def __init__(self, verbose=False, path=None, resume=False, searcher_args=None,
search_type=BayesianSearcher):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
search_type: A constant denoting the type of hyperparameter search algorithm that must be used.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = rand_temp_folder_generator()
self.path = path
ensure_dir(path)
if resume:
classifier = pickle_from_file(os.path.join(self.path, 'classifier'))
self.__dict__ = classifier.__dict__
self.cnn = pickle_from_file(os.path.join(self.path, 'module'))
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path, verbose, search_type)
def __init__(self, verbose=False, path=None, resume=False, searcher_args=None,
search_type=BayesianSearcher):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
search_type: A constant denoting the type of hyperparameter search algorithm that must be used.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = rand_temp_folder_generator()
self.path = path
ensure_dir(path)
if resume:
classifier = pickle_from_file(os.path.join(self.path, 'classifier'))
self.__dict__ = classifier.__dict__
self.cnn = pickle_from_file(os.path.join(self.path, 'module'))
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path, verbose, search_type)
def text_preprocess(x_train):
"""This is the text preprocess main method.
It takes an raw string, clean it and processing it into tokenlized numpy array.
"""
if Constant.STORE_PATH == '':
temp_path = temp_path_generator()
path = temp_path + '_store'
else:
path = Constant.STORE_PATH
ensure_dir(path)
x_train = [clean_str(x) for x in x_train]
x_train, word_index = tokenlize_text(
max_seq_length=Constant.MAX_SEQUENCE_LENGTH,
max_num_words=Constant.MAX_NB_WORDS,
x_train=x_train)
print("generating preprocessing model...")
x_train = processing(path=path,
word_index=word_index,
input_length=Constant.MAX_SEQUENCE_LENGTH,
x_train=x_train)
return x_train
def __init__(self, verbose=False, path=constant.DEFAULT_SAVE_PATH, resume=False,
searcher_args=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: An boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: An boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
"""
if searcher_args is None:
searcher_args = {}
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
ensure_dir(path)
def __init__(self, model_path=None, overwrite=False):
super(VoiceGenerator, self).__init__()
self.model_path = model_path if model_path is not None else temp_path_generator()
ensure_dir(self.model_path)
self.checkpoint_path = os.path.join(self.model_path, Constant.PRE_TRAIN_VOICE_GENERATOR_MODEL_NAME)
self.sample_rate = 0
self.hop_length = 0
self.overwrite = overwrite
self.device = get_device()
self.load()
def __init__(self, model_path=None, overwrite=False):
super(VoiceGenerator, self).__init__()
if model_path is None:
model_path = temp_path_generator()
self.model_path = model_path
ensure_dir(self.model_path)
self.checkpoint_path = os.path.join(self.model_path, Constant.PRE_TRAIN_VOICE_GENERATOR_MODEL_NAME)
self.sample_rate = 0
self.hop_length = 0
self.overwrite = overwrite
self.load()
def __init__(self, loss, metric, searcher_args=None, path=None, verbose=False, search_type=BayesianSearcher):
self.searcher_args = searcher_args if searcher_args is not None else {}
self.searcher = None
self.path = path if path is not None else rand_temp_folder_generator()
ensure_dir(self.path)
if verbose:
print('Saving Directory:', self.path)
self.verbose = verbose
self.loss = loss
self.metric = metric
self.generators = []
self.search_type = search_type
def __init__(self, loss, metric, searcher_args=None, path=None, verbose=False, search_type=BayesianSearcher):
self.searcher_args = searcher_args if searcher_args is not None else {}
self.searcher = None
self.path = path if path is not None else rand_temp_folder_generator()
ensure_dir(self.path)
if verbose:
print('Saving Directory:', self.path)
self.verbose = verbose
self.loss = loss
self.metric = metric
self.generators = []
self.search_type = search_type
def load(self, model_path=None):
temp_path = temp_path_generator()
ensure_dir(temp_path)
model_paths = [
f'{temp_path}/{file_name}'
for file_name in Constant.FACE_DETECTOR['MODEL_NAMES']
]
for google_id, file_name in zip(
Constant.FACE_DETECTOR['MODEL_GOOGLE_ID'],
Constant.FACE_DETECTOR['MODEL_NAMES']):
download_file_from_google_drive(
file_id=google_id, dest_path=f'{temp_path}/{file_name}')
return model_paths
def __init__(self, path=None, **kwargs):
"""
Initialization function for tabular supervised learner.
"""
super().__init__(**kwargs)
self.is_trained = False
self.clf = None
self.objective = None
self.tabular_preprocessor = None
self.path = path if path is not None else rand_temp_folder_generator()
ensure_dir(self.path)
if self.verbose:
print('Path:', path)
self.save_filename = os.path.join(self.path, 'lgbm.txt')
self.time_limit = None
self.lgbm = None
def __init__(self, path=None, **kwargs):
"""
Initialization function for tabular supervised learner.
