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- _base_ = [
- '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
- ]
-
- model = dict(
- type='SingleStageDetector',
- backbone=dict(
- type='MobileNetV2',
- out_indices=(4, 7),
- norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
- init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
- neck=dict(
- type='SSDNeck',
- in_channels=(96, 1280),
- out_channels=(96, 1280, 512, 256, 256, 128),
- level_strides=(2, 2, 2, 2),
- level_paddings=(1, 1, 1, 1),
- l2_norm_scale=None,
- use_depthwise=True,
- norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
- act_cfg=dict(type='ReLU6'),
- init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
- bbox_head=dict(
- type='SSDHead',
- in_channels=(96, 1280, 512, 256, 256, 128),
- num_classes=80,
- use_depthwise=True,
- norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
- act_cfg=dict(type='ReLU6'),
- init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
-
- # set anchor size manually instead of using the predefined
- # SSD300 setting.
- anchor_generator=dict(
- type='SSDAnchorGenerator',
- scale_major=False,
- strides=[16, 32, 64, 107, 160, 320],
- ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
- min_sizes=[48, 100, 150, 202, 253, 304],
- max_sizes=[100, 150, 202, 253, 304, 320]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.1, 0.1, 0.2, 0.2])),
- # model training and testing settings
- train_cfg=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0.,
- ignore_iof_thr=-1,
- gt_max_assign_all=False),
- smoothl1_beta=1.,
- allowed_border=-1,
- pos_weight=-1,
- neg_pos_ratio=3,
- debug=False),
- test_cfg=dict(
- nms_pre=1000,
- nms=dict(type='nms', iou_threshold=0.45),
- min_bbox_size=0,
- score_thr=0.02,
- max_per_img=200))
- cudnn_benchmark = True
-
- # dataset settings
- dataset_type = 'CocoDataset'
- data_root = 'data/coco/'
- img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
- train_pipeline = [
- dict(type='LoadImageFromFile', to_float32=True),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='PhotoMetricDistortion',
- brightness_delta=32,
- contrast_range=(0.5, 1.5),
- saturation_range=(0.5, 1.5),
- hue_delta=18),
- dict(
- type='Expand',
- mean=img_norm_cfg['mean'],
- to_rgb=img_norm_cfg['to_rgb'],
- ratio_range=(1, 4)),
- dict(
- type='MinIoURandomCrop',
- min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
- min_crop_size=0.3),
- dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Pad', size_divisor=320),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(320, 320),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=False),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=320),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
- ]
- data = dict(
- samples_per_gpu=24,
- workers_per_gpu=4,
- train=dict(
- _delete_=True,
- type='RepeatDataset', # use RepeatDataset to speed up training
- times=5,
- dataset=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_train2017.json',
- img_prefix=data_root + 'train2017/',
- pipeline=train_pipeline)),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
-
- # optimizer
- optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5)
- optimizer_config = dict(grad_clip=None)
-
- # learning policy
- lr_config = dict(
- policy='CosineAnnealing',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.001,
- min_lr=0)
- runner = dict(type='EpochBasedRunner', max_epochs=120)
-
- # Avoid evaluation and saving weights too frequently
- evaluation = dict(interval=5, metric='bbox')
- checkpoint_config = dict(interval=5)
- custom_hooks = [
- dict(type='NumClassCheckHook'),
- dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
- ]
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