Assignments/assignment3/Self_Supervised_Learning.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yiMY0rg1nlDs",
        "outputId": "5820de3b-2f7b-407f-cefb-0dfd6c380caa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/My Drive/cs231n/Assignments/assignment3/cs231n/datasets\n",
            "/content/drive/My Drive/cs231n/Assignments/assignment3\n"
          ]
        }
      ],
      "source": [
        "# This mounts your Google Drive to the Colab VM.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# TODO: Enter the foldername in your Drive where you have saved the unzipped\n",
        "# assignment folder, e.g. 'cs231n/assignments/assignment3/'\n",
        "FOLDERNAME = 'cs231n/Assignments/assignment3/'\n",
        "assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n",
        "\n",
        "# Now that we've mounted your Drive, this ensures that\n",
        "# the Python interpreter of the Colab VM can load\n",
        "# python files from within it.\n",
        "import sys\n",
        "sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))\n",
        "\n",
        "# This downloads the COCO dataset to your Drive\n",
        "# if it doesn't already exist.\n",
        "%cd /content/drive/My\\ Drive/$FOLDERNAME/cs231n/datasets/\n",
        "!bash get_datasets.sh\n",
        "%cd /content/drive/My\\ Drive/$FOLDERNAME"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-HBKXFynlDt"
      },
      "source": [
        "## Using GPU\n",
        "\n",
        "Go to `Runtime > Change runtime type` and set `Hardware accelerator` to `GPU`. This will reset Colab. **Rerun the top cell to mount your Drive again.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXtwio7U3mSI",
        "tags": [
          "pdf-title"
        ]
      },
      "source": [
        "# Self-Supervised Learning\n",
        "\n",
        "## What is self-supervised learning?\n",
        "Modern day machine learning requires lots of labeled data. But often times it's challenging and/or expensive to obtain large amounts of human-labeled data. Is there a way we could ask machines to automatically learn a model which can generate good visual representations without a labeled dataset? Yes, enter self-supervised learning!\n",
        "\n",
        "Self-supervised learning (SSL) allows models to automatically learn a \"good\" representation space using the data in a given dataset without the need for their labels. Specifically, if our dataset were a bunch of images, then self-supervised learning allows a model to learn and generate a \"good\" representation vector for images.\n",
        "\n",
        "The reason SSL methods have seen a surge in popularity is because the learnt model continues to perform well on other datasets as well i.e. new datasets on which the model was not trained on!\n",
        "\n",
        "## What makes a \"good\" representation?\n",
        "A \"good\" representation vector needs to capture the important features of the image as it relates to the rest of the dataset. This means that images in the dataset representing semantically similar entities should have similar representation vectors, and different  images in the dataset should have different representation vectors. For example, two images of an apple should have similar representation vectors, while an image of an apple and an image of a banana should have different representation vectors.\n",
        "\n",
        "## Contrastive Learning: SimCLR\n",
        "Recently, [SimCLR](https://arxiv.org/pdf/2002.05709.pdf) introduces a new architecture which uses **contrastive learning** to learn good visual representations. Contrastive learning aims to learn similar representations for similar images and different representations for different images. As we will see in this notebook, this simple idea allows us to train a surprisingly good model without using any labels.\n",
        "\n",
        "Specifically, for each image in the dataset, SimCLR generates two differently augmented views of that image, called a **positive pair**. Then, the model is encouraged to generate similar representation vectors for this pair of images. See below for an illustration of the architecture (Figure 2 from the paper)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "mGbw-mnonlDw",
        "outputId": "a84d7be4-99b2-4557-990b-5dc99f005fbe"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 3,
          "metadata": {
            "image/png": {
              "width": 500
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Run this cell to view the SimCLR architecture.\n",
        "from IPython.display import Image\n",
        "Image('images/simclr_fig2.png', width=500)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4SkNQ06nlDy"
      },
      "source": [
