Loading Data and Training the CNN.ipynb

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    "## Training the model\n",
    "\n",
    "The CNN architecture that I am gonna be using for training the model here in this notebook is saved in models.py file that I will import here, or anywhere it's required. \n",
    "\n",
    "#### Why models.py ?\n",
    "\n",
    "I have been taught this good practice that for most of the projects you can write your Neural Network architecture in `models.py` file so that any models I define can be saved and loaded by name in different notebooks in the project directory. For example, by defining a CNN class called `Net` in `models.py`, you can then create that same architecture in this and other notebooks by simply importing the class and instantiating a model:\n",
    "```\n",
    "    from models import Net\n",
    "    net = Net()\n",
    "```"
   ]
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      "mkdir: cannot create directory ‘/data’: File exists\n",
      "--2020-05-13 13:50:33--  https://s3.amazonaws.com/video.udacity-data.com/topher/2018/May/5aea1b91_train-test-data/train-test-data.zip\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.36.142\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.36.142|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 338613624 (323M) [application/zip]\n",
      "Saving to: ‘/data/train-test-data.zip’\n",
      "\n",
      "train-test-data.zip 100%[===================>] 322.93M  96.1MB/s    in 3.5s    \n",
      "\n",
      "2020-05-13 13:50:37 (91.0 MB/s) - ‘/data/train-test-data.zip’ saved [338613624/338613624]\n",
      "\n",
      "Archive:  /data/train-test-data.zip\n",
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      "  inflating: /data/training/Zhang_Ziyi_21.jpg  \n",
      "  inflating: /data/training/Zhang_Ziyi_22.jpg  \n",
      "  inflating: /data/training/Zhang_Ziyi_40.jpg  \n",
      "  inflating: /data/training/Zhang_Ziyi_41.jpg  \n",
      "  inflating: /data/training/Zhang_Ziyi_42.jpg  \n",
      "  inflating: /data/training/Zhong_Nanshan_00.jpg  \n",
      "  inflating: /data/training/Zhong_Nanshan_01.jpg  \n",
      "  inflating: /data/training/Zhong_Nanshan_02.jpg  \n",
      "  inflating: /data/training/Zhong_Nanshan_10.jpg  \n",
      "  inflating: /data/training/Zhong_Nanshan_11.jpg  \n",
      "  inflating: /data/training/Zhong_Nanshan_12.jpg  \n",
      "  inflating: /data/training/Zhong_Nanshan_50.jpg  \n",
      "  inflating: /data/training/Zhong_Nanshan_51.jpg  \n",
      "  inflating: /data/training/Zhong_Nanshan_52.jpg  \n",
      "  inflating: /data/training_frames_keypoints.csv  \n"
     ]
    }
   ],
   "source": [
    "# loading the data\n",
    "!mkdir /data\n",
    "!wget -P /data/ https://s3.amazonaws.com/video.udacity-data.com/topher/2018/May/5aea1b91_train-test-data/train-test-data.zip\n",
    "!unzip -n /data/train-test-data.zip -d /data\n",
    "\n",
    "#the output of the cell is gonna be very long, so you can scroll directly to the end of the page then scroll up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the usual resources\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# check if CUDA is available\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "# import utilities to keep workspaces alive during model training\n",
    "from workspace_utils import active_session\n",
    "\n",
    "# watch for any changes in model.py, if it changes, re-load it automatically\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(4, 4), stride=(1, 1))\n",
      "  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (conv4): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (conv5): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=8192, out_features=2000, bias=True)\n",
      "  (fc2): Linear(in_features=2000, out_features=1000, bias=True)\n",
      "  (fc3): Linear(in_features=1000, out_features=136, bias=True)\n",
      "  (dropout1): Dropout(p=0.1)\n",
      "  (dropout2): Dropout(p=0.2)\n",
      "  (dropout3): Dropout(p=0.3)\n",
      "  (dropout4): Dropout(p=0.4)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "#instantiating the model from models.py\n",
    "from models import Net\n",
    "\n",
    "net = Net()\n",
    "print(net)\n",
    "\n",
    "#move to GPU if available\n",
    "if use_cuda:\n",
    "    net.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Transform the dataset \n",
    "\n",
    "To prepare for training, create a transformed dataset of images and keypoints.\n",
    "These transformations have been defined in `data_load.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms, utils\n",
    "\n",
    "from data_load import FacialKeypointsDataset\n",
    "from data_load import Rescale, RandomCrop, Normalize, ToTensor\n",
    "\n",
    "data_transform = transforms.Compose([Rescale(240),\n",
    "                                     RandomCrop(224),\n",
    "                                     Normalize(),\n",
    "                                     ToTensor()])\n",
    "\n",
    "assert(data_transform is not None), 'Define a data_transform'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images:  3462\n",
      "0 torch.Size([1, 224, 224]) torch.Size([68, 2])\n",
      "1 torch.Size([1, 224, 224]) torch.Size([68, 2])\n",
      "2 torch.Size([1, 224, 224]) torch.Size([68, 2])\n",
      "3 torch.Size([1, 224, 224]) torch.Size([68, 2])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()\n"
     ]
    }
   ],
   "source": [
    "# create the transformed dataset\n",
    "transformed_dataset = FacialKeypointsDataset(csv_file='/data/training_frames_keypoints.csv',\n",
    "                                             root_dir='/data/training/',\n",
    "                                             transform=data_transform)\n",
    "\n",
    "\n",
    "print('Number of images: ', len(transformed_dataset))\n",
    "\n",
