415 lines
16 KiB
Python
415 lines
16 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains the Lambda layer."""
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import sys
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import textwrap
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import types as python_types
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import warnings
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import numpy as np
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import tensorflow.compat.v2 as tf
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from keras.engine.base_layer import Layer
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from keras.saving import serialization_lib
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from keras.saving.legacy import serialization as legacy_serialization
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from keras.utils import generic_utils
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from keras.utils import tf_inspect
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from keras.utils import tf_utils
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# isort: off
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from tensorflow.python.platform import tf_logging
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.Lambda")
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class Lambda(Layer):
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"""Wraps arbitrary expressions as a `Layer` object.
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The `Lambda` layer exists so that arbitrary expressions can be used
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as a `Layer` when constructing `Sequential`
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and Functional API models. `Lambda` layers are best suited for simple
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operations or quick experimentation. For more advanced use cases, follow
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[this guide](
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https://www.tensorflow.org/guide/keras/custom_layers_and_models)
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for subclassing `tf.keras.layers.Layer`.
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WARNING: `tf.keras.layers.Lambda` layers have (de)serialization limitations!
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The main reason to subclass `tf.keras.layers.Layer` instead of using a
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`Lambda` layer is saving and inspecting a Model. `Lambda` layers
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are saved by serializing the Python bytecode, which is fundamentally
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non-portable. They should only be loaded in the same environment where
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they were saved. Subclassed layers can be saved in a more portable way
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by overriding their `get_config` method. Models that rely on
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subclassed Layers are also often easier to visualize and reason about.
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Examples:
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```python
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# add a x -> x^2 layer
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model.add(Lambda(lambda x: x ** 2))
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```
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```python
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# add a layer that returns the concatenation
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# of the positive part of the input and
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# the opposite of the negative part
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def antirectifier(x):
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x -= K.mean(x, axis=1, keepdims=True)
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x = K.l2_normalize(x, axis=1)
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pos = K.relu(x)
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neg = K.relu(-x)
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return K.concatenate([pos, neg], axis=1)
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model.add(Lambda(antirectifier))
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```
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Variables:
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While it is possible to use Variables with Lambda layers, this practice is
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discouraged as it can easily lead to bugs. For instance, consider the
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following layer:
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```python
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scale = tf.Variable(1.)
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scale_layer = tf.keras.layers.Lambda(lambda x: x * scale)
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```
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Because scale_layer does not directly track the `scale` variable, it will
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not appear in `scale_layer.trainable_weights` and will therefore not be
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trained if `scale_layer` is used in a Model.
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A better pattern is to write a subclassed Layer:
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```python
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class ScaleLayer(tf.keras.layers.Layer):
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def __init__(self):
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super(ScaleLayer, self).__init__()
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self.scale = tf.Variable(1.)
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def call(self, inputs):
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return inputs * self.scale
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```
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In general, Lambda layers can be convenient for simple stateless
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computation, but anything more complex should use a subclass Layer
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instead.
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Args:
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function: The function to be evaluated. Takes input tensor as first
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argument.
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output_shape: Expected output shape from function. This argument can be
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inferred if not explicitly provided. Can be a tuple or function. If a
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tuple, it only specifies the first dimension onward;
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sample dimension is assumed either the same as the input:
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`output_shape = (input_shape[0], ) + output_shape` or, the input is
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`None` and the sample dimension is also `None`:
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`output_shape = (None, ) + output_shape` If a function, it specifies the
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entire shape as a function of the input shape:
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`output_shape = f(input_shape)`
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mask: Either None (indicating no masking) or a callable with the same
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signature as the `compute_mask` layer method, or a tensor that will be
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returned as output mask regardless of what the input is.
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arguments: Optional dictionary of keyword arguments to be passed to the
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function.
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Input shape: Arbitrary. Use the keyword argument input_shape (tuple of
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integers, does not include the samples axis) when using this layer as the
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first layer in a model.
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Output shape: Specified by `output_shape` argument
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"""
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@tf.__internal__.tracking.no_automatic_dependency_tracking
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def __init__(
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self, function, output_shape=None, mask=None, arguments=None, **kwargs
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):
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super().__init__(**kwargs)
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self.arguments = arguments or {}
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self.function = function
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if mask is not None:
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self.supports_masking = True
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self.mask = mask
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self._output_shape = output_shape
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# Warning on every invocation will be quite irksome in Eager mode.
