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mmsegmentation/tests/test_data/test_dataset.py
Siddharth Ancha 190063fbb4
[Fix] Fix reduce_zero_label in evaluation (#2504) 
## Motivation

Through this PR, I (1) fix a bug, and (2) perform some associated cleanup, and (3) add a unit test. The bug occurs during evaluation when two options -- `reduce_zero_label=True`, and custom classes are used. The bug was that the `reduce_zero_label` is not properly propagated (see details below).

## Modification

1. **Bugfix**

The bug occurs [in the initialization of `CustomDataset`](5d49918b3c/mmseg/datasets/custom.py (L108-L110)) where the `reduce_zero_label` flag is not propagated to its member `self.gt_seg_map_loader_cfg`:

```python
self.gt_seg_map_loader = LoadAnnotations(
) if gt_seg_map_loader_cfg is None else LoadAnnotations(
    **gt_seg_map_loader_cfg)
```

Because the `reduce_zero_label` flag was not being propagated, the zero label reduction was being [unnecessarily and explicitly duplicated during the evaluation](5d49918b3c/mmseg/core/evaluation/metrics.py (L66-L69)).

As pointed in a previous PR (#2500), `reduce_zero_label` must occur before applying the `label_map`. Due to this bug, the order gets reversed when both features are used simultaneously.

This has been fixed to:

```python
self.gt_seg_map_loader = LoadAnnotations(
    reduce_zero_label=reduce_zero_label, **gt_seg_map_loader_cfg)
```

2. **Cleanup**

Due to the bug fix, since both `reduce_zero_label` and `label_map` are being applied in `get_gt_seg_map_by_idx()` (i.e. `LoadAnnotations.__call__()`), the evaluation does not need perform them anymore. However, for backwards compatibility, the evaluation keeps previous input arguments.

This was pointed out for `label_map` in a previous issue (#1415) that the `label_map` should  not be applied in the evaluation. This was handled by [passing an empty dict](5d49918b3c/mmseg/datasets/custom.py (L306-L311)):

```python
# as the labels has been converted when dataset initialized
# in `get_palette_for_custom_classes ` this `label_map`
# should be `dict()`, see
# https://github.com/open-mmlab/mmsegmentation/issues/1415
# for more ditails
label_map=dict(),
reduce_zero_label=self.reduce_zero_label))
```

Similar to this, I now also set `reduce_label=False` since it is now also being handled by `get_gt_seg_map_by_idx()` (i.e. `LoadAnnotations.__call__()`).

3. **Unit test**

I've added a unit test that tests the `CustomDataset.pre_eval()` function when `reduce_zero_label=True` and custom classes are used. The test fails on the original `master` branch but passes with this fix.

## 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 test that passes when the fix is introduced, and fails on the original master branch._
- [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._
2023-01-30 12:01:20 +08:00

