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dataset.py
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dataset.py
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from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import os
image_size = 224
batch_size = 32
num_workers = 4
## Data directoris
# Local Paths
train_dir = "data/stanford-cars-dataset/data/car_data/car_data/train"
valid_dir = "data/stanford-cars-dataset/data/car_data/car_data/valid"
train_images = os.listdir(train_dir)
valid_images = os.listdir(valid_dir)
## Data Augmentation
# Training Data Transforms
def get_train_transform(image_size):
train_transform = transforms.Compose([
transforms.Resize((image_size, image_size)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomRotation(35),
transforms.RandomAdjustSharpness(sharpness_factor=2, p=0.5),
transforms.RandomGrayscale(p=0.5),
transforms.RandomPerspective(distortion_scale=0.5, p=0.5),
transforms.RandomPosterize(bits=2, p=0.5),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
return train_transform
# Validation Data Transforms
def get_valid_transform(image_size):
valid_transform = transforms.Compose([
transforms.Resize((image_size, image_size)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
return valid_transform
def get_datasets():
"""
Function to prepare the Datasets.
Returns the training and validation datasets along
with the class names.
"""
dataset_train = datasets.ImageFolder(
train_dir,
transform=(get_train_transform(image_size))
)
dataset_valid = datasets.ImageFolder(
valid_dir,
transform=(get_valid_transform(image_size))
)
return dataset_train, dataset_valid, dataset_train.classes
def get_data_loaders(dataset_train, dataset_valid):
"""
Input: the training and validation data.
Returns the training and validation data loaders.
"""
train_loader = DataLoader(
dataset_train, batch_size=batch_size,
shuffle=True, num_workers=num_workers
)
valid_loader = DataLoader(
dataset_valid, batch_size=batch_size,
shuffle=False, num_workers=num_workers
)
return train_loader, valid_loader