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74e8b89b17
This is an almost exact duplicate of #2500 (that was made to the `master` branch) now applied to the `1.x` branch. --- ## Motivation I want to fix a bug through this PR. The bug occurs when two options -- `reduce_zero_label=True`, and custom classes are used. `reduce_zero_label` remaps the GT seg labels by remapping the zero-class to 255 which is ignored. Conceptually, this should occur *before* the `label_map` is applied, which maps *already reduced labels*. However, currently, the `label_map` is applied before the zero label is reduced. ## Modification The modification is simple: - I've just interchanged the order of the two operations by moving a few lines from bottom to top. - I've added a test that passes when the fix is introduced, and fails on the original `master` branch. ## BC-breaking (Optional) I do not anticipate this change braking any backward-compatibility. ## Checklist - [x] Pre-commit or other linting tools are used to fix the potential lint issues. - _I've fixed all linting/pre-commit errors._ - [x] The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. - _I've added a unit test._ - [x] If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. - _I don't think this change affects MMDet or MMDet3D._ - [x] The documentation has been modified accordingly, like docstring or example tutorials. - _This change fixes an existing bug and doesn't require modifying any documentation/docstring._
279 lines
10 KiB
Python
279 lines
10 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import os.path as osp
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import tempfile
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import mmcv
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import numpy as np
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from mmcv.transforms import LoadImageFromFile
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from mmseg.datasets.transforms import (LoadAnnotations,
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LoadBiomedicalAnnotation,
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LoadBiomedicalData,
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LoadBiomedicalImageFromFile,
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LoadImageFromNDArray)
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class TestLoading:
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@classmethod
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def setup_class(cls):
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cls.data_prefix = osp.join(osp.dirname(__file__), '../data')
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def test_load_img(self):
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results = dict(img_path=osp.join(self.data_prefix, 'color.jpg'))
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transform = LoadImageFromFile()
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results = transform(copy.deepcopy(results))
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assert results['img_path'] == osp.join(self.data_prefix, 'color.jpg')
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assert results['img'].shape == (288, 512, 3)
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assert results['img'].dtype == np.uint8
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assert results['ori_shape'] == results['img'].shape[:2]
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assert repr(transform) == transform.__class__.__name__ + \
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"(ignore_empty=False, to_float32=False, color_type='color'," + \
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" imdecode_backend='cv2', file_client_args={'backend': 'disk'})"
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# to_float32
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transform = LoadImageFromFile(to_float32=True)
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results = transform(copy.deepcopy(results))
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assert results['img'].dtype == np.float32
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# gray image
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results = dict(img_path=osp.join(self.data_prefix, 'gray.jpg'))
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transform = LoadImageFromFile()
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results = transform(copy.deepcopy(results))
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assert results['img'].shape == (288, 512, 3)
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assert results['img'].dtype == np.uint8
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transform = LoadImageFromFile(color_type='unchanged')
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results = transform(copy.deepcopy(results))
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assert results['img'].shape == (288, 512)
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assert results['img'].dtype == np.uint8
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def test_load_seg(self):
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seg_path = osp.join(self.data_prefix, 'seg.png')
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results = dict(
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seg_map_path=seg_path, reduce_zero_label=True, seg_fields=[])
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transform = LoadAnnotations()
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results = transform(copy.deepcopy(results))
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assert results['gt_seg_map'].shape == (288, 512)
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assert results['gt_seg_map'].dtype == np.uint8
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assert repr(transform) == transform.__class__.__name__ + \
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"(reduce_zero_label=True,imdecode_backend='pillow')" + \
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"file_client_args={'backend': 'disk'})"
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# reduce_zero_label
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transform = LoadAnnotations(reduce_zero_label=True)
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results = transform(copy.deepcopy(results))
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assert results['gt_seg_map'].shape == (288, 512)
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assert results['gt_seg_map'].dtype == np.uint8
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def test_load_seg_custom_classes(self):
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test_img = np.random.rand(10, 10)
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test_gt = np.zeros_like(test_img)
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test_gt[2:4, 2:4] = 1
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test_gt[2:4, 6:8] = 2
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test_gt[6:8, 2:4] = 3
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test_gt[6:8, 6:8] = 4
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tmp_dir = tempfile.TemporaryDirectory()
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img_path = osp.join(tmp_dir.name, 'img.jpg')
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gt_path = osp.join(tmp_dir.name, 'gt.png')
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mmcv.imwrite(test_img, img_path)
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mmcv.imwrite(test_gt, gt_path)
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# test only train with label with id 3
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results = dict(
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img_path=img_path,
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seg_map_path=gt_path,
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label_map={
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0: 0,
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1: 0,
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2: 0,
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3: 1,
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4: 0
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},
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reduce_zero_label=False,
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seg_fields=[])
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load_imgs = LoadImageFromFile()
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results = load_imgs(copy.deepcopy(results))
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load_anns = LoadAnnotations()
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results = load_anns(copy.deepcopy(results))
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gt_array = results['gt_seg_map']
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true_mask = np.zeros_like(gt_array)
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true_mask[6:8, 2:4] = 1
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assert results['seg_fields'] == ['gt_seg_map']
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assert gt_array.shape == (10, 10)
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assert gt_array.dtype == np.uint8
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np.testing.assert_array_equal(gt_array, true_mask)
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# test only train with label with id 4 and 3
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results = dict(
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img_path=osp.join(self.data_prefix, 'color.jpg'),
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seg_map_path=gt_path,
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label_map={
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0: 0,
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1: 0,
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2: 0,
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3: 2,
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4: 1
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},
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reduce_zero_label=False,
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seg_fields=[])
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load_imgs = LoadImageFromFile()
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results = load_imgs(copy.deepcopy(results))
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load_anns = LoadAnnotations()
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results = load_anns(copy.deepcopy(results))
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gt_array = results['gt_seg_map']
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true_mask = np.zeros_like(gt_array)
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true_mask[6:8, 2:4] = 2
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true_mask[6:8, 6:8] = 1
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assert results['seg_fields'] == ['gt_seg_map']
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assert gt_array.shape == (10, 10)
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assert gt_array.dtype == np.uint8
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np.testing.assert_array_equal(gt_array, true_mask)
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# test with removing a class and reducing zero label simultaneously
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results = dict(
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img_path=img_path,
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seg_map_path=gt_path,
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# since reduce_zero_label is True, there are only 4 real classes.
