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03-GAN.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "pVAyqh-hccNc",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 22150,
+     "status": "ok",
+     "timestamp": 1726361588423,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "pVAyqh-hccNc",
+    "outputId": "c5eb1f4a-c3d1-4491-f677-6e4f87bd6a17"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting datasets\n",
+      "  Downloading datasets-3.0.0-py3-none-any.whl.metadata (19 kB)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.0)\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
+      "Collecting pyarrow>=15.0.0 (from datasets)\n",
+      "  Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n",
+      "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
+      "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
+      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n",
+      "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
+      "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.5)\n",
+      "Collecting xxhash (from datasets)\n",
+      "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
+      "Collecting multiprocess (from datasets)\n",
+      "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
+      "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
+      "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.5)\n",
+      "Requirement already satisfied: huggingface-hub>=0.22.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.24.6)\n",
+      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n",
+      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.0)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.11.1)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.22.0->datasets) (4.12.2)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.8)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.8.30)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
+      "Downloading datasets-3.0.0-py3-none-any.whl (474 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.3/474.3 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (39.9 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: xxhash, pyarrow, dill, multiprocess, datasets\n",
+      "  Attempting uninstall: pyarrow\n",
+      "    Found existing installation: pyarrow 14.0.2\n",
+      "    Uninstalling pyarrow-14.0.2:\n",
+      "      Successfully uninstalled pyarrow-14.0.2\n",
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
+      "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0mSuccessfully installed datasets-3.0.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2KMucD1rKwTz",
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 2060,
+     "status": "ok",
+     "timestamp": 1726361622660,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "2KMucD1rKwTz"
+   },
+   "outputs": [],
+   "source": [
+    "!pip freeze > requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "Z8RYK2ZSK42y",
+   "metadata": {
+    "id": "Z8RYK2ZSK42y"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "tVGMu6bhcdoz",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 4076,
+     "status": "ok",
+     "timestamp": 1726321470958,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "tVGMu6bhcdoz",
+    "outputId": "436e5f2d-46e8-4813-86d0-945606f3431b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ddb93c12-776c-43f3-87f2-566a61510042",
+   "metadata": {
+    "id": "ddb93c12-776c-43f3-87f2-566a61510042"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_from_disk\n",
+    "\n",
+    "import os\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.autograd import Variable\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
+    "import torchvision\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "import utils\n",
+    "\n",
+    "from utils import Utils, Logger"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2b42c1f-13d2-423b-a8c0-c1631d93b3fe",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 9,
+     "status": "ok",
+     "timestamp": 1726321476954,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "a2b42c1f-13d2-423b-a8c0-c1631d93b3fe",
+    "outputId": "b5f7a35f-201d-47fb-8d67-8f22e568e89c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x7988a01888d0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random_seed = 42\n",
+    "torch.manual_seed(random_seed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05efff77-160f-41e3-939a-0755f6986de0",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 9,
+     "status": "ok",
+     "timestamp": 1726321476955,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "05efff77-160f-41e3-939a-0755f6986de0",
+    "outputId": "a74d9600-0cc4-4c6a-e56b-cace164a82b7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "AVAIL_GPUS = min(1, torch.cuda.device_count())\n",
+    "NUM_WORKERS=int(os.cpu_count() / 2)\n",
+    "AVAIL_GPUS,NUM_WORKERS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fecca51-0f62-4060-9df1-ee94c54ebcaf",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 8,
+     "status": "ok",
+     "timestamp": 1726321476955,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "5fecca51-0f62-4060-9df1-ee94c54ebcaf",
+    "outputId": "6ad227e6-6d27-4ac5-bff1-56f7831b17ae"
+   },
+   "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset = load_from_disk(\"/content/drive/MyDrive/PreProcessedDataWithEmb\")\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4b75140-eb3d-425a-9769-b7a838b66a1d",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 7,
+     "status": "ok",
+     "timestamp": 1726321476955,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "c4b75140-eb3d-425a-9769-b7a838b66a1d",
+    "outputId": "cc744cf8-215a-481e-c041-142de4ee429d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cuda')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set device\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8808490-4734-470b-b330-470accf429b3",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 36
+    },
+    "executionInfo": {
+     "elapsed": 6,
+     "status": "ok",
+     "timestamp": 1726321476955,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "a8808490-4734-470b-b330-470accf429b3",
+    "outputId": "eccf8d8e-e9bc-4bc9-ee97-b01219332110"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      },
+      "text/plain": [
+       "'Tesla T4'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.get_device_name(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "K_3r1z0op4Ky",
+   "metadata": {
+    "id": "K_3r1z0op4Ky"
+   },
+   "outputs": [],
+   "source": [
+    "def get_wrong_image(dataset,correct_class):\n",
+    "        for data in dataset:\n",
+    "            if data['company'] != correct_class:\n",
+    "                return data['image']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "FOoGc_spmduX",
+   "metadata": {
+    "id": "FOoGc_spmduX"
+   },
+   "outputs": [],
+   "source": [
+    "# prompt: transform PIL Image to Tensor\\\n",
+    "\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "transform = transforms.PILToTensor()\n",
+    "\n",
+    "TrainTransformedImage = []\n",
+    "WorngTrainTransformedImage = []\n",
+    "TrainTextVector = []\n",
+    "for data in reloaded_dataset[\"train\"]:\n",
+    "  image_tensor = transform(data['image'].convert(\"RGB\"))\n",
+    "  wrong_image_tensor = transform(get_wrong_image(reloaded_dataset[\"train\"],data['company']).convert(\"RGB\"))\n",
+    "  TrainTransformedImage.append(image_tensor)\n",
+    "  WorngTrainTransformedImage.append(wrong_image_tensor)\n",
+    "  TrainTextVector.append(np.array(data['fulltext_vector'], dtype=\"float32\"))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "MxB4YVQKm8OV",
+   "metadata": {
+    "id": "MxB4YVQKm8OV"
+   },
+   "outputs": [],
+   "source": [
+    "# prompt: transform PIL Image to Tensor\\\n",
+    "\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "transform = transforms.PILToTensor()\n",
+    "\n",
+    "TestTransformedImage = []\n",
+    "WorngTestTransformedImage = []\n",
+    "TestTextVector = []\n",
+    "for data in reloaded_dataset[\"test\"]:\n",
+    "  image_tensor = transform(data['image'].convert(\"RGB\"))\n",
+    "  wrong_image_tensor = transform(get_wrong_image(reloaded_dataset[\"test\"],data['company']).convert(\"RGB\"))\n",
+    "  TestTransformedImage.append(image_tensor)\n",
+    "  WorngTestTransformedImage.append(wrong_image_tensor)\n",
+    "  TestTextVector.append(np.array(data['fulltext_vector'], dtype=\"float32\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "zVPu-CLLlEo5",
+   "metadata": {
+    "id": "zVPu-CLLlEo5"
+   },
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset\n",
+    "import numpy as np\n",
+    "\n",
+    "class EmojiDataset(Dataset):\n",
+    "    def __init__(self,transformed_image,wrong_transformed_image,text_vector):\n",
+    "        self.image_transform = transformed_image\n",
+    "        self.wrong_image_transform = wrong_transformed_image\n",
+    "        self.text_vector = text_vector\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.image_transform)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        image = self.image_transform[idx]\n",
