Python tensorflow.python.platform.gfile.MakeDirs() Examples
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Example #1
Source File: evaluation_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testFinalOpsOnEvaluationLoop(self): value_op, update_op = slim.metrics.streaming_accuracy( self._predictions, self._labels) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # Create Checkpoint and log directories chkpt_dir = os.path.join(self.get_temp_dir(), 'tmp_logs/') gfile.MakeDirs(chkpt_dir) logdir = os.path.join(self.get_temp_dir(), 'tmp_logs2/') gfile.MakeDirs(logdir) # Save initialized variables to checkpoint directory saver = tf.train.Saver() with self.test_session() as sess: init_op.run() saver.save(sess, os.path.join(chkpt_dir, 'chkpt')) # Now, run the evaluation loop: accuracy_value = slim.evaluation.evaluation_loop( '', chkpt_dir, logdir, eval_op=update_op, final_op=value_op, max_number_of_evaluations=1) self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
Example #2
Source File: gc_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testPathsWithParse(self): base_dir = os.path.join(tf.test.get_temp_dir(), "paths_parse") self.assertFalse(gfile.Exists(base_dir)) for p in xrange(3): gfile.MakeDirs(os.path.join(base_dir, "%d" % p)) # add a base_directory to ignore gfile.MakeDirs(os.path.join(base_dir, "ignore")) # create a simple parser that pulls the export_version from the directory. def parser(path): match = re.match("^" + base_dir + "/(\\d+)$", path.path) if not match: return None return path._replace(export_version=int(match.group(1))) self.assertEquals( gc.get_paths(base_dir, parser=parser), [gc.Path(os.path.join(base_dir, "0"), 0), gc.Path(os.path.join(base_dir, "1"), 1), gc.Path(os.path.join(base_dir, "2"), 2)])
Example #3
Source File: base.py From keras-lambda with MIT License | 6 votes |
def maybe_download(filename, work_directory, source_url): """Download the data from source url, unless it's already here. Args: filename: string, name of the file in the directory. work_directory: string, path to working directory. source_url: url to download from if file doesn't exist. Returns: Path to resulting file. """ if not gfile.Exists(work_directory): gfile.MakeDirs(work_directory) filepath = os.path.join(work_directory, filename) if not gfile.Exists(filepath): temp_file_name, _ = urlretrieve_with_retry(source_url) gfile.Copy(temp_file_name, filepath) with gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded', filename, size, 'bytes.') return filepath
Example #4
Source File: task.py From solutions-vision-search with Apache License 2.0 | 6 votes |
def maybe_download_and_extract(filename, data_dir, source_url): """Maybe download and extract a file.""" if not gfile.Exists(data_dir): gfile.MakeDirs(data_dir) filepath = os.path.join(data_dir, filename) if not gfile.Exists(filepath): print('Downloading from {}'.format(source_url)) temp_file_name, _ = urllib.request.urlretrieve(source_url) gfile.Copy(temp_file_name, filepath) with gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded \'{}\' of {} bytes'.format(filename, size)) if filename.endswith('.zip'): print('Extracting {}'.format(filename)) zipfile.ZipFile(file=filepath, mode='r').extractall(data_dir)
Example #5
Source File: base.py From lambda-packs with MIT License | 6 votes |
def maybe_download(filename, work_directory, source_url): """Download the data from source url, unless it's already here. Args: filename: string, name of the file in the directory. work_directory: string, path to working directory. source_url: url to download from if file doesn't exist. Returns: Path to resulting file. """ if not gfile.Exists(work_directory): gfile.MakeDirs(work_directory) filepath = os.path.join(work_directory, filename) if not gfile.Exists(filepath): temp_file_name, _ = urlretrieve_with_retry(source_url) gfile.Copy(temp_file_name, filepath) with gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded', filename, size, 'bytes.') return filepath
Example #6
Source File: graph_assets.py From tensorlang with Apache License 2.0 | 6 votes |