    "# iterate through the transformed dataset and print some stats about the first few samples\n",
    "for i in range(4):\n",
    "    sample = transformed_dataset[i]\n",
    "    print(i, sample['image'].size(), sample['keypoints'].size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batching and loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load training data in batches\n",
    "batch_size = 128\n",
    "\n",
    "train_loader = DataLoader(transformed_dataset, \n",
    "                          batch_size=batch_size,\n",
    "                          shuffle=True, \n",
    "                          num_workers=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before training\n",
    "\n",
    "Take a look at how this model performs before it trains. You should see that the keypoints it predicts start off in one spot and don't match the keypoints on a face at all! It's interesting to visualize this behavior so that you can compare it to the model after training and see how the model has improved.\n",
    "\n",
    "#### Load in the test dataset\n",
    "\n",
    "The test dataset is one that this model has *not* seen before, meaning it has not trained with these images. We'll load in this test data and before and after training, see how your model performs on this set!\n",
    "\n",
    "To visualize this test data, we have to go through some un-transformation steps to turn our images into python images from tensors and to turn our keypoints back into a recognizable range. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the test data, using the dataset class\n",
    "# AND apply the data_transform you defined above\n",
    "\n",
    "# create the test dataset\n",
    "test_dataset = FacialKeypointsDataset(csv_file='/data/test_frames_keypoints.csv',\n",
    "                                             root_dir='/data/test/',\n",
    "                                             transform=data_transform)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load test data in batches\n",
    "batch_size = 128\n",
    "\n",
    "test_loader = DataLoader(test_dataset, \n",
    "                          batch_size=batch_size,\n",
    "                          shuffle=True, \n",
    "                          num_workers=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the predicted keypoints\n",
    "\n",
    "Once we've had the model produce some predicted output keypoints, we can visualize these points, we have to \"un-transform\" the image/keypoint data to display it.\n",
    "\n",
    "I've defined a *new* function, `show_all_keypoints` that displays a grayscale image, its predicted keypoints and its ground truth keypoints (if provided)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_all_keypoints(image, predicted_key_pts, gt_pts=None):\n",
    "    \"\"\"Show image with predicted keypoints\"\"\"\n",
    "    # image is grayscale\n",
    "    plt.imshow(image, cmap='gray')\n",
    "    plt.scatter(predicted_key_pts[:, 0], predicted_key_pts[:, 1], s=20, marker='.', c='m')\n",
    "    # plot ground truth points as green pts\n",
    "    if gt_pts is not None:\n",
    "        plt.scatter(gt_pts[:, 0], gt_pts[:, 1], s=20, marker='.', c='g')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Un-transformation\n",
    "\n",
    "`visualize_output` that takes in a batch of images, predicted keypoints, and ground truth keypoints and displays a set of those images and their true/predicted keypoints.\n",
    "\n",
    "This function's main role is to take batches of image and keypoint data (the input and output of your CNN), and transform them into numpy images and un-normalized keypoints (x, y) for normal display. The un-transformation process turns keypoints and images into numpy arrays from Tensors *and* it undoes the keypoint normalization done in the Normalize() transform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize the output\n",
    "# I have set this to show only 10 outputs\n",
    "def visualize_output(test_images, test_outputs, gt_pts=None, batch_size=10):\n",
    "\n",
    "    for i in range(batch_size):\n",
    "        plt.figure(figsize=(20,10))\n",
    "        ax = plt.subplot(1, batch_size, i+1)\n",
    "        test_images = test_images.cpu()\n",
    "        test_outputs = test_outputs.cpu()\n",
    "        # un-transform the image data\n",
    "        image = test_images[i].data   # get the image from it's Variable wrapper\n",
    "        image = image.numpy()   # convert to numpy array from a Tensor\n",
    "        image = np.transpose(image, (1, 2, 0))   # transpose to go from torch to numpy image\n",
    "\n",
    "        # un-transform the predicted key_pts data\n",
    "        predicted_key_pts = test_outputs[i].data\n",
    "        predicted_key_pts = predicted_key_pts.numpy()\n",
    "        # undo normalization of keypoints  \n",
    "        predicted_key_pts = predicted_key_pts*50.0+100\n",
    "        \n",
    "        # plot ground truth points for comparison, if they exist\n",
    "        ground_truth_pts = None\n",
    "        if gt_pts is not None:\n",
    "            ground_truth_pts = gt_pts[i]         \n",
    "            ground_truth_pts = ground_truth_pts*50.0+100\n",
    "        \n",
    "        # call show_all_keypoints\n",
    "        show_all_keypoints(np.squeeze(image), predicted_key_pts, ground_truth_pts)\n",
    "            \n",
    "        plt.axis('off')\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = torch.nn.SmoothL1Loss()\n",
    "\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_net(n_epochs):\n",
    "\n",
    "    # prepare the net for training\n",
    "    net.train()\n",
    "\n",
    "    for epoch in range(n_epochs):  # loop over the dataset multiple times\n",
    "        \n",
    "        running_loss = 0.0\n",
    "\n",
    "        # train on batches of data, assumes you already have train_loader\n",
