{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Тестовое задание АНО \"ЦИСМ\" по направлению: «ML-разработка»\n\nВыполнил Карпов Даниил","metadata":{"id":"K-3k4CEOHb1o"}},{"cell_type":"markdown","source":"**Финальное решени представлено в Google Colab, т.к. я потратил время выделенное на графический ускоритель на платформе Kaggle.**\n\nПрикладываю ссылку на блокнот: https://colab.research.google.com/drive/1Fdr26vyiORAKwnHel8Izm6ORkhwsh0Mh?usp=sharing\n\nСсылка на скринкаст: https://drive.google.com/file/d/1xS8B2hRN_i9zfkJUrczY4e5WVm1uB8Bm/view?usp=share_link\n","metadata":{"id":"kRFaBTCcH3W5"}},{"cell_type":"code","source":"import torch\nimport os\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom torchvision.datasets import ImageFolder\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom torchvision import datasets, models, transforms\nimport torch.nn as nn\nfrom tqdm import tqdm\nfrom copy import deepcopy\nfrom torch.utils.tensorboard import SummaryWriter # print to tenserboard\nfrom sklearn.metrics import f1_score\nfrom PIL import Image\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix\nimport plotly.figure_factory as ff\nfrom IPython.display import clear_output","metadata":{"id":"8d6Dd5GJHdaA","execution":{"iopub.status.busy":"2023-01-25T11:11:52.077468Z","iopub.execute_input":"2023-01-25T11:11:52.077886Z","iopub.status.idle":"2023-01-25T11:11:58.302559Z","shell.execute_reply.started":"2023-01-25T11:11:52.077799Z","shell.execute_reply":"2023-01-25T11:11:58.301288Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from google.colab import drive\n# drive.mount('/content/drive')","metadata":{"id":"UBzD09cAJEev","outputId":"21d8a0f2-ac01-4b43-ffa8-9277354717aa"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Utils","metadata":{"id":"1z7I2j4uR9Kd"}},{"cell_type":"code","source":"def seed_everything(seed=42):\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False","metadata":{"id":"4eW9rZYwQlUs","execution":{"iopub.status.busy":"2023-01-25T11:12:03.184736Z","iopub.execute_input":"2023-01-25T11:12:03.185094Z","iopub.status.idle":"2023-01-25T11:12:03.192527Z","shell.execute_reply.started":"2023-01-25T11:12:03.185066Z","shell.execute_reply":"2023-01-25T11:12:03.189730Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def imshow(inp, title=None, plt_ax=plt, default=False):\n    \"\"\"Imshow для тензоров\"\"\"\n    inp = inp.numpy().transpose((1, 2, 0))\n    mean = np.array([0.5] * 3) # [0.485, 0.456, 0.406]\n    std = np.array([0.5] * 3) # [0.229, 0.224, 0.225]\n    inp = std * inp + mean\n    inp = np.clip(inp, 0, 1)\n    plt_ax.imshow(inp)\n    if title is not None:\n        plt_ax.set_title(title)\n    plt_ax.grid(False)","metadata":{"id":"1_yrNG3OR_wy","execution":{"iopub.status.busy":"2023-01-25T11:12:03.449499Z","iopub.execute_input":"2023-01-25T11:12:03.449922Z","iopub.status.idle":"2023-01-25T11:12:03.456710Z","shell.execute_reply.started":"2023-01-25T11:12:03.449889Z","shell.execute_reply":"2023-01-25T11:12:03.455594Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Вывод сетки фото\ndef show_grid(loader, dataset, classes=None, size=(4,4),):\n    axes = plt.subplots(nrows=size[0], ncols=size[1], figsize=(12, 9))[1]\n    t = next(iter(loader))\n    if len(t) == 3:\n        t = t[:-1]\n    data, labels = t\n    for ax, d, l in zip(axes.flatten(), data, labels):\n        ax.axis(\"off\")\n        if classes:        \n            imshow(d.data.cpu(), title=classes[l], plt_ax=ax)\n        else:\n            imshow(d.data.cpu(), plt_ax=ax)","metadata":{"id":"0837xHltSBi2","execution":{"iopub.status.busy":"2023-01-25T11:12:04.024512Z","iopub.execute_input":"2023-01-25T11:12:04.025560Z","iopub.status.idle":"2023-01-25T11:12:04.033420Z","shell.execute_reply.started":"2023-01-25T11:12:04.025510Z","shell.execute_reply":"2023-01-25T11:12:04.032334Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Функция разделения датасета на train и val\ndef split(data_dir, train_transforms, val_transforms, train_size=0.8, imagefolder=ImageFolder):\n    train_dataset = imagefolder(root=data_dir, transform=train_transforms)\n    val_dataset = imagefolder(root=data_dir, transform=val_transforms)\n    num_train = len(train_dataset)\n    indices = list(range(num_train))\n    split = int(np.floor(train_size * num_train))\n    np.random.shuffle(indices)\n    train_idx, valid_idx = indices[:split], indices[split:]\n\n    traindata = Subset(train_dataset, indices=train_idx)\n    valdata = Subset(val_dataset, indices=valid_idx)\n    return traindata, valdata","metadata":{"id":"ORTGL2kNeEv8","execution":{"iopub.status.busy":"2023-01-25T11:12:04.377751Z","iopub.execute_input":"2023-01-25T11:12:04.378768Z","iopub.status.idle":"2023-01-25T11:12:04.385884Z","shell.execute_reply.started":"2023-01-25T11:12:04.378720Z","shell.execute_reply":"2023-01-25T11:12:04.384904Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# функция сохранения \ndef save_checkpoint(model, optimizer, filename=\"my_checkpoint.pth.tar\"):\n    print(\"=> Saving checkpoint\")\n    checkpoint = {\n        \"state_dict\": model.state_dict(),\n        \"optimizer\": optimizer.state_dict(),\n    }\n    torch.save(checkpoint, filename)\n\n\ndef load_checkpoint(checkpoint_file, model, optimizer, lr):\n    print(\"=> Loading checkpoint\")\n    checkpoint = torch.load(checkpoint_file, map_location=DEVICE)\n    model.load_state_dict(checkpoint[\"state_dict\"])\n    optimizer.load_state_dict(checkpoint[\"optimizer\"])\n\n    # If we don't do this then it will just have learning rate of old checkpoint\n    # and it will lead to many hours of debugging \\:\n    for param_group in optimizer.param_groups:\n        param_group[\"lr\"] = lr","metadata":{"id":"L4KqdaIh1Xqj","execution":{"iopub.status.busy":"2023-01-25T11:12:04.906631Z","iopub.execute_input":"2023-01-25T11:12:04.907756Z","iopub.status.idle":"2023-01-25T11:12:04.914804Z","shell.execute_reply.started":"2023-01-25T11:12:04.907713Z","shell.execute_reply":"2023-01-25T11:12:04.913802Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def show_train_val_metric(losses, scores):\n    clear_output(wait=True)\n\n    axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 9))[1]\n    axes[0].plot(range(len(losses['train'])), losses['train'],color='blue',label='train loss')\n    axes[0].plot(range(len(losses['val'])), losses['val'],color='red',label='val loss')\n    axes[0].set_title('Loss') \n    \n    axes[1].plot(range(len(scores['train'])), scores['train'],color='blue',label='train f1')\n    axes[1].plot(range(len(scores['val'])), scores['val'],color='red',label='val f1')\n    axes[1].set_title('F1 macro')\n    plt.legend()\n    plt.suptitle('TRAIN: loss - %f, f1 macro - %f \\n VAL: loss - %f, f1 macro - %f' % (losses['train'][-1], scores['train'][-1], losses['val'][-1], scores['val'][-1]))\n    plt.show()","metadata":{"id":"NIxD28qsYgdk","execution":{"iopub.status.busy":"2023-01-25T11:12:06.151300Z","iopub.execute_input":"2023-01-25T11:12:06.151720Z","iopub.status.idle":"2023-01-25T11:12:06.161613Z","shell.execute_reply.started":"2023-01-25T11:12:06.151683Z","shell.execute_reply":"2023-01-25T11:12:06.160332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class ImageFolderWithPath(ImageFolder):\n  '''\n  Собственный класс ImageFolder.\n  Вдобавок к тензеру и метке возвращает путь\n  '''\n  def __getitem__(self,index):\n      original_tuple = super(ImageFolderWithPath,self).