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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/nateraw/c91fb548c3a749cfbe6436d555a547b0/ai_or_not_baseline_timm_datasets_lightning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# aiornot competition baseline\n", | |
| "\n", | |
| "Baseline for the [aiornot competition](https://huggingface.co/spaces/competitions/aiornot).\n", | |
| "\n", | |
| "If you found this notebook helpful, consider following me on GitHub [@nateraw](https://github.com/nateraw). ❤️" | |
| ], | |
| "metadata": { | |
| "id": "NflFpf1w7r-i" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "CtN7VwGlaXu4" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%%capture\n", | |
| "! pip install transformers timm pytorch_lightning datasets torchmetrics wandb" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Login to access" | |
| ], | |
| "metadata": { | |
| "id": "l18c6ZMe93Zt" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import os\n", | |
| "from google.colab import userdata\n", | |
| "from huggingface_hub import notebook_login\n", | |
| "\n", | |
| "if userdata.get(\"WANDB_API_KEY\"):\n", | |
| " os.environ['WANDB_API_KEY'] = userdata.get('WANDB_API_KEY')\n", | |
| "\n", | |
| "notebook_login()" | |
| ], | |
| "metadata": { | |
| "id": "8_DFmNsxsK8P" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Dataset Download and Prep\n", | |
| "\n", | |
| "here we download the competition dataset and convert it to a hugging face dataset. We hard code the revision because there is [a PR](https://huggingface.co/datasets/competitions/aiornot/discussions/6) that will change the dataset repo to be HF datasets compatible that will be merged soon. This way this notebook wont break when that gets merged.\n", | |
| "\n", | |
| "If using this after that PR is merged, you can skip the next few cells and just do:\n", | |
| "\n", | |
| "```python\n", | |
| "from datasets import load_dataset\n", | |
| "\n", | |
| "ds = load_dataset('competitions/aiornot')\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "54IwKksL9vvP" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from huggingface_hub import hf_hub_download\n", | |
| "from shutil import unpack_archive\n", | |
| "\n", | |
| "train_zip_file = hf_hub_download('competitions/aiornot', 'train.zip', repo_type='dataset', revision='b843a82bd712648b2fe0dc043cf8a04475491d38')\n", | |
| "unpack_archive(train_zip_file, './data')\n", | |
| "\n", | |
| "test_zip_file = hf_hub_download('competitions/aiornot', 'test.zip', repo_type='dataset', revision='b843a82bd712648b2fe0dc043cf8a04475491d38')\n", | |
| "unpack_archive(test_zip_file, './data')" | |
| ], | |
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| { | |
| "cell_type": "code", | |
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| "from pathlib import Path\n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "data_dir = Path('./data/train')\n", | |
| "\n", | |
| "df = pd.read_csv(hf_hub_download('competitions/aiornot', 'train.csv', repo_type='dataset', revision='b843a82bd712648b2fe0dc043cf8a04475491d38'))\n", | |
| "df['path'] = df['id'].apply(lambda x: str(data_dir / x))\n", | |
| "\n", | |
| "df.head()" | |
| ], | |
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| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "df", | |
| "summary": "{\n \"name\": \"df\",\n \"rows\": 18618,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 18618,\n \"samples\": [\n \"16072.jpg\",\n \"409.jpg\",\n \"13144.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"path\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 18618,\n \"samples\": [\n \"data/train/16072.jpg\",\n \"data/train/409.jpg\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 4 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from datasets import Dataset, Features, ClassLabel, DatasetDict, Value\n", | |
