Python tensorflow.python.training.saver.checkpoint_exists() Examples
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Example #1
Source File: projector_plugin.py From lambda-packs with MIT License | 5 votes |
def _read_latest_config_files(self, run_path_pairs): """Reads and returns the projector config files in every run directory.""" configs = {} config_fpaths = {} for run_name, assets_dir in run_path_pairs: config = projector_config_pb2.ProjectorConfig() config_fpath = os.path.join(assets_dir, PROJECTOR_FILENAME) if file_io.file_exists(config_fpath): file_content = file_io.read_file_to_string(config_fpath) text_format.Merge(file_content, config) has_tensor_files = False for embedding in config.embeddings: if embedding.tensor_path: if not embedding.tensor_name: embedding.tensor_name = os.path.basename(embedding.tensor_path) has_tensor_files = True break if not config.model_checkpoint_path: # See if you can find a checkpoint file in the logdir. logdir = _assets_dir_to_logdir(assets_dir) ckpt_path = _find_latest_checkpoint(logdir) if not ckpt_path and not has_tensor_files: continue if ckpt_path: config.model_checkpoint_path = ckpt_path # Sanity check for the checkpoint file. if (config.model_checkpoint_path and not checkpoint_exists(config.model_checkpoint_path)): logging.warning('Checkpoint file "%s" not found', config.model_checkpoint_path) continue configs[run_name] = config config_fpaths[run_name] = config_fpath return configs, config_fpaths
Example #2
Source File: plugin.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def _read_latest_config_files(self, run_path_pairs): """Reads and returns the projector config files in every run directory.""" configs = {} config_fpaths = {} for run_name, logdir in run_path_pairs: config = ProjectorConfig() config_fpath = os.path.join(logdir, PROJECTOR_FILENAME) if file_io.file_exists(config_fpath): file_content = file_io.read_file_to_string(config_fpath).decode('utf-8') text_format.Merge(file_content, config) has_tensor_files = False for embedding in config.embeddings: if embedding.tensor_path: has_tensor_files = True break if not config.model_checkpoint_path: # See if you can find a checkpoint file in the logdir. ckpt_path = latest_checkpoint(logdir) if not ckpt_path: # Or in the parent of logdir. ckpt_path = latest_checkpoint(os.path.join(logdir, os.pardir)) if not ckpt_path and not has_tensor_files: continue if ckpt_path: config.model_checkpoint_path = ckpt_path # Sanity check for the checkpoint file. if (config.model_checkpoint_path and not checkpoint_exists(config.model_checkpoint_path)): logging.warning('Checkpoint file %s not found', config.model_checkpoint_path) continue configs[run_name] = config config_fpaths[run_name] = config_fpath return configs, config_fpaths
Example #3
Source File: plugin.py From deep_image_model with Apache License 2.0 | 5 votes |
def _read_latest_config_files(self, run_path_pairs): """Reads and returns the projector config files in every run directory.""" configs = {} config_fpaths = {} for run_name, logdir in run_path_pairs: config = ProjectorConfig() config_fpath = os.path.join(logdir, PROJECTOR_FILENAME) if file_io.file_exists(config_fpath): file_content = file_io.read_file_to_string(config_fpath).decode('utf-8') text_format.Merge(file_content, config) has_tensor_files = False for embedding in config.embeddings: if embedding.tensor_path: has_tensor_files = True break if not config.model_checkpoint_path: # See if you can find a checkpoint file in the logdir. ckpt_path = latest_checkpoint(logdir) if not ckpt_path: # Or in the parent of logdir. ckpt_path = latest_checkpoint(os.path.join('../', logdir)) if not ckpt_path and not has_tensor_files: continue if ckpt_path: config.model_checkpoint_path = ckpt_path # Sanity check for the checkpoint file. if (config.model_checkpoint_path and not checkpoint_exists(config.model_checkpoint_path)): logging.warning('Checkpoint file %s not found', config.model_checkpoint_path) continue configs[run_name] = config config_fpaths[run_name] = config_fpath return configs, config_fpaths
Example #4
Source File: plugin.py From keras-lambda with MIT License | 5 votes |
