{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Install" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: uv in /Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages (0.1.42)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install uv" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!uv pip install dagshub setuptools accelerate toml torch torchvision transformers mlflow datasets ipywidgets python-dotenv evaluate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Initialized MLflow to track repo \"amaye15/CanineNet\"\n",
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\n" ], "text/plain": [ "Initialized MLflow to track repo \u001b[32m\"amaye15/CanineNet\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Repository amaye15/CanineNet initialized!\n",
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\n" ], "text/plain": [ "Repository amaye15/CanineNet initialized!\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os\n", "import toml\n", "import torch\n", "import mlflow\n", "import dagshub\n", "import datasets\n", "import evaluate\n", "from dotenv import load_dotenv\n", "from torchvision.transforms import v2\n", "from transformers import AutoImageProcessor, AutoModelForImageClassification, TrainingArguments, Trainer\n", "\n", "ENV_PATH = \"/Users/andrewmayes/Openclassroom/CanineNet/.env\"\n", "CONFIG_PATH = \"/Users/andrewmayes/Openclassroom/CanineNet/code/config.toml\"\n", "CONFIG = toml.load(CONFIG_PATH)\n", "\n", "load_dotenv(ENV_PATH)\n", "\n", "dagshub.init(repo_name=os.environ['MLFLOW_TRACKING_PROJECTNAME'], repo_owner=os.environ['MLFLOW_TRACKING_USERNAME'], mlflow=True, dvc=True)\n", "\n", "os.environ['MLFLOW_TRACKING_USERNAME'] = \"amaye15\"\n", "\n", "mlflow.set_tracking_uri(f'https://dagshub.com/' + os.environ['MLFLOW_TRACKING_USERNAME']\n", " + '/' + os.environ['MLFLOW_TRACKING_PROJECTNAME'] + '.mlflow')\n", "\n", "CREATE_DATASET = True\n", "ORIGINAL_DATASET = \"Alanox/stanford-dogs\"\n", "MODIFIED_DATASET = \"amaye15/stanford-dogs\"\n", "REMOVE_COLUMNS = [\"name\", \"annotations\"]\n", "RENAME_COLUMNS = {\"image\":\"pixel_values\", \"target\":\"label\"}\n", "SPLIT = 0.2\n", "\n", "METRICS = [\"accuracy\", \"f1\", \"precision\", \"recall\"]\n", "# MODELS = 'google/vit-base-patch16-224'\n", "# MODELS = \"google/siglip-base-patch16-224\"\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Affenpinscher: 0\n", "Afghan Hound: 1\n", "African Hunting Dog: 2\n", "Airedale: 3\n", "American Staffordshire Terrier: 4\n", "Appenzeller: 5\n", "Australian Terrier: 6\n", "Basenji: 7\n", "Basset: 8\n", "Beagle: 9\n", "Bedlington Terrier: 10\n", "Bernese Mountain Dog: 11\n", "Black And Tan Coonhound: 12\n", "Blenheim Spaniel: 13\n", "Bloodhound: 14\n", "Bluetick: 15\n", "Border Collie: 16\n", "Border Terrier: 17\n", "Borzoi: 18\n", "Boston Bull: 19\n", "Bouvier Des Flandres: 20\n", "Boxer: 21\n", "Brabancon Griffon: 22\n", "Briard: 23\n", "Brittany Spaniel: 24\n", "Bull Mastiff: 25\n", "Cairn: 26\n", "Cardigan: 27\n", "Chesapeake Bay Retriever: 28\n", "Chihuahua: 29\n", "Chow: 30\n", "Clumber: 31\n", "Cocker Spaniel: 32\n", "Collie: 33\n", "Curly Coated Retriever: 34\n", "Dandie Dinmont: 35\n", "Dhole: 36\n", "Dingo: 37\n", "Doberman: 38\n", "English Foxhound: 39\n", "English Setter: 40\n", "English Springer: 41\n", "Entlebucher: 42\n", "Eskimo Dog: 43\n", "Flat Coated Retriever: 44\n", "French Bulldog: 45\n", "German Shepherd: 46\n", "German Short Haired Pointer: 47\n", "Giant Schnauzer: 48\n", "Golden Retriever: 49\n", "Gordon Setter: 50\n", "Great Dane: 51\n", "Great Pyrenees: 52\n", "Greater Swiss Mountain Dog: 53\n", "Groenendael: 54\n", "Ibizan Hound: 55\n", "Irish Setter: 56\n", "Irish Terrier: 57\n", "Irish