Start building an example.

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python
+
+import gradio as gr
+
+import torch
+from torch import Tensor
+from torchvision import transforms
+
+import numpy as np
+
+import sys
+
+model = None
+
+def load_model():
+    global model
+    model = torch.jit.load("model.pt")
+
+def denormalize_and_clip_as_tensor (im: Tensor) -> Tensor:
+        return torch.clip(im * 0.5 + 0.5, 0.0, 1.0)
+
+def denormalize_and_clip_as_numpy (im: Tensor) -> np.ndarray:
+    im = im.squeeze(0)
+    return np.ascontiguousarray(denormalize_and_clip_as_tensor(im).permute(1,2,0).detach().cpu().numpy())
+
+def undo_antialiasing(im):
+    im_torch = torch.from_numpy (im).permute(2,0,1).unsqueeze(0).float() / 255.0
+    im_torch = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(im_torch)
+    with torch.no_grad():
+        output_torch = model(im_torch)
+    output = denormalize_and_clip_as_numpy(output_torch.rgb)
+    return (output*255.99).astype(np.uint8)
+
+load_model()
+iface = gr.Interface(fn=undo_antialiasing,
+                     inputs=gr.inputs.Image(),
+                     outputs=gr.outputs.Image(),
+                     examples=[['examples/Bowling.png'], ['examples/opencv.png']])
+iface.launch()

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+torch 
+torchvision 
+torchaudio