17. CNN for Regression - UTKface Dataset#
17.1. Download the Dataset#
!gdown --folder https://drive.google.com/drive/folders/1HROmgviy4jUUUaCdvvrQ8PcqtNg2jn3G?usp=sharing
Retrieving folder contents
Processing file 1mb5Z24TsnKI3ygNIlX6ZFiwUj0_PmpAW part1.tar.gz
Processing file 19vdaXVRtkP-nyxz1MYwXiFsh_m_OL72b part2.tar.gz
Processing file 1oj9ZWsLV2-k2idoW_nRSrLQLUP3hus3b part3.tar.gz
Retrieving folder contents completed
Building directory structure
Building directory structure completed
Downloading...
From (original): https://drive.google.com/uc?id=1mb5Z24TsnKI3ygNIlX6ZFiwUj0_PmpAW
From (redirected): https://drive.google.com/uc?id=1mb5Z24TsnKI3ygNIlX6ZFiwUj0_PmpAW&confirm=t&uuid=7a84271c-8ede-423d-942f-333f0fe68665
To: /content/UTKface_inthewild/part1.tar.gz
100% 874M/874M [00:12<00:00, 68.6MB/s]
Downloading...
From (original): https://drive.google.com/uc?id=19vdaXVRtkP-nyxz1MYwXiFsh_m_OL72b
From (redirected): https://drive.google.com/uc?id=19vdaXVRtkP-nyxz1MYwXiFsh_m_OL72b&confirm=t&uuid=85f2962b-5cd2-495a-a37d-b1df8731a3e7
To: /content/UTKface_inthewild/part2.tar.gz
100% 459M/459M [00:07<00:00, 63.0MB/s]
Downloading...
From (original): https://drive.google.com/uc?id=1oj9ZWsLV2-k2idoW_nRSrLQLUP3hus3b
From (redirected): https://drive.google.com/uc?id=1oj9ZWsLV2-k2idoW_nRSrLQLUP3hus3b&confirm=t&uuid=e91d4166-34df-4fdb-a190-d14ec5560130
To: /content/UTKface_inthewild/part3.tar.gz
100% 57.0M/57.0M [00:01<00:00, 47.7MB/s]
Download completed
!ls -lrth UTKface_inthewild
total 1.3G
-rw-r--r-- 1 root root 834M Sep 30 2021 part1.tar.gz
-rw-r--r-- 1 root root 55M Sep 30 2021 part3.tar.gz
-rw-r--r-- 1 root root 438M Sep 30 2021 part2.tar.gz
# Extract part1.tar.gz
!tar -xzvf UTKface_inthewild/part1.tar.gz -C UTKface_inthewild/
# Extract part2.tar.gz
!tar -xzvf UTKface_inthewild/part2.tar.gz -C UTKface_inthewild/
# Extract part3.tar.gz
!tar -xzvf UTKface_inthewild/part3.tar.gz -C UTKface_inthewild/
#!ls -lrth UTKface_inthewild/part1
!ls -lrth UTKface_inthewild
total 1.3G
drwxr-xr-x 2 22294 143 520K Jan 12 2017 part1
drwxr-xr-x 2 22294 143 528K Jan 18 2017 part2
drwxr-xr-x 2 22294 143 172K Jan 21 2017 part3
-rw-r--r-- 1 root root 834M Sep 30 2021 part1.tar.gz
-rw-r--r-- 1 root root 55M Sep 30 2021 part3.tar.gz
-rw-r--r-- 1 root root 438M Sep 30 2021 part2.tar.gz
# Move all .jpg files from subdirectories to UTKface_inthewild/
!find UTKface_inthewild/ -name '*.jpg' -exec mv {} UTKface_inthewild/ \;
# Remove the .tar.gz files
!rm UTKface_inthewild/part1.tar.gz UTKface_inthewild/part2.tar.gz UTKface_inthewild/part3.tar.gz
# Remove the extracted directories (assuming they were extracted into directories named part1, part2, and part3)
!rm -r UTKface_inthewild/part1 UTKface_inthewild/part2 UTKface_inthewild/part3
# List the remaining contents of UTKface_inthewild/
#!ls -lrth UTKface_inthewild/
!ls
sample_data utkface_dataset UTKface_inthewild
image_path = 'UTKface_inthewild/26_0_3_20170120134330671.jpg'
img = Image.open(image_path)
to_tensor = transforms.ToTensor()
img_tensor = to_tensor(img)
print("Pixel values before normalization (sample 5x5 from the first channel):")
print(img_tensor[0, :5, :5].numpy()) # Print the first 5x5 pixels from the first channel (R)
print(f"Min pixel value: {torch.min(img_tensor)}")
print(f"Max pixel value: {torch.max(img_tensor)}")
Pixel values before normalization (sample 5x5 from the first channel):
[[0.76862746 0.77254903 0.76862746 0.7764706 0.78431374]
[0.77254903 0.77254903 0.77254903 0.7764706 0.78431374]
[0.77254903 0.7764706 0.77254903 0.78039217 0.7882353 ]
[0.7764706 0.78039217 0.7764706 0.78431374 0.7882353 ]
[0.78039217 0.78431374 0.78039217 0.7882353 0.7921569 ]]
