Python tensorflow.python.platform.gfile.Walk() Examples
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code examples of tensorflow.python.platform.gfile.Walk().
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Example #1
Source File: io_wrapper.py From lambda-packs with MIT License | 6 votes |
def ListRecursively(top): """Walks a directory tree, yielding (dir_path, file_paths) tuples. For each of `top` and its subdirectories, yields a tuple containing the path to the directory and the path to each of the contained files. Note that unlike os.Walk()/gfile.Walk(), this does not list subdirectories and the file paths are all absolute. If the directory does not exist, this yields nothing. Args: top: A path to a directory.. Yields: A list of (dir_path, file_paths) tuples. """ for dir_path, _, filenames in gfile.Walk(top): yield (dir_path, (os.path.join(dir_path, filename) for filename in filenames))
Example #2
Source File: io_wrapper.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def ListRecursively(top): """Walks a directory tree, yielding (dir_path, file_paths) tuples. For each of `top` and its subdirectories, yields a tuple containing the path to the directory and the path to each of the contained files. Note that unlike os.Walk()/gfile.Walk(), this does not list subdirectories and the file paths are all absolute. If the directory does not exist, this yields nothing. Args: top: A path to a directory.. Yields: A list of (dir_path, file_paths) tuples. """ for dir_path, _, filenames in gfile.Walk(top): yield (dir_path, (os.path.join(dir_path, filename) for filename in filenames))
Example #3
Source File: io_wrapper.py From deep_image_model with Apache License 2.0 | 6 votes |
def ListRecursively(top): """Walks a directory tree, yielding (dir_path, file_paths) tuples. For each of `top` and its subdirectories, yields a tuple containing the path to the directory and the path to each of the contained files. Note that unlike os.Walk()/gfile.Walk(), this does not list subdirectories and the file paths are all absolute. If the directory does not exist, this yields nothing. Args: top: A path to a directory.. Yields: A list of (dir_path, file_paths) tuples. """ for dir_path, _, filenames in gfile.Walk(top): yield (dir_path, (os.path.join(dir_path, filename) for filename in filenames))
Example #4
Source File: io_wrapper.py From keras-lambda with MIT License | 6 votes |
def ListRecursively(top): """Walks a directory tree, yielding (dir_path, file_paths) tuples. For each of `top` and its subdirectories, yields a tuple containing the path to the directory and the path to each of the contained files. Note that unlike os.Walk()/gfile.Walk(), this does not list subdirectories and the file paths are all absolute. If the directory does not exist, this yields nothing. Args: top: A path to a directory.. Yields: A list of (dir_path, file_paths) tuples. """ for dir_path, _, filenames in gfile.Walk(top): yield (dir_path, (os.path.join(dir_path, filename) for filename in filenames))
Example #5
Source File: audio_train.py From Tensorflow-Audio-Classification with Apache License 2.0 | 4 votes |
def _wav_files_and_labels(): """Get wav files path and labels as a dict object. Args: None Returns: result = { label:wav_file_list } """ if not util.is_exists(FLAGS.wavfile_parent_dir): tf.logging.error("Can not find wav files at: {}, or you can download one at " "https://serv.cusp.nyu.edu/projects/urbansounddataset.".format( FLAGS.wavfile_parent_dir)) exit(1) wav_files = [] wav_labels = [] label_idx = 0 sub_dirs = [x[0] for x in gfile.Walk(FLAGS.wavfile_parent_dir)] # The root directory comes first, so skip it. is_root_dir = True for sub_dir in sub_dirs: if is_root_dir: is_root_dir = False continue extensions = ['wav'] file_list = [] dir_name = os.path.basename(sub_dir) if dir_name == FLAGS.wavfile_parent_dir: continue if dir_name[0] == '.': continue tf.logging.info("Looking for wavs in '" + dir_name + "'") for extension in extensions: file_glob = os.path.join(FLAGS.wavfile_parent_dir, dir_name, '*.' + extension) file_list.extend(gfile.Glob(file_glob)) if not file_list: tf.logging.warning('No files found') continue if len(file_list) < 20: tf.logging.warning('WARNING: Folder has less than 20 wavs,' 'which may cause issues.') elif len(file_list) > MAX_NUM_PER_CLASS: tf.logging.warning( 'WARNING: Folder {} has more than {} wavs. Some wavs will ' 'never be selected.'.format(dir_name, MAX_NUM_PER_CLASS)) # label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower()) wav_files.extend(file_list) wav_labels.extend([label_idx]*len(file_list)) label_idx += 1 assert len(wav_files) == len(wav_labels), \ 'Length of wav files and wav labels should be in consistent.' return wav_files, wav_labels
Example #6
Source File: debug_data.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
def _load_device_dumps(self, device_name, device_root): """Load `DebugTensorDatum` instances from the dump root of a given device. Populates a map {device_name: a list of `DebugTensorDatum`}, where the list is sorted by ascending timestamp. This sorting order reflects the order in which the TensorFlow executor processed the nodes of the graph. It is (one of many possible) topological sort of the nodes. This is useful for displaying tensors in the debugger frontend as well as for the use case in which the user wants to find a "culprit tensor", i.e., the first tensor in the graph that exhibits certain problematic properties, i.e., all zero values, or bad numerical values such as nan and inf. In addition, creates a map from node name to debug watches. In this Map, the key is the watched node name; the value is a dictionary. Of this dictionary, the key is the watched_output_slot. This method attempts to load the debug watches from the tensor dump files first, before loading the full set of debug watches from the partition graphs as done later. This is necessary because sometimes the partition graphs may not be available, e.g., when the run errors out. Args: device_name: (`str`) name of the device. device_root: (`str`) dump root directory of the given device. Raises: ValueError: If GraphDef for the device is not available. """ self._dump_tensor_data[device_name] = [] self._debug_watches[device_name] = collections.defaultdict( lambda: collections.defaultdict(set)) for root, _, files in gfile.Walk(device_root): for f in files: if _is_graph_file(f): self._dump_graph_file_paths[device_name] = os.path.join( device_root, root, f) else: datum = self._dump_file_name_to_datum(root, f) self._dump_tensor_data[device_name].append(datum) self._debug_watches[device_name][datum.node_name][ datum.output_slot].add(datum.debug_op) self._dump_tensor_data[device_name] = sorted( self._dump_tensor_data[device_name], key=lambda x: x.extended_timestamp) if self._dump_tensor_data[device_name]: self._t0s[device_name] = self._dump_tensor_data[device_name][0].timestamp else: self._t0s[device_name] = None