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upload segmentation code
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‎.pre-commit-config.yaml‎

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exclude: ^detection/configs
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exclude: ^detection/configs, ^segmentation/configs
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repos:
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- repo: https://gitlab.com/pycqa/flake8.git
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rev: 3.8.3

‎segmentation/configs/_base_/datasets/ade20k.py‎

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# dataset settings
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dataset_type = 'ADE20KDataset'
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data_root = 'data/ade/ADEChallengeData2016'
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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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crop_size = (512, 512)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', reduce_zero_label=True),
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dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(2048, 512),
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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='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=4,
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workers_per_gpu=4,
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train=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='images/training',
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ann_dir='annotations/training',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='images/validation',
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ann_dir='annotations/validation',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='images/validation',
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ann_dir='annotations/validation',
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pipeline=test_pipeline))

‎segmentation/configs/_base_/datasets/chase_db1.py‎

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# dataset settings
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dataset_type = 'ChaseDB1Dataset'
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data_root = 'data/CHASE_DB1'
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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 = (960, 999)
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crop_size = (128, 128)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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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, 2.0],
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]
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data = dict(
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samples_per_gpu=4,
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workers_per_gpu=4,
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train=dict(
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type='RepeatDataset',
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times=40000,
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='images/training',
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ann_dir='annotations/training',
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pipeline=train_pipeline)),
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val=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='images/validation',
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ann_dir='annotations/validation',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='images/validation',
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ann_dir='annotations/validation',
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pipeline=test_pipeline))

‎segmentation/configs/_base_/datasets/cityscapes.py‎

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# dataset settings
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dataset_type = 'CityscapesDataset'
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data_root = 'data/cityscapes/'
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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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crop_size = (512, 1024)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
9+
dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(2048, 1024),
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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='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='leftImg8bit/train',
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ann_dir='gtFine/train',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='leftImg8bit/val',
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ann_dir='gtFine/val',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='leftImg8bit/val',
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ann_dir='gtFine/val',
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pipeline=test_pipeline))

‎segmentation/configs/_base_/datasets/cityscapes_1024x1024.py‎

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_base_ = './cityscapes.py'
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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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crop_size = (1024, 1024)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
19+
dict(
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type='MultiScaleFlipAug',
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img_scale=(2048, 1024),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=False,
24+
transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
27+
dict(type='Normalize', **img_norm_cfg),
28+
dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))

‎segmentation/configs/_base_/datasets/cityscapes_768x768.py‎

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_base_ = './cityscapes.py'
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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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crop_size = (768, 768)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
9+
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
10+
dict(type='RandomFlip', prob=0.5),
11+
dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(2049, 1025),
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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='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
27+
dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))

‎segmentation/configs/_base_/datasets/cityscapes_769x769.py‎

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_base_ = './cityscapes.py'
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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)
4+
crop_size = (769, 769)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
10+
dict(type='RandomFlip', prob=0.5),
11+
dict(type='PhotoMetricDistortion'),
12+
dict(type='Normalize', **img_norm_cfg),
13+
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
19+
dict(
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type='MultiScaleFlipAug',
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img_scale=(2049, 1025),
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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=[
25+
dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
27+
dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))

‎segmentation/configs/_base_/datasets/cityscapes_832x832.py‎

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_base_ = './cityscapes.py'
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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)
4+
crop_size = (832, 832)
5+
train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
10+
dict(type='RandomFlip', prob=0.5),
11+
dict(type='PhotoMetricDistortion'),
12+
dict(type='Normalize', **img_norm_cfg),
13+
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
14+
dict(type='DefaultFormatBundle'),
15+
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
16+
]
17+
test_pipeline = [
18+
dict(type='LoadImageFromFile'),
19+
dict(
20+
type='MultiScaleFlipAug',
21+
img_scale=(2048, 1024),
22+
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
23+
flip=False,
24+
transforms=[
25+
dict(type='Resize', keep_ratio=True),
26+
dict(type='RandomFlip'),
27+
dict(type='Normalize', **img_norm_cfg),
28+
dict(type='ImageToTensor', keys=['img']),
29+
dict(type='Collect', keys=['img']),
30+
])
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))

‎segmentation/configs/_base_/datasets/cityscapes_896x896.py‎

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_base_ = './cityscapes.py'
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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)
4+
crop_size = (896, 896)
5+
train_pipeline = [
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dict(type='LoadImageFromFile'),
7+
dict(type='LoadAnnotations'),
8+
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
9+
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
10+
dict(type='RandomFlip', prob=0.5),
11+
dict(type='PhotoMetricDistortion'),
12+
dict(type='Normalize', **img_norm_cfg),
13+
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
14+
dict(type='DefaultFormatBundle'),
15+
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
16+
]
17+
test_pipeline = [
18+
dict(type='LoadImageFromFile'),
19+
dict(
20+
type='MultiScaleFlipAug',
21+
img_scale=(2048, 1024),
22+
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
23+
flip=False,
24+
transforms=[
25+
dict(type='Resize', keep_ratio=True),
26+
dict(type='RandomFlip'),
27+
dict(type='Normalize', **img_norm_cfg),
28+
dict(type='ImageToTensor', keys=['img']),
29+
dict(type='Collect', keys=['img']),
30+
])
31+
]
32+
data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))

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