def __init__(self, param_path=None):
assert os.path.isfile(
param_path), "pretrained VGG19 weights not found."
self.h5_file = param_path
if not os.path.exists(self.h5_file):
print(
"Pretrained VGG19 parameters not found. Downloading. Please wait..."
)
url = "https://nnabla.org/pretrained-models/nnabla-examples/GANs/first-order-model/vgg19.h5"
from nnabla.utils.data_source_loader import download
download(url, url.split('/')[-1], False)
with nn.parameter_scope("VGG19"):
logger.info('loading vgg19 parameters...')
nn.load_parameters(self.h5_file)
# drop all the affine layers.
drop_layers = [
'classifier/0/affine', 'classifier/3/affine',
'classifier/6/affine'
]
for layers in drop_layers:
nn.parameter.pop_parameter((layers + '/W'))
nn.parameter.pop_parameter((layers + '/b'))
self.mean = nn.Variable.from_numpy_array(
np.asarray([0.485, 0.456, 0.406]).reshape(1, 3, 1, 1))
self.std = nn.Variable.from_numpy_array(
np.asarray([0.229, 0.224, 0.225]).reshape(1, 3, 1, 1))
def load_checkpoint(self, args):
"""Load pretrained parameters and solver states
Args:
args (ArgumentParser): To check if tensorflow trained weights are to be used for testing and to get the path of the folder
from where the parameter and solver states are to be loaded
"""
if args.use_tf_weights:
if not os.path.isfile(
os.path.join(args.weights_path, 'gen_params.h5')):
os.makedirs(args.weights_path, exist_ok=True)
print(
"Downloading the pretrained tf-converted weights. Please wait..."
)
url = "https://nnabla.org/pretrained-models/nnabla-examples/GANs/stylegan2/styleGAN2_G_params.h5"
from nnabla.utils.data_source_loader import download
download(url, os.path.join(args.weights_path, 'gen_params.h5'),
False)
nn.load_parameters(os.path.join(args.weights_path,
'gen_params.h5'))
print('Loaded pretrained weights from tensorflow!')
else:
try:
nn.load_parameters(os.path.join(args.weights_path,
'params.h5'))
except:
if args.test:
warnings.warn(
"Testing Model without pretrained weights!!!")
else:
print('No Pretrained weights loaded.')
def load_checkpoint(self, args):
"""Load pretrained parameters and solver states
Args:
args (ArgumentParser): To check if tensorflow trained weights are to be used for testing and to get the path of the folder
from where the parameter and solver states are to be loaded
"""
if args.use_tf_weights:
if not os.path.isfile(os.path.join(args.weights_path, 'gen_params.h5')):
os.makedirs(args.weights_path, exist_ok=True)
print("Downloading the pretrained tf-converted weights. Please wait...")
url = "https://nnabla.org/pretrained-models/nnabla-examples/GANs/stylegan2/styleGAN2_G_params.h5"
from nnabla.utils.data_source_loader import download
download(url, os.path.join(
args.weights_path, 'gen_params.h5'), False)
nn.load_parameters(os.path.join(
args.weights_path, 'gen_params.h5'))
print('Loaded pretrained weights from tensorflow!')
else:
try:
if args.pre_trained_model is not None:
if os.path.isfile(args.pre_trained_model):
nn.load_parameters(args.pre_trained_model)
elif os.path.isfile(os.path.join(args.pre_trained_model, 'ffhq-slim-gen-256-config-e.h5')):
if args.train:
with nn.parameter_scope('Generator'):
nn.load_parameters(os.path.join(
args.pre_trained_model, 'ffhq-slim-gen-256-config-e.h5'))
with nn.parameter_scope('GeneratorEMA'):
nn.load_parameters(os.path.join(
args.pre_trained_model, 'ffhq-slim-gen-256-config-e.h5'))
else:
nn.load_parameters(os.path.join(
args.pre_trained_model, 'ffhq-slim-gen-256-config-e.h5'))
nn.load_parameters(os.path.join(
args.pre_trained_model, 'ffhq-slim-gen-256-config-e.h5'))
if os.path.isfile(os.path.join(args.pre_trained_model, 'ffhq-slim-disc-256-config-e-corrected.h5')):
nn.load_parameters(os.path.join(
args.pre_trained_model, 'ffhq-slim-disc-256-config-e-corrected.h5'))
if os.path.isdir(args.weights_path):
with nn.parameter_scope('Discriminator'):
nn.load_parameters(os.path.join(
args.weights_path, 'disc_params.h5'))
with nn.parameter_scope('Generator'):
nn.load_parameters(os.path.join(
args.weights_path, 'gen_params.h5'))
with nn.parameter_scope('GeneratorEMA'):
nn.load_parameters(os.path.join(
args.weights_path, 'gen_ema_params.h5'))
except:
if args.test:
warnings.warn(
"Testing Model without pretrained weights!!!")
else:
print('No Pretrained weights loaded.')
def download_provided_file(url, filepath=None, verbose=True):
if not filepath:
filepath = os.path.basename(url)
if not os.path.exists(filepath):
if verbose:
logger.info(f"{filepath} not found. Downloading...")
download(url, filepath, False)
if verbose:
logger.info(f"Downloaded {filepath}.")
