本文整理汇总了Python中tensorflow.gfile.MakeDirs方法的典型用法代码示例。如果您正苦于以下问题:Python gfile.MakeDirs方法的具体用法?Python gfile.MakeDirs怎么用?Python gfile.MakeDirs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.MakeDirs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: save_subvolume
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def save_subvolume(labels, origins, output_path, **misc_items):
"""Saves an FFN subvolume.
Args:
labels: 3d zyx number array with the segment labels
origins: dictionary mapping segment ID to origin information
output_path: path at which to save the segmentation in the form
of a .npz file
**misc_items: (optional) additional values to save
in the output file
"""
seg = segmentation.reduce_id_bits(labels)
gfile.MakeDirs(os.path.dirname(output_path))
with atomic_file(output_path) as fd:
np.savez_compressed(fd,
segmentation=seg,
origins=origins,
**misc_items)
示例2: save_checkpoint
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def save_checkpoint(self, path):
"""Saves a inference checkpoint to `path`."""
self.log_info('Saving inference checkpoint to %s.', path)
with timer_counter(self.counters, 'save_checkpoint'):
gfile.MakeDirs(os.path.dirname(path))
with storage.atomic_file(path) as fd:
seed_policy_state = None
if self.seed_policy is not None:
seed_policy_state = self.seed_policy.get_state()
np.savez_compressed(fd,
movement_policy=self.movement_policy.get_state(),
segmentation=self.segmentation,
seg_qprob=self.seg_prob,
seed=self.seed,
origins=self.origins,
overlaps=self.overlaps,
history=np.array(self.history),
history_deleted=np.array(self.history_deleted),
seed_policy_state=seed_policy_state,
counters=self.counters.dumps())
self.log_info('Inference checkpoint saved.')
示例3: main
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [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4: main
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [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5: get_target_path
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def get_target_path(request, point_num):
"""Computes the output path for a specific point.
Args:
request: ResegmentationRequest proto
point_num: index of the point of interest within the proto
Returns:
path to the output file where resegmentation results will be saved
"""
# Prepare the output directory.
output_dir = request.output_directory
id_a = request.points[point_num].id_a
id_b = request.points[point_num].id_b
if request.subdir_digits > 1:
m = hashlib.md5()
m.update(str(id_a))
m.update(str(id_b))
output_dir = os.path.join(output_dir, m.hexdigest()[:request.subdir_digits])
gfile.MakeDirs(output_dir)
# Terminate early if the output already exists.
dp = request.points[point_num].point
target_path = os.path.join(output_dir, '%d-%d_at_%d_%d_%d.npz' % (
id_a, id_b, dp.x, dp.y, dp.z))
if gfile.Exists(target_path):
logging.info('Output already exists: %s', target_path)
return
return target_path
示例6: save_flags
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def save_flags():
gfile.MakeDirs(FLAGS.train_dir)
with gfile.Open(os.path.join(FLAGS.train_dir,
'flags.%d' % time.time()), 'w') as f:
for mod, flag_list in FLAGS.flags_by_module_dict().items():
if (mod.startswith('google3.research.neuromancer.tensorflow') or
mod.startswith('/')):
for flag in flag_list:
f.write('%s\n' % flag.serialize())
示例7: dump_object
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def dump_object(object_to_dump, output_path):
"""Pickle the object and save to the output_path.
Args:
object_to_dump: Python object to be pickled
output_path: (string) output path which can be Google Cloud Storage
Returns:
None
"""
if not gfile.Exists(output_path):
gfile.MakeDirs(os.path.dirname(output_path))
with gfile.Open(output_path, 'w') as wf:
joblib.dump(object_to_dump, wf)
示例8: dump
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def dump(model_config, output_dir):
""" Dumps model configurations.
Args:
model_config: A dict.
output_dir: A string, the output directory.
"""
model_config_filename = os.path.join(output_dir, Constants.MODEL_CONFIG_YAML_FILENAME)
if not gfile.Exists(output_dir):
gfile.MakeDirs(output_dir)
with open_file(model_config_filename, mode="w") as file:
yaml.dump(model_config, file)
示例9: begin
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def begin(self):
super(DumpAttention, self).begin()
gfile.MakeDirs(self.params["output_dir"])
开发者ID:akanimax,项目名称:natural-language-summary-generation-from-structured-data,代码行数:5,代码来源:dump_attention.py
示例10: dump
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def dump(self, model_dir):
"""Dumps the options to a file in the model directory.
Args:
model_dir: Path to the model directory. The options will be
dumped into a file in this directory.
