pix2pix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from functools import partial\n",
    "from matplotlib.image import imread\n",
    "from matplotlib import pyplot as plt\n",
    "from glob import glob\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def placeholders():\n",
    "    return (\n",
    "        tf.placeholder(tf.float32, (None, 256, 256, 3), name=\"real_a\"),\n",
    "        tf.placeholder(tf.float32, (None, 256, 256, 3), name=\"real_b\"),\n",
    "        tf.placeholder(tf.float32, (None, 256, 256, 3), name=\"fake_b\"),\n",
    "        tf.placeholder(tf.float32, (None, 256, 256, 6), name=\"real_real\"),\n",
    "        tf.placeholder(tf.float32, (None, 256, 256, 6), name=\"real_fake\"),\n",
    "        tf.placeholder(tf.float32, (), name=\"lambda\"),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_init=tf.initializers.random_normal(mean=0, stddev=0.2)\n",
    "\n",
    "def conv_block(prev_layer, filters, dropout=False, activation_fn=None, norm=False, transpose=False, idx=None):\n",
    "    assert idx is not None, \"idx must be specified\"\n",
    "    \n",
    "    l = prev_layer\n",
    "    with tf.name_scope(f\"unet_block_{idx}\"):\n",
    "        \n",
    "        if activation_fn:\n",
    "            l = activation_fn(l)\n",
    "        if transpose:\n",
    "            l = tf.layers.conv2d_transpose(l, filters, kernel_size=4, strides=2, padding=\"same\", use_bias=False, kernel_initializer=conv_init)\n",
    "        else:\n",
    "            l = tf.layers.conv2d(l, filters, kernel_size=4, strides=2, padding=\"same\", dilation_rate=1, use_bias=False, kernel_initializer=conv_init)\n",
    "        \n",
    "        if norm:\n",
    "            l = tf.layers.batch_normalization(l)\n",
    "\n",
    "    return l\n",
    "\n",
    "def build_generator(x):\n",
    "    with tf.name_scope(\"generator\"):\n",
    "        with tf.name_scope(\"down\"):\n",
    "            activation = partial(tf.nn.leaky_relu, alpha=0.2)\n",
    "            \n",
    "            e1 = conv_block(x, 64, idx=0)\n",
    "            e2 = conv_block(e1, 128, activation_fn=activation, norm=True, idx=1)\n",
    "            e3 = conv_block(e2, 256, activation_fn=activation, norm=True, idx=2)\n",
    "            e4 = conv_block(e3, 512, activation_fn=activation, norm=True, idx=3)\n",
    "            e5 = conv_block(e4, 512, activation_fn=activation, norm=True, idx=4)\n",
    "            e6 = conv_block(e5, 512, activation_fn=activation, norm=True, idx=5)\n",
    "            e7 = conv_block(e6, 512, activation_fn=activation, norm=True, idx=6)\n",
    "            e8 = conv_block(e7, 512, idx=7)\n",
    "        \n",
    "        with tf.name_scope(\"up\"):\n",
    "            activation = partial(tf.nn.relu)\n",
    "            \n",
    "            d1_ = conv_block(e8, 512, transpose=True, activation_fn=activation, norm=True, dropout=True, idx=0)\n",
    "            d1 = tf.concat([d1_, e7], axis=3)\n",
    "            d2_ = conv_block(d1, 512, transpose=True, activation_fn=activation, norm=True, dropout=True, idx=1)\n",
    "            d2 = tf.concat([d2_, e6], axis=3)\n",
    "            d3_ = conv_block(d2, 512, transpose=True, activation_fn=activation, norm=True, dropout=True, idx=2)\n",
    "            d3 = tf.concat([d3_, e5], axis=3)\n",
    "            d4_ = conv_block(d3, 512, transpose=True, activation_fn=activation, norm=True, idx=3)\n",
    "            d4 = tf.concat([d4_, e4], axis=3)\n",
    "            d5_ = conv_block(d4, 512, transpose=True, activation_fn=activation, norm=True, idx=4)\n",
