{"cells": [{"cell_type": "markdown", "id": "636c4a3d", "metadata": {"papermill": {"duration": 0.009294, "end_time": "2023-03-15T10:17:04.100884", "exception": false, "start_time": "2023-03-15T10:17:04.091590", "status": "completed"}, "tags": []}, "source": ["\n", "# Barlow Twins Tutorial\n", "\n", "* **Author:** Ananya Harsh Jha\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-03-15T10:15:32.745667\n", "\n", "This notebook describes the self-supervised learning method Barlow Twins.\n", "Barlow Twins differs from other recently proposed algorithms as it doesn't\n", "fall under the category of either contrastive learning, or methods like knowledge\n", "distillation or clustering. The simplicity of the loss function and its effectiveness\n", "in comparison to the current state of the art makes Barlow Twins an interesting\n", "case study.\n", "\n", "\n", "---\n", "Open in [![Open In Colab](data:image/png;base64,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){height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/lightning_examples/barlow-twins.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/stable/)\n", "| Join us [on Slack](https://www.pytorchlightning.ai/community)"]}, {"cell_type": "markdown", "id": "ea7bda73", "metadata": {"papermill": {"duration": 0.003524, "end_time": "2023-03-15T10:17:04.108984", "exception": false, "start_time": "2023-03-15T10:17:04.105460", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "033687e1", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-03-15T10:17:04.117522Z", "iopub.status.busy": "2023-03-15T10:17:04.117046Z", "iopub.status.idle": "2023-03-15T10:17:07.370173Z", "shell.execute_reply": "2023-03-15T10:17:07.368604Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 3.260711, "end_time": "2023-03-15T10:17:07.373152", "exception": false, "start_time": "2023-03-15T10:17:04.112441", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", "\u001b[0m"]}], "source": ["! pip install --quiet \"setuptools==67.4.0\" \"ipython[notebook]>=8.0.0, <8.12.0\" \"pytorch-lightning>=1.4, <2.0.0\" \"lightning>=2.0.0rc0\" \"torch>=1.8.1, <1.14.0\" \"torchmetrics>=0.7, <0.12\" \"torchvision\" \"matplotlib\""]}, {"cell_type": "markdown", "id": "9b96f5ac", "metadata": {"papermill": {"duration": 0.006333, "end_time": "2023-03-15T10:17:07.388176", "exception": false, "start_time": "2023-03-15T10:17:07.381843", "status": "completed"}, "tags": []}, "source": ["## Barlow Twins\n", "\n", "Barlow Twins finds itself in unique place amongst the current state-of-the-art self-supervised learning methods. It does not fall under the existing categories of contrastive learning, knowledge distillation or clustering based methods. Instead, it creates its own category of redundancy reductionand achieves competitive performance with a simple yet effective loss function. In this tutorial, we look at coding up a small version of Barlow Twins algorithm using PyTorch Lightning."]}, {"cell_type": "code", "execution_count": 2, "id": "c292211b", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:07.397278Z", "iopub.status.busy": "2023-03-15T10:17:07.396895Z", "iopub.status.idle": "2023-03-15T10:17:10.093146Z", "shell.execute_reply": "2023-03-15T10:17:10.091433Z"}, "papermill": {"duration": 2.704698, "end_time": "2023-03-15T10:17:10.096449", "exception": false, "start_time": "2023-03-15T10:17:07.391751", "status": "completed"}, "tags": []}, "outputs": [], "source": ["from functools import partial\n", "from typing import Sequence, Tuple, Union\n", "\n", "import lightning as L\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torchvision.transforms as transforms\n", "import torchvision.transforms.functional as VisionF\n", "from lightning.pytorch.callbacks import Callback, ModelCheckpoint\n", "from torch import Tensor\n", "from torch.utils.data import DataLoader\n", "from torchmetrics.functional import accuracy\n", "from torchvision.datasets import CIFAR10\n", "from torchvision.models.resnet import resnet18\n", "from torchvision.utils import make_grid\n", "\n", "batch_size = 32\n", "num_workers = 0  # to run notebook on CPU\n", "max_epochs = 200\n", "z_dim = 128"]}, {"cell_type": "markdown", "id": "1c50d589", "metadata": {"papermill": {"duration": 0.004811, "end_time": "2023-03-15T10:17:10.109938", "exception": false, "start_time": "2023-03-15T10:17:10.105127", "status": "completed"}, "tags": []}, "source": ["### Transforms\n", "\n", "We first define the data augmentation pipeline used in Barlow Twins. Here, we use pipeline proposed in SimCLR, which generates two copies/views of an input image by applying the following transformations in a sequence.