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| _base_ = [ | |
| '../_base_/models/deeplabv3_r50-d8.py', '../_base_/default_runtime.py', | |
| '../_base_/schedules/schedule_20k.py' | |
| ] | |
| # dataset settings | |
| dataset_type = 'DSDLSegDataset' | |
| data_root = 'data/PASCAL_VOC2012' | |
| img_prefix = 'raw/VOCdevkit/VOC2012' | |
| train_ann = 'dsdl/dsdl_SemSeg_full/set-train/train.yaml' | |
| val_ann = 'dsdl/dsdl_SemSeg_full/set-val/val.yaml' | |
| 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=(2048, 512), 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') | |
| ] | |
| 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_prefix, seg_map_path=img_prefix), | |
| ann_file=train_ann, | |
| 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_prefix, seg_map_path=img_prefix), | |
| ann_file=val_ann, | |
| pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) | |
| test_evaluator = val_evaluator | |
| data_preprocessor = dict(size=crop_size) | |
| model = dict( | |
| data_preprocessor=data_preprocessor, | |
| decode_head=dict(num_classes=21), | |
| auxiliary_head=dict(num_classes=21)) | |