Final Project Filin.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Preparation"
      ],
      "metadata": {
        "id": "RIl5JeZDJjYH"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyRnPj1bD1po"
      },
      "source": [
        "\n",
        "## Import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CKiSOvexVqFL"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision.datasets import ImageFolder\n",
        "import torchvision.transforms.v2 as tt\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import gc\n",
        "import cv2\n",
        "from tqdm.notebook import tqdm\n",
        "from torchvision.utils import save_image\n",
        "from torchvision.utils import make_grid\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from tqdm import tqdm\n",
        "from pathlib import Path\n",
        "from PIL import Image\n",
        "%matplotlib inline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from IPython.display import clear_output\n",
        "import pandas as pd\n",
        "from torchvision import models\n",
        "from torch.nn import functional as F\n",
        "\n",
        "sns.set(style='darkgrid', font_scale=1.2)\n",
        "BATCH_SIZE = 32\n",
        "RESCALE_SIZE = 224"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zXvfTTwpD37P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d8931ed-82c9-49f4-91a6-5d1ec4353d45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA is available!  Training on GPU ...\n"
          ]
        }
      ],
      "source": [
        "train_on_gpu = torch.cuda.is_available()\n",
        "\n",
        "if not train_on_gpu:\n",
        "    print('CUDA is not available.  Training on CPU ...')\n",
        "    DEVICE = 'cpu'\n",
        "else:\n",
        "    print('CUDA is available!  Training on GPU ...')\n",
        "    DEVICE = 'cuda'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bkcRaKsJT6QQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "038fe4cf-532a-4b4b-a941-4ed7135070ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K6fnMKW1hgLF"
      },
      "outputs": [],
      "source": [
        "from torchvision.models import ResNet18_Weights, resnet18, efficientnet_b3, EfficientNet_B3_Weights, ResNet34_Weights, resnet34\n",
        "res_transform = ResNet18_Weights.IMAGENET1K_V1.transforms\n",
        "res_34_transform = ResNet34_Weights.IMAGENET1K_V1.transforms\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6qvQCDXIHKBP"
      },
      "outputs": [],
      "source": [
        "def evaluate(model, loader):\n",
        "    matched = 0\n",
        "    all = 0\n",
        "    model.eval()\n",
        "    for i, l in loader:\n",
        "        pred = model(i.to(DEVICE)).detach().to('cpu').argmax(dim=1)\n",
        "        matched += (pred == l).sum()\n",
        "        all += len(l)\n",
        "\n",
        "    return (matched/all).item()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tX7W_3yoURQ9"
      },
      "outputs": [],
      "source": [
        "def show_im(im):\n",
        "    im = im.detach().to('cpu').permute(1, 2, 0).numpy()\n",
        "    # print(im.shape)\n",
        "    plt.axis('off')\n",
        "    plt.imshow(im)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39LwXTyd7h1h"
      },
      "source": [
        "## Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Z_s3qxSUI_s"
      },
      "outputs": [],
      "source": [
        "\n",
        "!unzip -q \"/content/drive/MyDrive/celebA_train_500.zip\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!unzip -q \"/content/drive/MyDrive/celebA_ir.zip\"\n",
        "\n"
      ],
      "metadata": {
        "id": "IheCjVx6KI63"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KaU49xAMZ7N_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a23e4ba-04b2-41bf-f273-91fe30f058b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8544, 1878, 1589)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "\n",
        "anno = pd.read_csv('./celebA_train_500/celebA_anno.txt', sep=\" \", header=None, names=['name', 'label'])\n",
        "train_test_df = pd.read_csv('./celebA_train_500/celebA_train_split.txt', sep=\" \", header=None, names=['name', 'group'])\n",
        "train_test_df.groupby(by='group').count()\n",
        "train_df, val_df, test_df = train_test_df[train_test_df.group == 0], train_test_df[train_test_df.group == 1], train_test_df[train_test_df.group == 2]\n",
        "len(train_df), len(val_df), len(test_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i6jWtUDUVnFf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2f80f21-336f-4903-8ef4-d69da58fc459"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('celebA_train_500/celebA_imgs')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "\n",
        "path = Path('./celebA_train_500/celebA_imgs')\n",
        "path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jE7efKvkV9dD"
      },
      "outputs": [],
      "source": [
        "files = (list(path.rglob('*.jpg')))\n",
        "files_name = list(map(lambda x: str(x).split('/')[-1], files))\n",
        "df = pd.DataFrame({'name':files_name, 'file': files})\n",
        "data = pd.merge(df, anno, on='name')\n",
        "data\n",
        "\n",
        "\n",
        "train_data = pd.merge(data, train_df, on='name')\n",
        "test_data = pd.merge(data, test_df, on='name')\n",
        "val_data = pd.merge(data, val_df, on='name')\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYk6hmWT7nyf"
      },
      "source": [
        "##Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T8HRGM0NM0oQ"
      },
      "outputs": [],
      "source": [
        "class FaceDataset(Dataset):\n",
        "    \"\"\"Face Landmarks dataset.\"\"\"\n",
        "\n",
        "    def __init__(self, dataframe, mode='test', transform=None):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            csv_file (string): Path to the csv file with annotations.\n",
        "            root_dir (string): Directory with all the images.\n",
        "            transform (callable, optional): Optional transform to be applied\n",
        "                on a sample.\n",
        "        \"\"\"\n",
        "        self.data = dataframe\n",
        "        self.transform = transform\n",
        "\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    @classmethod\n",
        "    def load_sample(self, file):\n",
        "        image = Image.open(file)\n",
        "        image.load()\n",
        "        return image\n",
        "\n",
        "    @classmethod\n",
        "    def prepare_sample(self, image):\n",
        "        image = np.array(image)\n",
        "        image = Image.fromarray(image[77:-41, 45:-50])\n",
        "        # print(image.shape)\n",
        "        image = image.resize((RESCALE_SIZE, RESCALE_SIZE))\n",
        "\n",
        "        return image\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()\n",
        "        file_name = self.data.iloc[idx, 1]\n",
        "        label = self.data.iloc[idx, 2]\n",
        "        im = self.load_sample(file_name)\n",
        "        im = self.prepare_sample(im)\n",
        "        if self.transform:\n",
        "            im = self.transform(im)\n",
        "\n",
        "        # im = torch.FloatTensor(im).permute(2, 0, 1)\n",
        "\n",
        "        return im, label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rkBOfnl2xaDq"
      },
      "outputs": [],
      "source": [
        "# train_transform = tt.Compose([\n",
        "#     resTransform(),\n",
        "#     tt.RandomHorizontalFlip(p=0.5),\n",
        "# ])\n",
        "\n",
        "train_transform = tt.Compose([\n",
        "    # EfficientNet_B3_Weights.IMAGENET1K_V1.transforms(),\n",
        "    res_transform(),\n",
        "    tt.RandomHorizontalFlip(p=0.5),\n",
        "    # tt.RandomPerspective(distortion_scale=0.5, p=0.3,)\n",
        "])\n",
        "test_transform = res_transform()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MhlLpbr1OFPb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7287f9f-e56b-42cc-9784-ae9ffd1e916b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8544, 1878, 1589)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "\n",
        "train_dataset = FaceDataset(train_data, mode='train', transform=train_transform)\n",
        "val_dataset = FaceDataset(val_data, mode='val', transform=test_transform)\n",
        "test_dataset = FaceDataset(test_data, mode='test', transform=test_transform)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "len(train_data), len(val_data), len(test_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ie_vRnER-gQ3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "2743ebd2-0937-4bab-a20f-d5b6cde9c40e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for i in [1, 2, 3]:\n",
        "    # print(train_dataset[i][1])\n",
        "    plt.subplot(2, 3, i)\n",
        "    show_im(train_dataset[i][0])\n",
        "\n",
        "    plt.subplot(2, 3, i+3)\n",
        "    show_im(train_dataset[i][0]/train_dataset[i][0].abs().max())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WSrPGwSXM0dW"
      },
      "outputs": [],
      "source": [
        "\n",
        "sample = 0\n",
        "for i, v in train_loader:\n",
        "    sample = i\n",
        "    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rK_NejnTj3ks"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9jk7V4EEM0Xu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "de89d7e5-f9bd-4973-eda3-456f3901f029"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "show_im(sample[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4BHXo8KybY8M"
      },
      "outputs": [],
      "source": [
        "from torchvision.models._api import WeightsEnum\n",
        "from torch.hub import load_state_dict_from_url\n",
        "\n",
        "def get_state_dict(self, *args, **kwargs):\n",
        "    kwargs.pop(\"check_hash\")\n",
        "    return load_state_dict_from_url(self.url, *args, **kwargs)\n",
        "WeightsEnum.get_state_dict = get_state_dict"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4kybRd_kxWh"
      },
      "source": [
        "##utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ezWzVFKGi9Vi"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XYQfHoXCOvPz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clear_cuda():\n",
        "  torch.cuda.empty_cache()\n",
        "  torch.cuda.memory_summary(device=None, abbreviated=False)\n",
        "  # del unet_model\n",
        "\n",
        "  # del variables\n",
        "  gc.collect()"
      ],
      "metadata": {
        "id": "tJ1WF9RYOvhm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f_Klhiumi9nI"
      },
      "outputs": [],
      "source": [
        "class IdBlock(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "  def forward(self, x):\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hDXCO4d_k0ok"
      },
      "outputs": [],
      "source": [
        "def update_loader(base_transform, pre_transform):\n",
        "  train_transform = tt.Compose([\n",
        "      # EfficientNet_B3_Weights.IMAGENET1K_V1.transforms(),\n",
        "      base_transform,\n",
        "      *pre_transform\n",
        "  ])\n",
        "  test_transform = base_transform\n",
        "\n",
        "  train_dataset = FaceDataset(train_data, mode='train', transform=train_transform)\n",
        "  val_dataset = FaceDataset(val_data, mode='val', transform=test_transform)\n",
        "  test_dataset = FaceDataset(test_data, mode='test', transform=test_transform)\n",
        "\n",
        "  train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "  val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "  test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "\n",
        "  return train_loader, val_loader, test_loader\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6nw11vYyOuqZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e2mk7MNtcUhJ"
      },
      "outputs": [],
      "source": [
        "def fit_epoch(model, train_loader, criterion, optimizer):\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    processed_data = 0\n",
        "    model.to(DEVICE)\n",
        "    for inputs, labels in train_loader:\n",
        "        inputs = inputs.to(DEVICE)\n",
        "        labels = labels.to(DEVICE)\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        preds = torch.argmax(outputs, 1)\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "        processed_data += inputs.size(0)\n",
        "\n",
        "    train_loss = running_loss / processed_data\n",
        "    train_acc = running_corrects.cpu().numpy() / processed_data\n",
        "    return train_loss, train_acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w_CD9--hcUjs"
      },
      "outputs": [],
      "source": [
        "def eval_epoch(model, val_loader, criterion):\n",
        "    model.eval()\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    processed_size = 0\n",
