|
12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758 |
- _base_ = [
- '../_base_/models/mask_rcnn_r50_fpn.py',
- '../_base_/datasets/coco_instance.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- model = dict(
- 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))
- img_norm_cfg = 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],
- to_rgb=False)
- train_pipeline = [
- # Images are converted to float32 directly after loading in PyCls
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
- ]
- data = dict(
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
- optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
|
