15_resnet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "[[chapter_resnet]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resnets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Going back to Imagenette"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's going to be tough to judge any improvement we do to our models when we are already at an accuracy that is as high as we saw on MNIST in the previous chapter, so we will tackle a tougher problem by going back to Imagenette. We'll stick with small images to keep things reasonably fast.\n",
    "\n",
    "Let's grab the data--we'll use the already-resized 160px version to make things faster still, and will random crop to 128px:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(url, presize, resize):\n",
    "    path = untar_data(url)\n",
    "    return DataBlock(\n",
    "        blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, \n",
    "        splitter=GrandparentSplitter(valid_name='val'),\n",
    "        get_y=parent_label, item_tfms=Resize(presize),\n",
    "        batch_tfms=[*aug_transforms(min_scale=0.5, size=resize),\n",
    "                    Normalize.from_stats(*imagenet_stats)],\n",
    "    ).dataloaders(path, bs=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = get_data(URLs.IMAGENETTE_160, 160, 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch(max_n=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we looked at MNIST we were dealing with 28 x 28 pixel images. For Imagenette we are going to be training with 128 x 128 pixel images, and later on would like to be able to use larger images as well — at least as big as 224 x 224 pixels, the ImageNet standard. Do you recall how we managed to get a single vector of activations for each image out of the MNIST convolutional neural network?\n",
    "\n",
    "The approach we used was to ensure that there was enough stride two convolutions such that the final layer would have a grid size of one. Then we just flattened out the unit axes that we ended up with, to get a vector for each image (so a matrix of activations for a mini batch). We could do the same thing for Imagenette, but that's going to cause two problems:\n",
    "\n",
    "- We are going to need lots of stride two layers to make our grid one by one at the end — perhaps more than we would otherwise choose\n",
    "- The model will not work on images of any size other than the size we originally trained on.\n",
    "\n",
    "One approach to dealing with the first of these issues would be to flatten the final convolutional layer in a way that handles a grid size other than one by one. That is, we could simply flatten a matrix into a vector as we have done before, by laying out each row after the previous row. In fact, this is the approach that convolutional neural networks up until 2013 nearly always did. The most famous, still sometimes used today, is the 2013 ImageNet winner VGG. But there was another problem with this architecture: not only does it not work with images other than those of the same size as the training set, but it required a lot of memory, because flattening out the convolutional create resulted in many activations being fed into the final layers. Therefore, the weight matrices of the final layers were enormous.\n",
    "\n",
    "This problem was solved through the creation of *fully convolutional networks*. The trick in fully convolutional networks is to take the average of activations across a convolutional grid. In other words, we can simply use this function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg_pool(x): return x.mean((2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see, it is taking the mean over the X and Y axes. This function will always convert a grid of activations into a single activation per image. PyTorch provides a slightly more versatile module called `nn.AdaptiveAvgPool2d`, which averages a grid of activations into whatever sized destination you require (although we nearly always use the size of one).\n",
    "\n",
    "A fully convolutional network, therefore, has a number of convolutional layers, some of which will be stride two, at the end of which is an adaptive average pooling layer, a flatten layer to remove the unit axes, and finally a linear layer. Here is our first fully convolutional network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def block(ni, nf): return ConvLayer(ni, nf, stride=2)\n",
    "def get_model():\n",
    "    return nn.Sequential(\n",
    "        block(3, 16),\n",
    "        block(16, 32),\n",
    "        block(32, 64),\n",
    "        block(64, 128),\n",
    "        block(128, 256),\n",
    "        nn.AdaptiveAvgPool2d(1),\n",
    "        Flatten(),\n",
