2. Define the Network Architecture.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Convolutional Neural Network\n",
    "\n",
    "After you've looked at the data you're working with and, in this case, know the shapes of the images and of the keypoints, you are ready to define a convolutional neural network that can *learn* from this data.\n",
    "\n",
    "In this notebook and in `models.py`, you will:\n",
    "1. Define a CNN with images as input and keypoints as output\n",
    "2. Construct the transformed FaceKeypointsDataset, just as before\n",
    "3. Train the CNN on the training data, tracking loss\n",
    "4. See how the trained model performs on test data\n",
    "5. If necessary, modify the CNN structure and model hyperparameters, so that it performs *well* **\\***\n",
    "\n",
    "**\\*** What does *well* mean?\n",
    "\n",
    "\"Well\" means that the model's loss decreases during training **and**, when applied to test image data, the model produces keypoints that closely match the true keypoints of each face. And you'll see examples of this later in the notebook.\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN Architecture\n",
    "\n",
    "Recall that CNN's are defined by a few types of layers:\n",
    "* Convolutional layers\n",
    "* Maxpooling layers\n",
    "* Fully-connected layers\n",
    "\n",
    "You are required to use the above layers and encouraged to add multiple convolutional layers and things like dropout layers that may prevent overfitting. You are also encouraged to look at literature on keypoint detection, such as [this paper](https://arxiv.org/pdf/1710.00977.pdf), to help you determine the structure of your network.\n",
    "\n",
    "\n",
    "### TODO: Define your model in the provided file `models.py` file\n",
    "\n",
    "This file is mostly empty but contains the expected name and some TODO's for creating your model.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyTorch Neural Nets\n",
    "\n",
    "To define a neural network in PyTorch, you define the layers of a model in the function `__init__` and define the feedforward behavior of a network that employs those initialized layers in the function `forward`, which takes in an input image tensor, `x`. The structure of this Net class is shown below and left for you to fill in.\n",
    "\n",
    "Note: During training, PyTorch will be able to perform backpropagation by keeping track of the network's feedforward behavior and using autograd to calculate the update to the weights in the network.\n",
    "\n",
    "#### Define the Layers in ` __init__`\n",
    "As a reminder, a conv/pool layer may be defined like this (in `__init__`):\n",
    "```\n",
    "# 1 input image channel (for grayscale images), 32 output channels/feature maps, 3x3 square convolution kernel\n",
    "self.conv1 = nn.Conv2d(1, 32, 3)\n",
    "\n",
    "# maxpool that uses a square window of kernel_size=2, stride=2\n",
    "self.pool = nn.MaxPool2d(2, 2)      \n",
    "```\n",
    "\n",
    "#### Refer to Layers in `forward`\n",
    "Then referred to in the `forward` function like this, in which the conv1 layer has a ReLu activation applied to it before maxpooling is applied:\n",
    "```\n",
    "x = self.pool(F.relu(self.conv1(x)))\n",
    "```\n",
    "\n",
    "Best practice is to place any layers whose weights will change during the training process in `__init__` and refer to them in the `forward` function; any layers or functions that always behave in the same way, such as a pre-defined activation function, should appear *only* in the `forward` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Why models.py\n",
    "\n",
    "You are tasked with defining the network in the `models.py` file so that any models you define can be saved and loaded by name in different notebooks in this 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",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# load the data if you need to; if you have already loaded the data, you may comment this cell out\n",
    "# -- DO NOT CHANGE THIS CELL -- #\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">**Note:** Workspaces automatically close connections after 30 minutes of inactivity (including inactivity while training!). Use the code snippet below to keep your workspace alive during training. (The active_session context manager is imported below.)\n",
    "</div>\n",
    "```\n",
    "from workspace_utils import active_session\n",
    "\n",
    "with active_session():\n",
    "    train_model(num_epochs)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE :Template code provided by Udacity for this Notebook was left largely untouched and modified only if needed.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the usual resources\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=28224, out_features=1000, bias=True)\n",
      "  (fc2): Linear(in_features=1000, out_features=500, bias=True)\n",
      "  (fc3): Linear(in_features=500, out_features=136, bias=True)\n",
      "  (drop1): Dropout(p=0.4)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "## TODO: Define the Net in models.py\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "## TODO: Once you've define the network, you can instantiate it\n",
    "# one example conv layer has been provided for you\n",
    "from models import Net\n",
    "\n",
    "# I tried to use my laptop's gpu. It wasn't working properly.\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\") \n",
    "\n",
    "net = Net()\n",
    "net.to(device)\n",
    "print(net)"
   ]
  },
  {
   "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",
    "\n",
    "### TODO: Define a data transform\n",
    "\n",
    "In PyTorch, a convolutional neural network expects a torch image of a consistent size as input. For efficient training, and so your model's loss does not blow up during training, it is also suggested that you normalize the input images and keypoints. The necessary transforms have been defined in `data_load.py` and you **do not** need to modify these; take a look at this file (you'll see the same transforms that were defined and applied in Notebook 1).\n",
    "\n",
    "To define the data transform below, use a [composition](http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#compose-transforms) of:\n",
