mirror of https://github.com/hero-y/BHRL
211 lines
7.0 KiB
Python
211 lines
7.0 KiB
Python
# model settings
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test_seen_classes = False
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model = dict(
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type='BHRL',
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pretrained=None,
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backbone=dict(
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type='ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=True,
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style='pytorch'),
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neck=dict(
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type='FPN',
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in_channels=[256, 512, 1024, 2048],
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out_channels=256,
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num_outs=5),
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rpn_head=dict(
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type='RPNHead',
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in_channels=384,
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feat_channels=256,
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anchor_generator=dict(
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type='AnchorGenerator',
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scales=[8],
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ratios=[0.5, 1.0, 2.0],
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strides=[4, 8, 16, 32, 64]),
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[0.0, 0.0, 0.0, 0.0],
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target_stds=[1.0, 1.0, 1.0, 1.0]),
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
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roi_head=dict(
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type='BHRLRoIHead',
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bbox_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(
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type='DeformRoIPoolPack',
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output_size=7,
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output_channels=256),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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bbox_head=[
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dict(
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type='BHRLConvFCBBoxHead',
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use_shared_fc = True,
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num_fcs=2,
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in_channels=384,
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fc_out_channels=1024,
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roi_feat_size=7,
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num_classes=1,
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[0.0, 0.0, 0.0, 0.0],
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target_stds=[0.1, 0.1, 0.2, 0.2]),
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reg_class_agnostic=False,
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ihr = dict(
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metric_module_in_channel=256,
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metric_module_out_channel=384,
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),
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loss_cls=dict(
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type='RPLoss', use_sigmoid=False, loss_weight=1.0,alpha=0.25),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0))]),
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train_cfg = dict(
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rpn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.7,
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neg_iou_thr=0.3,
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min_pos_iou=0.3,
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match_low_quality=True,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=256,
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pos_fraction=0.5,
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neg_pos_ub=-1,
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add_gt_as_proposals=False),
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allowed_border=-1,
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pos_weight=-1,
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debug=False),
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rpn_proposal=dict(
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# nms_across_levels=False,
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nms_pre=2000,
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max_per_img=1000,
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nms=dict(type='nms', iou_threshold=0.7),
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min_bbox_size=0),
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rcnn=[
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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=0.5,
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match_low_quality=False,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=512,
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pos_fraction=0.25,
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neg_pos_ub=-1,
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add_gt_as_proposals=True),
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mask_size=28,
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pos_weight=-1,
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debug=False)]),
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test_cfg = dict(
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rpn=dict(
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# nms_across_levels=False,
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nms_pre=1000,
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max_per_img=1000,
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nms=dict(type='nms', iou_threshold=0.7),
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min_bbox_size=0),
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rcnn=dict(
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score_thr=0.05,
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nms=dict(type='nms', iou_threshold=0.5),
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max_per_img=100,
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mask_thr_binary=0.5)))
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# dataset settings
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dataset_type = 'OneShotVOCDataset'
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data_root = 'data/VOCdevkit/'
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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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# We use the same image size as the paper (One-Shot Instance Segmentation). It is the first to study one-shot object detection.
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
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dict(type='Resize', img_scale=(1024, 1024), 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=(1024, 1024)),
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dict(type='DefaultFormatBundle'),
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dict(type='LoadSiameseReference'),
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dict(type='ReferenceTransform', img_scale=(192, 192), keep_ratio=True, **img_norm_cfg),
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dict(type='SiameseFormatBundle'),
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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=(1024, 1024),
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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=(1024, 1024)),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='LoadSiameseReference'),
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dict(type='ReferenceTransform', img_scale=(192, 192), keep_ratio=True, **img_norm_cfg),
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dict(type='SiameseFormatBundle'),
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dict(type='Collect', keys=['img'], meta_keys=['img_info', 'filename', 'ori_shape',
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'img_shape', 'pad_shape', 'scale_factor',
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'flip', 'img_norm_cfg', 'label']),
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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 + 'voc_annotation/voc_train.json',
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img_prefix=data_root,
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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 + 'voc_annotation/voc_test.json',
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img_prefix=data_root,
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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 + 'voc_annotation/voc_test.json',
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img_prefix=data_root,
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pipeline=test_pipeline,
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test_seen_classes=test_seen_classes,
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position=0))
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evaluation = dict(interval=1, metric='bbox')
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# optimizer
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optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
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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='step',
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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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step=[6])
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runner = dict(type='EpochBasedRunner', max_epochs=9)
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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=20,
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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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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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work_dir = 'work_dirs/voc/BHRL'
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load_from = 'resnet_model/res50_loadfrom.pth'
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resume_from = None
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workflow = [('train', 1)]
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