| "from datasets import Image as ImageFeature\n", | |
| "\n", | |
| "def gen_examples():\n", | |
| " for i,row in df.iterrows():\n", | |
| " yield {\n", | |
| " 'id': row.id,\n", | |
| " 'image': {'path': row.path},\n", | |
| " 'label': row.label\n", | |
| " }\n", | |
| "\n", | |
| "def gen_examples_test():\n", | |
| " test_data_dir = Path('data/test')\n", | |
| " n_examples = len([x for x in list(test_data_dir.glob('*.jpg')) if x.is_file() and x.suffix == '.jpg'])\n", | |
| " for i in range(n_examples):\n", | |
| " fname = f\"{i}.jpg\"\n", | |
| " yield {\n", | |
| " 'id': fname, 'image': {'path': f\"data/test/{fname}\"}, 'label': -1\n", | |
| " }\n", | |
| "\n", | |
| "ds_train = Dataset.from_generator(gen_examples, features=Features(id=Value('string'), image=ImageFeature(), label=ClassLabel(names=['not-ai', 'ai'])))\n", | |
| "ds_test = Dataset.from_generator(gen_examples_test, features=Features(id=Value('string'), image=ImageFeature(), label=ClassLabel(names=['not-ai', 'ai'])))" | |
| ], | |
| "metadata": { | |
| "id": "IZfGjTUDeypM", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 81, | |
| "referenced_widgets": [ | |
| "5a29cadd40434d61bd3d868c2f1c4717", | |
| "0e8f5a831b324aec83b42aa97ebb989b", | |
| "5e63142f39a54ca9b312eecd975020fa", | |
| "f0efa9fc54aa4ae19887deec3dff1386", | |
| "17a1b05f58ae4ea3aeb2fe0a03bd55f3", | |
| "d01c769309254d7d96f4e4b1c0f7fecf", | |
| "b22a118e1181491d9d8bd6183a71e83a", | |
| "d5479d7c22884da4b0ed5104be8b20aa", | |
| "0c1be86ae3a84e0ebe711b31be7cf792", | |
| "d121e5dbb9574c9d83287df0d576f6fb", | |
| "5c9a0e771789450b8df29b8a415f8e31", | |
| "7ad0b0c5cc9a411bb637ac94ce90cfaf", | |
| "d943237bdb9d4fceb98888de469c9bc3", | |
| "7c9cb16a398147eabec0f0aebf7fb4ae", | |
| "ce8f74bdaa864b129a99433b9c030924", | |
| "3a0a5833c7764e99b4334362e9d7a027", | |
| "27ea37f671e24c60b64707d176f52881", | |
| "a2a0308437b946daa826ba412673502b", | |
| "55dc7cc0a5b741b5855fe3312558b505", | |
| "70ca44e588df4832a2523ef48d7d6471", | |
| "cb965fb0a66f4e43a978b4e1565a6a1c", | |
| "f9d5f43ee86247a3ad061d4ebb26bca3" | |
| ] | |
| }, | |
| "outputId": "243d6e26-7940-4a5f-cc92-d6bba6e1a5e6" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Generating train split: 0 examples [00:00, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "5a29cadd40434d61bd3d868c2f1c4717" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Generating train split: 0 examples [00:00, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "7ad0b0c5cc9a411bb637ac94ce90cfaf" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "ds = DatasetDict(train=ds_train, test=ds_test)" | |
| ], | |
| "metadata": { | |
| "id": "Tp5XfYxVtG7U" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Model Selection" | |
| ], | |
| "metadata": { | |
| "id": "jgmzI_cx9yrw" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Here you can play with this cell to find a model you might want to fine-tune from [`timm`](https://github.com/rwightman/pytorch-image-models).\n", | |
| "\n", | |
| "In my case, I wanna try a convnext. I already tried large while writing the notebook, so going for xlarge now." | |
| ], | |
| "metadata": { | |
| "id": "0RZ6W0Z9zrfr" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import timm\n", | |
| "\n", | |
| "timm.list_models('convnext*', pretrained=True)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000, | |
| "referenced_widgets": [ | |
| "30a0a78ca5434663922abcd9aa7d5d20", | |
| "322fbb1395984a0a817cfdacb6b0f463", | |
| "ac1c4e38603c42ff95f462b81a5c5a55", | |
| "063be434879949d18d148349a185f6ec", | |
| "9916b182304149b3ac47c5c88c7e5ff6", | |
