3A_Convolutional_Neural_Networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Convolutional Neural Networks (CNNs) in PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Representing images digitally\n",
    "\n",
    "While convolutional neural networks (CNNs) see a wide variety of uses, they were originally designed for images, and CNNs are still most commonly used for vision-related tasks.\n",
    "For today, we'll primarily be focusing on CNNs for images.\n",
    "Before we dive into convolutions and neural networks, it's worth prefacing with how images are represented by a computer, as this understanding will inform some of our design choices.\n",
    "\n",
    "Previously, we saw an example of a digitized MNIST handwritten digit.\n",
    "Specifically, we represent it as an $H \\times W$ table, with the value of each element storing the intensity of the corresponding pixel.\n",
    "\n",
    "<img src=\"Figures/mnist_digital.PNG\" alt=\"mnist_digital\" style=\"width: 600px;\"/>\n",
    "\n",
    "With a 2D representation as above, we for the most part can only efficiently represent grayscale images.\n",
    "What if we want color?\n",
    "There are many schemes for storing color, but one of the most common ones is the [RGB color model](https://en.wikipedia.org/wiki/RGB_color_model).\n",
    "In such a system, we store 3 tables of pixel intensities (each called a *channel*), one each for the colors red, green, and blue (hence RGB), resulting in an $H \\times W \\times 3$ tensor.\n",
    "Pixel values for a particular channel indicate how much of the corresponding color the image has at a particular location.\n",
    "\n",
    "Let's load an image and see this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the image tensor: (620, 1175, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7efe1b7573a0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import imageio\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Read image\n",
    "im = imageio.imread(\"./Figures/chapel.jpg\")\n",
    "print(\"Shape of the image tensor: {}\".format(im.shape))\n",
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the image we loaded has height and width of $620 \\times 1175$, with 3 channels corresponding to RGB.\n",
    "\n",
    "We can easily slice out and view individual color channels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(620, 1175)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7efe196f5d00>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Red channel\n",
    "im_red = im[:,:,0]\n",
    "print(im_red.shape)\n",
    "plt.imshow(im_red, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(620, 1175)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7efe196ead30>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Blue channel\n",
    "im_blue = im[:,:,2]\n",
    "print(im_red.shape)\n",
    "plt.imshow(im_blue, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we have so far considered only 3 channel RGB images, there are many settings in which we may consider a different number of channels.\n",
    "For example, [hyperspectral imaging](https://en.wikipedia.org/wiki/Hyperspectral_imaging) uses a wide range of the electromagnetic spectrum to characterize a scene.\n",
    "Such modalities may have hundreds of channels or more.\n",
    "Additionally, we'll soon see that certain intermediate representations in a CNN can be considered images with many channels.\n",
    "However, we'll focus much of today's discussion to 1 channel grayscale and 3 channel RGB images as our inputs, as is most commonly the case in computer vision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutions\n",
    "Convolutional neural networks (CNNs) are a class of neural networks that have convolutional layers.\n",
    "CNNs are particularly effective for data that have spatial structures and correlations (e.g. images).\n",
    "We'll focus on CNNs applied to images in this tutorial.\n",
    "Recall that a multilayer perceptron (MLP) is entirely composed of fully connected layers, which are each a matrix multiply operation (and addition of a bias) followed by a non-linearity (e.g. sigmoid, ReLU). \n",
    "A convolutional layer is similar, except the matrix multiply operation is replaced with a convolution operation (in practice a cross-correlation). \n",
    "Note that a CNN need not be entirely composed of convolutional layers; in fact, many popular CNN architectures end in fully connected layers.\n",
    "\n",
    "As before, since we're building neural networks, let's start by loading PyTorch. We'll find NumPy useful as well, so we'll also import that here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Review: Fully connected layer\n",
    "In a fully connected layer, the input $x \\in \\mathbb R^{M \\times C_{in}}$ is a vector (or, rather a batch of vectors), where $M$ is the minibatch size and $C_{in}$ is the dimensionality of the input. \n",
    "We first matrix multiply the input $x$ by a weight matrix $W$.\n",
    "This weight matrix has dimensions $W \\in \\mathbb R^{C_{in} \\times C_{out}}$, where $C_{out}$ is the number of output units.\n",
