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%% Cell type:markdown id:7edf7168 tags:
### **_Introduction au Machine Learning - Enise - Centrale Lyon_**
2024-2025
Emmanuel Dellandréa
%% Cell type:markdown id:4c69d182 tags:
# TD7 – Convolutional Neural Networks
%% Cell type:markdown id:fa71eda4 tags:
The objective of this tutorial is to use the PyTorch library for building, training, and evaluating CNN models.
%% Cell type:markdown id:23f266da tags:
## Sequence 1: Training a CNN to classify CIFAR10 images
The goal is to apply a Convolutional Neural Net (CNN) model on the CIFAR10 image dataset and test the accuracy of the model on the basis of image classification.
Be sure to check the PyTorch tutorials and documentation when needed:
https://pytorch.org/tutorials/
https://pytorch.org/docs/stable/index.html
%% Cell type:markdown id:4ba1c82d tags:
You can test if GPU is available on your machine and thus train on it to speed up the process
%% Cell type:code id:6e18f2fd tags:
``` python
import torch
# check if CUDA is available
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print("CUDA is not available. Training on CPU ...")
else:
print("CUDA is available! Training on GPU ...")
```
%% Cell type:markdown id:5cf214eb tags:
Next we load the CIFAR10 dataset
%% Cell type:code id:462666a2 tags:
``` python
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data.sampler import SubsetRandomSampler
# number of subprocesses to use for data loading
num_workers = 0
# how many samples per batch to load
batch_size = 20
# percentage of training set to use as validation
valid_size = 0.2
# convert data to a normalized torch.FloatTensor
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)
# choose the training and test datasets
train_data = datasets.CIFAR10("data", train=True, download=True, transform=transform)
test_data = datasets.CIFAR10("data", train=False, download=True, transform=transform)
# obtain training indices that will be used for validation
num_train = len(train_data)
indices = list(range(num_train))
np.random.shuffle(indices)
split = int(np.floor(valid_size * num_train))
train_idx, valid_idx = indices[split:], indices[:split]
# define samplers for obtaining training and validation batches
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(valid_idx)
# prepare data loaders (combine dataset and sampler)
train_loader = torch.utils.data.DataLoader(
train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers
)
valid_loader = torch.utils.data.DataLoader(
train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers
)
test_loader = torch.utils.data.DataLoader(
test_data, batch_size=batch_size, num_workers=num_workers
)
# specify the image classes
classes = [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
]
```
%% Cell type:markdown id:58ec3903 tags:
CNN definition (this one is an example)
%% Cell type:code id:317bf070 tags:
``` python
import torch.nn as nn
import torch.nn.functional as F
# define the CNN architecture
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# create a complete CNN
model = Net()
print(model)
# move tensors to GPU if CUDA is available
if train_on_gpu:
model.cuda()
```
%% Cell type:markdown id:a2dc4974 tags:
Loss function and training using SGD (Stochastic Gradient Descent) optimizer
%% Cell type:code id:4b53f229 tags:
``` python
import torch.optim as optim
criterion = nn.CrossEntropyLoss() # specify loss function
optimizer = optim.SGD(model.parameters(), lr=0.01) # specify optimizer
n_epochs = 30 # number of epochs to train the model
train_loss_list = [] # list to store loss to visualize
valid_loss_min = np.Inf # track change in validation loss
for epoch in range(n_epochs):
# Keep track of training and validation loss
train_loss = 0.0
valid_loss = 0.0
# Train the model
model.train()
for data, target in train_loader:
# Move tensors to GPU if CUDA is available
if train_on_gpu:
data, target = data.cuda(), target.cuda()
# Clear the gradients of all optimized variables
optimizer.zero_grad()
# Forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# Calculate the batch loss
loss = criterion(output, target)
# Backward pass: compute gradient of the loss with respect to model parameters
loss.backward()
# Perform a single optimization step (parameter update)
optimizer.step()
# Update training loss
train_loss += loss.item() * data.size(0)
# Validate the model
model.eval()
for data, target in valid_loader:
# Move tensors to GPU if CUDA is available
if train_on_gpu:
data, target = data.cuda(), target.cuda()
# Forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# Calculate the batch loss
loss = criterion(output, target)
# Update average validation loss
valid_loss += loss.item() * data.size(0)
# Calculate average losses
train_loss = train_loss / len(train_loader)
valid_loss = valid_loss / len(valid_loader)
train_loss_list.append(train_loss)
# Print training/validation statistics
print(
"Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}".format(
epoch, train_loss, valid_loss
)
)
# Save model if validation loss has decreased
if valid_loss <= valid_loss_min:
print(
"Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...".format(
valid_loss_min, valid_loss
)
)
torch.save(model.state_dict(), "model_cifar.pt")
valid_loss_min = valid_loss
```
%% Cell type:markdown id:13e1df74 tags:
Does overfit occur? If so, do an early stopping.
