Created by: mc261670164

Ubuntu机器上在训练PaddleDetection中的ppyolo时，我用的VOC格式的数据集，一直卡在一条log这不往下走，log的内容是“2020-09-04 15:33:29,268-WARNING: recv endsignal from outq with errmsg[consumer[consumer-310-1] failed to map with error:[target0 not in samples]]”。如何才能解决这个问题？我的yml如下: architecture: YOLOv3 use_gpu: false max_iters: 15000 #20000 log_smooth_window: 20 log_iter: 10 save_dir: save_models snapshot_iter: 200 metric: VOC pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar weights: save_models/model_final num_classes: 1 use_fine_grained_loss: false use_ema: true ema_decay: 0.9998

YOLOv3: backbone: ResNet yolo_head: YOLOv3Head use_fine_grained_loss: true

ResNet: norm_type: sync_bn freeze_at: 0 freeze_norm: false norm_decay: 0. depth: 50 feature_maps: [3, 4, 5] variant: d dcn_v2_stages: [5]

YOLOv3Head: anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] norm_decay: 0. coord_conv: true iou_aware: true iou_aware_factor: 0.4 scale_x_y: 1.05 spp: true yolo_loss: YOLOv3Loss nms: MatrixNMS drop_block: true

YOLOv3Loss: batch_size: 24 ignore_thresh: 0.7 scale_x_y: 1.05 label_smooth: false use_fine_grained_loss: true iou_loss: IouLoss iou_aware_loss: IouAwareLoss

IouLoss: loss_weight: 2.5 max_height: 608 max_width: 608

IouAwareLoss: loss_weight: 1.0 max_height: 608 max_width: 608

MatrixNMS: background_label: -1 keep_top_k: 100 normalized: false score_threshold: 0.01 post_threshold: 0.01

LearningRate: base_lr: 0.001 schedulers:

• !PiecewiseDecay gamma: 0.1 milestones:
  • 150000
  • 200000
• !LinearWarmup start_factor: 0. steps: 4000

OptimizerBuilder: optimizer: momentum: 0.9 type: Momentum regularizer: factor: 0.0005 type: L2

TrainReader: inputs_def: fields: ['image', 'gt_bbox', 'gt_class', 'gt_score'] num_max_boxes: 50 dataset: !VOCDataSet anno_path: train.txt dataset_dir: dataset/cartoon_face use_default_label: false with_background: false sample_transforms: - !DecodeImage to_rgb: True with_mixup: True - !MixupImage alpha: 1.5 beta: 1.5 - !ColorDistort {} - !RandomExpand fill_value: [123.675, 116.28, 103.53] - !RandomCrop {} - !RandomFlipImage is_normalized: false - !NormalizeBox {} - !PadBox num_max_boxes: 50 - !BboxXYXY2XYWH {} batch_transforms:

• !RandomShape sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608] random_inter: True
• !NormalizeImage mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] is_scale: True is_channel_first: false
• !Permute to_bgr: false channel_first: True
• !Gt2YoloTarget anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] downsample_ratios: [32, 16, 8] batch_size: 4 shuffle: true mixup_epoch: 25000 drop_last: true worker_num: 8 bufsize: 4 use_process: true

EvalReader: inputs_def: fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult'] num_max_boxes: 50 dataset: !VOCDataSet anno_path: test.txt dataset_dir: dataset/cartoon_face use_default_label: false with_background: false sample_transforms: - !DecodeImage to_rgb: True - !ResizeImage target_size: 608 interp: 2 - !NormalizeImage mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] is_scale: True is_channel_first: false - !PadBox num_max_boxes: 50 - !Permute to_bgr: false channel_first: True batch_size: 4 drop_empty: false worker_num: 8 bufsize: 4

TestReader: inputs_def: image_shape: [3, 608, 608] fields: ['image', 'im_size', 'im_id'] dataset: !ImageFolder anno_path: dataset/cartoon_face/label_list.txt with_background: false sample_transforms: - !DecodeImage to_rgb: True - !ResizeImage target_size: 608 interp: 2 - !NormalizeImage mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] is_scale: True is_channel_first: false - !Permute to_bgr: false channel_first: True batch_size: 1