    "        for batch_i, data in enumerate(train_loader):\n",
    "            # get the input images and their corresponding labels\n",
    "            images = data['image']\n",
    "            key_pts = data['keypoints']\n",
    "            \n",
    "            # flatten pts\n",
    "            key_pts = key_pts.view(key_pts.size(0), -1)\n",
    "\n",
    "            # convert variables to floats for regression loss\n",
    "            key_pts = key_pts.type(torch.FloatTensor)\n",
    "            images = images.type(torch.FloatTensor)\n",
    "            \n",
    "            if use_cuda:\n",
    "                images, key_pts = images.cuda(), key_pts.cuda()\n",
    "\n",
    "            # forward pass to get outputs\n",
    "            output_pts = net(images)\n",
    "\n",
    "            # calculate the loss between predicted and target keypoints\n",
    "            loss = torch.sqrt(criterion(output_pts, key_pts))\n",
    "\n",
    "            # zero the parameter (weight) gradients\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            # backward pass to calculate the weight gradients\n",
    "            loss.backward()\n",
    "\n",
    "            # update the weights\n",
    "            optimizer.step()\n",
    "\n",
    "            # print loss statistics\n",
    "            running_loss += loss.item()\n",
    "            if batch_i % 10 == 9:    # print every 10 batches\n",
    "                print('Epoch: {}, Batch: {}, Avg. Loss: {}'.format(epoch + 1, batch_i+1, running_loss/10))\n",
    "                running_loss = 0.0\n",
    "\n",
    "    print('Finished Training')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 10, Avg. Loss: 0.6189435720443726\n",
      "Epoch: 1, Batch: 20, Avg. Loss: 0.4420516431331635\n",
      "Epoch: 2, Batch: 10, Avg. Loss: 0.29841542840003965\n",
      "Epoch: 2, Batch: 20, Avg. Loss: 0.27847875356674195\n",
      "Epoch: 3, Batch: 10, Avg. Loss: 0.27933852672576903\n",
      "Epoch: 3, Batch: 20, Avg. Loss: 0.2766501665115356\n",
      "Epoch: 4, Batch: 10, Avg. Loss: 0.28088525533676145\n",
      "Epoch: 4, Batch: 20, Avg. Loss: 0.2713077560067177\n",
      "Epoch: 5, Batch: 10, Avg. Loss: 0.2739341765642166\n",
      "Epoch: 5, Batch: 20, Avg. Loss: 0.27186804413795473\n",
      "Epoch: 6, Batch: 10, Avg. Loss: 0.27126605063676834\n",
      "Epoch: 6, Batch: 20, Avg. Loss: 0.2653724417090416\n",
      "Epoch: 7, Batch: 10, Avg. Loss: 0.2702148735523224\n",
      "Epoch: 7, Batch: 20, Avg. Loss: 0.27224250733852384\n",
      "Epoch: 8, Batch: 10, Avg. Loss: 0.274235212802887\n",
      "Epoch: 8, Batch: 20, Avg. Loss: 0.27324173897504805\n",
      "Epoch: 9, Batch: 10, Avg. Loss: 0.25887638330459595\n",
      "Epoch: 9, Batch: 20, Avg. Loss: 0.27259342521429064\n",
      "Epoch: 10, Batch: 10, Avg. Loss: 0.2768922299146652\n",
      "Epoch: 10, Batch: 20, Avg. Loss: 0.2692341789603233\n",
      "Epoch: 11, Batch: 10, Avg. Loss: 0.26842895001173017\n",
      "Epoch: 11, Batch: 20, Avg. Loss: 0.2638102129101753\n",
      "Epoch: 12, Batch: 10, Avg. Loss: 0.2652289345860481\n",
      "Epoch: 12, Batch: 20, Avg. Loss: 0.26791703701019287\n",
      "Epoch: 13, Batch: 10, Avg. Loss: 0.2680212289094925\n",
      "Epoch: 13, Batch: 20, Avg. Loss: 0.26266615390777587\n",
      "Epoch: 14, Batch: 10, Avg. Loss: 0.25033301413059234\n",
      "Epoch: 14, Batch: 20, Avg. Loss: 0.2542695939540863\n",
      "Epoch: 15, Batch: 10, Avg. Loss: 0.2517216086387634\n",
      "Epoch: 15, Batch: 20, Avg. Loss: 0.24978393763303758\n",
      "Epoch: 16, Batch: 10, Avg. Loss: 0.25023782551288604\n",
      "Epoch: 16, Batch: 20, Avg. Loss: 0.245195934176445\n",
      "Epoch: 17, Batch: 10, Avg. Loss: 0.23356311172246932\n",
      "Epoch: 17, Batch: 20, Avg. Loss: 0.23219886422157288\n",
      "Epoch: 18, Batch: 10, Avg. Loss: 0.2332848221063614\n",
      "Epoch: 18, Batch: 20, Avg. Loss: 0.2290380820631981\n",
      "Epoch: 19, Batch: 10, Avg. Loss: 0.21737729907035827\n",
      "Epoch: 19, Batch: 20, Avg. Loss: 0.2176421880722046\n",
      "Epoch: 20, Batch: 10, Avg. Loss: 0.22418193966150285\n",
      "Epoch: 20, Batch: 20, Avg. Loss: 0.2140815258026123\n",
      "Epoch: 21, Batch: 10, Avg. Loss: 0.2198060095310211\n",
      "Epoch: 21, Batch: 20, Avg. Loss: 0.2036838099360466\n",
      "Epoch: 22, Batch: 10, Avg. Loss: 0.2134019300341606\n",
      "Epoch: 22, Batch: 20, Avg. Loss: 0.20642271190881728\n",
      "Epoch: 23, Batch: 10, Avg. Loss: 0.2096034198999405\n",
      "Epoch: 23, Batch: 20, Avg. Loss: 0.20443981736898423\n",
      "Epoch: 24, Batch: 10, Avg. Loss: 0.20488298833370208\n",
      "Epoch: 24, Batch: 20, Avg. Loss: 0.20153264105319976\n",
      "Epoch: 25, Batch: 10, Avg. Loss: 0.19982730448246003\n",
      "Epoch: 25, Batch: 20, Avg. Loss: 0.18699562251567842\n",
      "Epoch: 26, Batch: 10, Avg. Loss: 0.18805986493825913\n",
      "Epoch: 26, Batch: 20, Avg. Loss: 0.185680790245533\n",
      "Epoch: 27, Batch: 10, Avg. Loss: 0.17489138543605803\n",
      "Epoch: 27, Batch: 20, Avg. Loss: 0.19016765505075456\n",
      "Epoch: 28, Batch: 10, Avg. Loss: 0.1777550533413887\n",
      "Epoch: 28, Batch: 20, Avg. Loss: 0.17894623428583145\n",
      "Epoch: 29, Batch: 10, Avg. Loss: 0.18323658406734467\n",
      "Epoch: 29, Batch: 20, Avg. Loss: 0.18223708271980285\n",
      "Epoch: 30, Batch: 10, Avg. Loss: 0.17157752066850662\n",
      "Epoch: 30, Batch: 20, Avg. Loss: 0.17209681272506713\n",
      "Epoch: 31, Batch: 10, Avg. Loss: 0.17501881420612336\n",
      "Epoch: 31, Batch: 20, Avg. Loss: 0.16512602120637893\n",
      "Epoch: 32, Batch: 10, Avg. Loss: 0.164862696826458\n",
      "Epoch: 32, Batch: 20, Avg. Loss: 0.16369529366493224\n",
      "Epoch: 33, Batch: 10, Avg. Loss: 0.16517612040042878\n",
      "Epoch: 33, Batch: 20, Avg. Loss: 0.15598845481872559\n",
      "Epoch: 34, Batch: 10, Avg. Loss: 0.15714931041002272\n",
