96 lines
4.2 KiB
Python
96 lines
4.2 KiB
Python
# Copyright (c) 2019,20-22, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import torch
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from kaolin.io import utils
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from kaolin.utils.testing import contained_torch_equal
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class TestUtils:
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@pytest.mark.parametrize(
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'handler', [utils.heterogeneous_mesh_handler_naive_homogenize, utils.mesh_handler_naive_triangulate])
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@pytest.mark.parametrize(
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'face_assignment_mode', [0, 1, 2])
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def test_mesh_handler_naive_triangulate(self, handler, face_assignment_mode):
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N = 15
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vertices = torch.rand((N, 3), dtype=torch.float32)
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face_vertex_counts = torch.LongTensor([3, 4, 5, 3, 6])
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faces = torch.LongTensor(
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[0, 1, 2, # Face 0 -> 1 face idx [0]
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2, 1, 3, 4, # Face 1 -> 2 faces idx [1, 2]
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4, 5, 6, 7, 8, # Face 2 -> 3 faces idx [3, 4, 5]
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3, 4, 6, # Face 3 -> 1 face idx [6]
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8, 9, 10, 11, 12, 13]) # Face 4 -> 4 faces idx [7, 8, 9, 10]
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expected_faces = torch.LongTensor(
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[[0, 1, 2],
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[2, 1, 3], [2, 3, 4],
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[4, 5, 6], [4, 6, 7], [4, 7, 8],
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[3, 4, 6],
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[8, 9, 10], [8, 10, 11], [8, 11, 12], [8, 12, 13]])
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expected_num_faces = 11
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expected_face_vertex_counts = torch.LongTensor([3 for _ in range(expected_num_faces)])
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face_uvs_idx = torch.LongTensor(
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[0, 1, 2, # UVs for face 0
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10, 11, 12, 13, # UVs for face 1
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20, 21, 22, 23, 24, # UVs for face 2
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30, 31, 32, # UVs for face 3
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40, 41, 42, 43, 44, 45]) # UVs for face 4
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expected_face_uvs_idx = torch.LongTensor(
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[[0, 1, 2],
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[10, 11, 12], [10, 12, 13],
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[20, 21, 22], [20, 22, 23], [20, 23, 24],
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[30, 31, 32],
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[40, 41, 42], [40, 42, 43], [40, 43, 44], [40, 44, 45]])
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# assignments to faces
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face_assignments = None
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expected_face_assignments = None
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with_assignments = face_assignment_mode > 0
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if with_assignments:
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if face_assignment_mode == 1: # 1D tensors for face assignemtns replaced with new face indices
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face_assignments = {
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'1': torch.LongTensor([0, 2]),
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'2': torch.LongTensor([1, 3, 4])}
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expected_face_assignments = {
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'1': torch.LongTensor([0, 3, 4, 5]),
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'2': torch.LongTensor([1, 2, 6, 7, 8, 9, 10])}
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else: # 2D tensors of start and end face_idx, replaced with new start and end face_idx
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face_assignments = {
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'cat': torch.LongTensor([[0, 2], [3, 4], [2, 5]]),
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'dog': torch.LongTensor([[1, 3]])}
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expected_face_assignments = {
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'cat': torch.LongTensor([[0, 3], [6, 7], [3, 11]]),
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'dog': torch.LongTensor([[1, 6]])}
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res = handler(
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vertices, face_vertex_counts, faces, face_uvs_idx, face_assignments=face_assignments)
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assert len(res) == (5 if with_assignments else 4)
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new_vertices = res[0]
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new_face_vertex_counts = res[1]
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new_faces = res[2]
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new_face_uvs_idx = res[3]
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assert torch.allclose(new_vertices, vertices)
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assert torch.equal(new_face_vertex_counts, expected_face_vertex_counts)
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assert torch.equal(new_faces, expected_faces)
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assert torch.equal(new_face_uvs_idx, expected_face_uvs_idx)
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if with_assignments:
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new_face_assignments = res[4]
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assert contained_torch_equal(new_face_assignments, expected_face_assignments)
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