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- _base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- model = dict(
- type='YOLOF',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(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://detectron/resnet50_caffe')),
- neck=dict(
- type='DilatedEncoder',
- in_channels=2048,
- out_channels=512,
- block_mid_channels=128,
- num_residual_blocks=4),
- bbox_head=dict(
- type='YOLOFHead',
- num_classes=80,
- in_channels=512,
- reg_decoded_bbox=True,
- anchor_generator=dict(
- type='AnchorGenerator',
- ratios=[1.0],
- scales=[1, 2, 4, 8, 16],
- strides=[32]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[1., 1., 1., 1.],
- add_ctr_clamp=True,
- ctr_clamp=32),
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(
- type='UniformAssigner', pos_ignore_thr=0.15, neg_ignore_thr=0.7),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- test_cfg=dict(
- nms_pre=1000,
- min_bbox_size=0,
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.6),
- max_per_img=100))
- # optimizer
- optimizer = dict(
- type='SGD',
- lr=0.12,
- momentum=0.9,
- weight_decay=0.0001,
- paramwise_cfg=dict(
- norm_decay_mult=0., custom_keys={'backbone': dict(lr_mult=1. / 3)}))
- lr_config = dict(warmup_iters=1500, warmup_ratio=0.00066667)
-
- # use caffe img_norm
- 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='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='RandomShift', shift_ratio=0.5, max_shift_px=32),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
- ]
- 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(
- samples_per_gpu=8,
- workers_per_gpu=8,
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
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