Load Tensor and calculate distance

s4-calculate-distance.py

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+import numpy as np
+from numpy.linalg import norm
+import torch
+
+url1='http://farm3.staticflickr.com/2519/4126738647_cc436c111b_z.jpg'
+cap1='A motorcycle sits parked across from a herd of livestock'
+
+url2='http://farm3.staticflickr.com/2046/2003879022_1b4b466d1d_z.jpg'
+cap2='Motorcycle on platform to be worked on in garage'
+
+url3='https://i.natgeofe.com/n/548467d8-c5f1-4551-9f58-6817a8d2c45e/NationalGeographic_2572187_3x2.jpg'
+cap3='a cat laying down stretched out near a laptop'
+
+img1 = {
+  'flickr_url': url1,
+  'caption': cap1,
+  'image_path' : './shared_data/motorcycle_1.jpg',
+  'tensor_path' : './shared_data/motorcycle_1'
+}
+
+img2 = {
+    'flickr_url': url2,
+    'caption': cap2,
+    'image_path' : './shared_data/motorcycle_2.jpg',
+    'tensor_path' : './shared_data/motorcycle_2'
+}
+
+img3 = {
+    'flickr_url' : url3,
+    'caption': cap3,
+    'image_path' : './shared_data/cat_1.jpg',
+    'tensor_path' : './shared_data/cat_1'
+}
+
+def load_tensor(path):
+    return torch.load(path)
+
+def load_embeddings():
+    img1_tensor = load_tensor(img1['tensor_path'])
+    img2_tensor = load_tensor(img2['tensor_path'])
+    img3_tensor = load_tensor(img3['tensor_path'])
+    return np.array(img1_tensor), np.array(img2_tensor), np.array(img3_tensor)
+
+
+def cosine_similarity(vec1, vec2):
+    similarity = np.dot(vec1,vec2)/(norm(vec1)*norm(vec2))
+    return similarity
+
+def calculate_distance():
+    img1_tensor, img2_tensor, img3_tensor = load_embeddings()
+    similarity1 = cosine_similarity(img1_tensor, img2_tensor)
+    similarity2 = cosine_similarity(img1_tensor, img3_tensor)
+    similarity3 = cosine_similarity(img2_tensor, img3_tensor)
+    return similarity1, similarity2, similarity3
+
+print(calculate_distance())