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Modify torchFunction.H and update examples/df0DFoam/zeroD_cubicReactor/H2/pytorchIntegrator #364

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Merged
maorz1998 merged 1 commit into from
Oct 12, 2023
Merged

Modify torchFunction.H and update examples/df0DFoam/zeroD_cubicReactor/H2/pytorchIntegrator #364

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Binary file added examples/df0DFoam/zeroD_cubicReactor/H2/pytorchIntegrator/data_in_mean.npy
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161 changes: 83 additions & 78 deletions examples/df0DFoam/zeroD_cubicReactor/H2/pytorchIntegrator/inference.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
print ("Entering the first line")
from builtins import Exception, print
from calendar import prcal
import torch
Expand All @@ -11,7 +10,7 @@
import torch.profiler
import os

print("Entering Inference")

torch.set_printoptions(precision=10)


Expand All @@ -31,23 +30,21 @@ def json2Parser(json_path):
args = json.load(f)
return edict(args)


class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
neurons = layers
self.depth = len(neurons) - 1
self.actfun = MyGELU()
self.layers = []
for i in range(self.depth - 1):
self.layers.append(torch.nn.Linear(neurons[i], neurons[i + 1]))
self.layers.append(self.actfun)
self.layers.append(torch.nn.Linear(neurons[-2], neurons[-1])) # last layer
self.fc = torch.nn.Sequential(*self.layers)
class NN_MLP(torch.nn.Module):
def __init__(self, layer_info):
super(NN_MLP, self).__init__()
self.net = torch.nn.Sequential()
n = len(layer_info) - 1
for i in range(n - 1):
self.net.add_module('linear_layer_%d' %(i), torch.nn.Linear(layer_info[i], layer_info[i + 1]))
self.net.add_module('gelu_layer_%d' %(i), torch.nn.GELU())
if i <= 2:
self.net.add_module('batch_norm_%d' %(i), torch.nn.BatchNorm1d(layer_info[i + 1]))
self.net.add_module('linear_layer_%d' %(n - 1), torch.nn.Linear(layer_info[n - 1], layer_info[n]))

def forward(self, x):
x = self.fc(x)
return x
return self.net(x)

try:
#load variables from constant/CanteraTorchProperties
path_r = r"./constant/CanteraTorchProperties"
Expand Down Expand Up @@ -88,56 +85,57 @@ def forward(self, x):
#glbal variable will only init once when called interperter
#load parameters from json

norm0 = json2Parser(str(model1Path+"norm.json"))
norm1 = json2Parser(str(model2Path+"norm.json"))
norm2 = json2Parser(str(model3Path+"norm.json"))
setting0 = json2Parser(str(model1Path+"settings.json"))
lamda = setting0.power_transform
delta_t = setting0.delta_t
dim = setting0.dim
layers = setting0.layers
lamda = 0.1
delta_t = 1e-06
dim = 9
#layers = setting0.layers


Xmu0 = np.load('data_in_mean.npy')
Xstd0 = np.load('data_in_std.npy')
Ymu0 = np.load('data_target_mean.npy')
Ystd0 = np.load('data_target_std.npy')

Xmu0 = torch.tensor(Xmu0).unsqueeze(0).to(device=device)
Xstd0 = torch.tensor(Xstd0).unsqueeze(0).to(device=device)
Ymu0 = torch.tensor(Ymu0).unsqueeze(0).to(device=device)
Ystd0 = torch.tensor(Ystd0).unsqueeze(0).to(device=device)

Xmu1 = Xmu0
Xstd1 = Xstd0
Ymu1 = Ymu0
Ystd1 = Ystd0

Xmu2 = Xmu0
Xstd2 = Xstd0
Ymu2 = Ymu0
Ystd2 = Ystd0

Xmu0 = torch.tensor(norm0.input_mean).unsqueeze(0).to(device=device)
Xstd0 = torch.tensor(norm0.input_std).unsqueeze(0).to(device=device)
Ymu0 = torch.tensor(norm0.label_mean).unsqueeze(0).to(device=device)
Ystd0 = torch.tensor(norm0.label_std).unsqueeze(0).to(device=device)
#load model
layers = [9, 6400, 3200, 1600, 800, 400, 6]

Xmu1 = torch.tensor(norm1.input_mean).unsqueeze(0).to(device=device)
Xstd1 = torch.tensor(norm1.input_std).unsqueeze(0).to(device=device)
Ymu1 = torch.tensor(norm1.label_mean).unsqueeze(0).to(device=device)
Ystd1 = torch.tensor(norm1.label_std).unsqueeze(0).to(device=device)

