From 40f1a650067b4aa093e28385acb9354c6a128a38 Mon Sep 17 00:00:00 2001
From: Falko Habel <falko.habel@gmx.de>
Date: Mon, 17 Feb 2025 16:31:12 +0100
Subject: [PATCH] fix

---
 src/aiunn/inference.py | 72 +++++++++---------------------------------
 1 file changed, 15 insertions(+), 57 deletions(-)

diff --git a/src/aiunn/inference.py b/src/aiunn/inference.py
index 182c8da..991b708 100644
--- a/src/aiunn/inference.py
+++ b/src/aiunn/inference.py
@@ -7,6 +7,7 @@ import io
 from torch import nn
 from aiia import AIIABase
 
+
 class Upscaler(nn.Module):
     """
     Transforms the base model's final feature map using a transposed convolution.
@@ -30,19 +31,11 @@ class Upscaler(nn.Module):
     def forward(self, x):
         features = self.base_model(x)
         return self.last_transform(features)
-
 class ImageUpscaler:
     def __init__(self, model_path: str, device: str = 'cuda' if torch.cuda.is_available() else 'cpu'):
-        """
-        Initialize the ImageUpscaler with the trained model.
-        
-        Args:
-            model_path (str): Path to the trained model directory.
-            device (str): Device to run inference on ('cuda' or 'cpu').
-        """
         self.device = torch.device(device)
         self.model = self.load_model(model_path)
-        self.model.eval()  # Set the model to evaluation mode
+        self.model.eval()  # Set to evaluation mode
         
         # Define preprocessing transformations
         self.preprocess = Compose([
@@ -53,34 +46,20 @@ class ImageUpscaler:
     def load_model(self, model_path: str):
         """
         Load the trained model from the specified path.
-        
-        Args:
-            model_path (str): Path to the saved model.
-        
-        Returns:
-            nn.Module: Loaded PyTorch model.
         """
-        # Load the base model and wrap it with Upscaler
-        base_model = AIIABase.load(model_path)
-        model = Upscaler(base_model)
-        
-        # Move the model to the appropriate device
+        base_model = AIIABase.load(model_path)  # Load base model
+        model = Upscaler(base_model)           # Wrap with Upscaler
         return model.to(self.device)
     
     def preprocess_image(self, image: Image.Image):
         """
-        Preprocess the input image for inference.
-        
-        Args:
-            image (PIL.Image.Image): Input image in PIL format.
-        
-        Returns:
-            torch.Tensor: Preprocessed image tensor.
+        Preprocess input image for inference.
         """
-        # Convert PIL image to numpy array
-        image_array = np.array(image)
+        if not isinstance(image, Image.Image):
+            raise ValueError("Input must be a PIL.Image.Image object")
         
-        # Apply preprocessing transformations
+        # Convert to numpy array and apply preprocessing
+        image_array = np.array(image)
         augmented = self.preprocess(image=image_array)
         
         # Add batch dimension and move to device
@@ -88,48 +67,27 @@ class ImageUpscaler:
     
     def postprocess_image(self, output_tensor: torch.Tensor):
         """
-        Postprocess the output tensor to convert it back to an image.
-        
-        Args:
-            output_tensor (torch.Tensor): Model output tensor.
-        
-        Returns:
-            PIL.Image.Image: Upscaled image in PIL format.
+        Convert output tensor back to an image.
         """
-        # Remove batch dimension and move to CPU
-        output_tensor = output_tensor.squeeze(0).cpu()
-        
-        # Denormalize and convert to numpy array
-        output_array = (output_tensor * 0.5 + 0.5).clamp(0, 1).numpy()
-        
-        # Convert from CHW (Channels-Height-Width) to HWC (Height-Width-Channels) format
-        output_array = (output_array.transpose(1, 2, 0) * 255).astype(np.uint8)
-        
-        # Convert numpy array back to PIL image
+        output_tensor = output_tensor.squeeze(0).cpu()  # Remove batch dimension
+        output_array = (output_tensor * 0.5 + 0.5).clamp(0, 1).numpy() * 255
+        output_array = output_array.transpose(1, 2, 0).astype(np.uint8)  # CHW -> HWC
         return Image.fromarray(output_array)
     
     def upscale_image(self, input_image_path: str):
         """
         Perform upscaling on an input image.
-        
-        Args:
-            input_image_path (str): Path to the input low-resolution image.
-        
-        Returns:
-            PIL.Image.Image: Upscaled high-resolution image.
         """
-        # Load and preprocess the input image
-        input_image = Image.open(input_image_path).convert('RGB')
+        input_image = Image.open(input_image_path).convert('RGB')  # Ensure RGB format
         preprocessed_image = self.preprocess_image(input_image)
         
-        # Perform inference with the model
         with torch.no_grad():
             with torch.amp.autocast(device_type="cuda"):
                 output_tensor = self.model(preprocessed_image)
         
-        # Postprocess and return the upscaled image
         return self.postprocess_image(output_tensor)
 
+
 # Example usage:
 upscaler = ImageUpscaler(model_path="/root/vision/aiuNN/best_model")
 upscaled_image = upscaler.upscale_image("/root/vision/aiuNN/input.jpg")