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_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)
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