notebooks/Simpsons-Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "dWY5eflHfC1q",
    "outputId": "05b69912-d8b9-4325-ff16-947d6ba72263"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "    return false;\n",
       "}"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Отключим автоскролл\n",
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sOWQEdVxblxA"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from PIL import Image\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchvision import models, transforms\n",
    "from tqdm.autonotebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2RTHl9CueN_7"
   },
   "outputs": [],
   "source": [
    "DEVICE = torch.device(\"cuda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4uVRVD1Zd4aJ"
   },
   "source": [
    "DATA MODULE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N6JQFOIubt2S"
   },
   "outputs": [],
   "source": [
    "def sort_files(files_list):\n",
    "    files_dict = {}\n",
    "\n",
    "    for file in files_list:\n",
    "        files_dict.setdefault(file.parent.name, [])\n",
    "        files_dict[file.parent.name].append(file)\n",
    "\n",
    "    return files_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wliNCWRhbwS-"
   },
   "outputs": [],
   "source": [
    "def label_freq(files):\n",
    "    freq_dict = {}\n",
    "\n",
    "    for file in files:\n",
    "        freq_dict.setdefault(file.parent.name, 0)\n",
    "        freq_dict[file.parent.name] += 1\n",
    "\n",
    "    return freq_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SMr4WLuAbyIJ"
   },
   "outputs": [],
   "source": [
    "# undersampling\n",
    "def remove_images(files, sampler):\n",
    "    new_files = []\n",
    "\n",
    "    files_dict = dict_from_list(files)\n",
    "    freq = label_freq(files)\n",
    "\n",
    "    for label in sampler.keys():\n",
    "        if freq[label] >= sampler[label]:\n",
    "            indexes = np.random.permutation(freq[label])[: sampler[label]]\n",
    "            for index in indexes:\n",
    "                new_files.append(files_dict[label][index])\n",
    "\n",
    "    return new_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZDIANJ56cCiG"
   },
   "outputs": [],
   "source": [
    "# oversampling\n",
    "def add_images(files, new_images, sampler):\n",
    "    new_files = files.copy()\n",
    "\n",
    "    files_dict = dict_from_list(files)\n",
    "    new_images_dict = dict_from_list(new_images)\n",
    "    freq = label_freq(files)\n",
    "\n",
    "    for label in sampler.keys():\n",
    "        if freq[label] < sampler[label]:\n",
    "            all_images = files_dict[label] + new_images_dict[label]\n",
    "            new_files += new_images_dict[label]\n",
    "            to_add = sampler[label] - len(all_images)\n",
    "\n",
    "            if to_add > 0:\n",
    "                indexes = np.random.randint(0, len(all_images), to_add)\n",
    "                for index in indexes:\n",
    "                    new_files.append(all_images[index])\n",
    "\n",
    "    return new_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "J8R5ywPJcEiB"
   },
   "outputs": [],
   "source": [
    "# аугментация\n",
    "def resample(files, new_images, sampler):\n",
    "    new_files = add_images(files, new_images, sampler)\n",
    "    new_files = remove_images(new_files, sampler)\n",
    "    return new_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GIj67s8NcGs0"
   },
   "outputs": [],
   "source": [
    "# написал свой splitter, чтобы класс с малым количеством примеров не попадал полностью в валидационный датасет\n",
    "def train_test_split(files, train_size):\n",
    "    files_dict = dict_from_list(files)\n",
    "\n",
    "    train_files, test_files, = (\n",
    "        [],\n",
    "        [],\n",
    "    )\n",
    "\n",
    "    for label in files_dict.keys():\n",
    "\n",
    "        indexes = np.random.permutation(len(files_dict[label]))\n",
    "        train_indexes = indexes[: int(train_size * len(files_dict[label]))]\n",
    "        test_indexes = indexes[int(train_size * len(files_dict[label])) :]\n",
    "\n",
    "        for index in train_indexes:\n",
    "            train_files.append(files_dict[label][index])\n",
    "        for index in test_indexes:\n",
    "            test_files.append(files_dict[label][index])\n",
    "\n",
    "    return train_files, test_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Nn0oWBRQcI-I"
   },
   "outputs": [],
   "source": [
    "class SimpsonsDataset(Dataset):\n",
    "    def __init__(self, files, mode):\n",
    "        super().__init__()\n",
    "        self.files = sorted(files)\n",
    "        self.labels = [path.parent.name for path in self.files]\n",
    "        self.mode = mode\n",
    "        self.len_ = len(self.files)\n",
    "        self.label_encoder = LabelEncoder()\n",
    "\n",
    "        if self.mode != \"test\":\n",
    "            self.label_encoder.fit(self.labels)\n",
    "            with open(\"label_encoder.pkl\", \"wb\") as pickle_file:\n",
    "                pickle.dump(self.label_encoder, pickle_file)\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.len_\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        transform = transforms.Compose(\n",
