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_base_ = [ |
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'../_base_/datasets/coco_detection.py', |
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'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' |
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] |
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|
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model = dict( |
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type='FCOS', |
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backbone=dict( |
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type='ResNet', |
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depth=50, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type='BN', requires_grad=False), |
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norm_eval=True, |
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style='caffe', |
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init_cfg=dict( |
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type='Pretrained', |
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checkpoint='open-mmlab://detectron/resnet50_caffe')), |
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neck=dict( |
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type='FPN', |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=256, |
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start_level=1, |
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add_extra_convs='on_output', |
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num_outs=5, |
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relu_before_extra_convs=True), |
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bbox_head=dict( |
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type='FCOSHead', |
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num_classes=80, |
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in_channels=256, |
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stacked_convs=4, |
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feat_channels=256, |
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strides=[8, 16, 32, 64, 128], |
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loss_cls=dict( |
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type='FocalLoss', |
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use_sigmoid=True, |
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gamma=2.0, |
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alpha=0.25, |
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loss_weight=1.0), |
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loss_bbox=dict(type='IoULoss', loss_weight=1.0), |
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loss_centerness=dict( |
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), |
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|
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train_cfg=dict( |
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assigner=dict( |
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type='MaxIoUAssigner', |
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pos_iou_thr=0.5, |
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neg_iou_thr=0.4, |
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min_pos_iou=0, |
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ignore_iof_thr=-1), |
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allowed_border=-1, |
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pos_weight=-1, |
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debug=False), |
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test_cfg=dict( |
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nms_pre=1000, |
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min_bbox_size=0, |
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score_thr=0.05, |
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nms=dict(type='nms', iou_threshold=0.5), |
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max_per_img=100)) |
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img_norm_cfg = dict( |
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mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) |
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train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='LoadAnnotations', with_bbox=True), |
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dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), |
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dict(type='RandomFlip', flip_ratio=0.5), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), |
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] |
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict( |
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type='MultiScaleFlipAug', |
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img_scale=(1333, 800), |
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flip=False, |
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transforms=[ |
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dict(type='Resize', keep_ratio=True), |
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dict(type='RandomFlip'), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='ImageToTensor', keys=['img']), |
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dict(type='Collect', keys=['img']), |
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]) |
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] |
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data = dict( |
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samples_per_gpu=2, |
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workers_per_gpu=2, |
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train=dict(pipeline=train_pipeline), |
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val=dict(pipeline=test_pipeline), |
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test=dict(pipeline=test_pipeline)) |
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|
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optimizer = dict( |
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lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) |
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optimizer_config = dict( |
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_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) |
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|
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lr_config = dict( |
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policy='step', |
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warmup='constant', |
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warmup_iters=500, |
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warmup_ratio=1.0 / 3, |
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step=[8, 11]) |
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runner = dict(type='EpochBasedRunner', max_epochs=12) |
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|
