mirror of
https://github.com/open-mmlab/mmsegmentation.git
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bcafcdd2aa
* [Feature]Segformer re-implementation * Using act_cfg and norm_cfg to control activation and normalization * Split this PR into several little PRs * Fix lint error * Remove SegFormerHead * [Feature] Add segformer decode head and related train config * Add ade20K trainval support for segformer 1. Add related train and val configs; 2. Add AlignedResize; * Set arg: find_unused_parameters = True * parameters init refactor * 1. Refactor segformer backbone parameters init; 2. Remove rebundant functions and unit tests; * Remove rebundant codes * Replace Linear Layer to 1X1 Conv * Use nn.ModuleList to refactor segformer head. * Remove local to_xtuple * 1. Remove rebundant codes; 2. Modify module name; * Refactor the backbone of segformer using mmcv.cnn.bricks.transformer.py * Fix some code logic bugs. * Add mit_convert.py to match pretrain keys of segformer. * Resolve some comments. * 1. Add some assert to ensure right params; 2. Support flexible peconv position; * Add pe_index assert and fix unit test. * 1. Add doc string for MixVisionTransformer; 2. Add some unit tests for MixVisionTransformer; * Use hw_shape to pass shape of feature map. * 1. Fix doc string of MixVisionTransformer; 2. Simplify MixFFN; 3. Modify H, W to hw_shape; * Add more unit tests. * Add doc string for shape convertion functions. * Add some unit tests to improve code coverage. * Fix Segformer backbone pretrain weights match bug. * Modify configs of segformer. * resolve the shape convertion functions doc string. * Add pad_to_patch_size arg. * Support progressive test with fewer memory cost. * Modify default value of pad_to_patch_size arg. * 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. * Fix some bugs about model loading and eval hook. * Add ade20k 640x640 dataset. * Fix related segformer configs. * Depreciated efficient_test. * Fix training progress blocked by eval hook. * Depreciated old test api. * Modify error patch size. * Fix pretrain of mit_b0 * Fix the test api error. * Modify dataset base config. * 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. * Add part of benchmark results. * 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. * Update readme. * Update readme of segformer. * Updata readme of segformer. * Update segformer readme and fix segformer mit_b4. * Update readme of segformer. * Clean AlignedResize related config. * Clean code from pr #709 * Clean code from pr #709 * Add 512x512 segformer_mit-b5. * Fix lint. * Fix some segformer head bugs. * Add segformer unit tests. * Replace AlignedResize to ResizeToMultiple. * Modify readme of segformer. * Fix bug of ResizeToMultiple. * Add ResizeToMultiple unit tests. * Resolve conflict. * Simplify the implementation of ResizeToMultiple. * Update test results. * Fix multi-scale test error when resize_ratio=1.75 and input size=640x640. * Update segformer results. * Update Segformer results. * Fix some url bugs and pipelines bug. * Move ckpt convertion to tools. * Add segformer official pretrain weights usage. * Clean redundant codes. * Remove redundant codes. * Unfied format. * Add description for segformer converter. * Update workers.
499 lines
18 KiB
Python
499 lines
18 KiB
Python
import copy
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import os.path as osp
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import mmcv
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import numpy as np
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import pytest
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from mmcv.utils import build_from_cfg
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from PIL import Image
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from mmseg.datasets.builder import PIPELINES
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def test_resize_to_multiple():
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transform = dict(type='ResizeToMultiple', size_divisor=32)
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transform = build_from_cfg(transform, PIPELINES)
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img = np.random.randn(213, 232, 3)
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seg = np.random.randint(0, 19, (213, 232))
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results = dict()
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results['img'] = img
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results['gt_semantic_seg'] = seg
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results['seg_fields'] = ['gt_semantic_seg']
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results['img_shape'] = img.shape
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results['pad_shape'] = img.shape
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results = transform(results)
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assert results['img'].shape == (224, 256, 3)
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assert results['gt_semantic_seg'].shape == (224, 256)
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assert results['img_shape'] == (224, 256, 3)
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assert results['pad_shape'] == (224, 256, 3)
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def test_resize():
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# test assertion if img_scale is a list
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with pytest.raises(AssertionError):
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transform = dict(type='Resize', img_scale=[1333, 800], keep_ratio=True)
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build_from_cfg(transform, PIPELINES)
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# test assertion if len(img_scale) while ratio_range is not None
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with pytest.raises(AssertionError):
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transform = dict(
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type='Resize',
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img_scale=[(1333, 800), (1333, 600)],
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ratio_range=(0.9, 1.1),
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keep_ratio=True)
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build_from_cfg(transform, PIPELINES)
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# test assertion for invalid multiscale_mode
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with pytest.raises(AssertionError):
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transform = dict(
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type='Resize',
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img_scale=[(1333, 800), (1333, 600)],
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keep_ratio=True,
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multiscale_mode='2333')
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build_from_cfg(transform, PIPELINES)
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transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True)
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resize_module = build_from_cfg(transform, PIPELINES)
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results = dict()
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# (288, 512, 3)
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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results['img'] = img
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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resized_results = resize_module(results.copy())
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assert resized_results['img_shape'] == (750, 1333, 3)
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# test keep_ratio=False
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transform = dict(
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type='Resize',
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img_scale=(1280, 800),
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multiscale_mode='value',
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keep_ratio=False)
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resize_module = build_from_cfg(transform, PIPELINES)
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resized_results = resize_module(results.copy())
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assert resized_results['img_shape'] == (800, 1280, 3)
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# test multiscale_mode='range'
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transform = dict(
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type='Resize',
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img_scale=[(1333, 400), (1333, 1200)],
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multiscale_mode='range',
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keep_ratio=True)
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resize_module = build_from_cfg(transform, PIPELINES)
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resized_results = resize_module(results.copy())
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assert max(resized_results['img_shape'][:2]) <= 1333
