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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "do0o-c0P9wcM"
      },
      "source": [
        "# Preâmbulo\n",
        "\n",
        "Imports, argumentos e definição do device."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T4ATPZX-9qXK"
      },
      "source": [
        " # Basic imports.\n",
        "import os\n",
        "import time\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "from torch.nn import functional as F\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "from torchvision import models\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "\n",
        "from sklearn import metrics\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "# Setting predefined arguments.\n",
        "args = {\n",
        "    'epoch_num': 50,        # Number of epochs.\n",
        "    # 'lr': 1e-3,             # Learning rate.\n",
        "    'lr': 2e-4,             # Learning rate.\n",
        "    'beta': 0.5,            # Beta1 from Adam.\n",
        "    'num_workers': 2,       # Number of workers on data loader.\n",
        "    'batch_size': 1000,     # Mini-batch size.\n",
        "    'print_freq': 1,        # Printing frequency.\n",
        "    'z_dim': 100,           # Dimension of z input vector.\n",
        "    'num_samples': 3,       # Number of samples to be generated in evaluation.\n",
        "    # 'least_squares': True   # Least Squares optimization.\n",
        "    'least_squares': False  # Least Squares optimization.\n",
        "}\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    args['device'] = torch.device('cuda')\n",
        "else:\n",
        "    args['device'] = torch.device('cpu')\n",
        "\n",
        "print(args['device'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZA6okfQ98TP"
      },
      "source": [
        "# Carregando o conjunto de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXQAwioI99NJ",
        "outputId": "6ed76d64-bf02-4072-b4da-ee058d62ba4e",
        "colab": {
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            "be930768c5714bb68dae71bd9e460566"
          ]
        }
      },
      "source": [
        "# Root directory for the dataset (to be downloaded).\n",
        "root = './'\n",
        "\n",
        "# Transformations over the dataset.\n",
        "data_transforms = transforms.Compose([ # MNIST transforms.\n",
        "    transforms.Pad(2, fill=0),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5), (0.5)),\n",
        "])\n",
        "# data_transforms = transforms.Compose([ # CelebA transforms.\n",
        "#     # transforms.Pad(2, fill=0),\n",
        "#     transforms.Resize(64),\n",
        "#     transforms.CenterCrop(64),\n",
        "#     transforms.ToTensor(),\n",
        "#     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "# ])\n",
        "\n",
        "# Setting datasets and dataloaders.\n",
        "n_channels = 1\n",
        "train_set = datasets.MNIST(root,\n",
        "                           train=True,\n",
        "                           download=True,\n",
        "                           transform=data_transforms)\n",
        "\n",
        "# n_channels = 3\n",
        "# train_set = datasets.CelebA(root,\n",
        "#                             split='all',\n",
        "#                             download=True,\n",
        "#                             transform=data_transforms)\n",
        "\n",
        "# Setting dataloaders.\n",
        "train_loader = DataLoader(train_set,\n",
        "                          args['batch_size'],\n",
        "                          num_workers=args['num_workers'],\n",
        "                          shuffle=True) # MNIST\n",
        "# train_loader = DataLoader(train_set,\n",
        "#                           args['batch_size'],\n",
        "#                           num_workers=args['num_workers'],\n",
        "#                           shuffle=True,\n",
        "#                           drop_last=True) # CelebA\n",
        "\n",
        "# Printing training and testing dataset sizes.\n",