    "We then add a bias for each output, which we do by adding $b \\in \\mathbb{R}^{C_{out}}$.\n",
    "The output $y \\in \\mathbb{R}^{M \\times C_{out}}$ of the fully connected layer then:\n",
    "\n",
    "\\begin{align*}\n",
    "y = \\text{ReLU}(x W + b)\n",
    "\\end{align*}\n",
    "\n",
    "Remember, the values of $W$ and $b$ are variables that we are trying to learn for our model. \n",
    "Below we have a visualization of what the matrix operation looks like (bias term and activation function omitted).\n",
    "\n",
    "<img src=\"Figures/mnist_matmul.PNG\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: torch.Size([100, 784])\n",
      "Output shape: torch.Size([100, 10])\n"
     ]
    }
   ],
   "source": [
    "# Create a random flat input vector\n",
    "x_fc = torch.randn(100, 784)\n",
    "\n",
    "# Create weight matrix variable\n",
    "W = torch.randn(784, 10)/np.sqrt(784)\n",
    "W.requires_grad_()\n",
    "\n",
    "# Create bias variable\n",
    "b = torch.zeros(10, requires_grad=True)\n",
    "\n",
    "# Apply fully connected layer\n",
    "y_preact = torch.matmul(x_fc, W) + b\n",
    "y = F.relu(y_preact)\n",
    "\n",
    "# Print input/output shape\n",
    "print(\"Input shape: {}\".format(x_fc.shape))\n",
    "print(\"Output shape: {}\".format(y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convolutional layer\n",
    "\n",
    "In a convolutional layer, we convolve the input $x$ with a convolutional kernel (aka filter), which we also call $W$, producing output $y$:\n",
    "\n",
    "\\begin{align*}\n",
    "y = \\text{ReLU}(W*x + b)\n",
    "\\end{align*}\n",
    "\n",
    "In the context of CNNs, the output $y$ is often referred to as feature maps. As with a fully connected layer, the goal is to learn $W$ and $b$ for our model.\n",
    "\n",
    "Unlike the input of a fully connected layer, which is $x \\in \\mathbb R^{M\\times C_{in}}$, the dimensionality of an image input is 4D: $x \\in \\mathbb R^{M \\times C_{in} \\times H_{in} \\times W_{in}}$, where $M$ is still the batch size, $C_{in}$ is the number of channels of the input (e.g. 3 for RGB), and $H_{in}$ and $W_{in}$ are the height and width of the image.\n",
    "\n",
    "The weight parameter $W$ is also different in a convolutional layer.\n",
    "Unlike the 2-D weight matrix for fully connected layers, the kernel is 4-D with dimensions $W \\in \\mathbb R^{C_{out} \\times C_{in} \\times H_K \\times W_K }$, where $H_K$ and $W_K$ are the kernel height and weight, respectively.\n",
    "A common choice for $H_K$ and $W_K$ is $H_K = W_K = 3$ or $5$, but this tends to vary depending on the architecture.\n",
    "Convolving the input with the kernel and adding a bias then gives an output $y \\in \\mathbb R^{M \\times C_{out} \\times H_{out} \\times W_{out}}$.\n",
    "If we use \"same\" padding and a stride of $1$ in our convolution (more on this later), our output will have the same spatial dimensions as the input: $H_{out}=H_{in}$ and $W_{out}=W_{in}$.\n",
    "\n",
    "If you're having trouble visualizing this operation in 4D, it's easier to think about for a single member of the minibatch, one convolutional kernel at a time. \n",
    "Consider a stack of $C_{out}$ number of kernels, each of which are 3D ($C_{in} \\times H_K \\times W_K $). \n",
    "This 3D volume is then slid across the input (which is also 3D: $C_{in} \\times H_{in} \\times W_{in}$) in the two spatial dimensions (along $H_{in}$ and $W_{in}$). \n",
    "The outputs of the multiplication of the kernel and the input at every location creates a single feature map that is $H_{out} \\times W_{out}$. \n",
    "Stacking the feature maps generated by each kernel gives the 3D output $C_{out} \\times H_{out} \\times W_{out} $.\n",
    "Repeat the process for all $M$ inputs in the minibatch, and we get a 4D output $M  \\times C_{out} \\times H_{out} \\times W_{out}$.\n",
    "\n",
    "<img src=\"Figures/conv_filters.png\" alt=\"Convolutional filters\" style=\"width: 400px;\"/>\n",
    "\n",
    "A few more things to note:\n",
    "- Notice the ordering of the dimensions of the input (batch, channels in, height, width).\n",
    "This is commonly referred to as $NCHW$ ordering.\n",
    "Many other languages and libraries (e.g. MATLAB, TensorFlow, the image example at the beginning of this notebook) instead default to the slightly different $NHWC$ ordering.\n",
    "PyTorch defaults to $NCHW$, as it more efficient computationally, especially with CUDA. \n",
    "- An additional argument for the convolution is the *stride*, which controls the how far we slide the convolutional filter as we move it along the input image. \n",
    "The convolutional operator, from its signal processing roots, by default considers a stride length of 1 in all dimensions, but in some situations we would like to consider strides more than 1 (or even less than 1). \n",
    "More on this later.\n",
    "- In the context of signal processing, convolutions usually result in outputs that are larger than the input size, which results from when the kernel \"hangs off the edge\" of the input on both sides. \n",
