# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os.path as osp
import hydra
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import paddle
from omegaconf import DictConfig
import ppsci
from ppsci.utils import logger
def generate_data(data_dir):
train_ini1 = np.load(osp.join(data_dir, "train_ini2.npy")).astype(
paddle.get_default_dtype()
)
train_iniv1 = np.load(osp.join(data_dir, "train_iniv2.npy")).astype(
paddle.get_default_dtype()
)
train_xb1 = np.load(osp.join(data_dir, "train_xb2.npy")).astype(
paddle.get_default_dtype()
)
train_vb1 = np.load(osp.join(data_dir, "train_vb2.npy")).astype(
paddle.get_default_dtype()
)
xnode = np.linspace(12.47, 12.66, 191).astype(paddle.get_default_dtype())
ynode = np.linspace(-1, -0.0031, 998).astype(paddle.get_default_dtype())
znode = np.linspace(4.61, 4.82, 211).astype(paddle.get_default_dtype())
x0_train = train_ini1[:, 0:1]
y0_train = train_ini1[:, 1:2]
z0_train = train_ini1[:, 2:3]
t0_train = np.zeros_like(train_ini1[:, 0:1]).astype(paddle.get_default_dtype())
u0_train = train_iniv1[:, 0:1]
v0_train = train_iniv1[:, 1:2]
w0_train = train_iniv1[:, 2:3]
xb_train = train_xb1[:, 0:1]
yb_train = train_xb1[:, 1:2]
zb_train = train_xb1[:, 2:3]
tb_train = train_xb1[:, 3:4]
ub_train = train_vb1[:, 0:1]
vb_train = train_vb1[:, 1:2]
wb_train = train_vb1[:, 2:3]
x_train1 = xnode.reshape(-1, 1)[np.random.choice(191, 100000, replace=True), :]
y_train1 = ynode.reshape(-1, 1)[np.random.choice(998, 100000, replace=True), :]
z_train1 = znode.reshape(-1, 1)[np.random.choice(211, 100000, replace=True), :]
x_train = np.tile(x_train1, (17, 1))
y_train = np.tile(y_train1, (17, 1))
z_train = np.tile(z_train1, (17, 1))
total_times1 = (np.array(list(range(17))) * 0.0065).astype(
paddle.get_default_dtype()
)
t_train1 = total_times1.repeat(100000)
t_train = t_train1.reshape(-1, 1)
# test data
test_x = np.load(osp.join(data_dir, "test43_l.npy")).astype(
paddle.get_default_dtype()
)
test_v = np.load(osp.join(data_dir, "test43_vp.npy")).astype(
paddle.get_default_dtype()
)
t = np.array([0.0065, 4 * 0.0065, 7 * 0.0065, 10 * 0.0065, 13 * 0.0065]).astype(
paddle.get_default_dtype()
)
t_star = np.tile(t.reshape(5, 1), (1, 3000)).reshape(-1, 1)
x_star = np.tile(test_x[:, 0:1], (5, 1))
y_star = np.tile(test_x[:, 1:2], (5, 1))
z_star = np.tile(test_x[:, 2:3], (5, 1))
u_star = test_v[:, 0:1]
v_star = test_v[:, 1:2]
w_star = test_v[:, 2:3]
p_star = test_v[:, 3:4]
return (
x_train,
y_train,
z_train,
t_train,
x0_train,
y0_train,
z0_train,
t0_train,
u0_train,
v0_train,
w0_train,
xb_train,
yb_train,
zb_train,
tb_train,
ub_train,
vb_train,
wb_train,
x_star,
y_star,
z_star,
t_star,
u_star,
v_star,
w_star,
p_star,
)
class Transform:
def __init__(self, lowb, upb) -> None:
self.lowb = {"x": lowb[0], "y": lowb[1], "z": lowb[2], "t": lowb[3]}
self.upb = {"x": upb[0], "y": upb[1], "z": upb[2], "t": upb[3]}
def input_trans(self, input_dict):
for key, v in input_dict.items():
v = 2.0 * (v - self.lowb[key]) / (self.upb[key] - self.lowb[key]) - 1.0
input_dict[key] = v
return input_dict
def train(cfg: DictConfig):
# set model
model = ppsci.arch.MLP(**cfg.MODEL)
# load data
(
x_train,
y_train,
z_train,
t_train,
x0_train,
y0_train,
z0_train,
t0_train,
u0_train,
v0_train,
w0_train,
xb_train,
yb_train,
zb_train,
tb_train,
ub_train,
vb_train,
wb_train,
x_star,
y_star,
z_star,
t_star,
u_star,
v_star,
w_star,
p_star,
) = generate_data(cfg.data_dir)
# normalization
Xb = np.concatenate([xb_train, yb_train, zb_train, tb_train], 1)
lowb = Xb.min(0) # minimal number in each column
upb = Xb.max(0)