| "aa36760dbb0547a4978b710ffabeb57c", | |
| "0bf39a5d0cfe4261910833d167c6cafa", | |
| "8949822cf0bb4c458adca26cd134bb87", | |
| "8e9047f6f2b044daab72b1efab18be3b", | |
| "a87a9b4efad44e8aa4bc0e3ade8f8b97", | |
| "6a230132609a4d1290fd9083bcde9495" | |
| ] | |
| }, | |
| "id": "dCcsTvlUzdVc", | |
| "outputId": "946e90f2-fbfa-414e-bd3d-ac021fae796a" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "0it [00:00, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "30a0a78ca5434663922abcd9aa7d5d20" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['convnext_atto.d2_in1k',\n", | |
| " 'convnext_atto_ols.a2_in1k',\n", | |
| " 'convnext_base.clip_laion2b',\n", | |
| " 'convnext_base.clip_laion2b_augreg',\n", | |
| " 'convnext_base.clip_laion2b_augreg_ft_in1k',\n", | |
| " 'convnext_base.clip_laion2b_augreg_ft_in12k',\n", | |
| " 'convnext_base.clip_laion2b_augreg_ft_in12k_in1k',\n", | |
| " 'convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384',\n", | |
| " 'convnext_base.clip_laiona',\n", | |
| " 'convnext_base.clip_laiona_320',\n", | |
| " 'convnext_base.clip_laiona_augreg_320',\n", | |
| " 'convnext_base.clip_laiona_augreg_ft_in1k_384',\n", | |
| " 'convnext_base.fb_in1k',\n", | |
| " 'convnext_base.fb_in22k',\n", | |
| " 'convnext_base.fb_in22k_ft_in1k',\n", | |
| " 'convnext_base.fb_in22k_ft_in1k_384',\n", | |
| " 'convnext_femto.d1_in1k',\n", | |
| " 'convnext_femto_ols.d1_in1k',\n", | |
| " 'convnext_large.fb_in1k',\n", | |
| " 'convnext_large.fb_in22k',\n", | |
| " 'convnext_large.fb_in22k_ft_in1k',\n", | |
| " 'convnext_large.fb_in22k_ft_in1k_384',\n", | |
| " 'convnext_large_mlp.clip_laion2b_augreg',\n", | |
| " 'convnext_large_mlp.clip_laion2b_augreg_ft_in1k',\n", | |
| " 'convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384',\n", | |
| " 'convnext_large_mlp.clip_laion2b_augreg_ft_in12k_384',\n", | |
| " 'convnext_large_mlp.clip_laion2b_ft_320',\n", | |
| " 'convnext_large_mlp.clip_laion2b_ft_soup_320',\n", | |
| " 'convnext_large_mlp.clip_laion2b_soup_ft_in12k_320',\n", | |
| " 'convnext_large_mlp.clip_laion2b_soup_ft_in12k_384',\n", | |
| " 'convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320',\n", | |
| " 'convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384',\n", | |
| " 'convnext_nano.d1h_in1k',\n", | |
| " 'convnext_nano.in12k',\n", | |
| " 'convnext_nano.in12k_ft_in1k',\n", | |
| " 'convnext_nano_ols.d1h_in1k',\n", | |
| " 'convnext_pico.d1_in1k',\n", | |
| " 'convnext_pico_ols.d1_in1k',\n", | |
| " 'convnext_small.fb_in1k',\n", | |
| " 'convnext_small.fb_in22k',\n", | |
| " 'convnext_small.fb_in22k_ft_in1k',\n", | |
| " 'convnext_small.fb_in22k_ft_in1k_384',\n", | |
| " 'convnext_small.in12k',\n", | |
| " 'convnext_small.in12k_ft_in1k',\n", | |
| " 'convnext_small.in12k_ft_in1k_384',\n", | |
| " 'convnext_tiny.fb_in1k',\n", | |
| " 'convnext_tiny.fb_in22k',\n", | |
| " 'convnext_tiny.fb_in22k_ft_in1k',\n", | |
| " 'convnext_tiny.fb_in22k_ft_in1k_384',\n", | |
| " 'convnext_tiny.in12k',\n", | |
| " 'convnext_tiny.in12k_ft_in1k',\n", | |
| " 'convnext_tiny.in12k_ft_in1k_384',\n", | |
| " 'convnext_tiny_hnf.a2h_in1k',\n", | |
| " 'convnext_xlarge.fb_in22k',\n", | |
| " 'convnext_xlarge.fb_in22k_ft_in1k',\n", | |
| " 'convnext_xlarge.fb_in22k_ft_in1k_384',\n", | |
| " 'convnext_xxlarge.clip_laion2b_rewind',\n", | |
| " 'convnext_xxlarge.clip_laion2b_soup',\n", | |
| " 'convnext_xxlarge.clip_laion2b_soup_ft_in1k',\n", | |
| " 'convnext_xxlarge.clip_laion2b_soup_ft_in12k',\n", | |
| " 'convnextv2_atto.fcmae',\n", | |
| " 'convnextv2_atto.fcmae_ft_in1k',\n", | |
| " 'convnextv2_base.fcmae',\n", | |
| " 'convnextv2_base.fcmae_ft_in1k',\n", | |
| " 'convnextv2_base.fcmae_ft_in22k_in1k',\n", | |
| " 'convnextv2_base.fcmae_ft_in22k_in1k_384',\n", | |