    "            d5 = tf.concat([d5_, e3], axis=3)\n",
    "            d6_ = conv_block(d5, 256, transpose=True, activation_fn=activation, norm=True, idx=5)\n",
    "            d6 = tf.concat([d6_, e2], axis=3)\n",
    "            d7_ = conv_block(d6, 128, transpose=True, activation_fn=activation, norm=True, idx=6)\n",
    "            d7 = tf.concat([d7_, e1], axis=3)\n",
    "            d8 = conv_block(d7 ,   3, transpose=True, activation_fn=activation, norm=True, idx=7)\n",
    "    \n",
    "        out = tf.nn.tanh(d8)\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'generator/Tanh:0' shape=(1, 256, 256, 3) dtype=float32>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "with tf.get_default_graph().as_default():\n",
    "    model = build_generator(tf.constant(np.random.rand(1, 256, 256, 3), dtype=tf.float32))\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dis_conv_block(input, filters, strides=2, activation_fn=None, norm=False, idx=None):\n",
    "    assert idx is not None\n",
    "    \n",
    "    with tf.variable_scope(f\"dis_conv_block_{idx}\", reuse=tf.AUTO_REUSE):\n",
    "        l = input\n",
    "        l = tf.layers.conv2d(l, filters, kernel_size=4, strides=strides, padding=\"same\", dilation_rate=1, use_bias=False, kernel_initializer=conv_init)\n",
    "        \n",
    "        if norm:\n",
    "            l = tf.layers.batch_normalization(l)\n",
    "\n",
    "        if activation_fn:\n",
    "            l = activation_fn(l)\n",
    "        \n",
    "        return l\n",
    "\n",
    "def build_discriminator(x):\n",
    "    activation = partial(tf.nn.leaky_relu, alpha=0.2)\n",
    "    \n",
    "    with tf.name_scope(\"discriminator\"):\n",
    "        l = dis_conv_block(x, 64, activation_fn=activation, idx=0)\n",
    "        l = dis_conv_block(l, 128, activation_fn=activation, norm=True, idx=1)\n",
    "        l = dis_conv_block(l, 256, activation_fn=activation, norm=True, idx=2)\n",
    "        l = dis_conv_block(l, 512, activation_fn=activation, strides=1, norm=True, idx=3)\n",
    "        l = dis_conv_block(l, 512, activation_fn=activation, strides=1, norm=True, idx=4)\n",
    "        l = dis_conv_block(l, 1, activation_fn=activation, strides=1, idx=5)\n",
    "    \n",
    "    return tf.nn.sigmoid(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Sigmoid:0' shape=(1, 32, 32, 1) dtype=float32>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "with tf.get_default_graph().as_default():\n",
    "    model = build_discriminator(tf.constant(np.random.rand(1, 256, 256, 3), dtype=tf.float32))\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "if \"sess\" in globals():\n",
    "    sess.close()\n",
    "\n",
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "real_a_ph, real_b_ph, fake_b_ph, real_real_ph, real_fake_ph, lambda_ph = placeholders()\n",
    "\n",
    "real_real = tf.concat([real_a_ph, real_b_ph], axis=3)\n",
    "fake_discriminator = build_discriminator(real_fake_ph)\n",
    "real_discriminator = build_discriminator(real_real)\n",
    "\n",
    "zeros = tf.zeros(tf.shape(fake_discriminator))\n",
    "fake_dis_loss = tf.losses.sigmoid_cross_entropy(zeros, fake_discriminator)\n",
    "\n",
    "dis_ones = tf.ones(tf.shape(real_discriminator))\n",
    "real_dis_loss = tf.losses.sigmoid_cross_entropy(dis_ones, real_discriminator)\n",
    "\n",
    "with tf.variable_scope(\"discriminator_loss\"):\n",
    "    total_dis_loss = (real_dis_loss + fake_dis_loss) * 0.5\n",
    "    adam_dis = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5, beta2=0.999).minimize(total_dis_loss)\n",
    "\n",
