diff --git a/configs/cm-dgseg/cm_dgseg_b0_cityscapes.py b/configs/cm-dgseg/cm_dgseg_b0_cityscapes.py new file mode 100644 index 0000000000..035ee1300c --- /dev/null +++ b/configs/cm-dgseg/cm_dgseg_b0_cityscapes.py @@ -0,0 +1,197 @@ +# Copyright (c) OpenMMLab. All rights reserved. +"""CM-DGSeg B0 configuration for Cityscapes. + +Run command: + python tools/train.py configs/cm-dgseg/cm_dgseg_b0_cityscapes.py + python tools/test.py configs/cm-dgseg/cm_dgseg_b0_cityscapes.py --eval mIoU +""" + +_base_ = [ + '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', +] + +crop_size = (512, 1024) +num_classes = 19 +norm_cfg = dict(type='BN', requires_grad=True) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53, 0.0], + std=[58.395, 57.12, 57.375, 1.0], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size) + +model = dict( + type='DualStreamEncoderDecoder', + data_preprocessor=data_preprocessor, + backbone_rgb=dict( + type='MixVisionTransformer', + init_cfg=dict(type='Pretrained', checkpoint='https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth'), + in_channels=3, + embed_dims=32, + num_stages=4, + num_layers=[2, 2, 2, 2], + num_heads=[1, 2, 5, 8], + patch_sizes=[7, 3, 3, 3], + sr_ratios=[8, 4, 2, 1], + out_indices=(0, 1, 2, 3), + mlp_ratio=4, + qkv_bias=True, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.1), + backbone_disp=dict( + type='MixVisionTransformer', + in_channels=1, + embed_dims=16, + num_stages=4, + num_layers=[2, 2, 2, 2], + num_heads=[1, 1, 3, 4], + patch_sizes=[7, 3, 3, 3], + sr_ratios=[8, 4, 2, 1], + out_indices=(0, 1, 2, 3), + mlp_ratio=4, + qkv_bias=True, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.05), + decode_head=dict( + type='CMFSegFormerHead', + in_channels=[32, 64, 160, 256], + disp_in_channels=[16, 16, 48, 64], + in_index=[0, 1, 2, 3], + channels=256, + dropout_ratio=0.1, + num_classes=num_classes, + norm_cfg=norm_cfg, + align_corners=False, + with_boundary=False, + ignore_index=255, + loss_decode=[ + dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + avg_non_ignore=True + ), + dict( + type='LovaszLoss', + per_image=False, + reduction='none', + loss_weight=0.4 + ) + ] + ), + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(768, 768))) + +# Data pipelines: disable photometric distortion to keep disparity channel clean. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadCityscapesDisparity'), + dict(type='ConcatRGBDispTo4Ch', keep_rgb_dtype=False), + dict(type='LoadAnnotations'), + dict(type='RandomResize', scale=(2048, 1024), 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='PackSegInputs') +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadCityscapesDisparity'), + dict(type='ConcatRGBDispTo4Ch', keep_rgb_dtype=False, delete_disp=True), + dict(type='Resize', scale=(2048, 1024), keep_ratio=True), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] + +tta_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadCityscapesDisparity'), + dict(type='ConcatRGBDispTo4Ch', keep_rgb_dtype=False, delete_disp=True), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in [0.5, 0.75, 1.0, 1.25, 1.5] + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], + [dict(type='LoadAnnotations')], + [dict(type='PackSegInputs')] + ]) +] + +data_root = '/home/featurize/data/cityscapes' +train_dataloader = dict( + batch_size=4, + num_workers=4, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type='CityscapesDataset', + data_root=data_root, + data_prefix=dict(img_path='leftImg8bit/train', seg_map_path='gtFine/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type='CityscapesDataset', + data_root=data_root, + data_prefix=dict(img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + pipeline=test_pipeline)) +test_dataloader = val_dataloader + +test_evaluator = dict( + type='IoUMetric', + iou_metrics=['mIoU', 'mDice', 'mFscore', 'mAcc', 'mPrecision', 'mRecall'], +) + +val_evaluator = test_evaluator + +optim_wrapper = dict( + type='AmpOptimWrapper', + optimizer=dict(type='AdamW', lr=6e-5, betas=(0.9, 0.999), weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + pos_block=dict(decay_mult=0.), + norm=dict(decay_mult=0.), + head=dict(lr_mult=10.), + backbone_disp=dict(lr_mult=1.0)))) + +param_scheduler = [ + dict(type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=80000, + by_epoch=False) +] + +train_cfg = dict(type='IterBasedTrainLoop', max_iters=80000, val_interval=4000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') + +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=4000,save_best='mIoU', max_keep_ckpts=3), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +auto_scale_lr = dict(enable=False, base_batch_size=16) + + +work_dir = './work_dirs/ZCDataset-Segformer-20251129' \ No newline at end of file diff --git a/configs/cm-dgseg/cm_dgseg_b0_cityscapes_ft.py b/configs/cm-dgseg/cm_dgseg_b0_cityscapes_ft.py new file mode 100644 index 0000000000..c2d7e79dc3 --- /dev/null +++ b/configs/cm-dgseg/cm_dgseg_b0_cityscapes_ft.py @@ -0,0 +1,199 @@ +# Copyright (c) OpenMMLab. All rights reserved. +"""CM-DGSeg B0 configuration for Cityscapes. + +Run command: + python tools/train.py configs/cm-dgseg/cm_dgseg_b0_cityscapes.py + python tools/test.py configs/cm-dgseg/cm_dgseg_b0_cityscapes.py --eval mIoU +""" + +_base_ = [ + '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', +] + +crop_size = (512, 1024) +num_classes = 19 +norm_cfg = dict(type='BN', requires_grad=True) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53, 0.0], + std=[58.395, 57.12, 57.375, 1.0], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size) + +model = dict( + type='DualStreamEncoderDecoder', + data_preprocessor=data_preprocessor, + backbone_rgb=dict( + type='MixVisionTransformer', + init_cfg=dict(type='Pretrained', checkpoint='https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth'), + in_channels=3, + embed_dims=32, + num_stages=4, + num_layers=[2, 2, 2, 2], + num_heads=[1, 2, 5, 8], + patch_sizes=[7, 3, 3, 3], + sr_ratios=[8, 4, 2, 1], + out_indices=(0, 1, 2, 3), + mlp_ratio=4, + qkv_bias=True, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.1), + backbone_disp=dict( + type='MixVisionTransformer', + in_channels=1, + embed_dims=16, + num_stages=4, + num_layers=[2, 2, 2, 2], + num_heads=[1, 1, 3, 4], + patch_sizes=[7, 3, 3, 3], + sr_ratios=[8, 4, 2, 1], + out_indices=(0, 1, 2, 3), + mlp_ratio=4, + qkv_bias=True, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.05), + decode_head=dict( + type='CMFSegFormerHead', + in_channels=[32, 64, 160, 256], + disp_in_channels=[16, 16, 48, 64], + in_index=[0, 1, 2, 3], + channels=256, + dropout_ratio=0.1, + num_classes=num_classes, + norm_cfg=norm_cfg, + align_corners=False, + with_boundary=False, + ignore_index=255, + loss_decode=[ + dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + avg_non_ignore=True + ), + dict( + type='LovaszLoss', + per_image=False, + reduction='none', + loss_weight=0.4 + ) + ] + ), + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(768, 768))) + +# Data pipelines: disable photometric distortion to keep disparity channel clean. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadCityscapesDisparity'), + dict(type='ConcatRGBDispTo4Ch', keep_rgb_dtype=False), + dict(type='LoadAnnotations'), + dict(type='RandomResize', scale=(2048, 1024), 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='PackSegInputs') +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadCityscapesDisparity'), + dict(type='ConcatRGBDispTo4Ch', keep_rgb_dtype=False, delete_disp=True), + dict(type='Resize', scale=(2048, 1024), keep_ratio=True), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] + +tta_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadCityscapesDisparity'), + dict(type='ConcatRGBDispTo4Ch', keep_rgb_dtype=False, delete_disp=True), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in [0.5, 0.75, 1.0, 1.25, 1.5] + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], + [dict(type='LoadAnnotations')], + [dict(type='PackSegInputs')] + ]) +] + +data_root = '/home/featurize/data/cityscapes' +train_dataloader = dict( + batch_size=4, + num_workers=4, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type='CityscapesDataset', + data_root=data_root, + data_prefix=dict(img_path='leftImg8bit/train', seg_map_path='gtFine/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type='CityscapesDataset', + data_root=data_root, + data_prefix=dict(img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + pipeline=test_pipeline)) +test_dataloader = val_dataloader + +test_evaluator = dict( + type='IoUMetric', + iou_metrics=['mIoU', 'mDice', 'mFscore', 'mAcc', 'mPrecision', 'mRecall'], +) + +val_evaluator = test_evaluator + +optim_wrapper = dict( + type='AmpOptimWrapper', + optimizer=dict(type='AdamW', lr=2e-5, betas=(0.9, 0.999), weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + pos_block=dict(decay_mult=0.), + norm=dict(decay_mult=0.), + head=dict(lr_mult=10.), + backbone_disp=dict(lr_mult=1.0)))) + +param_scheduler = [ + dict(type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=20000, + by_epoch=False) +] + +train_cfg = dict(type='IterBasedTrainLoop', max_iters=20000, val_interval=2000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') + +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000,save_best='mIoU', max_keep_ckpts=3), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +auto_scale_lr = dict(enable=False, base_batch_size=16) + + +load_from = './work_dirs/ZCDataset-Segformer-20251129/iter_80000.pth' + +work_dir = './work_dirs/ZCDataset-Segformer-20251130-ft' \ No newline at end of file diff --git a/configs/cm-dgseg/cm_dgseg_b0_cityscapes_plus.py b/configs/cm-dgseg/cm_dgseg_b0_cityscapes_plus.py new file mode 100644 index 0000000000..7e6055e90b --- /dev/null +++ b/configs/cm-dgseg/cm_dgseg_b0_cityscapes_plus.py @@ -0,0 +1,202 @@ +# Copyright (c) OpenMMLab. All rights reserved. +"""CM-DGSeg B0 configuration for Cityscapes. + +Run command: + python tools/train.py configs/cm-dgseg/cm_dgseg_b0_cityscapes_plus.py + python tools/test.py configs/cm-dgseg/cm_dgseg_b0_cityscapes_plus.py --eval mIoU +""" + +_base_ = [ + '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', +] + +crop_size = (512, 1024) +num_classes = 19 +norm_cfg = dict(type='BN', requires_grad=True) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53, 0.0], + std=[58.395, 57.12, 57.375, 1.0], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size) + +model = dict( + type='DualStreamEncoderDecoder', + data_preprocessor=data_preprocessor, + backbone_rgb=dict( + type='MixVisionTransformer', + init_cfg=dict(type='Pretrained', checkpoint='https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth'), + in_channels=3, + embed_dims=32, + num_stages=4, + num_layers=[2, 2, 2, 2], + num_heads=[1, 2, 5, 8], + patch_sizes=[7, 3, 3, 3], + sr_ratios=[8, 4, 2, 1], + out_indices=(0, 1, 2, 3), + mlp_ratio=4, + qkv_bias=True, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.1), + backbone_disp=dict( + type='MixVisionTransformer', + in_channels=1, + embed_dims=16, + num_stages=4, + num_layers=[2, 2, 2, 2], + num_heads=[1, 1, 3, 4], + patch_sizes=[7, 3, 3, 3], + sr_ratios=[8, 4, 2, 1], + out_indices=(0, 1, 2, 3), + mlp_ratio=4, + qkv_bias=True, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.1), + decode_head=dict( + type='CMFSegFormerHead', + in_channels=[32, 64, 160, 256], + disp_in_channels=[16, 16, 48, 64], + in_index=[0, 1, 2, 3], + channels=256, + dropout_ratio=0.1, + num_classes=num_classes, + norm_cfg=norm_cfg, + align_corners=False, + alpha_init=0.1, + beta_init=0.1, + gamma_init=0.1, + with_boundary=True, + boundary_loss_weight=0.1, + ignore_index=255, + loss_decode=[ + dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + avg_non_ignore=True + ), + dict( + type='LovaszLoss', + per_image=False, + reduction='none', + loss_weight=0.5 + ) + ] + ), + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(512, 512))) + +# Data pipelines: keep disparity channel clean while giving RGB slight photometric jitter. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadCityscapesDisparity', max_disparity=300.0), + dict(type='ConcatRGBDispTo4Ch', keep_rgb_dtype=False), + dict(type='LoadAnnotations'), + dict(type='RandomResize', scale=(2048, 1024), 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='PackSegInputs') +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadCityscapesDisparity', max_disparity=300.0), + dict(type='ConcatRGBDispTo4Ch', keep_rgb_dtype=False, delete_disp=True), + dict(type='Resize', scale=(2048, 1024), keep_ratio=True), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] + +tta_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadCityscapesDisparity', max_disparity=300.0), + dict(type='ConcatRGBDispTo4Ch', keep_rgb_dtype=False, delete_disp=True), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in [0.5, 0.75, 1.0, 1.25, 1.5] + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], + [dict(type='LoadAnnotations')], + [dict(type='PackSegInputs')] + ]) +] + +data_root = '/home/featurize/data/cityscapes' +train_dataloader = dict( + batch_size=4, + num_workers=4, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type='CityscapesDataset', + data_root=data_root, + data_prefix=dict(img_path='leftImg8bit/train', seg_map_path='gtFine/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type='CityscapesDataset', + data_root=data_root, + data_prefix=dict(img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + pipeline=test_pipeline)) +test_dataloader = val_dataloader + +test_evaluator = dict( + type='IoUMetric', + iou_metrics=['mIoU', 'mDice', 'mFscore', 'mAcc', 'mPrecision', 'mRecall'], +) + +val_evaluator = test_evaluator + + +optim_wrapper = dict( + type='AmpOptimWrapper', + optimizer=dict(type='AdamW', lr=6e-5, betas=(0.9, 0.999), weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + pos_block=dict(decay_mult=0.), + norm=dict(decay_mult=0.), + head=dict(lr_mult=10.), + backbone_disp=dict(lr_mult=1.0)))) + +param_scheduler = [ + dict(type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=80000, + by_epoch=False) +] + +train_cfg = dict(type='IterBasedTrainLoop', max_iters=80000, val_interval=4000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') + +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=4000,save_best='mIoU', max_keep_ckpts=3), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +auto_scale_lr = dict(enable=False, base_batch_size=16) + + +work_dir = './work_dirs/ZCDataset-Segformer-20251201-2' \ No newline at end of file diff --git a/configs/cm-dgseg/cm_dgseg_b2_cityscapes.py b/configs/cm-dgseg/cm_dgseg_b2_cityscapes.py new file mode 100644 index 0000000000..31b1616beb --- /dev/null +++ b/configs/cm-dgseg/cm_dgseg_b2_cityscapes.py @@ -0,0 +1,56 @@ +# Copyright (c) OpenMMLab. All rights reserved. +"""CM-DGSeg B2 configuration for Cityscapes main results. + +Run command: + python tools/train.py configs/cm-dgseg/cm_dgseg_b2_cityscapes.py + python tools/test.py configs/cm-dgseg/cm_dgseg_b2_cityscapes.py --eval mIoU +""" + +_base_ = [ + './cm_dgseg_b0_cityscapes.py', +] + +# Switch MiT backbone to B2 and tighten optimization for the final run. +model = dict( + backbone_rgb=dict( + init_cfg=dict(type='Pretrained', checkpoint='{{PRETRAIN_B2}}'), + embed_dims=64, + num_layers=[3, 4, 6, 3], + num_heads=[1, 2, 5, 8], + drop_path_rate=0.2), + backbone_disp=dict( + embed_dims=32, + num_layers=[3, 4, 6, 3], + num_heads=[1, 2, 5, 8], + drop_path_rate=0.1), + decode_head=dict( + in_channels=[64, 128, 320, 512], + disp_in_channels=[32, 64, 160, 256], + channels=512, # set to 256 if GPU memory is limited. + dropout_ratio=0.0, + with_boundary=True, + boundary_loss_weight=0.2, + geometry_reg_weight=0.05)) + +# Training tweaks for the sprint setting. +# train_dataloader = dict(batch_size=4, num_workers=8) +# optim_wrapper = dict( +# optimizer=dict(lr=5e-5), +# paramwise_cfg=dict( +# custom_keys=dict( +# head=dict(lr_mult=12.), +# backbone_disp=dict(lr_mult=0.5)))) + +# param_scheduler = [ +# dict(type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), +# dict( +# type='PolyLR', +# eta_min=0.0, +# power=1.0, +# begin=1500, +# end=160000, +# by_epoch=False) +# ] + +# train_cfg = dict(type='IterBasedTrainLoop', max_iters=160000, val_interval=8000) +# default_hooks = dict(checkpoint=dict(interval=8000, max_keep_ckpts=5)) diff --git a/configs/cm-dgseg/segformer_b0_cityscapes.py b/configs/cm-dgseg/segformer_b0_cityscapes.py new file mode 100644 index 0000000000..f80e5d4318 --- /dev/null +++ b/configs/cm-dgseg/segformer_b0_cityscapes.py @@ -0,0 +1,249 @@ +# ------------------------------------------------------------ +# SegFormer B0 baseline for Cityscapes (for comparison with CM-DGSeg B0) +# Modified from your original B2 config +# ------------------------------------------------------------ + +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' + +crop_size = (512, 1024) + +data_preprocessor = dict( + type='SegDataPreProcessor', + bgr_to_rgb=True, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + pad_val=0, + seg_pad_val=255, + size=crop_size, +) + +data_root = '/home/featurize/data/cityscapes' +dataset_type = 'CityscapesDataset' + +default_hooks = dict( + checkpoint=dict( + type='CheckpointHook', + by_epoch=False, + interval=4000, + max_keep_ckpts=3, + save_best='mIoU'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook'), +) + +default_scope = 'mmseg' + +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0) +) + +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] + +log_level = 'INFO' +log_processor = dict(by_epoch=False) + +# ------------------------------------------------------------ +# SegFormer-B0 configuration +# ------------------------------------------------------------ +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + backbone=dict( + type='MixVisionTransformer', + # -------- change to B0 -------- + init_cfg=dict(type='Pretrained', + checkpoint='https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth'), + embed_dims=32, + num_layers=[2, 2, 2, 2], + num_heads=[1, 2, 5, 8], + patch_sizes=[7, 3, 3, 3], + sr_ratios=[8, 4, 2, 1], + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.1, + mlp_ratio=4, + qkv_bias=True, + + in_channels=3, + num_stages=4, + out_indices=(0, 1, 2, 3), + ), + + decode_head=dict( + type='SegformerHead', + # -------- B0 decode head channels -------- + in_channels=[32, 64, 160, 256], + in_index=[0, 1, 2, 3], + channels=256, + + dropout_ratio=0.1, + num_classes=19, + + norm_cfg=dict(type='BN', requires_grad=True), + align_corners=False, + + loss_decode=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0 + ) + ), + + pretrained=None, + + train_cfg=dict(), + test_cfg=dict( + mode='slide', + crop_size=crop_size, + stride=(768, 768), + ) +) + +norm_cfg = dict(type='BN', requires_grad=True) + +# ------------------------------------------------------------ +# Optimizer (same as B2 version) +# ------------------------------------------------------------ +optim_wrapper = dict( + type='AmpOptimWrapper', + optimizer=dict(type='AdamW', lr=6e-5, betas=(0.9, 0.999), weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0), + ) + ) +) + +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1e-6, + by_epoch=False, + begin=0, + end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=80000, + by_epoch=False), +] + +randomness = dict(seed=0) + +resume = False + +# ------------------------------------------------------------ +# Train / Val / Test +# ------------------------------------------------------------ +train_cfg = dict(type='IterBasedTrainLoop', max_iters=80000, val_interval=4000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') + +# ------------------- dataloaders ----------------------------- + +train_dataloader = dict( + batch_size=4, + num_workers=4, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + + dataset=dict( + type='CityscapesDataset', + data_root=data_root, + data_prefix=dict( + img_path='leftImg8bit/train', + seg_map_path='gtFine/train'), + reduce_zero_label=True, + + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict(type='RandomResize', scale=(2048, 1024), ratio_range=(0.5, 2.0), keep_ratio=True), + dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + # dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ] + ) +) + +val_dataloader = dict( + batch_size=1, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + + dataset=dict( + type='CityscapesDataset', + data_root=data_root, + data_prefix=dict( + img_path='leftImg8bit/val', + seg_map_path='gtFine/val'), + reduce_zero_label=True, + + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 1024), keep_ratio=True), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict(type='PackSegInputs'), + ] + ) +) + +test_dataloader = val_dataloader + +test_evaluator = dict( + type='IoUMetric', + iou_metrics=['mIoU', 'mDice', 'mFscore', 'mAcc', 'mPrecision', 'mRecall'], +) + +val_evaluator = test_evaluator + +# ------------------------------------------------------------ +# TTA +# ------------------------------------------------------------ +tta_model = dict(type='SegTTAModel') + +tta_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=s, keep_ratio=True) + for s in [0.5, 0.75, 1.0, 1.25, 1.5] + ], + [ + dict(type='RandomFlip', direction='horizontal', prob=0.0), + dict(type='RandomFlip', direction='horizontal', prob=1.0), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ] + ] + ) +] + +vis_backends = [dict(type='LocalVisBackend')] + +visualizer = dict( + type='SegLocalVisualizer', + name='visualizer', + vis_backends=vis_backends, +) + +work_dir = './work_dirs/ZCDataset-SegformerB0-compare-20251126' + diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index f8ad750d76..f40e29ab54 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -27,6 +27,7 @@ from .stare import STAREDataset from .synapse import SynapseDataset # yapf: disable +from .pipelines import ConcatRGBDispTo4Ch, LoadCityscapesDisparity from .transforms import (CLAHE, AdjustGamma, Albu, BioMedical3DPad, BioMedical3DRandomCrop, BioMedical3DRandomFlip, BioMedicalGaussianBlur, BioMedicalGaussianNoise, @@ -61,5 +62,6 @@ 'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset', 'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile', 'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset', - 'NYUDataset', 'HSIDrive20Dataset' + 'NYUDataset', 'HSIDrive20Dataset', 'LoadCityscapesDisparity', + 'ConcatRGBDispTo4Ch' ] diff --git a/mmseg/datasets/pipelines/__init__.py b/mmseg/datasets/pipelines/__init__.py new file mode 100644 index 0000000000..fe7847f177 --- /dev/null +++ b/mmseg/datasets/pipelines/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .concat_modalities import ConcatRGBDispTo4Ch +from .load_disparity import LoadCityscapesDisparity + +__all__ = ['LoadCityscapesDisparity', 'ConcatRGBDispTo4Ch'] diff --git a/mmseg/datasets/pipelines/concat_modalities.py b/mmseg/datasets/pipelines/concat_modalities.py new file mode 100644 index 0000000000..fd7c124687 --- /dev/null +++ b/mmseg/datasets/pipelines/concat_modalities.py @@ -0,0 +1,88 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Optional + +import numpy as np +from mmcv.transforms import BaseTransform + +from mmseg.registry import TRANSFORMS + + +@TRANSFORMS.register_module() +class ConcatRGBDispTo4Ch(BaseTransform): + """Concatenate RGB images and disparity maps to form 4-channel inputs. + + Required Keys: + - img (np.ndarray): RGB image with shape (H, W, 3). + - disp (np.ndarray): Normalized disparity map with shape (H, W). + + Modified Keys: + - img (np.ndarray): Concatenated 4-channel tensor with shape (H, W, 4). + + Args: + keep_rgb_dtype (bool): Whether to keep the RGB image dtype after + concatenation. If ``False``, the output will be converted to + ``np.float32``. Defaults to ``True``. + disp_scale (Optional[float]): Optional scaling factor applied to the + disparity channel after concatenation. Defaults to ``None`` (no + scaling). + delete_disp (bool): Whether to remove the standalone disparity entry + from the results dict after concatenation. Defaults to ``True``. + """ + + def __init__( + self, + keep_rgb_dtype: bool = True, + disp_scale: Optional[float] = None, + delete_disp: bool = True, + ) -> None: + super().__init__() + self.keep_rgb_dtype = keep_rgb_dtype + self.disp_scale = disp_scale + self.delete_disp = delete_disp + + def transform(self, results: dict) -> dict: + if 'img' not in results: + raise KeyError('`img` is required before applying ' + '`ConcatRGBDispTo4Ch`.') + if 'disp' not in results: + raise KeyError('`disp` is required before applying ' + '`ConcatRGBDispTo4Ch`. Please use ' + '`LoadCityscapesDisparity` first.') + + img = results['img'] + disp = results['disp'] + if disp.ndim == 2: + disp = disp[..., None] + elif disp.ndim == 3 and disp.shape[-1] != 1: + raise ValueError('Disparity map must be single channel. ' + f'Got shape {disp.shape}.') + + if not self.keep_rgb_dtype or img.dtype != np.float32: + img = img.astype(np.float32) + disp = disp.astype(img.dtype) + if self.disp_scale is not None: + disp = disp * self.disp_scale + + img_4ch = np.concatenate([img, disp], axis=2) + results['img'] = img_4ch + h, w, _ = img_4ch.shape + results['img_shape'] = (h, w, 4) + if 'ori_shape' in results: + ori = results['ori_shape'] + if isinstance(ori, tuple) and len(ori) >= 2: + results['ori_shape'] = (ori[0], ori[1], 4) + else: + results['ori_shape'] = (h, w, 4) + results['num_channels'] = 4 + if self.delete_disp: + results.pop('disp', None) + disp_fields = results.get('disp_fields', None) + if isinstance(disp_fields, list) and 'disp' in disp_fields: + disp_fields.remove('disp') + return results + + def __repr__(self) -> str: + repr_str = (f"{self.__class__.__name__}(keep_rgb_dtype={self.keep_rgb_dtype}, " + f"disp_scale={self.disp_scale}, " + f"delete_disp={self.delete_disp})") + return repr_str diff --git a/mmseg/datasets/pipelines/load_disparity.py b/mmseg/datasets/pipelines/load_disparity.py new file mode 100644 index 0000000000..2df0229823 --- /dev/null +++ b/mmseg/datasets/pipelines/load_disparity.py @@ -0,0 +1,112 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from typing import Optional + +import mmcv +import mmengine.fileio as fileio +import numpy as np +from mmcv.transforms import BaseTransform + +from mmseg.registry import TRANSFORMS + + +@TRANSFORMS.register_module() +class LoadCityscapesDisparity(BaseTransform): + """Load Cityscapes disparity maps and normalize them. + + This transform infers the disparity file path from the paired RGB image + path following the official Cityscapes naming convention. The disparity is + expected to be stored as a 16-bit PNG file. After loading, a logarithmic + normalization is applied so that the values fall into the range [0, 1]. + + Required Keys: + - img_path or filename + + Added Keys: + - disp (np.ndarray): Normalized disparity map with shape (H, W). + - disp_path (str): Absolute disparity file path. + - disp_shape (Tuple[int, int]): Height and width of the disparity map. + - disp_dtype (np.dtype): Original data type before normalization. + + Args: + max_disparity (Optional[float]): Optional prior upper bound for the + disparity values. When provided, the normalization uses this + constant instead of the per-image maximum for stability across the + dataset. Defaults to ``None``. + log_eps (float): Small epsilon added before taking logarithm to avoid + numerical issues. Defaults to 1e-3. + backend_args (Optional[dict]): Arguments to instantiate a file backend + when reading disparity files. Defaults to ``None``. + imdecode_backend (str): Backend used by :func:`mmcv.imfrombytes` to + decode PNG files. Defaults to ``'pillow'``. + """ + + def __init__( + self, + max_disparity: Optional[float] = None, + log_eps: float = 1e-3, + backend_args: Optional[dict] = None, + imdecode_backend: str = 'pillow', + ) -> None: + super().__init__() + self.max_disparity = max_disparity + self.log_eps = log_eps + self.backend_args = backend_args + self.imdecode_backend = imdecode_backend + + @staticmethod + def infer_disp_path(img_path: str) -> str: + """Infer the Cityscapes disparity path from the RGB image path.""" + if 'leftImg8bit' not in img_path: + raise ValueError('The provided img_path does not follow the ' + 'Cityscapes leftImg8bit naming convention: ' + f"{img_path}") + disp_path = img_path.replace('leftImg8bit', 'disparity') + disp_path = disp_path.replace('_leftImg8bit', '_disparity') + return disp_path + + def transform(self, results: dict) -> dict: + img_path = results.get('img_path') or results.get('filename') + if img_path is None: + raise KeyError('`img_path` or `filename` is required to locate the ' + 'paired Cityscapes disparity map.') + if not osp.isabs(img_path) and results.get('data_root'): + img_path = osp.join(results['data_root'], img_path) + + disp_path = self.infer_disp_path(img_path) + disp_bytes = fileio.get(disp_path, backend_args=self.backend_args) + disparity = mmcv.imfrombytes( + disp_bytes, flag='unchanged', backend=self.imdecode_backend) + if disparity.ndim == 3: + disparity = disparity[..., 0] + original_dtype = disparity.dtype + disparity = disparity.astype(np.float32) + + valid_mask = disparity > 0 + disp_norm = np.zeros_like(disparity, dtype=np.float32) + if np.any(valid_mask): + disparity_valid = disparity[valid_mask] + disp_log = np.log(disparity_valid + self.log_eps) + if self.max_disparity is not None: + denom = np.log(self.max_disparity + self.log_eps) + else: + denom = disp_log.max() + denom = max(denom, np.finfo(np.float32).eps) + disp_norm[valid_mask] = disp_log / denom + disp_norm = np.clip(disp_norm, 0.0, 1.0) + + results['disp'] = disp_norm + results['disp_path'] = disp_path + results['disp_shape'] = disp_norm.shape + results['disp_dtype'] = original_dtype + disp_fields = results.setdefault('disp_fields', []) + if 'disp' not in disp_fields: + disp_fields.append('disp') + return results + + def __repr__(self) -> str: + repr_str = (f"{self.