@dambo thank you for your response.
Yes, I am sure. The code for inference which I am using in python is:-
learn = load_learner("/data2/aditya/classifiers/LHSRHS/new_data801/", "final_lhs_rhs_model.pkl")
my_model = learn.model.cpu()
softmaxer = torch.nn.Softmax(dim=1)
my_model.eval()
image = Image.open("/data2/aditya/classifiers/LHSRHS/new_data801/RHS/100_5751_rhs_front_door_assy.png")
image = image.resize((448, 448))
x = TTF.to_tensor(image)
x = TTF.normalize(x, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
x.unsqueeze_(0)
print(x.size())
raw_out = my_model(x)
print(softmaxer(raw_out))

traced_cell = torch.jit.trace(my_model, x)
traced_cell.save('lhsrhs_script.zip')

I think there is some problem with normalize_transform function in c++ but I don’t know what it is because when I am printing tensor_image1 in c++ the values are not coming as expected.