      "Epoch: 34, Batch: 20, Avg. Loss: 0.1634998619556427\n",
      "Epoch: 35, Batch: 10, Avg. Loss: 0.15258720368146897\n",
      "Epoch: 35, Batch: 20, Avg. Loss: 0.1456226572394371\n",
      "Epoch: 36, Batch: 10, Avg. Loss: 0.155811870098114\n",
      "Epoch: 36, Batch: 20, Avg. Loss: 0.1601123183965683\n",
      "Epoch: 37, Batch: 10, Avg. Loss: 0.15305536091327668\n",
      "Epoch: 37, Batch: 20, Avg. Loss: 0.1531197965145111\n",
      "Epoch: 38, Batch: 10, Avg. Loss: 0.14913657307624817\n",
      "Epoch: 38, Batch: 20, Avg. Loss: 0.14516404867172242\n",
      "Epoch: 39, Batch: 10, Avg. Loss: 0.15273783802986146\n",
      "Epoch: 39, Batch: 20, Avg. Loss: 0.14939407855272294\n",
      "Epoch: 40, Batch: 10, Avg. Loss: 0.14842936843633653\n",
      "Epoch: 40, Batch: 20, Avg. Loss: 0.14743032231926917\n",
      "Epoch: 41, Batch: 10, Avg. Loss: 0.14120225608348846\n",
      "Epoch: 41, Batch: 20, Avg. Loss: 0.14795229732990264\n",
      "Epoch: 42, Batch: 10, Avg. Loss: 0.13713533580303192\n",
      "Epoch: 42, Batch: 20, Avg. Loss: 0.14046911597251893\n",
      "Epoch: 43, Batch: 10, Avg. Loss: 0.1397349141538143\n",
      "Epoch: 43, Batch: 20, Avg. Loss: 0.13992740958929062\n",
      "Epoch: 44, Batch: 10, Avg. Loss: 0.14178178906440736\n",
      "Epoch: 44, Batch: 20, Avg. Loss: 0.14839769303798675\n",
      "Epoch: 45, Batch: 10, Avg. Loss: 0.14034095853567125\n",
      "Epoch: 45, Batch: 20, Avg. Loss: 0.14554795995354652\n",
      "Epoch: 46, Batch: 10, Avg. Loss: 0.1401616021990776\n",
      "Epoch: 46, Batch: 20, Avg. Loss: 0.14058039486408233\n",
      "Epoch: 47, Batch: 10, Avg. Loss: 0.15334733575582504\n",
      "Epoch: 47, Batch: 20, Avg. Loss: 0.13664527535438536\n",
      "Epoch: 48, Batch: 10, Avg. Loss: 0.13334010392427445\n",
      "Epoch: 48, Batch: 20, Avg. Loss: 0.13391779214143754\n",
      "Epoch: 49, Batch: 10, Avg. Loss: 0.13538335636258125\n",
      "Epoch: 49, Batch: 20, Avg. Loss: 0.1402540922164917\n",
      "Epoch: 50, Batch: 10, Avg. Loss: 0.1380995385348797\n",
      "Epoch: 50, Batch: 20, Avg. Loss: 0.14377134367823602\n",
      "Epoch: 51, Batch: 10, Avg. Loss: 0.1434030368924141\n",
      "Epoch: 51, Batch: 20, Avg. Loss: 0.1321689747273922\n",
      "Epoch: 52, Batch: 10, Avg. Loss: 0.1457675114274025\n",
      "Epoch: 52, Batch: 20, Avg. Loss: 0.14039494469761848\n",
      "Epoch: 53, Batch: 10, Avg. Loss: 0.1345135122537613\n",
      "Epoch: 53, Batch: 20, Avg. Loss: 0.13600059151649474\n",
      "Epoch: 54, Batch: 10, Avg. Loss: 0.14483714252710342\n",
      "Epoch: 54, Batch: 20, Avg. Loss: 0.1493214726448059\n",
      "Epoch: 55, Batch: 10, Avg. Loss: 0.1294882357120514\n",
      "Epoch: 55, Batch: 20, Avg. Loss: 0.13439833149313926\n",
      "Epoch: 56, Batch: 10, Avg. Loss: 0.13267617225646972\n",
      "Epoch: 56, Batch: 20, Avg. Loss: 0.12877891436219216\n",
      "Epoch: 57, Batch: 10, Avg. Loss: 0.12706395089626313\n",
      "Epoch: 57, Batch: 20, Avg. Loss: 0.1300143249332905\n",
      "Epoch: 58, Batch: 10, Avg. Loss: 0.12778937295079232\n",
      "Epoch: 58, Batch: 20, Avg. Loss: 0.13360553458333016\n",
      "Epoch: 59, Batch: 10, Avg. Loss: 0.12485131621360779\n",
      "Epoch: 59, Batch: 20, Avg. Loss: 0.12069407179951668\n",
      "Epoch: 60, Batch: 10, Avg. Loss: 0.13031171187758445\n",
      "Epoch: 60, Batch: 20, Avg. Loss: 0.13042414039373398\n",
      "Epoch: 61, Batch: 10, Avg. Loss: 0.12802360504865645\n",
      "Epoch: 61, Batch: 20, Avg. Loss: 0.127623488008976\n",
      "Epoch: 62, Batch: 10, Avg. Loss: 0.13136030361056328\n",
      "Epoch: 62, Batch: 20, Avg. Loss: 0.13547028079628945\n",
      "Epoch: 63, Batch: 10, Avg. Loss: 0.1279095269739628\n",
      "Epoch: 63, Batch: 20, Avg. Loss: 0.12394444048404693\n",
      "Epoch: 64, Batch: 10, Avg. Loss: 0.1261570245027542\n",
      "Epoch: 64, Batch: 20, Avg. Loss: 0.12288684770464897\n",
      "Epoch: 65, Batch: 10, Avg. Loss: 0.12556208446621894\n",
      "Epoch: 65, Batch: 20, Avg. Loss: 0.12184173762798309\n",
      "Epoch: 66, Batch: 10, Avg. Loss: 0.12803722321987152\n",
      "Epoch: 66, Batch: 20, Avg. Loss: 0.13351177498698236\n",
      "Epoch: 67, Batch: 10, Avg. Loss: 0.12546580135822297\n",
      "Epoch: 67, Batch: 20, Avg. Loss: 0.12364714741706848\n",
      "Epoch: 68, Batch: 10, Avg. Loss: 0.12775811329483985\n",
      "Epoch: 68, Batch: 20, Avg. Loss: 0.11916478872299194\n",
      "Epoch: 69, Batch: 10, Avg. Loss: 0.12582916170358657\n",
      "Epoch: 69, Batch: 20, Avg. Loss: 0.1245804101228714\n",
      "Epoch: 70, Batch: 10, Avg. Loss: 0.13670952692627908\n",
      "Epoch: 70, Batch: 20, Avg. Loss: 0.1197363205254078\n",
      "Epoch: 71, Batch: 10, Avg. Loss: 0.1266719803214073\n",
      "Epoch: 71, Batch: 20, Avg. Loss: 0.1163291022181511\n",
      "Epoch: 72, Batch: 10, Avg. Loss: 0.1291121371090412\n",
      "Epoch: 72, Batch: 20, Avg. Loss: 0.12415442690253257\n",
      "Epoch: 73, Batch: 10, Avg. Loss: 0.11714610308408738\n",
      "Epoch: 73, Batch: 20, Avg. Loss: 0.12015517204999923\n",
      "Epoch: 74, Batch: 10, Avg. Loss: 0.12879123762249947\n",
      "Epoch: 74, Batch: 20, Avg. Loss: 0.11857607886195183\n",
      "Epoch: 75, Batch: 10, Avg. Loss: 0.12257435396313668\n",
      "Epoch: 75, Batch: 20, Avg. Loss: 0.12283714935183525\n",
      "Epoch: 76, Batch: 10, Avg. Loss: 0.13129547983407974\n",
      "Epoch: 76, Batch: 20, Avg. Loss: 0.11803888902068138\n",
      "Epoch: 77, Batch: 10, Avg. Loss: 0.1133531428873539\n",
      "Epoch: 77, Batch: 20, Avg. Loss: 0.115158911049366\n",
      "Epoch: 78, Batch: 10, Avg. Loss: 0.12449855208396912\n",
      "Epoch: 78, Batch: 20, Avg. Loss: 0.12068661227822304\n",
      "Epoch: 79, Batch: 10, Avg. Loss: 0.125364475697279\n",
      "Epoch: 79, Batch: 20, Avg. Loss: 0.12055029273033142\n",
      "Epoch: 80, Batch: 10, Avg. Loss: 0.11560068875551224\n",
      "Epoch: 80, Batch: 20, Avg. Loss: 0.12115855515003204\n",
