made chnages in models

Dockerfile

@@ -14,9 +14,9 @@ EXPOSE 8080
 
 # 3. Installing gdown, downloading models and immediately uninstalling it for a smaller image
 RUN pip install --no-cache-dir gdown && \
-     gdown 1h0N5fQtWwzdn4TEH_CJZDlE5xBI7-7X_ -O model_store/UNET.mar && \
-     gdown 1ozPgC_Cha99D5yYl4ES90LOd_Jo6p_fQ -O model_store/R-UNET.mar && \
-     gdown 1dr0Oe5mkpb_o7rarXGcolXgobqL5T9S0 -O model_store/A-R-UNET.mar && \
+     gdown 1yZfaeCagywUGfObCyhwNvd9hYVdXH84t -O model_store/UNET.mar && \
+     gdown 1632np236SU0ZFM9Li4EbcwpNyLPcio9A -O model_store/R-UNET.mar && \
+     gdown 1LyE6FQzY6wQI0nWwj3s1O29jseiF5-xo -O model_store/A-R-UNET.mar && \
      pip uninstall gdown -y
 
 USER model-server

image_handler.py

@@ -3,18 +3,29 @@ import torch
 import torchvision.transforms as transforms
 from PIL import Image
 import io
+import time
+import logging
+import torch.nn.functional as F
+
+logger = logging.getLogger(__name__)
 
 class ImageHandler(BaseHandler):
     def __init__(self):
         super(ImageHandler, self).__init__()
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.transform = transforms.Compose([transforms.ToTensor()])
+        self.input_tensor_for_metrics = None
+        self.start_time = 0
 
     def preprocess(self, data):
-        # TorchServe sends input as bytes → we decode into PIL image
+        self.start_time = time.time()
+
         image_bytes = data[0].get("body")
         image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        width, height = image.size
+        logger.info(f"DATA_QUALITY: resolution={width}x{height}, format={image.format}")
         tensor = self.transform(image).unsqueeze(0).to(self.device)
+        self.input_tensor_for_metrics = tensor.clone().detach()
         return tensor
 
     def inference(self, data, *args, **kwargs):
@@ -23,9 +34,27 @@ class ImageHandler(BaseHandler):
         return output
 
     def postprocess(self, data):
-        output_tensor = data.squeeze(0).cpu().clamp(0, 1)  # ensure valid range
-        output_image = transforms.ToPILImage()(output_tensor)
+        output_batched = data
+        input_batched = self.input_tensor_for_metrics
+        output_tensor = output_batched.squeeze(0).cpu().clamp(0, 1)
+        input_tensor = input_batched.squeeze(0).cpu()
+        output_tensor_resized = output_tensor
+        if output_tensor.shape != input_tensor.shape:
+             output_tensor_resized = F.interpolate(
+                 output_tensor.unsqueeze(0), 
+                 size=input_tensor.shape[-2:],
+                 mode='bilinear', 
+                 align_corners=False
+             ).squeeze(0)
+        
+        pixel_difference = torch.mean(torch.abs(input_tensor - output_tensor_resized)).item()
+        logger.info(f"OUTPUT_QUALITY: denoising_intensity={pixel_difference:.4f}")
 
+        end_time = time.time()
+        latency_ms = (end_time - self.start_time) * 1000
+        logger.info(f"OPERATIONAL_HEALTH: total_latency={latency_ms:.2f}ms")
+
+        output_image = transforms.ToPILImage()(output_tensor)
         buf = io.BytesIO()
         output_image.save(buf, format="PNG")
         return [buf.getvalue()]