From b783fd0cbf5e75f357a89aac626a142573289ce0 Mon Sep 17 00:00:00 2001 From: shgao Date: Thu, 12 Jan 2023 15:53:16 +0800 Subject: [PATCH 1/4] add imagenet-s dataset --- README.md | 1 + configs/_base_/datasets/imagenets.py | 61 ++ docs/en/dataset_prepare.md | 48 ++ docs/zh_cn/dataset_prepare.md | 49 ++ mmseg/datasets/__init__.py | 2 + mmseg/datasets/imagenets.py | 1004 ++++++++++++++++++++++++++ 6 files changed, 1165 insertions(+) create mode 100644 configs/_base_/datasets/imagenets.py create mode 100644 mmseg/datasets/imagenets.py diff --git a/README.md b/README.md index 72d5d6c12d..c6751ac138 100644 --- a/README.md +++ b/README.md @@ -188,6 +188,7 @@ Supported datasets: - [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-vaihingen) - [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isaid) - [x] [High quality synthetic face occlusion](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#delving-into-high-quality-synthetic-face-occlusion-segmentation-datasets) +- [x] [ImageNetS](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#imagenets) ## FAQ diff --git a/configs/_base_/datasets/imagenets.py b/configs/_base_/datasets/imagenets.py new file mode 100644 index 0000000000..09383de13a --- /dev/null +++ b/configs/_base_/datasets/imagenets.py @@ -0,0 +1,61 @@ +# dataset settings +dataset_type = 'ImageNetSDataset' +subset = 919 +data_root = 'data/ImageNetS/ImageNetS919' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (224, 224) +train_pipeline = [ + dict(type='LoadImageNetSImageFromFile', downsample_large_image=True), + dict(type='LoadImageNetSAnnotations', reduce_zero_label=False), + dict(type='Resize', img_scale=(1024, 256), ratio_range=(0.5, 2.0)), + dict( + type='RandomCrop', + crop_size=crop_size, + cat_max_ratio=0.75, + ignore_index=1000), + 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=1000), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageNetSImageFromFile', downsample_large_image=True), + dict( + type='MultiScaleFlipAug', + img_scale=(1024, 256), + 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 = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict( + type=dataset_type, + subset=subset, + data_root=data_root, + img_dir='train-semi', + ann_dir='train-semi-segmentation', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + subset=subset, + data_root=data_root, + img_dir='validation', + ann_dir='validation-segmentation', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + subset=subset, + data_root=data_root, + img_dir='validation', + ann_dir='validation-segmentation', + pipeline=test_pipeline)) \ No newline at end of file diff --git a/docs/en/dataset_prepare.md b/docs/en/dataset_prepare.md index a4878d0d83..abad882804 100644 --- a/docs/en/dataset_prepare.md +++ b/docs/en/dataset_prepare.md @@ -155,6 +155,25 @@ mmsegmentation │ │ │ ├── img │ │ │ ├── mask │ │ │ ├── split +│ ├── ImageNetS +│ │ ├── ImageNetS919 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +│ │ ├── ImageNetS300 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +│ │ ├── ImageNetS50 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test ``` ### Cityscapes @@ -580,3 +599,32 @@ OCCLUDER_DATASET.IMG_DIR "path/to/jw93/mmsegmentation/data_materials/DTD/images" ```python ``` + +### ImageNetS + +The ImageNet-S dataset is for [Large-scale unsupervised semantic segmentation](https://arxiv.org/abs/2106.03149). +In segmentation, we provide codes for semi-supervised training of large-scale semantic segmentation on the ImageNet-S dataset, with 50k high-quality semantic segmentation annotations. + +The images and annotations are available on [ImageNet-S](https://github.com/LUSSeg/ImageNet-S#imagenet-s-dataset-preparation). + +``` +│ ├── ImageNetS +│ │ ├── ImageNetS919 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +│ │ ├── ImageNetS300 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +│ │ ├── ImageNetS50 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +``` \ No newline at end of file diff --git a/docs/zh_cn/dataset_prepare.md b/docs/zh_cn/dataset_prepare.md index 6b9c8216e5..2a58425e64 100644 --- a/docs/zh_cn/dataset_prepare.md +++ b/docs/zh_cn/dataset_prepare.md @@ -119,6 +119,25 @@ mmsegmentation │ │ ├── ann_dir │ │ │ ├── train │ │ │ ├── val +│ ├── ImageNetS +│ │ ├── ImageNetS919 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +│ │ ├── ImageNetS300 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +│ │ ├── ImageNetS50 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test ``` ### Cityscapes @@ -317,3 +336,33 @@ python tools/convert_datasets/isaid.py /path/to/iSAID ``` 使用我们默认的配置 (`patch_width`=896, `patch_height`=896,　`overlap_area`=384)， 将生成 33978 张图片的训练集和 11644 张图片的验证集。 + +### ImageNetS + +ImageNet-S是用于[大规模无监督语义分割](https://arxiv.org/abs/2106.03149)任务的数据集。 + +利用五万张高质量的语义分割标注，我们提供了用于ImageNet-S数据集的半监督训练代码。 + +ImageNet-S数据集可在[ImageNet-S](https://github.com/LUSSeg/ImageNet-S#imagenet-s-dataset-preparation)处获取。 + +``` +│ ├── ImageNetS +│ │ ├── ImageNetS919 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +│ │ ├── ImageNetS300 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +│ │ ├── ImageNetS50 +│ │ │ ├── train-semi +│ │ │ ├── train-semi-segmentation +│ │ │ ├── validation +│ │ │ ├── validation-segmentation +│ │ │ ├── test +``` \ No newline at end of file diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index 9060564c0d..603dbc3ddd 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -11,6 +11,8 @@ from .drive import DRIVEDataset from .face import FaceOccludedDataset from .hrf import HRFDataset +from .imagenets import (ImageNetSDataset, LoadImageNetSAnnotations, + LoadImageNetSImageFromFile) from .isaid import iSAIDDataset from .isprs import ISPRSDataset from .loveda import LoveDADataset diff --git a/mmseg/datasets/imagenets.py b/mmseg/datasets/imagenets.py new file mode 100644 index 0000000000..9573b66a10 --- /dev/null +++ b/mmseg/datasets/imagenets.py @@ -0,0 +1,1004 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp + +import mmcv +import numpy as np +from PIL import Image + +from mmseg.core import intersect_and_union +from mmseg.datasets.pipelines import LoadAnnotations, LoadImageFromFile +from .builder import DATASETS, PIPELINES +from .custom import CustomDataset + + +@PIPELINES.register_module() +class LoadImageNetSImageFromFile(LoadImageFromFile): + """Load an image from the ImageNetS dataset. + + To avoid out of memory, images that are too large will + be downsampled to the scale of 1000. + + Args: + downsample_large_image (bool): Whether to downsample the large images. + False may cause out of memory. + Defaults to True. + """ + + def __init__(self, downsample_large_image=True, **kwargs): + super().