C1GAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function, division\n",
    "import scipy\n",
    "\n",
    "from keras.datasets import mnist\n",
    "#from keras_contrib.layers.normalization import InstanceNormalization\n",
    "from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate\n",
    "from keras.layers import BatchNormalization, Activation, ZeroPadding2D , merge, concatenate\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers.convolutional import UpSampling2D, Conv2D\n",
    "from keras.layers.core import RepeatVector, Permute\n",
    "from keras.models import Sequential, Model\n",
    "from keras.optimizers import Adam\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "from data_loader_ab import DataLoader\n",
    "from skimage.io import imsave\n",
    "from skimage.color import rgb2lab, lab2rgb, rgb2gray, gray2rgb\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class C1GAN():\n",
    "    def __init__(self):\n",
    "        # Input shape\n",
    "        self.img_rows = 256\n",
    "        self.img_cols = 256\n",
    "        self.G_channels = 1\n",
    "        self.D_channels = 2\n",
    "        self.G_img_shape = (self.img_rows, self.img_cols, self.G_channels)\n",
    "        self.D_img_shape = (self.img_rows, self.img_cols, self.D_channels)\n",
    "        # Configure data loader\n",
    "        self.dataset_name = 'place365'\n",
    "        self.data_loader = DataLoader(dataset_name=self.dataset_name,\n",
    "                                      img_res=(self.img_rows, self.img_cols))\n",
    "\n",
    "\n",
    "        # Calculate output shape of D (PatchGAN)\n",
    "        patch = int(self.img_rows / 2**4)\n",
    "        self.disc_patch = (patch, patch, 1)\n",
    "        \n",
    "        # Number of filters in the first layer of G and D\n",
    "        self.gf = 64\n",
    "        self.df = 64\n",
    "        \n",
    "        \n",
    "        \n",
    "        optimizer = Adam(lr=0.0002, beta_1=0.5)\n",
    "        optimizerD = Adam(lr=3.16e-5, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
    "        \n",
    "        # Build and compile the discriminator\n",
    "        self.discriminator = self.build_discriminator()\n",
    "        self.discriminator.compile(loss='mse',\n",
    "            optimizer=optimizerD,\n",
    "            metrics=['accuracy'])\n",
    "\n",
    "        #-------------------------\n",
    "        # Construct Computational\n",
    "        #   Graph of Generator\n",
    "        #-------------------------\n",
    "\n",
    "        # Build the generator\n",
    "        self.generator = self.build_generator()\n",
    "\n",
    "        # Input images and their conditioning images\n",
    "        img_A = Input(shape=self.D_img_shape) \n",
    "        img_B = Input(shape=self.G_img_shape)\n",
    "        embeddings = Input(shape=(1000,))\n",
    "        # By conditioning on B, throught inception embeddings, generate a fake version of A\n",
    "        fake_A = self.generator([img_B , embeddings])\n",
    "\n",
    "        # For the combined model we will only train the generator\n",
    "        self.discriminator.trainable = False\n",
    "\n",
    "        # Discriminators determines validity of translated images / condition pairs\n",
    "        valid = self.discriminator([fake_A, img_B])\n",
    "\n",
    "        self.combined = Model(inputs=[img_A, img_B , embeddings], outputs=[valid, fake_A])\n",
    "        self.combined.compile(loss=['mse', 'mse'],\n",
    "                              loss_weights=[1, 100],\n",
    "                              optimizer=optimizer)\n",
    "\n",
    "    def build_generator(self):\n",
    "\n",
    "        \n",
    "        embed_input = Input(shape=(1000,))\n",
    "        #Encoder\n",
