notebooks/2_model_creation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xrQh1kRqQ6Cx"
   },
   "source": [
    "# Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6U1r0q-tT3rV",
    "outputId": "7af589cf-0a17-46bb-dfc9-46aa47bc0376"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pytorch-ignite\n",
      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/db/4d/49a158b7e7ce3e31d2b921eea274c945e48eea07eb9df3da987b64ee87b6/pytorch_ignite-0.4.3-py3-none-any.whl (193kB)\n",
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      "\u001B[?25hRequirement already satisfied: torch<2,>=1.3 in /usr/local/lib/python3.6/dist-packages (from pytorch-ignite) (1.7.0+cu101)\n",
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      "Installing collected packages: pytorch-ignite\n",
      "Successfully installed pytorch-ignite-0.4.3\n"
     ]
    }
   ],
   "source": [
    "!pip install pytorch-ignite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aRirckb5Q85Q"
   },
   "source": [
    "### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mhGgBF1TQrnl",
    "outputId": "8013f3c6-f16a-4bc3-b54c-703a8b31f94a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7.0+cu101\n",
      "0.8.1+cu101\n"
     ]
    }
   ],
   "source": [
    "# Model creation\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms, models\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# Data manipulation\n",
    "import h5py\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "print(torch.__version__)\n",
    "print(torchvision.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tmf70sq1RGqM"
   },
   "source": [
    "### Device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OLOAkkZCRHsB",
    "outputId": "ebd95c14-e10e-42e7-e33b-af81715bc385"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fec6FlkXRJon",
    "outputId": "be212119-15a6-41bc-a53e-707ab3c3fe48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XGhYP6evQvQo"
   },
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "jiVLuxEkRE32"
   },
   "outputs": [],
   "source": [
    "root_path = \"/content/drive/MyDrive/datas/fruits/\"\n",
    "dataset_path = f\"{root_path}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "e9D2yZCCREzJ"
   },
   "outputs": [],
   "source": [
    "h5f = h5py.File(f'{dataset_path}/dataset.h5py', 'r')\n",
    "\n",
    "X_train = np.array(h5f['X_train'])\n",
    "X_test = np.array(h5f['X_test'])\n",
    "y_train = np.array(h5f['y_train'])\n",
    "y_test = np.array(h5f['y_test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "__jRuo0SRa15",
    "outputId": "cf1d314e-1544-4c7d-ec4f-532a4da0fa10"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3020, 3, 120, 120)"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BG3ojWazRbrD",
    "outputId": "cffd3ba3-55cc-4360-d99b-b41a30c2c93c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3020,)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "azdTl4h5h4IQ"
   },
   "outputs": [],
   "source": [
    "with open(f'{dataset_path}/labels.pickle', 'rb') as handle:\n",
    "    labels = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "JoZAaVcPinll"
   },
   "outputs": [],
   "source": [
    "OUT_SIZE = len(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-1ZatOJXRfSk"
   },
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MsH5YpjO0On6"
   },
   "source": [
    "### Ensure dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "KDb_Bx9m0QdS"
   },
   "outputs": [],
   "source": [
    "X_train = X_train.astype(np.float32)\n",
    "X_test = X_test.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Et_u7eEK0ZSD",
    "outputId": "19f9ac9f-b267-4a8d-c5b5-54bb17cb7431"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float32')"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JPby43fV0b_h",
    "outputId": "fa5f6a9e-8053-4e63-ff7e-657c1e3aba3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min 0.0\n",
      "max 1.0\n"
     ]
    }
   ],
   "source": [
    "print('min', X_train.min())\n",
    "print('max', X_train.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r16CcSG3RqPn"
   },
   "source": [
    "### Custom dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "VkOmZHVuRtEP"
   },
   "outputs": [],
   "source": [
    "transforms_pipe = transforms.Compose([\n",
    "                                     \n",
    "  # transforms.RandomSizedCrop(224),\n",