    "        nn.Linear(256, dls.c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to be replacing the implementation of `block` in the network with other variants in a moment, which is why we're not calling it `conv` any more. We're saving some time by taking advantage of fastai's `ConvLayer` that already provides the functionality of `conv` from the last chapter (plus a lot more!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> stop: Consider this question: Would this approach makes sense for an optical character recognition (OCR) problem such as MNIST? We see the vast majority of practitioners tackling OCR and similar problems tend to use fully convolutional networks, because that's what nearly everybody learns nowadays. But it really doesn't make any sense! You can't decide whether, for instance, whether a number is a \"3\" or an \"8\" by slicing it into small pieces, jumbling them up, and deciding whether on average each piece looks like a \"3\" or an \"8\". But that's what adaptive average pooling effectively does! Fully convolutional networks are only really a good choice for objects that don't have a single correct orientation or size (i.e. like most natural photos)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we are done with our convolutional layers, we will get activations of size `bs x ch x h x w` (batch size, a certain number of channels, height and width). We want to convert this to a tensor of size `bs x ch`, so we take the average over the last two dimensions and flatten the trailing `1 x 1` dimension like we did in our previous model. \n",
    "\n",
    "This is different from regular pooling in the sense that those layers will generally take the average (for average pooling) or the maximum (for max pooling) of a window of a given size: for instance max pooling layers of size 2 that were very popular in older CNNs reduce the size of our image by on each dimension by taking the maximum of each 2 by 2 window (with a stride of 2).\n",
    "\n",
    "As before, we can define a `Learner` with our custom model and the data we grabbed before then train it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_learner(m):\n",
    "    return Learner(dls, m, loss_func=nn.CrossEntropyLoss(), metrics=accuracy\n",
    "                  ).to_fp16()\n",
    "\n",
    "learn = get_learner(get_model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.47863011360168456, 3.981071710586548)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3e-3` is very often a good learning rate for CNNs, and that appears to be the case here too, so let's try that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.901582</td>\n",
       "      <td>2.155090</td>\n",
       "      <td>0.325350</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.559855</td>\n",
       "      <td>1.586795</td>\n",
       "      <td>0.507771</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.296350</td>\n",
       "      <td>1.295499</td>\n",
       "      <td>0.571720</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.144139</td>\n",
       "      <td>1.139257</td>\n",
       "      <td>0.639236</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.049770</td>\n",
       "      <td>1.092619</td>\n",
       "      <td>0.659108</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 3e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a pretty good start, considering we have to pick the correct one of ten categories, and we're training from scratch for just 5 epochs!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a modern CNN: ResNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have all the pieces needed to build the models we have been using in each computer vision task since the beginning of this book: ResNets. We introduce the main idea behind them and show how it improves accuracy Imagenette compared to our previous model, before building a version with all the recent tweaks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Skip-connections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2015 the authors of ResNet paper noticed something that they found curious. Even after using batchnorm, they saw that a network using more layers was doing less well than a network using less layers — and there were no other differences between the models. Most interestingly, the difference was observed not only in the validation set, but also in the training set; so, it wasn't just a generalisation issue, but a training issue. As the paper explains:\n",
    "\n",
    "> : Unexpectedly, such degradation is not caused by overfitting, and adding more layers to a suitably deep model leads to higher training error, as [previously reported] and thoroughly verified by our experiments.\n",
    "\n",
    "This is the graph they showed, with training error on the left, and test on the right:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Training of networks of different depth\" width=\"700\" caption=\"Training of networks of different depth\" id=\"resnet_depth\" src=\"images/att_00042.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the authors mention here, they are not the first people to have noticed this curious fact. But they were the 1st to make a very important leap:\n",
    "\n",
