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"_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YaSp23tE6Bjt", "outputId": "2da2eb23-f9e6-4d5b-e485-31d82b31b4aa" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/4.5 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/4.5 MB\u001b[0m \u001b[31m32.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m68.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!pip -q install --no-cache-dir \\\n", " \"kaggle\" \\\n", " \"transformers\" \\\n", " \"tensorboard==2.19.0\" \\\n", " \"pandas==2.2.2\" \\\n", " \"pillow==10.4.0\" \\\n", " \"scikit-learn\" \\\n", " \"tqdm\"" ] }, { "cell_type": "code", "source": [ "from google.colab import files\n", "uploaded = files.upload()\n", "print(uploaded.keys())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 97 }, "id": "Sbe5-8TiCKoR", "outputId": "c4624447-cd15-408b-bd3b-64e9b2393f33" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving kaggle.json to kaggle.json\n", "dict_keys(['kaggle.json'])\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "import glob\n", "import shutil\n", "\n", "json_files = glob.glob(\"*.json\")\n", "assert len(json_files) > 0, \"Сначала загрузи kaggle.json\"\n", "\n", "src = json_files[0]\n", "os.makedirs(os.path.expanduser(\"~/.kaggle\"), exist_ok=True)\n", "dst = os.path.expanduser(\"~/.kaggle/kaggle.json\")\n", "shutil.copy(src, dst)\n", "os.chmod(dst, 0o600)\n", "\n", "!kaggle --version\n", "!ls -lah ~/.kaggle" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FisLHAJ3CMPR", "outputId": "36461af8-bb05-447c-cb64-ca25a98b863d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Kaggle CLI 2.0.0\n", "total 16K\n", "drwxr-xr-x 2 root root 4.0K Apr 8 12:20 .\n", "drwx------ 1 root root 4.0K Apr 8 12:20 ..\n", "-rw------- 1 root root 72 Apr 8 12:20 kaggle.json\n" ] } ] }, { "cell_type": "code", "source": [ "!rm -rf data\n", "!mkdir -p data\n", "!kaggle datasets download -d amandam1/120-dog-breeds-breed-classification -p data --force\n", "!ls -lh data" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uUh4rIB3CNw2", "outputId": "2a106f51-8f84-4520-81f5-2b351ece50f3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset URL: https://www.kaggle.com/datasets/amandam1/120-dog-breeds-breed-classification\n", "License(s): CC0-1.0\n", "Downloading 120-dog-breeds-breed-classification.zip to data\n", "100% 740M/740M [00:06<00:00, 129MB/s]\n", "\n", "total 741M\n", "-rw-r--r-- 1 root root 741M Apr 20 2022 120-dog-breeds-breed-classification.zip\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "import zipfile\n", "\n", "zip_files = [f for f in os.listdir(\"data\") if f.endswith(\".zip\")]\n", "print(\"zip_files:\", zip_files)\n", "assert len(zip_files) > 0, \"Не найден zip-архив в папке data\"\n", "\n", "zip_path = os.path.join(\"data\", zip_files[0])\n", "print(\"zip_path:\", zip_path)\n", "print(\"size_gb:\", round(os.path.getsize(zip_path) / 1024**3, 3))\n", "\n", "with zipfile.ZipFile(zip_path, \"r\") as zf:\n", " bad = zf.testzip()\n", " print(\"testzip:\", bad)\n", " print(\"files_in_zip:\", len(zf.infolist()))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "K2MWGvBDCV7N", "outputId": "ab50fb77-ad97-4297-ebb9-1aa63c0af136" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "zip_files: ['120-dog-breeds-breed-classification.zip']\n", "zip_path: data/120-dog-breeds-breed-classification.zip\n", "size_gb: 0.723\n", "testzip: None\n", "files_in_zip: 20580\n" ] } ] }, { "cell_type": "code", "source": [ "!apt-get -qq update\n", "!apt-get -qq install -y unzip\n", "!mkdir -p data/dogs120\n", "!unzip -q \"$zip_path\" -d data/dogs120" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "o29mbFQpCZyc", "outputId": "742a90df-0b1b-45db-aea7-1e1d1c84e0d4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\n" ] } ] }, { "cell_type": "code", "source": [ "!find data -maxdepth 4 -type d | sort | head -120\n", "!du -sh data" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yXho63qaCa07", "outputId": "84aef5c4-4b3e-4b5e-a647-c8226f353083" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "data\n", "data/dogs120\n", "data/dogs120/Images\n", "data/dogs120/Images/n02085620-Chihuahua\n", "data/dogs120/Images/n02085782-Japanese_spaniel\n", "data/dogs120/Images/n02085936-Maltese_dog\n", "data/dogs120/Images/n02086079-Pekinese\n", "data/dogs120/Images/n02086240-Shih-Tzu\n", "data/dogs120/Images/n02086646-Blenheim_spaniel\n", "data/dogs120/Images/n02086910-papillon\n", "data/dogs120/Images/n02087046-toy_terrier\n", "data/dogs120/Images/n02087394-Rhodesian_ridgeback\n", "data/dogs120/Images/n02088094-Afghan_hound\n", "data/dogs120/Images/n02088238-basset\n", "data/dogs120/Images/n02088364-beagle\n", "data/dogs120/Images/n02088466-bloodhound\n", "data/dogs120/Images/n02088632-bluetick\n", "data/dogs120/Images/n02089078-black-and-tan_coonhound\n", "data/dogs120/Images/n02089867-Walker_hound\n", "data/dogs120/Images/n02089