mirror of
https://github.com/open-mmlab/mmsegmentation.git
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e235c1a824
* 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>
168 lines
8.2 KiB
Python
168 lines
8.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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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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from PIL import Image
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from .builder import DATASETS
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from .custom import CustomDataset
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@DATASETS.register_module()
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class ADE20KDataset(CustomDataset):
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"""ADE20K dataset.
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In segmentation map annotation for ADE20K, 0 stands for background, which
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is not included in 150 categories. ``reduce_zero_label`` is fixed to True.
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The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is fixed to
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'.png'.
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"""
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CLASSES = (
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'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
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'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
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'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
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'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
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'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
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'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
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'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
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'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
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'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
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'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
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'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
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'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
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'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
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'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
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'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
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'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
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'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
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'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
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'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
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'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
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'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
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'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
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'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
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'clock', 'flag')
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PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
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[4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
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[230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
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[150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
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[143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
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[0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
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[255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
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[255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
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[255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
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[224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
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[255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
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[6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
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[140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
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[255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
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[255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
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[11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
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[0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
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[255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
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[0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
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[173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
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[255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
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[255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
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[255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
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[0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
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[0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
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[143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
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[8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
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[255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
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[92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
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[163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
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[255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
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[255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
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[10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
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[255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
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[41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
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[71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
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[184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
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[102, 255, 0], [92, 0, 255]]
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def __init__(self, **kwargs):
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super(ADE20KDataset, self).__init__(
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img_suffix='.jpg',
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seg_map_suffix='.png',
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reduce_zero_label=True,
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**kwargs)
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def results2img(self, results, imgfile_prefix, to_label_id, indices=None):
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"""Write the segmentation results to images.
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Args:
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results (list[list | tuple | ndarray]): Testing results of the
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dataset.
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imgfile_prefix (str): The filename prefix of the png files.
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If the prefix is "somepath/xxx",
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the png files will be named "somepath/xxx.png".
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to_label_id (bool): whether convert output to label_id for
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submission.
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indices (list[int], optional): Indices of input results, if not
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set, all the indices of the dataset will be used.
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Default: None.
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Returns:
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list[str: str]: result txt files which contains corresponding
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semantic segmentation images.
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"""
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if indices is None:
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indices = list(range(len(self)))
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mmcv.mkdir_or_exist(imgfile_prefix)
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result_files = []
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for result, idx in zip(results, indices):
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filename = self.img_infos[idx]['filename']
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basename = osp.splitext(osp.basename(filename))[0]
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png_filename = osp.join(imgfile_prefix, f'{basename}.png')
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# The index range of official requirement is from 0 to 150.
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# But the index range of output is from 0 to 149.
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# That is because we set reduce_zero_label=True.
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result = result + 1
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output = Image.fromarray(result.astype(np.uint8))
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output.save(png_filename)
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result_files.append(png_filename)
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return result_files
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def format_results(self,
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results,
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imgfile_prefix,
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to_label_id=True,
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indices=None):
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"""Format the results into dir (standard format for ade20k evaluation).
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Args:
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results (list): Testing results of the dataset.
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imgfile_prefix (str | None): The prefix of images files. It
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includes the file path and the prefix of filename, e.g.,
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"a/b/prefix".
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to_label_id (bool): whether convert output to label_id for
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submission. Default: False
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indices (list[int], optional): Indices of input results, if not
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set, all the indices of the dataset will be used.
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Default: None.
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Returns:
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tuple: (result_files, tmp_dir), result_files is a list containing
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the image paths, tmp_dir is the temporal directory created
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for saving json/png files when img_prefix is not specified.
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"""
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if indices is None:
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indices = list(range(len(self)))
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assert isinstance(results, list), 'results must be a list.'
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assert isinstance(indices, list), 'indices must be a list.'
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result_files = self.results2img(results, imgfile_prefix, to_label_id,
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indices)
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return result_files
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