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assert min(resized_results['img_shape'][:2]) >= 400
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assert min(resized_results['img_shape'][:2]) <= 1200
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# test multiscale_mode='value'
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transform = dict(
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type='Resize',
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img_scale=[(1333, 800), (1333, 400)],
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multiscale_mode='value',
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keep_ratio=True)
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resize_module = build_from_cfg(transform, PIPELINES)
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resized_results = resize_module(results.copy())
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assert resized_results['img_shape'] in [(750, 1333, 3), (400, 711, 3)]
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# test multiscale_mode='range'
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transform = dict(
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type='Resize',
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img_scale=(1333, 800),
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ratio_range=(0.9, 1.1),
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keep_ratio=True)
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resize_module = build_from_cfg(transform, PIPELINES)
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resized_results = resize_module(results.copy())
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assert max(resized_results['img_shape'][:2]) <= 1333 * 1.1
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# test img_scale=None and ratio_range is tuple.
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# img shape: (288, 512, 3)
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transform = dict(
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type='Resize', img_scale=None, ratio_range=(0.5, 2.0), keep_ratio=True)
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resize_module = build_from_cfg(transform, PIPELINES)
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resized_results = resize_module(results.copy())
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assert int(288 * 0.5) <= resized_results['img_shape'][0] <= 288 * 2.0
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assert int(512 * 0.5) <= resized_results['img_shape'][1] <= 512 * 2.0
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def test_flip():
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# test assertion for invalid prob
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with pytest.raises(AssertionError):
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transform = dict(type='RandomFlip', prob=1.5)
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build_from_cfg(transform, PIPELINES)
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# test assertion for invalid direction
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with pytest.raises(AssertionError):
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transform = dict(type='RandomFlip', prob=1, direction='horizonta')
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build_from_cfg(transform, PIPELINES)
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transform = dict(type='RandomFlip', prob=1)
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flip_module = build_from_cfg(transform, PIPELINES)
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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original_img = copy.deepcopy(img)
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seg = np.array(
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Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
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original_seg = copy.deepcopy(seg)
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results['img'] = img
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results['gt_semantic_seg'] = seg
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results['seg_fields'] = ['gt_semantic_seg']
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = flip_module(results)
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flip_module = build_from_cfg(transform, PIPELINES)
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results = flip_module(results)
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assert np.equal(original_img, results['img']).all()
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assert np.equal(original_seg, results['gt_semantic_seg']).all()
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def test_random_crop():
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# test assertion for invalid random crop
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with pytest.raises(AssertionError):
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transform = dict(type='RandomCrop', crop_size=(-1, 0))
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build_from_cfg(transform, PIPELINES)
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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seg = np.array(
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Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
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results['img'] = img
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results['gt_semantic_seg'] = seg
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results['seg_fields'] = ['gt_semantic_seg']
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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h, w, _ = img.shape
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transform = dict(type='RandomCrop', crop_size=(h - 20, w - 20))
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crop_module = build_from_cfg(transform, PIPELINES)
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results = crop_module(results)
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assert results['img'].shape[:2] == (h - 20, w - 20)
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assert results['img_shape'][:2] == (h - 20, w - 20)
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assert results['gt_semantic_seg'].shape[:2] == (h - 20, w - 20)
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def test_pad():
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# test assertion if both size_divisor and size is None
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with pytest.raises(AssertionError):
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transform = dict(type='Pad')
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build_from_cfg(transform, PIPELINES)
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transform = dict(type='Pad', size_divisor=32)
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transform = build_from_cfg(transform, PIPELINES)
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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original_img = copy.deepcopy(img)
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results['img'] = img
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = transform(results)
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# original img already divisible by 32
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assert np.equal(results['img'], original_img).all()
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img_shape = results['img'].shape
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assert img_shape[0] % 32 == 0
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assert img_shape[1] % 32 == 0
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resize_transform = dict(
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type='Resize', img_scale=(1333, 800), keep_ratio=True)
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resize_module = build_from_cfg(resize_transform, PIPELINES)
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results = resize_module(results)
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results = transform(results)
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img_shape = results['img'].shape
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assert img_shape[0] % 32 == 0
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assert img_shape[1] % 32 == 0
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def test_rotate():
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# test assertion degree should be tuple[float] or float
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with pytest.raises(AssertionError):
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transform = dict(type='RandomRotate', prob=0.5, degree=-10)
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build_from_cfg(transform, PIPELINES)
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# test assertion degree should be tuple[float] or float
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with pytest.raises(AssertionError):
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transform = dict(type='RandomRotate', prob=0.5, degree=(10., 20., 30.))