| " 'convnextv2_femto.fcmae',\n", | |
| " 'convnextv2_femto.fcmae_ft_in1k',\n", | |
| " 'convnextv2_huge.fcmae',\n", | |
| " 'convnextv2_huge.fcmae_ft_in1k',\n", | |
| " 'convnextv2_huge.fcmae_ft_in22k_in1k_384',\n", | |
| " 'convnextv2_huge.fcmae_ft_in22k_in1k_512',\n", | |
| " 'convnextv2_large.fcmae',\n", | |
| " 'convnextv2_large.fcmae_ft_in1k',\n", | |
| " 'convnextv2_large.fcmae_ft_in22k_in1k',\n", | |
| " 'convnextv2_large.fcmae_ft_in22k_in1k_384',\n", | |
| " 'convnextv2_nano.fcmae',\n", | |
| " 'convnextv2_nano.fcmae_ft_in1k',\n", | |
| " 'convnextv2_nano.fcmae_ft_in22k_in1k',\n", | |
| " 'convnextv2_nano.fcmae_ft_in22k_in1k_384',\n", | |
| " 'convnextv2_pico.fcmae',\n", | |
| " 'convnextv2_pico.fcmae_ft_in1k',\n", | |
| " 'convnextv2_tiny.fcmae',\n", | |
| " 'convnextv2_tiny.fcmae_ft_in1k',\n", | |
| " 'convnextv2_tiny.fcmae_ft_in22k_in1k',\n", | |
| " 'convnextv2_tiny.fcmae_ft_in22k_in1k_384']" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Will see how `convnext_xlarge_in22k` works :)" | |
| ], | |
| "metadata": { | |
| "id": "dUGpm43A8msR" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model_name = 'convnext_xlarge_in22k'" | |
| ], | |
| "metadata": { | |
| "id": "Klk9xMkZ60lk" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Log in to access the dataset, since its private" | |
| ], | |
| "metadata": { | |
| "id": "rQpg9jJ5zypF" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Dataset Split + Transforms" | |
| ], | |
| "metadata": { | |
| "id": "azjCrZJH9_D1" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "HrJwwmGdD325" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from datasets import load_dataset\n", | |
| "\n", | |
| "split = ds[\"train\"].train_test_split(0.1)\n", | |
| "ds[\"train\"] = split[\"train\"]\n", | |
| "ds[\"validation\"] = split[\"test\"]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "zBk4ik1TLCNA", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 84, | |
| "referenced_widgets": [ | |
| "ae894a46688b4683b462af81577b6b3d", | |
| "e33212c10ac34c4d9ad85a9fc09350b2", | |
| "5ed43a5c7a114169bbbcc8ada7060488", | |
| "dcaa4554e7be4057a8dab1eb9c8c8f5e", | |
| "3c9098c256f240d8aff4273835a8bd6a", | |
| "9f7f934bbefb4915b8163f2cdf1557a2", | |
| "e13a371365b54b928d691f5f16a7360a", | |
| "4867b6f875f44418bdc0ab524a8580fb", | |
| "0002fdd76abd43a6b79a57b5b4110c65", | |
| "6f31e6ca741e403b900043bcbb469404", | |
| "a2d7d914c77e442fbb8175df0f907efd" | |
| ] | |
| }, | |
| "outputId": "dc89b29e-6c30-4755-8e2c-d0c9506c06cf" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/timm/models/_factory.py:117: UserWarning: Mapping deprecated model name convnext_xlarge_in22k to current convnext_xlarge.fb_in22k.\n", | |
| " model = create_fn(\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "model.safetensors: 0%| | 0.00/1.57G [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "ae894a46688b4683b462af81577b6b3d" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "import timm\n", | |
| "\n", | |
| "model = timm.create_model(model_name, pretrained=True, num_classes=1)\n", | |
| "data_cfg = timm.data.resolve_data_config(model.pretrained_cfg)\n", | |
| "train_transform = timm.data.create_transform(**data_cfg, is_training=True)\n", | |
| "val_transform = timm.data.create_transform(**data_cfg, is_training=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Not using this model instance so freeing it from memory" | |
| ], | |
| "metadata": { | |
| "id": "TtknXuuUUBVd" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import gc\n", | |
| "del model\n", | |
| "gc.collect()" | |
| ], | |
| "metadata": { | |