        "\n",
        "    for inputs, labels in val_loader:\n",
        "        inputs = inputs.to(DEVICE)\n",
        "        labels = labels.to(DEVICE)\n",
        "\n",
        "        with torch.set_grad_enabled(False):\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            preds = torch.argmax(outputs, 1)\n",
        "\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "        processed_size += inputs.size(0)\n",
        "    val_loss = running_loss / processed_size\n",
        "    val_acc = running_corrects.cpu().numpy() / processed_size\n",
        "    return val_loss, val_acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "joq76hcO902k"
      },
      "outputs": [],
      "source": [
        "def model_device(model_):\n",
        "    return next(model_.parameters()).device\n",
        "\n",
        "def freeze_layers(model_, numm=0, optimizer = torch.optim.Adam, lr1=1e-4, lr2=1e-5):\n",
        "    req_g = False\n",
        "    threshold = len(list(model_.features.parameters()))\n",
        "    print(f'{threshold=}')\n",
        "    for k, (name, module) in enumerate(model_.named_children()):\n",
        "        if name in ['classifier', 'avgpool']:\n",
        "            req_g = True\n",
        "        else:\n",
        "            req_g = False\n",
        "        c = 0\n",
        "        for k, i in enumerate(list(module.parameters())):\n",
        "            i.requires_grad = (k >= threshold-numm) or req_g\n",
        "            c += int(i.requires_grad)\n",
        "    print(count_graded(model_))\n",
        "    ret = {}\n",
        "    ret['model'] = model_\n",
        "    ret['optimizer'] = update_optimizer(model_, numm, optimizer, lr1, lr2)\n",
        "    return ret.values()\n",
        "\n",
        "def count_graded(model_):\n",
        "    d = {}\n",
        "    for name, mod in model_.named_children():\n",
        "        d[name] = {True:0, False:0}\n",
        "        for i in mod.parameters():\n",
        "            d[name][i.requires_grad] += 1\n",
        "    return d\n",
        "\n",
        "def update_optimizer(model_, num, optimizer, lr1, lr2):\n",
        "    return optimizer(\n",
        "        params=[\n",
        "            {\"params\": model_.classifier.parameters(), 'lr': lr1},\n",
        "            {\"params\": list(model_.features.parameters())[-num:], \"lr\": lr2,}\n",
        "        ])\n",
        "\n",
        "def train_w_freezed(model, train_datasset=train_dataset, val_dataset=val_dataset, unfreezed_nm=1, lr_classifier=1e-4, lr_feature=1e-4, loss_fn=nn.CrossEntropyLoss, epochs=5, batch_size=64):\n",
        "  train_w_freezed.called_times += 1\n",
        "  model.to(DEVICE)\n",
        "  model, optimizer = freeze_layers(model, numm = unfreezed_nm, lr1=lr_classifier, lr2=lr_feature)\n",
        "  history = train(train_dataset, val_dataset, model=model, epochs=epochs, batch_size=batch_size, loss_fn=loss_fn, opt=optimizer)\n",
        "  # torch.save(model, f'/content/gdrive/MyDrive/finefn{train_w_freezed.called_times}.pth')\n",
        "  return history\n",
        "\n",
        "train_w_freezed.called_times = 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ulT1yFGrCM3M"
      },
      "outputs": [],
      "source": [
        "def fit_epoch_arcane(model, train_loader, criterion, optimizer):\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    processed_data = 0\n",
        "    model.to(DEVICE)\n",
        "    # print(f\"{train_loader}\")\n",
        "    for inputs, labels in train_loader:\n",
        "        inputs = inputs.to(DEVICE)\n",
        "        labels = labels.to(DEVICE)\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        embed = model(inputs)\n",
        "        loss, outputs = criterion(embed, labels)\n",
        "        # print(loss.shape)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        preds = torch.argmax(outputs, 1)\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "        processed_data += inputs.size(0)\n",
        "\n",
        "    train_loss = running_loss / processed_data\n",
        "    train_acc = running_corrects.cpu().numpy() / processed_data\n",
        "    return train_loss, train_acc\n",
        "\n",
        "def eval_epoch_arcane(model, val_loader, criterion):\n",
        "    model.eval()\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    processed_size = 0\n",
        "\n",
        "    for inputs, labels in val_loader:\n",
        "        inputs = inputs.to(DEVICE)\n",
        "        labels = labels.to(DEVICE)\n",
        "\n",
        "        with torch.set_grad_enabled(False):\n",
        "            embed = model(inputs)\n",
        "            loss, outputs = criterion(embed, labels)\n",
        "            preds = torch.argmax(outputs, 1)\n",
        "\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "        processed_size += inputs.size(0)\n",
        "    val_loss = running_loss / processed_size\n",
        "    val_acc = running_corrects.cpu().numpy() / processed_size\n",
        "    return val_loss, val_acc\n",
        "\n",
        "def train_arcane(train_loader, val_loader, model, epochs, batch_size, opt, lr1=1e-4, lr2=1e-4, criterion = nn.CrossEntropyLoss()):\n",
        "    model.to(DEVICE)\n",
        "    criterion.to(DEVICE)\n",
        "    history = []\n",
        "    log_template = \"\\nEpoch {ep:03d} train_loss: {t_loss:0.4f} \\\n",
        "    val_loss {v_loss:0.4f} train_acc {t_acc:0.4f} val_acc {v_acc:0.4f}\"\n",
        "\n",
        "\n",
        "    with tqdm(desc=\"epoch\", total=epochs) as pbar_outer:\n",
        "\n",
        "\n",
        "        for epoch in range(epochs):\n",
        "            train_loss, train_acc = fit_epoch_arcane(model, train_loader, criterion, opt)\n",
        "\n",
        "            val_loss, val_acc = eval_epoch_arcane(model, val_loader, criterion)\n",
        "            history.append((train_loss, train_acc, val_loss, val_acc))\n",
        "\n",
        "            pbar_outer.update(1)\n",
        "            tqdm.write(log_template.format(ep=epoch+1, t_loss=train_loss,\\\n",
        "                                           v_loss=val_loss, t_acc=train_acc, v_acc=val_acc))\n",
        "\n",
        "    return history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BlPmF6mfi6_q"
      },
      "outputs": [],
      "source": [
        "def update_opt_resnet(model, opt, num, lr):\n",
        "    params = []\n",
        "    for name, i in list(model.named_children())[-num:]:\n",
        "        # print(name)\n",
        "        params.append({'params': i.parameters(), 'lr': lr})\n",
        "    # print(params)\n",
        "    return opt(params)\n",
        "\n",
        "def freeze_blocks(model, num, blocks, opt=torch.optim.Adam, lr=1e-4):\n",
        "    req = False\n",
        "    for name, module in model.named_children():\n",
        "        if name == blocks[-num]:\n",
        "            req = True\n",
        "        for param in module.parameters():\n",
        "            param.requires_grad = req\n",
        "        # print(name)\n",
        "    return update_opt_resnet(model, opt, num, lr)\n",
        "\n",
        "# opt = freeze_blocks(modelResNet, 3, blocks)\n",
        "# count_graded(modelResNet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uC07qfTc500M"
      },
      "outputs": [],
      "source": [
        "def train(train_loader, val_loader, model, epochs, batch_size, opt, lr1=1e-4, lr2=1e-4, criterion = nn.CrossEntropyLoss()):\n",
        "    model.to(DEVICE)\n",
        "    criterion.to(DEVICE)\n",
        "    history = []\n",
        "    log_template = \"\\nEpoch {ep:03d} train_loss: {t_loss:0.4f} \\\n",
        "    val_loss {v_loss:0.4f} train_acc {t_acc:0.4f} val_acc {v_acc:0.4f}\"\n",
        "\n",
        "\n",
        "    with tqdm(desc=\"epoch\", total=epochs) as pbar_outer:\n",
        "\n",
        "\n",
        "        for epoch in range(epochs):\n",
        "            train_loss, train_acc = fit_epoch(model, train_loader, criterion, opt)\n",
        "            print(\"loss\", train_loss)\n",
        "\n",
        "            val_loss, val_acc = eval_epoch(model, val_loader, criterion)\n",
        "            history.append((train_loss, train_acc, val_loss, val_acc))\n",
        "\n",
        "            pbar_outer.update(1)\n",
        "            tqdm.write(log_template.format(ep=epoch+1, t_loss=train_loss,\\\n",
        "                                           v_loss=val_loss, t_acc=train_acc, v_acc=val_acc))\n",
        "\n",
        "    return history\n",
        "\n",
        "def train_arcane(train_loader, val_loader, model, epochs, batch_size, opt, lr1=1e-4, lr2=1e-4, criterion = nn.CrossEntropyLoss()):\n",
        "    model.to(DEVICE)\n",
        "    criterion.to(DEVICE)\n",
        "    history = []\n",
        "    log_template = \"\\nEpoch {ep:03d} train_loss: {t_loss:0.4f} \\\n",
        "    val_loss {v_loss:0.4f} train_acc {t_acc:0.4f} val_acc {v_acc:0.4f}\"\n",
        "\n",
        "\n",
        "    with tqdm(desc=\"epoch\", total=epochs) as pbar_outer:\n",
        "\n",
        "\n",
        "        for epoch in range(epochs):\n",
        "            train_loss, train_acc = fit_epoch_arcane(model, train_loader, criterion, opt)\n",
        "\n",
        "            val_loss, val_acc = eval_epoch_arcane(model, val_loader, criterion)\n",
        "            history.append((train_loss, train_acc, val_loss, val_acc))\n",
        "\n",
        "            pbar_outer.update(1)\n",
        "            tqdm.write(log_template.format(ep=epoch+1, t_loss=train_loss,\\\n",
        "                                           v_loss=val_loss, t_acc=train_acc, v_acc=val_acc))\n",
        "\n",
        "    return history"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yn3KxX6FlwP-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYTY43YjlwtD"
      },
      "source": [
        "# Resnet -- получилось"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Сначала тренируем resnet на обычный CELoss, получаем accuracy 72%"
      ],
      "metadata": {
        "id": "Wevga6v-l0cr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "87LgM6WMlwtE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bd88dc4-86d5-4d95-99eb-ec04ed61fd21"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:135: UserWarning: Using 'weights' as positional parameter(s) is deprecated since 0.13 and may be removed in the future. Please use keyword parameter(s) instead.\n",
            "  warnings.warn(\n",
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
            "100%|██████████| 44.7M/44.7M [00:00<00:00, 316MB/s]\n"
          ]
        }
      ],
      "source": [
        "modelResNet1 = resnet18(ResNet18_Weights.IMAGENET1K_V1)\n",
        "modelResNet1;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EJLXzyVUlwtE"
      },
      "outputs": [],
      "source": [
        "class IdBlock(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "  def forward(self, x):\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_aVsFoPdlwtE"
      },
      "outputs": [],
      "source": [
        "# modelResNet = resnet18(ResNet18_Weights.IMAGENET1K_V1)\n",
        "modelResNet = torch.load('/content/drive/MyDrive/DLS project/modelResnet.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v2G9eGitlwtE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58eab0a3-d7fc-4e0d-dd52-f2bb0748cb1f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7293895483016968"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "modelResNet.eval()\n",
        "evaluate(modelResNet, test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M1BJHMBPlwtE"
      },
      "outputs": [],
      "source": [
        "blocks = [i[0] for i in modelResNet.named_children()]\n",
        "blocks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cairEQWAlwtE"
      },
      "outputs": [],
      "source": [
        "modelResNet.fc = nn.Sequential(\n",
        "    nn.Linear(512, 512)\n",
        "    , nn.ReLU()\n",
        "    , nn.Linear(512, 500)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xoy2wN7tlwtF"
      },
      "outputs": [],
      "source": [
        "def fit_epoch(model, train_loader, criterion, optimizer):\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    processed_data = 0\n",
        "    model.train()\n",
        "\n",
        "    for inputs, labels in train_loader:\n",
        "        inputs = inputs.to(DEVICE)\n",
        "        labels = labels.to(DEVICE)\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        preds = torch.argmax(outputs, 1)\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "        processed_data += inputs.size(0)\n",
        "\n",
        "    train_loss = running_loss / processed_data\n",
        "    train_acc = running_corrects.cpu().numpy() / processed_data\n",
        "    return train_loss, train_acc\n",
        "\n",
        "def eval_epoch(model, val_loader, criterion):\n",
