# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=invalid-name """Save and restore variables.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import os.path import re import time import uuid import numpy as np import six from google.protobuf import text_format from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import device as pydev from tensorflow.python.framework import errors from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops from tensorflow.python.lib.io import file_io from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_io_ops from tensorflow.python.ops import io_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import string_ops from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import training_util from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState from tensorflow.python.util import compat # Op names which identify variable reads which should be saved. _VARIABLE_OPS = set(["Variable", "VariableV2", "AutoReloadVariable", "ReadVariableOp", "ResourceGather"]) def _set_cpu0(device_string): """Creates a new device string based on `device_string` but using /CPU:0. If the device is already on /CPU:0, this is a no-op. Args: device_string: A device string. Returns: A device string. """ parsed_device = pydev.DeviceSpec.from_string(device_string) parsed_device.device_type = "CPU" parsed_device.device_index = 0 return parsed_device.to_string() class BaseSaverBuilder(object): """Base class for Savers. Can be extended to create different Ops. """ class SaveSpec(object): """Class used to describe tensor slices that need to be saved.""" def __init__(self, tensor, slice_spec, name): """Creates a `SaveSpec` object. Args: tensor: the tensor to save. slice_spec: the slice to be saved. See `Variable.SaveSliceInfo`. name: the name to save the tensor under. """ self.tensor = tensor self.slice_spec = slice_spec self.name = name class SaveableObject(object): """Base class for saving and restoring saveable objects.""" def __init__(self, op, specs, name): """Creates a `SaveableObject` object. Args: op: the "producer" object that this class wraps; it produces a list of tensors to save. E.g., a "Variable" object saving its backing tensor. specs: a list of SaveSpec, each element of which describes one tensor to save under this object. name: the name to save the object under. """ self.op = op self.specs = specs self.name = name # The device of this saveable. All tensors must be on the same device. self.device = specs[0].tensor.device def restore(self, restored_tensors, restored_shapes): """Restores this object from 'restored_tensors'. Args: restored_tensors: the tensors that were loaded from a checkpoint restored_shapes: the shapes this object should conform to after restore, or None. Returns: An operation that restores the state of the object. Raises: ValueError: If the object cannot be restored using the provided parameters. """ # pylint: disable=unused-argument raise ValueError("Calling an abstract method.") class VariableSaveable(SaveableObject): """SaveableObject implementation that handles Variables.""" def __init__(self, var, slice_spec, name): spec = BaseSaverBuilder.SaveSpec(var, slice_spec, name) super(BaseSaverBuilder.VariableSaveable, self).__init__(var, [spec], name) def restore(self, restored_tensors, restored_shapes): restored_tensor = restored_tensors[0] if restored_shapes is not None: restored_tensor = array_ops.reshape(restored_tensor, restored_shapes[0]) return state_ops.assign( self.op, restored_tensor, validate_shape=restored_shapes is None and self.op.get_shape().is_fully_defined()) class ResourceVariableSaveable(SaveableObject): """SaveableObject implementation that handles ResourceVariables.""" def __init__(self, var, slice_spec, name): if isinstance(var, ops.Tensor): self.handle_op = var.op.inputs[0] elif isinstance(var, resource_variable_ops.ResourceVariable): self.handle_op = var.handle else: raise ValueError( "Saveable is neither a resource variable nor a read operation." " Got: %s" % repr(var)) spec = BaseSaverBuilder.SaveSpec(var, slice_spec, name) super(BaseSaverBuilder.ResourceVariableSaveable, self).__init__( var, [spec], name) def restore(self, restored_tensors, restored_shapes): restored_tensor = restored_tensors[0] if restored_shapes is not None: restored_tensor = array_ops.reshape(restored_tensor, restored_shapes[0]) return resource_variable_ops.assign_variable_op( self.handle_op, restored_tensor) def __init__(self, write_version=saver_pb2.SaverDef.V2): self._write_version = write_version def save_op(self, filename_tensor, saveables): """Create an Op to save 'saveables'. This is intended to be overridden by subclasses that want to generate different Ops. Args: filename_tensor: String Tensor. saveables: A list of BaseSaverBuilder.SaveableObject objects. Returns: An Operation that save the variables. Raises: RuntimeError: (implementation detail) if "self._write_version" is an unexpected value. """ # pylint: disable=protected-access tensor_names = [] tensors = [] tensor_slices = [] for saveable in saveables: for spec in saveable.specs: tensor_names.append(spec.name) tensors.append(spec.tensor) tensor_slices.append(spec.slice_spec) if self._write_version == saver_pb2.SaverDef.V1: return io_ops._save( filename=filename_tensor, tensor_names=tensor_names, tensors=tensors, tensor_slices=tensor_slices) elif self._write_version == saver_pb2.SaverDef.V2: # "filename_tensor" is interpreted *NOT AS A FILENAME*, but as a prefix # of a V2 checkpoint: e.g. "/fs/train/ckpt-/tmp/worker-". return io_ops.save_v2(filename_tensor, tensor_names, tensor_slices, tensors) else: raise RuntimeError("Unexpected write_version: " + self._write_version) # pylint: disable=unused-argument def restore_op(self, filename_tensor, saveable, preferred_shard): """Create ops to restore 'saveable'. This is intended to be overridden by subclasses that want to generate different Ops. Args: filename_tensor: String Tensor. saveable: A BaseSaverBuilder.SaveableObject object. preferred_shard: Int. Shard to open first when loading a sharded file. Returns: A list of Tensors resulting from reading 'saveable' from 'filename'. """ # pylint: disable=protected-access tensors = [] for spec in saveable.specs: tensors.append( io_ops.restore_v2( filename_tensor, [spec.name], [spec.slice_spec], [spec.tensor.dtype])[0]) return tensors # pylint: enable=unused-argument def sharded_filename(self, filename_tensor, shard, num_shards): """Append sharding information to a filename. Args: filename_tensor: A string tensor. shard: Integer. The shard for the filename. num_shards: An int Tensor for the number of shards. Returns: A string tensor. """ # pylint: disable=protected-access return gen_io_ops._sharded_filename(filename_tensor, shard, num_shards) def _AddSaveOps(self, filename_tensor, saveables): """Add ops to save variables that are on the same shard. Args: filename_tensor: String Tensor. saveables: A list of SaveableObject objects. Returns: A tensor with the filename used to save. """ save = self.save_op(filename_tensor, saveables) return control_flow_ops.with_dependencies([save], filename_tensor) def _AddShardedSaveOpsForV2(self, checkpoint_prefix, per_device): """Add ops to save the params per shard, for the V2 format. Note that the sharded save procedure for the V2 format is different from V1: there is a special "merge" step that merges the small metadata produced from each device. Args: checkpoint_prefix: scalar String Tensor. Interpreted *NOT AS A FILENAME*, but as a prefix of a V2 checkpoint; per_device: A list of (device, BaseSaverBuilder.VarToSave) pairs, as returned by _GroupByDevices(). Returns: An op to save the variables, which, when evaluated, returns the prefix "" only and does not include the sharded spec suffix. """ # IMPLEMENTATION DETAILS: most clients should skip. # # Suffix for any well-formed "checkpoint_prefix", when sharded. # Transformations: # * Users pass in "save_path" in save() and restore(). Say "myckpt". # * checkpoint_prefix gets fed <_SHARDED_SUFFIX>. # # Example: # During runtime, a temporary directory is first created, which contains # files # # /myckpt_temp/ # part-?????-of-?????{.index, .data-00000-of-00001} # # Before .save() finishes, they will be (hopefully, atomically) renamed to # # / # myckpt{.index, .data-?????-of-?????} # # Users only need to interact with the user-specified prefix, which is # "/myckpt" in this case. Save() and Restore() work with the # prefix directly, instead of any physical pathname. (On failure and # subsequent restore, an outdated and orphaned temporary directory can be # safely removed.) _SHARDED_SUFFIX = "_temp_%s/part" % uuid.uuid4().hex tmp_checkpoint_prefix = string_ops.string_join( [checkpoint_prefix, _SHARDED_SUFFIX]) num_shards = len(per_device) sharded_saves = [] sharded_prefixes = [] num_shards_tensor = constant_op.constant(num_shards, name="num_shards") last_device = None for shard, (device, saveables) in enumerate(per_device): last_device = device with ops.device(device): sharded_filename = self.sharded_filename(tmp_checkpoint_prefix, shard, num_shards_tensor) sharded_prefixes.append(sharded_filename) sharded_saves.append(self._AddSaveOps(sharded_filename, saveables)) with ops.control_dependencies([x.op for x in sharded_saves]): # Co-locates the merge step with the last device. with ops.device(last_device): # V2 format write path consists of a metadata merge step. Once merged, # attempts to delete the temporary directory, "_temp". merge_step = gen_io_ops.merge_v2_checkpoints( sharded_prefixes, checkpoint_prefix, delete_old_dirs=True) with ops.control_dependencies([merge_step]): # Returns the prefix "" only. DOES NOT include the # sharded spec suffix. return array_ops.identity(checkpoint_prefix) def _AddShardedSaveOps(self, filename_tensor, per_device): """Add ops to save the params per shard. Args: filename_tensor: a scalar String Tensor. per_device: A list of (device, BaseSaverBuilder.SaveableObject) pairs, as returned by _GroupByDevices(). Returns: An op to save the variables. """ if self._write_version == saver_pb2.SaverDef.V2: return self._AddShardedSaveOpsForV2(filename_tensor, per_device) num_shards = len(per_device) sharded_saves = [] num_shards_tensor = constant_op.constant(num_shards, name="num_shards") for shard, (device, saveables) in enumerate(per_device): with ops.device(device): sharded_filename = self.sharded_filename(filename_tensor, shard, num_shards_tensor) sharded_saves.append(self._AddSaveOps(sharded_filename, saveables)) # Return the sharded name for the save path. with ops.control_dependencies([x.op for x in sharded_saves]): # pylint: disable=protected-access return gen_io_ops._sharded_filespec(filename_tensor, num_shards_tensor) def _AddRestoreOps(self, filename_tensor, saveables, restore_sequentially, reshape, preferred_shard=-1, name="restore_all"): """Add operations to restore saveables. Args: filename_tensor: Tensor for the path of the file to load. saveables: A list of SaveableObject objects. restore_sequentially: True if we want to restore variables sequentially within a shard. reshape: True if we want to reshape loaded tensors to the shape of the corresponding variable. preferred_shard: Shard to open first when loading a sharded file. name: Name for the returned op. Returns: An Operation that restores the variables. """ assign_ops = [] for saveable in saveables: restore_control_inputs = assign_ops[-1:] if restore_sequentially else [] # Load and optionally reshape on the CPU, as string tensors are not # available on the GPU. # TODO(touts): Re-enable restore on GPU when we can support annotating # string tensors as "HostMemory" inputs. with ops.device(_set_cpu0(saveable.device) if saveable.device else None): with ops.control_dependencies(restore_control_inputs): tensors = self.restore_op(filename_tensor, saveable, preferred_shard) shapes = None if reshape: # Compute the shapes, let the restore op decide if and how to do # the reshape. shapes = [] for spec in saveable.specs: v = spec.tensor shape = v.get_shape() if not shape.is_fully_defined(): shape = array_ops.shape(v) shapes.append(shape) assign_ops.append(saveable.restore(tensors, shapes)) # Create a Noop that has control dependencies from all the updates. return control_flow_ops.group(*assign_ops, name=name) def _AddShardedRestoreOps(self, filename_tensor, per_device, restore_sequentially, reshape): """Add Ops to restore variables from multiple devices. Args: filename_tensor: Tensor for the path of the file to load. per_device: A list of (device, SaveableObject) pairs, as returned by _GroupByDevices(). restore_sequentially: True if we want to restore variables sequentially within a shard. reshape: True if we want to reshape loaded tensors to the shape of the corresponding variable. Returns: An Operation that restores the variables. """ sharded_restores = [] for shard, (device, saveables) in enumerate(per_device): with ops.device(device): sharded_restores.append( self._AddRestoreOps( filename_tensor, saveables, restore_sequentially, reshape, preferred_shard=shard, name="restore_shard")) return control_flow_ops.group(*sharded_restores, name="restore_all") @staticmethod def _IsVariable(v): return isinstance(v, ops.Tensor) and v.op.type in _VARIABLE_OPS def _GroupByDevices(self, saveables): """Group Variable tensor slices per device. TODO(touts): Make sure that all the devices found are on different job/replica/task/cpu|gpu. It would be bad if 2 were on the same device. It can happen if the devices are unspecified. Args: saveables: A list of BaseSaverBuilder.SaveableObject objects. Returns: A list of tuples: (device_name, BaseSaverBuilder.SaveableObject) tuples. The list is sorted by ascending device_name. Raises: ValueError: If the tensors of a saveable are on different devices. """ per_device = collections.defaultdict(lambda: []) for saveable in saveables: canonical_device = set( pydev.canonical_name(spec.tensor.device) for spec in saveable.specs) if len(canonical_device) != 1: raise ValueError("All tensors of a saveable