| checkpoint_config = dict(interval=1) |
| log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) |
| custom_hooks = [dict(type='NumClassCheckHook')] |
| dist_params = dict(backend='nccl') |
| log_level = 'INFO' |
| load_from = None |
| resume_from = None |
| workflow = [('train', 1)] |
| opencv_num_threads = 0 |
| mp_start_method = 'fork' |
| auto_scale_lr = dict(enable=False, base_batch_size=16) |
| dataset_type = 'CocoDataset' |
| data_root = 'data/coco/' |
| img_norm_cfg = dict( |
| mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) |
| train_pipeline = [ |
| dict(type='LoadImageFromFile'), |
| dict(type='LoadAnnotations', with_bbox=True), |
| dict( |
| type='Resize', |
| img_scale=[(1333, 640), (1333, 800)], |
| multiscale_mode='range', |
| keep_ratio=True), |
| dict(type='RandomFlip', flip_ratio=0.5), |
| dict( |
| type='Normalize', |
| mean=[103.53, 116.28, 123.675], |
| std=[1.0, 1.0, 1.0], |
| to_rgb=False), |
| dict(type='Pad', size_divisor=32), |
| dict(type='DefaultFormatBundle'), |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) |
| ] |
| test_pipeline = [ |
| dict(type='LoadImageFromFile'), |
| dict( |
| type='MultiScaleFlipAug', |
| img_scale=(1333, 800), |
| flip=False, |
| transforms=[ |
| dict(type='Resize', keep_ratio=True), |
| dict(type='RandomFlip'), |
| dict( |
| type='Normalize', |
| mean=[103.53, 116.28, 123.675], |
| std=[1.0, 1.0, 1.0], |
| to_rgb=False), |
| dict(type='Pad', size_divisor=32), |
| dict(type='ImageToTensor', keys=['img']), |
| dict(type='Collect', keys=['img']) |
| ]) |
| ] |
| data = dict( |
| samples_per_gpu=2, |
| workers_per_gpu=2, |
| train=dict( |
| type='RepeatDataset', |
| times=3, |
| dataset=dict( |
| type='CocoDataset', |
| ann_file='data/coco/annotations/instances_train2017.json', |
| img_prefix='data/coco/train2017/', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict(type='LoadAnnotations', with_bbox=True), |
| dict( |
| type='Resize', |
| img_scale=[(1333, 640), (1333, 800)], |
| multiscale_mode='range', |
| keep_ratio=True), |
| dict(type='RandomFlip', flip_ratio=0.5), |
| dict( |
| type='Normalize', |
| mean=[103.53, 116.28, 123.675], |
| std=[1.0, 1.0, 1.0], |
| to_rgb=False), |
| dict(type='Pad', size_divisor=32), |
| dict(type='DefaultFormatBundle'), |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) |
| ])), |
| val=dict( |
| type='CocoDataset', |
| ann_file='data/coco/annotations/instances_val2017.json', |
| img_prefix='data/coco/val2017/', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict( |
| type='MultiScaleFlipAug', |
| img_scale=(1333, 800), |
| flip=False, |
| transforms=[ |
| dict(type='Resize', keep_ratio=True), |
| dict(type='RandomFlip'), |
| dict( |
| type='Normalize', |
| mean=[103.53, 116.28, 123.675], |
| std=[1.0, 1.0, 1.0], |
| to_rgb=False), |
| dict(type='Pad', size_divisor=32), |
| dict(type='ImageToTensor', keys=['img']), |
| dict(type='Collect', keys=['img']) |
| ]) |
| ]), |
| test=dict( |
| type='CocoDataset', |
| ann_file='data/coco/annotations/instances_val2017.json', |
| img_prefix='data/coco/val2017/', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict( |
| type='MultiScaleFlipAug', |
| img_scale=(1333, 800), |
| flip=False, |
| transforms=[ |
| dict(type='Resize', keep_ratio=True), |
| dict(type='RandomFlip'), |
| dict( |
| type='Normalize', |
| mean=[103.53, 116.28, 123.675], |
| std=[1.0, 1.0, 1.0], |
| to_rgb=False), |
| dict(type='Pad', size_divisor=32), |
| dict(type='ImageToTensor', keys=['img']), |
| dict(type='Collect', keys=['img']) |
