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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os.path as osp | |
| import tempfile | |
| import numpy as np | |
| import pytest | |
| import torch | |
| from mmdet.core.bbox import distance2bbox | |
| from mmdet.core.mask.structures import BitmapMasks, PolygonMasks | |
| from mmdet.core.utils import (center_of_mass, filter_scores_and_topk, | |
| flip_tensor, mask2ndarray, select_single_mlvl) | |
| from mmdet.utils import find_latest_checkpoint | |
| def dummy_raw_polygon_masks(size): | |
| """ | |
| Args: | |
| size (tuple): expected shape of dummy masks, (N, H, W) | |
| Return: | |
| list[list[ndarray]]: dummy mask | |
| """ | |
| num_obj, height, width = size | |
| polygons = [] | |
| for _ in range(num_obj): | |
| num_points = np.random.randint(5) * 2 + 6 | |
| polygons.append([np.random.uniform(0, min(height, width), num_points)]) | |
| return polygons | |
| def test_mask2ndarray(): | |
| raw_masks = np.ones((3, 28, 28)) | |
| bitmap_mask = BitmapMasks(raw_masks, 28, 28) | |
| output_mask = mask2ndarray(bitmap_mask) | |
| assert np.allclose(raw_masks, output_mask) | |
| raw_masks = dummy_raw_polygon_masks((3, 28, 28)) | |
| polygon_masks = PolygonMasks(raw_masks, 28, 28) | |
| output_mask = mask2ndarray(polygon_masks) | |
| assert output_mask.shape == (3, 28, 28) | |
| raw_masks = np.ones((3, 28, 28)) | |
| output_mask = mask2ndarray(raw_masks) | |
| assert np.allclose(raw_masks, output_mask) | |
| raw_masks = torch.ones((3, 28, 28)) | |
| output_mask = mask2ndarray(raw_masks) | |
| assert np.allclose(raw_masks, output_mask) | |
| # test unsupported type | |
| raw_masks = [] | |
| with pytest.raises(TypeError): | |
| output_mask = mask2ndarray(raw_masks) | |
| def test_distance2bbox(): | |
| point = torch.Tensor([[74., 61.], [-29., 106.], [138., 61.], [29., 170.]]) | |
| distance = torch.Tensor([[0., 0, 1., 1.], [1., 2., 10., 6.], | |
| [22., -29., 138., 61.], [54., -29., 170., 61.]]) | |
| expected_decode_bboxes = torch.Tensor([[74., 61., 75., 62.], | |
| [0., 104., 0., 112.], | |
| [100., 90., 100., 120.], | |
| [0., 120., 100., 120.]]) | |
| out_bbox = distance2bbox(point, distance, max_shape=(120, 100)) | |
| assert expected_decode_bboxes.allclose(out_bbox) | |
| out = distance2bbox(point, distance, max_shape=torch.Tensor((120, 100))) | |
| assert expected_decode_bboxes.allclose(out) | |
| batch_point = point.unsqueeze(0).repeat(2, 1, 1) | |
| batch_distance = distance.unsqueeze(0).repeat(2, 1, 1) | |
| batch_out = distance2bbox( | |
| batch_point, batch_distance, max_shape=(120, 100))[0] | |
| assert out.allclose(batch_out) | |
| batch_out = distance2bbox( | |
| batch_point, batch_distance, max_shape=[(120, 100), (120, 100)])[0] | |
| assert out.allclose(batch_out) | |
| batch_out = distance2bbox(point, batch_distance, max_shape=(120, 100))[0] | |
| assert out.allclose(batch_out) | |
| # test max_shape is not equal to batch | |
| with pytest.raises(AssertionError): | |
| distance2bbox( | |
| batch_point, | |
| batch_distance, | |
| max_shape=[(120, 100), (120, 100), (32, 32)]) | |
| rois = torch.zeros((0, 4)) | |
| deltas = torch.zeros((0, 4)) | |
| out = distance2bbox(rois, deltas, max_shape=(120, 100)) | |
| assert rois.shape == out.shape | |
| rois = torch.zeros((2, 0, 4)) | |
| deltas = torch.zeros((2, 0, 4)) | |
| out = distance2bbox(rois, deltas, max_shape=(120, 100)) | |
| assert rois.shape == out.shape | |
| def test_center_of_mass(mask): | |
| center_h, center_w = center_of_mass(mask) | |
| if mask.shape[0] == 4: | |