| "id": "184TFDQs0rnn", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "2aa1128b-bc07-4c0c-b1e2-992626cbe49c" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "208" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Transforms/collate fns for the different splits. Could be written better but it works." | |
| ], | |
| "metadata": { | |
| "id": "V6JH3LZ51xet" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "uZMW-QVzD9LF" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from torch.utils.data import DataLoader\n", | |
| "import torch\n", | |
| "\n", | |
| "def train_transform_fn(example_batch):\n", | |
| " # Take a list of PIL images and turn them to pixel values\n", | |
| " tensor_lst = []\n", | |
| " for im in example_batch['image']:\n", | |
| " im_tensor = train_transform(im)\n", | |
| " tensor_lst.append(im_tensor)\n", | |
| " x = torch.stack(tensor_lst)\n", | |
| "\n", | |
| " # Don't forget to include the labels!\n", | |
| " y = example_batch['label']\n", | |
| " return {'x': x, 'y': y}\n", | |
| "\n", | |
| "\n", | |
| "def val_transform_fn(example_batch):\n", | |
| " # Take a list of PIL images and turn them to pixel values\n", | |
| " tensor_lst = []\n", | |
| " for im in example_batch['image']:\n", | |
| " im_tensor = val_transform(im)\n", | |
| " tensor_lst.append(im_tensor)\n", | |
| " x = torch.stack(tensor_lst)\n", | |
| "\n", | |
| " # Don't forget to include the labels!\n", | |
| " y = example_batch['label']\n", | |
| " return {'x': x, 'y': y}\n", | |
| "\n", | |
| "\n", | |
| "def test_transform_fn(example_batch):\n", | |
| " # Take a list of PIL images and turn them to pixel values\n", | |
| " tensor_lst = []\n", | |
| " for im in example_batch['image']:\n", | |
| " im_tensor = val_transform(im)\n", | |
| " tensor_lst.append(im_tensor)\n", | |
| " x = torch.stack(tensor_lst)\n", | |
| " return {'x': x}\n", | |
| "\n", | |
| "def collate_fn(batch):\n", | |
| " x = torch.stack([ex['x'] for ex in batch])\n", | |
| " y = torch.tensor([ex['y'] for ex in batch])\n", | |
| " return x, y\n", | |
| "\n", | |
| "def collate_fn_test(batch):\n", | |
| " x = torch.stack([ex['x'] for ex in batch])\n", | |
| " return x" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Lightning Module\n", | |
| "\n", | |
| "- timm pretrained model backbone\n", | |
| "- torchmetrics for accuracy, which is calculated on val epoch end.\n", | |
| "- Adam optimizer\n", | |
| "- Cosine schedule w/ warmup" | |
| ], | |
| "metadata": { | |
| "id": "NQU2AwOS15qO" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "o3qpTZaHCbM-" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import torch\n", | |
| "import pytorch_lightning as pl\n", | |
| "from transformers import get_cosine_schedule_with_warmup\n", | |
| "import torchmetrics\n", | |
| "\n", | |
| "class LitModel(pl.LightningModule):\n", | |
| " def __init__(self, model_name, lr, weight_decay=1e-4, warmup_steps=0):\n", | |
| " super().__init__()\n", | |
| " self.save_hyperparameters()\n", | |
| " self.model = timm.create_model(self.hparams.model_name, pretrained=True, num_classes=1)\n", | |
| " self.criterion = torch.nn.BCEWithLogitsLoss()\n", | |
| " self.val_accuracy = torchmetrics.Accuracy('binary')\n", | |
| "\n", | |
| " def training_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " out = self.model(x).squeeze()\n", | |
| " loss = self.criterion(out, y.float())\n", | |
| " self.log('loss', loss)\n", | |
| " optimizer = self.optimizers()\n", | |
| " self.log('lr', optimizer.param_groups[0]['lr'], prog_bar=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def validation_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " out = self.model(x).squeeze()\n", | |
| " loss = self.criterion(out, y.float())\n", | |
| " self.val_accuracy.update(out.sigmoid(), y)\n", | |
| " self.log('val_loss', loss, prog_bar=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def on_validation_epoch_end(self):\n", | |
