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import os
import torch
from torchvision import datasets, transforms
from PIL import Image
import torchvision.transforms.functional as TF
def load_single_image(path="./image.jpeg"):
# Set up data transforms
data_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
# Normalize input channels using mean values and standard deviations of ImageNet.
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
# Load image
img = Image.open(path)
X = data_transforms(img)
return X
def load_dataset(data_dir="./data"):
# Set up data transforms
data_transforms = {
"train": transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
# Normalize input channels using mean values and standard deviations of ImageNet.
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]),
]
),
}
# Load image dataset
image_datasets = {
x if x == "train" else "validation": datasets.ImageFolder(
os.path.join(data_dir, x), data_transforms[x]
)
for x in ["train", "val"]
}
return image_datasets
def get_dataset_sizes(image_datasets):
dataset_sizes = {x: len(image_datasets[x]) for x in ["train", "validation"]}
return dataset_sizes
def get_class_names(image_datasets):
class_names = image_datasets["train"].classes
return class_names
def get_dataloaders(image_datasets, batch_size):
dataloaders = {
x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)
for x in ["train", "validation"]
}
return dataloaders
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