# 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. # ============================================================================== """Python utilities required by Keras.""" import binascii import codecs import importlib import marshal import os import re import sys import threading import time import types as python_types import warnings import weakref import numpy as np import tensorflow.compat.v2 as tf from keras.utils import io_utils from keras.utils import tf_contextlib from keras.utils import tf_inspect # isort: off from tensorflow.python.util.tf_export import keras_export _GLOBAL_CUSTOM_OBJECTS = {} _GLOBAL_CUSTOM_NAMES = {} # Flag that determines whether to skip the NotImplementedError when calling # get_config in custom models and layers. This is only enabled when saving to # SavedModel, when the config isn't required. _SKIP_FAILED_SERIALIZATION = False # If a layer does not have a defined config, then the returned config will be a # dictionary with the below key. _LAYER_UNDEFINED_CONFIG_KEY = "layer was saved without config" # Thread-local custom objects set by custom_object_scope. _THREAD_LOCAL_CUSTOM_OBJECTS = threading.local() @keras_export( "keras.utils.custom_object_scope", "keras.utils.CustomObjectScope", ) class CustomObjectScope: """Exposes custom classes/functions to Keras deserialization internals. Under a scope `with custom_object_scope(objects_dict)`, Keras methods such as `tf.keras.models.load_model` or `tf.keras.models.model_from_config` will be able to deserialize any custom object referenced by a saved config (e.g. a custom layer or metric). Example: Consider a custom regularizer `my_regularizer`: ```python layer = Dense(3, kernel_regularizer=my_regularizer) # Config contains a reference to `my_regularizer` config = layer.get_config() ... # Later: with custom_object_scope({'my_regularizer': my_regularizer}): layer = Dense.from_config(config) ``` Args: *args: Dictionary or dictionaries of `{name: object}` pairs. """ def __init__(self, *args): self.custom_objects = args self.backup = None def __enter__(self): self.backup = _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__.copy() for objects in self.custom_objects: _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__.update(objects) return self def __exit__(self, *args, **kwargs): _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__.clear() _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__.update(self.backup) @keras_export("keras.utils.get_custom_objects") def get_custom_objects(): """Retrieves a live reference to the global dictionary of custom objects. Custom objects set using using `custom_object_scope` are not added to the global dictionary of custom objects, and will not appear in the returned dictionary. Example: ```python get_custom_objects().clear() get_custom_objects()['MyObject'] = MyObject ``` Returns: Global dictionary of names to classes (`_GLOBAL_CUSTOM_OBJECTS`). """ return _GLOBAL_CUSTOM_OBJECTS # Store a unique, per-object ID for shared objects. # # We store a unique ID for each object so that we may, at loading time, # re-create the network properly. Without this ID, we would have no way of # determining whether a config is a description of a new object that # should be created or is merely a reference to an already-created object. SHARED_OBJECT_KEY = "shared_object_id" SHARED_OBJECT_DISABLED = threading.local() SHARED_OBJECT_LOADING = threading.local() SHARED_OBJECT_SAVING = threading.local() # Attributes on the threadlocal variable must be set per-thread, thus we # cannot initialize these globally. Instead, we have accessor functions with # default values. def _shared_object_disabled(): """Get whether shared object handling is disabled in a threadsafe manner.""" return getattr(SHARED_OBJECT_DISABLED, "disabled", False) def _shared_object_loading_scope(): """Get the current shared object saving scope in a threadsafe manner.""" return getattr(SHARED_OBJECT_LOADING, "scope", NoopLoadingScope()) def _shared_object_saving_scope(): """Get the current shared object saving scope in a threadsafe manner.""" return getattr(SHARED_OBJECT_SAVING, "scope", None) class DisableSharedObjectScope: """A context manager for disabling handling of shared objects. Disables shared object handling for both saving and loading. Created primarily for use with `clone_model`, which does extra surgery that is incompatible with shared objects. """ def __enter__(self): SHARED_OBJECT_DISABLED.disabled = True self._orig_loading_scope = _shared_object_loading_scope() self._orig_saving_scope = _shared_object_saving_scope() def __exit__(self, *args, **kwargs): SHARED_OBJECT_DISABLED.disabled = False SHARED_OBJECT_LOADING.scope = self._orig_loading_scope SHARED_OBJECT_SAVING.scope = self._orig_saving_scope class NoopLoadingScope: """The default shared object loading scope. It does nothing. Created to simplify