    "\n",
    "generator = build_generator(real_a_ph)\n",
    "real_fake = tf.concat([real_a_ph, generator], axis=3)\n",
    "gen_discriminator = build_discriminator(real_fake)\n",
    "\n",
    "\n",
    "gen_ones = tf.ones(tf.shape(gen_discriminator))\n",
    "gen_loss = tf.losses.sigmoid_cross_entropy(gen_ones, gen_discriminator)\n",
    "gen_l1_loss = tf.reduce_mean(tf.abs(generator - real_b_ph)) * lambda_ph\n",
    "\n",
    "with tf.variable_scope(\"generator_loss\"):\n",
    "    total_gen_loss = gen_loss + gen_l1_loss\n",
    "    adam_gen = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5, beta2=0.999).minimize(total_gen_loss)\n",
    "\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.5 s, sys: 7.24 s, total: 24.8 s\n",
      "Wall time: 24.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import cv2\n",
    "\n",
    "images = glob(\"/Users/alvaro/Downloads/CMP_facade_DB_base/base/*.jpg\")\n",
    "images = [(imread(image).astype(np.float32)-128)/128.0 for image in images]\n",
    "images = [cv2.resize(image, (256, 256)) for image in images]\n",
    "targets = glob(\"/Users/alvaro/Downloads/CMP_facade_DB_base/base/*.png\")\n",
    "targets = [(imread(target)*256-128)/128.0 for target in targets]\n",
    "targets = [cv2.resize(target, (256, 256)) for target in targets]\n",
    "data = list(zip(images, targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pick_image():\n",
    "    counter = 0\n",
    "    while True:\n",
    "        # print(counter)\n",
    "        image, target = data[counter % len(data)]\n",
    "        if random.random() < 0.5:\n",
    "            image = image[:,::-1,:]\n",
    "            target = target[:,::-1,:]\n",
    "        yield (image, target)\n",
    "        counter += 1\n",
    "\n",
    "BATCH_SIZE=3\n",
    "EPOCH_LENGTH=np.ceil(len(data)/float(BATCH_SIZE))\n",
    "\n",
    "ds = tf.data.Dataset.from_generator(pick_image, (tf.float32, tf.float32),\n",
    "                                    (tf.TensorShape([256, 256, 3]), tf.TensorShape([256, 256, 3])))\n",
    "o = ds.batch(3).shuffle(buffer_size=300)\n",
    "it = o.make_one_shot_iterator()\n",
    "images_it, targets_it = it.get_next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "discriminator loss 0.69536877\n",
      "generator loss 105.71336\n",
      "discriminator loss 0.7866654\n",
      "generator loss 106.698166\n"
     ]
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    image, target = images_it.eval(), targets_it.eval()\n",
    "    \n",
    "    for i in range(1):\n",
    "        fake = sess.run(generator, feed_dict={real_a_ph: image})\n",
    "        real_fake = np.concatenate([image, fake], axis=3)\n",
    "        \n",
    "        _, dis_loss = sess.run([adam_dis, total_dis_loss], feed_dict={\n",
    "            real_a_ph: image, real_b_ph: target, real_fake_ph: real_fake\n",
    "        })\n",
    "        print(\"discriminator loss\", dis_loss)\n",
    "    \n",
    "    for i in range(1):\n",
    "        _, gen_loss = sess.run([adam_gen, total_gen_loss], feed_dict={\n",
    "            real_a_ph: image, real_b_ph: target, lambda_ph: 100\n",
    "        })\n",
    "        print(\"generator loss\", gen_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = sess.run(generator, {real_a_ph: image})[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AaFYuWtM269CjrKPAcPCskxf1H5QFwj5MokErgsXbLAbe3sszQSRwnQxP56LN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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.floor(t*127 + 127).astype(np.int))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}