong_dark = load_rgb_image(
"test_simple_sphere_dark%s.png" % postfix
)
image_ref_phong_dark = load_rgb_image("test_simple_sphere_dark%s.png" % postfix)
self.assertTrue(torch.allclose(rgb, image_ref_phong_dark, atol=0.05))
def test_simple_sphere_elevated_camera(self):
......@@ -184,18 +169,14 @@ class TestRenderingMeshes(unittest.TestCase):
lights.location = torch.tensor([0.0, 0.0, +2.0], device=device)[None]
# Init renderer
rasterizer = MeshRasterizer(
cameras=cameras, raster_settings=raster_settings
)
rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings)
shaders = {
"phong": HardGouraudShader,
"gouraud": HardGouraudShader,
"flat": HardFlatShader,
}
for (name, shader_init) in shaders.items():
shader = shader_init(
lights=lights, cameras=cameras, materials=materials
)
shader = shader_init(lights=lights, cameras=cameras, materials=materials)
renderer = MeshRenderer(rasterizer=rasterizer, shader=shader)
images = renderer(sphere_meshes)
image_ref = load_rgb_image("test_simple_sphere_light_%s.png" % name)
......@@ -228,9 +209,7 @@ class TestRenderingMeshes(unittest.TestCase):
# Init renderer
renderer = MeshRenderer(
rasterizer=MeshRasterizer(
cameras=cameras, raster_settings=raster_settings
),
rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings),
shader=SoftSilhouetteShader(blend_params=blend_params),
)
images = renderer(sphere_mesh)
......@@ -258,9 +237,7 @@ class TestRenderingMeshes(unittest.TestCase):
The pupils in the eyes of the cow should always be looking to the left.
"""
device = torch.device("cuda:0")
DATA_DIR = (
Path(__file__).resolve().parent.parent / "docs/tutorials/data"
)
DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data"
obj_filename = DATA_DIR / "cow_mesh/cow.obj"
# Load mesh + texture
......@@ -283,9 +260,7 @@ class TestRenderingMeshes(unittest.TestCase):
# Init renderer
renderer = MeshRenderer(
rasterizer=MeshRasterizer(
cameras=cameras, raster_settings=raster_settings
),
rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings),
shader=TexturedSoftPhongShader(
lights=lights, cameras=cameras, materials=materials
),
......@@ -306,9 +281,7 @@ class TestRenderingMeshes(unittest.TestCase):
# Check grad exists
[verts] = mesh.verts_list()
verts.requires_grad = True
mesh2 = Meshes(
verts=[verts], faces=mesh.faces_list(), textures=mesh.textures
)
mesh2 = Meshes(verts=[verts], faces=mesh.faces_list(), textures=mesh.textures)
images = renderer(mesh2)
images[0, ...].sum().backward()
self.assertIsNotNone(verts.grad)
......
tests/test_rendering_utils.py
View file @ d57daa6f
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import numpy as np
import unittest
import torch
from pytorch3d.renderer.utils import TensorProperties
import numpy as np
import torch
from common_testing import TestCaseMixin
from pytorch3d.renderer.utils import TensorProperties
# Example class for testing
......@@ -81,9 +80,5 @@ class TestTensorProperties(TestCaseMixin, unittest.TestCase):
if inds.sum() > 0:
# Check the gathered points in the output have the same value from
# the input.
self.assertClose(
test_class_gathered.x[inds].mean(dim=0), x[i, ...]
)
self.assertClose(
test_class_gathered.y[inds].mean(dim=0), y[i, ...]
)
self.assertClose(test_class_gathered.x[inds].mean(dim=0), x[i, ...])
self.assertClose(test_class_gathered.y[inds].mean(dim=0), y[i, ...])
tests/test_rotation_conversions.py
View file @ d57daa6f
......@@ -4,8 +4,8 @@
import itertools
import math
import unittest
import torch
import torch
from pytorch3d.transforms.rotation_conversions import (
euler_angles_to_matrix,
matrix_to_euler_angles,
......@@ -45,9 +45,7 @@ class TestRandomRotation(unittest.TestCase):
)
# The 0.1 significance level for chisquare(8-1) is
# scipy.stats.chi2(7).ppf(0.9) == 12.017.
self.assertLess(
chisquare_statistic, 12, (counts, chisquare_statistic, k)
)
self.assertLess(chisquare_statistic, 12, (counts, chisquare_statistic, k))
class TestRotationConversion(unittest.TestCase):
......