serialization code that doesn't care about shared objects (e.g. when serializing a single object). """ def get(self, unused_object_id): return None def set(self, object_id, obj): pass class SharedObjectLoadingScope: """A context manager for keeping track of loaded objects. During the deserialization process, we may come across objects that are shared across multiple layers. In order to accurately restore the network structure to its original state, `SharedObjectLoadingScope` allows us to re-use shared objects rather than cloning them. """ def __enter__(self): if _shared_object_disabled(): return NoopLoadingScope() global SHARED_OBJECT_LOADING SHARED_OBJECT_LOADING.scope = self self._obj_ids_to_obj = {} return self def get(self, object_id): """Given a shared object ID, returns a previously instantiated object. Args: object_id: shared object ID to use when attempting to find already-loaded object. Returns: The object, if we've seen this ID before. Else, `None`. """ # Explicitly check for `None` internally to make external calling code a # bit cleaner. if object_id is None: return return self._obj_ids_to_obj.get(object_id) def set(self, object_id, obj): """Stores an instantiated object for future lookup and sharing.""" if object_id is None: return self._obj_ids_to_obj[object_id] = obj def __exit__(self, *args, **kwargs): global SHARED_OBJECT_LOADING SHARED_OBJECT_LOADING.scope = NoopLoadingScope() class SharedObjectConfig(dict): """A configuration container that keeps track of references. `SharedObjectConfig` will automatically attach a shared object ID to any configs which are referenced more than once, allowing for proper shared object reconstruction at load time. In most cases, it would be more proper to subclass something like `collections.UserDict` or `collections.Mapping` rather than `dict` directly. Unfortunately, python's json encoder does not support `Mapping`s. This is important functionality to retain, since we are dealing with serialization. We should be safe to subclass `dict` here, since we aren't actually overriding any core methods, only augmenting with a new one for reference counting. """ def __init__(self, base_config, object_id, **kwargs): self.ref_count = 1 self.object_id = object_id super().__init__(base_config, **kwargs) def increment_ref_count(self): # As soon as we've seen the object more than once, we want to attach the # shared object ID. This allows us to only attach the shared object ID # when it's strictly necessary, making backwards compatibility breakage # less likely. if self.ref_count == 1: self[SHARED_OBJECT_KEY] = self.object_id self.ref_count += 1 class SharedObjectSavingScope: """Keeps track of shared object configs when serializing.""" def __enter__(self): if _shared_object_disabled(): return None global SHARED_OBJECT_SAVING # Serialization can happen at a number of layers for a number of # reasons. We may end up with a case where we're opening a saving scope # within another saving scope. In that case, we'd like to use the # outermost scope available and ignore inner scopes, since there is not # (yet) a reasonable use case for having these nested and distinct. if _shared_object_saving_scope() is not None: self._passthrough = True return _shared_object_saving_scope() else: self._passthrough = False SHARED_OBJECT_SAVING.scope = self self._shared_objects_config = weakref.WeakKeyDictionary() self._next_id = 0 return self def get_config(self, obj): """Gets a `SharedObjectConfig` if one has already been seen for `obj`. Args: obj: The object for which to retrieve the `SharedObjectConfig`. Returns: The SharedObjectConfig for a given object, if already seen. Else, `None`. """ try: shared_object_config = self._shared_objects_config[obj] except (TypeError, KeyError): # If the object is unhashable (e.g. a subclass of # `AbstractBaseClass` that has not overridden `__hash__`), a # `TypeError` will be thrown. We'll just continue on without shared # object support. return None shared_object_config.increment_ref_count() return shared_object_config def create_config(self, base_config, obj): """Create a new SharedObjectConfig for a given object.""" shared_object_config = SharedObjectConfig(base_config, self._next_id) self._next_id += 1 try: self._shared_objects_config[obj] = shared_object_config except TypeError: # If the object is unhashable (e.g. a subclass of # `AbstractBaseClass` that has not overridden `__hash__`), a # `TypeError` will be thrown. We'll just continue on without shared # object support. pass return shared_object_config def __exit__(self, *args, **kwargs): if not getattr(self, "_passthrough", False): global SHARED_OBJECT_SAVING SHARED_OBJECT_SAVING.scope = None def serialize_keras_class_and_config( cls_name, cls_config, obj=None, shared_object_id=None ): """Returns the serialization of the class with the given config.""" base_config = {"class_name": cls_name, "config": cls_config} # We