def __init__(self):
self.net_dict = NNState(mode='eval')
# Data Augmentation operations
img_transforms = transforms.Compose([
transforms.RandomResizedCrop((64, 64), scale=(0.7, 1.0)),
transforms.ColorJitter(brightness=0.4,
contrast=0.3,
saturation=0.3,
hue=0.3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.eval_data = datasets.ImageFolder('./dataset_segmented/test',
transform=img_transforms)
def __init__(self):
self.net_dict = NNState('train')
# Data Augmentation operations
img_transforms = transforms.Compose([
transforms.RandomRotation((-30, 30)),
transforms.RandomResizedCrop((64, 64), scale=(0.7, 1.0)),
transforms.ColorJitter(brightness=0.4,
contrast=0.3,
saturation=0.3,
hue=0.3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.train_data = datasets.ImageFolder('./nn_dataset/train',
transform=img_transforms)
print(self.train_data.class_to_idx)
self.eval_data = datasets.ImageFolder('./nn_dataset/eval',
transform=img_transforms)
def __init__(self):
self.nn_state = NNState(mode='eval')
