Upload wizmap_diffusiondb_vidprom_final.py

wizmap_diffusiondb_vidprom_final.py

@@ -0,0 +1,283 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from VidProM.isc.io import read_descriptors
+
+
+# In[2]:
+
+
+vid_name, vid_feature = read_descriptors(['./VidProM/vidprom_embed.hdf5'])
+
+
+# In[3]:
+
+
+vid_feature.shape
+
+
+# In[4]:
+
+
+import re
+
+def remove_numbers_and_words(s):
+    # 删除所有数字
+    s = re.sub(r'\d+', '', s)
+    # 删除指定的单词
+    s = re.sub(r'(image|message|attachment|quot|make)', '', s, flags=re.IGNORECASE)
+    return s
+
+
+# In[5]:
+
+
+import pandas as pd
+df = pd.read_csv('./prompts4video_unique.csv')
+imdb_reviews = list(df['prompt'])
+imdb_reviews_clean = [i.split('-')[0] for i in imdb_reviews]
+vidprom_prompts = [remove_numbers_and_words(i) for i in imdb_reviews_clean]
+
+
+# In[6]:
+
+
+len(vidprom_prompts)
+
+
+# In[7]:
+
+
+diffdb_name, diffdb_feature = read_descriptors(['./DiffusionDB/diffusiondb_embed.hdf5'])
+
+
+# In[8]:
+
+
+diffdb_feature.shape
+
+
+# In[1]:
+
+
+import pandas as pd
+path_to_prompt_parquet = "DiffusionDB/metadata-large.parquet"
+prompts = pd.read_parquet(
+    path_to_prompt_parquet,
+    columns=['prompt']
+)
+diffdb_prompts = sorted(list(set(prompts['prompt'])))
+print("Length of prompts: ", len(diffdb_prompts))
+
+
+# In[2]:
+
+
+diffdb_prompts_1 = list(set(prompts['prompt']))
+
+
+# In[3]:
+
+
+with open("diffusiondb_prompts.txt", 'w', encoding='utf-8') as file:
+    for fruit in diffdb_prompts_1:
+        file.write(fruit + '\n')
+
+
+# In[5]:
+
+
+len(diffdb_prompts_1)
+
+
+# In[ ]:
+
+
+hf.upload_file(path_or_fileobj="./wizmap_vidprom_diffusiondb_final/data_vidprom_diffusiondb.ndjson", \
+               path_in_repo="data_vidprom_diffusiondb.ndjson", repo_id="WenhaoWang/VidProM", \
+               repo_type="dataset")
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+import umap
+import numpy as np
+embedding_0 = umap.UMAP(n_neighbors=60,
+                      min_dist=0.1,
+                      metric='correlation').fit_transform(np.concatenate([vid_feature,diffdb_feature]))
+
+
+# In[ ]:
+
+
+np.save('umap_diffusiondb_vidprom.npy', embedding_0)
+
+
+# In[10]:
+
+
+import numpy as np
+embedding_0 = np.load('umap_diffusiondb_vidprom.npy')
+
+
+# In[11]:
+
+
+texts = vidprom_prompts + diffdb_prompts
+xs = embedding_0[:, 0].astype(float).tolist()
+ys = embedding_0[:, 1].astype(float).tolist()
+
+
+# In[12]:
+
+
+from glob import glob
+from os.path import exists, join, basename
+from tqdm import tqdm
+from json import load, dump
+from matplotlib import pyplot as plt
+from collections import Counter
+
+from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
+from quadtreed3 import Quadtree, Node
+from scipy.sparse import csr_matrix
+from sklearn.neighbors import KernelDensity
+from scipy.stats import norm
+from typing import Tuple
+from io import BytesIO
+from umap import UMAP
+
+import pandas as pd
+import numpy as np
+import ndjson
+import requests
+import urllib
+import wizmap
+
+SEED = 20230501
+
+plt.rcParams['figure.dpi'] = 300
+
+
+# In[13]:
+
+
+labels = [0]*len(vidprom_prompts) + [1] *len(diffdb_prompts)
+
+
+# In[14]:
+
+
+len(labels)
+
+
+# In[15]:
+
+
+group_names = ["VidProM", "DiffusionDB"]
+
+
+# In[16]:
+
+
+grid_dict = wizmap.generate_grid_dict(embedding_0[:, 0].astype(float).tolist(), \
+                          embedding_0[:, 1].astype(float).tolist(), \
+                          texts, \
+                          embedding_name = 'VidProM_DiffusionDB', \
+                          labels = labels, \
+                          group_names = group_names)
+
+
+# In[17]:
+
+
+print(grid_dict.keys())
+
+
+# In[18]:
+
+
+data_list = wizmap.generate_data_list(xs, ys, texts, labels = labels)
+
+
+# In[19]:
+
+
+get_ipython().system('mkdir wizmap_vidprom_diffusiondb_final')
+
+
+# In[20]:
+
+
+wizmap.save_json_files(data_list, grid_dict, output_dir='./wizmap_vidprom_diffusiondb_final')
+
+
+# In[21]:
+
+
+get_ipython().system('mv ./wizmap_vidprom_diffusiondb_final/data.ndjson ./wizmap_vidprom_diffusiondb_final/data_vidprom_diffusiondb.ndjson')
+
+
+# In[22]:
+
+
+get_ipython().system('mv ./wizmap_vidprom_diffusiondb_final/grid.json ./wizmap_vidprom_diffusiondb_final/grid_vidprom_diffusiondb.json')
+
+
+# In[6]:
+
+
+import os
+
+# os.environ["HF_ENDPOINT"] = "http://localhost:5564"
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+from huggingface_hub import HfApi, logging
+
+logging.set_verbosity_debug()
+hf = HfApi(
+    endpoint="https://huggingface.co", # Can be a Private Hub endpoint.
+    token="xxxx", # Token is not persisted on the machine.
+)
+
+
+# In[ ]:
+
+
+hf.upload_file(path_or_fileobj="./wizmap_vidprom_diffusiondb_final/grid_vidprom_diffusiondb.json", \
+               path_in_repo="grid_vidprom_diffusiondb.json", repo_id="WenhaoWang/VidProM", \
+               repo_type="dataset")
+
+
+# In[24]:
+
+
+hf.upload_file(path_or_fileobj="./wizmap_vidprom_diffusiondb_final/grid_vidprom_diffusiondb.json", \
+               path_in_repo="grid_vidprom_diffusiondb.json", repo_id="WenhaoWang/VidProM", \
+               repo_type="dataset")
+
+
+# In[25]:
+
+
+hf.upload_file(path_or_fileobj="./wizmap_vidprom_diffusiondb_final/data_vidprom_diffusiondb.ndjson", \
+               path_in_repo="data_vidprom_diffusiondb.ndjson", repo_id="WenhaoWang/VidProM", \
+               repo_type="dataset")
+
+