From 89e795e320123dd7272049015de2a14cab6e456d Mon Sep 17 00:00:00 2001
From: Viner Abubakirov <viner96@yandex.ru>
Date: Tue, 31 Mar 2026 10:11:18 +0500
Subject: [PATCH] switch info to debug in logger

---
 main.py | 31 +++++++++++++++++--------------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/main.py b/main.py
index 2afba9f..9ebafab 100644
--- a/main.py
+++ b/main.py
@@ -84,44 +84,44 @@ class ImageInterpolator:
         self.vram_available = get_vram_available(device)
         self.embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).to(device)
         self.model_runner = model_runner
-        logging.info(
+        logging.debug(
             f"Initialized ImageInterpolator with device: {device}, anchor: {anchor}, available VRAM: {self.vram_available} bytes"
         )
 
     def interpolate(self, image1: Path, image2: Path, output_path: Path):
-        logging.info(f"Reading images: {image1} and {image2}")
+        logging.debug(f"Reading images: {image1} and {image2}")
         tensor1 = img2tensor(utils.read(image1)).to(self.device)
         tensor2 = img2tensor(utils.read(image2)).to(self.device)
-        logging.info(
+        logging.debug(
             f"Image shapes after conversion to tensors: {tensor1.shape}, {tensor2.shape}"
         )
         tensor1, tensor2 = check_dim_and_resize(tensor1, tensor2)
-        logging.info(f"Image shapes after resizing: {tensor1.shape}, {tensor2.shape}")
+        logging.debug(f"Image shapes after resizing: {tensor1.shape}, {tensor2.shape}")
         h, w = tensor1.shape[2], tensor1.shape[3]
-        logging.info(f"Interpolating images of size: {h}x{w}")
+        logging.debug(f"Interpolating images of size: {h}x{w}")
 
         scale = self.scale(h, w)
-        logging.info(f"Calculated scale factor: {scale:.2f}")
+        logging.debug(f"Calculated scale factor: {scale:.2f}")
         padding = int(16 / scale)
-        logging.info(f"Calculated padding: {padding} pixels")
+        logging.debug(f"Calculated padding: {padding} pixels")
         padder = InputPadder(tensor1.shape, divisor=padding)
         tensor1_padded, tensor2_padded = padder.pad(tensor1, tensor2)
-        logging.info(
+        logging.debug(
             f"Image shapes after padding: {tensor1_padded.shape}, {tensor2_padded.shape}"
         )
 
         tensor1_padded = tensor1_padded.to(self.device)
         tensor2_padded = tensor2_padded.to(self.device)
-        logging.info("Running model inference for interpolation")
+        logging.debug("Running model inference for interpolation")
         with torch.no_grad():
             interpolated = self.model_runner.model(
                 tensor1_padded, tensor2_padded, self.embt, scale_factor=scale, eval=True
             )["imgt_pred"]
-        logging.info(f"Interpolated image shape before unpadding: {interpolated.shape}")
+        logging.debug(f"Interpolated image shape before unpadding: {interpolated.shape}")
         (interpolated,) = padder.unpad(interpolated)
-        logging.info(f"Interpolated image shape after unpadding: {interpolated.shape}")
+        logging.debug(f"Interpolated image shape after unpadding: {interpolated.shape}")
         utils.write(output_path, tensor2img(interpolated.cpu()))
-        logging.info(f"Saved interpolated image to: {output_path}")
+        logging.debug(f"Saved interpolated image to: {output_path}")
 
     def scale(self, height: int, width: int) -> float:
         scale = (
@@ -145,7 +145,9 @@ def main():
     ckpt_path = Path("src/pretrained/amt-g.pth")
     image1_path = Path("source/img0.png")
     image2_path = Path("source/img1.png")
-    output_path = Path("output/interpolated_image.png")
+    image3_path = Path("source/img2.png")
+    output_path1 = Path("output/interpolated_image1.png")
+    output_path2 = Path("output/interpolated_image2.png")
 
     device = get_device()
     model_runner = ModelRunner(config_path, ckpt_path, device)
@@ -166,7 +168,8 @@ def main():
     else:
         raise Exception(f"Unsupported device type: {device.type}")
     interpolator = ImageInterpolator(device, anchor, model_runner)
-    interpolator.interpolate(image1_path, image2_path, output_path)
+    interpolator.interpolate(image1_path, image2_path, output_path1)
+    interpolator.interpolate(image2_path, image3_path, output_path2)
 
 
 if __name__ == "__main__":