mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
34bbb3ad32
Summary: Applies new import merging and sorting from µsort v1.0. When merging imports, µsort will make a best-effort to move associated comments to match merged elements, but there are known limitations due to the diynamic nature of Python and developer tooling. These changes should not produce any dangerous runtime changes, but may require touch-ups to satisfy linters and other tooling. Note that µsort uses case-insensitive, lexicographical sorting, which results in a different ordering compared to isort. This provides a more consistent sorting order, matching the case-insensitive order used when sorting import statements by module name, and ensures that "frog", "FROG", and "Frog" always sort next to each other. For details on µsort's sorting and merging semantics, see the user guide: https://usort.readthedocs.io/en/stable/guide.html#sorting Reviewed By: bottler Differential Revision: D35553814 fbshipit-source-id: be49bdb6a4c25264ff8d4db3a601f18736d17be1
273 lines
10 KiB
Python
273 lines
10 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the BSD-style license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
|
|
import unittest
|
|
|
|
import torch
|
|
from common_testing import get_random_cuda_device, TestCaseMixin
|
|
from pytorch3d.ops import packed_to_padded, padded_to_packed
|
|
from pytorch3d.structures.meshes import Meshes
|
|
|
|
|
|
class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
|
|
def setUp(self) -> None:
|
|
super().setUp()
|
|
torch.manual_seed(1)
|
|
|
|
@staticmethod
|
|
def init_meshes(
|
|
num_meshes: int = 10,
|
|
num_verts: int = 1000,
|
|
num_faces: int = 3000,
|
|
device: str = "cpu",
|
|
):
|
|
device = torch.device(device)
|
|
verts_list = []
|
|
faces_list = []
|
|
for _ in range(num_meshes):
|
|
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
|
|
)
|
|
verts_list.append(verts)
|
|
faces_list.append(faces)
|
|
meshes = Meshes(verts_list, faces_list)
|
|
|
|
return meshes
|
|
|
|
@staticmethod
|
|
def packed_to_padded_python(inputs, first_idxs, max_size, device):
|
|
"""
|
|
PyTorch implementation of packed_to_padded function.
|
|
"""
|
|
num_meshes = first_idxs.size(0)
|
|
D = inputs.shape[1] if inputs.dim() == 2 else 0
|
|
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)
|
|
for m in range(num_meshes):
|
|
s = first_idxs[m]
|
|
if m == num_meshes - 1:
|
|
f = inputs.shape[0]
|
|
else:
|
|
f = first_idxs[m + 1]
|
|
inputs_padded[m, :f] = inputs[s:f]
|
|
|
|
return inputs_padded
|
|
|
|
@staticmethod
|
|
def padded_to_packed_python(inputs, first_idxs, num_inputs, device):
|
|
"""
|
|
PyTorch implementation of padded_to_packed function.
|
|
"""
|
|
num_meshes = inputs.size(0)
|
|
D = inputs.shape[2] if inputs.dim() == 3 else 0
|
|
if D == 0:
|
|
inputs_packed = torch.zeros((num_inputs,), device=device)
|
|
else:
|
|
inputs_packed = torch.zeros((num_inputs, D), device=device)
|
|
for m in range(num_meshes):
|
|
s = first_idxs[m]
|
|
if m == num_meshes - 1:
|
|
f = num_inputs
|
|
else:
|
|
f = first_idxs[m + 1]
|
|
inputs_packed[s:f] = inputs[m, :f]
|
|
|
|
return inputs_packed
|
|
|
|
def _test_packed_to_padded_helper(self, D, device):
|
|
"""
|
|
Check the results from packed_to_padded and PyTorch implementations
|
|
are the same.
|
|
"""
|
|
meshes = self.init_meshes(16, 100, 300, device=device)
|
|
faces = meshes.faces_packed()
|
|
mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
|
|
max_faces = meshes.num_faces_per_mesh().max().item()
|
|
|
|
if D == 0:
|
|
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 = values.detach().clone()
|
|
values_torch.requires_grad = True
|
|
values_padded = packed_to_padded(
|
|
values, mesh_to_faces_packed_first_idx, max_faces
|
|
)
|
|
values_padded_torch = TestPackedToPadded.packed_to_padded_python(
|
|
values_torch, mesh_to_faces_packed_first_idx, max_faces, device
|
|
)
|
|
# check forward
|
|
self.assertClose(values_padded, values_padded_torch)
|
|
|
|
# check backward
|
|
if D == 0:
|
|
grad_inputs = torch.rand((len(meshes), max_faces), device=device)
|
|
else:
|
|
grad_inputs = torch.rand((len(meshes), max_faces, D), device=device)
|
|
values_padded.backward(grad_inputs)
|
|
grad_outputs = values.grad
|
|
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
|
|
)
|
|
self.assertClose(grad_outputs, grad_outputs_torch1)
|
|
self.assertClose(grad_outputs, grad_outputs_torch2)
|
|
|
|
def test_packed_to_padded_flat_cpu(self):
|
|
self._test_packed_to_padded_helper(0, "cpu")
|
|
|
|
def test_packed_to_padded_D1_cpu(self):
|
|
self._test_packed_to_padded_helper(1, "cpu")
|
|
|
|
def test_packed_to_padded_D16_cpu(self):
|
|
self._test_packed_to_padded_helper(16, "cpu")
|
|
|
|
def test_packed_to_padded_flat_cuda(self):
|
|
device = get_random_cuda_device()
|
|
self._test_packed_to_padded_helper(0, device)
|
|
|
|
def test_packed_to_padded_D1_cuda(self):
|
|
device = get_random_cuda_device()
|
|
self._test_packed_to_padded_helper(1, device)
|
|
|
|
def test_packed_to_padded_D16_cuda(self):
|
|
device = get_random_cuda_device()
|
|
self._test_packed_to_padded_helper(16, device)
|
|
|
|
def _test_padded_to_packed_helper(self, D, device):
|
|
"""
|
|
Check the results from packed_to_padded and PyTorch implementations
|
|
are the same.