__init__(**kwargs) + self.downsample_large_image = downsample_large_image + + def __call__(self, results): + """Call functions to load image and get image meta information. + + Args: + results (dict): Result dict from :obj:`mmseg.CustomDataset`. + + Returns: + dict: The dict contains loaded image and meta information. + """ + results = super().__call__(results) + if not self.downsample_large_image: + return results + + # Images that are too large + # (H * W > 1000 * 100, + # these images are included in ImageNetSDataset.LARGES) + # will be downsampled to 1000 along the longer side. + H, W = results['img_shape'][:2] + if H * W > pow(1000, 2): + if H > W: + target_size = (int(1000 * W / H), 1000) + else: + target_size = (1000, int(1000 * H / W)) + + results['img'] = mmcv.imresize( + results['img'], size=target_size, interpolation='bilinear') + if self.to_float32: + results['img'] = results['img'].astype(np.float32) + + results['img_shape'] = results['img'].shape + results['ori_shape'] = results['img'].shape + # Set initial values for default meta_keys + results['pad_shape'] = results['img'].shape + return results + + +@PIPELINES.register_module() +class LoadImageNetSAnnotations(LoadAnnotations): + """Load annotations for the ImageNetS dataset. The annotations in + ImageNet-S are saved as RGB images. + + The annotations with format of RGB should be + converted to the format of Gray as R + G * 256. + """ + + def __call__(self, results): + """Call function to load multiple types annotations. + + Args: + results (dict): Result dict from :obj:`mmseg.CustomDataset`. + + Returns: + dict: The dict contains loaded semantic segmentation annotations. + """ + results = super().__call__(results) + + # The annotations in ImageNet-S are saved as RGB images, + # due to 919 > 255 (upper bound of gray images). + + # For training, + # the annotations with format of RGB should be + # converted to the format of Gray as R + G * 256. + results['gt_semantic_seg'] = \ + results['gt_semantic_seg'][:, :, 1] * 256 + \ + results['gt_semantic_seg'][:, :, 2] + results['gt_semantic_seg'] = results['gt_semantic_seg'].astype( + np.int32) + return results + + +@DATASETS.register_module() +class ImageNetSDataset(CustomDataset): + """ImageNet-S dataset. + + In segmentation map annotation for ImageNet-S, 0 stands for others, which + is not included in 50/300/919 categories. ``ignore_index`` is fixed to + 1000. The ``img_suffix`` is fixed to '.JPEG' and ``seg_map_suffix`` is + fixed to '.png'. + """ + CLASSES50 = ('others', 'goldfish', 'tiger shark', 'goldfinch', 'tree frog', + 'kuvasz', 'red fox', 'siamese cat', 'american black bear', + 'ladybug', 'sulphur butterfly', 'wood rabbit', 'hamster', + 'wild boar', 'gibbon', 'african elephant', 'giant panda', + 'airliner', 'ashcan', 'ballpoint', 'beach wagon', 'boathouse', + 'bullet train', 'cellular telephone', 'chest', 'clog', + 'container ship', 'digital watch', 'dining table', + 'golf ball', 'grand piano', 'iron', 'lab coat', 'mixing bowl', + 'motor scooter', 'padlock', 'park bench', 'purse', + 'streetcar', 'table lamp', 'television', 'toilet seat', + 'umbrella', 'vase', 'water bottle', 'water tower', 'yawl', + 'street sign', 'lemon', 'carbonara', 'agaric') + CLASSES300 = ( + 'others', 'tench', 'goldfish', 'tiger shark', 'hammerhead', + 'electric ray', 'ostrich', 'goldfinch', 'house finch', + 'indigo bunting', 'kite', 'common newt', 'axolotl', 'tree frog', + 'tailed frog', 'mud turtle', 'banded gecko', 'american chameleon', + 'whiptail', 'african chameleon', 'komodo dragon', 'american alligator', + 'triceratops', 'thunder snake', 'ringneck snake', 'king snake', + 'rock python', 'horned viper', 'harvestman', 'scorpion', + 'garden spider', 'tick', 'african grey', 'lorikeet', + 'red-breasted merganser', 'wallaby', 'koala', 'jellyfish', + 'sea anemone', 'conch', 'fiddler crab', 'american lobster', + 'spiny lobster', 'isopod', 'bittern', 'crane', 'limpkin', 'bustard', + 'albatross', 'toy terrier', 'afghan hound', 'bluetick', 'borzoi', + 'irish wolfhound', 'whippet', 'ibizan hound', 'staffordshire ' + 'bullterrier', 'border terrier', 'yorkshire terrier', + 'lakeland terrier', 'giant schnauzer', 'standard schnauzer', + 'scotch terrier', 'lhasa', 'english setter', 'clumber', + 'english springer', 'welsh springer spaniel', 'kuvasz', 'kelpie', + 'doberman', 'miniature pinscher', 'malamute', 'pug', 'leonberg', + 'great pyrenees', 'samoyed', 'brabancon griffon', 'cardigan', 'coyote', + 'red fox', 'kit fox', 'grey fox', 'persian cat', 'siamese cat', + 'cougar', 'lynx', 'tiger', 'american black bear', 'sloth bear', + 'ladybug', 'leaf beetle', 'weevil', 'bee', 'cicada', 'leafhopper', + 'damselfly', 'ringlet', 'cabbage butterfly', 'sulphur butterfly', + 'sea cucumber', 'wood rabbit', 'hare', 'hamster', 'wild boar', + 'hippopotamus', 'bighorn', 'ibex', 'badger', 'three-toed sloth', + 'orangutan', 'gibbon', 'colobus', 'spider monkey', 'squirrel monkey', + 'madagascar cat', 'indian elephant', 'african elephant', 'giant panda', + 'barracouta', 'eel', 'coho', 'academic gown', 'accordion', 'airliner', + 'ambulance', 'analog clock', 'ashcan', 'backpack', 'balloon', + 'ballpoint', 'barbell', 'barn', 'bassoon', 'bath towel', 'beach wagon', + 'bicycle-built-for-two', 'binoculars', 'boathouse', 'bonnet', + 'bookcase', 'bow', 'brass', 'breastplate', 'bullet train', 'cannon', + 'can opener', "carpenter's kit", 'cassette', 'cellular telephone', + 'chain saw', 'chest', 'china cabinet', 'clog', 'combination lock', + 'container ship', 'corkscrew', 'crate', 'crock pot', 'digital watch', + 'dining table', 'dishwasher', 'doormat', 'dutch