Min pixel value: 0.0
Max pixel value: 1.0
17.2. Set up Dataset and DataLoader#
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import os
# Custom Dataset Class for UTKFace
class UTKFaceDataset(Dataset):
def __init__(self, image_dir, transform=None):
self.image_dir = image_dir
self.image_filenames = [f for f in os.listdir(image_dir) if f.endswith('.jpg')]
self.transform = transform
def __len__(self):
return len(self.image_filenames)
def __getitem__(self, idx):
img_path = os.path.join(self.image_dir, self.image_filenames[idx])
image = Image.open(img_path)
# Debugging: Print the mode of the image
#print(f"Loading image {img_path}, mode: {image.mode}")
# Convert image to RGB if it is grayscale or has an alpha channel
if image.mode != 'RGB':
image = image.convert('RGB')
print(f"Converted image to RGB mode")
# Age is the first part of the filename
age = int(self.image_filenames[idx].split('_')[0])
if self.transform:
image = self.transform(image)
return image, torch.tensor(age, dtype=torch.float32)
# Transformations (resize, normalize, etc.)
transform = transforms.Compose([
transforms.Resize((128, 128)), # Resize all images to 128x128
transforms.ToTensor(), # Convert images to tensor
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) # Normalize to [-1, 1] and image with pixel values contained in [0,1]
])
# Create dataset
image_dir = 'UTKface_inthewild/' # Directory where your .jpg files are located
dataset = UTKFaceDataset(image_dir=image_dir, transform=transform)
# Create DataLoader
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
17.3. Verify Data Loading#
# Example: Iterate through DataLoader
for images, ages in dataloader:
print(f'Image batch size: {images.size()}, Ages: {ages}')
break # Break after first batch for demonstration
Image batch size: torch.Size([32, 3, 128, 128]), Ages: tensor([26., 26., 28., 26., 2., 21., 16., 27., 45., 32., 39., 15., 65., 26.,
52., 2., 55., 37., 26., 1., 60., 3., 26., 17., 45., 33., 55., 28.,
8., 15., 26., 50.])
x = torch.ones((32, 3, 128, 128))
x.shape
torch.Size([32, 3, 128, 128])
17.4. Train your model#
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader, random_split
from torchvision import transforms
from PIL import Image
import os
class CNNRegression(nn.Module):
def __init__(self):
super().__init__() # This works just as well in Python 3
self.model = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
nn.Flatten(),
nn.Linear(64 * 16 * 16, 512), # Correct the input size here
nn.ReLU(),
nn.Dropout(p=0.5), # Add dropout with a probability of 50%
nn.Linear(512, 1),
nn.ReLU() # Apply ReLU to the output layer to ensure non-negative prediction
)
def forward(self, x):
return self.model(x)
# Example usage
model = CNNRegression()
x = torch.ones((32, 3, 128, 128)) # Batch size 32, 3 channels (RGB), 128x128 images
output = model(x)
print(f"{output.shape}\n\n") # Should output shape [32, 1]
# Create the full dataset
image_dir = 'UTKface_inthewild/' # Replace with your dataset directory
full_dataset = UTKFaceDataset(image_dir=image_dir, transform=transform)
train_size = int(0.7 * len(full_dataset)) # 70% for training
val_size = int(0.15 * len(full_dataset)) # 15% for validation
test_size = len(full_dataset) - train_size - val_size # 15% for testing
train_dataset, val_dataset, test_dataset = random_split(full_dataset, [train_size, val_size, test_size])
# Define DataLoaders for each dataset with a given batch size
batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# Example of using the DataLoader in a training loop
for images, ages in train_loader:
print(f"images.shape: {images.shape}") # Should be [batch_size, 3, 128, 128] for RGB images
