examples/human-seg_atten-unet-backbone_coco.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Human segmentation with attention U-net and transfer learning from ImageNet-trained VGG16 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example requires `keras-unet-collection`:\n",
    "```\n",
    "pip install keras-unet-collection\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras_unet_collection import models, utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the indicator of a fresh run\n",
    "first_time_running = False\n",
    "\n",
    "# user-specified working directory\n",
    "filepath = '/system/drive/COCO/'\n",
    "filepath_label = '/system/drive/COCO/stuffthingmaps/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The COCO-stuff dataset\n",
    "\n",
    "The [COCO-stuff](https://github.com/nightrome/cocostuff) is a segmentation benchmark dataset based on the full samples of [COCO 2017](https://cocodataset.org/#download) --- a large-scale object detection, segmentation, and captioning dataset, with pixel-level annotations.\n",
    "\n",
    "COCO-stuff consists of RGB image samples with both indoor and outdoor backgrounds. The segmentation focus of this example, human/person, is one of the COCO-stuff \"things\" class.\n",
    "\n",
    "The code cell below downloads the COCO 2017 samples (`train2017.zip` and `val2017.zip`) and COCO-stuff annotations (`stuffthingmaps_trainval2017.zip`). \n",
    "\n",
    "**Note**:\n",
    "* The total size of COCO 2017 is 20 GB. For saving its zipped and unzipped versions, a storage space of 40 GB is required.\n",
    "\n",
    "* `wget` is called through `subprocess.Popen`. Access download links mannually if the call failed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# downloading and executing data files\n",
    "if first_time_running:\n",
    "    \n",
    "    import zipfile\n",
    "    import urllib.request\n",
    "    import subprocess\n",
    "    \n",
    "    local_names = ['train2017.zip', 'val2017.zip', 'stuffthingmaps_trainval2017.zip']\n",
    "    \n",
    "    urls = ['http://images.cocodataset.org/zips/train2017.zip', \n",
    "            'http://images.cocodataset.org/zips/val2017.zip', \n",
    "            'http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip']\n",
    "    \n",
    "    for i, local_name in enumerate(local_names):\n",
    "        \n",
    "        print(\"Accessing <{}>\".format(local_name))\n",
    "        filename = filepath + local_name\n",
    "        \n",
    "        # calvin group labels\n",
    "        if i == 2:\n",
    "            subprocess.Popen(['wget', '-O', filename, urls[i]]) # <--- wget\n",
    "            with zipfile.ZipFile(filename, 'r') as zip_io:\n",
    "                zip_io.extractall(filepath_label) \n",
    "        # coco train/val images\n",
    "        else:\n",
    "            urllib.request.urlretrieve(urls[i], filename);    \n",
    "            with zipfile.ZipFile(filename, 'r') as zip_io:\n",
    "                zip_io.extractall(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# file path after data extraction\n",
    "path_trainval_img = filepath + 'train2017/'\n",
    "path_trainval_mask = filepath_label + 'train2017/'\n",
    "path_test_img = filepath + 'val2017/'\n",
    "path_test_mask = filepath_label + 'val2017/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting human samples\n",
    "\n",
    "For obtaining a better focus of the segmentation target, human samples are subsetted from COCO.\n",
    "\n",
    "The selection criteria is that after resizing to 128-by-128, human samples should have more than 33% of its 64-by-64 central pixels belong to the human/person category.\n",
    "\n",
    "As a binary segmentation problem, non-human COCO-stuff labels are grouped and labelled as \"background\". Accessory categories (e.g., ties) are also grouped into the background.\n",
    "\n",
    "**Note**: sample subsetting code cells are time-consuming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_human_samples(label_filenames, human_id=0, human_rate=1/3):\n",
    "    '''\n",
    "    Subsetting samples that contain \"person/human\" category from the COCO dataset\n",
    "    ----------\n",
    "    human_id = 0 : COCO stuffthingmaps label human as int 0\n",
    "    human_rate = 1/3: at least 33% of the pixels should belong to human.\n",
    "    ----------\n",
    "    '''\n",