| " self.log('val_acc', self.val_accuracy.compute(), prog_bar=True)\n", | |
| " self.val_accuracy.reset()\n", | |
| "\n", | |
| " def configure_optimizers(self):\n", | |
| " optimizer = torch.optim.Adam(\n", | |
| " self.parameters(), self.hparams.lr, weight_decay=self.hparams.weight_decay\n", | |
| " )\n", | |
| " scheduler = get_cosine_schedule_with_warmup(\n", | |
| " optimizer, self.hparams.warmup_steps, self.trainer.estimated_stepping_batches\n", | |
| " )\n", | |
| " return [optimizer], [{\"scheduler\": scheduler, \"interval\": \"step\"}]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Init DataLoaders" | |
| ], | |
| "metadata": { | |
| "id": "Bu7iGpDz2Dtr" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "uKvMe510LVak" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "batch_size = 64\n", | |
| "train_loader = DataLoader(ds['train'].with_transform(train_transform_fn), batch_size=batch_size, shuffle=True, collate_fn=collate_fn, num_workers=12, pin_memory=True)\n", | |
| "val_loader = DataLoader(ds['validation'].with_transform(val_transform_fn), batch_size=batch_size, shuffle=False, collate_fn=collate_fn, num_workers=12, pin_memory=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Training\n", | |
| "\n", | |
| "TODO - save top k ckpts and specify monitor val_loss. Just gotta add modelcheckpoint callback. too lazy." | |
| ], | |
| "metadata": { | |
| "id": "3g8rOfCr1PsG" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "1qCaMyqYDzzI", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 692, | |
| "referenced_widgets": [ | |
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| "20a4e9470b4a4989824d29d40382c58a", | |
| "cd86084dbf734d7ebe6200ee266bcdd5", | |
| "a9586acab77b4f639d9099a7cef36f10", | |
| "8ad4a2c194ab42d0ade26d470e8949ca", | |
| "3d3f5604c2584596bc71317a48fb4ba4", | |
| "a98ee2b7aadb431aa0626b2d34419a72", | |
| "196c91024cc4407c889b35421f32f957", | |
| "8600dc106e15431bacb4382676f0d9dc", | |
| "c1d025b8ad70479d8fda7158345eef0e", | |
| "ef2864fa6a064029adcbf3da5203cebc", | |
| "d02d96f7730c4b0097da2d975eb882f3", | |
| "72f33f0cbaea42399784776ee31b5e80" | |
| ] | |
| }, | |
| "outputId": "ca0c820c-019a-4afa-8bcc-6bb1e915c371" | |
| }, | |
| "outputs": [ | |
| { | |
| "metadata": { | |
| "tags": null | |
| }, | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/timm/models/_factory.py:117: UserWarning: Mapping deprecated model name convnext_xlarge_in22k to current convnext_xlarge.fb_in22k.\n", | |
| " model = create_fn(\n", | |
| "/usr/local/lib/python3.10/dist-packages/lightning_fabric/connector.py:571: `precision=16` is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!\n", | |
| "INFO:pytorch_lightning.utilities.rank_zero:Using 16bit Automatic Mixed Precision (AMP)\n", | |
| "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n", | |
| "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n", | |
| "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n", | |
| "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "INFO:pytorch_lightning.utilities.rank_zero:Loading `train_dataloader` to estimate number of stepping batches.\n", | |
| "INFO:pytorch_lightning.callbacks.model_summary:\n", | |
| " | Name | Type | Params | Mode \n", | |
| "-----------------------------------------------------------\n", | |
| "0 | model | ConvNeXt | 348 M | train\n", | |
| "1 | criterion | BCEWithLogitsLoss | 0 | train\n", | |
| "2 | val_accuracy | BinaryAccuracy | 0 | train\n", | |
| "-----------------------------------------------------------\n", | |
| "348 M Trainable params\n", | |
| "0 Non-trainable params\n", | |
| "348 M Total params\n", | |