trans = Transform(paddle.to_tensor(lowb), paddle.to_tensor(upb))
model.register_input_transform(trans.input_trans)
# set dataloader config
train_dataloader_cfg_b = {
"dataset": {
"name": "NamedArrayDataset",
"input": {"x": xb_train, "y": yb_train, "z": zb_train, "t": tb_train},
"label": {"u": ub_train, "v": vb_train, "w": wb_train},
},
"batch_size": cfg.nb_train,
"iters_per_epoch": cfg.TRAIN.lr_scheduler.iters_per_epoch,
"sampler": {
"name": "BatchSampler",
"drop_last": False,
"shuffle": True,
},
}
train_dataloader_cfg_ic = {
"dataset": {
"name": "NamedArrayDataset",
"input": {"x": x0_train, "y": y0_train, "z": z0_train, "t": t0_train},
"label": {"u": u0_train, "v": v0_train, "w": w0_train},
},
"batch_size": cfg.n0_train,
"iters_per_epoch": cfg.TRAIN.lr_scheduler.iters_per_epoch,
"sampler": {
"name": "BatchSampler",
"drop_last": False,
"shuffle": True,
},
}
valid_dataloader_cfg = {
"dataset": {
"name": "NamedArrayDataset",
"input": {"x": x_star, "y": y_star, "z": z_star, "t": t_star},
"label": {"u": u_star, "v": v_star, "w": w_star, "p": p_star},
},
"total_size": u_star.shape[0],
"batch_size": u_star.shape[0],
"sampler": {
"name": "BatchSampler",
"drop_last": False,
"shuffle": True,
},
}
geom = ppsci.geometry.PointCloud(
{"x": x_train, "y": y_train, "z": z_train, "t": t_train}, ("x", "y", "z", "t")
)
# supervised constraint s.t ||u-u_b||
sup_constraint_b = ppsci.constraint.SupervisedConstraint(
train_dataloader_cfg_b,
ppsci.loss.MSELoss("mean", cfg.alpha),
name="Sup_b",
)
# supervised constraint s.t ||u-u_0||
sup_constraint_0 = ppsci.constraint.SupervisedConstraint(
train_dataloader_cfg_ic,
ppsci.loss.MSELoss("mean", cfg.beta),
name="Sup_ic",
)
# set equation constarint s.t. ||F(u)||
equation = {
"NavierStokes": ppsci.equation.NavierStokes(
nu=1.0 / cfg.re, rho=1.0, dim=3, time=True
),
}
pde_constraint = ppsci.constraint.InteriorConstraint(
equation["NavierStokes"].equations,
{"continuity": 0, "momentum_x": 0, "momentum_y": 0, "momentum_z": 0},
geom,
{
"dataset": {"name": "NamedArrayDataset"},
"batch_size": cfg.ntrain,
"iters_per_epoch": cfg.TRAIN.lr_scheduler.iters_per_epoch,
"sampler": {
"name": "BatchSampler",
"drop_last": False,
"shuffle": True,
},
},
ppsci.loss.MSELoss("mean"),
name="EQ",
)
# wrap constraints
constraint = {
pde_constraint.name: pde_constraint,
sup_constraint_b.name: sup_constraint_b,
sup_constraint_0.name: sup_constraint_0,
}
residual_validator = ppsci.validate.SupervisedValidator(
valid_dataloader_cfg,
ppsci.loss.L2RelLoss(),
metric={"L2R": ppsci.metric.L2Rel()},
name="Residual",
)
# wrap validator
validator = {residual_validator.name: residual_validator}
# set optimizer
lr_scheduler = ppsci.optimizer.lr_scheduler.Piecewise(**cfg.TRAIN.lr_scheduler)()
optimizer = ppsci.optimizer.Adam(lr_scheduler)(model)
# initialize solver
solver = ppsci.solver.Solver(
model=model,
constraint=constraint,
output_dir=cfg.output_dir,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
epochs=cfg.epochs,
iters_per_epoch=cfg.TRAIN.lr_scheduler.iters_per_epoch,
log_freq=cfg.TRAIN.log_freq,
save_freq=cfg.TRAIN.save_freq,
eval_freq=cfg.TRAIN.eval_freq,
eval_during_train=True,
seed=cfg.seed,
equation=equation,
geom=geom,
validator=validator,
eval_with_no_grad=cfg.TRAIN.eval_with_no_grad,
)
# train model
solver.train()
# evaluate after finished training
solver.eval()
solver.plot_loss_history()
def evaluate(cfg: DictConfig):
# set model
model = ppsci.arch.MLP(**cfg.MODEL)
# test Data
test_x = np.load(osp.join(cfg.data_dir, "test43_l.npy")).astype(
paddle.get_default_dtype()
)
test_v = np.load(osp.join(cfg.data_dir, "test43_vp.npy")).astype(
