[Example] Add ldc 2d Re1000 and Re3200 example

docs/zh/examples/allen_cahn.md

@@ -40,7 +40,7 @@
 
 | 预训练模型  | 指标 |
 |:--| :--|
-| [allen_cahn_piratenet_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/AllenCahn/allen_cahn_piratenet_pretrained.pdparams) | L2Rel.u: 8.32403e-06 |
+| [allen_cahn_piratenet_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/AllenCahn/allen_cahn_piratenet_pretrained.pdparams) | L2Rel.u: 1.2e-05 |
 
 ## 1. 背景简介
 

examples/ldc/conf/ldc_2d_Re1000_plain.yaml

@@ -0,0 +1,88 @@
+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_ldc_2d_Re1000_plain/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+  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: train # running mode: train/eval
+seed: 42
+output_dir: ${hydra:run.dir}
+log_freq: 100
+use_tbd: false
+
+# working conditions
+Re: [100, 400, 1000]
+epochs: [20, 40, 140]
+EVAL_DATA_PATH: ./data/ldc_Re1000.mat
+
+# model settings
+MODEL:
+  input_keys: ["x", "y"]
+  output_keys: ["u", "v", "p"]
+  num_layers: 4
+  hidden_size: 256
+  activation: tanh
+
+# training settings
+TRAIN:
+  epochs: 20
+  iters_per_epoch: 1000
+  save_freq: 10
+  eval_during_train: true
+  eval_freq: 1
+  lr_scheduler:
+    epochs: ${sum:${epochs}}
+    iters_per_epoch: ${TRAIN.iters_per_epoch}
+    learning_rate: 1.0e-3
+    gamma: 0.9
+    decay_steps: 2000
+    by_epoch: false
+  batch_size:
+    pde: 1024
+    bc: 256
+  pretrained_model_path: null
+  checkpoint_path: null
+
+# evaluation settings
+EVAL:
+  pretrained_model_path: null
+  eval_with_no_grad: true
+  batch_size: 4096
+
+# inference settings
+INFER:
+  pretrained_model_path: null
+  export_path: ./inference/ldc_2d_re3200
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdiparams_path: ${INFER.export_path}.pdiparams
+  onnx_path: ${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
+  batch_size: 1024

examples/ldc/conf/ldc_2d_Re3200_piratenet.yaml

@@ -0,0 +1,99 @@
+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_ldc_2d_Re3200_piratenet/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+  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: train # running mode: train/eval
+seed: 42
+output_dir: ${hydra:run.dir}
+log_freq: 100
+use_tbd: false
+
+# working conditions
+Re: [100, 400, 1000, 1600, 3200]
+epochs: [10, 20, 50, 50, 500]
+EVAL_DATA_PATH: ./data/ldc_Re3200.mat
+
+# model settings
+MODEL:
+  input_keys: ["x", "y"]
+  output_keys: ["u", "v", "p"]
+  num_blocks: 4
+  hidden_size: 256
+  activation: tanh
+  fourier:
+    scale: 15.0
+    dim: 256
+  random_weight:
+    mean: 1.0
+    std: 0.1
+
+# training settings
+TRAIN:
+  epochs: 10
+  iters_per_epoch: 1000
+  save_freq: 100
+  eval_during_train: true
+  eval_freq: 1
+  lr_scheduler:
+    epochs: ${sum:${epochs}}
+    iters_per_epoch: ${TRAIN.iters_per_epoch}
+    learning_rate: 1.0e-3
+    gamma: 0.9
+    decay_steps: 10000
+    warmup_epoch: 5
+    by_epoch: false
+  batch_size:
+    pde: 4096
+    bc: 256
+  pretrained_model_path: null
+  checkpoint_path: null
+  grad_norm:
+    update_freq: 1000
+    momentum: 0.9
+    init_weights: [10, 1, 1, 100, 100]
+
+# evaluation settings
+EVAL:
+  pretrained_model_path: null
+  eval_with_no_grad: true
+  batch_size: 4096
+
+# inference settings
+INFER:
+  pretrained_model_path: null
+  export_path: ./inference/ldc_2d_re3200
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdiparams_path: ${INFER.export_path}.pdiparams
+  onnx_path: ${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
+  batch_size: 1024

examples/ldc/conf/ldc_2d_Re3200_sota.yaml

@@ -0,0 +1,97 @@
+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_ldc_2d_Re3200_sota/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+  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: train # running mode: train/eval
+seed: 42
+output_dir: ${hydra:run.dir}
+log_freq: 100
+use_tbd: false
+
+# working conditions
+Re: [100, 400, 1000, 3200]
+epochs: [50, 50, 100, 500]
+EVAL_DATA_PATH: ./data/ldc_Re1000.mat
+
+# model settings
+MODEL:
+  input_keys: ["x", "y"]
+  output_keys: ["u", "v", "p"]
+  num_layers: 5
+  hidden_size: 256
+  activation: tanh
+  fourier:
+    scale: 10.0
+    dim: 128
+  random_weight:
+    mean: 1.0
+    std: 0.1
+
+# training settings
+TRAIN:
+  epochs: 50
+  iters_per_epoch: 1000
+  save_freq: 100
+  eval_during_train: true
+  eval_freq: 1
+  lr_scheduler:
+    epochs: ${sum:${epochs}}
+    iters_per_epoch: ${TRAIN.iters_per_epoch}
+    learning_rate: 1.0e-3
+    gamma: 0.9
+    decay_steps: 10000
+    by_epoch: false
+  batch_size:
+    pde: 8192
+    bc: 256
+  pretrained_model_path: null
+  checkpoint_path: null
+  grad_norm:
+    update_freq: 1000
+    momentum: 0.9
+
+# evaluation settings
+EVAL:
+  pretrained_model_path: null
+  eval_with_no_grad: true
+  batch_size: 4096
+
+# inference settings
+INFER:
+  pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/ldc/ldc_re1000_sota_pretrained.pdparams
+  export_path: ./inference/ldc_2d_re1000
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdiparams_path: ${INFER.export_path}.pdiparams
+  onnx_path: ${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
+  batch_size: 1024