.. 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 :ref:`Go to the end ` 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:: Python # 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 .. rst-class:: sphx-glr-script-out .. code-block:: none .. 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 73-107 .. 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 = '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 # We want to be able to train our model on an `accelerator `__ # such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU. device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu" print(f"Using {device} device") .. rst-class:: sphx-glr-script-out .. code-block:: none Using cuda device .. GENERATED FROM PYTHON SOURCE LINES 108-112 Visualize a few images ^^^^^^^^^^^^^^^^^^^^^^ Let's visualize a few training images so as to understand the data augmentations. .. GENERATED FROM PYTHON SOURCE LINES 112-135 .. code-block:: Python 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]) .. image-sg:: /beginner/images/sphx_glr_transfer_learning_tutorial_001.png :alt: ['ants', 'bees', 'ants', 'ants'] :srcset: /beginner/images/sphx_glr_transfer_learning_tutorial_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 136-147 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 147-220 .. code-block:: Python 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, weights_only=True)) return model .. GENERATED FROM PYTHON SOURCE LINES 221-226 Visualizing the model predictions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Generic function to display predictions for a few images .. GENERATED FROM PYTHON SOURCE LINES 226-253 .. 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(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 254-259 Finetuning the ConvNet ---------------------- Load a pretrained model and reset final fully connected layer. .. GENERATED FROM PYTHON SOURCE LINES 259-276 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth 0%| | 0.00/44.7M [00:00`__. .. GENERATED FROM PYTHON SOURCE LINES 304-325 .. code-block:: Python 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 326-333 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 333-337 .. code-block:: Python model_conv = train_model(model_conv, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=25) .. rst-class:: sphx-glr-script-out .. code-block:: none Epoch 0/24 ---------- train Loss: 0.5895 Acc: 0.6926 val Loss: 0.2440 Acc: 0.8954 Epoch 1/24 ---------- train Loss: 0.5688 Acc: 0.7459 val Loss: 0.3939 Acc: 0.8627 Epoch 2/24 ---------- train Loss: 0.5887 Acc: 0.7623 val Loss: 0.3481 Acc: 0.8693 Epoch 3/24 ---------- train Loss: 0.5643 Acc: 0.7869 val Loss: 0.2008 Acc: 0.9346 Epoch 4/24 ---------- train Loss: 0.3695 Acc: 0.8525 val Loss: 0.1967 Acc: 0.9281 Epoch 5/24 ---------- train Loss: 0.3666 Acc: 0.8443 val Loss: 0.1831 Acc: 0.9477 Epoch 6/24 ---------- train Loss: 0.4185 Acc: 0.8279 val Loss: 0.2011 Acc: 0.9346 Epoch 7/24 ---------- train Loss: 0.4167 Acc: 0.8361 val Loss: 0.1949 Acc: 0.9412 Epoch 8/24 ---------- train Loss: 0.3161 Acc: 0.8443 val Loss: 0.1946 Acc: 0.9412 Epoch 9/24 ---------- train Loss: 0.3392 Acc: 0.8525 val Loss: 0.2035 Acc: 0.9412 Epoch 10/24 ---------- train Loss: 0.3916 Acc: 0.8525 val Loss: 0.2017 Acc: 0.9412 Epoch 11/24 ---------- train Loss: 0.3971 Acc: 0.8074 val Loss: 0.2062 Acc: 0.9346 Epoch 12/24 ---------- train Loss: 0.3772 Acc: 0.8443 val Loss: 0.1795 Acc: 0.9542 Epoch 13/24 ---------- train Loss: 0.3995 Acc: 0.8279 val Loss: 0.1840 Acc: 0.9346 Epoch 14/24 ---------- train Loss: 0.3359 Acc: 0.8402 val Loss: 0.1778 Acc: 0.9477 Epoch 15/24 ---------- train Loss: 0.3302 Acc: 0.8607 val Loss: 0.2040 Acc: 0.9346 Epoch 16/24 ---------- train Loss: 0.2961 Acc: 0.8566 val Loss: 0.1944 Acc: 0.9477 Epoch 17/24 ---------- train Loss: 0.4351 Acc: 0.8074 val Loss: 0.1983 Acc: 0.9346 Epoch 18/24 ---------- train Loss: 0.2795 Acc: 0.8811 val Loss: 0.1808 Acc: 0.9477 Epoch 19/24 ---------- train Loss: 0.4004 Acc: 0.8033 val Loss: 0.1861 Acc: 0.9477 Epoch 20/24 ---------- train Loss: 0.2431 Acc: 0.9180 val Loss: 0.1988 Acc: 0.9412 Epoch 21/24 ---------- train Loss: 0.3045 Acc: 0.8689 val Loss: 0.2003 Acc: 0.9477 Epoch 22/24 ---------- train Loss: 0.4579 Acc: 0.7828 val Loss: 0.2059 Acc: 0.9281 Epoch 23/24 ---------- train Loss: 0.3277 Acc: 0.8607 val Loss: 0.1977 Acc: 0.9412 Epoch 24/24 ---------- train Loss: 0.3773 Acc: 0.8320 val Loss: 0.1948 Acc: 0.9412 Training complete in 0m 28s Best val Acc: 0.954248 .. GENERATED FROM PYTHON SOURCE LINES 339-346 .. code-block:: Python visualize_model(model_conv) plt.ioff() plt.show() .. image-sg:: /beginner/images/sphx_glr_transfer_learning_tutorial_003.png :alt: predicted: bees, predicted: ants, predicted: ants, predicted: bees, predicted: bees, predicted: ants :srcset: /beginner/images/sphx_glr_transfer_learning_tutorial_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 347-353 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 353-374 .. code-block:: Python 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 376-386 .. code-block:: Python visualize_model_predictions( model_conv, img_path='data/hymenoptera_data/val/bees/72100438_73de9f17af.jpg' ) plt.ioff() plt.show() .. image-sg:: /beginner/images/sphx_glr_transfer_learning_tutorial_004.png :alt: Predicted: bees :srcset: /beginner/images/sphx_glr_transfer_learning_tutorial_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 387-394 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 4.825 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-jupyter :download:`Download Jupyter notebook: transfer_learning_tutorial.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: transfer_learning_tutorial.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: transfer_learning_tutorial.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_