    "    thres = int(64*64*human_rate) # pixel number thres after resizing\n",
    "    L = len(label_filenames)\n",
    "    flag = [] # return a list of booleans\n",
    "    for i in range(L):\n",
    "        sample_ = utils.image_to_array([label_filenames[i]], size=128, channel=1)\n",
    "        if np.sum(sample_[0, 32:-32, 32:-32, 0]==human_id) > thres:\n",
    "            flag.append(True)\n",
    "        else:\n",
    "            flag.append(False)\n",
    "        \n",
    "    return flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainval_input_names = np.array(sorted(glob(path_trainval_img+'*.jpg')))\n",
    "trainval_label_names = np.array(sorted(glob(path_trainval_mask+'*.png')))\n",
    "flag_human = split_human_samples(trainval_label_names, human_id=0)\n",
    "trainval_input_names = trainval_input_names[flag_human]\n",
    "trainval_label_names = trainval_label_names[flag_human]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_input_names = np.array(sorted(glob(path_test_img+'*.jpg')))\n",
    "test_label_names = np.array(sorted(glob(path_test_mask+'*.png')))\n",
    "flag_human_test = split_human_samples(test_label_names, human_id=0)\n",
    "test_input_names = test_input_names[flag_human_test]\n",
    "test_label_names = test_label_names[flag_human_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training-validation data split\n",
    "\n",
    "The COCO validation samples are treated as testing sets.\n",
    "\n",
    "The validation data of this example is split form the COCO training samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training:validation:testing = 18620:2069:862\n"
     ]
    }
   ],
   "source": [
    "L = len(trainval_input_names)\n",
    "ind_all = utils.shuffle_ind(L)\n",
    "\n",
    "L_train = int(0.9*L); L_valid = L - L_train\n",
    "ind_train = ind_all[:L_train]; ind_valid = ind_all[L_train:]\n",
    "\n",
    "train_input_names = trainval_input_names[ind_train]\n",
    "train_label_names = trainval_label_names[ind_train]\n",
    "valid_input_names = trainval_input_names[ind_valid]\n",
    "valid_label_names = trainval_label_names[ind_valid]\n",
    "\n",
    "print(\"Training:validation:testing = {}:{}:{}\".format(L_train, L_valid, len(test_label_names)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def ax_decorate_box(ax):\n",
    "    [j.set_linewidth(0) for j in ax.spines.values()]\n",
    "    ax.tick_params(axis=\"both\", which=\"both\", bottom=False, top=False, \\\n",
    "               labelbottom=False, left=False, right=False, labelleft=False)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "i_max = 10 # explore 10 images\n",
    "input_example = utils.image_to_array(train_input_names[:i_max], size=128, channel=3)\n",
    "label_example = utils.image_to_array(train_label_names[:i_max], size=128, channel=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i_example = 2\n",
    "\n",
    "fig, AX = plt.subplots(1, 2, figsize=(7, 3))\n",
    "plt.subplots_adjust(0, 0, 1, 1, hspace=0, wspace=0.1)\n",
    "\n",
    "for ax in AX:\n",
    "    ax = ax_decorate_box(ax)\n",
    "    \n",
    "AX[0].pcolormesh(np.mean(input_example[i_example, ...], axis=-1), cmap=plt.cm.gray)\n",
    "AX[1].pcolormesh(label_example[i_example, ..., 0]>0, cmap=plt.cm.gray)\n",
    "AX[0].set_title(\"Original\", fontsize=14);\n",
    "AX[1].set_title(\"Segmentation mask\", fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attention U-net with an ImageNet-trained backbone\n",
    "\n",
    "Attention U-net is applied for this segmentation task. This architecture is modified from the conventionally used U-net by assigning attention gates on each upsampling level. \n",
    "\n",
    "Attention gates take upsampled (i.e., decoded) and downsampled (i.e., encoded) tensors as queries and keys, respectively. These queries and keys are mapped to intermediate channel sizes and fed into the additive attention learning. The resulting vector is rescaled by a sigmoid function and multiplied with the downsampled tensor (keys, but here treated as \"values\" of self-attention). The attention gate output replaces the downsampled tensor and is concatenated with the upsampled tensor.\n",
    "\n",
    "Based on the amount and complexity of COCO samples, ImageNet-trained VGG16 is applied as an encoder backbone. This transfer learning strategy is expected to improve the segmentation performance based on two reasons: \n",
    "\n",
