[New configs]Deeplabv3plus loveda

mmseg/configs/_base_/datasets/loveda.py

@@ -0,0 +1,76 @@
+from mmcv.transforms.loading import LoadImageFromFile
+from mmcv.transforms import RandomResize, RandomFlip, Resize, TestTimeAug
+from mmengine.dataset.sampler import DefaultSampler, InfiniteSampler
+
+from mmseg.datasets.loveda import LoveDADataset
+from mmseg.datasets.transforms import LoadAnnotations, RandomCrop, PhotoMetricDistortion, PackSegInputs
+from mmseg.evaluation import IoUMetric
+
+dataset_type = LoveDADataset
+data_root = 'data/loveDA'
+crop_size = (512, 512)
+
+train_pipeline = [
+    dict(type=LoadImageFromFile),
+    dict(type=LoadAnnotations),
+    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),
+    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=4,
+    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'])
+test_evaluator = val_evaluator

mmseg/configs/_base_/default_runtime.py

@@ -0,0 +1,32 @@
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.runner import LogProcessor
+from mmengine.visualization import LocalVisBackend
+
+from mmseg.engine.hooks import SegVisualizationHook
+from mmseg.visualization import SegLocalVisualizer
+
+default_scope = None
+
+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))
+
+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(type=LogProcessor, window_size=50, by_epoch=False)
+
+log_level = 'INFO'
+load_from = None
+resume = False

mmseg/configs/_base_/models/deeplabv3plus_r50_d8.py

@@ -0,0 +1,60 @@
+from mmseg.models.data_preprocessor import SegDataPreProcessor
+from mmseg.models.segmentors import EncoderDecoder
+from mmseg.models.backbones import ResNetV1c
+from mmseg.models.decode_heads import DepthwiseSeparableASPPHead, FCNHead
+from mmseg.models.losses import CrossEntropyLoss
+
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+data_preprocessor = dict(
+    type=SegDataPreProcessor,
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255)
+model = dict(
+    type=EncoderDecoder,
+    data_preprocessor=data_preprocessor,
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type=ResNetV1c,
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type=DepthwiseSeparableASPPHead,
+        in_channels=2048,
+        in_index=3,
+        channels=512,
+        dilations=(1, 12, 24, 36),
+        c1_in_channels=256,
+        c1_channels=48,
+        dropout_ratio=0.1,
+        num_classes=19,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type=CrossEntropyLoss, use_sigmoid=False, loss_weight=1.0)),
+    auxiliary_head=dict(
+        type=FCNHead,
+        in_channels=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=19,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type=CrossEntropyLoss, use_sigmoid=False, loss_weight=0.4)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))

mmseg/configs/_base_/schedules/schedule_80k.py

@@ -0,0 +1,23 @@
+from torch.optim.sgd import SGD
+
+from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
+from mmengine.optim.scheduler.lr_scheduler import PolyLR
+from mmengine.runner.loops import IterBasedTrainLoop, TestLoop, ValLoop
+
+# optimizer
+optimizer = dict(type=SGD, lr=0.01, momentum=0.9, weight_decay=0.0001)
+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=80000,
+        by_epoch=False)
+]
+# training schedule for 80k
+train_cfg = dict(type=IterBasedTrainLoop, max_iters=80000, val_interval=8000)
+val_cfg = dict(type=ValLoop)
+test_cfg = dict(type=TestLoop)

mmseg/configs/deeplabv3plus/deeplabv3plus_r101_d8_4xb4_80k_loveda_512x512.py

@@ -0,0 +1,6 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .deeplabv3plus_r50_d8_4xb4_80k_loveda_512x512 import *
+
+model.update(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

mmseg/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py

@@ -0,0 +1,15 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .deeplabv3plus_r50_d8_4xb4_80k_loveda_512x512 import *
+
+model.update(
+    pretrained='open-mmlab://resnet18_v1c',
+    backbone=dict(depth=18),
+    decode_head=dict(
+        c1_in_channels=64,
+        c1_channels=12,
+        in_channels=512,
+        channels=128,
+    ),
+    auxiliary_head=dict(in_channels=256, channels=64))

mmseg/configs/deeplabv3plus/deeplabv3plus_r50_d8_4xb4_80k_loveda_512x512.py

@@ -0,0 +1,15 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.datasets.loveda import *
+    from .._base_.models.deeplabv3plus_r50_d8 import *
+    from .._base_.schedules.schedule_80k import *
+    from .._base_.default_runtime import *
+
+crop_size = (512, 512)
+data_preprocessor.update(size=crop_size)
+
+model.update(
+    data_preprocessor=data_preprocessor,
+    decode_head=dict(num_classes=7),
+    auxiliary_head=dict(num_classes=7))