deploy torch model

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+import importlib
+net = importlib.import_module("model.HGSRCNN").Net
+import torch
+
+model = net(multi_scale=True, group=1)
+model.load_state_dict(torch.load('checkpoint/HGSRCNN.pth',map_location=torch.device('cpu')))
+
+def np2torch(img):
+    img = torch.tensor(img)
+    img = img/255
+    img = img.permute(2, 0, 1)
+    img = img.unsqueeze(0)
+    img = img.cpu()
+    return img
+
+def torch2np(img):
+    return img.squeeze(0).mul(255).clamp(0, 255).byte().permute(1, 2, 0).numpy()
+
+
+
+
+def inference(img, scale):
+    img = np2torch(img)
+    if scale == 'scale x2':
+        out = model(img, 2)
+    elif scale == 'scale x3':
+        out = model(img, 3)
+    else:
+        out = model(img, 4)
+    return torch2np(out),str(out.shape)
+
+title = "AnimeGANv2"
+description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below."
+article = "Github Repo Pytorch "
+#examples=[['groot.jpeg','version 2 (🔺 robustness,🔻 stylization)'],['gongyoo.jpeg','version 1 (🔺 stylization, 🔻 robustness)']]
+
+demo = gr.Interface(
+    fn=inference, 
+    inputs=[
+        gr.inputs.Image(type="numpy"),
+        gr.inputs.Radio([
+            'scale x2',
+            'scale x3',
+            'scale x4',
+            ], 
+            type="value", 
+            default='scale x2', 
+            label='Scale'
+        )
+    ], 
+    outputs=[
+        gr.outputs.Image(type="numpy"),
+        'text'
+    ],
+    title=title,
+    description=description,
+    article=article,
+    #examples=examples
+    )
+
+demo.launch()