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[Hackathon 6th Code Camp No.15] support neuraloperator (#867)
* add-neuraloperator * add-neuraloperator * add-neuraloperator * add-neuraloperator * add-neuraloperator * add-neuraloperator * add-neuraloperator * add-neuraloperator * add-neuraloperator * add-neuraloperator * add-neuraloperator * add-neuraloperator
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‎docs/zh/api/arch.md

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- ChipDeepONets
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- AutoEncoder
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- CuboidTransformer
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- SFNONet
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- UNONet
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- TFNO1dNet
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- TFNO2dNet
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- TFNO3dNet
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show_root_heading: true
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heading_level: 3

‎docs/zh/api/data/dataset.md

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- RadarDataset
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- build_dataset
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- DGMRDataset
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- DarcyFlowDataset
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- SphericalSWEDataset
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show_root_heading: true

‎examples/neuraloperator/conf/sfno_swe_pretrain.yaml

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hydra:
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run:
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# dynamic output directory according to running time and override name
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dir: outputs_sfno_pretrain
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job:
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name: ${mode} # name of logfile
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chdir: false # keep current working direcotry unchaned
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config:
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override_dirname:
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exclude_keys:
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- TRAIN.checkpoint_path
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- TRAIN.pretrained_model_path
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- EVAL.pretrained_model_path
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- mode
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- output_dir
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- log_freq
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callbacks:
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init_callback:
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_target_: ppsci.utils.callbacks.InitCallback
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sweep:
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# output directory for multirun
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dir: ${hydra.run.dir}
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subdir: ./
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# general settings
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mode: train # running mode: train/eval/export/infer
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seed: 666
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output_dir: ${hydra:run.dir}
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log_freq: 20
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# set train and evaluate data path
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FILE_PATH: ./datasets/SWE/
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# dataset setting
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DATASET:
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label_keys: ["y"]
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train_resolution: "32x64"
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test_resolutions: ["32x64","64x128"]
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# model settings
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MODEL:
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input_keys: ["x"]
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output_keys: ["y"]
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in_channels: 3
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out_channels: 3
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n_modes: [32, 32]
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hidden_channels: 32
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projection_channels: 64
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n_layers: 4
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use_mlp: false
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mlp:
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expansion: 0.5
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dropout: 0.0
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norm: 'group_norm'
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fno_skip: "linear"
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mlp_skip: "soft-gating"
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separable: false
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preactivation: false
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factorization: null
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rank: 1.0
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joint_factorization: false
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fixed_rank_modes: null
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implementation: "factorized"
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domain_padding: null #0.078125
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domain_padding_mode: "one-sided" #symmetric
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fft_norm: 'forward'
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patching_levels: 0
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# training settings
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TRAIN:
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epochs: 300
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save_freq: 20
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eval_during_train: true
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eval_freq: 1
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lr_scheduler:
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epochs: ${TRAIN.epochs}
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learning_rate: 5e-3
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by_epoch: True
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type: "StepDecay"
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step_size: 60
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gamma: 0.5
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# ReduceOnPlateau only
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scheduler_patience: 5
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# CosineAnnealingLR
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scheduler_T_max: 30
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wd: 1e-4
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batch_size: 4
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pretrained_model_path: null
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checkpoint_path: null
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# evaluation settings
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EVAL:
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pretrained_model_path: ./outputs_sfno_pretrain/checkpoints/best_model.pdparams
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compute_metric_by_batch: false
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eval_with_no_grad: true
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batch_size: 10
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INFER:
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pretrained_model_path: ./outputs_sfno_pretrain/checkpoints/best_model.pdparams
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export_path: ./inference/sfno/sfno_darcyflow
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pdmodel_path: ${INFER.export_path}.pdmodel
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pdiparams_path: ${INFER.export_path}.pdiparams
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device: gpu
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engine: native
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precision: fp32
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onnx_path: ${INFER.export_path}.onnx
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ir_optim: true
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min_subgraph_size: 10
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gpu_mem: 4000
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gpu_id: 0
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max_batch_size: 16
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num_cpu_threads: 4
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batch_size: 1
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data_path: ./datasets/SWE/test_SWE_32x64.npy