\n", "\n", "First it takes a random crop of the image and resizes it to a fixed pre-specified size. Then, it applies a left-to-right random flip with a probability of 0.5. This step is followed by a composition of color jitter, conversion to grayscale with a probability of 0.2 and the application of a Gaussian blur filter. Finally, we normalize the image and convert it to a tensor.\n", "\n", "Within this transform, we add a third view for our online finetuner, which we explain later on. But, to explain things quickly here, we add a another transform to perform perform test our encoder on a downstream classification task."]}, {"cell_type": "code", "execution_count": 3, "id": "3f95ed87", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:10.122017Z", "iopub.status.busy": "2023-03-15T10:17:10.120628Z", "iopub.status.idle": "2023-03-15T10:17:10.136355Z", "shell.execute_reply": "2023-03-15T10:17:10.135536Z"}, "papermill": {"duration": 0.024148, "end_time": "2023-03-15T10:17:10.138739", "exception": false, "start_time": "2023-03-15T10:17:10.114591", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class BarlowTwinsTransform:\n", "    def __init__(self, train=True, input_height=224, gaussian_blur=True, jitter_strength=1.0, normalize=None):\n", "        self.input_height = input_height\n", "        self.gaussian_blur = gaussian_blur\n", "        self.jitter_strength = jitter_strength\n", "        self.normalize = normalize\n", "        self.train = train\n", "\n", "        color_jitter = transforms.ColorJitter(\n", "            0.8 * self.jitter_strength,\n", "            0.8 * self.jitter_strength,\n", "            0.8 * self.jitter_strength,\n", "            0.2 * self.jitter_strength,\n", "        )\n", "\n", "        color_transform = [transforms.RandomApply([color_jitter], p=0.8), transforms.RandomGrayscale(p=0.2)]\n", "\n", "        if self.gaussian_blur:\n", "            kernel_size = int(0.1 * self.input_height)\n", "            if kernel_size % 2 == 0:\n", "                kernel_size += 1\n", "\n", "            color_transform.append(transforms.RandomApply([transforms.GaussianBlur(kernel_size=kernel_size)], p=0.5))\n", "\n", "        self.color_transform = transforms.Compose(color_transform)\n", "\n", "        if normalize is None:\n", "            self.final_transform = transforms.ToTensor()\n", "        else:\n", "            self.final_transform = transforms.Compose([transforms.ToTensor(), normalize])\n", "\n", "        self.transform = transforms.Compose(\n", "            [\n", "                transforms.RandomResizedCrop(self.input_height),\n", "                transforms.RandomHorizontalFlip(p=0.5),\n", "                self.color_transform,\n", "                self.final_transform,\n", "            ]\n", "        )\n", "\n", "        self.finetune_transform = None\n", "        if self.train:\n", "            self.finetune_transform = transforms.Compose(\n", "                [\n", "                    transforms.RandomCrop(32, padding=4, padding_mode=\"reflect\"),\n", "                    transforms.RandomHorizontalFlip(),\n", "                    transforms.ToTensor(),\n", "                ]\n", "            )\n", "        else:\n", "            self.finetune_transform = transforms.ToTensor()\n", "\n", "    def __call__(self, sample):\n", "        return self.transform(sample), self.transform(sample), self.finetune_transform(sample)"]}, {"cell_type": "markdown", "id": "3618df65", "metadata": {"papermill": {"duration": 0.051171, "end_time": "2023-03-15T10:17:10.195254", "exception": false, "start_time": "2023-03-15T10:17:10.144083", "status": "completed"}, "tags": []}, "source": ["### Dataset\n", "\n", "We select CIFAR10 as the dataset to demonstrate the pre-training process for Barlow Twins. CIFAR10 images are 32x32 in size and we do not apply a Gaussian blur transformation on them. In this step, we create the training and validation dataloaders for CIFAR10."]