+    "        wrong_image = self.wrong_image_transform[idx]\n",
+    "        fulltext_vector = self.text_vector[idx]\n",
+    "        return image.float(), fulltext_vector, wrong_image\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "E2dcO4FIg4Pj",
+   "metadata": {
+    "id": "E2dcO4FIg4Pj"
+   },
+   "outputs": [],
+   "source": [
+    "train_data = EmojiDataset(TrainTransformedImage,WorngTrainTransformedImage,TrainTextVector)\n",
+    "train_dataloader = DataLoader(train_data, batch_size=64, shuffle=True)\n",
+    "test_data = EmojiDataset(TestTransformedImage,WorngTestTransformedImage,TestTextVector)\n",
+    "test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6-pRimNwMnl",
+   "metadata": {
+    "id": "e6-pRimNwMnl"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "O_oCUV-4f5I9",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 356
+    },
+    "executionInfo": {
+     "elapsed": 17115,
+     "status": "error",
+     "timestamp": 1726322100858,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "O_oCUV-4f5I9",
+    "outputId": "eb0d861c-1d9b-416e-b85c-c6475cdaf246"
+   },
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "mat1 and mat2 shapes cannot be multiplied (12288x64 and 786432x1024)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-32273bdaa5b7>\u001b[0m in \u001b[0;36m<cell line: 58>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0;31m# real_images = batch  # Replace with your real image data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0;31m# real_images = real_images.view(batch_size, -1) # flatten the real_images tensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0mreal_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiscriminator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m         \u001b[0md_loss_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-21-32273bdaa5b7>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;31m# Hyperparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (12288x64 and 786432x1024)"
+     ]
+    }
+   ],
+   "source": [
+    "# prompt: Write a python code to train a stackGan model to generate a image of size 64x64x4 using a fulltext_vector of size 100\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "class Generator(nn.Module):\n",
+    "    def __init__(self, input_size, output_size):\n",
+    "        super(Generator, self).__init__()\n",
+    "        self.model = nn.Sequential(\n",
+    "            nn.Linear(input_size, 256),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(256, 512),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(512, 1024),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(1024, output_size),\n",
+    "            nn.Tanh()\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.model(x)\n",
+    "\n",
+    "class Discriminator(nn.Module):\n",
+    "    def __init__(self, input_size):\n",
+    "        super(Discriminator, self).__init__()\n",
+    "        self.model = nn.Sequential(\n",
+    "            nn.Linear(input_size, 1024),\n",
+    "            nn.LeakyReLU(0.2),\n",
+    "            nn.Linear(1024, 512),\n",
+    "            nn.LeakyReLU(0.2),\n",
+    "            nn.Linear(512, 256),\n",
+    "            nn.LeakyReLU(0.2),\n",
+    "            nn.Linear(256, 1),\n",
+    "            nn.Sigmoid()\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.model(x)\n",
+    "\n",
+    "# Hyperparameters\n",
+    "input_size = 100  # Size of the fulltext_vector\n",
+    "output_size = 64* 64 * 64 * 3  # Size of the generated image (64x64x4)\n",
+    "batch_size = 64\n",
+    "learning_rate = 0.0002\n",
+    "num_epochs = 100\n",
+    "\n",
+    "# Create generator and discriminator\n",
+    "generator = Generator(input_size, output_size).to(device)\n",
+    "discriminator = Discriminator(output_size).to(device)\n",
+    "\n",
+    "# Define loss function and optimizers\n",
+    "criterion = nn.BCELoss()\n",
+    "optimizer_G = optim.Adam(generator.parameters(), lr=learning_rate)\n",
+    "optimizer_D = optim.Adam(discriminator.parameters(), lr=learning_rate)\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    #for batch in test_dataloader:\n",
+    "    for i, batch in enumerate(test_dataloader, 0):\n",
+    "\n",
+    "        real_images = batch[0].to(device)\n",
+    "        right_embed = batch[1].to(device)\n",
+    "        wrong_images = batch[2].to(device)\n",
+    "        # Generate random noise\n",
+    "        noise = torch.randn(batch_size, input_size).to(device)\n",
+    "\n",
+    "        # Generate fake images\n",
+    "        fake_images = generator(right_embed)\n",
+    "\n",
+    "        # Train discriminator\n",
+    "        optimizer_D.zero_grad()\n",
+    "        real_labels = torch.ones(batch_size, 1).to(device)\n",
+    "        fake_labels = torch.zeros(batch_size, 1).to(device)\n",
+    "\n",
+    "        # Real images (assuming you have real images in your dataset)\n",
+    "        # real_images = batch  # Replace with your real image data\n",
+    "        # real_images = real_images.view(batch_size, -1) # flatten the real_images tensor\n",
+    "        real_outputs = discriminator(real_images)\n",
+    "        d_loss_real = criterion(real_outputs, real_labels)\n",
+    "\n",
+    "        fake_outputs = discriminator(fake_images.detach())\n",
+    "        d_loss_fake = criterion(fake_outputs, fake_labels)\n",
+    "        d_loss = d_loss_real + d_loss_fake\n",
+    "        d_loss = d_loss_fake\n",
+    "        d_loss.backward()\n",
+    "        optimizer_D.step()\n",
+    "\n",
+    "        # Train generator\n",
+    "        optimizer_G.zero_grad()\n",
+    "        fake_outputs = discriminator(fake_images)\n",
+    "        g_loss = criterion(fake_outputs, real_labels)\n",
+    "        g_loss.backward()\n",
+    "        optimizer_G.step()\n",
+    "\n",
+    "    print(f\"Epoch [{epoch+1}/{num_epochs}], D Loss: {d_loss.item():.4f}, G Loss: {g_loss.item():.4f}\")\n",
+    "\n",
+    "print(\"Training finished!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "408f4137-3658-4b46-8034-2591773e70eb",
+   "metadata": {
+    "editable": true,
+    "id": "408f4137-3658-4b46-8034-2591773e70eb",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class generator(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(generator, self).__init__()\n",
+    "        self.image_size = 64\n",
+    "        self.num_channels = 3\n",
+    "        self.noise_dim = 100\n",
+    "        self.embed_dim = 100\n",
+    "        self.projected_embed_dim = 128\n",
+    "        self.latent_dim = self.noise_dim + self.projected_embed_dim\n",
+    "        self.ngf = 64\n",
+    "\n",
+    "        self.projection = nn.Sequential(\n",
+    "            nn.Linear(in_features=self.embed_dim, out_features=self.projected_embed_dim),\n",
+    "            nn.BatchNorm1d(num_features=self.projected_embed_dim),\n",
+    "            nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
+    "            )\n",
+    "\n",
+    "        self.netG = nn.Sequential(\n",
+    "            nn.ConvTranspose2d(self.latent_dim, self.ngf * 8, 4, 1, 0, bias=False),\n",
+    "            nn.BatchNorm2d(self.ngf * 8),\n",
+    "            nn.ReLU(True),\n",
+    "            # state size. (ngf*8) x 4 x 4\n",
+    "            nn.ConvTranspose2d(self.ngf * 8, self.ngf * 4, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ngf * 4),\n",
+    "            nn.ReLU(True),\n",
+    "            # state size. (ngf*4) x 8 x 8\n",
+    "            nn.ConvTranspose2d(self.ngf * 4, self.ngf * 2, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ngf * 2),\n",
+    "            nn.ReLU(True),\n",
+    "            # state size. (ngf*2) x 16 x 16\n",
+    "            nn.ConvTranspose2d(self.ngf * 2,self.ngf, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ngf),\n",
+    "            nn.ReLU(True),\n",
+    "            # state size. (ngf) x 32 x 32\n",
+    "            nn.ConvTranspose2d(self.ngf, self.num_channels, 4, 2, 1, bias=False),\n",
+    "            nn.Tanh()\n",
+    "             # state size. (num_channels) x 64 x 64\n",
+    "            )\n",
+    "\n",
+    "\n",
+    "    def forward(self, embed_vector, z):\n",
+    "\n",
+    "        projected_embed = self.projection(embed_vector).unsqueeze(2).unsqueeze(3)\n",
+    "        latent_vector = torch.cat([projected_embed, z], 1)\n",
+    "        output = self.netG(latent_vector)\n",
+    "\n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2ce6d2d-87c7-4cde-a7e2-e382d8b334b0",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 582,
+     "status": "ok",
+     "timestamp": 1726322184976,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "e2ce6d2d-87c7-4cde-a7e2-e382d8b334b0",
+    "outputId": "e79717ad-f16d-4e61-f988-102ac801f50d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "generator(\n",
+      "  (projection): Sequential(\n",
+      "    (0): Linear(in_features=100, out_features=128, bias=True)\n",
+      "    (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (2): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "  )\n",
+      "  (netG): Sequential(\n",
