| _base_ = 'mask_rcnn_r50_fpn_crop640_50e_coco.py' | |
| norm_cfg = dict(type='BN', requires_grad=True) | |
| model = dict( | |
| neck=dict( | |
| type='FPG', | |
| in_channels=[256, 512, 1024, 2048], | |
| out_channels=256, | |
| inter_channels=256, | |
| num_outs=5, | |
| stack_times=9, | |
| paths=['bu'] * 9, | |
| same_down_trans=None, | |
| same_up_trans=dict( | |
| type='conv', | |
| kernel_size=3, | |
| stride=2, | |
| padding=1, | |
| norm_cfg=norm_cfg, | |
| inplace=False, | |
| order=('act', 'conv', 'norm')), | |
| across_lateral_trans=dict( | |
| type='conv', | |
| kernel_size=1, | |
| norm_cfg=norm_cfg, | |
| inplace=False, | |
| order=('act', 'conv', 'norm')), | |
| across_down_trans=dict( | |
| type='interpolation_conv', | |
| mode='nearest', | |
| kernel_size=3, | |
| norm_cfg=norm_cfg, | |
| order=('act', 'conv', 'norm'), | |
| inplace=False), | |
| across_up_trans=None, | |
| across_skip_trans=dict( | |
| type='conv', | |
| kernel_size=1, | |
| norm_cfg=norm_cfg, | |
| inplace=False, | |
| order=('act', 'conv', 'norm')), | |
| output_trans=dict( | |
| type='last_conv', | |
| kernel_size=3, | |
| order=('act', 'conv', 'norm'), | |
| inplace=False), | |
| norm_cfg=norm_cfg, | |
| skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) | |