Create app.py

app.py

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+import torch
+from PIL import Image
+from torchvision import transforms
+import gradio as gr
+
+# === SEMI-WEAKLY SUPERVISED MODELSP RETRAINED WITH 940 HASHTAGGED PUBLIC CONTENT ===
+model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet18_swsl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet50_swsl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext50_32x4d_swsl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x4d_swsl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x8d_swsl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x16d_swsl')
+# ================= SEMI-SUPERVISED MODELS PRETRAINED WITH YFCC100M ==================
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet18_ssl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet50_ssl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext50_32x4d_ssl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x4d_ssl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x8d_ssl')
+# model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnext101_32x16d_ssl')
+model.eval()
+# Download an example image from the pytorch website
+import urllib
+url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+# sample execution (requires torchvision)
+
+def inference(input_image):
+    input_image = Image.open(filename)
+    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)
+    # Download ImageNet labels
+    !wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
+    # 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 = "SEMI-SUPERVISED AND SEMI-WEAKLY SUPERVISED IMAGENET MODELS"
+description = "Gradio demo for SEMI-SUPERVISED AND SEMI-WEAKLY SUPERVISED IMAGENET MODELS, ResNet and ResNext models introduced in the 'Billion scale semi-supervised learning for image classification' paper. 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/1905.00546'>Billion-scale semi-supervised learning for image classification</a> | <a href='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/blob/master/hubconf.py'>Github Repo</a></p>"
+
+examples = [
+            ['dog.jpg']
+]
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()