Remove Hugging Face download

requirements.txt

@@ -7,4 +7,5 @@ youtube_transcript_api
 torch
 transformers
 matplotlib
-seaborn
+seaborn
+datasets

s2_download_data.py

@@ -1,9 +1,6 @@
 import requests
 from PIL import Image
 from IPython.display import display
-import huggingface_hub
-from huggingface_hub import list_datasets
-from huggingface_hub import HfApi
 
 # You can use your own uploaded images and captions. 
 # You will be responsible for the legal use of images that 
@@ -49,19 +46,4 @@ def download_images():
         caption = img['caption']
         display(image)
         print(caption)
-        
-def load_data_from_huggingface(hf_dataset_name):
-        
-    api = HfApi()
-
-    #list models from huggingface
-
-    #models = list(api.list_models())
-
-    #list datasets from huggingface
-
-    #datasets = list(api.list_datasets())
-
-
-    return api.list_datasets(search=hf_dataset_name)
-   
+    

s3_data_to_vector_embedding.py

@@ -58,5 +58,4 @@ def save_embeddings():
         print(embedding['cross_modal_embeddings'][0].shape) #<class 'torch.Tensor'>
         torch.save(embedding['cross_modal_embeddings'][0], img['tensor_path'] + '.pt')
     
-save_embeddings()
 

s5-how-to-umap.py

@@ -6,12 +6,13 @@ import pandas as pd
 from tqdm import tqdm
 import matplotlib.pyplot as plt
 import seaborn as sns
-from s2_download_data import load_data_from_huggingface
-from utils import prepare_dataset_for_umap_visualization as data_prep
 from s3_data_to_vector_embedding import bt_embeddings_from_local
 import random
 import numpy as np
 import torch
+from sklearn.model_selection import train_test_split
+from datasets import load_dataset
+
 # prompt templates
 templates = [
     'a picture of {}',
@@ -23,10 +24,20 @@ templates = [
 def data_prep(hf_dataset_name, templates=templates, test_size=1000):
     # load Huggingface dataset (download if needed)
     
-    #dataset = load_dataset(hf_dataset_name, trust_remote_code=True)
-    dataset = load_data_from_huggingface(hf_dataset_name)
+    dataset = load_dataset(hf_dataset_name, trust_remote_code=True)
+    #dataset = load_data_from_huggingface(hf_dataset_name)
+    def display_list(lst, indent=0):
+        for item in lst:
+            if isinstance(item, list):
+                display_list(item, indent + 2)
+            else:
+                print(' ' * indent + str(item))
+
+    # Example usage:
+    display_list(dataset)
     # split dataset with specific test_size
-    train_test_dataset = dataset['train'].train_test_split(test_size=test_size)
+    train_test_dataset = train_test_split(dataset, test_size=test_size)
+
     # get the test dataset
     test_dataset = train_test_dataset['test']
     img_txt_pairs = []
@@ -36,30 +47,47 @@ def data_prep(hf_dataset_name, templates=templates, test_size=1000):
             'pil_img' : test_dataset[i]['image']
         })
     return img_txt_pairs
-    
-# compute BridgeTower embeddings for cat image-text pairs
-def load_cat_and_car_embeddings():
-        
-    # prepare image_text pairs 
 
-    # for the first 50 data of Huggingface dataset 
-    #  "yashikota/cat-image-dataset"
-    cat_img_txt_pairs = data_prep("yashikota/cat-image-dataset", 
-                                "cat", test_size=50)
+# load cat and car image-text pairs
+def load_pairs_from_dataset(dataset_name, file_name):
 
-    # for the first 50 data of Huggingface dataset 
-    #  "tanganke/stanford_cars"
-    car_img_txt_pairs = data_prep("tanganke/stanford_cars", 
-                                "car", test_size=50)
+    def load_dataset_locally(file_name):
+        with open(file_name, 'r') as f:
+            dataset = f.readlines()
+        return dataset
+    
+    def save_dataset_locally(dataset_list, file_name):
+        with open(file_name, 'w') as f:
+            for item in dataset_list:
+                f.write("%s\n" % item)
 