Xmu2 = torch.tensor(norm2.input_mean).unsqueeze(0).to(device=device)
Xstd2 = torch.tensor(norm2.input_std).unsqueeze(0).to(device=device)
Ymu2 = torch.tensor(norm2.label_mean).unsqueeze(0).to(device=device)
Ystd2 = torch.tensor(norm2.label_std).unsqueeze(0).to(device=device)
model0= NN_MLP(layers)
model1= NN_MLP(layers)
model2= NN_MLP(layers)

#load model
model0 = Net()
model1 = Net()
model2 = Net()

path_list=os.listdir(model1Path)
for filename in path_list:
if os.path.splitext(filename)[1] == '.pt':
modelname = filename


if torch.cuda.is_available()==False:
check_point0 = torch.load(str(model1Path+modelname), map_location='cpu')
check_point1 = torch.load(str(model2Path+modelname), map_location='cpu')
check_point2 = torch.load(str(model3Path+modelname), map_location='cpu')
else:
check_point0 = torch.load(str(model1Path+modelname))
check_point1 = torch.load(str(model2Path+modelname))
check_point2 = torch.load(str(model3Path+modelname))

model0.load_state_dict(check_point0)
model1.load_state_dict(check_point1)
model2.load_state_dict(check_point2)
state_dict = (torch.load('Temporary_Chemical.pt'))['state_dict']

new_state_dict = {}
for k, v in state_dict.items():
name = k[7:]
new_state_dict[name] = v
model0.load_state_dict(new_state_dict)
#model0.load_state_dict(state_dict)

model1 = model0
model2 = model0

model0.eval()
model0.to(device=device)
model1.eval()
model1.to(device=device)
model2.eval()
model2.to(device=device)

if len(device_ids) > 1:
Expand All @@ -152,67 +150,74 @@ def inference(vec0, vec1, vec2):
'''
use model to inference
'''
#args = np.reshape(args, (-1, 9)) #reshape to formed size
#vec0 = np.reshape(vec0, (-1, 24))
#vec1 = np.reshape(vec1, (-1, 24))
#vec2 = np.reshape(vec2, (-1, 24))
vec0 = np.reshape(vec0, (-1, 10))
vec0 = np.reshape(vec0, (-1, 10)) # T, P, Yi(7), Rho
vec1 = np.reshape(vec1, (-1, 10))
vec2 = np.reshape(vec2, (-1, 10))
vec0[:,1] *= 101325
vec1[:,1] *= 101325
vec2[:,1] *= 101325

try:
with torch.no_grad():
input0_ = torch.from_numpy(vec0).double().to(device=device) #cast ndarray to torch tensor
input1_ = torch.from_numpy(vec1).double().to(device=device) #cast ndarray to torch tensor
input2_ = torch.from_numpy(vec2).double().to(device=device) #cast ndarray to torch tensor


# pre_processing
rho0 = input0_[:, -1].unsqueeze(1)
input0_Y = input0_[:, 2:-1].clone()
input0_bct = input0_[:, 0:-1]
input0_bct[:, 2:] = (input0_bct[:, 2:]**(lamda) - 1) / lamda #BCT
input0_normalized = (input0_bct - Xmu0) / Xstd0
# input0_normalized[:, -1] = 0 #set Y_AR to 0
input0_normalized = (input0_bct - Xmu0) / Xstd0 #DimXmu0 = 9， DimXstd0 = 9， input0_bct =
input0_normalized = input0_normalized.float()


rho1 = input1_[:, -1].unsqueeze(1)
input1_Y = input1_[:, 2:-1].clone()
input1_bct = input1_[:, 0:-1]
input1_bct[:, 2:] = (input1_bct[:, 2:]**(lamda) - 1) / lamda #BCT
input1_normalized = (input1_bct - Xmu1) / Xstd1
# input1_normalized[:, -1] = 0 #set Y_AR to 0
input1_normalized = input1_normalized.float()

rho2 = input2_[:, -1].unsqueeze(1)
input2_Y = input2_[:, 2:-1].clone()
input2_bct = input2_[:, 0:-1]
input2_bct[:, 2:] = (input2_bct[:, 2:]**(lamda) - 1) / lamda #BCT
input2_normalized = (input2_bct - Xmu2) / Xstd2
# input2_normalized[:, -1] = 0 #set Y_AR to 0
input2_normalized = input2_normalized.float()



#inference

output0_normalized = model0(input0_normalized)
output1_normalized = model1(input1_normalized)
output2_normalized = model2(input2_normalized)