def maybe_download(filepath, source_url): """Download the data from source url, unless it's already here. Args: basename: string, name of the file in the directory. target_dir: string, path to working directory. source_url: url to download from if file doesn't exist. Returns: Path to resulting file. """ target_dir = path.dirname(filepath) if not gfile.Exists(target_dir): gfile.MakeDirs(target_dir) if not gfile.Exists(filepath): print('Downloading', source_url, 'to', filepath) temp_file_name, _ = _urlretrieve_with_retry(source_url) gfile.Copy(temp_file_name, filepath) with gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded', filepath, size, 'bytes.') return filepath
Example #7
Source File: base.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def maybe_download(filename, work_directory, source_url): """Download the data from source url, unless it's already here. Args: filename: string, name of the file in the directory. work_directory: string, path to working directory. source_url: url to download from if file doesn't exist. Returns: Path to resulting file. """ if not gfile.Exists(work_directory): gfile.MakeDirs(work_directory) filepath = os.path.join(work_directory, filename) if not gfile.Exists(filepath): temp_file_name, _ = urlretrieve_with_retry(source_url) gfile.Copy(temp_file_name, filepath) with gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded', filename, size, 'bytes.') return filepath
Example #8
Source File: evaluation_test.py From tf-slim with Apache License 2.0 | 6 votes |
def testReturnsSingleCheckpointIfOneCheckpointFound(self): checkpoint_dir = tempfile.mkdtemp('one_checkpoint_found') if not gfile.Exists(checkpoint_dir): gfile.MakeDirs(checkpoint_dir) global_step = variables.get_or_create_global_step() saver = saver_lib.Saver() # Saves the global step. with self.cached_session() as session: session.run(variables_lib.global_variables_initializer()) save_path = os.path.join(checkpoint_dir, 'model.ckpt') saver.save(session, save_path, global_step=global_step) num_found = 0 for _ in evaluation.checkpoints_iterator(checkpoint_dir, timeout=0): num_found += 1 self.assertEqual(num_found, 1)
Example #9
Source File: profile_context.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def _maybe_dump(self): if not (self._step in self._dump_steps or self._dump_next_step): return if not gfile.Exists(self._profiler_dir): gfile.MakeDirs(self._profiler_dir) print_mdl.WriteProfile( os.path.join(compat.as_bytes(self._profiler_dir), compat.as_bytes('profile_%d' % self._step)))
Example #10
Source File: trainer_lib.py From object_detection_with_tensorflow with MIT License | 5 votes |
def get_summary_writer(tensorboard_dir): """Creates a directory for writing summaries and returns a writer.""" tf.logging.info('TensorBoard directory: %s', tensorboard_dir) tf.logging.info('Deleting prior data if exists...') try: gfile.DeleteRecursively(tensorboard_dir) except errors.OpError as err: tf.logging.error('Directory did not exist? Error: %s', err) tf.logging.info('Deleted! Creating the directory again...') gfile.MakeDirs(tensorboard_dir) tf.logging.info('Created! Instatiating SummaryWriter...') summary_writer = tf.summary.FileWriter(tensorboard_dir) return summary_writer
Example #11
Source File: trainer_lib.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
def get_summary_writer(tensorboard_dir): """Creates a directory for writing summaries and returns a writer.""" tf.logging.info('TensorBoard directory: %s', tensorboard_dir) tf.logging.info('Deleting prior data if exists...') try: gfile.DeleteRecursively(tensorboard_dir) except errors.OpError as err: tf.logging.error('Directory did not exist? Error: %s', err) tf.logging.info('Deleted! Creating the directory again...') gfile.MakeDirs(tensorboard_dir) tf.logging.info('Created! Instatiating SummaryWriter...') summary_writer = tf.summary.FileWriter(tensorboard_dir) return summary_writer
Example #12
Source File: trainer_lib.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def get_summary_writer(tensorboard_dir): """Creates a directory for writing summaries and returns a writer.""" tf.logging.info('TensorBoard directory: %s', tensorboard_dir) tf.logging.info('Deleting prior data if exists...') try: gfile.DeleteRecursively(tensorboard_dir) except errors.OpError as err: tf.logging.error('Directory did not exist? Error: %s', err) tf.logging.info('Deleted! Creating the directory again...') gfile.MakeDirs(tensorboard_dir) tf.logging.info('Created! Instatiating SummaryWriter...') summary_writer = tf.summary.FileWriter(tensorboard_dir) return summary_writer