__getitem__(index)\n      path = self.imgs[index][0]\n      tuple_with_path = (original_tuple + (path,))\n      return tuple_with_path","metadata":{"id":"oMV4fw7bGZxm","execution":{"iopub.status.busy":"2023-01-25T11:12:07.043786Z","iopub.execute_input":"2023-01-25T11:12:07.044155Z","iopub.status.idle":"2023-01-25T11:12:07.050428Z","shell.execute_reply.started":"2023-01-25T11:12:07.044123Z","shell.execute_reply":"2023-01-25T11:12:07.049373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# репрезентативность результатов\nseed_everything()","metadata":{"id":"oXMB3VQ6Q8CM","execution":{"iopub.status.busy":"2023-01-25T11:12:12.878238Z","iopub.execute_input":"2023-01-25T11:12:12.878611Z","iopub.status.idle":"2023-01-25T11:12:12.885983Z","shell.execute_reply.started":"2023-01-25T11:12:12.878562Z","shell.execute_reply":"2023-01-25T11:12:12.884977Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Config","metadata":{"id":"o7McukNWTlok"}},{"cell_type":"code","source":"TRAIN_DATA_DIR = '/kaggle/input/footballteams-classification/Train/GrayScaleTrain'\nTEST_DATA_DIR = '/kaggle/input/footballteams-classification/Test/GrayScaleTest'\n\nBATCH_SIZE = 6\nNUM_EPOCHS = 50\nNUM_CORES = 2\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nCHECKPOINT_PATH = '/kaggle/working/efficientnet_b6_086.pth' # Путь к весам модели со скором 0.86 (использовался этап валидации). Необходим для анализа модели.","metadata":{"id":"V_nCH0mpTrwU","execution":{"iopub.status.busy":"2023-01-25T11:14:38.091333Z","iopub.execute_input":"2023-01-25T11:14:38.091740Z","iopub.status.idle":"2023-01-25T11:14:38.161476Z","shell.execute_reply.started":"2023-01-25T11:14:38.091706Z","shell.execute_reply":"2023-01-25T11:14:38.160324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# веса модели со скором 0.88112\n!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1P9uVwrt7X5hpQeEWD_-6uSgfnVu14nto' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1P9uVwrt7X5hpQeEWD_-6uSgfnVu14nto\" -O efficientnet_b6_088.pth && rm -rf /tmp/cookies.txt","metadata":{"id":"g4ijYbXsJJJ4","outputId":"74ad6ed3-1c62-4483-ebcf-0692b0df9228","execution":{"iopub.status.busy":"2023-01-25T11:12:33.189400Z","iopub.execute_input":"2023-01-25T11:12:33.189769Z","iopub.status.idle":"2023-01-25T11:12:38.681002Z","shell.execute_reply.started":"2023-01-25T11:12:33.189739Z","shell.execute_reply":"2023-01-25T11:12:38.679804Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# веса модели со скором 0.86613 (Использовался этап валидации)\n# Эти веса используются на этапе анализа модели, потому что присутствовал этап валидации.\n# Лучшие веса обучались на всем датасете, поэтому их не получится использовать\n!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1fuaLgh9XO4LN-NQA9xxwZZ6Jtj5lztNz' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1fuaLgh9XO4LN-NQA9xxwZZ6Jtj5lztNz\" -O efficientnet_b6_086.pth && rm -rf /tmp/cookies.txt","metadata":{"id":"3RA_04ikKQVH","outputId":"3314b8de-08da-4e99-a42a-a902252214df","execution":{"iopub.status.busy":"2023-01-25T11:13:10.723619Z","iopub.execute_input":"2023-01-25T11:13:10.724676Z","iopub.status.idle":"2023-01-25T11:13:19.486263Z","shell.execute_reply.started":"2023-01-25T11:13:10.724625Z","shell.execute_reply":"2023-01-25T11:13:19.485100Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.set(rc={'figure.figsize':(18,8)})","metadata":{"id":"wR3HaGdRcdzX","execution":{"iopub.status.busy":"2023-01-25T11:14:50.370967Z","iopub.execute_input":"2023-01-25T11:14:50.371341Z","iopub.status.idle":"2023-01-25T11:14:50.377025Z","shell.execute_reply.started":"2023-01-25T11:14:50.371306Z","shell.execute_reply":"2023-01-25T11:14:50.376066Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 1. Данные","metadata":{"id":"kznEfiZxRRNL"}},{"cell_type":"markdown","source":"### 1.1 Загрузка данных","metadata":{"id":"WMqg1HgVRYaW"}},{"cell_type":"code","source":"# Использовалось для загрузки датасета в Google Colab\n# !mkdir ~/.kaggle\n# !cp kaggle.json ~/.kaggle/\n# !chmod 600 ~/.kaggle/kaggle.json\n# !kaggle competitions download -c footballteams-classification","metadata":{"id":"qQoRhkCCQ9GQ","outputId":"6e17d1fb-6599-41f5-c277-e3169f08d9fa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# !mkdir images","metadata":{"id":"5IJvOlrERgxc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# !unzip -q -o footballteams-classification.zip -d images | awk 'BEGIN {ORS=\" \"} {if(NR%50==0)print \".\"}'","metadata":{"id":"gowA8ymtRs3D"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 1.2 Анализ данных","metadata":{"id":"dtCoGAmhUCjS"}},{"cell_type":"markdown","source":"Посмотрим на сбаланированность классов.","metadata":{"id":"snoSSKRKJbzM"}},{"cell_type":"code","source":"temp = datasets.ImageFolder(root=TRAIN_DATA_DIR)","metadata":{"id":"MxjX8FQfUFyz","execution":{"iopub.status.busy":"2023-01-25T11:14:53.586473Z","iopub.execute_input":"2023-01-25T11:14:53.587149Z","iopub.status.idle":"2023-01-25T11:14:54.034815Z","shell.execute_reply.started":"2023-01-25T11:14:53.587114Z","shell.execute_reply":"2023-01-25T11:14:54.033751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"CLASSES = temp.classes","metadata":{"id":"15wX_Wm7kHlx","execution":{"iopub.status.busy":"2023-01-25T11:14:54.120376Z","iopub.execute_input":"2023-01-25T11:14:54.120931Z","iopub.status.idle":"2023-01-25T11:14:54.127250Z","shell.execute_reply.started":"2023-01-25T11:14:54.120893Z","shell.execute_reply":"2023-01-25T11:14:54.126231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"NUM_CLASSES = len(CLASSES)\nNUM_CLASSES","metadata":{"id":"J4ry1lMb37-O","outputId":"c1f91ff4-afa3-4e24-c1c9-aca5535fb940","execution":{"iopub.status.busy":"2023-01-25T11:14:54.755835Z","iopub.execute_input":"2023-01-25T11:14:54.756196Z","iopub.status.idle":"2023-01-25T11:14:54.765311Z","shell.execute_reply.started":"2023-01-25T11:14:54.756166Z","shell.execute_reply":"2023-01-25T11:14:54.764214Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"count = np.unique(temp.targets, return_counts=True)\nsample_per_class_count = dict(zip(*count)) \nsample_per_class_count","metadata":{"id":"btb4mOhPUPfZ","outputId":"9eca886f-58a0-4b52-f051-744515416976","execution":{"iopub.status.busy":"2023-01-25T11:14:55.571523Z","iopub.execute_input":"2023-01-25T11:14:55.571902Z","iopub.status.idle":"2023-01-25T11:14:55.582838Z","shell.execute_reply.started":"2023-01-25T11:14:55.571870Z","shell.execute_reply":"2023-01-25T11:14:55.581157Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"names_count = {CLASSES[k]:v for k,v in sample_per_class_count.items()}\nnames_count","metadata":{"id":"U5KliefmaF9o","outputId":"1152bc54-bfb8-4d50-e992-4b9c48f3788b","execution":{"iopub.status.busy":"2023-01-25T11:14:58.074451Z","iopub.execute_input":"2023-01-25T11:14:58.075118Z","iopub.status.idle":"2023-01-25T11:14:58.082384Z","shell.execute_reply.started":"2023-01-25T11:14:58.075082Z","shell.execute_reply":"2023-01-25T11:14:58.081167Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.barplot(x=list(names_count.keys()), y=list(names_count.values()));","metadata":{"id":"MRu-2LeBa9DT","outputId":"19328472-2f18-4a50-c215-38add45e7c5f","execution":{"iopub.status.busy":"2023-01-25T11:15:00.173669Z","iopub.execute_input":"2023-01-25T11:15:00.174038Z","iopub.status.idle":"2023-01-25T11:15:00.491906Z","shell.execute_reply.started":"2023-01-25T11:15:00.174008Z","shell.execute_reply":"2023-01-25T11:15:00.490997Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"В каждом классе присутствует примерно равное количество фотографий. Это говорит о сбалансированности классов. Если бы классы не были сбалансированы, я бы применил WeightedRandomSampler. В таком случае количество фотографий в каждом классе будет равняться максимальному количеству фото среди рассматриваемых классов. Пример кода представлен ниже.