    "This might not always be desirable.\n",
    "We can control this by controlling the padding of the input.\n",
    "Typically, we use pad the input to ensure the output has the same spatial dimensions as the input (assuming stride of 1); this makes it easier for us to keep track of what the size of our model is.\n",
    "\n",
    "Let's implement this convolution operator in code.\n",
    "There is a convolution implementation in `torch.nn.functional`, which we use here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: torch.Size([100, 1, 28, 28])\n",
      "Convolution output shape: torch.Size([100, 16, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "# Create a random image input tensor\n",
    "x_cnn = torch.randn(100, 1, 28, 28)\n",
    "\n",
    "# Create convolutional kernel variable\n",
    "W1 = torch.randn(16, 1, 3, 3)/np.sqrt(1*3*3)\n",
    "W1.requires_grad_()\n",
    "\n",
    "# Create bias variable\n",
    "b1 = torch.zeros(16, requires_grad=True)\n",
    "\n",
    "# Apply convolutional layer\n",
    "conv1_preact = F.conv2d(x_cnn, W1, bias=b1, stride=1, padding=1)\n",
    "conv1 = F.relu(conv1_preact)\n",
    "\n",
    "# Print input/output shape\n",
    "print(\"Input shape: {}\".format(x_cnn.shape))\n",
    "print(\"Convolution output shape: {}\".format(conv1.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like in a MLP, we can stack multiple of these convolutional layers. \n",
    "In the *Representing Images Digitally* section, we briefly mentioned considering images with channels more than 3.\n",
    "Observe that the input to the second layer (i.e. the output of the first layer) can be viewed as an \"image\" with $C_{out}$ channels.\n",
    "Instead of each channel representing a color content though, each channel effectively represents how much the original input image activated a particular convolutional kernel.\n",
    "Given $C_{out}$ kernels that are each $C_{in} \\times H_K \\times W_K$, this results in $C_{out}$ channels for the output of the convolution.\n",
    "\n",
    "Note that we need to change the dimensions of the convolutional kernel such that its input channels matches the number of output channels of the previous layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second convolution output shape: torch.Size([100, 32, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "# 2nd layer variables\n",
    "W2 = torch.randn(32, 16, 3, 3)/np.sqrt(16*3*3)\n",
    "W2.requires_grad_()\n",
    "b2 = torch.zeros(32, requires_grad=True)\n",
    "\n",
    "# Apply 2nd convolutional layer\n",
    "conv2 = F.relu(F.conv2d(conv1, W2, bias=b2, stride=1, padding=1))\n",
    "\n",
    "# Print output shape\n",
    "print(\"Second convolution output shape: {}\".format(conv2.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, we typically perform these convolution operations many times. \n",
    "Popular CNN architectures for image analysis today can be 100+ layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshaping\n",
    "\n",
    "You'll commonly finding yourself needing to reshape tensors while building CNNs.\n",
    "The PyTorch function for doing so is `view()`. \n",
    "Anyone familiar with NumPy will find it very similar to `np.reshape()`.\n",
    "Importantly, the new dimensions must be chosen so that it is possible to rearrange the input into the shape of the output (i.e. the total number of elements must be the same).\n",
    "As with NumPy, you can optionally replace one of the dimensions with a `-1`, which tells `torch` to infer the missing dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = torch.zeros(4, 3)\n",
    "\n",
    "M2 = M.view(1,1,12)\n",
    "M3 = M.view(2,1,2,3)\n",
    "M4 = M.view(-1,2,3)\n",
    "M5 = M.view(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get an idea of why reshaping is need in a CNN, let's look at a diagram of a simple CNN.\n",
    "\n",
    "<img src=\"Figures/mnist_cnn_ex.png\" alt=\"mnist_cnn_ex\" style=\"width: 500px;\"/>\n",
    "\n",
    "First of all, the CNN expects a 4D input, with the dimensions corresponding to `[batch, channel, height, width]`.\n",
    "Your data may not come in this format, so you may have to reshape it yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([100, 1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "# Reshape flat input image into a 4D batched image input\n",
    "x_flat = torch.randn(100, 784)\n",
    "x_reshaped = x_flat.view(-1, 1, 28, 28)\n",
    "\n",
    "# Print input shape\n",
    "print(x_reshaped.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CNN architectures also commonly contain fully connected layers or a softmax, as we're often interested in classification.\n",
    "Both of these expect 2D inputs with dimensions `[batch, dim]`, so you have to \"flatten\" a CNN's 4D output to 2D.\n",
    "For example, to flatten the convolutional feature maps we created earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([100, 25088])\n"
     ]
    }
   ],