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# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import shutil
import tempfile
from typing import Generator
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
import torch
from PIL import Image
from mmseg.core.evaluation import get_classes, get_palette
from mmseg.datasets import (DATASETS, ADE20KDataset, CityscapesDataset,
COCOStuffDataset, ConcatDataset, CustomDataset,
ISPRSDataset, LoveDADataset, MultiImageMixDataset,
PascalVOCDataset, PotsdamDataset, RepeatDataset,
build_dataset, iSAIDDataset)
def test_classes():
assert list(CityscapesDataset.CLASSES) == get_classes('cityscapes')
assert list(PascalVOCDataset.CLASSES) == get_classes('voc') == get_classes(
'pascal_voc')
assert list(
ADE20KDataset.CLASSES) == get_classes('ade') == get_classes('ade20k')
assert list(COCOStuffDataset.CLASSES) == get_classes('cocostuff')
assert list(LoveDADataset.CLASSES) == get_classes('loveda')
assert list(PotsdamDataset.CLASSES) == get_classes('potsdam')
assert list(ISPRSDataset.CLASSES) == get_classes('vaihingen')
assert list(iSAIDDataset.CLASSES) == get_classes('isaid')
with pytest.raises(ValueError):
get_classes('unsupported')
def test_classes_file_path():
tmp_file = tempfile.NamedTemporaryFile()
classes_path = f'{tmp_file.name}.txt'
train_pipeline = [dict(type='LoadImageFromFile')]
kwargs = dict(pipeline=train_pipeline, img_dir='./', classes=classes_path)
# classes.txt with full categories
categories = get_classes('cityscapes')
with open(classes_path, 'w') as f:
f.write('\n'.join(categories))
assert list(CityscapesDataset(**kwargs).CLASSES) == categories
# classes.txt with sub categories
categories = ['road', 'sidewalk', 'building']
with open(classes_path, 'w') as f:
f.write('\n'.join(categories))
assert list(CityscapesDataset(**kwargs).CLASSES) == categories
# classes.txt with unknown categories
categories = ['road', 'sidewalk', 'unknown']
with open(classes_path, 'w') as f:
f.write('\n'.join(categories))
with pytest.raises(ValueError):
CityscapesDataset(**kwargs)
tmp_file.close()
os.remove(classes_path)
assert not osp.exists(classes_path)
def test_palette():
assert CityscapesDataset.PALETTE == get_palette('cityscapes')
assert PascalVOCDataset.PALETTE == get_palette('voc') == get_palette(
'pascal_voc')
assert ADE20KDataset.PALETTE == get_palette('ade') == get_palette('ade20k')
assert LoveDADataset.PALETTE == get_palette('loveda')
assert PotsdamDataset.PALETTE == get_palette('potsdam')
assert COCOStuffDataset.PALETTE == get_palette('cocostuff')
assert iSAIDDataset.PALETTE == get_palette('isaid')
with pytest.raises(ValueError):
get_palette('unsupported')
@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
@patch('mmseg.datasets.CustomDataset.__getitem__',
MagicMock(side_effect=lambda idx: idx))
def test_dataset_wrapper():
# CustomDataset.load_annotations = MagicMock()
# CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)
dataset_a = CustomDataset(img_dir=MagicMock(), pipeline=[])
len_a = 10
dataset_a.img_infos = MagicMock()
dataset_a.img_infos.__len__.return_value = len_a
dataset_b = CustomDataset(img_dir=MagicMock(), pipeline=[])
len_b = 20
dataset_b.img_infos = MagicMock()
dataset_b.img_infos.__len__.return_value = len_b
concat_dataset = ConcatDataset([dataset_a, dataset_b])
assert concat_dataset[5] == 5
assert concat_dataset[25] == 15
assert len(concat_dataset) == len(dataset_a) + len(dataset_b)
repeat_dataset = RepeatDataset(dataset_a, 10)
assert repeat_dataset[5] == 5
assert repeat_dataset[15] == 5
assert repeat_dataset[27] == 7
assert len(repeat_dataset) == 10 * len(dataset_a)
img_scale = (60, 60)
pipeline = [
dict(type='RandomMosaic', prob=1, img_scale=img_scale),
dict(type='RandomFlip', prob=0.5),
dict(type='Resize', img_scale=img_scale, keep_ratio=False),
