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| # model settings | |
| norm_cfg = dict(type='SyncBN', requires_grad=True) | |
| 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='CascadeEncoderDecoder', | |
| data_preprocessor=data_preprocessor, | |
| num_stages=2, | |
| pretrained='open-mmlab://resnet50_v1c', | |
| backbone=dict( | |
| type='ResNetV1c', | |
| 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=False, | |
| style='pytorch', | |
| contract_dilation=True), | |
| 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=-1, | |
| num_classes=19, | |
| norm_cfg=norm_cfg, | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | |
| dict( | |
| type='PointHead', | |
| in_channels=[256], | |
| in_index=[0], | |
| channels=256, | |
| num_fcs=3, | |
| coarse_pred_each_layer=True, | |
| dropout_ratio=-1, | |
| num_classes=19, | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)) | |
| ], | |
| # model training and testing settings | |
| train_cfg=dict( | |
| num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75), | |
| test_cfg=dict( | |
| mode='whole', | |
| subdivision_steps=2, | |
| subdivision_num_points=8196, | |
| scale_factor=2)) | |