   "source": [
    "# Flatten convolutional feature maps into a vector\n",
    "h_flat = conv2.view(-1, 28*28*32)\n",
    "\n",
    "# Print output shape\n",
    "print(h_flat.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pooling and striding\n",
    "\n",
    "Almost all CNN architectures incorporate either pooling or striding. This is done for a number of reasons, including:\n",
    "- Dimensionality reduction: pooling and striding operations reduces computational complexity by shrinking the number of values passed to the next layer.\n",
    "For example, a 2x2 maxpool reduces the size of the feature maps by a factor of 4.\n",
    "- Translational invariance: Oftentimes in computer vision, we'd prefer that shifting the input by a few pixels doesn't change the output. Pooling and striding reduces sensitivity to exact pixel locations.\n",
    "- Increasing receptive field: by summarizing a window with a single value, subsequent convolutional kernels are seeing a wider swath of the original input image. For example, a max pool on some input followed by a 3x3 convolution results in a kernel \"seeing\" a 6x6 region instead of 3x3.\n",
    "\n",
    "#### Pooling\n",
    "The two most common forms of pooling are max pooling and average pooling. \n",
    "Both reduce values within a window to a single value, on a per-feature-map basis.\n",
    "Max pooling takes the maximum value of the window as the output value; average pooling takes the mean.\n",
    "\n",
    "<img src=\"Figures/maxpool.PNG\" alt=\"avg_vs_max\" style=\"width: 400px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of conv2 feature maps before pooling: torch.Size([100, 32, 28, 28])\n",
      "Shape of conv2 feature maps after max pooling: torch.Size([100, 32, 14, 14])\n",
      "Shape of conv2 feature maps after avg pooling: torch.Size([100, 32, 14, 14])\n"
     ]
    }
   ],
   "source": [
    "# Taking the output we've been working with so far, first print its current size\n",
    "print(\"Shape of conv2 feature maps before pooling: {0}\".format(conv2.shape))\n",
    "\n",
    "# Max pool and then print new shape\n",
    "max_pool2 = F.max_pool2d(conv2, kernel_size=2)\n",
    "print(\"Shape of conv2 feature maps after max pooling: {0}\".format(max_pool2.shape))\n",
    "\n",
    "# Average pool and then print new shape\n",
    "avg_pool2 = F.avg_pool2d(conv2, kernel_size=2)\n",
    "print(\"Shape of conv2 feature maps after avg pooling: {0}\".format(avg_pool2.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can confirm that the two types of pooling behave how we'd expect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature map shape pre-pooling: torch.Size([1, 1, 4, 4])\n",
      "\n",
      "Max pool\n",
      "Shape: torch.Size([1, 1, 2, 2])\n",
      "tensor([[6., 8.],\n",
      "        [3., 4.]])\n",
      "\n",
      "Avg pool\n",
      "Shape: torch.Size([1, 1, 2, 2])\n",
      "tensor([[3.2500, 5.2500],\n",
      "        [2.0000, 2.0000]])\n"
     ]
    }
   ],
   "source": [
    "# Recreate values in pooling figure and make it 4D\n",
    "feature_map_fig = torch.tensor(np.array([[1,1,2,4],\n",
    "                                        [5,6,7,8],\n",
    "                                        [3,2,1,0],\n",
    "                                        [1,2,3,4]], dtype=np.float32))\n",
    "fmap_fig = feature_map_fig.view(1,1,4,4)\n",
    "print(\"Feature map shape pre-pooling: {}\".format(fmap_fig.shape))\n",
    "\n",
    "# Maxpool\n",
    "max_pool_fig = F.max_pool2d(fmap_fig, kernel_size=2)\n",
    "print(\"\\nMax pool\")\n",
    "print(\"Shape: {}\".format(max_pool_fig.shape))\n",
    "print(torch.squeeze(max_pool_fig))\n",
    "\n",
    "# Avgpool\n",
    "avg_pool_fig = F.avg_pool2d(fmap_fig, kernel_size=2)\n",
    "print(\"\\nAvg pool\")\n",
    "print(\"Shape: {}\".format(avg_pool_fig.shape))\n",
    "print(torch.squeeze(avg_pool_fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Striding\n",
    "One might expect that pixels in an image have high correlation with neighboring pixels, so we can save computation by skipping positions while sliding the convolutional kernel. \n",
    "By default, a CNN slides across the input one pixel at a time, which we call a stride of 1.\n",
    "By instead striding by 2, we skip calculating 75% of the values of the output feature map, which yields a feature map that's half the size in each spatial direction.\n",
    "Note, while pooling is an operation done after the convolution, striding is part of the convolution operation itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of conv1 feature maps: torch.Size([100, 16, 28, 28])\n",
      "Shape of conv2 feature maps with stride of 2: torch.Size([100, 32, 14, 14])\n"
     ]
    }
   ],
   "source": [
    "# Since striding is part of the convolution operation, we'll start with the feature maps before the 2nd convolution\n",
    "print(\"Shape of conv1 feature maps: {0}\".format(conv1.shape))\n",
    "\n",
    "# Apply 2nd convolutional layer, with striding of 2\n",
    "conv2_strided = F.relu(F.conv2d(conv1, W2, bias=b2, stride=2, padding=1))\n",
