cntk_unet_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cntk_unet\n",
    "import simulation\n",
    "%matplotlib inline\n",
    "import helper\n",
    "\n",
    "import cntk as C\n",
    "from cntk.learners import learning_rate_schedule, UnitType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 1, 192, 192) (3, 6, 192, 192)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vn+1TjqZoxxF1koNJTiU5MtD28SSHu8ejSQ537Zcm+d7AvA9OsnhJkubdMCPq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\nZlBLktQwg1qSpIYZ1JIkNez/Bye8B2lMUA7UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7604bf630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate some random images\n",
    "input_images, target_masks = simulation.generate_random_data(192, 192, count=3)\n",
    "\n",
    "print(input_images.shape, target_masks.shape)\n",
    "\n",
    "# Change channel-order and make 3 channels for matplot\n",
    "input_images_rgb = [(x.swapaxes(0, 2).swapaxes(0,1).repeat(3, axis=2) * -255 + 255).astype(np.uint8) for x in input_images]\n",
    "\n",
    "# Map each channel (i.e. class) to each color\n",
    "target_masks_rgb = [helper.masks_to_colorimg(x) for x in target_masks]\n",
    "\n",
    "# Left: Input image, Right: Target mask\n",
    "helper.plot_side_by_side([input_images_rgb, target_masks_rgb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cntk.device import try_set_default_device, gpu\n",
    "try_set_default_device(gpu(0))\n",
    "\n",
    "def slice_minibatch(data_x, data_y, i, minibatch_size):\n",
    "    sx = data_x[i * minibatch_size:(i + 1) * minibatch_size]\n",
    "    sy = data_y[i * minibatch_size:(i + 1) * minibatch_size]\n",
    "    \n",
    "    return sx, sy\n",
    "\n",
    "def measure_error(data_x, data_y, x, y, trainer, minibatch_size):\n",
    "    errors = []\n",
    "    for i in range(0, int(len(data_x) / minibatch_size)):\n",
    "        data_sx, data_sy = slice_minibatch(data_x, data_y, i, minibatch_size)        \n",
    "\n",
    "        errors.append(trainer.test_minibatch({x: data_sx, y: data_sy}))\n",
    "\n",
    "    return np.mean(errors)\n",
    "\n",
    "def train(images, masks, use_existing=False):  \n",
    "    shape = input_images[0].shape\n",
    "    data_size = input_images.shape[0]\n",
    "    \n",
    "    # Split data\n",
    "    test_portion = int(data_size * 0.1)\n",
    "    indices = np.random.permutation(data_size)\n",
    "    test_indices = indices[:test_portion]\n",
    "    training_indices = indices[test_portion:]\n",
    "\n",
    "    test_data = (images[test_indices], masks[test_indices])\n",
    "    training_data = (images[training_indices], masks[training_indices])\n",
    "\n",
    "    # Create model\n",
    "    x = C.input_variable(shape)\n",
    "    y = C.input_variable(masks[0].shape)\n",
    "\n",
    "    z = cntk_unet.create_model(x, masks.shape[1])\n",
    "    dice_coef = cntk_unet.dice_coefficient(z, y)\n",
    "\n",
    "    # Load the saved model if specified\n",
    "    checkpoint_file = \"cntk-unet.dnn\"\n",
    "    if use_existing:\n",
    "        z.load_model(checkpoint_file)\n",
    "\n",
    "    # Prepare model and trainer\n",
    "    lr = learning_rate_schedule(0.00001, UnitType.sample)\n",
    "    momentum = C.learners.momentum_as_time_constant_schedule(0.9)\n",
    "    trainer = C.Trainer(z, (-dice_coef, -dice_coef), C.learners.adam(z.parameters, lr=lr, momentum=momentum))\n",
    "\n",
    "    # Get minibatches of training data and perform model training\n",
    "    minibatch_size = 8\n",
    "    num_epochs = 75\n",
    "    \n",
    "    training_errors = []\n",
    "    test_errors = []\n",
    "\n",
    "    for e in range(0, num_epochs):\n",
    "        for i in range(0, int(len(training_data[0]) / minibatch_size)):\n",
    "            data_x, data_y = slice_minibatch(training_data[0], training_data[1], i, minibatch_size)\n",
    "\n",
    "            trainer.train_minibatch({x: data_x, y: data_y})\n",
    "        \n",
    "        # Measure training error\n",
    "        training_error = measure_error(training_data[0], training_data[1], x, y, trainer, minibatch_size)\n",
    "        training_errors.append(training_error)\n",
    "        \n",
    "        # Measure test error\n",
    "        test_error = measure_error(test_data[0], test_data[1], x, y, trainer, minibatch_size)\n",
    "        test_errors.append(test_error)\n",
    "\n",