        "print('Size of training set: ' + str(len(train_set)) + ' samples')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0cb30d796d442868038ed0de6f83acf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e53b6900f4ab44cf8ed90a738b77d4b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f990e19ded97474dbaa8eac791551168"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "1d52b9362cd84656b9a1165303f2bfa5"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "\n",
            "Size of training set: 60000 samples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHV0enX9-Ars"
      },
      "source": [
        "# Treinamento Adversarial Convolucional\n",
        "\n",
        "Como desde o começo GANs foram pensadas para imagens primariamente, era de se esperar que convoluções fossem inseridas em algum momento. O artigo original das [GANs](https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf), inclusive, contém testes entre arquiteturas parcialmente convolucionais e compostas apenas por camadas FC:\n",
        "\n",
        "*   Resultados Fully Connected no CIFAR;\n",
        "![FC GAN](https://www.dropbox.com/s/dbem2z7jjzodoyb/gan_fc_goodfellow.png?dl=1)\n",
        "*   Resultados Convolutionais no CIFAR.\n",
        "![Convolutional GAN](https://www.dropbox.com/s/z9ihvqb4a53eqvb/gan_conv_goodfellow.png?dl=1)\n",
        "\n",
        "A arquitetura de $G$ lembra o Decoder de um VAE com camadas que partem de um vetor aleatório $z$ e geram uma amostra final sintética $x$. Já a arquitetura de $D$ lembra uma CNN tradicional para classificação de imagens, como a AlexNet, VGG, ResNet ou DenseNet que já vimos previamente no curso:\n",
        "\n",
        "*   Arquitetura de uma Generativa $G$;\n",
        "![Rede generativa.](https://www.dropbox.com/s/q6vm8czd7shozdp/GAN_G.png?dl=1)\n",
        "\n",
        "*   Arquitetura de uma Generativa $D$;\n",
        "![Rede discriminativa.](https://www.dropbox.com/s/u4zsh5bgy5hzf4c/GAN_D.png?dl=1)\n",
        "\n",
        "Por muito tempo, porém, foi proibitivo criar GAN convolucionais com muitas camadas, pois haviam problemas sérios de convergência e instabilidade no treinamento. O artigo das [Deep Convolutional GANs](https://arxiv.org/pdf/1511.06434.pdf) mitigou a maior parte desses problemas, propondo uma arquitetura padrão, e hoje em dia é possível treinar uma Convolutional GAN com diversas camadas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nc44m3gCGYmx"
      },
      "source": [
        "# Prática: Implementando uma GAN Convolucional\n",
        "\n",
        "Nessa atividade implementaremos os principais elementos de uma DCGAN, incluindo sua arquitetura e procedimento de treino. Siga os passos delineados nos TO DO's das células de código para implementar a rede generativa, a rede discriminativa, os otimizadores e o procedimento de treino."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Definindo uma inicialização de pesos customizada."
      ],
      "metadata": {
        "id": "27qqI9bbx-tz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Customized weight initialization.\n",
        "def weights_init(m):\n",
        "    classname = m.__class__.__name__\n",
        "    if classname.find('Conv') != -1:\n",
        "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
        "    elif classname.find('BatchNorm') != -1:\n",
        "        nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
        "        nn.init.constant_(m.bias.data, 0)"
      ],
      "metadata": {
        "id": "OetkqB3Gx-aH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvzdYk6v-gdd"
      },
      "source": [
        "# Definindo o Gerador $G$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0FJQQU7-kP5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "846978c6-8b46-4b6b-8862-0913e1dfe77e"
      },
      "source": [
        "# Adversarial Generator.\n",
        "class Generator(nn.Module):\n",
        "    \n",
        "    def __init__(self, input_dim=100, output_ch=1):\n",
        "        \n",
        "        super(Generator, self).__init__()\n",
        "        \n",
        "        self.input_dim = input_dim\n",
        "        self.output_ch = output_ch\n",
        "\n",
        "        '''\n",
        "        TO DO: Implemente a arquitetura da rede generativa (G). Essa arquitetura\n",
        "        será composta por 4 blocos convolucionais, com cada bloco sendo composto\n",
        "        pelos seguintes elementos:\n",
        "        1) Convolução transposta 2d com kernel_size 4, stride 2, padding 1 e\n",
