423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-12-20 22:30:35 +08:00
Code Issues Packages Projects Releases Wiki Activity

Address black + isort fbsource linter warnings

Browse Source 
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
Download Patch File Download Diff File Expand all files Collapse all files

63
tests/test_packed_to_padded.py
View File

@@ -1,13 +1,12 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import unittest
import torch
import torch
from common_testing import TestCaseMixin
from pytorch3d.ops import packed_to_padded, padded_to_packed
from pytorch3d.structures.meshes import Meshes
from common_testing import TestCaseMixin
class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
@@ -25,9 +24,7 @@ class TestPackedToPadded(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
)
@@ -47,9 +44,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
if D == 0:
inputs_padded = torch.zeros((num_meshes, max_size), device=device)
else:
inputs_padded = torch.zeros(
(num_meshes, max_size, D), device=device
)
inputs_padded = torch.zeros((num_meshes, max_size, D), device=device)
for m in range(num_meshes):
s = first_idxs[m]
if m == num_meshes - 1:
@@ -92,13 +87,9 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
max_faces = meshes.num_faces_per_mesh().max().item()
if D == 0:
values = torch.rand(
(faces.shape[0],), device=device, requires_grad=True
)
values = torch.rand((faces.shape[0],), device=device, requires_grad=True)
else:
values = torch.rand(
(faces.shape[0], D), device=device, requires_grad=True
)
values = torch.rand((faces.shape[0], D), device=device, requires_grad=True)
values_torch = values.detach().clone()
values_torch.requires_grad = True
values_padded = packed_to_padded(
@@ -120,10 +111,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
values_padded_torch.backward(grad_inputs)
grad_outputs_torch1 = values_torch.grad
grad_outputs_torch2 = TestPackedToPadded.padded_to_packed_python(
grad_inputs,
mesh_to_faces_packed_first_idx,
values.size(0),
device=device,
grad_inputs, mesh_to_faces_packed_first_idx, values.size(0), device=device
)
self.assertClose(grad_outputs, grad_outputs_torch1)
self.assertClose(grad_outputs, grad_outputs_torch2)
@@ -165,9 +153,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
values_torch = values.detach().clone()
values_torch.requires_grad = True
values_packed = padded_to_packed(
values,
mesh_to_faces_packed_first_idx,
num_faces_per_mesh.sum().item(),
values, mesh_to_faces_packed_first_idx, num_faces_per_mesh.sum().item()
)
values_packed_torch = TestPackedToPadded.padded_to_packed_python(
values_torch,
@@ -180,9 +166,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
# check backward
if D == 0:
grad_inputs = torch.rand(
(num_faces_per_mesh.sum().item()), device=device
)
grad_inputs = torch.rand((num_faces_per_mesh.sum().item()), device=device)
else:
grad_inputs = torch.rand(
(num_faces_per_mesh.sum().item(), D), device=device
@@ -192,10 +176,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
values_packed_torch.backward(grad_inputs)
grad_outputs_torch1 = values_torch.grad
grad_outputs_torch2 = TestPackedToPadded.packed_to_padded_python(
grad_inputs,
mesh_to_faces_packed_first_idx,
values.size(1),
device=device,
grad_inputs, mesh_to_faces_packed_first_idx, values.size(1), device=device
)
self.assertClose(grad_outputs, grad_outputs_torch1)
self.assertClose(grad_outputs, grad_outputs_torch2)
@@ -219,34 +200,24 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
self._test_padded_to_packed_helper(16, "cuda:0")
def test_invalid_inputs_shapes(self, device="cuda:0"):
with self.assertRaisesRegex(
ValueError, "input can only be 2-dimensional."
):
with self.assertRaisesRegex(ValueError, "input can only be 2-dimensional."):
values = torch.rand((100, 50, 2), device=device)
first_idxs = torch.tensor([0, 80], dtype=torch.int64, device=device)
packed_to_padded(values, first_idxs, 100)
with self.assertRaisesRegex(
ValueError, "input can only be 3-dimensional."
):
with self.assertRaisesRegex(ValueError, "input can only be 3-dimensional."):
values = torch.rand((100,), device=device)
first_idxs = torch.tensor([0, 80], dtype=torch.int64, device=device)
padded_to_packed(values, first_idxs, 20)
with self.assertRaisesRegex(
ValueError, "input can only be 3-dimensional."
):
with self.assertRaisesRegex(ValueError, "input can only be 3-dimensional."):
values = torch.rand((100, 50, 2, 2), device=device)
first_idxs = torch.tensor([0, 80], dtype=torch.int64, device=device)
padded_to_packed(values, first_idxs, 20)
@staticmethod
def packed_to_padded_with_init(
num_meshes: int,
num_verts: int,
num_faces: int,
num_d: int,
device: str = "cpu",
num_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu"
):
meshes = TestPackedToPadded.init_meshes(
num_meshes, num_verts, num_faces, device
@@ -268,11 +239,7 @@ class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
@staticmethod
def packed_to_padded_with_init_torch(
num_meshes: int,
num_verts: int,
num_faces: int,
num_d: int,
device: str = "cpu",
num_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu"
):
meshes = TestPackedToPadded.init_meshes(
num_meshes, num_verts, num_faces, device