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# 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 pytorch3d.ops import packed_to_padded, padded_to_packed
from pytorch3d.structures.meshes import Meshes
from .common_testing import get_random_cuda_device, TestCaseMixin
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)
if inputs.dim() == 1:
inputs_padded = torch.zeros((num_meshes, max_size), device=device)
else:
inputs_padded = torch.zeros(
(num_meshes, max_size, *inputs.shape[1:]), 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 - s] = 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)
if inputs.dim() == 2:
inputs_packed = torch.zeros((num_inputs,), device=device)
else:
inputs_packed = torch.zeros((num_inputs, *inputs.shape[2:]), 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 - s]
return inputs_packed
def _test_packed_to_padded_helper(self, dims, 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 len(dims) == 0:
values = torch.rand((faces.shape[0],), device=device, requires_grad=True)
else:
values = torch.rand(
(faces.shape[0], *dims), 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 len(dims) == 0:
grad_inputs = torch.rand((len(meshes), max_faces), device=device)
else:
grad_inputs = torch.rand((len(meshes), max_faces, *dims), 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([], "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_D16_9_cpu(self):
self._test_packed_to_padded_helper([16, 9], "cpu")
def test_packed_to_padded_D16_3_2_cpu(self):
self._test_packed_to_padded_helper([16, 3, 2], "cpu")
def test_packed_to_padded_flat_cuda(self):
device = get_random_cuda_device()
self._test_packed_to_padded_helper([], 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_packed_to_padded_D16_9_cuda(self):
device = get_random_cuda_device()
self._test_packed_to_padded_helper([16, 9], device)
def test_packed_to_padded_D16_3_2_cuda(self):
device = get_random_cuda_device()
self._test_packed_to_padded_helper([16, 3, 2], device)
def _test_padded_to_packed_helper(self, dims, 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 len(dims) == 0:
values = torch.rand((len(meshes), max_faces), device=device)
else:
values = torch.rand((len(meshes), max_faces, *dims), 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)
if len(dims) > 0:
values_packed_dim2 = padded_to_packed(
values.transpose(1, 2),
mesh_to_faces_packed_first_idx,
num_faces_per_mesh.sum().item(),
max_size_dim=2,
)
# check forward
self.assertClose(values_packed_dim2, values_packed_torch)
# check backward
if len(dims) == 0:
grad_inputs = torch.rand((num_faces_per_mesh.sum().item()), device=device)
else:
grad_inputs = torch.rand(
(num_faces_per_mesh.sum().item(), *dims), 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([], "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_D16_9_cpu(self):
self._test_padded_to_packed_helper([16, 9], "cpu")
def test_padded_to_packed_D16_3_2_cpu(self):
self._test_padded_to_packed_helper([16, 3, 2], "cpu")
def test_padded_to_packed_flat_cuda(self):
device = get_random_cuda_device()
self._test_padded_to_packed_helper([], 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_padded_to_packed_D16_9_cuda(self):
device = get_random_cuda_device()
self._test_padded_to_packed_helper([16, 9], device)
def test_padded_to_packed_D16_3_2_cuda(self):
device = get_random_cuda_device()
self._test_padded_to_packed_helper([16, 3, 2], device)
@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
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