}, {"cell_type": "code", "execution_count": 4, "id": "558fb49d", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:10.205675Z", "iopub.status.busy": "2023-03-15T10:17:10.205290Z", "iopub.status.idle": "2023-03-15T10:17:56.848780Z", "shell.execute_reply": "2023-03-15T10:17:56.847355Z"}, "papermill": {"duration": 46.652164, "end_time": "2023-03-15T10:17:56.851579", "exception": false, "start_time": "2023-03-15T10:17:10.199415", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "d90dc21138c847f4b380495aaf79540c", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/170498071 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting ./cifar-10-python.tar.gz to .\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}], "source": ["def cifar10_normalization():\n", "    normalize = transforms.Normalize(\n", "        mean=[x / 255.0 for x in [125.3, 123.0, 113.9]], std=[x / 255.0 for x in [63.0, 62.1, 66.7]]\n", "    )\n", "    return normalize\n", "\n", "\n", "train_transform = BarlowTwinsTransform(\n", "    train=True, input_height=32, gaussian_blur=False, jitter_strength=0.5, normalize=cifar10_normalization()\n", ")\n", "train_dataset = CIFAR10(root=\".\", train=True, download=True, transform=train_transform)\n", "\n", "val_transform = BarlowTwinsTransform(\n", "    train=False, input_height=32, gaussian_blur=False, jitter_strength=0.5, normalize=cifar10_normalization()\n", ")\n", "val_dataset = CIFAR10(root=\".\", train=False, download=True, transform=train_transform)\n", "\n", "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, drop_last=True)\n", "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers, drop_last=True)"]}, {"cell_type": "markdown", "id": "ecb11640", "metadata": {"papermill": {"duration": 0.003955, "end_time": "2023-03-15T10:17:56.863491", "exception": false, "start_time": "2023-03-15T10:17:56.859536", "status": "completed"}, "tags": []}, "source": ["### Plot images\n", "\n", "To see how the CIFAR10 images look after the data augmentation pipeline, we load a few images from the dataloader and plot them here."]}, {"cell_type": "code", "execution_count": 5, "id": "bb076485", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:56.873797Z", "iopub.status.busy": "2023-03-15T10:17:56.873355Z", "iopub.status.idle": "2023-03-15T10:17:57.197704Z", "shell.execute_reply": "2023-03-15T10:17:57.196982Z"}, "papermill": {"duration": 0.334869, "end_time": "2023-03-15T10:17:57.202254", "exception": false, "start_time": "2023-03-15T10:17:56.867385", "status": "completed"}, "tags": []}, "outputs": [{"data": {"image/png": "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", "text/plain": ["<Figure size 640x480 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["for batch in val_loader:\n", "    (img1, img2, _), label = batch\n", "    break\n", "\n", "img_grid = make_grid(img1, normalize=True)\n", "\n", "\n", "def show(imgs):\n", "    if not isinstance(imgs, list):\n", "        imgs = [imgs]\n", "    fix, axs = plt.subplots(ncols=len(imgs), squeeze=False)\n", "    for i, img in enumerate(imgs):\n", "        img = img.detach()\n", "        img = VisionF.to_pil_image(img)\n", "        axs[0, i].imshow(np.asarray(img))\n", "        axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])\n", "\n", "\n", "show(img_grid)"]}, {"cell_type": "markdown", "id": "5fa7d593", "metadata": {"papermill": {"duration": 0.006015, "end_time": "2023-03-15T10:17:57.219237", "exception": false, "start_time": "2023-03-15T10:17:57.213222", "status": "completed"}, "tags": []}, "source": ["### Barlow Twins Loss\n", "\n", "Here we define the loss function for Barlow Twins. It first normalizes the D dimensinonal vectors from the projection head and then computes the DxD cross-correlation matrix between the normalized vectors of the 2 views of each image.\n", "\n", "Then it splits this cross-correlation matrix into two parts. The first part, the diagonal of this matrix is brought closer to 1, which pushes up the cosine similarity between the latent vectors of two views of each image, thus making the backbone invariant to the transformations applied to the views. The second part of the loss pushes the non-diagonal elements of the cross-corrlelation matrix closes to 0. This reduces the redundancy between the different dimensions of the latent vector."]