d

models/SRFlow/code/__init__.py

@@ -35,12 +35,7 @@ def predict(model, lr):
     return visuals.get('rlt', visuals.get("SR"))
 
 
-def t(array, divide): 
-    output = torch.Tensor(np.expand_dims(array.transpose([2, 0, 1]), axis=0).astype(np.float32))
-    if divide:
-        return output / 255
-    else:
-        return output
+def t(array): return torch.Tensor(np.expand_dims(array.transpose([2, 0, 1]), axis=0).astype(np.float32)) / 255
 
 
 def rgb(t): return (

models/SRFlow/srflow.py

@@ -20,7 +20,11 @@ def return_SRFlow_result(lr, divide, conf_path='models/SRFlow/code/confs/SRFlow_
     - sr: PIL Image
     """
     model, opt = load_model(conf_path)
+
     lr = PILToTensor()(lr).permute(1, 2, 0).numpy()
+    
+    if not divide:
+        lr *= 255
 
     scale = opt['scale']
     pad_factor = 2
@@ -29,7 +33,7 @@ def return_SRFlow_result(lr, divide, conf_path='models/SRFlow/code/confs/SRFlow_
     lr = impad(lr, bottom=int(np.ceil(h / pad_factor) * pad_factor - h),
                         right=int(np.ceil(w / pad_factor) * pad_factor - w))
     
-    lr_t = t(lr, divide)
+    lr_t = t(lr)
     heat = opt['heat']
     
     sr_t = model.get_sr(lq=lr_t, heat=heat)