aitodv1的复现结果问题

[Mr-Bigworth]

想请教下，因为想训练下detectors在aitodv1上的结果，所以使用aitodv2_detectors_rfla_kld_1x.py这一config，修改config为'../base/datasets/aitod_detection.py'，然后训练出来的结果为21.7map，请问可能是什么原因呢？ 谢谢！

[Chasel-Tsui]

你好，这个config是aitodv2的，训练数据使用的是aitodv1还是v2呢？测试使用的是val set还是test set呢？

[Mr-Bigworth]

你好，这个config是aitodv2的，训练数据使用的是aitodv1还是v2呢？测试使用的是val set还是test set呢？

您好，我是想复现这一结果 Training Set: AI-TOD trainval set, Validation Set: AI-TOD test set, 12 epochs.

DR w/ RFLA | R-50 | 24.8 | 55.2 | 18.5 | 9.3 | 24.8 | 30.3 | 38.2 -- | -- | -- | -- | -- | -- | -- | -- | --

所以我使用的是训练数据是aitodv1的trainval set，测试数据是aitodv1的test set，然后config是从aitodv2_detectors_rfla_kld_1x.py修改的，只修改了读取的数据集，就是把line2的'../base/datasets/aitodv2_detection.py',修改为'../base/datasets/aitod_detection.py'，其他未作修改进行复现。

[Mr-Bigworth]

你好，这个config是aitodv2的，训练数据使用的是aitodv1还是v2呢？测试使用的是val set还是test set呢？

顺便想请教一下，aitodv2的标注，我看网页上只提供了train和val的json（谷歌云盘），是用这两个就可以吗:-)

[Mr-Bigworth]

dataset_type = 'AITODDataset' data_root = '/share/home/wut_wanghn/data/AI-TOD/' 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'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(800, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), 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=(800, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], 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=4, train=dict( type='AITODDataset', ann_file= '/share/home/wut_wanghn/data/AI-TOD/annotations/aitod_trainval_v1.json', img_prefix='/share/home/wut_wanghn/data/AI-TOD/trainval/images/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(800, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ]), val=dict( type='AITODDataset', ann_file= '/share/home/wut_wanghn/data/AI-TOD/annotations/aitod_test_v1.json', img_prefix='/share/home/wut_wanghn/data/AI-TOD/test/images/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(800, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='AITODDataset', ann_file= '/share/home/wut_wanghn/data/AI-TOD/annotations/aitod_test_v1.json', img_prefix='/share/home/wut_wanghn/data/AI-TOD/test/images/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(800, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) evaluation = dict(interval=12, metric='bbox') optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='step', warmup='linear', warmup_iters=5000, warmup_ratio=0.001, step=[8, 11]) runner = dict(type='EpochBasedRunner', max_epochs=12) checkpoint_config = dict(interval=4) 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) model = dict( type='CascadeRCNN', pretrained='torchvision://resnet50', backbone=dict( type='DetectoRS_ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), conv_cfg=dict(type='ConvAWS'), norm_eval=True, sac=dict(type='SAC', use_deform=True), stage_with_sac=(False, True, True, True), output_img=True, style='pytorch'), neck=dict( type='RFP', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5, rfp_steps=2, aspp_out_channels=64, aspp_dilations=(1, 3, 6, 1), rfp_backbone=dict( rfp_inplanes=256, type='DetectoRS_ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, conv_cfg=dict(type='ConvAWS'), sac=dict(type='SAC', use_deform=True), stage_with_sac=(False, True, True, True), pretrained='torchvision://resnet50', style='pytorch')), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='RFGenerator', fpn_layer='p2', fraction=0.5, 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='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)), roi_head=dict( type='CascadeRoIHead', num_stages=3, stage_loss_weights=[1, 0.5, 0.25], 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=8, 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=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=8, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.05, 0.05, 0.1, 0.1]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=8, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.033, 0.033, 0.067, 0.067]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) ]), train_cfg=dict( rpn=dict( assigner=dict( type='HieAssigner', ignore_iof_thr=-1, gpu_assign_thr=512, iou_calculator=dict(type='BboxDistanceMetric'), assign_metric='kl', topk=[3, 1], ratio=0.9), 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=3000, max_per_img=3000, 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, gpu_assign_thr=256), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6, match_low_quality=False, ignore_iof_thr=-1, gpu_assign_thr=256), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.7, min_pos_iou=0.7, match_low_quality=False, ignore_iof_thr=-1, gpu_assign_thr=256), 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=3000, max_per_img=3000, 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=3000))) work_dir = './work_dirs/aitod_detectors_rfla_kld_1x' gpu_ids = range(0, 1)

