Update test_one_image.py

test_one_image.py

@@ -22,10 +22,10 @@ transformer_Arcface = transforms.Compose([
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
     ])
 
-# detransformer = transforms.Compose([
-#         transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
-#         transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
-#     ])
+detransformer = transforms.Compose([
+        transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
+        transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
+    ])
 if __name__ == '__main__':
     opt = TestOptions().parse()
 
@@ -35,51 +35,52 @@ if __name__ == '__main__':
     model = create_model(opt)
     model.eval()
 
+    with torch.no_grad():
+        
+        pic_a = opt.pic_a_path
+        img_a = Image.open(pic_a).convert('RGB')
+        img_a = transformer_Arcface(img_a)
+        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
 
-    pic_a = opt.pic_a_path
-    img_a = Image.open(pic_a).convert('RGB')
-    img_a = transformer_Arcface(img_a)
-    img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
+        pic_b = opt.pic_b_path
 
-    pic_b = opt.pic_b_path
+        img_b = Image.open(pic_b).convert('RGB')
+        img_b = transformer(img_b)
+        img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
 
-    img_b = Image.open(pic_b).convert('RGB')
-    img_b = transformer(img_b)
-    img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])
+        # convert numpy to tensor
+        img_id = img_id.cuda()
+        img_att = img_att.cuda()
 
-    # convert numpy to tensor
-    img_id = img_id.cuda()
-    img_att = img_att.cuda()
-
-    #create latent id
-    img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
-    latend_id = model.netArc(img_id_downsample)
-    latend_id = latend_id.detach().to('cpu')
-    latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)
-    latend_id = latend_id.to('cuda')
+        #create latent id
+        img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
+        latend_id = model.netArc(img_id_downsample)
+        latend_id = latend_id.detach().to('cpu')
+        latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)
+        latend_id = latend_id.to('cuda')
 
 
-    ############## Forward Pass ######################
-    img_fake = model(img_id, img_att, latend_id, latend_id, True)
+        ############## Forward Pass ######################
+        img_fake = model(img_id, img_att, latend_id, latend_id, True)
 
 
-    for i in range(img_id.shape[0]):
-        if i == 0:
-            row1 = img_id[i]
-            row2 = img_att[i]
-            row3 = img_fake[i]
-        else:
-            row1 = torch.cat([row1, img_id[i]], dim=2)
-            row2 = torch.cat([row2, img_att[i]], dim=2)
-            row3 = torch.cat([row3, img_fake[i]], dim=2)
+        for i in range(img_id.shape[0]):
+            if i == 0:
+                row1 = img_id[i]
+                row2 = img_att[i]
+                row3 = img_fake[i]
+            else:
+                row1 = torch.cat([row1, img_id[i]], dim=2)
+                row2 = torch.cat([row2, img_att[i]], dim=2)
+                row3 = torch.cat([row3, img_fake[i]], dim=2)
 
-    #full = torch.cat([row1, row2, row3], dim=1).detach()
-    full = row3.detach()
-    full = full.permute(1, 2, 0)
-    output = full.to('cpu')
-    output = np.array(output)
-    output = output[..., ::-1]
+        #full = torch.cat([row1, row2, row3], dim=1).detach()
+        full = row3.detach()
+        full = full.permute(1, 2, 0)
+        output = full.to('cpu')
+        output = np.array(output)
+        output = output[..., ::-1]
 
-    output = output*255
+        output = output*255
 
-    cv2.imwrite(opt.output_path + 'result.jpg',output)
+        cv2.imwrite(opt.output_path + 'result.jpg',output)