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feat(ppsci): support data_effient_nopt for inference
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‎examples/data_efficient_nopt/README.md

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

‎examples/data_efficient_nopt/config/inference_helmholtz.yaml

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

‎examples/data_efficient_nopt/config/operators_helmholtz.yaml

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# optimization
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optimizer: 'adam'
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scheduler: 'none'
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loss_style: 'mean'
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loss_func: 'mse'
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learning_rate: !!float 1.0
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max_epochs: 500
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scheduler_epochs: 500
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weight_decay: 0
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batch_size: 25
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# misc
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log_to_screen: !!bool False
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log_to_screen: !!bool True
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save_checkpoint: !!bool False
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seed: 0
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plot_figs: !!bool False
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accum_grad: 1
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enable_amp: !!bool False
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log_interval: 1
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checkpoint_save_interval: 10
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checkpoint_save_interval: 250
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debug_grad: False
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helmholtz: &helmholtz
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batch_size: 128
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nx: 128
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ny: 128
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log_to_wandb: !!bool True
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log_to_wandb: !!bool False
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save_checkpoint: !!bool True
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max_epochs: 500
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scheduler: 'cosine'
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helm-64-scale-o5_15: &helm_64_o5_15
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<<: *helmholtz
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train_path: '/path/to/helmholtz_64_o5_15_train.h5'
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val_path: '/path/to/helmholtz_64_o5_15_val.h5'
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test_path: '/path/to/helmholtz_64_o5_15_test.h5'
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scales_path: '/path/to/helmholtz_64_o5_15_train_scale.npy'
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# train_rand_idx_path: '/path/to/old_gen/train_rand_idx.npy'
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train_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_train.h5'
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val_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_val.h5'
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test_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_test.h5'
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scales_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_train_scale.npy'
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# train_rand_idx_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/old_gen/train_rand_idx.npy'
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batch_size: 128
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in_dim: 3
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out_dim: 1
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helm-64-pretrain-o1_20: &helm_64_o1_20_pt
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<<: *helmholtz
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train_path: '/path/to/helmholtz_64_o1_20_train.h5'
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val_path: '/path/to/helmholtz_64_o1_20_val.h5'
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test_path: '/path/to/helmholtz_64_o1_20_test.h5'
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scales_path: '/path/to/helmholtz_64_o1_20_train_scale.npy'
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train_rand_idx_path: '/path/to/train_rand_idx.npy'
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train_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o1_20_train.h5'
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val_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o1_20_val.h5'
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test_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o1_20_test.h5'
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scales_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o1_20_train_scale.npy'
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train_rand_idx_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/train_rand_idx.npy'
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batch_size: 128
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in_dim: 3
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out_dim: 1
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helm-64-finetune-o5_15: &helm_64_o5_15_ft
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<<: *helmholtz
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train_path: '/path/to/helmholtz_64_o5_15_train.h5'
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val_path: '/path/to/helmholtz_64_o5_15_val.h5'
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test_path: '/path/to/helmholtz_64_o5_15_test.h5'
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scales_path: '/path/to/helmholtz_64_o5_15_train_scale.npy'
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train_rand_idx_path: '/path/to/train_rand_idx.npy'
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train_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_train.h5'
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val_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_val.h5'
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test_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_test.h5'
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scales_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/helmholtz_64_o5_15_train_scale.npy'
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train_rand_idx_path: '/home/aistudio/data_efficient_nopt/data/helmholtz_64/helmholtz_64/train_rand_idx.npy'
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batch_size: 128
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in_dim: 3 #normal helmholtz has 3 dim, joint has 4
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out_dim: 1

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