oven', 'electric fan', + 'electric locomotive', 'envelope', 'file', 'folding chair', + 'football helmet', 'freight car', 'french horn', 'fur coat', + 'garbage truck', 'goblet', 'golf ball', 'grand piano', 'half track', + 'hamper', 'hard disc', 'harmonica', 'harvester', 'hook', + 'horizontal bar', 'horse cart', 'iron', "jack-o'-lantern", 'lab coat', + 'ladle', 'letter opener', 'liner', 'mailbox', 'megalith', + 'military uniform', 'milk can', 'mixing bowl', 'monastery', 'mortar', + 'mosquito net', 'motor scooter', 'mountain bike', 'mountain tent', + 'mousetrap', 'necklace', 'nipple', 'ocarina', 'padlock', 'palace', + 'parallel bars', 'park bench', 'pedestal', 'pencil sharpener', + 'pickelhaube', 'pillow', 'planetarium', 'plastic bag', + 'polaroid camera', 'pole', 'pot', 'purse', 'quilt', 'radiator', + 'radio', 'radio telescope', 'rain barrel', 'reflex camera', + 'refrigerator', 'rifle', 'rocking chair', 'rubber eraser', 'rule', + 'running shoe', 'sewing machine', 'shield', 'shoji', 'ski', 'ski mask', + 'slot', 'soap dispenser', 'soccer ball', 'sock', 'soup bowl', + 'space heater', 'spider web', 'spindle', 'sports car', + 'steel arch bridge', 'stethoscope', 'streetcar', 'submarine', + 'swimming trunks', 'syringe', 'table lamp', 'tank', 'teddy', + 'television', 'throne', 'tile roof', 'toilet seat', 'trench coat', + 'trimaran', 'typewriter keyboard', 'umbrella', 'vase', 'volleyball', + 'wardrobe', 'warplane', 'washer', 'water bottle', 'water tower', + 'whiskey jug', 'wig', 'wine bottle', 'wok', 'wreck', 'yawl', 'yurt', + 'street sign', 'traffic light', 'consomme', 'ice cream', 'bagel', + 'cheeseburger', 'hotdog', 'mashed potato', 'spaghetti squash', + 'bell pepper', 'cardoon', 'granny smith', 'strawberry', 'lemon', + 'carbonara', 'burrito', 'cup', 'coral reef', "yellow lady's slipper", + 'buckeye', 'agaric', 'gyromitra', 'earthstar', 'bolete') + CLASSES919 = ( + 'others', 'house finch', 'stupa', 'agaric', 'hen-of-the-woods', + 'wild boar', 'kit fox', 'desk', 'beaker', 'spindle', 'lipstick', + 'cardoon', 'ringneck snake', 'daisy', 'sturgeon', 'scorpion', + 'pelican', 'bustard', 'rock crab', 'rock beauty', 'minivan', 'menu', + 'thunder snake', 'zebra', 'partridge', 'lacewing', 'starfish', + 'italian greyhound', 'marmot', 'cardigan', 'plate', 'ballpoint', + 'chesapeake bay retriever', 'pirate', 'potpie', 'keeshond', 'dhole', + 'waffle iron', 'cab', 'american egret', 'colobus', 'radio telescope', + 'gordon setter', 'mousetrap', 'overskirt', 'hamster', 'wine bottle', + 'bluetick', 'macaque', 'bullfrog', 'junco', 'tusker', 'scuba diver', + 'pool table', 'samoyed', 'mailbox', 'purse', 'monastery', 'bathtub', + 'window screen', 'african crocodile', 'traffic light', 'tow truck', + 'radio', 'recreational vehicle', 'grey whale', 'crayfish', + 'rottweiler', 'racer', 'whistle', 'pencil box', 'barometer', + 'cabbage butterfly', 'sloth bear', 'rhinoceros beetle', 'guillotine', + 'rocking chair', 'sports car', 'bouvier des flandres', 'border collie', + 'fiddler crab', 'slot', 'go-kart', 'cocker spaniel', 'plate rack', + 'common newt', 'tile roof', 'marimba', 'moped', 'terrapin', 'oxcart', + 'lionfish', 'bassinet', 'rain barrel', 'american black bear', 'goose', + 'half track', 'kite', 'microphone', 'shield', 'mexican hairless', + 'measuring cup', 'bubble', 'platypus', 'saint bernard', 'police van', + 'vase', 'lhasa', 'wardrobe', 'teapot', 'hummingbird', 'revolver', + 'jinrikisha', 'mailbag', 'red-breasted merganser', 'assault rifle', + 'loudspeaker', 'fig', 'american lobster', 'can opener', 'arctic fox', + 'broccoli', 'long-horned beetle', 'television', 'airship', + 'black stork', 'marmoset', 'panpipe', 'drumstick', 'knee pad', + 'lotion', 'french loaf', 'throne', 'jeep', 'jersey', 'tiger cat', + 'cliff', 'sealyham terrier', 'strawberry', 'minibus', 'goldfinch', + 'goblet', 'burrito', 'harp', 'tractor', 'cornet', 'leopard', 'fly', + 'fireboat', 'bolete', 'barber chair', 'consomme', 'tripod', + 'breastplate', 'pineapple', 'wok', 'totem pole', 'alligator lizard', + 'common iguana', 'digital clock', 'bighorn', 'siamese cat', 'bobsled', + 'irish setter', 'zucchini', 'crock pot', 'loggerhead', + 'irish wolfhound', 'nipple', 'rubber eraser', 'impala', 'barbell', + 'snow leopard', 'siberian husky', 'necklace', 'manhole cover', + 'electric fan', 'hippopotamus', 'entlebucher', 'prison', 'doberman', + 'ruffed grouse', 'coyote', 'toaster', 'puffer', 'black swan', + 'schipperke', 'file', 'prairie chicken', 'hourglass', + 'greater swiss mountain dog', 'pajama', 'ear', 'pedestal', 'viaduct', + 'shoji', 'snowplow', 'puck', 'gyromitra', 'birdhouse', 'flatworm', + 'pier', 'coral reef', 'pot', 'mortar', 'polaroid camera', + 'passenger car', 'barracouta', 'banded gecko', + 'black-and-tan coonhound', 'safe', 'ski', 'torch', 'green lizard', + 'volleyball', 'brambling', 'solar dish', 'lawn mower', 'swing', + 'hyena', 'staffordshire bullterrier', 'screw', 'toilet tissue', + 'velvet', 'scale', 'stopwatch', 'sock', 'koala', 'garbage truck', + 'spider monkey', 'afghan hound', 'chain', 'upright', 'flagpole', + 'tree frog', 'cuirass', 'chest', 'groenendael', 'christmas stocking', + 'lakeland terrier', 'perfume', 'neck brace', 'lab coat', 'carbonara', + 'porcupine', 'shower curtain', 'slug', 'pitcher', + 'flat-coated retriever', 'pekinese', 'oscilloscope', 'church', 'lynx', + 'cowboy hat', 'table lamp', 'pug', 'crate', 'water buffalo', + 'labrador retriever', 'weimaraner', 'giant schnauzer', 'stove', + 'sea urchin', 'banjo', 'tiger', 'miniskirt', 'eft', + 'european gallinule', 'vending machine', 'miniature schnauzer', + 'maypole', 'bull mastiff', 'hoopskirt', 'coffeepot', 'four-poster', + 'safety pin', 'monarch', 'beer glass', 'grasshopper', 'head cabbage', + 'parking meter', 'bonnet', 'chiffonier', 'great dane', 'spider web', + 'electric locomotive', 'scotch terrier', 'australian terrier', + 'honeycomb', 'leafhopper', 'beer bottle', 'mud turtle', 'lifeboat', + 'cassette', "potter's wheel", 'oystercatcher', 'space heater', + 'coral fungus', 'sunglass', 'quail', 'triumphal arch', 'collie', + 'walker hound', 'bucket', 'bee', 'komodo dragon', 'dugong', 'gibbon', + 'trailer truck', 'king