08_pytorch_paper_replicating.ipynb

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    "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/08_pytorch_paper_replicating.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "# (WIP) 08. PyTorch Paper Replicating\n",
    "\n",
    "TK intro\n",
    "\n",
    "Want to recreate ViT paper: \"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale\" - https://arxiv.org/abs/2010.11929 - TK will refer to this as \"ViT paper\" throughout.\n",
    "\n",
    "* TK what is ViT?\n",
    "\n",
    "TK - The name Transformer comes from the architecture name in the paper where it was originally introduced, [*Attention is all you need*](https://arxiv.org/abs/1706.03762). An architecture is usually considered a Transformer variant if it uses attention layers in a specific pattern. Since the Transformer architecture originally focused on text data, the goal of the ViT paper was to bring it to the vision.\n",
    "\n",
    "TK - The original transformer was made to work on sequences of text (1D), Vision Transformer turns images into sequences of \"patches\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb53b99-277c-4ac0-a1fb-283c47576be1",
   "metadata": {},
   "source": [
    "## TK - What is paper replicating?\n",
    "\n",
    "It's no secret machine learning is advancing fast.\n",
    "\n",
    "Many of these advances get published in machine learning research papers.\n",
    "\n",
    "And the goal of **paper replicating** is to take replicate these advances with code so you can use the techniques for your own problem.\n",
    "\n",
    "For example, let's say a new model architecture gets released that performs better than any other architecture before on various benchmarks, wouldn't it be nice to try that architecture on your own problems?\n",
    "\n",
    "* TK image: paper replicating = research paper -> code (translate a research paper into usable code)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966b353c-c9d8-4568-ad64-c0df45a39442",
   "metadata": {},
   "source": [
    "## TK - What is a machine learning research paper?\n",
    "\n",
    "A machine learning research paper is a scientific paper that details findings of a research group on a specific area.\n",
    "\n",
    "The contents of a machine learning research paper can vary from paper to paper but they generally follow the structure:\n",
    "\n",
    "| **Section** | **Contents** |\n",
    "| ----- | ----- | \n",
    "| **Abstract** | An overview/summary of the paper's main findings/contributions. |\n",
    "| **Introduction** | What's the paper's main problem and what are previous methods used to try and solve it? |\n",
    "| **Method** | How did the researchers go about conducting their research? For example, what model(s) were used, data sources, training setups, etc. |\n",
    "| **Results** | What are the outcomes of the paper? If a new type of model or training setup was used, how did the results of findings compare to previous works (this is where **experiment tracking** comes in handy)? |\n",
    "| **Conclusion** | What are the limitations of the suggested methods? What are some next steps for the research community? |\n",
    "| **References** | What resources/other papers did the researchers look at to build their own body of work? |\n",
    "| **Appendix** | Are there any extra resources/findings to look at that weren't included in any of the above sections? |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ce67f6-0b00-448b-885d-b7d22bce4ff6",
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   "source": [
    "## TK - Why replicate a machine learning research paper?\n",
    "\n",
    "A machine learning research paper is often a presentation of months of work and experiments done by some of the best machine learning teams in the world condensed into a few pages of text.\n",
    "\n",
    "And if these experiments lead to better results in an area related to the problem you're working on, it'd be nice to them out.\n",
    "\n",
    "Also, replicating the work of others is a fantastic way to practice your skills.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-george-hotz-quote.png\" width=600 alt=\"george hotz quote saying to get better at being a machine learning engineer, download a paper, implement it and keep going until you have skills\"/>\n",
    "\n",
    "*George Hotz is founder of [comma.ai](https://comma.ai/), a self-driving car company and livestreams machine learning coding on [Twitch](https://www.twitch.tv/georgehotz) and those videos get posted in full to [YouTube](https://www.youtube.com/c/georgehotzarchive). I pulled this quote from one of his livestreams. The \"٭\" is to note that machine learning engineering often involves the extra step(s) of preprocessing data and making your models available for others to use (deployment).*\n",
    "    \n",
    "When you first start trying to replicate research papers, you'll likely be overwhelmed.\n",
    "\n",
    "That's normal.\n",
    "\n",
    "Research teams spend weeks, months and sometimes years creating these works so it makes sense if it takes you sometime to even read let alone reproduce the works.\n",
    "\n",
    "Replicating research is such a tough problem, phenomenal machine learning libraries and tools such as, [HuggingFace](https://huggingface.co/), [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models) (`timm` library) and [fast.ai](https://www.fast.ai/) have been born out of making machine learning research more accessible. "
   ]
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   "cell_type": "markdown",
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   "source": [
    "## TK - Where can you find code examples for machine learning research papers?\n",
    "\n",
    "One of the first things you'll notice when it comes to machine learning research is: there's a lot of it.\n",
    "\n",
    "So beware, trying to stay on top of it is like trying to outrun a hamster wheel.\n",
    "\n",
    "Follow your interest, pick a few things that stand out to you.\n",
    "\n",
    "In saying this, there are several places to find and read machine learning research papers:\n",
    "* [arXiv](https://arxiv.org/) - Pronounced \"archive\", arXiv is a free and open resource for reading technical articles on everything from physics to computer science (inlcuding machine learning).\n",
    "* [Papers with Code](https://paperswithcode.com/) - A curated collection of trending, active and greatest machine learning papers, many of which include code resources attached. Also includes a collection of common machine learning datasets, benchmarks and current state-of-the-art models.\n",
    "* [AK Twitter](https://twitter.com/ak92501) - The AK Twitter account publishes machine learning research highlights, often with live demos almost every day. I don't understand 9/10 posts but I find it fun to explore every so often.\n",
    "* [lucidrains' `vit-pytorch` GitHub repository](https://github.com/lucidrains/vit-pytorch) - Less of a place to find research papers and more of an example of what paper replicating with code on a larger-scale looks like. The `vit-pytorch` repository is a collection of Vision Transformer model architectures from various research papers replicated with PyTorch code (much of the inspiration for this notebook was gathered from this repository). \n",
    "\n",
    "TK image: showcase the above"
   ]
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    "## TK - What we're going to cover\n",
    "\n",
    "TODO\n",
    "\n",
    "* ViT -> FoodVision Mini\n",
    "* Layers = collections of functions to manipulate data -> Architectures = collections of layers (blocks) -> All layers (and blocks) have inputs and outputs\n",
    "    * Replicating research papers starts by figuring out the inputs and outputs of your layers -> blocks -> model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf677bb7-719a-447e-a8e8-c4f287146b62",
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   "source": [
    "## TK - Where can you get help?\n",
    "\n",
    "All of the materials for this course [are available on GitHub](https://github.com/mrdbourke/pytorch-deep-learning).\n",
    "\n",
    "If you run into trouble, you can ask a question on the course [GitHub Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions).\n",
    "\n",
    "And of course, there's the [PyTorch documentation](https://pytorch.org/docs/stable/index.html) and [PyTorch developer forums](https://discuss.pytorch.org/), a very helpful place for all things PyTorch. "
   ]
  },
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   "cell_type": "markdown",
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    "## TK 0. Getting setup \n",
    "\n",
    "As we've done previously, let's make sure we've got all of the modules we'll need for this section.\n",
    "\n",
    "We'll import the Python scripts (such as `data_setup.py` and `engine.py`) we created in [05. PyTorch Going Modular](https://www.learnpytorch.io/05_pytorch_going_modular/).\n",
    "\n",
    "To do so, we'll download [`going_modular`](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/going_modular) directory from the `pytorch-deep-learning` repository (if we don't already have it).\n",
    "\n",
    "We'll also get the [`torchinfo`](https://github.com/TylerYep/torchinfo) package if it's not available. \n",
    "\n",
    "`torchinfo` will help later on to give us a visual representation of our model.\n",
    "\n",
    "And since later on we'll be using a newer version of the `torchvision` package (as of June 2022), we'll make sure we've got the latest versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebe46d77-6c4d-4102-9994-2cb89f633f18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch version: 1.12.0+cu102\n",
      "torchvision version: 0.13.0+cu102\n"
     ]
    }
   ],
   "source": [
    "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
    "try:\n",
    "    import torch\n",
    "    import torchvision\n",
    "    assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
    "    assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
    "    print(f\"torch version: {torch.__version__}\")\n",
    "    print(f\"torchvision version: {torchvision.__version__}\")\n",
    "except:\n",
    "    print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
    "    !pip3 install -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu113\n",
    "    import torch\n",
    "    import torchvision\n",
    "    print(f\"torch version: {torch.__version__}\")\n",