object must be " "on the same device: %s" % saveable.name) per_device[canonical_device.pop()].append(saveable) return sorted(per_device.items(), key=lambda t: t[0]) @staticmethod def OpListToDict(op_list): """Create a dictionary of names to operation lists. Args: op_list: A list, tuple, or set of Variables or SaveableObjects. Returns: A dictionary of names to the operations that must be saved under that name. Variables with save_slice_info are grouped together under the same key in no particular order. Raises: TypeError: If the type of op_list or its elements is not supported. ValueError: If at least two saveables share the same name. """ if not isinstance(op_list, (list, tuple, set)): raise TypeError("Variables to save should be passed in a dict or a " "list: %s" % op_list) op_list = set(op_list) names_to_saveables = {} # pylint: disable=protected-access for var in op_list: if isinstance(var, BaseSaverBuilder.SaveableObject): names_to_saveables[var.name] = var elif isinstance(var, variables.PartitionedVariable): if var.name in names_to_saveables: raise ValueError("At least two variables have the same name: %s" % var.name) names_to_saveables[var.name] = var elif ((isinstance(var, variables.Variable) or isinstance(var, resource_variable_ops.ResourceVariable)) and var._save_slice_info): name = var._save_slice_info.full_name if name in names_to_saveables: if not isinstance(names_to_saveables[name], list): raise ValueError("Mixing slices and non-slices with the same name: " "%s" % name) names_to_saveables[name].append(var) else: names_to_saveables[name] = [var] else: var = ops.internal_convert_to_tensor(var, as_ref=True) if not BaseSaverBuilder._IsVariable(var): raise TypeError("Variable to save is not a Variable: %s" % var) if var.op.type == "ReadVariableOp": name = var.op.inputs[0].op.name else: name = var.op.name if name in names_to_saveables: raise ValueError("At least two variables have the same name: %s" % name) names_to_saveables[name] = var # pylint: enable=protected-access return names_to_saveables def _ValidateAndSliceInputs(self, names_to_saveables): """Returns the variables and names that will be used for a Saver. Args: names_to_saveables: A dict (k, v) where k is the name of an operation and v is an operation to save or a BaseSaverBuilder.Saver. Returns: A list of BaseSaverBuilder.SaveableObject objects. Raises: TypeError: If any of the keys are not strings or any of the values are not one of Tensor or Variable or a checkpointable operation. ValueError: If the same operation is given in more than one value (this also applies to slices of SlicedVariables). """ if not isinstance(names_to_saveables, dict): names_to_saveables = BaseSaverBuilder.OpListToDict(names_to_saveables) saveables = [] seen_ops = set() for name in sorted(names_to_saveables.keys()): if not isinstance(name, six.string_types): raise TypeError( "names_to_saveables must be a dict mapping string names to " "checkpointable operations. Name is not a string: %s" % name) op = names_to_saveables[name] if isinstance(op, BaseSaverBuilder.SaveableObject): self._AddSaveable(saveables, seen_ops, op) elif isinstance(op, (list, tuple, variables.PartitionedVariable)): if isinstance(op, variables.PartitionedVariable): op = list(op) # A set of slices. slice_name = None # pylint: disable=protected-access for variable in op: if (not isinstance(variable, variables.Variable) and not isinstance(variable, resource_variable_ops.ResourceVariable)): raise ValueError("Slices must all be Variables: %s" % variable) if not variable._save_slice_info: raise ValueError("Slices must all be slices: %s" % variable) if slice_name is None: slice_name = variable._save_slice_info.full_name elif slice_name != variable._save_slice_info.full_name: raise ValueError( "Slices must all be from the same tensor: %s != %s" % (slice_name, variable._save_slice_info.full_name)) if variable.op.type in ["Variable", "VariableV2", "AutoReloadVariable"]: saveable = BaseSaverBuilder.VariableSaveable( variable, variable._save_slice_info.spec, name) else: saveable = BaseSaverBuilder.ResourceVariableSaveable( variable, variable._save_slice_info.spec, name) self._AddSaveable(saveables, seen_ops, saveable) # pylint: enable=protected-access else: # A variable or tensor. variable = ops.internal_convert_to_tensor(op, as_ref=True) if not BaseSaverBuilder._IsVariable(variable): raise TypeError("names_to_saveables must be a dict mapping string " "names to Tensors/Variables. Not a variable: %s" % variable) if variable.op.type in ["Variable", "VariableV2", "AutoReloadVariable"]: saveable = BaseSaverBuilder.VariableSaveable(variable, "", name) else: saveable = BaseSaverBuilder.ResourceVariableSaveable( variable, "", name) self._AddSaveable(saveables, seen_ops, saveable) return saveables def _AddSaveable(self, saveables, seen_ops, saveable): """Adds the saveable to the saveables list. Args: saveables: List to append the SaveableObject to. seen_ops: Set of the ops of the saveables already processed. Used to check that each saveable is only saved once. saveable: The saveable. Raises: ValueError: If the saveable has already been processed. """ if saveable.op in seen_ops: raise ValueError("The same saveable will be restored with two names: %s" % saveable.name) saveables.append(saveable) seen_ops.add(saveable.op) def build(self, names_to_saveables, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, filename="model"): """Adds save/restore nodes to the graph and creates a SaverDef proto. Args: names_to_saveables: A dictionary mapping name to a Variable or SaveableObject. Each name will be associated with the corresponding variable in the checkpoint. reshape: If True, allow restoring parameters from a checkpoint that where the parameters have a different shape. This is only needed when you try to restore from a Dist-Belief checkpoint, and only some times. sharded: If True, shard the checkpoints, one per device that has Variable nodes. max_to_keep: Maximum number of checkpoints to keep. As new checkpoints are created, old ones are deleted. If None or 0, no checkpoints are deleted from the filesystem but only the last one is kept in the `checkpoint` file. Presently the number is only roughly enforced. For example in case of restarts more than max_to_keep checkpoints may be kept. keep_checkpoint_every_n_hours: How often checkpoints should be kept. Defaults to 10,000 hours. name: String. Optional name to use as a prefix when adding operations. restore_sequentially: A Bool, which if true, causes restore of different variables to happen sequentially within each device. filename: If known at graph construction time, filename used for variable loading/saving. Returns: A SaverDef proto. Raises: TypeError: If 'names_to_saveables' is not a dictionary mapping string keys to variable Tensors. ValueError: If any of the keys or values in 'names_to_saveables' is not unique. """ saveables = self._ValidateAndSliceInputs(names_to_saveables) if max_to_keep is None: max_to_keep = 0 with ops.name_scope(name, "save", [saveable.op