add examples

scripts/pt_example.py

@@ -0,0 +1,21 @@
+import requests
+from PIL import Image
+import matplotlib.pyplot as plt
+
+from transformers import CLIPProcessor, CLIPModel
+
+model = CLIPModel.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
+
+url = "https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcSjP8UWzpGqXKwlC1zPRhcJOXThfI4pXgg2Zhd1B-cstpnEDalY"
+image = Image.open(requests.get(url, stream=True).raw)
+text = ["Chest X-Ray", "Brain MRI"]
+
+inputs = processor(text=text, images=image, return_tensors="pt", padding=True)
+probs = model(**inputs).logits_per_image.softmax(dim=1).detach().numpy().flatten()
+
+plt.subplots()
+plt.imshow(image)
+plt.title("".join([x[0] + ": " + x[1] + " " for x in zip(text, [format(prob, ".4%") for prob in probs])]))
+plt.axis("off")
+plt.show()

scripts/tf_example.py

@@ -0,0 +1,22 @@
+import requests
+from PIL import Image
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+from transformers import CLIPProcessor, TFCLIPModel
+
+model = TFCLIPModel.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("flaviagiammarino/pubmed-clip-vit-base-patch32")
+
+url = "https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcSjP8UWzpGqXKwlC1zPRhcJOXThfI4pXgg2Zhd1B-cstpnEDalY"
+image = Image.open(requests.get(url, stream=True).raw)
+text = ["Chest X-Ray", "Brain MRI"]
+
+inputs = processor(text=text, images=image, return_tensors="tf", padding=True)
+probs = tf.nn.softmax(model(**inputs).logits_per_image, axis=-1).numpy().flatten()
+
+plt.subplots()
+plt.imshow(image)
+plt.title("".join([x[0] + ": " + x[1] + " " for x in zip(text, [format(prob, ".4%") for prob in probs])]))
+plt.axis("off")
+plt.show()