    "    print(f\"torchvision version: {torchvision.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30caf875-557e-410f-8dff-bd4a9f6c7ae4",
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    "> **Note:** If you're using Google Colab, you may have to restart your runtime after running the above cell. After restarting, you can run the cell again and verify you've got the right versions of `torch` and `torchvision`.\n",
    "\n",
    "Now we'll continue with the regular imports, setting up device agnostic code and this time we'll also get the [`helper_functions.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) script from GitHub.\n",
    "\n",
    "The `helper_functions.py` script contains several functions we created in previous sections:\n",
    "* `set_seeds()` to set the random seeds (created in [07. PyTorch Experiment Tracking section 0](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#create-a-helper-function-to-set-seeds)).\n",
    "* `download_data()` to download a data source given a link (created in [07. PyTorch Experiment Tracking section 1](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#1-get-data)).\n",
    "* `plot_loss_curves()` to inspect our model's training results (created in [04. PyTorch Custom Datasets section 7.8](https://www.learnpytorch.io/04_pytorch_custom_datasets/#78-plot-the-loss-curves-of-model-0))\n",
    "\n",
    "> **Note:** It may be a better idea for many of the functions in the `helper_functions.py` script to be merged into `going_modular/going_modular/utils.py`, perhaps that's an extension you'd like to try.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "960eb156-c1b1-4e76-a812-01bf045835bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Continue with regular imports\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from torch import nn\n",
    "from torchvision import transforms\n",
    "\n",
    "# Try to get torchinfo, install it if it doesn't work\n",
    "try:\n",
    "    from torchinfo import summary\n",
    "except:\n",
    "    print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
    "    !pip install -q torchinfo\n",
    "    from torchinfo import summary\n",
    "\n",
    "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
    "try:\n",
    "    from going_modular.going_modular import data_setup, engine\n",
    "    from helper_functions import download_data, set_seeds, plot_loss_curves\n",
    "except:\n",
    "    # Get the going_modular scripts\n",
    "    print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
    "    !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
    "    !mv pytorch-deep-learning/going_modular .\n",
    "    !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
    "    !rm -rf pytorch-deep-learning\n",
    "    from going_modular.going_modular import data_setup, engine\n",
    "    from helper_functions import download_data, set_seeds, plot_loss_curves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f9bdd26-26ac-4756-bd8e-b7a50799f28b",
   "metadata": {},
   "source": [
    "> **Note:** If you're using Google Colab, and you don't have a GPU turned on yet, it's now time to turn one on via `Runtime -> Change runtime type -> Hardware accelerator -> GPU`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e246f92-e509-474e-b6c7-c82cf11cb8ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a695192-2644-4aa7-beab-7222a24b1a1a",
   "metadata": {},
   "source": [
    "## TK 1. Get Data\n",
    "\n",
    "Since we're continuing on with FoodVision Mini, let's download the pizza, steak and sushi image dataset we've been using.\n",
    "\n",
    "To do so we can use the `download_data()` function from `helper_functions.py` that we created in [07. PyTorch Experiment Tracking section 1](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#1-get-data).\n",
    "\n",
    "We'll `source` to the raw GitHub link of the [`pizza_steak_sushi.zip` data](https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip) and the `destination` to `pizza_steak_sushi`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37b5ffc0-7093-481e-8081-dbdfac4c24f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('data/pizza_steak_sushi')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download pizza, steak, sushi images from GitHub\n",
    "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
    "                           destination=\"pizza_steak_sushi\")\n",
    "image_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a047b1-9f12-4dcf-8d97-83b7cbb37392",
   "metadata": {},
   "source": [
    "Beautiful! Data downloaded, let's setup the training and test directories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92a426b6-df22-4a58-9d5e-b382c73c6048",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup directory paths to train and test images\n",
    "train_dir = image_path / \"train\"\n",
    "test_dir = image_path / \"test\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e84624-6af8-4231-b606-3eabffd172db",
   "metadata": {},
   "source": [
    "## TK 2. Create Datasets and DataLoaders\n",
    "\n",
    "Since we've got some data, let's now turn it into `DataLoader`'s.\n",
    "\n",
    "To do so we can use the `create_dataloaders()` function in [`data_setup.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/data_setup.py).\n",
    "\n",
    "First, we'll create a transform to prepare our images.\n",
    "\n",
    "This where one of the first references to the ViT paper will come in.\n",
    "\n",
    "In Table 3, the training resolution is mentioned as being 224 (height=224, width=224). \n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-image-size-and-batch-size.png\" width=900 alt=\"Table 3 from the Vision Transformer paper showing the image size and batch size\"/>\n",
    "\n",
    "*You can often find various hyperparameter settings listed in a table. In this case we're still preparing our data, so we're mainly concerned with things like image size and batch size. Source: Table 3 in [ViT paper](https://arxiv.org/abs/2010.11929).*\n",
    "\n",
    "So we'll make sure our transform resizes our images appropriately.\n",
    "\n",
    "And since we'll be training our model from scratch (no transfer learning to begin with), we won't provide a `normalize` transform like we did in [06. PyTorch Transfer Learning section 2.1](https://www.learnpytorch.io/06_pytorch_transfer_learning/#21-creating-a-transform-for-torchvisionmodels-manual-creation).\n",
    "\n",
    "### 2.1 Prepare transforms for images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a45ea650-c3fa-479c-8767-48bc3a1f1267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manually created transforms: Compose(\n",
      "    Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
      "    ToTensor()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Create image size (from Table 3 in the ViT paper) \n",
    "IMG_SIZE = 224\n",
    "\n",
    "# Create transform pipeline manually\n",
    "manual_transforms = transforms.Compose([\n",
    "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
    "    transforms.ToTensor(),\n",
    "])           \n",
    "print(f\"Manually created transforms: {manual_transforms}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "437078c2-eb42-471f-8561-94845d0a878d",
   "metadata": {},
   "source": [
    "### 2.2 Turn images into `DataLoader`'s\n",
    "Transforms created!\n",
    "\n",
    "Let's now create our `DataLoader`'s.\n",
    "\n",
    "The ViT paper states the use of a batch size of 4096 which is 128x the size of the batch size we've been using (32).\n",
    "\n",
    "We're going to stick with a batch size of 32.\n",
    "\n",
    "Why?\n",
    "\n",
    "Because some hardware (including the free tier of Google Colab) may not be able to handle a batch size of 4096.\n",
    "\n",
    "Having a batch size of 4096 means that 4096 images need to fit into the GPU memory at a time.\n",
    "\n",
    "This works when you've got the hardware to handle it like a research team from Google often does but when you're running on a single GPU (such as using Google Colab), making sure things work with smaller batch size first is a good idea.\n",
    "\n",
    "An extension of this project could be to try a higher batch size value and see what happens.\n",
    "\n",
    "> **Note:** We're using the `pin_memory=True` parameter in the `create_dataloaders()` function to speed up computation. `pin_memory=True` avoids unnecessary copying of memory between the CPU and GPU memory by \"pinning\" examples that have been seen before. For more on this concept. Though the benefits of this will likely be seen with larger dataset sizes (our FoodVision Mini dataset is quite small). See the PyTorch [`torch.utils.data.DataLoader` documentation](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) or [Making Deep Learning Go Brrrr from First Principles](https://horace.io/brrr_intro.html) by Horace He for more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0ac8145-f89a-490f-82e3-d4b22225d163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<torch.utils.data.dataloader.DataLoader at 0x7f26746c80a0>,\n",
       " <torch.utils.data.dataloader.DataLoader at 0x7f2674408790>,\n",
       " ['pizza', 'steak', 'sushi'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the batch size\n",
    "BATCH_SIZE = 32 # this is lower than the ViT paper but it's because we're starting small\n",
    "\n",
    "# Create data loaders\n",
    "train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(\n",
    "    train_dir=train_dir,\n",
    "    test_dir=test_dir,\n",
    "    transform=manual_transforms, # use manually created transforms\n",
    "    batch_size=BATCH_SIZE\n",
    ")\n",
    "\n",
    "train_dataloader, test_dataloader, class_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a980a5b-0c3b-440d-87f5-c54f1ab143ab",
   "metadata": {},
   "source": [
    "### TK 2.3 Visualize a single image\n",
    "\n",
    "Now we've loaded our data, let's *visualize, visualize, visualize!*\n",
    "\n",
    "An important step in the ViT paper is preparing the images into patches.\n",
    "\n",
    "We'll get to what this means in a second but for now, let's view a single image and its label.\n",
    "\n",
    "To do so, let's get a single image and label from a batch of data and inspect their shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5734a22-ded5-403e-84f5-d7a90ed3f085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([3, 224, 224]), tensor(2))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a batch of images\n",
    "image_batch, label_batch = next(iter(train_dataloader))\n",
    "\n",