        "Given an image **x**, SimCLR uses two different data augmentation schemes **t** and **t'** to generate the positive pair of images **$\\tilde{x}_i$** and **$\\tilde{x}_j$**. $f$ is a basic encoder net that  extracts representation vectors from the augmented data samples, which yields **$h_i$** and **$h_j$**, respectively. Finally, a small neural network projection head $g$ maps the representation vectors to the space where the contrastive loss is applied. The goal of the contrastive loss is to maximize agreement between the final vectors **$z_i = g(h_i)$** and **$z_j = g(h_j)$**. We will discuss the contrastive loss in more detail later, and you will get to implement it.\n",
        "\n",
        "After training is completed, we throw away the projection head $g$ and only use $f$ and the representation $h$ to perform downstream tasks, such as classification. You will get a chance to finetune a layer on top of a trained SimCLR model for a classification task and compare its performance with a baseline model (without self-supervised learning)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNkPR-ernlDy"
      },
      "source": [
        "## Pretrained Weights\n",
        "For your convenience, we have given you pretrained weights (trained for ~18 hours on CIFAR-10) for the SimCLR model. Run the following cell to download pretrained model weights to be used later. (This will take ~1 minute)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ugRbRu_hnlDz",
        "vscode": {
          "languageId": "shellscript"
        }
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "DIR=pretrained_model/\n",
        "if [ ! -d \"$DIR\" ]; then\n",
        "    mkdir \"$DIR\"\n",
        "fi\n",
        "\n",
        "URL=http://downloads.cs.stanford.edu/downloads/cs231n/pretrained_simclr_model.pth\n",
        "FILE=pretrained_model/pretrained_simclr_model.pth\n",
        "if [ ! -f \"$FILE\" ]; then\n",
        "    echo \"Downloading weights...\"\n",
        "    wget \"$URL\" -O \"$FILE\"\n",
        "fi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uLwXMn_DnlD0",
        "outputId": "557cdee8-a3ca-404e-af2c-6d0e59cc380c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting thop\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from thop) (2.2.1+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->thop) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->thop) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->thop) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->thop) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->thop) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->thop) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch->thop)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch->thop)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch->thop)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch->thop)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch->thop)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch->thop)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch->thop)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch->thop)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch->thop)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch->thop)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch->thop)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch->thop) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch->thop)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->thop) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->thop) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, thop\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 thop-0.1.1.post2209072238\n"
          ]
        }
      ],
      "source": [
        "# Setup cell.\n",
        "%pip install thop\n",
        "import torch\n",
        "import os\n",
        "import importlib\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import random\n",
        "from thop import profile, clever_format\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision.datasets import CIFAR10\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Kssxyfx3mSP"
      },
      "source": [
        "# Data Augmentation\n",
        "\n",
        "Our first step is to perform data augmentation. Implement the `compute_train_transform()` function in `cs231n/simclr/data_utils.py` to apply the following random transformations:\n",
        "\n",
        "1. Randomly resize and crop to 32x32.\n",
        "2. Horizontally flip the image with probability 0.5\n",
        "3. With a probability of 0.8, apply color jitter (see `compute_train_transform()` for definition)\n",
        "4. With a probability of 0.2, convert the image to grayscale"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ciaPM09vnlD2"
      },
      "source": [
        "Now complete `compute_train_transform()` and `CIFAR10Pair.__getitem__()` in `cs231n/simclr/data_utils.py` to apply the data augmentation transform and generate **$\\tilde{x}_i$** and **$\\tilde{x}_j$**.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fx_VMEeT3mSP"
      },
      "source": [
        "Test to make sure that your data augmentation code is correct:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "9cBFOd_A3mSQ"
      },
      "outputs": [],