    "\n",
    "# Print output shape\n",
    "print(\"Shape of conv2 feature maps with stride of 2: {0}\".format(conv2_strided.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a custom CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's revisit MNIST digit classification, but this time, we'll use the following CNN as our classifier: $5 \\times 5$ convolution -> $2 \\times 2$ max pool -> $5 \\times 5$ convolution -> $2 \\times 2$ max pool -> fully connected to $\\mathbb R^{256}$ -> fully connected to $\\mathbb R^{10}$ (prediction). \n",
    "ReLU activation functions will be used to impose non-linearities.\n",
    "Remember, convolutions produce 4-D outputs, and fully connected layers expect 2-D inputs, so tensors must be reshaped when transitioning from one to the other.\n",
    "\n",
    "We can build this CNN with the components introduced before, but as with the logistic regression example, it may prove helpful to instead organize our model with a `nn.Module`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mInit signature:\u001b[0m\n",
      "\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0min_channels\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mout_channels\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mkernel_size\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mstride\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mpadding\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdilation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mgroups\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mbias\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mpadding_mode\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'zeros'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m     \n",
      "Applies a 2D convolution over an input signal composed of several input\n",
      "planes.\n",
      "\n",
      "In the simplest case, the output value of the layer with input size\n",
      ":math:`(N, C_{\\text{in}}, H, W)` and output :math:`(N, C_{\\text{out}}, H_{\\text{out}}, W_{\\text{out}})`\n",
      "can be precisely described as:\n",
      "\n",
      ".. math::\n",
      "    \\text{out}(N_i, C_{\\text{out}_j}) = \\text{bias}(C_{\\text{out}_j}) +\n",
      "    \\sum_{k = 0}^{C_{\\text{in}} - 1} \\text{weight}(C_{\\text{out}_j}, k) \\star \\text{input}(N_i, k)\n",
      "\n",
      "\n",
      "where :math:`\\star` is the valid 2D `cross-correlation`_ operator,\n",
      ":math:`N` is a batch size, :math:`C` denotes a number of channels,\n",
      ":math:`H` is a height of input planes in pixels, and :math:`W` is\n",
      "width in pixels.\n",
      "\n",
      "\n",
      "This module supports :ref:`TensorFloat32<tf32_on_ampere>`.\n",
      "\n",
      "On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.\n",
      "\n",
      "* :attr:`stride` controls the stride for the cross-correlation, a single\n",
      "  number or a tuple.\n",
      "\n",
      "* :attr:`padding` controls the amount of padding applied to the input. It\n",
      "  can be either a string {'valid', 'same'} or a tuple of ints giving the\n",
      "  amount of implicit padding applied on both sides.\n",
      "\n",
      "* :attr:`dilation` controls the spacing between the kernel points; also\n",
      "  known as the à trous algorithm. It is harder to describe, but this `link`_\n",
      "  has a nice visualization of what :attr:`dilation` does.\n",
      "\n",
      "* :attr:`groups` controls the connections between inputs and outputs.\n",
      "  :attr:`in_channels` and :attr:`out_channels` must both be divisible by\n",
      "  :attr:`groups`. For example,\n",
      "\n",
      "    * At groups=1, all inputs are convolved to all outputs.\n",
      "    * At groups=2, the operation becomes equivalent to having two conv\n",
      "      layers side by side, each seeing half the input channels\n",
      "      and producing half the output channels, and both subsequently\n",
      "      concatenated.\n",
      "    * At groups= :attr:`in_channels`, each input channel is convolved with\n",
      "      its own set of filters (of size\n",
      "      :math:`\\frac{\\text{out\\_channels}}{\\text{in\\_channels}}`).\n",
      "\n",
      "The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:\n",
      "\n",
      "    - a single ``int`` -- in which case the same value is used for the height and width dimension\n",
      "    - a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension,\n",
      "      and the second `int` for the width dimension\n",
      "\n",
      "Note:\n",
      "    When `groups == in_channels` and `out_channels == K * in_channels`,\n",
      "    where `K` is a positive integer, this operation is also known as a \"depthwise convolution\".\n",
      "\n",
      "    In other words, for an input of size :math:`(N, C_{in}, L_{in})`,\n",
      "    a depthwise convolution with a depthwise multiplier `K` can be performed with the arguments\n",
      "    :math:`(C_\\text{in}=C_\\text{in}, C_\\text{out}=C_\\text{in} \\times \\text{K}, ..., \\text{groups}=C_\\text{in})`.\n",
