# 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 @pytest.mark.parametrize('mask', [ torch.ones((28, 28)), torch.zeros((28, 28)), torch.rand(28, 28) > 0.5, torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]]) ]) 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')