    "1. Rescaling and/or cropping the data, such that you are left with a square image (the suggested size is 224x224px)\n",
    "2. Normalizing the images and keypoints; turning each RGB image into a grayscale image with a color range of [0, 1] and transforming the given keypoints into a range of [-1, 1]\n",
    "3. Turning these images and keypoints into Tensors\n",
    "\n",
    "These transformations have been defined in `data_load.py`, but it's up to you to call them and create a `data_transform` below. **This transform will be applied to the training data and, later, the test data**. It will change how you go about displaying these images and keypoints, but these steps are essential for efficient training.\n",
    "\n",
    "As a note, should you want to perform data augmentation (which is optional in this project), and randomly rotate or shift these images, a square image size will be useful; rotating a 224x224 image by 90 degrees will result in the same shape of output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms, utils\n",
    "\n",
    "# the dataset we created in Notebook 1 is copied in the helper file `data_load.py`\n",
    "from data_load import FacialKeypointsDataset\n",
    "# the transforms we defined in Notebook 1 are in the helper file `data_load.py`\n",
    "from data_load import Rescale, RandomCrop, Normalize, ToTensor\n",
    "\n",
    "\n",
    "## TODO: define the data_transform using transforms.Compose([all tx's, . , .])\n",
    "# order matters! i.e. rescaling should come before a smaller crop\n",
    "data_transform = transforms.Compose( [Rescale(100) , RandomCrop(96) , Normalize() , ToTensor()] )\n",
    "                                     \n",
    "# testing that you've defined a transform\n",
    "assert(data_transform is not None), 'Define a data_transform'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images:  3462\n",
      "0 torch.Size([1, 96, 96]) torch.Size([68, 2])\n",
      "1 torch.Size([1, 96, 96]) torch.Size([68, 2])\n",
      "2 torch.Size([1, 96, 96]) torch.Size([68, 2])\n",
      "3 torch.Size([1, 96, 96]) torch.Size([68, 2])\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\n",
    "\n",
    "Next, having defined the transformed dataset, we can use PyTorch's DataLoader class to load the training data in batches of whatever size as well as to shuffle the data for training the model. You can read more about the parameters of the DataLoader, in [this documentation](http://pytorch.org/docs/master/data.html).\n",
    "\n",
    "#### Batch size\n",
    "Decide on a good batch size for training your model. Try both small and large batch sizes and note how the loss decreases as the model trains. Too large a batch size may cause your model to crash and/or run out of memory while training.\n",
    "\n",
    "**Note for Windows users**: Please change the `num_workers` to 0 or you may face some issues with your DataLoader failing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load training data in batches\n",
    "batch_size = 20\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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load test data in batches\n",
    "batch_size = 20\n",
    "\n",
    "test_loader = DataLoader(test_dataset, \n",
    "                          batch_size=batch_size,\n",
    "                          shuffle=True, \n",
    "                          num_workers=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply the model on a test sample\n",
    "\n",
    "To test the model on a test sample of data, you have to follow these steps:\n",
    "1. Extract the image and ground truth keypoints from a sample\n",
    "2. Wrap the image in a Variable, so that the net can process it as input and track how it changes as the image moves through the network.\n",
    "3. Make sure the image is a FloatTensor, which the model expects.\n",
    "4. Forward pass the image through the net to get the predicted, output keypoints.\n",
    "\n",
    "This function test how the network performs on the first batch of test data. It returns the images, the transformed images, the predicted keypoints (produced by the model), and the ground truth keypoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test the model on a batch of test images\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",
    "        # I tried to use my laptop's gpu. It wasn't working properly.\n",
    "        images = images.to(device)\n",
    "        key_pts = key_pts.to(device)\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": "markdown",
   "metadata": {},
   "source": [
    "#### Debugging tips\n",
    "\n",
    "If you get a size or dimension error here, make sure that your network outputs the expected number of keypoints! Or if you get a Tensor type error, look into changing the above code that casts the data into float types: `images = images.type(torch.FloatTensor)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([20, 1, 96, 96])\n",
      "torch.Size([20, 68, 2])\n",
      "torch.Size([20, 68, 2])\n"
     ]
    }
   ],
   "source": [
    "# call the above function\n",
    "# returns: test images, test predicted keypoints, test ground truth keypoints\n",
    "test_images, test_outputs, gt_pts = net_sample_output()\n",
    "\n",
    "# print out the dimensions of the data to see if they make sense\n",
    "print(test_images.data.size())\n",
    "print(test_outputs.data.size())\n",
    "print(gt_pts.size())"
   ]
  },
  {
   "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 in a way that's similar to how we've displayed this data before, only this time, we have to \"un-transform\" the image/keypoint data to display it.\n",
    "\n",
    "Note that 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": 15,
   "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",
    "Next, you'll see a helper function. `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; it's assumed that you applied these transformations when you loaded your test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe399f27cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe399ea5630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe399de9710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe38852b780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3884e87b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the output\n",