    "# Get a single image from the batch\n",
    "image, label = image_batch[0], label_batch[0]\n",
    "\n",
    "# View the batch shapes\n",
    "image.shape, label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898cbb6d-b433-41be-9280-41de129077df",
   "metadata": {},
   "source": [
    "Wonderful!\n",
    "\n",
    "Now let's plot the image and its label with `matplotlib`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "afe85fae-38fd-4f34-a52c-29d02cce09c1",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot image with matplotlib\n",
    "plt.imshow(image.permute(1, 2, 0)) # rearrange image dimensions to suit matplotlib [color_channels, height, width] -> [height, width, color_channels]\n",
    "plt.title(class_names[label])\n",
    "plt.axis(False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8416fa-fb0c-4276-8405-12531ba78b71",
   "metadata": {},
   "source": [
    "Nice!\n",
    "\n",
    "Looks like our images are importing correctly, let's continue with the paper replication."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13bfb028-1afa-44ec-89e9-8890975311ea",
   "metadata": {},
   "source": [
    "## TK 3. Replicating the ViT paper: an overview\n",
    "\n",
    "Before we write anymore code, let's discuss what we're doing.\n",
    "\n",
    "We'd like to replicate the ViT paper for our own problem, FoodVision Mini.\n",
    "\n",
    "So our inputs are: images of pizza, steak and sushi.\n",
    "\n",
    "And our ideal model outputs are: predicted labels of pizza, steak or sushi.\n",
    "\n",
    "No different to what we've been doing throughout the previous sections.\n",
    "\n",
    "The question is: how do we go from our inputs to the desired outputs?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e7f0a12-cce9-45d0-a572-4ad612a98735",
   "metadata": {},
   "source": [
    "### 3.1 Inputs and outputs, layers and blocks\n",
    "\n",
    "ViT is a deep learning neural network architecture.\n",
    "\n",
    "And any neural network architecture is generally comprised of **layers**.\n",
    "\n",
    "And a collection of layers is often referred to as a **block**.\n",
    "\n",
    "And stacking many blocks together is what gives us the whole architecture.\n",
    "\n",
    "A **layer** takes an input (say an image tensor), performs some kind of function on it (for example what's in the layer's `forward()` method) and then returns an output.\n",
    "\n",
    "So if a **single layer** takes an input and gives an output, then a collection of layers or a **block** also takes an input and gives an output.\n",
    "\n",
    "Let's make this concrete:\n",
    "* **Layer** - takes an input, performs a function on it, returns an output.\n",
    "* **Block** - a collection of layers, takes an input, performs a series of functions on it, returns an output.\n",
    "* **Architecture (or model)** - a collection of blocks, takes an input, performs a series of functions on it, returns an output.\n",
    "\n",
    "This ideology is what we're going to be using to replicate the ViT paper.\n",
    "\n",
    "We're going to take it layer by layer, block by block, function by function putting the pieces of the puzzle together like Lego to get our desired overall architecture.\n",
    "\n",
    "The reason we do this is because looking at a whole research paper can be intimidating.\n",
    "\n",
    "So for a better understanding, we'll break it down, starting with the inputs and outputs of single layer and working up to the inputs and outputs of the whole model.\n",
    "\n",
    "TK image: stacking the network together like lego (functions + layers + blocks = model)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2852f3f-61f0-4dad-ae8c-49db54e28470",
   "metadata": {},
   "source": [
    "### 3.2 Getting specific: What's ViT made of?\n",
    "\n",
    "There are many little details about the ViT model sprinkled throughout the paper.\n",
    "\n",
    "Finding them all is like one big treasure hunt!\n",
    "\n",
    "Remember, a research paper is often months of work compressed into a few pages so it's understandable for it to take of practice to replicate.\n",
    "\n",
    "However, the main three resources we'll be looking at for the architecture design are:\n",
    "1. **Figure 1** - This gives an overview of the model in a graphical sense, you could *almost* recreate the architecture with this figure alone.\n",
    "2. **Four equations in section 3.1** - These equations give a little bit more of a mathematical grounding to the coloured blocks in Figure 1.\n",
    "3. **Table 1** - This table shows the various hyperparameter settings (such as number of layers and number of hidden units) for different ViT model variants. We'll be focused on the smallest version, ViT-Base."
   ]
  },
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   "id": "bac7c7d2-e58b-47b1-ae3e-7ae9f922326d",
   "metadata": {},
   "source": [
    "#### TK 3.2.1 Exploring Figure 1\n",
    "\n",
    "Let's start by going through Figure 1 of the ViT Paper.\n",
    "\n",
    "The main things we'll be paying attention to are:\n",
    "1. **Layers** - takes an **input**, performs an operation or function, produces an **output**.\n",
    "2. **Blocks** - a collection of layers, which in turn also takes an **input** and produces an **output**.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-figure-1-inputs-and-outputs.png\" width=900 alt=\"figure 1 from the original vision transformer paper\"/>\n",
    "\n",
    "*Figure 1 from the ViT Paper showcasing the different inputs, outputs, layers and blocks that create the architecture. Our goal will be to replicate each of these using PyTorch code.* \n",
    "\n",
    "The ViT architecture is comprised of several stages:\n",
    "* **Patch + Position Embedding (inputs)** - Turns the input image into a sequence of image patches and add a position number what order the patch comes in.\n",
    "* **Linear projection of flattened patches (Embedded Patches)** - The image patches get turned into an **embedding**, the benefit of using an embedding rather than just the image values is that an embedding is a *learnable* representation (typically in the form of a vector) of the image that can improve with training.\n",
    "* **Norm** - This is short for \"[Layer Normalization](https://paperswithcode.com/method/layer-normalization)\" or \"LayerNorm\", a technique for regularizing (reducing overfitting) a neural network, you can use LayerNorm via the PyTorch layer [`torch.nn.LayerNorm()`](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
    "* **Multi-Head Attention** - This is a [Multi-Headed Self-Attention layer](https://paperswithcode.com/method/multi-head-attention) or \"MSA\" for short. You can create an MSA layer via the PyTorch layer [`torch.nn.MultiheadAttention()`](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html).\n",
    "* **MLP (or [Multilayer perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron))** - A MLP can often refer to any collection of feedforward layers (or in PyTorch's case, a collection of layers with a `forward()` method). In the ViT Paper, the authors refer to the MLP as \"MLP block\" and it contains two [`torch.nn.Linear()`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layers with a [`torch.nn.GELU()`](https://pytorch.org/docs/stable/generated/torch.nn.GELU.html) non-linearity activation in between them (section 3.1) and a [`torch.nn.Dropout()`](https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html) layer after each (Appendex B.1). \n",
    "* **Transformer Encoder** - The Transformer Encoder, is a collection of the layers listed above. There are two skip connections inside the Transformer encoder (the \"+\" symbols) meaning the layer's inputs are fed directly to immediate layers as well as subsequent layers. The overall ViT architecture is comprised of a number of Transformer encoders stacked on top of eachother.\n",
    "* **MLP Head** - This is the output layer of the architecture, it converts the learned features of an input to a class output. Since we're working on image classification, you could also call this the \"classifier head\". The structure of the MLP Head is similar to the MLP block.\n",
    "\n",
    "You might notice that many of the pieces of the ViT architecture can be created with existing PyTorch layers.\n",
    "\n",
    "This is because of how PyTorch is designed, it's one of the main purposes of PyTorch to create reusable neural network layers for both researchers and machine learning practitioners.\n",
    "\n",
    "> **Question:** Why not code everything from scratch?\n",
    ">\n",
    "> You could definitely do that by reproducing all of the math equations from the paper with custom PyTorch layers and that would certainly be an educative exercise, however, using pre-existing PyTorch layers is usually favoured as pre-existing layers have often been extensively tested and performance checked to make sure they run correctly and fast. \n",
    "\n",
    "> **Note:** We're going to focused on write PyTorch code to create these layers, for the background on what each of these layers does, I'd suggest reading the ViT Paper in full or reading the linked resources for each layer.\n",
    "\n",
    "Let's take Figure 1 and adapt it to our FoodVision Mini problem of classifying images of food into pizza, steak or sushi.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-figure-1-inputs-and-outputs-food-mini.png\" width=900 alt=\"figure 1 from the original vision transformer paper adapted to work with food images, an image of pizza goes in and gets classified as 'pizza'\"/>\n",
    "\n",
    "*Figure 1 from the ViT Paper adapted for use with FoodVision Mini. An image of food goes in (pizza), the image gets turned into patches and then projected to an embedding. The embedding then travels through the various layers and blocks and (hopefully) the class \"pizza\" is returned.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add31aa8-2809-4f77-b94c-e34d865c12d0",
   "metadata": {},
   "source": [
    "#### TK - 3.2.2 Exploring the Four Equations\n",
    "\n",
    "The next main part(s) of the ViT paper we're going to look at are the four equations in section 3.1.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-four-equations.png\" width=650 alt=\"four mathematical equations from the vision transformer machine learning paper\"/>\n",
    "\n",
    "*These four equations represent the math behind the four major parts of the ViT architecture.*\n",
    "\n",
    "Section 3.1 describes each of these (some of the text has been omitted for brevity, bolded text is mine):\n",
    "\n",
