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| _base_ = [ | |
| '../_base_/models/fpn_poolformer_s12.py', '../_base_/default_runtime.py', | |
| '../_base_/schedules/schedule_40k.py' | |
| ] | |
| # dataset settings | |
| dataset_type = 'ADE20KDataset' | |
| data_root = 'data/ade/ADEChallengeData2016' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| crop_size = (512, 512) | |
| data_preprocessor = dict(size=crop_size) | |
| 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=(2048, 512), keep_ratio=True), | |
| dict(type='ResizeToMultiple', size_divisor=32), | |
| # 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') | |
| ] | |
| train_dataloader = dict( | |
| batch_size=4, | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(type='InfiniteSampler', shuffle=True), | |
| dataset=dict( | |
| type='RepeatDataset', | |
| times=50, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| data_prefix=dict( | |
| img_path='images/training', | |
| seg_map_path='annotations/training'), | |
| 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='images/validation', | |
| seg_map_path='annotations/validation'), | |
| pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) | |
| test_evaluator = val_evaluator | |
| # model settings | |
| model = dict( | |
| data_preprocessor=data_preprocessor, | |
| neck=dict(in_channels=[64, 128, 320, 512]), | |
| decode_head=dict(num_classes=150)) | |
| # optimizer | |
| # optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001) | |
| # optimizer_config = dict() | |
| # # learning policy | |
| # lr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False) | |
| optim_wrapper = dict( | |
| _delete_=True, | |
| type='AmpOptimWrapper', | |
| optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001)) | |
| param_scheduler = [ | |
| dict( | |
| type='PolyLR', | |
| power=0.9, | |
| begin=0, | |
| end=40000, | |
| eta_min=0.0, | |
| by_epoch=False, | |
| ) | |
| ] | |