[Mr-Bigworth]

dataset_type = 'AITODDataset' data_root = '/share/home/wut_wanghn/data/AI-TOD/' 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'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(800, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), 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=(800, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], 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=4, train=dict( type='AITODDataset', ann_file= '/share/home/wut_wanghn/data/AI-TOD/annotations/aitod_trainval_v1.json', img_prefix='/share/home/wut_wanghn/data/AI-TOD/trainval/images/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(800, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ]), val=dict( type='AITODDataset', ann_file= '/share/home/wut_wanghn/data/AI-TOD/annotations/aitod_test_v1.json', img_prefix='/share/home/wut_wanghn/data/AI-TOD/test/images/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(800, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='AITODDataset', ann_file= '/share/home/wut_wanghn/data/AI-TOD/annotations/aitod_test_v1.json', img_prefix='/share/home/wut_wanghn/data/AI-TOD/test/images/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(800, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) evaluation = dict(interval=12, metric='bbox') optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='step', warmup='linear', warmup_iters=5000, warmup_ratio=0.001, step=[8, 11]) runner = dict(type='EpochBasedRunner', max_epochs=12) checkpoint_config = dict(interval=4) 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) model = dict( type='CascadeRCNN', pretrained='torchvision://resnet50', backbone=dict( type='DetectoRS_ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), conv_cfg=dict(type='ConvAWS'), norm_eval=True, sac=dict(type='SAC', use_deform=True), stage_with_sac=(False, True, True, True), output_img=True, style='pytorch'), neck=dict( type='RFP', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5, rfp_steps=2, aspp_out_channels=64, aspp_dilations=(1, 3, 6, 1), rfp_backbone=dict( rfp_inplanes=256, type='DetectoRS_ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, conv_cfg=dict(type='ConvAWS'), sac=dict(type='SAC', use_deform=True), stage_with_sac=(False, True, True, True), pretrained='torchvision://resnet50', style='pytorch')), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='RFGenerator', fpn_layer='p2', fraction=0.5, 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='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)), roi_head=dict( type='CascadeRoIHead', num_stages=3, stage_loss_weights=[1, 0.5, 0.25], 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=8, 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=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=8, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.05, 0.05, 0.1, 0.1]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=8, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.033, 0.033, 0.067, 0.067]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) ]), train_cfg=dict( rpn=dict( assigner=dict( type='HieAssigner', ignore_iof_thr=-1, gpu_assign_thr=512, iou_calculator=dict(type='BboxDistanceMetric'), assign_metric='kl', topk=[3, 1], ratio=0.9), 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=3000, max_per_img=3000, 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, gpu_assign_thr=256), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6, match_low_quality=False, ignore_iof_thr=-1, gpu_assign_thr=256), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.7, min_pos_iou=0.7, match_low_quality=False, ignore_iof_thr=-1, gpu_assign_thr=256), 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=3000, max_per_img=3000, 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=3000))) work_dir = './work_dirs/aitod_detectors_rfla_kld_1x' gpu_ids = range(0, 1)

这是训练用的config

[Chasel-Tsui]

会不会是因为batchsize的原因呢？我在实验的时候batchsize设的是2，lr=0.005。另外，关于aitodv2,目前只公开了train和val的标签，test后续会公开，目前只需报告val上的结果即可~

[Mr-Bigworth]

会不会是因为batchsize的原因呢？我在实验的时候batchsize设的是2，lr=0.005。另外，关于aitodv2,目前只公开了train和val的标签，test后续会公开，目前只需报告val上的结果即可~

好的，我去尝试一下bs=2和lr=0.005，谢谢！

[Mr-Bigworth]

已解决，谢谢！

会不会是因为batchsize的原因呢？我在实验的时候batchsize设的是2，lr=0.005。另外，关于aitodv2,目前只公开了train和val的标签，test后续会公开，目前只需报告val上的结果即可~

复现结果： bbox_mAP: 0.2470, bbox_mAP_50: 0.5570, bbox_mAP_75: 0.1840, bbox_mAP_vt: 0.0960, bbox_mAP_t: 0.2500, bbox_mAP_s: 0.2990, bbox_mAP_m: 0.3810