    "1. ...The Transformer uses constant latent vector size $D$ through all of its layers, so we flatten the patches and map to $D$ dimensions with a **trainable linear projection** (Eq. 1). We refer to the output of this projection as the **patch embeddings**.\n",
    "2. The Transformer encoder (Vaswani et al., 2017) consists of alternating layers of multiheaded selfattention (MSA, see Appendix A) and MLP blocks (Eq. 2, 3). **Layernorm (LN) is applied before every block**, and **residual connections after every block** (Wang et al., 2019; Baevski \\& Auli, 2019).\n",
    "3. See above.\n",
    "4. Similar to BERT's [ class ] token, we **prepend a learnable embedding to the sequence of embedded patches** $\\left(\\mathbf{z}_{0}^{0}=\\mathbf{x}_{\\text {class }}\\right)$, whose state at the output of the Transformer encoder $\\left(\\mathbf{z}_{L}^{0}\\right)$ serves as the image representation $\\mathbf{y}$ (Eq. 4)...\n",
    "\n",
    "Let's map these descriptions to the ViT architecture in Figure 1.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-mapping-the-four-equations-to-figure-1.png\" width=1000 alt=\"mapping the vision transformer paper figure 1 to the four equations listed in the paper\"/>\n",
    "\n",
    "*Connecting Figure 1 from the ViT paper to the four equations from section 3.1 describing the math behind each of the layers/blocks. Some details such as \"residual connections after every block\" are referred to in Figure 1 and in the text but not in the equations.*\n",
    "\n",
    "There's a lot happening in the image above but following the coloured lines and arrows reveals the main concepts of the ViT architecture.\n",
    "\n",
    "**Equation 1**\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mathbf{z}_{0} &=\\left[\\mathbf{x}_{\\text {class }} ; \\mathbf{x}_{p}^{1} \\mathbf{E} ; \\mathbf{x}_{p}^{2} \\mathbf{E} ; \\cdots ; \\mathbf{x}_{p}^{N} \\mathbf{E}\\right]+\\mathbf{E}_{\\text {pos }}, & & \\mathbf{E} \\in \\mathbb{R}^{\\left(P^{2} \\cdot C\\right) \\times D}, \\mathbf{E}_{\\text {pos }} \\in \\mathbb{R}^{(N+1) \\times D}\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "This equation deals with the patch embedding and position embedding of the input image.\n",
    "\n",
    "In vector form, the embedding might look something like:\n",
    "\n",
    "TK - update the vector form to reflect a real exmaple\n",
    "\n",
    "```\n",
    "[class_token, image_patch_1, image_patch_2, image_patch_3...] + [0, image_patch_1_position, image_patch_2_position, image_patch_3_position...]\n",
    "```\n",
    "\n",
    "Where each of the elements in the vector is learnable (their `requires_grad=True`).\n",
    "\n",
    "**Equation 2**\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mathbf{z}_{\\ell}^{\\prime} &=\\operatorname{MSA}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell-1}\\right)\\right)+\\mathbf{z}_{\\ell-1}, & & \\ell=1 \\ldots L\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "This says that for every layer from $1$ through to $L$ (the total number of layers), there's a Multi-Head Attention layer (MSA) wrapping a LayerNorm layer (LN).\n",
    "\n",
    "The addition on the end is the equivalent of adding the input to the output and forming a [skip/residual connection](https://paperswithcode.com/method/residual-connection).\n",
    "\n",
    "We'll call this layer the \"MSA block\".\n",
    "\n",
    "In pseudocode, this might look like: \n",
    "\n",
    "```python\n",
    "# MSA block\n",
    "x_output_MSA_block = MSA_layer(LN_layer(x_input)) + x_input\n",
    "```\n",
    "\n",
    "**Equation 3**\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mathbf{z}_{\\ell} &=\\operatorname{MLP}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell}^{\\prime}\\right)\\right)+\\mathbf{z}_{\\ell}^{\\prime}, & & \\ell=1 \\ldots L \\\\\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "This says that for every layer from $1$ through to $L$ (the total number of layers), there's also a Multilayer Perceptron layer (MLP) wrapping a LayerNorm layer (LN).\n",
    "\n",
    "The addition on the end is showing the presence of a skip/residual connection.\n",
    "\n",
    "We'll call this layer the \"MLP block\".\n",
    "\n",
    "In pseudocode, this might look like: \n",
    "\n",
    "```python\n",
    "# MLP block\n",
    "# Notice how the layer(s) take the output of the MSA block (above) as input and use it as the skip connection on the end \n",
    "x_output_MLP_block = MLP_layer(LN_layer(x_output_MSA_block)) + x_output_MSA_block\n",
    "```\n",
    "\n",
    "**Equation 4**\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mathbf{y} &=\\operatorname{LN}\\left(\\mathbf{z}_{L}^{0}\\right) & &\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "This says for the last layer $L$, the output $y$ is the 0 index token of $z$ wrapped in a LayerNorm layer (LN).\n",
    "\n",
    "Or in our case, the 0 index of `x_output_MLP_block`:\n",
    "\n",
    "```python\n",
    "# MLP Head\n",
    "y = LN_layer(Linear_layer(x_output_MLP_block[0]))\n",
    "```\n",
    "\n",
    "Of course there are some simplifications above but we'll take care of those when we start to write PyTorch code for each section.\n",
    "\n",
    "> **Note:** The above section covers alot of information. But don't forget if something doesn't make sense, you can always research it further. By asking questions like \"what is a residual connection?\"."
   ]
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   "cell_type": "markdown",
   "id": "cd36899e-5bc7-411a-aab7-28e3a5a2c6cb",
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   "source": [
    "#### TK - 3.2.3 Exploring Table 1\n",
    "\n",
    "The final piece of the ViT architecture puzzle we'll focus on (for now) is Table 1.\n",
    "\n",
    "| Model | Layers | Hidden size $D$ | MLP size | Heads | Params |\n",
    "| :--- | :---: | :---: | :---: | :---: | :---: |\n",
    "| ViT-Base | 12 | 768 | 3072 | 12 | $86M$ |\n",
    "| ViT-Large | 24 | 1024 | 4096 | 16 | $307M$ |\n",
    "| ViT-Huge | 32 | 1280 | 5120 | 16 | $632M$ |\n",
    "\n",
    "<div align=center>\n",
    "    <i>Table 1: Details of Vision Transformer model variants. Source: <a href=\"https://arxiv.org/abs/2010.11929\"/>ViT paper.</i>\n",
    "</div>\n",
    "<br>\n",
    "\n",
    "This table showcasing the various hyperparameters of each of the ViT architectures.\n",
    "\n",
    "You can see the numbers gradually increase from ViT-Base to ViT-Huge.\n",
    "\n",
    "We're going to focus on replicating ViT-Base (start small and scale up when necessary) but we'll be writing code that could easily scale up to the larger variants.\n",
    "\n",
    "Breaking the hyperparameters down:\n",
    "* **Layers** - How many Transformer encoder blocks are there? (each of these will contain a MSA block and MLP block)\n",
    "* **Hidden size $D$** - This is the embedding dimension throughout the architecture, this will be the size of the vector that our image gets turned into when it gets patched and embedded. Generally, the larger the embedding dimension, the more information can be captured, the better results. However, a larger embedding comes at the cost of more compute.\n",
    "* **MLP size** - What are the number of hidden units in the MLP layers?\n",
    "* **Heads** - How many heads are there in the Multi-Head Attention layers?\n",
    "* **Params** - What are the total number of parameters of the model? Generally, more parameters leads to better performance but at the cost of more compute. You'll notice even ViT-Base has far more parameters than any other model we've used so far.\n",
    "\n",
    "We'll use these values as the hyperparameter settings for our ViT architecture. "
   ]
  },
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   "cell_type": "markdown",
   "id": "d9aedd15-5a98-431e-bd9e-9d18616e4bff",
   "metadata": {},
   "source": [
    "### TK - 3.3 My workflow for replicating papers\n",
    "\n",
    "When I start working on replicating a paper, I go through the following steps:\n",
    "\n",
    "1. Read the whole paper end-to-end once (to get an idea of the main concepts).\n",
    "2. Go back through each section and see how they line up with each other and start thinking about how they might be turned into code (just like above).\n",
    "3. Repeat step 2 until I've got a fairly good outline.\n",
    "4. Use [mathpix.com](https://mathpix.com/) (a very handy tool) to turn any sections of the paper into markdown/LaTeX to put into notebooks.\n",
    "5. Replicate the simplest version of the model possible.\n",
    "6. If I get stuck, look up other examples.\n",
    "\n",
    "TK - gif of mathpix\n",
    "\n",
    "We've already gone through the first few steps above (and if you haven't read the full paper yet, I'd encourage you to give it a go) but what we'll be focusing on next is step 5: replicating the simplest version fo the model possible.\n",
    "\n",
    "This is why we're starting with ViT-Base.\n",
    "\n",
    "Replicating the smallest version of the architecture possible, get it working and then we can scale up if we wanted to.\n",
    "\n",
    "> **Note:** If you've never read a research paper before, many of the above steps can be intimidating. But don't worry, like anything, your skills at reading *and* replicating papers will improve with practice. Don't forget, a research paper is often *months* of work by many people compressed into a few pages. So trying to replicate it on your own is no small feat. "
   ]
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   "source": [
    "## TK 4. Equation 1: Split data into patches and creating the patch embedding \n",
    "\n",
    "Let's start by creating the patch embedding.\n",
    "\n",
    "This means we'll be turning our input images in a sequence of patches and then embedding those patches.\n",
    "\n",
    "Recall that an **embedding** is a learnable representation of some form and is often a vector. The term learnable is important because this means the representation of an input image can be improved and learned over time.\n",
    "\n",
    "We'll begin by following the opening paragraph of section 3.1 of the ViT paper (bold mine):\n",
    "\n",
    "> The standard Transformer receives as input a 1D sequence of token embeddings. To handle 2D images, we reshape the image $\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times C}$ into a sequence of flattened 2D patches $\\mathbf{x}_{p} \\in \\mathbb{R}^{N \\times\\left(P^{2} \\cdot C\\right)}$, where $(H, W)$ is the resolution of the original image, $C$ is the number of channels, $(P, P)$ is the resolution of each image patch, and $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer. The Transformer uses constant latent vector size $D$ through all of its layers, so we flatten the patches and map to $D$ dimensions with a trainable linear projection (Eq. 1). We refer to the output of this projection as the **patch embeddings**.\n",
