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import torch
from torch.utils.data import DataLoader
from model.model import ExcitometerModel
from data.dataset import load_dataset, preprocess_data # Assuming you have these functions
# Configuration
batch_size = 32
num_classes = 10 # Adjust based on your specific use case
# Initialize the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ExcitometerModel(num_classes=num_classes)
model.to(device)
# Load model weights
model.load_state_dict(torch.load('excitometer_model.pth'))
model.eval() # Set model to evaluation mode
# Load data
def load_data():
# Load and preprocess dataset
test_data = load_dataset('test') # Replace with actual dataset loading
test_dataset = Dataset(test_data, preprocess_data) # Define Dataset class
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
return test_loader
test_loader = load_data()
# Evaluation loop
def evaluate():
running_loss = 0.0
correct = 0
total = 0
criterion = torch.nn.CrossEntropyLoss() # Assuming a classification problem
with torch.no_grad():
for inputs, labels in test_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
running_loss += loss.item() * inputs.size(0)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
avg_loss = running_loss / len(test_loader.dataset)
accuracy = correct / total
return avg_loss, accuracy
# Run evaluation
test_loss, test_accuracy = evaluate()
print(f'Test Loss: {test_loss:.4f}')
print(f'Test Accuracy: {test_accuracy:.4f}')
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