    "> : Let us consider a shallower architecture and its deeper counterpart that adds more layers onto it. There exists a solution by construction to the deeper model: the added layers are identity mapping, and the other layers are copied from the learned shallower model.\n",
    "\n",
    "Being an academic paper, this process written in a rather inaccessible way — but it's actually saying something very simple: start with the 20 layer neural network that is trained well, and add another 36 layers that do nothing at all (for instance, they linear layer with a single weight equal to one, and bias equal to 0). This would be a 56 layer network which does exactly the same thing as the 20 layer network. This shows that there are always deep networks which should be *at least as good* as any shallow network. But for some reason, SGD does not seem able to find them.\n",
    "\n",
    "> jargon: Identity mapping: a function that just returns its input without changing it at all. Also known as *identity function*.\n",
    "\n",
    "Actually, there is another way to create those extra 36 layers, which is much more interesting. What if we replaced every occurrence of `conv(x)` with `x + conv(x)`, where `conv` is the function from the previous chapter which does a 2nd convolution, then relu, then batchnorm. Furthermore, recall that batchnorm does `gamma*y + beta`. What if we initialized `gamma` for every one of these batchnorm layers to zero? Then our `conv(x)` for those extra 36 layers will always be equal to zero, which means `x+conv(x)` will always be equal to `x`.\n",
    "\n",
    "What has that gained us, then? The key thing is that those 36 extra layers, as they stand, are an *identity mapping*, but they have *parameters*, which means they are *trainable*. So, we can start with our best 20 layer model, add these 36 extra layers which initially do nothing at all, and then *fine tune the whole 56 layer model*. If those extra 36 layers can be useful, then they can learn parameters to do so!\n",
    "\n",
    "The ResNet paper actually proposed a variant of this, which is to instead \"skip over\" every 2nd convolution, so effectively we get `x+conv2(conv1(x))`. Or In diagram form (from the paper):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"A simple ResNet block\" width=\"331\" caption=\"A simple ResNet block\" id=\"resnet_block\" src=\"images/att_00043.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That arrow on the right is just the `x` part of `x+conv2(conv1(x))`, and is known as the *identity branch* or *skip connection*. The path on the left is the `conv2(conv1(x))` part. You can think of the identity path as providing a direct route from the input to the output.\n",
    "\n",
    "In a ResNet, we don't actually train it by first training a smaller number of layers, and then add new layers on the end and fine-tune. Instead, we use ResNet blocks (like the above) throughout the CNN, initialized from scratch in the usual way, and trained with SGD in the usual way. We rely on the skip connections to make the network easier to train for SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's another (largely equivalent) way to think of these \"ResNet blocks\". This is how the paper describes it:\n",
    "\n",
    "> : Instead of hoping each few stacked layers directly fit a desired underlying mapping, we explicitly let these layers fit a residual mapping. Formally, denoting the desired underlying mapping as H(x), we let the stacked nonlinear layers fit another mapping of F(x) := H(x)−x. The original mapping is recast into F(x)+x. We hypothesize that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.\n",
    "\n",
    "Again, this is rather inaccessible prose—so let's try to restate it in plain English! If the outcome of a given layer is `x`, when using a ResNet block that return `y = x+block(x)`, we're not asking the block to predict `y`, we are asking it to predict the difference between `y-x`. So the job of those blocks isn't to predict certain features anymore, but a little extra step that will minimize the error between `x` and the desired `y`. ResNet is, therefore, good at learning about slight differences between doing nothing and some other feature that the layer learns. Since we predict residuals (reminder: \"residual\" is predictions minus targets), this is why those kinds of models were named ResNets.\n",
    "\n",
    "One key concept that both of these two ways of thinking about ResNets share is the idea of \"easy to learn\". This is an important theme. Recall the universal approximation theorem, which states that a sufficiently large network *can* learn anything. This is still true. But there turns out to be a very important difference between what a network *can learn* in principle, and what it is *easy for it to learn* under realistic data and training regimes. Many of the advances in neural networks over the last decade have been like the ResNet block: the result of realizing how to make something which was always possible actually feasible.\n",
    "\n",