        "           bias=False. Esses parâmetros garantem que a convolução transposta\n",
        "           duplique as dimensões espaciais do tensor de entrada;\n",
        "        2) Batch normalization 2d;\n",
        "        3) ReLU.\n",
        "\n",
        "        Obs.1: O primeiro bloco convolucional deve receber self.input_dim canais\n",
        "        e retornar 256 canais. O segundo e terceiro blocos vão diminuindo essa\n",
        "        quantidade de canais pela metade.\n",
        "\n",
        "        Obs.2: O último (quarto) bloco será composto apenas pela convolução\n",
        "        transposta com uma ativação tangente hiperbólica e deve retornar 1 único\n",
        "        canal de saída, já ele estará tentando sintetizar uma amostra do MNIST\n",
        "        que é um conjunto de dados grayscale.\n",
        "        '''\n",
        "        self.tconv = nn.Sequential(\n",
        "            \n",
        "            # Bloco 1: (B, n_Z, 2, 2) -> (B, 256, 4, 4)\n",
        "            nn.ConvTranspose2d(self.input_dim, 256, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.ReLU(inplace=True),\n",
        "            \n",
        "            # Bloco 2: (B, 256, 4, 4) -> (B, 128, 8, 8)\n",
        "            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.ReLU(inplace=True),\n",
        "            \n",
        "            # Bloco 3: (B, 128, 8, 8) -> (B, 64, 16, 16)\n",
        "            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(inplace=True),\n",
        "            \n",
        "            # Bloco 4: (B, 64, 16, 16) -> (B, Ch, 32, 32)\n",
        "            nn.ConvTranspose2d(64, self.output_ch, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "        \n",
        "    def forward(self, z):\n",
        "        \n",
        "        '''TO DO: Implemente o forward do vetor de ruído z ao longo dessa\n",
        "        arquitetura. Lembre-se de retornar a predição gerada pela rede.'''\n",
        "        x = self.tconv(z)\n",
        "        \n",
        "        return x\n",
        "\n",
        "# Instantiating G.\n",
        "net_G = Generator(input_dim=args['z_dim'], output_ch=n_channels).to(args['device'])\n",
        "net_G.apply(weights_init)\n",
        "\n",
        "# Printing architecture.\n",
        "print(net_G)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generator(\n",
            "  (tconv): Sequential(\n",
            "    (0): ConvTranspose2d(100, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "    (3): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (8): ReLU(inplace=True)\n",
            "    (9): ConvTranspose2d(64, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (10): Tanh()\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsrfQ2eOsEZB"
      },
      "source": [
        "# Definindo o Discriminador $D$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pai5kiN6DW2R",
        "outputId": "8f8e5ff6-670d-4122-db5c-55b03b16934d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Adversarial Discriminator.\n",
        "class Discriminator(nn.Module):\n",
        "    \n",
        "    def __init__(self, input_ch=1, output_size=1, ls=False):\n",
        "        \n",
        "        super(Discriminator, self).__init__()\n",
        "        \n",
        "        self.input_ch = input_ch\n",
        "        self.output_size = output_size\n",
        "        self.ls = ls\n",
        "\n",
        "        '''\n",
        "        TO DO: Implemente a arquitetura da rede discriminativa (D). Essa\n",
        "        arquitetura será composta por 5 blocos convolucionais. Os 4 primeiros\n",
        "        blocos terão os seguintes elementos:\n",
        "        1) Convolução 2d com kernel_size 4, stride 2, padding 1 e\n",
        "           bias=False. Esses parâmetros garantem que a convolução divida as\n",
        "           dimensões espaciais do tensor de entrada por 2;\n",
        "        2) Batch normalization 2d;\n",
        "        3) LeakyReLU com parâmetro de negative slope de valor 0.2.\n",
        "\n",
        "        O último bloco deve ser composto de uma convolução que receba a imagem\n",
        "        inteira (kernel size igual às dimensões espaciais do tensor nesse ponto)\n",
