[New Configs] Add mmseg/configs folder & Support loveda, potsdam, sch…

…edules, default_runtime new configs (#3542)

# [New Configs] Add mmseg/configs folder & Support loveda, potsdam,
schedules, default_runtime new configs
- As the title , the new configs path is mmseg/configs/ 
- The configs files for the dataset have been tested. 
- The purpose of this PR is to enable other community members migrating
to the new config to reference the new configs files for schedules and
default runtime. Hoping for a quick merge~~~.
- Details of this task can be found at:
https://github.com/AI-Tianlong/mmseg-new-config

![image](https://github.com/AI-Tianlong/mmseg-new-config/assets/50650583/04d40057-ff2c-492c-be44-52c6d34d3676)

mmseg/configs/_base_/datasets/loveda.py

@@ -0,0 +1,79 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.transforms.loading import LoadImageFromFile
+from mmcv.transforms.processing import (RandomFlip, RandomResize, Resize,
+                                        TestTimeAug)
+from mmengine.dataset.sampler import DefaultSampler, InfiniteSampler
+
+from mmseg.datasets.loveda import LoveDADataset
+from mmseg.datasets.transforms.formatting import PackSegInputs
+from mmseg.datasets.transforms.loading import LoadAnnotations
+from mmseg.datasets.transforms.transforms import (PhotoMetricDistortion,
+                                                  RandomCrop)
+from mmseg.evaluation import IoUMetric
+
+# dataset settings
+dataset_type = LoveDADataset
+data_root = 'data/loveDA'
+crop_size = (512, 512)
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=LoadAnnotations, reduce_zero_label=True),
+    dict(
+        type=RandomResize,
+        scale=(2048, 512),
+        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=PackSegInputs)
+]
+test_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=Resize, scale=(1024, 1024), keep_ratio=True),
+    # add loading annotation after ``Resize`` because ground truth
+    # does not need to do resize data transform
+    dict(type=LoadAnnotations, reduce_zero_label=True),
+    dict(type=PackSegInputs)
+]
+img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
+tta_pipeline = [
+    dict(type=LoadImageFromFile, backend_args=None),
+    dict(
+        type=TestTimeAug,
+        transforms=[[
+            dict(type=Resize, scale_factor=r, keep_ratio=True)
+            for r in img_ratios
+        ],
+                    [
+                        dict(type=RandomFlip, prob=0., direction='horizontal'),
+                        dict(type=RandomFlip, prob=1., direction='horizontal')
+                    ], [dict(type=LoadAnnotations)],
+                    [dict(type=PackSegInputs)]])
+]
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=12,
+    persistent_workers=True,
+    sampler=dict(type=InfiniteSampler, shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='img_dir/train', seg_map_path='ann_dir/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=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
+        pipeline=test_pipeline))
+
+test_dataloader = val_dataloader
+val_evaluator = dict(type=IoUMetric, iou_metrics=['mIoU'])
+test_evaluator = val_evaluator

mmseg/configs/_base_/datasets/potsdam.py

@@ -0,0 +1,81 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.transforms.loading import LoadImageFromFile
+from mmcv.transforms.processing import (RandomFlip, RandomResize, Resize,
+                                        TestTimeAug)
+from mmengine.dataset.sampler import DefaultSampler, InfiniteSampler
+
+from mmseg.datasets.potsdam import PotsdamDataset
+from mmseg.datasets.transforms.formatting import PackSegInputs
+from mmseg.datasets.transforms.loading import LoadAnnotations
+from mmseg.datasets.transforms.transforms import (PhotoMetricDistortion,
+                                                  RandomCrop)
+from mmseg.evaluation import IoUMetric
+
+# dataset settings
+dataset_type = PotsdamDataset
+data_root = 'data/potsdam'
+crop_size = (512, 512)
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=LoadAnnotations, reduce_zero_label=True),
+    dict(
+        type=RandomResize,
+        scale=(512, 512),
+        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=PackSegInputs)
+]
+test_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=Resize, scale=(512, 512), keep_ratio=True),
+    # add loading annotation after ``Resize`` because ground truth
+    # does not need to do resize data transform
+    dict(type=LoadAnnotations, reduce_zero_label=True),
+    dict(type=PackSegInputs)
+]
+img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
+tta_pipeline = [
+    dict(type=LoadImageFromFile, backend_args=None),
+    dict(
+        type=TestTimeAug,
+        transforms=[[
+            dict(type=Resize, scale_factor=r, keep_ratio=True)
+            for r in img_ratios
+        ],
+                    [
+                        dict(type=RandomFlip, prob=0., direction='horizontal'),
+                        dict(type=RandomFlip, prob=1., direction='horizontal')
+                    ], [dict(type=LoadAnnotations)],
+                    [dict(type=PackSegInputs)]])
+]
+
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type=InfiniteSampler, shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='img_dir/train', seg_map_path='ann_dir/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=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type=IoUMetric, iou_metrics=['mIoU'])  # 'mDice', 'mFscore'
+test_evaluator = val_evaluator

mmseg/configs/_base_/default_runtime.py

@@ -0,0 +1,22 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+from mmengine.visualization import LocalVisBackend
+
+from mmseg.models import SegTTAModel
+from mmseg.visualization import SegLocalVisualizer
+
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+vis_backends = [dict(type=LocalVisBackend)]
+visualizer = dict(
+    type=SegLocalVisualizer, vis_backends=vis_backends, name='visualizer')
+log_processor = dict(by_epoch=False)
+log_level = 'INFO'
+load_from = None
+resume = False
+
+tta_model = dict(type=SegTTAModel)
+default_scope = None

