123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960 |
- _base_ = [
- '../_base_/models/mask-rcnn_r50_fpn.py',
- '../_base_/datasets/coco_instance.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- model = dict(
- data_preprocessor=dict(
- # The mean and std are used in PyCls when training RegNets
- mean=[103.53, 116.28, 123.675],
- std=[57.375, 57.12, 58.395],
- bgr_to_rgb=False),
- backbone=dict(
- _delete_=True,
- type='RegNet',
- arch='regnetx_3.2gf',
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch',
- init_cfg=dict(
- type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
- neck=dict(
- type='FPN',
- in_channels=[96, 192, 432, 1008],
- out_channels=256,
- num_outs=5))
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(
- type='RandomChoiceResize',
- scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
- (1333, 768), (1333, 800)],
- keep_ratio=True),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PackDetInputs')
- ]
- train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
- optim_wrapper = dict(
- optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005),
- clip_grad=dict(max_norm=35, norm_type=2))
- # learning policy
- max_epochs = 36
- train_cfg = dict(max_epochs=max_epochs)
- param_scheduler = [
- dict(
- type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
- dict(
- type='MultiStepLR',
- begin=0,
- end=max_epochs,
- by_epoch=True,
- milestones=[28, 34],
- gamma=0.1)
- ]
|