]
CustomDataset.load_annotations = MagicMock()
results = []
for _ in range(2):
height = np.random.randint(10, 30)
weight = np.random.randint(10, 30)
img = np.ones((height, weight, 3))
gt_semantic_seg = np.random.randint(5, size=(height, weight))
results.append(dict(gt_semantic_seg=gt_semantic_seg, img=img))
classes = ['0', '1', '2', '3', '4']
palette = [(0, 0, 0), (1, 1, 1), (2, 2, 2), (3, 3, 3), (4, 4, 4)]
CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: results[idx])
dataset_a = CustomDataset(
img_dir=MagicMock(),
pipeline=[],
test_mode=True,
classes=classes,
palette=palette)
len_a = 2
dataset_a.img_infos = MagicMock()
dataset_a.img_infos.__len__.return_value = len_a
multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline)
assert len(multi_image_mix_dataset) == len(dataset_a)
for idx in range(len_a):
results_ = multi_image_mix_dataset[idx]
# test skip_type_keys
multi_image_mix_dataset = MultiImageMixDataset(
dataset_a, pipeline, skip_type_keys=('RandomFlip'))
for idx in range(len_a):
results_ = multi_image_mix_dataset[idx]
assert results_['img'].shape == (img_scale[0], img_scale[1], 3)
skip_type_keys = ('RandomFlip', 'Resize')
multi_image_mix_dataset.update_skip_type_keys(skip_type_keys)
for idx in range(len_a):
results_ = multi_image_mix_dataset[idx]
assert results_['img'].shape[:2] != img_scale
# test pipeline
with pytest.raises(TypeError):
pipeline = [['Resize']]
multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline)
def test_custom_dataset():
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(128, 256), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(128, 256),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# with img_dir and ann_dir
train_dataset = CustomDataset(
train_pipeline,
data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
img_dir='imgs/',
ann_dir='gts/',
img_suffix='img.jpg',
seg_map_suffix='gt.png')
assert len(train_dataset) == 5
# with img_dir, ann_dir, split
train_dataset = CustomDataset(
train_pipeline,
data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
img_dir='imgs/',
ann_dir='gts/',
img_suffix='img.jpg',
seg_map_suffix='gt.png',
split='splits/train.txt')
assert len(train_dataset) == 4
# no data_root
train_dataset = CustomDataset(
train_pipeline,
img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
ann_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/gts'),
img_suffix='img.jpg',
seg_map_suffix='gt.png')
assert len(train_dataset) == 5
# with data_root but img_dir/ann_dir are abs path
train_dataset = CustomDataset(
train_pipeline,
data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
img_dir=osp.abspath(
osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs')),
ann_dir=osp.abspath(
osp.join(osp.dirname(__file__), '../data/pseudo_dataset/gts')),
img_suffix='img.jpg',
seg_map_suffix='gt.png')
assert len(train_dataset) == 5
# test_mode=True
test_dataset = CustomDataset(
test_pipeline,
img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
img_suffix='img.jpg',
test_mode=True,
classes=('pseudo_class', ))
assert len(test_dataset) == 5
# training data get
train_data = train_dataset[0]
assert isinstance(train_data, dict)
# test data get
test_data = test_dataset[0]
assert isinstance(test_data, dict)
# get gt seg map
gt_seg_maps = train_dataset.get_gt_seg_maps(efficient_test=True)
assert isinstance(gt_seg_maps, Generator)
gt_seg_maps = list(gt_seg_maps)
assert len(gt_seg_maps) == 5
# format_results not implemented
with pytest.raises(NotImplementedError):
test_dataset.format_results([], '')
pseudo_results = []
for gt_seg_map in gt_seg_maps:
h, w = gt_seg_map.shape
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
# test past evaluation without CLASSES
with pytest.raises(TypeError):
eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
with pytest.raises(TypeError):
eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
with pytest.raises(TypeError):
eval_results = train_dataset.evaluate(
pseudo_results, metric=['mDice', 'mIoU'])
# test past evaluation with CLASSES
train_dataset.CLASSES = tuple(['a'] * 7)
eval_results = train_dataset.evaluate(pseudo_results, metric='mIoU')
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
assert isinstance(eval_results, dict)
assert 'mDice' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results, metric='mFscore')
assert isinstance(eval_results, dict)
assert 'mRecall' in eval_results
assert 'mPrecision' in eval_results
assert 'mFscore' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(
pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mDice' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
assert 'mFscore' in eval_results
assert 'mPrecision' in eval_results
assert 'mRecall' in eval_results
assert not np.isnan(eval_results['mIoU'])
assert not np.isnan(eval_results['mDice'])
assert not np.isnan(eval_results['mAcc'])
assert not np.isnan(eval_results['aAcc'])
assert not np.isnan(eval_results['mFscore'])
assert not np.isnan(eval_results['mPrecision'])
assert not np.isnan(eval_results['mRecall'])
# test evaluation with pre-eval and the dataset.CLASSES is necessary
train_dataset.CLASSES = tuple(['a'] * 7)
pseudo_results = []
for idx in range(len(train_dataset)):
h, w = gt_seg_maps[idx].shape
pseudo_result = np.random.randint(low=0, high=7, size=(h, w))
pseudo_results.extend(train_dataset.pre_eval(pseudo_result, idx))
eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
assert isinstance(eval_results, dict)
assert 'mDice' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results, metric='mFscore')
assert isinstance(eval_results, dict)
assert 'mRecall' in eval_results
assert 'mPrecision' in eval_results
assert 'mFscore' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(
pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mDice' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
assert 'mFscore' in eval_results
assert 'mPrecision' in eval_results
assert 'mRecall' in eval_results
assert not np.isnan(eval_results['mIoU'])
assert not np.isnan(eval_results['mDice'])
assert not np.isnan(eval_results['mAcc'])
assert not np.isnan(eval_results['aAcc'])
assert not np.isnan(eval_results['mFscore'])
assert not np.isnan(eval_results['mPrecision'])
assert not np.isnan(eval_results['mRecall'])
def test_custom_dataset_pre_eval():
"""Test pre-eval function of custom dataset with reduce zero label and
removed classes.
The GT segmentation contain 4 classes: "A", "B", "C", "D", as well as
a zero label. Therefore, the labels go from 0 to 4.
Then, we will remove class "C" while instantiating the dataset. Therefore,
pre-eval must reduce the zero label and also apply label_map in the correct
order.
"""
# create a dummy dataset on disk
img = np.random.rand(10, 10)
ann = np.zeros_like(img)
ann[2:4, 2:4] = 1
ann[2:4, 6:8] = 2
ann[6:8, 2:4] = 3
ann[6:8, 6:8] = 4
tmp_dir = tempfile.TemporaryDirectory()
img_path = osp.join(tmp_dir.name, 'img', '00000.jpg')
ann_path = osp.join(tmp_dir.name, 'ann', '00000.png')
import mmcv
mmcv.imwrite(img, img_path)
mmcv.imwrite(ann, ann_path)
class FourClassDatasetWithZeroLabel(CustomDataset):
CLASSES = ['A', 'B', 'C', 'D'] # 4 classes
PALETTE = [(0, 0, 0)] * 4 # dummy palette
# with img_dir, ann_dir, split
dataset = FourClassDatasetWithZeroLabel(
[],
classes=['A', 'B', 'D'], # original classes with class "C" removed
reduce_zero_label=True, # reduce zero label set to True
data_root=osp.join(osp.dirname(__file__), tmp_dir.name),
img_dir='img/',
ann_dir='ann/',
img_suffix='.jpg',
seg_map_suffix='.png')
assert len(dataset) == 1