return
def load_imdb(vocab_size: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
file_name = 'imdb.npz'
url = f'https://s3.amazonaws.com/text-datasets/{file_name}'
download(url, open_file=False)
dataset_path = Path(get_data_home()) / file_name
unk_index = vocab_size - 1
raw = load_npy(dataset_path)
ret = dict()
for k, v in raw.items():
if 'x' in k:
for i, sentence in enumerate(v):
v[i] = [word if word < unk_index else unk_index for word in sentence]
ret[k] = v
return ret['x_train'], ret['x_test'], ret['y_train'], ret['y_test']
def __init__(self, train=True, shuffle=False, rng=None, output_dir=None):
super(STL10DataSource, self).__init__(shuffle=shuffle, rng=rng)
self._train = train
data_uri = 'http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz'
logger.info('Getting labbeled data from {}'.format(data_uri))
default_seed = 313
output_file = get_filename_to_download(output_dir, data_uri)
r = download(data_uri, output_file=output_file) # file object returned
print(r.name)
binary_dir = os.path.join(output_dir, "stl10_binary")
with tarfile.open(fileobj=r, mode="r:gz") as tar:
tar.extractall(path=output_dir)
for member in os.listdir(binary_dir):
if train:
if 'train_' not in member:
continue
print(member)
self.load_image_and_labels(os.path.join(binary_dir, member))
# Validation data
else:
print(member)
if 'test_' not in member:
continue
self.load_image_and_labels(os.path.join(binary_dir, member))
r.close
logger.info('Getting labeled data from {}'.format(data_uri))
self._size = self._labels.size
self._variables = ('x', 'y')
if rng is None:
rng = np.random.RandomState(default_seed)
self.rng = rng
self.reset()
def __init__(self, width, height, padding, train=True, shuffle=False, rng=None):
super(Caltech101DataSource, self).__init__(shuffle=shuffle, rng=rng)
data_uri = "http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz"
logger.info('Getting labeled data from {}.'.format(data_uri))
r = download(data_uri) # file object returned
label_dict = dict()
with tarfile.open(fileobj=r, mode="r:gz") as fpin:
images = []
labels = []
for name in fpin.getnames():
if ".jpg" not in name or "Google" in name:
continue
label, filename = name.split("/")[-2:]
if label not in label_dict:
label_dict[label] = len(label_dict)
im = imread(fpin.extractfile(name), num_channels=3)
arranged_images = self._resize_image(
im, width, height, padding)
images.append(arranged_images)
labels.append(label_dict[label])
self._size = len(images)
self._images = np.array(images)
self._labels = np.array(labels).reshape(-1, 1)
r.close()
logger.info('Getting labeled data from {}.'.format(data_uri))
self._size = self._labels.size
self._variables = ('x', 'y')
if rng is None:
rng = np.random.RandomState(313)
self.rng = rng
self._indexes = rng.permutation(self._size)
def __init__(self,
train=True,
shuffle=False,
rng=None,
label_shuffle=True,
label_shuffle_rate=0.1):
super(Cifar10DataSource, self).__init__(shuffle=shuffle, rng=rng)
self._train = train
data_uri = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
logger.info('Getting labeled data from {}.'.format(data_uri))
r = download(data_uri) # file object returned
with tarfile.open(fileobj=r, mode="r:gz") as fpin:
# Training data
if train:
images = []
labels = []
for member in fpin.getmembers():
if "data_batch" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes", allow_pickle=True)
images.append(data[b"data"])
labels.append(data[b"labels"])
self._size = 50000
self._images = np.concatenate(images).reshape(
self._size, 3, 32, 32)
self._labels = np.concatenate(labels).reshape(-1, 1)
self.raw_label = self._labels.copy()
if label_shuffle:
self.shuffle_rate = label_shuffle_rate
self.label_shuffle()
print(f"{self.shuffle_rate*100}% of data was shuffled ")
print(len(np.where(self._labels != self.raw_label)[0]))
# Validation data
else:
for member in fpin.getmembers():
if "test_batch" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes", allow_pickle=True)
images = data[b"data"]
labels = data[b"labels"]
self._size = 10000
self._images = images.reshape(self._size, 3, 32, 32)
self._labels = np.array(labels).reshape(-1, 1)
r.close()
logger.info('Getting labeled data from {}.'.format(data_uri))
self._size = self._labels.size
self._variables = ('x', 'y')
if rng is None:
rng = np.random.RandomState(313)
self.rng = rng
self.reset()
def main():
args = get_args()
nn.set_default_context(
get_extension_context(args.extension, device_id=args.device_id))
if args.nnp is None:
local_nnp_dir = os.path.join("asset", args.gym_env)
local_nnp_file = os.path.join(local_nnp_dir, "qnet.nnp")
if not find_local_nnp(args.gym_env):
logger.info("Downloading nnp data since you didn't specify...")
nnp_uri = os.path.join(
"https://nnabla.org/pretrained-models/nnp_models/examples/dqn",
args.gym_env, "qnet.nnp")
if not os.path.exists(local_nnp_dir):
os.mkdir(local_nnp_dir)
download(nnp_uri, output_file=local_nnp_file, open_file=False)
logger.info("Download done!")
