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216 lines
7.1 KiB
216 lines
7.1 KiB
# =========================================================
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# from 'mmdetection/configs/_base_/default_runtime.py'
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# =========================================================
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checkpoint_config = dict(interval=1)
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# yapf:disable
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log_config = dict(
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interval=50,
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hooks=[
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dict(type='TextLoggerHook'),
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# dict(type='TensorboardLoggerHook')
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])
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# yapf:enable
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custom_hooks = [dict(type='NumClassCheckHook')]
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# =========================================================
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# =========================================================
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# from 'mmdetection/configs/_base_/datasets/coco_detection.py'
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# =========================================================
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# dataset settings
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dataset_type = 'CocoDataset'
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data_root = 'data/coco/'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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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(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_train2017.json',
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img_prefix=data_root + 'train2017/',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_val2017.json',
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img_prefix=data_root + 'val2017/',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_val2017.json',
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img_prefix=data_root + 'val2017/',
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pipeline=test_pipeline))
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evaluation = dict(interval=1, metric='bbox')
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# =========================================================
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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model = dict(
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type='SingleStageDetector',
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backbone=dict(
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type='MobileNetV2',
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out_indices=(4, 7),
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norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
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init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
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neck=dict(
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type='SSDNeck',
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in_channels=(96, 1280),
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out_channels=(96, 1280, 512, 256, 256, 128),
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level_strides=(2, 2, 2, 2),
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level_paddings=(1, 1, 1, 1),
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l2_norm_scale=None,
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use_depthwise=True,
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norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
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act_cfg=dict(type='ReLU6'),
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init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
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bbox_head=dict(
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type='SSDHead',
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in_channels=(96, 1280, 512, 256, 256, 128),
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num_classes=80,
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use_depthwise=True,
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norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
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act_cfg=dict(type='ReLU6'),
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init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
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# set anchor size manually instead of using the predefined
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# SSD300 setting.
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anchor_generator=dict(
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type='SSDAnchorGenerator',
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scale_major=False,
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strides=[16, 32, 64, 107, 160, 320],
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ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
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min_sizes=[48, 100, 150, 202, 253, 304],
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max_sizes=[100, 150, 202, 253, 304, 320]),
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[.0, .0, .0, .0],
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target_stds=[0.1, 0.1, 0.2, 0.2])),
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# model training and testing settings
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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=0.5,
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min_pos_iou=0.,
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ignore_iof_thr=-1,
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gt_max_assign_all=False),
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smoothl1_beta=1.,
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allowed_border=-1,
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pos_weight=-1,
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neg_pos_ratio=3,
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debug=False),
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test_cfg=dict(
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nms_pre=1000,
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nms=dict(type='nms', iou_threshold=0.45),
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min_bbox_size=0,
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score_thr=0.02,
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max_per_img=200))
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cudnn_benchmark = True
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# dataset settings
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dataset_type = 'CocoDataset'
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data_root = 'data/coco/'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile', to_float32=True),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(
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type='PhotoMetricDistortion',
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brightness_delta=32,
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contrast_range=(0.5, 1.5),
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saturation_range=(0.5, 1.5),
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hue_delta=18),
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dict(
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type='Expand',
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mean=img_norm_cfg['mean'],
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to_rgb=img_norm_cfg['to_rgb'],
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ratio_range=(1, 4)),
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dict(
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type='MinIoURandomCrop',
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min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
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min_crop_size=0.3),
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dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='Pad', size_divisor=320),
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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=(320, 320),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=False),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=320),
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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=24,
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workers_per_gpu=4,
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train=dict(
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_delete_=True,
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type='RepeatDataset', # use RepeatDataset to speed up training
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times=5,
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dataset=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_train2017.json',
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img_prefix=data_root + 'train2017/',
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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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# optimizer
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optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5)
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optimizer_config = dict(grad_clip=None)
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# learning policy
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lr_config = dict(
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policy='CosineAnnealing',
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warmup='linear',
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warmup_iters=500,
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warmup_ratio=0.001,
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min_lr=0)
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runner = dict(type='EpochBasedRunner', max_epochs=120)
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# Avoid evaluation and saving weights too frequently
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evaluation = dict(interval=5, metric='bbox')
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checkpoint_config = dict(interval=5)
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custom_hooks = [
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dict(type='NumClassCheckHook'),
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dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
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]
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