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from torchvision import transforms
from torch.utils.data import DataLoader
# Define the path to your CSV file and images folder
csv_path = 'C:/Users/panda/Downloads/archive (3)/BIQ2021-main/train.csv'
images_folder = 'C:/Users/panda/Downloads/archive (3)/BIQ2021-main/Images'
# Create an instance of your custom dataset
train_dataset = CustomDataset(csv_path = 'C:/Users/panda/Downloads/archive (3)/BIQ2021-main/train.csv', images_folder = 'C:/Users/panda/Downloads/archive (3)/BIQ2021-main/Images', transform=None)
# Define a DataLoader without shuffling to go through the dataset in order
data_loader = DataLoader(custom_dataset, batch_size=1, shuffle=False)
# Calculate mean and standard deviation
mean = 0.0
std = 0.0
total_images = 0
for images, _ in data_loader:
# Assuming your images are normalized to [0, 1], which is common for neural networks
mean += images.mean(dim=(0, 2, 3))
std += images.std(dim=(0, 2, 3))
total_images += 1
# Calculate the mean and standard deviation across the entire dataset
mean /= total_images
std /= total_images
print(f"Mean: {mean}")
print(f"Standard Deviation: {std}")
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