| _base_ = [ | |
| '../_base_/models/faster-rcnn_r50_fpn.py', | |
| '../_base_/datasets/cityscapes_detection.py', | |
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py' | |
| ] | |
| model = dict( | |
| backbone=dict(init_cfg=None), | |
| roi_head=dict( | |
| bbox_head=dict( | |
| num_classes=8, | |
| loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))) | |
| # optimizer | |
| # lr is set for a batch size of 8 | |
| optim_wrapper = dict(optimizer=dict(lr=0.01)) | |
| # learning rate | |
| param_scheduler = [ | |
| dict( | |
| type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), | |
| dict( | |
| type='MultiStepLR', | |
| begin=0, | |
| end=8, | |
| by_epoch=True, | |
| # [7] yields higher performance than [6] | |
| milestones=[7], | |
| gamma=0.1) | |
| ] | |
| # actual epoch = 8 * 8 = 64 | |
| train_cfg = dict(max_epochs=8) | |
| # 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 | |
| # NOTE: `auto_scale_lr` is for automatically scaling LR, | |
| # USER SHOULD NOT CHANGE ITS VALUES. | |
| # base_batch_size = (8 GPUs) x (1 samples per GPU) | |
| # TODO: support auto scaling lr | |
| # auto_scale_lr = dict(base_batch_size=8) | |