      "source": [
        "from cs231n.simclr.data_utils import *\n",
        "from cs231n.simclr.contrastive_loss import *\n",
        "\n",
        "answers = torch.load('simclr_sanity_check.key')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "zoeu84i6nlD4",
        "outputId": "d5407d71-f2a7-4c12-fddb-7cfdb7400af4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Maximum error in data augmentation: 0\n"
          ]
        }
      ],
      "source": [
        "from PIL import Image\n",
        "import torchvision\n",
        "from torchvision.datasets import CIFAR10\n",
        "\n",
        "def test_data_augmentation(correct_output=None):\n",
        "    train_transform = compute_train_transform(seed=2147483647)\n",
        "    trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transform)\n",
        "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=2, shuffle=False, num_workers=2)\n",
        "    dataiter = iter(trainloader)\n",
        "    images, labels = next(dataiter)\n",
        "    img = torchvision.utils.make_grid(images)\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "    output = images\n",
        "\n",
        "    print(\"Maximum error in data augmentation: %g\"%rel_error( output.numpy(), correct_output.numpy()))\n",
        "\n",
        "# Should be less than 1e-07.\n",
        "test_data_augmentation(answers['data_augmentation'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i76TPfFL3mSQ"
      },
      "source": [
        "# Base Encoder and Projection Head\n",
        "The next steps are to apply the base encoder and projection head to the augmented samples **$\\tilde{x}_i$** and **$\\tilde{x}_j$**.\n",
        "\n",
        "The base encoder $f$ extracts representation vectors for the augmented samples. The SimCLR paper found that using deeper and wider models improved performance and thus chose [ResNet](https://arxiv.org/pdf/1512.03385.pdf) to use as the base encoder. The output of the base encoder are the representation vectors **$h_i = f(\\tilde{x}_i$)** and **$h_j = f(\\tilde{x}_j$)**.\n",
        "\n",
        "The projection head $g$ is a small neural network that maps the representation vectors **$h_i$** and **$h_j$** to the space where the contrastive loss is applied. The paper found that using a nonlinear projection head improved the representation quality of the layer before it. Specifically, they used a MLP with one hidden layer as the projection head $g$. The contrastive loss is then computed based on the outputs **$z_i = g(h_i$)** and **$z_j = g(h_j$)**.\n",
        "\n",
        "We provide implementations of these two parts in `cs231n/simclr/model.py`. Please skim through the file and make sure you understand the implementation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_9ORIPN3mSQ"
      },
      "source": [
        "# SimCLR: Contrastive Loss\n",
        "\n",
        "A mini-batch of $N$ training images yields a total of $2N$ data-augmented examples. For each positive pair $(i, j)$ of augmented examples, the contrastive loss function aims to maximize the agreement of vectors $z_i$ and $z_j$. Specifically, the loss is the normalized temperature-scaled cross entropy loss and aims to maximize the agreement of $z_i$ and $z_j$ relative to all other augmented examples in the batch:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHlLrv-SnlD6"
      },
      "source": [
        "$$\n",
        "l \\; (i, j) = -\\log \\frac{\\exp (\\;\\text{sim}(z_i, z_j)\\; / \\;\\tau) }{\\sum_{k=1}^{2N} \\mathbb{1}_{k \\neq i} \\exp (\\;\\text{sim} (z_i, z_k) \\;/ \\;\\tau) }\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4LsPkLtnlD6"
      },
      "source": [
        "where $\\mathbb{1} \\in \\{0, 1\\}$ is an indicator function that outputs $1$ if $k\\neq i$ and $0$ otherwise. $\\tau$ is a temperature parameter that determines how fast the exponentials increase.\n",
        "\n",
        "$sim(z_i, z_j) = \\frac{z_i \\cdot z_j}{|| z_i || || z_j ||}$ is the (normalized) dot product between vectors $z_i$ and $z_j$. The higher the similarity between $z_i$ and $z_j$, the larger the dot product is, and the larger the numerator becomes. The denominator normalizes the value by summing across $z_i$ and all other augmented examples $k$ in the batch. The range of the normalized value is $(0, 1)$, where a high score close to $1$ corresponds to a high similarity between the positive pair $(i, j)$ and low similarity between $i$ and other augmented examples $k$ in the batch. The negative log then maps the range $(0, 1)$ to the loss values $(\\inf, 0)$.\n",
        "\n",
        "The total loss is computed across all positive pairs $(i, j)$ in the batch. Let $z = [z_1, z_2, ..., z_{2N}]$ include all the augmented examples in the batch, where $z_{1}...z_{N}$ are outputs of the left branch, and $z_{N+1}...z_{2N}$ are outputs of the right branch. Thus, the positive pairs are $(z_{k}, z_{k + N})$ for $\\forall k \\in [1, N]$.\n",
        "\n",
        "Then, the total loss $L$ is:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uoi7kHmvnlD7"