__class__.__name__}(max_disparity={self.max_disparity}, " + f"log_eps={self.log_eps}, " + f"imdecode_backend='{self.imdecode_backend}', " + f"backend_args={self.backend_args})") + return repr_str diff --git a/mmseg/evaluation/metrics/iou_metric.py b/mmseg/evaluation/metrics/iou_metric.py index 16014c7400..a3c564990f 100644 --- a/mmseg/evaluation/metrics/iou_metric.py +++ b/mmseg/evaluation/metrics/iou_metric.py @@ -23,7 +23,8 @@ class IoUMetric(BaseMetric): ignore_index (int): Index that will be ignored in evaluation. Default: 255. iou_metrics (list[str] | str): Metrics to be calculated, the options - includes 'mIoU', 'mDice' and 'mFscore'. + includes 'mIoU', 'mDice', 'mFscore', 'mAcc', 'mPrecision' and + 'mRecall'. nan_to_num (int, optional): If specified, NaN values will be replaced by the numbers defined by the user. Default: None. beta (int): Determines the weight of recall in the combined score. @@ -217,8 +218,9 @@ def total_area_to_metrics(total_area_intersect: np.ndarray, all classes. total_area_label (np.ndarray): The ground truth histogram on all classes. - metrics (List[str] | str): Metrics to be evaluated, 'mIoU' and - 'mDice'. + metrics (List[str] | str): Metrics to be evaluated. Supported + options include 'mIoU', 'mDice', 'mFscore', 'mAcc', + 'mPrecision' and 'mRecall'. nan_to_num (int, optional): If specified, NaN values will be replaced by the numbers defined by the user. Default: None. beta (int): Determines the weight of recall in the combined score. @@ -246,12 +248,16 @@ def f_score(precision, recall, beta=1): if isinstance(metrics, str): metrics = [metrics] - allowed_metrics = ['mIoU', 'mDice', 'mFscore'] + allowed_metrics = ['mIoU', 'mDice', 'mFscore', 'mAcc', 'mPrecision', + 'mRecall'] if not set(metrics).issubset(set(allowed_metrics)): raise KeyError(f'metrics {metrics} is not supported') all_acc = total_area_intersect.sum() / total_area_label.sum() ret_metrics = OrderedDict({'aAcc': all_acc}) + acc = None + precision = None + recall = None for metric in metrics: if metric == 'mIoU': iou = total_area_intersect / total_area_union @@ -273,6 +279,18 @@ def f_score(precision, recall, beta=1): ret_metrics['Fscore'] = f_value ret_metrics['Precision'] = precision ret_metrics['Recall'] = recall + elif metric == 'mAcc': + acc = (acc if acc is not None else + total_area_intersect / total_area_label) + ret_metrics['Acc'] = acc + elif metric == 'mPrecision': + precision = (precision if precision is not None else + total_area_intersect / total_area_pred_label) + ret_metrics['Precision'] = precision + elif metric == 'mRecall': + recall = (recall if recall is not None else + total_area_intersect / total_area_label) + ret_metrics['Recall'] = recall ret_metrics = { metric: value.numpy() @@ -283,4 +301,4 @@ def f_score(precision, recall, beta=1): metric: np.nan_to_num(metric_value, nan=nan_to_num) for metric, metric_value in ret_metrics.items() }) - return ret_metrics + return ret_metrics \ No newline at end of file diff --git a/mmseg/models/decode_heads/__init__.py b/mmseg/models/decode_heads/__init__.py index 4229763816..4e7e3c8589 100644 --- a/mmseg/models/decode_heads/__init__.py +++ b/mmseg/models/decode_heads/__init__.py @@ -3,6 +3,7 @@ from .apc_head import APCHead from .aspp_head import ASPPHead from .cc_head import CCHead +from .cmf_segformer_head import CMFSegFormerHead from .da_head import DAHead from .ddr_head import DDRHead from .dm_head import DMHead @@ -44,5 +45,6 @@ 'SETRMLAHead', 'DPTHead', 'SETRMLAHead', 'SegmenterMaskTransformerHead', 'SegformerHead', 'ISAHead', 'STDCHead', 'IterativeDecodeHead', 'KernelUpdateHead', 'KernelUpdator', 'MaskFormerHead', 'Mask2FormerHead', - 'LightHamHead', 'PIDHead', 'DDRHead', 'VPDDepthHead', 'SideAdapterCLIPHead' + 'LightHamHead', 'PIDHead', 'DDRHead', 'VPDDepthHead', + 'SideAdapterCLIPHead', 'CMFSegFormerHead' ] diff --git a/mmseg/models/decode_heads/cmf_segformer_head.py b/mmseg/models/decode_heads/cmf_segformer_head.py new file mode 100644 index 0000000000..1c21e146ce --- /dev/null +++ b/mmseg/models/decode_heads/cmf_segformer_head.py @@ -0,0 +1,301 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule + +from mmseg.models.decode_heads.decode_head import BaseDecodeHead +from mmseg.registry import MODELS + + +class DepthwiseSobelConv(nn.Conv2d): + """Depth-wise convolution initialized with a Sobel/Laplacian kernel.""" + + def __init__(self, channels: int, kernel_size: int = 3) -> None: + super().__init__( + channels, + channels, + kernel_size=kernel_size, + padding=kernel_size // 2, + groups=channels, + bias=True) + self.reset_parameters() + + def reset_parameters(self) -> None: + super().reset_parameters() + base_kernel = torch.tensor([ + [0., 1., 0.], + [1., -4., 1.], + [0., 1., 0.], + ]) + kernel = base_kernel.view(1, 1, 3, 3) + with torch.no_grad(): + self.weight.copy_(kernel.repeat(self.in_channels, 1, 1, 1)) + if self.bias is not None: + self.bias.zero_() + + +class DGFE(nn.Module): + """Depth-Guided Feature Enhancement.""" + + def __init__( + self, + rgb_channels: int, + disp_channels: int, + reduction: int = 4, + alpha_init: float = 0.5, + beta_init: float = 0.5, + ) -> None: + super().__init__() + self.edge_conv = DepthwiseSobelConv(disp_channels) + self.edge_align = nn.Conv2d(disp_channels, rgb_channels, kernel_size=1) + mid_channels = max(disp_channels // reduction, 1) + self.conf_pool = nn.AdaptiveAvgPool2d(1) + self.conf_mlp = nn.Sequential( + nn.Linear(disp_channels, mid_channels), + nn.ReLU(inplace=True), + nn.Linear(mid_channels, rgb_channels)) + self.alpha = nn.Parameter(torch.tensor(alpha_init, dtype=torch.float32)) + self.beta = nn.Parameter(torch.tensor(beta_init, dtype=torch.float32)) + + def forward(self, rgb: torch.Tensor, + disp: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + edge_feat = self.edge_conv(disp) + edge_feat = self.edge_align(edge_feat) + a_edge = torch.tanh(edge_feat) + + pooled = self.conf_pool(disp).flatten(1) + conf = self.conf_mlp(pooled).view(rgb.size(0), -1, 1, 1) + a_conf = torch.sigmoid(conf) + + alpha = torch.sigmoid(self.alpha) + beta = torch.sigmoid(self.beta) + enhanced = rgb * (1 + alpha * a_edge) * (1 + beta * a_conf) + return enhanced, a_edge, a_conf + + +class CMG(nn.Module): + """Cross-Modality Gate for feature fusion.""" + + def __init__(self, + rgb_channels: int, + disp_channels: int, + gamma_init: float = 0.5) -> None: + super().__init__() + self.align_disp = nn.Conv2d(disp_channels, rgb_channels, kernel_size=1) + self.transform = nn.Conv2d(disp_channels, rgb_channels, kernel_size=1) + self.gate_conv = nn.Conv2d( + rgb_channels * 2, rgb_channels, kernel_size=3, padding=1) + self.gamma = nn.Parameter(torch.tensor(gamma_init, dtype=torch.float32)) + + def forward(self, rgb: torch.Tensor, + disp: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + disp_aligned = self.align_disp(disp) + gate_input = torch.cat([rgb, disp_aligned], dim=1) + gate = torch.sigmoid(self.gate_conv(gate_input)) + disp_transformed = self.transform(disp) + gamma = torch.sigmoid(self.gamma) + fused = (1 - gamma) * rgb + gamma * (rgb + gate * disp_transformed) + return fused, gate, disp_transformed + + +@MODELS.register_module() +class CMFSegFormerHead(BaseDecodeHead): + """SegFormer-style head with cross-modality fusion.""" + + def __init__( + self, + in_channels: List[int], + channels: int, + disp_in_channels: Optional[List[int]] = None, + reduction: int = 4, + alpha_init: float = 0.5, + beta_init: float = 0.5, + gamma_init: float = 0.5, + enable_dgfe: bool = True, + enable_cmg: bool = True, + use_disp: bool = True, + with_boundary: bool = False, + boundary_loss_weight: float = 0.3, + geometry_reg_weight: float = 0.0, + road_class_idx: int = 0, + **kwargs) -> None: + super().__init__( + in_channels=in_channels, + channels=channels, + input_transform='multiple_select', + **kwargs) + if disp_in_channels is None: + disp_in_channels = in_channels + assert len(disp_in_channels) == len(self.in_channels) + self.disp_in_channels = disp_in_channels + self.reduction = reduction + self.enable_dgfe = enable_dgfe + self.enable_cmg = enable_cmg + self.use_disp = use_disp + self.with_boundary = with_boundary + self.boundary_loss_weight = boundary_loss_weight + self.geometry_reg_weight = geometry_reg_weight + self.road_class_idx = road_class_idx + + self.dgfe_modules = nn.ModuleList() + self.cmg_modules = nn.ModuleList() + self.proj_layers = nn.ModuleList() + for rgb_c, disp_c in zip(self.in_channels, self.disp_in_channels): + self.dgfe_modules.append( + DGFE(rgb_c, disp_c, reduction, alpha_init, beta_init)) + self.cmg_modules.append(CMG(rgb_c, disp_c, gamma_init)) + self.proj_layers.append( + ConvModule( + in_channels=rgb_c, + out_channels=self.channels, + kernel_size=1, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg)) + + self.proj_fuse = ConvModule( + in_channels=self.channels, + out_channels=self.channels, + kernel_size=3, + padding=1, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + if self.with_boundary: + self.boundary_head = nn.Conv2d(self.channels, 1, kernel_size=1) + self.boundary_loss = nn.BCEWithLogitsLoss(reduction='mean') + + self.latest_results: Dict[str, Union[torch.Tensor, List[torch.Tensor]]] + self.latest_results = {} + + def forward(self, inputs: Tuple[List[torch.Tensor], List[torch.Tensor]]): + assert