    "        encoder_input = Input(shape=(256, 256, 1,))\n",
    "        encoder_output = Conv2D(64, (3,3), activation='relu', padding='same', strides=2)(encoder_input)\n",
    "        #encoder_output = BatchNormalization(momentum=0.8)(encoder_output)\n",
    "        encoder_output = Conv2D(128, (3,3), activation='relu', padding='same')(encoder_output)\n",
    "        encoder_output = Conv2D(128, (3,3), activation='relu', padding='same', strides=2)(encoder_output)\n",
    "        encoder_output = Conv2D(256, (3,3), activation='relu', padding='same')(encoder_output)\n",
    "        encoder_output = Conv2D(256, (3,3), activation='relu', padding='same', strides=2)(encoder_output)\n",
    "        encoder_output = Conv2D(512, (3,3), activation='relu', padding='same')(encoder_output)\n",
    "        encoder_output = Conv2D(512, (3,3), activation='relu', padding='same')(encoder_output)\n",
    "        encoder_output = Conv2D(256, (3,3), activation='relu', padding='same')(encoder_output)\n",
    "\n",
    "        #Fusion\n",
    "        fusion_output = RepeatVector(32 * 32)(embed_input) \n",
    "        fusion_output = Reshape(([32, 32, 1000]))(fusion_output)\n",
    "        fusion_output = concatenate([encoder_output, fusion_output], axis=3) \n",
    "        fusion_output = Conv2D(256, (1, 1), activation='relu', padding='same')(fusion_output) \n",
    "\n",
    "        #Decoder\n",
    "        decoder_output = Conv2D(128, (3,3), activation='relu', padding='same')(fusion_output)\n",
    "        decoder_output = UpSampling2D((2, 2))(decoder_output)\n",
    "        decoder_output = Conv2D(64, (3,3), activation='relu', padding='same')(decoder_output)\n",
    "        decoder_output = UpSampling2D((2, 2))(decoder_output)\n",
    "        decoder_output = Conv2D(32, (3,3), activation='relu', padding='same')(decoder_output)\n",
    "        decoder_output = Conv2D(16, (3,3), activation='relu', padding='same')(decoder_output)\n",
    "        decoder_output = Conv2D(2, (3, 3), activation='tanh', padding='same')(decoder_output)\n",
    "        decoder_output = UpSampling2D((2, 2))(decoder_output)\n",
    "\n",
    "        model = Model(inputs=[encoder_input, embed_input], outputs=decoder_output)\n",
    "\n",
    "        return model\n",
    "\n",
    "    def build_discriminator(self):\n",
    "\n",
    "        def d_layer(layer_input, filters, f_size=4, bn=True):\n",
    "            \"\"\"Discriminator layer\"\"\"\n",
    "            d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)\n",
    "            d = LeakyReLU(alpha=0.2)(d)\n",
    "            if bn:\n",
    "                d = BatchNormalization(momentum=0.8)(d)\n",
    "            return d\n",
    "\n",
    "        img_A = Input(shape=self.D_img_shape)\n",
    "        img_B = Input(shape=self.G_img_shape)\n",
    "\n",
    "        # Concatenate image and conditioning image by channels to produce input\n",
    "        combined_imgs = Concatenate(axis=-1)([img_A, img_B])\n",
    "\n",
    "        d1 = d_layer(combined_imgs, self.df, bn=False)\n",
    "        d2 = d_layer(d1, self.df*2)\n",
    "        d3 = d_layer(d2, self.df*4)\n",
    "        d4 = d_layer(d3, self.df*8)\n",
    "\n",
    "        validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)\n",
    "\n",
    "        return Model([img_A, img_B], validity)\n",
    "\n",
    "    def train(self, epochs, batch_size=1, sample_interval=50):\n",
    "\n",
    "        start_time = datetime.datetime.now()\n",
    "\n",
    "        # Adversarial loss ground truths\n",
    "        valid = np.ones((batch_size,) + self.disc_patch)\n",
    "        fake = np.zeros((batch_size,) + self.disc_patch)\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "            \n",