        "    model.eval()\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    processed_size = 0\n",
        "\n",
        "    for inputs, labels in val_loader:\n",
        "        inputs = inputs.to(DEVICE)\n",
        "        labels = labels.to(DEVICE)\n",
        "\n",
        "        with torch.set_grad_enabled(False):\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            preds = torch.argmax(outputs, 1)\n",
        "\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "        processed_size += inputs.size(0)\n",
        "    val_loss = running_loss / processed_size\n",
        "    val_acc = running_corrects.double().cpu().numpy() / processed_size\n",
        "    return val_loss, val_acc\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1wMYlGg_lwtF"
      },
      "outputs": [],
      "source": [
        "# opt = torch.optim.Adam(\n",
        "#         params=[\n",
        "#             {\"params\": modelResNet.layer4.parameters(), 'lr': 1e-4},\n",
        "#             {\"params\": modelResNet.fc.parameters(), \"lr\": 1e-4}\n",
        "#         ])\n",
        "hist = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c3fJkoOKlwtF"
      },
      "outputs": [],
      "source": [
        "loss, acc, val_loss, val_acc = zip(*hist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5djPFe7KlwtF"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(15, 9))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(loss, label=\"train_loss\")\n",
        "plt.plot(val_loss, label=\"val_loss\")\n",
        "plt.legend(loc='best')\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"loss\")\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(acc, label=\"train_acc\")\n",
        "plt.plot(val_acc, label=\"val_acc\")\n",
        "plt.legend(loc='best')\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GOlc0grklwtF"
      },
      "outputs": [],
      "source": [
        "print(count_graded(modelResNet))\n",
        "modelResNet.eval()\n",
        "print(evaluate(modelResNet, test_loader))\n",
        "print(evaluate(modelResNet, train_loader))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K_Pwj8kblwtF"
      },
      "outputs": [],
      "source": [
        "opt1 = freeze_blocks(modelResNet, 4, blocks)\n",
        "\n",
        "count_graded(modelResNet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "payHkLUslwtF"
      },
      "outputs": [],
      "source": [
        "hist1 = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ebzw8OoslwtF"
      },
      "outputs": [],
      "source": [
        "opt2 = freeze_blocks(modelResNet, 7, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist2 = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8y7Ph75klwtF"
      },
      "outputs": [],
      "source": [
        "opt3 = freeze_blocks(modelResNet, 8, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist3 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FZfozQKqlwtG"
      },
      "outputs": [],
      "source": [
        "opt4 = freeze_blocks(modelResNet, 9, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist4 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j9JBoiJ8lwtG"
      },
      "outputs": [],
      "source": [
        "opt5 = freeze_blocks(modelResNet, 9, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist5 = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g8PwM2QAlwtG"
      },
      "outputs": [],
      "source": [
        "train_transform = tt.Compose([\n",
        "    # EfficientNet_B3_Weights.IMAGENET1K_V1.transforms(),\n",
        "    res_transform(),\n",
        "    tt.RandomHorizontalFlip(p=0.5),\n",
        "    # tt.RandomPerspective(distortion_scale=0.5, p=0.3,)\n",
        "])\n",
        "test_transform = res_transform()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-IBxWwyElwtG"
      },
      "outputs": [],
      "source": [
        "\n",
        "train_dataset = FaceDataset(train_data, mode='train', transform=train_transform)\n",
        "val_dataset = FaceDataset(val_data, mode='val', transform=test_transform)\n",
        "test_dataset = FaceDataset(test_data, mode='test', transform=test_transform)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aOTOvK0klwtG"
      },
      "outputs": [],
      "source": [
        "opt6 = freeze_blocks(modelResNet, 9, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist6 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "34uV6RxJlwtG"
      },
      "outputs": [],
      "source": [
        "hist7 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TuPJ2Si6lwtG"
      },
      "outputs": [],
      "source": [
        "opt6 = freeze_blocks(modelResNet, 9, blocks, lr=1e-5)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist6 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qqvm5VWblwtG"
      },
      "outputs": [],
      "source": [
        "opt6 = freeze_blocks(modelResNet, 8, blocks, lr=1e-5)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist6 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt6)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# modelResNet = torch.load(\"./modelResnet.pth\")"
      ],
      "metadata": {
        "id": "9SgspC4rlwtG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##accuracy 72"
      ],
      "metadata": {
        "id": "3wGc_ctiw8_n"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ToaN-HxjlwtH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10ea85e6-90cc-4b3f-ca05-8394258f3481"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7293895483016968"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "evaluate(modelResNet, test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YSeXXNP9lwtH"
      },
      "outputs": [],
      "source": [
        "torch.save(modelResNet, \"/content/drive/MyDrive/DLS project/modelResnet.pth\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBgtXsSm8Etk"
      },
      "source": [
        "#ArcFaceLoss -- получилось"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Теперь обучаем resnet34 на ArcFaceLoss"
      ],
      "metadata": {
        "id": "m1EZ9-JZzcGe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PChN2_OUHaSu"
      },
      "outputs": [],
      "source": [
        "modelResNet1 = resnet34(ResNet34_Weights.IMAGENET1K_V1).to(DEVICE)\n",
        "modelResNet1;\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9XvKltx1HaS4"
      },
      "outputs": [],
      "source": [
        "class IdBlock(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "  def forward(self, x):\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TNBZkl5aGug1"
      },
      "outputs": [],
      "source": [
        "modelResNet1.fc = IdBlock()\n",
        "modelResNet1(sample).shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BiLix7bHEj4a"
      },
      "outputs": [],
      "source": [
        "x = torch.randn(size=(4, 32))\n",
        "nx = torch.norm(x, dim=(1))\n",
        "print(nx.shape, x.shape)\n",
        "xn = x/nx[:, None]\n",
        "print(torch.norm(x, dim=(1)))\n",
        "print(xn.norm(dim=(1)))\n",
        "print(xn[0].norm())\n",
        "l1 = nn.Linear(32, 16, bias=False)\n",
        "W = 0\n",
        "for i in l1.parameters():\n",
        "\n",
        "  W = i\n",
        "W.shape\n",
        "l1(x).shape\n",
        "print(l1)\n",
        "nw = torch.norm(W, dim=1)\n",
        "(W@x.T).shape\n",
        "\n",
        "wn = (W/nw[:, None])\n",
        "wn[0].shape, wn[0].norm()\n",
        "xn[0].shape, xn[0].norm()\n",
        "cos_theta = (xn[0]*wn[0]).sum()\n",
        "cosineses = (xn@wn.T)\n",
        "cosineses[0, 0]\n",
        "xx = x[0]\n",
        "ww = W[0]\n",
        "(xx*ww).sum()/xx.norm()/ww.norm()\n",
        "sinuses = torch.sqrt(1 - torch.pow(cosineses, 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D_Clb16IJhTx"
      },
      "outputs": [],
      "source": [
        "class ArcFaceLoss(nn.Module):\n",
        "    def __init__(self, num_classes, embedding_size, m, scale):\n",
        "        super().__init__()\n",
        "        self.num_classes = num_classes\n",
        "        self.embedding_size = embedding_size\n",
        "        self.m = m\n",
        "        self.scale = scale\n",
        "        self.cosm = np.cos(m)\n",
        "        self.sinm = np.sin(m)\n",
        "        self.W = torch.nn.Parameter(torch.Tensor(embedding_size, num_classes))\n",
        "        nn.init.xavier_normal_(self.W)\n",
        "\n",
        "    def forward(self, embeddings, labels):\n",
        "        cosine = self.fc(embeddings)\n",
        "        mask = self.onehot(labels)\n",
        "        cosine_of_target_classes = cosine[mask == 1]\n",
        "        modified_cosine_of_target_classes = self.transform_true(\n",
        "            cosine_of_target_classes\n",
        "        )\n",
        "        diff = (modified_cosine_of_target_classes - cosine_of_target_classes).unsqueeze(1)\n",
        "        logits = cosine + (mask * diff)\n",
        "        logits = self.scale_logits(logits)\n",
        "        return nn.CrossEntropyLoss()(logits, labels), logits\n",
        "\n",
        "    def fc(self, embeddings):\n",
        "\n",
        "        cosine = F.linear(F.normalize(embeddings), F.normalize(self.W.T))\n",
        "        return cosine\n",
        "\n",
        "    def onehot(self, labels):\n",
        "\n",
        "        batch_size = labels.size(0)\n",
        "        onehot_ = torch.zeros(batch_size, self.num_classes, device=DEVICE)\n",
        "        onehot_.scatter_(1, labels.unsqueeze(-1), 1)\n",
        "        return onehot_\n",
        "\n",
        "    def transform_true(self, cosineses):\n",
        "\n",
        "        eps = 1e-6\n",
        "        sinuses = 1.0 - cosineses**2\n",
        "        return cosineses*self.cosm - sinuses*self.sinm\n",
        "\n",
        "    def scale_logits(self, logits):\n",
        "\n",
        "        return logits * self.scale\n",
        "\n",
        "class ArcModel(nn.Module):\n",
        "  def __init__(self, model, arcloss):\n",
        "        super().__init__()\n",
        "        self.model = model\n",
        "        self.loss = arcloss\n",
        "\n",
        "        self.num_of_params = len(list(model.parameters()))\n",
        "\n",
        "  def forward(self, x):\n",
        "        emb12 = self.model(sample)\n",
        "        cos12 = self.loss.fc(emb12)\n",
        "        logits12 = self.loss.scale_logits(cos12)\n",
        "        probs = nn.Softmax(dim=1)(logits12)\n",
        "        return probs\n",
        "  def freeze_layers(self, ratio, opt, lr=1e-4, lr_loss=1e-5):\n",
        "        tr = int(ratio*self.num_of_params)\n",
        "        params = []\n",
        "        cur = 0\n",
        "        req = False\n",
        "\n",
        "        for i in self.model.parameters():\n",
        "            if cur >= tr:\n",
        "                i.requires_grad = True\n",
        "                params.append({'params':i, 'lr':lr})\n",
        "            else:\n",
        "                i.requires_grad = False\n",
        "            cur += 1\n",
        "        params.append({'params':self.loss.parameters(), 'lr':lr_loss})\n",
        "        # print(params)\n",
        "        return opt(params=params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u6uI7t-SUjDU"
      },
      "outputs": [],
      "source": [
        "# class ArcFaceLossSoftMax(nn.Module):\n",
        "#   def __init__(self, m:float, s:float)->None:\n",
        "#     super().__init__()\n",
        "\n",
        "#     self.m = m\n",
        "#     self.s = s\n",
        "\n",
        "#     self.cosm = np.cos(m)\n",
        "#     self.sinm = np.sin(m)\n",
        "\n",
        "#   def forward(self, cosineses):\n",
        "#     sinuses = torch.sqrt(1 - torch.pow(cosineses, 2))\n",
        "#     cos_tm  = cosineses*self.cosm - sinuses*self.sinm\n",
        "\n",
        "\n",
        "#     expon = torch.exp(self.s*cosineses)\n",
        "\n",
        "#     expon_pm = torch.exp(self.s*cos_tm)\n",
        "\n",
        "#     sum_exp = expon.sum(dim=1)\n",
        "#     sum_exp_exc = sum_exp[:, None]-expon\n",
        "\n",
        "#     SM = expon_pm/(expon_pm + sum_exp_exc)\n",
        "\n",
        "#     return SM\n",
        "\n",
        "# class ArcFaceLossFC(nn.Module):\n",
        "#   def __init__(self, in_, out_)->None:\n",
        "#     super().__init__()\n",
        "\n",
        "#     self.W = nn.Parameter(torch.randn(size = (out_, in_)))\n",
        "\n",
        "\n",
        "#   def forward(self, x):\n",
        "#     nx = torch.norm(x, dim=1)\n",
        "#     xn = x/nx[:, None]\n",
        "\n",
        "#     nw = torch.norm(self.W, dim=1)\n",
        "#     wn = (self.W/nw[:, None])\n",
        "#     cosineses = (xn@wn.T)\n",
        "#     return cosineses\n",
        "\n",
        "# class ArcFaceLossEntropy(nn.Module):\n",
        "#   def __init__(self, num_classes):\n",
