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【SCU】【PPSCI Export&Infer No.19】phycrnet #1029

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【SCU】【PPSCI Export&Infer No.19】phycrnet #1029

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21 changes: 21 additions & 0 deletions docs/zh/examples/phycrnet.md
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,27 @@

python main.py mode=eval DATA_PATH=./data/burgers_1501x2x128x128.mat EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/phycrnet/phycrnet_burgers.pdparams
```

=== "模型导出命令"

``` sh
wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyCRNet/burgers_1501x2x128x128.mat -P ./data/
# windows
# curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyCRNet/burgers_1501x2x128x128.mat --create-dirs -o ./data/burgers_1501x2x128x128.mat

python main.py mode=export DATA_PATH=./data/burgers_1501x2x128x128.mat EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/phycrnet/phycrnet_burgers.pdparams
```

=== "模型推理命令"

``` sh
wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyCRNet/burgers_1501x2x128x128.mat -P ./data/
# windows
# curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyCRNet/burgers_1501x2x128x128.mat --create-dirs -o ./data/burgers_1501x2x128x128.mat

python main.py mode=infer DATA_PATH=./data/burgers_1501x2x128x128.mat EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/phycrnet/phycrnet_burgers.pdparams ```


| 预训练模型 | 指标 |
|:--| :--|
| [phycrnet_burgers_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/phycrnet/phycrnet_burgers.pdparams) | a-RMSE: 3.20e-3 |
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18 changes: 18 additions & 0 deletions examples/phycrnet/conf/burgers_equations.yaml
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Expand Up @@ -63,3 +63,21 @@ EVAL:
eval_with_no_grad: true
TIME_BATCH_SIZE: 2000
TIME_STEPS: 2001

# inference settings
INFER:
pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/phycrnet/phycrnet_burgers.pdparams
export_path: ./inference/phycrnet_ide
pdmodel_path: ${INFER.export_path}.pdmodel
pdpiparams_path: ${INFER.export_path}.pdiparams
device: gpu
engine: native
precision: fp32
onnx_path: ${INFER.export_path}.onnx
ir_optim: true
min_subgraph_size: 10
gpu_mem: 4000
gpu_id: 0
max_batch_size: 64
num_cpu_threads: 4
batch_size: 16
18 changes: 18 additions & 0 deletions examples/phycrnet/conf/fitzhugh_nagumo_RD_equation.yaml
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Expand Up @@ -61,3 +61,21 @@ TRAIN:
EVAL:
pretrained_model_path: null
eval_with_no_grad: true

# inference settings
INFER:
pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/phycrnet/phycrnet_burgers.pdparams
export_path: ./inference/phycrnet_ide
pdmodel_path: ${INFER.export_path}.pdmodel
pdpiparams_path: ${INFER.export_path}.pdiparams
device: gpu
engine: native
precision: fp32
onnx_path: ${INFER.export_path}.onnx
ir_optim: true
min_subgraph_size: 10
gpu_mem: 4000
gpu_id: 0
max_batch_size: 64
num_cpu_threads: 4
batch_size: 16
18 changes: 18 additions & 0 deletions examples/phycrnet/conf/lambda_omega_RD_equation.yaml
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Expand Up @@ -61,3 +61,21 @@ TRAIN:
EVAL:
pretrained_model_path: null
eval_with_no_grad: true

# inference settings
INFER:
pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/phycrnet/phycrnet_burgers.pdparams
export_path: ./inference/phycrnet_ide
pdmodel_path: ${INFER.export_path}.pdmodel
pdpiparams_path: ${INFER.export_path}.pdiparams
device: gpu
engine: native
precision: fp32
onnx_path: ${INFER.export_path}.onnx
ir_optim: true
min_subgraph_size: 10
gpu_mem: 4000
gpu_id: 0
max_batch_size: 64
num_cpu_threads: 4
batch_size: 16
87 changes: 86 additions & 1 deletion examples/phycrnet/main.py
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Expand Up @@ -178,6 +178,85 @@ def _transform_out(_in, _out):
functions.output_graph(model, input_dict_val, cfg.output_dir, cfg.case_name)


def export(cfg: DictConfig):
# set random seed for reproducibility
ppsci.utils.misc.set_random_seed(cfg.seed)
# initialize logger
logger.init_logger("ppsci", osp.join(cfg.output_dir, f"{cfg.mode}.log"), "info")

# set initial states for convlstm
NUM_CONVLSTM = cfg.num_convlstm
(h0, c0) = (paddle.randn((1, 128, 16, 16)), paddle.randn((1, 128, 16, 16)))
initial_state = []
for _ in range(NUM_CONVLSTM):
initial_state.append((h0, c0))

# grid parameters
time_steps = cfg.TIME_STEPS
dx = cfg.DX[0] / cfg.DX[1]

steps = cfg.EVAL.TIME_BATCH_SIZE + 1
effective_step = list(range(0, steps))
num_time_batch = int(time_steps / cfg.EVAL.TIME_BATCH_SIZE)

functions.dt = cfg.DT
functions.dx = dx
functions.num_time_batch = num_time_batch
model = ppsci.arch.PhyCRNet(
dt=cfg.DT, step=steps, effective_step=effective_step, **cfg.MODEL
)

# initialize solver
solver = ppsci.solver.Solver(
model,
pretrained_model_path=cfg.INFER.pretrained_model_path,
)
# export model
from paddle.static import InputSpec

input_spec = [
{
key: InputSpec([None, 1], "float32", name=key)
for key in cfg.MODEL.input_keys
Comment on lines +219 to +220
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这里的形状应该是不对的，可以在PhyCrNet的forward里调试一下：
image
正确的形状和数据类型如下

'initial_state': [[65536]], float32,
'initial_state_shape': [[1,2,1,128,16,16]], int64
'input': [[1,2,127,128]], float32

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收到

},
]
solver.export(input_spec, cfg.INFER.export_path, with_onnx=False)


def inference(cfg: DictConfig):
from deploy.python_infer import pinn_predictor

predictor = pinn_predictor.PINNPredictor(cfg)

# use Burgers_2d_solver_HighOrder.py to generate data
data = scio.loadmat(cfg.DATA_PATH)
uv = data["uv"] # [t,c,h,w]
functions.uv = uv

initial_state = []
# generate input data
(_, _, input_dict, _,) = functions.Dataset(
paddle.to_tensor(initial_state),
paddle.to_tensor(
uv[
0:1,
],
dtype=paddle.get_default_dtype(),
),
).get()

output_dict = predictor.predict(
{key: input_dict[key] for key in cfg.INFER.input_keys}, cfg.INFER.batch_size
)
# mapping data to cfg.INFER.output_keys
output_dict = {
store_key: output_dict[infer_key]
for store_key, infer_key in zip(cfg.MODEL.output_keys, output_dict.keys())
}

functions.output_graph(predictor, input_dict, cfg.output_dir, cfg.case_name)


@hydra.main(
version_base=None, config_path="./conf", config_name="burgers_equations.yaml"
)
Expand All @@ -186,8 +265,14 @@ def main(cfg: DictConfig):
train(cfg)
elif cfg.mode == "eval":
evaluate(cfg)
elif cfg.mode == "export":
export(cfg)
elif cfg.mode == "infer":
inference(cfg)
else:
raise ValueError(f"cfg.mode should in ['train', 'eval'], but got '{cfg.mode}'")
raise ValueError(
f"cfg.mode should in ['train', 'eval', 'export', 'infer'], but got '{cfg.mode}'"
)


if __name__ == "__main__":
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