ResNet_version.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3.5\\Anaconda\\envs\\tensorFlow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.applications.inception_resnet_v2 import InceptionResNetV2\n",
    "from keras.preprocessing import image\n",
    "from keras.engine import Layer\n",
    "from keras.applications.inception_resnet_v2 import preprocess_input\n",
    "from keras.layers import Conv2D, UpSampling2D, InputLayer, Conv2DTranspose, Input, Reshape, merge, concatenate\n",
    "from keras.layers import Activation, Dense, Dropout, Flatten\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import TensorBoard \n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers.core import RepeatVector, Permute\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from skimage.color import rgb2lab, lab2rgb, rgb2gray, gray2rgb\n",
    "from skimage.transform import resize\n",
    "from skimage.io import imsave\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "from skimage import transform, data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get images\n",
    "X = []\n",
    "for filename in os.listdir('Train_France/'):\n",
    "    X.append(img_to_array(load_img('Train_France/'+filename)))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3.5\\Anaconda\\envs\\tensorFlow\\lib\\site-packages\\skimage\\transform\\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    }
   ],
   "source": [
    "lenth=[]\n",
    "width=[]\n",
    "for i in range(len(X)):\n",
    "    lenth.append(len(X[i]))\n",
    "    width.append(len(X[i][1]))\n",
    "    if len(X[i])==256:\n",
    "        X[i]=X[i]\n",
    "    else:\n",
    "        X[i]=np.array(X[i], dtype=float)\n",
    "        X[i]=transform.resize(X[i], (256, 256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(X, dtype=float)\n",
    "Xtrain = 1.0/255*X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load weights\n",
    "inception = InceptionResNetV2(weights='imagenet', include_top=True)\n",
    "inception.graph = tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "inception.save_weights(\"SaveModel/inception.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_input = Input(shape=(1000,))\n",
    "\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 = 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
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      "Epoch 21/200\n",
      "13/13 [==============================] - 137s 11s/step - loss: 0.0044\n",
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      "13/13 [==============================] - 126s 10s/step - loss: 0.0020\n",
      "Epoch 99/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 138s 11s/step - loss: 0.0024\n",
      "Epoch 100/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0023\n",
      "Epoch 101/200\n",
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      "Epoch 108/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0020\n",
      "Epoch 109/200\n",
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      "Epoch 110/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0019\n",
      "Epoch 111/200\n",
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      "Epoch 112/200\n",
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      "Epoch 113/200\n",
      "13/13 [==============================] - 139s 11s/step - loss: 0.0017\n",
      "Epoch 114/200\n",
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      "Epoch 115/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0016\n",
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      "13/13 [==============================] - 141s 11s/step - loss: 0.0014\n",
      "Epoch 154/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0013\n",
      "Epoch 155/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0013\n",
      "Epoch 156/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0012\n",
      "Epoch 157/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0012\n",
      "Epoch 158/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0013\n",
      "Epoch 159/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0016\n",
      "Epoch 160/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0016\n",
      "Epoch 161/200\n",
      "13/13 [==============================] - 139s 11s/step - loss: 0.0015\n",
      "Epoch 162/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0042\n",
      "Epoch 163/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0059\n",
      "Epoch 164/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0053\n",
      "Epoch 165/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0052\n",
      "Epoch 166/200\n",
      "13/13 [==============================] - 126s 10s/step - loss: 0.0048\n",
      "Epoch 167/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0053\n",
      "Epoch 168/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0051\n",
      "Epoch 169/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0051\n",
      "Epoch 170/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0058\n",
      "Epoch 171/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0050\n",
      "Epoch 172/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0052\n",
      "Epoch 173/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0050\n",
      "Epoch 174/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0048\n",
      "Epoch 175/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0053\n",
      "Epoch 176/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0052\n",
      "Epoch 177/200\n",
      "13/13 [==============================] - 137s 11s/step - loss: 0.0050\n",
      "Epoch 178/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0053\n",
      "Epoch 179/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0053\n",
      "Epoch 180/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0051\n",
