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【PPSCI Export&Infer No.30】heat_exchanger (PaddlePaddle#892)
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* 【PPSCI Export&Infer No.30】heat_exchanger

* fix codestyle bug

* update examples/heat_exchanger/heat_exchanger.py

* fix codestyle bugs

* Update heat_exchanger.py

Fix and simplify code

---------

Co-authored-by: HydrogenSulfate <[email protected]>
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@wufei2 @HydrogenSulfate
wufei2 and HydrogenSulfate authored May 13, 2024
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12 changes: 12 additions & 0 deletions docs/zh/examples/heat_exchanger.md
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Expand Up @@ -12,6 +12,18 @@
python heat_exchanger.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/HEDeepONet/HEDeepONet_pretrained.pdparams
```

=== "模型导出命令"

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

=== "模型推理命令"

``` sh
python heat_exchanger.py mode=infer
```

| 预训练模型 | 指标 |
|:--| :--|
| [heat_exchanger_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/HEDeepONet/HEDeepONet_pretrained.pdparams) | The L2 norm error between the actual heat exchanger efficiency and the predicted heat exchanger efficiency: 0.02087<br>MSE.heat_boundary(interior_mse): 0.52005<br>MSE.cold_boundary(interior_mse): 0.16590<br>MSE.wall(interior_mse): 0.01203 |
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19 changes: 19 additions & 0 deletions examples/heat_exchanger/conf/heat_exchanger.yaml
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Expand Up @@ -90,3 +90,22 @@ EVAL:
qm_h: 1
qm_c: 1
eta_true: 0.5

# inference settings
INFER:
pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/HEDeepONet/HEDeepONet_pretrained.pdparams
export_path: ./inference/ldc2d_steady_Re10
pdmodel_path: ${INFER.export_path}.pdmodel
pdiparams_path: ${INFER.export_path}.pdiparams
onnx_path: ${INFER.export_path}.onnx
device: gpu
engine: native
precision: fp32
ir_optim: true
min_subgraph_size: 5
gpu_mem: 2000
gpu_id: 0
max_batch_size: 1000
num_cpu_threads: 10
batch_size: 1000
input_keys: ['qm_h','qm_c',"x",'t']
140 changes: 72 additions & 68 deletions examples/heat_exchanger/heat_exchanger.py
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Expand Up @@ -373,65 +373,8 @@ def train(cfg: DictConfig):
# visualize prediction after finished training
visu_input["qm_c"] = np.full_like(visu_input["qm_c"], cfg.qm_h)
visu_input["qm_h"] = np.full_like(visu_input["qm_c"], cfg.qm_c)
pred = solver.predict(visu_input)
x = visu_input["x"][: cfg.NPOINT]
# plot temperature of heat boundary
plt.figure()
y = np.full_like(pred["T_h"][: cfg.NPOINT].numpy(), cfg.T_hin)
plt.plot(x, y, label="t = 0.0 s")
for i in range(10):
y = pred["T_h"][cfg.NPOINT * i * 2 : cfg.NPOINT * (i * 2 + 1)].numpy()
plt.plot(x, y, label=f"t = {(i+1)*0.1:,.1f} s")
plt.xlabel("A")
plt.ylabel(r"$T_h$")
plt.legend()
plt.grid()
plt.savefig("T_h.png")
# plot temperature of cold boundary
plt.figure()
y = np.full_like(pred["T_c"][: cfg.NPOINT].numpy(), cfg.T_cin)
plt.plot(x, y, label="t = 0.0 s")
for i in range(10):
y = pred["T_c"][cfg.NPOINT * i * 2 : cfg.NPOINT * (i * 2 + 1)].numpy()
plt.plot(x, y, label=f"t = {(i+1)*0.1:,.1f} s")
plt.xlabel("A")
plt.ylabel(r"$T_c$")
plt.legend()
plt.grid()
plt.savefig("T_c.png")
# plot temperature of wall
plt.figure()
y = np.full_like(pred["T_w"][: cfg.NPOINT].numpy(), cfg.T_win)
plt.plot(x, y, label="t = 0.0 s")
for i in range(10):
y = pred["T_w"][cfg.NPOINT * i * 2 : cfg.NPOINT * (i * 2 + 1)].numpy()
plt.plot(x, y, label=f"t = {(i+1)*0.1:,.1f} s")
plt.xlabel("A")
plt.ylabel(r"$T_w$")
plt.legend()
plt.grid()
plt.savefig("T_w.png")
# plot the heat exchanger efficiency as a function of time.
plt.figure()
qm_min = np.min((visu_input["qm_h"][0], visu_input["qm_c"][0]))
eta = (
visu_input["qm_h"][0]