    "  # transforms.RandomHorizontalFlip(),\n",
    "  # torchvision.transforms.RandomRotation(10),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7R69fZVlRy0N"
   },
   "source": [
    "We define two custom Dataset classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "1c4Gz6jzRymw"
   },
   "outputs": [],
   "source": [
    "class TrainDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"TensorDataset with support of transforms.\"\"\"\n",
    "\n",
    "    def __init__(self, arrays, transform=None):\n",
    "\n",
    "        # Convert to a tuple of tensors\n",
    "        self.tensors = (torch.Tensor(arrays[0]), torch.LongTensor(arrays[1]))\n",
    "        self.transform = transform\n",
    "\n",
    "        # Triggered if the tensors format is incorrect\n",
    "        assert all(self.tensors[0].size(0) == tensor.size(0) for tensor in self.tensors)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        x = self.tensors[0][index]\n",
    "\n",
    "        if self.transform:\n",
    "            x = self.transform(x)\n",
    "\n",
    "        y = self.tensors[1][index]\n",
    "\n",
    "        return x.to(device), y.to(device)\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.tensors[0].size(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "unlknAi_R4Vr"
   },
   "outputs": [],
   "source": [
    "class TestDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"TensorDataset without transforms.\"\"\"\n",
    "\n",
    "    def __init__(self, arrays):\n",
    "\n",
    "        # Convert to a tuple of tensors\n",
    "        self.tensors = (torch.Tensor(arrays[0]), torch.LongTensor(arrays[1]))\n",
    "\n",
    "        # Triggered if the tensors format is incorrect\n",
    "        assert all(self.tensors[0].size(0) == tensor.size(0) for tensor in self.tensors)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        x = self.tensors[0][index]\n",
    "        y = self.tensors[1][index]\n",
    "\n",
    "        return x.to(device), y.to(device)\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.tensors[0].size(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "ryHsUg1bR7uU"
   },
   "outputs": [],
   "source": [
    "train_dataset = TrainDataset((X_train, y_train), transforms_pipe)\n",
    "test_dataset = TestDataset((X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EjkfurXaR_-y"
   },
   "source": [
    "### Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "ifheeNRFR_ia"
   },
   "outputs": [],
   "source": [
    " BATCH_SIZE = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "hQaIgI_-SEAl"
   },
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "    train_dataset, \n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    test_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "forKVK-uSH7I"
   },
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qIEuHAnxr6Sp"
   },
   "source": [
    "### Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "Dquf8DVBSHfR"
   },
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "\n",
    "        # Features detector\n",
    "        self.features1 = nn.Sequential(\n",
    "            \n",
    "            # Hidden layer 1\n",
    "            nn.Conv2d(3, 16, kernel_size=3), nn.ReLU(),\n",
    "            nn.BatchNorm2d(16),\n",
    "\n",
    "            nn.MaxPool2d((2, 2)),\n",
    "            nn.Dropout(0.35)\n",
    "        )\n",
    "\n",
    "        # Features detector\n",
    "        self.features2 = nn.Sequential(\n",
    "            \n",
    "            # Hidden layer 3\n",
    "            nn.Conv2d(16, 32, kernel_size=3), nn.ReLU(),\n",
    "            nn.BatchNorm2d(32),\n",
    "\n",
    "            nn.MaxPool2d((2, 2)),\n",
    "            nn.Dropout(0.35)\n",
    "        )\n",
    "\n",
    "        # Features detector\n",
    "        self.features3 = nn.Sequential(\n",
    "            \n",
    "            # Hidden layer 3\n",
    "            nn.Conv2d(32, 64, kernel_size=3), nn.ReLU(),\n",
    "            nn.BatchNorm2d(64),\n",
    "\n",
    "            nn.MaxPool2d((2, 2)),\n",
    "            nn.Dropout(0.35)\n",
    "        )\n",
    "\n",
    "        # Classifier\n",
    "        self.classifier = nn.Sequential(\n",
    "            \n",
    "            nn.Linear(64*13*13, 512), nn.ReLU(),\n",
    "            nn.BatchNorm1d(512),\n",
    "            nn.Dropout(0.50),\n",
