Created by: joshuazm

/opt/anaconda3/bin/anaconda/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:17: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Mapping, defaultdict
EvalReader:
  batch_size: 32
  bufsize: 32
  dataset: !COCODataSet
    anno_path: dx_val.json
    dataset_dir: dataset/dx
    image_dir: images/val2020
    sample_num: -1
    with_background: false
  drop_empty: false
  inputs_def:
    fields:
    - image
    - im_size
    - im_id
    image_shape:
    - 3
    - 608
    - 608
    num_max_boxes: 50
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !ResizeImage
    interp: 2
    max_size: 0
    target_size: 608
    use_cv2: true
  - !NormalizeImage
    is_channel_first: false
    is_scale: false
    mean:
    - 0.485
    - 0.456
    - 0.406
    std:
    - 0.229
    - 0.224
    - 0.225
  - !Permute
    channel_first: true
    to_bgr: false
  worker_num: 8
IouLoss:
  loss_weight: 2.5
  max_height: 608
  max_width: 608
LearningRate:
  [32mbase_lr[0m: 0.0001
  [32mschedulers[0m:
  - !PiecewiseDecay
    gamma: 0.1
    milestones:
    - 55000
    - 75000
    values: null
  - !LinearWarmup
    start_factor: 0.0
    steps: 4000
OptimizerBuilder:
  [32mregularizer[0m:
    factor: 0.0005
    type: L2
  optimizer:
    momentum: 0.9
    type: Momentum
ResNet:
  [32mdcn_v2_stages[0m:
  - 5
  [32mfeature_maps[0m:
  - 3
  - 4
  - 5
  [32mfreeze_at[0m: 0
  [32mfreeze_norm[0m: false
  [32mnorm_type[0m: sync_bn
  [32mvariant[0m: d
  depth: 50
  gcb_params: {}
  gcb_stages: []
  nonlocal_stages: []
  norm_decay: 0.0
  weight_prefix_name: ''
TrainReader:
  batch_size: 16
  batch_transforms:
  - !RandomShape
    random_inter: true
    sizes:
    - 320
    - 352
    - 384
    - 416
    - 448
    - 480
    - 512
    - 544
    - 576
    - 608
  - !NormalizeImage
    is_channel_first: false
    is_scale: false
    mean:
    - 0.485
    - 0.456
    - 0.406
    std:
    - 0.229
    - 0.224
    - 0.225
  - !Permute
    channel_first: true
    to_bgr: false
  - !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
    num_classes: 80
  bufsize: 32
  dataset: !COCODataSet
    anno_path: dx_train.json
    dataset_dir: dataset/dx
    image_dir: images/train2020
    sample_num: -1
    with_background: false
  drop_last: true
  inputs_def:
    fields:
    - image
    - gt_bbox
    - gt_class
    - gt_score
    num_max_boxes: 50
  sample_transforms:
  - !DecodeImage
    to_rgb: true
    with_mixup: false
  - !RandomCrop
    allow_no_crop: true
    aspect_ratio:
    - 0.5
    - 2.0
    cover_all_box: false
    num_attempts: 50
    scaling:
    - 0.3
    - 1.0
    thresholds:
    - 0.0
    - 0.1
    - 0.3
    - 0.5
    - 0.7
    - 0.9
  - !RandomFlipImage
    is_mask_flip: false
    is_normalized: false
    prob: 0.5
  - !NormalizeBox {}
  - !PadBox
    num_max_boxes: 50
  - !BboxXYXY2XYWH {}
  shuffle: false
  use_fine_grained_loss: true
  use_process: true
  worker_num: 8
YOLOv3:
  [32mbackbone[0m: ResNet
  [32muse_fine_grained_loss[0m: false
  yolo_head: YOLOv3Head
YOLOv3Head:
  [32mdrop_block[0m: true
  [32mnms[0m:
    background_label: -1
    keep_top_k: 100
    nms_threshold: 0.45
    nms_top_k: 1000
    normalized: false
    score_threshold: 0.01
  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
  block_size: 3
  keep_prob: 0.9
  norm_decay: 0.0
  num_classes: 80
  weight_prefix_name: ''
  yolo_loss: YOLOv3Loss
YOLOv3Loss:
  [32mbatch_size[0m: 16
  [32miou_loss[0m: IouLoss
  [32mlabel_smooth[0m: false
  [32muse_fine_grained_loss[0m: true
  ignore_thresh: 0.7
architecture: YOLOv3
log_smooth_window: 20
max_iters: 85000
metric: COCO
num_classes: 1
pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar
save_dir: output
snapshot_iter: 200
use_fine_grained_loss: true
use_gpu: true
weights: output/yolov3_r50vd_dcn_db_iouloss_obj365_pretrained_coco/model_final

Traceback (most recent call last):
  File "tools/train.py", line 323, in <module>
    main()
  File "tools/train.py", line 116, in main
    train_fetches = model.train(feed_vars)
  File "/mnt/PaddleDetection/ppdet/modeling/architectures/yolov3.py", line 156, in train
    return self.build(feed_vars, mode='train')
  File "/mnt/PaddleDetection/ppdet/modeling/architectures/yolov3.py", line 85, in build
    gt_score, targets)
  File "/mnt/PaddleDetection/ppdet/modeling/anchor_heads/yolo_head.py", line 277, in get_loss
    self.prefix_name)
  File "/mnt/PaddleDetection/ppdet/modeling/losses/yolo_loss.py", line 57, in __call__
    mask_anchors, self._ignore_thresh)
  File "/mnt/PaddleDetection/ppdet/modeling/losses/yolo_loss.py", line 107, in _get_fine_grained_loss
    "YOLOv3 output layer number not equal target number"
AssertionError: YOLOv3 output layer number not equal target number

我是想用来训练自己的数据，且只有一个类