    " * The ImageNet and COCO containts (somewhat) similar kinds of natural images with a high overlap of data distribution; \n",
    "\n",
    " * The VGG16 architecture is a combination of same-padding convolution and max-pooling kernels, capable of extracting hierarchical features that can be processed by attention gates (ResNet backbone contains zero padding layers and is suboptimal in this case).\n",
    "\n",
    "The code cell below configures the attention U-net with an ImageNet-trained VGG16 backbone. Hyper-parameters are explained through the Python helper function:\n",
    "\n",
    "```python\n",
    "from keras_unet_collection import models\n",
    "\n",
    "help(models.att_unet_2d)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/work/ksha/py_env_20200417/lib/python3.6/site-packages/keras_unet_collection/_backbone_zoo.py:45: UserWarning: \n",
      "\n",
      "Backbone VGG16 does not use batch norm, but other layers received batch_norm=True\n",
      "  warnings.warn(param_mismatch);\n"
     ]
    }
   ],
   "source": [
    "model = models.att_unet_2d((128, 128, 3), filter_num=[64, 128, 256, 512, 1024], n_labels=2, \n",
    "                           stack_num_down=2, stack_num_up=2, activation='ReLU', \n",
    "                           atten_activation='ReLU', attention='add', output_activation='Sigmoid', \n",
    "                           batch_norm=True, pool=False, unpool=False, \n",
    "                           backbone='VGG16', weights='imagenet', \n",
    "                           freeze_backbone=True, freeze_batch_norm=True, \n",
    "                           name='attunet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second layer of the configured model, i.e., right after an input layer, is expected to be the VGG16 backbone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'VGG16_backbone'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers[1].name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity, this segmentation model is trained with cross-entropy loss with SGD optimizer and a learning rate of 1E-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD(lr=1e-2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "The segmentation model is trained with 200 epoches with early stopping. Each epoch containts 100 batches and each batch contains 32 samples.\n",
    "\n",
    "*The training process here is far from systematic, and is provided for illustration purposes only.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def input_data_process(input_array):\n",
    "    '''converting pixel vales to [0, 1]'''\n",
    "    return input_array/255.\n",
    "\n",
    "def target_data_process(target_array):\n",
    "    '''Converting human, non-human pixels into two categories.'''\n",
    "    target_array[target_array>0]=1 # grouping all other non-human categories \n",
    "    return keras.utils.to_categorical(target_array, num_classes=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_input = input_data_process(utils.image_to_array(valid_input_names, size=128, channel=3))\n",
    "valid_label = target_data_process(utils.image_to_array(valid_label_names, size=128, channel=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tInitial loss = 0.7699558138847351\n",
      "Validation performance is improved from 0.7699558138847351 to 0.7028995752334595\n",
      "Validation performance is improved from 0.7028995752334595 to 0.6433559656143188\n",
      "Validation performance is improved from 0.6433559656143188 to 0.5784065127372742\n",
      "Validation performance is improved from 0.5784065127372742 to 0.539142370223999\n",
      "Validation performance is improved from 0.539142370223999 to 0.5231527090072632\n",
      "Validation performance is improved from 0.5231527090072632 to 0.5181891322135925\n",
      "Validation performance is improved from 0.5181891322135925 to 0.5106982588768005\n",
      "Validation performance is improved from 0.5106982588768005 to 0.5057828426361084\n",
      "Validation performance is improved from 0.5057828426361084 to 0.5001973509788513\n",
      "Validation performance is improved from 0.5001973509788513 to 0.4992810785770416\n",
      "Validation performance is improved from 0.4992810785770416 to 0.49348872900009155\n",
      "Validation performance is improved from 0.49348872900009155 to 0.4890025556087494\n",
      "Validation performance is improved from 0.4890025556087494 to 0.4887843728065491\n",