    "> note: The original paper didn't actually do the trick of using zero for the initial value of gamma in the batchnorm layer; that came a couple of years later. So the original version of ResNet didn't quite begin training with a truly identity path through the ResNet blocks, but nonetheless having the ability to \"navigate through\" the skip connections did indeed make it train better. Adding the batchnorm gamma init trick made the models train at even higher learning rates.\n",
    "\n",
    "Here's the definition of a simple ResNet block (where `norm_type=NormType.BatchZero` causes fastai to init the `gamma` weights of that batchnorm layer to zero):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResBlock(Module):\n",
    "    def __init__(self, ni, nf):\n",
    "        self.convs = nn.Sequential(\n",
    "            ConvLayer(ni,nf),\n",
    "            ConvLayer(nf,nf, norm_type=NormType.BatchZero))\n",
    "        \n",
    "    def forward(self, x): return x + self.convs(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One problem with this, however, is that it can't handle a stride other than `1`, and it requires that `ni==nf`. Stop for a moment, to think carefully about why this is...\n",
    "\n",
    "The issue is that with a stride of, say, `2`, on one of the convolutions, the grid size of the output activations will be half the size on each axis of the input. So then we can't add that back to `x` in `forward` because `x` and the output activations have different dimensions. The same basic issue occurs if `ni!=nf`: the shapes of the input and output connections won't allow us to add them together.\n",
    "\n",
    "To fix this, we need a way to change the shape of `x` to match the result of `self.convs`. Halving the grid size can be done using an average pooling layer with a stride of 2: that is, a layer which takes 2x2 patches from the input, and replaces them with their average.\n",
    "\n",
    "Changing the number of channels can be done by using a convolution. We want this skip connection to be as close to an identity map as possible, however, which means making this convolution as simple as possible. The simplest possible convolution is one where the kernel size is `1`. That means that the kernel is size `ni*nf*1*1`, so it's only doing a dot product over the channels of each input pixel--it's not combining across pixels at all. This kind of *1x1 convolution* is very widely used in modern CNNs, so take a moment to think about how it works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> question: Create a `1x1 convolution` with `F.conv2d` or `nn.Conv2d` and apply it to an image. What happens to the `shape` of the image?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> jargon: 1x1 convolution: A convolution with a kernel size of one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a ResBlock using these tricks to handle changing shape in the skip connection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _conv_block(ni,nf,stride):\n",
    "    return nn.Sequential(\n",
    "        ConvLayer(ni, nf, stride=stride),\n",
    "        ConvLayer(nf, nf, act_cls=None, norm_type=NormType.BatchZero))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResBlock(Module):\n",
    "    def __init__(self, ni, nf, stride=1):\n",
    "        self.convs = _conv_block(ni,nf,stride)\n",
    "        self.idconv = noop if ni==nf else ConvLayer(ni, nf, 1, act_cls=None)\n",
    "        self.pool = noop if stride==1 else nn.AvgPool2d(2, ceil_mode=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return F.relu(self.convs(x) + self.idconv(self.pool(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we're using the `noop` function here, which simply returns its input unchanged (*noop* is a computer science term that stands for \"no operation\"). In this case, `idconv` does nothing at all if `nf==nf`, and `pool` does nothing if `stride==1`, which is what we wanted in our skip connection.\n",
    "\n",
    "Also, you'll see that we've removed relu (`act_cls=None`) from the final convolution in `convs` and from `idconv`, and moved it to *after* we add the skip connection. The thinking behind this is that the whole ResNet block is like a layer, and you want your activation to be *after* your layer.\n",
    "\n",
    "Let's replace our `block` with `ResBlock`, and try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def block(ni,nf): return ResBlock(ni, nf, stride=2)\n",
    "learn = get_learner(get_model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.973174</td>\n",
       "      <td>1.845491</td>\n",
       "      <td>0.373248</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.678627</td>\n",
       "      <td>1.778713</td>\n",
       "      <td>0.439236</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.386163</td>\n",
       "      <td>1.596503</td>\n",
       "      <td>0.507261</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.177839</td>\n",
       "      <td>1.102993</td>\n",
       "      <td>0.644841</td>\n",
