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【SCU】【PPSCI Export&Infer No.28】phylstm #1031

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38 changes: 38 additions & 0 deletions docs/zh/examples/phylstm.md
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Expand Up @@ -44,6 +44,44 @@
python phylstm3.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/phylstm/phylstm3_pretrained.pdparams
```

=== "模型导出命令"

=== "phylstm2"

``` sh
python phylstm2.py mode=export
```

=== "phylstm3"

``` sh
python phylstm3.py mode=export
```

=== "模型推理命令"

=== "phylstm2"

``` sh
# linux
wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyLSTM/data_boucwen.mat
# windows
# curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyLSTM/data_boucwen.mat -o data_boucwen.mat
python phylstm2.py mode=infer
```

=== "phylstm3"

``` sh
# linux
wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyLSTM/data_boucwen.mat
# windows
# curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyLSTM/data_boucwen.mat -o data_boucwen.mat
python phylstm3.py mode=infer
```



| 预训练模型 | 指标 |
|:--| :--|
| [phylstm2_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/phylstm/phylstm2_pretrained.pdparams) | loss(sup_valid): 0.00799 |
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18 changes: 18 additions & 0 deletions examples/phylstm/conf/phylstm2.yaml
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Expand Up @@ -48,3 +48,21 @@ 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/phylstm/phylstm2_pretrained.pdparams
export_path: ./inference/phylstm2
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: 10240
num_cpu_threads: 10
batch_size: 10240
18 changes: 18 additions & 0 deletions examples/phylstm/conf/phylstm3.yaml
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Expand Up @@ -48,3 +48,21 @@ 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/phylstm/phylstm3_pretrained.pdparams
export_path: ./inference/phylstm3
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: 10240
num_cpu_threads: 10
batch_size: 10240
132 changes: 131 additions & 1 deletion examples/phylstm/phylstm2.py
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Expand Up @@ -308,14 +308,144 @@ def evaluate(cfg: DictConfig):
solver.eval()


def export(cfg: DictConfig):
mat = scipy.io.loadmat(cfg.DATA_FILE_PATH)
u_data = mat["target_X_tf"]
u_data = u_data.reshape([u_data.shape[0], u_data.shape[1], 1])
u_all = u_data
eta_star = u_all[0:10]
eta = eta_star
# set model
model = ppsci.arch.DeepPhyLSTM(
cfg.MODEL.input_size,
eta.shape[2],
cfg.MODEL.hidden_size,
cfg.MODEL.model_type,
)
Comment on lines +319 to +324
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红框里的代码也需要加上
image

# initialize solver
solver = ppsci.solver.Solver(
model,
pretrained_model_path=cfg.INFER.pretrained_model_path,
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Suggested change
pretrained_model_path=cfg.INFER.pretrained_model_path,
cfg=cfg,

)
# export model
from paddle.static import InputSpec

input_spec = [
{key: InputSpec([None, 1], "float32", name=key) for key in model.input_keys},
]
Comment on lines +333 to +335
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这里的形状可以运行evaluate时，在DeepPhyLSTM.forward函数里打印输入的shape得到

solver.export(input_spec, cfg.INFER.export_path)


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

predictor = pinn_predictor.PINNPredictor(cfg)

mat = scipy.io.loadmat(cfg.DATA_FILE_PATH)
ag_data = mat["input_tf"] # ag, ad, av
u_data = mat["target_X_tf"]
ut_data = mat["target_Xd_tf"]
utt_data = mat["target_Xdd_tf"]
ag_data = ag_data.reshape([ag_data.shape[0], ag_data.shape[1], 1])
u_data = u_data.reshape([u_data.shape[0], u_data.shape[1], 1])
ut_data = ut_data.reshape([ut_data.shape[0], ut_data.shape[1], 1])
utt_data = utt_data.reshape([utt_data.shape[0], utt_data.shape[1], 1])

t = mat["time"]
dt = t[0, 1] - t[0, 0]

ag_all = ag_data
u_all = u_data
u_t_all = ut_data
u_tt_all = utt_data

# finite difference
N = u_data.shape[1]
phi1 = np.concatenate(
[
np.array([-3 / 2, 2, -1 / 2]),
np.zeros([N - 3]),
]
)
temp1 = np.concatenate([-1 / 2 * np.identity(N - 2), np.zeros([N - 2, 2])], axis=1)
temp2 = np.concatenate([np.zeros([N - 2, 2]), 1 / 2 * np.identity(N - 2)], axis=1)
phi2 = temp1 + temp2
phi3 = np.concatenate(
[
np.zeros([N - 3]),
np.array([1 / 2, -2, 3 / 2]),
]
