.. 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: 0.5119 Acc: 0.7705 val Loss: 0.2494 Acc: 0.9216 Epoch 1/24 ---------- train Loss: 0.4073 Acc: 0.8361 val Loss: 0.2335 Acc: 0.9150 Epoch 2/24 ---------- train Loss: 0.3775 Acc: 0.8443 val Loss: 0.2279 Acc: 0.9281 Epoch 3/24 ---------- train Loss: 0.5218 Acc: 0.7951 val Loss: 0.2613 Acc: 0.9020 Epoch 4/24 ---------- train Loss: 0.4783 Acc: 0.7869 val Loss: 0.3122 Acc: 0.9020 Epoch 5/24 ---------- train Loss: 0.4914 Acc: 0.7746 val Loss: 0.2654 Acc: 0.9216 Epoch 6/24 ---------- train Loss: 0.4815 Acc: 0.8279 val Loss: 0.2956 Acc: 0.9085 Epoch 7/24 ---------- train Loss: 0.3547 Acc: 0.8566 val Loss: 0.2662 Acc: 0.9150 Epoch 8/24 ---------- train Loss: 0.3241 Acc: 0.8770 val Loss: 0.2552 Acc: 0.9216 Epoch 9/24 ---------- train Loss: 0.2853 Acc: 0.8770 val Loss: 0.2434 Acc: 0.9150 Epoch 10/24 ---------- train Loss: 0.2662 Acc: 0.9057 val Loss: 0.2494 Acc: 0.9085 Epoch 11/24 ---------- train Loss: 0.3991 Acc: 0.8238 val Loss: 0.2500 Acc: 0.9281 Epoch 12/24 ---------- train Loss: 0.2888 Acc: 0.8770 val Loss: 0.2269 Acc: 0.9216 Epoch 13/24 ---------- train Loss: 0.2602 Acc: 0.9016 val Loss: 0.2200 Acc: 0.9216 Epoch 14/24 ---------- train Loss: 0.2599 Acc: 0.8811 val Loss: 0.2138 Acc: 0.9281 Epoch 15/24 ---------- train Loss: 0.2988 Acc: 0.8689 val Loss: 0.2228 Acc: 0.9281 Epoch 16/24 ---------- train Loss: 0.2522 Acc: 0.8934 val Loss: 0.2213 Acc: 0.9346 Epoch 17/24 ---------- train Loss: 0.2866 Acc: 0.8648 val Loss: 0.2153 Acc: 0.9216 Epoch 18/24 ---------- train Loss: 0.2613 Acc: 0.8770 val Loss: 0.2155 Acc: 0.9281 Epoch 19/24 ---------- train Loss: 0.1685 Acc: 0.9508 val Loss: 0.2171 Acc: 0.9281 Epoch 20/24 ---------- train Loss: 0.2950 Acc: 0.8770 val Loss: 0.2065 Acc: 0.9281 Epoch 21/24 ---------- train Loss: 0.2537 Acc: 0.8934 val Loss: 0.2229 Acc: 0.9216 Epoch 22/24 ---------- train Loss: 0.2344 Acc: 0.8975 val Loss: 0.2228 Acc: 0.9346 Epoch 23/24 ---------- train Loss: 0.2462 Acc: 0.9016 val Loss: 0.2210 Acc: 0.9216 Epoch 24/24 ---------- train Loss: 0.3504 Acc: 0.8320 val Loss: 0.2131 Acc: 0.9216 Training complete in 28m 41s Best val Acc: 0.934641 .. 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: 0.5573 Acc: 0.7049 val Loss: 0.2072 Acc: 0.9477 Epoch 1/24 ---------- train Loss: 0.5213 Acc: 0.7582 val Loss: 0.2360 Acc: 0.9020 Epoch 2/24 ---------- train Loss: 0.5114 Acc: 0.7992 val Loss: 0.2059 Acc: 0.9216 Epoch 3/24 ---------- train Loss: 0.3916 Acc: 0.8074 val Loss: 0.2740 Acc: 0.9020 Epoch 4/24 ---------- train Loss: 0.3955 Acc: 0.8484 val Loss: 0.1997 Acc: 0.9281 Epoch 5/24 ---------- train Loss: 0.3854 Acc: 0.8402 val Loss: 0.2046 Acc: 0.9412 Epoch 6/24 ---------- train Loss: 0.3578 Acc: 0.8238 val Loss: 0.2117 Acc: 0.9412 Epoch 7/24 ---------- train Loss: 0.3320 Acc: 0.8811 val Loss: 0.2364 Acc: 0.9216 Epoch 8/24 ---------- train Loss: 0.3562 Acc: 0.8566 val Loss: 0.2158 Acc: 0.9477 Epoch 9/24 ---------- train Loss: 0.3210 Acc: 0.8893 val Loss: 0.2069 Acc: 0.9477 Epoch 10/24 ---------- train Loss: 0.4188 Acc: 0.8197 val Loss: 0.2156 Acc: 0.9542 Epoch 11/24 ---------- train Loss: 0.3077 Acc: 0.8648 val Loss: 0.2080 Acc: 0.9477 Epoch 12/24 ---------- train Loss: 0.2559 Acc: 0.8852 val Loss: 0.2015 Acc: 0.9412 Epoch 13/24 ---------- train Loss: 0.3707 Acc: 0.8525 val Loss: 0.2713 Acc: 0.9020 Epoch 14/24 ---------- train Loss: 0.3064 Acc: 0.8770 val Loss: 0.2471 Acc: 0.9150 Epoch 15/24 ---------- train Loss: 0.3694 Acc: 0.8525 val Loss: 0.2292 Acc: 0.9346 Epoch 16/24 ---------- train Loss: 0.3417 Acc: 0.8566 val Loss: 0.2395 Acc: 0.9216 Epoch 17/24 ---------- train Loss: 0.2944 Acc: 0.8648 val Loss: 0.2368 Acc: 0.9346 Epoch 18/24 ---------- train Loss: 0.4056 Acc: 0.8033 val Loss: 0.2214 Acc: 0.9412 Epoch 19/24 ---------- train Loss: 0.3277 Acc: 0.8402 val Loss: 0.2000 Acc: 0.9477 Epoch 20/24 ---------- train Loss: 0.3533 Acc: 0.8361 val Loss: 0.2129 Acc: 0.9346 Epoch 21/24 ---------- train Loss: 0.3216 Acc: 0.8770 val Loss: 0.2039 Acc: 0.9412 Epoch 22/24 ---------- train Loss: 0.3297 Acc: 0.8648 val Loss: 0.2327 Acc: 0.9346 Epoch 23/24 ---------- train Loss: 0.2873 Acc: 0.8893 val Loss: 0.2184 Acc: 0.9346 Epoch 24/24 ---------- train Loss: 0.2921 Acc: 0.8443 val Loss: 0.2229 Acc: 0.9412 Training complete in 12m 55s 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:** ( 41 minutes 51.353 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 `_