       "      <td>00:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.052435</td>\n",
       "      <td>1.038013</td>\n",
       "      <td>0.667771</td>\n",
       "      <td>00:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 3e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not much better. But the whole point of this was to allow us to train *deeper* models, and we're not really taking advantage of that yet. To create a deeper model that's, say, twice as deep, all we need to do is replace our `block` with two `ResBlock`s in a row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def block(ni, nf):\n",
    "    return nn.Sequential(ResBlock(ni, nf, stride=2), ResBlock(nf, nf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.964076</td>\n",
       "      <td>1.864578</td>\n",
       "      <td>0.355159</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.636880</td>\n",
       "      <td>1.596789</td>\n",
       "      <td>0.502675</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.335378</td>\n",
       "      <td>1.304472</td>\n",
       "      <td>0.588535</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.089160</td>\n",
       "      <td>1.065063</td>\n",
       "      <td>0.663185</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.942904</td>\n",
       "      <td>0.963589</td>\n",
       "      <td>0.692739</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learner(get_model())\n",
    "learn.fit_one_cycle(5, 3e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're making good progress!\n",
    "\n",
    "The authors of the ResNet paper went on to win the 2015 ImageNet challenge. At the time, this was by far the most important annual event in computer vision. We have already seen another ImageNet winner: the 2013 winners, Zeiler and Fergus. It is interesting to note that in both cases the starting point for the breakthroughs were experimental observations. Observations about what layers actually learn, in the case of Zeiler and Fergus, and observations about which kind of networks can be trained, in the case of the ResNet authors. This ability to design and analyse thoughtful experiments, or even just to see an unexpected result say \"hmmm, that's interesting\" — and then, most importantly, to figure out what on earth is going on, with great tenacity, is at the heart of many scientific discoveries. Deep learning is not like pure mathematics. It is a heavily experimental field, so it's important to be strong practitioner, not just a theoretician.\n",
    "\n",
    "Since the ResNet was introduced, there's been many papers studying it and applying it to many domains. One of the most interesting, published in 2018, is [Visualizing the Loss Landscape of Neural Nets](https://arxiv.org/abs/1712.09913). It shows that using skip connections help smoothen the loss function, which makes training easier as it avoids us falling into a very sharp area. Here's a stunning picture from the paper, showing the bumpy terrain that SGD has to navigate to optimize a regular CNN (left) versus the smooth surface of a ResNet (right):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Impact of ResNet on loss landscape\" width=\"600\" caption=\"Impact of ResNet on loss landscape\" id=\"resnet_surface\" src=\"images/att_00044.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A state-of-the-art ResNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://arxiv.org/abs/1812.01187), the authors study different variations of the ResNet architecture that come at almost no additional cost in terms of number of parameters or computation. By using this tweaked ResNet50 architecture and Mixup they achieve 94.6% top-5 accuracy on ImageNet, instead of 92.2% with a regular ResNet50 without Mixup. This result is better than regular ResNet models that are twice as deep (and twice as slow, and much more likely to overfit)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> jargon: top-5 accuracy: A metric testing how often the label we want is in the top 5 predictions of our model. It was used in the Imagenet competition, since many images contained multiple objects, or contained objects that could be easily confused or may even have been mislabeled with a similar label. In these situations, looking at top-1 accuracy may be inappropriate. However, recently CNNs have been getting so good that top-5 accuracy is nearly 100%, so some researchers are using top-1 accuracy for Imagenet too now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, as we scale up to the full ResNet, we won't show the original one, but the tweaked one, since it's substantially better. It differs a little bit from our the implementation we had before in that it begins with a few convolutional layers followed by a max pooling layer, instead of just starting with ResNet blocks. This is what the first layers look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _resnet_stem(*sizes):\n",
    "    return [\n",
    "        ConvLayer(sizes[i], sizes[i+1], 3, stride = 2 if i==0 else 1)\n",