call `serialize_keras_class_and_config` for some branches of the load # path. In that case, we may already have a shared object ID we'd like to # retain. if shared_object_id is not None: base_config[SHARED_OBJECT_KEY] = shared_object_id # If we have an active `SharedObjectSavingScope`, check whether we've # already serialized this config. If so, just use that config. This will # store an extra ID field in the config, allowing us to re-create the shared # object relationship at load time. if _shared_object_saving_scope() is not None and obj is not None: shared_object_config = _shared_object_saving_scope().get_config(obj) if shared_object_config is None: return _shared_object_saving_scope().create_config(base_config, obj) return shared_object_config return base_config @keras_export("keras.utils.register_keras_serializable") def register_keras_serializable(package="Custom", name=None): """Registers an object with the Keras serialization framework. This decorator injects the decorated class or function into the Keras custom object dictionary, so that it can be serialized and deserialized without needing an entry in the user-provided custom object dict. It also injects a function that Keras will call to get the object's serializable string key. Note that to be serialized and deserialized, classes must implement the `get_config()` method. Functions do not have this requirement. The object will be registered under the key 'package>name' where `name`, defaults to the object name if not passed. Example: ```python # Note that `'my_package'` is used as the `package` argument here, and since # the `name` argument is not provided, `'MyDense'` is used as the `name`. @keras.utils.register_keras_serializable('my_package') class MyDense(keras.layers.Dense): pass assert keras.utils.get_registered_object('my_package>MyDense') == MyDense assert keras.utils.get_registered_name(MyDense) == 'my_package>MyDense' ``` Args: package: The package that this class belongs to. This is used for the `key` (which is 'package>name') to idenfify the class. Note that this is the first argument passed into the decorator. name: The name to serialize this class under in this package. If not provided or `None`, the class' name will be used (note that this is the case when the decorator is used with only one argument, which becomes the `package`). Returns: A decorator that registers the decorated class with the passed names. """ def decorator(arg): """Registers a class with the Keras serialization framework.""" class_name = name if name is not None else arg.__name__ registered_name = package + ">" + class_name if tf_inspect.isclass(arg) and not hasattr(arg, "get_config"): raise ValueError( "Cannot register a class that does not have a " "get_config() method." ) if registered_name in _GLOBAL_CUSTOM_OBJECTS: raise ValueError( f"{registered_name} has already been registered to " f"{_GLOBAL_CUSTOM_OBJECTS[registered_name]}" ) if arg in _GLOBAL_CUSTOM_NAMES: raise ValueError( f"{arg} has already been registered to " f"{_GLOBAL_CUSTOM_NAMES[arg]}" ) _GLOBAL_CUSTOM_OBJECTS[registered_name] = arg _GLOBAL_CUSTOM_NAMES[arg] = registered_name return arg return decorator @keras_export("keras.utils.get_registered_name") def get_registered_name(obj): """Returns the name registered to an object within the Keras framework. This function is part of the Keras serialization and deserialization framework. It maps objects to the string names associated with those objects for serialization/deserialization. Args: obj: The object to look up. Returns: The name associated with the object, or the default Python name if the object is not registered. """ if obj in _GLOBAL_CUSTOM_NAMES: return _GLOBAL_CUSTOM_NAMES[obj] else: return obj.__name__ @tf_contextlib.contextmanager def skip_failed_serialization(): global _SKIP_FAILED_SERIALIZATION prev = _SKIP_FAILED_SERIALIZATION try: _SKIP_FAILED_SERIALIZATION = True yield finally: _SKIP_FAILED_SERIALIZATION = prev @keras_export("keras.utils.get_registered_object") def get_registered_object(name, custom_objects=None, module_objects=None): """Returns the class associated with `name` if it is registered with Keras. This function is part of the Keras serialization and deserialization framework. It maps strings to the objects associated with them for serialization/deserialization. Example: ``` def from_config(cls, config, custom_objects=None): if 'my_custom_object_name' in config: config['hidden_cls'] = tf.keras.utils.get_registered_object( config['my_custom_object_name'], custom_objects=custom_objects) ``` Args: name: The name to look up. custom_objects: A dictionary of custom objects to look the name up in. Generally, custom_objects is provided by the user. module_objects: A dictionary of custom objects to look the name up in. Generally, module_objects is provided by midlevel library implementers. Returns: An instantiable class associated with 'name', or None if no such class exists. """ if name in _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__: return _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__[name] elif name in _GLOBAL_CUSTOM_OBJECTS: return _GLOBAL_CUSTOM_OBJECTS[name] elif custom_objects and name in custom_objects: return custom_objects[name] elif module_objects and name in module_objects: return module_objects[name] return None class CustomMaskWarning(Warning): pass @keras_export("keras.utils.serialize_keras_object") def serialize_keras_object(instance): """Serialize a Keras object into a JSON-compatible representation. Calls to `serialize_keras_object` while underneath the `SharedObjectSavingScope` context manager will cause any objects re-used across multiple layers to be saved with a special shared object ID. This allows the network to be re-created properly during deserialization. Args: instance: The object to serialize. Returns: A dict-like, JSON-compatible representation of the object's config. """ _, instance = tf.__internal__.decorator.unwrap(instance) if instance is None: return None # # For v1 layers, checking supports_masking is not enough. We have to also # check whether compute_mask has been overridden. supports_masking = getattr(instance, "supports_masking", False) or ( hasattr(instance, "compute_mask") and not is_default(instance.compute_mask) ) if supports_masking and is_default(instance.get_config): warnings.warn( "Custom mask layers require a config and must override " "get_config. When loading, the custom mask layer must be " "passed to the custom_objects argument.", category=CustomMaskWarning, stacklevel=2, ) if hasattr(instance, "get_config"): name = get_registered_name(instance.__class__) try: config = instance.get_config() except NotImplementedError as e: if _SKIP_FAILED_SERIALIZATION: return serialize_keras_class_and_config( name, {_LAYER_UNDEFINED_CONFIG_KEY: True} ) raise e serialization_config = {} for key, item in config.items(): if isinstance(item, str): serialization_config[key] = item continue # Any object of a different type needs to be converted to string or # dict for serialization (e.g. custom functions, custom classes) try: serialized_item = serialize_keras_object(item) if isinstance(serialized_item, dict) and not isinstance( item, dict ): serialized_item["__passive_serialization__"] = True serialization_config[key] = serialized_item except ValueError: serialization_config[key] = item name = get_registered_name(instance.__class__) return serialize_keras_class_and_config( name, serialization_config, instance ) if hasattr(instance, "__name__"): return get_registered_name(instance) raise ValueError( f"Cannot serialize {instance} since it doesn't implement " "`get_config()`, and also doesn\t have `__name__`" ) def get_custom_objects_by_name(item, custom_objects=None): """Returns the item if it is in either local or global custom objects.""" if item in _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__: return _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__[item] elif item in _GLOBAL_CUSTOM_OBJECTS: return _GLOBAL_CUSTOM_OBJECTS[item] elif custom_objects and item in custom_objects: return custom_objects[item] return None def class_and_config_for_serialized_keras_object( config, module_objects=None, custom_objects=None, printable_module_name="object", ): """Returns the class name and config for a serialized keras object.""" if ( not isinstance(config, dict) or "class_name" not in config or "config" not in config ): raise ValueError( f"Improper config format for {config}. " "Expecting python dict contains `class_name` and `config` as keys" ) class_name = config["class_name"] cls = get_registered_object(class_name, custom_objects, module_objects) if cls is None: raise ValueError( f"Unknown {printable_module_name}: {class_name}. " "Please ensure this " "object is passed to the `custom_objects` argument. See " "https://www.tensorflow.org/guide/keras/save_and_serialize" "#registering_the_custom_object for details." ) cls_config = config["config"] # Check if `cls_config` is a list. If it is a list, return the class and the # associated class configs for recursively deserialization. This case will # happen on the old version of sequential model (e.g. `keras_version` == # "2.0.6"), which is serialized in a different structure, for example # "{'class_name': 'Sequential', # 'config': [{'class_name': 'Embedding', 'config': ...}, {}, ...]