    "            for batch_i, (imgs_A, embeddings, imgs_B) in enumerate(self.data_loader.load_batch(batch_size)):\n",
    "\n",
    "                # ---------------------\n",
    "                #  Train Discriminator\n",
    "                # ---------------------\n",
    "\n",
    "                # Condition on B and generate a translated version\n",
    "                #imgs_B = np.expand_dims(imgs_B, axis=0)\n",
    "                fake_A = self.generator.predict([imgs_B , embeddings])\n",
    "\n",
    "                # Train the discriminators (original images = real / generated = Fake)\n",
    "                d_loss_real = self.discriminator.train_on_batch([imgs_A, imgs_B], valid)\n",
    "                d_loss_fake = self.discriminator.train_on_batch([fake_A, imgs_B], fake)\n",
    "                d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
    "\n",
    "                # -----------------\n",
    "                #  Train Generator\n",
    "                # -----------------\n",
    "\n",
    "                # Train the generators\n",
    "                g_loss = self.combined.train_on_batch([imgs_A, imgs_B , embeddings], [valid, imgs_A])\n",
    "\n",
    "                elapsed_time = datetime.datetime.now() - start_time\n",
    "                # Plot the progress\n",
    "                print (\"[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %3d%%] [G loss: %f] time: %s\" % (epoch, epochs,\n",
    "                                                                        batch_i, self.data_loader.n_batches,\n",
    "                                                                        d_loss[0], 100*d_loss[1],\n",
    "                                                                        g_loss[0],\n",
    "                                                                        elapsed_time))\n",
    "\n",
    "                # If at save interval => save generated image samples\n",
    "                if batch_i % sample_interval == 0:\n",
    "                    self.sample_images(epoch, batch_i)\n",
    "               \n",
    "            self.save(epoch)\n",
    "            print(\"weights saved...\")\n",
    "            \n",
    "            \n",
    "\n",
    "    def sample_images(self, epoch, batch_i):\n",
    "        os.makedirs('images/C1-%s-%d' % (epoch , batch_i), exist_ok=True)\n",
    "        r, c = 3, 3\n",
    "\n",
    "        imgs_A, embed ,imgs_B = self.data_loader.load_data(batch_size=40)\n",
    "        fake_A = self.generator.predict([imgs_B , embed])\n",
    "        fake_A = fake_A * 128\n",
    "\n",
    "        # Output colorizations\n",
    "        for i in range(len(fake_A)):\n",
    "            cur = np.zeros((256, 256, 3))\n",
    "            cur[:,:,0] = imgs_B[i][:,:,0]\n",
    "            cur[:,:,1:] = fake_A[i]\n",
    "            imsave(\"images/C1-%s-%d/gen_%d.png\" % (epoch,batch_i, i), lab2rgb(cur))\n",
    "            \n",
    "    def sample_imagesOLD(self, epoch, batch_i , save_model = True):    \n",
    "        os.makedirs('images/C1-%s-%d' % (epoch , batch_i), exist_ok=True)\n",
    "        imgs_A, embed ,imgs_B = self.data_loader.load_data(batch_size=25)\n",
    "        fake_A = self.generator.predict([imgs_B , embed])\n",
    "        fake_A = fake_A * 128\n",
    "        imgs_A = imgs_A*128\n",
    "        \n",
    "        titles = ['B&W', 'Generated', 'Original']\n",
    "        \n",
    "        \n",
    "        for i in range(len(fake_A)):\n",
    "\n",
    "            fig, axs = plt.subplots(3)\n",
    "            axs[0].set_title(titles[0])\n",
    "            axs[1].set_title(titles[1])\n",
    "            axs[2].set_title(titles[2])\n",
    "            \n",
    "            gen = np.zeros((256, 256, 3))\n",
    "            gen[:,:,0] = imgs_B[i][:,:,0]\n",
