2.7版本似乎训练不了MCMOT

[zouhan6806504]

问题确认 Search before asking

• [X] 我已经查询历史issue，没有发现相似的bug。I have searched the issues and found no similar bug report.

Bug组件 Bug Component

Training

Bug描述 Describe the Bug

来自于ikcest2024 baseline https://aistudio.baidu.com/projectdetail/7933300 在aistudio上运行，配置我全部放到一个文件里

use_gpu: true
use_xpu: false
use_mlu: false
use_npu: false
log_iter: 100
save_dir: output
snapshot_epoch: 1
print_flops: false
print_params: false

# Exporting the model
export:
  post_process: True  # Whether post-processing is included in the network when export model.
  nms: True           # Whether NMS is included in the network when export model.
  benchmark: False    # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
  fuse_conv_bn: False


metric: MCMOT

num_classes: 228

# for MCMOT training
TrainDataset:
  !MCMOTDataSet
    dataset_dir: /home/aistudio/work/dataset # 需要更改为自己对应的文件目录下
    image_lists: ['IKCEST.train']
    data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
    label_list: /home/aistudio/work/dataset/label_list.txt

EvalMOTDataset:
  !MOTImageFolder
    dataset_dir: /home/aistudio/work/dataset
    data_root: IKCEST/images/test/
    keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
    anno_path: /home/aistudio/work/dataset/label_list.txt

# for MCMOT video inference
#TestMOTDataset:
#  !MOTImageFolder
#    dataset_dir: /home/aistudio/work/dataset
#    keep_ori_im: True # set True if save visualization images or video
#    anno_path: /home/aistudio/work/dataset/label_list.txt


pretrain_weights: https://paddledet.bj.bcebos.com/models/centernet_dla34_140e_coco.pdparams

architecture: FairMOT
for_mot: True

FairMOT:
  detector: CenterNet
  reid: FairMOTEmbeddingHead
  loss: FairMOTLoss
  tracker: JDETracker # multi-class tracker

CenterNet:
  backbone: DLA
  neck: CenterNetDLAFPN
  head: CenterNetHead
  post_process: CenterNetPostProcess

CenterNetDLAFPN:
  down_ratio: 4
  last_level: 5
  out_channel: 0
  dcn_v2: True
  with_sge: False

CenterNetHead:
  head_planes: 256
  prior_bias: -2.19
  regress_ltrb: False
  size_loss: 'L1'
  loss_weight: {'heatmap': 1.0, 'size': 0.1, 'offset': 1.0, 'iou': 0.0}
  add_iou: False

FairMOTEmbeddingHead:
  ch_head: 256
  ch_emb: 128

CenterNetPostProcess:
  max_per_img: 200
  down_ratio: 4
  regress_ltrb: False

JDETracker:
  min_box_area: 0
  vertical_ratio: 0 # no need to filter bboxes according to w/h
  use_byte: True
  match_thres: 0.8
  conf_thres: 0.4
  low_conf_thres: 0.2
  tracked_thresh: 0.4
  metric_type: cosine

weights: output/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker/model_final

epoch: 30
LearningRate:
  base_lr: 0.0005
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [10, 20]
    use_warmup: False

OptimizerBuilder:
  optimizer:
    type: Adam
  regularizer: NULL

worker_num: 4
TrainReader:
  inputs_def:
    image_shape: [3, 608, 1088]
  sample_transforms:
    - Decode: {}
    - RGBReverse: {}
    - AugmentHSV: {}
    - LetterBoxResize: {target_size: [608, 1088]}
    - MOTRandomAffine: {reject_outside: False}
    - RandomFlip: {}
    - BboxXYXY2XYWH: {}
    - NormalizeBox: {}
    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1]}
    - RGBReverse: {}
    - Permute: {}
  batch_transforms:
    - Gt2FairMOTTarget: {}
  batch_size: 2
  shuffle: True
  drop_last: True
  use_shared_memory: True

EvalMOTReader:
  sample_transforms:
    - Decode: {}
    - LetterBoxResize: {target_size: [608, 1088]}
    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
    - Permute: {}
  batch_size: 1


#TestMOTReader:
#  inputs_def:
#    image_shape: [3, 608, 1088]
#  sample_transforms:
#    - Decode: {}
#    - LetterBoxResize: {target_size: [608, 1088]}
#    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
#    - Permute: {}
#  batch_size: 1

运行时报异常，

Traceback (most recent call last):
  File "/home/aistudio/PaddleDetection/tools/train.py", line 209, in <module>
    main()
  File "/home/aistudio/PaddleDetection/tools/train.py", line 205, in main
    run(FLAGS, cfg)
  File "/home/aistudio/PaddleDetection/tools/train.py", line 158, in run
    trainer.train(FLAGS.eval)
  File "/home/aistudio/PaddleDetection/ppdet/engine/trainer.py", line 463, in train
    self.cfg['EvalDataset'] = self.cfg.EvalDataset = create(
  File "/home/aistudio/PaddleDetection/ppdet/core/workspace.py", line 292, in create
    return cls(**cls_kwargs)
  File "/home/aistudio/PaddleDetection/ppdet/data/source/dataset.py", line 278, in __init__
    type = dataset_args.pop("name")
KeyError: 'name'

感觉就是EvalMOTDataset没能正确创建出来，而且我看代码里面对metrics MCMOT也没处理

复现环境 Environment

aistudio环境，直接运行项目就能复现

Bug描述确认 Bug description confirmation

• [X] 我确认已经提供了Bug复现步骤、代码改动说明、以及环境信息，确认问题是可以复现的。I confirm that the bug replication steps, code change instructions, and environment information have been provided, and the problem can be reproduced.

是否愿意提交PR？ Are you willing to submit a PR?

• [ ] 我愿意提交PR！I'd like to help by submitting a PR!