pytorch-blip-training.py

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    "<br>\n",
    "<h1 style = \"font-size:60px; font-family:Garamond ; font-weight : normal; background-color: #f6f5f5 ; color : #fe346e; text-align: center; border-radius: 100px 100px;\">BLIP Image Captioning Training</h1>\n",
    "<br>\n",
    "\n",
    "![](https://storage.googleapis.com/kaggle-competitions/kaggle/45917/logos/thumb76_76.png?t=2023-02-08-17-53-48)"
   ]
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  {
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   "source": [
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.5em; font-weight: 300;\">In this notebook we will train BLIP model by Salesforce for the Image Captioning task on the DiffusionDB dataset</span> <br>\n",
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\">Reference: https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdba2026",
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   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Install Required Libraries</h1></span>"
   ]
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  {
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     "text": [
      "/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)\r\n",
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      "Requirement already satisfied: smmap<6,>=3.0.1 in /opt/conda/lib/python3.7/site-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (3.0.5)\r\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->Click!=8.0.0,>=7.0->wandb) (3.8.1)\r\n",
      "Installing collected packages: wandb\r\n",
      "  Attempting uninstall: wandb\r\n",
      "    Found existing installation: wandb 0.12.21\r\n",
      "    Uninstalling wandb-0.12.21:\r\n",
      "      Successfully uninstalled wandb-0.12.21\r\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.\r\n",
      "allennlp 2.10.1 requires wandb<0.13.0,>=0.10.0, but you have wandb 0.13.10 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0mSuccessfully installed wandb-0.13.10\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)\r\n",
      "Processing /kaggle/input/lavis-pretrained/salesforce-lavis/transformers-4.26.1-py3-none-any.whl\r\n",
      "Installing collected packages: transformers\r\n",
      "  Attempting uninstall: transformers\r\n",
      "    Found existing installation: transformers 4.20.1\r\n",
      "    Uninstalling transformers-4.20.1:\r\n",
      "      Successfully uninstalled transformers-4.20.1\r\n",
      "Successfully installed transformers-4.26.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)\r\n",
      "Processing /kaggle/input/lavis-pretrained/salesforce-lavis/huggingface_hub-0.12.0-py3-none-any.whl\r\n",
      "Installing collected packages: huggingface-hub\r\n",
      "  Attempting uninstall: huggingface-hub\r\n",
      "    Found existing installation: huggingface-hub 0.10.1\r\n",
      "    Uninstalling huggingface-hub-0.10.1:\r\n",
      "      Successfully uninstalled huggingface-hub-0.10.1\r\n",
      "Successfully installed huggingface-hub-0.12.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade wandb\n",
    "!pip install --no-index --no-deps /kaggle/input/lavis-pretrained/salesforce-lavis/transformers* \n",
    "!pip install --no-index --no-deps /kaggle/input/lavis-pretrained/salesforce-lavis/hugging*"
   ]
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  {
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   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Import Required Libraries 📚</h1></span>"
   ]
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  {
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   },
   "outputs": [],
   "source": [
    "import os\n",
    "import gc\n",
    "import copy\n",
    "import time\n",
    "import random\n",
    "import joblib\n",
    "\n",
    "# For data manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Pytorch Imports\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "# Utils\n",
    "from tqdm import tqdm\n",
    "from collections import defaultdict\n",
    "\n",
    "# For Transformer Models\n",
    "from transformers import AutoProcessor, AdamW\n",
    "from transformers import BlipForConditionalGeneration\n",
    "\n",
    "# For colored terminal text\n",
    "from colorama import Fore, Back, Style\n",
    "b_ = Fore.BLUE\n",
    "y_ = Fore.YELLOW\n",
    "sr_ = Style.RESET_ALL\n",
    "\n",
    "# Suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# For descriptive error messages\n",