for saveable in saveables]) as name: # Add the Constant string tensor for the filename. filename_tensor = constant_op.constant(filename) # Add the save ops. if sharded: per_device = self._GroupByDevices(saveables) save_tensor = self._AddShardedSaveOps(filename_tensor, per_device) restore_op = self._AddShardedRestoreOps(filename_tensor, per_device, restore_sequentially, reshape) else: save_tensor = self._AddSaveOps(filename_tensor, saveables) restore_op = self._AddRestoreOps(filename_tensor, saveables, restore_sequentially, reshape) # In the following use case, it's possible to have restore_ops be called # something else: # - Build inference graph and export a meta_graph. # - Import the inference meta_graph # - Extend the inference graph to a train graph. # - Export a new meta_graph. # Now the second restore_op will be called "restore_all_1". # As such, comment out the assert for now until we know whether supporting # such usage model makes sense. # # assert restore_op.name.endswith("restore_all"), restore_op.name return saver_pb2.SaverDef( filename_tensor_name=filename_tensor.name, save_tensor_name=save_tensor.name, restore_op_name=restore_op.name, max_to_keep=max_to_keep, sharded=sharded, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, version=self._write_version) def _GetCheckpointFilename(save_dir, latest_filename): """Returns a filename for storing the CheckpointState. Args: save_dir: The directory for saving and restoring checkpoints. latest_filename: Name of the file in 'save_dir' that is used to store the CheckpointState. Returns: The path of the file that contains the CheckpointState proto. """ if latest_filename is None: latest_filename = "checkpoint" return os.path.join(save_dir, latest_filename) def generate_checkpoint_state_proto(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None): """Generates a checkpoint state proto. Args: save_dir: Directory where the model was saved. model_checkpoint_path: The checkpoint file. all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto. Returns: CheckpointState proto with model_checkpoint_path and all_model_checkpoint_paths updated to either absolute paths or relative paths to the current save_dir. """ if all_model_checkpoint_paths is None: all_model_checkpoint_paths = [] if (not all_model_checkpoint_paths or all_model_checkpoint_paths[-1] != model_checkpoint_path): logging.info("%s is not in all_model_checkpoint_paths. Manually adding it.", model_checkpoint_path) all_model_checkpoint_paths.append(model_checkpoint_path) # Relative paths need to be rewritten to be relative to the "save_dir" # if model_checkpoint_path already contains "save_dir". if not os.path.isabs(save_dir): if not os.path.isabs(model_checkpoint_path): model_checkpoint_path = os.path.relpath(model_checkpoint_path, save_dir) for i in range(len(all_model_checkpoint_paths)): p = all_model_checkpoint_paths[i] if not os.path.isabs(p): all_model_checkpoint_paths[i] = os.path.relpath(p, save_dir) coord_checkpoint_proto = CheckpointState( model_checkpoint_path=model_checkpoint_path, all_model_checkpoint_paths=all_model_checkpoint_paths) return coord_checkpoint_proto def update_checkpoint_state(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None, latest_filename=None): """Updates the content of the 'checkpoint' file. This updates the checkpoint file containing a CheckpointState proto. Args: save_dir: Directory where the model was saved. model_checkpoint_path: The checkpoint file. all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto. latest_filename: Optional name of the checkpoint file. Default to 'checkpoint'. Raises: RuntimeError: If the save paths conflict. """ # Writes the "checkpoint" file for the coordinator for later restoration. coord_checkpoint_filename = _GetCheckpointFilename(save_dir, latest_filename) ckpt = generate_checkpoint_state_proto( save_dir, model_checkpoint_path, all_model_checkpoint_paths=all_model_checkpoint_paths) if coord_checkpoint_filename == ckpt.model_checkpoint_path: raise RuntimeError("Save path '%s' conflicts with path used for " "checkpoint state. Please use a different save path." % model_checkpoint_path) # Preventing potential read/write race condition by *atomically* writing to a # file. file_io.atomic_write_string_to_file(coord_checkpoint_filename, text_format.MessageToString(ckpt)) def get_checkpoint_state(checkpoint_dir, latest_filename=None): """Returns CheckpointState proto from the "checkpoint" file. If the "checkpoint" file contains a valid CheckpointState proto, returns it. Args: checkpoint_dir: The directory of checkpoints. latest_filename: Optional name of the checkpoint file. Default to 'checkpoint'. Returns: A CheckpointState if the state was available, None otherwise. Raises: ValueError: if the checkpoint read doesn't have model_checkpoint_path set. """ ckpt = None coord_checkpoint_filename = _GetCheckpointFilename(checkpoint_dir, latest_filename) f = None try: # Check that the file exists before opening it to avoid # many lines of errors from colossus in the logs. if file_io.file_exists(coord_checkpoint_filename): file_content = file_io.read_file_to_string( coord_checkpoint_filename) ckpt = CheckpointState() text_format.Merge(file_content, ckpt) if not ckpt.model_checkpoint_path: raise ValueError("Invalid checkpoint state loaded from %s", checkpoint_dir) # For relative model_checkpoint_path and all_model_checkpoint_paths, # prepend checkpoint_dir. if not os.path.isabs(ckpt.model_checkpoint_path): ckpt.model_checkpoint_path = os.path.join(checkpoint_dir, ckpt.model_checkpoint_path) for i in range(len(ckpt.all_model_checkpoint_paths)): p = ckpt.all_model_checkpoint_paths[i] if not os.path.isabs(p): ckpt.all_model_checkpoint_paths[i] = os.path.join(checkpoint_dir, p) except errors.OpError as e: # It's ok if the file cannot be read logging.warning(str(e)) logging.warning("%s: Checkpoint ignored", coord_checkpoint_filename) return None except text_format.ParseError as e: logging.warning(str(e)) logging.warning("%s: Checkpoint ignored", coord_checkpoint_filename) return None finally: if f: f.close() return ckpt class Saver(object): """Saves and restores variables. See [Variables](../../how_tos/variables/index.md) for an overview of variables, saving and restoring. The `Saver` class adds ops to save and restore variables to and from *checkpoints*. It also provides convenience methods to run these ops. Checkpoints are binary files in a proprietary format which map variable names to tensor values. The best way to examine the contents of a checkpoint is to load it using a `Saver`. Savers can automatically number checkpoint filenames with a provided counter. This lets you keep multiple checkpoints at different steps while training a model. For example you can number the checkpoint filenames with the training step number. To avoid filling up disks, savers manage checkpoint files automatically. For example, they can keep only the N most recent files, or one checkpoint for every N hours of training. You