print(ages) # Tensor of ages corresponding to the batch of images
# Training code here...
break # Just to show the first batch; remove in actual training
torch.Size([32, 1])
images.shape: torch.Size([32, 3, 128, 128])
tensor([40., 43., 1., 20., 50., 65., 8., 26., 16., 61., 50., 68., 30., 26.,
30., 35., 46., 4., 32., 28., 35., 85., 53., 28., 2., 66., 30., 16.,
7., 22., 30., 24.])
device = torch.device("cuda:0")
# device = torch.device("cpu")
# Instantiate the model, loss function, and optimizer
model = CNNRegression()
model = model.to(device)
criterion = nn.MSELoss() # Mean Squared Error for regression
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# Training loop
num_epochs = 10
for epoch in range(num_epochs):
running_loss = 0.0
for images, ages in train_loader:
images = images.to(device)
ages = ages.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs.squeeze(), ages)
# Backward pass and optimization
loss.backward()
optimizer.step()
# Zero the parameter gradients
optimizer.zero_grad()
running_loss += loss.item()
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}')
print('Training finished!')
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Epoch [1/10], Loss: 382.6364
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Epoch [2/10], Loss: 334.8164
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Epoch [3/10], Loss: 302.5791
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Epoch [4/10], Loss: 269.1704
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Epoch [5/10], Loss: 243.6630
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Epoch [6/10], Loss: 222.7433
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Epoch [7/10], Loss: 202.3372
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Epoch [8/10], Loss: 176.7178
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Epoch [9/10], Loss: 158.4254
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Epoch [10/10], Loss: 136.2476
Training finished!
17.5. Testing#
import matplotlib.pyplot as plt
# Assuming `test_loader` is your DataLoader for the test dataset
# Collect true and predicted ages
true_ages = []
predicted_ages = []
# Put the model in evaluation mode
model.eval()
# Turn off gradient computation for testing
with torch.no_grad():
for images, ages in test_loader:
images = images.to(device)
ages = ages.to(device)
# Get predictions from the model
outputs = model(images).squeeze() # Assuming the model returns a single value for regression
# Collect the true and predicted ages
true_ages.extend(ages.cpu().numpy())
# Convert the outputs tensor to a 1D numpy array
predicted_ages.extend(outputs.cpu().numpy().flatten())
# Plot the correlation between true and predicted ages
plt.figure(figsize=(8, 8))
plt.scatter(true_ages, predicted_ages, alpha=0.5)
plt.plot([min(true_ages), max(true_ages)], [min(true_ages), max(true_ages)], 'r--', lw=2) # y=x line
plt.xlabel('True Ages')
plt.ylabel('Predicted Ages')
plt.title('Correlation between True and Predicted Ages')
plt.show()
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17.6. Displaying images with True and Predicted Ages#
import matplotlib.pyplot as plt
# Display 10 images with their true and predicted ages
def show_predictions(model, test_loader, device):
model.eval()
images_shown = 0
# Turn off gradients for validation
with torch.no_grad():
for images, ages in test_loader:
images = images.to(device)
ages = ages.to(device)
# Get model predictions
outputs = model(images).squeeze() # Predicted ages
# Display first 10 images with true and predicted ages
for i in range(min(10, images.size(0))): # Show up to 10 images
plt.figure(figsize=(2, 2))
img = images[i].cpu().numpy().transpose(1, 2, 0) # Convert to HWC format for plotting
img = (img * 0.5) + 0.5 # Denormalize the image to [0, 1] range
# Plot the image
plt.imshow(img)
plt.title(f'True Age: {ages[i].item():.1f}, Predicted Age: {outputs[i].item():.1f}')
plt.axis('off')
images_shown += 1
if images_shown == 10:
return # Stop after showing 10 images
# Call the function to show the predictions
show_predictions(model, test_loader, device)