paddle.get_default_dtype()
)
t = np.array([0.0065, 4 * 0.0065, 7 * 0.0065, 10 * 0.0065, 13 * 0.0065]).astype(
paddle.get_default_dtype()
)
t_star = paddle.to_tensor(np.tile(t.reshape(5, 1), (1, 3000)).reshape(-1, 1))
x_star = paddle.to_tensor(np.tile(test_x[:, 0:1], (5, 1)).reshape(-1, 1))
y_star = paddle.to_tensor(np.tile(test_x[:, 1:2], (5, 1)).reshape(-1, 1))
z_star = paddle.to_tensor(np.tile(test_x[:, 2:3], (5, 1)).reshape(-1, 1))
u_star = paddle.to_tensor(test_v[:, 0:1])
v_star = paddle.to_tensor(test_v[:, 1:2])
w_star = paddle.to_tensor(test_v[:, 2:3])
p_star = paddle.to_tensor(test_v[:, 3:4])
# wrap validator
ppsci.utils.load_pretrain(model, cfg.EVAL.pretrained_model_path)
# print the relative error
solution = model(
{
"x": x_star,
"y": y_star,
"z": z_star,
"t": t_star,
}
)
u_pred = solution["u"].reshape((5, -1))
v_pred = solution["v"].reshape((5, -1))
w_pred = solution["w"].reshape((5, -1))
p_pred = solution["p"].reshape((5, -1))
u_star = u_star.reshape((5, -1))
v_star = v_star.reshape((5, -1))
w_star = w_star.reshape((5, -1))
p_star = p_star.reshape((5, -1))
# NS equation can figure out pressure drop, need background pressure p_star.mean()
p_pred = p_pred - p_pred.mean() + p_star.mean()
u_error = paddle.linalg.norm(u_pred - u_star, axis=1) / np.linalg.norm(
u_star, axis=1
)
v_error = paddle.linalg.norm(v_pred - v_star, axis=1) / np.linalg.norm(
v_star, axis=1
)
w_error = paddle.linalg.norm(w_pred - w_star, axis=1) / np.linalg.norm(
w_star, axis=1
)
p_error = paddle.linalg.norm(p_pred - p_star, axis=1) / np.linalg.norm(
w_star, axis=1
)
t = np.array([0.0065, 4 * 0.0065, 7 * 0.0065, 10 * 0.0065, 13 * 0.0065])
plt.plot(t, np.array(u_error))
plt.plot(t, np.array(v_error))
plt.plot(t, np.array(w_error))
plt.plot(t, np.array(p_error))
plt.legend(["u_error", "v_error", "w_error", "p_error"])
plt.xlabel("t")
plt.ylabel("Relative l2 Error")
plt.title("Relative l2 Error, on test dataset")
plt.savefig(osp.join(cfg.output_dir, "error.jpg"))
logger.info("L2 error picture is saved")
grid_x, grid_y = np.mgrid[
x_star.min() : x_star.max() : 100j, y_star.min() : y_star.max() : 100j
].astype(paddle.get_default_dtype())
x_plot = paddle.to_tensor(grid_x.reshape(-1, 1))
y_plot = paddle.to_tensor(grid_y.reshape(-1, 1))
z_plot = paddle.to_tensor(z_star.min() * paddle.ones(y_plot.shape))
t_plot = paddle.to_tensor((t[-1]) * np.ones(x_plot.shape), paddle.float32)
sol = model({"x": x_plot, "y": y_plot, "z": z_plot, "t": t_plot})
fig, ax = plt.subplots(1, 4, figsize=(16, 4))
cmap = matplotlib.colormaps.get_cmap("jet")
ax[0].contourf(grid_x, grid_y, sol["u"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[0].set_title("u prediction")
ax[1].contourf(grid_x, grid_y, sol["v"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[1].set_title("v prediction")
ax[2].contourf(grid_x, grid_y, sol["w"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[2].set_title("w prediction")
ax[3].contourf(grid_x, grid_y, sol["p"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[3].set_title("p prediction")
norm = matplotlib.colors.Normalize(
vmin=sol["u"].min(), vmax=sol["u"].max()
) # set maximum and minimum
im = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
ax13 = fig.add_axes([0.125, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.325, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.525, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.725, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
plt.savefig(osp.join(cfg.output_dir, "z=0 plane"))
grid_y, grid_z = np.mgrid[