tests/test_sample_points_from_meshes.py
View file @ d57daa6f
......@@ -3,14 +3,13 @@
import unittest
from pathlib import Path
import torch
import torch
from common_testing import TestCaseMixin
from pytorch3d.ops import sample_points_from_meshes
from pytorch3d.structures.meshes import Meshes
from pytorch3d.utils.ico_sphere import ico_sphere
from common_testing import TestCaseMixin
class TestSamplePoints(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
......@@ -28,9 +27,7 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
verts_list = []
faces_list = []
for _ in range(num_meshes):
verts = torch.rand(
(num_verts, 3), dtype=torch.float32, device=device
)
verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
faces = torch.randint(
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
)
......@@ -48,13 +45,9 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
device = torch.device("cuda:0")
verts1 = torch.tensor([], dtype=torch.float32, device=device)
faces1 = torch.tensor([], dtype=torch.int64, device=device)
meshes = Meshes(
verts=[verts1, verts1, verts1], faces=[faces1, faces1, faces1]
)
meshes = Meshes(verts=[verts1, verts1, verts1], faces=[faces1, faces1, faces1])
with self.assertRaises(ValueError) as err:
sample_points_from_meshes(
meshes, num_samples=100, return_normals=True
)
sample_points_from_meshes(meshes, num_samples=100, return_normals=True)
self.assertTrue("Meshes are empty." in str(err.exception))
def test_sampling_output(self):
......@@ -67,12 +60,7 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
# Unit simplex.
verts_pyramid = torch.tensor(
[
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
],
[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
dtype=torch.float32,
device=device,
)
......@@ -113,12 +101,8 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
pyramid_verts = samples[2, :]
pyramid_normals = normals[2, :]
self.assertClose(
pyramid_verts.lt(1).float(), torch.ones_like(pyramid_verts)
)
self.assertClose(
(pyramid_verts >= 0).float(), torch.ones_like(pyramid_verts)
)
self.assertClose(pyramid_verts.lt(1).float(), torch.ones_like(pyramid_verts))
self.assertClose((pyramid_verts >= 0).float(), torch.ones_like(pyramid_verts))
# Face 1: z = 0, x + y <= 1, normals = (0, 0, 1).
face_1_idxs = pyramid_verts[:, 2] == 0
......@@ -126,14 +110,10 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
pyramid_verts[face_1_idxs, :],
pyramid_normals[face_1_idxs, :],
)
self.assertTrue(
torch.all((face_1_verts[:, 0] + face_1_verts[:, 1]) <= 1)
)
self.assertTrue(torch.all((face_1_verts[:, 0] + face_1_verts[:, 1]) <= 1))
self.assertClose(
face_1_normals,
torch.tensor([0, 0, 1], dtype=torch.float32).expand(
face_1_normals.size()
),
torch.tensor([0, 0, 1], dtype=torch.float32).expand(face_1_normals.size()),
)
# Face 2: x = 0, z + y <= 1, normals = (1, 0, 0).
......@@ -142,14 +122,10 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
pyramid_verts[face_2_idxs, :],
pyramid_normals[face_2_idxs, :],
)
self.assertTrue(
torch.all((face_2_verts[:, 1] + face_2_verts[:, 2]) <= 1)
)
self.assertTrue(torch.all((face_2_verts[:, 1] + face_2_verts[:, 2]) <= 1))
self.assertClose(
face_2_normals,
torch.tensor([1, 0, 0], dtype=torch.float32).expand(
face_2_normals.size()
),
torch.tensor([1, 0, 0], dtype=torch.float32).expand(face_2_normals.size()),
)