      },
      "source": [
        "$$\n",
        "L = \\frac{1}{2N} \\sum_{k=1}^N [ \\; l(k, \\;k+N) + l(k+N, \\;k)\\;]\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dw-CdpPlnlD7"
      },
      "source": [
        "**NOTE:** this equation is slightly different from the one in the paper. We've rearranged the ordering of the positive pairs in the batch, so the indices are different. The rearrangement makes it easier to implement the code in vectorized form.\n",
        "\n",
        "We'll walk through the steps of implementing the loss function in vectorized form. Implement the functions `sim`, `simclr_loss_naive` in `cs231n/simclr/contrastive_loss.py`. Test your code by running the sanity checks below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "b-CiVKoe3mSR"
      },
      "outputs": [],
      "source": [
        "from cs231n.simclr.contrastive_loss import *\n",
        "answers = torch.load('simclr_sanity_check.key')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9VMpPjXc3mSS",
        "outputId": "2d072b8e-b0d0-46f7-aeb8-75120ec62275"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Maximum error in sim: 3.81097e-08\n",
            "Maximum error in sim: 0\n"
          ]
        }
      ],
      "source": [
        "def test_sim(left_vec, right_vec, correct_output):\n",
        "    output = sim(left_vec, right_vec).cpu().numpy()\n",
        "    print(\"Maximum error in sim: %g\"%rel_error(correct_output.numpy(), output))\n",
        "\n",
        "# Should be less than 1e-07.\n",
        "test_sim(answers['left'][0], answers['right'][0], answers['sim'][0])\n",
        "test_sim(answers['left'][1], answers['right'][1], answers['sim'][1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SMpreOIw3mSS",
        "outputId": "3134d667-7d43-4912-bc70-ad0af1533d01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Maximum error in simclr_loss_naive: 0\n",
            "Maximum error in simclr_loss_naive: 1.13123e-07\n"
          ]
        }
      ],
      "source": [
        "def test_loss_naive(left, right, tau, correct_output):\n",
        "    naive_loss = simclr_loss_naive(left, right, tau).item()\n",
        "    print(\"Maximum error in simclr_loss_naive: %g\"%rel_error(correct_output, naive_loss))\n",
        "\n",
        "# Should be less than 1e-07.\n",
        "test_loss_naive(answers['left'], answers['right'], 5.0, answers['loss']['5.0'])\n",
        "test_loss_naive(answers['left'], answers['right'], 1.0, answers['loss']['1.0'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_cEoU6hM3mSS"
      },
      "source": [
        "Now implement the vectorized version by implementing `sim_positive_pairs`, `compute_sim_matrix`, `simclr_loss_vectorized` in `cs231n/simclr/contrastive_loss.py`.  Test your code by running the sanity checks below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uW4g-aB_3mSS",
        "outputId": "09541048-459a-43c0-b62e-21d6ef6df0cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Maximum error in sim_positive_pairs: 3.81097e-08\n"
          ]
        }
      ],
      "source": [
        "def test_sim_positive_pairs(left, right, correct_output):\n",
        "    sim_pair = sim_positive_pairs(left, right).cpu().numpy()\n",
        "    print(\"Maximum error in sim_positive_pairs: %g\"%rel_error(correct_output.numpy(), sim_pair))\n",
        "\n",
        "# Should be less than 1e-07.\n",
        "test_sim_positive_pairs(answers['left'], answers['right'], answers['sim'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fm5z3N7k3mST",
        "outputId": "019b58cc-e79d-4ef4-e01f-7eb867964b14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test passed!\n"
          ]
        }
      ],
      "source": [
        "def test_sim_matrix(left, right, correct_output):\n",
        "    out = torch.cat([left, right], dim=0)\n",
        "    sim_matrix = compute_sim_matrix(out).cpu()\n",
        "    assert torch.isclose(sim_matrix, correct_output).all(), \"correct: {}. got: {}\".format(correct_output, sim_matrix)\n",
        "    print(\"Test passed!\")\n",
        "\n",
        "test_sim_matrix(answers['left'], answers['right'], answers['sim_matrix'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5Eps9Pc3mST",
        "outputId": "f540c5c6-7bd9-4463-bd0c-611900442426"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Maximum error in loss_vectorized: 0\n",
            "Maximum error in loss_vectorized: 5.65617e-08\n"
          ]
        }
      ],
      "source": [
        "def test_loss_vectorized(left, right, tau, correct_output):\n",
        "    vec_loss = simclr_loss_vectorized(left, right, tau, device=left.device).item()\n",
        "    print(\"Maximum error in loss_vectorized: %g\"%rel_error(correct_output, vec_loss))\n",
        "\n",
        "# Should be less than 1e-07.\n",
        "test_loss_vectorized(answers['left'], answers['right'], 5.0, answers['loss']['5.0'])\n",
        "test_loss_vectorized(answers['left'], answers['right'], 1.0, answers['loss']['1.0'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwCbomo_nlD_"