973-English_foxhound\n", "data/dogs120/Images/n02090379-redbone\n", "data/dogs120/Images/n02090622-borzoi\n", "data/dogs120/Images/n02090721-Irish_wolfhound\n", "data/dogs120/Images/n02091032-Italian_greyhound\n", "data/dogs120/Images/n02091134-whippet\n", "data/dogs120/Images/n02091244-Ibizan_hound\n", "data/dogs120/Images/n02091467-Norwegian_elkhound\n", "data/dogs120/Images/n02091635-otterhound\n", "data/dogs120/Images/n02091831-Saluki\n", "data/dogs120/Images/n02092002-Scottish_deerhound\n", "data/dogs120/Images/n02092339-Weimaraner\n", "data/dogs120/Images/n02093256-Staffordshire_bullterrier\n", "data/dogs120/Images/n02093428-American_Staffordshire_terrier\n", "data/dogs120/Images/n02093647-Bedlington_terrier\n", "data/dogs120/Images/n02093754-Border_terrier\n", "data/dogs120/Images/n02093859-Kerry_blue_terrier\n", "data/dogs120/Images/n02093991-Irish_terrier\n", "data/dogs120/Images/n02094114-Norfolk_terrier\n", "data/dogs120/Images/n02094258-Norwich_terrier\n", "data/dogs120/Images/n02094433-Yorkshire_terrier\n", "data/dogs120/Images/n02095314-wire-haired_fox_terrier\n", "data/dogs120/Images/n02095570-Lakeland_terrier\n", "data/dogs120/Images/n02095889-Sealyham_terrier\n", "data/dogs120/Images/n02096051-Airedale\n", "data/dogs120/Images/n02096177-cairn\n", "data/dogs120/Images/n02096294-Australian_terrier\n", "data/dogs120/Images/n02096437-Dandie_Dinmont\n", "data/dogs120/Images/n02096585-Boston_bull\n", "data/dogs120/Images/n02097047-miniature_schnauzer\n", "data/dogs120/Images/n02097130-giant_schnauzer\n", "data/dogs120/Images/n02097209-standard_schnauzer\n", "data/dogs120/Images/n02097298-Scotch_terrier\n", "data/dogs120/Images/n02097474-Tibetan_terrier\n", "data/dogs120/Images/n02097658-silky_terrier\n", "data/dogs120/Images/n02098105-soft-coated_wheaten_terrier\n", "data/dogs120/Images/n02098286-West_Highland_white_terrier\n", "data/dogs120/Images/n02098413-Lhasa\n", "data/dogs120/Images/n02099267-flat-coated_retriever\n", "data/dogs120/Images/n02099429-curly-coated_retriever\n", "data/dogs120/Images/n02099601-golden_retriever\n", "data/dogs120/Images/n02099712-Labrador_retriever\n", "data/dogs120/Images/n02099849-Chesapeake_Bay_retriever\n", "data/dogs120/Images/n02100236-German_short-haired_pointer\n", "data/dogs120/Images/n02100583-vizsla\n", "data/dogs120/Images/n02100735-English_setter\n", "data/dogs120/Images/n02100877-Irish_setter\n", "data/dogs120/Images/n02101006-Gordon_setter\n", "data/dogs120/Images/n02101388-Brittany_spaniel\n", "data/dogs120/Images/n02101556-clumber\n", "data/dogs120/Images/n02102040-English_springer\n", "data/dogs120/Images/n02102177-Welsh_springer_spaniel\n", "data/dogs120/Images/n02102318-cocker_spaniel\n", "data/dogs120/Images/n02102480-Sussex_spaniel\n", "data/dogs120/Images/n02102973-Irish_water_spaniel\n", "data/dogs120/Images/n02104029-kuvasz\n", "data/dogs120/Images/n02104365-schipperke\n", "data/dogs120/Images/n02105056-groenendael\n", "data/dogs120/Images/n02105162-malinois\n", "data/dogs120/Images/n02105251-briard\n", "data/dogs120/Images/n02105412-kelpie\n", "data/dogs120/Images/n02105505-komondor\n", "data/dogs120/Images/n02105641-Old_English_sheepdog\n", "data/dogs120/Images/n02105855-Shetland_sheepdog\n", "data/dogs120/Images/n02106030-collie\n", "data/dogs120/Images/n02106166-Border_collie\n", "data/dogs120/Images/n02106382-Bouvier_des_Flandres\n", "data/dogs120/Images/n02106550-Rottweiler\n", "data/dogs120/Images/n02106662-German_shepherd\n", "data/dogs120/Images/n02107142-Doberman\n", "data/dogs120/Images/n02107312-miniature_pinscher\n", "data/dogs120/Images/n02107574-Greater_Swiss_Mountain_dog\n", "data/dogs120/Images/n02107683-Bernese_mountain_dog\n", "data/dogs120/Images/n02107908-Appenzeller\n", "data/dogs120/Images/n02108000-EntleBucher\n", "data/dogs120/Images/n02108089-boxer\n", "data/dogs120/Images/n02108422-bull_mastiff\n", "data/dogs120/Images/n02108551-Tibetan_mastiff\n", "data/dogs120/Images/n02108915-French_bulldog\n", "data/dogs120/Images/n02109047-Great_Dane\n", "data/dogs120/Images/n02109525-Saint_Bernard\n", "data/dogs120/Images/n02109961-Eskimo_dog\n", "data/dogs120/Images/n02110063-malamute\n", "data/dogs120/Images/n02110185-Siberian_husky\n", "data/dogs120/Images/n02110627-affenpinscher\n", "data/dogs120/Images/n02110806-basenji\n", "data/dogs120/Images/n02110958-pug\n", "data/dogs120/Images/n02111129-Leonberg\n", "data/dogs120/Images/n02111277-Newfoundland\n", "data/dogs120/Images/n02111500-Great_Pyrenees\n", "data/dogs120/Images/n02111889-Samoyed\n", "data/dogs120/Images/n02112018-Pomeranian\n", "data/dogs120/Images/n02112137-chow\n", "data/dogs120/Images/n02112350-keeshond\n", "data/dogs120/Images/n02112706-Brabancon_griffon\n", "data/dogs120/Images/n02113023-Pembroke\n", "data/dogs120/Images/n02113186-Cardigan\n", "data/dogs120/Images/n02113624-toy_poodle\n", "data/dogs120/Images/n02113712-miniature_poodle\n", "data/dogs120/Images/n02113799-standard_poodle\n", "data/dogs120/Images/n02113978-Mexican_hairless\n", "1.5G\tdata\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "import re\n", "import json\n", "import time\n", "import random\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from PIL import Image\n", "from tqdm.auto import tqdm\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score, f1_score\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torchvision.transforms as T\n", "from torch.utils.data import Dataset, DataLoader\n", "\n", "from transformers import AutoImageProcessor, AutoModelForImageClassification" ], "metadata": { "id": "4JnIpHEzCdTo" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "SEED = 42\n", "random.seed(SEED)\n", "np.random.seed(SEED)\n", "torch.manual_seed(SEED)\n", "torch.cuda.manual_seed_all(SEED)\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "print(\"device:\", device)\n", "\n", "RUB_PER_HOUR = 0.0\n", "\n", "def sec_to_rub(sec, rub_per_hour=0.0):\n", " return float(sec) / 3600.0 * float(rub_per_hour)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kZkpEI_bCfHo", "outputId": "703bb8ac-5d0d-48f9-d87c-467955ab58e1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "device: cuda\n" ] } ] }, { "cell_type": "code", "source": [ "HF_CKPT = \"wesleyacheng/dog-breeds-multiclass-image-classification-with-vit\"\n", "\n", "processor = AutoImageProcessor.from_pretrained(HF_CKPT)\n", "hf_model = AutoModelForImageClassification.from_pretrained(HF_CKPT).to(device)\n", "hf_model.eval()\n", "\n", "raw_id2label = hf_model.config.id2label\n", "if isinstance(next(iter(raw_id2label.keys())), str):\n", " hf_id2label = {int(k): v for k, v in raw_id2label.items()}\n", "else:\n", " hf_id2label = raw_id2label\n", "\n", "print(\"HF classes:\", len(hf_id2label))\n", "print(list(hf_id2label.items())[:5])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 366, "referenced_widgets": [ "df0fb89c4f9848d8b6bfbef3d4813321", "0f3ca3da94d84803855d6cc8ef39777a", "df9dee1f6d7b40c086abc5888217d9f3", "089c967f36554b7ebb163e2b5cdb49e2", "dad999ec7ef1496d9a53ec309ba5216f", "93f0cf53f013412c9fb1468b995328c7", "c203164619ba4cfbb388366975de1cec", "436f79f5ef7a4c4cb1b20975aa341b0d", "f7b456da1e2e424b8d686d4894637132", "124616b71c764c7d9f8479b4ffacaf1b", "746bc737ac414d57a3fab94aa22a5b7c", "4807772c13d04407bf51c8726f98066b", "56b10496d8e842d586e6a1906700396a", "c73d9d94b7d541138a081e5feeec5dcc", "1eab539b2ada48ce978081479f0942be", "e5b6f393202b4dae823a6073c829f680", "52e087b36f2e40e09a30af9b8b4924a1", "84e6ea4460cc45d191a68a80a31f8ba6", "e7bf444d03614d4c9b26f11aee448532", "53f7fae0cbab45e5b79e3c29a5ba2804", "2a1b56ef915749efa445eb8489f1e404", "cd571a6bfb8345479d4a9ab02707e76a", "05abf1700e904eed9a5a5705370682c5", "e6f5ba576f464249a6881395a6e2f231", "c7a735762c354f4bb7d6079ee1116c7f", "02caba808c3549dda08737dbccb2a4f0", "f2ad7d65b988424f8ab6a9ca0e73e3ed", "ce7c8d424c454e0e9f2f68aa8bf246ae", "0872cd612f0146fdbaceee17d569a290", "f82478f2215b41a69d23e2097a82583d", "b483c3b200a8494ab13f17ce0b5a691a", "137b6e65ab1345709e7be202cba72a1c", "e84851e2685643a49b2b24b005a22ef7", "108b83898feb4c1babb42f1e6210b154", "ab6ae9440c9e41c0acb1035570e4415a", "9751e6c0a3e94b44ab4793295b9c3606", "6fe3d494b4e1452cb0dc97c36fd35fa1", "eb9bedb5809244d08c1d649bcd837ee7", "f80d7fb427d9495ba4dc488011065429", "1d0f20d6a0744c3eadb20c8343d09786", "a424359a23e741e494dee465dd702317", "8262dc6500ac4b90b3f4baaaf270c2bd", "06173515d675425e9f33a09f257b08ff", "7e7f973890db4306849d40318b82b6a8" ] }, "id": "UAg3ry6ZCg-r", "outputId": "257c5c4a-fb8b-4b24-f105-6e92d5645819" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n", "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/325 [00:00 0:\n", " candidates.append((root, img_count))\n", "\n", "candidates = sorted(candidates, key=lambda x: x[0])\n", "print(\"sample image dirs:\", candidates[:20])\n", "\n", "def find_dataset_root(base=\"data/dogs120\"):\n", " best_root = None\n", " best_num_classes = -1\n", "\n", " for root, dirs, files in os.walk(base):\n", " class_dirs = 0\n", " for d in dirs:\n", " sub = os.path.join(root, d)\n", " sub_has_image = False\n", " for _, _, fs in os.walk(sub):\n", " if any(os.path.splitext(f.lower())[1] in IMAGE_EXTS for f in fs):\n", " sub_has_image = True\n", " break\n", " if sub_has_image:\n", " class_dirs += 1\n", "\n", " if class_dirs > best_num_classes:\n", " best_num_classes = class_dirs\n", " best_root = root\n", "\n", " return best_root, best_num_classes\n", "\n", "DATASET_ROOT, approx_classes = find_dataset_root(\"data/dogs120\")\n", "print(\"DATASET_ROOT:\", DATASET_ROOT)\n", "print(\"approx_classes:\", approx_classes)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-C2fUC5LCiuZ", "outputId": "05721076-9e1c-409a-96b7-ee5e22f16db5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "sample image dirs: [('data/dogs120/Images/n02085620-Chihuahua', 152), ('data/dogs120/Images/n02085782-Japanese_spaniel', 