    "\n",
    "            nn.Linear(512, 128), nn.ReLU(),\n",
    "\n",
    "            # Output layer\n",
    "            nn.Linear(128, OUT_SIZE)\n",
    "        )\n",
    "\n",
    "    def forward(self, X):\n",
    "\n",
    "        # Features\n",
    "        X = self.features1(X)\n",
    "        X = self.features2(X)\n",
    "        X = self.features3(X)\n",
    "\n",
    "        # print(X.shape)\n",
    "\n",
    "        # Classifier\n",
    "        X = X.view(-1, 64*13*13)\n",
    "        #X = X.view(X.size(0), -1)\n",
    "        X = self.classifier(X)\n",
    "\n",
    "        return F.log_softmax(X, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lrwrrkFQSf0Z",
    "outputId": "030534d7-7f1b-47cf-a59d-1c8a75f90e7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5629574"
      ]
     },
     "execution_count": 143,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = CNN()\n",
    "model.to(device)\n",
    "\n",
    "# Number of parameters\n",
    "sum(p.numel() for p in model.parameters() if p.requires_grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ql3GY0cdr78r"
   },
   "source": [
    "### Loss and optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "id": "bKIowo_esGqJ"
   },
   "outputs": [],
   "source": [
    "criterion = nn.NLLLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.00005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L9shChbmsTdM"
   },
   "source": [
    "### Train/test engines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "id": "0raBmk5bsVjk"
   },
   "outputs": [],
   "source": [
    "from ignite.engine import Engine\n",
    "from ignite.metrics import Accuracy, Loss\n",
    "\n",
    "def train_step(engine, batch):\n",
    "\n",
    "    x, y = batch\n",
    "\n",
    "    # Pass a batch\n",
    "    model.train()\n",
    "    y_pred = model(x)\n",
    "    loss = criterion(y_pred, y)\n",
    "\n",
    "    # Learn by backpropagation\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    return loss\n",
    "\n",
    "def validation_step(engine, batch):\n",
    "    model.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        x, y = batch[0], batch[1]\n",
    "        y_pred = model(x)\n",
    "\n",
    "        return y_pred, y\n",
    "\n",
    "# Define a trainer engine\n",
    "trainer = Engine(train_step)\n",
    "\n",
    "# Define an evaluator engine\n",
    "evaluator = Engine(validation_step)\n",
    "\n",
    "val_metrics = {\n",
    "  \"accuracy\": Accuracy(),\n",
    "  \"loss\": Loss(criterion)\n",
    "}\n",
    "\n",
    "# Attach metrics to the evaluator\n",
    "for name, metric in val_metrics.items():\n",
    "    metric.attach(evaluator, name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HXvjvk94tNBC"
   },
   "source": [
    "### Evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "id": "I0UPE2QLtOoj"
   },
   "outputs": [],
   "source": [
    "from ignite.metrics import Accuracy, Loss\n",
    "from ignite.engine import create_supervised_evaluator\n",
    "from ignite.contrib.handlers import ProgressBar\n",
    "from ignite.engine import Events\n",
    "\n",
    "# Accuracy and loss metrics are defined\n",
    "val_metrics = {\n",
    "  \"accuracy\": Accuracy(),\n",
    "  \"loss\": Loss(criterion)\n",
    "}\n",
    "\n",
    "train_evaluator = create_supervised_evaluator(model, metrics=val_metrics, device=device)\n",
    "\n",
    "# Run train_evaluator on train_loader every trainer's epoch completed\n",
    "@trainer.on(Events.EPOCH_COMPLETED)\n",
    "def run_train_validation():\n",
    "    train_evaluator.run(train_loader)\n",
    "\n",
    "# Run evaluator on val_loader every trainer's epoch completed\n",
    "@trainer.on(Events.EPOCH_COMPLETED)\n",
    "def run_validation():\n",
    "    evaluator.run(test_loader)\n",
    "\n",
    "\n",
    "@evaluator.on(Events.COMPLETED)\n",
    "def log_validation_results():\n",
    "    metrics = evaluator.state.metrics\n",
    "    print(\"       Validation - Accuracy: {:.3f} Loss: {:.4f} \\n\"\n",
    "          .format(metrics[\"accuracy\"], metrics[\"loss\"]))\n",
    "    \n",
    "@train_evaluator.on(Events.COMPLETED)\n",
    "def log_train_results():\n",
    "    metrics = train_evaluator.state.metrics\n",
    "    print(\"Epoch: {} Training - Accuracy: {:.3f} Loss: {:.4f}\"\n",
    "          .format(trainer.state.epoch, metrics[\"accuracy\"], metrics[\"loss\"]))\n",
