pyiqa

import pyiqa import torch # List all available metrics print("Available metrics:", pyiqa.list_models()) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Create a Full-Reference (FR) metric (e.g., LPIPS) # For gradient propagation (e.g., as a loss function), set as_loss=True iqa_fr_metric = pyiqa.create_metric('lpips', device=device, as_loss=True) print(f"LPIPS metric ('lower_better'): {iqa_fr_metric.lower_better}") # Create a No-Reference (NR) metric (e.g., NIQE) iqa_nr_metric = pyiqa.create_metric('niqe', device=device) print(f"NIQE metric ('lower_better'): {iqa_nr_metric.lower_better}") # Dummy image tensors (Batch, Channels, Height, Width), RGB, 0~1 range img1 = torch.rand(1, 3, 256, 256).to(device) img2 = torch.rand(1, 3, 256, 256).to(device) # Compute FR score score_fr = iqa_fr_metric(img1, img2) print(f"LPIPS score: {score_fr.item():.4f}") # Compute NR score score_nr = iqa_nr_metric(img1) print(f"NIQE score: {score_nr.item():.4f}") # Example of using a metric as a loss function (requires as_loss=True) loss = score_fr # LPIPS is lower_better, directly usable as loss loss.backward() # Gradients will be computed print("Backward pass complete for LPIPS loss.")