first commit

Author: XiaoQiangSHI

README.md

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+## OverView
+![overview](imgs\overview.jpg "OverView")
+## Result
+![](imgs\result.png)
+## 环境
+- mmcls>=1.0.0rc0
+- mmcv>=2.0.0rc1,<2.1.0
+- mmengine>=0.1.0,<1.0.0
+- pytorch >= 1.8
+## 训练
+- 指定tools/lane_with_angle_train.py中的config文件
+- python tools/lane_with_angle_train.py
+

imgs/overview.jpg

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+# ==========================================================================
+# Model
+# ==========================================================================
+num_head_list = [1, 1, 1]
+channels_list = [128, 256, 512]
+# channels_list = [512]
+backbone_stage_index = [1, 2, 3]
+# backbone_stage_index = [3]
+backbone_hw_list = [[60, 80], [30, 40], [15, 20]]
+norm_cfg = dict(type='BN', requires_grad=True)
+data_preprocessor = dict(
+    type='SegDataPreProcessor',
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255,
+    size = (480, 640))
+model = dict(
+    type='Angle_EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    pretrained=None,
+    # HACK:Whether to automatically balance multiple losses, only for with_angle_loss
+    # parse_loss=dict(type='ParseLoss', num_losses=2),
+    backbone=dict(
+        type='ResNet',
+        depth=18,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        strides=(1, 2, 2, 2),
+        norm_cfg=norm_cfg,
+        style='pytorch'),
+    # HACK:whether with angle fusion
+    # neck=dict(
+    #     type='Angle_Fusion_Neck',
+    #     hw_list=backbone_hw_list,
+    #     channels_list = channels_list,
+    #     step_list=[8, 4, 2, 1],
+    #     index_list=backbone_stage_index,
+    #     num_head_list=num_head_list),
+    decode_head=dict(
+        type='Angle_DAHead',
+        pam_channels=128,
+        in_channels=channels_list,
+        in_index=backbone_stage_index,
+        channels=256,
+        dropout_ratio=0.1,
+        num_classes=2,
+        norm_cfg=norm_cfg,
+        upsample_scale_factor_list=[2, 2, 2],
+        # HACK:whether with angle loss
+        #with_angle_loss=True,
+        #num_pre_angle_layers=4,
+        align_corners=False,
+        input_transform='resize_concat',
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0, class_weight=[0.4, 1])),
+    test_cfg=dict(mode='whole'))
+
+
+# ==========================================================================
+# Dataset
+# ==========================================================================
+data_root = '/root/autodl-tmp/MyDataWithAngle_all'
+crop_size = (480, 640)
+dataset_type = 'Laneimg_Withangle'
+train_pipeline = [
+    dict(type='AngleLoadImageFromFile'),
+    dict(type='AngleLoadAnnotations'),
+    dict(
+        type='RandomResize',
+        scale=(640, 480),
+        ratio_range=(0.5, 2.0),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='AnglePackSegInputs')
+]
+test_pipeline = [
+    dict(type='AngleLoadImageFromFile'),
+    dict(type='Resize', scale=(480, 640), keep_ratio=True),
+    dict(type='AngleLoadAnnotations'),
+    dict(type='AnglePackSegInputs')
+]
+train_dataloader = dict(
+    batch_size=4,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='train.txt',
+        data_root=data_root,
+        data_prefix=dict(img_path='data/img', seg_map_path='data/label'),
+        metainfo=dict(
+            classes=('background', 'lane'),
+            palette=[[0, 0, 0], [255, 255, 255]]),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='val.txt',
+        data_root=data_root,
+        data_prefix=dict(img_path='data/img', seg_map_path='data/label'),
+        metainfo=dict(
+            classes=('background', 'lane'),
+            palette=[[0, 0, 0], [255, 255, 255]]),
+        pipeline=test_pipeline))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='val.txt',
+        data_root=data_root,
+        data_prefix=dict(img_path='data/img', seg_map_path='data/label'),
+        metainfo=dict(
+            classes=('background', 'lane'),
+            palette=[[0, 0, 0], [255, 255, 255]]),
+        pipeline=test_pipeline))
+
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mFscore'])
+test_evaluator = val_evaluator
+
+# ==========================================================================
+# Default runtime
+# ==========================================================================
+default_scope = 'mmseg'
+env_cfg = dict(
+    cudnn_benchmark=True,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+vis_backends = [dict(type='TensorboardVisBackend')]
+visualizer = dict(
+    type='SegLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+log_processor = dict(type='LogProcessor', by_epoch=False)
+log_level = 'INFO'
+load_from = None
+resume = None
+
+# ==========================================================================
+# Shedules
+# ==========================================================================
+randomness = dict(seed=42)
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None)
+param_scheduler = [
+    dict(type='LinearLR', by_epoch=False, start_factor=0.1, begin=0, end=1000),
+    dict(
+        type='PolyLR',
+        eta_min=1e-4,
+        power=0.9,
+        begin=1000,
+        end=600000,
+        by_epoch=False)
+]
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=600000, val_interval=10000)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=200, log_metric_by_epoch=False),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, save_best='mIoU'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='SegVisualizationHook', draw=True, show=False, interval=400))
+launcher = 'none'