        "        e número de canais de saída do 4º bloco e retornar um tensor com as\n",
        "        dimensões (B, 1, 1, 1), ou seja, uma única predição binária de\n",
        "        classificação para cada amostra do batch. Nesse bloco, adicione uma\n",
        "        ativação sigmóide.\n",
        "\n",
        "        Obs.1: O primeiro bloco convolucional deve receber self.input_ch canais\n",
        "        (no caso do MNIST, 1 canal) e retornar 64 canais. Os blocos 2, 3 e 4\n",
        "        devem progressivamente duplicar esse número de canais do tensor de\n",
        "        entrada.\n",
        "        '''\n",
        "        self.conv = nn.Sequential(\n",
        "\n",
        "            # (B, Ch, 32, 32) -> (B, 64, 16, 16)\n",
        "            nn.Conv2d(self.input_ch, 64, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            \n",
        "            # (B, 64, 16, 16) -> (B, 128, 8, 8)\n",
        "            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            \n",
        "            # (B, 128, 8, 8) -> (B, 256, 4, 4)\n",
        "            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            \n",
        "            # (B, 256, 4, 4) -> (B, 512, 2, 2)\n",
        "            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(512),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "\n",
        "            # (B, 512, 2, 2) -> (B, 1, 1, 1)\n",
        "            nn.Conv2d(512, self.output_size, kernel_size=2, stride=1, bias=False),\n",
        "        )\n",
        "        \n",
        "    def forward(self, x):\n",
        "        \n",
        "        '''TO DO: Implemente o forward ao longo dessa arquitetura.'''\n",
        "        x = self.conv(x)\n",
        "        if not self.ls:\n",
        "            x = F.sigmoid(x)\n",
        "\n",
        "        '''TO DO: Antes de retornar o tensor de saída, aplique o método\n",
        "        `.squeeze()' nas dimensões 3 e 2 do tensor de saída, deixando-o apenas\n",
        "        com as dimensões (B, 1).\n",
        "        '''\n",
        "        return x.squeeze(3).squeeze(2)\n",
        "\n",
        "# Instantiating D.\n",
        "net_D = Discriminator(input_ch=n_channels, ls=args['least_squares']).to(args['device'])\n",
        "\n",
        "net_D.apply(weights_init)\n",
        "\n",
        "# Printing architecture.\n",
        "print(net_D)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Discriminator(\n",
            "  (conv): Sequential(\n",
            "    (0): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "    (3): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (5): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "    (6): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (8): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "    (9): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (10): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (11): LeakyReLU(negative_slope=0.2, inplace=True)\n",
            "    (12): Conv2d(512, 1, kernel_size=(2, 2), stride=(1, 1), bias=False)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeUPraJ3EyeP"
      },
      "source": [
        "# Definindo o otimizadores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I83m3L4FEzgY"
      },
      "source": [
        "'''TO DO: Defina um otimizador passando apenas os parâmetros do modelo\n",
        "generativo. O otimizador deve ser o Adam, com LR igual a args['lr'] e betas\n",
        "iguais a (args['beta'], 0.999).'''\n",
        "# G optimizer.\n",
        "opt_G = optim.Adam(net_G.parameters(),\n",
        "                   lr=args['lr'],\n",
        "                   betas=(args['beta'], 0.999))\n",
        "\n",
        "\n",
        "'''TO DO: Defina um otimizador passando apenas os parâmetros do modelo\n",
        "discriminativo. O otimizador deve ser o Adam, com LR igual a args['lr'] e betas\n",
        "iguais a (args['beta'], 0.999).'''\n",
        "# D optimizer.\n",
        "opt_D = optim.Adam(net_D.parameters(),\n",
        "                   lr=args['lr'],\n",
        "                   betas=(args['beta'], 0.999))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25Cvxt95E-A0"
      },
      "source": [
        "# Definindo a loss composta"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pg8SIpVnFCID"