crab', 'cheetah', 'rifle', 'stingray', 'bison', + 'ipod', 'modem', 'box turtle', 'motor scooter', 'container ship', + 'vestment', 'dingo', 'radiator', 'giant panda', 'nail', 'sea slug', + 'indigo bunting', 'trimaran', 'jacamar', 'chimpanzee', 'comic book', + 'odometer', 'dishwasher', 'bolo tie', 'barn', 'paddlewheel', + 'appenzeller', 'great white shark', 'green snake', 'jackfruit', + 'llama', 'whippet', 'hay', 'leaf beetle', 'sombrero', 'ram', + 'washbasin', 'cup', 'wall clock', 'acorn squash', 'spotted salamander', + 'boston bull', 'border terrier', 'doormat', 'cicada', 'kimono', + 'hand blower', 'ox', 'meerkat', 'space shuttle', 'african hunting dog', + 'violin', 'artichoke', 'toucan', 'bulbul', 'coucal', 'red wolf', + 'seat belt', 'bicycle-built-for-two', 'bow tie', 'pretzel', + 'bedlington terrier', 'albatross', 'punching bag', 'cocktail shaker', + 'diamondback', 'corn', 'ant', 'mountain bike', 'walking stick', + 'standard schnauzer', 'power drill', 'cardigan', 'accordion', + 'wire-haired fox terrier', 'streetcar', 'beach wagon', 'ibizan hound', + 'hair spray', 'car mirror', 'mountain tent', 'trench coat', + 'studio couch', 'pomeranian', 'dough', 'corkscrew', 'broom', + 'parachute', 'band aid', 'water tower', 'teddy', 'fire engine', + 'hornbill', 'hotdog', 'theater curtain', 'crane', 'malinois', 'lion', + 'african elephant', 'handkerchief', 'caldron', 'shopping basket', + 'gown', 'wolf spider', 'vizsla', 'electric ray', 'freight car', + 'pembroke', 'feather boa', 'wallet', 'agama', 'hard disc', 'stretcher', + 'sorrel', 'trilobite', 'basset', 'vulture', 'tarantula', 'hermit crab', + 'king snake', 'robin', 'bernese mountain dog', 'ski mask', + 'fountain pen', 'combination lock', 'yurt', 'clumber', 'park bench', + 'baboon', 'kuvasz', 'centipede', 'tabby', 'steam locomotive', 'badger', + 'irish water spaniel', 'picket fence', 'gong', 'canoe', + 'swimming trunks', 'submarine', 'echidna', 'bib', 'refrigerator', + 'hammer', 'lemon', 'admiral', 'chihuahua', 'basenji', 'pinwheel', + 'golfcart', 'bullet train', 'crib', 'muzzle', 'eggnog', + 'old english sheepdog', 'tray', 'tiger beetle', 'electric guitar', + 'peacock', 'soup bowl', 'wallaby', 'abacus', 'dalmatian', 'harvester', + 'aircraft carrier', 'snowmobile', 'welsh springer spaniel', + 'affenpinscher', 'oboe', 'cassette player', 'pencil sharpener', + 'japanese spaniel', 'plunger', 'black widow', 'norfolk terrier', + 'reflex camera', 'ice bear', 'redbone', 'mongoose', 'warthog', + 'arabian camel', 'bittern', 'mixing bowl', 'tailed frog', 'scabbard', + 'castle', 'curly-coated retriever', 'garden spider', 'folding chair', + 'mouse', 'prayer rug', 'red fox', 'toy terrier', 'leonberg', + 'lycaenid', 'poncho', 'goldfish', 'red-backed sandpiper', 'holster', + 'hair slide', 'coho', 'komondor', 'macaw', 'maltese dog', 'megalith', + 'sarong', 'green mamba', 'sea lion', 'water ouzel', 'bulletproof vest', + 'sulphur-crested cockatoo', 'scottish deerhound', 'steel arch bridge', + 'catamaran', 'brittany spaniel', 'redshank', 'otter', + 'brabancon griffon', 'balloon', 'rule', 'planetarium', 'trombone', + 'mitten', 'abaya', 'crash helmet', 'milk can', 'hartebeest', + 'windsor tie', 'irish terrier', 'african chameleon', 'matchstick', + 'water bottle', 'cloak', 'ground beetle', 'ashcan', 'crane', + 'gila monster', 'unicycle', 'gazelle', 'wombat', 'brain coral', + 'projector', 'custard apple', 'proboscis monkey', 'tibetan mastiff', + 'mosque', 'plastic bag', 'backpack', 'drum', 'norwich terrier', + 'pizza', 'carton', 'plane', 'gorilla', 'jigsaw puzzle', 'forklift', + 'isopod', 'otterhound', 'vacuum', 'european fire salamander', 'apron', + 'langur', 'boxer', 'african grey', 'ice lolly', 'toilet seat', + 'golf ball', 'titi', 'drake', 'ostrich', 'magnetic compass', + 'great pyrenees', 'rhodesian ridgeback', 'buckeye', 'dungeness crab', + 'toy poodle', 'ptarmigan', 'amphibian', 'monitor', 'school bus', + 'schooner', 'spatula', 'weevil', 'speedboat', 'sundial', 'borzoi', + 'bassoon', 'bath towel', 'pill bottle', 'acorn', 'tick', 'briard', + 'thimble', 'brass', 'white wolf', 'boathouse', 'yawl', + 'miniature pinscher', 'barn spider', 'jean', 'water snake', 'dishrag', + 'yorkshire terrier', 'hammerhead', 'typewriter keyboard', 'papillon', + 'ocarina', 'washer', 'standard poodle', 'china cabinet', 'steel drum', + 'swab', 'mobile home', 'german short-haired pointer', 'saluki', + 'bee eater', 'rock python', 'vine snake', 'kelpie', 'harmonica', + 'military uniform', 'reel', 'thatch', 'maraca', 'tricycle', + 'sidewinder', 'parallel bars', 'banana', 'flute', 'paintbrush', + 'sleeping bag', "yellow lady's slipper", 'three-toed sloth', + 'white stork', 'notebook', 'weasel', 'tiger shark', 'football helmet', + 'madagascar cat', 'dowitcher', 'wreck', 'king penguin', 'lighter', + 'timber wolf', 'racket', 'digital watch', 'liner', 'hen', + 'suspension bridge', 'pillow', "carpenter's kit", 'butternut squash', + 'sandal', 'sussex spaniel', 'hip', 'american staffordshire terrier', + 'flamingo', 'analog clock', 'black and gold garden spider', + 'sea cucumber', 'indian elephant', 'syringe', 'lens cap', 'missile', + 'cougar', 'diaper', 'chambered nautilus', 'garter snake', + 'anemone fish', 'organ', 'limousine', 'horse cart', 'jaguar', + 'frilled lizard', 'crutch', 'sea anemone', 'guenon', 'meat loaf', + 'slide rule', 'saltshaker', 'pomegranate', 'acoustic guitar', + 'shopping cart', 'drilling platform', 'nematode', 'chickadee', + 'academic gown', 'candle', 'norwegian elkhound', 'armadillo', + 'horizontal bar', 'orangutan', 'obelisk', 'stone wall', 'cannon', + 'rugby ball', 'ping-pong ball', 'window shade', 'trolleybus', + 'ice cream', 'pop bottle', 'cock', 'harvestman', 'leatherback turtle', + 'killer whale', 'spaghetti squash', 'chain saw', 'stinkhorn', + 'espresso maker', 'loafer', 'bagel', 'ballplayer', 'skunk', + 'chainlink fence', 'earthstar', 'whiptail', 'barrel', + 'kerry blue terrier', 'triceratops', 'chow', 'grey fox', 'sax', + 'binoculars', 'ladybug', 'silky terrier', 'gas pump', 'cradle', + 'whiskey jug', 'french bulldog', 'eskimo dog', 'hog', 'hognose snake', + 'pickup', 'indian cobra', 'hand-held computer', 