| label_convertor = dict( | |
| type='AttnConvertor', dict_type='DICT90', with_unknown=True) | |
| model = dict( | |
| type='MASTER', | |
| backbone=dict( | |
| type='ResNet', | |
| in_channels=3, | |
| stem_channels=[64, 128], | |
| block_cfgs=dict( | |
| type='BasicBlock', | |
| plugins=dict( | |
| cfg=dict( | |
| type='GCAModule', | |
| ratio=0.0625, | |
| n_head=1, | |
| pooling_type='att', | |
| is_att_scale=False, | |
| fusion_type='channel_add'), | |
| position='after_conv2')), | |
| arch_layers=[1, 2, 5, 3], | |
| arch_channels=[256, 256, 512, 512], | |
| strides=[1, 1, 1, 1], | |
| plugins=[ | |
| dict( | |
| cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)), | |
| stages=(True, True, False, False), | |
| position='before_stage'), | |
| dict( | |
| cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)), | |
| stages=(False, False, True, False), | |
| position='before_stage'), | |
| dict( | |
| cfg=dict( | |
| type='ConvModule', | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| norm_cfg=dict(type='BN'), | |
| act_cfg=dict(type='ReLU')), | |
| stages=(True, True, True, True), | |
| position='after_stage') | |
| ], | |
| init_cfg=[ | |
| dict(type='Kaiming', layer='Conv2d'), | |
| dict(type='Constant', val=1, layer='BatchNorm2d'), | |
| ]), | |
| encoder=None, | |
| decoder=dict( | |
| type='MasterDecoder', | |
| d_model=512, | |
| n_head=8, | |
| attn_drop=0., | |
| ffn_drop=0., | |
| d_inner=2048, | |
| n_layers=3, | |
| feat_pe_drop=0.2, | |
| feat_size=6 * 40), | |
| loss=dict(type='TFLoss', reduction='mean'), | |
| label_convertor=label_convertor, | |
| max_seq_len=30) | |