本文整理汇总了Python中tensorflow.gfile.Exists方法的典型用法代码示例。如果您正苦于以下问题:Python gfile.Exists方法的具体用法?Python gfile.Exists怎么用?Python gfile.Exists使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.gfile
的用法示例。
在下文中一共展示了gfile.Exists方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def main(unused_argv):
request = inference_flags.request_from_flags()
if not gfile.Exists(request.segmentation_output_dir):
gfile.MakeDirs(request.segmentation_output_dir)
bbox = bounding_box_pb2.BoundingBox()
text_format.Parse(FLAGS.bounding_box, bbox)
runner = inference.Runner()
runner.start(request)
runner.run((bbox.start.z, bbox.start.y, bbox.start.x),
(bbox.size.z, bbox.size.y, bbox.size.x))
counter_path = os.path.join(request.segmentation_output_dir, 'counters.txt')
if not gfile.Exists(counter_path):
runner.counters.dump(counter_path)
示例2: get_meta_filename
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def get_meta_filename(self, start_new_model, train_dir):
if start_new_model:
logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
task_as_string(self.task))
return None
latest_checkpoint = tf.train.latest_checkpoint(train_dir)
if not latest_checkpoint:
logging.info("%s: No checkpoint file found. Building a new model.",
task_as_string(self.task))
return None
meta_filename = latest_checkpoint + ".meta"
if not gfile.Exists(meta_filename):
logging.info("%s: No meta graph file found. Building a new model.",
task_as_string(self.task))
return None
else:
return meta_filename
示例3: get_meta_filename
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def get_meta_filename(self, start_new_model, train_dir):
if start_new_model:
logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
task_as_string(self.task))
return None
latest_checkpoint = tf.train.latest_checkpoint(train_dir)
if not latest_checkpoint:
logging.info("%s: No checkpoint file found. Building a new model.",
task_as_string(self.task))
return None
meta_filename = latest_checkpoint + ".meta"
if not gfile.Exists(meta_filename):
logging.info("%s: No meta graph file found. Building a new model.",
task_as_string(self.task))
return None
else:
return meta_filename
示例4: get_meta_filename
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def get_meta_filename(self, start_new_model, train_dir):
if start_new_model:
logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
task_as_string(self.task))
return None
latest_checkpoint = tf.train.latest_checkpoint(train_dir)
if not latest_checkpoint:
logging.info("%s: No checkpoint file found. Building a new model.",
task_as_string(self.task))
return None
meta_filename = latest_checkpoint + ".meta"
if not gfile.Exists(meta_filename):
logging.info("%s: No meta graph file found. Building a new model.",
task_as_string(self.task))
return None
else:
return meta_filename
示例5: get_meta_filename
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def get_meta_filename(self, start_new_model, train_dir):
if start_new_model:
logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
task_as_string(self.task))
return None
latest_checkpoint = tf.train.latest_checkpoint(train_dir)
if not latest_checkpoint:
logging.info("%s: No checkpoint file found. Building a new model.",
task_as_string(self.task))
return None
meta_filename = latest_checkpoint + ".meta"
if not gfile.Exists(meta_filename):
logging.info("%s: No meta graph file found. Building a new model.",
task_as_string(self.task))
return None
else:
return meta_filename
示例6: count_file
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def count_file(self, path, verbose=False, add_eos=False):
if verbose: print('counting file {} ...'.format(path))
assert exists(path)
sents = []
with open(path, 'r') as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
symbols = self.tokenize(line, add_eos=True)
self.counter.update(symbols)
sents.append(symbols)
return sents
# 更新counter 中的token
示例7: encode_file
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def encode_file(self, path, ordered=False, verbose=False,
add_double_eos=False):
if verbose: print('encoding file {} ...'.format(path))
assert exists(path)
encoded = []
with open(path, 'r') as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
symbols = self.tokenize(line, add_eos=True, add_double_eos=add_double_eos)
encoded.append(self.convert_to_nparray(symbols))
if ordered:
encoded = np.concatenate(encoded)
return encoded
#
示例8: main
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def main(unused_argv):
del unused_argv # Unused
corpus = get_lm_corpus(FLAGS.data_dir, FLAGS.dataset) #
save_dir = os.path.join(FLAGS.data_dir, "tfrecords")
if not exists(save_dir):
makedirs(save_dir)
# test mode
if FLAGS.per_host_test_bsz > 0:
corpus.convert_to_tfrecords("test", save_dir, FLAGS.per_host_test_bsz,
FLAGS.tgt_len, FLAGS.num_core_per_host,
FLAGS=FLAGS)
return
for split, batch_size in zip(
["train", "valid"],
[FLAGS.per_host_train_bsz, FLAGS.per_host_valid_bsz]):
if batch_size <= 0: continue
print("Converting {} set...".format(split))