)
phi_t0 = (
1
/ dt
* np.concatenate(
[
np.reshape(phi1, [1, phi1.shape[0]]),
phi2,
np.reshape(phi3, [1, phi3.shape[0]]),
],
axis=0,
)
)
phi_t0 = np.reshape(phi_t0, [1, N, N])

ag_star = ag_all[0:10]
eta_star = u_all[0:10]
eta_t_star = u_t_all[0:10]
eta_tt_star = u_tt_all[0:10]
ag_c_star = ag_all[0:50]
lift_star = -ag_c_star

eta = eta_star
ag = ag_star
lift = lift_star
eta_t = eta_t_star
eta_tt = eta_tt_star
ag_c = ag_c_star
g = -eta_tt - ag
phi_t = np.repeat(phi_t0, ag_c_star.shape[0], axis=0)

input_dict = {
"eta": eta,
"eta_t": eta_t,
"g": g,
"ag": ag,
"ag_c": ag_c,
"lift": lift,
"phi_t": phi_t,
}

output_dict = predictor.predict(input_dict, 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())
}

ppsci.visualize.save_vtu_from_dict(
"./phylstm2_pred.vtu",
{**input_dict, **output_dict},
input_dict.keys(),
cfg.MODEL.output_keys,
)


@hydra.main(version_base=None, config_path="./conf", config_name="phylstm2.yaml")
def main(cfg: DictConfig):
if cfg.mode == "train":
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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139 changes: 138 additions & 1 deletion examples/phylstm/phylstm3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -346,14 +346,151 @@ def evaluate(cfg: DictConfig):
solver.eval()


def export(cfg: DictConfig):
mat = scipy.io.loadmat(cfg.DATA_FILE_PATH)
u_data = mat["target_X_tf"]
u_data = u_data.reshape([u_data.shape[0], u_data.shape[1], 1])
u_all = u_data
eta_star = u_all[0:10]
eta = eta_star
# set model
model = ppsci.arch.DeepPhyLSTM(
cfg.MODEL.input_size,
eta.shape[2],
cfg.MODEL.hidden_size,
cfg.MODEL.model_type,
)
# 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 model.input_keys},
]
solver.export(input_spec, cfg.INFER.export_path)


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

predictor = pinn_predictor.PINNPredictor(cfg)

mat = scipy.io.loadmat(cfg.DATA_FILE_PATH)
t = mat["time"]
dt = 0.02
n1 = int(dt / 0.005)
t = t[::n1]

ag_data = mat["input_tf"][:, ::n1] # ag, ad, av
u_data = mat["target_X_tf"][:, ::n1]
ut_data = mat["target_Xd_tf"][:, ::n1]
utt_data = mat["target_Xdd_tf"][:, ::n1]
ag_data = ag_data.reshape([ag_data.shape[0], ag_data.shape[1], 1])
u_data = u_data.reshape([u_data.shape[0], u_data.shape[1], 1])
ut_data = ut_data.reshape([ut_data.shape[0], ut_data.shape[1], 1])
utt_data = utt_data.reshape([utt_data.shape[0], utt_data.shape[1], 1])

ag_pred = mat["input_pred_tf"][:, ::n1] # ag, ad, av
u_pred = mat["target_pred_X_tf"][:, ::n1]
ut_pred = mat["target_pred_Xd_tf"][:, ::n1]
utt_pred = mat["target_pred_Xdd_tf"][:, ::n1]
ag_pred = ag_pred.reshape([ag_pred.shape[0], ag_pred.shape[1], 1])
u_pred = u_pred.reshape([u_pred.shape[0], u_pred.shape[1], 1])
ut_pred = ut_pred.reshape([ut_pred.shape[0], ut_pred.shape[1], 1])
utt_pred = utt_pred.reshape([utt_pred.shape[0], utt_pred.shape[1], 1])

N = u_data.shape[1]
phi1 = np.concatenate(
[
np.array([-3 / 2, 2, -1 / 2]),
np.zeros([N - 3]),
]
)
temp1 = np.concatenate([-1 / 2 * np.identity(N - 2), np.zeros([N - 2, 2])], axis=1)
temp2 = np.concatenate([np.zeros([N - 2, 2]), 1 / 2 * np.identity(N - 2)], axis=1)
phi2 = temp1 + temp2
phi3 = np.concatenate(
[
np.zeros([N - 3]),
np.array([1 / 2, -2, 3 / 2]),
]
)
phi_t0 = (
1
/ dt
* np.concatenate(
[
np.reshape(phi1, [1, phi1.shape[0]]),
phi2,
np.reshape(phi3, [1, phi3.shape[0]]),
],
axis=0,
)
)
phi_t0 = np.reshape(phi_t0, [1, N, N])

ag_star = ag_data
eta_star = u_data
eta_t_star = ut_data
eta_tt_star = utt_data
ag_c_star = np.concatenate([ag_data, ag_pred[0:53]])
lift_star = -ag_c_star

eta = eta_star
ag = ag_star
lift = lift_star
eta_t = eta_t_star
eta_tt = eta_tt_star
g = -eta_tt - ag
ag_c = ag_c_star

phi_t = np.repeat(phi_t0, ag_c_star.shape[0], axis=0)

input_dict = {
"eta": eta,
"eta_t": eta_t,
"eta_tt": eta_tt,
"g": g,
"ag": ag,
"ag_c": ag_c,
"lift": lift,
"phi_t": phi_t,
}

output_dict = predictor.predict(input_dict, 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())
}

ppsci.visualize.save_vtu_from_dict(
"./phylstm3_pred.vtu",
{**input_dict, **output_dict},
input_dict.keys(),
cfg.MODEL.output_keys,
)


@hydra.main(version_base=None, config_path="./conf", config_name="phylstm3.yaml")
def main(cfg: DictConfig):
if cfg.mode == "train":
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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