args.nnp = local_nnp_file
from atari_utils import make_atari_deepmind
env = make_atari_deepmind(args.gym_env, valid=False)
print('Observation:', env.observation_space)
print('Action:', env.action_space)
obs_sampler = ObsSampler(args.num_frames)
val_replay_memory = ReplayMemory(env.observation_space.shape,
env.action_space.shape,
max_memory=args.num_frames)
# just play greedily
explorer = GreedyExplorer(env.action_space.n,
use_nnp=True,
nnp_file=args.nnp,
name='qnet')
validator = Validator(env,
val_replay_memory,
explorer,
obs_sampler,
num_episodes=1,
render=not args.no_render)
while True:
validator.step()
def __init__(self, train=True, shuffle=False, rng=None, output_dir=None):
super(MnistDataSource, self).__init__(shuffle=shuffle)
self._train = train
if self._train:
image_uri = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
label_uri = 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz'
else:
image_uri = 'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz'
label_uri = 'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
logger.info('Getting label data from {}.'.format(label_uri))
# With python3 we can write this logic as following, but with
# python2, gzip.object does not support file-like object and
# urllib.request does not support 'with statement'.
#
# with request.urlopen(label_uri) as r, gzip.open(r) as f:
# _, size = struct.unpack('>II', f.read(8))
# self._labels = numpy.frombuffer(f.read(), numpy.uint8).reshape(-1, 1)
#
label_output_file = get_filename_to_download(output_dir, label_uri)
# file object returned
r = download(label_uri, output_file=label_output_file)
data = zlib.decompress(r.read(), zlib.MAX_WBITS | 32)
_, size = struct.unpack('>II', data[0:8])
self._labels = numpy.frombuffer(data[8:], numpy.uint8).reshape(-1, 1)
r.close()
logger.info('Getting label data done.')
logger.info('Getting image data from {}.'.format(image_uri))
image_output_file = get_filename_to_download(output_dir, image_uri)
r = download(image_uri, output_file=image_output_file)
data = zlib.decompress(r.read(), zlib.MAX_WBITS | 32)
_, size, height, width = struct.unpack('>IIII', data[0:16])
self._images = numpy.frombuffer(data[16:], numpy.uint8).reshape(
size, 1, height, width)
r.close()
logger.info('Getting image data done.')
self._size = self._labels.size
self._variables = ('x', 'y')
if rng is None:
rng = numpy.random.RandomState(313)
self.rng = rng
self.reset()
def load_mnist(train=True):
'''
Load MNIST dataset images and labels from the original page by Yan LeCun or the cache file.
Args:
train (bool): The testing dataset will be returned if False. Training data has 60000 images, while testing has 10000 images.
Returns:
numpy.ndarray: A shape of (#images, 1, 28, 28). Values in [0.0, 1.0].
numpy.ndarray: A shape of (#images, 1). Values in {0, 1, ..., 9}.
'''
if train:
image_uri = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
label_uri = 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz'
else:
image_uri = 'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz'
label_uri = 'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
logger.info('Getting label data from {}.'.format(label_uri))
# With python3 we can write this logic as following, but with
# python2, gzip.object does not support file-like object and
# urllib.request does not support 'with statement'.
#
# with request.urlopen(label_uri) as r, gzip.open(r) as f:
# _, size = struct.unpack('>II', f.read(8))
# labels = numpy.frombuffer(f.read(), numpy.uint8).reshape(-1, 1)
#
r = download(label_uri)
data = zlib.decompress(r.read(), zlib.MAX_WBITS | 32)
_, size = struct.unpack('>II', data[0:8])
labels = numpy.frombuffer(data[8:], numpy.uint8).reshape(-1, 1)
r.close()
logger.info('Getting label data done.')
logger.info('Getting image data from {}.'.format(image_uri))
r = download(image_uri)
data = zlib.decompress(r.read(), zlib.MAX_WBITS | 32)
_, size, height, width = struct.unpack('>IIII', data[0:16])
images = numpy.frombuffer(data[16:], numpy.uint8).reshape(
size, 1, height, width)
r.close()
logger.info('Getting image data done.')
return images, labels
def main():
output_nnabla_file = 'nbla_bert_params.h5'
r = download(
"https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip", 'uncased_L-12_H-768_A-12.zip')
with zipfile.ZipFile("uncased_L-12_H-768_A-12.zip", "r") as zip_ref:
zip_ref.extractall(".")
input_ckpt_file = 'uncased_L-12_H-768_A-12/bert_model.ckpt'
output_nnabla_file = 'nbla_bert_params.h5'
convert(input_ckpt_file, output_nnabla_file)
def __init__(self, train=True, shuffle=False, rng=None):
super(MnistDataSource, self).__init__(shuffle=shuffle)
self._train = train
if self._train:
image_uri = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
label_uri = 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz'
else:
image_uri = 'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz'
label_uri = 'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
logger.info('Getting label data from {}.'.format(label_uri))
# With python3 we can write this logic as following, but with
# python2, gzip.object does not support file-like object and
# urllib.request does not support 'with statement'.
#
# with request.urlopen(label_uri) as r, gzip.open(r) as f:
# _, size = struct.unpack('>II', f.read(8))
# self._labels = numpy.frombuffer(f.read(), numpy.uint8).reshape(-1, 1)
#
r = download(label_uri)
data = zlib.decompress(r.read(), zlib.MAX_WBITS | 32)
_, size = struct.unpack('>II', data[0:8])
self._labels = numpy.frombuffer(data[8:], numpy.uint8).reshape(-1, 1)
r.close()
logger.info('Getting label data done.')
logger.info('Getting image data from {}.'.format(image_uri))
r = download(image_uri)
data = zlib.decompress(r.read(), zlib.MAX_WBITS | 32)
_, size, height, width = struct.unpack('>IIII', data[0:16])
self._images = numpy.frombuffer(data[16:], numpy.uint8).reshape(
size, 1, height, width)
r.close()
logger.info('Getting image data done.')
self._size = self._labels.size
self._variables = ('x', 'y')
if rng is None:
rng = numpy.random.RandomState(313)
self.rng = rng
self.reset()
def load_cyclegan_dataset(dataset="horse2zebra",
train=True,
domain="A",
normalize_method=lambda x: (x - 127.5) / 127.5):
'''
Load CycleGAN dataset from `here <https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/>`_
This function assumes that there are two domains in the dataset.