      "\n",
      "Note:\n",
      "    In some circumstances when given tensors on a CUDA device and using CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting ``torch.backends.cudnn.deterministic = True``. See :doc:`/notes/randomness` for more information.\n",
      "\n",
      "Note:\n",
      "    ``padding='valid'`` is the same as no padding. ``padding='same'`` pads\n",
      "    the input so the output has the shape as the input. However, this mode\n",
      "    doesn't support any stride values other than 1.\n",
      "\n",
      "Note:\n",
      "    This module supports complex data types i.e. ``complex32, complex64, complex128``.\n",
      "\n",
      "Args:\n",
      "    in_channels (int): Number of channels in the input image\n",
      "    out_channels (int): Number of channels produced by the convolution\n",
      "    kernel_size (int or tuple): Size of the convolving kernel\n",
      "    stride (int or tuple, optional): Stride of the convolution. Default: 1\n",
      "    padding (int, tuple or str, optional): Padding added to all four sides of\n",
      "        the input. Default: 0\n",
      "    padding_mode (string, optional): ``'zeros'``, ``'reflect'``,\n",
      "        ``'replicate'`` or ``'circular'``. Default: ``'zeros'``\n",
      "    dilation (int or tuple, optional): Spacing between kernel elements. Default: 1\n",
      "    groups (int, optional): Number of blocked connections from input\n",
      "        channels to output channels. Default: 1\n",
      "    bias (bool, optional): If ``True``, adds a learnable bias to the\n",
      "        output. Default: ``True``\n",
      "\n",
      "\n",
      "Shape:\n",
      "    - Input: :math:`(N, C_{in}, H_{in}, W_{in})` or :math:`(C_{in}, H_{in}, W_{in})`\n",
      "    - Output: :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(C_{out}, H_{out}, W_{out})`, where\n",
      "\n",
      "      .. math::\n",
      "          H_{out} = \\left\\lfloor\\frac{H_{in}  + 2 \\times \\text{padding}[0] - \\text{dilation}[0]\n",
      "                    \\times (\\text{kernel\\_size}[0] - 1) - 1}{\\text{stride}[0]} + 1\\right\\rfloor\n",
      "\n",
      "      .. math::\n",
      "          W_{out} = \\left\\lfloor\\frac{W_{in}  + 2 \\times \\text{padding}[1] - \\text{dilation}[1]\n",
      "                    \\times (\\text{kernel\\_size}[1] - 1) - 1}{\\text{stride}[1]} + 1\\right\\rfloor\n",
      "\n",
      "Attributes:\n",
      "    weight (Tensor): the learnable weights of the module of shape\n",
      "        :math:`(\\text{out\\_channels}, \\frac{\\text{in\\_channels}}{\\text{groups}},`\n",
      "        :math:`\\text{kernel\\_size[0]}, \\text{kernel\\_size[1]})`.\n",
      "        The values of these weights are sampled from\n",
      "        :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where\n",
      "        :math:`k = \\frac{groups}{C_\\text{in} * \\prod_{i=0}^{1}\\text{kernel\\_size}[i]}`\n",
      "    bias (Tensor):   the learnable bias of the module of shape\n",
      "        (out_channels). If :attr:`bias` is ``True``,\n",
      "        then the values of these weights are\n",
      "        sampled from :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where\n",
      "        :math:`k = \\frac{groups}{C_\\text{in} * \\prod_{i=0}^{1}\\text{kernel\\_size}[i]}`\n",
      "\n",
      "Examples:\n",
      "\n",
      "    >>> # With square kernels and equal stride\n",
      "    >>> m = nn.Conv2d(16, 33, 3, stride=2)\n",
      "    >>> # non-square kernels and unequal stride and with padding\n",
      "    >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))\n",
      "    >>> # non-square kernels and unequal stride and with padding and dilation\n",
      "    >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))\n",
      "    >>> input = torch.randn(20, 16, 50, 100)\n",
      "    >>> output = m(input)\n",
      "\n",
      ".. _cross-correlation:\n",
      "    https://en.wikipedia.org/wiki/Cross-correlation\n",
      "\n",
      ".. _link:\n",
      "    https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md\n",
      "\u001b[0;31mInit docstring:\u001b[0m Initializes internal Module state, shared by both nn.Module and ScriptModule.\n",
      "\u001b[0;31mFile:\u001b[0m           ~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/conv.py\n",
      "\u001b[0;31mType:\u001b[0m           type\n",
      "\u001b[0;31mSubclasses:\u001b[0m     LazyConv2d, Conv2d, ConvBn2d, Conv2d\n"
     ]
    }
   ],
   "source": [
    "?torch.nn.Conv2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "class MNIST_CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=5, padding=2)\n",
    "        self.fc1 = nn.Linear(7*7*64, 256)\n",
    "        self.fc2 = nn.Linear(256, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # conv layer 1\n",
    "        x = self.conv1(x)\n",
    "        x = F.relu(x)\n",
    "        x = F.max_pool2d(x, kernel_size=2)\n",
    "        \n",