      "Validation performance is improved from 0.4887843728065491 to 0.47941532731056213\n",
      "Validation performance is improved from 0.47941532731056213 to 0.4752771556377411\n",
      "Validation performance is improved from 0.4752771556377411 to 0.4722847640514374\n",
      "Validation performance 0.47451066970825195 is NOT improved\n",
      "Validation performance is improved from 0.4722847640514374 to 0.46411997079849243\n",
      "Validation performance is improved from 0.46411997079849243 to 0.4614582359790802\n",
      "Validation performance is improved from 0.4614582359790802 to 0.4586036205291748\n",
      "Validation performance is improved from 0.4586036205291748 to 0.4509889483451843\n",
      "Validation performance is improved from 0.4509889483451843 to 0.4480348229408264\n",
      "Validation performance is improved from 0.4480348229408264 to 0.4436340630054474\n",
      "Validation performance is improved from 0.4436340630054474 to 0.43725571036338806\n",
      "Validation performance is improved from 0.43725571036338806 to 0.43380051851272583\n",
      "Validation performance is improved from 0.43380051851272583 to 0.4308297038078308\n",
      "Validation performance is improved from 0.4308297038078308 to 0.42703384160995483\n",
      "Validation performance is improved from 0.42703384160995483 to 0.4211028814315796\n",
      "Validation performance is improved from 0.4211028814315796 to 0.41956689953804016\n",
      "Validation performance is improved from 0.41956689953804016 to 0.4167957007884979\n",
      "Validation performance is improved from 0.4167957007884979 to 0.41294363141059875\n",
      "Validation performance is improved from 0.41294363141059875 to 0.4103807806968689\n",
      "Validation performance is improved from 0.4103807806968689 to 0.40902209281921387\n",
      "Validation performance is improved from 0.40902209281921387 to 0.40660473704338074\n",
      "Validation performance is improved from 0.40660473704338074 to 0.4047784209251404\n",
      "Validation performance is improved from 0.4047784209251404 to 0.40342023968696594\n",
      "Validation performance 0.4063573479652405 is NOT improved\n",
      "Validation performance is improved from 0.40342023968696594 to 0.4007786512374878\n",
      "Validation performance 0.40146732330322266 is NOT improved\n",
      "Validation performance is improved from 0.4007786512374878 to 0.39823877811431885\n",
      "Validation performance is improved from 0.39823877811431885 to 0.3977005183696747\n",
      "Validation performance 0.400127112865448 is NOT improved\n",
      "Validation performance is improved from 0.3977005183696747 to 0.39322909712791443\n",
      "Validation performance 0.3940064311027527 is NOT improved\n",
      "Validation performance is improved from 0.39322909712791443 to 0.38941633701324463\n",
      "Validation performance 0.38951125741004944 is NOT improved\n",
      "Validation performance is improved from 0.38941633701324463 to 0.38805362582206726\n",
      "Validation performance is improved from 0.38805362582206726 to 0.3848157227039337\n",
      "Validation performance 0.38940656185150146 is NOT improved\n",
      "Validation performance is improved from 0.3848157227039337 to 0.38347187638282776\n",
      "Validation performance is improved from 0.38347187638282776 to 0.38133227825164795\n",
      "Validation performance 0.38768675923347473 is NOT improved\n",
      "Validation performance is improved from 0.38133227825164795 to 0.38111647963523865\n",
      "Validation performance 0.3832964301109314 is NOT improved\n",
      "Validation performance is improved from 0.38111647963523865 to 0.38044726848602295\n",
      "Validation performance is improved from 0.38044726848602295 to 0.37610095739364624\n",
      "Validation performance is improved from 0.37610095739364624 to 0.37480640411376953\n",
      "Validation performance is improved from 0.37480640411376953 to 0.37279537320137024\n",
      "Validation performance 0.3732231557369232 is NOT improved\n",
      "Validation performance is improved from 0.37279537320137024 to 0.37042149901390076\n",
      "Validation performance 0.3761703670024872 is NOT improved\n",
      "Validation performance 0.3758615553379059 is NOT improved\n",
      "Early stopping\n"
     ]
    }
   ],
   "source": [
    "N_epoch = 200 # number of epoches\n",
    "N_batch = 100 # number of batches per epoch\n",
    "N_sample = 32 # number of samples per batch\n",
    "\n",