    "\n",
    "And size we're dealing with image shapes, let's keep in mind the line from Table 3 of the ViT paper: \n",
    "\n",
    "> Training resolution is 224.\n",
    "\n",
    "TK - fill in the hyperparameters/shapes\n",
    "* D - ...\n",
    "* Image size - ...\n",
    "* patch embed size -... \n",
    "\n",
    "UPTOHERE: break down the above two paragraphs and pull out the important information, then: \n",
    "* TK image - breaking down the above two paragraphs (equation 1 being mapped to various sections of the paper)\n",
    "* TK - start with a dummy image and calculate the shapes by hand \n",
    "* TK - move towards visualizing... Want to visualize the data and see if it can be split into patches with a Conv2D layer.\n",
    "\n",
    "\n",
    "Also section 3.1:\n",
    "\n",
    "> The standard Transformer receives as input a 1D sequence of token embeddings. To handle 2D images, we reshape the image $\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times C}$ into a sequence of flattened 2D patches $\\mathbf{x}_{p} \\in \\mathbb{R}^{N \\times\\left(P^{2} \\cdot C\\right)}$, where $(H, W)$ is the resolution of the original image, $C$ is the number of channels, $(P, P)$ is the resolution of each image patch, and $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer.\n",
    "> \n",
    "> \"All models use an image size of 224\"\n",
    "\n",
    "To handle 2D images, we reshape the image `x` (HxWxC) -> `x` (Nx(P^2•C)) where:\n",
    "* (H, W) is the resolution of the original image\n",
    "* C is the number of channels\n",
    "* (P, P) is the resolution of each patch\n",
    "* N = HW/P^2\n",
    "\n",
    "Patch size from paper: \"ViT-B/16 means the “Large” variant with 16×16 input patch size\"\n",
    "\n",
    "Let's say I want patches of 16x16, I want a Conv2d with:\n",
    "* in_channels=3 (number of channels)\n",
    "* out_channels=D (number of hidden units)\n",
    "* kernel=patch_size (make the kernel the patch)\n",
    "* padding=\"same\" (want to keep the same dimensions of the input as the output)\n",
    "* stride=patch_size (stride the size of the patch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5b2be7ed-8d5f-41de-ad41-4319cf20b8bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "196.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of calculating N\n",
    "PATCH_SIZE = 16\n",
    "IMG_SIZE = 224\n",
    "height, width = IMG_SIZE, IMG_SIZE\n",
    "N = (height * width) / PATCH_SIZE ** 2\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "57f81861-5ff6-4e93-91fc-b41bee4c2117",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "768"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of calculating output shape\n",
    "16**2 * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d4fd046-6b51-4ac0-8d39-e67fb333a18a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "patch_size=16\n",
    "\n",
    "# Create the embedding and the patch in one hit \n",
    "conv2d = nn.Conv2d(in_channels=3,\n",
    "                   out_channels=768, # from Table 1: Hidden size D\n",
    "                   kernel_size=PATCH_SIZE,\n",
    "                   stride=PATCH_SIZE,\n",
    "                   padding=0)\n",
    "\n",
    "flatten = nn.Flatten(start_dim=2, \n",
    "                     end_dim=3) # https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a72a5614-f1cb-4c01-9696-24f07bf2a219",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 768, 14, 14])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 768, 196])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we have a patchified image of 256 tokens + an embedding for each patch\n",
    "patch_image_out_of_conv = conv2d(image.unsqueeze(0))\n",
    "print(patch_image_out_of_conv.shape)\n",
    "patch_image = flatten(conv2d(image.unsqueeze(0)))\n",
    "patch_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "336e1b36-9849-4104-8cb9-bb64a20ffc48",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.imshow(image.permute(1, 2, 0))\n",
    "plt.title(class_names[label])\n",
    "plt.axis(False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "af5b58ca-0d73-4c62-b4af-4e8867b764e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing 10 random Conv2d patches from index 89 to 99\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot random 10 conv patches\n",
    "import random\n",
    "random_start = random.randint(0, 758)\n",
    "random_end = random_start + 10\n",
    "print(f\"Showing 10 random Conv2d patches from index {random_start} to {random_end}\")\n",
    "patches = patch_image_out_of_conv[:, random_start:random_start+10, :, :]\n",
    "\n",
    "fig, axs = plt.subplots(ncols=10, squeeze=False, figsize=(16, 16))\n",
    "for i in range(10):\n",
    "    axs[0, i].imshow(patches[:, i, :, :].squeeze().detach().numpy())\n",
    "    axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "429d36f8-75e1-4361-ba41-25d8ceca892a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 256, 768])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get image in shape [batch_size, N, P^2*C]\n",
    "patch_image.transpose(-2, -1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "47f571a1-2303-4981-85f5-33936b39cf14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 256, 768])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get image in shape [batch_size, N, P^2*C]\n",
    "reshaped_patch_image = patch_image.permute(0, 2, 1)\n",
    "reshaped_patch_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bcd2e784-7989-40e5-b8f5-64de18f1fe3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Color channels, height, width\n",
    "# Want the top row\n",
    "plt.imshow(image[:, :patch_size, :].permute(1, 2, 0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7d45e15a-eb50-4c46-8055-2acaaacb881c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3240x3240 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PATCH_SIZE = 90\n",
    "IMG_SIZE = 270\n",
    "assert image.shape[1] % PATCH_SIZE == 0, \"Image size must be divisble by patch size.\" \n",
    "\n",
    "# Create a series of subplots\n",
    "fig, axs = plt.subplots(nrows=IMG_SIZE // PATCH_SIZE, # need int not float\n",
    "                        ncols=IMG_SIZE // PATCH_SIZE, \n",
    "                        figsize=(PATCH_SIZE // 2, PATCH_SIZE // 2),\n",
    "                        sharex=True,\n",
    "                        sharey=True)\n",
    "\n",
    "# Go from (C, H, W) -> (H, W, C) for matplotlib \n",
    "image_permuted = image.permute(1, 2, 0)\n",
    "for i, patch_h in enumerate(range(0, IMG_SIZE, PATCH_SIZE)):\n",
    "    for j, patch_w in enumerate(range(0, IMG_SIZE, PATCH_SIZE)):\n",
    "        # Plot coordinates = (i, j)\n",
    "        # print(f\"On patch: {i+1}, {j+1}\")\n",
    "        \n",
    "        # Plot the permuted image (image_permuted -> (Height, Width, Color Channels))\n",
    "        axs[i, j].imshow(image_permuted[patch_h:patch_h+PATCH_SIZE, # iterate through height \n",
    "                                        patch_w:patch_w+PATCH_SIZE, # iterate through width\n",
    "                                        :]) # get all color channels\n",
    "        \n",
    "        # Set up label information, remove the ticks for clarity and set labels to outside\n",
    "        # axs[i, j].set_ylabel(i+1, \n",
    "        #                      rotation=\"horizontal\", \n",
    "        #                      horizontalalignment=\"right\", \n",
    "        #                      verticalalignment=\"center\") \n",
    "        # axs[i, j].set_xlabel(j+1) \n",
    "        axs[i, j].set_xticks([])\n",
    "        axs[i, j].set_yticks([])\n",
    "        # axs[i, j].label_outer()\n",
    "\n",
    "# Set a super title\n",
    "fig.suptitle(f\"{class_names[label]} -> Patchified\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b165987a-8370-471a-a663-711e0c6e60db",
   "metadata": {},
   "source": [
    "### TK 4.1 Create patching layer\n",
    "\n",
    "TK - see \"Hybrid Architecture\" in section 3.1 in the ViT paper \n",
    "\n",
    "> **Hybrid Architecture.** As an alternative to raw image patches, the input sequence can be formed from feature maps of a CNN (LeCun et al., 1989). In this hybrid model, the patch embedding projection $\\mathbf{E}$ (Eq. 1) is applied to patches extracted from a CNN feature map. As a special case, the patches can have spatial size $1 \\times 1$, which means that the input sequence is obtained by simply flattening the spatial dimensions of the feature map and projecting to the Transformer dimension. The classification input embedding and position embeddings are added as described above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3ef75c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PatchEmbedding(nn.Module):\n",
    "    def __init__(self, \n",
    "                 in_channels=3,\n",
    "                 patch_size=16,\n",
    "                 embedding_dim=768):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.patcher = nn.Conv2d(in_channels=in_channels,\n",
    "                                 out_channels=embedding_dim,\n",
    "                                 kernel_size=patch_size,\n",
    "                                 stride=patch_size,\n",
    "                                 padding=0)\n",
    "\n",
    "        self.flatten = nn.Flatten(start_dim=2, # only flatten the 2nd last and last dim\n",
    "                                  end_dim=3)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # print(x.shape)\n",
    "        x_patched = self.patcher(x)\n",
    "        # print(x_patched.shape)\n",
    "        x_flattened = self.flatten(x_patched)\n",
    "        # print(x_flattened.shape)\n",
    "        return x_flattened.permute(0, 2, 1) # adjust so the embedding is on the final dimension [batch_size, P^2*C, N] -> [batch_size, N, P^2*C] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5599575-44cc-46c9-95a4-e65eb1379a59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 196, 768])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patchify = PatchEmbedding()\n",
    "patched_image = patchify(image.unsqueeze(0)) # add an extra batch dimension on the 0th index, otherwise will error\n",