}". if isinstance(cls_config, list): return (cls, cls_config) deserialized_objects = {} for key, item in cls_config.items(): if key == "name": # Assume that the value of 'name' is a string that should not be # deserialized as a function. This avoids the corner case where # cls_config['name'] has an identical name to a custom function and # gets converted into that function. deserialized_objects[key] = item elif isinstance(item, dict) and "__passive_serialization__" in item: deserialized_objects[key] = deserialize_keras_object( item, module_objects=module_objects, custom_objects=custom_objects, printable_module_name="config_item", ) # TODO(momernick): Should this also have 'module_objects'? elif isinstance(item, str) and tf_inspect.isfunction( get_registered_object(item, custom_objects) ): # Handle custom functions here. When saving functions, we only save # the function's name as a string. If we find a matching string in # the custom objects during deserialization, we convert the string # back to the original function. # Note that a potential issue is that a string field could have a # naming conflict with a custom function name, but this should be a # rare case. This issue does not occur if a string field has a # naming conflict with a custom object, since the config of an # object will always be a dict. deserialized_objects[key] = get_registered_object( item, custom_objects ) for key, item in deserialized_objects.items(): cls_config[key] = deserialized_objects[key] return (cls, cls_config) @keras_export("keras.utils.deserialize_keras_object") def deserialize_keras_object( identifier, module_objects=None, custom_objects=None, printable_module_name="object", ): """Turns the serialized form of a Keras object back into an actual object. This function is for mid-level library implementers rather than end users. Importantly, this utility requires you to provide the dict of `module_objects` to use for looking up the object config; this is not populated by default. If you need a deserialization utility that has preexisting knowledge of built-in Keras objects, use e.g. `keras.layers.deserialize(config)`, `keras.metrics.deserialize(config)`, etc. Calling `deserialize_keras_object` while underneath the `SharedObjectLoadingScope` context manager will cause any already-seen shared objects to be returned as-is rather than creating a new object. Args: identifier: the serialized form of the object. module_objects: A dictionary of built-in objects to look the name up in. Generally, `module_objects` is provided by midlevel library implementers. custom_objects: A dictionary of custom objects to look the name up in. Generally, `custom_objects` is provided by the end user. printable_module_name: A human-readable string representing the type of the object. Printed in case of exception. Returns: The deserialized object. Example: A mid-level library implementer might want to implement a utility for retrieving an object from its config, as such: ```python def deserialize(config, custom_objects=None): return deserialize_keras_object( identifier, module_objects=globals(), custom_objects=custom_objects, name="MyObjectType", ) ``` This is how e.g. `keras.layers.deserialize()` is implemented. """ if identifier is None: return None if isinstance(identifier, dict): # In this case we are dealing with a Keras config dictionary. config = identifier (cls, cls_config) = class_and_config_for_serialized_keras_object( config, module_objects, custom_objects, printable_module_name ) # If this object has already been loaded (i.e. it's shared between # multiple objects), return the already-loaded object. shared_object_id = config.get(SHARED_OBJECT_KEY) shared_object = _shared_object_loading_scope().get(shared_object_id) if shared_object is not None: return shared_object if hasattr(cls, "from_config"): arg_spec = tf_inspect.getfullargspec(cls.from_config) custom_objects = custom_objects or {} if "custom_objects" in arg_spec.args: deserialized_obj = cls.from_config( cls_config, custom_objects=dict( list(_GLOBAL_CUSTOM_OBJECTS.items()) + list(_THREAD_LOCAL_CUSTOM_OBJECTS.__dict__.items()) + list(custom_objects.items()) ), ) else: with CustomObjectScope(custom_objects): deserialized_obj = cls.from_config(cls_config) else: # Then `cls` may be a function returning a class. # in this case by convention `config` holds # the kwargs of the function. custom_objects = custom_objects or {} with CustomObjectScope(custom_objects): deserialized_obj = cls(**cls_config) # Add object to shared objects, in case we find it referenced again. _shared_object_loading_scope().set(shared_object_id, deserialized_obj) return deserialized_obj elif isinstance(identifier, str): object_name = identifier if custom_objects and object_name in custom_objects: obj = custom_objects.get(object_name) elif object_name in _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__: obj = _THREAD_LOCAL_CUSTOM_OBJECTS.