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00-Installations.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ae531c8-ea80-49d1-9513-4d4e926b7e7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install easydict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3836f6af-c2cc-438e-ba9b-812c9b7b590b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install datasets\n",
+    "!pip install pandas\n",
+    "!pip install matplotlib\n",
+    "!pip install seaborn\n",
+    "!pip install pillow\n",
+    "!pip install wordcloud\n",
+    "!pip install ipywidgets\n",
+    "!pip install sentence-transformers\n",
+    "!pip install easydict\n",
+    "!pip install --force-reinstall -v \"numpy==1.25.2\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1888801-9188-433e-8c23-ae3731901846",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install git+https://github.com/openai/CLIP.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8befa941-e491-4bd8-b898-55a2293c5f27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install typing-extensions>=4.8.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c37006e8-c0f4-428c-b884-95bfb7280b9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tensorflow[and-cuda]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

02-Work Embeddings.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "20631e0c-5f53-465b-8d9e-7b8072e26eda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\daparekh\\AppData\\Roaming\\Python\\Python311\\site-packages\\threadpoolctl.py:1214: RuntimeWarning: \n",
+      "Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at\n",
+      "the same time. Both libraries are known to be incompatible and this\n",
+      "can cause random crashes or deadlocks on Linux when loaded in the\n",
+      "same Python program.\n",
+      "Using threadpoolctl may cause crashes or deadlocks. For more\n",
+      "information and possible workarounds, please see\n",
+      "    https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md\n",
+      "\n",
+      "  warnings.warn(msg, RuntimeWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_from_disk\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "246a008c-a210-4bd6-99c4-0ada886cb11e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'fulltext'],\n",
+       "        num_rows: 33034\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'fulltext'],\n",
+       "        num_rows: 14158\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset = load_from_disk(\"PreProcessedData\")\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0c4dd5ce-701a-4afc-afdf-93e675147864",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "import torch\n",
+    "import torch.nn as nn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ab0deb7d-245d-4620-b9d6-dd71df74600a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "999296\n",
+      "1428686\n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_sentance = \"\"\n",
+    "for data in reloaded_dataset[\"train\"]:\n",
+    "    merged_sentance = merged_sentance + data[\"company\"]+\" \"+ data[\"content\"]+\" \"+ data[\"description\"]+\" \"\n",
+    "print(len(merged_sentance))\n",
+    "for data in reloaded_dataset[\"test\"]:\n",
+    "    merged_sentance = merged_sentance + data[\"company\"]+\" \"+ data[\"content\"]+\" \"+ data[\"description\"]+\" \"\n",
+    "print(len(merged_sentance))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "437fdfb1-c1db-4dff-888d-5c9b1029add7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "words = merged_sentance.split(' ')\n",
+    " \n",
+    "# create a dictionary\n",
+    "vocab = Counter(words) \n",
+    "vocab = sorted(vocab, key=vocab.get, reverse=True)\n",
+    "vocab_size = len(vocab)\n",
+    " \n",
+    "# create a word to index dictionary from our Vocab dictionary\n",
+    "word2idx = {word: ind for ind, word in enumerate(vocab)} \n",
+    "idx2word = {ind: word for ind, word in enumerate(vocab)} "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "862fbc17-ae03-48b5-b1ae-b6ed63eafb22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1790, 1790)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(word2idx),len(idx2word)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "008562d4-3789-4d26-8ab8-5d3b980b2438",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "words = reloaded_dataset[\"train\"][100]['fulltext']\n",
+    "words = words.split(' ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c7304275-11c7-4aa9-a929-3b6e9cd31f0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded_sentences = [word2idx[word] for word in words]\n",
+    " \n",
+    "# assign a value to your embedding_dim\n",
+    "e_dim = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "303ecce6-8560-44e6-8c62-e316315c3d04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 76, 34, 27, 0, 7, 1, 2]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoded_sentences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "82daf945-1bd5-4d75-be9b-bdfc1e8b1ee8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([12, 76, 34, 27,  0,  7,  1,  2])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.LongTensor(encoded_sentences)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "24a20907-1403-4677-ab62-725b25f5fa06",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8, 1])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialise an Embedding layer from Torch\n",
+    "emb = nn.Embedding(vocab_size, e_dim, padding_idx = 3)\n",
+    "word_vectors = emb(torch.LongTensor(encoded_sentences))\n",
+    " \n",
+    "#print the word_vectors\n",
+    "print(word_vectors.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9a5be641-14ee-45fa-8fa6-fd73834ac05d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_encoded_sentences(sentance):\n",
+    "    words = sentance.split(' ')\n",
+    "    encoded_words = [word2idx[word] for word in words]\n",
+    "    return encoded_words\n",
+    "\n",
+    "def get_decoded_sentences(encoded_words):\n",
+    "    sentance = ' '.join([idx2word[idx] for idx in encoded_words])\n",
+    "    return sentance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "df258a3a-0f6b-4f63-bfd3-f60a06f65471",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'facebook'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_decoded_sentences(get_encoded_sentences(\"facebook\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4cf66ff1-0d7d-4f1c-b0af-29b0396bf3c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=64x64>,\n",
+       " 'company': 'apple',\n",
+       " 'content': 'flag',\n",
+       " 'description': 'New Zealand',\n",
+       " 'fulltext': 'apple flag  New Zealand'}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset[\"train\"][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "91ac0e54-90bd-4c20-8cb8-7e333548f279",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fulltext_vector = []\n",
+    "for data in reloaded_dataset[\"train\"]:\n",
+    "    #print(data[\"fulltext\"])\n",
+    "    #print(get_encoded_sentences(data[\"fulltext\"]))\n",
+    "    encoded_sentences = get_encoded_sentences(data[\"fulltext\"])\n",
+    "    fulltext_vector.append(np.pad(encoded_sentences, [(0, 100-len(encoded_sentences))], mode='constant', constant_values=0))\n",
+    "    #print(fulltext_vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c913b2c3-8ba9-4eae-bcc3-93ef0998f40b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'fulltext', 'fulltext_vector'],\n",
+       "        num_rows: 33034\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'fulltext'],\n",
+       "        num_rows: 14158\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset[\"train\"]=reloaded_dataset[\"train\"].add_column(\"fulltext_vector\", fulltext_vector)\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "acdbc88f-1cba-4c75-86fd-db9f59454a50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fulltext_vector = []\n",
+    "for data in reloaded_dataset[\"test\"]:\n",
+    "    #print(data[\"fulltext\"])\n",
+    "    #print(get_encoded_sentences(data[\"fulltext\"]))\n",
+    "    encoded_sentences = get_encoded_sentences(data[\"fulltext\"])\n",
+    "    fulltext_vector.append(np.pad(encoded_sentences, [(0, 100-len(encoded_sentences))], mode='constant', constant_values=0))\n",
+    "    #print(fulltext_vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e51f138c-117c-4f79-8260-de1d56512d07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'fulltext', 'fulltext_vector'],\n",
+       "        num_rows: 33034\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'fulltext', 'fulltext_vector'],\n",
+       "        num_rows: 14158\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset[\"test\"]=reloaded_dataset[\"test\"].add_column(\"fulltext_vector\", fulltext_vector)\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a7c46e54-87c7-40c4-836c-b72dfbaea28e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93f1b3dc347847e89f0e18d9796fc2c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Saving the dataset (0/1 shards):   0%|          | 0/33034 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36765e95e88043a6aee8647b21900042",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Saving the dataset (0/1 shards):   0%|          | 0/14158 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reloaded_dataset.save_to_disk(\"PreProcessedDataWithEmb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "05367cd9-0afc-40cc-8dc7-2b9689eb1506",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'fulltext', 'fulltext_vector'],\n",
+       "        num_rows: 33034\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'fulltext', 'fulltext_vector'],\n",
+       "        num_rows: 14158\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset = load_from_disk(\"PreProcessedDataWithEmb\")\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3f8dfad-4dce-468d-bcbd-75743a6556f3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