"""
gfile.MakeDirs(model_dir)
options_dict = {
"model_class": self.model_class,
"model_params": self.model_params,
}
with gfile.GFile(TrainOptions.path(model_dir), "wb") as file:
file.write(json.dumps(options_dict).encode("utf-8"))
示例11: after_run
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def after_run(self, _run_context, run_values):
if not self.is_chief or self._done:
return
step_done = run_values.results
if self._active:
tf.logging.info("Captured full trace at step %s", step_done)
# Create output directory
gfile.MakeDirs(self._output_dir)
# Save run metadata
trace_path = os.path.join(self._output_dir, "run_meta")
with gfile.GFile(trace_path, "wb") as trace_file:
trace_file.write(run_values.run_metadata.SerializeToString())
tf.logging.info("Saved run_metadata to %s", trace_path)
# Save timeline
timeline_path = os.path.join(self._output_dir, "timeline.json")
with gfile.GFile(timeline_path, "w") as timeline_file:
tl_info = timeline.Timeline(run_values.run_metadata.step_stats)
tl_chrome = tl_info.generate_chrome_trace_format(show_memory=True)
timeline_file.write(tl_chrome)
tf.logging.info("Saved timeline to %s", timeline_path)
# Save tfprof op log
tf.contrib.tfprof.tfprof_logger.write_op_log(
graph=tf.get_default_graph(),
log_dir=self._output_dir,
run_meta=run_values.run_metadata)
tf.logging.info("Saved op log to %s", self._output_dir)
self._active = False
self._done = True
self._active = (step_done >= self.params["step"])
示例12: begin
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def begin(self):
self._iter_count = 0
self._global_step = tf.train.get_global_step()
self._pred_dict = graph_utils.get_dict_from_collection("predictions")
# Create the sample directory
if self._sample_dir is not None:
gfile.MakeDirs(self._sample_dir)
示例13: main
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def main(_argv):
"""Main functions. Runs all anaylses."""
# pylint: disable=W0212
tfprof_logger._merge_default_with_oplog = merge_default_with_oplog
FLAGS.model_dir = os.path.abspath(os.path.expanduser(FLAGS.model_dir))
output_dir = os.path.join(FLAGS.model_dir, "profile")
gfile.MakeDirs(output_dir)
run_meta, graph, op_log = load_metadata(FLAGS.model_dir)
param_arguments = [
param_analysis_options(output_dir),
micro_anaylsis_options(output_dir),
flops_analysis_options(output_dir),
device_analysis_options(output_dir),
]
for tfprof_cmd, params in param_arguments:
model_analyzer.print_model_analysis(
graph=graph,
run_meta=run_meta,
op_log=op_log,
tfprof_cmd=tfprof_cmd,
tfprof_options=params)
if params["dump_to_file"] != "":
print("Wrote {}".format(params["dump_to_file"]))
示例14: extract_holdout_model
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def extract_holdout_model(model):
game_output_path = OUTPUT_PATH.format(FLAGS.base_dir, 'games', model)
move_output_path = OUTPUT_PATH.format(FLAGS.base_dir, 'moves', model)
gfile.MakeDirs(os.path.basename(game_output_path))
gfile.MakeDirs(os.path.basename(move_output_path))
with gfile.GFile(game_output_path, 'w') as game_f, \
gfile.GFile(move_output_path, 'w') as move_f:
for sgf_name in tqdm(get_sgf_names(model)):
game_data, move_data = extract_data(sgf_name)
game_f.write(json.dumps(game_data) + '\n')
for move_datum in move_data:
move_f.write(json.dumps(move_datum) + '\n')
示例15: _prepare
# 需要导入模块: from tensorflow import gfile [as 别名]
# 或者: from tensorflow.gfile import MakeDirs [as 别名]
def _prepare(self):
""" Prepares for evaluation.
Builds the model with reuse=True, mode=EVAL and preprocesses
data file(s).
"""
features_file = self._dataset["features_file"]
labels_file = self._dataset["labels_file"]
vocab_source = self._dataset["vocab_source"]
vocab_target = self._dataset["vocab_target"]
self._model_configs = update_infer_params( # update inference parameters
self._model_configs,
beam_size=self._beam_size,
maximum_labels_length=self._maximum_labels_length,
length_penalty=self._length_penalty)
estimator_spec = model_fn(model_configs=self._model_configs,
mode=ModeKeys.INFER,
vocab_source=vocab_source,
vocab_target=vocab_target,
name=self._model_name, reuse=True,
verbose=False)
self._predict_ops = estimator_spec.predictions
text_inputter = TextLineInputter(
line_readers=LineReader(
data=features_file,
preprocessing_fn=lambda x: vocab_source.convert_to_idlist(x)),
padding_id=vocab_source.pad_id,
batch_size=self._batch_size)
self._infer_data = text_inputter.make_feeding_data(
input_fields=estimator_spec.input_fields)
tmp_trans_dir = os.path.join(self._model_configs["model_dir"], Constants.TMP_TRANS_DIRNAME)
if not gfile.Exists(tmp_trans_dir):
gfile.MakeDirs(tmp_trans_dir)
self._tmp_trans_file_prefix = os.path.join(tmp_trans_dir, Constants.TMP_TRANS_FILENAME_PREFIX)
self._read_ckpt_bleulog()
# load references
self._references = []
for rfile in access_multiple_files(labels_file):
with open_file(rfile) as fp:
if self._char_level:
self._references.append(to_chinese_char(fp.readlines()))
else:
self._references.append(fp.readlines())
self._references = list(map(list, zip(*self._references)))
with open_file(features_file) as fp:
self._sources = fp.readlines()
self._bad_count = 0
self._best_bleu_score = 0.