| norm_cfg = dict(type='BN', requires_grad=False) | |
| model = dict( | |
| type='FasterRCNNVMRN', | |
| backbone=dict( | |
| type='mmdet.ResNet', | |
| depth=101, | |
| num_stages=3, | |
| strides=(1, 2, 2), | |
| dilations=(1, 1, 1), | |
| out_indices=(2, ), | |
| 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/resnet101_caffe')), | |
| rpn_head=dict( | |
| type='mmdet.RPNHead', | |
| in_channels=1024, | |
| feat_channels=1024, | |
| anchor_generator=dict( | |
| type='AnchorGenerator', | |
| scales=[8, 16, 32], | |
| ratios=[0.33, 0.5, 1.0, 2.0, 3.0], | |
| strides=[16]), | |
| 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='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | |
| loss_bbox=dict(type='mmdet.L1Loss', loss_weight=1.0)), | |
| roi_head=dict( | |
| type='mmdet.StandardRoIHead', | |
| shared_head=dict( | |
| type='mmdet.ResLayer', | |
| depth=50, | |
| stage=3, | |
| stride=1, | |
| style='caffe', | |
| norm_cfg=dict(type='BN', requires_grad=False), | |
| norm_eval=True), | |
| bbox_roi_extractor=dict( | |
| type='mmdet.SingleRoIExtractor', | |
| roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | |
| out_channels=1024, | |
| featmap_strides=[16]), | |
| bbox_head=dict( | |
| type='mmdet.BBoxHead', | |
| with_avg_pool=True, | |
| roi_feat_size=7, | |
| in_channels=2048, | |
| num_classes=31, | |
| 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='mmdet.CrossEntropyLoss', | |
| use_sigmoid=False, | |
| loss_weight=1.0), | |
| loss_bbox=dict(type='mmdet.L1Loss', loss_weight=1.0))), | |
| vmrn_head=dict( | |
| type='invigorate.PairedRoIHead', | |
| shared_head=dict( | |
| type='invigorate.PairedResLayer', | |
| depth=50, | |
| stage=3, | |
| stride=1, | |
| style='caffe', | |
| norm_eval=False, | |
| share_weights=False), | |
| paired_roi_extractor=dict( | |
| type='invigorate.VMRNPairedRoIExtractor', | |
| roi_layer=dict(type='RoIPool', output_size=7), | |
| out_channels=1024, | |
| featmap_strides=[16]), | |
| relation_head=dict( | |
| type='invigorate.BBoxPairHead', | |
| with_avg_pool=True, | |
| roi_feat_size=7, | |
| in_channels=2048, | |
| num_relations=2, | |
| loss_cls=dict( | |
| type='mmdet.CrossEntropyLoss', | |
| use_sigmoid=False, | |
| loss_weight=1.0))), | |
| 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=0, | |
| pos_weight=-1, | |
| debug=False), | |
| rpn_proposal=dict( | |
| nms_pre=12000, | |
| max_per_img=2000, | |
| 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=256, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True), | |
| pos_weight=-1, | |
| debug=False), | |
| vmrn=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.5, | |
| min_pos_iou=0.7, | |
| match_low_quality=False, | |
| ignore_iof_thr=-1), | |
| relation_sampler=dict( | |
| type='RandomRelationSampler', | |
| num=32, | |
| pos_fraction=0.5, | |
| cls_ratio_ub=-1, | |
| add_gt_as_proposals=True, | |
| num_relation_cls=2), | |
| pos_weight=-1, | |
| online_data=True, | |
| online_start_iteration=0)), | |
| test_cfg=dict( | |
| rpn=dict( | |
| nms_pre=6000, | |
| max_per_img=300, | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| min_bbox_size=0), | |
| rcnn=dict( | |
| score_thr=0.05, | |
| nms=dict(type='nms', iou_threshold=0.3), | |
| max_per_img=100), | |
| vmrn=dict( | |
| bbox_score_thr=0.5, verbose_relation=False, average_scores=False))) | |
| dataset_type = 'VMRDDataset' | |
| data_root = 'data/vmrd/' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[1.0, 1.0, 1.0], to_rgb=True) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=['gt_bboxes', 'gt_labels', 'gt_relmats']), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='Expand', mean=[123.675, 116.28, 103.53]), | |
| dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relmats']), | |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relmats']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ] | |
| data = dict( | |
| samples_per_gpu=8, | |
| workers_per_gpu=8, | |
| train=dict( | |
| type='RepeatDataset', | |
| times=3, | |
| dataset=dict( | |
| type='VMRDDataset', | |
| ann_file='data/vmrd/ImageSets/Main/trainval.txt', | |
| img_prefix='data/vmrd/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=['gt_bboxes', 'gt_labels', 'gt_relmats']), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='Expand', mean=[123.675, 116.28, 103.53]), | |
| dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relmats']), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relmats']) | |
| ])), | |
| val=dict( | |
| type='VMRDDataset', | |
| ann_file='data/vmrd/ImageSets/Main/test.txt', | |
| img_prefix='data/vmrd/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ]), | |
| test=dict( | |
| type='VMRDDataset', | |
| ann_file='data/vmrd/ImageSets/Main/test.txt', | |
| img_prefix='data/vmrd/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ])) | |
| evaluation = dict(interval=1, metric=['mAP', 'ImgAcc']) | |
| optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) | |
| optimizer_config = dict(grad_clip=dict(max_norm=100, norm_type=2)) | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=500, | |
| warmup_ratio=0.001, | |
| step=[8]) | |
| runner = dict(type='EpochBasedRunner', max_epochs=20) | |
| checkpoint_config = dict(interval=1, max_keep_ckpts=3) | |
| 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) | |
| mmdet = None | |
| mmdet_root = '/data/home/hanbo/projects/alpha_vision/mmdetection/mmdet' | |
| work_dir = './work_dirs/faster_rcnn_vmrn_r101_caffe_c4_1x_vmrd4683' | |
| gpu_ids = range(0, 2) | |