def _read_latest_config_files(self, run_path_pairs): """Reads and returns the projector config files in every run directory.""" configs = {} config_fpaths = {} for run_name, logdir in run_path_pairs: config = ProjectorConfig() config_fpath = os.path.join(logdir, PROJECTOR_FILENAME) if file_io.file_exists(config_fpath): file_content = file_io.read_file_to_string(config_fpath).decode('utf-8') text_format.Merge(file_content, config) has_tensor_files = False for embedding in config.embeddings: if embedding.tensor_path: has_tensor_files = True break if not config.model_checkpoint_path: # See if you can find a checkpoint file in the logdir. ckpt_path = latest_checkpoint(logdir) if not ckpt_path: # Or in the parent of logdir. ckpt_path = latest_checkpoint(os.path.join(logdir, os.pardir)) if not ckpt_path and not has_tensor_files: continue if ckpt_path: config.model_checkpoint_path = ckpt_path # Sanity check for the checkpoint file. if (config.model_checkpoint_path and not checkpoint_exists(config.model_checkpoint_path)): logging.warning('Checkpoint file %s not found', config.model_checkpoint_path) continue configs[run_name] = config config_fpaths[run_name] = config_fpath return configs, config_fpaths
Example #5
Source File: exporter.py From DOTA_models with Apache License 2.0 | 4 votes |
def freeze_graph_with_def_protos( input_graph_def, input_saver_def, input_checkpoint, output_node_names, restore_op_name, filename_tensor_name, clear_devices, initializer_nodes, variable_names_blacklist=''): """Converts all variables in a graph and checkpoint into constants.""" del restore_op_name, filename_tensor_name # Unused by updated loading code. # 'input_checkpoint' may be a prefix if we're using Saver V2 format if not saver_lib.checkpoint_exists(input_checkpoint): raise ValueError( 'Input checkpoint "' + input_checkpoint + '" does not exist!') if not output_node_names: raise ValueError( 'You must supply the name of a node to --output_node_names.') # Remove all the explicit device specifications for this node. This helps to # make the graph more portable. if clear_devices: for node in input_graph_def.node: node.device = '' _ = importer.import_graph_def(input_graph_def, name='') with session.Session() as sess: if input_saver_def: saver = saver_lib.Saver(saver_def=input_saver_def) saver.restore(sess, input_checkpoint) else: var_list = {} reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: try: tensor = sess.graph.get_tensor_by_name(key + ':0') except KeyError: # This tensor doesn't exist in the graph (for example it's # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor saver = saver_lib.Saver(var_list=var_list) saver.restore(sess, input_checkpoint) if initializer_nodes: sess.run(initializer_nodes) variable_names_blacklist = (variable_names_blacklist.split(',') if variable_names_blacklist else None) output_graph_def = graph_util.convert_variables_to_constants( sess, input_graph_def, output_node_names.split(','), variable_names_blacklist=variable_names_blacklist) return output_graph_def
Example #6
Source File: exporter.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 4 votes |
def freeze_graph_with_def_protos( input_graph_def, input_saver_def, input_checkpoint, output_node_names, restore_op_name, filename_tensor_name, clear_devices, initializer_nodes, variable_names_blacklist=''): """Converts all variables in a graph and checkpoint into constants.""" del restore_op_name, filename_tensor_name # Unused by updated loading code. # 'input_checkpoint' may be a prefix if we're using Saver V2 format if not saver_lib.checkpoint_exists(input_checkpoint): raise ValueError( 'Input checkpoint "' + input_checkpoint + '" does not exist!') if not output_node_names: raise ValueError( 'You must supply the name of a node to --output_node_names.') # Remove all the explicit device specifications for this node. This helps to # make the graph more portable. if clear_devices: for node in input_graph_def.node: node.device = '' with tf.Graph().as_default(): tf.import_graph_def(input_graph_def, name='') config = tf.ConfigProto(graph_options=tf.GraphOptions()) with session.Session(config=config) as sess: if input_saver_def: saver = saver_lib.Saver(saver_def=input_saver_def) saver.restore(sess, input_checkpoint) else: var_list = {} reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: try: tensor = sess.graph.get_tensor_by_name(key + ':0') except KeyError: # This tensor doesn't exist in the graph (for example it's # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor saver = saver_lib.Saver(var_list=var_list) saver.restore(sess, input_checkpoint) if initializer_nodes: sess.run(initializer_nodes) variable_names_blacklist = (variable_names_blacklist.split(',') if variable_names_blacklist else None) output_graph_def = graph_util.convert_variables_to_constants( sess, input_graph_def, output_node_names.split(','), variable_names_blacklist=variable_names_blacklist) return output_graph_def
Example #7
Source File: exporter.py From Person-Detection-and-Tracking with MIT License | 4 votes |