    "            [transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]\n",
    "        )\n",
    "\n",
    "        image = Image.open(self.files[index])\n",
    "        image.load()\n",
    "        image = image.resize((224, 224))\n",
    "        x = np.array(image)\n",
    "        x = np.array(x / 255, dtype=\"float32\")\n",
    "        x = transform(x)\n",
    "\n",
    "        if self.mode == \"test\":\n",
    "            return x\n",
    "\n",
    "        label = self.labels[index]\n",
    "        encoded_label = self.label_encoder.transform([label])\n",
    "        y = encoded_label.item()\n",
    "\n",
    "        return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jCzez_VXfJKu"
   },
   "source": [
    "ML MODULE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dqIcZAtgcxLp"
   },
   "outputs": [],
   "source": [
    "def train_epoch(model, criterion, optimizer, train_loader):\n",
    "    model.train()\n",
    "\n",
    "    epoch_loss = 0.0\n",
    "    epoch_corrects = 0\n",
    "    processed_data = 0\n",
    "\n",
    "    for x_batch, y_batch in tqdm(train_loader, leave=False, desc=\"batch train:\"):\n",
    "        x_batch = x_batch.to(DEVICE)\n",
    "        y_batch = y_batch.to(DEVICE)\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        y_logits = model(x_batch)\n",
    "        y_pred = torch.argmax(y_logits, 1)\n",
    "\n",
    "        loss = criterion(y_logits, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        epoch_loss += loss.item() * x_batch.size(0)\n",
    "        epoch_corrects += torch.sum(y_pred == y_batch.data)\n",
    "        processed_data += x_batch.size(0)\n",
    "\n",
    "    epoch_loss /= processed_data\n",
    "    epoch_acc = epoch_corrects.cpu().numpy() / processed_data\n",
    "    return epoch_loss, epoch_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eIpz_wOdcziT"
   },
   "outputs": [],
   "source": [
    "def val_epoch(model, criterion, val_loader):\n",
    "    model.eval()\n",
    "\n",
    "    val_loss = 0.0\n",
    "    val_corrects = 0\n",
    "    processed_data = 0\n",
    "\n",
    "    for x_batch, y_batch in tqdm(val_loader, leave=False, desc=\"batch val:\"):\n",
    "        x_batch = x_batch.to(DEVICE)\n",
    "        y_batch = y_batch.to(DEVICE)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            y_logits = model(x_batch)\n",
    "            y_pred = torch.argmax(y_logits, 1)\n",
    "            loss = criterion(y_logits, y_batch)\n",
    "\n",
    "        val_loss += loss.item() * x_batch.size(0)\n",
    "        val_corrects += torch.sum(y_pred == y_batch.data)\n",
    "        processed_data += x_batch.size(0)\n",
    "\n",
    "    val_loss /= processed_data\n",
    "    val_acc = val_corrects.double() / processed_data\n",
    "    return val_loss, val_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7T-6lb5ic1oP"
   },
   "outputs": [],
   "source": [
    "def train(model, criterion, optimizer, epochs, batch_size):\n",
    "    log = []\n",
    "    log_template = (\n",
    "        \"\\nEpoch {ep:03d} train_loss: {t_loss:0.4f} val_loss {v_loss:0.4f} train_acc {t_acc:0.4f} val_acc {v_acc:0.4f}\"\n",
    "    )\n",
    "\n",
    "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)\n",
    "    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, drop_last=True)\n",
    "\n",
    "    for epoch in tqdm(range(epochs), desc=\"epoch:\"):\n",
    "        train_loss, train_acc = train_epoch(model, criterion, optimizer, train_loader)\n",
    "        val_loss, val_acc = val_epoch(model, criterion, val_loader)\n",
    "        log.append((train_loss, train_acc, val_loss, val_acc))\n",
    "        tqdm.write(\n",
    "            log_template.format(ep=epoch + 1, t_loss=train_loss, v_loss=val_loss, t_acc=train_acc, v_acc=val_acc)\n",
    "        )\n",
    "\n",
    "    return log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jBEYacUEc3xH"
   },
   "outputs": [],
   "source": [
    "def predict_proba(model, test_loader):\n",
    "    model.eval()\n",
    "    logits = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for x in tqdm(test_loader):\n",
    "            x = x.to(DEVICE)\n",
    "            y_logits = model(x).cpu()\n",
    "            logits.append(y_logits)\n",
    "\n",
    "    y_prob = nn.functional.softmax(torch.cat(logits), dim=-1).numpy()\n",
    "    return y_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KfFQxb4Jc7Xn"
   },
   "outputs": [],
   "source": [
    "def plot_loss(train_loss, val_loss):\n",
    "    plt.figure(figsize=(15, 9))\n",
    "    plt.plot(train_loss, label=\"train_loss\")\n",
    "    plt.plot(val_loss, label=\"val_loss\")\n",
    "    plt.xlabel(\"epochs\")\n",
    "    plt.ylabel(\"loss\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LQvqj-L-dDby"
   },
   "outputs": [],
   "source": [
    "def create_predictions_file(model, filename):\n",
    "    test_loader = DataLoader(test_dataset, shuffle=False, batch_size=64)\n",
    "    label_encoder = pickle.load(open(\"label_encoder.pkl\", \"rb\"))\n",
    "\n",