‎examples/neuraloperator/conf/tfno_darcyflow_pretrain.yaml

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hydra:
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run:
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# dynamic output directory according to running time and override name
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dir: outputs_tfno_pretrain
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job:
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name: ${mode} # name of logfile
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chdir: false # keep current working direcotry unchaned
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config:
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override_dirname:
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exclude_keys:
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- TRAIN.checkpoint_path
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- TRAIN.pretrained_model_path
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- EVAL.pretrained_model_path
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- mode
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- output_dir
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- log_freq
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callbacks:
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init_callback:
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_target_: ppsci.utils.callbacks.InitCallback
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sweep:
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# output directory for multirun
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dir: ${hydra.run.dir}
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subdir: ./
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# general settings
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mode: train # running mode: train/eval/export/infer
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seed: 666
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output_dir: ${hydra:run.dir}
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log_freq: 20
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# set train and evaluate data path
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FILE_PATH: ./datasets/darcyflow/
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# dataset setting
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DATASET:
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label_keys: ["y"]
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train_resolution: 16
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test_resolutions: [16,32]
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grid_boundaries: [[0, 1], [0, 1]]
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positional_encoding: True
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encode_input: False
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encode_output: False
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encoding: "channel-wise"
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channel_dim: 1
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# model settings
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MODEL:
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input_keys: ["x"]
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output_keys: ["y"]
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n_modes_height: 16
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n_modes_width: 16
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in_channels: 3
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out_channels: 1
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hidden_channels: 32
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projection_channels: 64
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n_layers: 4
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use_mlp: False
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mlp:
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expansion: 0.5
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dropout: 0.0
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norm: "group_norm"
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fno_skip: "linear"
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mlp_skip: "soft-gating"
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separable: false
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preactivation: false
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factorization: 'dense'
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rank: 1.0
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joint_factorization: false
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fixed_rank_modes: null
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implementation: "factorized"
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domain_padding: null #0.078125
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domain_padding_mode: "one-sided" #symmetric
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fft_norm: 'forward'
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patching_levels: 0
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# training settings
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TRAIN:
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epochs: 300
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save_freq: 20
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eval_during_train: true
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eval_freq: 1
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training_loss: "h1"
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lr_scheduler:
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epochs: ${TRAIN.epochs}
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learning_rate: 5e-3
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by_epoch: True
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type: "StepDecay"
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step_size: 60
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gamma: 0.5
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# ReduceOnPlateau only
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scheduler_patience: 5
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# CosineAnnealingLR
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scheduler_T_max: 500
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wd: 1.0e-4
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batch_size: 16
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pretrained_model_path: null
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checkpoint_path: null
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# evaluation settings
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EVAL:
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pretrained_model_path: ./outputs_tfno_pretrain/checkpoints/best_model.pdparams
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compute_metric_by_batch: false
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eval_with_no_grad: true
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batch_size: 16
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INFER:
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pretrained_model_path: ./outputs_tfno_pretrain/checkpoints/best_model.pdparams
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export_path: ./inference/tfno/tfno_darcyflow
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pdmodel_path: ${INFER.export_path}.pdmodel
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pdiparams_path: ${INFER.export_path}.pdiparams
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device: gpu
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engine: native
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precision: fp32
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onnx_path: ${INFER.export_path}.onnx
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ir_optim: true
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min_subgraph_size: 10
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gpu_mem: 4000
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gpu_id: 0
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max_batch_size: 16
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num_cpu_threads: 1
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batch_size: 1
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data_path: ./datasets/darcyflow/darcy_test_16.npy
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grid_boundaries: [[0, 1], [0, 1]]