    "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\n",
    "os.environ['TOKENIZERS_PARALLELISM'] = \"False\""
   ]
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<img src=\"https://i.imgur.com/gb6B4ig.png\" width=\"400\" alt=\"Weights & Biases\" />\n",
    "\n",
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\"> Weights & Biases (W&B) is a set of machine learning tools that helps you build better models faster. <strong>Kaggle competitions require fast-paced model development and evaluation</strong>. There are a lot of components: exploring the training data, training different models, combining trained models in different combinations (ensembling), and so on.</span>\n",
    "\n",
    "> <span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\">⏳ Lots of components = Lots of places to go wrong = Lots of time spent debugging</span>\n",
    "\n",
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\">W&B can be useful for Kaggle competition with it's lightweight and interoperable tools:</span>\n",
    "\n",
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\">To learn more about Weights and Biases check out this <strong><a href=\"https://www.kaggle.com/ayuraj/experiment-tracking-with-weights-and-biases\">kernel</a></strong>.</span>"
   ]
  },
  {
   "cell_type": "code",
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   "id": "cf7a2ec7",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
     ]
    }
   ],
   "source": [
    "import wandb\n",
    "\n",
    "try:\n",
    "    from kaggle_secrets import UserSecretsClient\n",
    "    user_secrets = UserSecretsClient()\n",
    "    api_key = user_secrets.get_secret(\"wandb_api\")\n",
    "    wandb.login(key=api_key)\n",
    "    anony = None\n",
    "except:\n",
    "    anony = \"must\"\n",
    "    print('If you want to use your W&B account, go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as wandb_api. \\nGet your W&B access token from here: https://wandb.ai/authorize')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185bad4b",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Training Configuration ⚙️</h1></span>"
   ]
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   "id": "884c4ee0",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f34f39ee4716440abc8031ee3d91f750",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
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      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f41215eacf04146a1ada153d0dd589f",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CONFIG = {\"seed\": 2023,\n",
    "          \"epochs\": 5,\n",
    "          \"model_name\": \"Salesforce/blip-image-captioning-base\",\n",
    "          \"train_batch_size\": 4,\n",
    "          \"valid_batch_size\": 8,\n",
    "          \"learning_rate\": 1e-4,\n",
    "          \"scheduler\": 'CosineAnnealingLR',\n",
    "          \"min_lr\": 1e-6,\n",
    "          \"T_max\": 500,\n",
    "          \"weight_decay\": 1e-6,\n",
    "          \"n_accumulate\": 1,\n",
    "          \"device\": torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"),\n",
    "          \"competition\": \"SD\",\n",
    "          \"_wandb_kernel\": \"deb\",\n",
    "          }\n",
    "\n",
    "CONFIG[\"processor\"] = AutoProcessor.from_pretrained(CONFIG['model_name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363b03a0",
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    "tags": []
   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Set Seed for Reproducibility</h1></span>"
   ]
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  {
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   "id": "e25445a2",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def set_seed(seed=42):\n",
    "    '''Sets the seed of the entire notebook so results are the same every time we run.\n",
    "    This is for REPRODUCIBILITY.'''\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    # When running on the CuDNN backend, two further options must be set\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    # Set a fixed value for the hash seed\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    \n",
    "set_seed(CONFIG['seed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda70cfe",
   "metadata": {
    "papermill": {
     "duration": 0.008652,
     "end_time": "2023-02-16T14:31:48.853009",
     "exception": false,