+      "    (0): ConvTranspose2d(228, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
+      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (2): ReLU(inplace=True)\n",
+      "    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (5): ReLU(inplace=True)\n",
+      "    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (8): ReLU(inplace=True)\n",
+      "    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (11): ReLU(inplace=True)\n",
+      "    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (13): Tanh()\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "netG = generator().to(device)\n",
+    "# Handle multi-gpu if desired\n",
+    "netG.apply(Utils.weights_init)\n",
+    "# Print the model\n",
+    "print(netG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ca1cf01-943e-47a8-acf7-9576243d5119",
+   "metadata": {
+    "id": "8ca1cf01-943e-47a8-acf7-9576243d5119"
+   },
+   "outputs": [],
+   "source": [
+    "class discriminator(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(discriminator, self).__init__()\n",
+    "        self.image_size = 64\n",
+    "        self.num_channels = 3\n",
+    "        self.embed_dim = 100\n",
+    "        self.projected_embed_dim = 128\n",
+    "        self.ndf = 64\n",
+    "        self.B_dim = 128\n",
+    "        self.C_dim = 16\n",
+    "\n",
+    "        self.netD_1 = nn.Sequential(\n",
+    "            # input is (nc) x 64 x 64\n",
+    "            nn.Conv2d(self.num_channels, self.ndf, 4, 2, 1, bias=False),\n",
+    "            nn.LeakyReLU(0.2, inplace=True),\n",
+    "            # state size. (ndf) x 32 x 32\n",
+    "            nn.Conv2d(self.ndf, self.ndf * 2, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ndf * 2),\n",
+    "            nn.LeakyReLU(0.2, inplace=True),\n",
+    "            # state size. (ndf*2) x 16 x 16\n",
+    "            nn.Conv2d(self.ndf * 2, self.ndf * 4, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ndf * 4),\n",
+    "            nn.LeakyReLU(0.2, inplace=True),\n",
+    "            # state size. (ndf*4) x 8 x 8\n",
+    "            nn.Conv2d(self.ndf * 4, self.ndf * 8, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ndf * 8),\n",
+    "            nn.LeakyReLU(0.2, inplace=True),\n",
+    "        )\n",
+    "\n",
+    "        self.projector = utils.Concat_embed(self.embed_dim, self.projected_embed_dim)\n",
+    "\n",
+    "        self.netD_2 = nn.Sequential(\n",
+    "            # state size. (ndf*8) x 4 x 4\n",
+    "            nn.Conv2d(self.ndf * 8 + self.projected_embed_dim, 1, 4, 1, 0, bias=False),\n",
+    "            nn.Sigmoid()\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, inp, embed):\n",
+    "        x_intermediate = self.netD_1(inp)\n",
+    "        x = self.projector(x_intermediate, embed)\n",
+    "        x = self.netD_2(x)\n",
+    "\n",
+    "        return x.view(-1, 1).squeeze(1) , x_intermediate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88574088-bcca-49dd-91c2-e9f6b40e4b8f",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 3,
+     "status": "ok",
+     "timestamp": 1726322190144,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "88574088-bcca-49dd-91c2-e9f6b40e4b8f",
+    "outputId": "d59ce15c-979b-4b9d-d4db-0bdc3526b7a2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "discriminator(\n",
+      "  (netD_1): Sequential(\n",
+      "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "  )\n",
+      "  (projector): Concat_embed(\n",
+      "    (projection): Sequential(\n",
+      "      (0): Linear(in_features=100, out_features=128, bias=True)\n",
+      "      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (2): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (netD_2): Sequential(\n",
+      "    (0): Conv2d(640, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
+      "    (1): Sigmoid()\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "netD_1 = discriminator().to(device)\n",
+    "# Handle multi-gpu if desired\n",
+    "netD_1.apply(Utils.weights_init)\n",
+    "# Print the model\n",
+    "print(netD_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "118be16d-27aa-41c1-a514-ca1fc95c54df",
+   "metadata": {
+    "id": "118be16d-27aa-41c1-a514-ca1fc95c54df"
+   },
+   "outputs": [],
+   "source": [
+    "class gan_factory(object):\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def generator_factory(type):\n",
+    "        if type == 'gan':\n",
+    "            return generator()\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def discriminator_factory(type):\n",
+    "        if type == 'gan':\n",
+    "            return discriminator()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16c6ac96-644c-4269-8731-ab447ad478fd",
+   "metadata": {
+    "editable": true,
+    "id": "16c6ac96-644c-4269-8731-ab447ad478fd",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "import yaml\n",
+    "from torch import nn\n",
+    "from torch.autograd import Variable\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "from utils import Utils, Logger\n",
+    "from PIL import Image\n",
+    "import os\n",
+    "\n",
+    "class Trainer(object):\n",
+    "    def __init__(self, type, dataset, split, lr,\n",
+    "                 save_path, l1_coef, l2_coef, batch_size, num_workers, epochs):\n",
+    "\n",
+    "        self.generator = torch.nn.DataParallel(gan_factory.generator_factory(type).cuda())\n",
+    "        self.discriminator = torch.nn.DataParallel(gan_factory.discriminator_factory(type).cuda())\n",
+    "\n",
+    "        self.discriminator.apply(Utils.weights_init)\n",
+    "\n",
+    "        self.generator.apply(Utils.weights_init)\n",
+    "\n",
+    "        self.dataset = dataset\n",
+    "\n",
+    "        #print \"Image = \",len(self.dataset)\n",
+    "        self.noise_dim = 100\n",
+    "        self.batch_size = batch_size\n",
+    "        self.num_workers = num_workers\n",
+    "        self.lr = lr\n",
+    "        self.beta1 = 0.5\n",
+    "        self.num_epochs = epochs\n",
+    "\n",
+    "\n",
+    "        self.l1_coef = l1_coef\n",
+    "        self.l2_coef = l2_coef\n",
+    "\n",
+    "        self.data_loader = DataLoader(self.dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers)\n",
+    "\n",
+    "        self.optimD = torch.optim.Adam(self.discriminator.parameters(), lr=self.lr, betas=(self.beta1, 0.999))\n",
+    "        self.optimG = torch.optim.Adam(self.generator.parameters(), lr=self.lr, betas=(self.beta1, 0.999))\n",
+    "\n",
+    "        self.logger = Logger()\n",
+    "        self.checkpoints_path = 'checkpoints'\n",
+    "        self.save_path = save_path\n",
+    "        self.type = type\n",
+    "\n",
+    "    def train(self, cls):\n",
+    "\n",
+    "        if self.type == 'gan':\n",
+    "            self._train_gan(cls)\n",
+    "\n",
+    "\n",
+    "    def _train_gan(self, cls):\n",
+    "        criterion = nn.BCELoss()\n",
+    "        l2_loss = nn.MSELoss()\n",
+    "        l1_loss = nn.L1Loss()\n",
+    "        #print(\"Started Training\")\n",
+    "        for epoch in range(self.num_epochs):\n",
+    "            iteration = 0\n",
+    "            #print(\"Starting Iter :\",iteration)\n",
+    "            for sample in self.data_loader:\n",
+    "                #print('Inside Dataloader loop is')\n",
+    "                iteration += 1\n",
+    "                right_images = sample[0]\n",
+    "                right_embed = sample[1]\n",
+    "                wrong_images = sample[2]\n",
+    "\n",
+    "\n",
+    "\n",
+    "                right_images = Variable(right_images.float()).cuda()\n",
+    "                right_embed = Variable(right_embed.float()).cuda()\n",
+    "                wrong_images = Variable(wrong_images.float()).cuda()\n",
+    "\n",
+    "                #print(\"Data Loaded\")\n",
+    "\n",
+    "                real_labels = torch.ones(right_images.size(0))\n",
+    "                fake_labels = torch.zeros(right_images.size(0))\n",
+    "\n",
+    "                smoothed_real_labels = torch.FloatTensor(Utils.smooth_label(real_labels.numpy(), -0.1))\n",
+    "\n",
+    "                real_labels = Variable(real_labels).cuda()\n",
+    "                smoothed_real_labels = Variable(smoothed_real_labels).cuda()\n",
+    "                fake_labels = Variable(fake_labels).cuda()\n",
+    "\n",
+    "                # Train the discriminator\n",
+    "                self.discriminator.zero_grad()\n",
+    "                outputs, activation_real = self.discriminator(right_images, right_embed)\n",
+    "                real_loss = criterion(outputs, smoothed_real_labels)\n",
+    "                real_score = outputs\n",
+    "\n",
+    "                if cls:\n",
+    "                    outputs, _ = self.discriminator(wrong_images, right_embed)\n",
+    "                    wrong_loss = criterion(outputs, fake_labels)\n",
+    "                    wrong_score = outputs\n",
+    "\n",
+    "                noise = Variable(torch.randn(right_images.size(0), 100)).cuda()\n",
+    "                noise = noise.view(noise.size(0), 100, 1, 1)\n",
+    "                fake_images = self.generator(right_embed, noise)\n",