        "#     super().__init__()\n",
        "#     self.num_classes = num_classes\n",
        "\n",
        "#   def forward(self, inp, target):\n",
        "#     one_hotted = F.one_hot(target, num_classes=self.num_classes)\n",
        "#     correct = inp[one_hotted==1]\n",
        "#     l = -torch.sum(torch.log(correct))/len(target)\n",
        "\n",
        "#     return l\n",
        "\n",
        "# class ArcFaceLoss(nn.Module):\n",
        "#   def __init__(self, in_, num_classes, m, s):\n",
        "#     super().__init__()\n",
        "#     self.fc = ArcFaceLossFC(in_=in_, out_=num_classes)\n",
        "#     self.sm =ArcFaceLossSoftMax(m=m, s=s)\n",
        "#     # self.entropy = ArcFaceLossEntropy(num_classes=num_classes)\n",
        "#     self.entropy = ArcFaceLossEntropy(num_classes)\n",
        "\n",
        "#   def forward(self, x, target):\n",
        "#     inp = self.sm(self.fc(x))\n",
        "#     return self.entropy(inp, target)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ARC = torch.load(\"/content/drive/MyDrive/DLS project/Копия jointModel1\")\n"
      ],
      "metadata": {
        "id": "61mwKpvUqKK-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PbHTAia5ysPy"
      },
      "outputs": [],
      "source": [
        "model = modelResNet1\n",
        "arcloss = ArcFaceLoss(embedding_size=512, num_classes=500, m=0.01, scale=20)\n",
        "model.to(DEVICE);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wTU808L63lxx"
      },
      "outputs": [],
      "source": [
        "ARC = ArcModel(model, arcloss).to(DEVICE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NSal9-m5_vXF"
      },
      "outputs": [],
      "source": [
        "opt14 = ARC.freeze_layers(0.6, torch.optim.Adam, 1e-4)\n",
        "count_graded(ARC.model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PD2eWAZEAM9V"
      },
      "outputs": [],
      "source": [
        "count_graded(ARC.model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cY_Aja6by71q"
      },
      "outputs": [],
      "source": [
        "hist_myModel = train_arcane(train_loader, val_loader, ARC.model, epochs=5, batch_size=32, opt=opt14, criterion=ARC.loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mSgiDE-SEQ3U"
      },
      "outputs": [],
      "source": [
        "opt14 = ARC.freeze_layers(0.2, torch.optim.Adam, 1e-4)\n",
        "count_graded(ARC.model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "trOIiyveERLI"
      },
      "outputs": [],
      "source": [
        "hist_myModel = train_arcane(train_loader, val_loader, ARC.model, epochs=5, batch_size=32, opt=opt14, criterion=ARC.loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iEJxvIYDEYJo"
      },
      "outputs": [],
      "source": [
        "opt14 = ARC.freeze_layers(0.0, torch.optim.Adam, 1e-4, 1e-6)\n",
        "count_graded(ARC.model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dsOGcfoDEVX8"
      },
      "outputs": [],
      "source": [
        "hist_myModel = train_arcane(train_loader, val_loader, ARC.model, epochs=5, batch_size=32, opt=opt14, criterion=ARC.loss)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "opt14 = ARC.freeze_layers(0.0, torch.optim.Adam, 1e-4, 1e-7)\n",
        "hist_myModel1 = train_arcane(train_loader, val_loader, ARC.model, epochs=5, batch_size=32, opt=opt14, criterion=ARC.loss)"
      ],
      "metadata": {
        "id": "t0VmCARoNy4N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pIr3C8UvEVkS"
      },
      "outputs": [],
      "source": [
        "opt14 = ARC.freeze_layers(0.0, torch.optim.Adam, 1e-4, 1e-7)\n",
        "hist_myModel1 = train_arcane(train_loader, val_loader, ARC.model, epochs=7, batch_size=32, opt=opt14, criterion=ARC.loss)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "opt14 = ARC.freeze_layers(0.0, torch.optim.Adam, 1e-5, 1e-8)\n",
        "hist_myModel1 = train_arcane(train_loader, val_loader, ARC.model, epochs=5, batch_size=32, opt=opt14, criterion=ARC.loss)"
      ],
      "metadata": {
        "id": "pqqxEdJTUwrm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kmqJBzQpBoHY"
      },
      "outputs": [],
      "source": [
        "en, den = 0, 0\n",
        "with torch.no_grad():\n",
        "  for sample, label in test_loader:\n",
        "    sample = sample.to(DEVICE)\n",
        "\n",
        "    probs = ARC(sample)\n",
        "    preds = torch.argmax(probs, dim=1).detach().cpu()\n",
        "    acc = (label==preds).sum().item()\n",
        "    en += acc\n",
        "    den += len(label)\n",
        "print(en, den, en/den)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bHUOL2oY_tz1"
      },
      "outputs": [],
      "source": [
        "torch.save(ARC.model, \"/content/drive/MyDrive/DLS project/jointModel1_model\")\n",
        "torch.save(ARC.loss, \"/content/drive/MyDrive/DLS project/jointModel1_loss\")\n",
        "torch.save(ARC, \"/content/drive/MyDrive/DLS project/jointModel1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##accuuracy 73"
      ],
      "metadata": {
        "id": "qPfMm4QB4sL9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w3u_x1yoCXIy"
      },
      "outputs": [],
      "source": [
        "ARC = torch.load(\"/content/drive/MyDrive/DLS project/jointModel1\")\n",
        "# ARC = torch.load(\"/content/drive/MyDrive/jointModel1\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "en, den = 0, 0\n",
        "with torch.no_grad():\n",
        "  for sample, label in test_loader:\n",
        "    sample = sample.to(DEVICE)\n",
        "    probs = ARC(sample)\n",
        "    preds = torch.argmax(probs, dim=1).detach().cpu()\n",
        "    acc = (label==preds).sum().item()\n",
        "    en += acc\n",
        "    den += len(label)\n",
        "\n",
        "print(en, den, en/den)"
      ],
      "metadata": {
        "id": "62aKL6Wp4rCW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83a3647b-f4de-439d-d559-5cbbf4989dbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1171 1589 0.736941472624292\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(ARC.model, \"/content/drive/MyDrive/DLS project/jointModel1_model\")\n",
        "torch.save(ARC.loss, \"/content/drive/MyDrive/DLS project/jointModel1_loss\")\n",
        "torch.save(ARC, \"/content/drive/MyDrive/DLS project/jointModel1\")\n"
      ],
      "metadata": {
        "id": "J4J9_VvtqyCA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#tpr@fpr -- получилось"
      ],
      "metadata": {
        "id": "F7fzeQ116BQg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Оценим модель обученную на ArcFaceLoss identification метрикой"
      ],
      "metadata": {
        "id": "VFmq-kZ0mOsG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install numba\n",
        "# # from numba import cuda\n",
        "# device1 = cuda.get_current_device()\n",
        "# device1.reset()"
      ],
      "metadata": {
        "id": "uUdt6LNrM7dh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "distance = torch.nn.CosineSimilarity(dim=0, eps=1e-08)\n"
      ],
      "metadata": {
        "id": "4kdm1Nz4TE40"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ARC= torch.load(\"/content/drive/MyDrive/DLS project/jointModel1\")\n",
        "model = ARC.model"
      ],
      "metadata": {
        "id": "ZfvWdmy26EaP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DEVICE"
      ],
      "metadata": {
        "id": "YXXRhXpvNdp3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d402d17a-257c-4e32-d530-41ece88aea0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "en, den = 0, 0\n",
        "with torch.no_grad():\n",
        "  for sample, label in test_loader:\n",
        "    sample = sample.to(DEVICE)\n",
        "    probs = ARC(sample)\n",
        "    preds = torch.argmax(probs, dim=1).detach().cpu()\n",
        "    acc = (label==preds).sum().item()\n",
        "    en += acc\n",
        "    den += len(label)\n",
        "\n",
        "print(en, den, en/den)"
      ],
      "metadata": {
        "id": "EaBQPf59C8z8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b380dba1-86c7-4d26-b0de-346882c8ca8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1171 1589 0.736941472624292\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clear_cuda()"
      ],
      "metadata": {
        "id": "LS5yzNNnPFhC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u2wau1RqF5HL"
      },
      "outputs": [],
      "source": [
        "# Если вы работаете с данными, которые даны по ссылке,\n",
        "# то эта ячейка поможет их загрузить\n",
        "from collections import defaultdict\n",
        "\n",
        "# file with query part annotations: which image belongs to which class\n",
        "# format:\n",
        "#     image_name_1.jpg 2678\n",
        "#     image_name_2.jpg 2679\n",
        "f = open('./celebA_ir/celebA_anno_query.csv', 'r')\n",
        "query_lines = f.readlines()[1:]\n",
        "f.close()\n",
        "query_lines = [x.strip().split(',') for x in query_lines]\n",
        "# plain list of image names from query. Neede to compute embeddings for query\n",
        "query_img_names = [x[0] for x in query_lines]\n",
        "\n",
        "# dictionary with info of which images from query belong to which class\n",
        "# format:\n",
        "#     {class: [image_1, image_2, ...]}\n",
        "query_dict = defaultdict(list)\n",
        "for img_name, img_class in query_lines:\n",
        "  query_dict[img_class].append(img_name)\n",
        "\n",
        "# list of distractor images\n",
        "distractors_img_names = os.listdir('./celebA_ir/celebA_distractors')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_dict;\n"
      ],
      "metadata": {
        "id": "fBW4_v0gDsBP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "im = FaceDataset.load_sample('./celebA_ir/celebA_query/001430.jpg')\n",
        "im = im.resize((RESCALE_SIZE, RESCALE_SIZE))\n",
        "im\n"
      ],
      "metadata": {
        "id": "4MBMdfVwD5nZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "a39ea782-83c2-48b7-c678-d3f9d80cc265"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imt = tt.Compose([tt.ToImage(), tt.ToDtype(torch.float32, scale=True)])(im)\n",
        "imt.shape"
      ],
      "metadata": {
        "id": "3yo4obuQFtEb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a5cd6ff-6d78-4c91-8366-9f42a1793573"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([3, 224, 224])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "emb = model(imt.unsqueeze(0).to(DEVICE))\n",
        "emb.shape"
      ],
      "metadata": {
        "id": "QSJSNZdEGZk4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cf1e9eb-853d-44a7-cc14-49d9c9416195"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 512])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample.shape"
      ],
      "metadata": {
        "id": "5gfutDWeGdvp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c0a77f5-49b1-4a20-9d52-11b2b15b1eb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([21, 3, 224, 224])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b1_DNpTC_lpQ"
      },
      "outputs": [],
      "source": [
        "def prepare(name):\n",
        "  im = FaceDataset.load_sample(name)\n",
        "  im = im.resize((RESCALE_SIZE, RESCALE_SIZE))\n",
        "  return tt.Compose([tt.ToImage(), tt.ToDtype(torch.float32, scale=True)])(im)\n",
        "\n",
        "\n",
        "def compute_embeddings(model, images_list, path):\n",
        "    '''\n",
        "    compute embeddings from the trained model for list of images.\n",
        "    params:\n",
        "    model: trained nn model that takes images and outputs embeddings\n",
        "    images_list: list of images paths to compute embeddings for\n",
        "    output:\n",
        "    list: list of model embeddings. Each embedding corresponds to images\n",
        "            names from images_list\n",
        "    '''\n",
        "    # YOUR CODE HERE\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "      inp = torch.stack([prepare(path + name) for name in images_list])\n",
        "      loader = DataLoader(inp, batch_size=32)\n",
        "\n",
        "      ret = []\n",
        "      for sample in loader:\n",
        "        ret.append(model(sample.to(DEVICE)).cpu())\n",
        "    return torch.cat(ret)\n",
        "    # #   clear_cuda()\n",
        "    # return ret\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clear_cuda()"
      ],
      "metadata": {
        "id": "JegQf7N6PYSE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AD2LXD87_jmR"
      },
      "outputs": [],
      "source": [
        "query_embeddings = compute_embeddings(model, query_img_names, '/content/celebA_ir/celebA_query/')\n",
        "clear_cuda()\n",
        "distractors_embeddings = compute_embeddings(model, distractors_img_names, '/content/celebA_ir/celebA_distractors/')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_embeddings.shape, distractors_embeddings.shape"
      ],
      "metadata": {