      },
      "source": [
        "'''TO DO: Definir a loss para treinamento da GAN. A loss utilizada\n",
        "tradicionalmente por GANs é a Binary Cross Entropy (BCE).\n",
        "Obs.1: A BCE no Pytorch tem implementação ligeiramente diferente da Cross\n",
        "Entropy multiclasse. A documentação sobre a classe da BCE é a seguinte:\n",
        "<https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html>'''\n",
        "# Defining adversarial loss.\n",
        "if args['least_squares']:\n",
        "    criterion = nn.MSELoss().to(args['device']) # LSGAN loss.\n",
        "else:\n",
        "    criterion = nn.BCELoss().to(args['device']) # NSGAN loss.\n",
        "\n",
        "'''TO DO (por último): Transforme sua NSGAN numa LSGAN ao simplesmente trocar a\n",
        "BCE pela MSE e remover a ativação sigmóide do final da rede discriminativa. Link\n",
        "para a loss MSE no Pytorch:\n",
        "<https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html>'''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-gUQrX7Fvf-"
      },
      "source": [
        "# Criando funções para Treino e Teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RUHQYKgFxz3"
      },
      "source": [
        "# Training procedure.\n",
        "def train(train_loader,\n",
        "          net_G, net_D,\n",
        "          opt_G, opt_D,\n",
        "          train_loss_G, train_loss_D,\n",
        "          criterion,\n",
        "          epoch):\n",
        "    \n",
        "    tic = time.time()\n",
        "    \n",
        "    # Predefining ones and zeros for batches.\n",
        "    y_real = torch.ones(args['batch_size'], 1).to(args['device'])\n",
        "    y_fake = torch.zeros(args['batch_size'], 1).to(args['device'])\n",
        "\n",
        "    # Setting networks to training mode.\n",
        "    net_D.train()\n",
        "    net_G.train()\n",
        "    \n",
        "    # Iterating over batches.\n",
        "    for i, batch_data in enumerate(train_loader):\n",
        "        \n",
        "        # Obtaining images and labels for batch.\n",
        "        x, labs = batch_data\n",
        "        \n",
        "        '''TO DO: Criar o vetor de ruído armazenado na variável `z' amostrado de\n",
        "        uma distribuição gaussiana utilizando a função `torch.randn()' com a\n",
        "        forma (batch_size, args['z_dim'], 2, 2) que será alimentado para a rede\n",
        "        generativa, resultando numa imagem sintética.'''\n",
        "        # Creating random vector z.\n",
        "        z = torch.randn((x.shape[0], args['z_dim'], 2, 2)) # MNIST (B, 1, 2, 2)\n",
        "        # z = torch.randn((x.shape[0], args['z_dim'], 4, 4)) # CelebA (B, 1, 4, 4)\n",
        "        \n",
        "        # Casting to correct device (x and z).\n",
        "        x = x.to(args['device'])\n",
        "        z = z.to(args['device'])\n",
        "        \n",
        "        ###############\n",
        "        # Updating D. #\n",
        "        ###############\n",
        "        \n",
        "        '''TO DO: Limpar os gradients do otimizador da rede discriminativa por\n",
        "        meio do método `.zero_grad()'.'''\n",
        "        # Clearing the gradients of D optimizer.\n",
        "        opt_D.zero_grad()\n",
        "\n",
        "        '''TO DO: Alimentar as imagens reais para a rede discriminativa e salvar\n",
        "        as predições na variável `D_real'.'''\n",
        "        # Forwarding real data through D.\n",
        "        D_real = net_D(x) # Through D.\n",
        "        \n",
        "        '''TO DO: Computar a loss das predições de D para o batch de imagens\n",
        "        reais em relação aos labels das imagens reais (`y_real') e armazenar na\n",
        "        variável `D_real_loss'.'''\n",
        "        # Computing loss for real data.\n",
        "        D_real_loss = criterion(D_real, y_real)\n",
        "\n",
        "        '''TO DO: Alimentar o vetor de ruído `z' para a rede generativa e salvar\n",
        "        as imagens sintéticas geradas na variável `G_out'.'''\n",
        "        # Forwarding noise through G.\n",
        "        G_out = net_G(z)\n",
        "        \n",
        "        '''TO DO: Alimentar as imagens sintéticas para a rede discriminativa e \n",