| "1,392.600 Total estimated model params size (MB)\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "91e98de1ade6455b976faa8d9572d23b", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Sanity Checking: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "483010a902c0432b9ed7ed30037873f4", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Training: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "bc37481d167242329758f2f72f5bcccc", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "2d623dddae664f1aaeac4c2e954ab55a", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "6a3a43ad6a8744d098a7798396623933", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "577cb6d4e6ea430880899524f53f4c86", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "105ef82eaa2d4fc5a59f7dd7029a8540", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "8600dc106e15431bacb4382676f0d9dc", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "c1d025b8ad70479d8fda7158345eef0e", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "ef2864fa6a064029adcbf3da5203cebc", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
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| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "d02d96f7730c4b0097da2d975eb882f3", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "72f33f0cbaea42399784776ee31b5e80", | |
| "version_major": 2, | |
| "version_minor": 0 | |
| }, | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "metadata": { | |
| "tags": null | |
| }, | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "lit_model = LitModel(model_name, 5e-5)\n", | |
| "trainer = pl.Trainer(devices=1, accelerator='gpu', max_epochs=10, precision=16)\n", | |
| "trainer.fit(lit_model, train_loader, val_loader)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Inference" | |
| ], | |
| "metadata": { | |
| "id": "aq_Jjzs-1OAg" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Here you'll wanna go to file browser and copy path to the checkpoint you want to use for inference, replacing the one shown here with yours.\n", | |
| "\n", | |
| "Again, this could be written better, but it works." | |
| ], | |
| "metadata": { | |
| "id": "FqUoJzN143lq" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "Jxupy2L3PoAU" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "lit_model = LitModel.load_from_checkpoint('/content/epoch=7-step=2096.ckpt')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "08KqItwKPs6a" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "lit_model.cuda()\n", | |
| "lit_model.freeze()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Test dataloader we'll bump up the batch size since we can...reduce if you go oom." | |
| ], | |
| "metadata": { | |
| "id": "_6xp-CJ15szs" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "test_loader = DataLoader(ds['test'].with_transform(test_transform_fn), batch_size=256, shuffle=False, collate_fn=collate_fn_test, num_workers=1, pin_memory=True)" | |
| ], | |
| "metadata": { | |
| "id": "05nB8rfV5sf9" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "Rq6hba37TFF8" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from tqdm.auto import tqdm\n", | |
| "\n", | |
| "all_preds = []\n", | |
| "for batch in tqdm(test_loader):\n", | |
| " x = batch.cuda()\n", | |
| " out = lit_model.model(x)\n", | |
| " all_preds.extend(out.squeeze().sigmoid().detach().cpu().numpy().tolist())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "IIttTsqIVK87" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "\n", | |
| "df = pd.DataFrame(ds['test'].remove_columns(['image']))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "sUPetDcCVt6j" | |
| }, | |
| "outputs": [], | |
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| "df['label'] = all_preds" | |
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