Args:
dataset (str): Dataset name excluding ".zip" extension, which you can find that `here <https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/>`_.
train (bool): The testing dataset will be returned if False. Training data has 60000 images, while testing has 10000 images.
domain (str): Domain name. It must be "A" or "B".
normalize_method: Function of how to normalize an image.
Returns:
(np.ndarray, list): Images and filenames.
'''
assert domain in ["A", "B"]
image_uri = 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/{}.zip'.format(
dataset)
logger.info('Getting {} data from {}.'.format(dataset, image_uri))
r = download(image_uri)
# Load unpaired images from zipfile.
with zipfile.ZipFile(r, "r") as zf:
images = []
filename_list = []
dirname = "{}{}".format("train" if train else "test", domain)
# filter images by name
zipinfos = filter(
lambda zinfo: dirname in zinfo.filename and ".jpg" in zinfo.
filename, zf.infolist())
for zipinfo in zipinfos:
with zf.open(zipinfo.filename, "r") as fp:
# filename
filename = zipinfo.filename
logger.info('loading {}'.format(filename))
# load image
image = scipy.misc.imread(fp, mode="RGB")
#image = scipy.misc.imread(fp)
image = np.transpose(image, (2, 0, 1))
image = normalize_method(image)
image_name, ext = os.path.splitext(filename.split("/")[-1])
images.append(image)
filename_list.append(image_name)
r.close()
logger.info('Getting image data done.')
return np.asarray(images), filename_list
def download_tiny_imagenet():
url = "http://cs231n.stanford.edu/tiny-imagenet-200.zip"
dir_data = os.path.join(get_data_home(), 'tiny-imagenet-200')
if not os.path.isdir(dir_data):
f = download(url)
logger.info('Extracting {} ...'.format(f.name))
z = zipfile.ZipFile(f)
d = get_data_home()
l = z.namelist()
for i in tqdm(range(len(l))):
z.extract(l[i], d)
z.close()
f.close()
return dir_data
def __init__(self, train=True, shuffle=False, rng=None):
super(Cifar100DataSource, self).__init__(shuffle=shuffle, rng=rng)
self._train = train
data_uri = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
logger.info('Getting labeled data from {}.'.format(data_uri))
r = download(data_uri) # file object returned
with tarfile.open(fileobj=r, mode="r:gz") as fpin:
# Training data
if train:
images = []
labels = []
for member in fpin.getmembers():
if "train" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes", allow_pickle=True)
images = data[b"data"]
labels = data[b"fine_labels"]
self._size = 50000
self._images = images.reshape(self._size, 3, 32, 32)
self._labels = np.array(labels).reshape(-1, 1)
# Validation data
else:
for member in fpin.getmembers():
if "test" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes", allow_pickle=True)
images = data[b"data"]
labels = data[b"fine_labels"]
self._size = 10000
self._images = images.reshape(self._size, 3, 32, 32)
self._labels = np.array(labels).reshape(-1, 1)
r.close()
logger.info('Getting labeled data from {} done.'.format(data_uri))
self._size = self._labels.size
self._variables = ('x', 'y')
if rng is None:
rng = np.random.RandomState(313)
self.rng = rng
self.reset()
def load_omniglot(test=False):
fname = "images_background.zip"
if test:
fname = "images_evaluation.zip"
dlname = OMNIGLOT_URL + fname
r = download(dlname)
f = zipfile.ZipFile(r, mode="r")
x = []
y = []
imgs = []
lang_dict = {}
n_letter = 0
for path in f.namelist():
# Four types of "path" is possible
# "image_xxx/"
# "image_xxx/Alphabet"
# "image_xxx/Alphabet/Letter"
# "image_xxx/Alphabet/Letter/img.png"
names = path.split('/')
if len(names) == 3: # i.e. [images_xxx, Alphabet, None]
alphabet = names[1]
print("loading alphabet: " + alphabet)
lang_dict[alphabet] = [n_letter, None]
if len(names) == 4:
if names[
3] is not '': # i.e. [images_xxx, Alphabet, Letter, Image]
imgs.append(imageio.imread(f.read(path)))
if len(imgs) == 20: # Number of images are limited to 20
x.append(np.stack(imgs))
y.append(np.ones(20, ) * n_letter)
n_letter += 1
lang_dict[alphabet][1] = n_letter - 1
imgs = []
x = np.stack(x)
y = np.stack(y)
return x, y, lang_dict
def download_tiny_imagenet():
"""
Downloads and unzips the tiny imagenet dataset.
