【Hackathon 8th No.16】 data_efficient_nopt 论文复现

examples/data_efficient_nopt/README.md

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+# Usage
+
+## 1. Data Download
+
+数据来源参考如下。
+
+|dataset| model | size| link |
+|---|---|---|---|
+|possion_64|Poisson (FNO)|6.2GB|https://drive.google.com/drive/folders/1crIsTZGxZULWhrXkwGDiWF33W6RHxJkf|
+|helmholtz_64|Helmholtz (FNO)|5.8GB|https://drive.google.com/drive/folders/1UjIaF6FsjmN_xlGGSUX-1K2V3EF2Zalw|
+
+## 2. Pretraining
+
+修改`config/operators_possion.yaml`或`config/operators_helmholtz.yaml`相关文件夹路径。
+
+修改`run_pretrain.sh`中config路径，采用`possion_64`或`helmholtz_64`配置，同时指定`--config`预训练配置。
+
+```shell
+bash run_pretrain.py
+```
+
+## 3. Fine-tuning
+
+修改`config/operators_possion.yaml`或`config/operators_helmholtz.yaml`相关文件夹路径。
+
+修改`run_pretrain.sh`中config路径，采用`possion_64`或`helmholtz_64`配置，同时指定`--config`微调配置。
+
+```shell
+bash run_pretrain.py
+```
+
+## 4. In-context Learning
+
+修改`config/inference_poisson.yaml`或`config/inference_helmholtz.yaml`相关文件夹路径。
+
+修改`run_pretrain.sh`中config路径，采用`possion_64`或`helmholtz_64`配置，同时指定`--ckpt`权重路径。
+
+```shell
+bash run_inference_possion.py
+```

examples/data_efficient_nopt/config/inference_helmholtz.yaml

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+default:
+  train_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o15_20_train.h5'
+  test_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o15_20_test.h5'
+  # scales_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o15_20_train_scale.npy'
+  # datapath: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o16_test.h5'
+  # datapath: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o15_test.h5'
+  # datapath: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o16_tensor15_test.h5'
+  # datapath: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_test.h5'
+  scales_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_train_scale.npy'
+
+  num_data_workers: 1
+  subsample: 1
+  # num_demos: 0
+  shuffle: False
+  nx: 64
+  nt: 64
+  Lx: !!float 1.0
+  Ly: !!float 1.0
+  pack_data: !!bool False
+
+  model: 'fno'
+  layers: [64, 64, 64, 64, 64]
+  modes1: [65, 65, 65, 65]
+  modes2: [65, 65, 65, 65]
+  fc_dim: 128
+
+  in_dim: 3
+  out_dim: 1
+  mode_cut: 32
+  embed_cut: 64
+  fc_cut: 2
+  dropout: 0
+
+  fix_backbone: True
+
+  loss_func: mse
+
+  batch_size: 1
+  loss_style: sum
+
+  log_to_wandb: !!bool False
+  logdir: ./log

examples/data_efficient_nopt/config/inference_poisson.yaml

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+default:
+  # datapath: '/home/aistudio/data_efficient_nopt/data/possion_64/poisson_64_e5_15_test.h5'
+  train_path: '/home/aistudio/data_efficient_nopt/data/possion_64/poisson_64_e15_50_train.h5' # pick demos
+  test_path: '/home/aistudio/data_efficient_nopt/data/possion_64/poisson_64_e15_50_test.h5'
+  # datapath: '/home/aistudio/data_efficient_nopt/data/possion_64/poisson_64_e20_test.h5'
+  scales_path: '/home/aistudio/data_efficient_nopt/data/possion_64/poisson_64_e5_15_train_scale.npy'
+
+  num_data_workers: 1
+  subsample: 1
+  # num_demos: 0
+  shuffle: False
+  nx: 64
+  nt: 64
+  Lx: !!float 1.0
+  Ly: !!float 1.0
+  pack_data: !!bool False
+
+  model: 'fno'
+  layers: [64, 64, 64, 64, 64]
+  modes1: [65, 65, 65, 65]
+  modes2: [65, 65, 65, 65]
+  fc_dim: 128
+
+  in_dim: 4
+  out_dim: 1
+  mode_cut: 32
+  embed_cut: 64
+  fc_cut: 2
+  dropout: 0
+
+  fix_backbone: True
+
+  loss_func: mse
+
+  batch_size: 1
+  loss_style: sum
+
+  log_to_wandb: !!bool False
+  logdir: ./log