# there are three classes ("A", "B", "D") that the network predicts
perfect_pred = np.zeros([10, 10], dtype=np.int64)
perfect_pred[2:4, 2:4] = 0 # 'A': 1 reduced to 0 that maps to 0
perfect_pred[2:4, 6:8] = 1 # 'B': 2 reduced to 1 that maps to 1
perfect_pred[6:8, 2:4] = 0 # 'C': 3 reduced to 2 that maps to -1, ignored
perfect_pred[6:8, 6:8] = 2 # 'D': 4 reduced to 3 that maps to 2
results = dataset.pre_eval([perfect_pred], [0])
from mmseg.core.evaluation.metrics import pre_eval_to_metrics
eval_results = pre_eval_to_metrics(results, ['mIoU', 'mDice', 'mFscore'])
# the results should be perfect
for metric in 'IoU', 'aAcc', 'Acc', 'Dice', 'Fscore', 'Precision', \
'Recall':
assert (eval_results[metric] == 1.0).all()
tmp_dir.cleanup()
@pytest.mark.parametrize('separate_eval', [True, False])
def test_eval_concat_custom_dataset(separate_eval):
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(128, 256),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data_root = osp.join(osp.dirname(__file__), '../data/pseudo_dataset')
img_dir = 'imgs/'
ann_dir = 'gts/'
cfg1 = dict(
type='CustomDataset',
pipeline=test_pipeline,
data_root=data_root,
img_dir=img_dir,
ann_dir=ann_dir,
img_suffix='img.jpg',
seg_map_suffix='gt.png',
classes=tuple(['a'] * 7))
dataset1 = build_dataset(cfg1)
assert len(dataset1) == 5
# get gt seg map
gt_seg_maps = dataset1.get_gt_seg_maps(efficient_test=True)
assert isinstance(gt_seg_maps, Generator)
gt_seg_maps = list(gt_seg_maps)
assert len(gt_seg_maps) == 5
# test past evaluation
pseudo_results = []
for gt_seg_map in gt_seg_maps:
h, w = gt_seg_map.shape
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
eval_results1 = dataset1.evaluate(
pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
# We use same dir twice for simplicity
# with ann_dir
cfg2 = dict(
type='CustomDataset',
pipeline=test_pipeline,
data_root=data_root,
img_dir=[img_dir, img_dir],
ann_dir=[ann_dir, ann_dir],
img_suffix='img.jpg',
seg_map_suffix='gt.png',
classes=tuple(['a'] * 7),
separate_eval=separate_eval)
dataset2 = build_dataset(cfg2)
assert isinstance(dataset2, ConcatDataset)
assert len(dataset2) == 10
eval_results2 = dataset2.evaluate(
pseudo_results * 2, metric=['mIoU', 'mDice', 'mFscore'])
if separate_eval:
assert eval_results1['mIoU'] == eval_results2[
'0_mIoU'] == eval_results2['1_mIoU']
assert eval_results1['mDice'] == eval_results2[
'0_mDice'] == eval_results2['1_mDice']
assert eval_results1['mAcc'] == eval_results2[
'0_mAcc'] == eval_results2['1_mAcc']
assert eval_results1['aAcc'] == eval_results2[
'0_aAcc'] == eval_results2['1_aAcc']
assert eval_results1['mFscore'] == eval_results2[
'0_mFscore'] == eval_results2['1_mFscore']
assert eval_results1['mPrecision'] == eval_results2[
'0_mPrecision'] == eval_results2['1_mPrecision']
assert eval_results1['mRecall'] == eval_results2[
'0_mRecall'] == eval_results2['1_mRecall']
else:
assert eval_results1['mIoU'] == eval_results2['mIoU']
assert eval_results1['mDice'] == eval_results2['mDice']
assert eval_results1['mAcc'] == eval_results2['mAcc']
assert eval_results1['aAcc'] == eval_results2['aAcc']
assert eval_results1['mFscore'] == eval_results2['mFscore']
assert eval_results1['mPrecision'] == eval_results2['mPrecision']
assert eval_results1['mRecall'] == eval_results2['mRecall']
# test get dataset_idx and sample_idx from ConcateDataset
dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(3)
assert dataset_idx == 0
assert sample_idx == 3
dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(7)
assert dataset_idx == 1
assert sample_idx == 2
dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(-7)
assert dataset_idx == 0
assert sample_idx == 3
# test negative indice exceed length of dataset
with pytest.raises(ValueError):
dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(-11)
