Update app.py

app.py

@@ -87,10 +87,10 @@ 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=(wl[i],hl[i]),mode='bilinear').squeeze().numpy().astype(np.uint8) 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=(hl[i],wl[i]),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)
+        #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)]
         print(pred_mask[0].shape)
         result = [((torch.from_numpy(pred_mask[i]).unsqueeze(2).repeat(1,1,3)).numpy()) for i in range(5)]