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hellockhellock
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Merge pull request open-mmlab#14 from OceanPang/dev
update fast rcnn configs
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‎configs/fast_mask_rcnn_r50_fpn_1x.py‎

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# model settings
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model = dict(
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type='FastRCNN',
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pretrained='modelzoo://resnet50',
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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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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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bbox_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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bbox_head=dict(
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type='SharedFCRoIHead',
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num_fcs=2,
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in_channels=256,
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fc_out_channels=1024,
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roi_feat_size=7,
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num_classes=81,
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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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reg_class_agnostic=False),
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mask_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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mask_head=dict(
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type='FCNMaskHead',
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num_convs=4,
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in_channels=256,
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conv_out_channels=256,
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num_classes=81))
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# model training and testing settings
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train_cfg = dict(
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rcnn=dict(
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mask_size=28,
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pos_iou_thr=0.5,
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neg_iou_thr=0.5,
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crowd_thr=1.1,
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roi_batch_size=512,
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add_gt_as_proposals=True,
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pos_fraction=0.25,
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pos_balance_sampling=False,
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neg_pos_ub=512,
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neg_balance_thr=0,
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min_pos_iou=0.5,
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pos_weight=-1,
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debug=False))
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test_cfg = dict(
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rcnn=dict(
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score_thr=0.05, max_per_img=100, nms_thr=0.5, mask_thr_binary=0.5))
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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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data = dict(
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imgs_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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img_scale=(1333, 800),
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img_norm_cfg=img_norm_cfg,
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size_divisor=32,
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proposal_file=data_root + 'proposals/train2017_r50_fpn_rpn_1x.pkl',
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flip_ratio=0.5,
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with_mask=True,
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with_crowd=True,
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with_label=True),
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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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img_scale=(1333, 800),
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img_norm_cfg=img_norm_cfg,
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proposal_file=data_root + 'proposals/val2017_r50_fpn_rpn_1x.pkl',
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size_divisor=32,
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flip_ratio=0,
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with_mask=True,
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with_crowd=True,
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with_label=True),
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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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img_scale=(1333, 800),
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img_norm_cfg=img_norm_cfg,
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proposal_file=data_root + 'proposals/val2017_r50_fpn_rpn_1x.pkl',
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size_divisor=32,
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flip_ratio=0,
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with_mask=False,
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with_label=False,
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test_mode=True))
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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=dict(max_norm=35, norm_type=2))
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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=1.0 / 3,
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step=[8, 11])
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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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# runtime settings
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total_epochs = 12
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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work_dir = './work_dirs/fast_mask_rcnn_r50_fpn_1x'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]

‎configs/fast_rcnn_r50_fpn_1x.py‎

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# model settings
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model = dict(
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type='FastRCNN',
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pretrained='modelzoo://resnet50',
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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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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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bbox_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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bbox_head=dict(
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type='SharedFCRoIHead',
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num_fcs=2,
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in_channels=256,
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fc_out_channels=1024,
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roi_feat_size=7,
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num_classes=81,
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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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reg_class_agnostic=False))
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# model training and testing settings
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train_cfg = dict(
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rcnn=dict(
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pos_iou_thr=0.5,
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neg_iou_thr=0.5,
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crowd_thr=1.1,
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roi_batch_size=512,
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add_gt_as_proposals=True,
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pos_fraction=0.25,
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pos_balance_sampling=False,
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neg_pos_ub=512,
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neg_balance_thr=0,
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min_pos_iou=0.5,
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pos_weight=-1,
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debug=False))
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test_cfg = dict(rcnn=dict(score_thr=0.05, max_per_img=100, nms_thr=0.5))
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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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data = dict(
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imgs_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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img_scale=(1333, 800),
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img_norm_cfg=img_norm_cfg,
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size_divisor=32,
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proposal_file=data_root + 'proposals/train2017_r50_fpn_rpn_1x.pkl',
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flip_ratio=0.5,
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with_mask=False,
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with_crowd=True,
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with_label=True),
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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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img_scale=(1333, 800),
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img_norm_cfg=img_norm_cfg,
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proposal_file=data_root + 'proposals/val2017_r50_fpn_rpn_1x.pkl',
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size_divisor=32,
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flip_ratio=0,
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with_mask=False,
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with_crowd=True,
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with_label=True),
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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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img_scale=(1333, 800),
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img_norm_cfg=img_norm_cfg,
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proposal_file=data_root + 'proposals/val2017_r50_fpn_rpn_1x.pkl',
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size_divisor=32,
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flip_ratio=0,
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with_mask=False,
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with_label=False,
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test_mode=True))
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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=dict(max_norm=35, norm_type=2))
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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=1.0 / 3,
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step=[8, 11])
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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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# runtime settings
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total_epochs = 12
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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work_dir = './work_dirs/fast_rcnn_r50_fpn_1x'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]

‎configs/faster_rcnn_r50_fpn_1x.py‎

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@@ -65,7 +65,7 @@
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pos_balance_sampling=False,
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neg_pos_ub=512,
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neg_balance_thr=0,
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min_pos_iou=1.1,
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min_pos_iou=0.5,
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pos_weight=-1,
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debug=False))
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test_cfg = dict(
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# yapf:enable
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# runtime settings
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total_epochs = 12
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device_ids = range(8)
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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work_dir = './work_dirs/faster_rcnn_r50_fpn_1x'

‎configs/mask_rcnn_r50_fpn_1x.py‎

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pos_balance_sampling=False,
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neg_pos_ub=512,
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neg_balance_thr=0,
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min_pos_iou=1.1,
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min_pos_iou=0.5,
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pos_weight=-1,
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debug=False))
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test_cfg = dict(
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# yapf:enable
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# runtime settings
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total_epochs = 12
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device_ids = range(8)
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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work_dir = './work_dirs/mask_rcnn_r50_fpn_1x'

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