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| _base_ = [ | |
| '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py', | |
| '../_base_/schedules/schedule_160k.py' | |
| ] | |
| norm_cfg = dict(type='SyncBN', requires_grad=True) | |
| crop_size = (512, 512) | |
| data_preprocessor = dict( | |
| type='SegDataPreProcessor', | |
| size=crop_size, | |
| 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_cfg | |
| num_classes = 150 | |
| model = dict( | |
| type='EncoderDecoder', | |
| data_preprocessor=data_preprocessor, | |
| backbone=dict( | |
| type='ResNet', | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3), | |
| dilations=(1, 1, 1, 1), | |
| strides=(1, 2, 2, 2), | |
| norm_cfg=norm_cfg, | |
| norm_eval=True, | |
| style='pytorch', | |
| contract_dilation=True, | |
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), | |
| decode_head=dict( | |
| type='MaskFormerHead', | |
| in_channels=[256, 512, 1024, | |
| 2048], # input channels of pixel_decoder modules | |
| feat_channels=256, | |
| in_index=[0, 1, 2, 3], | |
| num_classes=150, | |
| out_channels=256, | |
| num_queries=100, | |
| pixel_decoder=dict( | |
| type='mmdet.PixelDecoder', | |
| norm_cfg=dict(type='GN', num_groups=32), | |
| act_cfg=dict(type='ReLU')), | |
| enforce_decoder_input_project=False, | |
| positional_encoding=dict( # SinePositionalEncoding | |
| num_feats=128, normalize=True), | |
| transformer_decoder=dict( # DetrTransformerDecoder | |
| return_intermediate=True, | |
| num_layers=6, | |
| layer_cfg=dict( # DetrTransformerDecoderLayer | |
| self_attn_cfg=dict( # MultiheadAttention | |
| embed_dims=256, | |
| num_heads=8, | |
| attn_drop=0.1, | |
| proj_drop=0.1, | |
| dropout_layer=None, | |
| batch_first=True), | |
| cross_attn_cfg=dict( # MultiheadAttention | |
| embed_dims=256, | |
| num_heads=8, | |
| attn_drop=0.1, | |
| proj_drop=0.1, | |
| dropout_layer=None, | |
| batch_first=True), | |
| ffn_cfg=dict( | |
| embed_dims=256, | |
| feedforward_channels=2048, | |
| num_fcs=2, | |
| act_cfg=dict(type='ReLU', inplace=True), | |
| ffn_drop=0.1, | |
| dropout_layer=None, | |
| add_identity=True)), | |
| init_cfg=None), | |
| loss_cls=dict( | |
| type='mmdet.CrossEntropyLoss', | |
| use_sigmoid=False, | |
| loss_weight=1.0, | |
| reduction='mean', | |
| class_weight=[1.0] * num_classes + [0.1]), | |
| loss_mask=dict( | |
| type='mmdet.FocalLoss', | |
| use_sigmoid=True, | |
| gamma=2.0, | |
| alpha=0.25, | |
| reduction='mean', | |
| loss_weight=20.0), | |
| loss_dice=dict( | |
| type='mmdet.DiceLoss', | |
| use_sigmoid=True, | |
| activate=True, | |
| reduction='mean', | |
| naive_dice=True, | |
| eps=1.0, | |
| loss_weight=1.0), | |
| train_cfg=dict( | |
| assigner=dict( | |
| type='mmdet.HungarianAssigner', | |
| match_costs=[ | |
| dict(type='mmdet.ClassificationCost', weight=1.0), | |
| dict( | |
| type='mmdet.FocalLossCost', | |
| weight=20.0, | |
| binary_input=True), | |
| dict( | |
| type='mmdet.DiceCost', | |
| weight=1.0, | |
| pred_act=True, | |
| eps=1.0) | |
| ]), | |
| sampler=dict(type='mmdet.MaskPseudoSampler'))), | |
| # training and testing settings | |
| train_cfg=dict(), | |
| test_cfg=dict(mode='whole'), | |
| ) | |
| # optimizer | |
| optimizer = dict( | |
| type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001) | |
| optim_wrapper = dict( | |
| _delete_=True, | |
| type='OptimWrapper', | |
| optimizer=optimizer, | |
| clip_grad=dict(max_norm=0.01, norm_type=2), | |
| paramwise_cfg=dict(custom_keys={ | |
| 'backbone': dict(lr_mult=0.1), | |
| })) | |
| # learning policy | |
| param_scheduler = [ | |
| dict( | |
| type='PolyLR', | |
| eta_min=0, | |
| power=0.9, | |
| begin=0, | |
| end=160000, | |
| by_epoch=False) | |
| ] | |
| # In MaskFormer implementation we use batch size 2 per GPU as default | |
| train_dataloader = dict(batch_size=2, num_workers=2) | |
| val_dataloader = dict(batch_size=1, num_workers=4) | |
| test_dataloader = val_dataloader | |