.. 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 for Computer Vision Tutorial ============================================== **Author**: `Sasank Chilamkurthy `_ In this tutorial, you will learn how to train a convolutional neural network for image classification 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 initialization, 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:: default # 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 torch.backends.cudnn as cudnn import numpy as np import torchvision from torchvision import datasets, models, transforms import matplotlib.pyplot as plt import time import os import copy cudnn.benchmark = True 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:: default # 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 = 'data/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:: default 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:: default 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(f'Epoch {epoch}/{num_epochs - 1}') print('-' * 10) # Each epoch has a training and validation phase for phase in ['train', 'val']: if phase == 'train': 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) if phase == 'train': scheduler.step() epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}') # 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(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s') print(f'Best val Acc: {best_acc:4f}') # 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:: default 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(f'predicted: {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:: default model_ft = models.resnet18(pretrained=True) num_ftrs = model_ft.fc.in_features # Here the size of each output sample is set to 2. # Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)). 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:: default 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: 0.6104 Acc: 0.7008 val Loss: 0.6322 Acc: 0.7124 Epoch 1/24 ---------- train Loss: 0.4168 Acc: 0.8402 val Loss: 0.3723 Acc: 0.8105 Epoch 2/24 ---------- train Loss: 0.6424 Acc: 0.7090 val Loss: 0.8405 Acc: 0.6340 Epoch 3/24 ---------- train Loss: 0.4585 Acc: 0.8074 val Loss: 0.2838 Acc: 0.8889 Epoch 4/24 ---------- train Loss: 0.4374 Acc: 0.8361 val Loss: 0.2964 Acc: 0.8824 Epoch 5/24 ---------- train Loss: 0.5119 Acc: 0.7951 val Loss: 0.2815 Acc: 0.8954 Epoch 6/24 ---------- train Loss: 0.5043 Acc: 0.8402 val Loss: 0.2270 Acc: 0.9346 Epoch 7/24 ---------- train Loss: 0.2356 Acc: 0.9057 val Loss: 0.2393 Acc: 0.9150 Epoch 8/24 ---------- train Loss: 0.2722 Acc: 0.8893 val Loss: 0.2385 Acc: 0.9281 Epoch 9/24 ---------- train Loss: 0.3548 Acc: 0.8689 val Loss: 0.2182 Acc: 0.9346 Epoch 10/24 ---------- train Loss: 0.3702 Acc: 0.8648 val Loss: 0.2113 Acc: 0.9281 Epoch 11/24 ---------- train Loss: 0.2236 Acc: 0.9098 val Loss: 0.2301 Acc: 0.9281 Epoch 12/24 ---------- train Loss: 0.3456 Acc: 0.8566 val Loss: 0.2457 Acc: 0.9281 Epoch 13/24 ---------- train Loss: 0.2549 Acc: 0.9016 val Loss: 0.2738 Acc: 0.9085 Epoch 14/24 ---------- train Loss: 0.3553 Acc: 0.8607 val Loss: 0.2935 Acc: 0.8824 Epoch 15/24 ---------- train Loss: 0.2945 Acc: 0.8770 val Loss: 0.2390 Acc: 0.9216 Epoch 16/24 ---------- train Loss: 0.2350 Acc: 0.9016 val Loss: 0.2376 Acc: 0.9216 Epoch 17/24 ---------- train Loss: 0.3603 Acc: 0.8238 val Loss: 0.2450 Acc: 0.9281 Epoch 18/24 ---------- train Loss: 0.2551 