# Face 3: y = 0, x + z <= 1, normals = (0, -1, 0).
......@@ -158,14 +134,10 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
pyramid_verts[face_3_idxs, :],
pyramid_normals[face_3_idxs, :],
)
self.assertTrue(
torch.all((face_3_verts[:, 0] + face_3_verts[:, 2]) <= 1)
)
self.assertTrue(torch.all((face_3_verts[:, 0] + face_3_verts[:, 2]) <= 1))
self.assertClose(
face_3_normals,
torch.tensor([0, -1, 0], dtype=torch.float32).expand(
face_3_normals.size()
),
torch.tensor([0, -1, 0], dtype=torch.float32).expand(face_3_normals.size()),
)
# Face 4: x + y + z = 1, normals = (1, 1, 1)/sqrt(3).
......@@ -279,22 +251,15 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
num_faces = 50
for device in ["cpu", "cuda:0"]:
for invalid in ["nan", "inf"]:
verts = torch.rand(
(num_verts, 3), dtype=torch.float32, device=device
)
verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
# randomly assign an invalid type
verts[torch.randperm(num_verts)[:10]] = float(invalid)
faces = torch.randint(
num_verts,
size=(num_faces, 3),
dtype=torch.int64,
device=device,
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
)
meshes = Meshes(verts=[verts], faces=[faces])
with self.assertRaisesRegex(
ValueError, "Meshes contain nan or inf."
):
with self.assertRaisesRegex(ValueError, "Meshes contain nan or inf."):
sample_points_from_meshes(
meshes, num_samples=100, return_normals=True
)
......@@ -310,9 +275,7 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
verts_list = []
faces_list = []
for _ in range(num_meshes):
verts = torch.rand(
(num_verts, 3), dtype=torch.float32, device=device
)
verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
faces = torch.randint(
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
)
......
tests/test_so3.py
View file @ d57daa6f
# 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 pytorch3d.transforms.so3 import (
hat,
so3_exponential_map,
......@@ -26,9 +26,7 @@ class TestSO3(unittest.TestCase):
randomly generated logarithms of rotation matrices.
"""
device = torch.device("cuda:0")
log_rot = torch.randn(
(batch_size, 3), dtype=torch.float32, device=device
)
log_rot = torch.randn((batch_size, 3), dtype=torch.float32, device=device)
return log_rot
@staticmethod
......@@ -85,16 +83,12 @@ class TestSO3(unittest.TestCase):
log_rot = torch.randn(size=[5, 4], device=device)
with self.assertRaises(ValueError) as err:
so3_exponential_map(log_rot)
self.assertTrue(
"Input tensor shape has to be Nx3." in str(err.exception)
)
self.assertTrue("Input tensor shape has to be Nx3." in str(err.exception))
rot = torch.randn(size=[5, 3, 5], device=device)
with self.assertRaises(ValueError) as err:
so3_log_map(rot)
self.assertTrue(
"Input has to be a batch of 3x3 Tensors." in str(err.exception)
)
self.assertTrue("Input has to be a batch of 3x3 Tensors." in str(err.exception))
# trace of rot definitely bigger than 3 or smaller than -1
rot = torch.cat(
......
tests/test_struct_utils.py
View file @ d57daa6f
......@@ -2,11 +2,10 @@
import unittest
import torch
from pytorch3d.structures import utils as struct_utils
import torch
from common_testing import TestCaseMixin
from pytorch3d.structures import utils as struct_utils
class TestStructUtils(TestCaseMixin, unittest.TestCase):
......@@ -27,22 +26,16 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
self.assertEqual(x_padded.shape[1], K)
self.assertEqual(x_padded.shape[2], K)
for i in range(N):
self.assertClose(
x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i]
)
self.assertClose(x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i])
# check for no pad size (defaults to max dimension)
x_padded = struct_utils.list_to_padded(
x, pad_value=0.0, equisized=False
)
x_padded = struct_utils.list_to_padded(x, pad_value=0.0, equisized=False)
max_size0 = max(y.shape[0] for y in x)
max_size1 = max(y.shape[1] for y in x)
self.assertEqual(x_padded.shape[1], max_size0)
self.assertEqual(x_padded.shape[2], max_size1)
for i in range(N):
self.assertClose(
x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i]
)
self.assertClose(x_padded[i, : x[i].shape[0], : x[i].shape[1]], x[i])
# check for equisized
x = [torch.rand((K, 10), device=device) for _ in range(N)]
......@@ -88,9 +81,7 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
split_size = torch.randint(1, K, size=(2 * N,)).view(N, 2).unbind(0)
x_list = struct_utils.padded_to_list(x, split_size)
for i in range(N):
self.assertClose(
x_list[i], x[i, : split_size[i][0], : split_size[i][1]]
)
self.assertClose(x_list[i], x[i, : split_size[i][0], : split_size[i][1]])
with self.assertRaisesRegex(ValueError, "Supports only"):
x = torch.rand((N, K, K, K, K), device=device)