+    "                outputs, _ = self.discriminator(fake_images, right_embed)\n",
+    "                fake_loss = criterion(outputs, fake_labels)\n",
+    "                fake_score = outputs\n",
+    "\n",
+    "                d_loss = real_loss + fake_loss\n",
+    "\n",
+    "                if cls:\n",
+    "                    d_loss = d_loss + wrong_loss\n",
+    "\n",
+    "                d_loss.backward()\n",
+    "                self.optimD.step()\n",
+    "\n",
+    "                # Train the generator\n",
+    "                self.generator.zero_grad()\n",
+    "                noise = Variable(torch.randn(right_images.size(0), 100)).cuda()\n",
+    "                noise = noise.view(noise.size(0), 100, 1, 1)\n",
+    "                fake_images = self.generator(right_embed, noise)\n",
+    "                outputs, activation_fake = self.discriminator(fake_images, right_embed)\n",
+    "                _, activation_real = self.discriminator(right_images, right_embed)\n",
+    "\n",
+    "                activation_fake = torch.mean(activation_fake, 0)    #try with median and check if it converges\n",
+    "                activation_real = torch.mean(activation_real, 0)    #try with median and check if it converges\n",
+    "\n",
+    "\n",
+    "                g_loss = criterion(outputs, real_labels) + self.l2_coef * l2_loss(activation_fake, activation_real.detach()) + self.l1_coef * l1_loss(fake_images, right_images)\n",
+    "\n",
+    "                g_loss.backward()\n",
+    "                self.optimG.step()\n",
+    "\n",
+    "                #print('Completed Iter:', iteration)\n",
+    "\n",
+    "            self.logger.log_iteration_gan(epoch, iteration, d_loss, g_loss, real_score, fake_score)\n",
+    "\n",
+    "\n",
+    "            if (epoch) % 10 == 0:\n",
+    "                print('epoch', epoch, 'complete')\n",
+    "                Utils.save_checkpoint(self.discriminator, self.generator, self.checkpoints_path, self.save_path, epoch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d815894-238b-473d-8c29-cd2ebd948c59",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "editable": true,
+    "executionInfo": {
+     "elapsed": 2890277,
+     "status": "error",
+     "timestamp": 1726326443418,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "0d815894-238b-473d-8c29-cd2ebd948c59",
+    "outputId": "868608c0-e04e-4de7-ff06-f18455e12b59",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0, Iter: 222, d_loss= 0.701056, g_loss= 3538.156250, D(X)= 0.579759, D(G(X))= 0.064360\n",
+      "epoch 0 complete\n",
+      "Epoch: 1, Iter: 222, d_loss= 0.679630, g_loss= 3676.295898, D(X)= 0.604906, D(G(X))= 0.025995\n",
+      "Epoch: 2, Iter: 222, d_loss= 0.839086, g_loss= 3861.553955, D(X)= 0.576425, D(G(X))= 0.096675\n",
+      "Epoch: 3, Iter: 222, d_loss= 0.979223, g_loss= 3977.661377, D(X)= 0.955430, D(G(X))= 0.406845\n",
+      "Epoch: 4, Iter: 222, d_loss= 0.474595, g_loss= 3004.067383, D(X)= 0.799094, D(G(X))= 0.075874\n",
+      "Epoch: 5, Iter: 222, d_loss= 0.804994, g_loss= 3905.889160, D(X)= 0.574385, D(G(X))= 0.079843\n",
+      "Epoch: 6, Iter: 222, d_loss= 0.828463, g_loss= 3658.190430, D(X)= 0.960235, D(G(X))= 0.308461\n",
+      "Epoch: 7, Iter: 222, d_loss= 0.587771, g_loss= 2689.952148, D(X)= 0.787618, D(G(X))= 0.161049\n",
+      "Epoch: 8, Iter: 222, d_loss= 0.427244, g_loss= 3475.554688, D(X)= 0.849420, D(G(X))= 0.053325\n",
+      "Epoch: 9, Iter: 222, d_loss= 0.975016, g_loss= 2920.777100, D(X)= 0.410495, D(G(X))= 0.030045\n",
+      "Epoch: 10, Iter: 222, d_loss= 0.646374, g_loss= 2579.601074, D(X)= 0.710434, D(G(X))= 0.144055\n",
+      "epoch 10 complete\n",
+      "Epoch: 11, Iter: 222, d_loss= 0.468959, g_loss= 3792.114258, D(X)= 0.747990, D(G(X))= 0.037221\n",
+      "Epoch: 12, Iter: 222, d_loss= 0.467665, g_loss= 3418.384277, D(X)= 0.910842, D(G(X))= 0.091576\n",
+      "Epoch: 13, Iter: 222, d_loss= 0.612966, g_loss= 3741.182861, D(X)= 0.638289, D(G(X))= 0.025249\n",
+      "Epoch: 14, Iter: 222, d_loss= 1.720957, g_loss= 3652.041016, D(X)= 0.281481, D(G(X))= 0.001660\n",
+      "Epoch: 15, Iter: 222, d_loss= 0.531606, g_loss= 4409.900391, D(X)= 0.962485, D(G(X))= 0.101459\n",
+      "Epoch: 16, Iter: 222, d_loss= 0.491479, g_loss= 4025.999756, D(X)= 0.745472, D(G(X))= 0.061522\n",
+      "Epoch: 17, Iter: 222, d_loss= 0.766251, g_loss= 3645.494873, D(X)= 0.532194, D(G(X))= 0.012785\n",
+      "Epoch: 18, Iter: 222, d_loss= 0.695738, g_loss= 3654.352783, D(X)= 0.944553, D(G(X))= 0.253696\n",
+      "Epoch: 19, Iter: 222, d_loss= 0.867143, g_loss= 2578.100342, D(X)= 0.975738, D(G(X))= 0.324007\n",
+      "Epoch: 20, Iter: 222, d_loss= 0.509606, g_loss= 4646.008301, D(X)= 0.733054, D(G(X))= 0.066041\n",
+      "epoch 20 complete\n",
+      "Epoch: 21, Iter: 222, d_loss= 0.466746, g_loss= 3191.031250, D(X)= 0.721822, D(G(X))= 0.008881\n",
+      "Epoch: 22, Iter: 222, d_loss= 0.461730, g_loss= 4163.461914, D(X)= 0.811961, D(G(X))= 0.034651\n",
+      "Epoch: 23, Iter: 222, d_loss= 0.385916, g_loss= 3554.304443, D(X)= 0.799397, D(G(X))= 0.006166\n",
+      "Epoch: 24, Iter: 222, d_loss= 0.774094, g_loss= 4240.382812, D(X)= 0.922192, D(G(X))= 0.276380\n",
+      "Epoch: 25, Iter: 222, d_loss= 0.373230, g_loss= 3827.503174, D(X)= 0.847593, D(G(X))= 0.004507\n",
+      "Epoch: 26, Iter: 222, d_loss= 0.985485, g_loss= 4279.049805, D(X)= 0.439561, D(G(X))= 0.013861\n",
+      "Epoch: 27, Iter: 222, d_loss= 0.626296, g_loss= 3783.946533, D(X)= 0.593357, D(G(X))= 0.016181\n",
+      "Epoch: 28, Iter: 222, d_loss= 0.531090, g_loss= 3176.968018, D(X)= 0.680971, D(G(X))= 0.004957\n",
+      "Epoch: 29, Iter: 222, d_loss= 0.353129, g_loss= 2946.677979, D(X)= 0.880036, D(G(X))= 0.011091\n",
+      "Epoch: 30, Iter: 222, d_loss= 0.334022, g_loss= 3002.093018, D(X)= 0.919297, D(G(X))= 0.001554\n",
+      "epoch 30 complete\n",
+      "Epoch: 31, Iter: 222, d_loss= 0.413073, g_loss= 4168.980469, D(X)= 0.798591, D(G(X))= 0.026550\n",
+      "Epoch: 32, Iter: 222, d_loss= 0.430312, g_loss= 3704.318359, D(X)= 0.877181, D(G(X))= 0.043618\n",
+      "Epoch: 33, Iter: 222, d_loss= 0.540595, g_loss= 3159.942383, D(X)= 0.654681, D(G(X))= 0.013845\n",
+      "Epoch: 34, Iter: 222, d_loss= 0.613879, g_loss= 3588.398438, D(X)= 0.586779, D(G(X))= 0.002503\n",
+      "Epoch: 35, Iter: 222, d_loss= 0.419698, g_loss= 4234.351562, D(X)= 0.834483, D(G(X))= 0.048954\n",
+      "Epoch: 36, Iter: 222, d_loss= 0.397612, g_loss= 2739.781982, D(X)= 0.831707, D(G(X))= 0.021257\n",
+      "Epoch: 37, Iter: 222, d_loss= 0.965550, g_loss= 4611.081055, D(X)= 0.460998, D(G(X))= 0.003786\n",
+      "Epoch: 38, Iter: 222, d_loss= 0.394915, g_loss= 3028.919922, D(X)= 0.805663, D(G(X))= 0.004134\n",
+      "Epoch: 39, Iter: 222, d_loss= 0.448785, g_loss= 2665.940674, D(X)= 0.757539, D(G(X))= 0.001606\n",
+      "Epoch: 40, Iter: 222, d_loss= 0.546468, g_loss= 3599.658691, D(X)= 0.638130, D(G(X))= 0.007002\n",
+      "epoch 40 complete\n",
+      "Epoch: 41, Iter: 222, d_loss= 0.536460, g_loss= 3580.464111, D(X)= 0.779603, D(G(X))= 0.043807\n",
+      "Epoch: 42, Iter: 222, d_loss= 0.360860, g_loss= 4108.963379, D(X)= 0.906363, D(G(X))= 0.006131\n",
+      "Epoch: 43, Iter: 222, d_loss= 0.360820, g_loss= 3276.907959, D(X)= 0.858369, D(G(X))= 0.003254\n",
+      "Epoch: 44, Iter: 222, d_loss= 1.295341, g_loss= 4108.103516, D(X)= 0.266473, D(G(X))= 0.000534\n",
+      "Epoch: 45, Iter: 222, d_loss= 0.387217, g_loss= 4203.428711, D(X)= 0.788921, D(G(X))= 0.004543\n",
+      "Epoch: 46, Iter: 222, d_loss= 0.422805, g_loss= 3600.573242, D(X)= 0.745221, D(G(X))= 0.005458\n",
+      "Epoch: 47, Iter: 222, d_loss= 0.383310, g_loss= 3377.399170, D(X)= 0.967776, D(G(X))= 0.000569\n",
+      "Epoch: 48, Iter: 222, d_loss= 0.364424, g_loss= 2696.472900, D(X)= 0.817939, D(G(X))= 0.000297\n",
+      "Epoch: 49, Iter: 222, d_loss= 0.360125, g_loss= 3540.071045, D(X)= 0.881340, D(G(X))= 0.003572\n",
+      "Epoch: 50, Iter: 222, d_loss= 0.339098, g_loss= 3244.859131, D(X)= 0.919540, D(G(X))= 0.004547\n",
+      "epoch 50 complete\n",
+      "Epoch: 51, Iter: 222, d_loss= 0.363068, g_loss= 3698.898193, D(X)= 0.937415, D(G(X))= 0.007880\n",
+      "Epoch: 52, Iter: 222, d_loss= 0.375254, g_loss= 3719.618652, D(X)= 0.957973, D(G(X))= 0.005729\n",
+      "Epoch: 53, Iter: 222, d_loss= 0.438091, g_loss= 4305.036621, D(X)= 0.715028, D(G(X))= 0.000790\n",
+      "Epoch: 54, Iter: 222, d_loss= 0.354891, g_loss= 3700.000732, D(X)= 0.937225, D(G(X))= 0.000967\n",
+      "Epoch: 55, Iter: 222, d_loss= 0.364136, g_loss= 3272.887451, D(X)= 0.958542, D(G(X))= 0.001034\n",
+      "Epoch: 56, Iter: 222, d_loss= 0.666980, g_loss= 3945.071045, D(X)= 0.924580, D(G(X))= 0.263233\n",
+      "Epoch: 57, Iter: 222, d_loss= 0.356129, g_loss= 4415.549316, D(X)= 0.903652, D(G(X))= 0.003884\n",
+      "Epoch: 58, Iter: 222, d_loss= 0.355318, g_loss= 3713.228271, D(X)= 0.828642, D(G(X))= 0.003223\n",
+      "Epoch: 59, Iter: 222, d_loss= 0.333158, g_loss= 3780.786621, D(X)= 0.922390, D(G(X))= 0.000811\n",
+      "Epoch: 60, Iter: 222, d_loss= 0.331645, g_loss= 3918.236816, D(X)= 0.900606, D(G(X))= 0.001038\n",
+      "epoch 60 complete\n",