‎examples/neuraloperator/conf/uno_darcyflow_pretrain.yaml

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hydra:
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run:
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# dynamic output directory according to running time and override name
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dir: outputs_uno_pretrain
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job:
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name: ${mode} # name of logfile
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chdir: false # keep current working direcotry unchaned
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config:
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override_dirname:
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exclude_keys:
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- TRAIN.checkpoint_path
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- TRAIN.pretrained_model_path
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- EVAL.pretrained_model_path
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- mode
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- output_dir
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- log_freq
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callbacks:
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init_callback:
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_target_: ppsci.utils.callbacks.InitCallback
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sweep:
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# output directory for multirun
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dir: ${hydra.run.dir}
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subdir: ./
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# general settings
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mode: train # running mode: train/eval/export/infer
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seed: 666
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output_dir: ${hydra:run.dir}
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log_freq: 20
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# set train and evaluate data path
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FILE_PATH: ./datasets/darcyflow/
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# dataset setting
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DATASET:
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label_keys: ["y"]
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train_resolution: 16
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test_resolutions: [16,32]
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grid_boundaries: [[0, 1], [0, 1]]
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positional_encoding: True
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encode_input: False
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encode_output: False
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encoding: "channel-wise"
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channel_dim: 1
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# model settings
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MODEL:
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input_keys: ["x"]
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output_keys: ["y"]
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in_channels: 3
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out_channels: 1
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hidden_channels: 64
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projection_channels: 64
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n_layers: 5
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uno_out_channels: [32,64,64,64,32]
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uno_n_modes: [[16,16],[8,8],[8,8],[8,8],[16,16]]
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uno_scalings: [[1.0,1.0],[0.5,0.5],[1,1],[2,2],[1,1]]
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horizontal_skips_map: null
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incremental_n_modes: null
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use_mlp: false
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mlp:
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expansion: 0.5
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dropout: 0.0
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norm: "group_norm"
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fno_skip: "linear"
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horizontal_skip: "linear"
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mlp_skip: "soft-gating"
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separable: false
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preactivation: false
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factorization: null
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rank: 1.0
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joint_factorization: false
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fixed_rank_modes: null
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implementation: "factorized"
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domain_padding: 0.2 #0.078125
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domain_padding_mode: "one-sided" #symmetric
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fft_norm: 'forward'
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patching_levels: 0
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# training settings
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TRAIN:
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epochs: 300
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save_freq: 20
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eval_during_train: true
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eval_freq: 1
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training_loss: "h1"
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lr_scheduler:
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epochs: ${TRAIN.epochs}
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learning_rate: 5e-3
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by_epoch: True
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type: "StepDecay"
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step_size: 60
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gamma: 0.5
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# ReduceOnPlateau only
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scheduler_patience: 5
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# CosineAnnealingLR
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scheduler_T_max: 30
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wd: 1.0e-4
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batch_size: 16
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# /home/aistudio/darcy_flow_small.pdparams
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pretrained_model_path: null
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checkpoint_path: null
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# evaluation settings
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EVAL:
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pretrained_model_path: ./outputs_uno_pretrain/checkpoints/best_model.pdparams
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compute_metric_by_batch: false
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eval_with_no_grad: true
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batch_size: 16
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INFER:
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pretrained_model_path: ./outputs_uno_pretrain/checkpoints/best_model.pdparams
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export_path: ./inference/uno/uno_darcyflow
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pdmodel_path: ${INFER.export_path}.pdmodel
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pdiparams_path: ${INFER.export_path}.pdiparams
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device: gpu
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engine: native
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precision: fp32
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onnx_path: ${INFER.export_path}.onnx
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ir_optim: true
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min_subgraph_size: 10
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gpu_mem: 4000
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gpu_id: 0
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max_batch_size: 16
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num_cpu_threads: 4
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batch_size: 1
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data_path: ./datasets/darcyflow/darcy_test_16.npy
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grid_boundaries: [[0, 1], [0, 1]]

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