     "start_time": "2023-02-16T14:31:48.844357",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Data 📖</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c95dedb",
   "metadata": {
    "papermill": {
     "duration": 0.008935,
     "end_time": "2023-02-16T14:31:48.870799",
     "exception": false,
     "start_time": "2023-02-16T14:31:48.861864",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\">DiffusionDB is the first large-scale text-to-image prompt dataset. It contains 14 million images generated by Stable Diffusion using prompts and hyperparameters specified by real users.</span>\n",
    "<br>\n",
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\">DiffusionDB is publicly available at <a href=\"https://huggingface.co/datasets/poloclub/diffusiondb\">Hugging Face Dataset</a>.</span>\n",
    "<br><hr>\n",
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\">We will use the first 5k images of DiffusionDB-2M subset</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f7739ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:31:48.890138Z",
     "iopub.status.busy": "2023-02-16T14:31:48.889818Z",
     "iopub.status.idle": "2023-02-16T14:35:48.514412Z",
     "shell.execute_reply": "2023-02-16T14:35:48.513429Z"
    },
    "papermill": {
     "duration": 239.63723,
     "end_time": "2023-02-16T14:35:48.516961",
     "exception": false,
     "start_time": "2023-02-16T14:31:48.879731",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7458027dd4843528026ed2309117a47",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/15.0k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset diffusion_db/2m_first_5k to /root/.cache/huggingface/datasets/poloclub___diffusion_db/2m_first_5k/0.9.1/547894e3a57aa647ead68c9faf148324098f47f2bc1ab6705d670721de9d89d1...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d01ec517a5a43788c5b2ac0f7c4d8d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/581M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "839e2375745545388fe2db6cd40e09c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/585M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84d62e6d749849f2b730aed10ce09008",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/643M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67260f48c5e74cd2a61d1dc6ae7ca535",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/585M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e41c62969c7c492791fffb7ac90e4062",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/595M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47529ba61bc3496783ca914cddbe2243",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/195M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f86ea5208e8a44a5b97f8a5524d15cf3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset diffusion_db downloaded and prepared to /root/.cache/huggingface/datasets/poloclub___diffusion_db/2m_first_5k/0.9.1/547894e3a57aa647ead68c9faf148324098f47f2bc1ab6705d670721de9d89d1. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6975a073a0234b45a42ad533bbf89818",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "# Load the dataset with the `2m_first_5k` subset\n",
    "dataset = load_dataset('poloclub/diffusiondb', '2m_first_5k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cbeb0e3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:35:48.540211Z",
     "iopub.status.busy": "2023-02-16T14:35:48.539594Z",
     "iopub.status.idle": "2023-02-16T14:35:48.548663Z",
     "shell.execute_reply": "2023-02-16T14:35:48.547676Z"
    },
    "papermill": {
     "duration": 0.022645,
     "end_time": "2023-02-16T14:35:48.550732",
     "exception": false,
     "start_time": "2023-02-16T14:35:48.528087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'prompt', 'seed', 'step', 'cfg', 'sampler', 'width', 'height', 'user_name', 'timestamp', 'image_nsfw', 'prompt_nsfw'],\n",
       "        num_rows: 5000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "909582fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:35:48.573250Z",
     "iopub.status.busy": "2023-02-16T14:35:48.572975Z",
     "iopub.status.idle": "2023-02-16T14:36:54.214347Z",
     "shell.execute_reply": "2023-02-16T14:36:54.213414Z"
    },
    "papermill": {
     "duration": 65.654845,
     "end_time": "2023-02-16T14:36:54.216482",
     "exception": false,
     "start_time": "2023-02-16T14:35:48.561637",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9858217353a2402e96b5187fb79da8f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4984"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset['train']\n",