      },
      "source": [
        "# Implement the train function\n",
        "Complete the `train()` function in `cs231n/simclr/utils.py` to obtain the model's output and use `simclr_loss_vectorized` to compute the loss. (Please take a look at the `Model` class in `cs231n/simclr/model.py` to understand the model pipeline and the returned values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "eRbftXxRnlD_"
      },
      "outputs": [],
      "source": [
        "from cs231n.simclr.data_utils import *\n",
        "from cs231n.simclr.model import *\n",
        "from cs231n.simclr.utils import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwLN5rk9nlD_"
      },
      "source": [
        "### Train the SimCLR model\n",
        "\n",
        "Run the following cells to load in the pretrained weights and continue to train a little bit more. This part will take ~10 minutes and will output to `pretrained_model/trained_simclr_model.pth`.\n",
        "\n",
        "**NOTE:** Don't worry about logs such as '_[WARN] Cannot find rule for ..._'. These are related to another module used in the notebook. You can verify the integrity of your code changes through our provided prompts and comments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHXYbTba3mSU",
        "outputId": "8e719507-f469-4c17-e53f-4a96e24e1417"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
            "[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.\n",
            "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
            "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
            "[INFO] Register count_adap_avgpool() for <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>.\n",
            "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
            "[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm1d'>.\n",
            "# Model Params: 24.62M FLOPs: 1.31G\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Train Epoch: [1/1] Loss: 3.2582: 100%|██████████| 390/390 [02:36<00:00,  2.49it/s]\n",
            "Feature extracting: 100%|██████████| 782/782 [00:45<00:00, 17.08it/s]\n",
            "Test Epoch: [1/1] Acc@1:83.30% Acc@5:99.37%: 100%|██████████| 157/157 [00:12<00:00, 13.04it/s]\n"
          ]
        }
      ],
      "source": [
        "# Do not modify this cell.\n",
        "feature_dim = 128\n",
        "temperature = 0.5\n",
        "k = 200\n",
        "batch_size = 64\n",
        "epochs = 1\n",
        "temperature = 0.5\n",
        "percentage = 0.5\n",
        "pretrained_path = './pretrained_model/pretrained_simclr_model.pth'\n",
        "\n",
        "# Prepare the data.\n",
        "train_transform = compute_train_transform()\n",
        "train_data = CIFAR10Pair(root='data', train=True, transform=train_transform, download=True)\n",
        "train_data = torch.utils.data.Subset(train_data, list(np.arange(int(len(train_data)*percentage))))\n",
        "train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=16, pin_memory=True, drop_last=True)\n",
        "test_transform = compute_test_transform()\n",
        "memory_data = CIFAR10Pair(root='data', train=True, transform=test_transform, download=True)\n",
        "memory_loader = DataLoader(memory_data, batch_size=batch_size, shuffle=False, num_workers=16, pin_memory=True)\n",
        "test_data = CIFAR10Pair(root='data', train=False, transform=test_transform, download=True)\n",
        "test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=16, pin_memory=True)\n",
        "\n",
        "# Set up the model and optimizer config.\n",
        "model = Model(feature_dim)\n",
        "model.load_state_dict(torch.load(pretrained_path, map_location='cpu'), strict=False)\n",
        "model = model.to(device)\n",
        "flops, params = profile(model, inputs=(torch.randn(1, 3, 32, 32).to(device),))\n",
        "flops, params = clever_format([flops, params])\n",
        "print('# Model Params: {} FLOPs: {}'.format(params, flops))\n",
        "optimizer = optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-6)\n",
        "c = len(memory_data.classes)\n",
        "\n",
        "# Training loop.\n",
        "results = {'train_loss': [], 'test_acc@1': [], 'test_acc@5': []} #<< -- output\n",
        "\n",
        "if not os.path.exists('results'):\n",
        "    os.mkdir('results')\n",
        "best_acc = 0.0\n",
        "for epoch in range(1, epochs + 1):\n",
        "    train_loss = train(model, train_loader, optimizer, epoch, epochs, batch_size=batch_size, temperature=temperature, device=device)\n",
        "    results['train_loss'].append(train_loss)\n",
        "    test_acc_1, test_acc_5 = test(model, memory_loader, test_loader, epoch, epochs, c, k=k, temperature=temperature, device=device)\n",
        "    results['test_acc@1'].append(test_acc_1)\n",
        "    results['test_acc@5'].append(test_acc_5)\n",
        "\n",
        "    # Save statistics.\n",
        "    if test_acc_1 > best_acc:\n",
