gan_colored.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "gan_colored.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "Gjn2p-RsoeW9",
        "HUTo-0qNnB6E",
        "Tdkxz6hYpTYJ"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/darja/NeuralUni/blob/master/gan_colored.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "KBI6MugMm7vP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Environment"
      ]
    },
    {
      "metadata": {
        "id": "CvbS1nthmCvI",
        "colab_type": "code",
        "outputId": "f34ffbdc-f014-4c16-9871-71d15bb9f9b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from __future__ import print_function, division\n",
        "\n",
        "from keras.datasets import mnist\n",
        "from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate, MaxPool2D, Conv2DTranspose, ZeroPadding2D\n",
        "from keras.layers import BatchNormalization, Activation, ZeroPadding2D\n",
        "from keras.layers.merge import Concatenate\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras.layers.convolutional import UpSampling2D, Conv2D\n",
        "from keras.models import Sequential, Model\n",
        "from keras.optimizers import Adam, RMSprop\n",
        "from keras.utils import to_categorical\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import Image\n",
        "\n",
        "from keras import backend as K\n",
        "\n",
        "\n",
        "import os\n",
        "import random\n",
        "import sys\n",
        "import datetime\n",
        "import imageio\n",
        "\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Gjn2p-RsoeW9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Downloading Training Set from Gogle Drive"
      ]
    },
    {
      "metadata": {
        "id": "ipy4NiSXobvB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "872b6cc1-1eff-426b-d6a3-603c3fec1d44"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6WfhjVzlobuj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 20145
        },
        "outputId": "8d3f8ddc-0337-4843-a48e-a44084800e36"
      },
      "cell_type": "code",
      "source": [
        "!ls /content/gdrive/'My Drive'/neural\n",
        "!cp /content/gdrive/'My Drive'/neural/ships_256.zip ships_256.zip\n",
        "!unzip ships_256.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "03_cifar10_report_ryazhskikh.gdoc  cargo_256.zip  ships_400.zip\n",
            "boatset.zip\t\t\t   ships_256.zip\n",
            "Archive:  ships_256.zip\n",
            "   creating: ships_256/\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HUTo-0qNnB6E",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Input Files and Reading Operations"
      ]
    },
    {
      "metadata": {
        "id": "yYIMLp6Vl8_y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "K.clear_session()\n",
        "\n",
        "input_folder = \"ships_256\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9JztwFFgowsc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 20111
        },
        "outputId": "7be13777-604e-47e7-d80d-5e7441dac1ed"
      },
      "cell_type": "code",
      "source": [
        "!rm -rf {input_folder}\n",
        "!unzip {input_folder}.zip\n",
        "\n",
        "# !rm -rf __MACOSX\n",
        "# !rm -rf {input_folder}/.ipynb_checkpoints\n",
        "# !rm -rf {input_folder}/.DS_Store"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  ships_256.zip\n",
            "   creating: ships_256/\n",
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            "  inflating: ships_256/ferry_99.jpg  \n",
            "  inflating: ships_256/fishing_test_1.jpg  \n",
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            "  inflating: ships_256/tug_200.jpg   \n",
            "  inflating: ships_256/fishing_val_24.jpg  \n",
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            "  inflating: ships_256/fishing_val_18.jpg  \n",
            "  inflating: ships_256/tug_val_4.jpg  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_1_JlMT-pdoP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "01b4afd4-1db5-4eeb-d857-2f1b2c5c0a76"
      },
      "cell_type": "code",
      "source": [
        "def inputFiles():\n",
        "    return os.listdir(input_folder)\n",
        "\n",
        "def read_image(filename):\n",
        "#     print(filename)\n",
        "    path = \"%s/%s\" % (input_folder, filename)\n",
        "    return imageio.imread(path)\n",
        "\n",
        "def read_images(files):\n",
        "    imgs = []\n",
        "    for f in files:\n",
        "        im = read_image(f)\n",
        "#         print(im.shape)\n",
        "        imgs.append(im)\n",
        "    return np.array(imgs)\n",
        "\n",
        "\n",
        "files = inputFiles()\n",
        "print(\"Files count: \", len(files))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files count:  1180\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "S0ENCiRNpXGv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Sample image"
      ]
    },
    {
      "metadata": {
        "id": "doYZlPMipap2",
        "colab_type": "code",
        "outputId": "1c7dc22f-942a-46dd-8a16-4bcd3e33e6d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "cell_type": "code",
      "source": [
        "filename = files[5]\n",
        "print(filename)\n",
        "with open(\"%s/%s\" % (input_folder, filename),'rb') as f:\n",
        "    display(Image(data=f.read(), format='jpg'))\n",
        "    \n",
        "content = read_image(filename)\n",