%% Cell type:code id:d39df818 tags:
``` python
import matplotlib.pyplot as plt
plt.plot(range((len(train_loss_list))), train_loss_list)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("Performance of Model 1")
plt.show()
```
%% Cell type:markdown id:11df8fd4 tags:
Now loading the model with the lowest validation loss value
%% Cell type:code id:e93efdfc tags:
``` python
model.load_state_dict(torch.load("./model_cifar.pt"))
# track test loss
test_loss = 0.0
class_correct = list(0.0 for i in range(10))
class_total = list(0.0 for i in range(10))
model.eval()
# iterate over test data
for data, target in test_loader:
# move tensors to GPU if CUDA is available
if train_on_gpu:
data, target = data.cuda(), target.cuda()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the batch loss
loss = criterion(output, target)
# update test loss
test_loss += loss.item() * data.size(0)
# convert output probabilities to predicted class
_, pred = torch.max(output, 1)
# compare predictions to true label
correct_tensor = pred.eq(target.data.view_as(pred))
correct = (
np.squeeze(correct_tensor.numpy())
if not train_on_gpu
else np.squeeze(correct_tensor.cpu().numpy())
)
# calculate test accuracy for each object class
for i in range(batch_size):
label = target.data[i]
class_correct[label] += correct[i].item()
class_total[label] += 1
# average test loss
test_loss = test_loss / len(test_loader)
print("Test Loss: {:.6f}\n".format(test_loss))
for i in range(10):
if class_total[i] > 0:
print(
"Test Accuracy of %5s: %2d%% (%2d/%2d)"
% (
classes[i],
100 * class_correct[i] / class_total[i],
np.sum(class_correct[i]),
np.sum(class_total[i]),
)
)
else:
print("Test Accuracy of %5s: N/A (no training examples)" % (classes[i]))
print(
"\nTest Accuracy (Overall): %2d%% (%2d/%2d)"
% (
100.0 * np.sum(class_correct) / np.sum(class_total),
np.sum(class_correct),
np.sum(class_total),
)
)
```
%% Cell type:markdown id:944991a2 tags:
### Experiments:
Build a new network with the following structure.
- It has 3 convolutional layers of kernel size 3 and padding of 1.
- The first convolutional layer must output 16 channels, the second 32 and the third 64.
- At each convolutional layer output, we apply a ReLU activation then a MaxPool with kernel size of 2.
- Then, three fully connected layers, the first two being followed by a ReLU activation.
- The first fully connected layer will have an output size of 512.
- The second fully connected layer will have an output size of 64.
Compare the results obtained with this new network to those obtained previously.
%% Cell type:markdown id:201470f9 tags:
## Sequence 2: Working with pre-trained models.
PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.html
We will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files `imagenet-simple-labels.json` that contains the imagenet labels and the image dog.png that we will use as test.
%% Cell type:code id:b4d13080 tags:
``` python
import json
from PIL import Image
from torchvision import models
# Choose an image to pass through the model
test_image = "dog.png"
# Configure matplotlib for pretty inline plots
#%matplotlib inline
#%config InlineBackend.figure_format = 'retina'
# Prepare the labels
with open("imagenet-simple-labels.json") as f:
labels = json.load(f)
# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor
data_transform = transforms.Compose(
[
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
# Load the image
image = Image.open(test_image)
plt.imshow(image), plt.xticks([]), plt.yticks([])
# Now apply the transformation, expand the batch dimension, and send the image to the GPU
# image = data_transform(image).unsqueeze(0).cuda()
image = data_transform(image).unsqueeze(0)
# Download the model if it's not there already. It will take a bit on the first run, after that it's fast
model = models.resnet50(pretrained=True)
# Send the model to the GPU
# model.cuda()
# Set layers such as dropout and batchnorm in evaluation mode
model.eval()
# Get the 1000-dimensional model output
out = model(image)
# Find the predicted class
print("Predicted class is: {}".format(labels[out.argmax()]))
```
%% Cell type:markdown id:184cfceb tags:
### Experiments:
Study the code and the results obtained. Possibly add other images downloaded from the internet.
Experiment with other pre-trained CNN models.
%% Cell type:markdown id:5d57da4b tags:
## Sequence 3: Transfer Learning
For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.
Download and unzip in your working directory the dataset available at the address :
https://download.pytorch.org/tutorial/hymenoptera_data.zip
Execute the following code in order to display some images of the dataset.
%% Cell type:code id:be2d31f5 tags:
``` python
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision
from torchvision import datasets, transforms
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
"train": transforms.Compose(
[
transforms.RandomResizedCrop(
224
), # ImageNet models were trained on 224x224 images
transforms.RandomHorizontalFlip(), # flip horizontally 50% of the time - increases train set variability
transforms.ToTensor(), # convert it to a PyTorch tensor
transforms.Normalize(
[0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
), # ImageNet models expect this norm
]
),
"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"
# Create train and validation datasets and loaders
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=0
)
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")
# Helper function for displaying images
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])
# Un-normalize the images
inp = std * inp + mean
# Clip just in case
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
plt.show()
# 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])
```
%% Cell type:markdown id:bbd48800 tags:
Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer.
%% Cell type:code id:572d824c tags:
``` python
import copy
import time
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
epoch_time = [] # we'll keep track of the time needed for each epoch
for epoch in range(num_epochs):
epoch_start = time.time()
print("Epoch {}/{}".format(epoch + 1, num_epochs))
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 training phase
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())
# Add the epoch time
t_epoch = time.time() - epoch_start
epoch_time.append(t_epoch)
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, epoch_time
# Download a pre-trained ResNet18 model and freeze its weights
model = torchvision.models.resnet18(pretrained=True)
for param in model.parameters():
param.requires_grad = False
# Replace the final fully connected layer
# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 2)
# Send the model to the GPU
model = model.to(device)
# Set the loss function
criterion = nn.CrossEntropyLoss()
# Observe that only the parameters of the final layer are being optimized
optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)
model, epoch_time = train_model(
model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10
)
```
%% Cell type:markdown id:bbd48800 tags:
### Experiments:
Study the code and the results obtained.
Modify the code and add an "eval_model" function to allow
the evaluation of the model on a test set (different from the learning and validation sets used during the learning phase). Study the results obtained.
Now modify the code to replace the current classification layer with a set of two layers using a "relu" activation function for the middle layer. Renew the experiments and study the results obtained.
Experiment with other models and datasets.
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