'printer', 'pole', + 'bald eagle', 'american alligator', 'dumbbell', 'umbrella', 'mink', + 'shower cap', 'tank', 'quill', 'fox squirrel', 'ambulance', + 'lesser panda', 'frying pan', 'letter opener', 'hook', 'strainer', + 'pick', 'dragonfly', 'gar', 'piggy bank', 'envelope', 'stole', 'ibex', + 'american chameleon', 'bearskin', 'microwave', 'petri dish', + 'wood rabbit', 'beacon', 'dung beetle', 'warplane', 'ruddy turnstone', + 'knot', 'fur coat', 'hamper', 'beagle', 'ringlet', 'mask', + 'persian cat', 'cellular telephone', 'american coot', 'apiary', + 'shovel', 'coffee mug', 'sewing machine', 'spoonbill', 'padlock', + 'bell pepper', 'great grey owl', 'squirrel monkey', + 'sulphur butterfly', 'scoreboard', 'bow', 'malamute', 'siamang', + 'snail', 'remote control', 'sea snake', 'loupe', 'model t', + 'english setter', 'dining table', 'face powder', 'tench', + "jack-o'-lantern", 'croquet ball', 'water jug', 'airedale', 'airliner', + 'guinea pig', 'hare', 'damselfly', 'thresher', 'limpkin', 'buckle', + 'english springer', 'boa constrictor', 'french horn', + 'black-footed ferret', 'shetland sheepdog', 'capuchin', 'cheeseburger', + 'miniature poodle', 'spotlight', 'wooden spoon', + 'west highland white terrier', 'wig', 'running shoe', 'cowboy boot', + 'brown bear', 'iron', 'brassiere', 'magpie', 'gondola', 'grand piano', + 'granny smith', 'mashed potato', 'german shepherd', 'stethoscope', + 'cauliflower', 'soccer ball', 'pay-phone', 'jellyfish', 'cairn', + 'polecat', 'trifle', 'photocopier', 'shih-tzu', 'orange', 'guacamole', + 'hatchet', 'cello', 'egyptian cat', 'basketball', 'moving van', + 'mortarboard', 'dial telephone', 'street sign', 'oil filter', 'beaver', + 'spiny lobster', 'chime', 'bookcase', 'chiton', 'black grouse', 'jay', + 'axolotl', 'oxygen mask', 'cricket', 'worm fence', 'indri', + 'cockroach', 'mushroom', 'dandie dinmont', 'tennis ball', + 'howler monkey', 'rapeseed', 'tibetan terrier', 'newfoundland', + 'dutch oven', 'paddle', 'joystick', 'golden retriever', + 'blenheim spaniel', 'mantis', 'soft-coated wheaten terrier', + 'little blue heron', 'convertible', 'bloodhound', 'palace', + 'medicine chest', 'english foxhound', 'cleaver', 'sweatshirt', + 'mosquito net', 'soap dispenser', 'ladle', 'screwdriver', + 'fire screen', 'binder', 'suit', 'barrow', 'clog', 'cucumber', + 'baseball', 'lorikeet', 'conch', 'quilt', 'eel', 'horned viper', + 'night snake', 'angora', 'pickelhaube', 'gasmask', 'patas') + + # Some too large images are downsampled in LoadImageNetSImageFromFile. + # These images should be upsampled back in results2img. + LARGES = { + '00022800': [1225, 900], + '00037230': [2082, 2522], + '00011749': [1000, 1303], + '00040173': [1280, 960], + '00027045': [1880, 1330], + '00019424': [2304, 3072], + '00015496': [1728, 2304], + '00025715': [1083, 1624], + '00008260': [1400, 1400], + '00047233': [850, 1540], + '00043667': [2066, 1635], + '00024274': [1920, 2560], + '00028437': [1920, 2560], + '00018910': [1536, 2048], + '00046074': [1600, 1164], + '00021215': [1024, 1540], + '00034174': [960, 1362], + '00007361': [960, 1280], + '00030207': [1512, 1016], + '00015637': [1600, 1200], + '00013665': [2100, 1500], + '00028501': [1200, 852], + '00047237': [1624, 1182], + '00026950': [1200, 1600], + '00041704': [1920, 2560], + '00027074': [1200, 1600], + '00016473': [1200, 1200], + '00012206': [2448, 3264], + '00019622': [960, 1280], + '00008728': [2806, 750], + '00027712': [1128, 1700], + '00007195': [1290, 1824], + '00002942': [2560, 1920], + '00037032': [1954, 2613], + '00018543': [1067, 1600], + '00041570': [1536, 2048], + '00004422': [1728, 2304], + '00044827': [800, 1280], + '00046674': [1200, 1600], + '00017711': [1200, 1600], + '00048488': [1889, 2834], + '00000706': [1501, 2001], + '00032736': [1200, 1600], + '00024348': [1536, 2048], + '00023430': [1051, 1600], + '00030496': [1350, 900], + '00026543': [1280, 960], + '00010969': [2560, 1920], + '00025272': [1294, 1559], + '00019950': [1536, 1024], + '00004466': [1182, 1722], + '00029917': [3072, 2304], + '00014683': [1145, 1600], + '00013084': [1281, 2301], + '00039792': [1760, 1034], + '00046246': [2448, 3264], + '00004280': [984, 1440], + '00009435': [1127, 1502], + '00012860': [1673, 2500], + '00016702': [1444, 1000], + '00011278': [2048, 3072], + '00048174': [1605, 2062], + '00035451': [1225, 1636], + '00024769': [1200, 900], + '00032797': [1251, 1664], + '00027924': [1453, 1697], + '00010965': [1536, 2048], + '00020735': [1200, 1600], + '00027789': [853, 1280], + '00015113': [1324, 1999], + '00037571': [1251, 1586], + '00030120': [1536, 2048], + '00044219': [2448, 3264], + '00024604': [1535, 1955], + '00010926': [1200, 900], + '00017509': [1536, 2048], + '00042373': [924, 1104], + '00037066': [1536, 2048], + '00025494': [1880, 1060], + '00028610': [1377, 2204], + '00007196': [1202, 1600], + '00030788': [2592, 1944], + '00046865': [1920, 2560], + '00027141': [1600, 1200], + '00023215': [1200, 1600], + '00000218': [1439, 1652], + '00048126': [1516, 927], + '00030408': [1600, 2400], + '00038582': [1600, 1200], + '00046959': [1304, 900], + '00016988': [1242, 1656], + '00017201': [1629, 1377], + '00017658': [1000, 1035], + '00002766': [1495, 2383], + '00038573': [1600, 1071], + '00042297': [1200, 1200], + '00010564': [995, 1234], + '00001189': [1600, 1200], + '00007018': [1858, 2370], + '00043554': [1200, 1600], + '00000746': [1200, 1600], + '00001386': [960, 1280], + '00029975': [1600, 1200], + '00016221': [2877, 2089], + '00003152': [1200, 1600], + '00002552': [1200, 1600], + '00009402': [1125, 1500], + '00040672': [960, 1280], + '00024540': [960, 1280], + '00049770': [1457, 1589], + '00014533': [841, 1261], + '00006228': [1417, 1063], + '00034688': [1354, 2032], + '00032897': [1071, 1600], + '00024356': [2043, 3066], + '00019656': [1318, 1984], + '00035802': [2288, 2001], + '00017499': [1502, 1162], + '00046898': [1200, 1600], + '00040883': [1024, 1280], + '00031353': [1544, 1188], + '00028419': [1600, 1200], + '00048897': [2304, 3072], + '00040683': [1296, 1728], + '00042406': [848, 1200], + '00036007': [900, 1200], + '00010515': [1688, 1387], + '00048409': [5005, 