    "dataset = dataset.filter(lambda example: example[\"step\"] == 50)\n",
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0095214",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:36:54.239359Z",
     "iopub.status.busy": "2023-02-16T14:36:54.239065Z",
     "iopub.status.idle": "2023-02-16T14:36:54.259184Z",
     "shell.execute_reply": "2023-02-16T14:36:54.258135Z"
    },
    "papermill": {
     "duration": 0.033815,
     "end_time": "2023-02-16T14:36:54.261419",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.227604",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=512x768>,\n",
       " 'prompt': 'a renaissance portrait of dwayne johnson, art in the style of rembrandt!! intricate. ultra detailed, oil on canvas, wet - on - wet technique, pay attention to facial details, highly realistic, cinematic lightning, intricate textures, illusionistic detail, ',\n",
       " 'seed': 2480545905,\n",
       " 'step': 50,\n",
       " 'cfg': 16.0,\n",
       " 'sampler': 'k_euler_ancestral',\n",
       " 'width': 512,\n",
       " 'height': 768,\n",
       " 'user_name': 'e9dfc969d22cb9c5621ad075b3826c28f18ef3840c6dda59c4ac7daa55241393',\n",
       " 'timestamp': datetime.datetime(2022, 8, 20, 5, 28, tzinfo=<UTC>),\n",
       " 'image_nsfw': 0.16348764300346375,\n",
       " 'prompt_nsfw': 0.000792665290646255}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ae43dc",
   "metadata": {
    "papermill": {
     "duration": 0.010188,
     "end_time": "2023-02-16T14:36:54.282152",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.271964",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Data Split</h1></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d8beb86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:36:54.304302Z",
     "iopub.status.busy": "2023-02-16T14:36:54.303758Z",
     "iopub.status.idle": "2023-02-16T14:36:54.333924Z",
     "shell.execute_reply": "2023-02-16T14:36:54.333072Z"
    },
    "papermill": {
     "duration": 0.043385,
     "end_time": "2023-02-16T14:36:54.335921",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.292536",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = dataset.train_test_split(test_size=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18edf601",
   "metadata": {
    "papermill": {
     "duration": 0.010211,
     "end_time": "2023-02-16T14:36:54.356668",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.346457",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Dataset Class</h1></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "273d9089",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:36:54.379003Z",
     "iopub.status.busy": "2023-02-16T14:36:54.378353Z",
     "iopub.status.idle": "2023-02-16T14:36:54.384519Z",
     "shell.execute_reply": "2023-02-16T14:36:54.383628Z"
    },
    "papermill": {
     "duration": 0.019325,
     "end_time": "2023-02-16T14:36:54.386462",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.367137",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ImageCaptioningDataset(Dataset):\n",
    "    def __init__(self, dataset, processor):\n",
    "        self.dataset = dataset\n",
    "        self.processor = processor\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.dataset)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item = self.dataset[idx]\n",
    "        encoding = self.processor(images=item[\"image\"], text=item[\"prompt\"], \n",
    "                                  padding=\"max_length\", return_tensors=\"pt\")\n",
    "        # remove batch dimension\n",
    "        encoding = {k:v.squeeze() for k,v in encoding.items()}\n",
    "        return encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "08092c0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:36:54.408727Z",
     "iopub.status.busy": "2023-02-16T14:36:54.408050Z",
     "iopub.status.idle": "2023-02-16T14:36:54.412524Z",
     "shell.execute_reply": "2023-02-16T14:36:54.411632Z"
    },
    "papermill": {
     "duration": 0.017727,
     "end_time": "2023-02-16T14:36:54.414533",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.396806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset = ImageCaptioningDataset(dataset['train'], CONFIG['processor'])\n",
    "valid_dataset = ImageCaptioningDataset(dataset['test'], CONFIG['processor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62e3efa2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:36:54.436815Z",