        "        salvar as predições na variável `D_fake'.'''\n",
        "        # Forwarding synthetic samples through D.\n",
        "        D_fake = net_D(G_out)\n",
        "        \n",
        "        '''TO DO: Computar a loss das predições de D para o batch de imagens\n",
        "        sintéticas em relação aos labels das imagens sintéticas (`y_fake') e\n",
        "        armazenar na variável `D_fake_loss'.'''\n",
        "        # Computing loss for fake data.\n",
        "        D_fake_loss = criterion(D_fake, y_fake)\n",
        "\n",
        "        '''TO DO: Integrar as losses das imagens reais e sintéticas numa loss só\n",
        "        (`D_loss'). Essa integração das losses num único batch pode ser feita de\n",
        "        forma bastante simples no pytorch por meio da soma.'''\n",
        "        # Computing total loss for D.\n",
        "        D_loss = D_real_loss + D_fake_loss\n",
        "        \n",
        "        '''TO DO: Computar o backpropagation de acordo com a loss integrada da\n",
        "        rede discriminativa por meio do método `.backward()'.'''\n",
        "        # Computing backpropagation for D.\n",
        "        D_loss.backward()\n",
        "        \n",
        "        '''TO DO: Chamar o `.step()' no otimizador da rede discriminativa para a\n",
        "        atualização dos pesos.'''\n",
        "        # Taking step in D optimizer.\n",
        "        opt_D.step()\n",
        "\n",
        "        ###############\n",
        "        # Updating G. #\n",
        "        ###############\n",
        "        \n",
        "        '''TO DO: Limpar os gradients do otimizador da rede generativa por meio\n",
        "        do método `.zero_grad()'.'''\n",
        "        # Clearing the gradients of G optimizer.\n",
        "        opt_G.zero_grad()\n",
        "\n",
        "        '''TO DO: Alimentar o vetor de ruído `z' para G, produzindo um batch de\n",
        "        imagens sintéticas e salvando-o na variável `G_out'.'''\n",
        "        # Forwarding noise through G.\n",
        "        G_out = net_G(z)\n",
        "        \n",
        "        '''TO DO: Alimentar as imagens sintéticas geradas pela rede generativa\n",
        "        para a rede discriminativa, armazenando suas predições na variável\n",
        "        `D_fake'.'''\n",
        "        # Forwarding synthetic samples through D.\n",
        "        D_fake = net_D(G_out)\n",
        "        \n",
        "        '''TO DO: Computar a loss das predições da rede discriminativa passando\n",
        "        as predições sobre os dados sintéticos pela discriminativa, ***mas\n",
        "        enganando-a e passando rótulos reais para essas imagens***. Esse é o\n",
        "        ponto crucial das NSGANs, que otimizam a rede generativa passando\n",
        "        rótulos trocados para amostras sintéticas e assim achando o melhor\n",
        "        conjunto de parâmetros para a rede generativa de forma a enganar a rede\n",
        "        discriminativa com amostras mais e mais fotorrealistas! Armazenar a loss\n",
        "        gerada nesse processo na variável `G_loss'.'''\n",
        "        # Computing loss for G with swapped labels (passing fake data, but \n",
        "        # labels for real data).\n",
        "        G_loss = criterion(D_fake, y_real)\n",
        "        \n",
        "        '''TO DO: Computar os gradientes da armazenada em `G_loss' por meio do\n",
        "        método `.backward()'.'''\n",
        "        # Computing backpropagation for G.\n",
        "        G_loss.backward()\n",
        "        \n",
        "        '''TO DO: Chamar o `.step()' no otimizador da rede generativa.'''\n",
        "        # Taking step in G optimizer.\n",
        "        opt_G.step()\n",
        "        \n",
        "        # Updating lists.\n",
        "        train_loss_G.append(G_loss.data.item())\n",
        "        train_loss_D.append(D_loss.data.item())\n",
        "\n",
        "    toc = time.time()\n",
        "    \n",
        "    # Printing training epoch loss.\n",
        "    print('-------------------------------------------------------------------')\n",
        "    print('[epoch %d], [training time %.2f]' % (\n",
        "        epoch, (toc - tic)))\n",