imgs/result.png

@@ -0,0 +1,170 @@
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=dict(
+        type='SegDataPreProcessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_val=0,
+        seg_pad_val=255,
+        size=(480, 640)),
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='DAHead',
+        in_channels=2048,
+        in_index=3,
+        channels=512,
+        pam_channels=64,
+        dropout_ratio=0.1,
+        num_classes=2,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    auxiliary_head=dict(
+        type='FCNHead',
+        in_channels=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=2,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole')
+    )
+
+
+
+# ==========================================================================
+# Dataset
+# ==========================================================================
+data_root = '/root/autodl-tmp/MyDataWithAngle_all'
+crop_size = (480, 640)
+dataset_type = 'Laneimg_Withangle'
+train_pipeline = [
+    dict(type='AngleLoadImageFromFile'),
+    dict(type='AngleLoadAnnotations'),
+    # dict(
+    #     type='RandomResize',
+    #     scale=(640, 480),
+    #     ratio_range=(0.5, 2.0),
+    #     keep_ratio=True),
+    # dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    # dict(type='RandomFlip', prob=0.5),
+    # dict(type='PhotoMetricDistortion'),
+    dict(type='AnglePackSegInputs')
+]
+test_pipeline = [
+    dict(type='AngleLoadImageFromFile'),
+    # dict(type='Resize', scale=(480, 640), keep_ratio=True),
+    dict(type='AngleLoadAnnotations'),
+    dict(type='AnglePackSegInputs')
+]
+train_dataloader = dict(
+    batch_size=4,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='train.txt',
+        data_root=data_root,
+        data_prefix=dict(img_path='data/img', seg_map_path='data/label'),
+        metainfo=dict(
+            classes=('background', 'lane'),
+            palette=[[0, 0, 0], [255, 255, 255]]),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='val.txt',
+        data_root=data_root,
+        data_prefix=dict(img_path='data/img', seg_map_path='data/label'),
+        metainfo=dict(
+            classes=('background', 'lane'),
+            palette=[[0, 0, 0], [255, 255, 255]]),
+        pipeline=test_pipeline))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='val.txt',
+        data_root=data_root,
+        data_prefix=dict(img_path='data/img', seg_map_path='data/label'),
+        metainfo=dict(
+            classes=('background', 'lane'),
+            palette=[[0, 0, 0], [255, 255, 255]]),
+        pipeline=test_pipeline))
+
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mFscore'])
+test_evaluator = val_evaluator
+
+# ==========================================================================
+# Default runtime
+# ==========================================================================
+default_scope = 'mmseg'
+env_cfg = dict(
+    cudnn_benchmark=True,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+vis_backends = [dict(type='TensorboardVisBackend')]
+visualizer = dict(
+    type='SegLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+log_processor = dict(type='LogProcessor', by_epoch=False)
+log_level = 'INFO'
+load_from = None
+resume = '/root/angle-lane-mmseg/work_dirs/da_res_mmlab_alldata/best_mIoU_iter_95000.pth'
+
+# ==========================================================================
+# Shedules
+# ==========================================================================
+randomness = dict(seed=42)
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None)
+param_scheduler = [
+    dict(type='LinearLR', by_epoch=False, start_factor=0.1, begin=0, end=1000),
+    dict(
+        type='PolyLR',
+        eta_min=1e-4,
+        power=0.9,
+        begin=1000,
+        end=600000,
+        by_epoch=False)
+]
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=600000, val_interval=500)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=200, log_metric_by_epoch=False),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, save_best='mIoU'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='SegVisualizationHook', draw=True, show=False, interval=150))
+launcher = 'none'