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- _base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
-
- model = dict(
- type='CenterNet',
- backbone=dict(
- type='ResNet',
- depth=18,
- norm_eval=False,
- norm_cfg=dict(type='BN'),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
- neck=dict(
- type='CTResNetNeck',
- in_channel=512,
- num_deconv_filters=(256, 128, 64),
- num_deconv_kernels=(4, 4, 4),
- use_dcn=True),
- bbox_head=dict(
- type='CenterNetHead',
- num_classes=80,
- in_channel=64,
- feat_channel=64,
- loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0),
- loss_wh=dict(type='L1Loss', loss_weight=0.1),
- loss_offset=dict(type='L1Loss', loss_weight=1.0)),
- train_cfg=None,
- test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100))
-
- # We fixed the incorrect img_norm_cfg problem in the source code.
- 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, color_type='color'),
- 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='RandomCenterCropPad',
- crop_size=(512, 512),
- ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
- mean=[0, 0, 0],
- std=[1, 1, 1],
- to_rgb=True,
- test_pad_mode=None),
- dict(type='Resize', img_scale=(512, 512), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile', to_float32=True),
- dict(
- type='MultiScaleFlipAug',
- scale_factor=1.0,
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(
- type='RandomCenterCropPad',
- ratios=None,
- border=None,
- mean=[0, 0, 0],
- std=[1, 1, 1],
- to_rgb=True,
- test_mode=True,
- test_pad_mode=['logical_or', 31],
- test_pad_add_pix=1),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='DefaultFormatBundle'),
- dict(
- type='Collect',
- meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
- 'scale_factor', 'flip', 'flip_direction',
- 'img_norm_cfg', 'border'),
- keys=['img'])
- ])
- ]
-
- dataset_type = 'CocoDataset'
- data_root = 'data/coco/'
-
- # Use RepeatDataset to speed up training
- data = dict(
- samples_per_gpu=16,
- workers_per_gpu=4,
- train=dict(
- _delete_=True,
- type='RepeatDataset',
- 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
- # Based on the default settings of modern detectors, the SGD effect is better
- # than the Adam in the source code, so we use SGD default settings and
- # if you use adam+lr5e-4, the map is 29.1.
- optimizer_config = dict(
- _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
-
- # learning policy
- # Based on the default settings of modern detectors, we added warmup settings.
- lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=1000,
- warmup_ratio=1.0 / 1000,
- step=[18, 24]) # the real step is [18*5, 24*5]
- runner = dict(max_epochs=28) # the real epoch is 28*5=140
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