{"cells": [{"cell_type": "markdown", "id": "4b017e6e", "metadata": {"papermill": {"duration": 0.524855, "end_time": "2023-01-03T14:11:11.828462", "exception": false, "start_time": "2023-01-03T14:11:11.303607", "status": "completed"}, "tags": []}, "source": ["\n", "# Barlow Twins Tutorial\n", "\n", "* **Author:** Ananya Harsh Jha (ananya@pytorchlightning.ai)\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-01-03T15:09:26.174192\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": "9e7d262c", "metadata": {"papermill": {"duration": 0.023525, "end_time": "2023-01-03T14:11:11.869700", "exception": false, "start_time": "2023-01-03T14:11:11.846175", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "be4928e0", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-01-03T14:11:11.913461Z", "iopub.status.busy": "2023-01-03T14:11:11.912934Z", "iopub.status.idle": "2023-01-03T14:11:16.941423Z", "shell.execute_reply": "2023-01-03T14:11:16.940495Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 5.056331, "end_time": "2023-01-03T14:11:16.943449", "exception": false, "start_time": "2023-01-03T14:11:11.887118", "status": "completed"}, "tags": []}, "outputs": [], "source": ["! pip install --quiet \"setuptools==59.5.0\" \"matplotlib\" \"ipython[notebook]\" \"torch>=1.8\" \"torchvision\" \"torchmetrics>=0.7\" \"pytorch-lightning>=1.4\""]}, {"cell_type": "markdown", "id": "0c1031c1", "metadata": {"papermill": {"duration": 0.041149, "end_time": "2023-01-03T14:11:17.031116", "exception": false, "start_time": "2023-01-03T14:11:16.989967", "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": "b8260244", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:17.129802Z", "iopub.status.busy": "2023-01-03T14:11:17.129190Z", "iopub.status.idle": "2023-01-03T14:11:20.647421Z", "shell.execute_reply": "2023-01-03T14:11:20.646552Z"}, "papermill": {"duration": 3.551425, "end_time": "2023-01-03T14:11:20.649443", "exception": false, "start_time": "2023-01-03T14:11:17.098018", "status": "completed"}, "tags": []}, "outputs": [], "source": ["from functools import partial\n", "from typing import Sequence, Tuple, Union\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pytorch_lightning as pl\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 pytorch_lightning import Callback, LightningModule, Trainer\n", "from pytorch_lightning.callbacks import 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": "4bbc2b02", "metadata": {"papermill": {"duration": 0.003483, "end_time": "2023-01-03T14:11:20.717365", "exception": false, "start_time": "2023-01-03T14:11:20.713882", "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": "e1bc9fac", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:21.180488Z", "iopub.status.busy": "2023-01-03T14:11:21.179699Z", "iopub.status.idle": "2023-01-03T14:11:21.189128Z", "shell.execute_reply": "2023-01-03T14:11:21.188013Z"}, "papermill": {"duration": 0.373056, "end_time": "2023-01-03T14:11:21.191057", "exception": false, "start_time": "2023-01-03T14:11:20.818001", "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": "73b8f50f", "metadata": {"papermill": {"duration": 0.068577, "end_time": "2023-01-03T14:11:21.837189", "exception": false, "start_time": "2023-01-03T14:11:21.768612", "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": "c5203e71", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:21.892168Z", "iopub.status.busy": "2023-01-03T14:11:21.891508Z", "iopub.status.idle": "2023-01-03T14:11:30.340106Z", "shell.execute_reply": "2023-01-03T14:11:30.339349Z"}, "papermill": {"duration": 8.468487, "end_time": "2023-01-03T14:11:30.342113", "exception": false, "start_time": "2023-01-03T14:11:21.873626", "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": "9c7080f0c55149c584aca8c54558da86", "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": "3a224440", "metadata": {"papermill": {"duration": 0.032902, "end_time": "2023-01-03T14:11:30.528870", "exception": false, "start_time": "2023-01-03T14:11:30.495968", "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": "2dc18b5c", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:30.590427Z", "iopub.status.busy": "2023-01-03T14:11:30.589726Z", "iopub.status.idle": "2023-01-03T14:11:30.851179Z", "shell.execute_reply": "2023-01-03T14:11:30.850420Z"}, "papermill": {"duration": 0.3003, "end_time": "2023-01-03T14:11:30.854031", "exception": false, "start_time": "2023-01-03T14:11:30.553731", "status": "completed"}, "tags": []}, "outputs": [{"data": {"image/png": 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\n", "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": "4dd3612a", "metadata": {"papermill": {"duration": 0.104, "end_time": "2023-01-03T14:11:31.013943", "exception": false, "start_time": "2023-01-03T14:11:30.909943", "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": "a47bdc95", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:31.409899Z", "iopub.status.busy": "2023-01-03T14:11:31.408894Z", "iopub.status.idle": "2023-01-03T14:11:31.416240Z", "shell.execute_reply": "2023-01-03T14:11:31.415270Z"}, "papermill": {"duration": 0.016631, "end_time": "2023-01-03T14:11:31.417854", "exception": false, "start_time": "2023-01-03T14:11:31.401223", "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": "751b224d", "metadata": {"papermill": {"duration": 0.10387, "end_time": "2023-01-03T14:11:32.573956", "exception": false, "start_time": "2023-01-03T14:11:32.470086", "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": "f2423fbc", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:33.902175Z", "iopub.status.busy": "2023-01-03T14:11:33.901343Z", "iopub.status.idle": "2023-01-03T14:11:34.065025Z", "shell.execute_reply": "2023-01-03T14:11:34.064230Z"}, "papermill": {"duration": 