.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_beginner_transfer_learning_tutorial.py: Transfer Learning Tutorial ========================== **Author**: `Sasank Chilamkurthy `_ In this tutorial, you will learn how to train your network using transfer learning. You can read more about the transfer learning at `cs231n notes `__ Quoting these notes, In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. ImageNet, which contains 1.2 million images with 1000 categories), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest. These two major transfer learning scenarios look as follows: - **Finetuning the convnet**: Instead of random initializaion, we initialize the network with a pretrained network, like the one that is trained on imagenet 1000 dataset. Rest of the training looks as usual. - **ConvNet as fixed feature extractor**: Here, we will freeze the weights for all of the network except that of the final fully connected layer. This last fully connected layer is replaced with a new one with random weights and only this layer is trained. .. code-block:: python # License: BSD # Author: Sasank Chilamkurthy from __future__ import print_function, division import torch import torch.nn as nn import torch.optim as optim from torch.optim import lr_scheduler import numpy as np import torchvision from torchvision import datasets, models, transforms import matplotlib.pyplot as plt import time import os import copy plt.ion() # interactive mode Load Data --------- We will use torchvision and torch.utils.data packages for loading the data. The problem we're going to solve today is to train a model to classify **ants** and **bees**. We have about 120 training images each for ants and bees. There are 75 validation images for each class. Usually, this is a very small dataset to generalize upon, if trained from scratch. Since we are using transfer learning, we should be able to generalize reasonably well. This dataset is a very small subset of imagenet. .. Note :: Download the data from `here `_ and extract it to the current directory. .. code-block:: python # Data augmentation and normalization for training # Just normalization for validation data_transforms = { 'train': transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), 'val': transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), } data_dir = 'hymenoptera_data' image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']} dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4) for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} class_names = image_datasets['train'].classes device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") Visualize a few images ^^^^^^^^^^^^^^^^^^^^^^ Let's visualize a few training images so as to understand the data augmentations. .. code-block:: python def imshow(inp, title=None): """Imshow for Tensor.""" inp = inp.numpy().transpose((1, 2, 0)) mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) inp = std * inp + mean inp = np.clip(inp, 0, 1) plt.imshow(inp) if title is not None: plt.title(title) plt.pause(0.001) # pause a bit so that plots are updated # Get a batch of training data inputs, classes = next(iter(dataloaders['train'])) # Make a grid from batch out = torchvision.utils.make_grid(inputs) imshow(out, title=[class_names[x] for x in classes]) .. image:: /beginner/images/sphx_glr_transfer_learning_tutorial_001.png :class: sphx-glr-single-img Training the model ------------------ Now, let's write a general function to train a model. Here, we will illustrate: - Scheduling the learning rate - Saving the best model In the following, parameter ``scheduler`` is an LR scheduler object from ``torch.optim.lr_scheduler``. .. code-block:: python def train_model(model, criterion, optimizer, scheduler, num_epochs=25): since = time.time() best_model_wts = copy.deepcopy(model.state_dict()) best_acc = 0.0 for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) # Each epoch has a training and validation phase for phase in ['train', 'val']: if phase == 'train': scheduler.step() model.train() # Set model to training mode else: model.eval() # Set model to evaluate mode running_loss = 0.0 running_corrects = 0 # Iterate over data. for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) # zero