3646], + '00032654': [1200, 1600], + '00037955': [1200, 1600], + '00038471': [3072, 2048], + '00036201': [913, 1328], + '00038619': [1728, 2304], + '00038165': [926, 2503], + '00033240': [1061, 1158], + '00023086': [1200, 1600], + '00041385': [1200, 1600], + '00014066': [2304, 3072], + '00049973': [1211, 1261], + '00043188': [2000, 3000], + '00047186': [1535, 1417], + '00046975': [1560, 2431], + '00034402': [1776, 2700], + '00017033': [1392, 1630], + '00041068': [1280, 960], + '00011024': [1317, 900], + '00048035': [1800, 1200], + '00033286': [994, 1500], + '00016613': [1152, 1536], + '00044160': [888, 1200], + '00021138': [902, 1128], + '00022300': [798, 1293], + '00034300': [1920, 2560], + '00008603': [1661, 1160], + '00045173': [2312, 903], + '00048616': [960, 1280], + '00048317': [3872, 2592], + '00045470': [1920, 1800], + '00043934': [1667, 2500], + '00010699': [2240, 1488], + '00030550': [1200, 1600], + '00010516': [1704, 2272], + '00001779': [1536, 2048], + '00018389': [1084, 1433], + '00013889': [3072, 2304], + '00022440': [2112, 2816], + '00024005': [2592, 1944], + '00046620': [960, 1280], + '00035227': [960, 1280], + '00033636': [1110, 1973], + '00003624': [1165, 1600], + '00033400': [1200, 1600], + '00013891': [1200, 1600], + '00022593': [1472, 1456], + '00009546': [1936, 2592], + '00022022': [1182, 1740], + '00022982': [1200, 1600], + '00039569': [1600, 1067], + '00009276': [930, 1240], + '00026777': [960, 1280], + '00047680': [1425, 882], + '00040785': [853, 1280], + '00002037': [1944, 2592], + '00005813': [1098, 987], + '00018328': [1128, 1242], + '00022318': [1500, 1694], + '00026654': [790, 1285], + '00012895': [1600, 1067], + '00007882': [980, 1024], + '00043771': [1008, 1043], + '00032990': [3621, 2539], + '00034094': [1175, 1600], + '00034302': [1463, 1134], + '00025021': [1503, 1520], + '00000771': [900, 1200], + '00025149': [1600, 1200], + '00005211': [1063, 1600], + '00049544': [1063, 1417], + '00025378': [1800, 2400], + '00024287': [1200, 1600], + '00013550': [2448, 3264], + '00008076': [1200, 1600], + '00039536': [1000, 1500], + '00020331': [1024, 1280], + '00002623': [1050, 1400], + '00031071': [873, 1320], + '00025266': [1024, 1536], + '00015109': [1213, 1600], + '00027390': [1200, 1600], + '00018894': [1584, 901], + '00049009': [900, 1203], + '00026671': [1201, 1601], + '00018668': [1024, 990], + '00016942': [1024, 1024], + '00046430': [1944, 3456], + '00033261': [1341, 1644], + '00017363': [2304, 2898], + '00045935': [2112, 2816], + '00027084': [900, 1200], + '00037716': [1611, 981], + '00030879': [1200, 1600], + '00027539': [1534, 1024], + '00030052': [1280, 852], + '00011015': [2808, 2060], + '00037004': [1920, 2560], + '00044012': [2240, 1680], + '00049818': [1704, 2272], + '00003541': [1200, 1600], + '00000520': [2448, 3264], + '00028331': [3264, 2448], + '00030244': [1200, 1600], + '00039079': [1600, 1200], + '00033432': [1600, 1200], + '00010533': [1200, 1600], + '00005916': [899, 1200], + '00038903': [1052, 1592], + '00025169': [1895, 850], + '00049042': [1200, 1600], + '00021828': [1280, 988], + '00013420': [3648, 2736], + '00045201': [1381, 1440], + '00021857': [776, 1296], + '00048810': [1168, 1263], + '00047860': [2592, 3888], + '00046960': [2304, 3072], + '00039357': [1200, 1600], + '00019620': [1536, 2048], + '00026710': [1944, 2592], + '00021277': [1079, 1151], + '00028387': [1128, 1585], + '00028796': [990, 1320], + '00035149': [1064, 1600], + '00020182': [1843, 1707], + '00018286': [2592, 1944], + '00035658': [1488, 1984], + '00008180': [1024, 1633], + '00018740': [1200, 1600], + '00044356': [1536, 2048], + '00038857': [1252, 1676], + '00035014': [1200, 1600], + '00044824': [1200, 1600], + '00009912': [1200, 1600], + '00014572': [2400, 1800], + '00001585': [1600, 1067], + '00047704': [1200, 1600], + '00038537': [920, 1200], + '00027941': [2200, 3000], + '00028526': [2592, 1944], + '00042353': [1280, 1024], + '00043409': [2000, 1500], + '00002209': [2592, 1944], + '00040841': [1613, 1974], + '00038889': [900, 1200], + '00046941': [1200, 1600], + '00014029': [846, 1269], + '00023091': [900, 1200], + '00036184': [877, 1350], + '00006165': [1200, 1600], + '00033991': [868, 2034], + '00035078': [1680, 2240], + '00045681': [1467, 1134], + '00043867': [1200, 1600], + '00003586': [1200, 1600], + '00039024': [1283, 2400], + '00048990': [1200, 1200], + '00044334': [960, 1280], + '00020939': [960, 1280], + '00031529': [1302, 1590], + '00014867': [2112, 2816], + '00034239': [1536, 2048], + '00031845': [1200, 1600], + '00045721': [1536, 2048], + '00025336': [1441, 1931], + '00040323': [900, 1152], + '00009133': [876, 1247], + '00033687': [2357, 3657], + '00038351': [1306, 1200], + '00022618': [1060, 1192], + '00001626': [777, 1329], + '00039137': [1071, 1600], + '00034896': [1426, 1590], + '00048502': [1187, 1837], + '00048077': [1712, 2288], + '00026239': [1200, 1600], + '00032687': [857, 1280], + '00006639': [1498, 780], + '00037738': [2112, 2816], + '00035760': [1123, 1447], + '00004897': [1083, 1393], + '00012141': [3584, 2016], + '00016278': [3234, 2281], + '00006661': [1787, 3276], + '00033040': [1200, 1800], + '00009881': [960, 1280], + '00008240': [2592, 1944], + '00023506': [960, 1280], + '00046982': [1693, 2480], + '00049632': [2310, 1638], + '00005473': [960, 1280], + '00013491': [2000, 3008], + '00005581': [1593, 1200], + '00005196': [1417, 2133], + '00049433': [1207, 1600], + '00012323': [1200, 1800], + '00021883': [1600, 2400], + '00031877': [2448, 3264], + '00046428': [1200, 1600], + '00000725': [881, 1463], + '00044936': [894, 1344], + '00012054': [3040, 4048], + '00025447': [900, 1200], + '00005290': [1520, 2272], + '00023326': [984, 1312], + '00047891': [1067, 1600], + '00026115': [1067, 1600], + '00010051': [1062, 1275], + '00005999': [1123, 1600], + '00021752': [1071, 1600], + '00041559': [1200, 1600], + '00025931': [836, 1410], + '00009327': [2848, 4288], + '00029735': [1905, 1373], + '00012922': [1024, 1547], + '00042259': [1548, 1024], + '00024949': [1050, 956], + '00014669': [900, 1200], + '00028028': [1170, 1730], + '00003183': [1152, 1535], + '00039304': [1050, 1680], + '00014939': [1904, 