    "# by default this shows a batch of 5 images\n",
    "def visualize_output(test_images, test_outputs, gt_pts=None, batch_size=5):\n",
    "\n",
    "    for i in range(batch_size):\n",
    "        plt.figure(figsize=(20,10))\n",
    "        ax = plt.subplot( batch_size, 1, i+1)\n",
    "        \n",
    "        # un-transform the image data\n",
    "        image = test_images[i].data   # get the image from it's Variable wrapper\n",
    "        \n",
    "        # I tried to use my laptop's gpu. It wasn't working properly.\n",
    "        if torch.cuda.is_available():\n",
    "            image = image.cpu()\n",
    "            \n",
    "        image = image.data\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",
    "        \n",
    "        # I tried to use my laptop's gpu. It wasn't working properly.\n",
    "        if torch.cuda.is_available():\n",
    "            predicted_key_pts = predicted_key_pts.cpu()\n",
    "            \n",
    "        predicted_key_pts = predicted_key_pts.data\n",
    "        predicted_key_pts = predicted_key_pts.numpy()\n",
    "        \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",
    "        \n",
    "        if gt_pts is not None:\n",
    "            \n",
    "            ground_truth_pts = gt_pts[i] \n",
    "            \n",
    "            # I tried to use my laptop's gpu. It wasn't working properly.\n",
    "            if torch.cuda.is_available():\n",
    "                ground_truth_pts = ground_truth_pts.cpu()  \n",
    "                \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",
    "    \n",
    "# call it\n",
    "visualize_output(test_images, test_outputs, gt_pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "#### Loss function\n",
    "Training a network to predict keypoints is different than training a network to predict a class; instead of outputting a distribution of classes and using cross entropy loss, you may want to choose a loss function that is suited for regression, which directly compares a predicted value and target value. Read about the various kinds of loss functions (like MSE or L1/SmoothL1 loss) in [this documentation](http://pytorch.org/docs/master/_modules/torch/nn/modules/loss.html).\n",
    "\n",
    "### TODO: Define the loss and optimization\n",
    "\n",
    "Next, you'll define how the model will train by deciding on the loss function and optimizer.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: Define the loss and optimization\n",
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.SmoothL1Loss()\n",
    "# criterion = nn.MSELoss() # not giving much better accuracy as of the all attempts I made.\n",
    "\n",
    "optimizer = optim.Adam(net.parameters(), lr = 0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Initial Observation\n",
    "\n",
    "Now, you'll train on your batched training data from `train_loader` for a number of epochs. \n",
    "\n",
    "To quickly observe how your model is training and decide on whether or not you should modify it's structure or hyperparameters, you're encouraged to start off with just one or two epochs at first. As you train, note how your the model's loss behaves over time: does it decrease quickly at first and then slow down? Does it take a while to decrease in the first place? What happens if you change the batch size of your training data or modify your loss function? etc. \n",
    "\n",
    "Use these initial observations to make changes to your model and decide on the best architecture before you train for many epochs and create a final model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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",
    "        # print('Epoch: {}'.format(epoch + 1)) # for debugging\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",
    "            #print('Batch: {}'.format(batch_i+1)) # for debugging\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",
    "            key_pts, images = key_pts.to(device), images.to(device)\n",
    "\n",
    "            # forward pass to get outputs\n",
    "            output_pts = net(images)\n",
    "\n",
    "            # calculate the loss between predicted and target keypoints\n",
    "            loss = 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": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Batch: 10, Avg. Loss: 0.0734079658985138\n",
      "Epoch: 1, Batch: 20, Avg. Loss: 0.049135133251547815\n",
      "Epoch: 1, Batch: 30, Avg. Loss: 0.039182739704847334\n",
      "Epoch: 1, Batch: 40, Avg. Loss: 0.035831805877387525\n",
      "Epoch: 1, Batch: 50, Avg. Loss: 0.02754076961427927\n",
      "Epoch: 1, Batch: 60, Avg. Loss: 0.031598723493516445\n",
      "Epoch: 1, Batch: 70, Avg. Loss: 0.02635175921022892\n",
      "Epoch: 1, Batch: 80, Avg. Loss: 0.028875335678458215\n",
      "Epoch: 1, Batch: 90, Avg. Loss: 0.02790826726704836\n",
      "Epoch: 1, Batch: 100, Avg. Loss: 0.023227487318217756\n",
      "Epoch: 1, Batch: 110, Avg. Loss: 0.026174317859113217\n",
      "Epoch: 1, Batch: 120, Avg. Loss: 0.022281038388609887\n",
      "Epoch: 1, Batch: 130, Avg. Loss: 0.021194436773657798\n",
      "Epoch: 1, Batch: 140, Avg. Loss: 0.025286582112312318\n",
      "Epoch: 1, Batch: 150, Avg. Loss: 0.02870584763586521\n",
      "Epoch: 1, Batch: 160, Avg. Loss: 0.02290023658424616\n",
      "Epoch: 1, Batch: 170, Avg. Loss: 0.021589835267513992\n",
      "Epoch: 2, Batch: 10, Avg. Loss: 0.023671485669910907\n",
      "Epoch: 2, Batch: 20, Avg. Loss: 0.024026705138385295\n",
      "Epoch: 2, Batch: 30, Avg. Loss: 0.020050284825265406\n",
      "Epoch: 2, Batch: 40, Avg. Loss: 0.02519749868661165\n",
      "Epoch: 2, Batch: 50, Avg. Loss: 0.022013748437166213\n",
      "Epoch: 2, Batch: 60, Avg. Loss: 0.01872769184410572\n",
      "Epoch: 2, Batch: 70, Avg. Loss: 0.02090200325474143\n",
      "Epoch: 2, Batch: 80, Avg. Loss: 0.020148885436356068\n",
      "Epoch: 2, Batch: 90, Avg. Loss: 0.021002856269478797\n",
      "Epoch: 2, Batch: 100, Avg. Loss: 0.019760262034833432\n",
      "Epoch: 2, Batch: 110, Avg. Loss: 0.024226539954543113\n",
      "Epoch: 2, Batch: 120, Avg. Loss: 0.0209967571310699\n",
      "Epoch: 2, Batch: 130, Avg. Loss: 0.02201983593404293\n",
      "Epoch: 2, Batch: 140, Avg. Loss: 0.02131783775985241\n",
      "Epoch: 2, Batch: 150, Avg. Loss: 0.019238864444196223\n",