\n\n```Python\nBATCH_SIZE = 256\nNUM_CORES = 4\nclass_count = np.unique(train_dataset.targets, return_counts=True)\nteam_dict = dict(zip(*class_count))\nclass_weights = {k: 1/v for k, v in team_dict.items()}\nsample_weights = [0] * len(train_dataset)\nfor i, (data, label) in enumerate(tqdm(train_dataset)):\n    class_weight = class_weights[label]\n    sample_weights[i] = class_weight\n\nN = int(max(team_dict.values()) * len(team_dict))  # fit to max\ntrain_sampler = WeightedRandomSampler(sample_weights, num_samples=N, replacement=True)\ntrain_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, sampler=train_sampler, num_workers=NUM_CORES)\n```","metadata":{"id":"i0bR5Cl8U2ee"}},{"cell_type":"markdown","source":"### 1.3 Аугментации","metadata":{"id":"3v8ii55oTVO0"}},{"cell_type":"markdown","source":"Применим аугментации для входных изображений. Изменим их размер, преобразуем в тензоры, нормализуем, а так же добавим случайный поворот или отображение.","metadata":{"id":"kykzpW2rJu5n"}},{"cell_type":"code","source":"# RESCALE_SIZE = 256\n# augmentations = transforms.RandomChoice([\n#     transforms.RandomRotation(degrees=(-25, 25)),\n#     transforms.RandomHorizontalFlip(p=1),\n# ])\n# basic_transforms = transforms.Compose([\n#     transforms.Resize(size=(RESCALE_SIZE, RESCALE_SIZE)),\n#     transforms.Lambda(lambda x: np.array(x, dtype=\"float32\") / 255),\n#     transforms.ToTensor(),\n#     transforms.Normalize((0.5,), (0.5,)) , # [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n# ])\n# train_transforms = transforms.Compose([\n#     augmentations, # augmentations\n#     basic_transforms\n# ])\n# # функция возвращает объекты трнировочного и валидационного датасетов. Для них применяются разные аугментации (на валидации такие же аугментации как на тесте)\n# train_dataset, val_dataset = split(TRAIN_DATA_DIR, train_transforms, basic_transforms)","metadata":{"id":"bN6tBe1WThu9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"RESCALE_SIZE = 528\naugmentations = transforms.RandomChoice([\n    transforms.RandomRotation(degrees=(-25, 25)),\n    transforms.RandomHorizontalFlip(p=0.5),\n])\nbasic_transforms = transforms.Compose([\n    transforms.Resize(size=(RESCALE_SIZE, RESCALE_SIZE)),\n    transforms.Lambda(lambda x: np.array(x, dtype=\"float32\") / 255),\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,)), # [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n])\ntrain_transforms = transforms.Compose([\n    augmentations,\n    basic_transforms\n])\ntrain_dataset, val_dataset = split(TRAIN_DATA_DIR, train_transforms, basic_transforms)\n#train_dataset = datasets.ImageFolder(root=TRAIN_DATA_DIR, transform=train_transforms)","metadata":{"id":"DzYJ2X2GFY4I","execution":{"iopub.status.busy":"2023-01-25T11:15:18.638952Z","iopub.execute_input":"2023-01-25T11:15:18.639315Z","iopub.status.idle":"2023-01-25T11:15:18.687523Z","shell.execute_reply.started":"2023-01-25T11:15:18.639285Z","shell.execute_reply":"2023-01-25T11:15:18.686538Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# объекты тренировочного и валидационного даталоадеров\nloaders = {\n    'train': DataLoader(train_dataset, batch_size=BATCH_SIZE, num_workers=NUM_CORES),\n    'val': DataLoader(val_dataset, batch_size=BATCH_SIZE, num_workers=NUM_CORES)\n}","metadata":{"id":"eU9O2grXhMIW","execution":{"iopub.status.busy":"2023-01-25T11:15:19.494912Z","iopub.execute_input":"2023-01-25T11:15:19.495290Z","iopub.status.idle":"2023-01-25T11:15:19.500782Z","shell.execute_reply.started":"2023-01-25T11:15:19.495237Z","shell.execute_reply":"2023-01-25T11:15:19.499672Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Отобразим несколько фотографий из тренировочной выборки, чтобы посмотреть на то, как применились аугментации.","metadata":{"id":"mdND5AzRKucv"}},{"cell_type":"code","source":"show_grid(loaders['train'], train_dataset, classes=CLASSES, size=(2,3))","metadata":{"id":"VVKfomVhgykO","outputId":"7524f64a-87ea-4d44-a501-399d187cc412","execution":{"iopub.status.busy":"2023-01-25T11:15:22.159053Z","iopub.execute_input":"2023-01-25T11:15:22.159424Z","iopub.status.idle":"2023-01-25T11:15:23.702361Z","shell.execute_reply.started":"2023-01-25T11:15:22.159390Z","shell.execute_reply":"2023-01-25T11:15:23.701265Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2. Модель","metadata":{"id":"I_mQEvrBlv0d"}},{"cell_type":"markdown","source":"### Функция потерь","metadata":{"id":"aRtLkuiMK1aI"}},{"cell_type":"markdown","source":"В качестве функции потерь я ииспользовал кросс-энтропию (CrossEntropyLoss). Она задает близость двух вероятностных распределений. В контексте ML используется для многоклассовой классификации.","metadata":{"id":"VbB37J62K6zv"}},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,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)","metadata":{"id":"oYBwkniDLttv"}},{"cell_type":"markdown","source":"В первую очередь я попробовал архитектуру ResNet. В целях эксперимента написал архитектуру вручную.","metadata":{"id":"UNEjyNaLK_fM"}},{"cell_type":"markdown","source":"### 2.1 ResNet","metadata":{"id":"us7Q_z14rHa4"}},{"cell_type":"code","source":"class Block(nn.Module):\n    def __init__(self, in_channels, out_channels, identity_downsample=None, stride=1):\n        super(Block, self).__init__()\n        self.expansion = 4\n        self.conv1 = nn.Conv2d(in_channels, out_channels,\n                               kernel_size=1, stride=1, padding=0)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.conv2 = nn.Conv2d(out_channels, out_channels,\n                               kernel_size=3, stride=stride, padding=1)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.conv3 = nn.Conv2d(\n            out_channels, out_channels*self.expansion, kernel_size=1, stride=1, padding=0)\n        self.bn3 = nn.BatchNorm2d(out_channels*self.expansion)\n        self.relu = nn.ReLU()\n        self.identity_downsample = identity_downsample\n\n    def forward(self, x):\n        identity = x.clone()\n\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.conv2(x)\n        x = self.bn2(x)\n        x = self.relu(x)\n        x = self.conv3(x)\n        x = self.bn3(x)\n\n        if self.identity_downsample is not None:\n            identity = self.identity_downsample(identity)\n\n        x += identity\n        x = self.relu(x)\n        return x\n\n# ResNet implementation\n\n\nclass ResNet(nn.Module):\n    def __init__(self, Block, layers, image_channels, num_classes):\n        super(ResNet, self).__init__()\n        self.in_channels = 64\n        self.conv1 = nn.Conv2d(\n            image_channels, self.in_channels, kernel_size=7, stride=2, padding=3)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU()\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n        # ResNet layers\n        self.layer1 = self._make_layer(\n            Block, layers[0], out_channels=64, stride=1)\n        \n        self.layer2 = self._make_layer(\n            Block, layers[1], out_channels=128, stride=2)\n        \n        self.layer3 = self._make_layer(\n            Block, layers[2], out_channels=256, stride=2)\n        \n        self.layer4 = self._make_layer(\n            Block, layers[3], out_channels=512, stride=2)  # 2048 channels at the end\n\n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # 1x1x2048\n        self.fc = nn.Linear(512*4, num_classes)\n\n    def forward(self, x):\n        #x = reduce(lambda x, f: f(x), self.nlayers, x)\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        x = self.avgpool(x)\n        x = x.reshape(x.shape[0], -1)\n        x = self.fc(x)\n\n        return x\n\n    def _make_layer(self, Block, number_residual_blocks, out_channels, stride):\n        identity_downsample = None\n        layers = []\n\n        # when we've change number of channels in tensor\n        # we need to change number of channels in identity (shortcut)\n        if stride != 1 or self.in_channels != out_channels * 4:\n            identity_downsample = nn.Sequential(\n                nn.Conv2d(\n                    self.in_channels, \n                    out_channels *4, \n                    kernel_size=1, \n                    stride=stride\n                ),\n                nn.BatchNorm2d(out_channels*4)\n            )\n\n        # identity_downsample only for a first block in layer\n        layers.append(Block(self.in_channels, out_channels,\n                      identity_downsample, stride))\n        self.in_channels = out_channels * 4\n\n        for i in range(number_residual_blocks - 1):  # 256 -> 64 -> 64*4 (256) again\n            layers.append(Block(self.in_channels, out_channels))\n\n        return nn.Sequential(*layers)\n\ndef ResNet18(img_channels=3, num_classes=1000):\n    return ResNet(Block, [2, 2, 2, 2], img_channels, num_classes)\n\ndef ResNet50(img_channels=3, num_classes=1000):\n    return ResNet(Block, [3, 4, 6, 3], img_channels, num_classes)\n\ndef ResNet101(img_channels=3, num_classes=1000):\n    