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？那么恭喜您, 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模块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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: save_subvolume

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# 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)

开发者ID:google，项目名称:ffn，代码行数:20，

示例2: save_checkpoint

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# 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.')

开发者ID:google，项目名称:ffn，代码行数:24，

示例3: main

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# 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)

开发者ID:google，项目名称:ffn，代码行数:19，

示例4: main

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# 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()

开发者ID:zhaocq-nlp，项目名称:NJUNMT-tf，代码行数:19，

示例5: get_target_path

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# 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

开发者ID:google，项目名称:ffn，代码行数:34，

示例6: save_flags

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# 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())

开发者ID:google，项目名称:ffn，代码行数:11，

示例7: dump_object

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# 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)

开发者ID:GoogleCloudPlatform，项目名称:cloudml-samples，代码行数:17，

示例8: dump

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# 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)

开发者ID:zhaocq-nlp，项目名称:NJUNMT-tf，代码行数:14，

示例9: begin

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# 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，

示例10: dump

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# 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"))

开发者ID:akanimax，项目名称:natural-language-summary-generation-from-structured-data，代码行数:17，

示例11: after_run

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# 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"])

开发者ID:akanimax，项目名称:natural-language-summary-generation-from-structured-data，代码行数:36，

示例12: begin

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# 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)

开发者ID:akanimax，项目名称:natural-language-summary-generation-from-structured-data，代码行数:9，

示例13: main

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# 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"]))

开发者ID:pandegroup，项目名称:reaction_prediction_seq2seq，代码行数:30，

示例14: extract_holdout_model

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# 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')

开发者ID:mlperf，项目名称:training，代码行数:15，

示例15: _prepare

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# 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.

开发者ID:zhaocq-nlp，项目名称:NJUNMT-tf，代码行数:50，

注：本文中的tensorflow.gfile.MakeDirs方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。