      "Epoch: 2, Batch: 160, Avg. Loss: 0.019249544013291597\n",
      "Epoch: 2, Batch: 170, Avg. Loss: 0.021365309413522483\n",
      "Epoch: 3, Batch: 10, Avg. Loss: 0.019356720615178347\n",
      "Epoch: 3, Batch: 20, Avg. Loss: 0.01878951592370868\n",
      "Epoch: 3, Batch: 30, Avg. Loss: 0.016812180262058975\n",
      "Epoch: 3, Batch: 40, Avg. Loss: 0.01785306055098772\n",
      "Epoch: 3, Batch: 50, Avg. Loss: 0.016507181618362666\n",
      "Epoch: 3, Batch: 60, Avg. Loss: 0.015291535574942828\n",
      "Epoch: 3, Batch: 70, Avg. Loss: 0.016964637022465468\n",
      "Epoch: 3, Batch: 80, Avg. Loss: 0.015288078133016825\n",
      "Epoch: 3, Batch: 90, Avg. Loss: 0.021006084233522414\n",
      "Epoch: 3, Batch: 100, Avg. Loss: 0.018281518016010523\n",
      "Epoch: 3, Batch: 110, Avg. Loss: 0.016943785641342402\n",
      "Epoch: 3, Batch: 120, Avg. Loss: 0.01991813164204359\n",
      "Epoch: 3, Batch: 130, Avg. Loss: 0.017252401635050773\n",
      "Epoch: 3, Batch: 140, Avg. Loss: 0.018590879905968905\n",
      "Epoch: 3, Batch: 150, Avg. Loss: 0.01775635313242674\n",
      "Epoch: 3, Batch: 160, Avg. Loss: 0.016035569086670875\n",
      "Epoch: 3, Batch: 170, Avg. Loss: 0.014098163601011038\n",
      "Epoch: 4, Batch: 10, Avg. Loss: 0.013896413426846266\n",
      "Epoch: 4, Batch: 20, Avg. Loss: 0.014648806490004062\n",
      "Epoch: 4, Batch: 30, Avg. Loss: 0.014948631823062896\n",
      "Epoch: 4, Batch: 40, Avg. Loss: 0.015283545758575202\n",
      "Epoch: 4, Batch: 50, Avg. Loss: 0.01466248594224453\n",
      "Epoch: 4, Batch: 60, Avg. Loss: 0.015425478853285313\n",
      "Epoch: 4, Batch: 70, Avg. Loss: 0.016417692042887212\n",
      "Epoch: 4, Batch: 80, Avg. Loss: 0.012784596346318722\n",
      "Epoch: 4, Batch: 90, Avg. Loss: 0.013970444723963738\n",
      "Epoch: 4, Batch: 100, Avg. Loss: 0.016457697935402395\n",
      "Epoch: 4, Batch: 110, Avg. Loss: 0.01511653745546937\n",
      "Epoch: 4, Batch: 120, Avg. Loss: 0.013332029432058334\n",
      "Epoch: 4, Batch: 130, Avg. Loss: 0.015041685663163662\n",
      "Epoch: 4, Batch: 140, Avg. Loss: 0.014425065182149411\n",
      "Epoch: 4, Batch: 150, Avg. Loss: 0.014947396423667669\n",
      "Epoch: 4, Batch: 160, Avg. Loss: 0.01449602572247386\n",
      "Epoch: 4, Batch: 170, Avg. Loss: 0.015110976994037628\n",
      "Epoch: 5, Batch: 10, Avg. Loss: 0.013296484854072333\n",
      "Epoch: 5, Batch: 20, Avg. Loss: 0.014795213844627142\n",
      "Epoch: 5, Batch: 30, Avg. Loss: 0.012514554336667061\n",
      "Epoch: 5, Batch: 40, Avg. Loss: 0.016928316187113525\n",
      "Epoch: 5, Batch: 50, Avg. Loss: 0.013875136617571115\n",
      "Epoch: 5, Batch: 60, Avg. Loss: 0.016953667253255846\n",
      "Epoch: 5, Batch: 70, Avg. Loss: 0.013461390882730484\n",
      "Epoch: 5, Batch: 80, Avg. Loss: 0.012583428528159857\n",
      "Epoch: 5, Batch: 90, Avg. Loss: 0.0128846887499094\n",
      "Epoch: 5, Batch: 100, Avg. Loss: 0.0125469951890409\n",
      "Epoch: 5, Batch: 110, Avg. Loss: 0.01115883095189929\n",
      "Epoch: 5, Batch: 120, Avg. Loss: 0.01294743614271283\n",
      "Epoch: 5, Batch: 130, Avg. Loss: 0.01871082093566656\n",
      "Epoch: 5, Batch: 140, Avg. Loss: 0.0111890509724617\n",
      "Epoch: 5, Batch: 150, Avg. Loss: 0.01089253118261695\n",
      "Epoch: 5, Batch: 160, Avg. Loss: 0.0120202814694494\n",
      "Epoch: 5, Batch: 170, Avg. Loss: 0.010760869784280657\n",
      "Epoch: 6, Batch: 10, Avg. Loss: 0.01059567709453404\n",
      "Epoch: 6, Batch: 20, Avg. Loss: 0.012086751963943243\n",
      "Epoch: 6, Batch: 30, Avg. Loss: 0.010711697675287724\n",
      "Epoch: 6, Batch: 40, Avg. Loss: 0.014632789697498084\n",
      "Epoch: 6, Batch: 50, Avg. Loss: 0.009572688909247517\n",
      "Epoch: 6, Batch: 60, Avg. Loss: 0.01150004449300468\n",
      "Epoch: 6, Batch: 70, Avg. Loss: 0.010456453310325742\n",
      "Epoch: 6, Batch: 80, Avg. Loss: 0.010161112900823355\n",
      "Epoch: 6, Batch: 90, Avg. Loss: 0.011505838530138136\n",
      "Epoch: 6, Batch: 100, Avg. Loss: 0.010575595311820507\n",
      "Epoch: 6, Batch: 110, Avg. Loss: 0.0105177391320467\n",
      "Epoch: 6, Batch: 120, Avg. Loss: 0.011863618530333042\n",
      "Epoch: 6, Batch: 130, Avg. Loss: 0.00965529764071107\n",
      "Epoch: 6, Batch: 140, Avg. Loss: 0.008912904420867562\n",
      "Epoch: 6, Batch: 150, Avg. Loss: 0.010048831021413207\n",
      "Epoch: 6, Batch: 160, Avg. Loss: 0.010202153865247966\n",
      "Epoch: 6, Batch: 170, Avg. Loss: 0.00965369287878275\n",
      "Epoch: 7, Batch: 10, Avg. Loss: 0.011604556441307068\n",
      "Epoch: 7, Batch: 20, Avg. Loss: 0.009115518070757389\n",
      "Epoch: 7, Batch: 30, Avg. Loss: 0.01365530681796372\n",
      "Epoch: 7, Batch: 40, Avg. Loss: 0.008527451008558274\n",
      "Epoch: 7, Batch: 50, Avg. Loss: 0.010160889662802219\n",
      "Epoch: 7, Batch: 60, Avg. Loss: 0.009405485074967145\n",
      "Epoch: 7, Batch: 70, Avg. Loss: 0.009723330568522214\n",
      "Epoch: 7, Batch: 80, Avg. Loss: 0.008493180572986602\n",
      "Epoch: 7, Batch: 90, Avg. Loss: 0.008753558248281479\n",
      "Epoch: 7, Batch: 100, Avg. Loss: 0.009166707284748554\n",
      "Epoch: 7, Batch: 110, Avg. Loss: 0.009115779679268599\n",
      "Epoch: 7, Batch: 120, Avg. Loss: 0.009558981051668525\n",
      "Epoch: 7, Batch: 130, Avg. Loss: 0.00921910060569644\n",
      "Epoch: 7, Batch: 140, Avg. Loss: 0.008654218213632704\n",
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      "Epoch: 7, Batch: 170, Avg. Loss: 0.009471796872094273\n",
      "Epoch: 8, Batch: 10, Avg. Loss: 0.009197155991569162\n",
      "Epoch: 8, Batch: 20, Avg. Loss: 0.010325536783784627\n",
      "Epoch: 8, Batch: 30, Avg. Loss: 0.007761407690122723\n",
      "Epoch: 8, Batch: 40, Avg. Loss: 0.008776918239891528\n",
      "Epoch: 8, Batch: 50, Avg. Loss: 0.007859129970893265\n",
      "Epoch: 8, Batch: 60, Avg. Loss: 0.008720799488946796\n",
      "Epoch: 8, Batch: 70, Avg. Loss: 0.008037937013432384\n",
      "Epoch: 8, Batch: 80, Avg. Loss: 0.008357741916552187\n",
      "Epoch: 8, Batch: 90, Avg. Loss: 0.008701947843655944\n",
      "Epoch: 8, Batch: 100, Avg. Loss: 0.007755789952352643\n",
      "Epoch: 8, Batch: 110, Avg. Loss: 0.009841911541298033\n",
      "Epoch: 8, Batch: 120, Avg. Loss: 0.007150534447282553\n",
      "Epoch: 8, Batch: 130, Avg. Loss: 0.011563228676095605\n",
      "Epoch: 8, Batch: 140, Avg. Loss: 0.0074808936100453135\n",