......@@ -124,32 +115,24 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
# Add some random values in the input which are the same as the pad_value.
# These should not be filtered out.
x_list.append(
torch.randint(
low=pad_value, high=10, size=(dim, K), device=device
)
torch.randint(low=pad_value, high=10, size=(dim, K), device=device)
)
split_size.append(dim)
x_padded = struct_utils.list_to_padded(x_list, pad_value=pad_value)
x_packed = struct_utils.padded_to_packed(x_padded, pad_value=pad_value)
curr = 0
for i in range(N):
self.assertClose(
x_packed[curr : curr + split_size[i], ...], x_list[i]
)
self.assertClose(x_packed[curr : curr + split_size[i], ...], x_list[i])
self.assertClose(torch.cat(x_list), x_packed)
curr += split_size[i]
# Case 3: split_size is provided.
# Check each section of the packed tensor matches the corresponding
# unpadded elements.
x_packed = struct_utils.padded_to_packed(
x_padded, split_size=split_size
)
x_packed = struct_utils.padded_to_packed(x_padded, split_size=split_size)
curr = 0
for i in range(N):
self.assertClose(
x_packed[curr : curr + split_size[i], ...], x_list[i]
)
self.assertClose(x_packed[curr : curr + split_size[i], ...], x_list[i])
self.assertClose(torch.cat(x_list), x_packed)
curr += split_size[i]
......@@ -157,17 +140,13 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
# Raise an error.
split_size = torch.randint(1, K, size=(2 * N,)).view(N, 2).unbind(0)
with self.assertRaisesRegex(ValueError, "1-dimensional"):
x_packed = struct_utils.padded_to_packed(
x_padded, split_size=split_size
)
x_packed = struct_utils.padded_to_packed(x_padded, split_size=split_size)
split_size = torch.randint(1, K, size=(2 * N,)).view(N * 2).tolist()
with self.assertRaisesRegex(
ValueError, "same length as inputs first dimension"
):
x_packed = struct_utils.padded_to_packed(
x_padded, split_size=split_size
)
x_packed = struct_utils.padded_to_packed(x_padded, split_size=split_size)
# Case 5: both pad_value and split_size are provided.
# Raise an error.
......@@ -204,8 +183,6 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
for i in range(N):
self.assertTrue(num_items[i] == x_dims[i])
self.assertTrue(item_packed_first_idx[i] == cur)
self.assertTrue(
item_packed_to_list_idx[cur : cur + x_dims[i]].eq(i).all()
)
self.assertTrue(item_packed_to_list_idx[cur : cur + x_dims[i]].eq(i).all())
self.assertClose(x_packed[cur : cur + x_dims[i]], x[i])
cur += x_dims[i]
tests/test_subdivide_meshes.py
View file @ d57daa6f
......@@ -2,14 +2,13 @@
import unittest
import torch
import torch
from common_testing import TestCaseMixin
from pytorch3d.ops.subdivide_meshes import SubdivideMeshes
from pytorch3d.structures.meshes import Meshes
from pytorch3d.utils.ico_sphere import ico_sphere
from common_testing import TestCaseMixin
class TestSubdivideMeshes(TestCaseMixin, unittest.TestCase):
def test_simple_subdivide(self):
......@@ -72,25 +71,14 @@ class TestSubdivideMeshes(TestCaseMixin, unittest.TestCase):
)
faces1 = torch.tensor([[0, 1, 2]], dtype=torch.int64, device=device)
verts2 = torch.tensor(
[
[0.5, 1.0, 0.0],
[1.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[1.5, 1.0, 0.0],
],
[[0.5, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.5, 1.0, 0.0]],
dtype=torch.float32,
device=device,
requires_grad=True,
)
faces2 = torch.tensor(
[[0, 1, 2], [0, 3, 1]], dtype=torch.int64, device=device
)
faces3 = torch.tensor(
[[0, 1, 2], [0, 2, 3]], dtype=torch.int64, device=device
)
mesh = Meshes(
verts=[verts1, verts2, verts2], faces=[faces1, faces2, faces3]
)
faces2 = torch.tensor([[0, 1, 2], [0, 3, 1]], dtype=torch.int64, device=device)
faces3 = torch.tensor([[0, 1, 2], [0, 2, 3]], dtype=torch.int64, device=device)
mesh = Meshes(verts=[verts1, verts2, verts2], faces=[faces1, faces2, faces3])
subdivide = SubdivideMeshes()
new_mesh = subdivide(mesh.clone())
......@@ -218,9 +206,7 @@ class TestSubdivideMeshes(TestCaseMixin, unittest.TestCase):
self.assertTrue(new_feats.requires_grad == gt_feats.requires_grad)
@staticmethod
def subdivide_meshes_with_init(
num_meshes: int = 10, same_topo: bool = False
):
def subdivide_meshes_with_init(num_meshes: int = 10, same_topo: bool = False):
device = torch.device("cuda:0")
meshes = ico_sphere(0, device=device)
if num_meshes > 1:
......