      "Epoch 181/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0051\n",
      "Epoch 182/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0051\n",
      "Epoch 183/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0049\n",
      "Epoch 184/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0051\n",
      "Epoch 185/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0050\n",
      "Epoch 186/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0047\n",
      "Epoch 187/200\n",
      "13/13 [==============================] - 139s 11s/step - loss: 0.0052\n",
      "Epoch 188/200\n",
      "13/13 [==============================] - 126s 10s/step - loss: 0.0052\n",
      "Epoch 189/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0046\n",
      "Epoch 190/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0054\n",
      "Epoch 191/200\n",
      "13/13 [==============================] - 139s 11s/step - loss: 0.0052\n",
      "Epoch 192/200\n",
      "13/13 [==============================] - 128s 10s/step - loss: 0.0047\n",
      "Epoch 193/200\n",
      "13/13 [==============================] - 144s 11s/step - loss: 0.0049\n",
      "Epoch 194/200\n",
      "13/13 [==============================] - 128s 10s/step - loss: 0.0051\n",
      "Epoch 195/200\n",
      "13/13 [==============================] - 139s 11s/step - loss: 0.0053\n",
      "Epoch 196/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 126s 10s/step - loss: 0.0050\n",
      "Epoch 197/200\n",
      "13/13 [==============================] - 138s 11s/step - loss: 0.0048\n",
      "Epoch 198/200\n",
      "13/13 [==============================] - 125s 10s/step - loss: 0.0056\n",
      "Epoch 199/200\n",
      "13/13 [==============================] - 139s 11s/step - loss: 0.0047\n",
      "Epoch 200/200\n",
      "13/13 [==============================] - 127s 10s/step - loss: 0.0048\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2dd5198ada0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_inception_embedding(grayscaled_rgb):\n",
    "    grayscaled_rgb_resized = []\n",
    "    for i in grayscaled_rgb:\n",
    "        i = resize(i, (299, 299, 3), mode='constant')\n",
    "        grayscaled_rgb_resized.append(i)\n",
    "    grayscaled_rgb_resized = np.array(grayscaled_rgb_resized)\n",
    "    grayscaled_rgb_resized = preprocess_input(grayscaled_rgb_resized)\n",
    "    with inception.graph.as_default():\n",
    "        embed = inception.predict(grayscaled_rgb_resized)\n",
    "    return embed\n",
    "\n",
    "# Image transformer\n",
    "datagen = ImageDataGenerator(\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        rotation_range=20,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "#Generate training data\n",
    "batch_size = 10\n",
    "\n",
    "def image_a_b_gen(batch_size):\n",
    "    for batch in datagen.flow(Xtrain, batch_size=batch_size):\n",
    "        grayscaled_rgb = gray2rgb(rgb2gray(batch))\n",
    "        embed = create_inception_embedding(grayscaled_rgb)\n",
    "        lab_batch = rgb2lab(batch)\n",
    "        X_batch = lab_batch[:,:,:,0]\n",
    "        X_batch = X_batch.reshape(X_batch.shape+(1,))\n",
    "        Y_batch = lab_batch[:,:,:,1:] / 128\n",
    "        yield ([X_batch, create_inception_embedding(grayscaled_rgb)], Y_batch)\n",
    "\n",
    "\n",
    "#Train model      \n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.fit_generator(image_a_b_gen(batch_size), epochs=200, steps_per_epoch=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights(\"SaveModel/cifarCnnModel.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    model.load_weights(\"SaveModel/cifarCnnModel.h5\")\n",
    "    print(\"success\")\n",
    "except:\n",
    "    print(\"fail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3.5\\Anaconda\\envs\\tensorFlow\\lib\\site-packages\\skimage\\transform\\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    }
   ],
   "source": [
    "color_me = []\n",
    "for filename in os.listdir('Train_France/'):\n",
    "    color_me.append(img_to_array(load_img('Train_France/'+filename)))\n",
    "\n",
    "\n",
    "lenth=[]\n",
    "width=[]\n",
    "for i in range(len(color_me)):\n",
    "    lenth.append(len(color_me[i]))\n",
    "    width.append(len(color_me[i][1]))\n",
    "    if len(color_me[i])==256:\n",
    "        color_me[i]=color_me[i]\n",
    "    else:\n",
    "        color_me[i]=np.array(color_me[i], dtype=float)\n",
    "        color_me[i]=transform.resize(color_me[i], (256, 256))    \n",
    "\n",
    "color_me = np.array(color_me, dtype=float)\n",
    "gray_me = gray2rgb(rgb2gray(1.0/255*color_me))\n",
    "color_me_embed = create_inception_embedding(gray_me)\n",
    "color_me = rgb2lab(1.0/255*color_me)[:,:,:,0]\n",
    "color_me = color_me.reshape(color_me.shape+(1,))\n",
    "\n",
    "\n",
    "# Test model\n",
    "output = model.predict([color_me, color_me_embed])\n",
    "output = output * 128\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3.5\\Anaconda\\envs\\tensorFlow\\lib\\site-packages\\skimage\\transform\\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
      "F:\\Anaconda3.5\\Anaconda\\envs\\tensorFlow\\lib\\site-packages\\skimage\\util\\dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint8\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n"
     ]
    }
   ],
   "source": [
    "# Output colorizations\n",
    "for i in range(len(output)):\n",
    "    cur = np.zeros((256, 256, 3))\n",
    "    cur[:,:,0] = color_me[i][:,:,0]\n",
    "    cur[:,:,1:] = output[i]\n",
    "    cur=transform.resize(cur, (lenth[i], width[i]))\n",
    "    imsave(\"result_France/img_\"+str(i)+\".png\", lab2rgb(cur))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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