    "            for i in range(len(sizes)-1)\n",
    "    ] + [nn.MaxPool2d(kernel_size=3, stride=2, padding=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ConvLayer(\n",
       "   (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "   (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (2): ReLU()\n",
       " ), ConvLayer(\n",
       "   (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (2): ReLU()\n",
       " ), ConvLayer(\n",
       "   (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (2): ReLU()\n",
       " ), MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_output\n",
    "_resnet_stem(3,32,32,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "[ConvLayer(\n",
    "   (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "   (1): BatchNorm2d(32, eps=1e-05, momentum=0.1)\n",
    "   (2): ReLU()\n",
    " ), ConvLayer(\n",
    "   (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "   (1): BatchNorm2d(32, eps=1e-05, momentum=0.1)\n",
    "   (2): ReLU()\n",
    " ), ConvLayer(\n",
    "   (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "   (1): BatchNorm2d(64, eps=1e-05, momentum=0.1)\n",
    "   (2): ReLU()\n",
    " ), MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False)]\n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> jargon: Stem: The stem of a CNN are its first few layers. Generally, the stem has a different structure to the main body of the CNN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reason that we have a stem of plain convolutional layers, instead of ResNet blocks, is based on a very important insight about all deep convolutional neural networks: the vast majority of the computation occurs in the early layers. Therefore, we should keep the early layers as fast and simple as possible.\n",
    "\n",
    "To see why so much computation occurs in the early layers, consider the very first convolution on a 128 pixel input image. If it is a stride one convolution, then it will apply the kernel to every one of the 128×128 pixels. That's a lot of work! In the later layers, however, the grid size could be as small as 4x4 or even 2x2. So there are far fewer kernel applications to do.\n",
    "\n",
    "On the other hand, the first layer convolution only has three input features, and 32 output features. Since it is a 3x3 kernel, this is 3×32×3×3 = 864 parameters in the weights. On the other hand, the last convolution will be 256 input features and 512 output features, which will be 1,179,648 weights! So the first layers contain vast majority of the computation, but the last layers contain the vast majority of the parameters.\n",
    "\n",
    "A ResNet block takes more computation than a plain convolutional block, since (in the stride two case) a ResNet block has three convolutions and a pooling layer. That's why we want to have plain convolutions to start off our ResNet.\n",
    "\n",
    "We're now ready to show the implementation of a modern ResNet, with the \"bag of tricks\". The ResNet use four groups of ResNet blocks, with 64, 128, 256 then 512 filters. Each groups starts with a stride 2 block, except for the first one, since it's just after a `MaxPooling` layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResNet(nn.Sequential):\n",
    "    def __init__(self, n_out, layers, expansion=1):\n",
    "        stem = _resnet_stem(3,32,32,64)\n",
    "        self.block_szs = [64, 64, 128, 256, 512]\n",
    "        for i in range(1,5): self.block_szs[i] *= expansion\n",
    "        blocks = [self._make_layer(*o) for o in enumerate(layers)]\n",
    "        super().__init__(*stem, *blocks,\n",
    "                         nn.AdaptiveAvgPool2d(1), Flatten(),\n",
    "                         nn.Linear(self.block_szs[-1], n_out))\n",
    "    \n",
    "    def _make_layer(self, idx, n_layers):\n",
    "        stride = 1 if idx==0 else 2\n",
    "        ch_in,ch_out = self.block_szs[idx:idx+2]\n",
    "        return nn.Sequential(*[\n",
    "            ResBlock(ch_in if i==0 else ch_out, ch_out, stride if i==0 else 1)\n",
    "            for i in range(n_layers)\n",
    "        ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `_make_layer` function is just there to create a series of `nl` blocks. The first one is is going from `ch_in` to `ch_out` with the indicated `stride` and all the others are blocks of stride 1 with `ch_out` to `ch_out` tensors. Once the blocks are defined, our model is purely sequential, which is why we define it as a subclass of `nn.Sequential`. (Ignore the `expansion` parameter for now--we'll discuss it in the next section' for now, it'll be `1`, so it doesn't do anything.)\n",
    "\n",
    "The various versions of the models (ResNet 18, 34, 50, etc) just change the number of blocks in each of those groups. This is the definition of a ResNet18:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rn = ResNet(dls.c, [2,2,2,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train it for a little bit and see how it fares compared to the previous model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.673882</td>\n",