      "Epoch: 81, Batch: 10, Avg. Loss: 0.1170904852449894\n",
      "Epoch: 81, Batch: 20, Avg. Loss: 0.12417801171541214\n",
      "Epoch: 82, Batch: 10, Avg. Loss: 0.1217839129269123\n",
      "Epoch: 82, Batch: 20, Avg. Loss: 0.1245543047785759\n",
      "Epoch: 83, Batch: 10, Avg. Loss: 0.11907242983579636\n",
      "Epoch: 83, Batch: 20, Avg. Loss: 0.11717921048402787\n",
      "Epoch: 84, Batch: 10, Avg. Loss: 0.1253627896308899\n",
      "Epoch: 84, Batch: 20, Avg. Loss: 0.12673916220664977\n",
      "Epoch: 85, Batch: 10, Avg. Loss: 0.12109364494681359\n",
      "Epoch: 85, Batch: 20, Avg. Loss: 0.11974991261959075\n",
      "Epoch: 86, Batch: 10, Avg. Loss: 0.12059697806835175\n",
      "Epoch: 86, Batch: 20, Avg. Loss: 0.12257298529148102\n",
      "Epoch: 87, Batch: 10, Avg. Loss: 0.1238307498395443\n",
      "Epoch: 87, Batch: 20, Avg. Loss: 0.1226200632750988\n",
      "Epoch: 88, Batch: 10, Avg. Loss: 0.12223065868020058\n",
      "Epoch: 88, Batch: 20, Avg. Loss: 0.12207857817411423\n",
      "Epoch: 89, Batch: 10, Avg. Loss: 0.12092348337173461\n",
      "Epoch: 89, Batch: 20, Avg. Loss: 0.12196474000811577\n",
      "Epoch: 90, Batch: 10, Avg. Loss: 0.1222312644124031\n",
      "Epoch: 90, Batch: 20, Avg. Loss: 0.11285760700702667\n",
      "Epoch: 91, Batch: 10, Avg. Loss: 0.11445669904351234\n",
      "Epoch: 91, Batch: 20, Avg. Loss: 0.11670251414179802\n",
      "Epoch: 92, Batch: 10, Avg. Loss: 0.12003640532493591\n",
      "Epoch: 92, Batch: 20, Avg. Loss: 0.12098090276122093\n",
      "Epoch: 93, Batch: 10, Avg. Loss: 0.11492259576916694\n",
      "Epoch: 93, Batch: 20, Avg. Loss: 0.11412259042263032\n",
      "Epoch: 94, Batch: 10, Avg. Loss: 0.1143144428730011\n",
      "Epoch: 94, Batch: 20, Avg. Loss: 0.11689513027668\n",
      "Epoch: 95, Batch: 10, Avg. Loss: 0.11809179782867432\n",
      "Epoch: 95, Batch: 20, Avg. Loss: 0.1091231256723404\n",
      "Epoch: 96, Batch: 10, Avg. Loss: 0.1267546981573105\n",
      "Epoch: 96, Batch: 20, Avg. Loss: 0.11513920202851295\n",
      "Epoch: 97, Batch: 10, Avg. Loss: 0.12058257833123207\n",
      "Epoch: 97, Batch: 20, Avg. Loss: 0.11190622001886368\n",
      "Epoch: 98, Batch: 10, Avg. Loss: 0.1132437989115715\n",
      "Epoch: 98, Batch: 20, Avg. Loss: 0.12158381119370461\n",
      "Epoch: 99, Batch: 10, Avg. Loss: 0.11103884875774384\n",
      "Epoch: 99, Batch: 20, Avg. Loss: 0.11440228819847106\n",
      "Epoch: 100, Batch: 10, Avg. Loss: 0.12000268548727036\n",
      "Epoch: 100, Batch: 20, Avg. Loss: 0.11247154772281646\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "# train network\n",
    "n_epochs = 100\n",
    "\n",
    "with active_session():\n",
    "    train_net(n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test the model on a batch of test images \n",
    "# function to extract a test sample\n",
    "\n",
    "def net_sample_output():\n",
    "    \n",
    "    # iterate through the test dataset\n",
    "    for i, sample in enumerate(test_loader):\n",
    "        \n",
    "        # get sample data: images and ground truth keypoints\n",
    "        images = sample['image']\n",
    "        key_pts = sample['keypoints']\n",
    "\n",
    "        # convert images to FloatTensors\n",
    "        images = images.type(torch.FloatTensor)\n",
    "        \n",
    "        if use_cuda:\n",
    "                images, key_pts = images.cuda(), key_pts.cuda()\n",
    "\n",
    "        # forward pass to get net output\n",
    "        output_pts = net(images)\n",
    "        \n",
    "        # reshape to batch_size x 68 x 2 pts\n",
    "        output_pts = output_pts.view(output_pts.size()[0], 68, -1)\n",
    "        \n",
    "        # break after first image is tested\n",
    "        if i == 0:\n",
    "            return images, output_pts, key_pts\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 1, 224, 224])\n",
      "torch.Size([128, 68, 2])\n",
      "torch.Size([128, 68, 2])\n"
     ]
    }
   ],
   "source": [
    "# get a sample of test data\n",
    "test_images, test_outputs, gt_pts = net_sample_output()\n",
    "\n",
    "print(test_images.data.size())\n",
    "print(test_outputs.data.size())\n",
    "print(gt_pts.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4494168c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f9c5208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f97d828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f93d828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y2FBWIZfLkc/nAdQuBZQrmZiYGMl0JF7RQTkJDJMkUTGNxC2ASmMFbJNiYJIkuK7LYDBQFehyuYzjOLiuq0oAzWaTbrerlEOKfPl8XtWZxsbGWFxcBFDKIpbOMAx832dzc1MptuM4Cgvai+wLRRE/v7a2RqVSoVwuYxgGm5ub/PEf/zEPPPAAr3vd67hz5w6vfe1rWVlZ4caNGyp1DcOQyclJkiShVqtx8eJFlSrK4jqOg+M4DAYDhaaWSiWFCIsFMAyDfD5PkiTkcjkVl8jui6KITCZDsVhUsYquMKZpKiRWlEvwDVGiOI65desWqVSK6elpVf8Rl5jNZrFtm0ajQbPZpFqtsrm5qVL0KIpot9uq/iU1obm5OYIgoNPpYNs2vV6PXq9HLpejUCjQ6/WUlWm1Wqytre15jfaFonz1q19ldXWVRx55hA984AP82Z/9Gbdv3yZJEt75znfiui6bm5tsbm5y584dGo0Gk5OTXLp0iXQ6zaOPPspnP/tZfvAHf1DhC/1+H8uyyOVyI/hHkiSqgrqysqIWdWZmRgF07XZble0LhYKiChiGQalUwnEctra2VAosmIxYrSRJVF1KL+3n83kVQ3Q6Hc6fP0+n0+Gtb30r29vbLC0tMT09rYhUp06dolqtAnDt2jXiOObQoUNKCeT9AVy6dEnBCtVqldXVVUWaCsOQSqXCzMyMAiodx1Eo8V5kXyjKL//yL9Pr9VSNx7IsfN/n05/+NJ/5zGcUGirBqETuvu8rsy3Am6R8wmvJZDI4jqP+ttfrsb6+Tr1eV7sN4NatWwoa9zxPBaapVArP81SQOzk5qaxTp9OhVCrR6XRot9sKMJS4xjAMOp2OymIk8BSMI4oilpaWaLVaSvlc11XEIsnspGRQq9WYmppSytjr9ZTiS1UbYHFxkWq1ytbWlmIFfvKTnySbzTI2NsbrX/96Dh06pIqYe5F9oSiwU1+RB6gztiQr0GmJkqEMBgMcx2FiYoKxsTEVB2SzWSzLUkCTZB3tdltlRZIRyfX0uokU9OTv8vn8COYikL0guuKGpAosGYjELu12WymLpNUSLF+6dEnVZba2thQ+4zgOqVRK0SuCIGBra0tZE3F7cRyrZzEYDJicnGR+fp44jtXGEeu3vLzMysoKU1NTIxndS8m+UJSpqSkKhYLiaUgdQrAUMZMSnIly6HjH/Py8shiiVMLf6Ha7NJtNVSeS9FNih3w+z+zsLGNjY2QyGcrlMsViUSlJqVSiUCioGpDQCyWrkaxKMBPhtuZyOZrNJrVajXw+r2IGUSBBkSWwPX36NGtra1y8eFHB/eL6SqUSlUqFWq1Gu90e4dM4jkMul8P3fer1OmNjY5w9e5ZcLsfTTz9NGIZks1nFzqtWqywtLSnLtRfZF4rymc98hlKppDIJUY5Go6FM8WAwUAvS6/UolUpks