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build_from_cfg(transform, PIPELINES)
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transform = dict(type='RandomRotate', degree=10., prob=1.)
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transform = build_from_cfg(transform, PIPELINES)
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assert str(transform) == f'RandomRotate(' \
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f'prob={1.}, ' \
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f'degree=({-10.}, {10.}), ' \
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f'pad_val={0}, ' \
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f'seg_pad_val={255}, ' \
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f'center={None}, ' \
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f'auto_bound={False})'
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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h, w, _ = img.shape
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seg = np.array(
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Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
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results['img'] = img
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results['gt_semantic_seg'] = seg
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results['seg_fields'] = ['gt_semantic_seg']
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = transform(results)
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assert results['img'].shape[:2] == (h, w)
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assert results['gt_semantic_seg'].shape[:2] == (h, w)
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def test_normalize():
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True)
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transform = dict(type='Normalize', **img_norm_cfg)
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transform = build_from_cfg(transform, PIPELINES)
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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original_img = copy.deepcopy(img)
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results['img'] = img
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = transform(results)
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mean = np.array(img_norm_cfg['mean'])
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std = np.array(img_norm_cfg['std'])
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converted_img = (original_img[..., ::-1] - mean) / std
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assert np.allclose(results['img'], converted_img)
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def test_rgb2gray():
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# test assertion out_channels should be greater than 0
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with pytest.raises(AssertionError):
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transform = dict(type='RGB2Gray', out_channels=-1)
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build_from_cfg(transform, PIPELINES)
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# test assertion weights should be tuple[float]
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with pytest.raises(AssertionError):
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transform = dict(type='RGB2Gray', out_channels=1, weights=1.1)
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build_from_cfg(transform, PIPELINES)
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# test out_channels is None
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transform = dict(type='RGB2Gray')
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transform = build_from_cfg(transform, PIPELINES)
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assert str(transform) == f'RGB2Gray(' \
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f'out_channels={None}, ' \
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f'weights={(0.299, 0.587, 0.114)})'
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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h, w, c = img.shape
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seg = np.array(
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Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
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results['img'] = img
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results['gt_semantic_seg'] = seg
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results['seg_fields'] = ['gt_semantic_seg']
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = transform(results)
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assert results['img'].shape == (h, w, c)
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assert results['img_shape'] == (h, w, c)
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assert results['ori_shape'] == (h, w, c)
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# test out_channels = 2
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transform = dict(type='RGB2Gray', out_channels=2)
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transform = build_from_cfg(transform, PIPELINES)
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assert str(transform) == f'RGB2Gray(' \
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f'out_channels={2}, ' \
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f'weights={(0.299, 0.587, 0.114)})'
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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h, w, c = img.shape
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seg = np.array(
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Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
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results['img'] = img
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results['gt_semantic_seg'] = seg
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results['seg_fields'] = ['gt_semantic_seg']
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = transform(results)
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assert results['img'].shape == (h, w, 2)
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assert results['img_shape'] == (h, w, 2)
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assert results['ori_shape'] == (h, w, c)