Example #13
Source File: trainer_lib.py From hands-detection with MIT License | 5 votes |
def get_summary_writer(tensorboard_dir): """Creates a directory for writing summaries and returns a writer.""" tf.logging.info('TensorBoard directory: %s', tensorboard_dir) tf.logging.info('Deleting prior data if exists...') try: gfile.DeleteRecursively(tensorboard_dir) except errors.OpError as err: tf.logging.error('Directory did not exist? Error: %s', err) tf.logging.info('Deleted! Creating the directory again...') gfile.MakeDirs(tensorboard_dir) tf.logging.info('Created! Instatiating SummaryWriter...') summary_writer = tf.summary.FileWriter(tensorboard_dir) return summary_writer
Example #14
Source File: cifar10_train.py From TensorFlow-Playground with MIT License | 5 votes |
def main(argv=None): # pylint: disable=unused-argument cifar10.maybe_download_and_extract() if not gfile.Exists(FLAGS.train_dir): # gfile.DeleteRecursively(FLAGS.train_dir) gfile.MakeDirs(FLAGS.train_dir) train()
Example #15
Source File: exporter.py From deep_image_model with Apache License 2.0 | 5 votes |
def gfile_copy_callback(files_to_copy, export_dir_path): """Callback to copy files using `gfile.Copy` to an export directory. This method is used as the default `assets_callback` in `Exporter.init` to copy assets from the `assets_collection`. It can also be invoked directly to copy additional supplementary files into the export directory (in which case it is not a callback). Args: files_to_copy: A dictionary that maps original file paths to desired basename in the export directory. export_dir_path: Directory to copy the files to. """ logging.info("Write assets into: %s using gfile_copy.", export_dir_path) gfile.MakeDirs(export_dir_path) for source_filepath, basename in files_to_copy.items(): new_path = os.path.join( compat.as_bytes(export_dir_path), compat.as_bytes(basename)) logging.info("Copying asset %s to path %s.", source_filepath, new_path) if gfile.Exists(new_path): # Guard against being restarted while copying assets, and the file # existing and being in an unknown state. # TODO(b/28676216): Do some file checks before deleting. logging.info("Removing file %s.", new_path) gfile.Remove(new_path) gfile.Copy(source_filepath, new_path)
Example #16
Source File: event_file_writer.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def __init__(self, logdir, max_queue=10, flush_secs=120, filename_suffix=None): """Creates a `EventFileWriter` and an event file to write to. On construction the summary writer creates a new event file in `logdir`. This event file will contain `Event` protocol buffers, which are written to disk via the add_event method. The other arguments to the constructor control the asynchronous writes to the event file: * `flush_secs`: How often, in seconds, to flush the added summaries and events to disk. * `max_queue`: Maximum number of summaries or events pending to be written to disk before one of the 'add' calls block. Args: logdir: A string. Directory where event file will be written. max_queue: Integer. Size of the queue for pending events and summaries. flush_secs: Number. How often, in seconds, to flush the pending events and summaries to disk. filename_suffix: A string. Every event file's name is suffixed with `filename_suffix`. """ self._logdir = logdir if not gfile.IsDirectory(self._logdir): gfile.MakeDirs(self._logdir) self._event_queue = six.moves.queue.Queue(max_queue) self._ev_writer = pywrap_tensorflow.EventsWriter( compat.as_bytes(os.path.join(self._logdir, "events"))) self._flush_secs = flush_secs self._sentinel_event = self._get_sentinel_event() if filename_suffix: self._ev_writer.InitWithSuffix(compat.as_bytes(filename_suffix)) self._closed = False self._worker = _EventLoggerThread(self._event_queue, self._ev_writer, self._flush_secs, self._sentinel_event) self._worker.start()
Example #17