    "    y_prob = predict_proba(model, test_loader)\n",
    "    y_pred = label_encoder.inverse_transform(np.argmax(y_prob, axis=1))\n",
    "    test_filenames = [path.name for path in test_dataset.files]\n",
    "    predictions_df = pd.DataFrame({\"Id\": test_filenames, \"Expected\": y_pred})\n",
    "    predictions_df.to_csv(filename, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NTA_nvk-fXbz"
   },
   "source": [
    "DATA PREPARATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w1LgqixOdJy2"
   },
   "outputs": [],
   "source": [
    "!unzip -q /content/drive/My\\ Drive/journey-springfield.zip\n",
    "!unzip -q /content/drive/My\\ Drive/horizontal_flip_images.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Dc6xLZVsdNKK"
   },
   "outputs": [],
   "source": [
    "train_dir = Path(\"train/simpsons_dataset\")\n",
    "test_dir = Path(\"testset/testset\")\n",
    "new_images_dir = Path(\"horizontal_flip_images\")\n",
    "\n",
    "all_train_images_files = sorted(list(train_dir.rglob(\"*.jpg\")))\n",
    "test_images_files = sorted(list(test_dir.rglob(\"*.jpg\")))\n",
    "new_images_files = sorted(list(new_images_dir.rglob(\"*.jpg\")))\n",
    "labels = np.unique([file.parent.name for file in all_train_images_files])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IqOBzad0dQih"
   },
   "outputs": [],
   "source": [
    "original_sampler = label_freq(all_train_images_files)\n",
    "uniform_sampler = {label: 500 for label in labels}\n",
    "sampler_1 = {label: original_sampler[label] if original_sampler[label] > 300 else 300 for label in labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "WtF5OoDADH_a"
   },
   "outputs": [],
   "source": [
    "# создадим валидационный датасет из оригинальных данных\n",
    "_, val_images_files = train_test_split(all_train_images_files, train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w3DyxJzgOQVs"
   },
   "outputs": [],
   "source": [
    "# augmentation\n",
    "all_train_images_files_resampled = resample(all_train_images_files, new_images_files, sampler_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
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    },
    "id": "j876E2mTD9aA",
    "outputId": "b610665d-4a0e-40d0-9962-378f7af33b67"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0b7f1b6a8ed4df28fb7e8165a9ca8ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/26202 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# создадим обучающий датасет из аугментированных данных\n",
    "# не берем отсюда валидацинный датасет, потому что распределения классов в оргинальном датасете и в тестовом вероятнее всего одинаковые\n",
    "train_images_files_resampled = [\n",
    "    image_file for image_file in tqdm(all_train_images_files_resampled) if image_file not in val_images_files\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ytzMv5fcdVew"
   },
   "outputs": [],
   "source": [
    "train_dataset = SimpsonsDataset(train_images_files_resampled, mode=\"train\")\n",
    "val_dataset = SimpsonsDataset(val_images_files, mode=\"val\")\n",
    "test_dataset = SimpsonsDataset(test_images_files, mode=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yHJM2KllBpT8"
   },
   "source": [
    "SIMPLE CNN WITH BATCHNORM - 93.3%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5JFrz2oC41Xy"
   },
   "outputs": [],
   "source": [
    "class SimpleCnn(nn.Module):\n",
    "    def __init__(self, n_classes):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.BatchNorm2d(8),\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.BatchNorm2d(16),\n",
    "        )\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.BatchNorm2d(32),\n",
    "        )\n",
    "        self.conv4 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.BatchNorm2d(64),\n",
    "        )\n",
    "        self.conv5 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=64, out_channels=96, kernel_size=3),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.BatchNorm2d(96),\n",
    "        )\n",
    "\n",
    "        self.out = nn.Sequential(\n",
    "            nn.Linear(96 * 5 * 5, n_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "\n",
    "        x = x.view(x.size(0), -1)\n",
    "        logits = self.out(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Qgr024UWB7Tx"
   },
   "outputs": [],
   "source": [
    "simple_cnn = SimpleCnn(len(labels)).to(DEVICE)\n",
    "loss_func = nn.CrossEntropyLoss()\n",
    "optim_func = torch.optim.AdamW(simple_cnn.parameters(), lr=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "RESNET FINE-TUNING - 98.5%"
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   "execution_count": null,
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    "# заморозим первые 3 слоя и будем переобучать последний\n",
    "for param in resnet_model.parameters():\n",
    "    param.requires_grad = False\n",
    "for param in resnet_model.layer4.parameters():\n",
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    "# прогнал этот код дважды\n",
    "# сначала с lr=1e-4, потом обновил обучающую и валидацинную выборку, чтобы обучить на новых данных (по типу crossfoldа)\n",
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