185), ('data/dogs120/Images/n02085936-Maltese_dog', 252), ('data/dogs120/Images/n02086079-Pekinese', 149), ('data/dogs120/Images/n02086240-Shih-Tzu', 214), ('data/dogs120/Images/n02086646-Blenheim_spaniel', 188), ('data/dogs120/Images/n02086910-papillon', 196), ('data/dogs120/Images/n02087046-toy_terrier', 172), ('data/dogs120/Images/n02087394-Rhodesian_ridgeback', 172), ('data/dogs120/Images/n02088094-Afghan_hound', 239), ('data/dogs120/Images/n02088238-basset', 175), ('data/dogs120/Images/n02088364-beagle', 195), ('data/dogs120/Images/n02088466-bloodhound', 187), ('data/dogs120/Images/n02088632-bluetick', 171), ('data/dogs120/Images/n02089078-black-and-tan_coonhound', 159), ('data/dogs120/Images/n02089867-Walker_hound', 153), ('data/dogs120/Images/n02089973-English_foxhound', 157), ('data/dogs120/Images/n02090379-redbone', 148), ('data/dogs120/Images/n02090622-borzoi', 151), ('data/dogs120/Images/n02090721-Irish_wolfhound', 218)]\n", "DATASET_ROOT: data/dogs120/Images\n", "approx_classes: 120\n" ] } ] }, { "cell_type": "code", "source": [ "def normalize_breed_name(s: str) -> str:\n", " s = os.path.basename(str(s)).strip().lower()\n", "\n", " s = re.sub(r\"^n\\d{8}[-_ ]*\", \"\", s)\n", "\n", " s = s.replace(\"_\", \" \").replace(\"-\", \" \")\n", " s = re.sub(r\"[^a-z0-9 ]+\", \" \", s)\n", " s = re.sub(r\"\\s+\", \" \", s).strip()\n", " return s\n", "\n", "hf_name_to_idx = {\n", " normalize_breed_name(v): k\n", " for k, v in hf_id2label.items()\n", "}\n", "\n", "rows = []\n", "for root, _, files in os.walk(DATASET_ROOT):\n", " img_files = [\n", " f for f in files\n", " if os.path.splitext(f.lower())[1] in IMAGE_EXTS\n", " ]\n", " if not img_files:\n", " continue\n", "\n", " raw_class = os.path.basename(root)\n", " norm_class = normalize_breed_name(raw_class)\n", "\n", " if norm_class not in hf_name_to_idx:\n", " continue\n", "\n", " label = hf_name_to_idx[norm_class]\n", " class_name = hf_id2label[label]\n", "\n", " for f in img_files:\n", " rows.append({\n", " \"path\": os.path.join(root, f),\n", " \"raw_class\": raw_class,\n", " \"norm_class\": norm_class,\n", " \"label\": label,\n", " \"class_name\": class_name\n", " })\n", "\n", "df = pd.DataFrame(rows)\n", "\n", "print(\"rows:\", len(df))\n", "print(\"num_classes_mapped:\", df[\"label\"].nunique())\n", "print(df.head())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M6-6DQfSClDG", "outputId": "cfe29d31-6145-4b89-8f32-e7f7c277ae98" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "rows: 19439\n", "num_classes_mapped: 113\n", " path raw_class \\\n", "0 data/dogs120/Images/n02091635-otterhound/n0209... n02091635-otterhound \n", "1 data/dogs120/Images/n02091635-otterhound/n0209... n02091635-otterhound \n", "2 data/dogs120/Images/n02091635-otterhound/n0209... n02091635-otterhound \n", "3 data/dogs120/Images/n02091635-otterhound/n0209... n02091635-otterhound \n", "4 data/dogs120/Images/n02091635-otterhound/n0209... n02091635-otterhound \n", "\n", " norm_class label class_name \n", "0 otterhound 0 otterhound \n", "1 otterhound 0 otterhound \n", "2 otterhound 0 otterhound \n", "3 otterhound 0 otterhound \n", "4 otterhound 0 otterhound \n" ] } ] }, { "cell_type": "code", "source": [ "all_hf_classes = set(hf_id2label.keys())\n", "present_classes = set(df[\"label\"].unique())\n", "\n", "missing_hf = sorted(all_hf_classes - present_classes)\n", "print(\"missing_hf_classes_count:\", len(missing_hf))\n", "\n", "if len(missing_hf) > 0:\n", " print(\"First missing:\", [hf_id2label[x] for x in missing_hf[:15]])\n", "\n", "assert df[\"label\"].nunique() >= 100, \"\"\n", "print(\"OK\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KWfyFDLPCnI1", "outputId": "1f2cbb21-58e4-4565-f1a3-b68557002e99" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "missing_hf_classes_count: 7\n", "First missing: ['curly', 'wire', 'soft', 'flat', 'shih', 'black', 'german_short']\n", "OK\n" ] } ] }, { "cell_type": "code", "source": [ "idx_train, idx_tmp = train_test_split(\n", " df.index,\n", " test_size=0.30,\n", " random_state=42,\n", " stratify=df[\"label\"]\n", ")\n", "\n", "df_train = df.loc[idx_train].copy()\n", "df_tmp = df.loc[idx_tmp].copy()\n", "\n", "idx_val, idx_test = train_test_split(\n", " df_tmp.index,\n", " test_size=0.50,\n", " random_state=42,\n", " stratify=df_tmp[\"label\"]\n", ")\n", "\n", "df_val = df.loc[idx_val].copy()\n", "df_test = df.loc[idx_test].copy()\n", "\n", "df_train[\"split\"] = \"train\"\n", "df_val[\"split\"] = \"val\"\n", "df_test[\"split\"] = \"test\"\n", "\n", "df_all = pd.concat([df_train, df_val, df_test], ignore_index=True)\n", "df_all.to_csv(\"dogs120_splits.csv\", index=False)\n", "\n", "print(df_all[\"split\"].value_counts())\n", "print(\"num_classes:\", df_all[\"label\"].nunique())\n", "print(\"missing files:\", (~df_all[\"path\"].map(os.path.exists)).sum())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1wPHH-UACruo", "outputId": "44115f6f-37ca-48cb-c471-53d08d3a548b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "split\n", "train 