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SRnet.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2d225fc37e4908cf360e503ec7b34cebe9ce6b9905cc853a7d83b0ba026b1ba
+size 67952735

app.py

@@ -1,37 +1,49 @@
 import gradio as gr
-import onnxruntime as ort
 import numpy as np
 from PIL import Image
+import torch
+from torchvision.transforms import ToTensor
+from torchvision import transforms
+
 
 # Load the ONNX model
-model_path = "modelSR.onnx"  # Replace with your model path
-ort_session = ort.InferenceSession(model_path)
+model_path = "./SRnet.pth"  # Replace with your model path
+
+transforms = transforms.Compose([
+    #transforms.Resize(size=(50, 50), antialias=True),
+    transforms.ToTensor()
+])
+
+
 
 # Define the superresolution function
 def superresolve(image):
   # Preprocess the image
-  image = np.array(image.resize((256, 256)))  # Resize to expected input size
-  image = image[:, :, ::-1]  # Convert to BGR (some models expect this)
-  image = np.transpose(image, (2, 0, 1))  # Transpose to NHWC format
-  image = image[np.newaxis, ...]  # Add batch dimension
+  image = transforms(image)[None,...]
+
 
   # Run inference
-  ort_inputs = {ort_session.get_inputs()[0].name: image.astype(np.float32)}
-  ort_outs = ort_session.run(None, ort_inputs)
-  output = ort_outs[0][0]
+
+  net = torch.load(model_path)
+  net = net['arch']
+  net.eval()
+  net.cpu()
+
+  output = net(image)
 
   # Postprocess the output
-  output = output.transpose(1, 2, 0)  # Transpose back to HWC
-  output = np.clip(output, 0, 1)  # Clip values to [0, 1]
-  output = (output * 255).astype(np.uint8)  # Convert to uint8 for PIL
-  output = Image.fromarray(output[:, :, ::-1])  # Convert back to RGB
+  output = output.permute(0,2,3,1)[0].data.numpy()
+  output *= 255.0
+  output = output.clip(0, 255)
+  output = Image.fromarray(np.uint8(output))
+
 
   return output
 
 # Define the Gradio interface
 interface = gr.Interface(
   fn=superresolve,
-  inputs="image",
+  inputs=gr.Image(type="pil"),
   outputs="image",
   title="Super Resolution",
   description="Upload an image to upscale its resolution.",
@@ -41,4 +53,8 @@ interface = gr.Interface(
 )
 
 # Launch the Gradio app
-interface.launch()
+interface.launch()
+
+# impath = './image.jpg'
+# img = Image.open(impath).convert('RGB')
+# superresolve(img)