mmpose/demo/mmdetection_cfg/ssdlite_mobilenetv2_scratch_600e_onehand.py

# =========================================================
# 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')