        "        best_acc = test_acc_1\n",
        "        torch.save(model.state_dict(), './pretrained_model/trained_simclr_model.pth')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bm8P70VD3mST"
      },
      "source": [
        "# Finetune a Linear Layer for Classification!\n",
        "\n",
        "Now it's time to put the representation vectors to the test!\n",
        "\n",
        "We remove the projection head from the SimCLR model and slap on a linear layer to finetune for a simple classification task. All layers before the linear layer are frozen, and only the weights in the final linear layer are trained. We compare the performance of the SimCLR + finetuning model against a baseline model, where no self-supervised learning is done beforehand, and all weights in the model are trained. You will get to see for yourself the power of self-supervised learning and how the learned representation vectors improve downstream task performance.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ESAJ9bgXnlEB"
      },
      "source": [
        "## Baseline: Without Self-Supervised Learning\n",
        "First, let's take a look at the baseline model. We'll remove the projection head from the SimCLR model and slap on a linear layer to finetune for a simple classification task. No self-supervised learning is done beforehand, and all weights in the model are trained. Run the following cells.\n",
        "\n",
        "**NOTE:** Don't worry if you see low but reasonable performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "WiWM0NaJnlEG",
        "pycharm": {
          "is_executing": false
        }
      },
      "outputs": [],
      "source": [
        "class Classifier(nn.Module):\n",
        "    def __init__(self, num_class):\n",
        "        super(Classifier, self).__init__()\n",
        "\n",
        "        # Encoder.\n",
        "        self.f = Model().f\n",
        "\n",
        "        # Classifier.\n",
        "        self.fc = nn.Linear(2048, num_class, bias=True)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.f(x)\n",
        "        feature = torch.flatten(x, start_dim=1)\n",
        "        out = self.fc(feature)\n",
        "        return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaxM_6sCnlEH",
        "outputId": "8f62d951-109a-4940-ed2c-1edf8c945675"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
            "[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.\n",
            "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
            "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
            "[INFO] Register count_adap_avgpool() for <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>.\n",
            "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
            "# Model Params: 23.52M FLOPs: 1.31G\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Train Epoch: [1/10] Loss: 2.5539 ACC@1: 10.70% ACC@5: 51.30%: 100%|██████████| 40/40 [00:10<00:00,  3.97it/s]\n",
            "Test Epoch: [1/10] Loss: 2.3212 ACC@1: 11.48% ACC@5: 51.60%: 100%|██████████| 79/79 [00:10<00:00,  7.52it/s]\n",
            "Train Epoch: [2/10] Loss: 2.4299 ACC@1: 10.88% ACC@5: 51.42%: 100%|██████████| 40/40 [00:09<00:00,  4.13it/s]\n",
            "Test Epoch: [2/10] Loss: 2.7025 ACC@1: 10.18% ACC@5: 55.11%: 100%|██████████| 79/79 [00:10<00:00,  7.30it/s]\n",
            "Train Epoch: [3/10] Loss: 2.3950 ACC@1: 11.70% ACC@5: 53.12%: 100%|██████████| 40/40 [00:08<00:00,  4.65it/s]\n",
            "Test Epoch: [3/10] Loss: 2.5049 ACC@1: 10.24% ACC@5: 53.42%: 100%|██████████| 79/79 [00:11<00:00,  6.84it/s]\n",
            "Train Epoch: [4/10] Loss: 2.4029 ACC@1: 12.44% ACC@5: 54.02%: 100%|██████████| 40/40 [00:10<00:00,  3.88it/s]\n",
            "Test Epoch: [4/10] Loss: 2.5870 ACC@1: 10.34% ACC@5: 52.39%: 100%|██████████| 79/79 [00:10<00:00,  7.63it/s]\n",
            "Train Epoch: [5/10] Loss: 2.4127 ACC@1: 12.24% ACC@5: 54.48%: 100%|██████████| 40/40 [00:09<00:00,  4.39it/s]\n",
            "Test Epoch: [5/10] Loss: 2.7166 ACC@1: 14.82% ACC@5: 54.43%: 100%|██████████| 79/79 [00:11<00:00,  6.90it/s]\n",
            "Train Epoch: [6/10] Loss: 2.3939 ACC@1: 12.44% ACC@5: 54.02%: 100%|██████████| 40/40 [00:09<00:00,  4.43it/s]\n",
            "Test Epoch: [6/10] Loss: 2.3872 ACC@1: 13.67% ACC@5: 54.45%: 100%|██████████| 79/79 [00:10<00:00,  7.40it/s]\n",
            "Train Epoch: [7/10] Loss: 2.3648 ACC@1: 13.10% ACC@5: 54.66%: 100%|██████████| 40/40 [00:09<00:00,  4.15it/s]\n",
            "Test Epoch: [7/10] Loss: 2.4616 ACC@1: 11.80% ACC@5: 55.54%: 100%|██████████| 79/79 [00:10<00:00,  7.50it/s]\n",
            "Train Epoch: [8/10] Loss: 2.3864 ACC@1: 11.86% ACC@5: 55.12%: 100%|██████████| 40/40 [00:08<00:00,  4.66it/s]\n",
            "Test Epoch: [8/10] Loss: 2.4651 ACC@1: 14.32% ACC@5: 59.70%: 100%|██████████| 79/79 [00:11<00:00,  7.12it/s]\n",
            "Train Epoch: [9/10] Loss: 2.3793 ACC@1: 13.22% ACC@5: 56.60%: 100%|██████████| 40/40 [00:10<00:00,  3.95it/s]\n",
