additional cli

model.py

@@ -87,18 +87,22 @@ class Generator(nn.Module):
             nn.Tanh()
         )
         
-    def forward(self, input):
+    def forward(self, input, align_corners=True):
         out = self.block_a(input)
         half_size = out.size()[-2:]
         out = self.block_b(out)
         out = self.block_c(out)
         
-        out = F.interpolate(out, half_size, mode="bilinear", align_corners=True)
-#         out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
+        if align_corners:
+            out = F.interpolate(out, half_size, mode="bilinear", align_corners=True)
+        else:
+            out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
         out = self.block_d(out)
 
-        out = F.interpolate(out, input.size()[-2:], mode="bilinear", align_corners=True)
-#         out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
+        if align_corners:
+            out = F.interpolate(out, input.size()[-2:], mode="bilinear", align_corners=True)
+        else:
+            out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
         out = self.block_e(out)
 
         out = self.out_layer(out)

test.py

@@ -11,15 +11,16 @@ torch.backends.cudnn.enabled = False
 torch.backends.cudnn.benchmark = False
 torch.backends.cudnn.deterministic = True
     
-def load_image(image_path):
+def load_image(image_path, x32=False):
     img = cv2.imread(image_path).astype(np.float32)
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     h, w = img.shape[:2]
 
-    def to_32s(x):
-        return 256 if x < 256 else x - x%32
+    if x32: # resize image to multiple of 32s
+        def to_32s(x):
+            return 256 if x < 256 else x - x%32
+        img = cv2.resize(img, (to_32s(w), to_32s(h)))
 
-    img = cv2.resize(img, (to_32s(w), to_32s(h)))
     img = torch.from_numpy(img)
     img = img/127.5 - 1.0
     return img
@@ -36,14 +37,14 @@ def test(args):
     os.makedirs(args.output_dir, exist_ok=True)
 
     for image_name in sorted(os.listdir(args.input_dir)):
-        if os.path.splitext(image_name)[-1] not in [".jpg", ".png", ".bmp", ".tiff"]:
+        if os.path.splitext(image_name)[-1].lower() not in [".jpg", ".png", ".bmp", ".tiff"]:
             continue
             
-        image = load_image(os.path.join(args.input_dir, image_name))
+        image = load_image(os.path.join(args.input_dir, image_name), args.x32)
 
         with torch.no_grad():
             input = image.permute(2, 0, 1).unsqueeze(0).to(device)
-            out = net(input).squeeze(0).permute(1, 2, 0).cpu().numpy()
+            out = net(input, args.upsample_align).squeeze(0).permute(1, 2, 0).cpu().numpy()
             out = (out + 1)*127.5
             out = np.clip(out, 0, 255).astype(np.uint8)
             
@@ -74,6 +75,15 @@ if __name__ == '__main__':
         type=str,
         default='cuda:0',
     )
+    parser.add_argument(
+        '--upsample_align',
+        type=bool,
+        default=False,
+    )
+    parser.add_argument(
+        '--x32',
+        action="store_true",
+    )
     args = parser.parse_args()
     
     test(args)