     "iopub.status.busy": "2023-02-16T14:36:54.436048Z",
     "iopub.status.idle": "2023-02-16T14:36:54.593437Z",
     "shell.execute_reply": "2023-02-16T14:36:54.592283Z"
    },
    "papermill": {
     "duration": 0.171213,
     "end_time": "2023-02-16T14:36:54.596119",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.424906",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['pixel_values', 'input_ids', 'attention_mask'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[0].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff8fd35",
   "metadata": {
    "papermill": {
     "duration": 0.010438,
     "end_time": "2023-02-16T14:36:54.617520",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.607082",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Model</h1></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc5df20",
   "metadata": {
    "papermill": {
     "duration": 0.010408,
     "end_time": "2023-02-16T14:36:54.638512",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.628104",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\">BLIP is a model that is able to perform various multi-modal tasks including Image captioning </span> <br>\n",
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.2em; font-weight: 300;\">Model documentation: https://huggingface.co/docs/transformers/model_doc/blip </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a7d712a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:36:54.661659Z",
     "iopub.status.busy": "2023-02-16T14:36:54.660654Z",
     "iopub.status.idle": "2023-02-16T14:38:23.012450Z",
     "shell.execute_reply": "2023-02-16T14:38:23.011316Z"
    },
    "papermill": {
     "duration": 88.366068,
     "end_time": "2023-02-16T14:38:23.015065",
     "exception": false,
     "start_time": "2023-02-16T14:36:54.648997",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c913bc7f29b54b8fa83c8736e7444b5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/4.56k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed08c794b8a045079f326439c7db9c37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/990M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = BlipForConditionalGeneration.from_pretrained(CONFIG['model_name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db59d5fd",
   "metadata": {
    "papermill": {
     "duration": 0.011447,
     "end_time": "2023-02-16T14:38:23.038213",
     "exception": false,
     "start_time": "2023-02-16T14:38:23.026766",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Training Function</h1></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c9739d37",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:38:23.062685Z",
     "iopub.status.busy": "2023-02-16T14:38:23.061656Z",
     "iopub.status.idle": "2023-02-16T14:38:23.070634Z",
     "shell.execute_reply": "2023-02-16T14:38:23.069686Z"
    },
    "papermill": {
     "duration": 0.023773,
     "end_time": "2023-02-16T14:38:23.072976",
     "exception": false,
     "start_time": "2023-02-16T14:38:23.049203",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_one_epoch(model, optimizer, scheduler, dataloader, device, epoch):\n",
    "    model.train()\n",
    "    \n",
    "    dataset_size = 0\n",
    "    running_loss = 0.0\n",
    "    \n",
    "    bar = tqdm(enumerate(dataloader), total=len(dataloader))\n",
    "    for step, data in bar:\n",
    "        input_ids = data['input_ids'].to(device)\n",
    "        pixel_values = data['pixel_values'].to(device)\n",
    "        \n",
    "        batch_size = input_ids.size(0)\n",
    "\n",
    "        outputs = model(input_ids=input_ids, \n",
    "                        pixel_values=pixel_values, \n",
    "                        labels=input_ids)\n",
    "                \n",
    "        loss = outputs.loss\n",
    "        loss = loss / CONFIG['n_accumulate']\n",
    "        loss.backward()\n",
    "    \n",
    "        if (step + 1) % CONFIG['n_accumulate'] == 0:\n",
    "            optimizer.step()\n",
    "\n",
    "            # zero the parameter gradients\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            if scheduler is not None:\n",
    "                scheduler.step()\n",
    "                \n",
    "        running_loss += (loss.item() * batch_size)\n",
    "        dataset_size += batch_size\n",
    "        \n",
    "        epoch_loss = running_loss / dataset_size\n",
    "        \n",
    "        bar.set_postfix(Epoch=epoch, Train_Loss=epoch_loss,\n",