13607\n", "val 2916\n", "test 2916\n", "Name: count, dtype: int64\n", "num_classes: 113\n", "missing files: 0\n" ] } ] }, { "cell_type": "code", "source": [ "CNN_SIZE = 160\n", "CNN_BATCH_SIZE = 64 if device == \"cuda\" else 32\n", "HF_BATCH_SIZE = 24 if device == \"cuda\" else 8\n", "NUM_WORKERS = 0\n", "\n", "IMAGENET_MEAN = (0.485, 0.456, 0.406)\n", "IMAGENET_STD = (0.229, 0.224, 0.225)\n", "\n", "train_tfms = T.Compose([\n", " T.RandomResizedCrop(CNN_SIZE, scale=(0.7, 1.0)),\n", " T.RandomHorizontalFlip(),\n", " T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),\n", " T.ToTensor(),\n", " T.Normalize(IMAGENET_MEAN, IMAGENET_STD),\n", "])\n", "\n", "eval_tfms = T.Compose([\n", " T.Resize((CNN_SIZE, CNN_SIZE)),\n", " T.ToTensor(),\n", " T.Normalize(IMAGENET_MEAN, IMAGENET_STD),\n", "])\n", "\n", "class DogTensorDS(Dataset):\n", " def __init__(self, frame, tfms):\n", " self.df = frame.reset_index(drop=True)\n", " self.tfms = tfms\n", "\n", " def __len__(self):\n", " return len(self.df)\n", "\n", " def __getitem__(self, idx):\n", " row = self.df.iloc[idx]\n", " img = Image.open(row[\"path\"]).convert(\"RGB\")\n", " x = self.tfms(img)\n", " y = int(row[\"label\"])\n", " return x, y\n", "\n", "class DogPILDS(Dataset):\n", " def __init__(self, frame):\n", " self.df = frame.reset_index(drop=True)\n", "\n", " def __len__(self):\n", " return len(self.df)\n", "\n", " def __getitem__(self, idx):\n", " row = self.df.iloc[idx]\n", " img = Image.open(row[\"path\"]).convert(\"RGB\")\n", " y = int(row[\"label\"])\n", " return img, y\n", "\n", "def pil_collate(batch):\n", " imgs = [b[0] for b in batch]\n", " ys = torch.tensor([b[1] for b in batch], dtype=torch.long)\n", " return imgs, ys" ], "metadata": { "id": "RMfPoniYCtYu" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_df = df_all[df_all[\"split\"] == \"train\"].reset_index(drop=True)\n", "val_df = df_all[df_all[\"split\"] == \"val\"].reset_index(drop=True)\n", "test_df = df_all[df_all[\"split\"] == \"test\"].reset_index(drop=True)\n", "\n", "train_loader = DataLoader(\n", " DogTensorDS(train_df, train_tfms),\n", " batch_size=CNN_BATCH_SIZE,\n", " shuffle=True,\n", " num_workers=NUM_WORKERS,\n", " pin_memory=(device == \"cuda\")\n", ")\n", "\n", "val_loader = DataLoader(\n", " DogTensorDS(val_df, eval_tfms),\n", " batch_size=CNN_BATCH_SIZE,\n", " shuffle=False,\n", " num_workers=NUM_WORKERS,\n", " pin_memory=(device == \"cuda\")\n", ")\n", "\n", "test_loader = DataLoader(\n", " DogTensorDS(test_df, eval_tfms),\n", " batch_size=CNN_BATCH_SIZE,\n", " shuffle=False,\n", " num_workers=NUM_WORKERS,\n", " pin_memory=(device == \"cuda\")\n", ")\n", "\n", "hf_val_loader = DataLoader(\n", " DogPILDS(val_df),\n", " batch_size=HF_BATCH_SIZE,\n", " shuffle=False,\n", " num_workers=NUM_WORKERS,\n", " collate_fn=pil_collate\n", ")\n", "\n", "hf_test_loader = DataLoader(\n", " DogPILDS(test_df),\n", " batch_size=HF_BATCH_SIZE,\n", " shuffle=False,\n", " num_workers=NUM_WORKERS,\n", " collate_fn=pil_collate\n", ")\n", "\n", "print(len(train_df), len(val_df), len(test_df))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6EoWBG6DCvM-", "outputId": "338a681d-2af5-462a-ae11-0cf11062155c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "13607 2916 2916\n" ] } ] }, { "cell_type": "code", "source": [ "class DSConvBlock(nn.Module):\n", " def __init__(self, in_ch, out_ch, stride=1):\n", " super().__init__()\n", " self.depthwise = nn.Conv2d(\n", " in_ch, in_ch, kernel_size=3, stride=stride, padding=1,\n", " groups=in_ch, bias=False\n", " )\n", " self.dw_bn = nn.BatchNorm2d(in_ch)\n", "\n", " self.pointwise = nn.Conv2d(\n", " in_ch, out_ch, kernel_size=1, stride=1, padding=0, bias=False\n", " )\n", " self.pw_bn = nn.BatchNorm2d(out_ch)\n", "\n", " def forward(self, x):\n", " x = F.relu(self.dw_bn(self.depthwise(x)), inplace=True)\n", " x = F.relu(self.pw_bn(self.pointwise(x)), inplace=True)\n", " return x\n", "\n", "class DogLiteCNN(nn.Module):\n", " def __init__(self, num_classes):\n", " super().__init__()\n", " self.stem = nn.Sequential(\n", " nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),\n", " nn.BatchNorm2d(32),\n", " nn.ReLU(inplace=True)\n", " )\n", "\n", " self.features = nn.Sequential(\n", " DSConvBlock(32, 64, stride=1),\n", " DSConvBlock(64, 128, stride=2),\n", " DSConvBlock(128, 128, stride=1),\n", " DSConvBlock(128, 256, stride=2),\n", " DSConvBlock(256, 256, stride=1),\n", " DSConvBlock(256, 384, stride=2),\n", " DSConvBlock(384, 384, stride=1),\n", " )\n", "\n", " self.head = nn.Sequential(\n", " nn.AdaptiveAvgPool2d(1),\n", " nn.Flatten(),\n", " nn.Dropout(0.3),\n", " nn.Linear(384, num_classes)\n", " )\n", "\n", " def forward(self, x):\n", " x = self.stem(x)\n", " x = self.features(x)\n", " x = self.head(x)\n", " return x\n", "\n", "num_classes = len(hf_id2label)\n", "cnn = DogLiteCNN(num_classes).to(device)\n", "\n", "criterion = nn.CrossEntropyLoss(label_smoothing=0.1)\n", "optimizer = torch.optim.AdamW(cnn.parameters(), lr=3e-4, weight_decay=1e-4)\n", "\n", "print(\"params_million:\", round(sum(p.numel() for p in cnn.parameters()) / 1e6, 3))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fLm3l2KJCw3d", "outputId": "f0baa4b2-af50-443b-b553-66ab4b393bc1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "params_million: 0.435\n" ] } ] }, { "cell_type": "code", "source": [ "@torch.no_grad()\n", "def evaluate_cnn(model, loader):\n", " model.eval()\n", " ys, ps = [], []\n", " losses = []\n", "\n", " for x, y in tqdm(loader, leave=False):\n", " x = x.to(device, non_blocking=True)\n", " y = y.to(device, non_blocking=True)\n", "\n", " logits = model(x)\n", " loss = criterion(logits, y)\n", " pred = logits.argmax(1)\n", "\n", " ys.extend(y.cpu().numpy().tolist())\n", " ps.extend(pred.cpu().numpy().tolist())\n", " losses.append(float(loss.item()))\n", "\n", " return {\n", " \"loss\": float(np.mean(losses)),\n", " \"acc\": float(accuracy_score(ys, ps)),\n", " \"f1_macro\": float(f1_score(ys, ps, average=\"macro\"))\n", " }\n", "\n", "def train_one_epoch(model, loader):\n", " model.train()\n", " losses = []\n", " t0 = time.perf_counter()\n", "\n", " for x, y in tqdm(loader, leave=False):\n", " x = x.to(device, non_blocking=True)\n", " y = y.to(device, non_blocking=True)\n", "\n", " optimizer.zero_grad(set_to_none=True)\n", " logits = model(x)\n", " loss = criterion(logits, y)\n", " loss.backward()\n", " optimizer.step()\n", "\n", " losses.append(float(loss.item()))\n", "\n", " t1 = time.perf_counter()\n", " return {\n", " \"train_loss\": float(np.mean(losses)),\n", " \"time_sec\": float(t1 - t0)\n", " }\n", "\n", "@torch.no_grad()\n", "def hf_logits(imgs):\n", " batch = processor(images=imgs, return_tensors=\"pt\")\n", " pixel_values = batch[\"pixel_values\"].to(device)\n", " logits = hf_model(pixel_values=pixel_values).logits\n", " return logits\n", "\n", "@torch.no_grad()\n", "def evaluate_hf(loader):\n", " hf_model.eval()\n", " ys, ps = [], []\n", " t0 = time.perf_counter()\n", "\n", " for imgs, y in tqdm(loader, leave=False):\n", " logits = hf_logits(imgs)\n", " pred = logits.argmax(1).cpu().numpy()\n", "\n", " ys.extend(y.numpy().tolist())\n", " ps.extend(pred.tolist())\n", "\n", " t1 = time.perf_counter()\n", "\n", " return {\n", " \"acc\": float(accuracy_score(ys, ps)),\n", " \"f1_macro\": float(f1_score(ys, ps, average=\"macro\")),\n", " \"time_sec\": float(t1 - t0)\n", " }\n", "\n", "@torch.no_grad()\n", "def predict_proba_cnn(model, loader):\n", " model.eval()\n", " probs, ys = [], []\n", "\n", " for x, y in tqdm(loader, leave=False):\n", " x = x.to(device, non_blocking=True)\n", " logits = model(x)\n", " probs.append(F.softmax(logits, dim=1).cpu().numpy())\n", " ys.extend(y.numpy().tolist())\n", "\n", " return np.vstack(probs), np.array(ys)\n", "\n", "@torch.no_grad()\n", "def predict_proba_hf(loader):\n", " hf_model.eval()\n", " probs, ys = [], []\n", "\n", " for imgs, y in tqdm(loader, leave=False):\n", " logits = hf_logits(imgs)\n", " probs.append(torch.softmax(logits, dim=1).cpu().numpy())\n", " ys.extend(y.numpy().tolist())\n", "\n", " return np.vstack(probs), np.array(ys)" ], "metadata": { "id": "uW3e-HmfCy3O" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "step3_train = train_one_epoch(cnn, train_loader)\n", "cnn_val_step3 = evaluate_cnn(cnn, val_loader)\n", "hf_val_step3 = evaluate_hf(hf_val_loader)\n", "\n", "step3_report = {\n", " \"cnn_after_1_epoch\": {\n", " **step3_train,\n", " **cnn_val_step3,\n", " \"cost_rub\": sec_to_rub(step3_train[\"time_sec\"], RUB_PER_HOUR)\n", " },\n", " \"hf_on_val\": {\n", " **hf_val_step3,\n", " \"cost_rub\": sec_to_rub(hf_val_step3[\"time_sec\"], RUB_PER_HOUR)\n", " },\n", " \"total_step3_cost_rub\": sec_to_rub(\n", " step3_train[\"time_sec\"] + hf_val_step3[\"time_sec\"],\n", " RUB_PER_HOUR\n", " )\n", "}\n", "\n", "with open(\"step3_metrics.json\", \"w\", encoding=\"utf-8\") as f:\n", " json.dump(step3_report, f, ensure_ascii=False, indent=2)\n", "\n", "torch.save(cnn.state_dict(), \"doglite_epoch1.pt\")\n", "\n", "print(json.dumps(step3_report, ensure_ascii=False, indent=2))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 328, "referenced_widgets": [ "8131b7f2af1340b4b7ecf8144a8d293d", "843d968c451d4450b73a66576e3df16b", "1b289c00e43e4c55ae61c785b17e4c8b", "e14c36d18f4f4dfc9ab4ced2f36d01c9", "a66d9560db0a431c821cd1b7b590a000", "d69f72e87cb44739a0f5d228d842d23b", "b6412930ed9744d88d44f6a0e0efc002", "5b1ee7ed422b42c1a0b466c3bd20c240", "e06b0685c7a141aa9e893ebabc2e5279", "958b97e28db0468c91d39dec0f8e7f4a", "be1db19cbf094accaa4b8abbe01981bc", "9a8c329e25134f24b3724dbe6e50d0bc", "269b22d6ccab46d6ac62d41f637ab7e5", "2d29c4aad0754d25a84f25db094b7be2", "9ef45a73fa034b6099dee8cc21e65faa", "4208ef81a6014eb6a1a4a5fbdb1f28e3", "8a464e59449642d8b0a829b1b5f1e92e", "bab152e64fd5437897aeeea4da321848", "3a1506cece6d40eebae4afb16833300b", "e04abc87d4a243d69819a3cc82754c4e", "beea469fc595495bbd81c4f254ae10eb", "d56cd23820a54de8891f872d7e4e4f5d", "e57da70ee29340b3880358a3996dd963", "2e6c3badb08f43bc9d4ed5df7346283c", "046f93fe252e449894ef20d418b7c4d1", "2c3518f8fad345b89e3652d3e7ca098f", "295356a619024c8d8c773a4a4e9c63db", "b585f41c7d0c414e9e4c80b7f8b881f0", "aa1d59f5e38b4167ada7312eb3871037", "370bb3c15e7247a7a56ff281bb15d8ab", "e8012326dae1430cb6e24fea56b2d264", "b70c9b997e224920bae2cce2abe3f546", "3c0b361e5f6a417a87e68eea8fec35cc" ] }, "id": "ZPYVcV6pC1PO", "outputId": "ebeb6415-d43e-4ab1-a62c-510d8764714e" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/213 [00:00 best_acc:\n", " best_acc = val_m[\"acc\"]\n", " torch.save(cnn.state_dict(), best_state_path)\n", "\n", "writer.close()\n", "\n", "history_df = pd.DataFrame(history)\n", "history_df.to_csv(\"step4_cnn_history.csv\", index=False)\n", "\n", "with open(\"step4_cnn_history.json\", \"w\", encoding=\"utf-8\") as f:\n", " json.dump(history, f, ensure_ascii=False, indent=2)\n", "\n", "print(\"best val acc:\", best_acc)\n", "history_df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 268, "referenced_widgets": [ "7dcab62aea8643279a8b647495cc7986", "67b9ba80a1e84321bdd90835bbb3fa4e", "a54af15246164c7fb9882a18e55b7ec6", "a24f6bcbf82f47bab6a1f7f81b281a5f", "73f6b6cdde2146e79fb44be6da19ce51", "cb569392d10f4235b6769885d9700825", "af555b654f9a4132aba10433216cfe8a", "4c8b71a40bda4c54a72c9d1aa8f2a6d7", "763bf4ed3f774fbba330b16295bb482e", "9bf3fbbce5aa420fa3f8ea2a18a54ac4", "8084e60b6b874b829afb9eac22eff9a9", "1cfb9a685b8a491793a872cf1dc4861e", "9e76c7da2adf4bcebce9dde2984fa579", "7a2de14dd79d4a33869c4ca1c70c6049", "2c034573baef492cb3ff990d0ed96bc8", "2307e4581c60457fb788a4f7aefc4ca5", "892183a96dde4cf98e1452db3f27bb5c", "ed3fb8f280bd4a5dae5246545ba24ec6", "670f016ff1244f08999380357cdf5aff", "f3ceb9a5820b413dbc4578fbafcdeead", "8eee145858504c919fa4d57270c3dfde", "bd8cd8f3a6164cf2878ebcc15d515356", "8de5817d2b5e4a3c8480a03fd265cdc7", "171db4ab574b462ba7052063800e848d", "8eea2650eb134102a54e2503b492ba5f", "6bd03afd39b148cdbef4a7874f147c87", "908f8bededab4dad918817f32751d0a0", "6ce47b3a03ae4eb0a25714786d8568fa", "61b0e9edc9e4460ea58b2d8ca6b55f1c", "a77588b2f6f24b8180dfc279ba8e5b70", "b14b22ade74a465f9f3a582afaea41c9", "c31b68bd57d44ecabf54390a52a230cc", "745148a3eeed4d3ebed5d41a521aae98", "06c4c9e0e7144a33b034c3e5374960c8", "d110a65aad9f475ba52bfc79d3132847", "89c2c6fc1ad54c56b6077e7abf5eaff3", "df7e3a32dfac4b9e802cc8249b33008b", "3b6dc4907a334418a5ae26a66a81bccb", "8eeafab0938e4fc1ba26ced7707bbea8", "3fb16abc94ef47c28f23bd6b6080405e", "07ac215fa73443cba8bc0d64a625e72d", "c196a960cd00477280cecc7ad75d3aae", "73bfc643e0014f729b68be7c31b7267e", "6314b00495b94449ad66b3b8e3b4a617", "139aa9d1ed8947ad8df4aeaa7db0d192", "da94f2f0242e44c38b3b131bf4c52f08", "1659154cdf5745c6bf01dc987b9010cb", "dad39e84e02e4aef97afca962259b4ec", "e5d45acda7764585bc7324caa504432d", "905ab12d227347df9f772759471b378b", "62d5d3a296ae4e219a81c82222b7d269", "76fabcacdb034c11b767cc7d15346238", "05e9741a33344cf393d7effd178120c9", "90938f8e0f7a4544a54b28433440cc4a", "32b6bdec3e284e0cb9a35374a25c8def", "26d21e13ed894149bfa191475c4d23d2", "7809444a5bcb4010a0738d1456cb737c", "bc681125d445435ab993f4cd7ef2613b", "e4b898e9c8f5438faef2190e377fead5", "c6d5d9ae44874b78af831de26f3a4335", "ca2660877ed843098a01ca2eb096837e", "833d4bf08a414cfcbf05063bc4a27d61", "9ebcc0e596354bb486a5858dff3b70c6", "632fa263884144f5a32ee1721d163772", "8536ae13144f4e668e718e66c9820265", "c5cfcd62d4934e3cbc3728adf03e55b7" ] }, "id": "ryQr3ThnC28Y", "outputId": "3c31be93-42e9-45f8-b985-e132cf1d070b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/213 [00:00\n", "
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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "history_df", "summary": "{\n \"name\": \"history_df\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"epoch\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 4,\n \"num_unique_values\": 4,\n \"samples\": [\n 2,\n 4,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"train_loss\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.18034866841210262,\n \"min\": 4.302493411050716,\n \"max\": 4.7214702798726975,\n \"num_unique_values\": 4,\n \"samples\": [\n 4.553371630923849,\n 4.302493411050716,\n 4.7214702798726975\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"val_loss\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.15309298019754133,\n \"min\": 4.283579629400502,\n \"max\": 4.629003690636677,\n \"num_unique_values\": 4,\n \"samples\": [\n 4.489863582279371,\n 4.283579629400502,\n 4.629003690636677\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"val_acc\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0166762134402876,\n \"min\": 0.029492455418381344,\n \"max\": 0.0668724279835391,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.03772290809327846,\n 