| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| ''' Testing customized ops. ''' | |
| import torch | |
| from torch.autograd import gradcheck | |
| import numpy as np | |
| import os | |
| import sys | |
| BASE_DIR = os.path.dirname(os.path.abspath(__file__)) | |
| sys.path.append(BASE_DIR) | |
| import pointnet2_utils | |
| def test_interpolation_grad(): | |
| batch_size = 1 | |
| feat_dim = 2 | |
| m = 4 | |
| feats = torch.randn(batch_size, feat_dim, m, requires_grad=True).float().cuda() | |
| def interpolate_func(inputs): | |
| idx = torch.from_numpy(np.array([[[0,1,2],[1,2,3]]])).int().cuda() | |
| weight = torch.from_numpy(np.array([[[1,1,1],[2,2,2]]])).float().cuda() | |
| interpolated_feats = pointnet2_utils.three_interpolate(inputs, idx, weight) | |
| return interpolated_feats | |
| assert (gradcheck(interpolate_func, feats, atol=1e-1, rtol=1e-1)) | |
| if __name__=='__main__': | |
| test_interpolation_grad() | |