    "        print(\"epoch #{}: training_error={}, test_error={}\".format(e, training_errors[-1], test_errors[-1]))\n",
    "        \n",
    "        trainer.save_checkpoint(checkpoint_file)\n",
    "\n",
    "    return trainer, training_errors, test_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch #0: training_error=-0.019768690868564275, test_error=-0.020717654066781204\n",
      "epoch #1: training_error=-0.14359762888887653, test_error=-0.14444618796308836\n",
      "epoch #2: training_error=-0.16253145324147267, test_error=-0.16211109856764475\n",
      "epoch #3: training_error=-0.19506786180579144, test_error=-0.19313516840338707\n",
      "epoch #4: training_error=-0.22743441371814063, test_error=-0.2299772041539351\n",
      "epoch #5: training_error=-0.22651763014171433, test_error=-0.2276181268195311\n",
      "epoch #6: training_error=-0.25330155660276826, test_error=-0.2570686799784501\n",
      "epoch #7: training_error=-0.27093734209952147, test_error=-0.2745807444055875\n",
      "epoch #8: training_error=-0.32097409859947534, test_error=-0.3199269076188405\n",
      "epoch #9: training_error=-0.32212045840595077, test_error=-0.3231043294072151\n",
      "epoch #10: training_error=-0.4180802334909854, test_error=-0.41908330221970874\n",
      "epoch #11: training_error=-0.5081395947414896, test_error=-0.5119023198882738\n",
      "epoch #12: training_error=-0.5355584644753, test_error=-0.5396901319424311\n",
      "epoch #13: training_error=-0.5596165833265885, test_error=-0.5651968071858088\n",
      "epoch #14: training_error=-0.6433821154677349, test_error=-0.6459879775842031\n",
      "epoch #15: training_error=-0.6609660775765128, test_error=-0.6660722494125366\n",
      "epoch #16: training_error=-0.6821304870688397, test_error=-0.6858411679665247\n",
      "epoch #17: training_error=-0.6975538927575816, test_error=-0.7021944771210352\n",
      "epoch #18: training_error=-0.7028526124746903, test_error=-0.7015786518653234\n",
      "epoch #19: training_error=-0.7167597547821377, test_error=-0.720397874712944\n",
      "epoch #20: training_error=-0.7156574731287749, test_error=-0.7193131099144617\n",
      "epoch #21: training_error=-0.7252500445946403, test_error=-0.7287015269200007\n",
      "epoch #22: training_error=-0.7334559886351876, test_error=-0.7369491656621298\n",
      "epoch #23: training_error=-0.7400820079057113, test_error=-0.74381023645401\n",
      "epoch #24: training_error=-0.7529412295507348, test_error=-0.7539715717236201\n",
      "epoch #25: training_error=-0.7605505870736163, test_error=-0.7639513413111368\n",
      "epoch #26: training_error=-0.7628906576529794, test_error=-0.7664126455783844\n",
      "epoch #27: training_error=-0.7715898026590762, test_error=-0.7751793265342712\n",
      "epoch #28: training_error=-0.7752938420876213, test_error=-0.7786444971958796\n",
      "epoch #29: training_error=-0.7786822977273361, test_error=-0.7816734810670217\n",
      "epoch #30: training_error=-0.782146718191064, test_error=-0.7854048907756805\n",
      "epoch #31: training_error=-0.7842959367710611, test_error=-0.7869394868612289\n",
      "epoch #32: training_error=-0.7898872852325439, test_error=-0.7923590739568075\n",
      "epoch #33: training_error=-0.7931342622508173, test_error=-0.7958166400591532\n",
      "epoch #34: training_error=-0.7994443261105081, test_error=-0.8019372572501501\n",
      "epoch #35: training_error=-0.7979994908623074, test_error=-0.8004395614067713\n",
      "epoch #36: training_error=-0.7975412876709648, test_error=-0.8000765045483907\n",
      "epoch #37: training_error=-0.8006633597871532, test_error=-0.8025749226411184\n",
      "epoch #38: training_error=-0.8104041752607926, test_error=-0.812293271223704\n",
      "epoch #39: training_error=-0.8078771876252215, test_error=-0.8101015339295069\n",
      "epoch #40: training_error=-0.8104296383650407, test_error=-0.8116654952367147\n",
      "epoch #41: training_error=-0.812252676486969, test_error=-0.8135889967282613\n",
      "epoch #42: training_error=-0.8167183248893074, test_error=-0.8186343262592951\n",
      "epoch #43: training_error=-0.8183319314666416, test_error=-0.8183851490418116\n",
      "epoch #44: training_error=-0.8180797893068065, test_error=-0.8185829023520151\n",
      "epoch #45: training_error=-0.8236719385437343, test_error=-0.8233668257792791\n",
      "epoch #46: training_error=-0.8258916523145593, test_error=-0.8249997198581696\n",