"""
super().__init__(**kwargs)
self.is_trained = False
self.clf = None
self.objective = None
self.tabular_preprocessor = None
self.path = path if path is not None else rand_temp_folder_generator()
ensure_dir(self.path)
if self.verbose:
print('Path:', path)
self.save_filename = os.path.join(self.path, 'lgbm.txt')
self.time_limit = None
self.lgbm = None
def __init__(self,
verbose=False,
path=None,
resume=False,
searcher_args=None,
augment=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
augment: A boolean value indicating whether the data needs augmentation. If not define, then it
will use the value of Constant.DATA_AUGMENTATION which is True by default.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path,
verbose)
if augment is None:
augment = Constant.DATA_AUGMENTATION
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
self.augment = augment
ensure_dir(path)
def __init__(self, verbose=True, model_path=None):
"""Initialize the instance."""
self.verbose = verbose
self.model = None
self.device = get_device()
self.model_path = model_path if model_path is not None else temp_path_generator(
)
ensure_dir(self.model_path)
self.local_paths = [
os.path.join(self.model_path, x.local_name)
for x in self._google_drive_files
]
for path, x in zip(self.local_paths, self._google_drive_files):
if not os.path.exists(path):
download_file_from_google_drive(file_id=x.google_drive_id,
dest_path=path,
verbose=True)
def load(self, model_path=None):
if model_path is None:
model_file_name = Constant.OBJECT_DETECTOR['MODEL_NAME']
temp_path = temp_path_generator()
ensure_dir(temp_path)
model_path = f'{temp_path}/{model_file_name}'
download_file_from_google_drive(file_id=Constant.OBJECT_DETECTOR['MODEL_GOOGLE_ID'], dest_path=model_path)
# load net
num_classes = len(VOC_CLASSES) + 1 # +1 for background
self.model = self._build_ssd('test', 300, num_classes) # initialize SSD
if self.device.startswith("cuda"):
self.model.load_state_dict(torch.load(model_path))
else:
self.model.load_state_dict(torch.load(model_path, map_location=lambda storage, loc: storage))
self.model.eval()
print('Finished loading model!')
self.model = self.model.to(self.device)
def text_preprocess(x_train):
"""This is the text preprocess main method.
It takes an raw string, clean it and processing it into tokenlized numpy array.
"""
if Constant.STORE_PATH == '':
temp_path = temp_path_generator()
path = temp_path + '_store'
else:
path = Constant.STORE_PATH
ensure_dir(path)
x_train = [clean_str(x) for x in x_train]
x_train, word_index = tokenlize_text(max_seq_length=Constant.MAX_SEQUENCE_LENGTH,
max_num_words=Constant.MAX_NB_WORDS,
x_train=x_train)
print("generating preprocessing model...")
x_train = processing(path=path, word_index=word_index, input_length=Constant.MAX_SEQUENCE_LENGTH, x_train=x_train)
return x_train
def __init__(self,
verbose=False,
searcher_type=None,
path=constant.DEFAULT_SAVE_PATH,
resume=False):
"""Initialize the instance.
The classifier will be loaded from file if the directory in 'path' has a saved classifier.
Otherwise it would create a new one.
"""
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle.load(
open(os.path.join(path, 'classifier'), 'rb'))
self.__dict__ = classifier.__dict__
else:
self.y_encoder = None
self.verbose = verbose
self.searcher = False
self.searcher_type = searcher_type
self.path = path
ensure_dir(path)
def __init__(self, verbose=False, path=None, resume=False, searcher_args=None, augment=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
augment: A boolean value indicating whether the data needs augmentation. If not define, then it
will use the value of Constant.DATA_AUGMENTATION which is True by default.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
if augment is None:
augment = Constant.DATA_AUGMENTATION
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
self.augment = augment
ensure_dir(path)
def load(self, model_path=None):
temp_path = temp_path_generator()
ensure_dir(temp_path)
for model_link, file_path in zip(Constant.FACE_DETECTION_PRETRAINED['PRETRAINED_MODEL_LINKS'],
Constant.FACE_DETECTION_PRETRAINED['FILE_NAMES']):
download_file(model_link, f'{temp_path}/{file_path}')
from keras.datasets import mnist
import os
from pandas import DataFrame
from PIL import Image
from autokeras.utils import ensure_dir
ensure_dir('mnist/train')
ensure_dir('mnist/test')
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# x_train = x_train.reshape(x_train.shape + (1,))
# x_test = x_test.reshape(x_test.shape + (1,))
# file_names = []
# for i in range(len(x_train)):
# file_name = ("%05d" % (i, )) + '.jpg'
# Image.fromarray(x_train[i]).save(os.path.join('mnist', 'train', file_name))
# file_names.append(file_name)
#
# csv_data = {'File Name': file_names, 'Label': y_train}
# DataFrame(csv_data).to_csv('mnist/train/label.csv', index=False)
file_names = []
for i in range(len(x_test)):
file_name = ("%05d" % (i, )) + '.jpg'
Image.fromarray(x_test[i]).save(os.path.join('mnist', 'test', file_name))
file_names.append(file_name)
csv_data = {'File Name': file_names, 'Label': y_test}
DataFrame(csv_data).to_csv('mnist/test/label.csv', index=False)