1.486797, "end_time": "2023-01-03T14:11:34.067003", "exception": false, "start_time": "2023-01-03T14:11:32.580206", "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": "46df99c9", "metadata": {"papermill": {"duration": 0.008469, "end_time": "2023-01-03T14:11:34.109849", "exception": false, "start_time": "2023-01-03T14:11:34.101380", "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": "0c9060b2", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:34.202989Z", "iopub.status.busy": "2023-01-03T14:11:34.202663Z", "iopub.status.idle": "2023-01-03T14:11:34.207863Z", "shell.execute_reply": "2023-01-03T14:11:34.207200Z"}, "papermill": {"duration": 0.045601, "end_time": "2023-01-03T14:11:34.209453", "exception": false, "start_time": "2023-01-03T14:11:34.163852", "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": "fe18b6c1", "metadata": {"papermill": {"duration": 0.052472, "end_time": "2023-01-03T14:11:34.281587", "exception": false, "start_time": "2023-01-03T14:11:34.229115", "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": "8e6d5b1f", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:34.424092Z", "iopub.status.busy": "2023-01-03T14:11:34.423356Z", "iopub.status.idle": "2023-01-03T14:11:34.428598Z", "shell.execute_reply": "2023-01-03T14:11:34.427900Z"}, "papermill": {"duration": 0.112423, "end_time": "2023-01-03T14:11:34.430187", "exception": false, "start_time": "2023-01-03T14:11:34.317764", "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": "3e56f841", "metadata": {"papermill": {"duration": 1.425889, "end_time": "2023-01-03T14:11:35.951834", "exception": false, "start_time": "2023-01-03T14:11:34.525945", "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": "f35509dc", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:36.011264Z", "iopub.status.busy": "2023-01-03T14:11:36.010484Z", "iopub.status.idle": "2023-01-03T14:11:36.019724Z", "shell.execute_reply": "2023-01-03T14:11:36.019051Z"}, "papermill": {"duration": 0.033904, "end_time": "2023-01-03T14:11:36.021300", "exception": false, "start_time": "2023-01-03T14:11:35.987396", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class BarlowTwins(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": "c3413765", "metadata": {"papermill": {"duration": 0.020526, "end_time": "2023-01-03T14:11:36.052734", "exception": false, "start_time": "2023-01-03T14:11:36.032208", "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": "0e793c50", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:36.113318Z", "iopub.status.busy": "2023-01-03T14:11:36.112998Z", "iopub.status.idle": "2023-01-03T14:11:36.124201Z", "shell.execute_reply": "2023-01-03T14:11:36.123471Z"}, "papermill": {"duration": 0.028007, "end_time": "2023-01-03T14:11:36.125800", "exception": false, "start_time": "2023-01-03T14:11:36.097793", "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: pl.Trainer, pl_module: pl.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: pl.Trainer,\n", "        pl_module: pl.LightningModule,\n", "        outputs: Sequence,\n", "        batch: Sequence,\n", "        batch_idx: int,\n", "        dataloader_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)\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: pl.Trainer,\n", "        pl_module: pl.LightningModule,\n", "        outputs: Sequence,\n", "        batch: Sequence,\n", "        batch_idx: int,\n", "        dataloader_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)\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": "cff362c6", "metadata": {"papermill": {"duration": 0.047791, "end_time": "2023-01-03T14:11:36.196803", "exception": false, "start_time": "2023-01-03T14:11:36.149012", "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": "ee9a9323", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:36.221931Z", "iopub.status.busy": "2023-01-03T14:11:36.221141Z", "iopub.status.idle": "2023-01-03T14:11:36.603716Z", "shell.execute_reply": "2023-01-03T14:11:36.603041Z"}, "papermill": {"duration": 0.399597, "end_time": "2023-01-03T14:11:36.605272", "exception": false, "start_time": "2023-01-03T14:11:36.205675", "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 = Trainer(\n", "    max_epochs=max_epochs,\n", "    accelerator=\"auto\",\n", "    devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs\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": "c1bcf484", "metadata": {"papermill": {"duration": 0.044822, "end_time": "2023-01-03T14:11:38.291529", "exception": false, "start_time": "2023-01-03T14:11:38.246707", "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": "9cd56c8d", "metadata": {"execution": {"iopub.execute_input": "2023-01-03T14:11:38.352115Z", "iopub.status.busy": "2023-01-03T14:11:38.351337Z", "iopub.status.idle": "2023-01-03T14:11:38.802463Z", "shell.execute_reply": "2023-01-03T14:11:38.801722Z"}, "papermill": {"duration": 0.490473, "end_time": "2023-01-03T14:11:38.804040", "exception": false, "start_time": "2023-01-03T14:11:38.313567", "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": "1df0d4d8", "metadata": {"papermill": {"duration": 0.207975, "end_time": "2023-01-03T14:11:39.102011", "exception": false, "start_time": "2023-01-03T14:11:38.894036", "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": "colab,colab_type,id,-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": 30.194704, "end_time": "2023-01-03T14:11:39.969641", "environment_variables": {}, "exception": null, "input_path": "lightning_examples/barlow-twins/barlow_twins.ipynb", "output_path": ".notebooks/lightning_examples/barlow-twins.ipynb", "parameters": {}, "start_time": "2023-01-03T14:11:09.774937", "version": "2.4.0"}, "widgets": {"application/vnd.jupyter.widget-state+json": {"state": 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