    "        # conv layer 2\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "        x = F.max_pool2d(x, kernel_size=2)\n",
    "        \n",
    "        # fc layer 1\n",
    "        x = x.view(-1, 7*7*64)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        \n",
    "        # fc layer 2\n",
    "        x = self.fc2(x)\n",
    "        return x        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how our `nn.Module` contains several operation chained together.\n",
    "The code for submodule initialization, which creates all the stateful parameters associated with each operation, is placed in the `__init__()` function, where it is run once during object instantiation.\n",
    "Meanwhile, the code describing the forward pass, which is used every time the model is run, is placed in the `forward()` method.\n",
    "Printing an instantiated model shows the model summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNIST_CNN(\n",
      "  (conv1): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "  (fc1): Linear(in_features=3136, out_features=256, bias=True)\n",
      "  (fc2): Linear(in_features=256, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = MNIST_CNN()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can drop this model into our logistic regression training code, with few modifications beyond changing the model itself.\n",
    "A few other changes:\n",
    "- CNNs expect a 4-D input, so we no longer have to reshape the images before feeding them to our neural network.\n",
    "- Since CNNs are a little more complex than models we've worked with before, we're going to increase the number of epochs (complete passes through the training data) during training.\n",
    "- We switch from a vanilla stochastic gradient descent optimizer to the [Adam](https://arxiv.org/abs/1412.6980) optimizer, which tends to do well for neural networks."
   ]
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     "text": [
      "Test accuracy: 0.9882000088691711\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, transforms\n",
    "from tqdm.notebook import tqdm, trange\n",
    "\n",
    "# Load the data\n",
    "mnist_train = datasets.MNIST(root=\"./datasets\", train=True, transform=transforms.ToTensor(), download=True)\n",
    "mnist_test = datasets.MNIST(root=\"./datasets\", train=False, transform=transforms.ToTensor(), download=True)\n",
    "train_loader = torch.utils.data.DataLoader(mnist_train, batch_size=100, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(mnist_test, batch_size=100, shuffle=False)\n",
    "\n",
    "## Training\n",
    "# Instantiate model  \n",
    "model = MNIST_CNN()  # <---- change here\n",
    "\n",
    "# Loss and Optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)  # <---- change here\n",
    "\n",
    "# Iterate through train set minibatchs \n",
    "for epoch in trange(3):  # <---- change here\n",
    "    for images, labels in tqdm(train_loader):\n",
    "        # Zero out the gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass\n",
    "        x = images  # <---- change here \n",
    "        y = model(x)\n",
    "        loss = criterion(y, labels)\n",
    "        # Backward pass\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "## Testing\n",
    "correct = 0\n",
    "total = len(mnist_test)\n",
    "\n",
    "with torch.no_grad():\n",
    "    # Iterate through test set minibatchs \n",
    "    for images, labels in tqdm(test_loader):\n",
    "        # Forward pass\n",
    "        x = images  # <---- change here \n",
    "        y = model(x)\n",
    "\n",
    "        predictions = torch.argmax(y, dim=1)\n",
    "        correct += torch.sum((predictions == labels).float())\n",
    "\n",
    "print('Test accuracy: {}'.format(correct/total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are running this notebook on CPU, training this CNN might take a while.\n",
    "On the other hand, if you use a GPU, this model should train in seconds.\n",
    "This is why we usually prefer to use GPUs when we have them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Torchvision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Datasets and transforms\n",
    "\n",
    "As any experienced ML practioner will say, data wrangling is often half (sometimes even 90%) of the battle when building a model.\n",
    "Often, we have to write significant code to handle downloading, organizing, formatting, shuffling, pre-processing, augmenting, and batching examples. \n",
    "For popular datasets, we'd like to standardize data handling so that the comparisons we make are specific to the models themselves.\n",
    "\n",
    "Enter [Torchvision](https://pytorch.org/docs/stable/torchvision/index.html).\n",
    "Torchvision includes easy-to-use APIs for downloading and loading many popular vision datasets.\n",
    "We've previously seen this in action for downloading the MNIST dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import datasets\n",
    "\n",
    "mnist_train = datasets.MNIST(root=\"./datasets\", train=True, transform=transforms.ToTensor(), download=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, there's [many more](https://pytorch.org/docs/stable/torchvision/datasets.html).\n",
