mirror of https://github.com/alibaba/EasyCV.git
133 lines
5.1 KiB
Python
133 lines
5.1 KiB
Python
_base_ = './segformer_b0_coco.py'
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CLASSES = [
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'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
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'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
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'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
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'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
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'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
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'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
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'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
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'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
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'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
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'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush', 'banner', 'blanket', 'branch', 'bridge',
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'building-other', 'bush', 'cabinet', 'cage', 'cardboard', 'carpet',
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'ceiling-other', 'ceiling-tile', 'cloth', 'clothes', 'clouds', 'counter',
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'cupboard', 'curtain', 'desk-stuff', 'dirt', 'door-stuff', 'fence',
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'floor-marble', 'floor-other', 'floor-stone', 'floor-tile', 'floor-wood',
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'flower', 'fog', 'food-other', 'fruit', 'furniture-other', 'grass',
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'gravel', 'ground-other', 'hill', 'house', 'leaves', 'light', 'mat',
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'metal', 'mirror-stuff', 'moss', 'mountain', 'mud', 'napkin', 'net',
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'paper', 'pavement', 'pillow', 'plant-other', 'plastic', 'platform',
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'playingfield', 'railing', 'railroad', 'river', 'road', 'rock', 'roof',
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'rug', 'salad', 'sand', 'sea', 'shelf', 'sky-other', 'skyscraper', 'snow',
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'solid-other', 'stairs', 'stone', 'straw', 'structural-other', 'table',
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'tent', 'textile-other', 'towel', 'tree', 'vegetable', 'wall-brick',
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'wall-concrete', 'wall-other', 'wall-panel', 'wall-stone', 'wall-tile',
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'wall-wood', 'water-other', 'waterdrops', 'window-blind', 'window-other',
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'wood'
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]
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model = dict(
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pretrained=
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'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth',
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backbone=dict(
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embed_dims=64,
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num_layers=[3, 6, 40, 3],
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),
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decode_head=dict(
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in_channels=[64, 128, 320, 512],
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channels=768,
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),
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)
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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img_scale = (2048, 640)
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crop_size = (640, 640)
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train_pipeline = [
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dict(type='MMResize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
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dict(type='SegRandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='MMRandomFlip', flip_ratio=0.5),
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dict(type='MMPhotoMetricDistortion'),
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dict(type='MMNormalize', **img_norm_cfg),
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dict(type='MMPad', size=crop_size, pad_val=dict(img=0, masks=0, seg=255)),
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dict(type='DefaultFormatBundle'),
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dict(
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type='Collect',
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keys=['img', 'gt_semantic_seg'],
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meta_keys=('filename', 'ori_filename', 'ori_shape', 'img_shape',
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'pad_shape', 'scale_factor', 'flip', 'flip_direction',
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'img_norm_cfg')),
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]
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test_pipeline = [
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dict(
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type='MMMultiScaleFlipAug',
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img_scale=img_scale,
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
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transforms=[
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dict(type='MMResize', keep_ratio=True),
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dict(type='MMRandomFlip'),
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dict(type='MMNormalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(
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type='Collect',
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keys=['img'],
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meta_keys=('filename', 'ori_filename', 'ori_shape',
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'img_shape', 'pad_shape', 'scale_factor', 'flip',
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'flip_direction', 'img_norm_cfg')),
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])
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]
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data_root = './data/coco_stuff164k/'
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# dataset settings
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data_type = 'SegSourceRaw'
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data_root = 'data/VOCdevkit/VOC2012'
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train_img_root = data_root + 'JPEGImages'
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train_label_root = data_root + 'SegmentationClass'
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train_list_file = data_root + 'ImageSets/Segmentation/train.txt'
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val_img_root = data_root + 'JPEGImages'
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val_label_root = data_root + 'SegmentationClass'
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val_list_file = data_root + 'ImageSets/Segmentation/val.txt'
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test_batch_size = 2
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data = dict(
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imgs_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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type='SegDataset',
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ignore_index=255,
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data_source=dict(
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type=data_type,
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img_suffix='.jpg',
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label_suffix='.png',
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img_root=train_img_root,
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label_root=train_label_root,
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split=train_list_file,
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classes=CLASSES,
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),
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pipeline=train_pipeline),
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val=dict(
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imgs_per_gpu=test_batch_size,
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ignore_index=255,
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type='SegDataset',
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data_source=dict(
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type=data_type,
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img_suffix='.jpg',
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label_suffix='.png',
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img_root=val_img_root,
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label_root=val_label_root,
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split=val_list_file,
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classes=CLASSES,
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),
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pipeline=test_pipeline),
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)
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