tests/test_texturing.py
View file @ d57daa6f
......@@ -2,9 +2,10 @@
import unittest
import torch
import torch.nn.functional as F
from common_testing import TestCaseMixin
from pytorch3d.renderer.mesh.rasterizer import Fragments
from pytorch3d.renderer.mesh.texturing import (
interpolate_face_attributes,
......@@ -13,8 +14,6 @@ from pytorch3d.renderer.mesh.texturing import (
)
from pytorch3d.structures import Meshes, Textures
from pytorch3d.structures.utils import list_to_padded
from common_testing import TestCaseMixin
from test_meshes import TestMeshes
......@@ -68,12 +67,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
dists=torch.ones_like(pix_to_face),
)
grad_vert_tex = torch.tensor(
[
[0.3, 0.3, 0.3],
[0.9, 0.9, 0.9],
[0.5, 0.5, 0.5],
[0.3, 0.3, 0.3],
],
[[0.3, 0.3, 0.3], [0.9, 0.9, 0.9], [0.5, 0.5, 0.5], [0.3, 0.3, 0.3]],
dtype=torch.float32,
)
texels = interpolate_vertex_colors(fragments, mesh)
......@@ -115,9 +109,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
[[0.5, 0.3, 0.2], [0.3, 0.6, 0.1]], dtype=torch.float32
).view(1, 1, 1, 2, -1)
dummy_verts = torch.zeros(4, 3)
vert_uvs = torch.tensor(
[[1, 0], [0, 1], [1, 1], [0, 0]], dtype=torch.float32
)
vert_uvs = torch.tensor([[1, 0], [0, 1], [1, 1], [0, 0]], dtype=torch.float32)
face_uvs = torch.tensor([[0, 1, 2], [1, 2, 3]], dtype=torch.int64)
interpolated_uvs = torch.tensor(
[[0.5 + 0.2, 0.3 + 0.2], [0.6, 0.3 + 0.6]], dtype=torch.float32
......@@ -137,9 +129,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
dists=pix_to_face,
)
tex = Textures(
maps=tex_map,
faces_uvs=face_uvs[None, ...],
verts_uvs=vert_uvs[None, ...],
maps=tex_map, faces_uvs=face_uvs[None, ...], verts_uvs=vert_uvs[None, ...]
)
meshes = Meshes(verts=[dummy_verts], faces=[face_uvs], textures=tex)
texels = interpolate_texture_map(fragments, meshes)
......@@ -151,9 +141,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
tex_map = tex_map.permute(0, 3, 1, 2)
tex_map = torch.cat([tex_map, tex_map], dim=0)
expected_out = F.grid_sample(tex_map, pixel_uvs, align_corners=False)
self.assertTrue(
torch.allclose(texels.squeeze(), expected_out.squeeze())
)
self.assertTrue(torch.allclose(texels.squeeze(), expected_out.squeeze()))
def test_init_rgb_uv_fail(self):
V = 20
......@@ -183,9 +171,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
Textures(verts_rgb=torch.ones((5, 16, 16, 3)))
# maps provided without verts/faces uvs
with self.assertRaisesRegex(
ValueError, "faces_uvs and verts_uvs are required"
):
with self.assertRaisesRegex(ValueError, "faces_uvs and verts_uvs are required"):
Textures(maps=torch.ones((5, 16, 16, 3)))
def test_padded_to_packed(self):