    "\n",
    "ProgressBar().attach(trainer, output_transform=lambda x: {'batch loss': x})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N2AnmSrWwEd3"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 459,
     "referenced_widgets": [
      "454b038fed404ee5a0c06426e0d782af",
      "42a3eb6557f943db95c999f2822f4a09",
      "451ce9b476d240baab05f35c7b7a428a",
      "20c1a8cdc2de42279a990a44a0e74a6a",
      "1ae64910c86b4e0caf32e0b1f0cf2b12",
      "4af530932af445238e6c8d330bf61195",
      "acfcedc1e928449993a40e121c6491f9",
      "d9cde46b0000436b86fb631505ca859b",
      "20ec7dee5dc94400b34bcded839cc9df",
      "14eed110f61249d7974c72afa82c1be3",
      "75e6fb1cadaf4813bc46447b0a70268e",
      "a7cfb8fec1e74e018b2c8460fcff616a",
      "ef2ae6d1ca2941ffa0d1ec7bd93dfb6c",
      "e8aed2f7245e4aa3974032cbe1c11f14",
      "80ed6b568f03431b8d0c39fc2090c2e3",
      "08d7492e1de647fa95c067bec40fd006",
      "857e9ea7bca24dd0bf45c9e9c00094e3",
      "34164a873cf04cffa540042e120f44ce",
      "e425b74aa8b34da7bb5135cc027e1781",
      "ca61e7873de5425b8792c0f53cfda183",
      "2590a729f3d74f698048705b1ec2fb59",
      "a8b4c4f8386c48818f2ff7bd9b4f475b",
      "40b875d9293a43269bbbb0b4389fb3e2",
      "42cc80cc4329406ea3d79bef87d0849a",
      "9688d222bbba47bb98b1f873fe54e941",
      "b4db2490fd0d432988f448580d812891",
      "050961b69a1647a2873d8053a22b4c5f",
      "1c549515388f49f5b15c1b4442150750",
      "f3cb70aa9ef747f5b9baae43b9de7e44",
      "55b50c1e98e84b6d97133ecd2b44f177",
      "373a4a1f743742c9b86d763cb6645308",
      "227d5d6e889846ca989f6303052a0cab",
      "e58675717e5b4002b9d46d52b19be373",
      "951d7ace388e42aebe9c95913ee7e5ca",
      "215627e991e04ebb928ce7c6e11b341b",
      "6745de25a43244909a636895043fbd15",
      "6f53b1718e684f4d98e44431b4e3a79b",
      "0eaa5f1e036d44559c6c1440d7a9dcda",
      "4cb88de237fa4c73a2e6710349692712",
      "a60432d52c444d798203ccb1b642bfca"
     ]
    },
    "id": "Anvg2wJuuTs3",
    "outputId": "51d213b5-3d19-4f43-d0e0-1261fd30c3ea"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "454b038fed404ee5a0c06426e0d782af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=95.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Training - Accuracy: 0.681 Loss: 0.8963\n",
      "       Validation - Accuracy: 0.636 Loss: 0.9755 \n",
      "\n",
      "\r"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20ec7dee5dc94400b34bcded839cc9df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=95.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2 Training - Accuracy: 0.793 Loss: 0.6294\n",
      "       Validation - Accuracy: 0.747 Loss: 0.7136 \n",
      "\n",
      "\r"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "857e9ea7bca24dd0bf45c9e9c00094e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=95.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3 Training - Accuracy: 0.819 Loss: 0.5110\n",
      "       Validation - Accuracy: 0.783 Loss: 0.5856 \n",
      "\n",
      "\r"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9688d222bbba47bb98b1f873fe54e941",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=95.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4 Training - Accuracy: 0.838 Loss: 0.4373\n",
      "       Validation - Accuracy: 0.804 Loss: 0.5062 \n",
      "\n",
      "\r"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e58675717e5b4002b9d46d52b19be373",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=95.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5 Training - Accuracy: 0.882 Loss: 0.3253\n",
      "       Validation - Accuracy: 0.852 Loss: 0.3795 \n",
      "\n",
      "\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "State:\n",
       "\titeration: 475\n",
       "\tepoch: 5\n",
       "\tepoch_length: 95\n",
       "\tmax_epochs: 5\n",
       "\toutput: <class 'torch.Tensor'>\n",
       "\tbatch: <class 'list'>\n",
       "\tmetrics: <class 'dict'>\n",
       "\tdataloader: torch.utils.data.dataloader.DataLoader\n",
       "\tseed: <class 'NoneType'>\n",
       "\ttimes: <class 'dict'>"
      ]
     },
     "execution_count": 147,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.run(train_loader, max_epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HmgXb5Hb_qnv"
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "id": "R1oUbTQ-_s26"