__dict__[object_name] elif object_name in _GLOBAL_CUSTOM_OBJECTS: obj = _GLOBAL_CUSTOM_OBJECTS[object_name] else: obj = module_objects.get(object_name) if obj is None: raise ValueError( f"Unknown {printable_module_name}: {object_name}. Please " "ensure this object is passed to the `custom_objects` " "argument. See " "https://www.tensorflow.org/guide/keras/save_and_serialize" "#registering_the_custom_object for details." ) # Classes passed by name are instantiated with no args, functions are # returned as-is. if tf_inspect.isclass(obj): return obj() return obj elif tf_inspect.isfunction(identifier): # If a function has already been deserialized, return as is. return identifier else: raise ValueError( f"Could not interpret serialized " f"{printable_module_name}: {identifier}" ) def func_dump(func): """Serializes a user defined function. Args: func: the function to serialize. Returns: A tuple `(code, defaults, closure)`. """ if os.name == "nt": raw_code = marshal.dumps(func.__code__).replace(b"\\", b"/") code = codecs.encode(raw_code, "base64").decode("ascii") else: raw_code = marshal.dumps(func.__code__) code = codecs.encode(raw_code, "base64").decode("ascii") defaults = func.__defaults__ if func.__closure__: closure = tuple(c.cell_contents for c in func.__closure__) else: closure = None return code, defaults, closure def func_load(code, defaults=None, closure=None, globs=None): """Deserializes a user defined function. Args: code: bytecode of the function. defaults: defaults of the function. closure: closure of the function. globs: dictionary of global objects. Returns: A function object. """ if isinstance(code, (tuple, list)): # unpack previous dump code, defaults, closure = code if isinstance(defaults, list): defaults = tuple(defaults) def ensure_value_to_cell(value): """Ensures that a value is converted to a python cell object. Args: value: Any value that needs to be casted to the cell type Returns: A value wrapped as a cell object (see function "func_load") """ def dummy_fn(): value # just access it so it gets captured in .__closure__ cell_value = dummy_fn.__closure__[0] if not isinstance(value, type(cell_value)): return cell_value return value if closure is not None: closure = tuple(ensure_value_to_cell(_) for _ in closure) try: raw_code = codecs.decode(code.encode("ascii"), "base64") except (UnicodeEncodeError, binascii.Error): raw_code = code.encode("raw_unicode_escape") code = marshal.loads(raw_code) if globs is None: globs = globals() return python_types.FunctionType( code, globs, name=code.co_name, argdefs=defaults, closure=closure ) def has_arg(fn, name, accept_all=False): """Checks if a callable accepts a given keyword argument. Args: fn: Callable to inspect. name: Check if `fn` can be called with `name` as a keyword argument. accept_all: What to return if there is no parameter called `name` but the function accepts a `**kwargs` argument. Returns: bool, whether `fn` accepts a `name` keyword argument. """ arg_spec = tf_inspect.getfullargspec(fn) if accept_all and arg_spec.varkw is not None: return True return name in arg_spec.args or name in arg_spec.kwonlyargs @keras_export("keras.utils.Progbar") class Progbar: """Displays a progress bar. Args: target: Total number of steps expected, None if unknown. width: Progress bar width on screen. verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose) stateful_metrics: Iterable of string names of metrics that should *not* be averaged over time. Metrics in this list will be displayed as-is. All others will be averaged by the progbar before display. interval: Minimum visual progress update interval (in seconds). unit_name: Display name for step counts (usually "step" or "sample"). """ def __init__( self, target, width=30, verbose=1, interval=0.05, stateful_metrics=None, unit_name="step", ): self.target = target self.width = width self.verbose = verbose self.interval = interval self.unit_name = unit_name if stateful_metrics: self.stateful_metrics = set(stateful_metrics) else: self.stateful_metrics = set() self._dynamic_display = ( (hasattr(sys.stdout, "isatty") and sys.stdout.isatty()) or "ipykernel" in sys.modules or "posix" in sys.modules or "PYCHARM_HOSTED" in os.environ ) self._total_width = 0 self._seen_so_far = 0 # We use a dict + list to avoid garbage collection # issues found in OrderedDict self._values = {} self._values_order = [] self._start = time.time() self._last_update = 0 self._time_at_epoch_start = self._start self._time_at_epoch_end = None self._time_after_first_step = None def update(self, current, values=None, finalize=None): """Updates the progress bar. Args: current: Index of current step. values: List of tuples: `(name, value_for_last_step)`. If `name` is in `stateful_metrics`, `value_for_last_step` will be displayed as-is. Else, an average of the metric over time will be displayed. finalize: Whether this is the last update for the progress bar. If `None`, defaults to `current >= self.target`. """ if finalize is None: if self.target is None: finalize = False else: finalize = current >= self.target values = values or [] for k, v in values: if k not in self._values_order: self._values_order.append(k) if k not in self.stateful_metrics: # In the case that progress bar doesn't have a target value