Returns:
dir_data: the directory of the data containing the tiny imagenet
"""
url = "http://cs231n.stanford.edu/tiny-imagenet-200.zip"
dir_data = os.path.join('datasets', 'tiny-imagenet-200')
if not os.path.isdir(dir_data):
f = download(url)
logger.info('Extracting {} ...'.format(f.name))
z = zipfile.ZipFile(f)
d = 'datasets/'
l = z.namelist()
for i in tqdm(range(len(l))):
z.extract(l[i], d)
z.close()
f.close()
return dir_data
def _load_data(url: str):
with download(url, open_file=True) as f:
lines = f.read().decode('utf-8').replace('\n', ' <eos> ')
words = lines.strip().split()
dataset = np.ndarray((len(words), ), dtype=np.int32)
for i, word in enumerate(words):
if word not in w2i:
w2i[word] = len(w2i)
if w2i[word] not in i2w:
i2w[w2i[word]] = word
dataset[i] = w2i[word]
sentences = []
sentence = []
for index in dataset:
if i2w[index] != '<eos>':
sentence.append(index)
else:
sentences.append(sentence)
sentence = []
return sentences
def load_pix2pix_dataset(dataset="edges2shoes", train=True, num_samples=-1):
image_uri = 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/{}.tar.gz'\
.format(dataset)
logger.info('Getting {} data from {}.'.format(dataset, image_uri))
r = download(image_uri)
# Load concatenated images, then save separately
# TODO: how to do for test
img_A_list = []
img_B_list = []
img_names = []
with tarfile.open(fileobj=r, mode="r") as tar:
cnt = 0
for tinfo in tar.getmembers():
if not ".jpg" in tinfo.name:
continue
if not ((train == True and "train" in tinfo.name) or
(train == False and "val" in tinfo.name)):
continue
logger.info("Loading {} ...".format(tinfo.name))
f = tar.extractfile(tinfo)
img = scipy.misc.imread(f, mode="RGB")
h, w, c = img.shape
img_A = img[:, 0:w // 2, :].transpose((2, 0, 1))
img_B = img[:, w // 2:, :].transpose((2, 0, 1))
img_A_list.append(img_A)
img_B_list.append(img_B)
img_names.append(tinfo.name.split("/")[-1])
cnt += 1
if num_samples != -1 and cnt >= num_samples:
break
r.close()
logger.info('Getting image data done.')
img_A, img_B = np.asarray(img_A_list), np.asarray(img_B_list)
return img_A, img_B, img_names
def load_ptbset(ptbfile):
"""
Load Penn Treebank Corpus
"""
f = download(ptbfile)
itow = {} # index to word
wtoi = {} # word to index
dataset = []
# extract vocabraries from corpus
for line in f:
for w in line.split():
# register the new word as an index number of first appearance
if w not in wtoi:
i = len(wtoi)
wtoi[w] = i
itow[i] = w
# translate words into numbers
dataset.append(wtoi[w])
return itow, wtoi, dataset
def _load_data(self, type_name: str) -> List[List[int]]:
url = self.ptb_url.format(type_name)
with download(url, open_file=True) as f:
lines: str = f.read().decode('utf-8').replace('\n', '<eos>')
if self.return_char_info:
for char in set(lines):
if char not in self.c2i:
self.c2i[char] = len(self.c2i)
if self.c2i[char] not in self.i2c:
self.i2c[self.c2i[char]] = char
words = lines.strip().split()
dataset = np.ndarray((len(words), ), dtype=np.int32)
for i, word in enumerate(words):
if word not in self.w2i:
self.w2i[word] = len(self.w2i)
if self.w2i[word] not in self.i2w:
self.i2w[self.w2i[word]] = word
dataset[i] = self.w2i[word]
sentences = []
sentence = []
if self.with_bos:
sentence.append(self.w2i['<bos>'])
for index in dataset:
if self.i2w[index] != '<eos>':
sentence.append(index)
else:
sentence.append(index)
sentences.append(sentence)
sentence = []
if self.with_bos:
sentence.append(self.w2i['<bos>'])
return sentences
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--output-filename',
'-o',
type=str,
default=None,
help="name of an output image file.")
parser.add_argument('--output-dir',
'-d',
type=str,
default="results",
help="directory where the generated image is saved.")
parser.add_argument('--seed',
type=int,
required=True,
help="seed for primal style noise.")
parser.add_argument('--stochastic-seed',
type=int,
default=1,
help="seed for noises added to intermediate features.")
parser.add_argument('--truncation-psi',
default=0.5,
type=float,
help="value for truncation trick.")
parser.add_argument('--batch-size',
type=int,
default=1,
help="Number of images to generate.")
parser.add_argument(
'--mixing',
action='store_true',
help="if specified, apply style mixing with additional seed.")
parser.add_argument('--seed-mix',
type=int,
default=None,
help="seed for another / secondary style noise.")
parser.add_argument('--mix-after',
type=int,
default=7,
help="after this layer, style mixing is applied.")
parser.add_argument('--context',
'-c',
type=str,
default="cudnn",
help="context. cudnn is recommended.")
args = parser.parse_args()
assert 0 < args.mix_after < 17, "specify --mix-after from 1 to 16."
if not os.path.isfile("styleGAN2_G_params.h5"):
print("Downloading the pretrained weight. Please wait...")
url = "https://nnabla.org/pretrained-models/nnabla-examples/GANs/stylegan2/styleGAN2_G_params.h5"
from nnabla.utils.data_source_loader import download
download(url, url.split('/')[-1], False)
ctx = get_extension_context(args.context)
nn.set_default_context(ctx)
batch_size = args.batch_size
num_layers = 18
rnd = np.random.RandomState(args.seed)
z = rnd.randn(batch_size, 512)
print("Generation started...")
print(f"truncation value: {args.truncation_psi}")
print(f"seed for additional noise: {args.stochastic_seed}")
# Inference via nn.NdArray utilizes significantly less memory
if args.mixing:
# apply style mixing
assert args.seed_mix
print(
f"using style noise seed {args.seed} for layers 0-{args.mix_after - 1}"
)
print(
f"using style noise seed {args.seed_mix} for layers {args.mix_after}-{num_layers}."