1k2Nja4cOGCApYETRXX0W63lfINBgNlnnO53Ah8L24jm83iuq5aaNnZopS6yEILvC+1okwmo64ntEzZBMIJlv/P5XIK0ygWi+TzeUUHEAuaz+dxHId0Oq2QaEF9Jc3VObKtVouxsTHOnTtHtVrl+vXrqghqmiYzMzMqG9yr7AtFkYqo7BIxp/Jw9B0oxB8JFjc3N7l27RqNRmMkBRZIvtFoKLBNvnccR7kBcSPZbJZsNquCPQkoxUq02208zxvBSqRsIB/CuhfATWIVSadlkUVpxEpKLHPt2jUeffRRFhYWuH79uioJSK1LCoKNRkORpIMgoNvtKizFcRzW19ep1WpMT0/zkz/5k5w6dYpMJsOXv/xlPv/5z/Ptb39bbYi9yr5QlA996EMq0Eun0wAjwR+gshZAQfiCadi2TalUUrtY0mbf95WSiWWRBZTgs1QqkUqllFWR19FL9nrrhiCy8ho60AajtE5RSFEYgdiljURSZ3EBvu/TbDaZmZmhVCrRbDZVnct1XaXY4t7EGsq9SeorXQWdTodbt27R7XZJp9M0m00eeughHnzwQXK5nHqee5F9oSiSXkpqKgGo/kakQiq1FsuyFBlJKrxhGKqsSedyiJkWDKXX6zEYDEbSZWHoS0YhVk2UR7IouT6gcBcdoRUlk+vr9y9xk65EQnmQIHl7e5t0Oo3nedTrdQXT64w5IW1L8Cx4kSitQAJBEFCpVPj617/O2tqaej7CELwX2ReKUiwWAZSpl69lEeSB67vccRxmZ2fxPE8hjK7rjiiKZEySnoqSibmX6vHY2BjlcplMJqPgeikC9vt9FffIwkjlVRZfV2KJk/r9vnJBIvJ6gt1I3NButxXKKijssWPHyOVy9Pt9ut2u6i/KZDKq/UPn7YpL1nnHAl6m02lV+2m1Wqq95L5DZsV8yg6GHfhZAjCdZyFBmzRMyUPS4XPx/zpJWhZRjyckYJX/h1HKgiis3J8okK4Asjv1OEp/PfkbUXpBi8XNCfXBcRxKpZJSAlFUCeTFbcm9SMwj71myPXmOck3h5khjXLfbZWJiYqS29ZJr9PdZ4H8o0U2nLIpOZNKrtRJvlEolhV34vk8+nx+B0KUoJg9dSv5yPd1MS0egHqBKPCAfcj+7lUKsAoxaRL1mJMojSiKiB9+9Xk/RAwR5lVJCGIaKJiG/FyshVmxra4tms6k2XZIklMtl+v2+am+V+pPADfdCM9gXTeq6guz+uSyYLIbUaeThSkVX3A6gwK1ms6kQWNhpoRQLlMvlKBaLqrYiFkn+TtoqZEElsJVdLhZDAk5RRjHzohzSAam/H7mu1JN832dra0spsaCu6XRaAXPyXvRUVzaLWArh4EobiLSizszM4Lquisn6/b56LnuRfWFRdLR1t8LcjfwtplsC1Gw2q5BOiUksy1KKJMiuxCymaSpFEThe0mEB+mTX6nxeXSQQ3s1PAdTr6O2bepuskJBkkcU6yGKLKxkMBqqbUcjT4nr05yJKI68l1kwQZimKirKKZdn9nr6T7AuLIrJXAEh2qRTYBOrWqZPizlKp1EiQC6g4R/gZUn8xTIN20h7+jWXSpj1iacQSSBovH3JNsYxiLfQYRxZGj7fETQgwJ4GtuC9ReKE3xnGsQDu5pii3/F54t91uV9XHJI6T+5Pr33fpscheW0fieDjSwXVdBa3D8MFWq1W1UIKv9Ho9arUajUaDKIqYm5tjYmJCZTq5XI5sLsuvbf4aV3tXOekOGfxXOlc4nT3Nhxc+jJNy1IPWdy2glFZ/H7IYOo9XFFfY/dIGKgsppf/5+XmVptfr9ZFNIKRv/drCL5GqudSLBOyTOE1c4L0QlkT2haLoZhleehyDnt7qVEQx5a7rKlxFCnLS5O04DoVCgXw+r8yybds0wgZXe1eJibnqX8XAICbmcucyt7duD2tAqbJaJHFh4n7EBcn96aSlu70/eV3YwVikltTpdJQ7FIXzPE8FtBLU7n4d+ZnQB+R15N4En5Ki5Es9Z132haJIJqFXh+HFTaO+s3e3eUiKK4BbEASK6GxZlqoCS0ocxRHVoEo+yHMkOcIt4xYz4QxJlLDurDMTzfDbm7/NirnCsdQx/uXsv6QZNskbeWXyhWitx1p6e0RCQiNokG/lMVyDhIQ2bRzbIe2mqfVrJJ1kp+2j28HMm2SDLCQ74zekUUxSZ3nP8hzEtekZpO/7ylVKbOT7/l1rV99J9oWifLciD0XMNuxgCp1OR+0+sS4yecB1XVJ2ikyQ4RPuJ1huL3Nm/Qz//v/692yc26D2T2tEzYh1Y50bSzd4fOFxEhKu9a/x4RsfZsPa4LB5mHdl3oWbdpVlAyAGo2nQz/WJwoiwFvIb/AbL8TLnls7xgT//AO9/5/vZSG8w3Z8GFzYKG0xNTPH6O6+n1WrxN8f/hkahwVw0xz/u/WP8rq9QXZmcJACixB56IVRacwV/kf4nARMFrb4X2TeKcreM5zv9re52BHwS9FLSVYkV5MGIq0k7aV7zp6/BaBrc+Rd3iI2Yy5OXaWVazF+YZ/K3J3FuOpw8dpJHg0e58vorXFi8QKaeYb20TmIk3I5vs93bZj49r+4l7Icc/a2jeDc82keGjWf9rT7L/2KZyIy4uHCRXtRjw9kgMRI23U0wUF83wyZWzqJRaJAYCavWKl2jSxQMLa4E5pKSw6gblmcj6bRkQhKfCIB3r0oC+0RRdruclxId7RRidT6fp1AoqBZNAb+2trYUxcCxHY4Wj1JKSoyvjmMkBueWznHh8AUeqDxAqVeie7iLd9PDiA286x6e4fHR3/so1VyVL73lS/xx5o/ZdDc5lBzCS7yRGMVqWng3hv+bvTlEPdVrLFzg3PI5Ck6BhXCBFWuFs8tnMTC4uHCRk+snoQOtXovCVIFmocnMYAY3cgnToQpaPc9TrDmxKDrbX7AZ+Zm0iug8GL2TcK+yL5rUX/GKV9zzTQixWCrI5XKZQqGA7/usr68TBAFra2uK0zoIBvzb4N9ywj9BdbYKBoytjlGZrfBf/8l/JZNkyId50lNpzv7fZ8nezOIf98EA95pL63CLJ974BINowCA1oOSUSDtpxsbGFOeEBOb/j3myN7PU5mrESczY6hjrU+t89o2fZTqeZuL4BHbKZu4P5jj07KFh/OI1KHaL/Me3/kdWeisE/YCu0eXk3EnK5TJduliBhWVaqkFeWHUSpOppvkD14oLlf3RkWizSt771rfunSf1eRaBzHcyCHVhdRzflgYzb4xyvH8fEZGxtjCf+2RP4fZ9aUiNdTxNZERvdDfrNPtded41iUsQf8/HwSPfSNPNN4kaMgYHRNeiFPUxjJ3AUN/jcP3+OfqVPZTBsmk91UqQmU5zyTikSdyqVov8Lfa5XrzP/8XlK10vU5msYYwbprWFl1/Ed2u02X5z/IpvuJtPBNG/efrN6BjrKLPRLcTdSFJTBgjqPWDCoe7HgcJ8qiphNqcLqlVxAxSidTkeZ5kF2wPJgmcXmIvVDdaxJi9QghV0dMsJW11b55oPfpDPWIVvLYps2TbdJvpkHE1p2i7Jb5rGrj5GyU2qYngwJFMJVGIbU03UIIU5iuuku2WgHK5FGLIC+3Sd4T0B7pc1GsIHRMRR9E2Cru8Wmu0liJGykN2jHbTJkFBaiZ3n6VAUdXNTBx90b7P/3WY9gA8AIuqlPRdDTxSRJcFyHTzz2CV4x9Qpy8znyhfwIE83Mm3TGOmBCpzykS2JCs9BUX1ezVXpmD9M1SUgUdVJGSrQ7bZpxk4QhltHr9jAbJqvOsHXCbA4V1nZsEi/BSzzGxsZoW+0XTFaI45jGdoNis0ij0GC6P40bu2rhJYjXeSU6WivPRbequ3Gqewk77ktF0SvM+nwQeB68Mi0c3yEcDBVnEA5Y+ZEVnht/juvhdd6VeZfidmTSGXKDHFkvy+rqKtdmr3F6+TS3pm/RdbuYkQkJxKkYO7Z5+vTTbGe2mY/m+fno5zGNYd3ITtl88MoHudy9zBGO8BOpn+ANX3gDExsT3MrewjAMDrcPs1Je4f0/83628lsU20Xc2GXD2WAqnuJHaj+irKFpmBTCAo88/QiFuQJj6THiJFYUCr0YqaO+8iE1LFEUURadKXgvsi8URfe38r24jN1vSt8tkpZKSpzJZLBMi3d8/R3MVme5mrrKL5V+acdaGLBmr2EXbfKpPJl0hrmPzGFdsugudnnb7bfR9JrExLzjve8AA7CABDAgsiK2vW0SEtbMNY7/xnFKh0u4v+NSD+tc6V4hJuZ2cpuXf+7lTG5MYiYmi51FGl4DMxmCaNu5bRIzoZ57fpq0ARvpDZaqSxSMArZl8/a/fTuz1VlWSiv8+dSf06c/UoAUqyLZkFSQYacjUZiAwmjTi5b3msTsi6KgBIR6nKEX8HZ/CDEniiLF4Wg0GsMH07KYrc5iJRYn+yfJx3noQLFRxEgM5qI5MtGQJW93bKxnrWEqfMvDSBuUO2XK3TLnls9hRRYPLj2ovl6MFjlmHcPE5NzSOcZaY5jPmhiN4f2fcc9gRRbnls8xf3WexEmIjIj3vfN9vOO97+AX3vkL5Lt5Mv0MJGBFFl7NU4r4zKlnCKOQbD+r3sN8fR7Xd5Uy6EQsqd0IWUmI1tVqlfX1dVZWVtjY2KBerysXpfNp7jvXIwGZBFq76YO6iJnVS+ay04IgIEklrI13Ife2AAAdpElEQVStMbs9tChb4RZRHPHIM48wfXSa+fK8ohnYeZv4TIz5rEn/aB/nhoOBgWmY/Prv/ToNr0GpUyIxElYeWGH7n21TLBdpx21O/tFJMCE+F/Oe6+/hmfoznM2f5Q/+8x8w/fQ03cUu2TtZal6NS4cuEVlDwG15YhnfGabdsR1z7NIxLnz/BTAYAm1eQhAFw/fwvEXpOl1MTEWalrKAb/lk4swI5UGqx3r9SYJ/+V6sy33XrqFPZNTL5npkrqfAUjrXGfdSkTVNk8++7rNsX9/m8vplovbwoYSDkHSYxsBQhxa4rkv/f+2T9tMkhYTy/17GvmITng4hgfKVMv0H+gT/W8DY+BjFuEin08HFpf7hOpl+BmPR4JmvPEOURFxqXqLz/g6r26vU4hrHPnaM0o0SD1Qe4PLUZc4tn6PoFjkUH2LZXGY+maf6vcPeYhKYjWaZyc8Q+AGfeuxTGHWDqBDh4alnYts2Pb/HN859g0a+QaFZ4MFvPjhiKQDVnSDDgUSJZJPdq+vZF4qixyi7iUCyW3SRhyIjpgSiliExKSdF4AWYlgb1P78Do3iHUBRFEYNogJW3MA2Txi82MJoG5phJyk6BD6nxFGY8ZJINwoHCKlzXxc7bjPvjPFx+mGdqz3A2d5aSU8Iv+9gtmzvvvUOqm+Jd6XdRD+qUjpW4dfYW7+bddBgOGPzV/q8On0Fi8lP8FFEuIo5iTMukm+mSIrWD/D7vbn3Tp5FvkJgJzUKTwA6ww53F14cZCjlciFI6qeq+m48iA/H0moUOnu1mkOkEY51JJsGb3n4qWc/5R87z1fJXmQvneFf4LhUPyTWiKMJyLcxxU1EtzexQwSTtFl6LZEvehzyCSwEffdlH6f2fPZJ2QqPR2ElbLTDGDPJmfoidjEPeGE56nLGH0xOObxznev86R6wjjFvjdLKdYdXbDzAHJl2jq7oLJXBNDVIqbS40CqT6KbUBALVpYHTSZhzHCqmVURl7lX2hKA8//PBIkQ9GU+DdxGsYWiHp8JeufdWM3vPJ9DLE0fMAkxfTKQ2znlV7dQhcGZkXtVY6hVG4t1EUUa1Wd5RrO8K4ZEAE0dMRST3BdExViBTCkM5s203SdhyHD0x9gK3eFm7oKkuQslO89ctvZXprmpXSCv/p1f9JvfcoiiCBV118FT2jh+mbxEZMRKQ4LhLkikUWspPMUJmcnFS9RXuVfaEojz/++AiNEHbik7ulyNKIJRRCwQ3iOMYyLd7+t29nrjbHc85z/Cv3XxEHMYV6gVa5xVw4h5M4I30+OsilM+2lDVWObKlWqxw6dGhI7LZ7RA9EmJdN3vvP38uFpy5wJnuG9028byRD0RVRMjt57SRJcFIOk0wq9lqSJOTDPDNbM5iJqbIeEYWqGiaZOEOSShgwUNiKWF8hUEvdR6ZDBEHAxsYG6XRazV7Zi+wLRZERFbtZYuJupNh1N4URvoWQkFPtFHO1uWF6HJyklCnRMBuc+fYZpo9NM1ecIy7EineqUxr1g5OE4S7di9VqdaTDruf3WP5fljGaBhfaF4bBbPsSq6lVvNhT1k1Y9KIcuhIJ4Ukv7gHExZjKdIWJjQmWC8vUkhpT0dSQiO2maSUt4lasuDAyykuIWsBIjBKGIcvLyyPdjBK37FX2haLox6HADjdFfK3eIaibVMl8AGVuW1aLldIK8/V5bmRu0LbbWMYLGfTyWQev9A9pzRCys8QHUj8yEoND/+EQ3k2PB/6nB7g0fYnD5mHc0CVmhw3f7/cVq1/vetT7fcRdidIbpsGTb3mSjcsbbMfbQ/cWRSQkfOnwl6jn6hQaBV7+9MuHlE0tRpNhQ2JVhA03MzODaZqK7X/Pa/TdLe0/vHwnfoT4UllU8fWymHpsk5DwR9/zR3h9j6ubVzEqQ+rhhVde4KulrzIbzvLT0U+PKIss1G7MRhrbDcNQ80dkFGcpLuHdHIJlVs/CSIauxA98bMtWfUdCotbZ+CIq89J6hFQ3o2nQSXeIuzGm8XxHodOnnqsPs51ik77dxxk4CniUa+vX2t1SIpCC3n+0F9kXitJut0fohHoQprebwk7xD1CLq8+wFzCtn+3jZlyOHTtG1+hyvniexEhYs9do9pvDyvHzu1z+d3f5Xa4nMUy73WZldYXV+iqZJMPC7AJT9SkuLl4kNmJux7f5xsVvULSL6qxAqWLr6a24BJ10La4IgATSnTQpO6U2QRiGWLFFsVUc4ieNAnbfxjCNkUBXFl+ge1EgsTQ6jH/fAW5zc3Mj2AYMTbMEYwKqiYg7kvYEmWMvpld21rGjx5hMTXK1cpU77TvU8jUme5OkwzSRvZNO6jsPdpRSYhWJM1JOiq+d/hor9gp2ZPPl7/syDy49yIn1E1yZvUKpXaLdbRNnYo6kj6gYQO9JhhdWbwUWMAwDEvhH//kfMbY6xkp5hd85+TtYqWHdxrZtHnnmEbp0cUJHIdP6h4iMSZf3J4Grzt+57yYuPfLIIy9oZBKRgFbHRaRZW3a84zhcvHhRxSkA4SDkzV99M3O1OW56N4exwliaYqpIMjvaYqG/hi4CrEmMEaUi1qI1MCC0QjDgwsIFTq+eBqDpNfniw18E4Lxxnvc478Fgp6tRyg6624PRE9BSnRRjq2OYiclcbY7sIEvbaO+AkXGCEz3fMhLvxHC7XY3jOHiex/T0NBMTEyNoNzByH3uRfaEov/u7v6uUQLccOpdWFEXMt8yMlQcgs18nJiaGw/nCvMp+jnaOYtQNvLKH3/OV2RWSD6DgbRGd2Z4kCUmU8NjvP8aD3/8gFxYvkCJFmIScWjnF1UNXVXVZ5HZyG7tok02y6rQOHQ2V19ctaZIkBJmA7dltxtfGWSmt0HN7JFFCs9nEtEwGzgAndiBh5H2IuxYSlxCl1tbW2NzcfAEGJcnCXmVfKIqYTH2KkVRDYbTVVApagjvo3XKAGopjpA1Wy6vM1eZYLiyTFJ9HWE2DNm3yUX6kzVKuLaLvwDiOoQXZG1k+ev2j1Lwa+W6e23O3ObJ6hPf+0/dycf4i7sCllx42fh9PHceLPQxzp7ayu1Anyi8paxiGw8zmx79EXI25tnUNgud3fxJz8ZUXaZVa5Ot5zv6/Z5WC7LYq8uzkDKPdlgsYiVf2IvtCUQDVMCWafjfUVHyytFvqTVawc06OKNgnHv4EreUW9oTN4swipmXypcUvselujmQ/+msL8CbYhmrh9MA/4eNecyl1S7z3Z97LxYWLQyZ9bGBicmL9BB/61HDM2Nr710aOmpP715vVYCfekvhqMBgM+Sn9DTrdzo57yEG71AZz+HmQGpBKdubBSDwkuI/UeHT3oqPdolR7lX2hKLJQstvEnOp9svrOA0YCXFGWMAzVIdKu6zI5OcnZ7zvLYDDgueeew8gbbJ7cHMl+nMgZ4Z5K2igKq8aGkvCN/+EbrC6t8rIvvoyLCxeJrIgLCxcwDZPIjLi0cAnLtEhNpOg6Xex49PHq97s7DpP3PugPMBsmljm0aHI2cdEpUmgUaBQb5Ot5UoOUWj2hFoThzkkecj1RzN3pucR2e5V9pSg6jK7vOFEcaYkUNtfuepCOdPZ6PdrtNsRwYvIEbtqlVWuRq+Voj7WZ7k/jDBz6Zn9kpJUonIzp2tzcpNVq0Ww1eWLuCeqH6mTekiFfz1MfG2IaVjK8/6POUWofruG7Pv16n/6gr3qKdXANRt2ACqgNk9f8yWsYXxtndWyV3z7+2+q+wkHIA19/gHbSppQqERGpeScyF04ISjqBWj7rLl2e2X034vzNb37zyKwOfRCfvgv0OoYolfhZ+Znec9ysN/mZp3+GE/4JrqSu8KtTv8rk5yd57OWPMZWdop/tE0fD3Tw+Pj5Sjd7Y2KBar3Jp8xKN6w3aUZvaO2tgQne8y/c+9b385dhfggF9+rzu0uvwAo8vjH2B49PHKVKklW+RiTLqlPW7NbrplsXpOoyvjWMmJrPVWdxDLlvBFoZpEGUinIGDndhq1ZrN5kiNbGZmhomJCWV9dd7J7tcV5Hivsi8U5dOf/jSwcyC27ELLslhcXBxpqNapB7opl9PCpBgWhiFe4HHCP4GFxanBKbyBx2Z3k85GB46ioGzx83It27bJ5rN8fPLj9E/1sb/X5lV/+SouVS/RHG9ydPkof3v2b4c3/7xh+PIDX8YZOPiOz8k7J3Fjl0vpS0xFU7wxfiOzqVmy9s7MND1FlvfRS/fYmtliYn2C1fIqLatFQsKd193Bn/LJVrMc+fIRdc/yfmWshcRTeifCbjabrjD3XXo8MTGB67qMj4+rfhnf9xUKK6ip7II4jtXxanKyuswo02eoVQYVrjpXOdk/yRX7CpVBRSmVuDSZEiBAnaCwFSr0jb7CTN5aeyudToeL4xexLZuu2x2Sr2HI0rdifHNIcbwxf4OEhNiMWTPW+DgfJ02aD8UfwrF3ZpuIRZSdHfQDnnjjE5hNk6XmEmxB4iX4U/6wjWSsQ3oszcAfqPlzYRiqTsUgCNRpZrq72e3m5Pv7Lj1+6KGHAFRaJ6O8ZeasPutEMoSbN29SKBTU3JNMJqNiGTW204B/M/ZvyIU5qkmVOIqVWymVSxRmC0MoPxiaaMFmTNNkPBnHCzy66S5e4HF8+TiXfuoSsRnz3OxzZIKMSoWN2MDAwB24dNNdjlWOkfbTXFy4SGImYECQBFTiCnPJHDDKe9GHE4ZRSCtuMQiHsZbRNchWs3TGOhTqOyQl6afWC3y2bTM5OcnExISyMHrwKp91GH+vsi8URaY4DwYDDh06RKvVGuGmACNYiTRhHzp0aKSmAahZbVJmj4mpUoUEinGRWlKj2+vytdNfo1VuMeVP8eO1H1evIUXHQrXAn37iT1meWOZw5TD1iTpnl89y/vB5MOHY+jF6do8bszc4u3yWX3z8F/ml//6XuHD4As9NP8fh7mH+6Nf/iJ97z8/RcTu4icuUMfWCWEGVDaIYp+PQSToKiGs2m7TbbbKfyjI+Pk7ezNMIGyNuUq63u2th94hT/X/kNe87moGcWBWGIYcOHeLKlSsAqo6zW6Sgtrm5qc4wll3iuq6iIwpaaSQGv9j8RU6Hp3nWepYPuh9UjDcZNyF9xCKNYgMcOFo5SuiE/PXb/5ofbP4g5zkPBlxeuExCAtbw62amybMLzyp3tOQtYTgGf/Jrf8LNuZssvXPpBfC9AHDhIFT1nY3JDT712KdUO4ZYxyiMiDOjuIhOc9Qrx/I7aXLTWzr0171vBxIDaoqjmE5ZbL3VQA5gbDabqloqiyzBrB68jZljnA5PY2NzJjqDV/cIN0P6U33GumMYPYPYGgX4DMvgqx/4KrlqjkaxwWt+/zWUVkp88X/8IldnrnJu+RyxEfPsoWc5tXGKglng3PI5njk8PADigfUHeOZdz5DbytGeGA4Q1CcljSDLDVPVd6Yr0zhdR1V8ZeH1SUtqegKj8IFeT9Kb1PWZdjJjReKzPa/Rd7Wy/8Ai7CwYst1k4I2w1oRuIJ9V4e95lFa+l/ONhVEmGUXDbHA1dZVTg1NcMi7RoMHU56Y4+uBRxt1x4lSsCEqpVEpZo3Q6TXe6S6qWorxaxkxMfus//BY1r0aUiYiIcHoOUT4CEz7yex+hnq1jJAalXomv/PxXaE+1VfYhlnA3vuFbPpXpCpMbk6yOrdIwGli2RZgOGdQGkOwc3iBDcmDnhA/YKZ5K24pOVNcHBMomFJ7MXmVfKIos/nczCUhEpxnKQ1Jm2oB/XfzXpLoptqNtSMDAID1Iq5lqQSogTXqExCQk6bgY0zjUoLhcxExMJjoTxN3nKQOJOfL1eHucxEhoLDToZ4eBpt7hp38odxGFfOENX8BqWdSSGvTg6Zc/TeO1DYxlA+P3R7EknZagd07uRXQOzL3IvlAUMZ3697CDMezuatODOBHBFSTFlZ+pCQdGQt2okzA6TyUh4UuLX2I7u82UP8UbNt6gGGqyAJZlcfHnLmK1LB7+k4fxbnr4x4ev5d3w6B3rQQLeTY/O0Q6Xf+oyrVQL4h1Wnt5Po2pZcYLX9whSwx4kP+NDD0InpFlsggnJoQQ8SIKdQqI8Ez2TeanuP1FOPci979JjGL3p3XQD/effqRtfajS64omV0q+ZJAnE4PkeQTFgO7s9nEHibrBtbjMejWNjq8W1LAvTNjEnTG7+zzdxeg5JMRkO1WkaDLID4ijGalkEXkDUj7ACiyQcremMWJUo5vX/z+uZ3Jhkc2qTL7zhC8N5KmYXq29RaBZoFBpwB5J2AqlRAtRuq7DXSrDOv7nvADfdDOu7Qk+JX0r0WAZ2/LcEtjrD30gMPlj5IKdWT7E6tso3jnyDrewWdmzzuYXPMelP8qNrP6qwCr1QGQwCBs4Aw3++0uzGGPHz7ssNiAajhCjBSXS2PUC6m1bTDqY2p3B6Dp8/8nkqmQqlVomHv/UwF65foLk+nK4AO9VnubaeEt8NeX0pue9wFD2v39jYUGMboihSAa1hGOo4uN0UBHk4cj6fuBs9SBaLYhgGJaPEyf5JLCzmqnO8+dqb2Rrb4nMLnyMxEipuhVbUgvbO4d1yT/pRdLtNvaSz+gEK8qHKDhi4PZdOqsPm1CZTm1NsTm3SzDWpZCpDF5mrM7AHRM0ho01EKr5SRAVeAB/oblee6+4ZMrrL2qvsC0WRN7175IWkw4LW6m9+t0iJPY5jNUtWFmi3K6tT57J1mTPxGdbH1xl4A8bjcSb9SSpuhYnuBG7sEhGxHWyT6+dw067KOOR6u+tPOkYhpYYRLmsCP/rnP8rkxiTrU+v86Rv/lHKvjJ/2yZpZpoIpNtOblDtljJ6h3J5YRyGC6xQL2Th6K+7d5G7B7n3nenTWF4xqujxo6c5/sYqn3u+jn/GnWxJlAQz4oPNBXnXyVcyemR2y3TH56Cc/itNx6Ht9PvfDn+OJ+SfYymwx5U/xY5s/RsbNqAnQIoohr7HNBFbfTajO9DJMbgxrWf/ux/4d5xfPM9Of4S3Vt5CyU7yt8TZacYuwEbJirihrKhtEsJDdVWG9HffFFGU3zfNeZV8oio4ryPd6BVRmpO0mCOui72YBlmDHf8tnxV1JIupGnVljduimfJeZzWEbZ9yJSRkptjJbalhwK25h+MYI9pCQ0DE6pI30CA9EDzSjeDh9wMOj5/aoTFew2hYXFi+QGMMx6oEd4KU87MTGSRya7