def freeze_graph_with_def_protos( input_graph_def, input_saver_def, input_checkpoint, output_node_names, restore_op_name, filename_tensor_name, clear_devices, initializer_nodes, variable_names_blacklist=''): """Converts all variables in a graph and checkpoint into constants.""" del restore_op_name, filename_tensor_name # Unused by updated loading code. # 'input_checkpoint' may be a prefix if we're using Saver V2 format if not saver_lib.checkpoint_exists(input_checkpoint): raise ValueError( 'Input checkpoint "' + input_checkpoint + '" does not exist!') if not output_node_names: raise ValueError( 'You must supply the name of a node to --output_node_names.') # Remove all the explicit device specifications for this node. This helps to # make the graph more portable. if clear_devices: for node in input_graph_def.node: node.device = '' with tf.Graph().as_default(): tf.import_graph_def(input_graph_def, name='') config = tf.ConfigProto(graph_options=tf.GraphOptions()) with session.Session(config=config) as sess: if input_saver_def: saver = saver_lib.Saver(saver_def=input_saver_def) saver.restore(sess, input_checkpoint) else: var_list = {} reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: try: tensor = sess.graph.get_tensor_by_name(key + ':0') except KeyError: # This tensor doesn't exist in the graph (for example it's # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor saver = saver_lib.Saver(var_list=var_list) saver.restore(sess, input_checkpoint) if initializer_nodes: sess.run(initializer_nodes) variable_names_blacklist = (variable_names_blacklist.split(',') if variable_names_blacklist else None) output_graph_def = graph_util.convert_variables_to_constants( sess, input_graph_def, output_node_names.split(','), variable_names_blacklist=variable_names_blacklist) return output_graph_def
Example #8
Source File: exporter.py From Traffic-Rule-Violation-Detection-System with MIT License | 4 votes |
def freeze_graph_with_def_protos( input_graph_def, input_saver_def, input_checkpoint, output_node_names, restore_op_name, filename_tensor_name, clear_devices, initializer_nodes, variable_names_blacklist=''): """Converts all variables in a graph and checkpoint into constants.""" del restore_op_name, filename_tensor_name # Unused by updated loading code. # 'input_checkpoint' may be a prefix if we're using Saver V2 format if not saver_lib.checkpoint_exists(input_checkpoint): raise ValueError( 'Input checkpoint "' + input_checkpoint + '" does not exist!') if not output_node_names: raise ValueError( 'You must supply the name of a node to --output_node_names.') # Remove all the explicit device specifications for this node. This helps to # make the graph more portable. if clear_devices: for node in input_graph_def.node: node.device = '' with tf.Graph().as_default(): tf.import_graph_def(input_graph_def, name='') config = tf.ConfigProto(graph_options=tf.GraphOptions()) with session.Session(config=config) as sess: if input_saver_def: saver = saver_lib.Saver(saver_def=input_saver_def) saver.restore(sess, input_checkpoint) else: var_list = {} reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: try: tensor = sess.graph.get_tensor_by_name(key + ':0') except KeyError: # This tensor doesn't exist in the graph (for example it's # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor saver = saver_lib.Saver(var_list=var_list) saver.restore(sess, input_checkpoint) if initializer_nodes: sess.run(initializer_nodes) variable_names_blacklist = (variable_names_blacklist.split(',') if variable_names_blacklist else None) output_graph_def = graph_util.convert_variables_to_constants( sess, input_graph_def, output_node_names.split(','), variable_names_blacklist=variable_names_blacklist) return output_graph_def
Example #9
Source File: exporter.py From Gun-Detector with Apache License 2.0 | 4 votes |
def freeze_graph_with_def_protos( input_graph_def, input_saver_def, input_checkpoint, output_node_names, restore_op_name, filename_tensor_name, clear_devices, initializer_nodes, variable_names_blacklist=''): """Converts all variables in a graph and checkpoint into constants.""" del restore_op_name, filename_tensor_name # Unused by updated loading code. # 'input_checkpoint' may be a prefix if we're using Saver V2 format if not saver_lib.checkpoint_exists(input_checkpoint): raise ValueError( 'Input checkpoint "' + input_checkpoint + '" does not exist!') if not output_node_names: raise ValueError( 'You must supply the name of a node to --output_node_names.') # Remove all the explicit device specifications for this node. This helps to # make the graph more portable. if clear_devices: for node in input_graph_def.node: node.device = '' with tf.Graph().as_default(): tf.import_graph_def(input_graph_def, name='') config = tf.ConfigProto(graph_options=tf.GraphOptions()) with session.Session(config=config) as sess: if input_saver_def: saver = saver_lib.Saver(saver_def=input_saver_def) saver.restore(sess, input_checkpoint) else: var_list = {} reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: try: tensor = sess.graph.get_tensor_by_name(key + ':0') except KeyError: # This tensor doesn't exist in the graph (for example it's # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor saver = saver_lib.Saver(var_list=var_list) saver.restore(sess, input_checkpoint) if initializer_nodes: sess.run(initializer_nodes) variable_names_blacklist = (variable_names_blacklist.split(',') if variable_names_blacklist else None) output_graph_def = graph_util.convert_variables_to_constants( sess, input_graph_def, output_node_names.split(','), variable_names_blacklist=variable_names_blacklist) return output_graph_def
Example #10
Source File: exporter.py From ros_tensorflow with Apache License 2.0 | 4 votes |
def freeze_graph_with_def_protos( input_graph_def, input_saver_def, input_checkpoint, output_node_names, restore_op_name, filename_tensor_name, clear_devices, initializer_nodes, variable_names_blacklist=''): """Converts all variables in a graph and checkpoint into constants.""" del restore_op_name, filename_tensor_name # Unused by updated loading code. # 'input_checkpoint' may be a prefix if we're using Saver V2 format if not saver_lib.checkpoint_exists(input_checkpoint): raise ValueError( 'Input checkpoint "' + input_checkpoint + '" does not exist!') if not output_node_names: raise ValueError( 'You must supply the name of a node to --output_node_names.') # Remove all the explicit device specifications for this node. This helps to # make the graph more portable. if clear_devices: for node in input_graph_def.node: node.device = '' with tf.Graph().as_default(): tf.import_graph_def(input_graph_def, name='') config = tf.ConfigProto(graph_options=tf.GraphOptions()) with session.Session(config=config) as sess: if input_saver_def: saver = saver_lib.Saver(saver_def=input_saver_def) saver.restore(sess, input_checkpoint) else: var_list = {} reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: try: tensor = sess.graph.get_tensor_by_name(key + ':0') except KeyError: # This tensor doesn't exist in the graph (for example it's # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor saver = saver_lib.Saver(var_list=var_list) saver.restore(sess, input_checkpoint) if initializer_nodes: sess.run(initializer_nodes) variable_names_blacklist = (variable_names_blacklist.split(',') if variable_names_blacklist else None) output_graph_def = graph_util.convert_variables_to_constants( sess, input_graph_def, output_node_names.split(','), variable_names_blacklist=variable_names_blacklist) return output_graph_def
Example #11
Source File: exporter.py From hands-detection with MIT License | 4 votes |
def freeze_graph_with_def_protos( input_graph_def, input_saver_def, input_checkpoint, output_node_names, restore_op_name, filename_tensor_name, clear_devices, initializer_nodes, variable_names_blacklist=''): """Converts all variables in a graph and checkpoint into constants.""" del restore_op_name, filename_tensor_name # Unused by updated loading code. # 'input_checkpoint' may be a prefix if we're using Saver V2 format if not saver_lib.checkpoint_exists(input_checkpoint): raise ValueError( 'Input checkpoint "' + input_checkpoint + '" does not exist!') if not output_node_names: raise ValueError( 'You must supply the name of a node to --output_node_names.') # Remove all the explicit device specifications for this node. This helps to # make the graph more portable. if clear_devices: for node in input_graph_def.node: node.device = '' _ = importer.import_graph_def(input_graph_def, name='') with session.Session() as sess: if input_saver_def: saver = saver_lib.Saver(saver_def=input_saver_def) saver.restore(sess, input_checkpoint) else: var_list = {} reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: try: tensor = sess.graph.get_tensor_by_name(key + ':0') except KeyError: # This tensor doesn't exist in the graph (for example it's # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor saver = saver_lib.Saver(var_list=var_list) saver.restore(sess, input_checkpoint) if initializer_nodes: sess.run(initializer_nodes) variable_names_blacklist = (variable_names_blacklist.split(',') if variable_names_blacklist else None) output_graph_def = graph_util.convert_variables_to_constants( sess, input_graph_def, output_node_names.split(','), variable_names_blacklist=variable_names_blacklist) return output_graph_def