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64 lines
1.9 KiB
64 lines
1.9 KiB
1 year ago
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from utils.data_standardizer import standardization
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from utils.data_loader import data_loader
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import matplotlib.pyplot as plt
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def test(model_load_path, X, standard = False, device = 0):
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model = torch.load(model_load_path)
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if standard:
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X = standardization(X)
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device = f'cuda:{device}' if torch.cuda.is_available() else 'cpu'
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X = torch.from_numpy(X).type(torch.float32).to(device)
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with torch.no_grad():
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return model(X)
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if __name__=='__main__':
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# Load datasets
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# test data select:
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dataload_mod='mod1' # opt: mod1 mod2 mod3
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# pretrained model select:
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pretrained_mod='mod1' # opt: mod1 mod2 mod3
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dst_path='datasets/top88_'+ dataload_mod + '_xPhys_180_60.npy'
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U_path='datasets/top88_'+ dataload_mod + '_u_180_60.npy'
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global_density, global_displace, coarse_density, coarse_displace, fine_displace = data_loader(dst_path, U_path)
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X = np.hstack((coarse_density[:,:] , coarse_displace[:,:,0] , coarse_displace[:,:,1]))
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Y = fine_displace[:,:]
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# Set loss function
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loss_function = nn.MSELoss()
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# Predict
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pred = test('checkpoints/ANN_' + pretrained_mod + '_opt.pt', X)
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# Calculate loss
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pred_loss=[]
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device = f'cuda:{0}' if torch.cuda.is_available() else 'cpu'
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Y = torch.from_numpy(Y).type(torch.float32).to(device)
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for i in range(pred.shape[0]):
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pred_loss.append(loss_function(pred[i,:],Y[i,:]).item())
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print('Total loss: '+ str(loss_function(pred,Y).item()))
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# Plot
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plt.plot(range(pred.shape[0]),pred_loss)
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plt.ylabel('Loss')
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plt.xlabel('Coarse mesh id')
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plt.title("Linear graph")
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plt.show()
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loss_metrix = np.asarray(pred_loss)
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loss_metrix = loss_metrix.reshape(int(60/5), int(180/5))
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plt.matshow(loss_metrix)
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plt.title("Show loss value in grid")
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plt.show()
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