nBunJQhpHCoWxLdot0tA3wx0f/2vnU9equG/Mz3fY4fP64OhtY7A3W520PT/bIU+0TiOB7hkw6yAwV6VWYq2K7NlD/FprvJTHeGTJTBtLXg2oD/kv8vrNqrzIaz/GTrJ7EMa4RiGMcxX5j4AuveOtPBNG/aehNPvPEJXN9lejA9nLXfnyFv7szVF0WWgyeTJBlBdO/Wj72bdiFW6KXkvnM9+khumTotC9LpdEYWFEaVQky/NDgVCgU1PrPVaimqguAhooAy0VFE2ji9vkeQCUiR4s1bb+bRJx/lxK0TbE1v8eRbnlTX8k2fVXtV0Sp7Rg8v8dT1kjjh0Scf5ZP/5JNq/Gc/1SdHjjgd87bm2wisgJJTwvbsEau6trZGtVpV1jGTyQxd1/N9OMKY0wunuouS74GRLFCsqjS83Yui7IsZbrATlO1misPOkfbw4iMvJUYYDAbqlHTd1ejcDIHpK5WKenDAkA7gBaqwl+1nOXnrJFZiMbkxieu7O30zocPsYHbIRhvM4oTOC9LfE7dOqPlvs71ZsuwcSWdbNjkjp7oBRYIgGOkPlnsW3oxOstotu4FL3Qrtjl/uS2QWvvONSzYjbC4YZZQDqubT7/fJZrOKyyKiK4sEmaIw4qL0uMgwDIJMoBhnWzNbBJmAJH7+97HBRz7xEeyWTVyI+Zu3/Q1YO3hO6IVsTW/xkd/7CNePXOfbP/JtrJT1Avhdn2WiT0+QniVRIr3rcDelUt9EdwtU5dnpv78vAbcXsxJxHOO6Ls3mMLh7sZqQVEKlczCKItVcvvv6Yq6jKCKbzappRuKWBMiSBfrrt/016V6aIBNgJuaIxZhan1K1nmJSHCK0cawYeV9+65dxfZeu01WuT/+Q19AXulKpUK1WVQuGKJQovU4rEHxptxLcyzPeq+wb1yOiL64e0Moi3m3qAOxgGbZtq6PqXyxLkuPa1tfX1Q7dvbsUqGaZDHIDNQ9OzH/f67M9u01sxGxObnKrfovNzU2CIFBlfMM0CLwAyx49S1AH6vQ+HxmzIaesyr3pze16Gn43+fsqxIvJvrMownYzjJ3TNGHnICNgJIAT0bvzhZ0uorsc4WoMBgNqtZo6M3m38gmuo/9crx+l3TR/9t/9Gc2lJlc2rmBeGHYvZrNDNtrs7Cz5fF6ZfXEXcg/69VOpFJ1Oh5WVFW7duqXYeoKdSEAu7a8vqFtpIhZGV6Tdwf93I/tCUWQ3J0mi5qXptRIdTBMF0gtberQvyqVPcJJj7nu9nnpo+XxeNYwJc10epA6SwQtxClmI5TvL3Lh1Y+QQ6k6nQ7fbpdFosLi4yMzMzIjL3B0rCfbT6/WoVCrq7GO9G1Kywvn5eba2ttSxd/pGkaxHd1V6PKOjx/dKTYB9oijwwp2hLxiMBruS1chstbv5Zz091A9W0LOr3eCTXnC7m7/XSw3b29usr6+rQFjS9CQZHmotC1oul1WmomMl8lq7OTR6m6ycwQNQKBQ4cuQIjuNw8+bNF+BCu2Uv6Ou9ILT7RlHutmgvFpnPzMwMG7yej0V0POHFrqu7Jt1aye90JPduHX06qCUBa61WA3ZiqEwmQxiGbG5uYlk7B3TLjBedtyr3JoVG/fBK+b1kcqZp0mw2WV1dVSWMvS7y3aD7+xbCB16wMPDib6hSqYw8bPk/HZmEF85Z0Xe0pNLASJ+zKIHOVtddkp5diQjN8cSJE8zMzOD7Pt/85jfV70X5dEump6lRFPHcc89x7do19TdyOoauLHEcK+WUzE2XkXrWLvlulEOXfaEoeoYDLxwws1u2t7dHAkL972WXCnc0l8sp9puYdr1PSB+1IRmFPibrbiQffZarnGk4GAy4du0a6+vr6m8MY6cCrOM1ehFPYghpLtct3+7iqDSu6TGYXF8fQvRisYcODdxLfAL7LD3erfV3czuSBchC6A9z9/8HQUAul3sB21wesAwA1i2Hbl30HSoLLJnI+Pg4p0+fVjSIJBlOrbZtm8XFRcrlMqVSaSSAlPu3bRvP8xSRfGtri42NDaVYspB6xicjyqXB7aUmO94tGwJGgup7kX2jKLv9M9xbdfNub1yQXJkFJ4uk805lzpluoXYjn7tRUAHpFhYWmJ2dHVHezc1N1tbW1KToYrE40li22zXYts3S0pJS2N2kKF1ZhOMC3xkv0V3b3eS7SZH3het5sQxjL1ovZv1ufy9Trbvdrkqz9b+xLItms0mn06FQKIxkNfqu2/1Z4ptMJsOrXvUqms0mN2/eZH19nUajQb/fZ2FhQR3qIIubyWSGs1a0eCwMQ1ZWVpRr3A3M6eQowVcklvr7xh33IvtCUWCUH6uTcvYiL7aDZmdnFS1RTK7sWvnwfZ9ut6t+Lkqn81fk/uR3Ev/EcczY2BiFQoETJ04o1Pj69etcvnyZdrvN+vo6hw8fxvM8VQVW9aAw5Pbt29RqNXq9noo1dPqlPBO5VykO7mWo8Iul+vf6fGGfuB49Zdwtu4Oul3pzu8G3RqMxcuYv7HA6BKXVg1fdqgiiuvuz3hohQa2ehc3OzjIxMUG73aZSqdBsNpUS6LhOEASsrq4q4E2UR0A2mfWvn2msK87dno88z7vFKLuThntxQfvCovT7fTU9CEYrvbsro7uh690pos7G9zyPer2OYRgjcYpeYJNUVDfzujW5m0sTa6LXbfTgs1AocPz4cW7fvs2dO3fUSeYTExPMzs6qSrDv+6ytrY2cwpHL5cjlcuqMZJnWoKO5IvI+9OBXD4jlWerxju727qV6vC9OUj+Q/S/7wvUcyP6XA0U5kD3JgaIcyJ7kQFEOZE9yoCgHsic5UJQD2ZMcKMqB7EkOFOVA9iQHinIge5IDRTmQPcmBohzInuRAUQ5kT3KgKAeyJzlQlAPZkxwoyoHsSQ4U5UD2JAeKciB7kgNFOZA9yYGiHMie5EBRDmRPcqAoB7InOVCUA9mTHCjKgexJ/j+mzC2wDcl53AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f9070b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f8b9e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f874dd8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f834f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f7f4f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f7bc128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_output(test_images, test_outputs, gt_pts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "model_dir = 'saved_models/'\n",
    "model_name = 'keypoints_model_1.pt'\n",
    "\n",
    "torch.save(net.state_dict(), model_dir+model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, you can see how to extract a single filter (by index) from your first convolutional layer. The filter should appear as a grayscale grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.14795384 -0.12784955  0.25300175  0.20602921 -0.07494157]\n",
      " [-0.06281316  0.2249728   0.12335959  0.07569068  0.09757778]\n",
      " [-0.13440916  0.03242321  0.13428009 -0.02102039 -0.07204004]\n",
      " [ 0.1179764  -0.10290004 -0.17937526 -0.24274522  0.15383279]\n",
      " [-0.03617718 -0.13041027 -0.03506539 -0.24086389 -0.19910464]]\n",
      "(5, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f44941d5780>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f7e2438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#transfer model to cpu\n",
    "net = net.cpu()\n",
    "\n",
    "# Get the weights in the first conv layer, \"conv1\"\n",
    "weights1 = net.conv1.weight.data\n",
    "\n",
    "w = weights1.numpy()\n",
    "\n",
    "filter_index = 10\n",
    "\n",
    "print(w[filter_index][0])\n",
    "print(w[filter_index][0].shape)\n",
    "\n",
    "# display the filter weights\n",
    "plt.imshow(w[filter_index][0], cmap='gray')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature maps: Filter an image to see the effect of a convolutional kernel\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'filtered image')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f448f9ceef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load in and display any image from the transformed test dataset\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "some_image = mpimg.imread('images/mona_lisa.jpg')\n",
    "some_img_copy = some_image.copy()\n",
    "some_img_gray = cv2.cvtColor(some_img_copy, cv2.COLOR_RGB2GRAY)\n",
    "\n",
    "# Using cv's filter2D function,\n",
    "## apply a specific set of filter weights (like the one displayed above) to the test image\n",
    "feature_map = cv2.filter2D(some_img_gray, -1, w[10][0])\n",
    "\n",
    "f, ax = plt.subplots(1, 2)\n",
    "ax[0].imshow(some_img_gray, cmap='gray')\n",
    "ax[0].set_title('original image')\n",
    "ax[1].imshow(feature_map, cmap='gray')\n",
    "ax[1].set_title('filtered image')"
   ]
  }
 ],
 "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
}