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4e9c26bbbc
mmsegmentation/tests/test_data/test_dataset.py
sennnnn 4e9c26bbbc [Refactor] Support progressive test with fewer memory cost () 
* Support progressive test with fewer memory cost.

* Temp code

* Using processor to refactor evaluation workflow.

* refactor eval hook.

* Fix process bar.

* Fix middle save argument.

* Modify some variable name of dataset evaluate api.

* Modify some viriable name of eval hook.

* Fix some priority bugs of eval hook.

* Depreciated efficient_test.

* Fix training progress blocked by eval hook.

* Depreciated old test api.

* Fix test api error.

* Modify outer api.

* Build a sampler test api.

* TODO: Refactor format_results.

* Modify variable names.

* Fix num_classes bug.

* Fix sampler index bug.

* Fix grammaly bug.

* Support batch sampler.

* More readable test api.

* Remove some command arg and fix eval hook bug.

* Support format-only arg.

* Modify format_results of datasets.

* Modify tool which use test apis.

* support cityscapes eval

* fixed cityscapes

* 1. Add comments for batch_sampler;

2. Keep eval hook api same and add deprecated warning;

3. Add doc string for dataset.pre_eval;

* Add efficient_test doc string.

* Modify test tool to compat old version.

* Modify eval hook to compat with old version.

* Modify test api to compat old version api.

* Sampler explanation.

* update warning

* Modify deploy_test.py

* compatible with old output, add efficient test back

* clear logic of exclusive

* Warning about efficient_test.

* Modify format_results save folder.

* Fix bugs of format_results.

* Modify deploy_test.py.

* Update doc

* Fix deploy test bugs.

* Fix custom dataset unit tests.

* Fix dataset unit tests.

* Fix eval hook unit tests.

* Fix some imcompatible.

* Add pre_eval argument for eval hooks.

* Update eval hook doc string.

* Make pre_eval false in default.

* Add unit tests for dataset format_results.

* Fix some comments and bc-breaking bug.

* Fix pre_eval set cfg field.

* Remove redundant codes.

Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com>
2021-08-19 20:44:58 -07:00

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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import shutil
from typing import Generator
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
from PIL import Image
from mmseg.core.evaluation import get_classes, get_palette
from mmseg.datasets import (DATASETS, ADE20KDataset, CityscapesDataset,
ConcatDataset, CustomDataset, PascalVOCDataset,
RepeatDataset)
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')
with pytest.raises(ValueError):
get_classes('unsupported')
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')
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)
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)
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([], '')
# 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_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=['mDice', 'mIoU'])
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
# 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
# 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
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')
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')
@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
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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