y_star.min() : y_star.max() : 100j, z_star.min() : z_star.max() : 100j
].astype(paddle.get_default_dtype())
z_plot = paddle.to_tensor(grid_z.reshape(-1, 1))
y_plot = paddle.to_tensor(grid_y.reshape(-1, 1))
x_plot = paddle.to_tensor(x_star.min() * paddle.ones(y_plot.shape))
t_plot = paddle.to_tensor((t[-1]) * np.ones(x_plot.shape), paddle.float32)
sol = model({"x": x_plot, "y": y_plot, "z": z_plot, "t": t_plot})
fig, ax = plt.subplots(1, 4, figsize=(16, 4))
cmap = matplotlib.colormaps.get_cmap("jet")
ax[0].contourf(grid_y, grid_z, sol["u"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[0].set_title("u prediction")
ax[1].contourf(grid_y, grid_z, sol["v"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[1].set_title("v prediction")
ax[2].contourf(grid_y, grid_z, sol["w"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[2].set_title("w prediction")
ax[3].contourf(grid_y, grid_z, sol["p"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[3].set_title("p prediction")
norm = matplotlib.colors.Normalize(
vmin=sol["u"].min(), vmax=sol["u"].max()
) # set maximum and minimum
im = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
ax13 = fig.add_axes([0.125, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.325, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.525, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.725, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
plt.savefig(osp.join(cfg.output_dir, "x=0 plane"))
def export(cfg: DictConfig):
from paddle.static import InputSpec
# set models
model = ppsci.arch.MLP(**cfg.MODEL)
# load pretrained model
solver = ppsci.solver.Solver(
model=model, pretrained_model_path=cfg.INFER.pretrained_model_path
)
# export models
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
# set model predictor
predictor = pinn_predictor.PINNPredictor(cfg)
# infer Data
test_x = np.load(osp.join(cfg.data_dir, "test43_l.npy")).astype(np.float32)
test_v = np.load(osp.join(cfg.data_dir, "test43_vp.npy")).astype(np.float32)
t = np.array([0.0065, 4 * 0.0065, 7 * 0.0065, 10 * 0.0065, 13 * 0.0065]).astype(
np.float32
)
t_star = np.tile(t.reshape(5, 1), (1, 3000)).reshape(-1, 1)
x_star = np.tile(test_x[:, 0:1], (5, 1)).reshape(-1, 1)
y_star = np.tile(test_x[:, 1:2], (5, 1)).reshape(-1, 1)
z_star = np.tile(test_x[:, 2:3], (5, 1)).reshape(-1, 1)
u_star = test_v[:, 0:1]
v_star = test_v[:, 1:2]
w_star = test_v[:, 2:3]
p_star = test_v[:, 3:4]
pred = predictor.predict(
{
"x": x_star,
"y": y_star,
"z": z_star,
"t": t_star,
},
cfg.INFER.batch_size,
)
pred = {
store_key: pred[infer_key]
for store_key, infer_key in zip(cfg.INFER.output_keys, pred.keys())
}
u_pred = pred["u"].reshape((5, -1))
v_pred = pred["v"].reshape((5, -1))
w_pred = pred["w"].reshape((5, -1))
p_pred = pred["p"].reshape((5, -1))
u_star = u_star.reshape((5, -1))
v_star = v_star.reshape((5, -1))
w_star = w_star.reshape((5, -1))
p_star = p_star.reshape((5, -1))
# NS equation can figure out pressure drop, need background pressure p_star.mean()
p_pred = p_pred - p_pred.mean() + p_star.mean()
u_error = np.linalg.norm(u_pred - u_star, axis=1) / np.linalg.norm(u_star, axis=1)
v_error = np.linalg.norm(v_pred - v_star, axis=1) / np.linalg.norm(v_star, axis=1)
w_error = np.linalg.norm(w_pred - w_star, axis=1) / np.linalg.norm(w_star, axis=1)
p_error = np.linalg.norm(p_pred - p_star, axis=1) / np.linalg.norm(w_star, axis=1)
t = np.array([0.0065, 4 * 0.0065, 7 * 0.0065, 10 * 0.0065, 13 * 0.0065])
plt.plot(t, np.array(u_error))
plt.plot(t, np.array(v_error))
plt.plot(t, np.array(w_error))
plt.plot(t, np.array(p_error))