    "Currently, datasets for image classification (e.g. MNIST, CIFAR, ImageNet), object detection (VOC, COCO, Cityscapes), and video action recognition (UCF101, Kinetics) are included.\n",
    "\n",
    "For formatting, pre-processing, and augmenting, [transforms](https://pytorch.org/docs/stable/torchvision/transforms.html) can come in handy.\n",
    "Again, we've seen this before (see above), when we used a transform to convert the MNIST data from PIL images to PyTorch tensors.\n",
    "However, transforms can be used for much more. \n",
    "Preprocessing steps like data whitening are common before feeding the data into the model.\n",
    "Also, in many cases, we use data augmentations to artificially inflate our dataset and learn invariances.\n",
    "Transforms are a versatile tool for all of these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Leveraging popular convolutional neural networks\n",
    "\n",
    "While you certainly can build your own custom CNNs like we did above, more often than not, it's better to use one of the popular existing architectures. \n",
    "The Torchvision documentation has a [list of supported CNNs](https://pytorch.org/docs/stable/torchvision/models.html), as well as some performance characteristics. \n",
    "There's a number of reasons for using one of these CNNs instead of designing your own.\n",
    "\n",
    "First, for image datasets larger and more complex than MNIST (which is basically all of them), a fair amount network depth and width is often necessary.\n",
    "For example, some of the popular CNNs can be over 100 layers deep, with several tricks and details beyond what we've covered in this notebook.\n",
    "Coding all of this yourself has a high potential for error, especially when you're first getting started.\n",
    "Instead, you can create the CNN architecture using Torchvision, using a couple lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.models as models\n",
    "resnet18 = models.resnet18()\n",
    "print(resnet18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading a working CNN architecture in a couple lines can save a significant amount of time both implementing and debugging.\n",
    "\n",
    "The second, perhaps even more important, reason to use one of these existing architectures is the ability to use pre-trained weights.\n",
    "Early on in the recent resurgence of deep learning, people discovered that the weights of a CNN trained for ImageNet classification were highly transferable. \n",
    "For example, it is common to use the weights of an ImageNet-trained CNN as a weight initialization for other vision tasks, or even to freeze the bulk of the weights and only re-train the final classification layer(s) on a new task.\n",
    "This is significant, as in most settings, we rarely have enough labeled data to train a powerful CNN from scratch without overfitting.\n",
    "Loading pre-trained CNN is also pretty simple, involving an additional argument to the previous cell block:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resnet18 = models.resnet18(pretrained=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1\">*Note that this line of code is commented out, as running it will initiate a download of the pre-trained weights, which is a fairly large file.*</font>\n",
    "\n",
    "A full tutorial on using pre-trained CNNs is a little beyond the scope of this notebook.\n",
    "See [this tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) for an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other computer vision tasks\n",
    "The base CNN architectures were often designed for image classification, but the same CNNs are often used as the backbone of most modern computer vision models.\n",
    "These other models often take this base CNN and include additional networks or make other architecture changes to adapt them to other tasks, such as object detection.\n",
    "Torchvision contains a few models (and pre-trained weights) for object detection, segmentation, and video action recognition.\n",
    "For example, to load a [Faster R-CNN](https://arxiv.org/abs/1506.01497) with a [ResNet50](https://arxiv.org/abs/1512.03385) convolutional feature extractor with [Feature Pyramid Networks](https://arxiv.org/abs/1612.03144) pre-trained on [MS COCO](http://cocodataset.org/#home):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# object_detector = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1\">*Again, this line has been commented out to prevent loading a large network for this demo.*</font>\n",
    "\n",
    "Torchvision's selection of non-classification models is relatively light, and not particularly flexible.\n",
    "A number of other libraries are available, depending on the task.\n",
    "For example, for object detection and segmentation, Facebook AI Research's [Detectron2](https://github.com/facebookresearch/detectron2) is highly recommend."
   ]
  }
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