)
rnd = np.random.RandomState(args.seed_mix)
z2 = rnd.randn(batch_size, 512)
style_noises = [nn.NdArray.from_numpy_array(z)]
style_noises += [nn.NdArray.from_numpy_array(z2)]
else:
# no style mixing (single noise / style is used)
print(f"using style noise seed {args.seed} for entire layers.")
style_noises = [nn.NdArray.from_numpy_array(z) for _ in range(2)]
nn.set_auto_forward(True)
nn.load_parameters("styleGAN2_G_params.h5")
rgb_output = generate(batch_size, style_noises, args.stochastic_seed,
args.mix_after, args.truncation_psi)
# convert to uint8 to save an image file
image = convert_images_to_uint8(rgb_output, drange=[-1, 1])
if args.output_filename is None:
if not args.mixing:
filename = f"seed{args.seed}"
else:
filename = f"seed{args.seed}_{args.seed_mix}"
else:
filename = args.output_filename
os.makedirs(args.output_dir, exist_ok=True)
for i in range(batch_size):
filepath = os.path.join(args.output_dir, f'{filename}_{i}.png')
imsave(filepath, image[i], channel_first=True)
print(f"Genetation completed. Saved {filepath}.")
nn.set_default_context(ctx)
"""
"""
embedding_size: int = 2
batch_size: int = 1
max_epoch: int = 50
negative_sample_size = 10
file_url = 'https://raw.githubusercontent.com/qiangsiwei/poincare_embedding/master/data/mammal_subtree.tsv'
from functools import reduce
import operator
import random
with download(file_url, open_file=True) as f:
lines: str = f.read().decode('utf-8').split('\n')
pdata = list(map(lambda l: l.split('\t'), filter(None, lines)))
pdict = {w: i for i, w in enumerate(set(reduce(operator.add, pdata)))}
vocab_size: int = len(pdict)
num_train_batch = len(pdata) // batch_size
def load_train_func(index):
x, y = pdata[index]
negative_sample_prob = np.ones(len(pdict))
negative_sample_prob[pdict[x]] = 0.0
negative_sample_prob[pdict[y]] = 0.0
negative_sample_prob /= len(pdict) - 2
def main():
args = get_args()
# Get context
from nnabla.ext_utils import get_extension_context
logger.info("Running in %s" % args.context)
ctx = get_extension_context(args.context,
device_id=args.device_id,
type_config=args.type_config)
nn.set_default_context(ctx)
nn.set_auto_forward(True)
image = io.imread(args.test_image)
if image.ndim == 2:
image = color.gray2rgb(image)
elif image.shape[-1] == 4:
image = image[..., :3]
if args.context == 'cudnn':
if not os.path.isfile(args.cnn_face_detction_model):
# Block of bellow code will download the cnn based face-detection model file provided by dlib for face detection
# and will save it in the directory where this script is executed.
print("Downloading the face detection CNN. Please wait...")
url = "http://dlib.net/files/mmod_human_face_detector.dat.bz2"
from nnabla.utils.data_source_loader import download
download(url, url.split('/')[-1], False)
# get the decompressed data.
data = bz2.BZ2File(url.split('/')[-1]).read()
# write to dat file.
open(url.split('/')[-1][:-4], 'wb').write(data)
face_detector = dlib.cnn_face_detection_model_v1(
args.cnn_face_detction_model)
detected_faces = face_detector(
cv2.cvtColor(image[..., ::-1].copy(), cv2.COLOR_BGR2GRAY))
detected_faces = [[
d.rect.left(),
d.rect.top(),
d.rect.right(),
d.rect.bottom()
] for d in detected_faces]
else:
face_detector = dlib.get_frontal_face_detector()
detected_faces = face_detector(
cv2.cvtColor(image[..., ::-1].copy(), cv2.COLOR_BGR2GRAY))
detected_faces = [[d.left(), d.top(),
d.right(), d.bottom()] for d in detected_faces]
if len(detected_faces) == 0:
print("Warning: No faces were detected.")
return None
# Load FAN weights
with nn.parameter_scope("FAN"):
print("Loading FAN weights...")
nn.load_parameters(args.model)
# Load ResNetDepth weights
if args.landmarks_type_3D:
with nn.parameter_scope("ResNetDepth"):
print("Loading ResNetDepth weights...")
