Update app.py

app.py

@@ -4,7 +4,7 @@ import gradio as gr
 import os
 #os.system("sudo apt-get install nvIDia-cuda-toolkit")
 os.system("pip3 install torch")
-os.system("/usr/local/bin/python -m pip install --upgrade pip")
+#os.system("/usr/local/bin/python -m pip install --upgrade pip")
 os.system("pip3 install collections")
 os.system("pip3 install torchvision")
 os.system("pip3 install einops")
@@ -87,8 +87,8 @@ def test(gpu_id, net, img_list, group_size, img_size):
           group_img[i]=img_transform(Image.fromarray(img_list[i]))
         _,pred_mask=net(group_img*1)
         pred_mask=(pred_mask.detach().squeeze()*255)#.numpy().astype(np.uint8)
-        pred_mask=[F.interpolate(pred_mask[i].reshape(1,1,pred_mask[i].shape[-2],pred_mask[i].shape[-1]),size=(size,size),mode='bilinear').squeeze().numpy().astype(np.uint8) for i in range(5)]
-        #pred_mask=[crf_refine(((group_img[i]-group_img[i].min())/(group_img[i].max()-group_img[i].min())*255).permute(1,2,0).contiguous().numpy().astype(np.uint8),pred_mask[i]) for i in range(5)]
+        #pred_mask=[F.interpolate(pred_mask[i].reshape(1,1,pred_mask[i].shape[-2],pred_mask[i].shape[-1]),size=(size,size),mode='bilinear').squeeze().numpy().astype(np.uint8) for i in range(5)]
+        pred_mask=[crf_refine(((group_img[i]-group_img[i].min())/(group_img[i].max()-group_img[i].min())*255).permute(1,2,0).contiguous().numpy().astype(np.uint8),pred_mask[i]) for i in range(5)]
         #for i in range(5):
         #    print(img_list[i].shape,pred_mask[i].shape)
         pred_mask=[crf_refine(img_list[i],pred_mask[i]) for i in range(5)]