si23traktor/agent/neural_network/datasets.py

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import torchvision
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
BATCH_SIZE = 64
train_transform = transforms.Compose([
transforms.Resize((224, 224)), #validate that all images are 224x244
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomVerticalFlip(p=0.5),
transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)),
transforms.RandomRotation(degrees=(30, 70)), #random effects are applied to prevent overfitting
transforms.ToTensor(),
transforms.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]
)
])
valid_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]
)
])
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train_dataset = torchvision.datasets.ImageFolder(root='./images/train', transform=train_transform)
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validation_dataset = torchvision.datasets.ImageFolder(root='./images/validation', transform=valid_transform)
train_loader = DataLoader(
train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0, pin_memory=True
)
valid_loader = DataLoader(
validation_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0, pin_memory=True
)