Sound Classification Inference

sound_classification_inference.py

# ----------------------------
# Inference
# ----------------------------
def inference (model, val_dl):
  correct_prediction = 0
  total_prediction = 0

  # Disable gradient updates
  with torch.no_grad():
    for data in val_dl:
      # Get the input features and target labels, and put them on the GPU
      inputs, labels = data[0].to(device), data[1].to(device)

      # Normalize the inputs
      inputs_m, inputs_s = inputs.mean(), inputs.std()
      inputs = (inputs - inputs_m) / inputs_s

      # Get predictions
      outputs = model(inputs)

      # Get the predicted class with the highest score
      _, prediction = torch.max(outputs,1)
      # Count of predictions that matched the target label
      correct_prediction += (prediction == labels).sum().item()
      total_prediction += prediction.shape[0]
    
  acc = correct_prediction/total_prediction
  print(f'Accuracy: {acc:.2f}, Total items: {total_prediction}')

# Run inference on trained model with the validation set
inference(myModel, val_dl)