{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":33768,"databundleVersionId":3083964,"sourceType":"competition"}],"dockerImageVersionId":31011,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import datasets, transforms, models\nfrom PIL import Image\nimport pandas as pd\nfrom tqdm import tqdm\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-04-22T10:13:50.398816Z","iopub.execute_input":"2025-04-22T10:13:50.399351Z","iopub.status.idle":"2025-04-22T10:13:50.403764Z","shell.execute_reply.started":"2025-04-22T10:13:50.399328Z","shell.execute_reply":"2025-04-22T10:13:50.402926Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_dir = '/kaggle/input/classification-of-butterflies/train_butterflies/train_split'\n\ntransform_train = transforms.Compose([\n    transforms.RandomResizedCrop(224),\n    transforms.RandomHorizontalFlip(),\n    transforms.ToTensor(),\n    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n\ntrain_dataset = datasets.ImageFolder(train_dir, transform=transform_train)\n\ntrain_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-22T10:13:51.058104Z","iopub.execute_input":"2025-04-22T10:13:51.058352Z","iopub.status.idle":"2025-04-22T10:13:51.121817Z","shell.execute_reply.started":"2025-04-22T10:13:51.058334Z","shell.execute_reply":"2025-04-22T10:13:51.121297Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test_dir = '/kaggle/input/classification-of-butterflies/test_butterflies/valid'\n\ntransform_test = transforms.Compose([\n    transforms.Resize(256),\n    transforms.CenterCrop(224),\n    transforms.ToTensor(),\n    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n\nclass TestDataset(Dataset):\n    def __init__(self, root_dir, transform=None):\n        self.root_dir = root_dir\n        self.transform = transform\n        self.image_files = [f for f in os.listdir(root_dir) if f.endswith('.jpg')]\n\n    def __len__(self):\n        return len(self.image_files)\n\n    def __getitem__(self, idx):\n        img_name = self.image_files[idx]\n        img_path = os.path.join(self.root_dir, img_name)\n        image = Image.open(img_path).convert('RGB')\n        if self.transform:\n            image = self.transform(image)\n\n        img_id = int(img_name.split('.')[0])\n        return image, img_id\n\ntest_dataset = TestDataset(test_dir, transform=transform_test)\n\n\ntest_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-22T10:13:52.158138Z","iopub.execute_input":"2025-04-22T10:13:52.158807Z","iopub.status.idle":"2025-04-22T10:13:52.166845Z","shell.execute_reply.started":"2025-04-22T10:13:52.158785Z","shell.execute_reply":"2025-04-22T10:13:52.166310Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model = models.resnet18(pretrained=True)\n\nnum_ftrs = model.fc.in_features\nmodel.fc = nn.Linear(num_ftrs, 50)\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = model.to(device)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-22T10:13:55.241714Z","iopub.execute_input":"2025-04-22T10:13:55.241974Z","iopub.status.idle":"2025-04-22T10:13:55.481084Z","shell.execute_reply.started":"2025-04-22T10:13:55.241955Z","shell.execute_reply":"2025-04-22T10:13:55.480465Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"criterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\ndef train_model(model, train_loader, criterion, optimizer, epochs=10):\n    model.train()\n    for epoch in range(epochs):\n        running_loss = 0.0\n        for inputs, labels in tqdm(train_loader):\n            inputs, labels = inputs.to(device), labels.to(device)\n            \n            optimizer.zero_grad()\n            outputs = model(inputs)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n            \n            running_loss += loss.item()\n        \n        print(f'Epoch [{epoch+1}/{epochs}], Loss: {running_loss/len(train_loader):.4f}')\n\ntrain_model(model, train_loader, criterion, optimizer, epochs=20)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-22T10:13:57.635409Z","iopub.execute_input":"2025-04-22T10:13:57.635674Z","iopub.status.idle":"2025-04-22T10:19:35.742918Z","shell.execute_reply.started":"2025-04-22T10:13:57.635654Z","shell.execute_reply":"2025-04-22T10:19:35.742067Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def predict(model, dataloader):\n    model.eval()\n    all_preds = []\n    all_ids = []\n    with torch.no_grad():\n        for inputs, ids in tqdm(dataloader):\n            inputs = inputs.to(device)\n            outputs = model(inputs)\n            _, preds = torch.max(outputs, 1)\n            all_preds.extend(preds.cpu().numpy())\n            all_ids.extend(ids.numpy())\n    return all_ids, all_preds\n\ntest_ids, test_preds = predict(model, test_loader)\n\nsubmission = pd.DataFrame({'index': test_ids, 'label': test_preds})\nsubmission.to_csv('submission.csv', index=False)\nprint(\"Файл submission.csv успешно создан!\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-22T10:21:01.493806Z","iopub.execute_input":"2025-04-22T10:21:01.494138Z","iopub.status.idle":"2025-04-22T10:21:02.256498Z","shell.execute_reply.started":"2025-04-22T10:21:01.494114Z","shell.execute_reply":"2025-04-22T10:21:02.255627Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}