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def get_all_preds(model, loader):\n",
    "    all_preds = torch.tensor([], device=device)\n",
    "\n",
    "    for images, labels in loader:\n",
    "        preds = model(images)\n",
    "\n",
    "        all_preds = torch.cat((all_preds, preds),dim=0)\n",
    "    return all_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "id": "25vXoP52B25Y"
   },
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    test_preds = get_all_preds(model, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "id": "-ERIxfNgB-Ag"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "prediction = test_preds.argmax(dim=1).to('cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WfJvpuOJJS_m"
   },
   "source": [
    "### Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 500
    },
    "id": "1FqIjDuYCBQA",
    "outputId": "8fcdc298-5cdd-4508-b76e-6821c1fb90eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d62891390>"
      ]
     },
     "execution_count": 151,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, prediction)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0uGas_XJJMt2"
   },
   "source": [
    "# Exportation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KxNC9C_VL6_y"
   },
   "source": [
    "This model has been trained on GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "id": "i7oxNsYeLH_n"
   },
   "outputs": [],
   "source": [
    "export_path = \"./model/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bEd6OmDEMR-A",
    "outputId": "01b864ad-3da1-4990-c9e1-57cff34ad17f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNN(\n",
       "  (features1): Sequential(\n",
       "    (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
       "    (4): Dropout(p=0.35, inplace=False)\n",
       "  )\n",
       "  (features2): Sequential(\n",
       "    (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
       "    (4): Dropout(p=0.35, inplace=False)\n",
       "  )\n",
       "  (features3): Sequential(\n",
       "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
       "    (4): Dropout(p=0.35, inplace=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=10816, out_features=512, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.5, inplace=False)\n",
       "    (4): Linear(in_features=512, out_features=128, bias=True)\n",
       "    (5): ReLU()\n",
       "    (6): Linear(in_features=128, out_features=6, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 153,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GPjhHpDnK4BR"
   },
   "source": [
    "### State dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "id": "5T5s5K0RK3ge"
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), export_path + 'state_dict.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2A4C8RKyK5hz"
   },
   "source": [
    "### Full model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "id": "oKsxfP9hK61r"
   },
   "outputs": [],
   "source": [
    "torch.save(model, export_path + 'full_model.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sVEi8VZWK7Gn"
   },
   "source": [
    "### Trace script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HQY_hUPoMAmC"
   },
   "source": [
    "Create a traced script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "id": "jSpkRK7eK816"
   },
   "outputs": [],
   "source": [
    "sample = torch.rand(1, 3, 120, 120).to(device)\n",
    "traced_script_module = torch.jit.trace(model, sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RM__dCYRMZpD"
   },
   "source": [
    "Test the script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "id": "1xXz8wTGMbR5"
   },
   "outputs": [],
   "source": [
    "y_pred_sample = traced_script_module(torch.ones(1, 3, 120, 120).to(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HE9GVvTlMhgI",
    "outputId": "83e9086a-caa3-4bda-a2d3-db16fe1c48ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0], device='cuda:0')"
      ]
     },
     "execution_count": 158,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_sample.argmax(dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "quVS0z1DMzX0"
   },
   "source": [
    "Save to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "id": "brUzOF68M03O"
   },
   "outputs": [],
   "source": [
    "traced_script_module.save(export_path + \"torchscript_model.pt\")"
   ]
  }
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