+      "Epoch: 61, Iter: 222, d_loss= 0.342588, g_loss= 3963.584961, D(X)= 0.866488, D(G(X))= 0.000639\n",
+      "Epoch: 62, Iter: 222, d_loss= 0.335670, g_loss= 3823.017090, D(X)= 0.909363, D(G(X))= 0.000514\n",
+      "Epoch: 63, Iter: 222, d_loss= 0.332328, g_loss= 3192.711914, D(X)= 0.871337, D(G(X))= 0.000163\n",
+      "Epoch: 64, Iter: 222, d_loss= 0.352667, g_loss= 4211.349121, D(X)= 0.830729, D(G(X))= 0.000308\n",
+      "Epoch: 65, Iter: 222, d_loss= 0.375499, g_loss= 3618.913574, D(X)= 0.785201, D(G(X))= 0.001551\n",
+      "Epoch: 66, Iter: 222, d_loss= 0.422351, g_loss= 2822.109131, D(X)= 0.796270, D(G(X))= 0.030600\n",
+      "Epoch: 67, Iter: 222, d_loss= 0.532200, g_loss= 3551.816895, D(X)= 0.684085, D(G(X))= 0.037150\n",
+      "Epoch: 68, Iter: 222, d_loss= 0.468477, g_loss= 3234.406982, D(X)= 0.808089, D(G(X))= 0.020581\n",
+      "Epoch: 69, Iter: 222, d_loss= 0.357905, g_loss= 3858.374023, D(X)= 0.955218, D(G(X))= 0.000775\n",
+      "Epoch: 70, Iter: 222, d_loss= 0.377006, g_loss= 3424.719727, D(X)= 0.787619, D(G(X))= 0.000361\n",
+      "epoch 70 complete\n",
+      "Epoch: 71, Iter: 222, d_loss= 0.332974, g_loss= 2816.813477, D(X)= 0.919339, D(G(X))= 0.000862\n",
+      "Epoch: 72, Iter: 222, d_loss= 0.337460, g_loss= 2565.474121, D(X)= 0.928413, D(G(X))= 0.000817\n",
+      "Epoch: 73, Iter: 222, d_loss= 0.328905, g_loss= 4435.673340, D(X)= 0.912533, D(G(X))= 0.000075\n",
+      "Epoch: 74, Iter: 222, d_loss= 0.353846, g_loss= 4069.165283, D(X)= 0.819689, D(G(X))= 0.000205\n",
+      "Epoch: 75, Iter: 222, d_loss= 0.456906, g_loss= 4124.631836, D(X)= 0.978655, D(G(X))= 0.027646\n",
+      "Epoch: 76, Iter: 222, d_loss= 0.351983, g_loss= 3220.302490, D(X)= 0.854669, D(G(X))= 0.000532\n",
+      "Epoch: 77, Iter: 222, d_loss= 0.331386, g_loss= 3644.930908, D(X)= 0.883734, D(G(X))= 0.000370\n",
+      "Epoch: 78, Iter: 222, d_loss= 0.335561, g_loss= 2944.252441, D(X)= 0.914969, D(G(X))= 0.000500\n",
+      "Epoch: 79, Iter: 222, d_loss= 0.329391, g_loss= 3497.903320, D(X)= 0.882267, D(G(X))= 0.000115\n",
+      "Epoch: 80, Iter: 222, d_loss= 0.342888, g_loss= 3254.711914, D(X)= 0.845431, D(G(X))= 0.000475\n",
+      "epoch 80 complete\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-43-d0d8e74db11c>\u001b[0m in \u001b[0;36m<cell line: 31>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m                   )\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0;31m#trainer.predict()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-41-1bfb2b9620b8>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, cls)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'gan'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_gan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-41-1bfb2b9620b8>\u001b[0m in \u001b[0;36m_train_gan\u001b[0;34m(self, cls)\u001b[0m\n\u001b[1;32m    106\u001b[0m                     \u001b[0md_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md_loss\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mwrong_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m                 \u001b[0md_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    523\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m             )\n\u001b[0;32m--> 525\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    526\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         )\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m     _engine_run_backward(\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py\u001b[0m in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    742\u001b[0m         \u001b[0munregister_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_register_logging_hooks_on_whole_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    743\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m         return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    745\u001b[0m             \u001b[0mt_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m         )  # Calls into the C++ engine to run the backward pass\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import argparse\n",
+    "from PIL import Image   #This may not be used\n",
+    "import os    ##This may not be used\n",
+    "import easydict\n",
+    "\n",
+    "args = easydict.EasyDict({'type': 'gan',\n",
+    "                        'lr': 0.0002,\n",
+    "                        'l1_coef': 50,\n",
+    "                        'l2_coef': 100,\n",
+    "                        'cls': True,\n",
+    "                        'save_path':'Result',\n",
+    "                        'inference': True,\n",
+    "                        'dataset': test_data,\n",
+    "                        'split': 2,\n",
+    "                        'batch_size':64,\n",
+    "                        'num_workers':1,\n",
+    "                        'epochs':600})\n",
+    "\n",
+    "trainer = Trainer(type=args.type,\n",
+    "                  dataset=args.dataset,\n",
+    "                  split=args.split,\n",
+    "                  lr=args.lr,\n",
+    "                  save_path=args.save_path,\n",
+    "                  l1_coef=args.l1_coef,\n",
+    "                  l2_coef=args.l2_coef,\n",
+    "                  batch_size=args.batch_size,\n",
+    "                  num_workers=args.num_workers,\n",
+    "                  epochs=args.epochs\n",
+    "                  )\n",
+    "\n",
+    "trainer.train(args.cls)\n",
+    "#trainer.predict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "242ee9a9-47df-4c50-b7d9-ae181543f75f",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 211
+    },
+    "executionInfo": {
+     "elapsed": 759,
+     "status": "error",
+     "timestamp": 1726326479692,
+     "user": {
+      "displayName": "Darshil Parekh",
+      "userId": "08764169128860999444"
+     },
+     "user_tz": -330
+    },
+    "id": "242ee9a9-47df-4c50-b7d9-ae181543f75f",
+    "outputId": "1c0df61d-a9a3-461f-9ea1-a0eb63c92be1"
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Logger' object has no attribute 'd_loss_list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-45-b7e01a63074d>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Extract the data from the logger\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0md_loss_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md_loss_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mg_loss_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg_loss_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0md_x_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md_x_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Logger' object has no attribute 'd_loss_list'"
+     ]
+    }
+   ],
+   "source": [
+    "# prompt: draw a graph for the d_loss, g_loss, d(X) and d(g(x))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have lists or arrays named d_loss_list, g_loss_list, d_x_list, d_gx_list\n",
+    "# containing the values for each metric over the training epochs.\n",
+    "\n",
+    "# Extract the data from the logger\n",
+    "d_loss_list = trainer.logger.d_loss_list\n",
+    "g_loss_list = trainer.logger.g_loss_list\n",
+    "d_x_list = trainer.logger.d_x_list\n",
+    "d_gx_list = trainer.logger.d_gx_list\n",
+    "\n",
+    "# Create the figure and axes\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
+    "\n",
+    "# Plot D Loss\n",
+    "axs[0, 0].plot(d_loss_list)\n",
+    "axs[0, 0].set_title(\"Discriminator Loss\")\n",
+    "axs[0, 0].set_xlabel(\"Epoch\")\n",
+    "axs[0, 0].set_ylabel(\"Loss\")\n",
+    "\n",
+    "# Plot G Loss\n",
+    "axs[0, 1].plot(g_loss_list)\n",
+    "axs[0, 1].set_title(\"Generator Loss\")\n",
+    "axs[0, 1].set_xlabel(\"Epoch\")\n",
+    "axs[0, 1].set_ylabel(\"Loss\")\n",
+    "\n",
+    "# Plot D(X)\n",
+    "axs[1, 0].plot(d_x_list)\n",
+    "axs[1, 0].set_title(\"Discriminator Output for Real Images (D(X))\")\n",
+    "axs[1, 0].set_xlabel(\"Epoch\")\n",
+    "axs[1, 0].set_ylabel(\"Score\")\n",
+    "\n",
+    "# Plot D(G(X))\n",
+    "axs[1, 1].plot(d_gx_list)\n",
+    "axs[1, 1].set_title(\"Discriminator Output for Fake Images (D(G(X)))\")\n",
+    "axs[1, 1].set_xlabel(\"Epoch\")\n",
+    "axs[1, 1].set_ylabel(\"Score\")\n",
+    "\n",
+    "# Adjust layout and display the plot\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e2bf862-35fe-433e-b1e2-012cbd5ee47d",
+   "metadata": {
+    "editable": true,
+    "id": "1e2bf862-35fe-433e-b1e2-012cbd5ee47d",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!cp -r checkpoints /content/drive/MyDrive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "JDN71GvsFTyn",
+   "metadata": {
+    "id": "JDN71GvsFTyn"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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
+}

03_GAN_1.ipynb

@@ -0,0 +1,921 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "pVAyqh-hccNc",
+   "metadata": {
+    "id": "pVAyqh-hccNc"
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "jxrRXwMPRZkB",
+   "metadata": {
+    "id": "jxrRXwMPRZkB"
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install google-colab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "tVGMu6bhcdoz",
+   "metadata": {
+    "id": "tVGMu6bhcdoz"
+   },
