PaddleScience/examples/confild/conf/confild_case2.yaml at 46a52608aa658e3408ac4c436dd694d32cd5474e · PaddlePaddle/PaddleScience · GitHub

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defaults:
  - ppsci_default
  - TRAIN: train_default
  - TRAIN/ema: ema_default
  - TRAIN/swa: swa_default
  - EVAL: eval_default
  - INFER: infer_default
  - hydra/job/config/override_dirname/exclude_keys: exclude_keys_default
  - _self_

hydra:
  run:
    # dynamic output directory according to running time and override name
    # dir: outputs_confild_case2/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
    dir: ./outputs_confild_case2
  job:
    name: ${mode} # name of logfile
    chdir: false # keep current working directory unchanged
  callbacks:
    init_callback:
      _target_: ppsci.utils.callbacks.InitCallback
  sweep:
    # output directory for multirun
    dir: ${hydra.run.dir}
    subdir: ./

# general settings
mode: infer # running mode: infer
seed: 2025
output_dir: ${hydra:run.dir}
log_freq: 20

TRAIN:
  batch_size: 40
  test_batch_size: 40
  epochs: 44500
  mutil_GPU: 1
  lr:
    cnf: 1.e-4
    latents: 1.e-5

EVAL:
  confild_pretrained_model_path: ./outputs_confild_case2/confild_case2/epoch_99999
  latent_pretrained_model_path: ./outputs_confild_case2/latent_case2/epoch_99999

CONFILD:
  input_keys: ["confild_x", "latent_z"]
  output_keys: ["confild_output"]
  num_hidden_layers: 10
  out_features: 4
  hidden_features: 256
  in_coord_features: 2
  in_latent_features: 256

Latent:
  input_keys: ["latent_x"]
  output_keys: ["latent_z"]
  N_samples: 1200
  lumped: False
  N_features: 256
  dims: 2

INFER:
  Latent:
    INFER:
      pretrained_model_path: null
      export_path: ./inference/latent_case2
      pdmodel_path: ${INFER.Latent.INFER.export_path}.pdmodel
      pdiparams_path: ${INFER.Latent.INFER.export_path}.pdiparams
      onnx_path: ${INFER.Latent.INFER.export_path}.onnx
      device: gpu
      engine: native
      precision: fp32
      ir_optim: true
      min_subgraph_size: 5
      gpu_mem: 2000
      gpu_id: 0
      max_batch_size: 1024
      num_cpu_threads: 10
    log_freq: 20
  Confild:
    INFER:
      pretrained_model_path: null
      export_path: ./inference/confild_case2
      pdmodel_path: ${INFER.Confild.INFER.export_path}.pdmodel
      pdiparams_path: ${INFER.Confild.INFER.export_path}.pdiparams
      onnx_path: ${INFER.Confild.INFER.export_path}.onnx
      device: gpu
      engine: native
      precision: fp32
      ir_optim: true
      min_subgraph_size: 5
      gpu_mem: 2000
      gpu_id: 0
      max_batch_size: 1024
      num_cpu_threads: 10
      coord_shape: [400, 100, 2]
      latents_shape: [1, 1, 256]
    log_freq: 20
  batch_size: 40

Data:
  data_path: /home/aistudio/work/extracted/data/Case2/data.npy
  # coor_path: ../case2/coords.npy
  normalizer:
    method: "-11"
    dim: 0
  load_data_fn: load_channel_flow