plt.legend(["u_error", "v_error", "w_error", "p_error"])
plt.xlabel("t")
plt.ylabel("Relative l2 Error")
plt.title("Relative l2 Error, on test dataset")
plt.savefig(osp.join(cfg.output_dir, "error.jpg"))
grid_x, grid_y = np.mgrid[
x_star.min() : x_star.max() : 100j, y_star.min() : y_star.max() : 100j
].astype(np.float32)
x_plot = grid_x.reshape(-1, 1)
y_plot = grid_y.reshape(-1, 1)
z_plot = (z_star.min() * np.ones(y_plot.shape)).astype(np.float32)
t_plot = ((t[-1]) * np.ones(x_plot.shape)).astype(np.float32)
sol = predictor.predict(
{"x": x_plot, "y": y_plot, "z": z_plot, "t": t_plot}, cfg.INFER.batch_size
)
sol = {
store_key: sol[infer_key]
for store_key, infer_key in zip(cfg.INFER.output_keys, sol.keys())
}
fig, ax = plt.subplots(1, 4, figsize=(16, 4))
cmap = matplotlib.colormaps.get_cmap("jet")
ax[0].contourf(grid_x, grid_y, sol["u"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[0].set_title("u prediction")
ax[1].contourf(grid_x, grid_y, sol["v"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[1].set_title("v prediction")
ax[2].contourf(grid_x, grid_y, sol["w"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[2].set_title("w prediction")
ax[3].contourf(grid_x, grid_y, sol["p"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[3].set_title("p prediction")
norm = matplotlib.colors.Normalize(
vmin=sol["u"].min(), vmax=sol["u"].max()
) # set maximum and minimum
im = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
ax13 = fig.add_axes([0.125, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.325, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.525, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.725, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
plt.savefig(osp.join(cfg.output_dir, "z=0 plane"))
grid_y, grid_z = np.mgrid[
y_star.min() : y_star.max() : 100j, z_star.min() : z_star.max() : 100j
].astype(np.float32)
z_plot = grid_z.reshape(-1, 1)
y_plot = grid_y.reshape(-1, 1)
x_plot = (x_star.min() * np.ones(y_plot.shape)).astype(np.float32)
t_plot = ((t[-1]) * np.ones(x_plot.shape)).astype(np.float32)
sol = predictor.predict(
{"x": x_plot, "y": y_plot, "z": z_plot, "t": t_plot}, cfg.INFER.batch_size
)
sol = {
store_key: sol[infer_key]
for store_key, infer_key in zip(cfg.INFER.output_keys, sol.keys())
}
fig, ax = plt.subplots(1, 4, figsize=(16, 4))
cmap = matplotlib.colormaps.get_cmap("jet")
ax[0].contourf(grid_y, grid_z, sol["u"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[0].set_title("u prediction")
ax[1].contourf(grid_y, grid_z, sol["v"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[1].set_title("v prediction")
ax[2].contourf(grid_y, grid_z, sol["w"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[2].set_title("w prediction")
ax[3].contourf(grid_y, grid_z, sol["p"].reshape(grid_x.shape), levels=50, cmap=cmap)
ax[3].set_title("p prediction")
norm = matplotlib.colors.Normalize(
vmin=sol["u"].min(), vmax=sol["u"].max()
) # set maximum and minimum
im = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
ax13 = fig.add_axes([0.125, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.325, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.525, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
ax13 = fig.add_axes([0.725, 0.0, 0.175, 0.02])
plt.colorbar(im, cax=ax13, orientation="horizontal")
plt.savefig(osp.join(cfg.output_dir, "x=0 plane"))
@hydra.main(version_base=None, config_path="./conf", config_name="VP_NSFNet4.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', 'export', 'infer'], but got '{cfg.mode}'"
)
if __name__ == "__main__":
main()