        "print(content.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fishing_181.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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tOenp6fz2fnn8N3IX9IPycc1px/8AXzn9GGfr+vVQBhtCuTjA9c49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\nvsttZDS9Pjud88y3HmxTyHF4bhnNquXRw+NxmYLF4KssTyS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iWLaELxTvg6LWI5ZRh8Dp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fTWutWn/FJSa0viLxnY6Ze2WntCt3c2c2h6WgvruzsX05tSQ3Sh4o5fk8rowWb067pyqVqfK6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\npXk8PKnOm99VKDdpO65kpJvyt73/2Q==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(256, 256, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Tdkxz6hYpTYJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Input Parameters"
      ]
    },
    {
      "metadata": {
        "id": "bi72yllrmzc8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "epochs = 100\n",
        "latent_dim = 100\n",
        "batch_size = 6\n",
        "steps_per_epoch = len(files) // batch_size\n",
        "\n",
        "img_rows = 256\n",
        "img_cols = 256\n",
        "channels = 3\n",
        "\n",
        "lr = 1e-4\n",
        "# optimizer = Adam(lr)\n",
        "optimizer = RMSprop(lr)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qVURvbFYnHDP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Output Сonfiguration"
      ]
    },
    {
      "metadata": {
        "id": "N_iT-kKhIz5q",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Utilities"
      ]
    },
    {
      "metadata": {
        "id": "7hjZtfU0IzMX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def optimizer_string():\n",
        "    if (type(optimizer) == Adam):\n",
        "        return \"adam_%.0E\" % lr\n",
        "    elif (type(optimizer) == RMSprop):\n",
        "        return \"rmsprop_%.0E\" % lr\n",
        "    else:\n",
        "        return optimizer\n",
        "\n",
        "def config_string(prefix):\n",
        "    dt = datetime.datetime.now().strftime('%Y%m%d-%H%M')\n",
        "    return '%s_%s_latent_%s-batch_%s_%s' % (prefix, optimizer_string(), latent_dim, batch_size, dt)\n",
        "\n",
        "def folder_to_gif(folder, output_file):\n",
        "    images = []\n",
        "    for index in np.arange(0, epochs):\n",
        "        images.append(imageio.imread(\"%s/%s.jpg\" % (folder, index)))\n",
        "\n",
        "    imageio.mimsave(output_file, images)\n",
        "    \n",
        "def generate_config_name(prefix):\n",
        "    return \"%s_%s_%s\" % (prefix, config_string, dt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gJjgC0i5I6Lv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Output Folder"
      ]
    },
    {
      "metadata": {
        "id": "mRKn324zmvfA",
        "colab_type": "code",
        "outputId": "ef7e6b37-1dd0-4b6a-80f1-ac9de7edf7dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "output_folder = config_string(\"gen\")\n",
        "print(output_folder)\n",
        "!rm -fr {output_folder}\n",
        "!mkdir {output_folder}"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gen_rmsprop_1E-04_latent_100-batch_6_20190328-1229\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3kvYvOP4nTN2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# GAN"
      ]
    },
    {
      "metadata": {
        "id": "gJXUhbLGnUok",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# del gan\n",
        "import itertools\n",
        "\n",
        "class GAN():\n",
        "    def __init__(self):\n",
        "        self.img_rows = img_rows\n",
        "        self.img_cols = img_cols\n",
        "        self.channels = channels\n",
        "        self.img_shape = (self.img_rows, self.img_cols, self.channels)\n",
        "        self.latent_dim = latent_dim\n",
        "\n",
        "        # Build and compile the discriminator\n",
        "        self.discriminator = self.build_discriminator()       \n",
        "        self.discriminator.compile(loss='binary_crossentropy',\n",
        "            optimizer=optimizer,\n",
        "            metrics=['accuracy'])\n",
        "\n",
        "        # Build the generator\n",
        "        self.generator = self.build_generator()\n",
        "\n",
        "        # The generator takes noise as input and generates imgs\n",
        "        z = Input(shape=(self.latent_dim,))\n",
        "        img = self.generator(z)\n",
        "\n",
        "        # For the combined model we will only train the generator\n",
        "        self.discriminator.trainable = False\n",
        "\n",
        "        # The discriminator takes generated images as input and determines validity\n",
        "        validity = self.discriminator(img)\n",
        "\n",
        "        # The combined model  (stacked generator and discriminator)\n",
        "        # Trains the generator to fool the discriminator\n",
        "        self.combined = Model(z, validity)\n",
        "        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)\n",
        "        self.combined.summary()\n",
        "\n",
        "\n",
        "    def build_generator(self):\n",
        "        print(\"Creating generator\")\n",
        "        \n",
        "        model = Sequential(name=\"generator\")\n",
        "\n",
        "        model.add(Dense(self.img_rows * self.img_cols * self.channels, activation=\"relu\", input_dim=self.latent_dim))\n",
        "        model.add(Reshape((self.img_rows // 4, self.img_cols // 4, 16 * self.channels)))\n",
        "        model.add(UpSampling2D())\n",
        "        model.add(Conv2D(48, kernel_size=3, padding=\"same\"))\n",
