data_transforms = {
'test': transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
}
# Image and label directories
labels_dir = './ClothingAttributeDataset/labels/'
images_dir = './ClothingAttributeDataset/images/'
# Load the data
image_datasets = {x: ClothingAttributeDataset(labels_dir, images_dir, x, data_transforms[x]) for x in ['test']}
dataloaders = {x: DataLoader(image_datasets[x], batch_size=TEST_BATCH_SIZE, shuffle=True) for x in ['test']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['test']}
main_task = 5 # gender
class_names = ['Male', 'Female']
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def imshow(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
}
# Image and label directories
labels_dir = './ClothingAttributeDataset/labels/'
images_dir = './ClothingAttributeDataset/images/'
# Load the data
image_datasets = {
x: ClothingAttributeDataset(labels_dir, images_dir, x, data_transforms[x])
for x in ['test']
}
dataloaders = {
x: DataLoader(image_datasets[x], batch_size=TEST_BATCH_SIZE)
for x in ['test']
}
dataset_sizes = {x: len(image_datasets[x]) for x in ['test']}
main_task = 5 # gender
auxiliary_task = 20 # skin exposure
class_names = ['Male', 'Female']
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")