isinstance(inputs, (tuple, list)) and len(inputs) == 2, ( + 'CMFSegFormerHead expects a tuple of (rgb_feats, disp_feats).') + feats_rgb, feats_disp = inputs + feats_rgb = self._transform_inputs(feats_rgb) + feats_disp = self._transform_inputs(feats_disp) + assert len(feats_rgb) == len(feats_disp) + + upsampled = [] + edge_list, conf_list, gate_list, disp_trans_list = [], [], [], [] + for idx, (fr, fd) in enumerate(zip(feats_rgb, feats_disp)): + if self.use_disp and self.enable_dgfe: + fr_enhanced, a_edge, a_conf = self.dgfe_modules[idx](fr, fd) + else: + fr_enhanced = fr + a_edge = fr.new_zeros(fr.shape) + a_conf = fr.new_zeros(fr.size(0), fr.size(1), 1, 1) + + if self.use_disp and self.enable_cmg: + fr_fused, gate, fd_trans = self.cmg_modules[idx](fr_enhanced, fd) + else: + fr_fused = fr_enhanced + gate = fr.new_zeros(fr.shape) + fd_trans = fr.new_zeros(fr.shape) + proj = self.proj_layers[idx](fr_fused) + if proj.shape[2:] != feats_rgb[0].shape[2:]: + proj = F.interpolate( + proj, + size=feats_rgb[0].shape[2:], + mode='bilinear', + align_corners=self.align_corners) + upsampled.append(proj) + edge_list.append(a_edge) + conf_list.append(a_conf) + gate_list.append(gate) + disp_trans_list.append(fd_trans) + + fused = torch.stack(upsampled, dim=0).sum(dim=0) + fused = self.proj_fuse(fused) + feature_for_cls = fused + if self.dropout is not None: + feature_for_cls = self.dropout(feature_for_cls) + seg_logits = self.conv_seg(feature_for_cls) + + boundary_pred = None + if self.with_boundary: + boundary_pred = self.boundary_head(feature_for_cls) + + self.latest_results = { + 'fused_feature': fused, + 'a_edge': edge_list, + 'a_conf': conf_list, + 'gate': gate_list, + 'fd_trans': disp_trans_list, + 'boundary_pred': boundary_pred, + } + return seg_logits + + def loss_by_feat(self, seg_logits: torch.Tensor, + batch_data_samples) -> Dict[str, torch.Tensor]: + losses = super().loss_by_feat(seg_logits, batch_data_samples) + device = seg_logits.device + if self.with_boundary and self.latest_results.get('boundary_pred') is not None: + boundary_pred = self.latest_results['boundary_pred'] + edge_targets = [] + for data_sample in batch_data_samples: + edge_data = None + if hasattr(data_sample, 'edge_map'): + edge_data = data_sample.edge_map.data + elif hasattr(data_sample, 'gt_edge_map'): + edge_data = data_sample.gt_edge_map.data + if edge_data is not None: + edge = edge_data.float() + if edge.dim() == 2: + edge = edge.unsqueeze(0).unsqueeze(0) + elif edge.dim() == 3 and edge.size(0) != 1: + edge = edge.mean(dim=0, keepdim=True).unsqueeze(0) + elif edge.dim() == 3: + edge = edge.unsqueeze(0) + edge_targets.append(edge) + else: + zero_edge = torch.zeros(1, 1, *boundary_pred.shape[2:], + device=device) + edge_targets.append(zero_edge) + edge_target = torch.cat(edge_targets, dim=0).to(device) + if boundary_pred.shape[2:] != edge_target.shape[2:]: + boundary_pred = F.interpolate( + boundary_pred, + size=edge_target.shape[2:], + mode='bilinear', + align_corners=self.align_corners) + losses['loss_edge'] = self.boundary_loss(boundary_pred, + edge_target) * \ + self.boundary_loss_weight + + if self.geometry_reg_weight > 0: + road_masks, disp_grads = [], [] + for data_sample in batch_data_samples: + if hasattr(data_sample, 'road_mask') and hasattr( + data_sample, 'disp_grad'): + road = data_sample.road_mask.data.float() + grad = data_sample.disp_grad.data.float() + if road.dim() == 2: + road = road.unsqueeze(0).unsqueeze(0) + elif road.dim() == 3 and road.size(0) != 1: + road = road.mean(dim=0, keepdim=True).unsqueeze(0) + elif road.dim() == 3: + road = road.unsqueeze(0) + if grad.dim() == 2: + grad = grad.unsqueeze(0).unsqueeze(0) + elif grad.dim() == 3 and grad.size(0) != 1: + grad = grad.mean(dim=0, keepdim=True).unsqueeze(0) + elif grad.dim() == 3: + grad = grad.unsqueeze(0) + road_masks.append(road) + disp_grads.append(grad) + if road_masks: + road_mask = torch.stack(road_masks, dim=0).to(device) + disp_grad = torch.stack(disp_grads, dim=0).to(device) + prob = seg_logits.softmax(dim=1) + road_prob = prob[:, self.road_class_idx:self.road_class_idx + 1] + weight = torch.exp(-disp_grad.abs()) + geo_loss = (road_prob * weight * road_mask).mean() + losses['loss_geo'] = geo_loss * self.geometry_reg_weight + + return losses + + def get_latest_results(self) -> Dict[str, Union[torch.Tensor, List[torch.Tensor]]]: + return self.latest_results diff --git a/mmseg/models/decode_heads/decode_head.py b/mmseg/models/decode_heads/decode_head.py index fd53afe22d..49d0e7a1c8 100644 --- a/mmseg/models/decode_heads/decode_head.py +++ b/mmseg/models/decode_heads/decode_head.py @@ -6,6 +6,7 @@ import torch import torch.nn as nn from mmengine.model import BaseModule +from mmengine.structures import BaseDataElement from torch import Tensor from mmseg.registry import MODELS @@ -349,7 +350,8 @@ def predict_by_feat(self, seg_logits: Tensor, Returns: Tensor: Outputs segmentation logits map. """ - + if isinstance(batch_img_metas[0], BaseDataElement): + batch_img_metas = [data_sample.metainfo for data_sample in batch_img_metas] if isinstance(batch_img_metas[0]['img_shape'], torch.Size): # slide inference size = batch_img_metas[0]['img_shape'] diff --git a/mmseg/models/segmentors/__init__.py b/mmseg/models/segmentors/__init__.py index 59b012f417..c57b1dcc89 100644 --- a/mmseg/models/segmentors/__init__.py +++ b/mmseg/models/segmentors/__init__.py @@ -2,11 +2,12 @@ from .base import BaseSegmentor from .cascade_encoder_decoder import CascadeEncoderDecoder from .depth_estimator import DepthEstimator +from .dual_stream_encoder_decoder import DualStreamEncoderDecoder from .encoder_decoder import EncoderDecoder from .multimodal_encoder_decoder import MultimodalEncoderDecoder from .seg_tta import SegTTAModel __all__ = [ 'BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder', 'SegTTAModel', - 'MultimodalEncoderDecoder', 'DepthEstimator' + 'MultimodalEncoderDecoder', 'DepthEstimator', 'DualStreamEncoderDecoder' ] diff --git a/mmseg/models/segmentors/base.py b/mmseg/models/segmentors/base.py index 17a0bb2b33..2b1b950e9d 100644 --- a/mmseg/models/segmentors/base.py +++ b/mmseg/models/segmentors/base.py @@ -177,7 +177,7 @@ def postprocess_result(self, # resize as original shape i_seg_logits = resize( i_seg_logits, - size=img_meta['ori_shape'], + size=img_meta['ori_shape'][:2], mode='bilinear', align_corners=self.align_corners, warning=False).squeeze(0) diff --git a/mmseg/models/segmentors/dual_stream_encoder_decoder.py b/mmseg/models/segmentors/dual_stream_encoder_decoder.py new file mode 100644 index 0000000000..db67e2e96e --- /dev/null +++ b/mmseg/models/segmentors/dual_stream_encoder_decoder.py @@ -0,0 +1,151 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import List, Optional, Sequence, Tuple + +import torch +import torch.nn as nn +from torch import Tensor + +from mmseg.registry import MODELS +from mmseg.utils import (ConfigType, OptConfigType, OptMultiConfig, + OptSampleList, SampleList, add_prefix) +from .base import BaseSegmentor + + +@MODELS.register_module() +class DualStreamEncoderDecoder(BaseSegmentor): + """Dual-stream encoder-decoder for cross-modality segmentation. + + This segmentor maintains individual backbones for RGB and disparity inputs + and merges their multi-scale features inside the decode head. + + Args: + backbone_rgb (ConfigType): Config dict for the RGB backbone. + backbone_disp (ConfigType): Config dict for the disparity backbone. + decode_head (ConfigType): Config dict for the decode head that accepts + two feature lists ``(feats_rgb, feats_disp)``. + auxiliary_head (OptConfigType): Optional auxiliary head config. The + auxiliary head receives the concatenated tuple of features similar + to the decode head. Defaults to None. + train_cfg (OptConfigType): Training config. Defaults to None. + test_cfg (OptConfigType): Test config. Defaults to None. + data_preprocessor (OptConfigType): Data preprocessor config. Defaults + to None. + init_cfg (OptMultiConfig): Initialization config. Defaults to None. + """ + + def __init__( + self, + backbone_rgb: ConfigType, + decode_head: ConfigType, + backbone_disp: Optional[ConfigType] = None, + auxiliary_head: OptConfigType = None, + train_cfg: OptConfigType = None, + test_cfg: OptConfigType = None, + data_preprocessor: OptConfigType = None, + init_cfg: OptMultiConfig = None, + ) -> None: + super().