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Zero
Running
on
Zero
| _base_ = '../common/ms-90k_coco.py' | |
| # model settings | |
| model = dict( | |
| type='BoxInst', | |
| data_preprocessor=dict( | |
| type='BoxInstDataPreprocessor', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| bgr_to_rgb=True, | |
| pad_size_divisor=32, | |
| mask_stride=4, | |
| pairwise_size=3, | |
| pairwise_dilation=2, | |
| pairwise_color_thresh=0.3, | |
| bottom_pixels_removed=10), | |
| backbone=dict( | |
| type='ResNet', | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3), | |
| frozen_stages=1, | |
| norm_cfg=dict(type='BN', requires_grad=True), | |
| norm_eval=True, | |
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), | |
| style='pytorch'), | |
| neck=dict( | |
| type='FPN', | |
| in_channels=[256, 512, 1024, 2048], | |
| out_channels=256, | |
| start_level=1, | |
| add_extra_convs='on_output', # use P5 | |
| num_outs=5, | |
| relu_before_extra_convs=True), | |
| bbox_head=dict( | |
| type='BoxInstBboxHead', | |
| num_params=593, | |
| num_classes=80, | |
| in_channels=256, | |
| stacked_convs=4, | |
| feat_channels=256, | |
| strides=[8, 16, 32, 64, 128], | |
| norm_on_bbox=True, | |
| centerness_on_reg=True, | |
| dcn_on_last_conv=False, | |
| center_sampling=True, | |
| conv_bias=True, | |
| loss_cls=dict( | |
| type='FocalLoss', | |
| use_sigmoid=True, | |
| gamma=2.0, | |
| alpha=0.25, | |
| loss_weight=1.0), | |
| loss_bbox=dict(type='GIoULoss', loss_weight=1.0), | |
| loss_centerness=dict( | |
| type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), | |
| mask_head=dict( | |
| type='BoxInstMaskHead', | |
| num_layers=3, | |
| feat_channels=16, | |
| size_of_interest=8, | |
| mask_out_stride=4, | |
| topk_masks_per_img=64, | |
| mask_feature_head=dict( | |
| in_channels=256, | |
| feat_channels=128, | |
| start_level=0, | |
| end_level=2, | |
| out_channels=16, | |
| mask_stride=8, | |
| num_stacked_convs=4, | |
| norm_cfg=dict(type='BN', requires_grad=True)), | |
| loss_mask=dict( | |
| type='DiceLoss', | |
| use_sigmoid=True, | |
| activate=True, | |
| eps=5e-6, | |
| loss_weight=1.0)), | |
| # model training and testing settings | |
| test_cfg=dict( | |
| nms_pre=1000, | |
| min_bbox_size=0, | |
| score_thr=0.05, | |
| nms=dict(type='nms', iou_threshold=0.6), | |
| max_per_img=100, | |
| mask_thr=0.5)) | |
| # optimizer | |
| optim_wrapper = dict(optimizer=dict(lr=0.01)) | |
| # evaluator | |
| val_evaluator = dict(metric=['bbox', 'segm']) | |
| test_evaluator = val_evaluator | |