    "                        LR=optimizer.param_groups[0]['lr'])\n",
    "    gc.collect()\n",
    "    \n",
    "    return epoch_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f121ad",
   "metadata": {
    "papermill": {
     "duration": 0.012242,
     "end_time": "2023-02-16T14:38:23.096437",
     "exception": false,
     "start_time": "2023-02-16T14:38:23.084195",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Validation Function</h1></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72d7eb0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:38:23.119485Z",
     "iopub.status.busy": "2023-02-16T14:38:23.119194Z",
     "iopub.status.idle": "2023-02-16T14:38:23.126580Z",
     "shell.execute_reply": "2023-02-16T14:38:23.125520Z"
    },
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     "duration": 0.021512,
     "end_time": "2023-02-16T14:38:23.128797",
     "exception": false,
     "start_time": "2023-02-16T14:38:23.107285",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def valid_one_epoch(model, dataloader, device, epoch):\n",
    "    model.eval()\n",
    "    \n",
    "    dataset_size = 0\n",
    "    running_loss = 0.0\n",
    "    \n",
    "    bar = tqdm(enumerate(dataloader), total=len(dataloader))\n",
    "    for step, data in bar:        \n",
    "        input_ids = data['input_ids'].to(device)\n",
    "        pixel_values = data['pixel_values'].to(device)\n",
    "        \n",
    "        batch_size = input_ids.size(0)\n",
    "\n",
    "        outputs = model(input_ids=input_ids, \n",
    "                        pixel_values=pixel_values, \n",
    "                        labels=input_ids)\n",
    "                \n",
    "        loss = outputs.loss\n",
    "        \n",
    "        running_loss += (loss.item() * batch_size)\n",
    "        dataset_size += batch_size\n",
    "        \n",
    "        epoch_loss = running_loss / dataset_size\n",
    "        \n",
    "        bar.set_postfix(Epoch=epoch, Valid_Loss=epoch_loss,\n",
    "                        LR=optimizer.param_groups[0]['lr'])   \n",
    "    \n",
    "    gc.collect()\n",
    "    \n",
    "    return epoch_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d6d7a1",
   "metadata": {
    "papermill": {
     "duration": 0.012221,
     "end_time": "2023-02-16T14:38:23.153206",
     "exception": false,
     "start_time": "2023-02-16T14:38:23.140985",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Run Training</h1></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97c4b6e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:38:23.178255Z",
     "iopub.status.busy": "2023-02-16T14:38:23.177202Z",
     "iopub.status.idle": "2023-02-16T14:38:23.188028Z",
     "shell.execute_reply": "2023-02-16T14:38:23.187136Z"
    },
    "papermill": {
     "duration": 0.025606,
     "end_time": "2023-02-16T14:38:23.190025",
     "exception": false,
     "start_time": "2023-02-16T14:38:23.164419",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def run_training(model, optimizer, scheduler, device, num_epochs):\n",
    "    # To automatically log gradients\n",
    "    wandb.watch(model, log_freq=100)\n",
    "    \n",
    "    if torch.cuda.is_available():\n",
    "        print(\"[INFO] Using GPU: {}\\n\".format(torch.cuda.get_device_name()))\n",
    "    \n",
    "    start = time.time()\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_epoch_loss = np.inf\n",
    "    history = defaultdict(list)\n",
    "    \n",
    "    for epoch in range(1, num_epochs + 1): \n",
    "        train_epoch_loss = train_one_epoch(model, optimizer, scheduler, \n",
    "                                           dataloader=train_loader, \n",
    "                                           device=CONFIG['device'], epoch=epoch)\n",
    "        \n",
    "        val_epoch_loss = valid_one_epoch(model, valid_loader, device=CONFIG['device'], \n",
    "                                         epoch=epoch)\n",
    "    \n",
    "        history['Train Loss'].append(train_epoch_loss)\n",
    "        history['Valid Loss'].append(val_epoch_loss)\n",
    "        \n",
    "        # Log the metrics\n",
    "        wandb.log({\"Train Loss\": train_epoch_loss})\n",
    "        wandb.log({\"Valid Loss\": val_epoch_loss})\n",
    "        \n",
    "        # deep copy the model\n",
    "        if val_epoch_loss <= best_epoch_loss:\n",
    "            print(f\"{b_}Validation Loss Improved ({best_epoch_loss} ---> {val_epoch_loss})\")\n",