    "patched_image.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23bf2bb8-b2ae-48e8-8d5a-37ae5cb4a6e6",
   "metadata": {},
   "source": [
    "## TK. 5. Create MSA (Multihead Self-Attention)\n",
    "\n",
    "See docs - https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html\n",
    "\n",
    "Heads = 12 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b76ae98c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiheadSelfAttentionBlock(nn.Module):\n",
    "    \"\"\"Creates a multi-head self-attention block (\"MSABlock\" for short).\n",
    "    \n",
    "    Uses PyTorch's torch.nn.MultiheadAttention: https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html\n",
    "    Source code for what's happening behind the scenes: https://github.com/pytorch/pytorch/blob/master/torch/nn/functional.py#L4909\n",
    "    \"\"\"\n",
    "    def __init__(self,\n",
    "                 embedding_dim=768, # From Table 1\n",
    "                 num_heads=12, # From Table 1\n",
    "                 attn_dropout=0): # Doesn't look like the paper uses any dropout in MSABlocks\n",
    "        super().__init__()\n",
    "        self.multihead_attn = nn.MultiheadAttention(embed_dim=embedding_dim,\n",
    "                                                    num_heads=num_heads,\n",
    "                                                    dropout=attn_dropout,\n",
    "                                                    batch_first=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        attn_output, _ = self.multihead_attn(query=x,\n",
    "                                             key=x,\n",
    "                                             value=x,\n",
    "                                             need_weights=False)\n",
    "        return attn_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ceb1dfc0-40ad-4cee-bc54-e9a5cec56895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 196, 768])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multihead_self_attention_block = MultiheadSelfAttentionBlock(embedding_dim=768, \n",
    "                                                             num_heads=12)\n",
    "patched_image_through_msa_block = multihead_self_attention_block(patched_image)\n",
    "patched_image_through_msa_block.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0168ef51-ab1f-4ee7-8e9d-2ee7ecca3c7b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## TK 6. Create feedforward model (MLP)\n",
    "\n",
    "> Dropout, when used, is applied **after every dense layer except for the the qkv-projections and directly after adding positional- to patch embeddings.**\n",
    "\n",
    "> The MLP contains two layers with a GELU non-linearity\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mathbf{z}_{\\ell} &=\\operatorname{MLP}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell}^{\\prime}\\right)\\right)+\\mathbf{z}_{\\ell}^{\\prime}, & & \\ell=1 \\ldots L\n",
    "\\end{aligned}\n",
    "$$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "68d9dbfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Could also call this \"FeedForward\"\n",
    "class MLPBlock(nn.Module):\n",
    "    \"\"\"Creates an MLPBlock of the Vision Transformer architecture.\"\"\"\n",
    "    def __init__(self,\n",
    "                 embedding_dim, # embedding dimension (Hidden Size D in Table 1)\n",
    "                 mlp_size, # MLP size in Table 1\n",
    "                 dropout=0): # \"Dropout... is applied to every dense layer... (Appendix B.1)\"\n",
    "        super().__init__()\n",
    "        \n",
    "        self.mlp = nn.Sequential(\n",
    "            nn.Linear(in_features=embedding_dim,\n",
    "                      out_features=mlp_size),\n",
    "            nn.GELU(), # \"The MLP contains two layers with a GELU non-linearity (section 3.1).\"\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(in_features=mlp_size, # needs to take same in_features as out_features of layer above\n",
    "                      out_features=embedding_dim) # take back to embedding_dim\n",
    "            nn.Dropout(p=dropout)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.mlp(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "442fb987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 196, 768])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp_block = MLPBlock(embedding_dim=768, # Table 1 \n",
    "                     mlp_size=3072) # Table 1\n",
    "patched_image_through_mlp_block = mlp_block(patched_image_through_msa_block)\n",
    "patched_image_through_mlp_block.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "812ccd0a-554f-4722-9987-f03c079a269f",
   "metadata": {},
   "source": [
    "## TK 7. Create transformer block\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2c43855c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerEncoderBlock(nn.Module):\n",
    "    \"\"\"Creates a Transformer Encoder block.\"\"\"\n",
    "    def __init__(self,\n",
    "                 embedding_dim=768, # From Table 1\n",
    "                 num_heads=12, # From Table 1\n",
    "                 mlp_size=3072, # From Table 1\n",
    "                 mlp_dropout=0.1,\n",
    "                 attn_dropout=0):\n",
    "        super().__init__()\n",
    "\n",
    "        # Create MSA Block (for equation 2)\n",
    "        self.msa_block = nn.Sequential(\n",
    "            nn.LayerNorm(normalized_shape=embedding_dim),\n",
    "            MultiheadSelfAttentionBlock(embedding_dim=embedding_dim,\n",
    "                                                      num_heads=num_heads,\n",
    "                                                      attn_dropout=attn_dropout))\n",
    "        \n",
    "        # Create MLP Block (for equation 3)\n",
    "        self.mlp_block = nn.Sequential(\n",
    "            nn.LayerNorm(normalized_shape=embedding_dim),\n",
    "            MLPBlock(embedding_dim=embedding_dim,\n",
    "                     mlp_size=mlp_size,\n",
    "                     dropout=mlp_dropout))\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.msa_block(x) + x # Create skip connection\n",
    "        x = self.mlp_block(x) + x # Create skip connection\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f9041c-a0af-4fbe-8969-a2bde8637821",
   "metadata": {},
   "source": [
    "## TK 8. Putting it all together to create ViT\n",
    "\n",
    "Combine the transformer blocks and patched embedding into a ViT architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2df890d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ViT(nn.Module):\n",
    "    \"\"\"Creates a Vision Transformer architecture.\"\"\"\n",
    "    def __init__(self,\n",
    "                 img_size=224, # From Table 3 in ViT paper\n",
    "                 in_channels=3,\n",
    "                 patch_size=16,\n",
    "                 num_transformer_layers=12, # From Table 1 in ViT paper\n",
    "                 embedding_dim=768,\n",
    "                 mlp_size=3072,\n",
    "                 num_heads=12,\n",
    "                 attn_dropout=0,\n",
    "                 mlp_dropout=0.1,\n",
    "                 embedding_dropout=0.1,\n",
    "                 num_classes=1000): # default for ImageNet\n",
    "        super().__init__() # don't for the super().__init__()!\n",
    "    \n",
    "        # Get image size\n",
    "        self.img_height, self.img_width = img_size, img_size\n",
    "        \n",
    "        # Calculate number of patches (height * width/patch^2)\n",
    "        self.num_patches = (self.img_height * self.img_width) // patch_size**2\n",
    "        \n",
    "        # Create position embedding (from index 1 -> last of patch embedding size)\n",
    "        self.position_embedding = nn.Parameter(data=torch.randn(1, self.num_patches+1, embedding_dim),\n",
    "                                               requires_grad=True)\n",
    "                 \n",
    "        # Create class embedding (needs to go at front of sequence embedding)\n",
    "        self.class_embedding = nn.Parameter(data=torch.randn(1, 1, embedding_dim),\n",
    "                                            requires_grad=True)\n",
    "                \n",
    "        # Create embedding dropout\n",
    "        self.embedding_dropout = nn.Dropout(p=embedding_dropout)\n",
    "        \n",
    "        # Create patch embedding layer\n",
    "        self.patch_embedding = PatchEmbedding(in_channels=in_channels,\n",
    "                                              patch_size=patch_size,\n",
    "                                              embedding_dim=embedding_dim)\n",
    "        \n",
    "        # Create transformer encoder blocks\n",
    "        self.transformer_enedoder = nn.Sequential(*[TransformerEncoderBlock(embedding_dim=embedding_dim,\n",
    "                                                                            num_heads=num_heads,\n",
    "                                                                            mlp_size=mlp_size,\n",
    "                                                                            mlp_dropout=mlp_dropout) for _ in range(num_transformer_layers)])\n",
    "       \n",
    "        # Create classifier head (equation 4)\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.LayerNorm(normalized_shape=embedding_dim),\n",
    "            nn.Linear(in_features=embedding_dim, \n",
    "                      out_features=num_classes)\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        # Get batch size\n",
    "        batch_size = x.shape[0]\n",
    "        # Create class token embedding\n",
    "        class_token = self.class_embedding.expand(batch_size, -1, -1)\n",
    "\n",
    "        # Create patch embedding\n",
    "        x = self.patch_embedding(x)\n",
    "\n",
    "        # Concat class embedding and patch embedding (equation 1)\n",
    "        x = torch.cat((class_token, x), dim=1)\n",
    "\n",
    "        # Add position embedding to patch embedding (equation 1)\n",
    "        x = self.position_embedding + x\n",
    "\n",
    "        # Run embedding dropout\n",
    "        x = self.embedding_dropout(x)\n",
    "\n",
    "        # Pass patch, position and class embedding through transformer encoder layers (equations 2 & 3)\n",
    "        x = self.transformer_enedoder(x)\n",
    "\n",
    "        # Put 0 index logit through classifier (equation 4)\n",
    "        x = self.classifier(x[:, 0]) # run on each sample in a batch at 0 index\n",
    "\n",
    "        return x\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7dc9f8ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 1, 768])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 32\n",