+   "outputs": [],
+   "source": [
+    "#from google.colab import drive\n",
+    "#drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ddb93c12-776c-43f3-87f2-566a61510042",
+   "metadata": {
+    "id": "ddb93c12-776c-43f3-87f2-566a61510042"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_from_disk\n",
+    "\n",
+    "import os\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.autograd import Variable\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
+    "import torchvision\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "import utils\n",
+    "\n",
+    "from utils import Utils, Logger"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a2b42c1f-13d2-423b-a8c0-c1631d93b3fe",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a2b42c1f-13d2-423b-a8c0-c1631d93b3fe",
+    "outputId": "48c61bf7-c6f0-4999-be41-9343ece1c42d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x269e8d33c10>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random_seed = 42\n",
+    "torch.manual_seed(random_seed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "05efff77-160f-41e3-939a-0755f6986de0",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "05efff77-160f-41e3-939a-0755f6986de0",
+    "outputId": "8a37fe4f-0e64-454e-fa11-7fda2854ab4a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 11)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "AVAIL_GPUS = min(1, torch.cuda.device_count())\n",
+    "NUM_WORKERS=int(os.cpu_count() / 2)\n",
+    "AVAIL_GPUS,NUM_WORKERS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "Dt3BJgH_RkaV",
+   "metadata": {
+    "id": "Dt3BJgH_RkaV"
+   },
+   "outputs": [],
+   "source": [
+    "colab = False\n",
+    "colabUrl = \"/content/drive/MyDrive/PreProcessedDataWithEmb\"\n",
+    "\n",
+    "localUrl = \"C:\\\\Users\\\\daparekh\\\\OneDrive - OpenText\\\\Desktop\\\\Final Thesis\\\\Code\\\\PreProcessedDataWithEmb\"\n",
+    "\n",
+    "if colab:\n",
+    "  data_path = colabUrl\n",
+    "else:\n",
+    "  data_path = localUrl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5fecca51-0f62-4060-9df1-ee94c54ebcaf",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5fecca51-0f62-4060-9df1-ee94c54ebcaf",
+    "outputId": "47be9553-c7cb-4c31-e231-9f761e338a44"
+   },
+   "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset = load_from_disk(data_path)\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c4b75140-eb3d-425a-9769-b7a838b66a1d",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "c4b75140-eb3d-425a-9769-b7a838b66a1d",
+    "outputId": "ccf919fd-c5e9-4e1d-9c7a-f49eecbac827"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cuda')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set device\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a8808490-4734-470b-b330-470accf429b3",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a8808490-4734-470b-b330-470accf429b3",
+    "outputId": "47144284-4221-4def-87fd-fb05fb375345"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'NVIDIA RTX 500 Ada Generation Laptop GPU'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.get_device_name(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "K_3r1z0op4Ky",
+   "metadata": {
+    "id": "K_3r1z0op4Ky"
+   },
+   "outputs": [],
+   "source": [
+    "def get_wrong_image(dataset,correct_class):\n",
+    "        for data in dataset:\n",
+    "            if data['company'] != correct_class:\n",
+    "                return data['image']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "FOoGc_spmduX",
+   "metadata": {
+    "id": "FOoGc_spmduX"
+   },
+   "outputs": [],
+   "source": [
+    "# prompt: transform PIL Image to Tensor\\\n",
+    "\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "transform = transforms.PILToTensor()\n",
+    "\n",
+    "TrainTransformedImage = []\n",
+    "WorngTrainTransformedImage = []\n",
+    "TrainTextVector = []\n",
+    "for data in reloaded_dataset[\"train\"]:\n",
+    "  image_tensor = transform(data['image'].convert(\"RGB\"))\n",
+    "  wrong_image_tensor = transform(get_wrong_image(reloaded_dataset[\"train\"],data['company']).convert(\"RGB\"))\n",
+    "  TrainTransformedImage.append(image_tensor)\n",
+    "  WorngTrainTransformedImage.append(wrong_image_tensor)\n",
+    "  TrainTextVector.append(np.array(data['fulltext_vector'], dtype=\"float32\"))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "MxB4YVQKm8OV",
+   "metadata": {
+    "id": "MxB4YVQKm8OV"
+   },
+   "outputs": [],
+   "source": [
+    "# prompt: transform PIL Image to Tensor\\\n",
+    "\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "transform = transforms.PILToTensor()\n",
+    "\n",
+    "TestTransformedImage = []\n",
+    "WorngTestTransformedImage = []\n",
+    "TestTextVector = []\n",
+    "for data in reloaded_dataset[\"test\"]:\n",
+    "  image_tensor = transform(data['image'].convert(\"RGB\"))\n",
+    "  wrong_image_tensor = transform(get_wrong_image(reloaded_dataset[\"test\"],data['company']).convert(\"RGB\"))\n",
+    "  TestTransformedImage.append(image_tensor)\n",
+    "  WorngTestTransformedImage.append(wrong_image_tensor)\n",
+    "  TestTextVector.append(np.array(data['fulltext_vector'], dtype=\"float32\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "zVPu-CLLlEo5",
+   "metadata": {
+    "id": "zVPu-CLLlEo5"
+   },
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset\n",
+    "import numpy as np\n",
+    "\n",
+    "class EmojiDataset(Dataset):\n",
+    "    def __init__(self,transformed_image,wrong_transformed_image,text_vector):\n",
+    "        self.image_transform = transformed_image\n",
+    "        self.wrong_image_transform = wrong_transformed_image\n",
+    "        self.text_vector = text_vector\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.image_transform)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        image = self.image_transform[idx]\n",
+    "        wrong_image = self.wrong_image_transform[idx]\n",
+    "        fulltext_vector = self.text_vector[idx]\n",
+    "        return image.float(), fulltext_vector, wrong_image\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "HSAg0I6jaaSW",
+   "metadata": {
+    "id": "HSAg0I6jaaSW"
+   },
+   "outputs": [],
+   "source": [
+    "batch_size = 64"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "E2dcO4FIg4Pj",
+   "metadata": {
+    "id": "E2dcO4FIg4Pj"
+   },
+   "outputs": [],
+   "source": [
+    "train_data = EmojiDataset(TrainTransformedImage,WorngTrainTransformedImage,TrainTextVector)\n",
+    "train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
+    "test_data = EmojiDataset(TestTransformedImage,WorngTestTransformedImage,TestTextVector)\n",
+    "test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "408f4137-3658-4b46-8034-2591773e70eb",
+   "metadata": {
+    "editable": true,
+    "id": "408f4137-3658-4b46-8034-2591773e70eb",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class generator(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(generator, self).__init__()\n",
+    "        self.image_size = 64\n",
+    "        self.num_channels = 3\n",
+    "        self.noise_dim = 100\n",
+    "        self.embed_dim = 100\n",
+    "        self.projected_embed_dim = 128\n",
+    "        self.latent_dim = self.noise_dim + self.projected_embed_dim\n",
+    "        self.ngf = 64\n",
+    "\n",
+    "        self.projection = nn.Sequential(\n",
+    "            nn.Linear(in_features=self.embed_dim, out_features=self.projected_embed_dim),\n",
+    "            nn.BatchNorm1d(num_features=self.projected_embed_dim),\n",
+    "            nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
+    "            )\n",
+    "\n",
+    "        self.netG = nn.Sequential(\n",
+    "            nn.ConvTranspose2d(self.latent_dim, self.ngf * 8, 4, 1, 0, bias=False),\n",
+    "            nn.BatchNorm2d(self.ngf * 8),\n",
+    "            nn.ReLU(True),\n",
+    "            # state size. (ngf*8) x 4 x 4\n",
+    "            nn.ConvTranspose2d(self.ngf * 8, self.ngf * 4, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ngf * 4),\n",
+    "            nn.ReLU(True),\n",
+    "            # state size. (ngf*4) x 8 x 8\n",
+    "            nn.ConvTranspose2d(self.ngf * 4, self.ngf * 2, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ngf * 2),\n",
+    "            nn.ReLU(True),\n",
+    "            # state size. (ngf*2) x 16 x 16\n",
+    "            nn.ConvTranspose2d(self.ngf * 2,self.ngf, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ngf),\n",
+    "            nn.ReLU(True),\n",
+    "            # state size. (ngf) x 32 x 32\n",
+    "            nn.ConvTranspose2d(self.ngf, self.num_channels, 4, 2, 1, bias=False),\n",
+    "            nn.Tanh()\n",
+    "             # state size. (num_channels) x 64 x 64\n",
+    "            )\n",
+    "\n",
+    "\n",
+    "    def forward(self, embed_vector, z):\n",
+    "\n",
+    "        projected_embed = self.projection(embed_vector).unsqueeze(2).unsqueeze(3)\n",
+    "        latent_vector = torch.cat([projected_embed, z], 1)\n",
+    "        output = self.netG(latent_vector)\n",
+    "\n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e2ce6d2d-87c7-4cde-a7e2-e382d8b334b0",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "e2ce6d2d-87c7-4cde-a7e2-e382d8b334b0",