        "    print('-------------------------------------------------------------------')\n",
        "    \n",
        "    if epoch % args['print_freq'] == 0:\n",
        "        \n",
        "        # Plotting losses.\n",
        "        fig, ax = plt.subplots(1, 2, figsize=(16, 4))\n",
        "\n",
        "        ax[0].plot(np.asarray(train_loss_G), 'r-', label='G loss')\n",
        "        ax[0].legend()\n",
        "        \n",
        "        ax[1].plot(np.asarray(train_loss_D), 'b--', label='D loss')\n",
        "        ax[1].legend()\n",
        "\n",
        "        plt.show()\n",
        "        \n",
        "    return train_loss_G, train_loss_D"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0t2N1kgQNMTl"
      },
      "source": [
        "# Evaluating procedure.\n",
        "def evaluate(net_G, criterion, epoch, sample_z):\n",
        "    \n",
        "    # Setting networks to training mode.\n",
        "    net_D.eval()\n",
        "    net_G.eval()\n",
        "    \n",
        "    # Casting z to correct device.\n",
        "    z = sample_z.to(args['device'])\n",
        "    \n",
        "    # Generating new samples.\n",
        "    G_out = net_G(z)\n",
        "    \n",
        "    # Plotting.\n",
        "    fig, ax = plt.subplots(args['num_samples'],\n",
        "                           args['num_samples'],\n",
        "                           figsize=(8, 8))\n",
        "    \n",
        "    for i in range(args['num_samples']):\n",
        "        \n",
        "        for j in range(args['num_samples']):\n",
        "            \n",
        "            sample = G_out[j * args['num_samples'] + i]\n",
        "            \n",
        "            ax[j, i].imshow(sample.detach().cpu().numpy().squeeze(), cmap=plt.get_cmap('gray')) # MNIST\n",
        "            # ax[j, i].imshow(np.transpose(sample.detach().cpu().numpy().squeeze(), axes=(1, 2, 0))) # CelebA\n",
        "            ax[j, i].set_yticks([])\n",
        "            ax[j, i].set_xticks([])\n",
        "            \n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhC6uqUpJO8Y"
      },
      "source": [
        "# Iterando sobre epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnbf0HVDJKTC",
        "outputId": "9be5bcf5-d767-47fb-85d4-881d3909d96a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Lists for losses.\n",
        "train_loss_G = []\n",
        "train_loss_D = []\n",
        "\n",
        "# Creating random vector z.\n",
        "sample_z = torch.randn((args['num_samples'] * args['num_samples'], args['z_dim'], 2, 2)) # MNIST\n",
        "# sample_z = torch.randn((args['num_samples'] * args['num_samples'], args['z_dim'], 4, 4)) # CelebA\n",
        "\n",
        "# Iterating over epochs.\n",
        "for epoch in range(1, args['epoch_num'] + 1):\n",
        "\n",
        "    # Training function.\n",
        "    train_loss_G, train_loss_D = train(train_loader,\n",
        "                                       net_G, net_D,\n",
        "                                       opt_G, opt_D,\n",
        "                                       train_loss_G, train_loss_D,\n",
        "                                       criterion,\n",
        "                                       epoch)\n",
        "\n",
        "    if epoch % args['print_freq'] == 0:\n",
        "        \n",
        "        # Testing function for sample generation.\n",
        "        evaluate(net_G, criterion, epoch, sample_z)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1967: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
            "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Challenge\n",
        "Modifique o código atual para realizar síntese de imagens no dataset CelebA. O CelebA, assim como o MNIST, também é feito disponível no torchvision com interfaces de acesso simplificado. Para padronizar os tamanhos do dataset para imagens com dimensões (64, 64), adicione um `transforms.Resize(size=(64, 64))`, remova o padding e fixe o `transforms.Normalize()` para operar em imagens RGB no data_transforms.\n",
        "\n",
        "Será necessário realizar alterações em várias outras áreas do código para portar o código para o CelebA!"
      ],
      "metadata": {
        "id": "p5xHbi81pTkF"
      }
    }
  ]
}