            "Test Epoch: [9/10] Loss: 2.6685 ACC@1: 10.05% ACC@5: 57.28%: 100%|██████████| 79/79 [00:10<00:00,  7.54it/s]\n",
            "Train Epoch: [10/10] Loss: 2.4030 ACC@1: 12.96% ACC@5: 57.64%: 100%|██████████| 40/40 [00:08<00:00,  4.69it/s]\n",
            "Test Epoch: [10/10] Loss: 2.4337 ACC@1: 15.30% ACC@5: 58.28%: 100%|██████████| 79/79 [00:11<00:00,  6.85it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best top-1 accuracy without self-supervised learning:  15.299999999999999\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Do not modify this cell.\n",
        "feature_dim = 128\n",
        "temperature = 0.5\n",
        "k = 200\n",
        "batch_size = 128\n",
        "epochs = 10\n",
        "percentage = 0.1\n",
        "\n",
        "train_transform = compute_train_transform()\n",
        "train_data = CIFAR10(root='data', train=True, transform=train_transform, download=True)\n",
        "trainset = torch.utils.data.Subset(train_data, list(np.arange(int(len(train_data)*percentage))))\n",
        "train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=16, pin_memory=True)\n",
        "test_transform = compute_test_transform()\n",
        "test_data = CIFAR10(root='data', train=False, transform=test_transform, download=True)\n",
        "test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=16, pin_memory=True)\n",
        "\n",
        "model = Classifier(num_class=len(train_data.classes)).to(device)\n",
        "for param in model.f.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "flops, params = profile(model, inputs=(torch.randn(1, 3, 32, 32).to(device),))\n",
        "flops, params = clever_format([flops, params])\n",
        "print('# Model Params: {} FLOPs: {}'.format(params, flops))\n",
        "optimizer = optim.Adam(model.fc.parameters(), lr=1e-3, weight_decay=1e-6)\n",
        "no_pretrain_results = {'train_loss': [], 'train_acc@1': [], 'train_acc@5': [],\n",
        "           'test_loss': [], 'test_acc@1': [], 'test_acc@5': []}\n",
        "\n",
        "best_acc = 0.0\n",
        "for epoch in range(1, epochs + 1):\n",
        "    train_loss, train_acc_1, train_acc_5 = train_val(model, train_loader, optimizer, epoch, epochs, device='cuda')\n",
        "    no_pretrain_results['train_loss'].append(train_loss)\n",
        "    no_pretrain_results['train_acc@1'].append(train_acc_1)\n",
        "    no_pretrain_results['train_acc@5'].append(train_acc_5)\n",
        "    test_loss, test_acc_1, test_acc_5 = train_val(model, test_loader, None, epoch, epochs)\n",
        "    no_pretrain_results['test_loss'].append(test_loss)\n",
        "    no_pretrain_results['test_acc@1'].append(test_acc_1)\n",
        "    no_pretrain_results['test_acc@5'].append(test_acc_5)\n",
        "    if test_acc_1 > best_acc:\n",
        "        best_acc = test_acc_1\n",
        "\n",
        "# Print the best test accuracy.\n",
        "print('Best top-1 accuracy without self-supervised learning: ', best_acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q677cNEcnlEH"
      },
      "source": [
        "## With Self-Supervised Learning\n",
        "\n",
        "Let's see how much improvement we get with self-supervised learning. Here, we pretrain the SimCLR model using the simclr loss you wrote, remove the projection head from the SimCLR model, and use a linear layer to finetune for a simple classification task."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hoD0hI38nlEH",
        "outputId": "00ff6c35-e957-40b0-b7b3-150b57af145f",
        "test": "ssl_best_accuracy"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.\n",
            "[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.\n",
            "[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.\n",
            "[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.\n",
            "[INFO] Register count_adap_avgpool() for <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>.\n",
            "[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.\n",
            "# Model Params: 23.52M FLOPs: 1.31G\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Train Epoch: [1/10] Loss: 1.8214 ACC@1: 64.86% ACC@5: 93.62%: 100%|██████████| 40/40 [00:09<00:00,  4.39it/s]\n",
            "Test Epoch: [1/10] Loss: 1.3352 ACC@1: 78.05% ACC@5: 98.22%: 100%|██████████| 79/79 [00:10<00:00,  7.58it/s]\n",
            "Train Epoch: [2/10] Loss: 1.1873 ACC@1: 75.86% ACC@5: 97.52%: 100%|██████████| 40/40 [00:08<00:00,  4.74it/s]\n",
            "Test Epoch: [2/10] Loss: 0.9419 ACC@1: 79.22% ACC@5: 98.26%: 100%|██████████| 79/79 [00:11<00:00,  6.97it/s]\n",
            "Train Epoch: [3/10] Loss: 0.9338 ACC@1: 76.50% ACC@5: 98.06%: 100%|██████████| 40/40 [00:10<00:00,  3.96it/s]\n",
            "Test Epoch: [3/10] Loss: 0.7827 ACC@1: 79.90% ACC@5: 98.70%: 100%|██████████| 79/79 [00:10<00:00,  7.67it/s]\n",
            "Train Epoch: [4/10] Loss: 0.8417 ACC@1: 76.90% ACC@5: 97.64%: 100%|██████████| 40/40 [00:08<00:00,  4.78it/s]\n",
            "Test Epoch: [4/10] Loss: 0.7106 ACC@1: 79.72% ACC@5: 98.57%: 100%|██████████| 79/79 [00:10<00:00,  7.54it/s]\n",
