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add config support
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‎README.md

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‎config/ade20k.json

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{
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"bag_file":"/home/hrz/project/fdata/indoor/2022-12-05-17-14-13.bag",
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"pose_file":"/home/hrz/project/fdata/indoor/result/pose6d.csv",
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"start_time":0,
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"play_time":-1,
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"LiDAR_topic":"/velodyne_points",
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"camera_topic":"/zed2/zed_node/left/image_rect_color/compressed",
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"image_compressed":true,
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"extrinsic":
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[
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[ 1.0102, -0.0026, -0.0087, 0.1135],
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[-0.0033, -0.0030, -0.9963, -0.1617],
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[ 0.0049, 0.9962, -0.0287, 0.0516],
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[ 0.0000, 0.0000, 0.0000, 1.0000]
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],
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"intrinsic":
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[
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[543.5046, 0, 630.7183],
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[0, 540.5383, 350.9063],
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[0, 0, 1]
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],
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"distortion_matrix":[-1.05873889e-01, 1.32265629e-01, -8.55667814e-05,-1.04098281e-03, -7.01241428e-02],
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"save_folder":"result/indoor",
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"mode":"indoor",
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"model_config":"imseg/mmseg/configs/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k.py",
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"model_file":"imseg/mmseg/model/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
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"lane_class":24,
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"pole_class":45,
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"predict_func":"get_predict_func_mmlab",
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"cmap":"ade20k"
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}

‎config/indoor_config.json

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"model_file":"imseg/mask2former/model/model.pkl",
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"lane_class":24,
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"pole_class":45,
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"predict_func":"get_predict_func",
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"predict_func":"get_predict_func_detectron",
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"cmap":"mapillary"
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}

‎config/outdoor_config.json

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"model_file":"imseg/mask2former/model/model.pkl",
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"lane_class":24,
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"pole_class":45,
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"predict_func":"get_predict_func",
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"predict_func":"get_predict_func_detectron",
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"cmap":"mapillary"
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}

‎config/outdoor_distortion_config.json

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"model_file":"imseg/mask2former/model/model.pkl",
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"lane_class":24,
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"pole_class":45,
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"predict_func":"get_predict_func",
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"predict_func":"get_predict_func_detectron",
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"cmap":"mapillary"
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}

‎imseg/mmseg/_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))

‎imseg/mmseg/_base_/datasets/ade20k_640x640.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 = (640, 640)
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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=(2560, 640), 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=(2560, 640),
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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))

‎imseg/mmseg/_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))

‎imseg/mmseg/_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)
7+
train_pipeline = [
8+
dict(type='LoadImageFromFile'),
9+
dict(type='LoadAnnotations'),
10+
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
11+
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'),
14+
dict(type='Normalize', **img_norm_cfg),
15+
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 = [
20+
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'),
29+
dict(type='Normalize', **img_norm_cfg),
30+
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))

‎imseg/mmseg/_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)
5+
train_pipeline = [
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dict(type='LoadImageFromFile'),
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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),
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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'),
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))

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