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- _base_ = '../fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py'
- model = dict(
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=False),
- norm_eval=True,
- style='caffe',
- init_cfg=dict(
- type='Pretrained',
- checkpoint='open-mmlab://detectron2/resnet50_caffe')),
- roi_head=dict(
- bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
- # model training and testing settings
- train_cfg=dict(
- rcnn=dict(
- assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
- sampler=dict(num=256))),
- test_cfg=dict(rcnn=dict(score_thr=1e-3)))
- dataset_type = 'CocoDataset'
- data_root = 'data/coco/'
- img_norm_cfg = dict(
- mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
- train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadProposals', num_max_proposals=300),
- dict(type='LoadAnnotations', with_bbox=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', 'proposals', 'gt_bboxes', 'gt_labels']),
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadProposals', num_max_proposals=None),
- 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', 'proposals']),
- ])
- ]
- # TODO: support loading proposals
- data = dict(
- train=dict(
- proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_train2017.pkl',
- pipeline=train_pipeline),
- val=dict(
- proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
- pipeline=test_pipeline),
- test=dict(
- proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
- pipeline=test_pipeline))
- optimizer_config = dict(
- _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
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