PaddleScience/examples/confild/conf/confild_case1.yaml at cd2b32bc9e89b618f2c310117753af06b32def14 · 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:
    dir: outputs_confild_case1/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
  job:
    name: ${mode}
    chdir: false
  callbacks:
    init_callback:
      _target_: ppsci.utils.callbacks.InitCallback
  sweep:
    dir: ${hydra.run.dir}
    subdir: ./

mode: infer
seed: 2025
output_dir: ${hydra:run.dir}
log_freq: 20

TRAIN:
  batch_size: 64
  test_batch_size: 256
  epochs: 9800
  mutil_GPU: 1
  lr:
    cnf: 1.e-4
    latents: 1.e-5

EVAL:
  confild_pretrained_model_path: ./outputs_confild_case1/confild_case1/epoch_99999
  latent_pretrained_model_path: ./outputs_confild_case1/latent_case1/epoch_99999

CONFILD:
  input_keys: ["confild_x", "latent_z"]
  output_keys: ["confild_output"]
  num_hidden_layers: 10
  out_features: 3
  hidden_features: 128
  in_coord_features: 2
  in_latent_features: 128

Latent:
  input_keys: ["latent_x"]
  output_keys: ["latent_z"]
  N_samples: 16000
  lumped: True
  N_features: 128
  dims: 2

INFER:
  Latent:
    INFER:
      pretrained_model_path: null
      export_path: ./inference/latent_case1
      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_case1
      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: [918, 2]
      latents_shape: [1, 128]
    log_freq: 20
  batch_size: 64

Uncondiction_INFER:
  batch_size : 16
  test_batch_size : 16
  time_length : 128
  latent_length : 128
  image_size : 128
  num_channels: 128
  num_res_blocks: 2
  num_heads: 4
  num_head_channels: 64
  attention_resolutions: "32,16,8"
  channel_mult: null
  steps: 1000
  noise_schedule: "cosine"

Data:
  data_path: data/Case1/case1_data.npy
  coor_path: data/Case1/case1_coords.npy
  normalizer:
    method: "-11"
    dim: 0
  load_data_fn: load_elbow_flow