| import torch
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| import torch.nn as nn
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| import torch.optim as optim
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| from torch.utils.data import DataLoader, TensorDataset
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| import numpy as np
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|
|
|
|
| def testModel(model, test_features, test_labels, dataset, num_frames=32):
|
| """
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| "Test the LSTM model on the test set."
|
| """
|
| model.eval()
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| test_loss = 0.0
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| test_total = 0
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| test_correct = 0
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|
|
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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| model.to(device)
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| criterion = nn.CrossEntropyLoss()
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|
|
| test_dataset = TensorDataset(test_features, test_labels)
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| test_loader = DataLoader(test_dataset, batch_size=num_frames, shuffle=False)
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|
|
| predicted_labels = []
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| all_outputs = []
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|
|
| with torch.no_grad():
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| for batch_x, batch_y in test_loader:
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| batch_x, batch_y = batch_x.to(device), batch_y.to(device)
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| outputs = model(batch_x)
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|
|
| loss = criterion(outputs, batch_y.long())
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| test_loss += loss.item()
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|
|
| _, predicted = torch.max(outputs, 1)
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| predicted_labels.extend(predicted.cpu().numpy())
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|
|
|
|
| softmax_outputs = torch.nn.functional.softmax(outputs, dim=1)
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| all_outputs.append(softmax_outputs.cpu().numpy())
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|
|
| test_correct += (predicted == batch_y).sum().item()
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| test_total += batch_y.size(0)
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|
|
| y_pred = predicted_labels
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| y_pred_proba = np.vstack(all_outputs)
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|
|
| test_loss /= len(test_loader)
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| test_accuracy = 100 * test_correct / test_total
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|
|
|
|
| print(f'Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.2f}%')
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|
|
|
|
| with open(f'./benchmarks/{dataset}/benchmark.txt', 'a') as f:
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| f.write(f"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.2f}%\n")
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|
|
| return y_pred, y_pred_proba
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|
|
|
|