      "Epoch: 8, Batch: 150, Avg. Loss: 0.007879551639780403\n",
      "Epoch: 8, Batch: 160, Avg. Loss: 0.008201061515137553\n",
      "Epoch: 8, Batch: 170, Avg. Loss: 0.008659153990447522\n",
      "Epoch: 9, Batch: 10, Avg. Loss: 0.00709116505458951\n",
      "Epoch: 9, Batch: 20, Avg. Loss: 0.007371772266924381\n",
      "Epoch: 9, Batch: 30, Avg. Loss: 0.007016996992751956\n",
      "Epoch: 9, Batch: 40, Avg. Loss: 0.011845487961545586\n",
      "Epoch: 9, Batch: 50, Avg. Loss: 0.007478782115504146\n",
      "Epoch: 9, Batch: 60, Avg. Loss: 0.007121454831212759\n",
      "Epoch: 9, Batch: 70, Avg. Loss: 0.006699949200265109\n",
      "Epoch: 9, Batch: 80, Avg. Loss: 0.006574948597699404\n",
      "Epoch: 9, Batch: 90, Avg. Loss: 0.007316707447171211\n",
      "Epoch: 9, Batch: 100, Avg. Loss: 0.006895190477371216\n",
      "Epoch: 9, Batch: 110, Avg. Loss: 0.009376392001286149\n",
      "Epoch: 9, Batch: 120, Avg. Loss: 0.006702829711139202\n",
      "Epoch: 9, Batch: 130, Avg. Loss: 0.006995123578235507\n",
      "Epoch: 9, Batch: 140, Avg. Loss: 0.0075031477492302654\n",
      "Epoch: 9, Batch: 150, Avg. Loss: 0.00795242008753121\n",
      "Epoch: 9, Batch: 160, Avg. Loss: 0.007201387500390411\n",
      "Epoch: 9, Batch: 170, Avg. Loss: 0.007792541943490505\n",
      "Epoch: 10, Batch: 10, Avg. Loss: 0.008795878826640546\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10, Batch: 20, Avg. Loss: 0.007095467997714877\n",
      "Epoch: 10, Batch: 30, Avg. Loss: 0.0073270248249173164\n",
      "Epoch: 10, Batch: 40, Avg. Loss: 0.012376247439533472\n",
      "Epoch: 10, Batch: 50, Avg. Loss: 0.007030392345041036\n",
      "Epoch: 10, Batch: 60, Avg. Loss: 0.006862525828182697\n",
      "Epoch: 10, Batch: 70, Avg. Loss: 0.0066708606667816635\n",
      "Epoch: 10, Batch: 80, Avg. Loss: 0.005936416145414114\n",
      "Epoch: 10, Batch: 90, Avg. Loss: 0.007882191706448794\n",
      "Epoch: 10, Batch: 100, Avg. Loss: 0.006926198909059167\n",
      "Epoch: 10, Batch: 110, Avg. Loss: 0.0061365925706923004\n",
      "Epoch: 10, Batch: 120, Avg. Loss: 0.009471888560801745\n",
      "Epoch: 10, Batch: 130, Avg. Loss: 0.006122748320922256\n",
      "Epoch: 10, Batch: 140, Avg. Loss: 0.006923566851764917\n",
      "Epoch: 10, Batch: 150, Avg. Loss: 0.006521404348313808\n",
      "Epoch: 10, Batch: 160, Avg. Loss: 0.007160494336858392\n",
      "Epoch: 10, Batch: 170, Avg. Loss: 0.005737501219846309\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "# train your network\n",
    "n_epochs = 10 # start small, and increase when you've decided on your model structure and hyperparams\n",
    "\n",
    "# this is a Workspaces-specific context manager to keep the connection\n",
    "# alive while training your model, not part of pytorch\n",
    "with active_session():\n",
    "    train_net(n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test data\n",
    "\n",
    "See how your model performs on previously unseen, test data. We've already loaded and transformed this data, similar to the training data. Next, run your trained model on these images to see what kind of keypoints are produced. You should be able to see if your model is fitting each new face it sees, if the points are distributed randomly, or if the points have actually overfitted the training data and do not generalize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([20, 1, 96, 96])\n",
      "torch.Size([20, 68, 2])\n",
      "torch.Size([20, 68, 2])\n"
     ]
    }
   ],
   "source": [
    "# get a sample of test data again\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": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11b145f668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WaqLdbt8Rp9Bp9j0dyyAZXCnNE8kgIWrTNFW9SRRF9xW6P1ZmqFarCs/X7/cJwwNYdxhOUD+y8IJX+F6DOQIdK5VKU9a27Hp5Twp6Zu8h0mPWkBPJdlg8Qc+rCMQfJthGycDCBLa2vr4+lWzT7yF02H0EEOu6LnNzc6oKSy8oks93Op0HFxAL01wuKF+Y6GDBPkpw53uN7knwSOoR5PdsllAmTy/5F5qt9DrM0tfvJ9fVcRWWZali20wmo5JKnU5HIbnvJ7+iP7/UlEpQLpPJUC6XabfbeJ6n6kce2CKatbU1pQN931cizvM8JcKljlDHRd4vSbKmVCoxNzfHuXPnlDrQd48gsC1r0tNBnucwfS5S4W6RS733gpt08XY8MsWMWjhZlOFwyIULF9jZ2SEMJ7WgR6UwDFW5YK/Xo1KpqMSe4Dzk+aSE4CgurdCxMoNe/yDVzlIkou+SOI6ndub9xBJkl4qvXSwWGQ6HCmlMBHbPJsgFSkLoXoIke/QeC3JtsQkAAj/A7ttEhWiqACbwA77zQ9+h/5/7pB9LU/1yVSXkAObn5wnDkEajwa1bt6aY6l7j0n9LUbJcUxqKSAh6OBwqNTwr4e5Frxkz6KL6Xurge4nvC04CDnooRFFEGIRc+pVL5F/O01vtUftHNSzbIvRDwnpIvByTTCaJo5iwHuIsOFNGmpBpmNz6G7cYPDEg81iGC//mAgCjndEEd/ifexBA/xt9koMktf0a6aX01DibzSanT59mf39fQfiOQjJvun0xaxfMejdHpWM3IHV3UtyeMAynCkh1+l6Mx/n5eRXPkGuYpgl98Pd9iCC1lmLtpTXm5ud46P94iMQLCfbfvM/p/+00jfc12P/uPguXFzj1lVMk7MTUBEf7EYMnBhBA9xtdnvjLJ3A/7bL/4j6VixUSb0wwfnrM8PEh3/2p7zJ6coT3/R6XvnAJq22pNHM+n6dQKCgpORv6PmwOdC9E7Cp9g81uLukJcRQ6VmawrAn4Qpggk8ngeZ4qnZcdIhHH+3EpRUwuLCwoo200Gim7oNfv8Vup32L353d5+OWH8W2f553nmd+c50vPf4mPvvejXF+5zpXfuIL5RpPrP3KdqxtX+creVxjmJ0ZuuVym2WwSGzHmG0yC7wR85Gc/wrVvXiP936Xx3u1xdf0qP5r7Ub78P3yZW9zi6u2rGA8bXF+5zuv/5ev5F5//FzhvcnA/5HLhwoWpNPXseA6TkuPxmFarpVDkYn/Jd8QukpzLA8sMMHGrYLofk1jDMOk7IAaZMMT92AyJREJVOkvllpty2Y122Xa3iY2YZ84+gxEbxEbMXmaPJ848wfWV64RWyLPzz4IBkRlxfeU6a/4axU5R3cP3fer1Ojv/cIfNlze5Hl4nNmL6bh8MuHbmGq/bfx234ltExuQaRmwQWiHXytdouS3KT5Xxdie2kuQQjsL4ovJmWxroKkOfO/nfUelYm4IKMEOMG0BlCCUjB9O67n4YQSZIeh2USiW8kcdX3a/ypdSXSHtpzMhk3ptnYbyAGZucjk6z9e4tLu9cxgotLm5fZH40jxmbrNqrGD0Db8tThT5hGLK+vs5ubZf13XWq4yrE4MTO5Hqc5tEzj3LePo+JyRnzDGeNs5iYPLz/MAWvQOOvNEhWkyrBJcbpUcYoc6a/ljGLmpDYiR4YOwodq2QQ90eKX3RD6LBgjm4x32vn6Baz6E/Xdel0OrSCFrv2LrEZM3SG/Ornf5X1H1+HMmQqGRLDSfHJu/138/bn305UjKANSSPJariK9QkL8wWT6JGI+Ndj+v0+q6urhGFILpvjb/b+JkZkUKwUycxnmHPnqFarfCb8DPVhneXCMq12C8/0KL6hyEfPfZTrg+s8VHuIj6x8BLtns3p2dQrxfVgQTBZeXNRUKkWj0VChaHF7w3BS1V0qlV414ztLx8oMogokCJTNZun1evT7fRzHUb8Bpffh/gpgpcpI7pPtZ6kMK+yn9nn9+uspJUvsLeyBAekwzYUvXMB90SVMhphjk/1T+/zRD/8Rb/vNt1HeLmNEk0rm8OmQbJBl4A7Y3d2dQM5sh8d+7zGKm0W6Z7psf2KbRCJBFEQU/SLFQZGoElEqlibudNjm+uA6YRzyXP85Cl8tcP7b5zlXOsfPxT83NY5Zm0He0/s66RJC2gYKUsy2bTqdzn0xw7GqiUKhoEAjEmSCA0kh6CaAxcVFisXikdOwImGkcEQ6piXMBO+68S7e8e138FjvMf70x/6UXn8S4TS6Bv3tPsRgeRZmZDK3Ocf89jzlrTJmNJme2Iwx32gSFSb+vWmaLC4u4nouxY0iZmSSu52jv9mn2+zifNQh/h9jeD+YP2bi2i6DwYCSXeKR8iNYhsUj+UeoPFXBjEyWGktU3TsRSYclxGYDYhKnGY1GKhopn5E+UEelY5UMkqwxTVOVg8G0ASTNNtrtNtlslu3t7SOFo2WXSHFKEARYpsU7//07qdaq7FR2+OMf+WOarSZDb8jQG/IrS79C7edrvP7l1/PPv/jPaaVbBImAd/z7dxDYAQk/gXfRI/6fYqpXq6w315lLzqkGWp7rsTO/Q7KfpDAoUP5amWK/iL1t08w0KfVLRIOI29++zamrp0ilUvzmD/0mjVGDvJmn9cYW4dMhO3M7xNkYZ3BgMOubQB+/DoDN5/NTHoTv+wqjKfbCA+tNSL+FKIrY29uj3++r8CqgjCgJ8txPBbFOw+FwAv3yM1RrVczYZLG+yHBrSMts4Y086mad3XO7YMK1lWt8+H0f5rmV5wB44+038tkvfZZvffBbFN9UZL4wzwf/8oM81XiKN5XfxGde/5kJ7jBh8PH3fpxddxd37OI5Hg+vP4zBxJV8eP1hDMPgev86V65f4fPf/3lG3ohKqoJlWYw+P+KpP3mKP3vmzzizcYZma9KzScdCHpa8kv9J/wUJOkmavNPpEEWRgvIdlV6TIhrRuZ7nKZj8bLfWV+uuNksxMaQnv8Ue6Zt9WqdbFDeL7J/aZ5Ac0NhrcOOtN+iWuvJFLm1emjDCK41Orq1c4/aF23iLHmEU0g26PNV4ijAOearxFC2/NXm+NOzGu0RGxMAdTFzLs9cmF9H/Bp7rP4dnemTIHLQpcpO4p1wytzLMVeZYOLdAv9Zna3Pr0DEKpkJXH7qbaVmWgsrLvDWbTVZWVl59cThmZpCkjN6YM5vNKqg5oDgbUN1LJB5xNzJMg+F7hsTLMZu1TS6uXQTAdmy++ZPfJNFL0IgaXEheYPHSIt/IfUMtvBmZfPzffpyf+bmfUddbjpdZe88aqUQKJ+Gw+OlFrr7lKtdXrvPmhTdzfuE8+9Y+hmFwfv083w2+q66H3jnHgDeW3sj11nUeXXiU73vd9ymkkgTXoiii1+/x5xf/nL2rezi7DpkvZ+j3+lNoKkFrX758WfWqEKieZGj1Fj6z6fWj0LGjo2u1Gr1eTyGfxdATFdFutzFNk2w2S61WO5o1nIF4OQYLvAWPcHsy0W7SJR/lGRQHlI1JB7WkmeR0fJoNNsCAc/Y5wp8PuRhe5KXgJU6bp/npxE+rS1tdi8SNBJ+9/llerr7M47/zOAN/oHbbZwqfYae7w+e2P8fzg+d5OPswpmVyvXOdN1fezG+8/TcYmkPOzp1VRcZwICVNTOJxzF5yj9iIGS2MyBQy0Du819WsEWkYxqFqRNDUR4lfqPU58if/K5C03RkOh6qPod6QQ+8cOx6PKRQKyp++J/XB2DCIV2KSe0mcwCGbyfKWL7+FwkaBzkqHWx+6RRhPDLNPGJ+gE3VIp9KUnTKpVIpfDH6R2qCGG0yaaImPPs6M8a/4fOItn5hIhqffzBd/8IsqhZzJZCgVS3zu1Odo+S0qboWF6gK9qEfRLk5g+KmqcqMlqmpZFgYG8/90nve98D6e/Jknee7Uc2SaGRLjxNSC62lyCSrpGV44wIPoNGtvvBodKzPU63WVYBHjUMcxCABWIPPCMPqhIYeRgYH9FRu7aHNm7gzGqkHUiChsFDAjk8J6AXfkEuYnB5pYlkXJLCnGS6VSMISyU2bMBFoWRdEEJDIesfOpHa49e42IiCdrT7L98jaVxYpKOCl3k0mBi5t0ydm5KfQRMFWQ0+/36W/0Sb6YxIgNPvd/fo6vf+DrPP3s09yybk15CeJdLC0tAQdBNqnQVom4V0jiNY1G48H1JqRVje4LCzhD+go4jkOz2cRxHHK5HMViUVVi3YsSVgJ7ZBOFEwRQ1+rSXGpS2izRP90nzIZ3WNZ3iOyZVLVQwS6QslL0wz6pcQp+BLpv6lL4tcn1BDQr19IxkLNnZMBBlNSatwgfCjFvmHSWO6RzaXLZ3KHPIAVHmUzmDqyFPo9yfckEP7C5CTjoQ6QDUfTeTJ1OR9U5Sn5Bgk/3IlEpYodYCYtv/eS36J3ukd3Icv5fnccfTdSRwOIOm/TBYDDl7ibtJP7/4uONJyl2z/Jou22CpwKG2wfNOaX4R4ze2ec1TVON0XXdCexv8Arkj4hOqoObcqdOtdHHtrS0pLwkkZ46SWxFx4no1dpHoWNlhvX1dYXPg4OUrARYhsOhaowpndIty1LYwXsZk8PhkCAMCNyAsT85TsjoGGQ3sxiRQepmiqSXPBQ4Kilzy7LY2NhQqCvXdVlMLTL31BxX169ihRZvaL6B0qhE6i0p2nGbzu2OUmfS2V0/7yqRSEzp8ymvqAXG8wYfe+/H+MB7PsBXEl9hobqA53lqwwhjFQoFyuUyQRBMheql8adIAlG3Ylc8sOhoCTcXCoUpJhB7YLY+QD9VZraR6CzZjk3rR1uMF8e0O23O7Z9jnB7TPt2msFHAu+ARZAOicXQHQ+jBHdHT4s+PkiNyr8vx2S99lp237/DY//oYqQ9M3F3v/R7RMxHDq0OcX3OUqydS4V7SzLIsnKrD7iO7Kn1+i1uMEiP1fLrRKKnrRCKhgEBi16TTaWWYCqpLcKUPLNLJ8zzlXuk1EbITRQ/qjCAMI4igu1nIiXyC8eIYTOjkOwTdgDAKeeInniA1SuEsOJT9Mo5xZ/WW1C0K4lg1IvVD0r+QJnwp5GMf/BjXK9e5+qtX+aXf/SWMgQHhxHiNr8f0Nno4SUe17BMb5G5kWRaGYWD9I4vlby+zYW1w0bnIoDZQTJ9KpSgWiyQSCQqFgiq6ld9hOOn3JHMkmV/JVYg6OiodO7il0Wio8i+JOcg5EYPBQO2A2Uqrs2fP0mw22draUpJEj9fbvo29Y+Mv+uRaOQp2QUmhoTEEH5XvEENMlwY6bEzSyePdMW/57ltopVtcn7tOaIZcX7lOI2owF85Nop4mRFci+mafeH+i3q5cuXIH+ERHZrfbbbrd7qQvxdjjJ+KfIFPIEHUivtX+llJbUgQkjUt0yCBMsI/9fn8KHCPnZUl11QPrWgpyV8SnLEwURXS7XdUtXge5wGQybdumUChM6UF9oFEYUfr9EkbW4EzlDPtX9lnILEzskxjcoQvunccECgnsznEc9vf32dreYqe9w/eXvp9TzVNc3rzM88vPc3X9KulRmr3cHv6qT/ofpgnzIYwgOU6qFPJs0a5OlmUxHAzZf3GfulfHTbrYI5vmoKkWMp/Ps7i4qFxhyenoBqLYWHrdKhwEqwRVdlQ6dnS09BOQtnRystzc3JwalN4pXeyMdDqtOq7LaW86hWFIFEa4Y5dnH32Wb1a+ySn/FH979Lf5gd/5AfLreXpne6x9eA1mSjJ01WNZFv1Bn7+8/Jd0ih0+9NYPkbJS7GX2uLR1iY//24/zUx/7KfbcCSbi9e3X848r/5iRN1KLoyOuDmOIwA8YfnBI5ekK9opN831NGqPJYaqDwYBSqaSadIkKm31W/ZlHoxHpdFpJH73l3wObqIJJlzdATVoYhipZJQN0nAlEPY5jZVNI6bmcuSABLB3gEYYhkRvRn5vgEbftbaJuRH49jxmZZG9nsQc2ZKYXSoA1ClVljyeJLAM6hQ4dOmDAC8sv8NGf/ih1t65yEM92n2VoDsmkMgqcKnGSTCYzJd6lvmKwOSD8TogRGWTXs3wx/CK3rducck+x6q8q3IeekxEPQ7CdMI1pECNSQLCFQgHHce44JO1edKyupUQe9WCP6EYplJWBSuRMQtTyPcdxuHAeMqHTAAAVXklEQVThAqurq2r36KrD9EwyjQxGbDDvzWM6Jp2VDrEZ01/tE+bCKbCttOcdjUZ0Wh2MpkExUaQyrGDEBuV+mVwnBzFgQCPZ4KGNhyavY7joXCRqRUT7EePhmOHWcOrEGN0uERR4kA8Ir4TEZszGpQ1um7eJiNi2trHyloo06hJGVMOpU6coFouqA730xZqVbsIU5XL5yOtz7LC3WbEpFq+ccCsTIJlNYOpsKqnGEgh8vV5XotSyLBJWgqtPXGVhdYE0aaKliGc/8CxpP80oNcIaWaTDg7OgwvCVowf9kKv/11XKW2Xqi3WCHwrwHZ/99X16/R7P/pVn6Rf7XF2/yme/+Fla6UlUdP0j6xQ+XSDzUoZcMofpm+y/YZ/k/53EykzHM8bjSSfXfr/Pcz/zHMF+QMtoccY8w8vxy5wKT7FUWCLwp+0akTKiNnSJKvM668HIJrsfOnabYTAYTBXCCsdL5lKQTq7r0m63lYs5W4grp7no7pNEMjvtDundNFEmYn+0z5wxxyA5mBTPOCn1OVFBzWaTaD/i8a3HMWOT8k4Zdzgxdg0MCrkCn/rapyiEBc7VzmFhUelXJt7E/w6ZZgYDA3NkTlzNazF+zacZN9XCyW/Vuc2EttnGsR1+rP5jDIwB4+aYYXTn+ZpyjoTjOCrwpPeAGAwGzM3NqVS25EpEGh2VjpUZTNMkiiIajQbpdFohenVEs+6CiQGpxx/gQHzK4OWzcJDmjYn5T6/7TzRzTU6PT/M+3kfKS9EO2jjJiYqQk1wkIbVV2qLarPLRn/ooN07f4NL2Jd5gvoE/W/0z/sPj/wGAN9x+A5/6nU9R7pUxMSm1SjQyDcr9iTiOjZjgdQGj9AgnmIa0S8Fso9Gg3+2T9bP06NGLemTNLHEixkhPjJHBYHCoeyrjTiQSKvooqkLGr0ctH9g4g4RRdYkAB+1oUqmUCufOwup1CJw+MYuLiwoxpRtLvu3TzE6qnzbY4MyXz3Du5jk6Kx2e/DtPMvSGihHa7Tbj0SSn0U63uXH6BqEV8uKpF3n/n7+frz38NWUwPnP2Gf7Wx/4WD60/xOd+/XN8/L0f59rKNS5vXubDf/JhWn+jxdlHzuL2XXVarWwCQR416g0u/colCpsFPvz3PsyN6g2WwiX+eu+vq6CSFP/K/LjuQd5Csp5xHE+Fo+UcS8FG6u7mUehYDchKpTLlTYhPnclkVIBEVIDAueQ9gcnp9YXCTIZhqOYYkpzJWTmKvSJGZLDQW2D1u6uYkUl+PY+/5yvjsdfrUavV6G52CZIBpX5J5SEeXn+YLz7+xcnDv2IwTm4Iz608xz/4wD/gmZVniKyI51ae4+//nb/PL1m/RLffVda/pMPb7Tb1ep16vU6wH1DaLNFOtXl+/nliI2bb2ob0ZOGTyaRCkZdKpUmk8pW5aDabE4ZqNFSOR89jyMGn4o08sEU058+fVxVVMH3CChz0LxIwrLSsEd9Z1MlsOFk8jbm5OSVJRqMRj3z7ERL5BJVUhdpCjYW9BVrLrUk5vndw8OjIH/Hkf/8kf5r4UzJehl/+wi/TqrQ4vXWaH//Yj0+kwivehM4QNxdvsmQssRlvKsmxl9zDtw+6qwjoV87cbDQaDK0h7ZU2xfUiD9Ue4kb1BsvxMlkzi29NPJHZPIxIQ/ktklOXmMJI+rw+sJ1blpeXVahXegrMBo/0BI2IRhmUDFA43rIsdSCHnNckxwQnk0mazSZp0vi2z++9+/eYH89jV2yS3ST9UZ+9vT183ycoBoSJEAzou30++fc+SdNtUh1VpzGNQq8wxKXmJX7xr/4iv7D2C9wc34QYTvmniHsx49x4+kDTCGhCGIREccQz73sGd+Tyd4t/l9qwRtyP6Q/7yjCWZJSMcTAYYJqmKpbRcyiZTEbVTEiOQjy3B7bBl4Bbpbmmzvly3qNIBsEbxHGswC9SIZRMJpVFLWcsVKtVHMdRdkm/32c0GhGEAU8+8iSdQocFb4Ff/q1fZn5rnp3KDp+//HmiOOJi8SLPRM8QmAGJKMF+ch+AbWebpWiJbXOb1CjF0BlydeMqg/MDXgpe4vnK8/yT7X/CP7vyz9hobtDcb9LaaqmjiiXoM+wPKf7PRVZvrlJeKfP0e5/GcAz22COuxSSsBOPw4CxsYaAgCFQzMP1YZF2ddrtd5XbqKW+YMNIDG4G0rElJ/urqqhKbegBIRJ/oQhHjEubNZA5g5sJIevxBjKt2u62MKS/h0cl3JhXX7t6kP8IrdRTuWRdKUMwXec/ae2gn2sw15/iDS39APV1nwVsgNENwYOyMiY2Y9pk2G8GGGtOz3WfphT1W51YpmAWe3nr6IBP7ShOQkTfi1M1TGJFBfj2P7dl8wfoCL/MyK9kV3jN8zx2bQz+wVULyetRUsB5ig+lgHZmHKIoeXGbY2prUA0hPZtnN9Xod27ZxXZeXXnpJfV4Mollgi0gXORA9DCf9mYSZdMTTeDAm3UjTL/fJd/L0Ej1KRom1zBods8NSdgnLsijmirz7a++mWq/yjvI7+Nfv/NdUFir8WvHXJu6iMXFrN9nkQvIC3x19F4DXpV/HfHpeiefV1VVu3rxJv9fn0d9+lPJ2mdZSi+Zyk+JmkcapBnWnzsvxy0RErLOOZ3qkOAgxDwYDcrmcOt1WTrLVT78tFArKUxH7qlQqTR3kIpHMo9KxMsN4PFY1ErL7das3l8spF1NOuZfYgu5qSRIrDMOp0K9UXstrmOANLv75Rcb2mKJd5Etv+hJmx2Q/3MfreKycWZlkS1sGi/XFSVl94zSXM5ex0har5iq349s4hsMoHnHZvcynz32aXtwjYSXImZNFk3QyvCLRtoaTes3YpLhZ5I8+8EeYCZMwF5JP5DljTKKOZ42z2L7NcHxgO0nSScYrKX2RFrZtq4BSPp8nk8lg27YyHgUyeL+u5bFD5eFA/Ev6Gg7iCdKrQULMMpgoihRjSHGtfhCqHuWT74pNMRqNSPpJhv4kojiIBvT602jrRtSgvlinslOhdbpFbiVHNpflk5lP0ot6zKXmiFMxlVQF13UpU1b3lxrHTCajDgRZH62zVd5iqbFErVqj7/YJ3ICCVcAyLd5vvh/f8cmSZWN/Q7mhs+l1AcHIfcTTklP+ZgNweiBvMBg8uHUTcHAaq6SxYfqAUjhAN+nGkCCrZXAiAXQJoe9Q2UFSV+D7Pgk7QTts4/U84ihW6mQ8HpMv5HniJ54gH+WJ8hGZ9GS3pVNpFrILdxhnotZ2d3cPEl3aeVpjf8xvP/rb5MIcUSHiP2b/I3vOHivGCh/KfIjAD4i9GN/ylc0k0VZZbPG2BPTTarUoFosqqCQRW/G8stmsylYKPbDgFnEbZ+FgsqvFKvZ9X8HchEEEzyCkN6eYpdnJjaKImJgX/+qLDCoDnF2HhT9YUEzX6XTUgaHDzJBknCRuxHBqcj1JG8uzu0kXs2PSNiaWfm23RtgIqQd1mq2mOp+7VCpRC2okggS7zi6xEbMer9OP+xRTxTueGQ5aGcvrUqmkYgvSf0FK7YvFonK/0+m0MhzFUB+Px8zPzx95fY7dtRTvQVSEXiAj0kDKzWHiWfT7fZLJJP1+fypEOwv8EJIJkc86joNv+AwqAzBhXB3jO74yTtvtNplMhnQ6Tb/X57F/8xiF9QK91R6bH9skk5ugibrdLkk7SfThiPiZmPB1IXwSrnzhCoX1Arvzu/z+238fOEBch2FI0S2y4C2w5+6xHC+TiiZjLJcnqqZer9Nut1XUVewDkUb62dapVEqlpSUPISpCb288GAxIp9P3xGHO0rEnqvRDzk3TVEgngXVJNZB+ELp+nJ/43KPRSDGXHqGEA7UjiaFer4cf+Lh7Lt6Ch7PjYI9tQjukG3UpU8b3/cnhIakl8ut5jMggs5YhakT4rq8Qx0bTwH7GhhASzycI18JJ5VZsslBboL/Zx07bVOYrdMMuSZIYGPzw7g8Tp2OWCktq0eXZZTPoarJarU4ZjaIq9FS2ZCh1htCLbMQrOSodKzNsbBwYSrJ4ElmUxZf39E6nOsZPAjOyq4XEzYQDH11Om4cJRrL6/1Rp+S2SQZLQCqn/SJ1bC7codAr8tRf/GgDtdJvGqQbl7TLt0212Rju4O+7BgR9zFskrSawbFv5lH87C/ql9yltlXs69TM2vkRgmeOb7nqFb7JLv5HnX9rswMMgaWQI/oNPpqIipjE3mQEg8E/GsBHWtHwYrm0b6XghMXgJy9+NJwGvgTcjAdTi4VCEFQcBwOCSZTKpdr+P8dMSPLjpnSaDiMtFSu2mZFnkzj52xafktvAVPQevtgk02maUVtvjdt/0u89Y8USHC6lhgHOjwoTeET0NQD+gmugy6A57+8ae5/tR12oM2tmVj5k26xS6xGdPJd4hakfIIYLq6SvcgZJGTyaTaJDoqTIJQYl/poFuBysGEgWq12hSU7yh07HUTlmWp4JPrusroEW6WTKLuUYhKkboGgcbpEyW/DcOY6l+gJ8IkpNtoNHBTrlIb+U6esBfy75b/HfV0nfniPO/ceidG11D2Rz6fV5ImlUoRFSPc0KXZbvJV96ts/cAWuWaOq9+8StgKybVydItdSr0ScS9mYA2UehAXUU+8OY6jxg0TJrFtW2VpRS3IHIq9JRJCalWlDEE8jQe2kbhYxhJZE/2mT4DuIciCClpHpMUsWvgwkkkXaaJfU+Bxy3+8jDvnTiKIlZB6uk5sxNTcGq1xi5w50bfj8aRKa2AMKIwKUzC2TthhK7FFbMR0i11CN8ToG5z9k7NEqYhKqkJUijBtUzGBXjQrUlKHs8mCi+stzy9HMEhySmwNYYp8Pq8ilRLqvx86VmaYDSSJmBOLV/L+gjIG6Ha7alCiY3W6lxgMguDQlLeQgUEqTGEnbHJmjoXRAnvJPUrdEsbQIEpN9K9pmfx6/Ots97ZZNVf52fHPkk5NEmKpMMVSsMRWYovKsELeyhOmJ9IoFabwx76yX2afW4zhWcNXTrqRzK7sdoED6mdLwEHqOgwPWg6IPaHXZb4aHXtPJ5iIsFKppFykdDqtxKK4mrZtq50hoBURj+JuipUtbmcQBMrglFNYxOAajSYd3+W1ZVmqujsIAvyxzw/e/kHW99dhAF7KIwoj8vk8tX6NrdIWMTFr0Rq1QY2SPzktzvM8fjL4SV64/QJhJ6QX9FQ6WZi+Xq/T7XY5ffq0CpBJFHVubk7tcL2qS/ItwiiiKiTvEIahmj99k/R6PfL5/CRbOhwqRjoKHSszrK+vUygUmJubU4MSoyebzdJutxWcXOINEq6+m78s15FJk8ym/E+OUtYPI5MIn/R/EA8mkUiQjtMMGarP+75PNsqyOF5k19llOVrGHJt4oafOmPDHPsuFZTpGB3/sT4XJAYX+FtUoNo3jOEplWpZFp9NRJ9eKVIODTTTbw0mYWgxPHUuaTCYf7MLbra0tpdPkaOFaraZK2UU16HaBzgSHxe0FwCHQMGBqZ8p7AgyxbZt0Ok06nWZubk7FN2QBxJCdaokTw7t23oVTckjHabzQwx/7SsVJhZe4g5JO1w1eMQQF9SQZRt2Y1Bt4ytjEnhB3W4xpx3HodDoq9iCFNAKMkRZJdzu64TA6Vgzk2tqasuhloHI4qBxQJhFBOdBTgkFyppNUS4shJYstsHHZrRKXaLfbCocopLtpMqH6scLi1sEBA9oJm7yZx3gF+iS7XcS9eBxyWu78/Lyq7YCDtLpuJArzHIZV1JNuus1hWZayC+S7+nVnDfD7oWOVDFJxLTpOz0gOBgMV5YNpA0sGZdu2khT694Mg4Bvf+IbaFTJRFy9eVJ/VJ0afsPF4rE7Ns6zJCTGtVkvFJmCSL5AKL7HypcuaMKQ8jyxgKpWaApa0Wi0FT5PxS6e7fD6vkNRC8sy6xBDkkhT3SiBMH5PkRRYXF9U8HNVuOHY8gxSRiCslXdAE+St4B8dxpg7V0L0IXZ3IYeQ3b95U14RJEGZvb08ZbDKxeqc0YQBdUomUEGi7RDVFjemfF0aVXa8/b7vdplwuq1iBqAx5Fl1iCAPPgnjkb1FhEkw77Bk6nY6qsazVaoopHcfh7NmzR1qfY3ctxdDp9/vYtj0lLaReQnclJVonGEn9lBrP87h58yZra2tTiRzHcSgWiwokIqV6Eq6VvIbu74/HYwUelYCNDjeDAyBOGIaKgQXWrjODhMt7vZ4KVOnPJ/cXG0IYXSSDziw6XkGYV68u0zeJoMnT6TQvvfQSKysrShUdhY6VGSQTKa6UGI5iTQtyWiYTUDtK7IZGo0G/36fVarG/vz/VElCyfK7rsry8rKSOZU1aFEs0Tp5Db7rdbDanmFOkiDCdiHTZwYPBgGKxqPICAkKRxRM4nrizkpYfDoeT2gntenJIu854OknqelZq6DkbYSCZp3w+z+bm5oPrTSQSiamaCXGn9MRKt9tVfRhkB3qex+7uLnt7e7RaranchvwIqENiEc1mU4Fv9eSO7/tqx0uL3fX1deVJZDIZFcGDgz5Us66tHt0UI1fyIWEYqnpSPc186dIlyuUyf/iHf8jLL788tcNLpdIdwBRd7Ym7rUtFAfDImPTPCfrqga2bUDdNJJToFrdMdwNlBzcaDba2ttTC6Iagrjcty5rqMy0Q80qlogIzIk7FJtHR2BLAEvWlN9L0fV8ZtzrEX1oNCsMKaFWuI4sqzLCyssLjjz9OKpXi2WefVYbq3t4eL7zwAvl8nitXrkydyS32iJAU5cABglziDSLlRGKIC/rAnngrWUndUNrc3FTiv9Pp0G63lRupi0z9b50h5G89eif32draYmVlhZWVFbVw4lLK9/QSdrm2FO3Id+Re0iFFN+QGgwFxHE+looEp9bW8vMxb3/pWHn30UTqdDqdPn1a2xI0bN7h27ZoqAl5YWCCfzyvXVNScBMbEVhGpk8/nSafTk+TbK8ho2SRRFN2zs+4sHSszSKmZkMQN6vW6ylbqTKAvhD7R8j99l+o7QERnr9fj5s2b5PN5bNtWTCExjJ2dnSnQiDyTeA2j0UjFNPQMou/7yj7wPO/QU2ULhQKVSoWlpSXm5+d529vepqSUnB8ldZRra2sqUrq1tcXe3h5ra2sUCgWq1cmJuYJaEgkqgTnP81S5vjC2qIzZSOirkaHjCk/o/9907O2CT+jBpRNmOCFFJ8xwQopOmOGEFJ0wwwkpOmGGE1J0wgwnpOiEGU5I0QkznJCiE2Y4IUUnzHBCik6Y4YQUnTDDCSk6YYYTUnTCDCek6IQZTkjRCTOckKITZjghRSfMcEKKTpjhhBSdMMMJKTphhhNSdMIMJ6To/wU4GoVt5OokOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11b13adeb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11b136bf98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11b133a3c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11b13f2470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## TODO: visualize your test output\n",
    "# you can use the same function as before, by un-commenting the line below:\n",
    "\n",
    "visualize_output(test_images, test_outputs, gt_pts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you've found a good model (or two), save your model so you can load it and use it later!\n",
    "\n",
    "Save your models but please **delete any checkpoints and saved models before you submit your project** otherwise your workspace may be too large to submit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: change the name to something uniqe for each new model\n",
    "model_dir = 'saved_models/'\n",
    "model_name = 'keypoints_model_1.pt'\n",
    "\n",
    "# NOTE : saved_models/keypoints_model_1.pt is for the modified LeNet I am finally using.\n",
    "#        saved_models/keypoints_model_0.pt is a previous model that I had used. It is no longer needed.\n",
    "\n",
    "# after training, save your model parameters in the dir 'saved_models'\n",
    "torch.save(net.state_dict(), model_dir+model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you've trained a well-performing model, answer the following questions so that we have some insight into your training and architecture selection process. Answering all questions is required to pass this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1: What optimization and loss functions did you choose and why?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: I used the Adam optimizer . It appeared to be giving better results than SGD.\n",