}, {"cell_type": "code", "execution_count": 6, "id": "a58346bc", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:57.232610Z", "iopub.status.busy": "2023-03-15T10:17:57.232425Z", "iopub.status.idle": "2023-03-15T10:17:57.239404Z", "shell.execute_reply": "2023-03-15T10:17:57.238845Z"}, "papermill": {"duration": 0.016432, "end_time": "2023-03-15T10:17:57.241653", "exception": false, "start_time": "2023-03-15T10:17:57.225221", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class BarlowTwinsLoss(nn.Module):\n", "    def __init__(self, batch_size, lambda_coeff=5e-3, z_dim=128):\n", "        super().__init__()\n", "\n", "        self.z_dim = z_dim\n", "        self.batch_size = batch_size\n", "        self.lambda_coeff = lambda_coeff\n", "\n", "    def off_diagonal_ele(self, x):\n", "        # taken from: https://github.com/facebookresearch/barlowtwins/blob/main/main.py\n", "        # return a flattened view of the off-diagonal elements of a square matrix\n", "        n, m = x.shape\n", "        assert n == m\n", "        return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()\n", "\n", "    def forward(self, z1, z2):\n", "        # N x D, where N is the batch size and D is output dim of projection head\n", "        z1_norm = (z1 - torch.mean(z1, dim=0)) / torch.std(z1, dim=0)\n", "        z2_norm = (z2 - torch.mean(z2, dim=0)) / torch.std(z2, dim=0)\n", "\n", "        cross_corr = torch.matmul(z1_norm.T, z2_norm) / self.batch_size\n", "\n", "        on_diag = torch.diagonal(cross_corr).add_(-1).pow_(2).sum()\n", "        off_diag = self.off_diagonal_ele(cross_corr).pow_(2).sum()\n", "\n", "        return on_diag + self.lambda_coeff * off_diag"]}, {"cell_type": "markdown", "id": "1da0791d", "metadata": {"papermill": {"duration": 0.005874, "end_time": "2023-03-15T10:17:57.258039", "exception": false, "start_time": "2023-03-15T10:17:57.252165", "status": "completed"}, "tags": []}, "source": ["### Backbone\n", "\n", "This is a standard Resnet backbone that we pre-train using the Barlow Twins method. To accommodate the 32x32 CIFAR10 images, we replace the first 7x7 convolution of the Resnet backbone by a 3x3 filter. We also remove the first Maxpool layer from the network for CIFAR10 images."]}, {"cell_type": "code", "execution_count": 7, "id": "58250a78", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:57.270905Z", "iopub.status.busy": "2023-03-15T10:17:57.270739Z", "iopub.status.idle": "2023-03-15T10:17:57.414693Z", "shell.execute_reply": "2023-03-15T10:17:57.413480Z"}, "papermill": {"duration": 0.153123, "end_time": "2023-03-15T10:17:57.417187", "exception": false, "start_time": "2023-03-15T10:17:57.264064", "status": "completed"}, "tags": []}, "outputs": [], "source": ["encoder = resnet18()\n", "\n", "# for CIFAR10, replace the first 7x7 conv with smaller 3x3 conv and remove the first maxpool\n", "encoder.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n", "encoder.maxpool = nn.MaxPool2d(kernel_size=1, stride=1)\n", "\n", "# replace classification fc layer of Resnet to obtain representations from the backbone\n", "encoder.fc = nn.Identity()"]}, {"cell_type": "markdown", "id": "f886affa", "metadata": {"papermill": {"duration": 0.006082, "end_time": "2023-03-15T10:17:57.434282", "exception": false, "start_time": "2023-03-15T10:17:57.428200", "status": "completed"}, "tags": []}, "source": ["### Projection head\n", "\n", "Unlike SimCLR and BYOL, the downstream performance of Barlow Twins greatly benefits from having a larger projection head after the backbone network. The paper utilizes a 3 layer MLP with 8192 hidden dimensions and 8192 as the output dimenion of the projection head. For the purposes of the tutorial, we use a smaller projection head. But, it is imperative to mention here that in practice, Barlow Twins needs to be trained using a bigger projection head as it is highly sensitive to its architecture and output dimensionality."]