    "class_tokens = nn.Parameter(data=torch.randn(1, 1, 768))\n",
    "class_tokens.expand(batch_size, -1, -1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f86190a3-ed1f-4ab3-881c-4eecea996912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6055, 1.3140, 0.2506]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_seeds()\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "rand_image = torch.randn(1, 3, 224, 224)\n",
    "# vit = ViT(num_classes=len(class_names)) \n",
    "vit = ViT(num_classes=3)\n",
    "vit(rand_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0a0c9c-6d98-47fd-a152-8a06be99b5fa",
   "metadata": {},
   "source": [
    "## TK 9. Inspect the model\n",
    "\n",
    "> **Note:** If you go too big, your hardware might not be able to handle it... (e.g. too high of a batch size...)\n",
    "\n",
    "TK - Number of parameters should be equivalent to: https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16 (`num_params=86,567,656`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "494bde26-ed1e-45dc-b615-78ac268ca20e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===========================================================================================================================================================\n",
       "Layer (type (var_name))                                                     Input Shape          Output Shape         Param #              Trainable\n",
       "===========================================================================================================================================================\n",
       "ViT (ViT)                                                                   [128, 3, 224, 224]   [128, 3]             152,064              True\n",
       "├─Dropout (embedding_dropout)                                               [128, 197, 768]      [128, 197, 768]      --                   --\n",
       "├─PatchEmbedding (patch_embedding)                                          [128, 3, 224, 224]   [128, 196, 768]      --                   True\n",
       "│    └─Conv2d (patcher)                                                     [128, 3, 224, 224]   [128, 768, 14, 14]   590,592              True\n",
       "│    └─Flatten (flatten)                                                    [128, 768, 14, 14]   [128, 768, 196]      --                   --\n",
       "├─Dropout (embedding_dropout)                                               [128, 197, 768]      [128, 197, 768]      --                   --\n",
       "├─Sequential (transformer_enedoder)                                         [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    └─TransformerEncoderBlock (0)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (1)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (2)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (3)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (4)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (5)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (6)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (7)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (8)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (9)                                          [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (10)                                         [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "│    └─TransformerEncoderBlock (11)                                         [128, 197, 768]      [128, 197, 768]      --                   True\n",
       "│    │    └─Sequential (msa_block)                                          [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
       "│    │    └─Sequential (mlp_block)                                          [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
       "├─Sequential (classifier)                                                   [128, 768]           [128, 3]             --                   True\n",
       "│    └─LayerNorm (0)                                                        [128, 768]           [128, 768]           1,536                True\n",
       "│    └─Linear (1)                                                           [128, 768]           [128, 3]             2,307                True\n",
       "===========================================================================================================================================================\n",
       "Total params: 85,800,963\n",
       "Trainable params: 85,800,963\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (G): 22.08\n",
       "===========================================================================================================================================================\n",
       "Input size (MB): 77.07\n",
       "Forward/backward pass size (MB): 13168.81\n",
       "Params size (MB): 257.55\n",
       "Estimated Total Size (MB): 13503.43\n",
       "==========================================================================================================================================================="
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchinfo import summary\n",
    "\n",
    "# TK - clean up the summary so it looks nice when it prints out \n",
    "# Print a summary using torchinfo (uncomment for actual output)\n",
    "summary(model=vit, \n",
    "        input_size=(128, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\"\n",
    "        # col_names=[\"input_size\"], # uncomment for smaller output\n",
    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "        col_width=20,\n",
    "        row_settings=[\"var_names\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0279251-5cc1-42a5-bebc-8391ef911343",
   "metadata": {},
   "source": [
    "* TK - same number of parameters as: https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16 -> 86567656"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3791f963-7d51-418b-b8bd-4c77341560e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1, 1, 768]), torch.Size([32, 1, 768]))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 32\n",
    "cls_embedding = nn.Parameter(torch.randn(1, 1, 768))\n",
    "# See here: https://pytorch.org/docs/stable/generated/torch.Tensor.expand.html\n",
    "cls_embedding.shape, cls_embedding.expand(batch_size, -1, -1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd92b752-22f0-4a13-95ac-e085d20013ac",
   "metadata": {},
   "source": [
    "## TK 10. Train model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9107b068-f253-4026-ad21-83be41404043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd05568d17844f95a57415f7f46f0dd2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 3.8765 | train_acc: 0.3516 | test_loss: 1.8075 | test_acc: 0.2604\n",
      "Epoch: 2 | train_loss: 1.7652 | train_acc: 0.2500 | test_loss: 1.8281 | test_acc: 0.1979\n",
      "Epoch: 3 | train_loss: 1.7685 | train_acc: 0.2930 | test_loss: 1.0586 | test_acc: 0.5417\n",
      "Epoch: 4 | train_loss: 1.3442 | train_acc: 0.3906 | test_loss: 1.6053 | test_acc: 0.1979\n",
      "Epoch: 5 | train_loss: 1.2500 | train_acc: 0.2969 | test_loss: 1.0597 | test_acc: 0.4811\n",
      "Epoch: 6 | train_loss: 1.1027 | train_acc: 0.4258 | test_loss: 1.2707 | test_acc: 0.1979\n",
      "Epoch: 7 | train_loss: 1.1864 | train_acc: 0.2578 | test_loss: 1.3656 | test_acc: 0.1979\n",
      "Epoch: 8 | train_loss: 1.1701 | train_acc: 0.3047 | test_loss: 1.2117 | test_acc: 0.2604\n",
      "Epoch: 9 | train_loss: 1.1279 | train_acc: 0.4219 | test_loss: 1.0078 | test_acc: 0.5417\n",
      "Epoch: 10 | train_loss: 1.1814 | train_acc: 0.2344 | test_loss: 1.1760 | test_acc: 0.2604\n"
     ]
    }
   ],
   "source": [
    "from going_modular.going_modular import engine\n",
    "\n",
    "optimizer = torch.optim.Adam(params=vit.parameters(), \n",
    "                             lr=1e-3,\n",
    "                             betas=(0.9, 0.999), # default\n",
    "                             weight_decay=0.1) # from the ViT paper section 4.1\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "set_seeds()\n",
    "results = engine.train(model=vit,\n",
    "                       train_dataloader=train_dataloader,\n",
    "                       test_dataloader=test_dataloader,\n",
    "                       optimizer=optimizer,\n",
    "                       loss_fn=loss_fn,\n",
    "                       epochs=10,\n",
    "                       device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32cb3ad2-6eea-4b69-9ce8-322270643919",
   "metadata": {},
   "source": [
    "## TK 11. Evaluate model\n",
    "\n",
    "TK - plot the loss curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fcca1148-6475-4012-bfc9-cbad2706c22d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "plot_loss_curves(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c370cae-9854-474c-be05-51dabe62c204",
   "metadata": {},
   "source": [
    "TK - why do the loss curves look the way they do? (too big of a model, not enough data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9bc3d75-028d-4d48-bcfb-d0dd4f53d2ca",
   "metadata": {},
   "source": [
    "## TK 12. Bring in pretrained ViT from `torchvision.models` on same dataset \n",
    "\n",
    "* Get a similar model from here - https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "30de8333-74b0-49ae-a81e-0266e6325f26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.13.0.dev20220606\n",
      "0.14.0.dev20220606\n"
     ]
    }
   ],
   "source": [
    "# The following requires torch v0.12+ and torchvision v0.13+\n",
    "import torch\n",
    "import torchvision\n",
    "print(torch.__version__) \n",
    "print(torchvision.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b0b87f68-98cc-49f8-89bd-ff220a757f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "29887400-2f92-4c75-961e-0541bea6b73a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set seeds\n",
    "def set_seeds(seed=42):\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b8e2dda6-8af0-4255-815f-4d885fa4b477",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Requires torchvision >= 0.13\n",
    "pretrained_vit_weights = torchvision.models.ViT_B_16_Weights.DEFAULT\n",
    "pretrained_vit = torchvision.models.vit_b_16(weights=pretrained_vit_weights).to(device)\n",
    "\n",
    "# Freeze the base parameters\n",
    "for parameter in pretrained_vit.parameters():\n",
    "    parameter.requires_grad = False\n",
    "    \n",
    "# Change the classifier head\n",
    "set_seeds()\n",