__init__( + data_preprocessor=data_preprocessor, init_cfg=init_cfg) + self.backbone_rgb = MODELS.build(backbone_rgb) + if backbone_disp is not None: + self.backbone_disp = MODELS.build(backbone_disp) + self.use_disp_branch = True + else: + self.backbone_disp = None + self.use_disp_branch = False + self._init_decode_head(decode_head) + self._init_auxiliary_head(auxiliary_head) + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + assert self.with_decode_head + + def _init_decode_head(self, decode_head: ConfigType) -> None: + self.decode_head = MODELS.build(decode_head) + self.align_corners = self.decode_head.align_corners + self.num_classes = self.decode_head.num_classes + self.out_channels = self.decode_head.out_channels + + def _init_auxiliary_head(self, auxiliary_head: OptConfigType) -> None: + if auxiliary_head is None: + self.auxiliary_head = None + return + if isinstance(auxiliary_head, Sequence): + module_list = [] + for head_cfg in auxiliary_head: + module_list.append(MODELS.build(head_cfg)) + self.auxiliary_head = nn.ModuleList(module_list) + else: + self.auxiliary_head = MODELS.build(auxiliary_head) + + @property + def with_auxiliary_head(self) -> bool: + if isinstance(self.auxiliary_head, nn.ModuleList): + return len(self.auxiliary_head) > 0 + return self.auxiliary_head is not None + + def _forward_auxiliary(self, inputs: Tuple[List[Tensor], List[Tensor]], + data_samples: SampleList) -> dict: + losses = dict() + if isinstance(self.auxiliary_head, nn.ModuleList): + for idx, aux_head in enumerate(self.auxiliary_head): + loss_aux = aux_head.loss(inputs, data_samples, self.train_cfg) + losses.update(add_prefix(loss_aux, f'aux_{idx}')) + else: + loss_aux = self.auxiliary_head.loss(inputs, data_samples, + self.train_cfg) + losses.update(add_prefix(loss_aux, 'aux')) + return losses + + def extract_feat(self, inputs: Tensor) -> Tuple[List[Tensor], List[Tensor]]: + """Extract multi-scale features from RGB and disparity inputs.""" + rgb = inputs[:, :3, :, :] + feats_rgb = self.backbone_rgb(rgb) + if not isinstance(feats_rgb, (list, tuple)): + feats_rgb = [feats_rgb] + if self.use_disp_branch and inputs.size(1) > 3: + disp = inputs[:, 3:4, :, :] + feats_disp = self.backbone_disp(disp) if self.backbone_disp else [] + if not isinstance(feats_disp, (list, tuple)): + feats_disp = [feats_disp] + else: + feats_disp = [torch.zeros_like(feat) for feat in feats_rgb] + if not isinstance(feats_rgb, (list, tuple)): + feats_rgb = [feats_rgb] + return list(feats_rgb), list(feats_disp) + + def encode_decode(self, inputs: Tensor, + batch_img_metas: List[dict]) -> Tensor: + feats = self.extract_feat(inputs) + seg_logits = self.decode_head.predict( + feats, batch_img_metas, self.test_cfg) + return seg_logits + + def _decode_head_forward_train( + self, inputs: Tuple[List[Tensor], List[Tensor]], + data_samples: SampleList) -> dict: + losses = dict() + loss_decode = self.decode_head.loss(inputs, data_samples, + self.train_cfg) + losses.update(add_prefix(loss_decode, 'decode')) + return losses + + def loss(self, inputs: Tensor, data_samples: SampleList) -> dict: + feats = self.extract_feat(inputs) + losses = self._decode_head_forward_train(feats, data_samples) + if self.with_auxiliary_head: + losses.update(self._forward_auxiliary(feats, data_samples)) + return losses + + def predict(self, + inputs: Tensor, + data_samples: OptSampleList = None) -> SampleList: + feats = self.extract_feat(inputs) + seg_logits = self.decode_head.predict(feats, data_samples, self.test_cfg) + return self.postprocess_result(seg_logits, data_samples) + + def _forward(self, + inputs: Tensor, + data_samples: OptSampleList = None): + feats = self.extract_feat(inputs) + return self.decode_head.forward(feats) diff --git a/notebooks/CM-DGSeg_Report.py b/notebooks/CM-DGSeg_Report.py new file mode 100644 index 0000000000..eefde1b14b --- /dev/null +++ b/notebooks/CM-DGSeg_Report.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +"""Offline report generator for CM-DGSeg results.""" + +import argparse +import csv +from pathlib import Path +from typing import Dict, List + +import matplotlib + +matplotlib.use('Agg') +import matplotlib.pyplot as plt + + +def read_csv(path: Path) -> List[Dict[str, str]]: + with open(path, 'r') as f: + reader = csv.DictReader(f) + return list(reader) + + +def to_float(value: str) -> float: + try: + return float(value) + except (TypeError, ValueError): + return 0.0 + + +def plot_bar(rows: List[Dict[str, str]], metric: str, out_path: Path, title: str) -> None: + names = [row.get('experiment', row.get('preset', 'exp')) for row in rows] + values = [to_float(row.get(metric, 0.0)) for row in rows] + plt.figure(figsize=(10, 5)) + plt.bar(names, values, color='#2ca02c') + plt.ylabel(metric) + plt.title(title) + plt.xticks(rotation=30, ha='right') + plt.tight_layout() + plt.savefig(out_path) + plt.close() + + +def plot_param_flops(rows: List[Dict[str, str]], out_path: Path) -> None: + params = [to_float(row.get('Params(M)', 0.0)) for row in rows] + flops = [to_float(row.get('FLOPs(G)', 0.0)) for row in rows] + names = [row.get('experiment', row.get('preset', 'exp')) for row in rows] + plt.figure(figsize=(10, 5)) + plt.scatter(params, flops, c='#d62728') + for name, x, y in zip(names, params, flops): + plt.annotate(name, (x, y)) + plt.xlabel('Params (M)') + plt.ylabel('FLOPs (G)') + plt.title('Parameter vs FLOPs trade-off') + plt.tight_layout() + plt.savefig(out_path) + plt.close() + + +def main() -> None: + parser = argparse.ArgumentParser(description='Generate CM-DGSeg report figures') + parser.add_argument('--main-csv', type=str, default='outputs/summary/metrics.csv', + help='CSV file containing main results.') + parser.add_argument('--ablation-csv', type=str, default='work_dirs/cm_dgseg_ablation/ablation_results.csv', + help='CSV file containing ablation results.') + parser.add_argument('--output-dir', type=str, default='paper_figs', + help='Directory to store generated figures.') + args = parser.parse_args() + + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + if Path(args.main_csv).exists(): + main_rows = read_csv(Path(args.main_csv)) + if main_rows: + plot_bar(main_rows, 'mIoU', output_dir / 'main_mIoU.pdf', 'Main Results mIoU') + plot_param_flops(main_rows, output_dir / 'params_flops.pdf') + else: + print(f'Main CSV not found: {args.main_csv}') + + if Path(args.ablation_csv).exists(): + ablation_rows = read_csv(Path(args.ablation_csv)) + if ablation_rows: + plot_bar(ablation_rows, 'mIoU', output_dir / 'ablation_mIoU.pdf', 'Ablation mIoU') + else: + print(f'Ablation CSV not found: {args.ablation_csv}') + + print(f'Figures saved to {output_dir}') + + +if __name__ == '__main__': + main() diff --git a/run_train_val.ps1 b/run_train_val.ps1 new file mode 100644 index 0000000000..dbcc4b61f7 --- /dev/null +++ b/run_train_val.ps1 @@ -0,0 +1,44 @@ +param( + [string]$Config = 'configs/cm-dgseg/cm_dgseg_b0_cityscapes.py', + [int]$Gpus = 1, + [string]$WorkDir = '', + [string]$Checkpoint = 'latest.pth' +) + +if (-not $WorkDir) { + $WorkDir = Join-Path 'work_dirs' ([IO.Path]::GetFileNameWithoutExtension($Config)) +} + +$logDir = Join-Path $WorkDir 'logs' +$smiLog = Join-Path $logDir 'nvidia_smi.csv' +New-Item -ItemType Directory -Path $logDir -Force | Out-Null + +$job = $null +if (Get-Command 'nvidia-smi' -ErrorAction SilentlyContinue) { + "timestamp,index,name,utilization.gpu [%],utilization.memory [%],memory.total [MiB],memory.used [MiB],memory.free [MiB]" | Set-Content $smiLog + $job = Start-Job -ScriptBlock { + param($Path) + & nvidia-smi --query-gpu=timestamp,index,name,utilization.gpu,utilization.memory,memory.total,memory.used,memory.free ` + --format=csv,noheader -l 60 >> $Path + } -ArgumentList $smiLog +} else { + Write-Warning 'nvidia-smi not available; skipping telemetry.' +} + +try { + $trainArgs = @('tools/train.py', $Config, '--work-dir', $WorkDir, '--auto-resume') + if ($Gpus -gt 1) { + $trainArgs += @('--launcher', 'pytorch', '--devices', $Gpus) + } + & python @trainArgs + + $ckptPath = Join-Path $WorkDir $Checkpoint + & python 'tools/test.py' $Config $ckptPath '--eval' 'mIoU' +} +finally { + if ($job) { + Stop-Job $job -ErrorAction SilentlyContinue | Out-Null + Receive-Job $job | Out-Null + Remove-Job $job | Out-Null + } +} diff --git a/run_train_val.sh b/run_train_val.sh new file mode 100755 index 0000000000..f30f784366 --- /dev/null +++ b/run_train_val.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +set -euo pipefail + +CONFIG=${1:-configs/cm-dgseg/cm_dgseg_b0_cityscapes.py} +GPUS=${2:-1} +WORK_DIR=${3:-work_dirs/$(basename "${CONFIG}" .py)} +CHECKPOINT=${4:-latest.pth} + +mkdir -p "${WORK_DIR}" +LOG_DIR="${WORK_DIR}/logs" +mkdir -p "${LOG_DIR}" +SMI_LOG="${LOG_DIR}/nvidia_smi.csv" + +if command -v nvidia-smi >/dev/null 2>&1; then + echo "timestamp,index,name,utilization.gpu [%],utilization.memory [%],memory.total [MiB],memory.used [MiB],memory.free [MiB]" > "${SMI_LOG}" + nvidia-smi --query-gpu=timestamp,index,name,utilization.gpu,utilization.memory,memory.total,memory.used,memory.free \ + --format=csv,noheader -l 60 >> "${SMI_LOG}" & + SMI_PID=$! +else + echo "nvidia-smi not found; skipping GPU telemetry." >&2 + SMI_PID=0 +fi + +cleanup() { + if [[ ${SMI_PID} -ne 0 ]]; then + kill ${SMI_PID} >/dev/null 2>&1 || true + fi +} +trap cleanup EXIT + +TRAIN_CMD=(python tools/train.py "${CONFIG}" --work-dir "${WORK_DIR}" --auto-resume) +if [[ ${GPUS} -gt 1 ]]; then + TRAIN_CMD+=(--launcher pytorch --devices "${GPUS}") +fi +"${TRAIN_CMD[@]}" + +TEST_CMD=(python tools/test.py "${CONFIG}" "${WORK_DIR}/${CHECKPOINT}" --eval mIoU) +"${TEST_CMD[@]}" diff --git a/tools/gen_cityscapes_edges.py b/tools/gen_cityscapes_edges.py new file mode 100644 index 0000000000..cf7c7c1611 --- /dev/null +++ b/tools/gen_cityscapes_edges.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python3 +# Copyright (c) OpenMMLab. All rights reserved. +"""Generate thin binary edge maps from Cityscapes annotations.""" + +import argparse +from pathlib import Path + +import cv2 +import numpy as np +from tqdm import tqdm + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description='Generate Cityscapes edge maps') + parser.add_argument('--ann-root', type=str, required=True, + help='Root directory to Cityscapes gtFine annotations.') + parser.add_argument('--out-root', type=str, required=True, + help='Output directory for generated edge maps.') + parser.add_argument('--splits', nargs='+', default=['train', 'val'], + help='Dataset splits to process (default: train val).') + parser.add_argument('--low', type=float, default=5.0, + help='Low threshold for Canny edge detector.') + parser.add_argument('--high', type=float, default=15.0, + help='High threshold for Canny edge detector.') + parser.add_argument('--kernel', type=int, default=3, + help='Kernel size for morphological refinement.') + return parser.parse_args() + + +def build_edge_map(label: np.ndarray, low: float, high: float, + kernel_size: int) -> np.ndarray: + if label.ndim == 3: + label = label[..., 0] + label_u8 = label.astype(np.uint8) + canny = cv2.Canny(label_u8, low, high) + kernel = np.ones((kernel_size, kernel_size), np.uint8) + gradient = cv2.morphologyEx(label_u8, cv2.MORPH_GRADIENT, kernel) + combined = np.clip(canny.astype(np.uint16) + gradient.astype(np.uint16), 0, + 255) + refined = cv2.dilate(combined.astype(np.uint8), kernel, iterations=1) + edge = (refined > 0).astype(np.uint8) * 255 + return edge + + +def main() -> None: + args = parse_args() + ann_root = Path(args.ann_root) + out_root = Path(args.out_root) + out_root.mkdir(parents=True, exist_ok=True) + + for split in args.splits: + split_dir = ann_root / split + if not split_dir.exists(): + print(f'[Skip] Split {split} not found in {ann_root}.') + continue + files = sorted(split_dir.rglob('*_labelIds.png')) + if not files: + print(f'[Skip] No label files under {split_dir}.') + continue + for ann_path in tqdm(files, desc=f'Processing {split}'): + rel_path = ann_path.relative_to(ann_root) + out_path = out_root / rel_path + out_path.parent.mkdir(parents=True, exist_ok=True) + label = cv2.imread(str(ann_path), cv2.IMREAD_UNCHANGED) + edge = build_edge_map(label, args.low, args.high, args.kernel) + cv2.imwrite(str(out_path), edge) + + print(f'Edge maps saved to: {out_root}') + + +if __name__ == '__main__': + main() diff --git a/tools/run_ablation.py b/tools/run_ablation.py new file mode 100644 index 0000000000..1fa3d0c004 --- /dev/null +++ b/tools/run_ablation.