.. 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 .. 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 73-103 .. 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]) .. image-sg:: /beginner/images/sphx_glr_transfer_learning_tutorial_001.png :alt: ['ants', 'ants', 'ants', 'ants'] :srcset: /beginner/images/sphx_glr_transfer_learning_tutorial_001.png :class: sphx-glr-single-img .. 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) .. 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 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) .. rst-class:: sphx-glr-script-out .. code-block:: none Epoch 0/24 ---------- train Loss: 0.6996 Acc: 0.6516 val Loss: 0.2014 Acc: 0.9346 Epoch 1/24 ---------- train Loss: 0.4233 Acc: 0.8033 val Loss: 0.2656 Acc: 0.8758 Epoch 2/24 ---------- train Loss: 0.4603 Acc: 0.7869 val Loss: 0.1847 Acc: 0.9477 Epoch 3/24 ---------- train Loss: 0.3096 Acc: 0.8566 val Loss: 0.1747 Acc: 0.9477 Epoch 4/24 ---------- train Loss: 0.4427 Acc: 0.8156 val Loss: 0.1630 Acc: 0.9477 Epoch 5/24 ---------- train Loss: 0.5505 Acc: 0.7828 val Loss: 0.1643 Acc: 0.9477 Epoch 6/24 ---------- train Loss: 0.3004 Acc: 0.8607 val Loss: 0.1744 Acc: 0.9542 Epoch 7/24 ---------- train Loss: 0.4083 Acc: 0.8361 val Loss: 0.1892 Acc: 0.9412 Epoch 8/24 ---------- train Loss: 0.4483 Acc: 0.7910 val Loss: 0.1984 Acc: 0.9477 Epoch 9/24 ---------- train Loss: 0.3335 Acc: 0.8279 val Loss: 0.1942 Acc: 0.9412 Epoch 10/24 ---------- train Loss: 0.2413 Acc: 0.8934 val Loss: 0.2001 Acc: 0.9477 Epoch 11/24 ---------- train Loss: 0.3107 Acc: 0.8689 val Loss: 0.1801 Acc: 0.9412 Epoch 12/24 ---------- train Loss: 0.3032 Acc: 0.8689 val Loss: 0.1669 Acc: 0.9477 Epoch 13/24 ---------- train Loss: 0.3587 Acc: 0.8525 val Loss: 0.1900 Acc: 0.9477 Epoch 14/24 ---------- train Loss: 0.2771 Acc: 0.8893 val Loss: 0.2317 Acc: 0.9216 Epoch 15/24 ---------- train Loss: 0.3064 Acc: 0.8852 val Loss: 0.1909 Acc: 0.9477 Epoch 16/24 ---------- train Loss: 0.4243 Acc: 0.8238 val Loss: 0.2227 Acc: 0.9346 Epoch 17/24 ---------- train Loss: 0.3297 Acc: 0.8238 val Loss: 0.1916 Acc: 0.9412 Epoch 18/24 ---------- train Loss: 0.4235 Acc: 0.8238 val Loss: 0.1766 Acc: 0.9477 Epoch 19/24 ---------- train Loss: 0.2500 Acc: 0.8934 val Loss: 0.2003 Acc: 0.9477 Epoch 20/24 ---------- train Loss: 0.2413 Acc: 0.8934 val Loss: 0.1821 Acc: 0.9477 Epoch 21/24 ---------- train Loss: 0.3762 Acc: 0.8115 val Loss: 0.1842 Acc: 0.9412 Epoch 22/24 ---------- train Loss: 0.3485 Acc: 0.8566 val Loss: 0.2166 Acc: 0.9281 Epoch 23/24 ---------- train Loss: 0.3625 Acc: 0.8361 val Loss: 0.1747 Acc: 0.9412 Epoch 24/24 ---------- train Loss: 0.3840 Acc: 0.8320 val Loss: 0.1768 Acc: 0.9412 Training complete in 0m 31s Best val Acc: 0.954248 .. GENERATED FROM PYTHON SOURCE LINES 335-342 .. code-block:: default 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: bees, predicted: bees, predicted: ants, predicted: ants :srcset: /beginner/images/sphx_glr_transfer_learning_tutorial_003.png :class: sphx-glr-single-img .. 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() .. 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 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:** ( 1 minutes 36.878 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 `_