corpus.convert_to_tfrecords(split, save_dir, batch_size, FLAGS.tgt_len,
FLAGS.num_core_per_host, FLAGS=FLAGS)
示例9: count_file
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def count_file(self, path, verbose=False, add_eos=False):
if verbose: print('counting file {} ...'.format(path))
assert exists(path)
sents = []
with open(path, 'r') as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
symbols = self.tokenize(line, add_eos=add_eos)
self.counter.update(symbols)
sents.append(symbols)
return sents
# 更新counter 中的token
示例10: encode_file
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def encode_file(self, path, ordered=False, verbose=False, add_eos=True,
add_double_eos=False):
if verbose: print('encoding file {} ...'.format(path))
assert exists(path)
encoded = []
with open(path, 'r') as f:
for idx, line in enumerate(f):
if verbose and idx > 0 and idx % 500000 == 0:
print(' line {}'.format(idx))
symbols = self.tokenize(line, add_eos=add_eos,
add_double_eos=add_double_eos)
encoded.append(self.convert_to_nparray(symbols))
if ordered:
encoded = np.concatenate(encoded)
return encoded
示例11: main
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def main(unused_argv):
del unused_argv # Unused
corpus = get_lm_corpus(FLAGS.data_dir, FLAGS.dataset)
save_dir = os.path.join(FLAGS.data_dir, "tfrecords")
if not exists(save_dir):
makedirs(save_dir)
# test mode
if FLAGS.per_host_test_bsz > 0:
corpus.convert_to_tfrecords("test", save_dir, FLAGS.per_host_test_bsz,
FLAGS.tgt_len, FLAGS.num_core_per_host,
FLAGS=FLAGS)
return
for split, batch_size in zip(
["train", "valid"],
[FLAGS.per_host_train_bsz, FLAGS.per_host_valid_bsz]):
if batch_size <= 0: continue
print("Converting {} set...".format(split))
corpus.convert_to_tfrecords(split, save_dir, batch_size, FLAGS.tgt_len,
FLAGS.num_core_per_host, FLAGS=FLAGS)
示例12: main
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def main(_argv):
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths)
# replace parameters in configs_file with tf FLAGS
model_configs = update_configs_from_flags(model_configs, FLAGS, TRAIN_ARGS.keys())
model_dir = model_configs["model_dir"]
if not gfile.Exists(model_dir):
gfile.MakeDirs(model_dir)
if "CUDA_VISIBLE_DEVICES" not in os.environ.keys():
raise OSError("need CUDA_VISIBLE_DEVICES environment variable")
tf.logging.info("CUDA_VISIBLE_DEVICES={}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
training_runner = TrainingExperiment(
model_configs=model_configs)
training_runner.run()
示例13: access_multiple_files
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def access_multiple_files(name):
""" Gets the list of files.
Args:
name: A string, the prefix of the files.
Returns: A list or None.
"""
assert name
ret = []
if gfile.Exists(name):
ret.append(name)
else:
idx = 0
while gfile.Exists(name + str(idx)):
ret.append(name + str(idx))
idx += 1
assert len(ret) > 0, (
"Fail to access file {} or {}0...".format(name, name))
return ret
示例14: load_from_config_path
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def load_from_config_path(config_paths):
""" Loads configurations from files of yaml format.
Args:
config_paths: A string (each file name is seperated by ",") or
a list of strings (file names).
Returns: A dictionary of model configurations, parsed from config files.
"""
if isinstance(config_paths, six.string_types):
config_paths = config_paths.strip().split(",")
assert isinstance(config_paths, list) or isinstance(config_paths, tuple)
model_configs = dict()
for config_path in config_paths:
config_path = config_path.strip()
if not config_path:
continue
if not gfile.Exists(config_path):
raise OSError("config file does not exist: {}".format(config_path))
config_path = os.path.abspath(config_path)
tf.logging.info("loading configurations from {}".format(config_path))
with open_file(config_path, mode="r") as config_file:
config_flags = yaml.load(config_file)
model_configs = deep_merge_dict(model_configs, config_flags)
return model_configs
示例15: get_existing_subvolume_path
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import Exists [as 别名]
def get_existing_subvolume_path(segmentation_dir, corner, allow_cpoint=False):
"""Returns the path to an existing FFN subvolume.
This like `get_subvolume_path`, but returns paths to existing data only.
Args:
segmentation_dir: directory containing FFN subvolumes
corner: lower corner of the FFN subvolume as a (z, y, x) tuple
allow_cpoint: whether to return a checkpoint path in case the final
segmentation is not ready
Returns:
path to an existing FFN subvolume (string) or None if no such subvolume
is found
"""
target_path = segmentation_path(segmentation_dir, corner)
if gfile.Exists(target_path):
return target_path
target_path = legacy_segmentation_path(segmentation_dir, corner)
if gfile.Exists(target_path):
return target_path
if allow_cpoint:
target_path = checkpoint_path(segmentation_dir, corner)
if gfile.Exists(target_path):
return target_path
return None