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self._already_warned = False
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function_args = tf_inspect.getfullargspec(function).args
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self._fn_expects_training_arg = "training" in function_args
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self._fn_expects_mask_arg = "mask" in function_args
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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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if self._output_shape is None:
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# Make use of existing autocomputation but provide Lambda-specific
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# error message. This is always safe to run even when the outer
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# context is Graph mode because Lambda layers don't have side
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# effects such as `add_loss`.
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with tf.__internal__.eager_context.eager_mode():
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try:
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return super().compute_output_shape(input_shape)
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except NotImplementedError:
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raise NotImplementedError(
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"We could not automatically infer the shape of "
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"the Lambda's output. Please specify `output_shape` "
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"for this Lambda."
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)
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if callable(self._output_shape):
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output_shapes = self._output_shape(input_shape)
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return tf_utils.convert_shapes(output_shapes, to_tuples=False)
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# Output shapes are passed directly and don't include batch dimension.
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input_tensor_shape = tf_utils.convert_shapes(
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input_shape, to_tuples=False
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)
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batch_size = (
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tf.nest.flatten(input_tensor_shape)[0][0] if input_shape else None
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)
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def _add_batch(shape):
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return tf.TensorShape([batch_size] + shape.as_list())
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output_shapes = tf_utils.convert_shapes(
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self._output_shape, to_tuples=False
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)
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return tf.nest.map_structure(_add_batch, output_shapes)
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def call(self, inputs, mask=None, training=None):
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# We must copy for thread safety, but it only needs to be a shallow
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# copy.
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kwargs = {k: v for k, v in self.arguments.items()}
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if self._fn_expects_mask_arg:
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kwargs["mask"] = mask
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if self._fn_expects_training_arg:
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kwargs["training"] = training
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created_variables = []
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def _variable_creator(next_creator, **kwargs):
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var = next_creator(**kwargs)
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created_variables.append(var)
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return var
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with tf.GradientTape(
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watch_accessed_variables=True
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) as tape, tf.variable_creator_scope(_variable_creator):
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result = self.function(inputs, **kwargs)
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self._check_variables(created_variables, tape.watched_variables())
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return result
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def _check_variables(self, created_variables, accessed_variables):
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if not created_variables and not accessed_variables:
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# In the common case that a Lambda layer does not touch a Variable,
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# we don't want to incur the runtime cost of assembling any state
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# used for checking only to immediately discard it.
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return
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# Filter out the state variable in the tf.random.Generator, which is
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# commonly used for initializer or droput. The variable is intentionally
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# not tracked and it is not a trainable variable.
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created_variables = [
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v for v in created_variables if "StateVar" not in v.name
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]
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tracked_weights = set(v.ref() for v in self.weights)
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untracked_new_vars = [
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v for v in created_variables if v.ref() not in tracked_weights
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]
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if untracked_new_vars:
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variable_str = "\n".join(f" {i}" for i in untracked_new_vars)
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error_str = textwrap.dedent(
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"""
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The following Variables were created within a Lambda layer ({name})
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but are not tracked by said layer:
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{variable_str}
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The layer cannot safely ensure proper Variable reuse across multiple
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calls, and consequently this behavior is disallowed for safety. Lambda
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layers are not well suited to stateful computation; instead, writing a
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subclassed Layer is the recommend way to define layers with
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Variables."""
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).format(name=self.name, variable_str=variable_str)
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raise ValueError(error_str)
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untracked_used_vars = [
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v for v in accessed_variables if v.ref() not in tracked_weights
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]
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if untracked_used_vars and not self._already_warned:
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variable_str = "\n".join(f" {i}" for i in untracked_used_vars)
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self._warn(
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textwrap.dedent(
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"""
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The following Variables were used a Lambda layer's call ({name}), but
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are not present in its tracked objects:
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{variable_str}
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It is possible that this is intended behavior, but it is more likely
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an omission. This is a strong indication that this layer should be
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formulated as a subclassed Layer rather than a Lambda layer."""
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).format(name=self.name, variable_str=variable_str)
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)
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self._already_warned = True
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def _warn(self, msg):
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# This method will be overridden in a unit test to raise an error,
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# because self.assertWarns is not universally implemented.