        "id": "nLdI00dHQsh_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb666cc7-8359-4207-c3b4-598933a35602"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1222, 512]), torch.Size([2001, 512]))"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ksOfuF_C_fWI"
      },
      "outputs": [],
      "source": [
        "def compute_cosine_query_pos(query_dict, query_img_names, query_embeddings):\n",
        "  '''\n",
        "  compute cosine similarities between positive pairs from query (stage 1)\n",
        "  params:\n",
        "    query_dict: dict {class: [image_name_1, image_name_2, ...]}. Key: class in\n",
        "                the dataset. Value: images corresponding to that class\n",
        "    query_img_names: list of images names\n",
        "    query_embeddings: list of embeddings corresponding to query_img_names\n",
        "  output:\n",
        "    list of floats: similarities between embeddings corresponding\n",
        "                    to the same people from query list\n",
        "  '''\n",
        "  # YOUR CODE HERE\n",
        "  distances = []\n",
        "  labels = {l: k for k, v in query_dict.items() for l in v}\n",
        "  emb_dict = dict.fromkeys(query_dict.keys())\n",
        "  for l in emb_dict.keys():\n",
        "    emb_dict[l] = []\n",
        "  for name, emb in zip(query_img_names, query_embeddings):\n",
        "    emb_dict[labels[name]].append(emb)\n",
        "  for label, embs in emb_dict.items():\n",
        "    for i in range(len(embs)):\n",
        "      for j in range(i):\n",
        "        distances.append(distance(embs[i], embs[j]))\n",
        "\n",
        "\n",
        "  return torch.FloatTensor(distances)\n",
        "\n",
        "def compute_cosine_query_neg(query_dict, query_img_names, query_embeddings):\n",
        "  '''\n",
        "  compute cosine similarities between negative pairs from query (stage 2)\n",
        "  params:\n",
        "    query_dict: dict {class: [image_name_1, image_name_2, ...]}. Key: class in\n",
        "                the dataset. Value: images corresponding to that class\n",
        "    query_img_names: list of images names\n",
        "    query_embeddings: list of embeddings corresponding to query_img_names\n",
        "  output:\n",
        "    list of floats: similarities between embeddings corresponding\n",
        "                    to different people from query list\n",
        "  '''\n",
        "  # YOUR CODE HERE\n",
        "\n",
        "  distances = []\n",
        "  labels = {l: k for k, v in query_dict.items() for l in v}\n",
        "  emb_dict = dict.fromkeys(query_dict.keys(), )\n",
        "  for l in emb_dict.keys():\n",
        "    emb_dict[l] = []\n",
        "  for name, emb in zip(query_img_names, query_embeddings):\n",
        "    emb_dict[labels[name]].append(emb)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  l_n = len(emb_dict)\n",
        "  keys = list(emb_dict.keys())\n",
        "  for i in range(l_n):\n",
        "    for j in range(i):\n",
        "      # print(keys[i], keys[j])\n",
        "      for k in emb_dict[keys[i]]:\n",
        "        for l in emb_dict[keys[j]]:\n",
        "          distances.append(distance(k, l))\n",
        "\n",
        "  return torch.FloatTensor(distances)\n",
        "\n",
        "def compute_cosine_query_distractors(query_embeddings, distractors_embeddings):\n",
        "  '''\n",
        "  compute cosine similarities between negative pairs from query and distractors\n",
        "  (stage 3)\n",
        "  params:\n",
        "    query_embeddings: list of embeddings corresponding to query_img_names\n",
        "    distractors_embeddings: list of embeddings corresponding to distractors_img_names\n",
        "  output:\n",
        "    list of floats: similarities between pairs of people (q, d), where q is\n",
        "                    embedding corresponding to photo from query, d —\n",
        "                    embedding corresponding to photo from distractors\n",
        "  '''\n",
        "  distances = []\n",
        "  for q in query_embeddings:\n",
        "    for d in distractors_embeddings:\n",
        "      distances.append(distance(q, d))\n",
        "  return torch.FloatTensor(distances)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sL5eieLz_a6T"
      },
      "outputs": [],
      "source": [
        "\n",
        "cosine_query_pos = compute_cosine_query_pos(query_dict, query_img_names,\n",
        "                                            query_embeddings)\n",
        "cosine_query_neg = compute_cosine_query_neg(query_dict, query_img_names,\n",
        "                                            query_embeddings)\n",
        "cosine_query_distractors = compute_cosine_query_distractors(query_embeddings,\n",
        "                                                            distractors_embeddings)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(cosine_query_neg)"
      ],
      "metadata": {
        "id": "Qys0AL6Aut3i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0d7b04a-0e18-4ef8-a78a-1ac1dcb4fa88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.3378, 0.3625, 0.2646,  ..., 0.4058, 0.3745, 0.2816])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYSlzv5s_0-F"
      },
      "source": [
        "Ячейка ниже проверяет, что код работает верно:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OEFojrOY_44r"
      },
      "outputs": [],
      "source": [
        "\n",
        "test_query_dict = {\n",
        "    2876: ['1.jpg', '2.jpg', '3.jpg'],\n",
        "    5674: ['5.jpg'],\n",
        "    864:  ['9.jpg', '10.jpg'],\n",
        "}\n",
        "test_query_img_names = ['1.jpg', '2.jpg', '3.jpg', '5.jpg', '9.jpg', '10.jpg']\n",
        "test_query_embeddings = torch.FloatTensor([\n",
        "                    [1.56, 6.45,  -7.68],\n",
        "                    [-1.1 , 6.11,  -3.0],\n",
        "                    [-0.06,-0.98,-1.29],\n",
        "                    [8.56, 1.45,  1.11],\n",
        "                    [0.7,  1.1,   -7.56],\n",
        "                    [0.05, 0.9,   -2.56],\n",
        "])\n",
        "\n",
        "test_distractors_img_names = ['11.jpg', '12.jpg', '13.jpg', '14.jpg', '15.jpg']\n",
        "\n",
        "test_distractors_embeddings = torch.FloatTensor([\n",
        "                    [0.12, -3.23, -5.55],\n",
        "                    [-1,   -0.01, 1.22],\n",
        "                    [0.06, -0.23, 1.34],\n",
        "                    [-6.6, 1.45,  -1.45],\n",
        "                    [0.89,  1.98, 1.45],\n",
        "])\n",
        "\n",
        "test_cosine_query_pos = compute_cosine_query_pos(test_query_dict, test_query_img_names,\n",
        "                                            test_query_embeddings)\n",
        "test_cosine_query_neg = compute_cosine_query_neg(test_query_dict, test_query_img_names,\n",
        "                                            test_query_embeddings)\n",
        "test_cosine_query_distractors = compute_cosine_query_distractors(test_query_embeddings,\n",
        "                                                            test_distractors_embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iQIyWXmE_5tS"
      },
      "outputs": [],
      "source": [
        "true_cosine_query_pos = [0.8678237233650096, 0.21226104378511604,\n",
        "                         -0.18355866977496182, 0.9787437979250561]\n",
        "assert np.allclose(sorted(test_cosine_query_pos), sorted(true_cosine_query_pos)), \\\n",
        "      \"A mistake in compute_cosine_query_pos function\"\n",
        "\n",
        "true_cosine_query_neg = [0.15963231223161822, 0.8507997093616965, 0.9272761484302097,\n",
        "                         -0.0643994061127092, 0.5412660901220571, 0.701307100338029,\n",
        "                         -0.2372575528216902, 0.6941032794522218, 0.549425446066643,\n",
        "                         -0.011982733001947084, -0.0466679194884999]\n",
        "assert np.allclose(sorted(test_cosine_query_neg), sorted(true_cosine_query_neg)), \\\n",
        "      \"A mistake in compute_cosine_query_neg function\"\n",
        "\n",
        "true_cosine_query_distractors = [0.3371426578637511, -0.6866465610863652, -0.8456563512871669,\n",
        "                                 0.14530087113136106, 0.11410510307646118, -0.07265097629002357,\n",
        "                                 -0.24097699660707042,-0.5851992679925766, 0.4295494455718534,\n",
        "                                 0.37604478596058194, 0.9909483738948858, -0.5881093317868022,\n",
        "                                 -0.6829712976642919, 0.07546364489032083, -0.9130970963915521,\n",
        "                                 -0.17463101988684684, -0.5229363015558941, 0.1399896725311533,\n",
        "                                 -0.9258034013399499, 0.5295114163723346, 0.7811585442749943,\n",
        "                                 -0.8208760031249596, -0.9905139680301821, 0.14969764653247228,\n",
        "                                 -0.40749654525418444, 0.648660814944824, -0.7432584300096284,\n",
        "                                 -0.9839696492435877, 0.2498741082804709, -0.2661183373780491]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "assert np.allclose(sorted(test_cosine_query_distractors), sorted(true_cosine_query_distractors)), \\\n",
        "      \"A mistake in compute_cosine_query_distractors function\""
      ],
      "metadata": {
        "id": "gzvCnrm8qSao"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HgfGS4PzBXog"
      },
      "source": [
        "И, наконец, финальная функция, которая считает IR metric:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rJJ0ef6t_VLJ"
      },
      "outputs": [],
      "source": [
        "def compute_ir(cosine_query_pos, cosine_query_neg, cosine_query_distractors,\n",
        "               fpr=0.1):\n",
        "  '''\n",
        "  compute identification rate using precomputer cosine similarities between pairs\n",
        "  at given fpr\n",
        "  params:\n",
        "    cosine_query_pos: cosine similarities between positive pairs from query\n",
        "    cosine_query_neg: cosine similarities between negative pairs from query\n",
        "    cosine_query_distractors: cosine similarities between negative pairs\n",
        "                              from query and distractors\n",
        "    fpr: false positive rate at which to compute TPR\n",
        "  output:\n",
        "    float: threshold for given fpr\n",
        "    float: TPR at given FPR\n",
        "  '''\n",
        "  false_pair_amount = len(cosine_query_neg) + len(cosine_query_distractors)\n",
        "  N = int(fpr*false_pair_amount)\n",
        "  # print(f\"{N=}\")\n",
        "  false_pairs = torch.sort(\n",
        "      torch.cat(\n",
        "        (cosine_query_neg, cosine_query_distractors)\n",
        "      )\n",
        "      , descending=True\n",
        "    ).values\n",
        "\n",
        "  # print(false_pairs)\n",
        "  threshold = false_pairs[N]\n",
        "  # print(f\"{threshold=}\")\n",
        "  en = ((cosine_query_pos > threshold).sum())\n",
        "  acc = en/len(cosine_query_pos)\n",
        "  # print(acc)\n",
        "  # print()\n",
        "  return threshold, acc\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZz8_1vbFtKE"
      },
      "source": [
        "И ячейки для ее проверки:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DmwpM3FH_VpR"
      },
      "outputs": [],
      "source": [
        "test_thr = []\n",
        "test_tpr = []\n",
        "for fpr in [0.5, 0.3, 0.1]:\n",
        "  x, y = compute_ir(test_cosine_query_pos, test_cosine_query_neg,\n",
        "                    test_cosine_query_distractors, fpr=fpr)\n",
        "  test_thr.append(x)\n",
        "  test_tpr.append(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nVYigU0jFvc9"
      },
      "outputs": [],
      "source": [
        "\n",
        "true_thr = [-0.011982733001947084, 0.3371426578637511, 0.701307100338029]\n",
        "assert np.allclose(np.array(test_thr), np.array(true_thr)), \"A mistake in computing threshold\"\n",
        "\n",
        "true_tpr = [0.75, 0.5, 0.5]\n",
        "assert np.allclose(np.array(test_tpr), np.array(true_tpr)), \"A mistake in computing tpr\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksuMMLQOFxJ1"
      },
      "source": [
        "А в ячейке ниже вы можете посчитать TPR@FPR для датасета с лицами. Давайте, например, посчитаем для значений fpr = [0.5, 0.2, 0.1, 0.05]."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QhMhAza2GDL_"
      },
      "outputs": [],
      "source": [
        "res = []\n",
        "for fpr in [0.5, 0.2, 0.1, 0.05]:\n",
        "  res.append(compute_ir(cosine_query_pos, cosine_query_neg, cosine_query_distractors, fpr))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for (tr, idm), fpr in zip(res, [0.5, 0.2, 0.1, 0.05]):\n",