number checkpoint filenames by passing a value to the optional `global_step` argument to `save()`: ```python saver.save(sess, 'my-model', global_step=0) ==> filename: 'my-model-0' ... saver.save(sess, 'my-model', global_step=1000) ==> filename: 'my-model-1000' ``` Additionally, optional arguments to the `Saver()` constructor let you control the proliferation of checkpoint files on disk: * `max_to_keep` indicates the maximum number of recent checkpoint files to keep. As new files are created, older files are deleted. If None or 0, all checkpoint files are kept. Defaults to 5 (that is, the 5 most recent checkpoint files are kept.) * `keep_checkpoint_every_n_hours`: In addition to keeping the most recent `max_to_keep` checkpoint files, you might want to keep one checkpoint file for every N hours of training. This can be useful if you want to later analyze how a model progressed during a long training session. For example, passing `keep_checkpoint_every_n_hours=2` ensures that you keep one checkpoint file for every 2 hours of training. The default value of 10,000 hours effectively disables the feature. Note that you still have to call the `save()` method to save the model. Passing these arguments to the constructor will not save variables automatically for you. A training program that saves regularly looks like: ```python ... # Create a saver. saver = tf.train.Saver(...variables...) # Launch the graph and train, saving the model every 1,000 steps. sess = tf.Session() for step in xrange(1000000): sess.run(..training_op..) if step % 1000 == 0: # Append the step number to the checkpoint name: saver.save(sess, 'my-model', global_step=step) ``` In addition to checkpoint files, savers keep a protocol buffer on disk with the list of recent checkpoints. This is used to manage numbered checkpoint files and by `latest_checkpoint()`, which makes it easy to discover the path to the most recent checkpoint. That protocol buffer is stored in a file named 'checkpoint' next to the checkpoint files. If you create several savers, you can specify a different filename for the protocol buffer file in the call to `save()`. @@__init__ @@save @@restore Other utility methods. @@last_checkpoints @@set_last_checkpoints_with_time @@recover_last_checkpoints @@as_saver_def """ def __init__(self, var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False, allow_empty=False, write_version=saver_pb2.SaverDef.V2, pad_step_number=False): """Creates a `Saver`. The constructor adds ops to save and restore variables. `var_list` specifies the variables that will be saved and restored. It can be passed as a `dict` or a list: * A `dict` of names to variables: The keys are the names that will be used to save or restore the variables in the checkpoint files. * A list of variables: The variables will be keyed with their op name in the checkpoint files. For example: ```python v1 = tf.Variable(..., name='v1') v2 = tf.Variable(..., name='v2') # Pass the variables as a dict: saver = tf.train.Saver({'v1': v1, 'v2': v2}) # Or pass them as a list. saver = tf.train.Saver([v1, v2]) # Passing a list is equivalent to passing a dict with the variable op names # as keys: saver = tf.train.Saver({v.op.name: v for v in [v1, v2]}) ``` The optional `reshape` argument, if `True`, allows restoring a variable from a save file where the variable had a different shape, but the same number of elements and type. This is useful if you have reshaped a variable and want to reload it from an older checkpoint. The optional `sharded` argument, if `True`, instructs the saver to shard checkpoints per device. Args: var_list: A list of `Variable`/`SaveableObject`, or a dictionary mapping names to `SaveableObject`s. If `None`, defaults to the list of all saveable objects. reshape: If `True`, allows restoring parameters from a checkpoint where the variables have a different shape. sharded: If `True`, shard the checkpoints, one per device. max_to_keep: Maximum number of recent checkpoints to keep. Defaults to 5. keep_checkpoint_every_n_hours: How often to keep checkpoints. Defaults to 10,000 hours. name: String. Optional name to use as a prefix when adding operations. restore_sequentially: A `Bool`, which if true, causes restore of different variables to happen sequentially within each device. This can lower memory usage when restoring very large models. saver_def: Optional `SaverDef` proto to use instead of running the builder. This is only useful for specialty code that wants to recreate a `Saver` object for a previously built `Graph` that had a `Saver`. The `saver_def` proto should be the one returned by the `as_saver_def()` call of the `Saver` that was created for that `Graph`. builder: Optional `SaverBuilder` to use if a `saver_def` was not provided. Defaults to `BaseSaverBuilder()`. defer_build: If `True`, defer adding the save and restore ops to the `build()` call. In that case `build()` should be called before finalizing the graph or using the saver. allow_empty: If `False` (default) raise an error if there are no variables in the graph. Otherwise, construct the saver anyway and make it a no-op. write_version: controls what format to use when saving checkpoints. It also affects certain filepath matching logic. The V2 format is the recommended choice: it is much more optimized than V1 in terms of memory required and latency incurred during restore. Regardless of this flag, the Saver is able to restore from both V2 and V1 checkpoints. pad_step_number: if True, pads the global step number in the checkpoint filepaths to some fixed width (8 by default). This is turned off by default. Raises: TypeError: If `var_list` is invalid. ValueError: If any of the keys or values in `var_list` are not unique. """ if defer_build and var_list: raise ValueError( "If `var_list` is provided then build cannot be deferred. " "Either set defer_build=False or var_list=None.") self._var_list = var_list self._reshape = reshape self._sharded = sharded self._max_to_keep = max_to_keep self._keep_checkpoint_every_n_hours = keep_checkpoint_every_n_hours self._name = name self._restore_sequentially = restore_sequentially self.saver_def = saver_def self._builder = builder self._is_built = False self._allow_empty = allow_empty self._is_empty = None self._write_version = write_version self._pad_step_number = pad_step_number if not defer_build: self.build() if self.saver_def: self._check_saver_def() self._write_version = self.saver_def.version def build(self): """Builds saver_def.""" if self._is_built: return self._is_built = True if not self.saver_def: if self._builder is None: self._builder = BaseSaverBuilder(self._write_version) if self._var_list is None: # pylint: disable=protected-access self._var_list = variables._all_saveable_objects() if not self._var_list: if self._allow_empty: self._is_empty = True return else: raise ValueError("No variables to save") self._is_empty = False self.saver_def = self._builder.build( self._var_list, reshape=self._reshape, sharded=self._sharded, max_to_keep=self._max_to_keep, keep_checkpoint_every_n_hours=self._keep_checkpoint_every_n_hours, name=self._name, restore_sequentially=self._restore_sequentially) elif self.saver_def and self._name: # Since self._name is used as a name_scope by builder(), we are # overloading the use of this field to represent the "import_scope" as # well. self.saver_def.filename_tensor_name = ops.prepend_name_scope( self.saver_def.filename_tensor_name, self._name) self.saver_def.save_tensor_name = ops.prepend_name_scope( self.saver_def.save_tensor_name, self._name) self.saver_def.restore_op_name = ops.prepend_name_scope( self.saver_def.restore_op_name, self._name) self._check_saver_def() # Updates next checkpoint time. self._next_checkpoint_time = ( time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600) self._last_checkpoints = [] def _check_saver_def(self): if not isinstance(self.saver_def, saver_pb2.SaverDef): raise ValueError("saver_def must be a saver_pb2.SaverDef: %s" % self.saver_def) if not self.saver_def.save_tensor_name: raise ValueError("saver_def must specify the save_tensor_name: %s" % str(self.saver_def)) if not self.saver_def.restore_op_name: raise ValueError("saver_def must specify the restore_op_name: %s" % str(self.saver_def)) def _CheckpointFilename(self, p): """Returns the checkpoint filename given a `(filename, time)` pair. Args: p: (filename, time) pair. Returns: Checkpoint file name. """ name, _ = p return name def _MetaGraphFilename(self, checkpoint_filename, meta_graph_suffix="meta"): """Returns the meta graph filename. Args: checkpoint_filename: Name of the checkpoint file. meta_graph_suffix: Suffix for `MetaGraphDef` file. Defaults to 'meta'. Returns: MetaGraph file name. """ # If the checkpoint_filename is sharded, the checkpoint_filename could # be of format model.ckpt-step#-?????-of-shard#. For example, # model.ckpt-123456-?????-of-00005, or model.ckpt-123456-00001-of-00002. basename = re.sub(r"-[\d\?]+-of-\d+$", "", checkpoint_filename) meta_graph_filename = ".".join([basename, meta_graph_suffix]) return meta_graph_filename def _MaybeDeleteOldCheckpoints(self, latest_save_path, meta_graph_suffix="meta"): """Deletes old checkpoints if necessary. Always keep the last `max_to_keep` checkpoints. If `keep_checkpoint_every_n_hours` was specified, keep an additional checkpoint every `N` hours. For example, if `N` is 0.5, an additional checkpoint is kept for every 0.5 hours of training; if `N` is 10, an additional checkpoint is kept for every 10 hours of training. Args: latest_save_path: Name including path of checkpoint file to save. meta_graph_suffix: Suffix for `MetaGraphDef` file. Defaults to 'meta'. """ if not self.saver_def.max_to_keep: return # Remove first from list if the same name was used before. for p in self._last_checkpoints: if latest_save_path == self._CheckpointFilename(p): self._last_checkpoints.remove(p) # Append new path to list self._last_checkpoints.append((latest_save_path, time.time())) # If more than max_to_keep, remove oldest. if len(self._last_checkpoints) > self.saver_def.max_to_keep: p = self._last_checkpoints.pop(0) # Do not delete the file if we keep_checkpoint_every_n_hours is set and we # have reached N hours of training. should_keep = p[1] > self._next_checkpoint_time if should_keep: self._next_checkpoint_time += ( self.saver_def.keep_checkpoint_every_n_hours * 3600) return # Otherwise delete the files. try: checkpoint_prefix = self._CheckpointFilename(p) self._delete_file_if_exists( self._MetaGraphFilename(checkpoint_prefix, meta_graph_suffix)) if self.saver_def.version == saver_pb2.SaverDef.V2: # V2 has a metadata file and some data files. self._delete_file_if_exists(checkpoint_prefix + ".index") self._delete_file_if_exists(checkpoint_prefix + ".data-?????-of-?????") else: # V1, Legacy. Exact match on the data file. self._delete_file_if_exists(checkpoint_prefix) except Exception as e: # pylint: disable=broad-except logging.warning("Ignoring: %s", str(e)) def _delete_file_if_exists(self, filespec): for pathname in file_io.get_matching_files(filespec): file_io.delete_file(pathname) def as_saver_def(self): """Generates a `SaverDef` representation of this saver. Returns: A `SaverDef` proto. """ return self.saver_def def to_proto(self, export_scope=None): """Converts this `Saver` to a `SaverDef` protocol buffer. Args: export_scope: Optional `string`. Name scope to remove. Returns: A `SaverDef` protocol buffer. """ if (export_scope is None or self._name.startswith(export_scope)): return self.saver_def else: return None @staticmethod def from_proto(saver_def, import_scope=None): """Returns a `Saver` object created from `saver_def`. Args: saver_def: a `SaveDef` protocol buffer. import_scope: Optional `string`. Name scope to use. Returns: A `Saver` built from saver_def. """ return Saver(saver_def=saver_def, name=import_scope) @property def last_checkpoints(self): """List of not-yet-deleted checkpoint filenames. You can pass any of the returned values to `restore()`. Returns: A list of checkpoint filenames, sorted from oldest to newest. """ return list(self._CheckpointFilename(p) for p in self._last_checkpoints) def set_last_checkpoints(self, last_checkpoints): """DEPRECATED: Use set_last_checkpoints_with_time. Sets the list of old checkpoint filenames. Args: last_checkpoints: A list of checkpoint filenames. Raises: AssertionError: If last_checkpoints is not a list. """ assert isinstance(last_checkpoints, list) # We use a timestamp of +inf so that this checkpoint will never be # deleted. This is both safe and backwards compatible to a previous # version of the code which used s[1] as the "timestamp". self._last_checkpoints = [(s, np.inf) for s in last_checkpoints] def set_last_checkpoints_with_time(self, last_checkpoints_with_time): """Sets the list of old checkpoint filenames and timestamps. Args: last_checkpoints_with_time: A list of tuples of checkpoint filenames and timestamps. Raises: AssertionError: If last_checkpoints_with_time is not a list. """ assert isinstance(last_checkpoints_with_time, list) self._last_checkpoints = last_checkpoints_with_time def recover_last_checkpoints(self, checkpoint_paths): """Recovers the internal saver state after a crash. This method is useful for recovering the "self._last_checkpoints" state. Globs for the checkpoints pointed to by `checkpoint_paths`. If the files exist, use their mtime as the checkpoint timestamp. Args: checkpoint_paths: a list of checkpoint paths. """ mtimes = get_checkpoint_mtimes(checkpoint_paths) self.set_last_checkpoints_with_time(list(zip(checkpoint_paths, mtimes))) def save(self, sess, save_path, global_step=None, latest_filename=None, meta_graph_suffix="meta", write_meta_graph=True, write_state=True): """Saves variables. This method runs the ops added by the constructor for saving variables. It requires a session in which the graph was launched. The variables to save must also have been initialized. The method returns the path of the newly created checkpoint file. This path can be passed directly to a call to `restore()`. Args: sess: A Session to use to save the variables. save_path: String. Path to the checkpoint filename. If the saver is `sharded`, this is the prefix of the sharded checkpoint filename. global_step: If provided the global step number is appended to `save_path` to create the checkpoint filename. The optional argument can be a `Tensor`, a `Tensor` name or an integer. latest_filename: Optional name for the protocol buffer file that will contains the list of most recent checkpoint filenames. That file, kept in the same directory as the checkpoint files, is automatically managed by the saver to keep track of recent checkpoints. Defaults to 'checkpoint'. meta_graph_suffix: Suffix for `MetaGraphDef` file. Defaults to 'meta'. write_meta_graph: `Boolean` indicating whether or not to write the meta graph file. write_state: `Boolean` indicating whether or not to write the `CheckpointStateProto`. Returns: A string: path at which the variables were saved. If the saver is sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn' is the number of shards created. If the saver is empty, returns None. Raises: TypeError: If `sess` is not a `Session`. ValueError: If `latest_filename` contains path components, or if it collides with `save_path`. RuntimeError: If save and restore ops weren't built. """ if not self._is_built: raise RuntimeError( "`build()` should be called before save if defer_build==True") if latest_filename is None: latest_filename = "checkpoint" if self._write_version != saver_pb2.SaverDef.V2: logging.warning("*******************************************************") logging.warning("TensorFlow's V1 checkpoint format has been deprecated.") logging.warning("Consider switching to the more efficient V2 format:") logging.warning(" `tf.train.Saver(write_version=tf.train.SaverDef.V2)`") logging.warning("now on by default.") logging.warning("*******************************************************") if os.path.split(latest_filename)[0]: raise ValueError("'latest_filename' must not contain path components") if global_step is not None: if not isinstance(global_step, compat.integral_types): global_step = training_util.global_step(sess, global_step) checkpoint_file = "%s-%d" % (save_path, global_step) if self._pad_step_number: # Zero-pads the step numbers, so that they are sorted when listed. checkpoint_file = "%s-%s" % (save_path, "{:08d}".format(global_step)) else: checkpoint_file = save_path if os.path.basename( save_path) == latest_filename and not self.saver_def.sharded: # Guard against collision between data file and checkpoint state file. raise ValueError( "'latest_filename' collides with 'save_path': '%s' and '%s'" % (latest_filename, save_path)) if not gfile.IsDirectory(os.path.dirname(save_path)): raise ValueError( "Parent directory of {} doesn't exist, can't save.".format(save_path)) save_path = os.path.dirname(save_path) if not isinstance(sess, session.SessionInterface): raise TypeError("'sess' must be a Session; %s" % sess) if not self._is_empty: model_checkpoint_path = sess.run( self.saver_def.save_tensor_name, {self.saver_def.filename_tensor_name: checkpoint_file}) model_checkpoint_path = compat.as_str(model_checkpoint_path) if write_state: self._MaybeDeleteOldCheckpoints( model_checkpoint_path, meta_graph_suffix=meta_graph_suffix) update_checkpoint_state(save_path, model_checkpoint_path, self.last_checkpoints, latest_filename) if write_meta_graph: meta_graph_filename = self._MetaGraphFilename( checkpoint_file, meta_graph_suffix=meta_graph_suffix) with sess.graph.as_default(): self.export_meta_graph(meta_graph_filename) if self._is_empty: return None else: return model_checkpoint_path def export_meta_graph(self, filename=None, collection_list=None, as_text=False, export_scope=None, clear_devices=False): """Writes `MetaGraphDef` to save_path/filename. Args: filename: Optional meta_graph filename including the path. collection_list: List of string keys to collect. as_text: If `True`, writes the meta_graph as an ASCII proto. export_scope: Optional `string`. Name scope to remove. clear_devices: Whether or not to clear the device field for an `Operation` or `Tensor` during export. Returns: A `MetaGraphDef` proto. """ return export_meta_graph( filename=filename, graph_def=ops.get_default_graph().as_graph_def(add_shapes=True), saver_def=self.saver_def, collection_list=collection_list, as_text=as_text, export_scope=export_scope, clear_devices=clear_devices) def restore(self, sess, save_path): """Restores previously saved variables. This method runs the ops added by the constructor for restoring variables. It requires a session in which the graph was launched. The variables to restore do not have to have been initialized, as restoring is itself a way to initialize variables. The `save_path` argument is typically a value previously returned from a `save()` call, or a call to `latest_checkpoint()`. Args: sess: A `Session` to use to restore the parameters. save_path: Path where parameters were previously saved. """ if self._is_empty: return logging.info("Restoring parameters from %s", save_path) sess.run(self.saver_def.restore_op_name, {self.saver_def.filename_tensor_name: save_path}) @staticmethod def _add_collection_def(meta_graph_def, key, export_scope=None): """Adds a collection to MetaGraphDef protocol buffer. Args: meta_graph_def: MetaGraphDef protocol buffer. key: One of the GraphKeys or user-defined string. export_scope: Optional `string`. Name scope to remove. """ meta_graph.add_collection_def(meta_graph_def, key, export_scope=export_scope) def _prefix_to_checkpoint_path(prefix, format_version): """Returns the pathname of a checkpoint file, given the checkpoint prefix. For V1 checkpoint, simply returns the prefix itself (the data file). For V2, returns the pathname to the index file. Args: prefix: a string, the prefix of a checkpoint. format_version: the checkpoint format version that corresponds to the prefix. Returns: The pathname of a checkpoint file, taking into account the checkpoint format version. """ if format_version == saver_pb2.SaverDef.V2: return prefix + ".index" # The index file identifies a checkpoint. return prefix # Just the data file. def latest_checkpoint(checkpoint_dir, latest_filename=None): """Finds the filename of latest saved checkpoint file. Args: checkpoint_dir: Directory where the variables were saved. latest_filename: Optional name for the protocol buffer file that contains the list of most recent checkpoint filenames. See the corresponding argument to `Saver.save()`. Returns: The full path to the latest checkpoint or `None` if no checkpoint was found. """ # Pick the latest checkpoint based on checkpoint state. ckpt = get_checkpoint_state(checkpoint_dir, latest_filename) if ckpt and ckpt.model_checkpoint_path: # Look for either a V2 path or a V1 path, with priority for V2. v2_path = _prefix_to_checkpoint_path(ckpt.model_checkpoint_path, saver_pb2.SaverDef.V2) v1_path = _prefix_to_checkpoint_path(ckpt.model_checkpoint_path, saver_pb2.SaverDef.V1) if