# test negative indice value
indice = -6
dataset_idx1, sample_idx1 = dataset2.get_dataset_idx_and_sample_idx(indice)
dataset_idx2, sample_idx2 = dataset2.get_dataset_idx_and_sample_idx(
len(dataset2) + indice)
assert dataset_idx1 == dataset_idx2
assert sample_idx1 == sample_idx2
# test evaluation with pre-eval and the dataset.CLASSES is necessary
pseudo_results = []
eval_results1 = []
for idx in range(len(dataset1)):
h, w = gt_seg_maps[idx].shape
pseudo_result = np.random.randint(low=0, high=7, size=(h, w))
pseudo_results.append(pseudo_result)
eval_results1.extend(dataset1.pre_eval(pseudo_result, idx))
assert len(eval_results1) == len(dataset1)
assert isinstance(eval_results1[0], tuple)
assert len(eval_results1[0]) == 4
assert isinstance(eval_results1[0][0], torch.Tensor)
eval_results1 = dataset1.evaluate(
eval_results1, metric=['mIoU', 'mDice', 'mFscore'])
pseudo_results = pseudo_results * 2
eval_results2 = []
for idx in range(len(dataset2)):
eval_results2.extend(dataset2.pre_eval(pseudo_results[idx], idx))
assert len(eval_results2) == len(dataset2)
assert isinstance(eval_results2[0], tuple)
assert len(eval_results2[0]) == 4
assert isinstance(eval_results2[0][0], torch.Tensor)
eval_results2 = dataset2.evaluate(
eval_results2, metric=['mIoU', 'mDice', 'mFscore'])
if separate_eval:
assert eval_results1['mIoU'] == eval_results2[
'0_mIoU'] == eval_results2['1_mIoU']
assert eval_results1['mDice'] == eval_results2[
'0_mDice'] == eval_results2['1_mDice']
assert eval_results1['mAcc'] == eval_results2[
'0_mAcc'] == eval_results2['1_mAcc']
assert eval_results1['aAcc'] == eval_results2[
'0_aAcc'] == eval_results2['1_aAcc']
assert eval_results1['mFscore'] == eval_results2[
'0_mFscore'] == eval_results2['1_mFscore']
assert eval_results1['mPrecision'] == eval_results2[
'0_mPrecision'] == eval_results2['1_mPrecision']
assert eval_results1['mRecall'] == eval_results2[
'0_mRecall'] == eval_results2['1_mRecall']
else:
assert eval_results1['mIoU'] == eval_results2['mIoU']
assert eval_results1['mDice'] == eval_results2['mDice']
assert eval_results1['mAcc'] == eval_results2['mAcc']
assert eval_results1['aAcc'] == eval_results2['aAcc']
assert eval_results1['mFscore'] == eval_results2['mFscore']
assert eval_results1['mPrecision'] == eval_results2['mPrecision']
assert eval_results1['mRecall'] == eval_results2['mRecall']
# test batch_indices for pre eval
eval_results2 = dataset2.pre_eval(pseudo_results,
list(range(len(pseudo_results))))
assert len(eval_results2) == len(dataset2)
assert isinstance(eval_results2[0], tuple)
assert len(eval_results2[0]) == 4
assert isinstance(eval_results2[0][0], torch.Tensor)
eval_results2 = dataset2.evaluate(
eval_results2, metric=['mIoU', 'mDice', 'mFscore'])
if separate_eval:
assert eval_results1['mIoU'] == eval_results2[
'0_mIoU'] == eval_results2['1_mIoU']
assert eval_results1['mDice'] == eval_results2[
'0_mDice'] == eval_results2['1_mDice']
assert eval_results1['mAcc'] == eval_results2[
'0_mAcc'] == eval_results2['1_mAcc']
assert eval_results1['aAcc'] == eval_results2[
'0_aAcc'] == eval_results2['1_aAcc']
assert eval_results1['mFscore'] == eval_results2[
'0_mFscore'] == eval_results2['1_mFscore']
assert eval_results1['mPrecision'] == eval_results2[
'0_mPrecision'] == eval_results2['1_mPrecision']
assert eval_results1['mRecall'] == eval_results2[
'0_mRecall'] == eval_results2['1_mRecall']
else:
assert eval_results1['mIoU'] == eval_results2['mIoU']
assert eval_results1['mDice'] == eval_results2['mDice']
assert eval_results1['mAcc'] == eval_results2['mAcc']
assert eval_results1['aAcc'] == eval_results2['aAcc']
assert eval_results1['mFscore'] == eval_results2['mFscore']
assert eval_results1['mPrecision'] == eval_results2['mPrecision']
assert eval_results1['mRecall'] == eval_results2['mRecall']