        "        model.add(BatchNormalization(momentum=0.8))\n",
        "        model.add(LeakyReLU(alpha=0.2))\n",
        "        model.add(UpSampling2D())\n",
        "        model.add(Conv2D(96, kernel_size=3, padding=\"same\"))\n",
        "        model.add(BatchNormalization(momentum=0.8))\n",
        "        model.add(LeakyReLU(alpha=0.2))\n",
        "        model.add(Conv2D(self.channels, kernel_size=3, padding=\"same\"))\n",
        "        model.add(Activation(\"tanh\"))\n",
        "\n",
        "        return model\n",
        "    \n",
        "    def build_discriminator(self):\n",
        "        print(\"Creating discriminator\")\n",
        "        \n",
        "        model = Sequential(name=\"discriminator\")\n",
        "\n",
        "        model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding=\"same\"))\n",
        "        model.add(LeakyReLU(alpha=0.2))\n",
        "        model.add(Dropout(0.25))\n",
        "        model.add(Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n",
        "        model.add(BatchNormalization(momentum=0.8))\n",
        "        model.add(LeakyReLU(alpha=0.2))\n",
        "        model.add(Dropout(0.25))\n",
        "        model.add(Conv2D(32, kernel_size=3, strides=2, padding=\"same\"))\n",
        "        model.add(BatchNormalization(momentum=0.8))\n",
        "        model.add(LeakyReLU(alpha=0.2))\n",
        "#         model.add(Dropout(0.25))\n",
        "        model.add(Flatten())\n",
        "        model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "        return model\n",
        "    \n",
        "    def train(self, epochs, batch_size=16, sample_interval=50):\n",
        "        # Adversarial ground truths\n",
        "        valid = np.ones((batch_size, 1))\n",
        "        fake = np.zeros((batch_size, 1))\n",
        "\n",
        "        for epoch in range(epochs):\n",
        "            arr = np.arange(len(files)) \n",
        "            np.random.shuffle(arr) \n",
        "            rand_files = np.asarray(files)[arr] \n",
        "            t = itertools.cycle(rand_files) \n",
        "        \n",
        "            for i in range(steps_per_epoch):\n",
        "                print('\\rEpoch ', end='%s: %s of %s ' % (epoch, i, steps_per_epoch))\n",
        "                # ---------------------\n",
        "                #  Train Discriminator\n",
        "                # ---------------------\n",
        "\n",
        "                # Select a random batch of images\n",
        "                files_batch = []\n",
        "                for i in range(batch_size):\n",
        "                    files_batch.append(next(t))\n",
        "                imgs = read_images(files_batch)\n",
        "                imgs = imgs / 127.5 - 1\n",
        "\n",
        "                noise = np.random.normal(0, 1, (batch_size, self.latent_dim))\n",
        "\n",
        "                # Generate a batch of new images\n",
        "                gen_imgs = self.generator.predict(noise)\n",
        "\n",
        "                # Train the discriminator\n",
        "                d_loss_real = self.discriminator.train_on_batch(imgs, valid)\n",
        "                d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)\n",
        "                d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "    \n",
        "                # ---------------------\n",
        "                #  Train Generator\n",
        "                # ---------------------\n",
        "\n",
        "                noise = np.random.normal(0, 1, (batch_size, self.latent_dim))\n",
        "\n",
        "                # Train the generator (to have the discriminator label samples as valid)\n",
        "                g_loss = self.combined.train_on_batch(noise, valid)\n",
        "\n",
        "#                 print (\"%d [D loss: %f] [D acc.: %.2f%%, fake %.2f%%, real %.2f%%] [G loss: %f]\" % (epoch, d_loss[0], 100*d_loss[1], 100*d_loss_fake[1], 100*d_loss_real[1], g_loss))\n",
        "\n",
        "            # Generate sample image for every epoch\n",
        "            self.sample_images(epoch)\n",
        "\n",
        "\n",
        "    def sample_images(self, epoch):\n",
        "        r, c = 1, 1\n",
        "        noise = np.random.normal(0, 1, (r * c, self.latent_dim))\n",
        "        gen_imgs = self.generator.predict(noise)\n",
        "        gen_imgs = 0.5 * gen_imgs + 0.5\n",
        "    \n",
        "        imageio.imwrite(\"%s/%d.jpg\" % (output_folder, epoch), gen_imgs[0])\n",
        "\n",
        "gan = GAN()\n",
        "gan.train(epochs=epochs + 1, batch_size=batch_size, sample_interval=sample_interval)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hnBRr0hyJQVo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# View Result"
      ]
    },
    {
      "metadata": {
        "id": "wAlr1E0XnqKv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training Samples GIF"
      ]
    },
    {
      "metadata": {
        "id": "Lti4ClYPEB4F",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "output_file = output_folder + \".gif\"\n",
        "folder_to_gif(output_folder, output_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "B4mEbYrgJVhv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training Samples Archive"
      ]
    },
    {
      "metadata": {
        "id": "Vjzdt_dEd56i",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!zip -r {output_folder}.zip {output_folder}"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}