nn.load_parameters(args.resnet_depth_model)
landmarks = []
for i, d in enumerate(detected_faces):
center = [d[2] - (d[2] - d[0]) / 2.0, d[3] - (d[3] - d[1]) / 2.0]
center[1] = center[1] - (d[3] - d[1]) * 0.12
scale = (d[2] - d[0] + d[3] - d[1]) / args.reference_scale
inp = crop(image, center, scale)
inp = nn.Variable.from_numpy_array(inp.transpose((2, 0, 1)))
inp = F.reshape(F.mul_scalar(inp, 1 / 255.0), (1, ) + inp.shape)
with nn.parameter_scope("FAN"):
out = fan(inp, args.network_size)[-1]
pts, pts_img = get_preds_fromhm(out, center, scale)
pts, pts_img = F.reshape(pts, (68, 2)) * \
4, F.reshape(pts_img, (68, 2))
if args.landmarks_type_3D:
heatmaps = np.zeros((68, 256, 256), dtype=np.float32)
for i in range(68):
if pts.d[i, 0] > 0:
heatmaps[i] = draw_gaussian(heatmaps[i], pts.d[i], 2)
heatmaps = nn.Variable.from_numpy_array(heatmaps)
heatmaps = F.reshape(heatmaps, (1, ) + heatmaps.shape)
with nn.parameter_scope("ResNetDepth"):
depth_pred = F.reshape(
resnet_depth(F.concatenate(inp, heatmaps, axis=1)),
(68, 1))
pts_img = F.concatenate(pts_img,
depth_pred * (1.0 / (256.0 /
(200.0 * scale))),
axis=1)
landmarks.append(pts_img.d)
visualize(landmarks, image, args.output)
def __init__(self, train=True, shuffle=False, rng=None):
super(Cifar100DataSource, self).__init__(shuffle=shuffle)
# Lock
lockfile = os.path.join(get_data_home(), "cifar100.lock")
start_time = time.time()
while True: # busy-lock due to communication between process spawn by mpirun
try:
fd = os.open(lockfile, os.O_CREAT | os.O_EXCL | os.O_RDWR)
break
except OSError as e:
if e.errno != errno.EEXIST:
raise
if (time.time() - start_time) >= 60 * 30: # wait for 30min
raise Exception(
"Timeout occured. If there are cifar10.lock in $HOME/nnabla_data, it should be deleted."
)
time.sleep(5)
self._train = train
data_uri = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
logger.info('Getting labeled data from {}.'.format(data_uri))
r = download(data_uri) # file object returned
with tarfile.open(fileobj=r, mode="r:gz") as fpin:
# Training data
if train:
images = []
labels = []
for member in fpin.getmembers():
if "train" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes")
images = data[b"data"]
labels = data[b"fine_labels"]
self._size = 50000
self._images = images.reshape(self._size, 3, 32, 32)
self._labels = np.array(labels).reshape(-1, 1)
# Validation data
else:
for member in fpin.getmembers():
if "test" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes")
images = data[b"data"]
labels = data[b"fine_labels"]
self._size = 10000
self._images = images.reshape(self._size, 3, 32, 32)
self._labels = np.array(labels).reshape(-1, 1)
r.close()
logger.info('Getting labeled data from {} done.'.format(data_uri))
self._size = self._labels.size
self._variables = ('x', 'y')
if rng is None:
rng = np.random.RandomState(313)
self.rng = rng
self.reset()
# Unlock
os.close(fd)
os.unlink(lockfile)
def generate_attribute_direction(args, attribute_prediction_model):
if not os.path.isfile(os.path.join(args.weights_path, 'gen_params.h5')):
os.makedirs(args.weights_path, exist_ok=True)
print(
"Downloading the pretrained tf-converted weights. Please wait...")
url = "https://nnabla.org/pretrained-models/nnabla-examples/GANs/stylegan2/styleGAN2_G_params.h5"
from nnabla.utils.data_source_loader import download
download(url, os.path.join(args.weights_path, 'gen_params.h5'), False)
nn.load_parameters(os.path.join(args.weights_path, 'gen_params.h5'))
print('Loaded pretrained weights from tensorflow!')
nn.load_parameters(args.classifier_weight_path)
print(f'Loaded {args.classifier_weight_path}')
batches = [
args.batch_size for _ in range(args.num_images // args.batch_size)
]
if args.num_images % args.batch_size != 0:
batches.append(args.num_images -
(args.num_images // args.batch_size) * args.batch_size)
w_plus, w_minus = 0.0, 0.0
w_plus_count, w_minus_count = 0.0, 0.0
pbar = trange(len(batches))
for i in pbar:
batch_size = batches[i]
z = [F.randn(shape=(batch_size, 512)).data]
z = [z[0], z[0]]
for i in range(len(z)):
z[i] = F.div2(
z[i],
F.pow_scalar(F.add_scalar(
F.mean(z[i]**2., axis=1, keepdims=True), 1e-8),
0.5,
inplace=True))
# get latent code
w = [mapping_network(z[0], outmaps=512, num_layers=8)]
w += [mapping_network(z[1], outmaps=512, num_layers=8)]
# truncation trick
dlatent_avg = nn.parameter.get_parameter_or_create(name="dlatent_avg",
shape=(1, 512))
w = [lerp(dlatent_avg, _, 0.7) for _ in w]
constant_bc = nn.parameter.get_parameter_or_create(
name="G_synthesis/4x4/Const/const", shape=(1, 512, 4, 4))
constant_bc = F.broadcast(constant_bc,
(batch_size, ) + constant_bc.shape[1:])
gen = synthesis(w, constant_bc, noise_seed=100, mix_after=7)
classifier_score = F.softmax(attribute_prediction_model(gen, True))
confidence, class_pred = F.max(classifier_score,
axis=1,
with_index=True,
keepdims=True)
w_plus += np.sum(w[0].data * (class_pred.data == 0) *
(confidence.data > 0.65),
axis=0,
keepdims=True)
w_minus += np.sum(w[0].data * (class_pred.data == 1) *
(confidence.data > 0.65),
axis=0,
keepdims=True)
w_plus_count += np.sum(
(class_pred.data == 0) * (confidence.data > 0.65))
w_minus_count += np.sum(
(class_pred.data == 1) * (confidence.data > 0.65))
pbar.set_description(f'{w_plus_count} {w_minus_count}')
# save attribute direction
attribute_variation_direction = (w_plus / w_plus_count) - (w_minus /
w_minus_count)
print(w_plus_count, w_minus_count)
np.save(f'{args.classifier_weight_path.split("/")[0]}/direction.npy',
attribute_variation_direction)
def generate_data(args):
if not os.path.isfile(os.path.join(args.weights_path, 'gen_params.h5')):
os.makedirs(args.weights_path, exist_ok=True)
print(
"Downloading the pretrained tf-converted weights. Please wait...")