+    "outputId": "94a5adf9-6d0b-4f55-c61b-2ff189d4c7d9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "generator(\n",
+      "  (projection): Sequential(\n",
+      "    (0): Linear(in_features=100, out_features=128, bias=True)\n",
+      "    (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (2): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "  )\n",
+      "  (netG): Sequential(\n",
+      "    (0): ConvTranspose2d(228, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
+      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (2): ReLU(inplace=True)\n",
+      "    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (5): ReLU(inplace=True)\n",
+      "    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (8): ReLU(inplace=True)\n",
+      "    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (11): ReLU(inplace=True)\n",
+      "    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (13): Tanh()\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "netG = generator().to(device)\n",
+    "# Handle multi-gpu if desired\n",
+    "netG.apply(Utils.weights_init)\n",
+    "# Print the model\n",
+    "print(netG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8ca1cf01-943e-47a8-acf7-9576243d5119",
+   "metadata": {
+    "id": "8ca1cf01-943e-47a8-acf7-9576243d5119"
+   },
+   "outputs": [],
+   "source": [
+    "class discriminator(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(discriminator, self).__init__()\n",
+    "        self.image_size = 64\n",
+    "        self.num_channels = 3\n",
+    "        self.embed_dim = 100\n",
+    "        self.projected_embed_dim = 128\n",
+    "        self.ndf = 64\n",
+    "        self.B_dim = 128\n",
+    "        self.C_dim = 16\n",
+    "\n",
+    "        self.netD_1 = nn.Sequential(\n",
+    "            # input is (nc) x 64 x 64\n",
+    "            nn.Conv2d(self.num_channels, self.ndf, 4, 2, 1, bias=False),\n",
+    "            nn.LeakyReLU(0.2, inplace=True),\n",
+    "            # state size. (ndf) x 32 x 32\n",
+    "            nn.Conv2d(self.ndf, self.ndf * 2, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ndf * 2),\n",
+    "            nn.LeakyReLU(0.2, inplace=True),\n",
+    "            # state size. (ndf*2) x 16 x 16\n",
+    "            nn.Conv2d(self.ndf * 2, self.ndf * 4, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ndf * 4),\n",
+    "            nn.LeakyReLU(0.2, inplace=True),\n",
+    "            # state size. (ndf*4) x 8 x 8\n",
+    "            nn.Conv2d(self.ndf * 4, self.ndf * 8, 4, 2, 1, bias=False),\n",
+    "            nn.BatchNorm2d(self.ndf * 8),\n",
+    "            nn.LeakyReLU(0.2, inplace=True),\n",
+    "        )\n",
+    "\n",
+    "        self.projector = utils.Concat_embed(self.embed_dim, self.projected_embed_dim)\n",
+    "\n",
+    "        self.netD_2 = nn.Sequential(\n",
+    "            # state size. (ndf*8) x 4 x 4\n",
+    "            nn.Conv2d(self.ndf * 8 + self.projected_embed_dim, 1, 4, 1, 0, bias=False),\n",
+    "            nn.Sigmoid()\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, inp, embed):\n",
+    "        x_intermediate = self.netD_1(inp)\n",
+    "        x = self.projector(x_intermediate, embed)\n",
+    "        x = self.netD_2(x)\n",
+    "\n",
+    "        return x.view(-1, 1).squeeze(1) , x_intermediate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "88574088-bcca-49dd-91c2-e9f6b40e4b8f",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "88574088-bcca-49dd-91c2-e9f6b40e4b8f",
+    "outputId": "8d364989-a60f-4606-c934-f559b0c7fe87"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "discriminator(\n",
+      "  (netD_1): Sequential(\n",
+      "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "  )\n",
+      "  (projector): Concat_embed(\n",
+      "    (projection): Sequential(\n",
+      "      (0): Linear(in_features=100, out_features=128, bias=True)\n",
+      "      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (2): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (netD_2): Sequential(\n",
+      "    (0): Conv2d(640, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
+      "    (1): Sigmoid()\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "netD_1 = discriminator().to(device)\n",
+    "# Handle multi-gpu if desired\n",
+    "netD_1.apply(Utils.weights_init)\n",
+    "# Print the model\n",
+    "print(netD_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "118be16d-27aa-41c1-a514-ca1fc95c54df",
+   "metadata": {
+    "id": "118be16d-27aa-41c1-a514-ca1fc95c54df"
+   },
+   "outputs": [],
+   "source": [
+    "class gan_factory(object):\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def generator_factory(type):\n",
+    "        if type == 'gan':\n",
+    "            return generator()\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def discriminator_factory(type):\n",
+    "        if type == 'gan':\n",
+    "            return discriminator()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "16c6ac96-644c-4269-8731-ab447ad478fd",
+   "metadata": {
+    "editable": true,
+    "id": "16c6ac96-644c-4269-8731-ab447ad478fd",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "import yaml\n",
+    "from torch import nn\n",
+    "from torch.autograd import Variable\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "from utils import Utils, Logger\n",
+    "from PIL import Image\n",
+    "import os\n",
+    "\n",
+    "class Trainer(object):\n",
+    "    def __init__(self, type, dataset, lr, save_path, l1_coef, l2_coef, batch_size, num_workers, epochs):\n",
+    "\n",
+    "        self.generator = torch.nn.DataParallel(gan_factory.generator_factory(type).cuda())\n",
+    "        self.discriminator = torch.nn.DataParallel(gan_factory.discriminator_factory(type).cuda())\n",
+    "\n",
+    "        self.discriminator.apply(Utils.weights_init)\n",
+    "\n",
+    "        self.generator.apply(Utils.weights_init)\n",
+    "\n",
+    "        self.dataset = dataset\n",
+    "\n",
+    "        #print \"Image = \",len(self.dataset)\n",
+    "        self.noise_dim = 100\n",
+    "        self.batch_size = batch_size\n",
+    "        self.num_workers = num_workers\n",
+    "        self.lr = lr\n",
+    "        self.beta1 = 0.5\n",
+    "        self.num_epochs = epochs\n",
+    "\n",
+    "\n",
+    "        self.l1_coef = l1_coef\n",
+    "        self.l2_coef = l2_coef\n",
+    "\n",
+    "        self.data_loader = DataLoader(self.dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers)\n",
+    "\n",
+    "        self.optimD = torch.optim.Adam(self.discriminator.parameters(), lr=self.lr, betas=(self.beta1, 0.999))\n",
+    "        self.optimG = torch.optim.Adam(self.generator.parameters(), lr=self.lr, betas=(self.beta1, 0.999))\n",
+    "\n",
+    "        self.logger = Logger()\n",
+    "        self.checkpoints_path = 'checkpoints'\n",
+    "        self.save_path = save_path\n",
+    "        self.type = type\n",
+    "\n",
+    "    def train(self, cls):\n",
+    "\n",
+    "        if self.type == 'gan':\n",
+    "            self._train_gan(cls)\n",
+    "\n",
+    "\n",
+    "    def _train_gan(self, cls):\n",
+    "        criterion = nn.BCELoss()\n",
+    "        l2_loss = nn.MSELoss()\n",
+    "        l1_loss = nn.L1Loss()\n",
+    "        #print(\"Started Training\")\n",
+    "        for epoch in range(self.num_epochs):\n",
+    "            iteration = 0\n",
+    "            #print(\"Starting Iter :\",iteration)\n",
+    "            for sample in self.data_loader:\n",
+    "                #print('Inside Dataloader loop is')\n",
+    "                iteration += 1\n",
+    "                right_images = sample[0]\n",
+    "                right_embed = sample[1]\n",
+    "                wrong_images = sample[2]\n",
+    "\n",
+    "\n",
+    "\n",
+    "                right_images = Variable(right_images.float()).cuda()\n",
+    "                right_embed = Variable(right_embed.float()).cuda()\n",
+    "                wrong_images = Variable(wrong_images.float()).cuda()\n",
+    "\n",
+    "                #print(\"Data Loaded\")\n",
+    "\n",
+    "                real_labels = torch.ones(right_images.size(0))\n",
+    "                fake_labels = torch.zeros(right_images.size(0))\n",
+    "\n",
+    "                smoothed_real_labels = torch.FloatTensor(Utils.smooth_label(real_labels.numpy(), -0.1))\n",
+    "\n",
+    "                real_labels = Variable(real_labels).cuda()\n",
+    "                smoothed_real_labels = Variable(smoothed_real_labels).cuda()\n",
+    "                fake_labels = Variable(fake_labels).cuda()\n",
+    "\n",
+    "                # Train the discriminator\n",
+    "                self.discriminator.zero_grad()\n",
+    "                outputs, activation_real = self.discriminator(right_images, right_embed)\n",
+    "                real_loss = criterion(outputs, smoothed_real_labels)\n",
+    "                real_score = outputs\n",
+    "\n",
+    "                if cls:\n",
+    "                    outputs, _ = self.discriminator(wrong_images, right_embed)\n",