| assert center_h == 1.5 | |
| assert center_w == 1.5 | |
| assert isinstance(center_h, torch.Tensor) \ | |
| and isinstance(center_w, torch.Tensor) | |
| assert 0 <= center_h <= 28 \ | |
| and 0 <= center_w <= 28 | |
| def test_flip_tensor(): | |
| img = np.random.random((1, 3, 10, 10)) | |
| src_tensor = torch.from_numpy(img) | |
| # test flip_direction parameter error | |
| with pytest.raises(AssertionError): | |
| flip_tensor(src_tensor, 'flip') | |
| # test tensor dimension | |
| with pytest.raises(AssertionError): | |
| flip_tensor(src_tensor[0], 'vertical') | |
| hfilp_tensor = flip_tensor(src_tensor, 'horizontal') | |
| expected_hflip_tensor = torch.from_numpy(img[..., ::-1, :].copy()) | |
| expected_hflip_tensor.allclose(hfilp_tensor) | |
| vfilp_tensor = flip_tensor(src_tensor, 'vertical') | |
| expected_vflip_tensor = torch.from_numpy(img[..., ::-1].copy()) | |
| expected_vflip_tensor.allclose(vfilp_tensor) | |
| diag_filp_tensor = flip_tensor(src_tensor, 'diagonal') | |
| expected_diag_filp_tensor = torch.from_numpy(img[..., ::-1, ::-1].copy()) | |
| expected_diag_filp_tensor.allclose(diag_filp_tensor) | |
| def test_select_single_mlvl(): | |
| mlvl_tensors = [torch.rand(2, 1, 10, 10)] * 5 | |
| mlvl_tensor_list = select_single_mlvl(mlvl_tensors, 1) | |
| assert len(mlvl_tensor_list) == 5 and mlvl_tensor_list[0].ndim == 3 | |
| def test_filter_scores_and_topk(): | |
| score = torch.tensor([[0.1, 0.3, 0.2], [0.12, 0.7, 0.9], [0.02, 0.8, 0.08], | |
| [0.4, 0.1, 0.08]]) | |
| bbox_pred = torch.tensor([[0.2, 0.3], [0.4, 0.7], [0.1, 0.1], [0.5, 0.1]]) | |
| score_thr = 0.15 | |
| nms_pre = 4 | |
| # test results type error | |
| with pytest.raises(NotImplementedError): | |
| filter_scores_and_topk(score, score_thr, nms_pre, (score, )) | |
| filtered_results = filter_scores_and_topk( | |
| score, score_thr, nms_pre, results=dict(bbox_pred=bbox_pred)) | |
| filtered_score, labels, keep_idxs, results = filtered_results | |
| assert filtered_score.allclose(torch.tensor([0.9, 0.8, 0.7, 0.4])) | |
| assert labels.allclose(torch.tensor([2, 1, 1, 0])) | |
| assert keep_idxs.allclose(torch.tensor([1, 2, 1, 3])) | |
| assert results['bbox_pred'].allclose( | |
| torch.tensor([[0.4, 0.7], [0.1, 0.1], [0.4, 0.7], [0.5, 0.1]])) | |
| def test_find_latest_checkpoint(): | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| path = tmpdir | |
| latest = find_latest_checkpoint(path) | |
| # There are no checkpoints in the path. | |
| assert latest is None | |
| path = osp.join(tmpdir, 'none') | |
| latest = find_latest_checkpoint(path) | |
| # The path does not exist. | |
| assert latest is None | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| with open(osp.join(tmpdir, 'latest.pth'), 'w') as f: | |
| f.write('latest') | |
| path = tmpdir | |
| latest = find_latest_checkpoint(path) | |
| assert latest == osp.join(tmpdir, 'latest.pth') | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| with open(osp.join(tmpdir, 'iter_4000.pth'), 'w') as f: | |
| f.write('iter_4000') | |
| with open(osp.join(tmpdir, 'iter_8000.pth'), 'w') as f: | |
| f.write('iter_8000') | |
| path = tmpdir | |
| latest = find_latest_checkpoint(path) | |
| assert latest == osp.join(tmpdir, 'iter_8000.pth') | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| with open(osp.join(tmpdir, 'epoch_1.pth'), 'w') as f: | |
| f.write('epoch_1') | |
| with open(osp.join(tmpdir, 'epoch_2.pth'), 'w') as f: | |
| f.write('epoch_2') | |
| path = tmpdir | |
| latest = find_latest_checkpoint(path) | |
| assert latest == osp.join(tmpdir, 'epoch_2.pth') | |