file_io.get_matching_files(v2_path) or file_io.get_matching_files( v1_path): return ckpt.model_checkpoint_path else: logging.error("Couldn't match files for checkpoint %s", ckpt.model_checkpoint_path) return None def import_meta_graph(meta_graph_or_file, clear_devices=False, import_scope=None, **kwargs): """Recreates a Graph saved in a `MetaGraphDef` proto. This function takes a `MetaGraphDef` protocol buffer as input. If the argument is a file containing a `MetaGraphDef` protocol buffer , it constructs a protocol buffer from the file content. The function then adds all the nodes from the `graph_def` field to the current graph, recreates all the collections, and returns a saver constructed from the `saver_def` field. In combination with `export_meta_graph()`, this function can be used to * Serialize a graph along with other Python objects such as `QueueRunner`, `Variable` into a `MetaGraphDef`. * Restart training from a saved graph and checkpoints. * Run inference from a saved graph and checkpoints. ```Python ... # Create a saver. saver = tf.train.Saver(...variables...) # Remember the training_op we want to run by adding it to a collection. tf.add_to_collection('train_op', train_op) sess = tf.Session() for step in xrange(1000000): sess.run(train_op) if step % 1000 == 0: # Saves checkpoint, which by default also exports a meta_graph # named 'my-model-global_step.meta'. saver.save(sess, 'my-model', global_step=step) ``` Later we can continue training from this saved `meta_graph` without building the model from scratch. ```Python with tf.Session() as sess: new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta') new_saver.restore(sess, 'my-save-dir/my-model-10000') # tf.get_collection() returns a list. In this example we only want the # first one. train_op = tf.get_collection('train_op')[0] for step in xrange(1000000): sess.run(train_op) ``` NOTE: Restarting training from saved `meta_graph` only works if the device assignments have not changed. Args: meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including the path) containing a `MetaGraphDef`. clear_devices: Whether or not to clear the device field for an `Operation` or `Tensor` during import. import_scope: Optional `string`. Name scope to add. Only used when initializing from protocol buffer. **kwargs: Optional keyed arguments. Returns: A saver constructed from `saver_def` in `MetaGraphDef` or None. A None value is returned if no variables exist in the `MetaGraphDef` (i.e., there are no variables to restore). """ if not isinstance(meta_graph_or_file, meta_graph_pb2.MetaGraphDef): meta_graph_def = meta_graph.read_meta_graph_file(meta_graph_or_file) else: meta_graph_def = meta_graph_or_file meta_graph.import_scoped_meta_graph(meta_graph_def, clear_devices=clear_devices, import_scope=import_scope, **kwargs) if meta_graph_def.HasField("saver_def"): return Saver(saver_def=meta_graph_def.saver_def, name=import_scope) else: if variables._all_saveable_objects(): # pylint: disable=protected-access # Return the default saver instance for all graph variables. return Saver() else: # If no graph variables exist, then a Saver cannot be constructed. logging.info("Saver not created because there are no variables in the" " graph to restore") return None def export_meta_graph(filename=None, meta_info_def=None, graph_def=None, saver_def=None, collection_list=None, as_text=False, graph=None, export_scope=None, clear_devices=False, **kwargs): """Returns `MetaGraphDef` proto. Optionally writes it to filename. This function exports the graph, saver, and collection objects into `MetaGraphDef` protocol buffer with the intention of it being imported at a later time or location to restart training, run inference, or be a subgraph. Args: filename: Optional filename including the path for writing the generated `MetaGraphDef` protocol buffer. meta_info_def: `MetaInfoDef` protocol buffer. graph_def: `GraphDef` protocol buffer. saver_def: `SaverDef` protocol buffer. collection_list: List of string keys to collect. as_text: If `True`, writes the `MetaGraphDef` as an ASCII proto. graph: The `Graph` to import into. If `None`, use the default graph. export_scope: Optional `string`. Name scope under which to extract the subgraph. The scope name will be striped from the node definitions for easy import later into new name scopes. If `None`, the whole graph is exported. graph_def and export_scope cannot both be specified. clear_devices: Whether or not to clear the device field for an `Operation` or `Tensor` during export. **kwargs: Optional keyed arguments. Returns: A `MetaGraphDef` proto. Raises: ValueError: When the `GraphDef` is larger than 2GB. """ meta_graph_def, _ = meta_graph.export_scoped_meta_graph( filename=filename, meta_info_def=meta_info_def, graph_def=graph_def, saver_def=saver_def, collection_list=collection_list, as_text=as_text, graph=graph, export_scope=export_scope, clear_devices=clear_devices, **kwargs) return meta_graph_def def checkpoint_exists(checkpoint_prefix): """Checks whether a V1 or V2 checkpoint exists with the specified prefix. This is the recommended way to check if a checkpoint exists, since it takes into account the naming difference between V1 and V2 formats. Args: checkpoint_prefix: the prefix of a V1 or V2 checkpoint, with V2 taking priority. Typically the result of `Saver.save()` or that of `tf.train.latest_checkpoint()`, regardless of sharded/non-sharded or V1/V2. Returns: A bool, true iff a checkpoint referred to by `checkpoint_prefix` exists. """ pathname = _prefix_to_checkpoint_path(checkpoint_prefix, saver_pb2.SaverDef.V2) if file_io.get_matching_files(pathname): return True elif file_io.get_matching_files(checkpoint_prefix): return True else: return False def get_checkpoint_mtimes(checkpoint_prefixes): """Returns the mtimes (modification timestamps) of the checkpoints. Globs for the checkpoints pointed to by `checkpoint_prefixes`. If the files exist, collect their mtime. Both V2 and V1 checkpoints are considered, in that priority. This is the recommended way to get the mtimes, since it takes into account the naming difference between V1 and V2 formats. Args: checkpoint_prefixes: a list of checkpoint paths, typically the results of `Saver.save()` or those of `tf.train.latest_checkpoint()`, regardless of sharded/non-sharded or V1/V2. Returns: A list of mtimes (in microseconds) of the found checkpoints. """ mtimes = [] def match_maybe_append(pathname): fnames = file_io.get_matching_files(pathname) if fnames: mtimes.append(file_io.stat(fnames[0]).mtime_nsec / 1e9) return True return False for checkpoint_prefix in checkpoint_prefixes: # Tries V2's metadata file first. pathname = _prefix_to_checkpoint_path(checkpoint_prefix, saver_pb2.SaverDef.V2) if match_maybe_append(pathname): continue # Otherwise, tries V1, where the prefix is the complete pathname. match_maybe_append(checkpoint_prefix) return mtimes ops.register_proto_function( ops.GraphKeys.SAVERS, proto_type=saver_pb2.SaverDef, to_proto=Saver.to_proto, from_proto=Saver.from_proto)