        "  print(f\"@fpr = {fpr}:  {idm}\")"
      ],
      "metadata": {
        "id": "lNW-Iefuzc0e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a927593-f776-4b13-b3be-0a577a4f8af4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "@fpr = 0.5:  0.8250797986984253\n",
            "@fpr = 0.2:  0.5538346767425537\n",
            "@fpr = 0.1:  0.39263635873794556\n",
            "@fpr = 0.05:  0.2707696557044983\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Оценим модель обученную на CE"
      ],
      "metadata": {
        "id": "6Wnw-dbOuhmX"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nauk8SGQuhh7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "obUR3QqWuxjM"
      },
      "outputs": [],
      "source": [
        "# modelResNet = resnet18(ResNet18_Weights.IMAGENET1K_V1)\n",
        "modelResNet = torch.load('/content/drive/MyDrive/DLS project/modelResnet.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "824793af-00e8-4255-bd5f-0be3a7c5d0f5",
        "id": "9SZuhCxJuxjM"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7293895483016968"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "modelResNet.eval()\n",
        "evaluate(modelResNet, test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelResNet.fc = IdBlock()"
      ],
      "metadata": {
        "id": "ZpasLrszu0xS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clear_cuda()"
      ],
      "metadata": {
        "id": "KVnUN57fuoGK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Hq6QHPouoGQ"
      },
      "outputs": [],
      "source": [
        "query_embeddings = compute_embeddings(modelResNet, query_img_names, '/content/celebA_ir/celebA_query/')\n",
        "clear_cuda()\n",
        "distractors_embeddings = compute_embeddings(modelResNet, distractors_img_names, '/content/celebA_ir/celebA_distractors/')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_embeddings.shape, distractors_embeddings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be0e21ec-0f87-4dbe-b056-287827432760",
        "id": "7Htmjx9_uoGQ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1222, 512]), torch.Size([2001, 512]))"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "cosine_query_pos = compute_cosine_query_pos(query_dict, query_img_names,\n",
        "                                            query_embeddings)\n",
        "cosine_query_neg = compute_cosine_query_neg(query_dict, query_img_names,\n",
        "                                            query_embeddings)\n",
        "cosine_query_distractors = compute_cosine_query_distractors(query_embeddings,\n",
        "                                                            distractors_embeddings)\n"
      ],
      "metadata": {
        "id": "8TNl_yZTuhdV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resCE = []\n",
        "for fpr in [0.5, 0.2, 0.1, 0.05]:\n",
        "  resCE.append(compute_ir(cosine_query_pos, cosine_query_neg, cosine_query_distractors, fpr))\n"
      ],
      "metadata": {
        "id": "VPPEO-NAug_H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for (tr, idm), fpr in zip(resCE, [0.5, 0.2, 0.1, 0.05]):\n",
        "  print(f\"@fpr = {fpr}:  {idm}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DbwGM8_v1xv",
        "outputId": "1cad4e3d-6e9f-48f0-fb64-4afe3696aef7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "@fpr = 0.5:  0.7443788051605225\n",
            "@fpr = 0.2:  0.478771835565567\n",
            "@fpr = 0.1:  0.34155288338661194\n",
            "@fpr = 0.05:  0.24162760376930237\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Сравним качество эмбедингов специализированной и обычной моделями\n",
        "\n"
      ],
      "metadata": {
        "id": "5NqT_2kJvpYh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for (tr, idm), (trCE, idmCE), fpr in zip(res, resCE, [0.5, 0.2, 0.1, 0.05]):\n",
        "  print(f\"@fpr = {fpr}:  {idm} ||| {idmCE}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_N6I9AewBaV",
        "outputId": "37e4f7d4-e4ed-414a-8480-58e6f1c56eff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "@fpr = 0.5:  0.8250797986984253 ||| 0.7443788051605225\n",
            "@fpr = 0.2:  0.5538346767425537 ||| 0.478771835565567\n",
            "@fpr = 0.1:  0.39263635873794556 ||| 0.34155288338661194\n",
            "@fpr = 0.05:  0.2707696557044983 ||| 0.24162760376930237\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Видим, что модель обученная на специализированный лосс представляет изображения лучше"
      ],
      "metadata": {
        "id": "fA2p9s1PwXSA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ниже можно не смотреть"
      ],
      "metadata": {
        "id": "vmANojgKwwJ5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#triple loss"
      ],
      "metadata": {
        "id": "mNxyqY-dukXP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rAfyl3n6voMt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class TripletLoss(nn.Module):\n",
        "    def __init__(self, m = 0, distance = None):\n",
        "        super().__init__()\n",
        "        self.m = m\n",
        "        if distance is None:\n",
        "            self.distance = torch.nn.CosineSimilarity(dim=1, eps=1e-08)\n",
        "        else:\n",
        "            self.distance = distance\n",
        "    def forward(self, anchor, positive, negative):\n",
        "\n",
        "        return nn.ReLU()(self.distance(anchor.norm(dim=1), positive.norm(dim=1)) - self.distance(anchor.norm(dim=1), negative.norm(dim=1)) + self.m)"
      ],
      "metadata": {
        "id": "nWU6ROPT0Kw8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "distance = torch.nn.CosineSimilarity(dim=1, eps=1e-08)\n"
      ],
      "metadata": {
        "id": "J17BpXipu-u6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.FloatTensor([[0, 1, 1], [0, 1, 1]])\n",
        "b = torch.FloatTensor([[0, 2, 1], [0, 2, 2]])\n",
        "distance(a, b)"
      ],
      "metadata": {
        "id": "j_eBFeB2uljO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zAsug1afulc-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "os1v9s8wyGce"
      },
      "outputs": [],
      "source": [
        "model = resnet34(ResNet34_Weights.IMAGENET1K_V1)\n",
        "model;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j4_iSxcVyGc0"
      },
      "outputs": [],
      "source": [
        "class IdBlock(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "  def forward(self, x):\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PnXKkQ3gyGc0"
      },
      "outputs": [],
      "source": [
        "model.fc = IdBlock()\n",
        "model(sample).shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = []\n",
        "k = 0\n",
        "for i, label in train_loader:\n",
        "    pred.append(model(i))\n",
        "    k += 1\n",
        "    if k == 3:\n",
        "        break\n"
      ],
      "metadata": {
        "id": "dKf_3IgMulWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(pred)"
      ],
      "metadata": {
        "id": "YrVbufv212y5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l = TripletLoss()(*pred)\n",
        "l"
      ],
      "metadata": {
        "id": "Q5YWFVlczRmV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ejclQcr9zLP6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5g_5G3YdulPG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ktG2iobbulIn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-s1hLKpeulAP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FSZk44emujxU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ptuqNqNuujnE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bde3_AHIujeK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tcqf0cxeui7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3TxI-xJ7vrz"
      },
      "source": [
        "\n",
        "# Effnet -- не подошло"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9JTjs5xnuh6h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hFsCHAcZM0R0"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "# modelResNet = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)\n",
        "# modelResNet\n",
        "\n",
        "modelEff = efficientnet_b3(weights=EfficientNet_B3_Weights)\n",
        "modelEff"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wb6i-YuNwjUu"
      },
      "outputs": [],
      "source": [
        "prev_in = modelEff.classifier[1].in_features\n",
        "prev_in"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RLwNRl59wjUu"
      },
      "outputs": [],
      "source": [
        "modelEff.classifier =  nn.Sequential(\n",
        "    nn.Linear(prev_in, 512),\n",
        "    nn.BatchNorm1d(512),\n",
        "    nn.SiLU(),\n",
        "    nn.Linear(512, 500)\n",
        ")\n",
        "modelEff.classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bj6vNPBNdKm2"
      },
      "outputs": [],
      "source": [
        "modelEff.classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-JHuZnInAsDM"
      },
      "outputs": [],
      "source": [
        "import gc\n",
        "torch.cuda.empty_cache()\n",
        "torch.cuda.memory_summary(device=None, abbreviated=False)\n",
        "# del unet_model\n",
        "\n",
        "# del variables\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clear_cuda():\n",
        "  torch.cuda.empty_cache()\n",
        "  torch.cuda.memory_summary(device=None, abbreviated=False)\n",
        "  # del unet_model\n",
        "\n",
        "  # del variables\n",
        "  gc.collect()"
      ],
      "metadata": {
        "id": "0k5jwGNYOoow"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i9wyWR5NFZ6r"
      },
      "outputs": [],
      "source": [
        "DEVICE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s8bJHJ7MDCyt"
      },
      "outputs": [],
      "source": [
        "a = torch.FloatTensor([1, 0, 0])\n",
        "b = torch.FloatTensor([0.7, 0.1, 0.2])\n",
        "nn.CrossEntropyLoss()(a, b)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IWrE_19ucqFg"
      },
      "outputs": [],
      "source": [
        "effhist = train_w_freezed(modelEff, train_dataset, val_dataset, unfreezed_nm=10, epochs=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "quVe1MJ2ArDR"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yAw64dk-cqvR"
      },
      "outputs": [],
      "source": [
        "effhist1 = train_w_freezed(modelEff, train_dataset, val_dataset, 300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Apw-nLwEcqzf"
      },
      "outputs": [],
      "source": [
        "effhist3 = train_w_freezed(modelEff, train_dataset, val_dataset, 300, epochs=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mnTu1Zi5cq3r"
      },
      "outputs": [],
      "source": [
        "torch.save(modelEff, f'effnet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-aX3iP5h3hRM"
      },
      "outputs": [],
      "source": [
        "evaluate(modelEff, test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8O-p-7eHdjiX"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "km7FoDCwdjd8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_OcAyMnc72Wm"
      },
      "source": [
        "# Resnet -- получилось"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A_nwGyC0djZQ"
      },
      "outputs": [],
      "source": [
        "modelResNet1 = resnet18(ResNet18_Weights.IMAGENET1K_V1)\n",
        "modelResNet1;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gxS7gJBmGaGz"
      },
      "outputs": [],
      "source": [
        "class IdBlock(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "  def forward(self, x):\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JgpjuGmhdi8k"
      },
      "outputs": [],
      "source": [
        "# modelResNet = resnet18(ResNet18_Weights.IMAGENET1K_V1)\n",
        "modelResNet = torch.load('/content/drive/MyDrive/DLS project/modelResnet.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ri826KWV6i4Y"
      },
      "outputs": [],
      "source": [
        "modelResNet.eval()\n",
        "evaluate(modelResNet, test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DUSkCkiCiHYm"
      },
      "outputs": [],
      "source": [
        "blocks = [i[0] for i in modelResNet.named_children()]\n",
        "blocks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Shk0gvoQi7OW"
      },
      "outputs": [],
      "source": [
        "modelResNet.fc = nn.Sequential(\n",
        "    nn.Linear(512, 512)\n",
        "    , nn.ReLU()\n",
        "    , nn.Linear(512, 500)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZgQduQZBDn2h"
      },
      "outputs": [],
      "source": [
        "def fit_epoch(model, train_loader, criterion, optimizer):\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    processed_data = 0\n",
        "    model.train()\n",
        "\n",
        "    for inputs, labels in train_loader:\n",
        "        inputs = inputs.to(DEVICE)\n",