return ResNet(Block, [3, 4, 23, 3], img_channels, num_classes)\n\ndef ResNet152(img_channels=3, num_classes=1000):\n    return ResNet(Block, [3, 8, 36, 3], img_channels, num_classes)","metadata":{"id":"2eyqKzKWiBuD","execution":{"iopub.status.busy":"2023-01-25T11:15:29.787924Z","iopub.execute_input":"2023-01-25T11:15:29.788310Z","iopub.status.idle":"2023-01-25T11:15:29.813236Z","shell.execute_reply.started":"2023-01-25T11:15:29.788274Z","shell.execute_reply":"2023-01-25T11:15:29.812121Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### 2.1.1 ResNet18\n","metadata":{"id":"41Vx4C_BLYq4"}},{"cell_type":"markdown","source":"- optimizer - Adam (lr=1e-3)\n- scheduler - StepLR (step=10, gamma=0.1)\n","metadata":{"id":"PBhhkrQhLv6Y"}},{"cell_type":"code","source":"# model = ResNet18(num_classes=NUM_CLASSES).to(DEVICE)\n# criterion = torch.nn.CrossEntropyLoss() \n# optimizer = torch.optim.Adam(model.parameters(), lr=1.0e-3)\n# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)","metadata":{"id":"ePRWNPj5YuiD"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![resnet18_relu_train_val.PNG](data:image/png;base64,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)","metadata":{"id":"Iu2YGKN6Lp2Q"}},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABGgAAAA4CAYAAAChS87UAAAfhklEQVR4nO3dfVjUVf7/8ecuxaTRtOoXV1rQCtx2GdsC3QqygjTSwmbNQFuJVo0yvEm0NWJj1a+t4r3lTRnqFkGplO6oKVEElgvWT2BLmd+WULlgsM1PXCcW90NL/v6Y4UZuFJCim9fjuryc+cz5nHM+o//M6zrnfX50+vTp04iIiIiIiIiISI/5cU9PQERERERERETkh04BjYiIiIiIiIhID1NAIyIiIiIiIiLSwxTQiIiIiIiIiIj0MAU0IiIiIiIiIiI9TAGNiIiIiIiIiEgPu6CnJyBfB4PSXenYSp3QP4y4iUGYm3/sLCN3VzrbthVRji9Do6O4b0wYAeb2+utmzmLSNuXhwEJ0QgR+39CwIiIiIiIiIt9WPzp9+vTpr6Vno5KS94so/PgAe/91lPJTRyl3f+R34WD8LhnC6IE3ExoUTMBPvpYZfL8VbyJy/k64cz67Hwxqul5VwNq501mx3+l6H7aI9zZH4d3weUUmk0clkVsLpgGBBFBGSZUBvcNIyUplvO83MPeqTCaHJpHLFF79OJHgb2BIERERERERkW+zbl9BY1QWsTVnI885ShoDmZbKvzxCefUR8qt3wN/A7+KRzLgxgfEWr+6ezvdXvYOSQ3YIaXbNsYvJobPJ7R3I+Hv6s/WVvBY3Ocla5gpnwhfmsnmiK40pz4jj5uQ8EpdlM/qpCL6phTQiIiIiIiIi4tJ9NWi+rCTrpQe4cUsCyWcJZ9pS/u83mZt9J8OeXU/+8W6bUac49iQRabWSuKeC8pwUHrZaSdzjcH1Y76Rk1ypmxVqJtFqZOGcTuWXOMzto0Sby4QWk7Xc0GyCbRKuVyKRsHM5i0ubENvaVX8VZ+2o5XtFzViIfz3S9yXzcPW8H1P8H0x3z2J1vIyXav42nNDj5BUAYt49oWirjNyKCcIAvTmJ08Psqes41t9RiJ0UZs5lotZJa3DB/B/kZC3jY6mrzcEomRS2f8QzFpFqtRFqTyHI0v7zJ9V0+V9zunW2Ol5ze+jutKiAteZqrP2sss1btoqThK63YxSyrlcjYTZTUN7vHKGCF1UqkNYXcFv/cIiIiIiIiIt2pewKayrdJTp3AQ/88QtPvay+C+45l6XXryYl5jY8e2cfRBNefj+J2kBORzEJvCwHNZuA4tZV702J5sqimW6bVKbWuFSnlr69kctwmsg7Z+bwWwIHtkXAiH1lPVqmr6ZHXU5h8WzSLDjbEGQZFy6LdbUwEBPhivJfOvNhwJu9oCHlO8vkhOyVHs/jTuGgWFZRSfshO/o4UJo6cTdaJhol0ZLx2eEfxzNoYLO0ugfFmyK/9gTxez6tovFqel00uEPBrS9NWqHOptlNyyE7R8/P5bfIu8g/ZcdQDRjFrreFMTE4nv9b13eQ/l8S4kXFsrWi/O8chOyWHHJxsHpC4VwmVVJ9lHs3Gy6owoN5BbsYCJo60kmp3t6nIZPLIWOZlHMC4zJ8AUylZa2YTOW4VRQbgG0TwBXZK9qeT+2GzrguyWXvITsnlwQzVsiIRERERERH5Gp3/FqfK15i5bSm2rxoueBLa7wFm3DaWUB/PNm8xefUlwDKSAMtIYo1K8rOWMvfjIveqm6Ok7huL8Z8dLAz95rc85ed8wYLdhcQGun6RG/sX8NgeJ6axK3ln6Ri8PWis45I6exPW3HgsHnaynisDprD5nURCPYDaAhZNzYQTpTjwbgo+Coq5NDWfv4/whvoK0uLCmZe3i215jzNqrDfG/vXnHC/4QRu7g1O4InoTRC1md2JQm8/SFsuDG1n92Wz+mDSK69L96Y+DUrtB8MR1rH4wsNPfV5b9UjbnHyF8gOt9ecZiVtgNghNsvDQjEBNgHEwhMnoTiSm7GL12TLduoSp/xTVe8+/LuT+FyNhNLNqQzfinIjAOZJNbC6GLbGRMcK0cKt0yjRUlUF5mEBzoy+h7w5hXnMe2fXamBwYCBoX7XKuUYsfeom1fIiIiIiIi8rU6vxU01W+T3Dyc+bGFGddt5PnY8e2GM62YfAi1riLHOpdYU8PFOtLejWdNSd15Ta9LJkxuDGcACvPSMfAmbuwtmJxOnCecOC8OI3osUHGAw5WudqbeAHns3FJAaZUTwxRCUtpKkiaHtFiVcjvWEe4rHr6ER7iLyNR3brwuq7RTZC/DiYFhAIaBgZNSewElXeg7dMqUxnAGKsjfUwxEEH2HL8YJ1/wN/7uIHgbsKeFwfft9dV7DeIEkxbvDLMA8PJE3Cgt5f747WHFfL9yeie1QBc5aCJiwjmcWJmANdP2n8x7u2uZVnlVEKUC9nbxMA3rHcHuIqdXIIiIiIiIiIt2p6ytovqrEZltIWrNw5omIpcT9smurXkxX3snCGB/6pyew3AA4yvI3lzDEJ5nwvl2eZeddcFGzNw7KP3b9vTZ2KGtbNS6grArwDWL68wnkP7SKrcmxbE0GMBNw+xQW/G88oWfZN3TRBc1//Hd0vM4/FgBGMSsmTSOtzJ+4jFySQlxBlLMghXETN/HwJDOv7k4guBN5hKnF/MsKALJJvC2bxFatyyh3tLp4HhrG64/p4hbz6mOmYWbed81jdV4Jj+1azyzretfnA4IYP3sxSff4u9oNuB3rHUnk7tlJ/tEYApwFZNWC6f4IhiqfERERERERka9ZlwMa54GNLPxXwwqXQcT+OrnL4UwjczAzxs7lSMOqnK/e5Pdv3sZfo2+gZ34jm7j0EgB/5mzbRmwbdXdN7sU2pmHxvPreFByf2jlcXETezvWkvb6KiaWwOyseSzeP1yX2PFLLgJAp3BfS1JE5JIYHQjaRWLCJXHsCwR3fMdVydph9gYoxrH57PuEXt9HCDHQ0pPmyo+MZ8N+zNPPwxfpUPtbFFZQUFVOYl8u2LbtIm2vlZO98Vt9hBsyE3x4Be7LJ+z8OhjoyKcdE3J0hPfR/T0RERERERH5IurbF6asjbC16s/F3dvCgx3gi1Kfrs/iqmqK/luAE8LmTZTeObdwW5Di2kb982vWuz48Zy3VBQBm5JQ7MfczuPybK979OYUkZn/8XOFFGfk4mqTvLMPsHEX7PFBb8eS1xAGXFHO7wqpEOjtfcf//T8ccxmegP8L6d0tpm12tLsb8P4I3pvNIIf4JHmIBsikpNTfM3OynM2cfhEgeGR1v3mfG9GqCUsk+aCi/n57zeuumJMkoaT7RqGK+AbTllTW3K0hl35WCumJhJOQblxQXYMjaR6/TFMnwMsU+s5JknQgAD2wdN95lvsWIFcvcsJvXlCvCNx9rlsEpERERERESk47q0gsZ5YAtPNqxuuHAkM2+0dH2VwVfV5L8yi3uPHcW7bCFvxd6MeVgsye/vYKYT4Ahz3zvAby7vmVU0fmMfJ+75aFIXRDOxMpEHhl9EaeZGVuyyYwTN4+1Xg4DPef2RJNJq/SlyTOO+wEsoz1tPGkDQLYQOAM56zHRnxwN6m/EDyrekMIswRt2XwKhB5+g8MIo5I9YzKyedh6NPEve7uwimGNvzm7DVgmnEbMZ3vk5wMyZCp8wjPDOJtOnRnJz5ANH+/yHvhRRS9zsx3Z/G+8PbOv7bn6Gj/OFQGalx4RTefgP9P9jH52Z/oNnRT/XFLLopmtRaE9NfPcycoKbxchdEM7E0huC+TorS0ynCRPg9Yfhhoty+ilnJxZhsDhY8dAt+tXZe3FQAmIgd3uyBzbdgnQi2jF3YAL+EMCxtBkoiIiIiIiIi3asLAU0NhaVvNr4L9rub0J92cfRm4QyA43gyD+3ayPNjBjMqaCze+3a4VukcO0DhlzcQemEXxzkfvYNIeikN85zprHguifznAExYxsxjyeIY/ABMISRtXwSPp5CWMpssXG28RyTy7Ap3m+4cDyBwCs8kHuC3KQXYNttxWmIYNehch2R7Y302F+/1SczasIu1c3e5x/QmPGEFS+JbFjTuAt8ontkO82YvYGvKbGwAmAl9MJWU37e/Xchy/zoW/P0+Fu1yULQjG8uElTwzpoibJxY3NfIw4325CT4NxrehLlHz8TLWkw/QO5DxS1eyYKzrafwmrCOjOolZGzaRGLfJdV+fIGKfWsmC4c1nZCL09hhMGekY+BI94rzSKhEREREREZEO+9Hp06dPd+oO4wBPrn+MVAAG8cSdacT9vAsjtwhnAIJ/upCnJ9yM34+Br0pYsyae5e4ixI+O3MeMq7swTncynDhrwWQ2Y2pvZUVH2nTnePUGTsOEuXdXundiYMJsbh5SFLPoymj3v29r4Uvz2XxPB2OcWidOo5PfRb2Bgemsz9vu57VO13fRp/21VsYJJ4ZHy2cWERERERH5nqt3Upq3kxdfyaTwM/D7VRTRv7uLcP8OFDp1lpG7K51t24oox5eh0VHcNyaMgJa31jso2pFO2s48Sk+efQzHwUz+YsvD9kEFXBZM9D0x/CbMH/MZv/WcFL2widzqtiblTfiUGILbmr5hx7b+ddcJvWdr9y3T+YDmoxe547WNlABcOJ4dcfGdOvUHOHc4A0Ad+Vtu41730c/hV2/h+ZHnUedGOqiCrFWZrn/fNnjfNIXYYd+B/9kiIiIiIiLiUl/B1qmjSMwxWnxgInxpFpvvOctRwRW7eNg6m6wTLa73iWC1bR3WhltPFDAvOpa0spYdtBzDoGhVNL9dY6fVbEbMY/faGAIaM4azLSAIIyU/lfEDWn9S8swoIpeVnbPdt03ntzid+qLpx3uvQfh9LeEMgCd+fQdD5REAyk/VdHqq0hW+jEpIYFRPT0NERERERES6RWn6bHc440/sU+uY/mvI3zCNWS+UkTv3AdYGZjG9zQoPDrb+0RXOmAKjSEoYQwBl7ExZwNaybGb9MZPQzVF4Y5D/dJwrnPGPYfWz8YT3NTi8YTYTnysmd+5KsiJWMsoMxsFVzFpjx8BE+LxtLLnLFyp28ti9C8jNWcDDm4N542H3ZIyT7hAnkLinEgnr03xuF+HXp+V8gbJ0/risVUr0ndDpgMZR3RSscOElrWqKlL/3Jk7LSCxtHLHc8XDG5aIL+wGugKb0RCVOBqO1GyIiIiIiIiIdVF/MtmWu2p5+CStZMMZ1cIv1iZWU51hZUVHG2p3FTA9s4wjbQ5msywMIY8HGRe5VKCGEXt4f752upRvlTvA2OzD6TmH6DNeuC6t7S1PozHjGPxfHVnZR9NFKRg2DknfSKQe4YzGr7w90/cbvE8PqxQVc80g2pet2UvRgIMEewAmHqy0hjBoTQvA5n7WCrX9aQBEQPnkKxuZNrjql3xFdOsWpPeVvPcbY9w/gOPgRO+6PJ7h5SNPJcEZEREREREREztOnJeTXApiwNj/F1iOQ0LEmVqwxMApKKCWIgBa3lha97gpIQiII9XZSmrMT2wcOuNiX8HsTCG7cNuRL+IwEwlvc7yw64D7IJYrghtq1hntj04D/OWMBhumS/q4XtXaOVEKwL1DtcNWR8TVDVQFpL2dTVm3G95owfjM2CO8WtUnLdyxgXh4QNI+k35lYtLlzX1VP63RA4913EHDA9ebLLxr3jBklG/nd+wdcpy4ZWxn7wiXsibsPy4V0OZz5z5fHG19b+vho9YyIiIiIiIhIZ5yocJcpCcG3RVlXP78QIA8OVeBs41Znpd314pcOsqyhLLI3VY1Z+3Q605/fxpxhLffVFJNqnY+ttoKSMifmoBhSFicyyv2D3i8gzDXmls1svS+E8YOA+gr+kpHpvr+AsirAF6h1ugKi6vX8NtRoqlmTsZ4Vz8fz0rYEghsOzKnaxbz5eRj4M2dhDAEXNPT33dH5tSu9LsHS8Np51L3cCEyWB3j+2huajmk2NnJH6ouUfNnVlTN1lFcfaXzXv5dXp6cqIiIiIiIiIudp8yq2/XotbxcW8n5uGtOHmaDWztrZmyipb93ccchOSZkr8jEqS7B/UNEYrnjfEU+cP1CbR2L4UG6zWrntunDmVQc2ZQ1uRm8L0ydGYPGPIMmWz/uFhbydFk9wbzDs6/nTjgp3SydZix8ntxYCZiwmrs16Ot9+nQ9oBg0mtPHN2xR+1PSRX/gSdrQMadaO7dq2pq+OUPjPhjc+DP1pv05PVUREREREROQHzcPkrh37Oca/z/zoP/91xya9z3X6TwxJiWH49TFjHhTCnMWJrjCl4gCHK1u2DSLp4yN88vER3k+bgl9VMWlzrTy2x71Gp3cQSS+lkTQ2CDNOSisMAn6Xxjsro7gUgDD83Qc+mQLHMGfhOnbbVhJ7tTfmPmb8hifwvw+5GhTlFOAAjLxVzNplgO8UlsQHtaqV+13R+YDGNIQbG7OSSjb83zMPZG4V0jTTmZozxt/e4IWv3G8uvIGhgzw7PVURERERERGRHzR/i/uUXjsF9uYbmZyUFBS4Xo6wtKo/A+B9eYj7lYlLm6ceF5twVYw56SopU2/gPOHEecKJ0WxFjXn4FB4IAzCwfeA+Wclw4rzAwvh5L/L+x0f4pDCLZ2aEYPogz1WvxjeIIQ21bWpdfTprz5yX2dvvjPclB9JdK3QqNjHul4O54srBXBGaRC4AeSSGDuaKlOKzf0/fAl0oz+vF0ICRje8cH28nt8VmNb/wJey45syQpnMFgavJKt7hqmcDWPrfwBAVoBERERERERHpHPMNhN/hepn19HqK3GGHcXA9K3a5Xo8aeYOr5mttBbkZm7B95FpZ4xcS4T456XWy9jf98C/dk+kKP3oHE+AL/LeAFUOHcs3QoUQuK8DZENIcfZ3X81wv/dwJj1GwimuGDuWaa6eT2hAYVeWx4ulsAALuDWvc6lSaeZ+r7agUd6FjoNaOLdMVLJl+HtDm4pDvqh+dPn36dKfvMkpYkxrP8i9db70Hrefg3S13izWd6uTXydOajIOrufGdhoDGwtKx6xl/eadnKSIiIiIiIiIVmUwelURuLdDHH4svlB8qcxUGDprHG9tiCPCAkmfCiVxWAb2nkFGYSKjJoGiVlXFrygAT3oH+9Dcq3PVlTIQvzWLzPa7tRiXPWRmXYnetZOntjcXHaKxDQ+8wVr+ZinUArqOwp44iMcdoPU//KbxqS2wq/Hsim1k3TcNW6+7T3xujwk7piRZ9tqUqk8mhSeQSRkp+qvuI8G+3rh1wbbIwodkKGcfRWSTn17Rq5nfrErbcuKRzR2lXvsbv/9q0eib4pxO4VeGMiIiIiIiISNf4RvHM9kWMDzTBiTJKDpXhxIT3mHm88aIrnAHwuzIEM2AKsTDYBGAieOY2di+NIbiPgcPuLv7bJ4jYp5rCGQDLg9vY/dQUwgeYoNbhDmfMBNyeQEZusyDFw5fxT9lImRjU7KRmE94jEnn11WbhDECfCFbnpjFnhDemWgclh+yUnjDhPSKBjLOFM99RXVtBA/BVJbYXYpn5rzr3hUE8cUcacVedx2ycRaxJT2B5Q5BmupPnx84l3Oesd4mIiIiIiIhIBxhOd50Ykxlz7zYa1BrtFg02nE4MTJjN5yjDW+vEaYDJbMbkcZZ29QZOp3Hudp1t+x3V9YAGoPptkl9MJq2hmC+DiL1+CQtDO5+oGB+/xpNZS0lrCGd+PJgZN63m0WAdry0iIiIiIiIi32/nF9AAVL7GzG1LsX3VdMnPHM/T48YT/JMO3G9Ukp+1lLkfF1HeeLHrQY+IiIiIiIiIyHfN+Qc0AJVvk7wjuWn1CwCeBFx8M/dbJhB+9SD8zE3HZBs11ZQfLSK/eDsvHC+htFm4w48tzLgxmUeH9UA4U1/DkewNvJBRzGd4cfmISUyPvZ6+3+DyqcMbYzh8fToTrj5Hw1M11Hl64dmtc6vh6N4NpGYU85nXICImzWHC9X3bburYy5L1EDdvNO20EBEREREREZEOuqBbevG5mYVxW7gxM5kn/nnEXeC3jtJ/v0nye2/Cex3rxs88noUR8YT7nbttt6uvYnfCZF4b/CiJyx+iX69qPt2+hkkxdpanT2LwNxXS1FRxvI1i1i0d3vAb9g1/k2nDunHo7MX8oXAEy1Jn0O9UKbvnzyTVczNxQZ6tG9fXcfzz7htbRERERERE5IfMY/78+fO7p6dLCLj6Lu6//Br6/7OC0loHznPfBXgS8JO7SAyZz+JRtxLQkW1RX4O6vz7N7P8Xwwu/v4V+Xp54XnQp/YMiGPb5Al4+Gckt/q6QouaTt9idvpv9h6owXfYL+rtL5NQUZ1Lw70F8+W4G27Ps/GjQr/CpL+WN9AzeKqrG7D+YfhcBJ4vZ/vaX+P83n8yXsygohSssvvRyn3L1+YE0jg6M5brLgPoajuZmkvnaPo4c8+Syn/vQ68dQmb2GjOwP+fizk1Q6PLlqiA+eZ5lbR32U+2d+MmIOIT/zwKOXN/6eH/DnTwYT+atLXQ2q3mX7n3ewv/TfXNavjgPvwvA7f0GvNvqq/nAve1/OanMuNR/uZfvLWRw+5sllP61i7ztf8kv/hjGK2b3lFfb9tZS6/lfi16eNcEhERERERETke6Zrx2yfhcknmNiY9eyP38KeG+ey0OdmQnsNomlRjCcBpsGM8h7PwmuXsGfCa+RMmsX4a30wdftsOu6jwreIvG04LeOAwfF/4Y8RrnSh7uAaZi4qwWd0FJHX1vFawkx2H3O1q/vkHVKXb+CQ9wgih1aTOnUqj6x+B58IKyHsZeqTe6kBOPUPDmQsIsUGIVFWQjz28ujUTI7Wt5xRDYfXTCXF7kNElJUhJ19iUoKNauCSK6/j5/2h31XXMfQqVzhz5tyq2TJ1Jm9Ude47GPLAmVurjn5o5/IB7g1M9g3EzNwLIVYig75g+6oMPm2nn2rbTCYuL8VndBTjQuvYPvMP7Kt2P1X2H4haXsqg0VZCBvyN1KQNbLEfd314zMbcBBt111oZN9qHw4umsu5g6+PbRURERERERL5vumeLU1tMPliuuxPLdXcS+7UN0n0+PRLAoKiztfgHu9cXEzl/MyEDAaJIiD+CNW0/EY8PB6DfiIncPWwAEMWdP3uXuthJDBkIPDCJu8P38ykwBOBkEPfOGc0gD2BCEtM/+Q3b860k3NQsHvrHXtYdm8iyZbfiBTBpHomfxLDbbiU28HqG/AyOB15PcJBrbtvX/4N7ly8jpC/AJP444ygxNju3PRR45mPUV3Pk3Wr6XR9w1to6dQfXsPjvE/nTQ15AHQXbbYTM3cPdwwAGMm1aFfuebfvevpHLsUV6uuvjRBE5bAMFH8Mtff9BdtoXTFv+J0IGAAGTmHbSzugPAeooStuAT/xfuHuYJzCQuOTjPDx/L5Wbo1C5aBEREREREfk++/oCmu+Yfj6lFFUCP2uvxXEqPwniloFNVzwHW7h6y3Ea1niceRZ7Xzzb2vsDMMziCmdcvXD5VQFscVQDA5rafF7F4ZJ3mBmT0XiprrqGkLvam1sJ22fG8HLDpVPVVN5kbd30WA4r/5DDnenPEtnOs9YdXMPUFTA71YqPB0A1xysDGOTbrJH3AC5v5/E4VUWhLZM33inm05Nw/CRE3Oqe57HB3NLsMb28B8KHrjE+qwhg8OBmIdXAAIZ8sp/joIBGREREREREvtcU0LhdFRjIqvfsTBt25oqTyu2/Z4vX4yREANRRVw80hCv1dRgeXaiRUukKdRpOP/qiphoubaOfsDlsnnt9BzsdwewX5hB8rmLGA6N4JvcsS4WO2XhixRfEPZ3EkDNq2NRB821Y9XUYrTaEAdSwb+mjFA1fzuyn5+DlCYdXj2Sf+1NPzy/44hQ0Fq6przvrGG0OISIiIiIiIvI904NVX75d+o6exG1581mV01S4pebDDFZt9CR4WF/AQkhEDltsDZ/XcDgzk7rQoM4fM23PIf9YQzfF7N7tyQ1BLXqxBHF3Xg4FJ93v66t4Y8UaihqG9/Ck5mTD2h3X3PblNdVrqbStIDW/k0Vojr3FkkdzuHl5EiHezT8YwNCb6tiSWUxDnHI0ey9FbXZSwxfH+zLYMhAvT6DeTlF+40MREvk3dmwppaYeOFnK9ldy2h2j0pbB7rAgft65pxARERERERH5ztEKmga9gohbN4e05MnctMKLwZ41VF46nGmrHueWvgCeBM9YzkePTsWa6UW/uhr41Uz+NGPguXpuLWAQlWtimFwBdTVwdfxy7m7ZTa/hTFtyhN/f/xtSvbygpoZLrItY5t4eNGT0JFJnRmMteJSMJ251zy0G60Yv+lHD8UutLF8+oNXQ7avmjSWL2F0Fu2NGsqThsnUZ78wKwidqEff/YSpR47zohydXxVuZcLCtfgYQMcnC1GnRbPfyBM/rGfqLhs88GRL/LBM2LGbm/ZXQ/yam3zoCSl2f+kQtIu7JR4kaB/08Xc/w9PLWhZtFREREREREvm9+dPr06dM9PYlvnfo6auo9XStA2nKqhhoPr/Y/P5sqG3MXQuI6K31P1VDn6eUuptu+upoa6HXudu7G1NDFuXVEB+dMfR01NeDVcutWwxamhvvfXcFNBbfyzqygpjZ1NdTUe+HVXg0fERERERERke8ZraBpi4cnXmcLIHp54XWWjzusl1eHVod4enViNM9umlt7OjhnPDzxurTlxTqObJzMgmNWpk2w8D+VJWzfWEzs3BlnNvu6n0FERERERETkW0YraL5pp6o4/Hf4edCAH+zWnWr7XvJySjneayBDR4wg+ArFMSIiIiIiIvLDpoBGRERERERERKSH6RQnEREREREREZEepoBGRERERERERKSHKaAREREREREREelhCmhERERERERERHqYAhoRERERERERkR6mgEZEREREREREpIcpoBERERERERER6WEKaEREREREREREepgCGhERERERERGRHvb/Aa/GvyaFohwhAAAAAElFTkSuQmCC)","metadata":{"id":"0tajbUU_Lurc"}},{"cell_type":"markdown","source":"Далее я пробовал заменить функцию активации ReLU на LeakyReLU, т.к. когда значение меньше нуля, LeakyReLU не обнуляет его, что может положительно сказаться на решении проблемы затухающего градиента.\n\nТак же пробовал более большую модель ResNet, а именно ResNet152.\n\nК сожалению, большого прироста метрики эксперименты с ResNet не дали, поэтому я решил попробовать другие архитектуры.","metadata":{"id":"sjcDaDksMQ7F"}},{"cell_type":"markdown","source":"### 2.2 EfficientNet","metadata":{"id":"XxyMOgkzLmkg"}},{"cell_type":"markdown","source":"#### 2.2.1 EfficientNet b6","metadata":{"id":"aUcNLEOBNu4x"}},{"cell_type":"markdown","source":"- optimizer - SGD (lr=1e-3, weight_decay=2e-4)\n- scheduler - StepLR (step=10, gamma=0.1)","metadata":{"id":"w-4LpkTRO2W0"}},{"cell_type":"code","source":"# model = models.efficientnet_b6(pretrained=False).to(DEVICE)\n# model.classifier = nn.Sequential(\n#     nn.Dropout(p=0.5, inplace=True),\n#     nn.Linear(in_features=2304, out_features=NUM_CLASSES, bias=True)\n#   )\n# criterion = torch.nn.CrossEntropyLoss()\n# optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=0.9, weight_decay=2e-4)\n# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.1)\n\nmodel = models.efficientnet_b6(pretrained=False).to(DEVICE)\nmodel.classifier[1] = nn.Linear(in_features=2304, out_features=NUM_CLASSES, bias=True)\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n\nload_checkpoint(CHECKPOINT_PATH, model, optimizer, lr=1e-4)\nmodel = model.to(DEVICE)\n\n# optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n# scheduler = torch.optim.lr_scheduler.StepLR(optimizer,step_size=20, gamma=0.1)","metadata":{"id":"NZLfOpz2Y8WT","outputId":"cfb9145b-c203-4bb4-ce6f-dbabed612159","execution":{"iopub.status.busy":"2023-01-25T11:15:56.026272Z","iopub.execute_input":"2023-01-25T11:15:56.026894Z","iopub.status.idle":"2023-01-25T11:16:01.539160Z","shell.execute_reply.started":"2023-01-25T11:15:56.026846Z","shell.execute_reply":"2023-01-25T11:16:01.538176Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"К сожалению, я столкнулся с проблемой совместимости tenserboard и kaggle, поэтому для kaggle реализовал свою фунцию вывода графиков во время обучения.","metadata":{"id":"SjAW3zj0OEd5"}},{"cell_type":"markdown","source":"![Efficientnetb6_wd2-4.PNG](data:image/png;base64,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)","metadata":{"id":"6lIvDLlLN4zA"}},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,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)","metadata":{"id":"KZWh4UmEOAE8"}},{"cell_type":"markdown","source":"Проанализировав результаты, я внес несколько изменений, а именно:\n\n- Изменил оптимизатор на Adam (lr=1e-4), scheduler (step=20, gamma=0.1)\n- Изменил входной размер изображений на 528x528 (такой размер является правильным для архитектуры b6, согласно документации pytorch)\n- batch_size = 6. Пришлось уменьшить из-за недостатка видеопамяти.