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return tf_logging.warning(msg)
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def compute_mask(self, inputs, mask=None):
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if callable(self.mask):
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return self.mask(inputs, mask)
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return self.mask
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def get_config(self):
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function_config = self._serialize_function_to_config(self.function)
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output_shape_config = self._serialize_function_to_config(
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self._output_shape, allow_raw=True
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)
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config = {
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"function": function_config[0],
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"function_type": function_config[1],
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"module": function_config[2],
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"output_shape": output_shape_config[0],
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"output_shape_type": output_shape_config[1],
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"output_shape_module": output_shape_config[2],
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}
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if self.mask is not None:
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mask_config = self._serialize_function_to_config(self.mask)
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config.update(
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{
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"mask": mask_config[0],
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"mask_type": mask_config[1],
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"mask_module": mask_config[2],
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}
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)
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config["arguments"] = self.arguments
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def _serialize_function_to_config(self, inputs, allow_raw=False):
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if isinstance(inputs, python_types.LambdaType):
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output = generic_utils.func_dump(inputs)
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output_type = "lambda"
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module = inputs.__module__
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elif callable(inputs):
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output = inputs.__name__
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output_type = "function"
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module = inputs.__module__
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elif allow_raw:
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output = inputs
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output_type = "raw"
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module = None
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else:
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raise ValueError(
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f"Invalid input for serialization, type: {type(inputs)} "
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)
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return output, output_type, module
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@classmethod
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def from_config(cls, config, custom_objects=None):
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config = config.copy()
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function = cls._parse_function_from_config(
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config, custom_objects, "function", "module", "function_type"
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)
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output_shape = cls._parse_function_from_config(
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config,
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custom_objects,
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"output_shape",
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"output_shape_module",
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"output_shape_type",
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)
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if "mask" in config:
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mask = cls._parse_function_from_config(
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config, custom_objects, "mask", "mask_module", "mask_type"
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)
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else:
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mask = None
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config["function"] = function
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config["output_shape"] = output_shape
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config["mask"] = mask
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# If arguments were numpy array, they have been saved as
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# list. We need to recover the ndarray
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if "arguments" in config:
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for key in config["arguments"]:
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if isinstance(config["arguments"][key], dict):
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arg_dict = config["arguments"][key]
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if "type" in arg_dict and arg_dict["type"] == "ndarray":
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# Overwrite the argument with its numpy translation
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config["arguments"][key] = np.array(arg_dict["value"])
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return cls(**config)
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@classmethod
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def _parse_function_from_config(
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cls,
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config,
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custom_objects,
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func_attr_name,
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module_attr_name,
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func_type_attr_name,
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):
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globs = globals().copy()
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module = config.pop(module_attr_name, None)
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if module in sys.modules:
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globs.update(sys.modules[module].__dict__)
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elif module is not None:
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# Note: we don't know the name of the function if it's a lambda.
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warnings.warn(
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"{} is not loaded, but a Lambda layer uses it. "
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"It may cause errors.".format(module),
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UserWarning,
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stacklevel=2,
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)
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if custom_objects:
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globs.update(custom_objects)
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function_type = config.pop(func_type_attr_name)
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if function_type == "function":
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# Simple lookup in custom objects
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function = legacy_serialization.deserialize_keras_object(
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config[func_attr_name],
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custom_objects=custom_objects,
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printable_module_name="function in Lambda layer",
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)
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elif function_type == "lambda":
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if serialization_lib.in_safe_mode():
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raise ValueError(
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"Requested the deserialization of a Lambda layer with a "
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"Python `lambda` inside it. "
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"This carries a potential risk of arbitrary code execution "
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"and thus it is disallowed by default. If you trust the "
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"source of the saved model, you can pass `safe_mode=False` "
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"to the loading function in order to allow "
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"Lambda layer loading."
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)
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# /!\ Unsafe deserialization from bytecode! Danger! /!\
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function = generic_utils.func_load(
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config[func_attr_name], globs=globs
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)
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elif function_type == "raw":
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function = config[func_attr_name]
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else:
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supported_types = ["function", "lambda", "raw"]
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raise TypeError(
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"Unsupported value for `function_type` argument. Received: "
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f"function_type={function_type}. "
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f"Expected one of {supported_types}"
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)
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return function
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