    "            best_epoch_loss = val_epoch_loss\n",
    "            run.summary[\"Best Loss\"] = best_epoch_loss\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())\n",
    "            PATH = f\"BestLoss.bin\"\n",
    "            torch.save(model.state_dict(), PATH)\n",
    "            # Save a model file from the current directory\n",
    "            print(f\"Model Saved{sr_}\")\n",
    "            \n",
    "        print()\n",
    "    \n",
    "    end = time.time()\n",
    "    time_elapsed = end - start\n",
    "    print('Training complete in {:.0f}h {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 3600, (time_elapsed % 3600) // 60, (time_elapsed % 3600) % 60))\n",
    "    print(\"Best Loss: {:.4f}\".format(best_epoch_loss))\n",
    "    \n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    \n",
    "    return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb0417a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:38:23.215024Z",
     "iopub.status.busy": "2023-02-16T14:38:23.214716Z",
     "iopub.status.idle": "2023-02-16T14:38:23.220530Z",
     "shell.execute_reply": "2023-02-16T14:38:23.219509Z"
    },
    "papermill": {
     "duration": 0.02107,
     "end_time": "2023-02-16T14:38:23.222666",
     "exception": false,
     "start_time": "2023-02-16T14:38:23.201596",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def fetch_scheduler(optimizer):\n",
    "    if CONFIG['scheduler'] == 'CosineAnnealingLR':\n",
    "        scheduler = lr_scheduler.CosineAnnealingLR(optimizer,T_max=CONFIG['T_max'], \n",
    "                                                   eta_min=CONFIG['min_lr'])\n",
    "    elif CONFIG['scheduler'] == 'CosineAnnealingWarmRestarts':\n",
    "        scheduler = lr_scheduler.CosineAnnealingWarmRestarts(optimizer,T_0=CONFIG['T_0'], \n",
    "                                                             eta_min=CONFIG['min_lr'])\n",
    "    elif CONFIG['scheduler'] == None:\n",
    "        return None\n",
    "        \n",
    "    return scheduler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd71c4b",
   "metadata": {
    "papermill": {
     "duration": 0.010724,
     "end_time": "2023-02-16T14:38:23.244768",
     "exception": false,
     "start_time": "2023-02-16T14:38:23.234044",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.5em; font-weight: 300;\">Start Training</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0dadc7b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-16T14:38:23.268160Z",
     "iopub.status.busy": "2023-02-16T14:38:23.267640Z",
     "iopub.status.idle": "2023-02-16T16:12:58.302075Z",
     "shell.execute_reply": "2023-02-16T16:12:58.300520Z"
    },
    "papermill": {
     "duration": 5675.049367,
     "end_time": "2023-02-16T16:12:58.305091",
     "exception": false,
     "start_time": "2023-02-16T14:38:23.255724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mdchanda\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.13.10"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/kaggle/working/wandb/run-20230216_143823-uuxun11s</code>"
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    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/dchanda/SD/runs/uuxun11s' target=\"_blank\">BLIP-baseline</a></strong> to <a href='https://wandb.ai/dchanda/SD' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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       "<IPython.core.display.HTML object>"
      ]
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    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/dchanda/SD' target=\"_blank\">https://wandb.ai/dchanda/SD</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/dchanda/SD/runs/uuxun11s' target=\"_blank\">https://wandb.ai/dchanda/SD/runs/uuxun11s</a>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Using GPU: Tesla P100-PCIE-16GB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1122/1122 [18:02<00:00,  1.04it/s, Epoch=1, LR=8.62e-5, Train_Loss=1.74]\n",
      "100%|██████████| 63/63 [00:41<00:00,  1.53it/s, Epoch=1, LR=8.62e-5, Valid_Loss=1.53]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mValidation Loss Improved (inf ---> 1.533640047830188)\n",
      "Model Saved\u001b[0m\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1122/1122 [17:57<00:00,  1.04it/s, Epoch=2, LR=5.24e-5, Train_Loss=1.48]\n",
