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May 16, 2024
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[Upadte]update plotting of hpinn's inference #903

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[Upadte]update plotting of hpinn's inference
lijialin03 May 16, 2024
6cbcda1
update1
lijialin03 May 16, 2024
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1 change: 0 additions & 1 deletion docs/zh/examples/hpinns.md
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Expand Up @@ -45,7 +45,6 @@
```



| 预训练模型 | 指标 |
|:--| :--|
| [hpinns_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/hPINNs/hpinns_pretrained.pdparams) | loss(opt_sup): 0.05352<br>MSE.eval_metric(opt_sup): 0.00002<br>loss(val_sup): 0.02205<br>MSE.eval_metric(val_sup): 0.00001 |
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17 changes: 12 additions & 5 deletions examples/hpinns/holography.py
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Expand Up @@ -460,12 +460,19 @@ def inference(cfg: DictConfig):
for store_key, infer_key in zip(cfg.INFER.output_keys, output_dict.keys())
}

ppsci.visualize.save_vtu_from_dict(
"./hpinns_pred.vtu",
{**input_dict, **output_dict},
input_dict.keys(),
cfg.INFER.output_keys,
# plotting E and eps
N = ((func_module.l_BOX[1] - func_module.l_BOX[0]) / 0.05).astype(int)
input_eval = np.stack((input_dict["x"], input_dict["y"]), axis=-1).reshape(
N[0], N[1], 2
)
e_re = output_dict["e_re"].reshape(N[0], N[1])
e_im = output_dict["e_im"].reshape(N[0], N[1])
eps = output_dict["eps"].reshape(N[0], N[1])
v_visual = e_re**2 + e_im**2
field_visual = np.stack((v_visual, eps), axis=-1)
plot_module.field_name = ["Fig7_E", "Fig7_eps"]
plot_module.FIGNAME = "hpinns_pred"
plot_module.plot_field_holo(input_eval, field_visual)


@hydra.main(version_base=None, config_path="./conf", config_name="hpinns.yaml")
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11 changes: 6 additions & 5 deletions examples/hpinns/plotting.py
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Expand Up @@ -20,6 +20,7 @@
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional

import functions as func_module
import matplotlib.pyplot as plt
Expand Down Expand Up @@ -114,16 +115,16 @@ def prepare_data(solver: ppsci.solver.Solver, expr_dict: Dict[str, Callable]):
def plot_field_holo(
coord_visual: np.ndarray,
field_visual: np.ndarray,
coord_lambda: np.ndarray,
field_lambda: np.ndarray,
coord_lambda: Optional[np.ndarray] = None,
field_lambda: Optional[np.ndarray] = None,
):
"""Plot fields of of holography example.

Args:
coord_visual (np.ndarray): The coord of epsilon and |E|**2.
field_visual (np.ndarray): The filed of epsilon and |E|**2.
coord_lambda (np.ndarray): The coord of lambda.
field_lambda (np.ndarray): The filed of lambda.
coord_lambda (Optional[np.ndarray], optional): The coord of lambda. Defaults to None.
field_lambda (Optional[np.ndarray], optional): The filed of lambda. Defaults to None.
"""
fmin, fmax = np.array([0, 1.0]), np.array([0.6, 12])
cmin, cmax = coord_visual.min(axis=(0, 1)), coord_visual.max(axis=(0, 1))
Expand Down Expand Up @@ -168,7 +169,7 @@ def plot_field_holo(
cb = plt.colorbar()
plt.axis((emin[0], emax[0], emin[1], emax[1]))
plt.clim(vmin=fmin[fi], vmax=fmax[fi])
else:
elif coord_lambda is not None and field_lambda is not None:
# Fig_6C_lambda_
plt.figure(fi * 100 + 101, figsize=(8, 6))
plt.clf()
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