......@@ -209,9 +195,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
# This is set inside Meshes when textures is passed as an input.
# Here we set _num_faces_per_mesh and _num_verts_per_mesh explicity.
tex1 = tex.clone()
tex1._num_faces_per_mesh = (
faces_uvs_padded.gt(-1).all(-1).sum(-1).tolist()
)
tex1._num_faces_per_mesh = faces_uvs_padded.gt(-1).all(-1).sum(-1).tolist()
tex1._num_verts_per_mesh = torch.tensor([5, 4])
faces_packed = tex1.faces_uvs_packed()
verts_packed = tex1.verts_uvs_packed()
......@@ -245,16 +229,12 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
for i in range(N):
self.assertTrue(
(faces_list[i] == faces_uvs_padded[i, ...].squeeze())
.all()
.item()
(faces_list[i] == faces_uvs_padded[i, ...].squeeze()).all().item()
)
for i in range(N):
self.assertTrue(
(verts_list[i] == verts_uvs_padded[i, ...].squeeze())
.all()
.item()
(verts_list[i] == verts_uvs_padded[i, ...].squeeze()).all().item()
)
def test_clone(self):
......@@ -344,9 +324,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
verts_uvs=torch.randn((B, V, 2)),
)
tex_mesh = Meshes(
verts=mesh.verts_padded(),
faces=mesh.faces_padded(),
textures=tex_uv,
verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex_uv
)
N = 20
new_mesh = tex_mesh.extend(N)
......@@ -359,12 +337,10 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
for i in range(len(tex_mesh)):
for n in range(N):
self.assertClose(
tex_init.faces_uvs_list()[i],
new_tex.faces_uvs_list()[i * N + n],
tex_init.faces_uvs_list()[i], new_tex.faces_uvs_list()[i * N + n]
)
self.assertClose(
tex_init.verts_uvs_list()[i],
new_tex.verts_uvs_list()[i * N + n],
tex_init.verts_uvs_list()[i], new_tex.verts_uvs_list()[i * N + n]
)
self.assertAllSeparate(
[
......@@ -384,9 +360,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
# 2. Texture vertex RGB
tex_rgb = Textures(verts_rgb=torch.randn((B, V, 3)))
tex_mesh_rgb = Meshes(
verts=mesh.verts_padded(),
faces=mesh.faces_padded(),
textures=tex_rgb,
verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex_rgb
)
N = 20
new_mesh_rgb = tex_mesh_rgb.extend(N)
......@@ -399,8 +373,7 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
for i in range(len(tex_mesh_rgb)):
for n in range(N):
self.assertClose(
tex_init.verts_rgb_list()[i],
new_tex.verts_rgb_list()[i * N + n],
tex_init.verts_rgb_list()[i], new_tex.verts_rgb_list()[i * N + n]
)
self.assertAllSeparate(
[tex_init.verts_rgb_padded(), new_tex.verts_rgb_padded()]
......
tests/test_transforms.py
View file @ d57daa6f
......@@ -3,8 +3,8 @@
import math
import unittest
import torch
import torch
from pytorch3d.transforms.so3 import so3_exponential_map
from pytorch3d.transforms.transform3d import (
Rotate,
......@@ -18,9 +18,7 @@ from pytorch3d.transforms.transform3d import (
class TestTransform(unittest.TestCase):
def test_to(self):
tr = Translate(torch.FloatTensor([[1.0, 2.0, 3.0]]))
R = torch.FloatTensor(
[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]]
)
R = torch.FloatTensor([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
R = Rotate(R)
t = Transform3d().compose(R, tr)
for _ in range(3):
......@@ -36,9 +34,7 @@ class TestTransform(unittest.TestCase):
the same as composition of clones of translation and rotation.
"""
tr = Translate(torch.FloatTensor([[1.0, 2.0, 3.0]]))
R = torch.FloatTensor(
[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]]
)
R = torch.FloatTensor([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
R = Rotate(R)
# check that the _matrix property of clones of
......@@ -63,9 +59,9 @@ class TestTransform(unittest.TestCase):
def test_translate(self):
t = Transform3d().translate(1, 2, 3)
points = torch.tensor(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]
).view(1, 3, 3)
points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(
1, 3, 3
)
normals = torch.tensor(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
).view(1, 3, 3)
......@@ -82,9 +78,9 @@ class TestTransform(unittest.TestCase):
def test_scale(self):
t = Transform3d().scale(2.0).scale(0.5, 0.25, 1.0)
points = torch.tensor(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]
).view(1, 3, 3)
points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(
1, 3, 3
)
normals = torch.tensor(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
).view(1, 3, 3)
......@@ -101,9 +97,9 @@ class TestTransform(unittest.TestCase):
def test_scale_translate(self):
t = Transform3d().scale(2, 1, 3).translate(1, 2, 3)
points = torch.tensor(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]
).view(1, 3, 3)
points = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 0.5, 0.0]]).view(
1, 3, 3
)
normals = torch.tensor(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
).view(1, 3, 3)
......@@ -120,9 +116,9 @@ class TestTransform(unittest.TestCase):
def test_rotate_axis_angle(self):
t = Transform3d().rotate_axis_angle(90.0, axis="Z")
points = torch.tensor(
[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 1.0]]
).view(1, 3, 3)
points = torch.tensor([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 1.0]]).view(
1, 3, 3
)
normals = torch.tensor(
[[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]
).view(1, 3, 3)
......@@ -194,9 +190,7 @@ class TestTransform(unittest.TestCase):
t_ = Rotate(
so3_exponential_map(
torch.randn(
(batch_size, 3),
dtype=torch.float32,
device=device,
(batch_size, 3), dtype=torch.float32, device=device
)
),
device=device,
......@@ -717,9 +711,7 @@ class TestRotate(unittest.TestCase):
def test_inverse(self, batch_size=5):
device = torch.device("cuda:0")
log_rot = torch.randn(
(batch_size, 3), dtype=torch.float32, device=device
)
log_rot = torch.randn((batch_size, 3), dtype=torch.float32, device=device)
R = so3_exponential_map(log_rot)
t = Rotate(R)
im = t.inverse()._matrix
......@@ -749,9 +741,7 @@ class TestRotateAxisAngle(unittest.TestCase):
transformed_points = t.transform_points(points)
expected_points = torch.tensor([0.0, 0.0, 1.0])
self.assertTrue(
torch.allclose(
transformed_points.squeeze(), expected_points, atol=1e-7
)
torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
)
self.assertTrue(torch.allclose(t._matrix, matrix))
......@@ -775,9 +765,7 @@ class TestRotateAxisAngle(unittest.TestCase):
transformed_points = t.transform_points(points)
expected_points = torch.tensor([0.0, 0.0, 1.0])
self.assertTrue(
torch.allclose(
transformed_points.squeeze(), expected_points, atol=1e-7
)
torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
)
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
......@@ -835,9 +823,7 @@ class TestRotateAxisAngle(unittest.TestCase):
transformed_points = t.transform_points(points)
expected_points = torch.tensor([0.0, 0.0, -1.0])
self.assertTrue(
torch.allclose(
transformed_points.squeeze(), expected_points, atol=1e-7
)
torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
)
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
......@@ -866,9 +852,7 @@ class TestRotateAxisAngle(unittest.TestCase):
transformed_points = t.transform_points(points)
expected_points = torch.tensor([0.0, 0.0, -1.0])
self.assertTrue(
torch.allclose(
transformed_points.squeeze(), expected_points, atol=1e-7
)
torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
)
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
......@@ -923,9 +907,7 @@ class TestRotateAxisAngle(unittest.TestCase):
transformed_points = t.transform_points(points)
expected_points = torch.tensor([0.0, 1.0, 0.0])
self.assertTrue(
torch.allclose(
transformed_points.squeeze(), expected_points, atol=1e-7
)
torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
)
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
......@@ -949,9 +931,7 @@ class TestRotateAxisAngle(unittest.TestCase):
transformed_points = t.transform_points(points)
expected_points = torch.tensor([0.0, 1.0, 0.0])
self.assertTrue(
torch.allclose(
transformed_points.squeeze(), expected_points, atol=1e-7
)
torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
)
self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
......
