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| _base_ = [ | |
| '../_base_/models/faster_rcnn_r50_fpn.py', | |
| '../_base_/datasets/cityscapes_detection.py', | |
| '../_base_/default_runtime.py' | |
| ] | |
| model = dict( | |
| pretrained=None, | |
| roi_head=dict( | |
| bbox_head=dict( | |
| type='Shared2FCBBoxHead', | |
| in_channels=256, | |
| fc_out_channels=1024, | |
| roi_feat_size=7, | |
| num_classes=8, | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0., 0., 0., 0.], | |
| target_stds=[0.1, 0.1, 0.2, 0.2]), | |
| reg_class_agnostic=False, | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | |
| loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))) | |
| # optimizer | |
| # lr is set for a batch size of 8 | |
| optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) | |
| optimizer_config = dict(grad_clip=None) | |
| # learning policy | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=500, | |
| warmup_ratio=0.001, | |
| # [7] yields higher performance than [6] | |
| step=[7]) | |
| runner = dict( | |
| type='EpochBasedRunner', max_epochs=8) # actual epoch = 8 * 8 = 64 | |
| log_config = dict(interval=100) | |
| # For better, more stable performance initialize from COCO | |
| load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' # noqa | |