Create app.py

app.py

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+import torch
+from PIL import Image
+from torchvision import transforms
+import gradio as gr
+import os
+
+os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
+
+target_platform = "proxyless_cpu"
+# proxyless_gpu, proxyless_mobile, proxyless_mobile14 are also avaliable.
+model = torch.hub.load('mit-han-lab/ProxylessNAS', target_platform, pretrained=True)
+model.eval()
+# Download an example image from the pytorch website
+torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+# sample execution (requires torchvision)
+
+def inference(input_image):
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model.to('cuda')
+
+    with torch.no_grad():
+        output = model(input_batch)
+    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+    # Read the categories
+    with open("imagenet_classes.txt", "r") as f:
+        categories = [s.strip() for s in f.readlines()]
+    # Show top categories per image
+    top5_prob, top5_catid = torch.topk(probabilities, 5)
+    result = {}
+    for i in range(top5_prob.size(0)):
+        result[categories[top5_catid[i]]] = top5_prob[i].item()
+    return result
+
+inputs = gr.inputs.Image(type='pil')
+outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
+
+title = "PROXYLESSNAS"
+description = "Gradio demo for PROXYLESSNAS, Proxylessly specialize CNN architectures for different hardware platforms. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1812.00332'>ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware</a> | <a href='https://github.com/mit-han-lab/ProxylessNAS'>Github Repo</a></p>"
+
+examples = [
+            ['dog.jpg']
+]
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()