How to see the accurancy of the results after Gaitgraph training under the GREW dataset.

[BerLancelot]

I'm a beginner in gait recognition, and I'd like to ask how to see the accuracy of the results after gaitgraph training under the GREW dataset, at the moment I can only see the supervised comparison loss function. Thanks for the answer!

Currently during training the progress bar prompt will only appear something like "[2024-01-06 18:42:25] [INFO]: Iteration 150000, Cost 53.59s, SupConLoss_loss=0.0237" with no accuracy related information.

[ChaoFan996]

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[BerLancelot]

Many thanks! I'm using the previous version of Opengait with the Gaitgraph algorithm, but I find that the Rank-1 accuracy is only 5.5% when I'm reproducing it using the GREW dataset. Noticing that the original Gaitgraph paper was not tested using the GREW dataset, I wonder if the low accuracy was due to the difference in the dataset, or if the algorithm itself was not working well?

Here are my parameter settings:

data_cfg:
  dataset_name: GREW
  dataset_root: E:/PycharmProjects/datasets/processed_datasets/GREW/GREW-pose-pkl
  dataset_partition: ./datasets/GREW/GREW.json
  test_dataset_name: GREW
  num_workers: 1
  remove_no_gallery: false
  frame_threshold: 16

evaluator_cfg:
  enable_float16: false
  restore_ckpt_strict: false
  restore_hint: 150000
  save_name: GaitGraph1_phase1
  sampler:
    batch_size: 256
    frames_num_fixed: 501
    frames_num_max: 50
    frames_num_min: 25
    sample_type: fixed_ordered
    frames_skip_num: 0
  metric: euc
  # eval_func: GREW_submission
  eval_func: evaluate_real_scene
  transform:
    - type: Compose
      trf_cfg:
        - type: SelectSequenceCenter
          sequence_length: 16
        - type: GaitGraph1Input

loss_cfg:
  - loss_term_weight: 1
    temperature: 0.01
    type: SupConLoss_Re
    log_prefix: SupConLoss

model_cfg:
  model: GaitGraph1
  joint_format: coco
  input_num: 1
  reduction: 8
  block: Bottleneck # Basic, initial
  input_branch:
    - 3
    - 64
    - 64
    - 32
  main_stream:
    - 32
    - 128
    - 128
    - 256
    - 256
  num_class: 256
  tta: true

optimizer_cfg:
  lr: 0.01
  solver: Adam
  weight_decay: 0.00001

scheduler_cfg:
  max_lr: 0.01
  total_steps: 150000
  scheduler: OneCycleLR

trainer_cfg:
  enable_float16: false
  log_iter: 100
  with_test: true
  restore_ckpt_strict: true
  restore_hint: 0
  save_iter: 5000
  save_name: GaitGraph1_phase1
  sync_BN: true
  total_iter: 150000
  sampler:
    batch_shuffle: true
    frames_num_fixed: 501
    frames_num_max: 50
    frames_num_min: 25
    sample_type: fixed_ordered #Repeat sample
    frames_skip_num: 0
    batch_size: 128
    type: CommonSampler
  transform:
    - type: TwoView
      trf_cfg:
        - type: MirrorPoses
          probability: 0.5
        - type: FlipSequence
          probability: 0.5
        - type: RandomSelectSequence
          sequence_length: 16
        - type: PointNoise
          std: 0.05
        - type: JointNoise
          std: 0.1
        - type: GaitGraph1Input

And the test results are as follows:

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