|
|
"""
|
|
meshes = self.init_meshes(16, 100, 300, device=device)
|
|
mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
|
|
num_faces_per_mesh = meshes.num_faces_per_mesh()
|
|
max_faces = num_faces_per_mesh.max().item()
|
|
if D == 0:
|
|
values = torch.rand((len(meshes), max_faces), device=device)
|
|
else:
|
|
values = torch.rand((len(meshes), max_faces, D), device=device)
|
|
for i, num in enumerate(num_faces_per_mesh):
|
|
values[i, num:] = 0
|
|
values.requires_grad = True
|
|
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_packed_torch = TestPackedToPadded.padded_to_packed_python(
|
|
values_torch,
|
|
mesh_to_faces_packed_first_idx,
|
|
num_faces_per_mesh.sum().item(),
|
|
device,
|
|
)
|
|
# check forward
|
|
self.assertClose(values_packed, values_packed_torch)
|
|
|
|
# check backward
|
|
if D == 0:
|
|
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
|
|
)
|
|
values_packed.backward(grad_inputs)
|
|
grad_outputs = values.grad
|
|
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
|
|
)
|
|
self.assertClose(grad_outputs, grad_outputs_torch1)
|
|
self.assertClose(grad_outputs, grad_outputs_torch2)
|
|
|
|
def test_padded_to_packed_flat_cpu(self):
|
|
self._test_padded_to_packed_helper(0, "cpu")
|
|
|
|
def test_padded_to_packed_D1_cpu(self):
|
|
self._test_padded_to_packed_helper(1, "cpu")
|
|
|
|
def test_padded_to_packed_D16_cpu(self):
|
|
self._test_padded_to_packed_helper(16, "cpu")
|
|
|
|
def test_padded_to_packed_flat_cuda(self):
|
|
device = get_random_cuda_device()
|
|
self._test_padded_to_packed_helper(0, device)
|
|
|
|
def test_padded_to_packed_D1_cuda(self):
|
|
device = get_random_cuda_device()
|
|
self._test_padded_to_packed_helper(1, device)
|
|
|
|
def test_padded_to_packed_D16_cuda(self):
|
|
device = get_random_cuda_device()
|
|
self._test_padded_to_packed_helper(16, device)
|
|
|
|
def test_invalid_inputs_shapes(self, device="cuda:0"):
|
|
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."):
|
|
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."):
|
|
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"
|
|
):
|
|
meshes = TestPackedToPadded.init_meshes(
|
|
num_meshes, num_verts, num_faces, device
|
|
)
|
|
faces = meshes.faces_packed()
|
|
mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
|
|
max_faces = meshes.num_faces_per_mesh().max().item()
|
|
if num_d == 0:
|
|
values = torch.rand((faces.shape[0],), device=meshes.device)
|
|
else:
|
|
values = torch.rand((faces.shape[0], num_d), device=meshes.device)
|
|
torch.cuda.synchronize()
|
|
|
|
def out():
|
|
packed_to_padded(values, mesh_to_faces_packed_first_idx, max_faces)
|
|
torch.cuda.synchronize()
|
|
|
|
return out
|
|
|
|
@staticmethod
|
|
def packed_to_padded_with_init_torch(
|
|
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
|
|
)
|
|
faces = meshes.faces_packed()
|
|
mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
|
|
max_faces = meshes.num_faces_per_mesh().max().item()
|
|
if num_d == 0:
|
|
values = torch.rand((faces.shape[0],), device=meshes.device)
|
|
else:
|
|
values = torch.rand((faces.shape[0], num_d), device=meshes.device)
|
|
torch.cuda.synchronize()
|
|
|
|
def out():
|
|
TestPackedToPadded.packed_to_padded_python(
|
|
values, mesh_to_faces_packed_first_idx, max_faces, device
|
|
)
|
|
torch.cuda.synchronize()
|
|
|
|
return out
|