-    # display an example of a cat image-text pair data
-    display(cat_img_txt_pairs[0]['caption'])
-    display(cat_img_txt_pairs[0]['pil_img'])
 
-    # display an example of a car image-text pair data
-    display(car_img_txt_pairs[0]['caption'])
-    display(car_img_txt_pairs[0]['pil_img'])
+    def check_dataset_locally(file_name):
+        if (path.exists(file_name)):
+            return True
+        return False
+    
+    if (check_dataset_locally(file_name)):
+        print('Dataset already exists')
+        img_txt_pairs = load_dataset_locally(file_name)
+    else:
+        print('Downloading dataset')
+            
+        img_txt_pairs = data_prep(dataset_name, test_size=50)
+        save_dataset_locally(img_txt_pairs, file_name)
+    return img_txt_pairs
+        
 
+def load_all_dataset():
+    
+    cat_img_txt_pairs = load_pairs_from_dataset("yashikota/cat-image-dataset", './shared_data/cat_img_txt_pairs.txt')
+    car_img_txt_pairs = load_pairs_from_dataset("tanganke/stanford_cars", './shared_data/car_img_txt_pairs.txt')
+    
+    return cat_img_txt_pairs, car_img_txt_pairs
+# compute BridgeTower embeddings for cat image-text pairs
+def load_cat_and_car_embeddings():
+    # prepare image_text pairs 
+    cat_img_txt_pairs, car_img_txt_pairs = load_all_dataset()
     def save_embeddings(embedding, path):
         torch.save(embedding, path)
 
@@ -68,18 +96,18 @@ def load_cat_and_car_embeddings():
         caption = img_txt_pair['caption']
         return bt_embeddings_from_local(caption, pil_img)
     
-    def load_all_embeddings_from_image_text_pairs(file_name):
-        cat_embeddings = []
+    def load_all_embeddings_from_image_text_pairs(img_txt_pairs, file_name):
+        embeddings = []
         for img_txt_pair in tqdm(
-                            cat_img_txt_pairs, 
-                            total=len(cat_img_txt_pairs)
+                            img_txt_pairs, 
+                            total=len(img_txt_pairs)
                         ):
             pil_img = img_txt_pair['pil_img']
             caption = img_txt_pair['caption']
             embedding = load_embeddings(caption, pil_img)
-            cat_embeddings.append(embedding)
+            embeddings.append(embedding)
             save_embeddings(cat_embeddings, file_name)
-            return cat_embeddings
+            return embeddings
     
 
     cat_embeddings = []
@@ -87,12 +115,12 @@ def load_cat_and_car_embeddings():
     if (path.exists('./shared_data/cat_embeddings.pt')):
         cat_embeddings = torch.load('./shared_data/cat_embeddings.pt')
     else:
-        cat_embeddings = load_all_embeddings_from_image_text_pairs('./shared_data/cat_embeddings.pt')
+        cat_embeddings = load_all_embeddings_from_image_text_pairs(cat_img_txt_pairs, './shared_data/cat_embeddings.pt')
     
     if (path.exists('./shared_data/car_embeddings.pt')):
         car_embeddings = torch.load('./shared_data/car_embeddings.pt') 
     else:
-        car_embeddings = load_all_embeddings_from_image_text_pairs('./shared_data/car_embeddings.pt')
+        car_embeddings = load_all_embeddings_from_image_text_pairs(car_img_txt_pairs, './shared_data/car_embeddings.pt')
     
     return cat_embeddings, car_embeddings
                         
@@ -135,5 +163,15 @@ def show_umap_visualization():
     plt.xlabel('X')
     plt.ylabel('Y')
     plt.show()
-    
-load_cat_and_car_embeddings()
+
+def run():
+    cat_img_txt_pairs, car_img_txt_pairs = load_all_dataset()
+    # display an example of a cat image-text pair data
+    display(cat_img_txt_pairs[0]['caption'])
+    display(cat_img_txt_pairs[0]['pil_img'])
+
+    # display an example of a car image-text pair data
+    display(car_img_txt_pairs[0]['caption'])
+    display(car_img_txt_pairs[0]['pil_img'])
+
+run()