Create app.py

app.py

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+from PIL import Image
+
+import torch
+import timm
+import torchvision
+import torchvision.transforms as T
+
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+import gradio as gr
+
+torch.set_grad_enabled(False);
+
+with open("imagenet_classes.txt", "r") as f:
+    imagenet_categories = [s.strip() for s in f.readlines()]
+
+transform = T.Compose([
+    T.Resize(256, interpolation=3),
+    T.CenterCrop(224),
+    T.ToTensor(),
+    T.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
+])
+
+model = torch.hub.load('facebookresearch/deit:main', 'deit_base_patch16_224', pretrained=True)
+
+def detr(im):
+  img = transform(im).unsqueeze(0)
+
+  # compute the predictions
+  out = model(img)
+
+  # and convert them into probabilities
+  scores = torch.nn.functional.softmax(out, dim=-1)[0]
+
+  # finally get the index of the prediction with highest score
+  topk_scores, topk_label = torch.topk(scores, k=5, dim=-1)
+
+
+  d = {}
+  for i in range(5):
+      pred_name = imagenet_categories[topk_label[i]]
+      pred_name = f"{pred_name:<25}"
+      score = topk_scores[i]
+      score = f"{score:.3f}"
+      d[pred_name] = score
+  return d
+
+inputs = gr.inputs.Image(type='pil', label="Original Image")
+outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
+
+title = "Deit"
+description = "demo for Facebook DeiT: Data-efficient Image Transformers. 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/2012.12877'>Training data-efficient image transformers & distillation through attention</a> | <a href='https://github.com/facebookresearch/deit'>Github Repo</a></p>"
+
+examples = [
+    ['deer.jpg'],
+    ['cat.jpg']
+]
+
+gr.Interface(detr, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()