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import numpy as np
from numpy.linalg import norm
import torch
from IPython.display import display
import cv2
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():
ex1_embed = load_tensor(img1['tensor_path'] + '.pt')
ex2_embed = load_tensor(img2['tensor_path'] + '.pt')
ex3_embed = load_tensor(img3['tensor_path'] + '.pt')
return ex1_embed.data.numpy(), ex2_embed.data.numpy(), ex3_embed.data.numpy()
def cosine_similarity(vec1, vec2):
similarity = np.dot(vec1,vec2)/(norm(vec1)*norm(vec2))
return similarity
def calculate_cosine_distance():
ex1_embed, ex2_embed, ex3_embed = load_embeddings()
similarity1 = cosine_similarity(ex1_embed, ex2_embed)
similarity2 = cosine_similarity(ex1_embed, ex3_embed)
similarity3 = cosine_similarity(ex2_embed, ex3_embed)
return [similarity1, similarity2, similarity3]
def calcuate_euclidean_distance():
ex1_embed, ex2_embed, ex3_embed = load_embeddings()
distance1 = cv2.norm(ex1_embed,ex2_embed, cv2.NORM_L2)
distance2 = cv2.norm(ex1_embed,ex3_embed, cv2.NORM_L2)
distance3 = cv2.norm(ex2_embed,ex3_embed, cv2.NORM_L2)
return [distance1, distance2, distance3]
def show_cosine_distance():
distances = calculate_cosine_distance()
print("Cosine similarity between ex1_embeded and ex2_embeded is:")
display(distances[0])
print("Cosine similarity between ex1_embeded and ex3_embeded is:")
display(distances[1])
print("Cosine similarity between ex2_embeded and ex2_embeded is:")
display(distances[2])
def show_euclidean_distance():
distances = calcuate_euclidean_distance()
print("Euclidean distance between ex1_embeded and ex2_embeded is:")
display(distances[0])
print("Euclidean distance between ex1_embeded and ex3_embeded is:")
display(distances[1])
print("Euclidean distance between ex2_embeded and ex2_embeded is:")
display(distances[2])
show_cosine_distance()
show_euclidean_distance() |