\n\n","metadata":{"id":"hto2IwrctZGF"}},{"cell_type":"markdown","source":"Процесс обучения:","metadata":{"id":"mfCsp2pSumXC"}},{"cell_type":"markdown","source":"![efficientnet_b6_085.PNG](data:image/png;base64,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)","metadata":{"id":"1IjCvvG6uoxF"}},{"cell_type":"markdown","source":"Результат:\n","metadata":{"id":"f-LuBGJAurxU"}},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABE4AAAA3CAYAAAASXg+WAAAf90lEQVR4nO3df1yUZb7/8dcuG5Nm41EPrp6F0BhrD6O1g64FWcJaZqbOWqG2Eq0/2M2fhWaLbK562swspV2NTqFuImwKmSFmaLlgGZibsKbDObtAZdDicb7hcZalborm+8cMvxkYUPtxej8fDx6PmXuu+7qu+xr8g4+f63N9x+12uxERERERERERkXa++1VPQERERERERETk60qBExERERERERERHxQ4ERERERERERHxQYETEREREREREREfFDgREREREREREfFBgZOuGC5cZ10YDV1cbzBw1fWk+w76lp6rymb28GH88P5cnP6ua+M9Cbk4L+rkRERERERE5JvmOzqO2BcDx3MJ/GxtES6AyU9z/HfjMXd0fV0QaZOmsak6krWvpDM91L8RnLsTuHFpAabZ6Rx9OBLTxXuYr5kq8lKycWBlWuJ4Qrpq3uCivGAP21/M5tjf+2KJnkz83bFEDGrb0EXe/VHMyw1n9WtZxIe5KM7cQv4ZXx0HETMnjggzODbGMCnFSfzWY6yO/vZ8EyIiIiIiItK57513D0Y1juPFHHvvCK/+7ykqPzlFpfejkEuGEXL5cG674iaibBFY/uW8R/vylG5h3toiXL1tTJ8TSfiN12P2df17VZhNJjAF09fs/xCm3kGYMBEysO9FCZo49yUz61kHw3+5mbUTgy7CCAAlpNlXkcMU/iNnDhH+zYzijamkMYeYrgInDVXkLZ/LvBcrmi45ThSRsyWDhc9nsXRUi5U7tYe0XANsU4gJAzAoO5jKpgJfnUcTfLcncGKdOAdrymrSN2YzNzqu62COiIiIiIiIfCv0OHBiVBez8+BmnnM6mgIlbVV+VkZlTRmFNbvhLxBy2c0suiGR6dY+PR32y1Pn8jxX3HLWJtq6uB5Owt6TJHRzCPOtazj+3poLMduO1TlxnChlYA+2EHWH80QpDiIvSt+u/Rt44MUKTONWsmtdHFazQfme5cxemsum5RnYX5uDxdvWsW8LxYB1SqQ38BHET586xm1ttuwYFVv42bRUysNsDG+MJ4VFMCEYHCXZ5FfEER92UR5HREREREREvmG6X+Pks2ry/jiXG3YksqKToElHKv/5Og8duJ1R/5lK4cfdHvnCaXDhyE3hgXg7k+x2Zi7dQn6Fq+nj4ufsTFqe7XmTvZxJdjtJ+5w+r3uyLuxMsieT17JIRoOTwszVzLN7xpm3IoPC080fO/clM8luZ9JzJa2m5yrNZf3SeM9n8UtIO1iBq+mP/8axtlDcUEX+2gWedvPWklPa+AxO8pLtzNpQBEDhhrlMsttJK/H3fj/nUrKFSfbl7PQsCL+y25mUfKB7dULqKsjxOYcKXn42FwMbyQ/HYe0HBJiwTF3FUyvns3CiCVdTcyflxVWAiZhrmqMeJrMZc7+WPyZO5myhHBP2xDisAY0tw4kYD1BKwbtdP0HbdVmfW9riO6Ld79ikeatJP9zYr0HxM9OYZLezcn/rNa980bMWszMrEBERERERka9e9zJOqt9gxe4VpBstL/Yhov8tzLDcwsirQgkZ0AeTNxxj1NZQeaqYwpKX2Paxg/IvPNedn+zk7vQjJIxN5eGILzv7xEnO/RN4YJ8L06BwLEFQtn8ts3dnk5CVQ/Konm2a8WRdDORc4x/PRgmb7ryH9aUG9AvDOthFfuZq8nZnk5yVQ0I4TRkhLZM1nLkLuPn+A7h6B2ENC4LyA6xJyCXrF1nsTbJhahormPQlKeQcNhNkOHGeKOWB/QVU7s1jYbg/c+36fn/mcn4qSV9uJ+/PYViowOGdg6Pxe3CWUnACiIwlJthF+cE95LzrhMuCiZmSyNJWNU6qcBwEiCR4cCdDlm5hTaYBYYkk3Np6X1WIJRIoovCDKsD31qamdcFEUHgYfR25bDqcS/qbT/PmOk8dnOInpnHncxWYBtmYEBmEoyCDlfuzKVifz9apQVhHWCl/IgPHviMsvXW8ZxsYFeRvP4DjRDATHlPKi4iIiIiIyNeC219/3+te9NRN7is2NP7c7J6xbYf7rb8b/t3/6d/db738gPuGDa37ePitf/g9hQvh0zdXua8eanFfvWSP+8zn3ouVWe5ZVot7yI1Pu082XvvzY+4hQy3uIY8Vt+6gw+vF7keHWtxDhs5176j2XPkwI9Y9pM045958zH3jUIt7yOL97nNut/tM9tzWfX1a6P6N1eIeYk10v3zG2/Xnle4dc63uIUOj3RvfbTmWxT1rS7n7U7fb7f78nPutR6I9421onldj/7Oyz7ib+Xm/X3Np2d9j7mN+fwvNc7j5sWLPHNxu94cvzHVfPdTiHnLHdveHbrfbXbzB837ZZve2uVbPWjX93Op+9M+fNnd5br/7fu883vq8gyE9jdyvLra6hwy1un+T/2m7T9t9Hx1pXJehse6N737atC7bZlk86+JovSZNc/lnofvRexLdj24pdJ9xu93uz4vdj1ot7iFDE92vnvO2+WC7+46hFveQKdvdZf4so4iIiIiIiFx0/m3VqXmDFVnryPFmjPBdK4tGb+b5+OlEDQ70L0JjGkyUPYWD9oeIb0pVqCf97flsdNR3N97TY8cKMjAIImHqWEwuz5HCrsuimTYVqDrCyeoLMUoVhftKgHCS508myLsdxDwmideOHeP4qrF0WEO2+BDpdRB0bywx3/POzWXmJ3fFAlUUOapaNI7m1olhnqyPADNR424FwDA66LdDXdzfrbn0VDRzf96cuRISO5t4gJJDFDuBBgMD4MW1bA/exNGyMt4vO8lr6ycTQgVpS1Iobpxv3TkaN71cGkDHSjNYn2uALYl7buxhvox3XZg5n4QR3j4Cgol/5hjHj+U01UYx9QYoYM+OIspPuzBMkSSnbyB5dqQnlyXAxoQ4E5BLkfchnH8+RDEQMS26qW6LiIiIiIiIfLW6Dpx8UU1OziOktwiaPDx+HQ/eENqjrRqmK2/nkbgUHmy6+RRPvv44+TU96KwHKt8DcLIpfiTXjmz8iWJeJkARFac7v98/TiqKAAZiuqz1JyZvnY0O7/rIU9fC+Ux8i7mNZPS8DAAKP+ik9sYl5znlNvef11z8FsawltttAiyERXteftoABJi8v2NxJCdFewJQASYsU5ezIBqoyiC/1HvvIIs32HCGM+1LtQAu8p5NpRwT8YtisfgKrnShcV24rM1JSCZvDRUTgI2FzycS0a+CnSviuSVqJD8cNpJb5qVS2GLZIm6fTwiw81AxBi4KCwoAG/YxwT2bnIiIiIiIiFxwXdY4cR3ZzCP/25gREkr8j1eQ8O/nWZfEHMGiqQ9R1pjF8sXrLHv9Ft6adv1FOZa3pb6XA4SxNCurw5NTTN04Ttg3E+ZgoMqAz7txV+/LAbAsy2LXDF+TK2l//SLwby7ny+CcAc1fuhNneYuPgy1EAfmY6NvqF8PMwB947jeaCrI2rnkFZ5zQLqWnKdtkpc9skzPewa2DfT9b47rw+aedPplp1Hx2HZ2D84NSTpYUU7AnlfT9Kcwsh7158z1FacOjmRacwvrsQzgW/4P8fUB0LLeFdtq1iIiIiIiIfIk6zzj5ooydxa83nZISEforHo7qrPJmF76oofgth2dLxeDbeeKGqU0lOJ0fbeblD3retb+so21ABfkOZ6uTVioP7+eYo4Iz3Qh0+BZGxDgTUETWwRano1RkcOeVwxg6M7vD04jM1ggigPLXHZxpcRqMqeoQrxY7KK/xex9OK8bn3b+vJ3P5tKF9P53LpqCouR+jaA9ZVUCwjeGDgKCx2CcC7CevRTucB8jZDTCZiKsaL4ZhHQtQSvFf26ac+JNt0nwqT5S1RaCowaDyRClO77M1rgs7DpDfdMyzQf6K4Qy9chhrioCzFRQezCZtTwXmMBsxd81h9R82eY6rrijhZOM/qIBwYu4OhroM0lflkANMmHprJ2VpRURERERE5MvWacaJ68gOfvuZ980lN7P4BmvPM0K+qKHwxQe4+6NTBFU8wp/ib8I8Kp4Vx3ez2AVQxkNHj/DTIRc36yRk6nISnp9G2uppzKxOYu6YSynP3sz63FIM20re2GW7AKOYiJqzkpjsZPJXT2NmeRwR/V0UZ2RQjImYu6IJgfbH9obG8utfZHDnc6u5c1YVyfeO5dKKbNJ+n4ujzsbq/CzPH+3+zsKbHVH4RDIrq8djnxNHhL+JIn7PpTHTI4M1SyBmYhxLb/V3q0kwhY/amXf4VixUkr8jl0pMxCTGYgXAzIQ587HsSyVtZhSOmc3rWFgHlmVzmdD0PCZGRk6GzFzy3q2AiS2+xxMZrOki2wTDQfFBoHcs0SOaLzt3L+Smhwpg3AaOpk0mqGldMpg37Rzx40LggwLScw0Im8+ECIAz7L8/mfS6MIqdC7gn/HIqC1JJB7CNJarF9iTr+DlYn1hNTu4BYDwTxlyQlCcRERERERG5QDrJOKnlWPnrTe8iQu4g6vs9HKVF0ATA+fEKfplbhkF/Jtias0746AjHPvPZy4XR20byH9NZOgYKn0tmdvwS1uRWYJm8kr3b4wi5UOMEx/LMS2uYHm5QmJnKpo0ZFBrhTF+XwzNTfeUUmIhYtp3MxEg4vIWkhHgeWJtL+ZDJrM7ZTnw3t3CYb13CMzPCMZ0tIn3jWvIrur6n+3MJJ+HpJKL6GThyt7DpxZL2ASGfoln9hwWwO5VNW3Nx1JmJSspqtT4mWyK79q4h3ka7ddz1i9bnLpvHxRLfG4yMPRQ2Jai4yHk2hcouapsYRYdIB0J+GUtUi9iKqV8QZsAc+q/e3T/N62IqzSVtYyppuaWYxiSS+cdEIkyAKZLkl9YQb3OSt3YJM+MTSNpainlcErv+0OZ3LCyaaY0xnsl2Yvr5vXgiIiIiIiLyJfiO2+12d/iJcYTfpv6KNABCefj2dBKu6rBl59oETQAivv8Iv59xEyHfBb5wsHHjfJ70Fp998OZDLBrRcVcXnOHCVQcmsxlTD4uF+qXOhcsw+SwK26EGA5fL8BQd7X2e4zcYuD43eQuX9vB+P+ZiuAxMZhPFa4dx53M+GkWv4ejW2HbbUYyzLujqe/BjHZ27E7hxaQExvzvGM5P9zd5wsnN2FElHJ/PMmxuY0DZ4YRhg6mDMxnXpbfa9tl/W75iIiIiIiEgLzneySX8hl/zyc/S1RDPl7jimj/KjMICrgvzcDLKyiqmkL5Yfj2faz2OJCu7gj55WbYMZOS2WeyZHY+ngTzG/53O6iPQXjuAEgm6cQ/yo1p25Kgp4+flsst6tgr5hxEy5h/iptqbTbC8G34GTv21n4iubcQBcMp3dCfM9/5veHV0FTQCop3DHLdztPQY4ZsQOnr/5POqoyFeucn8KWaU+PhwYTcJMW8fHMV8IDU4cReWcC+jLsIhwgvz4nTWcpRz76zkYaCXqKm2VERERERGRbzKD4rV27nyu/ZYDyy+y2Jtk810eoyqb2ROSW9RzbGRmwvocnpnaoixDVS7z7EvIO9umab/xPJXzNPampgbFKdP42cZSWlfKNGFdtJ1did75NDgpTF3KvJQiGqtWxqwrZOtdQU39lGcuZNKKAtpV3Aybw66cJCLON+nAB9+Bk+OphP5pp+e1+SHemXN794pW+hU08ag8MJcxjjIALJbNHJw8rDsjiYiIiIiIiAhgFKzm2tkZGJiIWbad1TOCcO5Yzc+eKMDAhH1TIU9N7Og/jL2Z+AVAdBJ718/B2ruK/LVzmb2tAnrHkXlspbe0QXNbU3gsyYmTsVDBnrWr2VlBq50GxjtruWXaFioxE5W4igW2f8X55xR+tbEEg2ASsvJIHmWiOGU4d240oJ8Z81kXLtoETk5lcGfMak/d0GXbWT0jjEur9vPogmRyqiBkWQ5vzAvv4LnOn8/isM6a5oAHl1zeLiJVefR1XNabsV7Wwc3dCJoAXHrJAMATOCk/W42LYRcvI0FERERERETk/yQnL6dneDIyJj7GU/M82f4h89bzuGMkD+wzyHlxP7+e2L58AlRRddTzKmbiFKz9AIKJmTOHqG3JFNZVUXkWGAScyObpAoBoVm9ew/RBAJFEDRlI0B4HAJUuCDK7yHt+C5VAyC82s3WRN7tkjJWQ/lvIrwHTOScQDIaJqMQ0npp/KWnDpnnLhjRz1ZiIWjSfKKxMm2fz1I7sF8vCn28h57cVVP7ZgXNe+EU5pbTTU3V8qfzTr5h6/AjOd/7G7nvnE9EyeNLNoImIiIiIiIiIXACGg9ICz8uYcde3SEgwEzUuGvYVQEEpJw2IabdfJ5zIWBObthkUHnfgmhqNOQAqDx2gECDMxnBvVKK8eD+VAJHjiQpyUX5wDznvOuGyYGLuTiSi8SRRo5jifZ6XE8baoKqEnfsKqPonBF0zhYS4MMze2iTW+flkms1ASYePZrbFsrTtIbgNVRQe9mxJslwfcVGCJtBJ4CSofyhwxPPms3807SEyHJv5+XFPoRaMnUzddjn7Eu7Begk9Dpp8+tnHTa+t/QYr20RERERERESku846PQENwBLSOowQFBIGFABVnGnMHGnFRNSyHNZ+vpw1mQlcuy8Ma38njgqDoFFx/MeG+Vi9QQ5Xtbeo5b87ybNHsaa0uerIpt9nsPD5LJaOMrWYTzTmE8mMnpndVL8EUlk/cQN7fzeZkADPgRr+cu5LZtazDs5UlOIkiJjE9Tw+J8zv+7vLdzij1+VYG1+7TjUtvsk6l+d/dH1zJMfYzMS07Tg+62mmST2VNWVN7wb26tP9pxARERERERGR8+MspbS0whPcOFuBo8IFGLiqqiivdLZvvzWFrB9v4o1jxzien87CUSaoK2XTki04Glo2LGD9750szSnk+LFC9q6LxQK49i1hzT5X+367UufEcaIUZx1QZ1BZWkJZTQ+e10++Qxqhw4hqevMGx/7W/FFIzOPsbhs82TS1Z9tzvijj2P80vhnMyO8P6M78RURERERERAQg4NKml0Ztm7NnPmt+eWlHR/caRay8fQnpJS4sv0jneFkZ779XxtFn4hh4uoD1M+9hU7vTU+NIToompJ8Zc2gkSx9L8iRgVB3hZHXrltZlScSPCMLcLwjrXStJnum5nnfc0e3HDLorjfffK+P9/8pj7TiD8v0pzPxZaptgzYXjO6xhGs4NTTGMap79r9YP0y540kJ3apoYf3mNbV9431xyPSNDA/2Zt4iIiIiIiIi0FBRGhPcY4PwTrY8jdvxlv+dFsA1LR3/InzhEeh1AJHPjIptqjwTdOoUJAFSQdcgTOQkaEum9yUTflrVSLjMxEIBzGAYQFIzF+9HAy1puxTHRt7FWqmG0P164I3UuXGdduFwtWpvCmP7LOM/rihJOdpAUcyF0Etrow0jLzU3vnO+9RH6bDJqQmMfZfW3r4En3CsHWkFeym8Znsw68nuEqcCIiIiIiIiLSA+HExHoiJ5XPprCzynu5Kpv1T3vehMRGe7JCGpwUv7iF9CLvX+QBJu9pusWUlrcITlSVU+59ae7taRESOZ4IAPaTd7g5UFC+L5t8gN4RWIKBgAii7/Xck79vP5WNGSGuAnIyPC8jfmhpd4pvR8qz7+HakSO5NmohOxs3uzS4KMzzBoS4nL69/eioB77jdrvdPj81HGxMm8+T3pSeoNBU3rnD2q5Z4yk7Id08Pcd45ylueLMxcGJl3dRUpg/p9jOIiIiIiIiICEBdCWvs00irAHoHYQ0L8hRRrQN6x7H16Epienvqi1y7MBeIZO0b6UwfXMXO+yaQdNAAzERMjSWqdwU7dxd47u0XyzOvr2FCPwCD4hQ7d26sAEwEhYcx0Kjy1kQxEbMuj613eVNfTucy++Yl5NcB/cKwBpua5xM2h105SUS0CniUsOZKz3HEMesK2XqXN1Xj7AGSbl7AzrOet+awcEzV3n4Ay6Is9iba/ArCdFfnIQ6TlRktMkqcpx5gRWFtu2YhP3mcHTc83r0jh6tfYdlbzdkmEd+fwU8UNBERERERERHpud42kv+YztIx5lZFVM22OWzN9wRNAMxDrFh7A2E2hgUBAcFM/898MhPHY+nnonj3FjZlFuCsMxMxdSW78huDJgAmIhZnsXddHBH9DJylpZ6gST8b8b9rETQBGDSZra+ns3RcEKazFd75mAgal8SuXW2DJp3oN561eekkT7VhBlze4ItpkI34dXkXLWgCXWWcAHxRTc62eBb/b733QigPT0wn4erzGNVVzMaMRJ5szP4x3c7zUx8iZvB59CkiIiIiIiIizepcuAwgwITZ3EFYwTAwvmfC1EGxWMPlwmgwYe7XdTjCcLkw8DFGSw0GLpeByWzucEy/efvBZMZ8kbbntNR14ASg5g1WbF9BemMRV0KJv+5xHonqfqTDeO8Vfpu3jvTGoMl3h7Hoxqd4MELHEIuIiIiIiIjI14t/gROA6ldYnLWOnC+aL4WY5/P7O6cT8S9+3G9UU5i3jofeK6ay6WLPAzAiIiIiIiIiIheb/4ETgOo3WLF7RXO2CACBWC67iXutM4gZEUqIufk4YaO2hspTxRSWvMS2jx2Utwi68F0ri25YwYOjvoKgSUMtZQeeZVtmCX+nD0PGzWJh/HX0P59UoW46uTmOk9dlMGNEFw0/qaU+sA+BX+LcRERERERERMSje4ETgM+qyctewcP/U0ZPj0gOMU/nkfHziQnpYQfno+E0exNn88qwB0mKHc2AXjV88NJGfn0gnCczZjHsSwpQnHzqZg6NeZ0Foy5MOxERERERERG58AJWrVq1qnt3XI5lxBTuHXItA/+nivI6J66u7wICsfzLFJIiV/HYhJ9g8Wd7z0VQ/9bvWfL/4ti2bCwD+gQSeGlfBtrGM+rMal44N4mxYZ6Mmdr3/8TejL0cPnEa07/9kIHeEiy1JdkU/TOUz97O5KW8Ur4Teg2DG8p5LSOTPxXXYA4bxoBLgXMlvPTGZ4R9Xkj2C3kUlcNQazC9vKcOnTmSzqkr4hn9b0BDLafys8l+5RBlHwXyb1cNptd3ofrARjIP/JX3/n6OamcgVw8fTGAnc/NX9YFsTpkHUPXyH9h7qJz6wVcS0rc5U6jD/k+/zY6cjxhyTTCBAOWvknagtmlO9aU5ZL7bm2uv7Hte34+IiIiIiIjI14m/hwe3YxocQXxcKofn72DfDQ/xyOCbiOoVSnMSSSAW0zAmBE3nkR89zr4Zr3Bw1gNM/9FgTD0e9fz97difmHTLGALbXB82/2V+M94Tgah/ZyOL1zgYfFssk35UzyuJi9n7kadd/ftvkvbks5wIGsekkTWk3Xcf9z/1JoPH24nkVe777avUAnzyIUcy17A2ByJj7UQGvMqD92VzqqHtjGo5ufE+1pYOZnysneHn/sisxBxqgMuvHM1VA2HA1aMZebU3QNFqbjXsuG8xr53u3hp8XJrJ2hWZfHxdLJMi69mdkMxebx8+n33Q5dRn7+HYOU+7soPPkv7sq5z4xLuuB57l48BB3ZuIiIiIiIiIyNfc9867B9NgrKNvxzr6duIvwIQutg/KLITGdtbiQ/amljBp1VYirwCIJXF+Gfb0w4xfPgaAAeNmcseoQUAst//gberjZzH8CmDuLO6IOcwHwHCAczbuXnoboQHAjGQWvv9TXiq0k3hji7DNh6/y9EczeeKJn9AHYNZKkt6PY2+pnfjw6xj+A/g4/DoibJ65vZT6IXc/+QSR/QFm8ZtFp4jLKeWWX4a3foyGGsrermHAdZYOarfUEjlrKbdYPH0k3JHDC+/WMmlQTafPHjnewd6SesZGn+bE2+NIiH2TYgdEjiqluGAM1/+ybThKRERERERE5Jvt/AMn3zADBpdTXA38wFeLj6l+38bYK5qvBA6zMmLHx55MEmhz3nR/Anv56GqU1RM08fTCkKst7HDWAC0yM86c5qTjTRbHZTZdqq+pJXKKr7k5eGlxHC80Xvqkhuob7e2bfnSQDb8+yO0Z/8mkjp61xTMMCLqC2tpaunr2YaPHceCwg8QffsiRK238djTMPlbKggElFIy6iRm+1kFERERERETkG+pbFzi5OjyclKOlLBjVOkOj+qVl7OiznMTxAPXUN9AcXGioxwjoQTZFtSfg0N/79h+1NdC3g36il7L1oev87HQcS7YtJaKrIrZXxPJMfqepNT508uxWG+PXHaUo/EP6jF5OoO0yIlNLKAp6myGj7e22P4mIiIiIiIh8032F1Ua+Gv1vm8UtBatIOdhcGKT2r5mkbA4kYlR/wErk+IPsyGn8vJaT2dnUR9maAiB+Kz1I4UeN3ZSwd28g19va9GK1cUfBQYq8tUNoOM1r6zdS3Dh8QCC15xpzXTxzO1RQ23R7dc560gq7WeTEpy6evddorr/mVVJS/0HENX0gwErElTk8vcNE5KhuVqgVERERERER+Qb41mWc0MtGwtNLSV8xmxvX92FYYC3VfcewIGU5Y/sDBBKx6En+9uB92LP7MKC+Fq5ZzKOLruiq5/YsoVRvjGN2FdTXwoj5T3JH2256jWHB42Usu/enpPXpA7W1XG5fwxPe3TzDb5tF2uJp2IseJPPhn3jnFod9cx8GUMvHfe08+eSFKsra1bMHMnLMaKr/O5QRgzzvR4wexqnq0YzsdlRJRERERERE5OvvO2632/1VT+Ir01BPbUMgfXztMfmkltqAPr4/78zpHB56BJKettP/k1rqA/sQ2MX2mvraWujVdTtvY2rp4dz8cT7PLiIiIiIiIvJ/xLcv46SlgED6dBak6NWHC7IBpVcfv+p/BPbpxmiBF2huvlyoZxcRERERERH5Bvt2Z5xcTJ+c5uR/w1W2QSqaKiIiIiIiIvINpcCJiIiIiIiIiIgP37pTdURERERERERE/KXAiYiIiIiIiIiIDwqciIiIiIiIiIj4oMCJiIiIiIiIiIgPCpyIiIiIiIiIiPigwImIiIiIiIiIiA8KnIiIiIiIiIiI+KDAiYiIiIiIiIiID/8ftg8j5q7dKUwAAAAASUVORK5CYII=)","metadata":{"id":"RohNm3aXu1OD"}},{"cell_type":"markdown","source":"После чего я пожертвовал этапом валидации и дообучил модель на всём датасете. Финальный результат представлен ниже.","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:575dbf34-2449-4143-8fb2-dd2c1ff36189.png)","metadata":{},"attachments":{"575dbf34-2449-4143-8fb2-dd2c1ff36189.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Результат на лидерборде отличается, т.к. он формировался на другой половине тестовых данных.","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:5116f0ea-cb78-4e46-a039-f764c2623af2.png)","metadata":{},"attachments":{"5116f0ea-cb78-4e46-a039-f764c2623af2.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### 2.3 DenseNet","metadata":{"id":"I9XT0OtUOyZy"}},{"cell_type":"markdown","source":"#### 2.3.1 DenseNet161","metadata":{"id":"Md_SrvWYPp_q"}},{"cell_type":"markdown","source":"- optimizer - Adam (lr=1e-4)\n- scheduler - StepLR (step=10, gamma=0.1)","metadata":{"id":"BpEHqY30QIRE"}},{"cell_type":"code","source":"# model = models.densenet161(pretrained=False)\n# model.classifier=nn.Linear(in_features=2208, out_features=NUM_CLASSES)\n# model=model.to(DEVICE)\n\n# criterion = nn.CrossEntropyLoss()\n# optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n# scheduler = torch.optim.lr_scheduler.StepLR(optimizer,step_size=10, gamma=0.1)\n# # optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=0.9, weight_decay=2e-4)\n# # scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)","metadata":{"id":"VTw_EV3kY_s7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![densenet161.PNG](data:image/png;base64,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)","metadata":{"id":"MTc49TZuPyTX"}},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,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)","metadata":{"id":"3PAj97TwP8zA"}},{"cell_type":"markdown","source":"DenseNet показала результат, превосходящий ResNet, поэтому я решил продолжить эксперементировать с данной архитектурой.\n\nПосмотрев на графики функции потерь и метрики f1, я заметил, что они выходят на \"плато\" после 15-20 эпохи. Поэтому я решил обучить модель на 15-20 эпохах, не исползуя этап валидации. Это увеличит количество данных в тренировчной выборке и тем самым повысит качество финального результата.\n\nПосле обучения без валидации скор был увеличен:","metadata":{"id":"UBgtXpi0QDBW"}},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,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)","metadata":{"id":"ePrgQwG0RTdq"}},{"cell_type":"markdown","source":"## 3. Обучение модели","metadata":{"id":"VLHKMEEmrRpn"}},{"cell_type":"code","source":"losses = {}\nscores = {}\ndef train(model, criterion, optimizer, scheduler, epochs, loaders, model_name):\n    losses[model_name] = {'train': [], 'val':[]}\n    scores[model_name] = {'train': [], 'val':[]}\n\n    model.to(DEVICE)\n\n    #best_model_wts = deepcopy(model.state_dict())\n    best_score = 0.0\n    #writer = SummaryWriter(f'logs/resnet')\n    loop = tqdm(range(epochs))\n    #step = 14\n    for epoch in loop:\n        train_running_loss = 0.0\n        train_running_score = 0\n\n        val_running_loss = 0.0\n        val_running_score = 0\n\n        model.train()  # Set model to training mode\n\n        for inputs, labels in loaders['train']:\n            inputs = inputs.to(DEVICE)\n            labels = labels.to(DEVICE)\n            \n            optimizer.zero_grad()\n            \n            with torch.set_grad_enabled(True):\n                outputs = model(inputs)\n                loss = criterion(outputs, labels)\n                preds = outputs.argmax(dim=1)\n                loss.backward()\n                optimizer.step()\n            # compute f1 macro\n            train_running_score += f1_score(labels.cpu(), preds.cpu(), average='macro')\n            train_running_loss += loss.cpu().item()\n\n        train_running_loss /= len(loaders['train'])\n        train_running_score /= len(loaders['train'])\n\n        losses[model_name]['train'].append(train_running_loss)\n        scores[model_name]['train'].append(train_running_score)\n        \n        print(f'\\n TRAIN Loss: {train_running_loss:.4f} f1 macro: {train_running_score:.4f}')\n\n\n        if scheduler:\n            scheduler.step()\n\n        if loaders['val']:\n            model.eval() # Set model to eval mode\n\n            for inputs, labels in loaders['val']:\n                inputs = inputs.to(DEVICE)\n                labels = labels.to(DEVICE)\n\n                optimizer.zero_grad()\n\n                with torch.no_grad():\n                    outputs = model(inputs)\n                    loss = criterion(outputs, labels)\n                    preds = outputs.argmax(dim=1)\n                # compute f1 macro\n                val_running_score += f1_score(labels.cpu(), preds.cpu(), average='macro')\n                val_running_loss += loss.cpu().item()\n\n            val_running_loss /= len(loaders['val'])\n            val_running_score /= len(loaders['val'])\n\n            losses[model_name]['val'].append(val_running_loss)\n            scores[model_name]['val'].append(val_running_score)\n            \n            print(f'\\n VAL Loss: {val_running_loss:.4f} f1 macro: {val_running_score:.4f}')\n            \n            if val_running_score > best_score:\n                best_score = val_running_score\n                #best_model_wts = deepcopy(model.state_dict())\n                save_checkpoint(model, optimizer, filename=CHECKPOINT_PATH)\n        else:\n            save_checkpoint(model, optimizer, filename=CHECKPOINT_PATH)\n\n\n        #writer.add_scalars('Loss', {'train':train_running_loss, 'val':val_running_loss}, global_step=step)\n        #writer.add_scalars('f1 macro', {'train':train_running_score, 'val':val_running_score}, global_step=step)\n        \n        #step += 1\n        \n        #show_train_val_metric(losses[model_name], scores[model_name])\n       \n            \n    load_checkpoint(CHECKPOINT_PATH, model, optimizer, lr=1e-6)\n","metadata":{"id":"HRLYNjAYrV5M","execution":{"iopub.status.busy":"2023-01-25T11:15:39.408055Z","iopub.execute_input":"2023-01-25T11:15:39.408426Z","iopub.status.idle":"2023-01-25T11:15:39.425555Z","shell.execute_reply.started":"2023-01-25T11:15:39.408394Z","shell.execute_reply":"2023-01-25T11:15:39.424450Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# %reload_ext tensorboard\n# %tensorboard --logdir='./logs/resnet/'\ntrain(model, criterion, optimizer, scheduler, NUM_EPOCHS, loaders, model_name='EfficientNet_b6_after')","metadata":{"id":"dULa6lnK4jmA","outputId":"accfa047-61df-44f9-d089-327d16d6567c"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 4. Получение предсказаний","metadata":{"id":"emBb5_DkT2M8"}},{"cell_type":"code","source":"class TestDataset(Dataset):\n    '''\n    Класс тестового датасета.