{ "cells": [ { "cell_type": "markdown", "id": "b5cf5c32", "metadata": { "papermill": { "duration": 0.01025, "end_time": "2023-02-16T14:31:06.482504", "exception": false, "start_time": "2023-02-16T14:31:06.472254", "status": "completed" }, "tags": [] }, "source": [ "
\n", "

BLIP Image Captioning Training

\n", "
\n", "\n", "![](https://storage.googleapis.com/kaggle-competitions/kaggle/45917/logos/thumb76_76.png?t=2023-02-08-17-53-48)" ] }, { "cell_type": "markdown", "id": "bf5e3eb0", "metadata": { "papermill": { "duration": 0.006668, "end_time": "2023-02-16T14:31:06.496010", "exception": false, "start_time": "2023-02-16T14:31:06.489342", "status": "completed" }, "tags": [] }, "source": [ "In this notebook we will train BLIP model by Salesforce for the Image Captioning task on the DiffusionDB dataset
\n", "Reference: https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb" ] }, { "cell_type": "markdown", "id": "cdba2026", "metadata": { "papermill": { "duration": 0.006494, "end_time": "2023-02-16T14:31:06.509075", "exception": false, "start_time": "2023-02-16T14:31:06.502581", "status": "completed" }, "tags": [] }, "source": [ "#

Install Required Libraries

" ] }, { "cell_type": "code", "execution_count": 1, "id": "40bf4ea5", "metadata": { "_kg_hide-output": true, "execution": { "iopub.execute_input": "2023-02-16T14:31:06.524647Z", "iopub.status.busy": "2023-02-16T14:31:06.523876Z", "iopub.status.idle": "2023-02-16T14:31:31.643878Z", "shell.execute_reply": "2023-02-16T14:31:31.642709Z" }, "papermill": { "duration": 25.130763, "end_time": "2023-02-16T14:31:31.646468", "exception": false, "start_time": "2023-02-16T14:31:06.515705", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)\r\n", "Requirement already satisfied: wandb in /opt/conda/lib/python3.7/site-packages (0.12.21)\r\n", "Collecting wandb\r\n", " Downloading wandb-0.13.10-py3-none-any.whl (2.0 MB)\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[?25hRequirement already satisfied: sentry-sdk>=1.0.0 in /opt/conda/lib/python3.7/site-packages (from wandb) (1.15.0)\r\n", "Requirement already satisfied: GitPython>=1.0.0 in /opt/conda/lib/python3.7/site-packages (from wandb) (3.1.27)\r\n", "Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from wandb) (59.8.0)\r\n", "Requirement already satisfied: requests<3,>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from wandb) (2.28.1)\r\n", "Requirement already satisfied: protobuf!=4.21.0,<5,>=3.12.0 in /opt/conda/lib/python3.7/site-packages (from wandb) (3.20.3)\r\n", "Requirement already satisfied: PyYAML in /opt/conda/lib/python3.7/site-packages (from wandb) (6.0)\r\n", "Requirement already satisfied: psutil>=5.0.0 in /opt/conda/lib/python3.7/site-packages (from wandb) (5.9.2)\r\n", "Requirement already satisfied: docker-pycreds>=0.4.0 in /opt/conda/lib/python3.7/site-packages (from wandb) (0.4.0)\r\n", "Requirement already satisfied: setproctitle in /opt/conda/lib/python3.7/site-packages (from wandb) (1.3.2)\r\n", "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from wandb) (4.1.1)\r\n", "Requirement already satisfied: appdirs>=1.4.3 in /opt/conda/lib/python3.7/site-packages (from wandb) (1.4.4)\r\n", "Requirement already satisfied: Click!=8.0.0,>=7.0 in /opt/conda/lib/python3.7/site-packages (from wandb) (8.1.3)\r\n", "Requirement already satisfied: pathtools in /opt/conda/lib/python3.7/site-packages (from wandb) (0.1.2)\r\n", "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from Click!=8.0.0,>=7.0->wandb) (6.0.0)\r\n", "Requirement already satisfied: six>=1.4.0 in /opt/conda/lib/python3.7/site-packages (from docker-pycreds>=0.4.0->wandb) (1.16.0)\r\n", "Requirement already satisfied: gitdb<5,>=4.0.1 in /opt/conda/lib/python3.7/site-packages (from GitPython>=1.0.0->wandb) (4.0.9)\r\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.0.0->wandb) (2022.12.7)\r\n", "Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.0.0->wandb) (2.1.1)\r\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.0.0->wandb) (1.26.11)\r\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.0.0->wandb) (3.3)\r\n", "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*" ] }, { "cell_type": "markdown", "id": "f290f229", "metadata": { "papermill": { "duration": 0.008123, "end_time": "2023-02-16T14:31:31.663453", "exception": false, "start_time": "2023-02-16T14:31:31.655330", "status": "completed" }, "tags": [] }, "source": [ "#