Source File: evaluation_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testEvaluationLoopTimeout(self): _, update_op = slim.metrics.streaming_accuracy( self._predictions, self._labels) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # Create checkpoint and log directories. chkpt_dir = os.path.join(self.get_temp_dir(), 'tmp_logs/') gfile.MakeDirs(chkpt_dir) logdir = os.path.join(self.get_temp_dir(), 'tmp_logs2/') gfile.MakeDirs(logdir) # Save initialized variables to checkpoint directory. saver = tf.train.Saver() with self.test_session() as sess: init_op.run() saver.save(sess, os.path.join(chkpt_dir, 'chkpt')) # Run the evaluation loop with a timeout. with self.test_session() as sess: start = time.time() slim.evaluation.evaluation_loop( '', chkpt_dir, logdir, eval_op=update_op, eval_interval_secs=2.0, timeout=6.0) end = time.time() # Check we've waited for the timeout. self.assertGreater(end - start, 6.0) # Then the timeout kicked in and stops the loop. self.assertLess(end - start, 7.5)
Example #18
Source File: cifar10_eval.py From TensorFlow-Playground with MIT License | 5 votes |
def main(argv=None): # pylint: disable=unused-argument cifar10.maybe_download_and_extract() if gfile.Exists(FLAGS.eval_dir): gfile.DeleteRecursively(FLAGS.eval_dir) gfile.MakeDirs(FLAGS.eval_dir) evaluate()
Example #19
Source File: session_manager_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testRecoverSession(self): # Create a checkpoint. checkpoint_dir = os.path.join(self.get_temp_dir(), "recover_session") try: gfile.DeleteRecursively(checkpoint_dir) except errors.OpError: pass # Ignore gfile.MakeDirs(checkpoint_dir) with tf.Graph().as_default(): v = tf.Variable(1, name="v") sm = tf.train.SessionManager(ready_op=tf.report_uninitialized_variables()) saver = tf.train.Saver({"v": v}) sess, initialized = sm.recover_session("", saver=saver, checkpoint_dir=checkpoint_dir) self.assertFalse(initialized) sess.run(v.initializer) self.assertEquals(1, sess.run(v)) saver.save(sess, os.path.join(checkpoint_dir, "recover_session_checkpoint")) # Create a new Graph and SessionManager and recover. with tf.Graph().as_default(): v = tf.Variable(2, name="v") with self.test_session(): self.assertEqual(False, tf.is_variable_initialized(v).eval()) sm2 = tf.train.SessionManager( ready_op=tf.report_uninitialized_variables()) saver = tf.train.Saver({"v": v}) sess, initialized = sm2.recover_session("", saver=saver, checkpoint_dir=checkpoint_dir) self.assertTrue(initialized) self.assertEqual( True, tf.is_variable_initialized( sess.graph.get_tensor_by_name("v:0")).eval(session=sess)) self.assertEquals(1, sess.run(v))
Example #20
Source File: trainer_lib.py From HumanRecognition with MIT License | 5 votes |
def get_summary_writer(tensorboard_dir): """Creates a directory for writing summaries and returns a writer.""" tf.logging.info('TensorBoard directory: %s', tensorboard_dir) tf.logging.info('Deleting prior data if exists...') try: gfile.DeleteRecursively(tensorboard_dir) except errors.OpError as err: tf.logging.error('Directory did not exist? Error: %s', err) tf.logging.info('Deleted! Creating the directory again...') gfile.MakeDirs(tensorboard_dir) tf.logging.info('Created! Instatiating SummaryWriter...') summary_writer = tf.summary.FileWriter(tensorboard_dir) return summary_writer
Example #21
Source File: cifar10_multi_gpu_train.py From TensorFlow-Playground with MIT License | 5 votes |
def main(argv=None): # pylint: disable=unused-argument cifar10.maybe_download_and_extract() if gfile.Exists(FLAGS.train_dir): gfile.DeleteRecursively(FLAGS.train_dir) gfile.MakeDirs(FLAGS.train_dir) train()
Example #22
Source File: meta_graph_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def _TestDir(test_name): test_dir = os.path.join(tf.test.get_temp_dir(), test_name) if os.path.exists(test_dir): shutil.rmtree(test_dir) gfile.MakeDirs(test_dir) return test_dir # pylint: enable=invalid-name
Example #23
Source File: writer.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def _write_plugin_assets(self, graph): plugin_assets = plugin_asset.get_all_plugin_assets(graph) logdir = self.event_writer.get_logdir() for asset_container in plugin_assets: plugin_name = asset_container.plugin_name plugin_dir = os.path.join(logdir, _PLUGINS_DIR, plugin_name) gfile.MakeDirs(plugin_dir) assets = asset_container.assets() for (asset_name, content) in assets.items(): asset_path = os.path.join(plugin_dir, asset_name) with gfile.Open(asset_path, "w") as f: f.write(content)
Example #24
Source File: event_file_writer.py From deep_image_model with Apache License 2.0 | 5 votes |