    "            gen[:,:,1:] = fake_A[i]\n",
    "            rgb_gen = lab2rgb(gen)\n",
    "            \n",
    "            real = np.zeros((256, 256, 3))\n",
    "            real[:,:,0] = imgs_B[i][:,:,0]\n",
    "            real[:,:,1:] = imgs_A[i]\n",
    "            rgb_real = lab2rgb(real)\n",
    "            \n",
    "            \n",
    "            gen = np.zeros((256, 256, 3))\n",
    "            gen[:,:,0] = imgs_B[i][:,:,0]\n",
    "            gen[:,:,1:] = imgs_A[i]/128\n",
    "            rgb_bew = lab2rgb(gen)\n",
    "\n",
    "            \n",
    "            \n",
    "            axs[0].imshow(rgb_bew)\n",
    "            axs[1].imshow(rgb_gen)\n",
    "            axs[2].imshow(rgb_real)\n",
    "            \n",
    "            #imsave(\"images/C1-%s-%d/%d_bew.png\" % (epoch,batch_i, i), rgb_bew)\n",
    "            #imsave(\"images/C1-%s-%d/%d_real.png\" % (epoch,batch_i, i), rgb_real)\n",
    "            #imsave(\"images/C1-%s-%d/%d_gen.png\" % (epoch,batch_i, i), rgb_gen)\n",
    "            \n",
    "            fig.savefig(\"images/C1-%s-%d/turing%d.png\" % (epoch,batch_i, i))\n",
    "            plt.close()\n",
    "            \n",
    "        if(save_model):    \n",
    "            self.save(epoch)\n",
    "        \n",
    "    def save(self,ep):\n",
    "        os.makedirs('saved_model/C1-%s' % ep, exist_ok=True)\n",
    "        self.combined.save_weights(\"./saved_model/C1-%s/modelW_epoch_%d.h5\" % (ep , ep))\n",
    "        self.generator.save_weights(\"./saved_model/C1-%s/genW_epoch_%d.h5\" % (ep, ep))\n",
    "        self.discriminator.save_weights(\"./saved_model/C1-%s/disW_epoch_%d.h5\" % (ep, ep))\n",
    "        \n",
    "    def load(self,ep):\n",
    "        self.combined.load_weights(\"./saved_model/C1-%s/modelW_epoch_%d.h5\" % (ep , ep))\n",
    "        self.generator.load_weights(\"./saved_model/C1-%s/genW_epoch_%d.h5\" % (ep, ep))\n",
    "        self.discriminator.load_weights(\"./saved_model/C1-%s/disW_epoch_%d.h5\" % (ep, ep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gan = C1GAN()\n",
    "gan.load(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\miche\\Anaconda3\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n",
      "C:\\Users\\miche\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0/10] [Batch 0/350] [D loss: 0.266566, acc:  32%] [G loss: 1.143466] time: 0:00:16.366317\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\miche\\Anaconda3\\lib\\site-packages\\skimage\\util\\dtype.py:141: UserWarning: Possible precision loss when converting from float64 to uint8\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0/10] [Batch 1/350] [D loss: 0.259885, acc:  53%] [G loss: 1.130720] time: 0:00:29.419494\n",
      "[Epoch 0/10] [Batch 2/350] [D loss: 0.256177, acc:  57%] [G loss: 1.258416] time: 0:00:35.805417\n",
      "[Epoch 0/10] [Batch 3/350] [D loss: 0.253612, acc:  50%] [G loss: 1.476810] time: 0:00:42.192338\n",
      "[Epoch 0/10] [Batch 4/350] [D loss: 0.252900, acc:  48%] [G loss: 1.317581] time: 0:00:48.661043\n",
      "[Epoch 0/10] [Batch 5/350] [D loss: 0.253167, acc:  46%] [G loss: 1.326573] time: 0:00:55.065917\n",
      "[Epoch 0/10] [Batch 6/350] [D loss: 0.253152, acc:  50%] [G loss: 1.300932] time: 0:01:01.481731\n",
      "[Epoch 0/10] [Batch 7/350] [D loss: 0.252400, acc:  49%] [G loss: 1.284121] time: 0:01:07.862670\n",
      "[Epoch 0/10] [Batch 8/350] [D loss: 0.252998, acc:  43%] [G loss: 1.077934] time: 0:01:14.258569\n",
      "[Epoch 0/10] [Batch 9/350] [D loss: 0.252083, acc:  50%] [G loss: 1.191026] time: 0:01:20.645523\n",
      "[Epoch 0/10] [Batch 10/350] [D loss: 0.251629, acc:  47%] [G loss: 1.300182] time: 0:01:27.015459\n",
      "[Epoch 0/10] [Batch 11/350] [D loss: 0.252638, acc:  48%] [G loss: 1.472468] time: 0:01:33.408398\n",
