.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "beginner/transfer_learning_tutorial.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. 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. .. GENERATED FROM PYTHON SOURCE LINES 33-53 .. code-block:: default # License: BSD # Author: Sasank Chilamkurthy 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 from PIL import Image from tempfile import TemporaryDirectory cudnn.benchmark = True plt.ion() # interactive mode .. GENERATED FROM PYTHON SOURCE LINES 54-73 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. .. GENERATED FROM PYTHON SOURCE LINES 73-103 .. 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") .. GENERATED FROM PYTHON SOURCE LINES 104-108 Visualize a few images ^^^^^^^^^^^^^^^^^^^^^^ Let's visualize a few training images so as to understand the data augmentations. .. GENERATED FROM PYTHON SOURCE LINES 108-131 .. code-block:: default def imshow(inp, title=None): """Display image 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]) .. GENERATED FROM PYTHON SOURCE LINES 132-143 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``. .. GENERATED FROM PYTHON SOURCE LINES 143-216 .. code-block:: default def train_model(model, criterion, optimizer, scheduler, num_epochs=25): since = time.time() # Create a temporary directory to save training checkpoints with TemporaryDirectory() as tempdir: best_model_params_path = os.path.join(tempdir, 'best_model_params.pt') torch.save(model.state_dict(), best_model_params_path) 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 torch.save(model.state_dict(), best_model_params_path) 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(torch.load(best_model_params_path)) return model .. GENERATED FROM PYTHON SOURCE LINES 217-222 Visualizing the model predictions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Generic function to display predictions for a few images .. GENERATED FROM PYTHON SOURCE LINES 222-249 .. 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) .. GENERATED FROM PYTHON SOURCE LINES 250-255 Finetuning the ConvNet ---------------------- Load a pretrained model and reset final fully connected layer. .. GENERATED FROM PYTHON SOURCE LINES 255-272 .. code-block:: default model_ft = models.resnet18(weights='IMAGENET1K_V1') 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) .. GENERATED FROM PYTHON SOURCE LINES 273-279 Train and evaluate ^^^^^^^^^^^^^^^^^^ It should take around 15-25 min on CPU. On GPU though, it takes less than a minute. .. GENERATED FROM PYTHON SOURCE LINES 279-283 .. code-block:: default model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25) .. GENERATED FROM PYTHON SOURCE LINES 285-289 .. code-block:: default visualize_model(model_ft) .. GENERATED FROM PYTHON SOURCE LINES 290-300 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 `__. .. GENERATED FROM PYTHON SOURCE LINES 300-321 .. code-block:: default model_conv = torchvision.models.resnet18(weights='IMAGENET1K_V1') 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) .. GENERATED FROM PYTHON SOURCE LINES 322-329 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. .. GENERATED FROM PYTHON SOURCE LINES 329-333 .. code-block:: default model_conv = train_model(model_conv, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=25) .. GENERATED FROM PYTHON SOURCE LINES 335-342 .. code-block:: default visualize_model(model_conv) plt.ioff() plt.show() .. GENERATED FROM PYTHON SOURCE LINES 343-349 Inference on custom images -------------------------- Use the trained model to make predictions on custom images and visualize the predicted class labels along with the images. .. GENERATED FROM PYTHON SOURCE LINES 349-370 .. code-block:: default def visualize_model_predictions(model,img_path): was_training = model.training model.eval() img = Image.open(img_path) img = data_transforms['val'](img) img = img.unsqueeze(0) img = img.to(device) with torch.no_grad(): outputs = model(img) _, preds = torch.max(outputs, 1) ax = plt.subplot(2,2,1) ax.axis('off') ax.set_title(f'Predicted: {class_names[preds[0]]}') imshow(img.cpu().data[0]) model.train(mode=was_training) .. GENERATED FROM PYTHON SOURCE LINES 372-382 .. code-block:: default visualize_model_predictions( model_conv, img_path='data/hymenoptera_data/val/bees/72100438_73de9f17af.jpg' ) plt.ioff() plt.show() .. GENERATED FROM PYTHON SOURCE LINES 383-390 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:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_beginner_transfer_learning_tutorial.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: transfer_learning_tutorial.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: transfer_learning_tutorial.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_