}, {"cell_type": "code", "execution_count": 8, "id": "ea05fb87", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:57.447305Z", "iopub.status.busy": "2023-03-15T10:17:57.447111Z", "iopub.status.idle": "2023-03-15T10:17:57.452667Z", "shell.execute_reply": "2023-03-15T10:17:57.451898Z"}, "papermill": {"duration": 0.013833, "end_time": "2023-03-15T10:17:57.454000", "exception": false, "start_time": "2023-03-15T10:17:57.440167", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class ProjectionHead(nn.Module):\n", "    def __init__(self, input_dim=2048, hidden_dim=2048, output_dim=128):\n", "        super().__init__()\n", "\n", "        self.projection_head = nn.Sequential(\n", "            nn.Linear(input_dim, hidden_dim, bias=True),\n", "            nn.BatchNorm1d(hidden_dim),\n", "            nn.ReLU(),\n", "            nn.Linear(hidden_dim, output_dim, bias=False),\n", "        )\n", "\n", "    def forward(self, x):\n", "        return self.projection_head(x)"]}, {"cell_type": "markdown", "id": "f732ceef", "metadata": {"papermill": {"duration": 0.005938, "end_time": "2023-03-15T10:17:57.470391", "exception": false, "start_time": "2023-03-15T10:17:57.464453", "status": "completed"}, "tags": []}, "source": ["### Learning rate warmup\n", "\n", "For the purposes of this tutorial, we keep things simple and use a linear warmup schedule with Adam optimizer. In our previous experiments we have found that linear warmup part is much more important for the final performance of a model than the cosine decay component of the schedule."]}, {"cell_type": "code", "execution_count": 9, "id": "9c76696c", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:57.483473Z", "iopub.status.busy": "2023-03-15T10:17:57.483159Z", "iopub.status.idle": "2023-03-15T10:17:57.487653Z", "shell.execute_reply": "2023-03-15T10:17:57.486894Z"}, "papermill": {"duration": 0.012493, "end_time": "2023-03-15T10:17:57.488968", "exception": false, "start_time": "2023-03-15T10:17:57.476475", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def fn(warmup_steps, step):\n", "    if step < warmup_steps:\n", "        return float(step) / float(max(1, warmup_steps))\n", "    else:\n", "        return 1.0\n", "\n", "\n", "def linear_warmup_decay(warmup_steps):\n", "    return partial(fn, warmup_steps)"]}, {"cell_type": "markdown", "id": "82a4ff69", "metadata": {"papermill": {"duration": 0.00783, "end_time": "2023-03-15T10:17:57.503250", "exception": false, "start_time": "2023-03-15T10:17:57.495420", "status": "completed"}, "tags": []}, "source": ["### Barlow Twins Lightning Module\n", "\n", "We keep the LightningModule for Barlow Twins neat and simple. It takes in an backbone encoder and initializes the projection head and the loss function. We configure the optimizer and the learning rate scheduler in the ``configure_optimizers`` method."]}, {"cell_type": "code", "execution_count": 10, "id": "ec48d4b6", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:57.516294Z", "iopub.status.busy": "2023-03-15T10:17:57.515981Z", "iopub.status.idle": "2023-03-15T10:17:57.525416Z", "shell.execute_reply": "2023-03-15T10:17:57.524533Z"}, "papermill": {"duration": 0.01854, "end_time": "2023-03-15T10:17:57.527757", "exception": false, "start_time": "2023-03-15T10:17:57.509217", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class BarlowTwins(L.LightningModule):\n", "    def __init__(\n", "        self,\n", "        encoder,\n", "        encoder_out_dim,\n", "        num_training_samples,\n", "        batch_size,\n", "        lambda_coeff=5e-3,\n", "        z_dim=128,\n", "        learning_rate=1e-4,\n", "        warmup_epochs=10,\n", "        max_epochs=200,\n", "    ):\n", "        super().__init__()\n", "\n", "        self.encoder = encoder\n", "        self.projection_head = ProjectionHead(input_dim=encoder_out_dim, hidden_dim=encoder_out_dim, output_dim=z_dim)\n", "        self.loss_fn = BarlowTwinsLoss(batch_size=batch_size, lambda_coeff=lambda_coeff, z_dim=z_dim)\n", "\n", "        self.learning_rate = learning_rate\n", "        self.warmup_epochs = warmup_epochs\n", "        self.max_epochs = max_epochs\n", "\n", "        self.train_iters_per_epoch = num_training_samples // batch_size\n", "\n", "    def forward(self, x):\n", "        return self.encoder(x)\n", "\n", "    def shared_step(self, batch):\n", "        (x1, x2, _), _ = batch\n", "\n", "        z1 = self.projection_head(self.encoder(x1))\n", "        