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import unittest |
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import torch |
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from pytorch3d.ops import packed_to_padded, padded_to_packed |
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from pytorch3d.structures.meshes import Meshes |
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from .common_testing import get_random_cuda_device, TestCaseMixin |
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class TestPackedToPadded(TestCaseMixin, unittest.TestCase): |
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def setUp(self) -> None: |
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super().setUp() |
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torch.manual_seed(1) |
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@staticmethod |
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def init_meshes( |
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num_meshes: int = 10, |
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num_verts: int = 1000, |
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num_faces: int = 3000, |
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device: str = "cpu", |
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): |
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device = torch.device(device) |
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verts_list = [] |
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faces_list = [] |
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for _ in range(num_meshes): |
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verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device) |
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faces = torch.randint( |
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num_verts, size=(num_faces, 3), dtype=torch.int64, device=device |
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) |
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verts_list.append(verts) |
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faces_list.append(faces) |
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meshes = Meshes(verts_list, faces_list) |
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return meshes |
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@staticmethod |
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def packed_to_padded_python(inputs, first_idxs, max_size, device): |
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""" |
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PyTorch implementation of packed_to_padded function. |
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""" |
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num_meshes = first_idxs.size(0) |
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if inputs.dim() == 1: |
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inputs_padded = torch.zeros((num_meshes, max_size), device=device) |
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else: |
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inputs_padded = torch.zeros( |
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(num_meshes, max_size, *inputs.shape[1:]), device=device |
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) |
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for m in range(num_meshes): |
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s = first_idxs[m] |
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if m == num_meshes - 1: |
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f = inputs.shape[0] |
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else: |
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f = first_idxs[m + 1] |
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inputs_padded[m, : f - s] = inputs[s:f] |
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return inputs_padded |
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@staticmethod |
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def padded_to_packed_python(inputs, first_idxs, num_inputs, device): |
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""" |
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PyTorch implementation of padded_to_packed function. |
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""" |
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num_meshes = inputs.size(0) |
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if inputs.dim() == 2: |
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inputs_packed = torch.zeros((num_inputs,), device=device) |
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else: |
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inputs_packed = torch.zeros((num_inputs, *inputs.shape[2:]), device=device) |
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for m in range(num_meshes): |
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s = first_idxs[m] |
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if m == num_meshes - 1: |
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f = num_inputs |
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else: |
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f = first_idxs[m + 1] |
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inputs_packed[s:f] = inputs[m, : f - s] |
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return inputs_packed |
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def _test_packed_to_padded_helper(self, dims, device): |
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""" |
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Check the results from packed_to_padded and PyTorch implementations |
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are the same. |
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""" |
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meshes = self.init_meshes(16, 100, 300, device=device) |
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faces = meshes.faces_packed() |
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mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx() |
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max_faces = meshes.num_faces_per_mesh().max().item() |
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if len(dims) == 0: |
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values = torch.rand((faces.shape[0],), device=device, requires_grad=True) |
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else: |
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values = torch.rand( |
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(faces.shape[0], *dims), device=device, requires_grad=True |
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) |
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values_torch = values.detach().clone() |
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values_torch.requires_grad = True |
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values_padded = packed_to_padded( |
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values, mesh_to_faces_packed_first_idx, max_faces |
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) |
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values_padded_torch = TestPackedToPadded.packed_to_padded_python( |
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values_torch, mesh_to_faces_packed_first_idx, max_faces, device |
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) |
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self.assertClose(values_padded, values_padded_torch) |
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if len(dims) == 0: |
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grad_inputs = torch.rand((len(meshes), max_faces), device=device) |
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else: |
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grad_inputs = torch.rand((len(meshes), max_faces, *dims), device=device) |
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values_padded.backward(grad_inputs) |
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grad_outputs = values.grad |
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values_padded_torch.backward(grad_inputs) |
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grad_outputs_torch1 = values_torch.grad |
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grad_outputs_torch2 = TestPackedToPadded.padded_to_packed_python( |
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grad_inputs, mesh_to_faces_packed_first_idx, values.size(0), device=device |
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) |
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self.assertClose(grad_outputs, grad_outputs_torch1) |
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self.assertClose(grad_outputs, grad_outputs_torch2) |
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def test_packed_to_padded_flat_cpu(self): |
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self._test_packed_to_padded_helper([], "cpu") |
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def test_packed_to_padded_D1_cpu(self): |
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self._test_packed_to_padded_helper([1], "cpu") |
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def test_packed_to_padded_D16_cpu(self): |
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self._test_packed_to_padded_helper([16], "cpu") |
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def test_packed_to_padded_D16_9_cpu(self): |
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self._test_packed_to_padded_helper([16, 9], "cpu") |
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def test_packed_to_padded_D16_3_2_cpu(self): |
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self._test_packed_to_padded_helper([16, 3, 2], "cpu") |
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def test_packed_to_padded_flat_cuda(self): |
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device = get_random_cuda_device() |
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self._test_packed_to_padded_helper([], device) |
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def test_packed_to_padded_D1_cuda(self): |
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device = get_random_cuda_device() |
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self._test_packed_to_padded_helper([1], device) |
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def test_packed_to_padded_D16_cuda(self): |
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device = get_random_cuda_device() |
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self._test_packed_to_padded_helper([16], device) |
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def test_packed_to_padded_D16_9_cuda(self): |
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device = get_random_cuda_device() |