utils.py

@@ -0,0 +1,71 @@
+
+# coding: utf-8
+
+# In[2]:
+
+
+# import nbimporter
+
+
+# In[3]:
+
+
+import numpy as np
+from torch import nn
+from torch import  autograd
+import torch
+import os
+import pdb
+
+
+# In[ ]:
+
+
+class Concat_embed(nn.Module):
+
+    def __init__(self, embed_dim, projected_embed_dim):
+        super(Concat_embed, self).__init__()
+        self.projection = nn.Sequential(
+            nn.Linear(in_features=embed_dim, out_features=projected_embed_dim),
+            nn.BatchNorm1d(num_features=projected_embed_dim),
+            nn.LeakyReLU(negative_slope=0.2, inplace=True)
+            )
+
+    def forward(self, inp, embed):
+        projected_embed = self.projection(embed)
+        replicated_embed = projected_embed.repeat(4, 4, 1, 1).permute(2,  3, 0, 1)
+        hidden_concat = torch.cat([inp, replicated_embed], 1)
+
+        return hidden_concat
+
+class Utils(object):
+
+    @staticmethod
+    def smooth_label(tensor, offset):
+        return tensor + offset
+
+    @staticmethod
+    def save_checkpoint(netD, netG, dir_path, subdir_path, epoch):
+        path =  os.path.join(dir_path, subdir_path)
+        if not os.path.exists(path):
+            os.makedirs(path)
+
+        torch.save(netD.state_dict(), '{0}/disc_{1}.pth'.format(path, epoch))
+        torch.save(netG.state_dict(), '{0}/gen_{1}.pth'.format(path, epoch))
+
+    @staticmethod
+    def weights_init(m):
+        classname = m.__class__.__name__
+        if classname.find('Conv') != -1:
+            m.weight.data.normal_(0.0, 0.02)
+        elif classname.find('BatchNorm') != -1:
+            m.weight.data.normal_(1.0, 0.02)
+            m.bias.data.fill_(0)
+
+
+class Logger(object):
+
+    def log_iteration_gan(self, epoch, iteration, d_loss, g_loss, real_score, fake_score):
+        print("Epoch: %d, Iter: %d, d_loss= %f, g_loss= %f, D(X)= %f, D(G(X))= %f" % (
+            epoch, iteration, d_loss.data.cpu().mean(), g_loss.data.cpu().mean(), real_score.data.cpu().mean(),
+            fake_score.data.cpu().mean()))