    "            The loss function I chose was Smooth L1 loss as it is a regression problem and this particular function appeared to  do better than just L1 loss or MSE Loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: What kind of network architecture did you start with and how did it change as you tried different architectures? Did you decide to add more convolutional layers or any layers to avoid overfitting the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: I initially took a very large architecture that was taking too long to even load on cpu. So, I removed multiple layers and simplified it greatly before attempting to train again. The next time, the architecture had very few layers and was very inaccurate in detecting the keypoints. So, I tinkered with the architecture again and ended up with soemthing very close to the LeNet architecture. Also, to avoid overfitting, I used a dropout layer. Initially, I had tried to use a Batch Normalization layer as well, but it was taking too long to train the network using only cpu. I did not use gpu for the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3: How did you decide on the number of epochs and batch_size to train your model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: I decided on the number of epochs by pre-determining that I want the average loss to be around 0.005 within a reasonable training time on cpu. The batch size was similarly considered for given cpu and available training time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Visualization\n",
    "\n",
    "Sometimes, neural networks are thought of as a black box, given some input, they learn to produce some output. CNN's are actually learning to recognize a variety of spatial patterns and you can visualize what each convolutional layer has been trained to recognize by looking at the weights that make up each convolutional kernel and applying those one at a time to a sample image. This technique is called feature visualization and it's useful for understanding the inner workings of a CNN."
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter :\n",
      "[[-0.06949034 -0.01351957  0.1381871   0.1212634   0.03488846]\n",
      " [-0.06216648 -0.02420375 -0.17664941 -0.09523497  0.1818258 ]\n",
      " [-0.11924231  0.02145204  0.08623429  0.0206835  -0.06430721]\n",
      " [-0.0617352   0.12546031  0.09886093 -0.12245367 -0.1259132 ]\n",
      " [-0.15647189  0.13410188  0.16948618  0.07021599 -0.13701792]]\n",
      "Shape : (5, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe3883c1c50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3884699b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the weights in the first conv layer, \"conv1\"\n",
    "# if necessary, change this to reflect the name of your first conv layer\n",
    "weights1 = net.conv1.weight.data\n",
    "\n",
    "w = weights1.numpy()\n",
    "\n",
    "filter_index = 11\n",
    "\n",
    "print(\"Filter :\")\n",
    "print(w[filter_index][0])\n",
    "print(\"Shape : \" + str(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\n",
    "\n",
    "Each CNN has at least one convolutional layer that is composed of stacked filters (also known as convolutional kernels). As a CNN trains, it learns what weights to include in it's convolutional kernels and when these kernels are applied to some input image, they produce a set of **feature maps**. So, feature maps are just sets of filtered images; they are the images produced by applying a convolutional kernel to an input image. These maps show us the features that the different layers of the neural network learn to extract. For example, you might imagine a convolutional kernel that detects the vertical edges of a face or another one that detects the corners of eyes. You can see what kind of features each of these kernels detects by applying them to an image. One such example is shown below; from the way it brings out the lines in an the image, you might characterize this as an edge detection filter.\n",
    "\n",
    "<img src='images/feature_map_ex.png' width=50% height=50%/>\n",
    "\n",
    "\n",
    "Next, choose a test image and filter it with one of the convolutional kernels in your trained CNN; look at the filtered output to get an idea what that particular kernel detects.\n",
    "\n",
    "### TODO: Filter an image to see the effect of a convolutional kernel\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11b145be48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##TODO: load in and display any image from the transformed test dataset\n",
    "\n",
    "## TODO: Using cv's filter2D function,\n",
    "## apply a specific set of filter weights (like the one displayed above) to the test image\n",
    "import cv2\n",
    "\n",
    "# Loading the image\n",
    "img = cv2.imread('images/mona_lisa.jpg')\n",
    "\n",
    "#convert colorspace\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# Applying the filter to image\n",
    "filtered = cv2.filter2D(img, -1, w[filter_index][0])\n",
    "\n",
    "# Visualize the effects and the corresponding filter\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = fig.add_subplot(222, xticks = [], yticks = [])\n",
    "ax.imshow(filtered)\n",
    "ax.set_title(\"Filtered Image\")\n",
    "\n",
    "ax = fig.add_subplot(224, xticks = [], yticks = [])\n",
    "ax.imshow(w[filter_index][0], cmap = 'gray')\n",
    "ax.set_title(\"Filter\")\n",
    "\n",
    "ax = fig.add_subplot(221, xticks = [], yticks = [])\n",
    "ax.imshow(img)\n",
    "ax.set_title(\"Original Image\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4: Choose one filter from your trained CNN and apply it to a test image; what purpose do you think it plays? What kind of feature do you think it detects?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: The feature indexed 11 is an edge-detection filter . It mainly identifies a basic outline of the entire object as can be seen from the filtered image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Moving on!\n",
    "\n",
    "Now that you've defined and trained your model (and saved the best model), you are ready to move on to the last notebook, which combines a face detector with your saved model to create a facial keypoint detection system that can predict the keypoints on *any* face in an 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": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}