python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls loss_bbox --legend loss_cls loss_bbox --out losses.pdf 注:--keys 这里的参数可以替换成log.json中的数据参数。(3)在相同图像中比较两次运行结果的 bbox mAP python tools/analysis_tools/analyze_logs.py plot_curve...
上述初始化参数比较简单,就两个参数:reduction默认是’mean’,即返回损失的均值,loss_weight控制L1 Loss总的权重值。但在forward部分参数就多了:pred和target不必多说,二者shape应该一致,假设在处理bbox二者shape为[1000,4];weight的shape应该和pred的shape一样,控制每个样本对总的损失的权重值;avg_factor和reduct...
# loss of proposal generated from encode feature map. if enc_cls_scores is not None: # NOTE The enc_loss calculation of the DINO is # different from that of Deformable DETR. enc_loss_cls, enc_losses_bbox, enc_losses_iou = \ self.loss_by_feat_single( enc_cls_scores, enc_bbox_pre...
AI代码解释 train_pipeline=[dict(type='LoadPointsFromFile',coord_type='DEPTH',shift_height=False,use_color=True,load_dim=6,use_dim=[0,1,2,3,4,5]),dict(type='LoadAnnotations3D',with_bbox_3d=False,with_label_3d=False,with_mask_3d=False,with_seg_3d=True),dict(type='PointSegClassMapp...
充分理解前向推理流程后,下一步深入阅读训练过程中 loss 计算流程了,重点关注正负样本定义规则、bbox 编解码规则和 loss 计算函数。 阅读顺序:深入理解 RetinaNet 后,再看 Faster R-CNN。 1.3 遇到问题如何求助 ①在阅读过程中,可以再次去查看知乎相关源码解读文章,对照看,多看几遍,应该会加快理解。 ②存在问题,...
train_pipeline=[dict(type='LoadPointsFromFile',coord_type='DEPTH',shift_height=False,use_color=True,load_dim=6,use_dim=[0,1,2,3,4,5]),dict(type='LoadAnnotations3D',with_bbox_3d=False,with_label_3d=False,with_mask_3d=False,with_seg_3d=True),dict(type='PointSegClassMapping',valid...
train_pipeline = [dict(type='LoadPointsFromFile',coord_type='DEPTH',shift_height=False,use_color=True,load_dim=6,use_dim=[0, 1, 2, 3, 4, 5]),dict(type='LoadAnnotations3D',with_bbox_3d=False,with_label_3d=False,with_mask_3d=False,with_seg_3d=True),dict(type='PointSegClassMappi...
/mmdet/core: anchor和bbox等操作具体实现,并被包裹到registry /mmdet/core/anchor/anchor_generator.py: anchor构建 # 此类是传统anchor生成方法,比如RCNNclassAnchorGenerator(object):# --base_sizes: anchor面积大小,如果为None就使用strides代替# --scales: anchor大小,base_sizes、strides类似# --ratios: ancho...
/mmdet/core: anchor和bbox等操作具体实现,并被包裹到registry /mmdet/core/anchor/anchor_generator.py: anchor构建 # 此类是传统anchor生成方法,比如RCNNclassAnchorGenerator(object):# --base_sizes: anchor面积大小,如果为None就使用strides代替# --scales: anchor大小,base_sizes、strides类似# --ratios: ancho...
self.loss_bbox = build_loss(dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) self.loss_cls = build_loss(dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)) def forward_train(self, x, sampling_results, gt_bboxes, gt_labels, gt_masks=None, img_metas, cfg=...