def test_ade():
test_dataset = ADE20KDataset(
pipeline=[],
img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
assert len(test_dataset) == 5
# Test format_results
pseudo_results = []
for _ in range(len(test_dataset)):
h, w = (2, 2)
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
file_paths = test_dataset.format_results(pseudo_results, '.format_ade')
assert len(file_paths) == len(test_dataset)
temp = np.array(Image.open(file_paths[0]))
assert np.allclose(temp, pseudo_results[0] + 1)
shutil.rmtree('.format_ade')
@pytest.mark.parametrize('separate_eval', [True, False])
def test_concat_ade(separate_eval):
test_dataset = ADE20KDataset(
pipeline=[],
img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
assert len(test_dataset) == 5
concat_dataset = ConcatDataset([test_dataset, test_dataset],
separate_eval=separate_eval)
assert len(concat_dataset) == 10
# Test format_results
pseudo_results = []
for _ in range(len(concat_dataset)):
h, w = (2, 2)
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
# test format per image
file_paths = []
for i in range(len(pseudo_results)):
file_paths.extend(
concat_dataset.format_results([pseudo_results[i]],
'.format_ade',
indices=[i]))
assert len(file_paths) == len(concat_dataset)
temp = np.array(Image.open(file_paths[0]))
assert np.allclose(temp, pseudo_results[0] + 1)
shutil.rmtree('.format_ade')
# test default argument
file_paths = concat_dataset.format_results(pseudo_results, '.format_ade')
assert len(file_paths) == len(concat_dataset)
temp = np.array(Image.open(file_paths[0]))
assert np.allclose(temp, pseudo_results[0] + 1)
shutil.rmtree('.format_ade')
def test_cityscapes():
test_dataset = CityscapesDataset(
pipeline=[],
img_dir=osp.join(
osp.dirname(__file__),
'../data/pseudo_cityscapes_dataset/leftImg8bit'),
ann_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_cityscapes_dataset/gtFine'))
assert len(test_dataset) == 1
gt_seg_maps = list(test_dataset.get_gt_seg_maps())
# Test format_results
pseudo_results = []
for idx in range(len(test_dataset)):
h, w = gt_seg_maps[idx].shape
pseudo_results.append(np.random.randint(low=0, high=19, size=(h, w)))
file_paths = test_dataset.format_results(pseudo_results, '.format_city')
assert len(file_paths) == len(test_dataset)
temp = np.array(Image.open(file_paths[0]))
assert np.allclose(temp,
test_dataset._convert_to_label_id(pseudo_results[0]))
# Test cityscapes evaluate
test_dataset.evaluate(
pseudo_results, metric='cityscapes', imgfile_prefix='.format_city')
shutil.rmtree('.format_city')
@pytest.mark.parametrize('separate_eval', [True, False])
def test_concat_cityscapes(separate_eval):
cityscape_dataset = CityscapesDataset(
pipeline=[],
img_dir=osp.join(
osp.dirname(__file__),
'../data/pseudo_cityscapes_dataset/leftImg8bit'),
ann_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_cityscapes_dataset/gtFine'))
assert len(cityscape_dataset) == 1
with pytest.raises(NotImplementedError):
_ = ConcatDataset([cityscape_dataset, cityscape_dataset],
separate_eval=separate_eval)
ade_dataset = ADE20KDataset(
pipeline=[],
img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
assert len(ade_dataset) == 5
with pytest.raises(NotImplementedError):
_ = ConcatDataset([cityscape_dataset, ade_dataset],
separate_eval=separate_eval)
def test_loveda():
test_dataset = LoveDADataset(
pipeline=[],
img_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_loveda_dataset/img_dir'),
ann_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_loveda_dataset/ann_dir'))
assert len(test_dataset) == 3
gt_seg_maps = list(test_dataset.get_gt_seg_maps())
# Test format_results
pseudo_results = []
for idx in range(len(test_dataset)):
h, w = gt_seg_maps[idx].shape
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
