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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import numpy as np | |
| import pytest | |
| import torch | |
| from mmpose.models.necks import PoseWarperNeck | |
| def test_posewarper_neck(): | |
| """Test PoseWarperNeck.""" | |
| with pytest.raises(AssertionError): | |
| # test value of trans_conv_kernel | |
| _ = PoseWarperNeck( | |
| out_channels=3, | |
| in_channels=512, | |
| inner_channels=128, | |
| trans_conv_kernel=2) | |
| with pytest.raises(TypeError): | |
| # test type of res_blocks_cfg | |
| _ = PoseWarperNeck( | |
| out_channels=3, | |
| in_channels=512, | |
| inner_channels=128, | |
| res_blocks_cfg=2) | |
| with pytest.raises(AssertionError): | |
| # test value of dilations | |
| neck = PoseWarperNeck( | |
| out_channels=3, in_channels=512, inner_channels=128, dilations=[]) | |
| in_channels = 48 | |
| out_channels = 17 | |
| inner_channels = 128 | |
| neck = PoseWarperNeck( | |
| in_channels=in_channels, | |
| out_channels=out_channels, | |
| inner_channels=inner_channels) | |
| with pytest.raises(TypeError): | |
| # the forward require two arguments: inputs and frame_weight | |
| _ = neck(1) | |
| with pytest.raises(AssertionError): | |
| # the inputs to PoseWarperNeck must be list or tuple | |
| _ = neck(1, [0.1]) | |
| # test the case when num_frames * batch_size if larger than | |
| # the default value of 'im2col_step' but can not be divided | |
| # by it in mmcv.ops.deform_conv | |
| b_0 = 8 # batch_size | |
| b_1 = 16 | |
| h_0 = 4 # image height | |
| h_1 = 2 | |
| num_frame_0 = 2 | |
| num_frame_1 = 5 | |
| # test input format | |
| # B, C, H, W | |
| x0_shape = (b_0, in_channels, h_0, h_0) | |
| x1_shape = (b_1, in_channels, h_1, h_1) | |
| # test concat_tensors case | |
| # at the same time, features output from backbone like ResNet is Tensors | |
| x0_shape = (b_0 * num_frame_0, in_channels, h_0, h_0) | |
| x0 = _demo_inputs(x0_shape, length=1) | |
| frame_weight_0 = np.random.uniform(0, 1, num_frame_0) | |
| # test forward | |
| y = neck(x0, frame_weight_0) | |
| assert y.shape == torch.Size([b_0, out_channels, h_0, h_0]) | |
| # test concat_tensors case | |
| # this time, features output from backbone like HRNet | |
| # is list of Tensors rather than Tensors | |
| x0_shape = (b_0 * num_frame_0, in_channels, h_0, h_0) | |
| x0 = _demo_inputs(x0_shape, length=2) | |
| x0 = [x0] | |
| frame_weight_0 = np.random.uniform(0, 1, num_frame_0) | |
| # test forward | |
| y = neck(x0, frame_weight_0) | |
| assert y.shape == torch.Size([b_0, out_channels, h_0, h_0]) | |
| # test not concat_tensors case | |
| # at the same time, features output from backbone like ResNet is Tensors | |
| x1_shape = (b_1, in_channels, h_1, h_1) | |
| x1 = _demo_inputs(x1_shape, length=num_frame_1) | |
| frame_weight_1 = np.random.uniform(0, 1, num_frame_1) | |
| # test forward | |
| y = neck(x1, frame_weight_1) | |
| assert y.shape == torch.Size([b_1, out_channels, h_1, h_1]) | |
| # test not concat_tensors case | |
| # this time, features output from backbone like HRNet | |
| # is list of Tensors rather than Tensors | |
| x1_shape = (b_1, in_channels, h_1, h_1) | |
| x1 = _demo_inputs(x1_shape, length=2) | |
| x1 = [x1 for _ in range(num_frame_1)] | |
| frame_weight_1 = np.random.uniform(0, 1, num_frame_1) | |
| # test forward | |
| y = neck(x1, frame_weight_1) | |
| assert y.shape == torch.Size([b_1, out_channels, h_1, h_1]) | |
| # test special case that when in concat_tensors case, | |
| # batch_size * num_frames is larger than the default value | |
| # 'im2col_step' in mmcv.ops.deform_conv, but can not be divided by it | |
| # see https://github.com/open-mmlab/mmcv/issues/1440 | |
| x1_shape = (b_1 * num_frame_1, in_channels, h_1, h_1) | |
| x1 = _demo_inputs(x1_shape, length=2) | |
| x1 = [x1] | |
| frame_weight_0 = np.random.uniform(0, 1, num_frame_1) | |
| y = neck(x1, frame_weight_1) | |
| assert y.shape == torch.Size([b_1, out_channels, h_1, h_1]) | |
| # test the inappropriate value of `im2col_step` | |
| neck = PoseWarperNeck( | |
| in_channels=in_channels, | |
| out_channels=out_channels, | |
| inner_channels=inner_channels, | |
| im2col_step=32) | |
| with pytest.raises(AssertionError): | |
| _ = neck(x1, frame_weight_1) | |
| def _demo_inputs(input_shape=(80, 48, 4, 4), length=1): | |
| """Create a superset of inputs needed to run backbone. | |
| Args: | |
| input_shape (tuple): input batch dimensions. | |
| Default: (1, 3, 64, 64). | |
| length (int): the length of output list | |
| nested (bool): whether the output Tensor is double-nested list. | |
| """ | |
| imgs = [ | |
| torch.FloatTensor(np.random.random(input_shape)) for _ in range(length) | |
| ] | |
| return imgs | |