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python3 +# Copyright (c) OpenMMLab. All rights reserved. +"""Automate CM-DGSeg ablation experiments.""" + +import argparse +import csv +import os +import re +import subprocess +import sys +import tempfile +from copy import deepcopy +from pathlib import Path +from typing import Dict, List + +from mmengine.config import Config + +ABLATIONS: Dict[str, Dict[str, bool]] = { + 'rgb_only': dict(use_disp=False, enable_dgfe=False, enable_cmg=False, + with_boundary=False, geometry_reg=False), + 'early4ch': dict(use_disp=True, enable_dgfe=False, enable_cmg=False, + with_boundary=False, geometry_reg=False, early_fusion=True), + 'dual_no_dgfe': dict(use_disp=True, enable_dgfe=False, enable_cmg=False, + with_boundary=False, geometry_reg=False), + '+dgfe': dict(use_disp=True, enable_dgfe=True, enable_cmg=False, + with_boundary=False, geometry_reg=False), + '+cmg': dict(use_disp=True, enable_dgfe=True, enable_cmg=True, + with_boundary=False, geometry_reg=False), + '+edge_loss': dict(use_disp=True, enable_dgfe=True, enable_cmg=True, + with_boundary=True, geometry_reg=False), + '+geo_reg': dict(use_disp=True, enable_dgfe=True, enable_cmg=True, + with_boundary=True, geometry_reg=True), +} + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description='Run CM-DGSeg ablations') + parser.add_argument('config', type=str, help='Base CM-DGSeg config file.') + parser.add_argument('--work-dir', type=str, default='work_dirs/cm_dgseg_ablation', + help='Root directory for ablation experiments.') + parser.add_argument('--iters', type=int, default=None, + help='Override training iterations for quick experiments.') + parser.add_argument('--launcher', type=str, default='none', + help='Launcher for distributed training (default: none).') + parser.add_argument('--devices', type=int, default=1, + help='Number of GPUs to use.') + parser.add_argument('--skip-train', action='store_true', + help='Only run evaluation assuming checkpoints exist.') + return parser.parse_args() + + +def _filter_pipeline(pipeline: List[dict], keep_disp: bool) -> List[dict]: + filtered = [] + for step in pipeline: + if not keep_disp and step['type'] in { + 'LoadCityscapesDisparity', 'ConcatRGBDispTo4Ch'}: + continue + filtered.append(step) + return filtered + + +def _update_dataset_pipeline(cfg: Config, pipeline_key: str, pipeline: List[dict]) -> None: + cfg[pipeline_key] = pipeline + dataloader_key = pipeline_key.replace('pipeline', 'dataloader') + if dataloader_key in cfg and 'dataset' in cfg[dataloader_key]: + cfg[dataloader_key]['dataset']['pipeline'] = pipeline + + +def _convert_to_early_fusion(cfg: Config) -> None: + backbone = deepcopy(cfg.model['backbone_rgb']) + backbone['in_channels'] = 4 + decode_head = deepcopy(cfg.model['decode_head']) + cfg.model = dict( + type='EncoderDecoder', + data_preprocessor=cfg.model['data_preprocessor'], + backbone=backbone, + decode_head=dict( + type='SegformerHead', + in_channels=decode_head['in_channels'], + in_index=decode_head['in_index'], + channels=decode_head['channels'], + dropout_ratio=decode_head['dropout_ratio'], + num_classes=decode_head['num_classes'], + norm_cfg=decode_head['norm_cfg'], + align_corners=decode_head['align_corners'], + loss_decode=decode_head['loss_decode']), + train_cfg=cfg.get('train_cfg', dict()), + test_cfg=cfg.get('test_cfg', dict())) + cfg.model.pop('backbone_disp', None) + + +def apply_ablation(cfg: Config, tag: str) -> Config: + options = ABLATIONS[tag] + cfg = cfg.copy() + keep_disp = options.get('use_disp', True) + + for pipeline_key in ['train_pipeline', 'test_pipeline', 'tta_pipeline']: + if pipeline_key in cfg: + new_pipeline = _filter_pipeline(cfg[pipeline_key], keep_disp) + _update_dataset_pipeline(cfg, pipeline_key, new_pipeline) + + if options.get('early_fusion'): + _convert_to_early_fusion(cfg) + else: + cfg.model['decode_head']['use_disp'] = options['use_disp'] + cfg.model['decode_head']['enable_dgfe'] = options['enable_dgfe'] + cfg.model['decode_head']['enable_cmg'] = options['enable_cmg'] + cfg.model['decode_head']['with_boundary'] = options['with_boundary'] + if options['with_boundary']: + cfg.model['decode_head'].setdefault('boundary_loss_weight', 0.3) + cfg.model['decode_head']['geometry_reg_weight'] = ( + 0.05 if options['geometry_reg'] else 0.0) + + if not keep_disp: + cfg.model.pop('backbone_disp', None) + dp = cfg.model.get('data_preprocessor', {}) + if 'mean' in dp and len(dp['mean']) > 3: + dp['mean'] = dp['mean'][:3] + if 'std' in dp and len(dp['std']) > 3: + dp['std'] = dp['std'][:3] + + return cfg + + +def adjust_iterations(cfg: Config, max_iters: int) -> None: + cfg.train_cfg['max_iters'] = max_iters + cfg.train_cfg['val_interval'] = max(100, max_iters // 4) + for sched in cfg.param_scheduler: + if sched.get('type') == 'PolyLR': + sched['end'] = max_iters + if sched.get('end', None) and sched['end'] > max_iters: + sched['end'] = max_iters + + +def run_command(cmd: List[str], capture: bool = False) -> subprocess.CompletedProcess: + if capture: + return subprocess.run(cmd, check=True, text=True, capture_output=True) + return subprocess.run(cmd, check=True) + + +def parse_metrics(output: str) -> Dict[str, float]: + metrics = {} + for match in re.finditer(r'(mIoU|mAcc|aAcc):\s*([0-9.]+)', output): + metrics[match.group(1)] = float(match.group(2)) + return metrics + + +def main() -> None: + args = parse_args() + base_cfg = Config.fromfile(args.config) + work_root = Path(args.work_dir) + work_root.mkdir(parents=True, exist_ok=True) + results_path = work_root / 'ablation_results.csv' + + with open(results_path, 'w', newline='') as csv_file: + writer = csv.DictWriter(csv_file, fieldnames=['preset', 'work_dir', 'mIoU']) + writer.writeheader() + for preset in ABLATIONS: + cfg = apply_ablation(base_cfg, preset) + if args.iters: + adjust_iterations(cfg, args.iters) + with tempfile.NamedTemporaryFile('w', suffix=f'_{preset}.py', delete=False) as tmp: + cfg.dump(tmp.name) + cfg_path = tmp.name + preset_work_dir = work_root / preset + preset_work_dir.mkdir(parents=True, exist_ok=True) + + if not args.skip_train: + train_cmd = [ + sys.executable, 'tools/train.py', cfg_path, + f'--work-dir={preset_work_dir}', + f'--launcher={args.launcher}' + ] + if args.launcher != 'none': + train_cmd.extend(['--devices', str(args.devices)]) + run_command(train_cmd) + + checkpoint = preset_work_dir / 'latest.pth' + if not checkpoint.exists(): + raise FileNotFoundError(f'Checkpoint not found: {checkpoint}') + test_cmd = [ + sys.executable, 'tools/test.py', cfg_path, str(checkpoint), '--eval', 'mIoU' + ] + test_result = run_command(test_cmd, capture=True) + metrics = parse_metrics(test_result.stdout) + writer.writerow({ + 'preset': preset, + 'work_dir': str(preset_work_dir), + 'mIoU': metrics.get('mIoU', 0.0) + }) + print(f'[{preset}] mIoU: {metrics.get("mIoU", 0.0):.4f}') + os.remove(cfg_path) + + print(f'Ablation results saved to {results_path}') + + +if __name__ == '__main__': + main() diff --git a/tools/summarize_results.py b/tools/summarize_results.py new file mode 100644 index 0000000000..e7931a1ebb --- /dev/null +++ b/tools/summarize_results.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python3 +# Copyright (c) OpenMMLab. All rights reserved. +"""Summarize CM-DGSeg evaluation results into CSV, Markdown, and plots.""" + +import argparse +import json +import os +from pathlib import Path +from typing import Dict, List, Tuple + +import matplotlib.pyplot as plt + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description='Summarize evaluation metrics') + parser.add_argument('paths', nargs='+', help='Evaluation JSON files or directories.') + parser.add_argument('--output-dir', type=str, default='outputs/summary', + help='Directory to store aggregated summaries.') + parser.add_argument('--metric-key', type=str, default='mIoU', + help='Primary metric key to plot (default: mIoU).') + return parser.parse_args() + + +def find_result_files(paths: List[str]) -> List[Path]: + files: List[Path] = [] + for path in paths: + p = Path(path) + if p.is_dir(): + files.extend(sorted(p.rglob('*.json'))) + elif p.suffix == '.json': + files.append(p) + return files + + +def extract_metrics(path: Path) -> Tuple[str, Dict[str, float], Dict[str, float]]: + with open(path, 'r') as f: + data = json.load(f) + if isinstance(data, list): + data = data[-1] + if 'metrics' in data: + metrics = data['metrics'] + elif 'metric_values' in data: + metrics = data['metric_values'] + else: + metrics = data + class_iou = {} + if 'class_iou' in metrics: + class_iou = {k: float(v) for k, v in metrics['class_iou'].items()} + elif 