      "[Epoch 0/10] [Batch 12/350] [D loss: 0.252429, acc:  52%] [G loss: 1.223845] time: 0:01:39.785315\n",
      "[Epoch 0/10] [Batch 13/350] [D loss: 0.251340, acc:  48%] [G loss: 1.112201] time: 0:01:46.151294\n",
      "[Epoch 0/10] [Batch 14/350] [D loss: 0.251220, acc:  48%] [G loss: 1.162718] time: 0:01:52.487384\n",
      "[Epoch 0/10] [Batch 15/350] [D loss: 0.251652, acc:  47%] [G loss: 1.119345] time: 0:01:58.837405\n",
      "[Epoch 0/10] [Batch 16/350] [D loss: 0.251891, acc:  52%] [G loss: 1.104950] time: 0:02:05.123598\n",
      "[Epoch 0/10] [Batch 17/350] [D loss: 0.252228, acc:  45%] [G loss: 1.529463] time: 0:02:11.392836\n",
      "[Epoch 0/10] [Batch 18/350] [D loss: 0.251548, acc:  50%] [G loss: 1.278983] time: 0:02:17.758782\n",
      "[Epoch 0/10] [Batch 19/350] [D loss: 0.251988, acc:  50%] [G loss: 1.245606] time: 0:02:24.069908\n",
      "[Epoch 0/10] [Batch 20/350] [D loss: 0.251936, acc:  50%] [G loss: 1.404367] time: 0:02:30.396990\n",
      "[Epoch 0/10] [Batch 21/350] [D loss: 0.251638, acc:  48%] [G loss: 1.279584] time: 0:02:36.703161\n",
      "[Epoch 0/10] [Batch 22/350] [D loss: 0.251858, acc:  48%] [G loss: 1.106699] time: 0:02:43.045171\n",
      "[Epoch 0/10] [Batch 23/350] [D loss: 0.251413, acc:  47%] [G loss: 1.376021] time: 0:02:49.349316\n",
      "[Epoch 0/10] [Batch 24/350] [D loss: 0.251810, acc:  51%] [G loss: 1.127939] time: 0:02:55.646478\n",
      "[Epoch 0/10] [Batch 25/350] [D loss: 0.251901, acc:  49%] [G loss: 1.138409] time: 0:03:01.961593\n",
      "[Epoch 0/10] [Batch 26/350] [D loss: 0.251641, acc:  46%] [G loss: 1.194580] time: 0:03:08.262776\n",
      "[Epoch 0/10] [Batch 27/350] [D loss: 0.251856, acc:  52%] [G loss: 1.256117] time: 0:03:14.563928\n",
      "[Epoch 0/10] [Batch 28/350] [D loss: 0.251993, acc:  48%] [G loss: 1.149622] time: 0:03:20.893010\n",
      "[Epoch 0/10] [Batch 29/350] [D loss: 0.251567, acc:  47%] [G loss: 1.213789] time: 0:03:27.198150\n",
      "[Epoch 0/10] [Batch 30/350] [D loss: 0.251371, acc:  48%] [G loss: 1.156102] time: 0:03:33.516254\n",
      "[Epoch 0/10] [Batch 31/350] [D loss: 0.251467, acc:  48%] [G loss: 1.236449] time: 0:03:39.846297\n",
      "[Epoch 0/10] [Batch 32/350] [D loss: 0.251578, acc:  47%] [G loss: 1.196718] time: 0:03:46.196349\n",
      "[Epoch 0/10] [Batch 33/350] [D loss: 0.251670, acc:  50%] [G loss: 1.241807] time: 0:03:52.501459\n",
      "[Epoch 0/10] [Batch 34/350] [D loss: 0.251946, acc:  50%] [G loss: 1.174577] time: 0:03:58.806632\n",
      "[Epoch 0/10] [Batch 35/350] [D loss: 0.251651, acc:  51%] [G loss: 1.068402] time: 0:04:05.117758\n",
      "[Epoch 0/10] [Batch 36/350] [D loss: 0.251545, acc:  48%] [G loss: 1.049722] time: 0:04:11.405913\n",
      "[Epoch 0/10] [Batch 37/350] [D loss: 0.251660, acc:  49%] [G loss: 1.343138] time: 0:04:17.700116\n",
      "[Epoch 0/10] [Batch 38/350] [D loss: 0.251745, acc:  48%] [G loss: 1.367854] time: 0:04:24.037140\n",
      "[Epoch 0/10] [Batch 39/350] [D loss: 0.251516, acc:  47%] [G loss: 1.193794] time: 0:04:30.495901\n",
      "[Epoch 0/10] [Batch 40/350] [D loss: 0.251723, acc:  46%] [G loss: 1.321678] time: 0:04:36.896755\n",
      "[Epoch 0/10] [Batch 41/350] [D loss: 0.252123, acc:  47%] [G loss: 1.296183] time: 0:04:43.221876\n",
      "[Epoch 0/10] [Batch 42/350] [D loss: 0.251669, acc:  47%] [G loss: 1.073487] time: 0:04:49.535995\n",
      "[Epoch 0/10] [Batch 43/350] [D loss: 0.251870, acc:  49%] [G loss: 1.272326] time: 0:04:55.851107\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "[Epoch 0/10] [Batch 334/350] [D loss: 0.251547, acc:  46%] [G loss: 1.135541] time: 0:35:35.387559\n",
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      "weights saved...\n",
