Merge branch 'main' of https://github.com/chaofengc/IQA-Toolbox-Python …

…into main

README.md

@@ -83,7 +83,7 @@ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cp
 # create metric with default setting
 iqa_metric = pyiqa.create_metric('lpips', device=device)
 # Note that gradient propagation is disabled by default. set as_loss=True to enable it as a loss function.
-iqa_loss = pyiqa.create_metric('lpips', device=device, as_loss=True)
+# iqa_loss = pyiqa.create_metric('lpips', device=device, as_loss=True)
 
 # create metric with custom setting
 iqa_metric = pyiqa.create_metric('psnr', test_y_channel=True, color_space='ycbcr').to(device)
@@ -94,7 +94,6 @@ print(iqa_metric.lower_better)
 # example for iqa score inference
 # Tensor inputs, img_tensor_x/y: (N, 3, H, W), RGB, 0 ~ 1
 score_fr = iqa_metric(img_tensor_x, img_tensor_y)
-score_nr = iqa_metric(img_tensor_x)
 
 # img path as inputs.
 score_fr = iqa_metric('./ResultsCalibra/dist_dir/I03.bmp', './ResultsCalibra/ref_dir/I03.bmp')

pyiqa/models/inference_model.py

@@ -76,6 +76,7 @@ def forward(self, target, ref=None, **kwargs):
                         ref = ref.unsqueeze(0)
 
                 if self.metric_mode == 'FR':
+                    assert ref is not None, 'Please specify reference image for Full Reference metric'
                     output = self.net(target.to(self.device), ref.to(self.device), **kwargs)
                 elif self.metric_mode == 'NR':
                     output = self.net(target.to(self.device), **kwargs)