      "epoch #47: training_error=-0.8239949169366256, test_error=-0.8246004184087118\n",
      "epoch #48: training_error=-0.8278672368630119, test_error=-0.8268091479937235\n",
      "epoch #49: training_error=-0.832696767475294, test_error=-0.831729864080747\n",
      "epoch #50: training_error=-0.8263022370960401, test_error=-0.8262786467870077\n",
      "epoch #51: training_error=-0.842771373624387, test_error=-0.8406341224908829\n",
      "epoch #52: training_error=-0.857192787916764, test_error=-0.8564214507738749\n",
      "epoch #53: training_error=-0.8568549975104954, test_error=-0.8593804885943731\n",
      "epoch #54: training_error=-0.86151617091635, test_error=-0.8591275463501612\n",
      "epoch #55: training_error=-0.8698923546334971, test_error=-0.8672014276186625\n",
      "epoch #56: training_error=-0.899405440558558, test_error=-0.8971130202213923\n",
      "epoch #57: training_error=-0.8627254413521808, test_error=-0.8596462905406952\n",
      "epoch #58: training_error=-0.9112692019213801, test_error=-0.9090506235758463\n",
      "epoch #59: training_error=-0.9077493258144544, test_error=-0.9010724077622095\n",
      "epoch #60: training_error=-0.9106351442958998, test_error=-0.9055953522523245\n",
      "epoch #61: training_error=-0.9199885093647501, test_error=-0.913592591881752\n",
      "epoch #62: training_error=-0.924489686800086, test_error=-0.9214814503987631\n",
      "epoch #63: training_error=-0.9274574082830678, test_error=-0.9210271586974462\n",
      "epoch #64: training_error=-0.9304829752963523, test_error=-0.9223643392324448\n",
      "epoch #65: training_error=-0.9349522372950678, test_error=-0.9265173921982447\n",
      "epoch #66: training_error=-0.9366193714349166, test_error=-0.9279018491506577\n",
      "epoch #67: training_error=-0.9376024194385695, test_error=-0.9277462114890417\n",
      "epoch #68: training_error=-0.9381804207096929, test_error=-0.928676704565684\n",
      "epoch #69: training_error=-0.9367524255876956, test_error=-0.9292152722676595\n",
      "epoch #70: training_error=-0.9380349750104158, test_error=-0.9298784782489141\n",
      "epoch #71: training_error=-0.9406253742135089, test_error=-0.9317039549350739\n",
      "epoch #72: training_error=-0.944081843935925, test_error=-0.9340653717517853\n",
      "epoch #73: training_error=-0.9468802089276521, test_error=-0.9356546898682913\n",
      "epoch #74: training_error=-0.9458474998888762, test_error=-0.9347378015518188\n"
     ]
    }
   ],
   "source": [
    "input_images, target_masks = input_images, target_masks = simulation.generate_random_data(192, 192, count=1024)\n",
    "\n",
    "trainer, training_errors, test_errors = train(input_images, target_masks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VgW6v/RUT/UO5rtZi/po4jbqjXuO/su4BHM/XmMjYdtvCXaqIiJxgCtuKEnIpUIo/Gbpe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe70ddb4940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helper.plot_errors({\"training\": training_errors, \"test\": test_errors}, title=\"Simulation Learning Curve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 192, 192) (10, 6, 192, 192) (10, 6, 192, 192)\n"
     ]
    }
   ],
   "source": [
    "# Generate some random images\n",
    "input_images, target_masks = input_images, target_masks = simulation.generate_random_data(192, 192, count=10)\n",
    "\n",
    "# Predict\n",
    "pred = trainer.model.eval(input_images)\n",
    "\n",
    "print(input_images.shape, target_masks.shape, pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7e3NjY+OgZcCB7ReQu/sbj3k+56jQs6zSfifcUy+65BuPeT7nqNCvrNJR6dcD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\nLcmHktw5c/2JJC/r7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe70ddb47f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change channel-order and make 3 channels for matplot\n",
    "input_images_rgb = [(x.swapaxes(0, 2).swapaxes(0,1).repeat(3, axis=2) * -255 + 255).astype(np.uint8) for x in input_images]\n",
    "\n",
    "# Map each channel (i.e. class) to each color\n",
    "target_masks_rgb = [helper.masks_to_colorimg(x) for x in target_masks]\n",
    "pred_rgb = [helper.masks_to_colorimg(x) for x in pred]\n",
    "\n",
    "# Left: Input image, Middle: Correct mask (Ground-truth), Rigth: Predicted mask\n",
    "helper.plot_side_by_side([input_images_rgb, target_masks_rgb, pred_rgb])"
   ]
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}