in # the first epoch, both on_batch_end and on_epoch_end will be # called, which will cause 'current' and 'self._seen_so_far' to # have the same value. Force the minimal value to 1 here, # otherwise stateful_metric will be 0s. value_base = max(current - self._seen_so_far, 1) if k not in self._values: self._values[k] = [v * value_base, value_base] else: self._values[k][0] += v * value_base self._values[k][1] += value_base else: # Stateful metrics output a numeric value. This representation # means "take an average from a single value" but keeps the # numeric formatting. self._values[k] = [v, 1] self._seen_so_far = current message = "" now = time.time() info = " - %.0fs" % (now - self._start) if current == self.target: self._time_at_epoch_end = now if self.verbose == 1: if now - self._last_update < self.interval and not finalize: return prev_total_width = self._total_width if self._dynamic_display: message += "\b" * prev_total_width message += "\r" else: message += "\n" if self.target is not None: numdigits = int(np.log10(self.target)) + 1 bar = ("%" + str(numdigits) + "d/%d [") % (current, self.target) prog = float(current) / self.target prog_width = int(self.width * prog) if prog_width > 0: bar += "=" * (prog_width - 1) if current < self.target: bar += ">" else: bar += "=" bar += "." * (self.width - prog_width) bar += "]" else: bar = "%7d/Unknown" % current self._total_width = len(bar) message += bar time_per_unit = self._estimate_step_duration(current, now) if self.target is None or finalize: info += self._format_time(time_per_unit, self.unit_name) else: eta = time_per_unit * (self.target - current) if eta > 3600: eta_format = "%d:%02d:%02d" % ( eta // 3600, (eta % 3600) // 60, eta % 60, ) elif eta > 60: eta_format = "%d:%02d" % (eta // 60, eta % 60) else: eta_format = "%ds" % eta info = " - ETA: %s" % eta_format for k in self._values_order: info += " - %s:" % k if isinstance(self._values[k], list): avg = np.mean( self._values[k][0] / max(1, self._values[k][1]) ) if abs(avg) > 1e-3: info += " %.4f" % avg else: info += " %.4e" % avg else: info += " %s" % self._values[k] self._total_width += len(info) if prev_total_width > self._total_width: info += " " * (prev_total_width - self._total_width) if finalize: info += "\n" message += info io_utils.print_msg(message, line_break=False) message = "" elif self.verbose == 2: if finalize: numdigits = int(np.log10(self.target)) + 1 count = ("%" + str(numdigits) + "d/%d") % (current, self.target) info = count + info for k in self._values_order: info += " - %s:" % k avg = np.mean( self._values[k][0] / max(1, self._values[k][1]) ) if avg > 1e-3: info += " %.4f" % avg else: info += " %.4e" % avg if self._time_at_epoch_end: time_per_epoch = ( self._time_at_epoch_end - self._time_at_epoch_start ) avg_time_per_step = time_per_epoch / self.target self._time_at_epoch_start = now self._time_at_epoch_end = None info += " -" + self._format_time(time_per_epoch, "epoch") info += " -" + self._format_time( avg_time_per_step, self.unit_name ) info += "\n" message += info io_utils.print_msg(message, line_break=False) message = "" self._last_update = now def add(self, n, values=None): self.update(self._seen_so_far + n, values) def _format_time(self, time_per_unit, unit_name): """format a given duration to display to the user. Given the duration, this function formats it in either milliseconds or seconds and displays the unit (i.e. ms/step or s/epoch) Args: time_per_unit: the duration to display unit_name: the name of the unit to display Returns: a string with the correctly formatted duration and units """ formatted = "" if time_per_unit >= 1 or time_per_unit == 0: formatted += " %.0fs/%s" % (time_per_unit, unit_name) elif time_per_unit >= 1e-3: formatted += " %.0fms/%s" % (time_per_unit * 1e3, unit_name) else: formatted += " %.0fus/%s" % (time_per_unit * 1e6, unit_name) return formatted def _estimate_step_duration(self, current, now): """Estimate the duration of a single step. Given the step number `current` and the corresponding time `now` this function returns an estimate for how long a single step takes. If this is called before one step has been completed (i.e. `current == 0`) then zero is given as an estimate. The duration estimate ignores the duration of the (assumed to be non-representative) first step for estimates when more steps are available (i.e. `current>1`). Args: current: Index of current step. now: The current time. Returns: Estimate of the duration of a single step. """ if current: # there are a few special scenarios here: # 1) somebody is calling the progress bar without ever supplying # step 1 # 2) somebody is calling the progress bar and supplies step one # multiple times, e.g. as part of a finalizing call # in these cases, we just fall back to the simple calculation if self._time_after_first_step is not None and current > 1: time_per_unit = (now - self._time_after_first_step) / ( current - 1 ) else: time_per_unit = (now - self._start) / current if current == 1: self._time_after_first_step = now return time_per_unit else: return 0 def _update_stateful_metrics(self, stateful_metrics): self.stateful_metrics = self.stateful_metrics.union(stateful_metrics) def make_batches(size, batch_size): """Returns a list of batch indices (tuples of indices). Args: size: Integer, total size of the data to slice into batches. batch_size: Integer, batch size. Returns: A list of tuples of array indices. """ num_batches = int(np.ceil(size / float(batch_size))) return [ (i * batch_size, min(size, (i + 1) * batch_size)) for i in range(0, num_batches) ] def slice_arrays(arrays, start=None, stop=None): """Slice an array or list of arrays. This takes an array-like, or a list of array-likes, and outputs: - arrays[start:stop] if `arrays` is an array-like - [x[start:stop] for x in arrays] if `arrays` is a list Can also work on list/array of indices: `slice_arrays(x, indices)` Args: arrays: Single array or list of arrays. start: can be an integer index (start index) or a list/array of indices stop: integer (stop index); should be None if `start` was a list. Returns: A slice of the array(s). Raises: ValueError: If the value of start is a list and stop is not None. """ if arrays is None: return [None] if isinstance(start, list) and stop is not None: raise ValueError( "The stop argument has to be None if the value of start " f"is a list. Received start={start}, stop={stop}" ) elif isinstance(arrays, list): if hasattr(start, "__len__"): # hdf5 datasets only support list objects as indices if hasattr(start, "shape"): start = start.tolist() return [None if x is None else x[start] for x in arrays] return [ None if x is None else None if not hasattr(x, "__getitem__") else x[start:stop] for x in arrays ] else: if hasattr(start, "__len__"): if hasattr(start, "shape"): start = start.tolist() return arrays[start] if hasattr(start, "__getitem__"): return arrays[start:stop] return [None] def to_list(x): """Normalizes a list/tensor into a list. If a tensor is passed, we return a list of size 1 containing the tensor. Args: x: target object to be normalized. Returns: A list. """ if isinstance(x, list): return x return [x] def to_snake_case(name): intermediate = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name) insecure = re.sub("([a-z])([A-Z])", r"\1_\2", intermediate).lower() # If the class is private the name starts with "_" which is not secure # for creating scopes. We prefix the name with "private" in this case. if insecure[0] != "_": return insecure return "private" + insecure def is_all_none(structure): iterable = tf.nest.flatten(structure) # We cannot use Python's `any` because the iterable may return Tensors. for element in iterable: if element is not None: return False return True def check_for_unexpected_keys(name, input_dict, expected_values): unknown = set(input_dict.keys()).difference(expected_values) if unknown: raise ValueError( f"Unknown entries in {name} dictionary: {list(unknown)}. " f"Only expected following keys: {expected_values}" ) def validate_kwargs( kwargs, allowed_kwargs, error_message="Keyword argument not understood:" ): """Checks that all keyword arguments are in the set of allowed keys.""" for kwarg in kwargs: if kwarg not in allowed_kwargs: raise TypeError(error_message, kwarg) def validate_config(config): """Determines whether config appears to be a valid layer config.""" return ( isinstance(config, dict) and _LAYER_UNDEFINED_CONFIG_KEY not in config ) def default(method): """Decorates a method to detect overrides in subclasses.""" method._is_default = True return method def is_default(method): """Check if a method is decorated with the `default` wrapper.""" return getattr(method, "_is_default", False) def populate_dict_with_module_objects(target_dict, modules, obj_filter): for module in modules: for name in dir(module): obj = getattr(module, name) if obj_filter(obj): target_dict[name] = obj class LazyLoader(python_types.ModuleType): """Lazily import a module, mainly to avoid pulling in large dependencies.""" def __init__(self, local_name, parent_module_globals, name): self._local_name = local_name self._parent_module_globals = parent_module_globals super().__init__(name) def _load(self): """Load the module and insert it into the parent's globals.""" # Import the target module and insert it into the parent's namespace module = importlib.import_module(self.__name__) self._parent_module_globals[self._local_name] = module # Update this object's dict so that if someone keeps a reference to the # LazyLoader, lookups are efficient (__getattr__ is only called on # lookups that fail). self.__dict__.update(module.__dict__) return module def __getattr__(self, item): module = self._load() return getattr(module, item) # Aliases custom_object_scope = CustomObjectScope