# post_processing
output0_bct = (output0_normalized * Ystd0 + Ymu0) * delta_t + input0_bct
output0_Y = (lamda * output0_bct[:, 2:] + 1)**(1 / lamda)
#output0_bct = (output0_normalized * Ystd0 + Ymu0) * delta_t + input0_bct
output0_bct = output0_normalized * Ystd0 + Ymu0 + input0_bct[:, 2:-1]
output0_Y = input0_Y.clone()
output0_Y[:, :-1] = (lamda * output0_bct + 1)**(1 / lamda)
output0_Y = output0_Y / torch.sum(input=output0_Y, dim=1, keepdim=True)
output0 = (output0_Y - input0_Y) * rho0 / delta_t
output0 = output0.cpu().numpy()

output1_bct = (output1_normalized * Ystd1 + Ymu1) * delta_t + input1_bct
output1_Y = (lamda * output1_bct[:, 2:] + 1)**(1 / lamda)

output1_bct = output1_normalized * Ystd1 + Ymu1 + input1_bct[:, 2:-1]
output1_Y = input1_Y.clone()
output1_Y[:, :-1] = (lamda * output1_bct + 1)**(1 / lamda)
output1_Y = output1_Y / torch.sum(input=output1_Y, dim=1, keepdim=True)
output1 = (output1_Y - input1_Y) * rho1 / delta_t
output1 = (output1_Y - input1_Y) * rho1 / delta_t
output1 = output1.cpu().numpy()

output2_bct = (output2_normalized * Ystd2 + Ymu2) * delta_t + input2_bct
output2_Y = (lamda * output2_bct[:, 2:] + 1)**(1 / lamda)
output2_bct = output2_normalized * Ystd2 + Ymu2 + input2_bct[:, 2:-1]
output2_Y = input2_Y.clone()
output2_Y[:, :-1] = (lamda * output2_bct + 1)**(1 / lamda)
output2_Y = output2_Y / torch.sum(input=output2_Y, dim=1, keepdim=True)
output2 = (output2_Y - input2_Y) * rho2 / delta_t
output2 = output2_Y - output2_Y
output2 = output2.cpu().numpy()

result = np.append(output0, output1, axis=0)
Expand Down
1 change: 0 additions & 1 deletion examples/df0DFoam/zeroD_cubicReactor/H2/pytorchIntegrator/mechanisms
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65 changes: 15 additions & 50 deletions src/dfChemistryModel/torchFunctions.H
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
template <class ThermoType>
template<class DeltaTType>

void Foam::dfChemistryModel<ThermoType>::getGPUProblems
(
const DeltaTType &deltaT,
Expand All @@ -18,15 +19,15 @@ void Foam::dfChemistryModel<ThermoType>::getGPUProblems
scalar rhoi = rho_[cellI];

// if T < 700, set RR=0
// if (T_[cellI] < 700)
// {
// Qdot_[cellI] = 0;
// for (int i = 0; i < mixture_.nSpecies(); i++)
// {
// RR_[i][cellI] = 0.0;
// }
// continue;
// }
if (T_[cellI] < 700)
{
Qdot_[cellI] = 0;
for (int i = 0; i < mixture_.nSpecies(); i++)
{
RR_[i][cellI] = 0.0;
}
continue;
}

// set problems
GpuProblem problem(mixture_.nSpecies());
Expand All @@ -40,46 +41,10 @@ void Foam::dfChemistryModel<ThermoType>::getGPUProblems
}
problem.rhoi = rhoi;

// choose DNN module
if (((Qdot_[cellI] < 3e7) && (T_[cellI] < 2000) && ( T_[cellI] >= 700)) || (T_[cellI] < 700))//choose1
{
// if use CVODE
// ode_problem.Y = problem.Y;
// ode_problem.Ti = Ti;
// ode_problem.pi = pi;
// ode_problem.rhoi = rhoi;
// ode_problem.deltaT = deltaT[cellI];
// ode_problem.cpuTime = cpuTimes_[cellI];
// ode_problem.cellid = cellI;
// if (!(Pstream::myProcNo() % cores_)) // submaster
// {
// ode_problem.local = false;
// }
// CPUproblemList.append(ode_problem);

// selectDNN_[cellI]=0;
// continue;

// if use DNN
problem.DNNid = 0;
GPUproblemList.append(problem);
continue;
}
if(((Qdot_[cellI] >= 3e7) && (T_[cellI] < 2000)&&(T_[cellI] >= 700))||((Qdot_[cellI] > 7e8) && T_[cellI] > 2000)) //choose2
{
problem.DNNid = 1;
GPUproblemList.append(problem);

selectDNN_[cellI]=1;
continue;
}
if ((Qdot_[cellI] < 7e8) && (T_[cellI] >= 2000) && (Qdot_[cellI]!=0)) //choose3
{
problem.DNNid = 2;
GPUproblemList.append(problem);
selectDNN_[cellI]=2;
continue;
}
problem.DNNid = 0;
GPUproblemList.append(problem);
selectDNN_[cellI]=0;
continue;

}

Expand Down Expand Up @@ -244,4 +209,4 @@ void Foam::dfChemistryModel<ThermoType>::updateSolutionBuffer
solutionList.clear();
}
return;
}
}