tests/test_vert_align.py
View file @ d57daa6f
......@@ -2,22 +2,18 @@
import unittest
import torch
import torch.nn.functional as F
from common_testing import TestCaseMixin
from pytorch3d.ops.vert_align import vert_align
from pytorch3d.structures.meshes import Meshes
from common_testing import TestCaseMixin
class TestVertAlign(TestCaseMixin, unittest.TestCase):
@staticmethod
def vert_align_naive(
feats,
verts_or_meshes,
return_packed: bool = False,
align_corners: bool = True,
feats, verts_or_meshes, return_packed: bool = False, align_corners: bool = True
):
"""
Naive implementation of vert_align.
......@@ -60,16 +56,13 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
return out_feats
@staticmethod
def init_meshes(
num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
):
def init_meshes(num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000):
device = torch.device("cuda:0")
verts_list = []
faces_list = []
for _ in range(num_meshes):
verts = (
torch.rand((num_verts, 3), dtype=torch.float32, device=device)
* 2.0
torch.rand((num_verts, 3), dtype=torch.float32, device=device) * 2.0
- 1.0
) # verts in the space of [-1, 1]
faces = torch.randint(
......@@ -82,15 +75,11 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
return meshes
@staticmethod
def init_feats(
batch_size: int = 10, num_channels: int = 256, device: str = "cuda"
):
def init_feats(batch_size: int = 10, num_channels: int = 256, device: str = "cuda"):
H, W = [14, 28], [14, 28]
feats = []
for (h, w) in zip(H, W):
feats.append(
torch.rand((batch_size, num_channels, h, w), device=device)
)
feats.append(torch.rand((batch_size, num_channels, h, w), device=device))
return feats
def test_vert_align_with_meshes(self):
......@@ -102,16 +91,12 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
# feats in list
out = vert_align(feats, meshes, return_packed=True)
naive_out = TestVertAlign.vert_align_naive(
feats, meshes, return_packed=True
)
naive_out = TestVertAlign.vert_align_naive(feats, meshes, return_packed=True)
self.assertClose(out, naive_out)
# feats as tensor
out = vert_align(feats[0], meshes, return_packed=True)
naive_out = TestVertAlign.vert_align_naive(
feats[0], meshes, return_packed=True
)
naive_out = TestVertAlign.vert_align_naive(feats[0], meshes, return_packed=True)
self.assertClose(out, naive_out)
def test_vert_align_with_verts(self):
......@@ -120,30 +105,21 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
"""
feats = TestVertAlign.init_feats(10, 256)
verts = (
torch.rand(
(10, 100, 3), dtype=torch.float32, device=feats[0].device
)
* 2.0
torch.rand((10, 100, 3), dtype=torch.float32, device=feats[0].device) * 2.0
- 1.0
)
# feats in list
out = vert_align(feats, verts, return_packed=True)
naive_out = TestVertAlign.vert_align_naive(
feats, verts, return_packed=True
)
naive_out = TestVertAlign.vert_align_naive(feats, verts, return_packed=True)
self.assertClose(out, naive_out)
# feats as tensor
out = vert_align(feats[0], verts, return_packed=True)
naive_out = TestVertAlign.vert_align_naive(
feats[0], verts, return_packed=True
)
naive_out = TestVertAlign.vert_align_naive(feats[0], verts, return_packed=True)
self.assertClose(out, naive_out)
out2 = vert_align(
feats[0], verts, return_packed=True, align_corners=False
)
out2 = vert_align(feats[0], verts, return_packed=True, align_corners=False)
naive_out2 = TestVertAlign.vert_align_naive(
feats[0], verts, return_packed=True, align_corners=False
)
......@@ -158,9 +134,7 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
verts_list = []
faces_list = []
for _ in range(num_meshes):
verts = torch.rand(
(num_verts, 3), dtype=torch.float32, device=device
)
verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
faces = torch.randint(
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
)
......
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