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| # model settings | |
| norm_cfg = dict(type='SyncBN', requires_grad=True) | |
| checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s12_3rdparty_32xb128_in1k_20220414-f8d83051.pth' # noqa | |
| # TODO: delete custom_imports after mmpretrain supports auto import | |
| # please install mmpretrain >= 1.0.0rc7 | |
| # import mmpretrain.models to trigger register_module in mmpretrain | |
| custom_imports = dict( | |
| imports=['mmpretrain.models'], allow_failed_imports=False) | |
| 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, | |
| backbone=dict( | |
| type='mmpretrain.PoolFormer', | |
| arch='s12', | |
| init_cfg=dict( | |
| type='Pretrained', checkpoint=checkpoint_file, prefix='backbone.'), | |
| in_patch_size=7, | |
| in_stride=4, | |
| in_pad=2, | |
| down_patch_size=3, | |
| down_stride=2, | |
| down_pad=1, | |
| drop_rate=0., | |
| drop_path_rate=0., | |
| out_indices=(0, 2, 4, 6), | |
| frozen_stages=0, | |
| ), | |
| neck=dict( | |
| type='FPN', | |
| in_channels=[256, 512, 1024, 2048], | |
| out_channels=256, | |
| num_outs=4), | |
| decode_head=dict( | |
| type='FPNHead', | |
| in_channels=[256, 256, 256, 256], | |
| in_index=[0, 1, 2, 3], | |
| feature_strides=[4, 8, 16, 32], | |
| channels=128, | |
| 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)), | |
| # model training and testing settings | |
| train_cfg=dict(), | |
| test_cfg=dict(mode='whole')) | |