| ]) |
| ])) |
| evaluation = dict(interval=1, metric='bbox') |
| optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) |
| optimizer_config = dict(grad_clip=None) |
| lr_config = dict( |
| policy='step', |
| warmup='linear', |
| warmup_iters=500, |
| warmup_ratio=0.001, |
| step=[9, 11]) |
| runner = dict(type='EpochBasedRunner', max_epochs=12) |
| model = dict( |
| type='FasterRCNN', |
| backbone=dict( |
| type='ResNet', |
| depth=50, |
| num_stages=4, |
| out_indices=(0, 1, 2, 3), |
| frozen_stages=1, |
| norm_cfg=dict(type='BN', requires_grad=False), |
| norm_eval=True, |
| style='caffe', |
| init_cfg=dict( |
| type='Pretrained', |
| checkpoint='open-mmlab://detectron2/resnet50_caffe')), |
| neck=dict( |
| type='FPN', |
| in_channels=[256, 512, 1024, 2048], |
| out_channels=256, |
| num_outs=5), |
| rpn_head=dict( |
| type='RPNHead', |
| in_channels=256, |
| feat_channels=256, |
| anchor_generator=dict( |
| type='AnchorGenerator', |
| scales=[8], |
| ratios=[0.5, 1.0, 2.0], |
| strides=[4, 8, 16, 32, 64]), |
| bbox_coder=dict( |
| type='DeltaXYWHBBoxCoder', |
| target_means=[0.0, 0.0, 0.0, 0.0], |
| target_stds=[1.0, 1.0, 1.0, 1.0]), |
| loss_cls=dict( |
| type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), |
| loss_bbox=dict(type='L1Loss', loss_weight=1.0)), |
| roi_head=dict( |
| type='StandardRoIHead', |
| bbox_roi_extractor=dict( |
| type='SingleRoIExtractor', |
| roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), |
| out_channels=256, |
| featmap_strides=[4, 8, 16, 32]), |
| bbox_head=dict( |
| type='Shared2FCBBoxHead', |
| in_channels=256, |
| fc_out_channels=1024, |
| roi_feat_size=7, |
| num_classes=80, |
| bbox_coder=dict( |
| type='DeltaXYWHBBoxCoder', |
| target_means=[0.0, 0.0, 0.0, 0.0], |
| target_stds=[0.1, 0.1, 0.2, 0.2]), |
| reg_class_agnostic=False, |
| loss_cls=dict( |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), |
| loss_bbox=dict(type='L1Loss', loss_weight=1.0)), |
| train_cfg=dict( |
| assigner=dict( |
| type='MaxIoUAssigner', |
| pos_iou_thr=0.5, |
| neg_iou_thr=0.5, |
| min_pos_iou=0.5, |
| match_low_quality=False, |
| ignore_iof_thr=-1), |
| sampler=dict( |
| type='RandomSampler', |
| num=512, |
| pos_fraction=0.25, |
| neg_pos_ub=-1, |
| add_gt_as_proposals=True), |
| pos_weight=-1, |
| debug=False), |
| test_cfg=dict( |
| score_thr=0.05, |
| nms=dict(type='nms', iou_threshold=0.5), |
| max_per_img=100), |
| pretrained=None), |
| train_cfg=dict( |
| rpn=dict( |
| assigner=dict( |
| type='MaxIoUAssigner', |
| pos_iou_thr=0.7, |
| neg_iou_thr=0.3, |
| min_pos_iou=0.3, |
| match_low_quality=True, |
| ignore_iof_thr=-1), |
| sampler=dict( |
| type='RandomSampler', |
| num=256, |
| pos_fraction=0.5, |
| neg_pos_ub=-1, |
| add_gt_as_proposals=False), |
| allowed_border=-1, |
| pos_weight=-1, |
| debug=False), |
| rpn_proposal=dict( |
| nms_pre=2000, |
| max_per_img=1000, |
| nms=dict(type='nms', iou_threshold=0.7), |
| min_bbox_size=0), |
| rcnn=dict( |
| assigner=dict( |
| type='MaxIoUAssigner', |
| pos_iou_thr=0.5, |
| neg_iou_thr=0.5, |
| min_pos_iou=0.5, |
| match_low_quality=False, |
| ignore_iof_thr=-1), |
| sampler=dict( |
| type='RandomSampler', |
| num=512, |
| pos_fraction=0.25, |
| neg_pos_ub=-1, |
| add_gt_as_proposals=True), |
| pos_weight=-1, |
| debug=False)), |
| test_cfg=dict( |
| rpn=dict( |
| nms_pre=1000, |
| max_per_img=1000, |
| nms=dict(type='nms', iou_threshold=0.7), |
| min_bbox_size=0), |
| rcnn=dict( |
| score_thr=0.05, |
| nms=dict(type='nms', iou_threshold=0.5), |
| max_per_img=100))) |
|
|