       "      <td>1.828394</td>\n",
       "      <td>0.413758</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.331675</td>\n",
       "      <td>1.572685</td>\n",
       "      <td>0.518217</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.087224</td>\n",
       "      <td>1.086102</td>\n",
       "      <td>0.650701</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.900428</td>\n",
       "      <td>0.968219</td>\n",
       "      <td>0.684331</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.760280</td>\n",
       "      <td>0.782558</td>\n",
       "      <td>0.757197</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learner(rn)\n",
    "learn.fit_one_cycle(5, 3e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even although we have more channels (and our model is therefore even more accurate), our training is just as fast as before, thanks to our optimized stem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bottleneck layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things are a tiny bit more complicated for deeper models like `resnet50` as they don't use the same resnet blocks: instead of stacking two convolutions with a kernel size of 3, they use three different convolutions: two 1x1 (at the beginning and the end) and one 3x3, as shown in the right of this image from the ResNet paper (using an example of 64 channel output, comparing to the regular ResBlock on the left):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Comparison of regular and bottleneck ResNet blocks\" width=\"550\" caption=\"Comparison of regular and bottleneck ResNet blocks\" id=\"resnet_compare\" src=\"images/att_00045.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why? 1x1 convolutions are much faster, so even if this seems to be a more complex design, this block executes faster than the first resnet block we saw. This then lets us use more filters: as we see on the illustration, the number of filters in and out is 4 times higher (256) and the 1 by 1 convs are here to diminish then restore the number of channels (hence the name bottleneck). The overall impact is that we can use more filters in the same amount of time.\n",
    "\n",
    "Let's try replacing our ResBlock with this bottleneck design:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _conv_block(ni,nf,stride):\n",
    "    return nn.Sequential(\n",
    "        ConvLayer(ni, nf//4, 1),\n",
    "        ConvLayer(nf//4, nf//4, stride=stride), \n",
    "        ConvLayer(nf//4, nf, 1, act_cls=None, norm_type=NormType.BatchZero))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use this to create a ResNet50, which uses this bottleneck block, and uses group sizes of `(3,4,6,3)`. We now need to pass `4` in to the `expansion` parameter of `ResNet`, since we need to start with four times less channels, and we'll end with four times more channels.\n",
    "\n",
    "Deeper networks like this don't generally show improvements when training for only 5 epochs, so we'll bump it up to 20 epochs this time to make the most of our bigger model. And to really get great results, let's use bigger images too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = get_data(URLs.IMAGENETTE_320, presize=320, resize=224)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't have to do anything to account for the larger 224 pixel images--thanks to our fully convolutional network, it just works. This is also why we were able to do *progressive resizing* earlier in the book--the models we used were fully convolutional, so we were even able to fine-tune models trained with different sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rn = ResNet(dls.c, [3,4,6,3], 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.613448</td>\n",
       "      <td>1.473355</td>\n",
       "      <td>0.514140</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.359604</td>\n",
       "      <td>2.050794</td>\n",
       "      <td>0.397452</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.253112</td>\n",
       "      <td>4.511735</td>\n",
       "      <td>0.387006</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.133450</td>\n",
       "      <td>2.575221</td>\n",
       "      <td>0.396178</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.054752</td>\n",
       "      <td>1.264525</td>\n",
       "      <td>0.613758</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.927930</td>\n",
       "      <td>2.670484</td>\n",
       "      <td>0.422675</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.838268</td>\n",
       "      <td>1.724588</td>\n",
       "      <td>0.528662</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.748289</td>\n",
       "      <td>1.180668</td>\n",
       "      <td>0.666497</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.688637</td>\n",
       "      <td>1.245039</td>\n",
       "      <td>0.650446</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.645530</td>\n",