Import Required Libraries 📚

" ] }, { "cell_type": "code", "execution_count": 2, "id": "cb621077", "metadata": { "execution": { "iopub.execute_input": "2023-02-16T14:31:31.681788Z", "iopub.status.busy": "2023-02-16T14:31:31.681416Z", "iopub.status.idle": "2023-02-16T14:31:41.222887Z", "shell.execute_reply": "2023-02-16T14:31:41.221543Z" }, "papermill": { "duration": 9.554096, "end_time": "2023-02-16T14:31:41.225822", "exception": false, "start_time": "2023-02-16T14:31:31.671726", "status": "completed" }, "tags": [] }, "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\"" ] }, { "cell_type": "markdown", "id": "cb921899", "metadata": { "papermill": { "duration": 0.009533, "end_time": "2023-02-16T14:31:41.245168", "exception": false, "start_time": "2023-02-16T14:31:41.235635", "status": "completed" }, "tags": [] }, "source": [ "\"Weights\n", "\n", " Weights & Biases (W&B) is a set of machine learning tools that helps you build better models faster. Kaggle competitions require fast-paced model development and evaluation. There are a lot of components: exploring the training data, training different models, combining trained models in different combinations (ensembling), and so on.\n", "\n", "> ⏳ Lots of components = Lots of places to go wrong = Lots of time spent debugging\n", "\n", "W&B can be useful for Kaggle competition with it's lightweight and interoperable tools:\n", "\n", "To learn more about Weights and Biases check out this kernel." ] }, { "cell_type": "code", "execution_count": 3, "id": "cf7a2ec7", "metadata": { "execution": { "iopub.execute_input": "2023-02-16T14:31:41.264970Z", "iopub.status.busy": "2023-02-16T14:31:41.263962Z", "iopub.status.idle": "2023-02-16T14:31:44.300089Z", "shell.execute_reply": "2023-02-16T14:31:44.298958Z" }, "papermill": { "duration": 3.048201, "end_time": "2023-02-16T14:31:44.302465", "exception": false, "start_time": "2023-02-16T14:31:41.254264", "status": "completed" }, "tags": [] }, "outputs": [ { "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", "metadata": { "papermill": { "duration": 0.008461, "end_time": "2023-02-16T14:31:44.319838", "exception": false, "start_time": "2023-02-16T14:31:44.311377", "status": "completed" }, "tags": [] }, "source": [ "#

Training Configuration ⚙️

" ] }, { "cell_type": "code", "execution_count": 4, "id": "884c4ee0", "metadata": { "execution": { "iopub.execute_input": "2023-02-16T14:31:44.338851Z", "iopub.status.busy": "2023-02-16T14:31:44.337985Z", "iopub.status.idle": "2023-02-16T14:31:48.784003Z", "shell.execute_reply": "2023-02-16T14:31:48.782990Z" }, "papermill": { "duration": 4.458692, "end_time": "2023-02-16T14:31:48.786816", "exception": false, "start_time": "2023-02-16T14:31:44.328124", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f34f39ee4716440abc8031ee3d91f750", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading (…)rocessor_config.json: 0%| | 0.00/287 [00:00

Set Seed for Reproducibility

" ] }, { "cell_type": "code", "execution_count": 5, "id": "e25445a2", "metadata": { "execution": { "iopub.execute_input": "2023-02-16T14:31:48.824340Z", "iopub.status.busy": "2023-02-16T14:31:48.824010Z", "iopub.status.idle": "2023-02-16T14:31:48.833530Z", "shell.execute_reply": "2023-02-16T14:31:48.832684Z" }, "papermill": { "duration": 0.021404, "end_time": "2023-02-16T14:31:48.835520", "exception": false, "start_time": "2023-02-16T14:31:48.814116", "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": [ "#

Data 📖

" ] }, { "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": [ "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.\n", "
\n", "DiffusionDB is publicly available at Hugging Face Dataset.\n", "
\n", "We will use the first 5k images of DiffusionDB-2M subset" ] }, { "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,\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=),\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": [ "#

Data Split

" ] }, { "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": [ "#

Dataset Class

" ] }, { "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": [ "#

Model

" ] }, { "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": [ "BLIP is a model that is able to perform various multi-modal tasks including Image captioning
\n", "Model documentation: https://huggingface.co/docs/transformers/model_doc/blip " ] }, { "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

Training Function

" ] }, { "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": [ "#

Validation Function

" ] }, { "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" }, "papermill": { "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": [ "#

Run Training

" ] }, { "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": [ "Start Training" ] }, { "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": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /kaggle/working/wandb/run-20230216_143823-uuxun11s" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run BLIP-baseline to Weights & Biases (docs)
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/dchanda/SD" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/dchanda/SD/runs/uuxun11s" ], "text/plain": [ "" ] }, "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... (success)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

Run history:


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Run summary:


Best Loss1.45262
Train Loss1.40263
Valid Loss1.45262

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run BLIP-baseline at: https://wandb.ai/dchanda/SD/runs/uuxun11s
Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Find logs at: ./wandb/run-20230216_143823-uuxun11s/logs" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "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, "end_time": "2023-02-16T16:12:59.764979", "exception": false, "start_time": "2023-02-16T16:12:59.118963", "status": "completed" }, "tags": [] }, "source": [ "#

Visualizations

\n", "\n", "View the Complete Dashboard Here ⮕" ] }, { "cell_type": "code", "execution_count": 20, "id": "e600bd3c", "metadata": { "execution": { "iopub.execute_input": "2023-02-16T16:13:01.196498Z", "iopub.status.busy": "2023-02-16T16:13:01.195610Z", "iopub.status.idle": "2023-02-16T16:13:01.205307Z", "shell.execute_reply": "2023-02-16T16:13:01.204154Z" }, "papermill": { "duration": 0.696293, "end_time": "2023-02-16T16:13:01.207388", "exception": false, "start_time": "2023-02-16T16:13:00.511095", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "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" ] }, { "cell_type": "markdown", "id": "72e134ef", 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