0.0668724279835391,\n 0.029492455418381344\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"val_f1_macro\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.011623361385446031,\n \"min\": 0.007218133505086909,\n \"max\": 0.0336248987465368,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.012524422483321786,\n 0.0336248987465368,\n 0.007218133505086909\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"epoch_time_sec\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.589041969427287,\n \"min\": 83.85175714400009,\n \"max\": 95.73960929400005,\n \"num_unique_values\": 4,\n \"samples\": [\n 95.73960929400005,\n 83.85175714400009,\n 90.28594455099994\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 21 } ] }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(7,4))\n", "plt.plot(history_df[\"epoch\"], history_df[\"val_acc\"], marker=\"o\")\n", "plt.title(\"Val accuracy по эпохам\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.grid(True)\n", "plt.show()\n", "\n", "plt.figure(figsize=(7,4))\n", "plt.plot(history_df[\"epoch\"], history_df[\"train_loss\"], marker=\"o\")\n", "plt.title(\"Train loss по эпохам\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.grid(True)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 803 }, "id": "gHntLrR9C4vX", "outputId": "62b1db0f-a8cd-4bbb-d632-19966267e833" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "best_cnn = DogLiteCNN(num_classes).to(device)\n", "best_cnn.load_state_dict(torch.load(\"doglite_best.pt\", map_location=device))\n", "\n", "cnn_val_p, y_val1 = predict_proba_cnn(best_cnn, val_loader)\n", "hf_val_p, y_val2 = predict_proba_hf(hf_val_loader)\n", "assert np.all(y_val1 == y_val2)\n", "\n", "best_w = 0.0\n", "best_val_acc = -1.0\n", "\n", "for w in np.linspace(0.0, 1.0, 21):\n", " pred = (w * cnn_val_p + (1.0 - w) * hf_val_p).argmax(axis=1)\n", " acc = accuracy_score(y_val1, pred)\n", " if acc > best_val_acc:\n", " best_val_acc = float(acc)\n", " best_w = float(w)\n", "\n", "print(\"best_w:\", best_w, \"best_val_acc:\", best_val_acc)\n", "\n", "cnn_test_p, y_test1 = predict_proba_cnn(best_cnn, test_loader)\n", "hf_test_p, y_test2 = predict_proba_hf(hf_test_loader)\n", "assert np.all(y_test1 == y_test2)\n", "\n", "ens_pred = (best_w * cnn_test_p + (1.0 - best_w) * hf_test_p).argmax(axis=1)\n", "\n", "ensemble_metrics = {\n", " \"best_cnn_weight\": best_w,\n", " \"val_acc_for_best_weight\": best_val_acc,\n", " \"test_acc\": float(accuracy_score(y_test1, ens_pred)),\n", " \"test_f1_macro\": float(f1_score(y_test1, ens_pred, average=\"macro\")),\n", " \"cnn_train_time_sec_total\": float(total_cnn_train_sec),\n", " \"total_pipeline_time_sec\": float(total_cnn_train_sec + hf_val_step3[\"time_sec\"]),\n", " \"approx_total_cost_rub\": float(sec_to_rub(total_cnn_train_sec + hf_val_step3[\"time_sec\"], RUB_PER_HOUR))\n", "}\n", "\n", "with open(\"ensemble_metrics.json\", \"w\", encoding=\"utf-8\") as f:\n", " json.dump(ensemble_metrics, f, ensure_ascii=False, indent=2)\n", "\n", "print(json.dumps(ensemble_metrics, ensure_ascii=False, indent=2))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 200, "referenced_widgets": [ "817c3cd642994f98af2a53ce8f6fd5f4", "9d2c9ebb98714ceb921af005dd0af9ad", "95b8ba438a284d3e91b1001f9c47ca08", "6035f01339ec456fb4ea79f542da8146", "deb80ee1ec444b4694ba2f39bdcda711", "e5bb2b6d16c84c399fec4218de6b0458", "5a842e1b07584945ba798ca5cf8e6a58", "07f91e4fbb2c408186d3ee846cd2eb48", "1e305861ad3649568947a906372d8394", "13e00d1e3a33458a9ce06f2c511ebe8a", "ec3b389fb4c548958ed7c235e8133f89", "824fc19c2da64fa38c658f6f78fde028", "eb498ba7350a47cdb82bdebd5ca2c0e8", "ef76ea860195445aa04253e529fbb9b5", "f80c416288484ada8a389275f674d41d", "79cb46023d814f2fb9226e447dcdd23f", "183553a256b14ee1984f9aa51980bf35", "d5630e232b974e2eb0d6a9175f894fd8", "39c132eed80344d9816d7e70dcae6398", "5f7cef270a2f407e9fe2a51e58889a85", "4b5c8e7e625443078ec3bb2982acfcb7", "75125a538bb446228b128ca5492de616", "7cbd453cc72746ce89698a3a0357a46f", "d5ddcc8458494654952ea3a90d246d4a", "a44f3ce55d6545e6bb1ea78e55559adf", "30642eaafb3f408e88ef02833b7e6081", "df00d59a8ba14cb1bc056e94f1c29483", "f9f8266f1ce445f9af967df8a92eeb9c", "e496644905dd416687bcca3d336f9d62", "1f23cc9e8bdc427dac55d0ebc02ec4a5", "a409dde8f01a480a80fb35bb14eac3a5", "a908bb0317d34019abb65573d37862f7", "7ecd633873cc4962b44dc2c80bc55946", "cc4cbbeac1474fc1971633b225894fd2", "9efb84a0e3ba46c7a9fa94b124ce8861", "d678aebe10dd455a9132c5ed36c64bcf", "5a0614a7aeef4a8db4150b875db180d6", "f3e821895b0c455faa830dd6d670cf7d", "d9878e779617478c9a043e1da145610b", "f52852f33b41440faa25932d88c662d7", "f4ee25ffdf0a404882fc1406e42de69d", "87c1a207130949c7b271db503b47d361", "9c4752b5121a46438afb9dd07fa2aac7", "c7e9c117274f437a85c008729d2ee8b0" ] }, "id": "mGaB8hUsDnDY", "outputId": "aa701351-5763-4b05-b75f-cd9b515b6292" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/46 [00:00