def __init__(self, config: Config):
config = config.overwrite(config[config.dataset])
config = config.overwrite(Config.read_from_cli())
config.exp_name = f'zsl_{config.dataset}_{config.hp.compute_hash()}_{config.random_seed}'
if not config.get('silent'):
print(config.hp)
self.random = np.random.RandomState(config.random_seed)
super().__init__(config)
def get_transform(config: Config) -> Callable:
if config.name == 'mnist':
return transforms.Compose([
transforms.Resize(config.target_img_size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
elif config.name in {'cifar10', 'single_image'}:
return transforms.Compose([
transforms.Resize(
(config.target_img_size, config.target_img_size)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
elif config.name.startswith('lsun_') or config.name in {
'ffhq_thumbs', 'celeba_thumbs', 'ffhq_256', 'ffhq_1024'
}:
if config.get('concat_patches.enabled'):
return transforms.Compose([
CenterCropToMin(),
transforms.RandomHorizontalFlip(),
PatchConcatAndResize(config.target_img_size,
config.concat_patches.ratio),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
else:
return transforms.Compose([
CenterCropToMin(),
transforms.RandomHorizontalFlip(),
transforms.Resize(config.target_img_size),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
elif config.name == 'imagenet_vs':
return transforms.Compose([
PadToSquare(),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5),
inplace=True),
])
else:
raise NotImplementedError(f'Unknown dataset: {config.name}')
def __init__(self, config: Config):
super(ConvModel, self).__init__()
conv_sizes = config.get('conv_sizes', [1, 8, 32, 64])
dense_sizes = config.get('dense_sizes', [576, 128])
use_bn = config.get('use_bn', False)
use_dropout = config.get('use_dropout', False)
use_maxpool = config.get('use_maxpool', False)
use_skip_connection = config.get('use_skip_connection', False)
activation = config.get('activation', 'relu')
adaptive_pool_size = config.get('adaptive_pool_size', (4, 4))
if activation == 'relu':
self.activation = lambda: nn.ReLU(inplace=True)
elif activation == 'selu':
self.activation = lambda: nn.SELU(inplace=True)
elif activation == 'tanh':
self.activation = lambda: nn.Tanh()
elif activation == 'sigmoid':
self.activation = lambda: nn.Sigmoid()
else:
raise NotImplementedError(
f'Unknown activation function: {activation}')
conv_body = nn.Sequential(*[
ConvBlock(conv_sizes[i], conv_sizes[i+1], use_bn, use_skip_connection, \
use_maxpool, self.activation) for i in range(len(conv_sizes) - 1)])
dense_head = nn.Sequential(*[
self._create_dense_block(dense_sizes[i], dense_sizes[
i + 1], use_dropout) for i in range(len(dense_sizes) - 1)
])
self.nn = nn.Sequential(conv_body,
nn.AdaptiveAvgPool2d(adaptive_pool_size),
Flatten(), dense_head,
nn.Linear(dense_sizes[-1], 10))
def load_data(
config: Config,
img_target_shape: Tuple[int, int] = None
) -> Tuple[ImageDataset, ImageDataset, np.ndarray]:
if config.name == 'CUB':
ds_train = cub.load_dataset(config.dir,
split='train',
target_shape=img_target_shape,
in_memory=config.get('in_memory', False))
ds_test = cub.load_dataset(config.dir,
split='test',
target_shape=img_target_shape,
in_memory=config.get('in_memory', False))
class_attributes = cub.load_class_attributes(config.dir).astype(
np.float32)
elif config.name == 'CUB_EMBEDDINGS':
ds_train = feats.load_dataset(config.dir,
config.input_type,
split='train')
ds_test = feats.load_dataset(config.dir,
config.input_type,
split='test')
class_attributes = cub.load_class_attributes(config.dir).astype(
np.float32)
elif config.name == 'AWA':
ds_train = awa.load_dataset(config.dir,
split='train',
target_shape=img_target_shape)
ds_test = awa.load_dataset(config.dir,
split='test',
target_shape=img_target_shape)
class_attributes = awa.load_class_attributes(config.dir).astype(
np.float32)
elif config.name == 'SUN':
ds_train = sun.load_dataset(config.dir,
split='train',
target_shape=img_target_shape)
ds_test = sun.load_dataset(config.dir,
split='val',
target_shape=img_target_shape)
class_attributes = sun.load_class_attributes(config.dir).astype(
np.float32)
elif config.name == 'TinyImageNet':
ds_train = tiny_imagenet.load_dataset(config.dir,
split='train',
target_shape=img_target_shape)
ds_test = tiny_imagenet.load_dataset(config.dir,
split='val',
target_shape=img_target_shape)
class_attributes = None
elif config.name in SIMPLE_LOADERS.keys():
ds_train = SIMPLE_LOADERS[config.name](config.dir, split='train')
ds_test = SIMPLE_LOADERS[config.name](config.dir, split='test')
class_attributes = None
elif config.name.endswith('EMBEDDINGS'):
ds_train = feats.load_dataset(config.dir,
config.input_type,
split='train')
ds_test = feats.load_dataset(config.dir,
config.input_type,
split='test')
class_attributes = None
else:
raise NotImplementedError(f'Unkown dataset: {config.name}')
# if embed_data:
# ds_train = extract_resnet_features_for_dataset(ds_train, input_type=18)
# ds_test = extract_resnet_features_for_dataset(ds_test, input_type=18)
# np.save(f'/tmp/{config.name}_train', ds_train)
# np.save(f'/tmp/{config.name}_test', ds_test)
# ds_train = np.load(f'/tmp/{config.name}_train.npy', allow_pickle=True)
# ds_test = np.load(f'/tmp/{config.name}_test.npy', allow_pickle=True)
return ds_train, ds_test, class_attributes