        "        labels = labels.to(DEVICE)\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        preds = torch.argmax(outputs, 1)\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "        processed_data += inputs.size(0)\n",
        "\n",
        "    train_loss = running_loss / processed_data\n",
        "    train_acc = running_corrects.cpu().numpy() / processed_data\n",
        "    return train_loss, train_acc\n",
        "\n",
        "def eval_epoch(model, val_loader, criterion):\n",
        "    model.eval()\n",
        "    running_loss = 0.0\n",
        "    running_corrects = 0\n",
        "    processed_size = 0\n",
        "\n",
        "    for inputs, labels in val_loader:\n",
        "        inputs = inputs.to(DEVICE)\n",
        "        labels = labels.to(DEVICE)\n",
        "\n",
        "        with torch.set_grad_enabled(False):\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            preds = torch.argmax(outputs, 1)\n",
        "\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "        processed_size += inputs.size(0)\n",
        "    val_loss = running_loss / processed_size\n",
        "    val_acc = running_corrects.double().cpu().numpy() / processed_size\n",
        "    return val_loss, val_acc\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2F8Vrnbxi7TC"
      },
      "outputs": [],
      "source": [
        "# opt = torch.optim.Adam(\n",
        "#         params=[\n",
        "#             {\"params\": modelResNet.layer4.parameters(), 'lr': 1e-4},\n",
        "#             {\"params\": modelResNet.fc.parameters(), \"lr\": 1e-4}\n",
        "#         ])\n",
        "hist = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g2ks-xRzl8LN"
      },
      "outputs": [],
      "source": [
        "loss, acc, val_loss, val_acc = zip(*hist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9zqyD42hxDgK"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(15, 9))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(loss, label=\"train_loss\")\n",
        "plt.plot(val_loss, label=\"val_loss\")\n",
        "plt.legend(loc='best')\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"loss\")\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(acc, label=\"train_acc\")\n",
        "plt.plot(val_acc, label=\"val_acc\")\n",
        "plt.legend(loc='best')\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iSr6GqF7hF19"
      },
      "outputs": [],
      "source": [
        "print(count_graded(modelResNet))\n",
        "modelResNet.eval()\n",
        "print(evaluate(modelResNet, test_loader))\n",
        "print(evaluate(modelResNet, train_loader))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NTALitUahFxl"
      },
      "outputs": [],
      "source": [
        "opt1 = freeze_blocks(modelResNet, 4, blocks)\n",
        "\n",
        "count_graded(modelResNet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aKXaW3C-hFsT"
      },
      "outputs": [],
      "source": [
        "hist1 = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_SAsLKb3hFnT"
      },
      "outputs": [],
      "source": [
        "opt2 = freeze_blocks(modelResNet, 7, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist2 = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w2hFxar2rLp6"
      },
      "outputs": [],
      "source": [
        "opt3 = freeze_blocks(modelResNet, 8, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist3 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZQ8vny6YrINo"
      },
      "outputs": [],
      "source": [
        "opt4 = freeze_blocks(modelResNet, 9, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist4 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8oVeY-5h07LE"
      },
      "outputs": [],
      "source": [
        "opt5 = freeze_blocks(modelResNet, 9, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist5 = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uNcJR2u35ZS1"
      },
      "outputs": [],
      "source": [
        "train_transform = tt.Compose([\n",
        "    # EfficientNet_B3_Weights.IMAGENET1K_V1.transforms(),\n",
        "    res_transform(),\n",
        "    tt.RandomHorizontalFlip(p=0.5),\n",
        "    # tt.RandomPerspective(distortion_scale=0.5, p=0.3,)\n",
        "])\n",
        "test_transform = res_transform()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UUz0gH5i5ZS8"
      },
      "outputs": [],
      "source": [
        "\n",
        "train_dataset = FaceDataset(train_data, mode='train', transform=train_transform)\n",
        "val_dataset = FaceDataset(val_data, mode='val', transform=test_transform)\n",
        "test_dataset = FaceDataset(test_data, mode='test', transform=test_transform)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hbNvHcDf07zP"
      },
      "outputs": [],
      "source": [
        "opt6 = freeze_blocks(modelResNet, 9, blocks)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist6 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OuCc-ZsH07m1"
      },
      "outputs": [],
      "source": [
        "hist7 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gW9RbreDhFct"
      },
      "outputs": [],
      "source": [
        "opt6 = freeze_blocks(modelResNet, 9, blocks, lr=1e-5)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist6 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KPd5be7l_irQ"
      },
      "outputs": [],
      "source": [
        "opt6 = freeze_blocks(modelResNet, 8, blocks, lr=1e-5)\n",
        "\n",
        "count_graded(modelResNet)\n",
        "\n",
        "hist6 = train(train_loader, val_loader, modelResNet, epochs=5, batch_size=32, opt=opt6)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# modelResNet = torch.load(\"./modelResnet.pth\")"
      ],
      "metadata": {
        "id": "ALCoXzJj4BEL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wiZhPaMDBthF"
      },
      "outputs": [],
      "source": [
        "evaluate(modelResNet, test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AGjge2OtFrUE"
      },
      "outputs": [],
      "source": [
        "torch.save(modelResNet, \"/content/drive/MyDrive/DLS project/modelResnet.pth\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdwZQtD37_Fs"
      },
      "source": [
        "# Embedings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_RMDS1eDLcix"
      },
      "outputs": [],
      "source": [
        "class Identity(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Identity, self).__init__()\n",
        "\n",
        "    def forward(self, x):\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ooja_T6KLdMT"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A-CrU_OtLdRh"
      },
      "outputs": [],
      "source": [
        "import copy\n",
        "model_copy = copy.deepcopy(modelResNet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h8yoEJYZLdWF"
      },
      "outputs": [],
      "source": [
        "model_copy.fc = Identity()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "boGgZswRM1XS"
      },
      "outputs": [],
      "source": [
        "model_copy(sample.to(DEVICE)).shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JE9WfLFeMwxr"
      },
      "outputs": [],
      "source": [
        "len(test_dataset), (test_dataset[0][0]).shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y8GNt5dZU5Ca"
      },
      "outputs": [],
      "source": [
        "d = [i.item() for i in torch.randint(500, (20, ))]\n",
        "d"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jTI_s3xyMw5u"
      },
      "outputs": [],
      "source": [
        "samples = []\n",
        "instances = {l:None for l in d}\n",
        "labels = []\n",
        "for i, l in train_dataset:\n",
        "    if l in d:\n",
        "        samples.append(i)\n",
        "        labels.append(l)\n",
        "        instances[l] = i\n",
        "samples = torch.stack(samples)\n",
        "preds = model_copy(samples.to(DEVICE)).detach().cpu()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p6owumXJXrIQ"
      },
      "outputs": [],
      "source": [
        "labels = torch.Tensor(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dCEBzWTuXW1s"
      },
      "outputs": [],
      "source": [
        "preds.shape, labels.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MY8LYJS9Mw9y"
      },
      "outputs": [],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "t = TSNE(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xECGZo0xUXCh"
      },
      "outputs": [],
      "source": [
        "preds = preds.numpy()\n",
        "labels = labels.numpy()\n",
        "preds.shape, labels.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HkOhKE7oVxOc"
      },
      "outputs": [],
      "source": [
        "k = 365\n",
        "preds[:k].shape, labels[:k].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "naguVueSUiyn"
      },
      "outputs": [],
      "source": [
        "pred_clustered = t.fit_transform(preds[:k])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O0UQRnXgUtBo"
      },
      "outputs": [],
      "source": [
        "sc = plt.scatter(pred_clustered[:, 0], pred_clustered[:, 1],c=labels[:k], cmap='Spectral')\n",
        "plt.colorbar()\n",
        "plt.show();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I8XT0h4IUi-c"
      },
      "outputs": [],
      "source": [
        "for k, (l, i) in enumerate(instances.items()):\n",
        "    # print(k)\n",
        "    plt.subplot(4, 5, k+1)\n",
        "    show_im(i)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yI_ANCfiaNFZ"
      },
      "source": [
        "### train arcane"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2RuDqb9cZL4x"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tq_Ca-ARasQi"
      },
      "outputs": [],
      "source": [
        "block_names = []\n",
        "for i , m in modelResNet1.named_children():\n",
        "  block_names.append(i)\n",
        "\n",
        "\n",
        "loss_ = ArcFaceLoss(\n",
        "                num_classes=500,\n",
        "                embedding_size=512,\n",
        "                m=0.2,\n",
        "                scale=50.0\n",
        "            )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YSHLiHukWx1d"
      },
      "outputs": [],
      "source": [
        "modelResNet1 = torch.load(\"/content/drive/MyDrive/ArcFace3.pth\")\n",
        "count_graded(modelResNet1)\n",
        "# evaluate(modelResNet1, test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dyrAnDviaQcw"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=5, blocks=block_names, lr=1e-4)\n",
        "count_graded(modelResNet1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ClPkNHTjW-gy"
      },
      "outputs": [],
      "source": [
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3CEc5Un2clxd"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=9, blocks=block_names, lr=1e-3)\n",
        "count_graded(modelResNet1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zT55Al16dgWi"
      },
      "outputs": [],
      "source": [
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IdtzRSP2dk_i"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=9, blocks=block_names, lr=1e-4)\n",
        "count_graded(modelResNet1)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hWHN2swVghO2"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=10, blocks=block_names, lr=1e-5)\n",
        "count_graded(modelResNet1)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gmyVDNhmgoFi"
      },
      "outputs": [],
      "source": [
        "train_loader, val_loader, test_loader =\\\n",
        "update_loader(base_transform=res_transform(), pre_transform=[tt.RandomHorizontalFlip(p=0.5), tt.RandomPerspective(distortion_scale=0.2, p=0.3,)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0iYoCkfxitU3"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=10, blocks=block_names, lr=1e-6)\n",
        "count_graded(modelResNet1)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7sYOtuQgiu-F"
      },
      "outputs": [],
      "source": [
        "torch.save(modelResNet1, './drive/MyDrive/ArcFace2.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NYztH75HkeJg"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=10, blocks=block_names, lr=1e-4)\n",
        "count_graded(modelResNet1)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7uuUPyWloUzT"
      },
      "outputs": [],
      "source": [
        "train_loader, val_loader, test_loader =\\\n",
        "update_loader(base_transform=res_transform(), pre_transform=[tt.RandomHorizontalFlip(p=0.5),])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9UXkCdWuoUro"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=10, blocks=block_names, lr=1e-4)\n",
        "count_graded(modelResNet1)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TXD_t13Yoea7"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=10, blocks=block_names, lr=1e-3)\n",
        "count_graded(modelResNet1)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "98AIYxIMqBy1"
      },
      "outputs": [],
      "source": [