      "100%|██████████| 63/63 [00:41<00:00,  1.54it/s, Epoch=2, LR=5.24e-5, Valid_Loss=1.49]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mValidation Loss Improved (1.533640047830188 ---> 1.4860287522505184)\n",
      "Model Saved\u001b[0m\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1122/1122 [18:02<00:00,  1.04it/s, Epoch=3, LR=1.75e-5, Train_Loss=1.44]\n",
      "100%|██████████| 63/63 [00:41<00:00,  1.53it/s, Epoch=3, LR=1.75e-5, Valid_Loss=1.46]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mValidation Loss Improved (1.4860287522505184 ---> 1.4617982918848256)\n",
      "Model Saved\u001b[0m\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1122/1122 [18:01<00:00,  1.04it/s, Epoch=4, LR=1.14e-6, Train_Loss=1.42]\n",
      "100%|██████████| 63/63 [00:41<00:00,  1.53it/s, Epoch=4, LR=1.14e-6, Valid_Loss=1.46]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mValidation Loss Improved (1.4617982918848256 ---> 1.4550576501475547)\n",
      "Model Saved\u001b[0m\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1122/1122 [18:01<00:00,  1.04it/s, Epoch=5, LR=1.24e-5, Train_Loss=1.4]\n",
      "100%|██████████| 63/63 [00:42<00:00,  1.50it/s, Epoch=5, LR=1.24e-5, Valid_Loss=1.45]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mValidation Loss Improved (1.4550576501475547 ---> 1.4526172795133265)\n",
      "Model Saved\u001b[0m\n",
      "\n",
      "Training complete in 1h 33m 48s\n",
      "Best Loss: 1.4526\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Train Loss</td><td>█▃▂▁▁</td></tr><tr><td>Valid Loss</td><td>█▄▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Best Loss</td><td>1.45262</td></tr><tr><td>Train Loss</td><td>1.40263</td></tr><tr><td>Valid Loss</td><td>1.45262</td></tr></table><br/></div></div>"
      ],
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">BLIP-baseline</strong> at: <a href='https://wandb.ai/dchanda/SD/runs/uuxun11s' target=\"_blank\">https://wandb.ai/dchanda/SD/runs/uuxun11s</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20230216_143823-uuxun11s/logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "run = wandb.init(project=CONFIG['competition'], \n",
    "                 config=CONFIG,\n",
    "                 job_type='Train',\n",
    "                 tags=[CONFIG['model_name']],\n",
    "                 name=\"BLIP-baseline\",\n",
    "                 anonymous='must')\n",
    "\n",
    "# Create Dataloaders\n",
    "train_loader = DataLoader(train_dataset, shuffle=True, batch_size=CONFIG['train_batch_size'])\n",
    "valid_loader = DataLoader(valid_dataset, shuffle=False, batch_size=CONFIG['valid_batch_size'])\n",
    "\n",
    "model.to(CONFIG['device'])\n",
    "\n",
    "# Define Optimizer and Scheduler\n",
    "optimizer = AdamW(model.parameters(), lr=CONFIG['learning_rate'], weight_decay=CONFIG['weight_decay'])\n",
    "scheduler = fetch_scheduler(optimizer)\n",
    "\n",
    "model, history = run_training(model, optimizer, scheduler,\n",
    "                              device=CONFIG['device'],\n",
    "                              num_epochs=CONFIG['epochs'])\n",
    "\n",
    "run.finish()\n",
    "\n",
    "del model, history, train_loader, valid_loader\n",
    "_ = gc.collect()\n",
    "print()\n",
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fc4dee9",
   "metadata": {
    "papermill": {
     "duration": 0.646016,
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     "exception": false,
     "start_time": "2023-02-16T16:12:59.118963",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style = \"font-family: garamond; font-size: 40px; font-style: normal; letter-spcaing: 3px; background-color: #f6f5f5; color :#fe346e; border-radius: 100px 100px; text-align:center\">Visualizations</h1></span>\n",
    "\n",
    "<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.5em; font-weight: 300;\"><a href=\"https://wandb.ai/dchanda/SD\">View the Complete Dashboard Here ⮕</a></span>"
   ]
  },
  {
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       "        <iframe\n",
       "            width=\"1080\"\n",
       "            height=\"720\"\n",
       "            src=\"https://wandb.ai/dchanda/SD/runs/uuxun11s\"\n",
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   "source": [
    "# This is just to display the W&B run page in this interactive session.\n",
    "from IPython import display\n",
    "\n",
    "# we create an IFrame and set the width and height\n",
    "iF = display.IFrame(run.url, width=1080, height=720)\n",
    "iF"
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