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| import os | |
| import numpy as np | |
| import requests | |
| import zipfile | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch import optim | |
| from torch.autograd import Variable | |
| from torchvision import datasets, transforms | |
| import torchvision | |
| from PIL import Image | |
| from .glove import GloVe | |
| from tqdm import tqdm | |
| AD_DIRECTORY = os.path.dirname(__file__) | |
| def obtain_vector(inputs, glove): | |
| vector_im = glove.embedding.get(inputs) | |
| if vector_im is None: | |
| vector_im = glove.embedding.get(inputs.lower()) | |
| if vector_im is None: | |
| vector_im = glove.embedding.get(inputs.title()) | |
| if vector_im is None: | |
| vector_im = glove.embedding.get(inputs.upper()) | |
| return vector_im | |
| def generate_glove(): | |
| print("Generating glove similarity...") | |
| #download glove file | |
| os.makedirs("./knowledge", exist_ok=True) | |
| glove_file = './knowledge/glove.840B.300d.txt' | |
| if not os.path.exists(glove_file): | |
| print("Downloading glove files...") | |
| print("") | |
| print("Gonna take a while") | |
| print("") | |
| url_path = "http://nlp.stanford.edu/data/glove.840B.300d.zip" | |
| r = requests.get(url_path) | |
| with open("./knowledge/glove.840B.300d.zip","wb") as f: | |
| f.write(r.content) | |
| filename = './knowledge/glove.840B.300d.zip' | |
| fz = zipfile.ZipFile(filename, 'r') | |
| for file in fz.namelist(): | |
| fz.extract(file, './knowledge/.') | |
| if os.path.exists(filename): | |
| os.remove(filename) | |
| glove = GloVe('./knowledge/glove.840B.300d.txt') | |
| filepath = os.path.join(AD_DIRECTORY, "label_name.txt") | |
| vec_list = [] | |
| vec_list_np = [] | |
| cos_similarity = np.zeros((1000,1000)) | |
| index = 0 | |
| #the labels could be a word or a phrase with multi words | |
| #we also tested on average of every words | |
| #But we assume the last word should be more important, so in our final version | |
| #we assign a higher weight to last word in a phrase and average the fornt words | |
| #w2v for last word of multi-words | |
| for line in tqdm(open(filepath)): | |
| a = line.strip('\n') | |
| b = a.split(',') | |
| cnt = 0 | |
| vector = torch.zeros(300) | |
| vec_front = torch.zeros(300) | |
| vec_b_average = torch.zeros(300) | |
| cnt_b = 0 | |
| for i in range(len(b)): | |
| b[i] = b[i].lstrip() | |
| c = b[i].split(' ') | |
| if obtain_vector(c[-1], glove) is not None: | |
| vec_b_average += obtain_vector(c[-1], glove) | |
| cnt_b += 1 | |
| if cnt_b == 0: | |
| print('index ', index,' generatint word_vector failure') | |
| continue | |
| vec_b_average = vec_b_average / cnt_b | |
| for i in range(len(b)): | |
| b[i] = b[i].lstrip() | |
| c = b[i].split(' ') | |
| cnt_f = 0 | |
| for j in range(len(c) - 1): | |
| if obtain_vector(c[j], glove) is not None: | |
| vec_front += obtain_vector(c[j], glove) | |
| cnt_f += 1 | |
| if obtain_vector(c[-1], glove) is not None: | |
| vec_back =obtain_vector(c[-1], glove) | |
| else: | |
| vec_back = vec_b_average | |
| if cnt_f == 0: | |
| vector += vec_back | |
| else: | |
| vector += (vec_front / cnt_f )* 0.1 + vec_back * 0.9 | |
| cnt += 1 | |
| vector = torch.div(vector,cnt) | |
| vec_list_np.append(np.array(vector)) | |
| vec_list.append(vector) | |
| index += 1 | |
| vec_list_np_stacked = np.stack(vec_list_np) | |
| vec_list_torch = torch.from_numpy(vec_list_np_stacked) | |
| cos_similarity = F.cosine_similarity(vec_list_torch[None, :], vec_list_torch[:, None], dim=-1) | |
| cos_similarity = cos_similarity.numpy() | |
| # np.save('./knowledge/golve_vec_list', np.array(vec_list_np)) | |
| # for i in range(len(vec_list)): | |
| # for j in range(len(vec_list)): | |
| # cos_similarity[i,j] = F.cosine_similarity(vec_list[i], vec_list[j],dim=0).type(torch.half) | |
| # if i != j: | |
| # cos_similarity[i,j] = cos_similarity[i,j] | |
| # cos_similarity = np.array(cos_similarity) | |
| np.save(os.path.join(AD_DIRECTORY, "imagenet_cos_similarity_glove"), cos_similarity) | |
| print("Glove cos_similarity finished") | |
| return cos_similarity |