123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153 |
- # =========================================================
- # from 'mmdetection/configs/_base_/default_runtime.py'
- # =========================================================
- default_scope = 'mmdet'
- checkpoint_config = dict(interval=1)
- # yapf:disable
- log_config = dict(
- interval=50,
- hooks=[
- dict(type='TextLoggerHook'),
- # dict(type='TensorboardLoggerHook')
- ])
- # yapf:enable
- custom_hooks = [dict(type='NumClassCheckHook')]
- # =========================================================
- # model settings
- data_preprocessor = dict(
- type='DetDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- bgr_to_rgb=True,
- pad_size_divisor=1)
- model = dict(
- type='SingleStageDetector',
- data_preprocessor=data_preprocessor,
- 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=1,
- 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),
- sampler=dict(type='PseudoSampler'),
- 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
- file_client_args = dict(backend='disk')
- dataset_type = 'CocoDataset'
- data_root = 'data/onehand10k/'
- classes = ('hand', )
- input_size = 320
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='Resize', scale=(input_size, input_size), keep_ratio=False),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor'))
- ]
- val_dataloader = dict(
- batch_size=8,
- num_workers=2,
- persistent_workers=True,
- drop_last=False,
- sampler=dict(type='DefaultSampler', shuffle=False),
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file='annotations/onehand10k_test.json',
- test_mode=True,
- pipeline=test_pipeline))
- test_dataloader = val_dataloader
- # 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')
- ]
- log_config = dict(interval=5)
- # NOTE: `auto_scale_lr` is for automatically scaling LR,
- # USER SHOULD NOT CHANGE ITS VALUES.
- # base_batch_size = (8 GPUs) x (24 samples per GPU)
- auto_scale_lr = dict(base_batch_size=192)
- load_from = 'https://download.openmmlab.com/mmdetection/'
- 'v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/'
- 'ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth'
- vis_backends = [dict(type='LocalVisBackend')]
- visualizer = dict(
- type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
|