    "tol = 0 # current early stopping patience\n",
    "max_tol = 2 # the max-allowed early stopping patience\n",
    "min_del = 0 # the lowest acceptable loss value reduction \n",
    "\n",
    "# loop over epoches\n",
    "for epoch in range(N_epoch):    \n",
    "    # initial loss record\n",
    "    if epoch == 0:\n",
    "        y_pred = model.predict([valid_input])\n",
    "        record = np.mean(keras.losses.categorical_crossentropy(valid_label, y_pred))\n",
    "        print('\\tInitial loss = {}'.format(record))\n",
    "    \n",
    "    # loop over batches\n",
    "    for step in range(N_batch):\n",
    "        # selecting smaples for the current batch\n",
    "        ind_train_shuffle = utils.shuffle_ind(L_train)[:N_sample]\n",
    "        \n",
    "        # batch data formation\n",
    "        ## augmentation is not applied\n",
    "        train_input = input_data_process(utils.image_to_array(train_input_names[ind_train_shuffle], size=128, channel=3))\n",
    "        train_label = target_data_process(utils.image_to_array(train_label_names[ind_train_shuffle], size=128, channel=1))\n",
    "        \n",
    "        # train on batch\n",
    "        loss_ = model.train_on_batch([train_input,], [train_label,])\n",
    "        # ** training loss is not stored ** #\n",
    "        \n",
    "    # epoch-end validation\n",
    "    y_pred = model.predict([valid_input])\n",
    "    record_temp = np.mean(keras.losses.categorical_crossentropy(valid_label, y_pred))\n",
    "    # ** validation loss is not stored ** #\n",
    "    \n",
    "    # if loss is reduced\n",
    "    if record - record_temp > min_del:\n",
    "        print('Validation performance is improved from {} to {}'.format(record, record_temp))\n",
    "        record = record_temp; # update the loss record\n",
    "        tol = 0; # refresh early stopping patience\n",
    "        # ** model checkpoint is not stored ** #\n",
    "        \n",
    "    # if loss not reduced\n",
    "    else:\n",
    "        print('Validation performance {} is NOT improved'.format(record_temp))\n",
    "        tol += 1\n",
    "        if tol >= max_tol:\n",
    "            print('Early stopping')\n",
    "            break;\n",
    "        else:\n",
    "            # Pass to the next epoch\n",
    "            continue;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "The testing set performance is evaluated with cross-entropy and example outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_input = input_data_process(utils.image_to_array(test_input_names, size=128, channel=3))\n",
    "test_label = target_data_process(utils.image_to_array(test_label_names, size=128, channel=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict([test_input])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing set cross-entropy = 0.3868310749530792\n"
     ]
    }
   ],
   "source": [
    "print('Testing set cross-entropy = {}'.format(np.mean(keras.losses.categorical_crossentropy(test_label, y_pred))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example of outputs**\n",
    "\n",
    "As a common practice in computer vision projects, only nice looking samples are plotted : |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x307.2 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i_sample = 12\n",
    "\n",
    "fig, AX = plt.subplots(1, 3, figsize=(13, (13-0.2)/3))\n",
    "plt.subplots_adjust(0, 0, 1, 1, hspace=0, wspace=0.1)\n",
    "for ax in AX:\n",
    "    ax = ax_decorate_box(ax)\n",
    "AX[0].pcolormesh(np.mean(test_input[i_sample, ...,], axis=-1), cmap=plt.cm.gray)\n",
    "AX[1].pcolormesh(y_pred[i_sample, ..., 0], cmap=plt.cm.jet)\n",
    "AX[2].pcolormesh(test_label[i_sample, ..., 0], cmap=plt.cm.jet)\n",
    "\n",
    "AX[0].set_title(\"Original\", fontsize=14);\n",
    "AX[1].set_title(\"Pixels belong to human (red for high probabilities)\", fontsize=14);\n",
    "AX[2].set_title(\"Labeled truth\", fontsize=14);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "A segmentation model is proposed based on the architecture of UNET 3+ and is trained using the Oxford-IIIT Pets dataset. Result evaluation indicates that this segmentation model can distinguish pixes of a pet from image backgrounds.\n",
    "\n",
    "Many technical details of this work, for example, network hyper-parameters and training strategy, can be improved for achieving better performance. "
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