the parameter gradients optimizer.zero_grad() # forward # track history if only in train with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) # backward + optimize only if in training phase if phase == 'train': loss.backward() optimizer.step() # statistics running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print('{} Loss: {:.4f} Acc: {:.4f}'.format( phase, epoch_loss, epoch_acc)) # deep copy the model if phase == 'val' and epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = copy.deepcopy(model.state_dict()) print() time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) print('Best val Acc: {:4f}'.format(best_acc)) # load best model weights model.load_state_dict(best_model_wts) return model Visualizing the model predictions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Generic function to display predictions for a few images .. code-block:: python def visualize_model(model, num_images=6): was_training = model.training model.eval() images_so_far = 0 fig = plt.figure() with torch.no_grad(): for i, (inputs, labels) in enumerate(dataloaders['val']): inputs = inputs.to(device) labels = labels.to(device) outputs = model(inputs) _, preds = torch.max(outputs, 1) for j in range(inputs.size()[0]): images_so_far += 1 ax = plt.subplot(num_images//2, 2, images_so_far) ax.axis('off') ax.set_title('predicted: {}'.format(class_names[preds[j]])) imshow(inputs.cpu().data[j]) if images_so_far == num_images: model.train(mode=was_training) return model.train(mode=was_training) Finetuning the convnet ---------------------- Load a pretrained model and reset final fully connected layer. .. code-block:: python model_ft = models.resnet18(pretrained=True) num_ftrs = model_ft.fc.in_features model_ft.fc = nn.Linear(num_ftrs, 2) model_ft = model_ft.to(device) criterion = nn.CrossEntropyLoss() # Observe that all parameters are being optimized optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9) # Decay LR by a factor of 0.1 every 7 epochs exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1) Train and evaluate ^^^^^^^^^^^^^^^^^^ It should take around 15-25 min on CPU. On GPU though, it takes less than a minute. .. code-block:: python model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Epoch 0/24 ---------- train Loss: 0.5540 Acc: 0.7336 val Loss: 0.2588 Acc: 0.8954 Epoch 1/24 ---------- train Loss: 0.5288 Acc: 0.7910 val Loss: 0.3113 Acc: 0.9020 Epoch 2/24 ---------- train Loss: 0.5715 Acc: 0.8074 val Loss: 0.2625 Acc: 0.8954 Epoch 3/24 ---------- train Loss: 0.4685 Acc: 0.8361 val Loss: 0.5189 Acc: 0.8105 Epoch 4/24 ---------- train Loss: 0.4609 Acc: 0.7951 val Loss: 0.3270 Acc: 0.8693 Epoch 5/24 ---------- train Loss: 0.4258 Acc: 0.8402 val Loss: 0.2755 Acc: 0.8627 Epoch 6/24 ---------- train Loss: 0.4301 Acc: 0.8320 val Loss: 0.2516 Acc: 0.9150 Epoch 7/24 ---------- train Loss: 0.2645 Acc: 0.8975 val Loss: 0.2003 Acc: 0.9216 Epoch 8/24 ---------- train Loss: 0.4231 Acc: 0.8197 val Loss: 0.1995 Acc: 0.9216 Epoch 9/24 ---------- train Loss: 0.4237 Acc: 0.8484 val Loss: 0.2001 Acc: 0.9150 Epoch 10/24 ---------- train Loss: 0.2910 Acc: 0.8607 val Loss: 0.1986 Acc: 0.9150 Epoch 11/24 ---------- train Loss: 0.3015 Acc: 0.8852 val Loss: 0.1777 Acc: 0.9216 Epoch 12/24 ---------- train Loss: 0.2800 Acc: 0.8934 val Loss: 0.1939 Acc: 0.9216 Epoch 13/24 ---------- train Loss: 0.3545 Acc: 0.8320 val Loss: 0.2028 Acc: 0.9150 Epoch 14/24 ---------- train Loss: 0.2652 Acc: 0.8893 val Loss: 0.1801 Acc: 0.9216 Epoch 15/24 ---------- train Loss: 0.1955 Acc: 0.9221 val Loss: 0.1856 Acc: 0.9216 Epoch 16/24 ---------- train Loss: 0.3078 Acc: 0.8402 val Loss: 0.2034 Acc: 0.9216 Epoch 17/24 ---------- train Loss: 0.2575 Acc: 0.8893 val Loss: 0.1991 Acc: 0.9150 Epoch 18/24 ---------- train Loss: 0.2993 Acc: 0.8730 val Loss: 0.1834 Acc: 0.9216 Epoch 19/24 ---------- train Loss: 0.2655 Acc: 0.8852 val Loss: 0.1861 Acc: 0.9150 Epoch 20/24 ---------- train Loss: 0.2295 Acc: 0.9098 val Loss: 0.2009 Acc: 0.9085 Epoch 21/24 ---------- train Loss: 0.2719 Acc: 0.8811 val Loss: 0.1781 Acc: 0.9150 Epoch 22/24 ---------- train Loss: 0.2683 Acc: 0.9057 val Loss: 0.1872 Acc: 0.9216 Epoch 23/24 ---------- train Loss: 0.1990 Acc: 0.9262 val Loss: 0.2038 Acc: 0.9150 Epoch 24/24 ---------- train Loss: 0.3078 Acc: 0.8443 val Loss: 0.1842 Acc: 0.9281 Training complete in 1m 15s Best val Acc: 0.928105 .. code-block:: python visualize_model(model_ft) .. image:: /beginner/images/sphx_glr_transfer_learning_tutorial_002.png :class: sphx-glr-single-img ConvNet as fixed feature extractor ---------------------------------- Here, we need to freeze all the network except the final layer. We need to set ``requires_grad == False`` to freeze the parameters so that the gradients are not computed in ``backward()``. You can read more about this in the documentation `here `__. .. code-block:: python model_conv = torchvision.models.resnet18(pretrained=True) for param in model_conv.parameters(): param.requires_grad = False # Parameters of newly constructed modules have requires_grad=True by default num_ftrs = model_conv.fc.in_features model_conv.fc = nn.Linear(num_ftrs, 2) model_conv = model_conv.to(device) criterion = nn.CrossEntropyLoss() # Observe that only parameters of final layer are being optimized as # opoosed to before. optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9) # Decay LR by a factor of 0.1 every 7 epochs exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1) Train and evaluate ^^^^^^^^^^^^^^^^^^ On CPU this will take about half the time compared to previous scenario. This is expected as gradients don't need to be computed for most of the network. However, forward does need to be computed. .. code-block:: python model_conv = train_model(model_conv, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=25) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Epoch 0/24 ---------- train Loss: 0.5684 Acc: 0.7008 val Loss: 0.2154 Acc: 0.9216 Epoch 1/24 ---------- train Loss: 0.6135 Acc: 0.7213 val Loss: 0.2339 Acc: 0.9150 Epoch 2/24 ---------- train Loss: 0.5376 Acc: 0.7746 val Loss: 0.1760 Acc: 0.9281 Epoch 3/24 ---------- train Loss: 0.5482 Acc: 0.7746 val Loss: 0.4500 Acc: 0.8301 Epoch 4/24 ---------- train Loss: 0.6307 Acc: 0.7336 val Loss: 0.1816 Acc: 0.9542 Epoch 5/24 ---------- train Loss: 0.5348 Acc: 0.7541 val Loss: 0.2523 Acc: 0.9216 Epoch 6/24 ---------- train Loss: 0.3509 Acc: 0.8566 val Loss: 0.2389 Acc: 0.9281 Epoch 7/24 ---------- train Loss: 0.4197 Acc: 0.8320 val Loss: 0.1908 Acc: 0.9477 Epoch 8/24 ---------- train Loss: 0.4030 Acc: 0.8156 val Loss: 0.1792 Acc: 0.9477 Epoch 9/24 ---------- train Loss: 0.3229 Acc: 0.8484 val Loss: 0.1783 Acc: 0.9477 Epoch 10/24 ---------- train Loss: 0.3085 Acc: 0.8648 val Loss: 0.1814 Acc: 0.9477 Epoch 11/24 ---------- train Loss: 0.3339 Acc: 0.8402 val Loss: 0.1918 Acc: 0.9477 Epoch 12/24 ---------- train Loss: 0.3329 Acc: 0.8402 val Loss: 0.1790 Acc: 0.9412 Epoch 13/24 ---------- train Loss: 0.3591 Acc: 0.8320 val Loss: 0.1726 Acc: 0.9412 Epoch 14/24 ---------- train Loss: 0.2591 Acc: 0.8975 val Loss: 0.1900 Acc: 0.9477 Epoch 15/24 ---------- train Loss: 0.3220 Acc: 0.8484 val Loss: 0.1870 Acc: 0.9477 Epoch 16/24 ---------- train Loss: 0.4379 Acc: 0.7910 val Loss: 0.1763 Acc: 0.9477 Epoch 17/24 ---------- train Loss: 0.3358 Acc: 0.8402 val Loss: 0.2181 Acc: 0.9477 Epoch 18/24 ---------- train Loss: 0.3809 Acc: 0.8279 val Loss: 0.2109 Acc: 0.9542 Epoch 19/24 ---------- train Loss: 0.3471 Acc: 0.8484 val Loss: 0.1883 Acc: 0.9477 Epoch 20/24 ---------- train Loss: 0.3506 Acc: 0.8525 val Loss: 0.1922 Acc: 0.9477 Epoch 21/24 ---------- train Loss: 0.2658 Acc: 0.8975 val Loss: 0.1818 Acc: 0.9412 Epoch 22/24 ---------- train Loss: 0.3664 Acc: 0.8279 val Loss: 0.1808 Acc: 0.9477 Epoch 23/24 ---------- train Loss: 0.3049 Acc: 0.8852 val Loss: 0.2089 Acc: 0.9477 Epoch 24/24 ---------- train Loss: 0.2753 Acc: 0.8811 val Loss: 0.1806 Acc: 0.9477 Training complete in 0m 38s Best val Acc: 0.954248 .. code-block:: python visualize_model(model_conv) plt.ioff() plt.show() .. image:: /beginner/images/sphx_glr_transfer_learning_tutorial_003.png :class: sphx-glr-single-img **Total running time of the script:** ( 1 minutes 58.669 seconds) .. _sphx_glr_download_beginner_transfer_learning_tutorial.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: transfer_learning_tutorial.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: transfer_learning_tutorial.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_