1240], + '00048366': [1600, 1200], + '00022406': [3264, 2448], + '00033363': [1125, 1500], + '00041230': [1125, 1500], + '00044222': [2105, 2472], + '00021950': [1200, 1200], + '00028475': [2691, 3515], + '00002149': [900, 1600], + '00033356': [1080, 1920], + '00041158': [960, 1280], + '00029672': [1536, 2048], + '00045816': [1023, 1153], + '00020471': [2076, 2716], + '00012398': [1067, 1600], + '00017884': [2048, 3072], + '00025132': [1200, 1600], + '00042429': [1362, 1980], + '00021285': [1127, 1200], + '00045113': [2792, 2528], + '00047915': [1200, 891], + '00009481': [1097, 924], + '00025448': [1760, 2400], + '00033911': [1759, 2197], + '00044684': [1200, 1600], + '00033754': [2304, 1728], + '00002733': [1536, 2048], + '00027371': [936, 1128], + '00019941': [685, 1591], + '00028479': [1944, 2592], + '00018451': [1028, 1028], + '00024067': [1000, 1352], + '00016524': [1704, 2272], + '00048926': [1944, 2592], + '00020992': [1024, 1280], + '00044576': [1024, 1280], + '00031796': [960, 1280], + '00043540': [2448, 3264], + '00049250': [1056, 1408], + '00030602': [2592, 3872], + '00046571': [1118, 1336], + '00024908': [1442, 1012], + '00018903': [3072, 2304], + '00032370': [1944, 2592], + '00043445': [1050, 1680], + '00030791': [2228, 3168], + '00046866': [2057, 3072], + '00047293': [1800, 2400], + '00024853': [1296, 1936], + '00014344': [1125, 1500], + '00041327': [960, 1280], + '00017867': [2592, 3872], + '00037615': [1664, 2496], + '00011247': [1605, 2934], + '00034664': [2304, 1728], + '00013733': [1024, 1280], + '00009125': [1200, 1600], + '00035163': [1654, 1233], + '00017537': [1200, 1600], + '00043423': [1536, 2048], + '00035755': [1154, 900], + '00021712': [1600, 1200], + '00000597': [2792, 1908], + '00033579': [882, 1181], + '00035830': [2112, 2816], + '00005917': [920, 1380], + '00029722': [2736, 3648], + '00039979': [1200, 1600], + '00040854': [1606, 2400], + '00039884': [2848, 4288], + '00003508': [1128, 1488], + '00019862': [1200, 1600], + '00041813': [1226, 1160], + '00007121': [985, 1072], + '00013315': [883, 1199], + '00049822': [922, 1382], + '00027622': [1434, 1680], + '00047689': [1536, 2048], + '00017415': [1491, 2283], + '00023713': [927, 1287], + '00001632': [1200, 1600], + '00033104': [1200, 1600], + '00017643': [1002, 1200], + '00038396': [1330, 1999], + '00027614': [2166, 2048], + '00025962': [1600, 1200], + '00015915': [1067, 1600], + '00008940': [1942, 2744], + '00012468': [2000, 2000], + '00046953': [828, 1442], + '00002084': [1067, 1600], + '00040245': [2657, 1898], + '00023718': [900, 1440], + '00022770': [924, 1280], + '00028957': [960, 1280], + '00001054': [2048, 3072], + '00040541': [1369, 1809], + '00024869': [960, 1280], + '00037655': [900, 1440], + '00037200': [2171, 2575], + '00037390': [1394, 1237], + '00025318': [1054, 1024], + '00021634': [1800, 2400], + '00044217': [1003, 1024], + '00014877': [1200, 1600], + '00029504': [1224, 1632], + '00016422': [960, 1280], + '00028015': [1944, 2592], + '00006235': [967, 1291], + '00045909': [2272, 1704] + } + + def __init__(self, subset=919, **kwargs): + + assert subset in (50, 300, 919), \ + 'ImageNet-S has three subsets, i.e., '\ + 'ImageNet-S50, ImageNet-S300 and ImageNet-S919.' + if subset == 50: + self.CLASSES = self.CLASSES50 + elif subset == 300: + self.CLASSES = self.CLASSES300 + else: + self.CLASSES = self.CLASSES919 + + super(ImageNetSDataset, self).__init__( + img_suffix='.JPEG', + seg_map_suffix='.png', + reduce_zero_label=False, + ignore_index=1000, + **kwargs) + + self.subset = subset + gt_seg_map_loader_cfg = kwargs.get('gt_seg_map_loader_cfg', None) + self.gt_seg_map_loader = LoadImageNetSAnnotations( + ) if gt_seg_map_loader_cfg is None else LoadImageNetSAnnotations( + **gt_seg_map_loader_cfg) + + def pre_eval(self, preds, indices): + """Collect eval result for ImageNet-S. In LoadImageNetSImageFromFile, + the too large images have been downsampled. Here the preds should be + upsampled back after argmax. + + Args: + preds (list[torch.Tensor] | torch.Tensor): the segmentation logit + after argmax, shape (N, H, W). + indices (list[int] | int): the prediction related ground truth + indices. + + Returns: + list[torch.Tensor]: (area_intersect, area_union, area_prediction, + area_ground_truth). + """ + # In order to compat with batch inference + if not isinstance(indices, list): + indices = [indices] + if not isinstance(preds, list): + preds = [preds] + + pre_eval_results = [] + + for pred, index in zip(preds, indices): + seg_map = self.get_gt_seg_map_by_idx(index) + pred = mmcv.imresize( + pred, + size=(seg_map.shape[1], seg_map.shape[0]), + interpolation='nearest') + pre_eval_results.append( + intersect_and_union( + pred, + seg_map, + len(self.CLASSES), + self.ignore_index, + # 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)) + + return pre_eval_results + + def results2img(self, results, imgfile_prefix, to_label_id, indices=None): + """Write the segmentation results to images for ImageNetS. The results + should be converted as RGB images due to 919 (>256) categroies. In + LoadImageNetSImageFromFile, the too large images have been downsampled. + Here the results should be upsampled back after argmax. + + Args: + results (list[ndarray]): Testing results of the + dataset. + imgfile_prefix (str): The filename prefix of the png files. + If the prefix is "somepath/xxx", + the png files will be named "somepath/xxx.png". + to_label_id (bool): whether convert output to label_id for + submission. + indices (list[int], optional): Indices of input results, if not + set, all the indices of the dataset will be used. + Default: None. + + Returns: + list[str: str]: result txt files which contains corresponding + semantic segmentation images. + """ + if indices is None: + indices = list(range(len(self))) + + result_files = [] + for result, idx in zip(results, indices): + + filename = self.img_infos[idx]['filename'] + + directory = filename.split('/')[-2] + basename = osp.splitext(osp.basename(filename))[0] + + png_filename = osp.join(imgfile_prefix, directory, + f'{basename}.png') + + # The index range of output is from 0 to 919/300/50. + result_rgb = np.zeros(shape=(result.shape[0], result.shape[1], 3)) + result_rgb[:, :, 0] = result % 256 + result_rgb[:, :, 1] = result // 256 + + if basename.split('_')[2] in self.LARGES.keys(): + result_rgb = mmcv.imresize( + result_rgb, + size=(self.LARGES[basename.split('_')[2]][1], + self.LARGES[basename.split('_')[2]][0]), + interpolation='nearest') + + mmcv.mkdir_or_exist(osp.join(imgfile_prefix, directory)) + output = Image.fromarray(result_rgb.astype(np.uint8)) + output.save(png_filename) + result_files.append(png_filename) + + return result_files + + def format_results(self, + results, + imgfile_prefix, + to_label_id=True, + indices=None): + """Format the results into dir (standard format for ImageNetS + evaluation). + + Args: + results (list): Testing results of the dataset. + imgfile_prefix (str | None): The prefix of images files. It + includes the file path and the prefix of filename, e.g., + "a/b/prefix". + to_label_id (bool): whether convert output to label_id for + submission. Default: False + indices (list[int], optional): Indices of input results, if not + set, all the indices of the dataset will be used. + Default: None. + + Returns: + tuple: (result_files, tmp_dir), result_files is a list containing + the image paths, tmp_dir is the temporal directory created + for saving json/png files when img_prefix is not specified. + """ + + if indices is None: + indices = list(range(len(self))) + + assert isinstance(results, list), 'results must be a list.' + assert isinstance(indices, list), 'indices must be a list.' + + result_files = self.results2img(results, imgfile_prefix, to_label_id, + indices) + + return result_files \ No newline at end of file From 955c9f98e5debd2a9f78ab2d82227e22bf46b33b Mon Sep 17 00:00:00 2001 From: shgao Date: Thu, 12 Jan 2023 15:58:39 +0800 Subject: [PATCH 2/4] update describ --- docs/en/dataset_prepare.md | 3 +-- docs/zh_cn/dataset_prepare.md | 6 ++---- mmseg/datasets/__init__.py | 4 +++- 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/docs/en/dataset_prepare.md b/docs/en/dataset_prepare.md index abad882804..199e16a7d8 100644 --- a/docs/en/dataset_prepare.md +++ b/docs/en/dataset_prepare.md @@ -602,8 +602,7 @@ OCCLUDER_DATASET.IMG_DIR "path/to/jw93/mmsegmentation/data_materials/DTD/images" ### ImageNetS -The ImageNet-S dataset is for [Large-scale unsupervised semantic segmentation](https://arxiv.org/abs/2106.03149). -In segmentation, we provide codes for semi-supervised training of large-scale semantic segmentation on the ImageNet-S dataset, with 50k high-quality semantic segmentation annotations. +The ImageNet-S dataset is for [Large-scale unsupervised/semi-supervised semantic segmentation](https://arxiv.org/abs/2106.03149). The images and annotations are available on [ImageNet-S](https://github.com/LUSSeg/ImageNet-S#imagenet-s-dataset-preparation). diff --git a/docs/zh_cn/dataset_prepare.md b/docs/zh_cn/dataset_prepare.md index 2a58425e64..5abc99cf84 100644 --- a/docs/zh_cn/dataset_prepare.md +++ b/docs/zh_cn/dataset_prepare.md @@ -339,11 +339,9 @@ python tools/convert_datasets/isaid.py /path/to/iSAID ### ImageNetS -ImageNet-S是用于[大规模无监督语义分割](https://arxiv.org/abs/2106.03149)任务的数据集。 +ImageNet-S是用于[大规模无监督/半监督语义分割](https://arxiv.org/abs/2106.03149)任务的数据集。 -利用五万张高质量的语义分割标注，我们提供了用于ImageNet-S数据集的半监督训练代码。 - -ImageNet-S数据集可在[ImageNet-S](https://github.com/LUSSeg/ImageNet-S#imagenet-s-dataset-preparation)处获取。 +ImageNet-S数据集可在[ImageNet-S](https://github.com/LUSSeg/ImageNet-S#imagenet-s-dataset-preparation)获取。 ``` │ ├── ImageNetS diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index 603dbc3ddd..95ba76e45e 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -29,5 +29,7 @@ 'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset', 'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset', 'COCOStuffDataset', 'LoveDADataset', 'MultiImageMixDataset', - 'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset', 'FaceOccludedDataset' + 'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset', 'FaceOccludedDataset', + 'ImageNetSDataset', 'LoadImageNetSAnnotations', + 'LoadImageNetSImageFromFile' ] From cb620e9387716ca7c944d7e238b78f7f9bf2854d Mon Sep 17 00:00:00 2001 From: shgao Date: Thu, 12 Jan 2023 16:41:22 +0800 Subject: [PATCH 3/4] format code --- docs/en/dataset_prepare.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/en/dataset_prepare.md b/docs/en/dataset_prepare.md index 199e16a7d8..bc32bddc5f 100644 --- a/docs/en/dataset_prepare.md +++ b/docs/en/dataset_prepare.md @@ -626,4 +626,4 @@ The images and annotations are available on [ImageNet-S](https://github.com/LUSS │ │ │ ├── validation │ │ │ ├── validation-segmentation │ │ │ ├── test -``` \ No newline at end of file +``` From 6e400ae844ec53c33fb3c93c7316f89987dea500 Mon Sep 17 00:00:00 2001 From: shgao Date: Thu, 12 Jan 2023 16:47:40 +0800 Subject: [PATCH 4/4] format --- configs/_base_/datasets/imagenets.py | 2 +- docs/zh_cn/dataset_prepare.md | 2 +- mmseg/datasets/imagenets.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/configs/_base_/datasets/imagenets.py b/configs/_base_/datasets/imagenets.py index 09383de13a..b7b55aab78 100644 --- a/configs/_base_/datasets/imagenets.py +++ b/configs/_base_/datasets/imagenets.py @@ -58,4 +58,4 @@ data_root=data_root, img_dir='validation', ann_dir='validation-segmentation', - pipeline=test_pipeline)) \ No newline at end of file + pipeline=test_pipeline)) diff --git a/docs/zh_cn/dataset_prepare.md b/docs/zh_cn/dataset_prepare.md index 5abc99cf84..da64561cad 100644 --- a/docs/zh_cn/dataset_prepare.md +++ b/docs/zh_cn/dataset_prepare.md @@ -363,4 +363,4 @@ ImageNet-S数据集可在[ImageNet-S](https://github.com/LUSSeg/ImageNet-S#image │ │ │ ├── validation │ │ │ ├── validation-segmentation │ │ │ ├── test -``` \ No newline at end of file +``` diff --git a/mmseg/datasets/imagenets.py b/mmseg/datasets/imagenets.py index 9573b66a10..77fbb388d0 100644 --- a/mmseg/datasets/imagenets.py +++ b/mmseg/datasets/imagenets.py @@ -1001,4 +1001,4 @@ def format_results(self, result_files = self.results2img(results, imgfile_prefix, to_label_id, indices) - return result_files \ No newline at end of file + return result_files