       "      <td>1.053691</td>\n",
       "      <td>0.674904</td>\n",
       "      <td>00:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.593401</td>\n",
       "      <td>1.180786</td>\n",
       "      <td>0.676433</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.536634</td>\n",
       "      <td>0.879937</td>\n",
       "      <td>0.713885</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.479208</td>\n",
       "      <td>0.798356</td>\n",
       "      <td>0.741656</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.440071</td>\n",
       "      <td>0.600644</td>\n",
       "      <td>0.806879</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.402952</td>\n",
       "      <td>0.450296</td>\n",
       "      <td>0.858599</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.359117</td>\n",
       "      <td>0.486126</td>\n",
       "      <td>0.846369</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.313642</td>\n",
       "      <td>0.442215</td>\n",
       "      <td>0.861911</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.294050</td>\n",
       "      <td>0.485967</td>\n",
       "      <td>0.853503</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.270583</td>\n",
       "      <td>0.408566</td>\n",
       "      <td>0.875924</td>\n",
       "      <td>00:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.266003</td>\n",
       "      <td>0.411752</td>\n",
       "      <td>0.872611</td>\n",
       "      <td>00:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learner(rn)\n",
    "learn.fit_one_cycle(20, 3e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're getting a great result now! Try adding Mixup, and then training this for a hundred epochs while you go get lunch. You'll have yourself a very accurate image classifier, trained from scratch.\n",
    "\n",
    "The bottleneck design we've shown here is only used in ResNet50, 101, and 152 in all official models we've seen. ResNet18 and 34 use the non-bottleneck design seen in the previous section. However, we've noticed that the bottleneck layer generally works better even for the shallower networks. This just goes to show that the little details in papers tend to stick around for years, even if they're actually not quite the best design! Questioning assumptions and \"stuff everyone knows\" is always a good idea, because this is still a new field, and there's lots of details that aren't always done well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questionnaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. How did we get to a single vector of activations in the convnets used for MNIST in previous chapters? Why isn't that suitable for Imagenette?\n",
    "1. What do we do for Imagenette instead?\n",
    "1. What is adaptive pooling?\n",
    "1. What is average pooling?\n",
    "1. Why do we need `Flatten` after an adaptive average pooling layer?\n",
    "1. What is a skip connection?\n",
    "1. Why do skip connections allow us to train deeper models?\n",
    "1. What does <<resnet_depth>> show? How did that lead to the idea of skip connections?\n",
    "1. What is an identity mapping?\n",
    "1. What is the basic equation for a resnet block (ignoring batchnorm and relu layers)?\n",
    "1. What do ResNets have to do with \"residuals\"?\n",
    "1. How do we deal with the skip connection when there is a stride 2 convolution? How about when the number of filters changes?\n",
    "1. How can we express a 1x1 convolution in terms of a vector dot product?\n",
    "1. What does the `noop` function return?\n",
    "1. Explain what is shown in <<resnet_surface>>.\n",
    "1. When is top-5 accuracy a better metric than top-1 accuracy?\n",
    "1. What is the stem of a CNN?\n",
    "1. Why use plain convs in the CNN stem, instead of resnet blocks?\n",
    "1. How does a bottleneck block differ from a plain resnet block?\n",
    "1. Why is a bottleneck block faster?\n",
    "1. How do fully convolution nets (and nets with adaptive pooling in general) allow for progressive resizing?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Try creating a fully convolutional net with adaptive average pooling for MNIST (note that you'll need fewer stride 2 layers). How does it compare to a network without such a pooling layer?\n",
    "1. In <<chapter_foundations>> we introduce *Einstein summation notation*. Skip ahead to see how this works, and then write an implementation of the 1x1 convolution operation using `torch.einsum`. Compare it to the same operation using `torch.conv2d`.\n",
    "1. Write a \"top 5 accuracy\" function using plain PyTorch or plain Python.\n",
    "1. Train a model on Imagenette for more epochs, with and without label smoothing. Take a look at the Imagenette leaderboards and see how close you can get to the best results shown. Read the linked pages describing the leading approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s"
   ]
  }
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