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# if the full set of classes is ["A", "B", "C", "D"], the
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# following label map simulates the dataset option
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# classes=["A", "C", "D"] which removes class "B".
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label_map={
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0: 0,
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1: 255, # simulate removing class 1
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2: 1,
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3: 2
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},
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reduce_zero_label=True, # reduce zero label
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seg_fields=[])
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load_imgs = LoadImageFromFile()
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results = load_imgs(copy.deepcopy(results))
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# reduce zero label
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load_anns = LoadAnnotations()
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results = load_anns(copy.deepcopy(results))
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gt_array = results['gt_seg_map']
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true_mask = np.ones_like(gt_array) * 255 # all zeros get mapped to 255
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true_mask[2:4, 2:4] = 0 # 1s are reduced to class 0 mapped to class 0
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true_mask[2:4, 6:8] = 255 # 2s are reduced to class 1 which is removed
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true_mask[6:8, 2:4] = 1 # 3s are reduced to class 2 mapped to class 1
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true_mask[6:8, 6:8] = 2 # 4s are reduced to class 3 mapped to class 2
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assert results['seg_fields'] == ['gt_seg_map']
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assert gt_array.shape == (10, 10)
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assert gt_array.dtype == np.uint8
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np.testing.assert_array_equal(gt_array, true_mask)
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# test no custom classes
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results = dict(
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img_path=img_path,
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seg_map_path=gt_path,
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reduce_zero_label=False,
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seg_fields=[])
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load_imgs = LoadImageFromFile()
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results = load_imgs(copy.deepcopy(results))
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load_anns = LoadAnnotations()
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results = load_anns(copy.deepcopy(results))
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gt_array = results['gt_seg_map']
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assert results['seg_fields'] == ['gt_seg_map']
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assert gt_array.shape == (10, 10)
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assert gt_array.dtype == np.uint8
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np.testing.assert_array_equal(gt_array, test_gt)
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tmp_dir.cleanup()
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def test_load_image_from_ndarray(self):
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results = {'img': np.zeros((256, 256, 3), dtype=np.uint8)}
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transform = LoadImageFromNDArray()
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results = transform(results)
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assert results['img'].shape == (256, 256, 3)
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assert results['img'].dtype == np.uint8
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assert results['img_shape'] == (256, 256)
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assert results['ori_shape'] == (256, 256)
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# to_float32
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transform = LoadImageFromNDArray(to_float32=True)
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results = transform(copy.deepcopy(results))
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assert results['img'].dtype == np.float32
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# test repr
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transform = LoadImageFromNDArray()
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assert repr(transform) == ('LoadImageFromNDArray('
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'ignore_empty=False, '
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'to_float32=False, '
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"color_type='color', "
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"imdecode_backend='cv2', "
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"file_client_args={'backend': 'disk'})")
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def test_load_biomedical_img(self):
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results = dict(
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img_path=osp.join(self.data_prefix, 'biomedical.nii.gz'))
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transform = LoadBiomedicalImageFromFile()
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results = transform(copy.deepcopy(results))
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assert results['img_path'] == osp.join(self.data_prefix,
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'biomedical.nii.gz')
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assert len(results['img'].shape) == 4
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assert results['img'].dtype == np.float32
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assert results['ori_shape'] == results['img'].shape[1:]
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assert repr(transform) == ('LoadBiomedicalImageFromFile('
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"decode_backend='nifti', "
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'to_xyz=False, '
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'to_float32=True, '
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"file_client_args={'backend': 'disk'})")
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def test_load_biomedical_annotation(self):
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results = dict(
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seg_map_path=osp.join(self.data_prefix, 'biomedical_ann.nii.gz'))
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transform = LoadBiomedicalAnnotation()
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results = transform(copy.deepcopy(results))
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assert len(results['gt_seg_map'].shape) == 3
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assert results['gt_seg_map'].dtype == np.float32
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def test_load_biomedical_data(self):
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input_results = dict(
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img_path=osp.join(self.data_prefix, 'biomedical.npy'))
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transform = LoadBiomedicalData(with_seg=True)
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results = transform(copy.deepcopy(input_results))
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assert results['img_path'] == osp.join(self.data_prefix,
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'biomedical.npy')
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assert results['img'][0].shape == results['gt_seg_map'].shape
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assert results['img'].dtype == np.float32
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assert results['ori_shape'] == results['img'].shape[1:]
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assert repr(transform) == ('LoadBiomedicalData('
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'with_seg=True, '
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"decode_backend='numpy', "
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'to_xyz=False, '
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"file_client_args={'backend': 'disk'})")
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transform = LoadBiomedicalData(with_seg=False)
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results = transform(copy.deepcopy(input_results))
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assert len(results['img'].shape) == 4
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assert results.get('gt_seg_map') is None
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assert repr(transform) == ('LoadBiomedicalData('
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'with_seg=False, '
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"decode_backend='numpy', "
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'to_xyz=False, '
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"file_client_args={'backend': 'disk'})")
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