z2 = self.projection_head(self.encoder(x2))\n", "\n", "        return self.loss_fn(z1, z2)\n", "\n", "    def training_step(self, batch, batch_idx):\n", "        loss = self.shared_step(batch)\n", "        self.log(\"train_loss\", loss, on_step=True, on_epoch=False)\n", "        return loss\n", "\n", "    def validation_step(self, batch, batch_idx):\n", "        loss = self.shared_step(batch)\n", "        self.log(\"val_loss\", loss, on_step=False, on_epoch=True)\n", "\n", "    def configure_optimizers(self):\n", "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n", "\n", "        warmup_steps = self.train_iters_per_epoch * self.warmup_epochs\n", "\n", "        scheduler = {\n", "            \"scheduler\": torch.optim.lr_scheduler.LambdaLR(\n", "                optimizer,\n", "                linear_warmup_decay(warmup_steps),\n", "            ),\n", "            \"interval\": \"step\",\n", "            \"frequency\": 1,\n", "        }\n", "\n", "        return [optimizer], [scheduler]"]}, {"cell_type": "markdown", "id": "9b77cfb1", "metadata": {"papermill": {"duration": 0.007469, "end_time": "2023-03-15T10:17:57.544592", "exception": false, "start_time": "2023-03-15T10:17:57.537123", "status": "completed"}, "tags": []}, "source": ["### Evaluation\n", "\n", "We define a callback which appends a linear layer on top of the encoder and trains the classification evaluation head in an online manner. We make sure not to backpropagate the gradients back to the encoder while tuning the linear layer. This technique was used in SimCLR as well and they showed that the final downstream classification peformance is pretty much similar to the results on online finetuning as the training progresses."]}, {"cell_type": "code", "execution_count": 11, "id": "7370d0e8", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:57.557764Z", "iopub.status.busy": "2023-03-15T10:17:57.557441Z", "iopub.status.idle": "2023-03-15T10:17:57.569207Z", "shell.execute_reply": "2023-03-15T10:17:57.568339Z"}, "papermill": {"duration": 0.021182, "end_time": "2023-03-15T10:17:57.571787", "exception": false, "start_time": "2023-03-15T10:17:57.550605", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class OnlineFineTuner(Callback):\n", "    def __init__(\n", "        self,\n", "        encoder_output_dim: int,\n", "        num_classes: int,\n", "    ) -> None:\n", "        super().__init__()\n", "\n", "        self.optimizer: torch.optim.Optimizer\n", "\n", "        self.encoder_output_dim = encoder_output_dim\n", "        self.num_classes = num_classes\n", "\n", "    def on_fit_start(self, trainer: L.Trainer, pl_module: L.LightningModule) -> None:\n", "        # add linear_eval layer and optimizer\n", "        pl_module.online_finetuner = nn.Linear(self.encoder_output_dim, self.num_classes).to(pl_module.device)\n", "        self.optimizer = torch.optim.Adam(pl_module.online_finetuner.parameters(), lr=1e-4)\n", "\n", "    def extract_online_finetuning_view(\n", "        self, batch: Sequence, device: Union[str, torch.device]\n", "    ) -> Tuple[Tensor, Tensor]:\n", "        (_, _, finetune_view), y = batch\n", "        finetune_view = finetune_view.to(device)\n", "        y = y.to(device)\n", "\n", "        return finetune_view, y\n", "\n", "    def on_train_batch_end(\n", "        self,\n", "        trainer: L.Trainer,\n", "        pl_module: L.LightningModule,\n", "        outputs: Sequence,\n", "        batch: Sequence,\n", "        batch_idx: int,\n", "    ) -> None:\n", "        x, y = self.extract_online_finetuning_view(batch, pl_module.device)\n", "\n", "        with torch.no_grad():\n", "            feats = pl_module(x)\n", "\n", "        feats = feats.detach()\n", "        preds = pl_module.online_finetuner(feats)\n", "        loss = F.cross_entropy(preds, y)\n", "\n", "        loss.backward()\n", "        self.optimizer.step()\n", "        self.optimizer.zero_grad()\n", "\n", "        acc = accuracy(F.softmax(preds, dim=1), y, task=\"multiclass\", num_classes=10)\n", "        pl_module.log(\"online_train_acc\", acc, on_step=True, on_epoch=False)\n", "        pl_module.log(\"online_train_loss\", loss, on_step=True, on_epoch=False)\n", "\n", "    def on_validation_batch_end(\n", "        