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def test_adjust_gamma():
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# test assertion if gamma <= 0
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with pytest.raises(AssertionError):
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transform = dict(type='AdjustGamma', gamma=0)
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build_from_cfg(transform, PIPELINES)
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# test assertion if gamma is list
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with pytest.raises(AssertionError):
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transform = dict(type='AdjustGamma', gamma=[1.2])
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build_from_cfg(transform, PIPELINES)
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# test with gamma = 1.2
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transform = dict(type='AdjustGamma', gamma=1.2)
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transform = build_from_cfg(transform, PIPELINES)
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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original_img = copy.deepcopy(img)
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results['img'] = img
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = transform(results)
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inv_gamma = 1.0 / 1.2
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table = np.array([((i / 255.0)**inv_gamma) * 255
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for i in np.arange(0, 256)]).astype('uint8')
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converted_img = mmcv.lut_transform(
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np.array(original_img, dtype=np.uint8), table)
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assert np.allclose(results['img'], converted_img)
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assert str(transform) == f'AdjustGamma(gamma={1.2})'
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def test_rerange():
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# test assertion if min_value or max_value is illegal
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with pytest.raises(AssertionError):
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transform = dict(type='Rerange', min_value=[0], max_value=[255])
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build_from_cfg(transform, PIPELINES)
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# test assertion if min_value >= max_value
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with pytest.raises(AssertionError):
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transform = dict(type='Rerange', min_value=1, max_value=1)
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build_from_cfg(transform, PIPELINES)
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|
|
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# test assertion if img_min_value == img_max_value
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|
with pytest.raises(AssertionError):
|
|
transform = dict(type='Rerange', min_value=0, max_value=1)
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|
transform = build_from_cfg(transform, PIPELINES)
|
|
results = dict()
|
|
results['img'] = np.array([[1, 1], [1, 1]])
|
|
transform(results)
|
|
|
|
img_rerange_cfg = dict()
|
|
transform = dict(type='Rerange', **img_rerange_cfg)
|
|
transform = build_from_cfg(transform, PIPELINES)
|
|
results = dict()
|
|
img = mmcv.imread(
|
|
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
|
|
original_img = copy.deepcopy(img)
|
|
results['img'] = img
|
|
results['img_shape'] = img.shape
|
|
results['ori_shape'] = img.shape
|
|
# Set initial values for default meta_keys
|
|
results['pad_shape'] = img.shape
|
|
results['scale_factor'] = 1.0
|
|
|
|
results = transform(results)
|
|
|
|
min_value = np.min(original_img)
|
|
max_value = np.max(original_img)
|
|
converted_img = (original_img - min_value) / (max_value - min_value) * 255
|
|
|
|
assert np.allclose(results['img'], converted_img)
|
|
assert str(transform) == f'Rerange(min_value={0}, max_value={255})'
|
|
|
|
|
|
def test_CLAHE():
|
|
# test assertion if clip_limit is None
|
|
with pytest.raises(AssertionError):
|
|
transform = dict(type='CLAHE', clip_limit=None)
|
|
build_from_cfg(transform, PIPELINES)
|
|
|
|
# test assertion if tile_grid_size is illegal
|
|
with pytest.raises(AssertionError):
|
|
transform = dict(type='CLAHE', tile_grid_size=(8.0, 8.0))
|
|
build_from_cfg(transform, PIPELINES)
|
|
|
|
# test assertion if tile_grid_size is illegal
|
|
with pytest.raises(AssertionError):
|
|
transform = dict(type='CLAHE', tile_grid_size=(9, 9, 9))
|
|
build_from_cfg(transform, PIPELINES)
|
|
|
|
transform = dict(type='CLAHE', clip_limit=2)
|
|
transform = build_from_cfg(transform, PIPELINES)
|
|
results = dict()
|
|
img = mmcv.imread(
|
|
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
|
|
original_img = copy.deepcopy(img)
|
|
results['img'] = img
|
|
results['img_shape'] = img.shape
|
|
results['ori_shape'] = img.shape
|
|
# Set initial values for default meta_keys
|
|
results['pad_shape'] = img.shape
|
|
results['scale_factor'] = 1.0
|
|
|
|
results = transform(results)
|
|
|
|
converted_img = np.empty(original_img.shape)
|
|
for i in range(original_img.shape[2]):
|
|
converted_img[:, :, i] = mmcv.clahe(
|
|
np.array(original_img[:, :, i], dtype=np.uint8), 2, (8, 8))
|
|
|
|
assert np.allclose(results['img'], converted_img)
|
|
assert str(transform) == f'CLAHE(clip_limit={2}, tile_grid_size={(8, 8)})'
|
|
|
|
|
|
def test_seg_rescale():
|
|
results = dict()
|
|
seg = np.array(
|
|
Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
|
|
results['gt_semantic_seg'] = seg
|
|
results['seg_fields'] = ['gt_semantic_seg']
|
|
h, w = seg.shape
|
|
|
|
transform = dict(type='SegRescale', scale_factor=1. / 2)
|
|
rescale_module = build_from_cfg(transform, PIPELINES)
|
|
rescale_results = rescale_module(results.copy())
|
|
assert rescale_results['gt_semantic_seg'].shape == (h // 2, w // 2)
|
|
|
|
transform = dict(type='SegRescale', scale_factor=1)
|
|
rescale_module = build_from_cfg(transform, PIPELINES)
|
|
rescale_results = rescale_module(results.copy())
|
|
assert rescale_results['gt_semantic_seg'].shape == (h, w)
|