def __init__(self, logdir, max_queue=10, flush_secs=120): """Creates a `EventFileWriter` and an event file to write to. On construction the summary writer creates a new event file in `logdir`. This event file will contain `Event` protocol buffers, which are written to disk via the add_event method. The other arguments to the constructor control the asynchronous writes to the event file: * `flush_secs`: How often, in seconds, to flush the added summaries and events to disk. * `max_queue`: Maximum number of summaries or events pending to be written to disk before one of the 'add' calls block. Args: logdir: A string. Directory where event file will be written. max_queue: Integer. Size of the queue for pending events and summaries. flush_secs: Number. How often, in seconds, to flush the pending events and summaries to disk. """ self._logdir = logdir if not gfile.IsDirectory(self._logdir): gfile.MakeDirs(self._logdir) self._event_queue = six.moves.queue.Queue(max_queue) self._ev_writer = pywrap_tensorflow.EventsWriter( compat.as_bytes(os.path.join(self._logdir, "events"))) self._closed = False self._worker = _EventLoggerThread(self._event_queue, self._ev_writer, flush_secs) self._worker.start()
Example #25
Source File: saver_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def setUp(self): self._base_dir = os.path.join(self.get_temp_dir(), "saver_utils_test") gfile.MakeDirs(self._base_dir)
Example #26
Source File: saver_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def _TestDir(test_name): test_dir = os.path.join(tf.test.get_temp_dir(), test_name) if os.path.exists(test_dir): shutil.rmtree(test_dir) gfile.MakeDirs(test_dir) return test_dir # pylint: enable=invalid-name
Example #27
Source File: session_manager_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testRecoverSession(self): # Create a checkpoint. checkpoint_dir = os.path.join(self.get_temp_dir(), "recover_session") try: gfile.DeleteRecursively(checkpoint_dir) except errors.OpError: pass # Ignore gfile.MakeDirs(checkpoint_dir) with tf.Graph().as_default(): v = tf.Variable(1, name="v") sm = tf.train.SessionManager(ready_op=tf.assert_variables_initialized()) saver = tf.train.Saver({"v": v}) sess, initialized = sm.recover_session("", saver=saver, checkpoint_dir=checkpoint_dir) self.assertFalse(initialized) sess.run(v.initializer) self.assertEquals(1, sess.run(v)) saver.save(sess, os.path.join(checkpoint_dir, "recover_session_checkpoint")) # Create a new Graph and SessionManager and recover. with tf.Graph().as_default(): v = tf.Variable(2, name="v") with self.test_session(): self.assertEqual(False, tf.is_variable_initialized(v).eval()) sm2 = tf.train.SessionManager(ready_op=tf.assert_variables_initialized()) saver = tf.train.Saver({"v": v}) sess, initialized = sm2.recover_session("", saver=saver, checkpoint_dir=checkpoint_dir) self.assertTrue(initialized) self.assertEqual( True, tf.is_variable_initialized( sess.graph.get_tensor_by_name("v:0")).eval(session=sess)) self.assertEquals(1, sess.run(v))
Example #28
Source File: mscnn_train.py From mscnn with GNU General Public License v3.0 | 5 votes |
def main(argv=None): if gfile.Exists(FLAGS.train_log): gfile.DeleteRecursively(FLAGS.train_log) gfile.MakeDirs(FLAGS.train_log) if not gfile.Exists(FLAGS.model_dir): gfile.MakeDirs(FLAGS.model_dir) if not gfile.Exists(FLAGS.output_dir): gfile.MakeDirs(FLAGS.output_dir) train()
Example #29
Source File: mscnn_eval.py From mscnn with GNU General Public License v3.0 | 5 votes |
def main(argv=None): if gfile.Exists(FLAGS.eval_dir): gfile.DeleteRecursively(FLAGS.eval_dir) gfile.MakeDirs(FLAGS.eval_dir) evaluate()
Example #30
Source File: trainer_lib.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def get_summary_writer(tensorboard_dir): """Creates a directory for writing summaries and returns a writer.""" tf.logging.info('TensorBoard directory: %s', tensorboard_dir) tf.logging.info('Deleting prior data if exists...') try: gfile.DeleteRecursively(tensorboard_dir) except errors.OpError as err: tf.logging.error('Directory did not exist? Error: %s', err) tf.logging.info('Deleted! Creating the directory again...') gfile.MakeDirs(tensorboard_dir) tf.logging.info('Created! Instatiating SummaryWriter...') summary_writer = tf.summary.FileWriter(tensorboard_dir) return summary_writer