self,\n", "        trainer: L.Trainer,\n", "        pl_module: L.LightningModule,\n", "        outputs: Sequence,\n", "        batch: Sequence,\n", "        batch_idx: int,\n", "    ) -> None:\n", "        x, y = self.extract_online_finetuning_view(batch, pl_module.device)\n", "\n", "        with torch.no_grad():\n", "            feats = pl_module(x)\n", "\n", "        feats = feats.detach()\n", "        preds = pl_module.online_finetuner(feats)\n", "        loss = F.cross_entropy(preds, y)\n", "\n", "        acc = accuracy(F.softmax(preds, dim=1), y, task=\"multiclass\", num_classes=10)\n", "        pl_module.log(\"online_val_acc\", acc, on_step=False, on_epoch=True, sync_dist=True)\n", "        pl_module.log(\"online_val_loss\", loss, on_step=False, on_epoch=True, sync_dist=True)"]}, {"cell_type": "markdown", "id": "da4103e9", "metadata": {"papermill": {"duration": 0.006051, "end_time": "2023-03-15T10:17:57.589781", "exception": false, "start_time": "2023-03-15T10:17:57.583730", "status": "completed"}, "tags": []}, "source": ["Finally, we define the trainer for training the model. We pass in the ``train_loader`` and ``val_loader`` we had initialized earlier to the ``fit`` function."]}, {"cell_type": "code", "execution_count": 12, "id": "3ba99c80", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:57.602719Z", "iopub.status.busy": "2023-03-15T10:17:57.602551Z", "iopub.status.idle": "2023-03-15T10:17:58.113146Z", "shell.execute_reply": "2023-03-15T10:17:58.112039Z"}, "papermill": {"duration": 0.51993, "end_time": "2023-03-15T10:17:58.115737", "exception": false, "start_time": "2023-03-15T10:17:57.595807", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["ModelCheckpoint(save_last=True, save_top_k=-1, monitor=None) will duplicate the last checkpoint saved.\n"]}, {"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}], "source": ["encoder_out_dim = 512\n", "\n", "model = BarlowTwins(\n", "    encoder=encoder,\n", "    encoder_out_dim=encoder_out_dim,\n", "    num_training_samples=len(train_dataset),\n", "    batch_size=batch_size,\n", "    z_dim=z_dim,\n", ")\n", "\n", "online_finetuner = OnlineFineTuner(encoder_output_dim=encoder_out_dim, num_classes=10)\n", "checkpoint_callback = ModelCheckpoint(every_n_epochs=100, save_top_k=-1, save_last=True)\n", "\n", "trainer = L.Trainer(\n", "    max_epochs=max_epochs,\n", "    accelerator=\"auto\",\n", "    devices=1,\n", "    callbacks=[online_finetuner, checkpoint_callback],\n", ")\n", "\n", "# uncomment this to train the model\n", "# this is done for the tutorial so that the notebook compiles\n", "# trainer.fit(model, train_loader, val_loader)"]}, {"cell_type": "markdown", "id": "da7ab591", "metadata": {"papermill": {"duration": 0.007408, "end_time": "2023-03-15T10:17:58.131109", "exception": false, "start_time": "2023-03-15T10:17:58.123701", "status": "completed"}, "tags": []}, "source": ["### Using the trained encoder for downstream tasks\n", "\n", "Once the encoder is pretrained on CIFAR10, we can use it to get image embeddings and use them further downstream on tasks like classification, detection, segmentation etc.\n", "\n", "In this tutorial, we did not completely train our encoder for 100s of epochs using the Barlow Twins pretraining method. So, we will load the pretrained encoder weights from a checkpoint and show the image embeddings obtained from that.\n", "\n", "To create this checkpoint, the encoder was pretrained for 200 epochs, and obtained a online finetune accuracy of x% on CIFAR-10."]}, {"cell_type": "code", "execution_count": 13, "id": "13d028f6", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:17:58.152561Z", "iopub.status.busy": "2023-03-15T10:17:58.151484Z", "iopub.status.idle": "2023-03-15T10:17:58.536740Z", "shell.execute_reply": "2023-03-15T10:17:58.535792Z"}, "papermill": {"duration": 0.396014, "end_time": "2023-03-15T10:17:58.539687", "exception": false, "start_time": "2023-03-15T10:17:58.143673", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["torch.Size([4, 512])\n"]}], "source": ["# ckpt_model = torch.load('')  # upload checkpoint to aws\n", "# encoder = ckpt_model.encoder\n", "encoder = model.encoder\n", "\n", "downstream_dataset = CIFAR10(root=\".