70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
import logging
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import time
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import torch
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def evaluate(model, data_loader, device):
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"""Performs evaluation on the dataset for one epoch.
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Args:
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model (torch.nn.Module): The model to evaluate.
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data_loader (torch.utils.data.DataLoader): DataLoader for the evaluation data.
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device (torch.device): The device to run evaluation on.
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Returns:
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dict: A dictionary containing evaluation metrics (e.g., average loss).
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"""
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model.eval() # Set model to evaluation mode
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total_loss = 0.0
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num_batches = len(data_loader)
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eval_start_time = time.time()
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status_interval = max(1, num_batches // 10) # Log status roughly 10 times
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logging.info("--- Starting Evaluation --- ")
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with torch.no_grad(): # Disable gradient calculations
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for i, (images, targets) in enumerate(data_loader):
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images = list(image.to(device) for image in images)
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targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
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# To handle the different behavior of Mask R-CNN in eval mode,
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# we explicitly reset the model to training mode to compute losses,
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# then switch back to eval mode for the rest of the evaluation
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model.train()
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loss_dict = model(images, targets)
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model.eval()
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losses = sum(loss for loss in loss_dict.values())
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loss_value = losses.item()
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total_loss += loss_value
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if (i + 1) % status_interval == 0:
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logging.info(f" Evaluated batch {i + 1}/{num_batches}")
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avg_loss = total_loss / num_batches if num_batches > 0 else 0
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eval_duration = time.time() - eval_start_time
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logging.info("--- Evaluation Finished ---")
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logging.info(f" Average Evaluation Loss: {avg_loss:.4f}")
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logging.info(f" Evaluation Duration: {eval_duration:.2f}s")
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# Return metrics (currently just average loss)
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metrics = {"average_loss": avg_loss}
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return metrics
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# Example usage (can be removed or kept for testing):
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if __name__ == "__main__":
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# This is a dummy test and requires a model, dataloader, device
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print(
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"This script contains the evaluate function and cannot be run directly for testing without setup."
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)
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# Example:
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# device = torch.device('cpu')
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# # Create dummy model and dataloader
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# model = ...
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# data_loader = ...
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# metrics = evaluate(model, data_loader, device)
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# print(f"Dummy evaluation metrics: {metrics}")
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