mmseg/configs/_base_/schedules/schedule_160k.py

@@ -0,0 +1,43 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
+from mmengine.optim.scheduler.lr_scheduler import PolyLR
+from mmengine.runner.loops import IterBasedTrainLoop, TestLoop, ValLoop
+from torch.optim.sgd import SGD
+
+from mmseg.engine import SegVisualizationHook
+
+# optimizer
+optimizer = dict(
+    type=SGD,
+    #  lr=0.01,
+    #  momentum=0.9,
+    #  weight_decay=0.0005
+)
+
+optim_wrapper = dict(type=OptimWrapper, optimizer=optimizer, clip_grad=None)
+
+# learning policy
+param_scheduler = [
+    dict(
+        type=PolyLR,
+        eta_min=1e-4,
+        power=0.9,
+        begin=0,
+        end=160000,
+        by_epoch=False)
+]
+# training schedule for 160k
+
+train_cfg = dict(type=IterBasedTrainLoop, max_iters=160000, val_interval=8000)
+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=8000),
+    sampler_seed=dict(type=DistSamplerSeedHook),
+    visualization=dict(type=SegVisualizationHook))

mmseg/configs/_base_/schedules/schedule_20k.py

@@ -0,0 +1,36 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
+from mmengine.optim.scheduler.lr_scheduler import PolyLR
+from mmengine.runner.loops import IterBasedTrainLoop, TestLoop, ValLoop
+from torch.optim.sgd import SGD
+
+from mmseg.engine import SegVisualizationHook
+
+# optimizer
+optimizer = dict(type=SGD, lr=0.01, momentum=0.9, weight_decay=0.0005)
+optim_wrapper = dict(type=OptimWrapper, optimizer=optimizer, clip_grad=None)
+
+# learning policy
+param_scheduler = [
+    dict(
+        type=PolyLR,
+        eta_min=1e-4,
+        power=0.9,
+        begin=0,
+        end=20000,
+        by_epoch=False)
+]
+# training schedule for 20k
+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),
+    sampler_seed=dict(type=DistSamplerSeedHook),
+    visualization=dict(type=SegVisualizationHook))

mmseg/configs/_base_/schedules/schedule_240k.py

@@ -0,0 +1,34 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
+from mmengine.optim.scheduler.lr_scheduler import PolyLR
+from mmengine.runner.loops import IterBasedTrainLoop, TestLoop, ValLoop
+# from mmengine.runner.loops import EpochBasedTrainLoop
+from torch.optim.sgd import SGD
+
+from mmseg.engine import SegVisualizationHook
+
+optimizer = dict(type=SGD, lr=0.01, momentum=0.9, weight_decay=0.0005)
+optim_wrapper = dict(type=OptimWrapper, optimizer=optimizer, clip_grad=None)
+# learning policy
+param_scheduler = [
+    dict(
+        type=PolyLR,
+        eta_min=1e-4,
+        power=0.9,
+        begin=0,
+        end=240000,
+        by_epoch=False)
+]
+# training schedule for 240k
+train_cfg = dict(type=IterBasedTrainLoop, max_iters=240000, val_interval=24000)
+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=24000),
+    sampler_seed=dict(type=DistSamplerSeedHook),
+    visualization=dict(type=SegVisualizationHook))

mmseg/configs/_base_/schedules/schedule_25k.py

@@ -0,0 +1,43 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
+from mmengine.optim.scheduler.lr_scheduler import ConstantLR, LinearLR
+from mmengine.runner.loops import IterBasedTrainLoop, TestLoop, ValLoop
+# from mmengine.runner.loops import EpochBasedTrainLoop
+from torch.optim.adamw import AdamW
+
+from mmseg.engine import SegVisualizationHook
+from mmseg.engine.schedulers import PolyLRRatio
+
+# optimizer
+optimizer = dict(type=AdamW, lr=0.01, weight_decay=0.1)
+
+optim_wrapper = dict(type=OptimWrapper, optimizer=optimizer, clip_grad=None)
+# learning policy
+
+# learning policy
+param_scheduler = [
+    dict(type=LinearLR, start_factor=3e-2, begin=0, end=12000, by_epoch=False),
+    dict(
+        type=PolyLRRatio,
+        eta_min_ratio=3e-2,
+        power=0.9,
+        begin=12000,
+        end=24000,
+        by_epoch=False),
+    dict(type=ConstantLR, by_epoch=False, factor=1, begin=24000, end=25000)
+]
+
+# training schedule for 25k
+train_cfg = dict(type=IterBasedTrainLoop, max_iters=25000, val_interval=1000)
+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=1000),
+    sampler_seed=dict(type=DistSamplerSeedHook),
+    visualization=dict(type=SegVisualizationHook))

mmseg/configs/_base_/schedules/schedule_320k.py

@@ -0,0 +1,36 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
+from mmengine.optim.scheduler.lr_scheduler import PolyLR
+from mmengine.runner.loops import IterBasedTrainLoop, TestLoop, ValLoop
+# from mmengine.runner.loops import EpochBasedTrainLoop
+from torch.optim.sgd import SGD
+
+from mmseg.engine import SegVisualizationHook
+
+# optimizer
+optimizer = dict(type=SGD, lr=0.01, momentum=0.9, weight_decay=0.0005)
+optim_wrapper = dict(type=OptimWrapper, optimizer=optimizer, clip_grad=None)
+
+# learning policy
+param_scheduler = [
+    dict(
+        type=PolyLR,
+        eta_min=1e-4,
+        power=0.9,
+        begin=0,
+        end=320000,
+        by_epoch=False)
+]
+# training schedule for 320k
+train_cfg = dict(type=IterBasedTrainLoop, max_iters=320000, val_interval=32000)
+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=32000),
+    sampler_seed=dict(type=DistSamplerSeedHook),
+    visualization=dict(type=SegVisualizationHook))