      "[Epoch 1/10] [Batch 0/350] [D loss: 0.251376, acc:  48%] [G loss: 1.153749] time: 0:37:10.839411\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 1/10] [Batch 69/350] [D loss: 0.252044, acc:  46%] [G loss: 1.465897] time: 0:44:30.376179\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 1/10] [Batch 319/350] [D loss: 0.251450, acc:  50%] [G loss: 1.158798] time: 1:10:45.439834\n",
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      "weights saved...\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Epoch 2/10] [Batch 54/350] [D loss: 0.251423, acc:  52%] [G loss: 1.224285] time: 1:19:37.768496\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 2/10] [Batch 138/350] [D loss: 0.251405, acc:  54%] [G loss: 1.439442] time: 1:28:24.537024\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 2/10] [Batch 221/350] [D loss: 0.252032, acc:  47%] [G loss: 1.054479] time: 1:37:10.432886\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 2/10] [Batch 304/350] [D loss: 0.251877, acc:  47%] [G loss: 1.014306] time: 1:45:51.274292\n",
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      "weights saved...\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
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      "weights saved...\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 4/10] [Batch 23/350] [D loss: 0.250979, acc:  47%] [G loss: 1.304015] time: 2:29:51.140794\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 4/10] [Batch 273/350] [D loss: 0.251183, acc:  50%] [G loss: 1.300423] time: 2:56:10.931755\n",
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      "weights saved...\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 5/10] [Batch 7/350] [D loss: 0.251316, acc:  45%] [G loss: 1.192974] time: 3:04:59.797723\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 5/10] [Batch 340/350] [D loss: 0.251344, acc:  39%] [G loss: 1.018480] time: 3:40:02.586293\n",
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      "weights saved...\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 6/10] [Batch 75/350] [D loss: 0.252354, acc:  48%] [G loss: 1.258148] time: 3:48:57.304566\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 6/10] [Batch 325/350] [D loss: 0.251046, acc:  44%] [G loss: 1.186646] time: 4:15:17.758468\n",
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      "weights saved...\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Epoch 7/10] [Batch 60/350] [D loss: 0.251443, acc:  52%] [G loss: 1.245707] time: 4:24:12.340105\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 7/10] [Batch 144/350] [D loss: 0.251145, acc:  48%] [G loss: 1.195626] time: 4:33:00.967663\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 7/10] [Batch 227/350] [D loss: 0.251330, acc:  47%] [G loss: 1.203652] time: 4:41:49.038708\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 7/10] [Batch 310/350] [D loss: 0.251079, acc:  48%] [G loss: 1.111763] time: 4:50:31.372095\n",
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      "weights saved...\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 8/10] [Batch 45/350] [D loss: 0.251222, acc:  48%] [G loss: 1.074386] time: 4:59:25.826073\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[Epoch 8/10] [Batch 129/350] [D loss: 0.251401, acc:  48%] [G loss: 1.156682] time: 5:08:14.792756\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "weights saved...\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 9/10] [Batch 29/350] [D loss: 0.251035, acc:  46%] [G loss: 1.006233] time: 5:34:32.318371\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\miche\\Anaconda3\\lib\\site-packages\\skimage\\color\\colorconv.py:993: UserWarning: Color data out of range: Z < 0 in 1 pixels\n",