    "pretrained_vit.heads = nn.Linear(in_features=768, out_features=len(class_names)).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8fbd83a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "======================================================================================================================================================\n",
       "Layer (type (var_name))                                                Input Shape          Output Shape         Param #              Trainable\n",
       "======================================================================================================================================================\n",
       "VisionTransformer (VisionTransformer)                                  [128, 3, 224, 224]   [128, 3]             768                  Partial\n",
       "├─Conv2d (conv_proj)                                                   [128, 3, 224, 224]   [128, 768, 14, 14]   (590,592)            False\n",
       "├─Encoder (encoder)                                                    [128, 197, 768]      [128, 197, 768]      151,296              False\n",
       "│    └─Dropout (dropout)                                               [128, 197, 768]      [128, 197, 768]      --                   --\n",
       "│    └─Sequential (layers)                                             [128, 197, 768]      [128, 197, 768]      --                   False\n",
       "│    │    └─EncoderBlock (encoder_layer_0)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_1)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_2)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_3)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_4)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_5)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_6)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_7)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_8)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_9)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_10)                            [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    │    └─EncoderBlock (encoder_layer_11)                            [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
       "│    └─LayerNorm (ln)                                                  [128, 197, 768]      [128, 197, 768]      (1,536)              False\n",
       "├─Linear (heads)                                                       [128, 768]           [128, 3]             2,307                True\n",
       "======================================================================================================================================================\n",
       "Total params: 85,800,963\n",
       "Trainable params: 2,307\n",
       "Non-trainable params: 85,798,656\n",
       "Total mult-adds (G): 22.08\n",
       "======================================================================================================================================================\n",
       "Input size (MB): 77.07\n",
       "Forward/backward pass size (MB): 13322.95\n",
       "Params size (MB): 257.55\n",
       "Estimated Total Size (MB): 13657.57\n",
       "======================================================================================================================================================"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print a summary using torchinfo (uncomment for actual output)\n",
    "summary(model=pretrained_vit, \n",
    "        input_size=(128, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\"\n",
    "        # col_names=[\"input_size\"], # uncomment for smaller output\n",
    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
    "        col_width=20,\n",
    "        row_settings=[\"var_names\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2e4ed730-de72-415f-99d1-5dffd57e9dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TK - the above output has the same number of parameters as our own created model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "94cb3900",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('data/pizza_steak_sushi')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download pizza, steak, sushi images from GitHub\n",
    "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
    "                           destination=\"pizza_steak_sushi\")\n",
    "image_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2e6ae0fe-73c0-4930-988a-e4df903084b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PosixPath('data/pizza_steak_sushi/train'),\n",
       " PosixPath('data/pizza_steak_sushi/test'))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dir = image_path / \"train\"\n",
    "test_dir = image_path / \"test\" \n",
    "train_dir, test_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dd2f58ff-6182-453a-a802-70ff98c09557",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ImageClassification(\n",
      "    crop_size=[224]\n",
      "    resize_size=[256]\n",
      "    mean=[0.485, 0.456, 0.406]\n",
      "    std=[0.229, 0.224, 0.225]\n",
      "    interpolation=InterpolationMode.BILINEAR\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Create dataset for pretrained ViT\n",
    "pretrained_vit_transforms = pretrained_vit_weights.transforms()\n",
    "print(pretrained_vit_transforms)\n",
    "\n",
    "train_dataloader_pretrained, test_dataloader_pretrained, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
    "                                                                                                     test_dir=test_dir,\n",
    "                                                                                                     transform=pretrained_vit_transforms,\n",
    "                                                                                                     batch_size=1024) # From here: https://arxiv.org/abs/2205.01580 (there are other improvements there too...)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a49408b4-24d9-4bb1-90a2-dd61c08f78a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "169ffd0e842f47cf88ec4a7d83aa377f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | train_loss: 1.1490 | train_acc: 0.2356 | test_loss: 1.0584 | test_acc: 0.4667\n",
      "Epoch: 2 | train_loss: 1.0017 | train_acc: 0.5289 | test_loss: 0.9194 | test_acc: 0.6400\n",
      "Epoch: 3 | train_loss: 0.8716 | train_acc: 0.7244 | test_loss: 0.7983 | test_acc: 0.6667\n",
      "Epoch: 4 | train_loss: 0.7583 | train_acc: 0.8089 | test_loss: 0.6942 | test_acc: 0.7733\n",
      "Epoch: 5 | train_loss: 0.6608 | train_acc: 0.8622 | test_loss: 0.6060 | test_acc: 0.8800\n",
      "Epoch: 6 | train_loss: 0.5777 | train_acc: 0.8889 | test_loss: 0.5318 | test_acc: 0.8933\n",
      "Epoch: 7 | train_loss: 0.5076 | train_acc: 0.9156 | test_loss: 0.4700 | test_acc: 0.9067\n",
      "Epoch: 8 | train_loss: 0.4487 | train_acc: 0.9244 | test_loss: 0.4188 | test_acc: 0.9333\n",
      "Epoch: 9 | train_loss: 0.3993 | train_acc: 0.9378 | test_loss: 0.3765 | test_acc: 0.9333\n",
      "Epoch: 10 | train_loss: 0.3580 | train_acc: 0.9378 | test_loss: 0.3417 | test_acc: 0.9467\n"
     ]
    }
   ],
   "source": [
    "# Train pretrained feature extractor ViT for 5 epochs on Pizza, Steak, Sushi\n",
    "# TK - can probably increase the batch_size here because we're using feature extraction and not \n",
    "# training the whole model\n",
    "from going_modular.going_modular import engine\n",
    "\n",
    "optimizer = torch.optim.Adam(params=pretrained_vit.parameters(), \n",
    "                             lr=1e-3)\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "set_seeds()\n",
    "pretrained_vit_results = engine.train(model=pretrained_vit,\n",
    "                                      train_dataloader=train_dataloader_pretrained,\n",
    "                                      test_dataloader=test_dataloader_pretrained,\n",
    "                                      optimizer=optimizer,\n",
    "                                      loss_fn=loss_fn,\n",
    "                                      epochs=10,\n",
    "                                      device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3c0af18e-6419-4dd6-b8ea-f5830bbd63d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss curves\n",
    "from helper_functions import plot_loss_curves\n",
    "\n",
    "plot_loss_curves(pretrained_vit_results) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0fd00943-01aa-4ef4-b366-3cb859a25b6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Saving model to: models/08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\n"
     ]
    }
   ],
   "source": [
    "# Save the model\n",
    "from going_modular.going_modular import utils\n",
    "\n",
    "utils.save_model(model=pretrained_vit,\n",
    "                 target_dir=\"models\",\n",
    "                 model_name=\"08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f52ef12c-b88e-4796-84eb-981491a84334",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrained ViT feature extractor model size: 327 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get the model size in bytes then convert to megabytes\n",
    "pretrained_vit_model_size = Path(\"models/08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\").stat().st_size // (1024*1024)\n",
    "print(f\"Pretrained ViT feature extractor model size: {pretrained_vit_model_size} MB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "277a08e0-bc0e-453d-bf0f-b56164ae71ab",
   "metadata": {},
   "source": [
    "## TK - Things this replication misses out on\n",
    "\n",
    "TK Put down the difference in the paper vs this replication\n",
    "* Many of these things are in Table 3:\n",
    "    * training data (ImageNet from scratch vs FoodVision Mini data) \n",
    "    * LR warmup\n",
    "    * LR decay\n",
    "    * Weight decay\n",
    "    * Number of epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04b1569b-117e-43fd-9e0b-324157cb82a4",
   "metadata": {},
   "source": [
    "## TK - Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd69be46-cb68-4391-9834-8f87d8814722",
   "metadata": {},
   "source": [
    "## TK - Extra-curriculum\n",
    "\n",
    "* See the illustrated transformer for an overview of the Transformer model:  https://jalammar.github.io/illustrated-transformer/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ecc8ddc-fd70-4e4b-b888-430f01de73c9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "03bc13acfc4e8139fb32f411c6712485d4605f3bdd6569f6973c62d6adcc8291"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}