Acc: 0.8852 val Loss: 0.2362 Acc: 0.9281 Epoch 19/24 ---------- train Loss: 0.3012 Acc: 0.8689 val Loss: 0.2332 Acc: 0.9281 Epoch 20/24 ---------- train Loss: 0.3427 Acc: 0.8689 val Loss: 0.2539 Acc: 0.9281 Epoch 21/24 ---------- train Loss: 0.3070 Acc: 0.8525 val Loss: 0.2490 Acc: 0.9281 Epoch 22/24 ---------- train Loss: 0.2947 Acc: 0.8770 val Loss: 0.2319 Acc: 0.9281 Epoch 23/24 ---------- train Loss: 0.2480 Acc: 0.9016 val Loss: 0.2276 Acc: 0.9281 Epoch 24/24 ---------- train Loss: 0.3127 Acc: 0.8648 val Loss: 0.2424 Acc: 0.9150 Training complete in 1m 7s Best val Acc: 0.934641 .. code-block:: default 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:: default 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 # opposed 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:: default 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: 0.8283 Acc: 0.6680 val Loss: 0.2313 Acc: 0.9412 Epoch 1/24 ---------- train Loss: 0.3411 Acc: 0.8566 val Loss: 0.2130 Acc: 0.9085 Epoch 2/24 ---------- train Loss: 0.4557 Acc: 0.7951 val Loss: 0.2120 Acc: 0.9346 Epoch 3/24 ---------- train Loss: 0.3622 Acc: 0.8402 val Loss: 0.2383 Acc: 0.9085 Epoch 4/24 ---------- train Loss: 0.5417 Acc: 0.7623 val Loss: 0.1973 Acc: 0.9412 Epoch 5/24 ---------- train Loss: 0.4069 Acc: 0.8402 val Loss: 0.2052 Acc: 0.9477 Epoch 6/24 ---------- train Loss: 0.4072 Acc: 0.7869 val Loss: 0.2844 Acc: 0.8824 Epoch 7/24 ---------- train Loss: 0.3897 Acc: 0.8361 val Loss: 0.1749 Acc: 0.9477 Epoch 8/24 ---------- train Loss: 0.3548 Acc: 0.8197 val Loss: 0.1757 Acc: 0.9477 Epoch 9/24 ---------- train Loss: 0.3292 Acc: 0.8484 val Loss: 0.1728 Acc: 0.9477 Epoch 10/24 ---------- train Loss: 0.4108 Acc: 0.8115 val Loss: 0.1748 Acc: 0.9412 Epoch 11/24 ---------- train Loss: 0.4019 Acc: 0.8033 val Loss: 0.1815 Acc: 0.9477 Epoch 12/24 ---------- train Loss: 0.4609 Acc: 0.7869 val Loss: 0.1933 Acc: 0.9281 Epoch 13/24 ---------- train Loss: 0.4383 Acc: 0.7828 val Loss: 0.1774 Acc: 0.9477 Epoch 14/24 ---------- train Loss: 0.2799 Acc: 0.8730 val Loss: 0.1831 Acc: 0.9412 Epoch 15/24 ---------- train Loss: 0.3141 Acc: 0.8443 val Loss: 0.1811 Acc: 0.9477 Epoch 16/24 ---------- train Loss: 0.2609 Acc: 0.9139 val Loss: 0.1956 Acc: 0.9346 Epoch 17/24 ---------- train Loss: 0.3234 Acc: 0.8279 val Loss: 0.1788 Acc: 0.9477 Epoch 18/24 ---------- train Loss: 0.3325 Acc: 0.8607 val Loss: 0.1541 Acc: 0.9477 Epoch 19/24 ---------- train Loss: 0.3555 Acc: 0.8361 val Loss: 0.1735 Acc: 0.9477 Epoch 20/24 ---------- train Loss: 0.3300 Acc: 0.8443 val Loss: 0.1767 Acc: 0.9608 Epoch 21/24 ---------- train Loss: 0.3155 Acc: 0.8607 val Loss: 0.1737 Acc: 0.9477 Epoch 22/24 ---------- train Loss: 0.3697 Acc: 0.8443 val Loss: 0.1803 Acc: 0.9281 Epoch 23/24 ---------- train Loss: 0.2814 Acc: 0.8648 val Loss: 0.1667 Acc: 0.9477 Epoch 24/24 ---------- train Loss: 0.3470 Acc: 0.8525 val Loss: 0.2084 Acc: 0.9281 Training complete in 0m 40s Best val Acc: 0.960784 .. code-block:: default visualize_model(model_conv) plt.ioff() plt.show() .. image:: /beginner/images/sphx_glr_transfer_learning_tutorial_003.png :class: sphx-glr-single-img Further Learning ----------------- If you would like to learn more about the applications of transfer learning, checkout our `Quantized Transfer Learning for Computer Vision Tutorial `_. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 1 minutes 53.724 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 `_