      "  warn('Color data out of range: Z < 0 in %s pixels' % invalid[0].size)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 9/10] [Batch 259/350] [D loss: 0.250811, acc:  46%] [G loss: 0.935684] time: 5:58:50.288664\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 9/10] [Batch 342/350] [D loss: 0.251307, acc:  46%] [G loss: 1.254246] time: 6:07:36.027147\n",
      "[Epoch 9/10] [Batch 343/350] [D loss: 0.251288, acc:  49%] [G loss: 1.079780] time: 6:07:42.421084\n",
      "[Epoch 9/10] [Batch 344/350] [D loss: 0.251662, acc:  45%] [G loss: 1.148721] time: 6:07:48.735200\n",
      "[Epoch 9/10] [Batch 345/350] [D loss: 0.251563, acc:  51%] [G loss: 1.037053] time: 6:07:55.051312\n",
      "[Epoch 9/10] [Batch 346/350] [D loss: 0.251328, acc:  47%] [G loss: 1.027043] time: 6:08:01.367393\n",
      "[Epoch 9/10] [Batch 347/350] [D loss: 0.251465, acc:  55%] [G loss: 1.016329] time: 6:08:07.667548\n",
      "[Epoch 9/10] [Batch 348/350] [D loss: 0.251553, acc:  49%] [G loss: 0.952170] time: 6:08:13.970727\n",
      "weights saved...\n"
     ]
    }
   ],
   "source": [
    "gan.train(epochs=10, batch_size=100, sample_interval=175)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\miche\\Anaconda3\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n",
      "C:\\Users\\miche\\Anaconda3\\lib\\site-packages\\skimage\\util\\dtype.py:141: UserWarning: Possible precision loss when converting from float64 to uint8\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n",
      "C:\\Users\\miche\\Anaconda3\\lib\\site-packages\\skimage\\color\\colorconv.py:993: UserWarning: Color data out of range: Z < 0 in 1 pixels\n",
      "  warn('Color data out of range: Z < 0 in %s pixels' % invalid[0].size)\n"
     ]
    }
   ],
   "source": [
    "#gan.save(10)\n",
    "gan.sample_images(11,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.misc import imsave\n",
    "data = DataLoader(dataset_name=\"val_256\",\n",
    "                                      img_res=(256, 256))\n",
    "imgs_A, imgs_B = data.load_data(batch_size=60, is_testing=True)\n",
    "\n",
    "r, c = 3, 3\n",
    "\n",
    "fake_A = gan.generator.predict(imgs_B)\n",
    "\n",
    "gen_imgs = np.concatenate([imgs_B, fake_A, imgs_A])\n",
    "\n",
    "# Rescale images 0 - 1\n",
    "gen_imgs = 0.5 * gen_imgs + 0.5\n",
    "fake_A = 0.5 * fake_A + 0.5\n",
    "imgs_B = 0.5 * imgs_B + 0.5\n",
    "imgs_A = 0.5 * imgs_A + 0.5\n",
    "titles = ['Originale', 'B&W', 'Generata']\n",
    "\n",
    "\n",
    "ic = 0\n",
    "f=0\n",
    "cnt = 0\n",
    "for img in fake_A:\n",
    "    f += 1\n",
    "    imsave('images/test9000/testOutput%d.png' % (f), img)\n",
    "    \n",
    "    if ic==2:\n",
    "        fig, axs = plt.subplots(r, c)\n",
    "        axs[0, 0].set_title(titles[0])\n",
    "        axs[0, 1].set_title(titles[1])\n",
    "        axs[0, 2].set_title(titles[2])\n",
    "        for i in range(r):\n",
    "            axs[i,0].imshow(imgs_A[cnt])\n",
    "            axs[i,1].imshow(imgs_B[cnt])\n",
    "            axs[i,2].imshow(fake_A[cnt])\n",
    "            cnt += 1\n",
    "        \n",
    "        fig.savefig(\"images/test9000/%d.png\" % (cnt))\n",
    "        plt.close()\n",
    "    if ic == 2:\n",
    "        ic=0\n",
    "    else:\n",
    "        ic += 1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs_A, embed ,imgs_B = gan.data_loader.load_data(batch_size=2)\n",
    "fake_A = gan.generator.predict([imgs_B , embed])\n",
    "fake_A = fake_A * 128\n",
    "\n",
    "# Output colorizations\n",
    "for i in range(len(fake_A)):\n",
    "    cur = np.zeros((256, 256, 3))\n",
    "    cur[:,:,0] = imgs_B[i][:,:,0]\n",
    "    cur[:,:,1:] = fake_A[i]\n",
    "    imsave(\"images/test-c1/img_final_\"+str(i)+\".png\", lab2rgb(cur))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
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 },
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