'IoUs' in metrics and 'classes' in metrics: + class_iou = {cls: float(iou) for cls, iou in zip(metrics['classes'], metrics['IoUs'])} + elif 'per_class_results' in metrics: + class_iou = {k: float(v.get('IoU', 0.0)) for k, v in metrics['per_class_results'].items()} + scalars = {k: float(v) for k, v in metrics.items() if isinstance(v, (int, float))} + experiment = path.stem + return experiment, scalars, class_iou + + +def save_csv(output_dir: Path, rows: List[Dict[str, float]], fieldnames: List[str]) -> None: + import csv + csv_path = output_dir / 'metrics.csv' + with open(csv_path, 'w', newline='') as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + for row in rows: + writer.writerow(row) + + +def save_markdown(output_dir: Path, rows: List[Dict[str, float]], fieldnames: List[str]) -> None: + md_path = output_dir / 'metrics.md' + with open(md_path, 'w') as f: + f.write('| ' + ' | '.join(fieldnames) + ' |\n') + f.write('| ' + ' | '.join(['---'] * len(fieldnames)) + ' |\n') + for row in rows: + f.write('| ' + ' | '.join(f'{row.get(col, 0):.4f}' if isinstance(row.get(col), float) + else str(row.get(col, '')) + for col in fieldnames) + ' |\n') + + +def plot_metric(output_dir: Path, rows: List[Dict[str, float]], metric_key: str) -> None: + plt.figure(figsize=(10, 5)) + names = [row['experiment'] for row in rows] + values = [row.get(metric_key, 0.0) for row in rows] + plt.bar(names, values, color='#1f77b4') + plt.ylabel(metric_key) + plt.xticks(rotation=30, ha='right') + plt.tight_layout() + plt.savefig(output_dir / f'{metric_key}_bar.png') + plt.close() + + +def main() -> None: + args = parse_args() + files = find_result_files(args.paths) + if not files: + raise FileNotFoundError('No evaluation JSON files found.') + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + rows: List[Dict[str, float]] = [] + class_rows: Dict[str, Dict[str, float]] = {} + + for path in files: + experiment, scalars, class_iou = extract_metrics(path) + row = {'experiment': experiment} + row.update(scalars) + rows.append(row) + for cls, value in class_iou.items(): + class_rows.setdefault(cls, {})[experiment] = value + + fieldnames = sorted({key for row in rows for key in row.keys()}) + save_csv(output_dir, rows, fieldnames) + save_markdown(output_dir, rows, fieldnames) + plot_metric(output_dir, rows, args.metric_key) + + # Save per-class IoU tables + for cls, values in class_rows.items(): + class_path = output_dir / f'class_{cls}_iou.csv' + with open(class_path, 'w') as f: + f.write('experiment,IoU\n') + for exp, val in values.items(): + f.write(f'{exp},{val:.4f}\n') + + print(f'Summary saved to {output_dir}') + + +if __name__ == '__main__': + main() diff --git a/tools/vis_attention_and_results.py b/tools/vis_attention_and_results.py new file mode 100644 index 0000000000..0f513af48a --- /dev/null +++ b/tools/vis_attention_and_results.py @@ -0,0 +1,138 @@ +#!/usr/bin/env python3 +# Copyright (c) OpenMMLab. All rights reserved. +"""Visualize CM-DGSeg attention and gating maps.""" + +import argparse +import os +from pathlib import Path +from typing import List + +import cv2 +import mmcv +import numpy as np +import torch +import torch.nn.functional as F +from mmengine.config import Config + +from mmseg.apis import init_model +from mmseg.datasets.pipelines.load_disparity import LoadCityscapesDisparity +from mmseg.registry import DATASETS + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description='Visualize CM-DGSeg outputs') + parser.add_argument('config', type=str, help='Path to config file.') + parser.add_argument('checkpoint', type=str, help='Path to checkpoint file.') + parser.add_argument('--output-dir', type=str, default='outputs/vis', + help='Directory to save visualization results.') + parser.add_argument('--indices', type=int, nargs='+', default=[0, 1, 2], + help='Dataset indices to visualize.') + parser.add_argument('--split', type=str, choices=['train', 'val'], default='val', + help='Dataset split to sample from.') + return parser.parse_args() + + +def colorize_mask(mask: np.ndarray, palette: List[List[int]]) -> np.ndarray: + color = np.zeros((*mask.shape, 3), dtype=np.uint8) + for idx, value in enumerate(palette): + if idx >= len(palette): + break + color[mask == idx] = value + return color + + +def tensor_to_heatmap(tensor: torch.Tensor, target_size: tuple) -> np.ndarray: + tensor = tensor.float().mean(dim=1, keepdim=True) + tensor = F.interpolate(tensor, size=target_size, mode='bilinear', align_corners=False) + tensor = tensor.sigmoid() + array = tensor.squeeze().cpu().numpy() + array = (array - array.min()) / (array.max() - array.min() + 1e-6) + array = (array * 255).astype(np.uint8) + return array + + +def main() -> None: + args = parse_args() + cfg = Config.fromfile(args.config) + cfg.model.pretrained = None + device = 'cuda' if torch.cuda.is_available() else 'cpu' + model = init_model(cfg, args.checkpoint, device=device) + model.eval() + + dataset_cfg = cfg.val_dataloader if args.split == 'val' else cfg.train_dataloader + dataset = DATASETS.build(dataset_cfg['dataset']) + palette = dataset.PALETTE if hasattr(dataset, 'PALETTE') else dataset.METAINFO['palette'] + + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + disp_loader = LoadCityscapesDisparity() + + for idx in args.indices: + sample = dataset[idx] + inputs = sample['inputs'] + data_sample = sample['data_samples'] + batch = model.data_preprocessor(dict(inputs=[inputs], data_samples=[data_sample]), + training=False) + with torch.no_grad(): + seg_logits = model._forward(batch['inputs'], batch['data_samples']) + results = model.postprocess_result(seg_logits, batch['data_samples']) + latest = model.decode_head.get_latest_results() + + pred_sample = results[0] + pred_mask = pred_sample.pred_sem_seg.data.squeeze().cpu().numpy() + gt_mask = pred_sample.gt_sem_seg.data.squeeze().cpu().numpy() + height, width = pred_mask.shape + target_size = (height, width) + + img_path = pred_sample.metainfo.get('img_path', None) + data_root = dataset.data_root if hasattr(dataset, 'data_root') else '' + if img_path is None: + img_path = data_sample.metainfo.get('img_path') + if img_path is None: + raise RuntimeError('Image path metadata is missing.') + if not os.path.isabs(img_path): + img_path = os.path.join(data_root, img_path) + rgb = mmcv.imread(img_path) + + disp_dict = {'img_path': img_path} + disp_info = disp_loader.transform(disp_dict) + disp_norm = disp_info['disp'] + disp_img = (disp_norm * 255).astype(np.uint8) + disp_color = cv2.applyColorMap(disp_img, cv2.COLORMAP_TURBO) + + rgb_out = rgb.copy() + pred_color = colorize_mask(pred_mask, palette) + gt_color = colorize_mask(gt_mask, palette) + + edge_maps = latest.get('a_edge', []) + conf_maps = latest.get('a_conf', []) + gate_maps = latest.get('gate', []) + edge_heat = tensor_to_heatmap(edge_maps[0], target_size) if edge_maps else None + base_feat = edge_maps[0] if edge_maps else (gate_maps[0] if gate_maps else None) + if conf_maps: + conf_tensor = conf_maps[0].mean(dim=1, keepdim=True) + if base_feat is not None: + conf_tensor = conf_tensor.expand(-1, 1, base_feat.shape[2], base_feat.shape[3]) + conf_heat = tensor_to_heatmap(conf_tensor, target_size) + else: + conf_heat = None + gate_heat = tensor_to_heatmap(gate_maps[0], target_size) if gate_maps else None + + stem = Path(img_path).stem + mmcv.imwrite(rgb_out, out_dir / f'{stem}_rgb.png') + mmcv.imwrite(disp_color, out_dir / f'{stem}_disp.png') + mmcv.imwrite(pred_color[:, :, ::-1], out_dir / f'{stem}_pred.png') + mmcv.imwrite(gt_color[:, :, ::-1], out_dir / f'{stem}_gt.png') + if edge_heat is not None: + mmcv.imwrite(edge_heat, out_dir / f'{stem}_a_edge.png') + if conf_heat is not None: + mmcv.imwrite(conf_heat, out_dir / f'{stem}_a_conf.png') + if gate_heat is not None: + mmcv.imwrite(gate_heat, out_dir / f'{stem}_gate.png') + + print(f'Visualizations saved to {out_dir}') + + +if __name__ == '__main__': + main() diff --git "a/\345\233\276\350\241\250/\346\265\213\350\257\225\351\233\206\345\210\206\347\261\273\350\257\204\344\274\260\346\214\207\346\240\207.pdf" "b/\345\233\276\350\241\250/\346\265\213\350\257\225\351\233\206\345\210\206\347\261\273\350\257\204\344\274\260\346\214\207\346\240\207.pdf" new file mode 100644 index 0000000000..3a7be265e4 Binary files /dev/null and "b/\345\233\276\350\241\250/\346\265\213\350\257\225\351\233\206\345\210\206\347\261\273\350\257\204\344\274\260\346\214\207\346\240\207.pdf" differ diff --git "a/\345\233\276\350\241\250/\346\265\213\350\257\225\351\233\206\345\210\206\347\261\273\350\257\204\344\274\260\346\214\207\346\240\20729.pdf" "b/\345\233\276\350\241\250/\346\265\213\350\257\225\351\233\206\345\210\206\347\261\273\350\257\204\344\274\260\346\214\207\346\240\20729.pdf" new file mode 100644 index 0000000000..cdcd113f4d Binary files /dev/null and "b/\345\233\276\350\241\250/\346\265\213\350\257\225\351\233\206\345\210\206\347\261\273\350\257\204\344\274\260\346\214\207\346\240\20729.pdf" differ diff --git "a/\345\233\276\350\241\250/\350\256\255\347\273\203\346\227\245\345\277\227-\346\265\213\350\257\225\351\233\206cm-segformer-1117.csv" "b/\345\233\276\350\241\250/\350\256\255\347\273\203\346\227\245\345\277\227-\346\265\213\350\257\225\351\233\206cm-segformer-1117.csv" new file mode 100644 index 0000000000..1ea3240b48 --- /dev/null +++ "b/\345\233\276\350\241\250/\350\256\255\347\273\203\346\227\245\345\277\227-\346\265\213\350\257\225\351\233\206cm-segformer-1117.csv" @@ -0,0 +1,20 @@ +aAcc,mIoU,mAcc,data_time,time,step +91.66,53.93,65.16,0.009838526725769042,0.28409515953063963,4000.0 +92.75,60.15,74.43,0.009079122257803728,0.28041057767506367,8000.0 +93.06,61.03,70.99,0.009355526484415204,0.2811149775149104,12000.0 +94.08,66.32,78.45,0.008869783607071744,0.2801906972112294,16000.0 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