            "Train Epoch: [5/10] Loss: 0.7680 ACC@1: 78.00% ACC@5: 97.78%: 100%|██████████| 40/40 [00:08<00:00,  4.70it/s]\n",
            "Test Epoch: [5/10] Loss: 0.6467 ACC@1: 81.06% ACC@5: 98.85%: 100%|██████████| 79/79 [00:10<00:00,  7.51it/s]\n",
            "Train Epoch: [6/10] Loss: 0.7347 ACC@1: 77.64% ACC@5: 97.90%: 100%|██████████| 40/40 [00:10<00:00,  3.98it/s]\n",
            "Test Epoch: [6/10] Loss: 0.6093 ACC@1: 81.57% ACC@5: 98.90%: 100%|██████████| 79/79 [00:10<00:00,  7.59it/s]\n",
            "Train Epoch: [7/10] Loss: 0.6942 ACC@1: 78.32% ACC@5: 98.30%: 100%|██████████| 40/40 [00:08<00:00,  4.81it/s]\n",
            "Test Epoch: [7/10] Loss: 0.5894 ACC@1: 81.66% ACC@5: 98.89%: 100%|██████████| 79/79 [00:11<00:00,  6.93it/s]\n",
            "Train Epoch: [8/10] Loss: 0.6770 ACC@1: 78.56% ACC@5: 98.42%: 100%|██████████| 40/40 [00:09<00:00,  4.26it/s]\n",
            "Test Epoch: [8/10] Loss: 0.5671 ACC@1: 82.31% ACC@5: 98.98%: 100%|██████████| 79/79 [00:10<00:00,  7.67it/s]\n",
            "Train Epoch: [9/10] Loss: 0.6681 ACC@1: 78.82% ACC@5: 98.36%: 100%|██████████| 40/40 [00:08<00:00,  4.58it/s]\n",
            "Test Epoch: [9/10] Loss: 0.5551 ACC@1: 82.23% ACC@5: 98.90%: 100%|██████████| 79/79 [00:10<00:00,  7.44it/s]\n",
            "Train Epoch: [10/10] Loss: 0.6435 ACC@1: 79.40% ACC@5: 98.08%: 100%|██████████| 40/40 [00:08<00:00,  4.62it/s]\n",
            "Test Epoch: [10/10] Loss: 0.5411 ACC@1: 82.38% ACC@5: 98.95%: 100%|██████████| 79/79 [00:11<00:00,  7.18it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best top-1 accuracy with self-supervised learning:  82.38\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Do not modify this cell.\n",
        "feature_dim = 128\n",
        "temperature = 0.5\n",
        "k = 200\n",
        "batch_size = 128\n",
        "epochs = 10\n",
        "percentage = 0.1\n",
        "pretrained_path = './pretrained_model/trained_simclr_model.pth'\n",
        "\n",
        "train_transform = compute_train_transform()\n",
        "train_data = CIFAR10(root='data', train=True, transform=train_transform, download=True)\n",
        "trainset = torch.utils.data.Subset(train_data, list(np.arange(int(len(train_data)*percentage))))\n",
        "train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=16, pin_memory=True)\n",
        "test_transform = compute_test_transform()\n",
        "test_data = CIFAR10(root='data', train=False, transform=test_transform, download=True)\n",
        "test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=16, pin_memory=True)\n",
        "\n",
        "model = Classifier(num_class=len(train_data.classes))\n",
        "model.load_state_dict(torch.load(pretrained_path, map_location='cpu'), strict=False)\n",
        "model = model.to(device)\n",
        "for param in model.f.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "flops, params = profile(model, inputs=(torch.randn(1, 3, 32, 32).to(device),))\n",
        "flops, params = clever_format([flops, params])\n",
        "print('# Model Params: {} FLOPs: {}'.format(params, flops))\n",
        "optimizer = optim.Adam(model.fc.parameters(), lr=1e-3, weight_decay=1e-6)\n",
        "pretrain_results = {'train_loss': [], 'train_acc@1': [], 'train_acc@5': [],\n",
        "           'test_loss': [], 'test_acc@1': [], 'test_acc@5': []}\n",
        "\n",
        "best_acc = 0.0\n",
        "for epoch in range(1, epochs + 1):\n",
        "    train_loss, train_acc_1, train_acc_5 = train_val(model, train_loader, optimizer, epoch, epochs)\n",
        "    pretrain_results['train_loss'].append(train_loss)\n",
        "    pretrain_results['train_acc@1'].append(train_acc_1)\n",
        "    pretrain_results['train_acc@5'].append(train_acc_5)\n",
        "    test_loss, test_acc_1, test_acc_5 = train_val(model, test_loader, None, epoch, epochs)\n",
        "    pretrain_results['test_loss'].append(test_loss)\n",
        "    pretrain_results['test_acc@1'].append(test_acc_1)\n",
        "    pretrain_results['test_acc@5'].append(test_acc_5)\n",
        "    if test_acc_1 > best_acc:\n",
        "        best_acc = test_acc_1\n",
        "\n",
        "# Print the best test accuracy. You should see a best top-1 accuracy of >=70%.\n",
        "print('Best top-1 accuracy with self-supervised learning: ', best_acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdNuVOKhnlEI"
      },
      "source": [
        "### Plot your Comparison\n",
        "\n",
        "Plot the test accuracies between the baseline model (no pretraining) and same model pretrained with self-supervised learning."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "faN966GpnlEI",
        "outputId": "1edb690c-7d73-4898-9f50-09cdc281165d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(no_pretrain_results['test_acc@1'], label=\"Without Pretrain\")\n",
        "plt.plot(pretrain_results['test_acc@1'], label=\"With Pretrain\")\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Test Top-1 Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}