+    "                    wrong_loss = criterion(outputs, fake_labels)\n",
+    "                    wrong_score = outputs\n",
+    "\n",
+    "                noise = Variable(torch.randn(right_images.size(0), 100)).cuda()\n",
+    "                noise = noise.view(noise.size(0), 100, 1, 1)\n",
+    "                fake_images = self.generator(right_embed, noise)\n",
+    "                outputs, _ = self.discriminator(fake_images, right_embed)\n",
+    "                fake_loss = criterion(outputs, fake_labels)\n",
+    "                fake_score = outputs\n",
+    "\n",
+    "                d_loss = real_loss + fake_loss\n",
+    "\n",
+    "                if cls:\n",
+    "                    d_loss = d_loss + wrong_loss\n",
+    "\n",
+    "                d_loss.backward()\n",
+    "                self.optimD.step()\n",
+    "\n",
+    "                # Train the generator\n",
+    "                self.generator.zero_grad()\n",
+    "                noise = Variable(torch.randn(right_images.size(0), 100)).cuda()\n",
+    "                noise = noise.view(noise.size(0), 100, 1, 1)\n",
+    "                fake_images = self.generator(right_embed, noise)\n",
+    "                outputs, activation_fake = self.discriminator(fake_images, right_embed)\n",
+    "                _, activation_real = self.discriminator(right_images, right_embed)\n",
+    "\n",
+    "                activation_fake = torch.mean(activation_fake, 0)    #try with median and check if it converges\n",
+    "                activation_real = torch.mean(activation_real, 0)    #try with median and check if it converges\n",
+    "\n",
+    "\n",
+    "                g_loss = criterion(outputs, real_labels) + self.l2_coef * l2_loss(activation_fake, activation_real.detach()) + self.l1_coef * l1_loss(fake_images, right_images)\n",
+    "\n",
+    "                g_loss.backward()\n",
+    "                self.optimG.step()\n",
+    "\n",
+    "                #print('Completed Iter:', iteration)\n",
+    "\n",
+    "            self.logger.log_iteration_gan(epoch, iteration, d_loss, g_loss, real_score, fake_score)\n",
+    "\n",
+    "\n",
+    "            if (epoch) % 10 == 0:\n",
+    "                print('epoch', epoch, 'complete')\n",
+    "                Utils.save_checkpoint(self.discriminator, self.generator, self.checkpoints_path, self.save_path, epoch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0d815894-238b-473d-8c29-cd2ebd948c59",
+   "metadata": {
+    "editable": true,
+    "id": "0d815894-238b-473d-8c29-cd2ebd948c59",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Trainer' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[18], line 15\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01measydict\u001b[39;00m\n\u001b[0;32m      4\u001b[0m args \u001b[38;5;241m=\u001b[39m easydict\u001b[38;5;241m.\u001b[39mEasyDict({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgan\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      5\u001b[0m                         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m0.001\u001b[39m,\n\u001b[0;32m      6\u001b[0m                         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ml1_coef\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m50\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     12\u001b[0m                         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_workers\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;241m6\u001b[39m,\n\u001b[0;32m     13\u001b[0m                         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mepochs\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m100\u001b[39m})\n\u001b[1;32m---> 15\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mtype,\n\u001b[0;32m     16\u001b[0m                   dataset\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mdataset,\n\u001b[0;32m     17\u001b[0m                   lr\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mlr,\n\u001b[0;32m     18\u001b[0m                   save_path\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39msave_path,\n\u001b[0;32m     19\u001b[0m                   l1_coef\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39ml1_coef,\n\u001b[0;32m     20\u001b[0m                   l2_coef\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39ml2_coef,\n\u001b[0;32m     21\u001b[0m                   batch_size\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mbatch_size,\n\u001b[0;32m     22\u001b[0m                   num_workers\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mnum_workers,\n\u001b[0;32m     23\u001b[0m                   epochs\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mepochs\n\u001b[0;32m     24\u001b[0m                   )\n\u001b[0;32m     26\u001b[0m trainer\u001b[38;5;241m.\u001b[39mtrain(args\u001b[38;5;241m.\u001b[39mcls)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'Trainer' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "import argparse\n",
+    "import easydict\n",
+    "\n",
+    "args = easydict.EasyDict({'type': 'gan',\n",
+    "                        'lr': 0.001,\n",
+    "                        'l1_coef': 50,\n",
+    "                        'l2_coef': 100,\n",
+    "                        'cls': True,\n",
+    "                        'save_path':'Result',\n",
+    "                        'dataset': test_data,\n",
+    "                        'batch_size': batch_size,\n",
+    "                        'num_workers':6,\n",
+    "                        'epochs': 100})\n",
+    "\n",
+    "trainer = Trainer(type=args.type,\n",
+    "                  dataset=args.dataset,\n",
+    "                  lr=args.lr,\n",
+    "                  save_path=args.save_path,\n",
+    "                  l1_coef=args.l1_coef,\n",
+    "                  l2_coef=args.l2_coef,\n",
+    "                  batch_size=args.batch_size,\n",
+    "                  num_workers=args.num_workers,\n",
+    "                  epochs=args.epochs\n",
+    "                  )\n",
+    "\n",
+    "trainer.train(args.cls)\n",
+    "#trainer.predict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "242ee9a9-47df-4c50-b7d9-ae181543f75f",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 211
+    },
+    "id": "242ee9a9-47df-4c50-b7d9-ae181543f75f",
+    "outputId": "1c0df61d-a9a3-461f-9ea1-a0eb63c92be1"
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Logger' object has no attribute 'd_loss_list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-45-b7e01a63074d>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Extract the data from the logger\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0md_loss_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md_loss_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mg_loss_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg_loss_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0md_x_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md_x_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Logger' object has no attribute 'd_loss_list'"
+     ]
+    }
+   ],
+   "source": [
+    "# prompt: draw a graph for the d_loss, g_loss, d(X) and d(g(x))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have lists or arrays named d_loss_list, g_loss_list, d_x_list, d_gx_list\n",
+    "# containing the values for each metric over the training epochs.\n",
+    "\n",
+    "# Extract the data from the logger\n",
+    "d_loss_list = trainer.logger.d_loss_list\n",
+    "g_loss_list = trainer.logger.g_loss_list\n",
+    "d_x_list = trainer.logger.d_x_list\n",
+    "d_gx_list = trainer.logger.d_gx_list\n",
+    "\n",
+    "# Create the figure and axes\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
+    "\n",
+    "# Plot D Loss\n",
+    "axs[0, 0].plot(d_loss_list)\n",
+    "axs[0, 0].set_title(\"Discriminator Loss\")\n",
+    "axs[0, 0].set_xlabel(\"Epoch\")\n",
+    "axs[0, 0].set_ylabel(\"Loss\")\n",
+    "\n",
+    "# Plot G Loss\n",
+    "axs[0, 1].plot(g_loss_list)\n",
+    "axs[0, 1].set_title(\"Generator Loss\")\n",
+    "axs[0, 1].set_xlabel(\"Epoch\")\n",
+    "axs[0, 1].set_ylabel(\"Loss\")\n",
+    "\n",
+    "# Plot D(X)\n",
+    "axs[1, 0].plot(d_x_list)\n",
+    "axs[1, 0].set_title(\"Discriminator Output for Real Images (D(X))\")\n",
+    "axs[1, 0].set_xlabel(\"Epoch\")\n",
+    "axs[1, 0].set_ylabel(\"Score\")\n",
+    "\n",
+    "# Plot D(G(X))\n",
+    "axs[1, 1].plot(d_gx_list)\n",
+    "axs[1, 1].set_title(\"Discriminator Output for Fake Images (D(G(X)))\")\n",
+    "axs[1, 1].set_xlabel(\"Epoch\")\n",
+    "axs[1, 1].set_ylabel(\"Score\")\n",
+    "\n",
+    "# Adjust layout and display the plot\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e2bf862-35fe-433e-b1e2-012cbd5ee47d",
+   "metadata": {
+    "editable": true,
+    "id": "1e2bf862-35fe-433e-b1e2-012cbd5ee47d",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!cp -r checkpoints /content/drive/MyDrive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "JDN71GvsFTyn",
+   "metadata": {
+    "id": "JDN71GvsFTyn"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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
+}