\n    '''\n    def __init__(self, files):\n        self.files = files\n        self.transforms = transforms.Compose([\n            transforms.Resize(size=(RESCALE_SIZE, RESCALE_SIZE)),\n            transforms.Lambda(lambda x: np.array(x, dtype=\"float32\") / 255),\n            transforms.ToTensor(),\n            transforms.Normalize((0.5), (0.5)) ,\n        ])\n\n    def __getitem__(self, index):\n        \n        img = Image.open(self.files[index]).convert('RGB')\n        return self.transforms(img)\n    \n    def __len__(self):\n        return len(self.files)","metadata":{"id":"KpfmibP744OX","execution":{"iopub.status.busy":"2023-01-25T11:15:45.722676Z","iopub.execute_input":"2023-01-25T11:15:45.723041Z","iopub.status.idle":"2023-01-25T11:15:45.730879Z","shell.execute_reply.started":"2023-01-25T11:15:45.723010Z","shell.execute_reply":"2023-01-25T11:15:45.729670Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Получим список всех путей тестовых фотографий \nfrom pathlib import Path\nTEST_DIR = Path(TEST_DATA_DIR)\ntest_files = list(TEST_DIR.rglob('*.jpg'))\n#test_files","metadata":{"id":"T83SmuzBtpN_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_dataset = TestDataset(test_files)","metadata":{"id":"81A_Wbgmocj5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_CORES)","metadata":{"id":"-LZ3Gl8loHyP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def predict(model, test_loader):\n    '''\n    model - модель нс\n    test_loader - тестовый даталоадер\n    return: список предсказаний (номер класса для каждого объекта в тестовой выборке)\n    '''\n    model.eval()\n    with torch.no_grad():\n        logits = []\n        for inputs in test_loader:\n            inputs = inputs.to(DEVICE)\n            \n            outputs = model(inputs).cpu()\n            logits.append(outputs)\n            \n    probs = nn.functional.softmax(torch.cat(logits), dim=-1).numpy()\n    return probs.argmax(axis=1)","metadata":{"id":"7i3nPppio4pm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"probs = predict(model,test_dataloader)","metadata":{"id":"zJviJ-E4VAyr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_filenames = [path.name for path in test_dataset.files]","metadata":{"id":"9M5t-YzkVCyK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission = pd.DataFrame({'img_name':test_filenames, 'label':probs})","metadata":{"id":"PHA17H8TVFB2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission","metadata":{"id":"LYLGpJE5VIaJ","outputId":"95e573aa-2565-4043-e685-1da3281e4ff7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('/content/drive/MyDrive/resnet/efficientnet_b6(7)_test.csv', index=False)","metadata":{"id":"sQwsuo4sVKXn"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 5. Анализ предсказаний модели","metadata":{"id":"h4uDi4wWT9gm"}},{"cell_type":"code","source":"train_dataset, val_dataset = split(TRAIN_DATA_DIR, train_transforms, basic_transforms, imagefolder=ImageFolderWithPath)\n\nloaders = {\n    'train': DataLoader(train_dataset, batch_size=BATCH_SIZE, num_workers=NUM_CORES),\n    'val': DataLoader(val_dataset, batch_size=BATCH_SIZE, num_workers=NUM_CORES)\n}","metadata":{"id":"KRHqqCsQSlIf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = {'label': [], 'prediction': [], 'path':[]}\nmodel.eval()\nwith torch.no_grad():\n    for inputs, label, path in loaders['val']:\n        inputs = inputs.to(DEVICE)\n\n        outputs = model(inputs).cpu()\n\n        data['label'].extend([x.cpu().detach().item() for x in label])\n        data['prediction'].append(outputs)\n        data['path'].extend([p for p in path])\n\ndata['prediction'] = nn.functional.softmax(torch.cat(data['prediction']), dim=-1).numpy().argmax(axis=1)\ndf = pd.DataFrame.from_dict(data)\ndf","metadata":{"id":"G2-71TFmgIm2","outputId":"eeaf7e28-ad55-47ca-a1ea-f8d0e95b734a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"teams_dict = {\n    0:'Arsenal',\n    1:'Barcelona',\n    2:'Bayern',\n    3:'Chelsea',\n    4:'Juventus',\n    5:'Liverpool',\n    6:'ManchesterCity',\n    7:'ManchesterUnited',\n    8:'PSG',\n    9:'Real',\n}","metadata":{"id":"u6E7GrL4ljX9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df.replace(teams_dict)","metadata":{"id":"X1eA2NemjhTG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"id":"vvelgM46lw9c","outputId":"2d66de2e-2525-408b-d6e4-f1e9ebdd9bb8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Создадим датафрейм, который содержит пары предсказаине, лейбл на которых модель ошиблась.  ","metadata":{"id":"dqTpQg4wI-3s"}},{"cell_type":"code","source":"df_errors = df[df['label'] != df['prediction']].reset_index(drop=True)","metadata":{"id":"EjppOgRRVNlg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_errors.nunique()","metadata":{"id":"TPbVtND0ar_d","outputId":"d8fe0e04-6853-4874-86f6-f3fa582232a7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_errors","metadata":{"id":"cvqWUsqk7a89","outputId":"d48799e5-8c0b-49af-f94a-dcff411d61df"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Построим матрицу путанности (confusion_matrix), чтобы посмотреть какие ошибки допускает обученная модель.","metadata":{"id":"OondMSR2JUj0"}},{"cell_type":"code","source":"fig = ff.create_annotated_heatmap(\n    confusion_matrix(df_errors['label'], df_errors['prediction']), \n    x=list(teams_dict.values()), \n    y=list(teams_dict.values()), \n    colorscale='Viridis')\nfig.show()","metadata":{"id":"PlVbH7R9pxuU"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,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)","metadata":{"id":"wMKfEPlVdSI0"}},{"cell_type":"markdown","source":"Можно заметить, что чаще всего модель путает формы команд PSG и Barcelona, PSG и Chelsea. В большинстве случаев модель дает правильные предсказания","metadata":{}},{"cell_type":"code","source":"cols = 5\nrows = 4\naxes = plt.subplots(nrows=rows, ncols=cols, figsize=(20, 20))[1]\nrandom_indices = np.random.choice(np.arange(df_errors.shape[0]), cols * rows, replace=False)\nfor ax, index in zip(axes.flatten(), random_indices):\n    label, pred = df_errors.iloc[index]['label'], df_errors.iloc[index]['prediction']\n    ax.set_title(f'label:{label} \\n prediction: {pred}')\n    image = Image.open(df_errors.iloc[index]['path'])\n    ax.imshow(image)\n    ax.axis('off')","metadata":{"id":"kZFQ0P7BfYne","outputId":"ada1b457-a3b1-409f-fe18-414f57ab9a97"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Итог","metadata":{}},{"cell_type":"markdown","source":"Для улучшения результатов можно:\n   - Использовать более сложную модель, например, EfficientNet b7\n   - Более тонкая настройка гиперпараметров\n   - Использовать другой оптимизатор. SGD с momentum может дать лучший результат, чем Adam, при более точной настройке гиперпараметров\n   - Применить другие аугментации\n   - Внести изменения в архитектуру используемой модели: добавить полносвязных слоев и т.д.","metadata":{}}]}