        "torch.save(modelResNet1, './drive/MyDrive/ArcFace3.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TYRSBXoGrYXA"
      },
      "outputs": [],
      "source": [
        "loss_ = ArcFaceLoss(\n",
        "                num_classes=500,\n",
        "                embedding_size=512,\n",
        "                m=0.02,\n",
        "                scale=30.0\n",
        "            ).to(DEVICE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ZH8FZfZrk8o"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZSA_ASwwriAh"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=10, blocks=block_names, lr=1e-3)\n",
        "count_graded(modelResNet1)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3kbaYEKErOmx"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=10, blocks=block_names, lr=1e-4)\n",
        "count_graded(modelResNet1)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q5XjgiGQrOzP"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OnaeM8_XrO8f"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dhP06nYqoUf-"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ypKeqgd9QvAG"
      },
      "outputs": [],
      "source": [
        "# train_transform = tt.Compose([\n",
        "#     resTransform(),\n",
        "#     tt.RandomHorizontalFlip(p=0.5),\n",
        "# ])\n",
        "\n",
        "train_transform = tt.Compose([\n",
        "    # EfficientNet_B3_Weights.IMAGENET1K_V1.transforms(),\n",
        "    res_transform(),\n",
        "    tt.RandomHorizontalFlip(p=0.5),\n",
        "    tt.RandomPerspective(distortion_scale=0.2, p=0.3,)\n",
        "])\n",
        "test_transform = res_transform()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h_hZhDBBQvAG"
      },
      "outputs": [],
      "source": [
        "train_transform = tt.Compose([\n",
        "    # EfficientNet_B3_Weights.IMAGENET1K_V1.transforms(),\n",
        "    res_transform(),\n",
        "    tt.RandomHorizontalFlip(p=0.5),\n",
        "    tt.RandomPerspective(distortion_scale=0.2, p=0.3,)\n",
        "])\n",
        "test_transform = res_transform()\n",
        "\n",
        "train_dataset = FaceDataset(train_data, mode='train', transform=train_transform)\n",
        "val_dataset = FaceDataset(val_data, mode='val', transform=test_transform)\n",
        "test_dataset = FaceDataset(test_data, mode='test', transform=test_transform)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
        "len(train_data), len(val_data), len(test_data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mPorN8tzfCsu"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EjfXKyXjcmOK"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IJz8TRVdcmbB"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YE4KwqyJcmkh"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1b8q807ZcmsP"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LHECfyjOZLzm"
      },
      "outputs": [],
      "source": [
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0GT1dbZNJWtk"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=5, blocks=block_names, lr=1e-4)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8ev29XgENT-8"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=5, blocks=block_names, lr=1e-3)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xa_x103MNWbO"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=5, blocks=block_names, lr=1e-5)\n",
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MohlO7NgRj9e"
      },
      "outputs": [],
      "source": [
        "torch.save(modelResNet1, './drive/MyDrive/ArcFace1.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nj3lj0geQtcl"
      },
      "outputs": [],
      "source": [
        "opt11 = freeze_blocks(modelResNet1, num=8, blocks=block_names, lr=1e-5)\n",
        "count_graded(modelResNet1)\n",
        "# hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "#               opt=opt11, criterion=loss_, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "du9pqvbzQtmm"
      },
      "outputs": [],
      "source": [
        "hist_arc = train_arcane(model=modelResNet1, batch_size=30, train_loader=train_loader, val_loader=val_loader,\n",
        "              opt=opt11, criterion=loss_, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vn0jfKmIXrUf"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AL8EiC6iQtu4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WoQ7L9ymQt2r"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JYXqrVwIZMju"
      },
      "outputs": [],
      "source": [
        "for i in train_loader:\n",
        "  print(f\"{i=}\")\n",
        "  break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DwyS8hDWYQrm"
      },
      "outputs": [],
      "source": [
        "labels = torch.randint(5, size=(32,))\n",
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W8UvDAzPGukG"
      },
      "outputs": [],
      "source": [
        "print(f\"{inp.shape=}\")\n",
        "# logits = ArcFaceLossFC(in_=512, out_=5)(inp)\n",
        "# probs = ArcFaceLossSoftMax(m=0., s=1)(logits)\n",
        "losss = ArcFaceLossEntropy(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l_j0F5o-bpYQ"
      },
      "outputs": [],
      "source": [
        "probs = torch.FloatTensor([[1, 0, 0], [0, 1, 0]])\n",
        "probs2 = torch.FloatTensor([[0.5, 0.5, 0], [0, 1, 0]])\n",
        "\n",
        "labels = torch.LongTensor([0, 1, 0, 2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lttz1LVAbpj1"
      },
      "outputs": [],
      "source": [
        "print(losss(probs, labels))\n",
        "print(losss(probs2, labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YwQt4cGkYQaP"
      },
      "outputs": [],
      "source": [
        "probs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XGUWyfH7YQys"
      },
      "outputs": [],
      "source": [
        "oh = F.one_hot(labels)\n",
        "oh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uDw-1-2wYQ5K"
      },
      "outputs": [],
      "source": [
        "oh*probs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QjSO4U5PGum4"
      },
      "outputs": [],
      "source": [
        "inp.shape, logits.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ohk4yJmGupk"
      },
      "outputs": [],
      "source": [
        "probs.sum(dim=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qZAOl3aMGusl"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kk4MSf41GuvM"
      },
      "outputs": [],
      "source": [
        "A = torch.Tensor([[1, 2, 3, 10], [4, 5, 6, 20], [7, 8, 9, 100]])\n",
        "print(A.shape)\n",
        "sA = A.sum(dim=1)\n",
        "print(sA)\n",
        "iiii = sA[:, None]-A\n",
        "print(iiii)\n",
        "print(iiii.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "99EV-6dxGux8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rIr7w3NGGu0r"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CvyVgW6gGu3u"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pvifiXgpi7YS"
      },
      "outputs": [],
      "source": [
        "inst = test_dataset[-7]\n",
        "same = 0\n",
        "other = 0\n",
        "for i in train_dataset:\n",
        "    if i[1] == inst[1]:\n",
        "        same = i\n",
        "        break\n",
        "    else:\n",
        "        other = i\n",
        "k = 1\n",
        "for i, l in (same, inst, other):\n",
        "    print(l)\n",
        "    plt.subplot(1, 3, k)\n",
        "    k += 1\n",
        "    show_im(i)\n",
        "# show_im(same[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GNH8kQJmi7cq"
      },
      "outputs": [],
      "source": [
        "inst_pred = torch.sigmoid(modelResNet(inst[0].unsqueeze(0).to(DEVICE)).detach().to('cpu').squeeze()).numpy()\n",
        "same_pred = torch.sigmoid(modelResNet(same[0].unsqueeze(0).to(DEVICE)).detach().to('cpu').squeeze()).numpy()\n",
        "other_pred = torch.sigmoid(modelResNet(other[0].unsqueeze(0).to(DEVICE)).detach().to('cpu').squeeze()).numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NwYAJrwzExAW"
      },
      "outputs": [],
      "source": [
        "k = 0\n",
        "for i in (inst_pred, same_pred, other_pred):\n",
        "    k += 1\n",
        "    plt.subplot(3, 1, k)\n",
        "    print(i.argmax())\n",
        "    plt.scatter(np.linspace(0, 499, 500), i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gXdFwQ1fHJ84"
      },
      "outputs": [],
      "source": [
        "evaluate(modelResNet, train_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZNQtF7XHJ4e"
      },
      "outputs": [],
      "source": [
        "for name, module in modelResNet.named_children():\n",
        "    for param in module.parameters():\n",
        "        param.requires_grad = True\n",
        "    print(name)\n",
        "print()\n",
        "count_graded(modelResNet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dTJt-J97HJ0I"
      },
      "outputs": [],
      "source": [
        "num = (len(list(modelResNet.fc.parameters())))\n",
        "opt = torch.optim.Adam(\n",
        "        params=[\n",
        "            {\"params\": modelResNet.fc.parameters(), 'lr': 1e-4},\n",
        "            {\"params\": list(modelResNet.parameters())[:-num], \"lr\": 1e-5,}\n",
        "        ])\n",
        "\n",
        "hist_full = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LJ4n8VwyHJvw"
      },
      "outputs": [],
      "source": [
        "hist_full2 = train(train_loader, val_loader, modelResNet, epochs=10, batch_size=32, opt=opt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5hzR_KAgHJrt"
      },
      "outputs": [],
      "source": [
        "evaluate(modelResNet, test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lIjRaLmrHJnY"
      },
      "outputs": [],
      "source": [
        "torch.save(modelResNet, \"firstresnet\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xHhd4aTmHJi-"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7JEvgRekHJen"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mxa0NXZw_ryD"
      },
      "outputs": [],
      "source": [
        "from torchvision.models import efficientnet_b3, EfficientNet_B3_Weights\n",
        "from torchvision.models._api import WeightsEnum\n",
        "from torch.hub import load_state_dict_from_url\n",
        "\n",
        "def get_state_dict(self, *args, **kwargs):\n",
        "    kwargs.pop(\"check_hash\")\n",
        "    return load_state_dict_from_url(self.url, *args, **kwargs)\n",
        "WeightsEnum.get_state_dict = get_state_dict\n",
        "\n",
        "# efficientnet_b3(weights=EfficientNet_B3_Weights.DEFAULT)\n",
        "model = efficientnet_b3(weights=\"DEFAULT\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zfUWvWxq_sY_"
      },
      "outputs": [],
      "source": [
        "freeze_layers(model, 10);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AVmpnOe5_scI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gqiSRuMv_se9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7JFi8EoT4pYJ"
      },
      "outputs": [],
      "source": [
        "print(count_graded(modelResNet))\n",
        "# print(freeze_layers(modelResNet, 10))\n",
        "# print(count_graded(modelResNet))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3e95dhMj7H1U"
      },
      "outputs": [],
      "source": [
        "!pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OByaHgwV56zI"
      },
      "outputs": [],
      "source": [
        "effnet = models.efficientnet_b3(weights='DEFAULT')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c1ol01xN4pS1"
      },
      "outputs": [],
      "source": [
        "efficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "obwfMz0Q4pM9"
      },
      "outputs": [],
      "source": [
        "for i, k in efficientnet.named_children():\n",
        "    print(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i6cy680a4pGX"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ubD8nxEM4pB_"
      },
      "outputs": [],
      "source": [
        "efficientnet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jfmCACjo4oBL"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "06HQRZMvEZEm"
      },
      "outputs": [],
      "source": [
        "def train_w_freezed(model, train_datasset=train_dataset, val_dataset=val_dataset, unfreezed_nm=1, lr_classifier=1e-4, lr_feature=1e-4, epochs=5, batch_size=64):\n",
        "  train_w_freezed.called_times += 1\n",
        "  model, optimizer = freeze_layers(model, numm = unfreezed_nm, lr1=lr_classifier, lr2=lr_feature)\n",
        "  history = train(train_dataset, val_dataset, model=model, epochs=epochs, batch_size=batch_size, opt=optimizer)\n",
        "  torch.save(model, f'/content/gdrive/MyDrive/efn{train_w_freezed.called_times}.pth')\n",
        "  return history\n",
        "\n",
        "train_w_freezed.called_times = 0\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "smrszRFuEcjh"
      },
      "outputs": [],
      "source": [
        "hist = train_w_freezed(modelResNet, epochs=2 )"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "toc_visible": true,
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}