* (pred["T_h"][:: cfg.NPOINT] - pred["T_h"][cfg.NPOINT - 1 :: cfg.NPOINT])
/ (
qm_min
* (pred["T_h"][:: cfg.NPOINT] - pred["T_c"][cfg.NPOINT - 1 :: cfg.NPOINT])
)
).numpy()
x = list(range(1, cfg.NTIME + 1))
plt.plot(x, eta)
plt.xlabel("time")
plt.ylabel(r"$\eta$")
plt.grid()
plt.savefig("eta.png")
error = np.square(eta[-1] - cfg.eta_true)
logger.info(
f"The L2 norm error between the actual heat exchanger efficiency and the predicted heat exchanger efficiency is {error}"
)
pred = solver.predict(visu_input, return_numpy=True)
plot(visu_input, pred, cfg)


def evaluate(cfg: DictConfig):
Expand Down Expand Up @@ -593,14 +536,69 @@ def evaluate(cfg: DictConfig):
# visualize prediction after finished training
visu_input["qm_c"] = np.full_like(visu_input["qm_c"], cfg.qm_h)
visu_input["qm_h"] = np.full_like(visu_input["qm_c"], cfg.qm_c)
pred = solver.predict(visu_input)
pred = solver.predict(visu_input, return_numpy=True)
plot(visu_input, pred, cfg)


def export(cfg: DictConfig):
# set model
model = ppsci.arch.HEDeepONets(**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 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)

# set time-geometry
timestamps = np.linspace(0.0, 2, cfg.NTIME + 1, endpoint=True)
geom = {
"time_rect": ppsci.geometry.TimeXGeometry(
ppsci.geometry.TimeDomain(0.0, 1, timestamps=timestamps),
ppsci.geometry.Interval(0, cfg.DL),
)
}
input_dict = geom["time_rect"].sample_interior(cfg.NPOINT * cfg.NTIME, evenly=True)
test_h = np.random.rand(1).reshape([-1, 1]).astype("float32")
test_c = np.random.rand(1).reshape([-1, 1]).astype("float32")
# rearrange train data and eval data
input_dict["qm_h"] = np.tile(test_h, (cfg.NPOINT * cfg.NTIME, 1))
input_dict["qm_c"] = np.tile(test_c, (cfg.NPOINT * cfg.NTIME, 1))
input_dict["qm_c"] = np.full_like(input_dict["qm_c"], cfg.qm_h)
input_dict["qm_h"] = np.full_like(input_dict["qm_c"], cfg.qm_c)
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())
}
plot(input_dict, output_dict, cfg)


def plot(visu_input, pred, cfg: DictConfig):
x = visu_input["x"][: cfg.NPOINT]
# plot temperature of heat boundary
plt.figure()
y = np.full_like(pred["T_h"][: cfg.NPOINT].numpy(), cfg.T_hin)
y = np.full_like(pred["T_h"][: cfg.NPOINT], cfg.T_hin)
plt.plot(x, y, label="t = 0.0 s")
for i in range(10):
y = pred["T_h"][cfg.NPOINT * i * 2 : cfg.NPOINT * (i * 2 + 1)].numpy()
y = pred["T_h"][cfg.NPOINT * i * 2 : cfg.NPOINT * (i * 2 + 1)]
plt.plot(x, y, label=f"t = {(i+1)*0.1:,.1f} s")
plt.xlabel("A")
plt.ylabel(r"$T_h$")
Expand All @@ -609,10 +607,10 @@ def evaluate(cfg: DictConfig):
plt.savefig("T_h.png")
# plot temperature of cold boundary
plt.figure()
y = np.full_like(pred["T_c"][: cfg.NPOINT].numpy(), cfg.T_cin)
y = np.full_like(pred["T_c"][: cfg.NPOINT], cfg.T_cin)
plt.plot(x, y, label="t = 0.0 s")
for i in range(10):
y = pred["T_c"][cfg.NPOINT * i * 2 : cfg.NPOINT * (i * 2 + 1)].numpy()
y = pred["T_c"][cfg.NPOINT * i * 2 : cfg.NPOINT * (i * 2 + 1)]
plt.plot(x, y, label=f"t = {(i+1)*0.1:,.1f} s")
plt.xlabel("A")
plt.ylabel(r"$T_c$")
Expand All @@ -621,10 +619,10 @@ def evaluate(cfg: DictConfig):
plt.savefig("T_c.png")
# plot temperature of wall
plt.figure()
y = np.full_like(pred["T_w"][: cfg.NPOINT].numpy(), cfg.T_win)
y = np.full_like(pred["T_w"][: cfg.NPOINT], cfg.T_win)
plt.plot(x, y, label="t = 0.0 s")
for i in range(10):
y = pred["T_w"][cfg.NPOINT * i * 2 : cfg.NPOINT * (i * 2 + 1)].numpy()
y = pred["T_w"][cfg.NPOINT * i * 2 : cfg.NPOINT * (i * 2 + 1)]
plt.plot(x, y, label=f"t = {(i+1)*0.1:,.1f} s")
plt.xlabel("A")
plt.ylabel(r"$T_w$")
Expand All @@ -641,7 +639,7 @@ def evaluate(cfg: DictConfig):
qm_min
* (pred["T_h"][:: cfg.NPOINT] - pred["T_c"][cfg.NPOINT - 1 :: cfg.NPOINT])
)
).numpy()
)
x = list(range(1, cfg.NTIME + 1))
plt.plot(x, eta)
plt.xlabel("time")
Expand All @@ -660,8 +658,14 @@ 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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