url = "https://nnabla.org/pretrained-models/nnabla-examples/GANs/stylegan2/styleGAN2_G_params.h5"
from nnabla.utils.data_source_loader import download
download(url, os.path.join(args.weights_path, 'gen_params.h5'), False)
nn.load_parameters(os.path.join(args.weights_path, 'gen_params.h5'))
print('Loaded pretrained weights from tensorflow!')
os.makedirs(args.save_image_path, exist_ok=True)
batches = [
args.batch_size for _ in range(args.num_images // args.batch_size)
]
if args.num_images % args.batch_size != 0:
batches.append(args.num_images -
(args.num_images // args.batch_size) * args.batch_size)
for idx, batch_size in enumerate(batches):
z = [
F.randn(shape=(batch_size, 512)).data,
F.randn(shape=(batch_size, 512)).data
]
for i in range(len(z)):
z[i] = F.div2(
z[i],
F.pow_scalar(F.add_scalar(
F.mean(z[i]**2., axis=1, keepdims=True), 1e-8),
0.5,
inplace=True))
# get latent code
w = [mapping_network(z[0], outmaps=512, num_layers=8)]
w += [mapping_network(z[1], outmaps=512, num_layers=8)]
# truncation trick
dlatent_avg = nn.parameter.get_parameter_or_create(name="dlatent_avg",
shape=(1, 512))
w = [lerp(dlatent_avg, _, 0.7) for _ in w]
# Load direction
if not args.face_morph:
attr_delta = nn.NdArray.from_numpy_array(
np.load(args.attr_delta_path))
attr_delta = F.reshape(attr_delta[0], (1, -1))
w_plus = [w[0] + args.coeff * attr_delta, w[1]]
w_minus = [w[0] - args.coeff * attr_delta, w[1]]
else:
w_plus = [w[0], w[0]] # content
w_minus = [w[1], w[1]] # style
constant_bc = nn.parameter.get_parameter_or_create(
name="G_synthesis/4x4/Const/const", shape=(1, 512, 4, 4))
constant_bc = F.broadcast(constant_bc,
(batch_size, ) + constant_bc.shape[1:])
gen_plus = synthesis(w_plus, constant_bc, noise_seed=100, mix_after=8)
gen_minus = synthesis(w_minus,
constant_bc,
noise_seed=100,
mix_after=8)
gen = synthesis(w, constant_bc, noise_seed=100, mix_after=8)
image_plus = convert_images_to_uint8(gen_plus, drange=[-1, 1])
image_minus = convert_images_to_uint8(gen_minus, drange=[-1, 1])
image = convert_images_to_uint8(gen, drange=[-1, 1])
for j in range(batch_size):
filepath = os.path.join(args.save_image_path,
f'image_{idx*batch_size+j}')
imsave(f'{filepath}_o.png', image_plus[j], channel_first=True)
imsave(f'{filepath}_y.png', image_minus[j], channel_first=True)
imsave(f'{filepath}.png', image[j], channel_first=True)
print(f"Genetated. Saved {filepath}")
def __init__(self, train=True, shuffle=False, rng=None):
super(Cifar10DataSource, self).__init__(shuffle=shuffle)
# Lock
lockfile = os.path.join(get_data_home(), "cifar10.lock")
start_time = time.time()
while True: # busy-lock due to communication between process spawn by mpirun
try:
fd = os.open(lockfile, os.O_CREAT | os.O_EXCL | os.O_RDWR)
break
except OSError as e:
if e.errno != errno.EEXIST:
raise
if (time.time() - start_time) >= 60 * 30: # wait for 30min
raise Exception(
"Timeout occured. If there are cifar10.lock in $HOME/nnabla_data, it should be deleted.")
time.sleep(5)
self._train = train
data_uri = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
logger.info('Getting labeled data from {}.'.format(data_uri))
r = download(data_uri) # file object returned
with tarfile.open(fileobj=r, mode="r:gz") as fpin:
# Training data
if train:
images = []
labels = []
for member in fpin.getmembers():
if "data_batch" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes")
images.append(data[b"data"])
labels.append(data[b"labels"])
self._size = 50000
self._images = np.concatenate(
images).reshape(self._size, 3, 32, 32)
self._labels = np.concatenate(labels).reshape(-1, 1)
# Validation data
else:
for member in fpin.getmembers():
if "test_batch" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes")
images = data[b"data"]
labels = data[b"labels"]
self._size = 10000
self._images = images.reshape(self._size, 3, 32, 32)
self._labels = np.array(labels).reshape(-1, 1)
r.close()
logger.info('Getting labeled data from {}.'.format(data_uri))
self._size = self._labels.size
self._variables = ('x', 'y')
if rng is None:
rng = np.random.RandomState(313)
self.rng = rng
self.reset()
# Unlock
os.close(fd)
os.unlink(lockfile)
