Update app.py

app.py

@@ -24,8 +24,11 @@ os.makedirs(Ref_path)
 os.makedirs(Ref_path_T)
 os.makedirs('result')
 
-os.system("wget https://www.dropbox.com/s/xv6inxwy0so4ni0/LR.png -O LR.png")
-os.system("wget https://www.dropbox.com/s/abydd1oczs1163l/Ref.png -O Ref.png")
+#os.system("wget https://www.dropbox.com/s/xv6inxwy0so4ni0/LR.png -O LR.png")
+#os.system("wget https://www.dropbox.com/s/abydd1oczs1163l/Ref.png -O Ref.png")
+os.system("wget https://www.dropbox.com/s/vqekqdz80d85gi4/UW.png -O LR.png")
+os.system("wget https://www.dropbox.com/s/lsopmquhpm87v83/W.png -O Ref.png")
+
 
 def resize(img):
     max_side = 512
@@ -39,18 +42,18 @@ def resize(img):
     return img
   
 def inference(LR, Ref):
-    LR = resize(LR)
-    Ref = resize(Ref)
+    #LR = resize(LR)
+    #Ref = resize(Ref)
 
     LR.save(os.path.join(LR_path, '0000.png'))
     Ref.save(os.path.join(Ref_path, '0000.png'))
     Ref.save(os.path.join(Ref_path_T, '0000.png'))
 
     os.system("python -B run.py \
-                --mode amp_RefVSR_small_MFID_8K \
-                --config config_RefVSR_small_MFID_8K \
+                --mode RefVSR_MFID_8K \
+                --config config_RefVSR_MFID_8K \
                 --data RealMCVSR \
-                --ckpt_abs_name ckpt/RefVSR_small_MFID_8K.pytorch \
+                --ckpt_abs_name ckpt/RefVSR_MFID_8K.pytorch \
                 --data_offset ./test \
                 --output_offset ./result \
                 --qualitative_only \
@@ -59,7 +62,7 @@ def inference(LR, Ref):
 
     return "result/0000.png"
 
-title="RefVSR"
+title="RefVSR (under construction)"
 description="Demo application for Reference-based Video Super-Resolution (RefVSR). Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively."
 
 article = "<p style='text-align: center'><b>To check the full capability of the module, we recommend to clone Github repository and run RefVSR models on videos using GPUs.</b></p><p style='text-align: center'>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity. Hence, the model will not take advantage of temporal LR and Ref frames.</p><p style='text-align: center'>The model is the small-sized model trained with the proposed two-stage training strategy.</p><p style='text-align: center'>The sample frames are in HD resolution (1920x1080) and in the PNG format. </p><p style='text-align: center'><a href='https://junyonglee.me/projects/RefVSR' target='_blank'>Project</a> | <a href='https://arxiv.org/abs/2203.14537' target='_blank'>arXiv</a> | <a href='https://github.com/codeslake/RefVSR' target='_blank'>Github</a></p>"