file_paths = test_dataset.format_results(pseudo_results, '.format_loveda')
assert len(file_paths) == len(test_dataset)
# Test loveda evaluate
test_dataset.evaluate(
pseudo_results, metric='mIoU', imgfile_prefix='.format_loveda')
shutil.rmtree('.format_loveda')
def test_potsdam():
test_dataset = PotsdamDataset(
pipeline=[],
img_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_potsdam_dataset/img_dir'),
ann_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_potsdam_dataset/ann_dir'))
assert len(test_dataset) == 1
def test_vaihingen():
test_dataset = ISPRSDataset(
pipeline=[],
img_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_vaihingen_dataset/img_dir'),
ann_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_vaihingen_dataset/ann_dir'))
assert len(test_dataset) == 1
def test_isaid():
test_dataset = iSAIDDataset(
pipeline=[],
img_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_isaid_dataset/img_dir'),
ann_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_isaid_dataset/ann_dir'))
assert len(test_dataset) == 2
isaid_info = test_dataset.load_annotations(
img_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_isaid_dataset/img_dir'),
img_suffix='.png',
ann_dir=osp.join(
osp.dirname(__file__), '../data/pseudo_isaid_dataset/ann_dir'),
seg_map_suffix='.png',
split=osp.join(
osp.dirname(__file__),
'../data/pseudo_isaid_dataset/splits/train.txt'))
assert len(isaid_info) == 1
@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
@patch('mmseg.datasets.CustomDataset.__getitem__',
MagicMock(side_effect=lambda idx: idx))
@pytest.mark.parametrize('dataset, classes', [
('ADE20KDataset', ('wall', 'building')),
('CityscapesDataset', ('road', 'sidewalk')),
('CustomDataset', ('bus', 'car')),
('PascalVOCDataset', ('aeroplane', 'bicycle')),
])
def test_custom_classes_override_default(dataset, classes):
dataset_class = DATASETS.get(dataset)
original_classes = dataset_class.CLASSES
# Test setting classes as a tuple
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=classes,
test_mode=True)
assert custom_dataset.CLASSES != original_classes
assert custom_dataset.CLASSES == classes
# Test setting classes as a list
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=list(classes),
test_mode=True)
assert custom_dataset.CLASSES != original_classes
assert custom_dataset.CLASSES == list(classes)
# Test overriding not a subset
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=[classes[0]],
test_mode=True)
assert custom_dataset.CLASSES != original_classes
assert custom_dataset.CLASSES == [classes[0]]
# Test default behavior
if dataset_class is CustomDataset:
with pytest.raises(AssertionError):
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=None,
test_mode=True)
else:
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=None,
test_mode=True)
assert custom_dataset.CLASSES == original_classes
@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
@patch('mmseg.datasets.CustomDataset.__getitem__',
MagicMock(side_effect=lambda idx: idx))
def test_custom_dataset_random_palette_is_generated():
dataset = CustomDataset(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=('bus', 'car'),
test_mode=True)
assert len(dataset.PALETTE) == 2
for class_color in dataset.PALETTE:
assert len(class_color) == 3
assert all(x >= 0 and x <= 255 for x in class_color)
@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
@patch('mmseg.datasets.CustomDataset.__getitem__',
MagicMock(side_effect=lambda idx: idx))
def test_custom_dataset_custom_palette():
dataset = CustomDataset(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=('bus', 'car'),
palette=[[100, 100, 100], [200, 200, 200]],
test_mode=True)
assert tuple(dataset.PALETTE) == tuple([[100, 100, 100], [200, 200, 200]])
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