Water Spaniel: 58\n", "Irish Wolfhound: 59\n", "Italian Greyhound: 60\n", "Japanese Spaniel: 61\n", "Keeshond: 62\n", "Kelpie: 63\n", "Kerry Blue Terrier: 64\n", "Komondor: 65\n", "Kuvasz: 66\n", "Labrador Retriever: 67\n", "Lakeland Terrier: 68\n", "Leonberg: 69\n", "Lhasa: 70\n", "Malamute: 71\n", "Malinois: 72\n", "Maltese Dog: 73\n", "Mexican Hairless: 74\n", "Miniature Pinscher: 75\n", "Miniature Poodle: 76\n", "Miniature Schnauzer: 77\n", "Newfoundland: 78\n", "Norfolk Terrier: 79\n", "Norwegian Elkhound: 80\n", "Norwich Terrier: 81\n", "Old English Sheepdog: 82\n", "Otterhound: 83\n", "Papillon: 84\n", "Pekinese: 85\n", "Pembroke: 86\n", "Pomeranian: 87\n", "Pug: 88\n", "Redbone: 89\n", "Rhodesian Ridgeback: 90\n", "Rottweiler: 91\n", "Saint Bernard: 92\n", "Saluki: 93\n", "Samoyed: 94\n", "Schipperke: 95\n", "Scotch Terrier: 96\n", "Scottish Deerhound: 97\n", "Sealyham Terrier: 98\n", "Shetland Sheepdog: 99\n", "Shih Tzu: 100\n", "Siberian Husky: 101\n", "Silky Terrier: 102\n", "Soft Coated Wheaten Terrier: 103\n", "Staffordshire Bullterrier: 104\n", "Standard Poodle: 105\n", "Standard Schnauzer: 106\n", "Sussex Spaniel: 107\n", "Tibetan Mastiff: 108\n", "Tibetan Terrier: 109\n", "Toy Poodle: 110\n", "Toy Terrier: 111\n", "Vizsla: 112\n", "Walker Hound: 113\n", "Weimaraner: 114\n", "Welsh Springer Spaniel: 115\n", "West Highland White Terrier: 116\n", "Whippet: 117\n", "Wire Haired Fox Terrier: 118\n", "Yorkshire Terrier: 119\n" ] } ], "source": [ "if CREATE_DATASET:\n", " ds = datasets.load_dataset(ORIGINAL_DATASET, token=os.getenv(\"HF_TOKEN\"), split=\"full\", trust_remote_code=True)\n", " ds = ds.remove_columns(REMOVE_COLUMNS).rename_columns(RENAME_COLUMNS)\n", "\n", " labels = ds.select_columns(\"label\").to_pandas().sort_values(\"label\").get(\"label\").unique().tolist()\n", " numbers = range(len(labels))\n", " label2int = dict(zip(labels, numbers))\n", " int2label = dict(zip(numbers, labels))\n", "\n", " for key, val in label2int.items():\n", " print(f\"{key}: {val}\")\n", "\n", " ds = ds.class_encode_column(\"label\")\n", " ds = ds.align_labels_with_mapping(label2int, \"label\")\n", "\n", " ds = ds.train_test_split(test_size=SPLIT, stratify_by_column = \"label\")\n", " #ds.push_to_hub(MODIFIED_DATASET, token=os.getenv(\"HF_TOKEN\"))\n", "\n", " CONFIG[\"label2int\"] = str(label2int)\n", " CONFIG[\"int2label\"] = str(int2label)\n", "\n", " # with open(\"output.toml\", \"w\") as toml_file:\n", " # toml.dump(toml.dumps(CONFIG), toml_file)\n", "\n", " #ds = datasets.load_dataset(MODIFIED_DATASET, token=os.getenv(\"HF_TOKEN\"), trust_remote_code=True, streaming=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/andrewmayes/Openclassroom/CanineNet/env/lib/python3.12/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n", "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration. Please open a PR/issue to update `preprocessor_config.json` to use `image_processor_type` instead of `feature_extractor_type`. This warning will be removed in v4.40.\n", "Some weights of Swinv2ForImageClassification were not initialized from the model checkpoint at microsoft/swinv2-base-patch4-window16-256 and are newly initialized because the shapes did not match:\n", "- classifier.weight: found shape torch.Size([1000, 1024]) in the checkpoint and torch.Size([120, 1024]) in the model instantiated\n", "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([120]) in the model instantiated\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", "max_steps is given, it will override any value given in num_train_epochs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6b6d7e2cfe13400eb1a322641a85ed43", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00