\", train=False, transform=transforms.ToTensor())\n", "dataloader = DataLoader(downstream_dataset, batch_size=4, shuffle=False)\n", "\n", "for batch in dataloader:\n", "    img, label = batch\n", "    print(encoder(img).shape)\n", "    break"]}, {"cell_type": "markdown", "id": "9d6287a2", "metadata": {"papermill": {"duration": 0.006401, "end_time": "2023-03-15T10:17:58.559796", "exception": false, "start_time": "2023-03-15T10:17:58.553395", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/Lightning-AI/lightning) on GitHub\n", "The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n", "tools we're building.\n", "\n", "### Join our [Slack](https://www.pytorchlightning.ai/community)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\n", "\n", "### Contributions !\n", "The best way to contribute to our community is to become a code contributor! At any time you can go to\n", "[Lightning](https://github.com/Lightning-AI/lightning) or [Bolt](https://github.com/Lightning-AI/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/Lightning-AI/lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/Lightning-AI/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* You can also contribute your own notebooks with useful examples !\n", "\n", "### Great thanks from the entire Pytorch Lightning Team for your interest !\n", "\n", "[![Pytorch Lightning](data:image/png;base64,NDA0OiBOb3QgRm91bmQ=){height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", "   :header: Barlow Twins Tutorial\n", "   :card_description: This notebook describes the self-supervised learning method Barlow Twins. Barlow Twins differs from other recently proposed algorithms as it doesn't fall under the category of...\n", "   :tags: Image,Self-Supervised,GPU/TPU,Lightning-Examples"]}], "metadata": {"jupytext": {"cell_metadata_filter": "id,colab,colab_type,-all", "formats": "ipynb,py:percent", "main_language": "python"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16"}, "papermill": {"default_parameters": {}, "duration": 56.709385, "end_time": "2023-03-15T10:17:59.486021", "environment_variables": {}, "exception": null, "input_path": "lightning_examples/barlow-twins/barlow_twins.ipynb", "output_path": ".notebooks/lightning_examples/barlow-twins.ipynb", "parameters": {}, "start_time": "2023-03-15T10:17:02.776636", "version": "2.4.0"}, "widgets": {"application/vnd.jupyter.widget-state+json": {"state": {"2091c9f87a1a42029f7a7e016384c0bc": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "3d9362ccb5d34c399157d5398c76a07e": {"model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5c7edd67371348a4a3f68eca312db6cf": {"model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "64a7e761a91d455cb347d3bc9f9f2576": {"model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7b9e6b1be4074f1ba6bce914a5034941": {"model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a5a88b90515f45268fbcf861a1ed2431": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "bf3026ad59f34865b04917ac5f539d66": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5c7edd67371348a4a3f68eca312db6cf", "placeholder": "\u200b", "style": "IPY_MODEL_2091c9f87a1a42029f7a7e016384c0bc", "tabbable": null, "tooltip": null, "value": " 170498071/170498071 [00:41&lt;00:00, 1597381.59it/s]"}}, "d90dc21138c847f4b380495aaf79540c": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_dc0615b9648b4a26822edffce51c6326", "IPY_MODEL_e80f807dc00e4b4e88c0a38d01741045", "IPY_MODEL_bf3026ad59f34865b04917ac5f539d66"], "layout": "IPY_MODEL_7b9e6b1be4074f1ba6bce914a5034941", "tabbable": null, "tooltip": null}}, "d98cdb7e9b0c4730b299cc505f2c6b6c": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "dc0615b9648b4a26822edffce51c6326": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_64a7e761a91d455cb347d3bc9f9f2576", "placeholder": "\u200b", "style": "IPY_MODEL_a5a88b90515f45268fbcf861a1ed2431", "tabbable": null, "tooltip": null, "value": "100%"}}, "e80f807dc00e4b4e88c0a38d01741045": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3d9362ccb5d34c399157d5398c76a07e", "max": 170498071.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_d98cdb7e9b0c4730b299cc505f2c6b6c", "tabbable": null, "tooltip": null, "value": 170498071.0}}}, "version_major": 2, "version_minor": 0}}}, "nbformat": 4, "nbformat_minor": 5}