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self._test_packed_to_padded_helper([16, 9], device) |
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def test_packed_to_padded_D16_3_2_cuda(self): |
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device = get_random_cuda_device() |
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self._test_packed_to_padded_helper([16, 3, 2], device) |
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def _test_padded_to_packed_helper(self, dims, device): |
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""" |
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Check the results from packed_to_padded and PyTorch implementations |
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are the same. |
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""" |
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meshes = self.init_meshes(16, 100, 300, device=device) |
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mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx() |
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num_faces_per_mesh = meshes.num_faces_per_mesh() |
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max_faces = num_faces_per_mesh.max().item() |
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if len(dims) == 0: |
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values = torch.rand((len(meshes), max_faces), device=device) |
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else: |
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values = torch.rand((len(meshes), max_faces, *dims), device=device) |
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for i, num in enumerate(num_faces_per_mesh): |
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values[i, num:] = 0 |
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values.requires_grad = True |
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values_torch = values.detach().clone() |
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values_torch.requires_grad = True |
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values_packed = padded_to_packed( |
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values, mesh_to_faces_packed_first_idx, num_faces_per_mesh.sum().item() |
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) |
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values_packed_torch = TestPackedToPadded.padded_to_packed_python( |
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values_torch, |
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mesh_to_faces_packed_first_idx, |
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num_faces_per_mesh.sum().item(), |
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device, |
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) |
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self.assertClose(values_packed, values_packed_torch) |
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if len(dims) > 0: |
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values_packed_dim2 = padded_to_packed( |
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values.transpose(1, 2), |
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mesh_to_faces_packed_first_idx, |
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num_faces_per_mesh.sum().item(), |
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max_size_dim=2, |
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) |
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self.assertClose(values_packed_dim2, values_packed_torch) |
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if len(dims) == 0: |
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grad_inputs = torch.rand((num_faces_per_mesh.sum().item()), device=device) |
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else: |
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grad_inputs = torch.rand( |
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(num_faces_per_mesh.sum().item(), *dims), device=device |
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) |
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values_packed.backward(grad_inputs) |
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grad_outputs = values.grad |
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values_packed_torch.backward(grad_inputs) |
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grad_outputs_torch1 = values_torch.grad |
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grad_outputs_torch2 = TestPackedToPadded.packed_to_padded_python( |
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grad_inputs, mesh_to_faces_packed_first_idx, values.size(1), device=device |
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) |
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self.assertClose(grad_outputs, grad_outputs_torch1) |
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self.assertClose(grad_outputs, grad_outputs_torch2) |
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def test_padded_to_packed_flat_cpu(self): |
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self._test_padded_to_packed_helper([], "cpu") |
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def test_padded_to_packed_D1_cpu(self): |
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self._test_padded_to_packed_helper([1], "cpu") |
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def test_padded_to_packed_D16_cpu(self): |
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self._test_padded_to_packed_helper([16], "cpu") |
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def test_padded_to_packed_D16_9_cpu(self): |
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self._test_padded_to_packed_helper([16, 9], "cpu") |
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def test_padded_to_packed_D16_3_2_cpu(self): |
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self._test_padded_to_packed_helper([16, 3, 2], "cpu") |
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def test_padded_to_packed_flat_cuda(self): |
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device = get_random_cuda_device() |
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self._test_padded_to_packed_helper([], device) |
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def test_padded_to_packed_D1_cuda(self): |
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device = get_random_cuda_device() |
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self._test_padded_to_packed_helper([1], device) |
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def test_padded_to_packed_D16_cuda(self): |
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device = get_random_cuda_device() |
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self._test_padded_to_packed_helper([16], device) |
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def test_padded_to_packed_D16_9_cuda(self): |
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device = get_random_cuda_device() |
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self._test_padded_to_packed_helper([16, 9], device) |
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def test_padded_to_packed_D16_3_2_cuda(self): |
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device = get_random_cuda_device() |
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self._test_padded_to_packed_helper([16, 3, 2], device) |
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@staticmethod |
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def packed_to_padded_with_init( |
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num_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu" |
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): |
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meshes = TestPackedToPadded.init_meshes( |
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num_meshes, num_verts, num_faces, device |
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) |
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faces = meshes.faces_packed() |
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mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx() |
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max_faces = meshes.num_faces_per_mesh().max().item() |
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if num_d == 0: |
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values = torch.rand((faces.shape[0],), device=meshes.device) |
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else: |
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values = torch.rand((faces.shape[0], num_d), device=meshes.device) |
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torch.cuda.synchronize() |
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def out(): |
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packed_to_padded(values, mesh_to_faces_packed_first_idx, max_faces) |
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torch.cuda.synchronize() |
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return out |
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@staticmethod |
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def packed_to_padded_with_init_torch( |
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num_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu" |
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): |
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meshes = TestPackedToPadded.init_meshes( |
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num_meshes, num_verts, num_faces, device |
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) |
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faces = meshes.faces_packed() |
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mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx() |
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max_faces = meshes.num_faces_per_mesh().max().item() |
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if num_d == 0: |
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values = torch.rand((faces.shape[0],), device=meshes.device) |
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else: |
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values = torch.rand((faces.shape[0], num_d), device=meshes.device) |
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torch.cuda.synchronize() |
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def out(): |
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TestPackedToPadded.packed_to_padded_python( |
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values, mesh_to_faces_packed_first_idx, max_faces, device |
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) |
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torch.cuda.synchronize() |
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return out |
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