317 lines
12 KiB
Python
317 lines
12 KiB
Python
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# Copyright 2018 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 InputSpec class."""
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import tensorflow.compat.v2 as tf
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from keras import backend
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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from tensorflow.python.util.tf_export import tf_export
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@keras_export(
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"keras.layers.InputSpec",
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v1=["keras.layers.InputSpec", "keras.__internal__.legacy.layers.InputSpec"],
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)
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@tf_export(v1=["layers.InputSpec"])
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class InputSpec:
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"""Specifies the rank, dtype and shape of every input to a layer.
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Layers can expose (if appropriate) an `input_spec` attribute:
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an instance of `InputSpec`, or a nested structure of `InputSpec` instances
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(one per input tensor). These objects enable the layer to run input
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compatibility checks for input structure, input rank, input shape, and
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input dtype.
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A None entry in a shape is compatible with any dimension,
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a None shape is compatible with any shape.
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Args:
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dtype: Expected DataType of the input.
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shape: Shape tuple, expected shape of the input
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(may include None for unchecked axes). Includes the batch size.
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ndim: Integer, expected rank of the input.
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max_ndim: Integer, maximum rank of the input.
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min_ndim: Integer, minimum rank of the input.
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axes: Dictionary mapping integer axes to
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a specific dimension value.
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allow_last_axis_squeeze: If True, then allow inputs of rank N+1 as long
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as the last axis of the input is 1, as well as inputs of rank N-1
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as long as the last axis of the spec is 1.
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name: Expected key corresponding to this input when passing data as
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a dictionary.
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Example:
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```python
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class MyLayer(Layer):
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def __init__(self):
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super(MyLayer, self).__init__()
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# The layer will accept inputs with
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# shape (?, 28, 28) & (?, 28, 28, 1)
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# and raise an appropriate error message otherwise.
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self.input_spec = InputSpec(
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shape=(None, 28, 28, 1),
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allow_last_axis_squeeze=True)
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```
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"""
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def __init__(
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self,
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dtype=None,
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shape=None,
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ndim=None,
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max_ndim=None,
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min_ndim=None,
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axes=None,
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allow_last_axis_squeeze=False,
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name=None,
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):
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self.dtype = tf.as_dtype(dtype).name if dtype is not None else None
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shape = tf.TensorShape(shape)
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if shape.rank is None:
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shape = None
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else:
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shape = tuple(shape.as_list())
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if shape is not None:
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self.ndim = len(shape)
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self.shape = shape
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else:
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self.ndim = ndim
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self.shape = None
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self.max_ndim = max_ndim
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self.min_ndim = min_ndim
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self.name = name
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self.allow_last_axis_squeeze = allow_last_axis_squeeze
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try:
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axes = axes or {}
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self.axes = {int(k): axes[k] for k in axes}
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except (ValueError, TypeError):
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raise TypeError(
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"Argument `axes` must be a dict with integer keys. "
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f"Received: axes={axes}"
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)
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if self.axes and (self.ndim is not None or self.max_ndim is not None):
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max_dim = (self.ndim if self.ndim else self.max_ndim) - 1
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max_axis = max(self.axes)
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if max_axis > max_dim:
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raise ValueError(
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"Axis {} is greater than the maximum "
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"allowed value: {}".format(max_axis, max_dim)
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)
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def __repr__(self):
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spec = [
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("dtype=" + str(self.dtype)) if self.dtype else "",
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("shape=" + str(self.shape)) if self.shape else "",
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("ndim=" + str(self.ndim)) if self.ndim else "",
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("max_ndim=" + str(self.max_ndim)) if self.max_ndim else "",
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("min_ndim=" + str(self.min_ndim)) if self.min_ndim else "",
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("axes=" + str(self.axes)) if self.axes else "",
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]
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return f"InputSpec({', '.join(x for x in spec if x)})"
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def get_config(self):
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return {
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"dtype": self.dtype,
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"shape": self.shape,
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"ndim": self.ndim,
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"max_ndim": self.max_ndim,
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"min_ndim": self.min_ndim,
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"axes": self.axes,
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}
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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def to_tensor_shape(spec):
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"""Returns a tf.TensorShape object that matches the shape specifications.
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If the InputSpec's shape or ndim is defined, this method will return a fully
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or partially-known shape. Otherwise, the returned TensorShape is None.
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Args:
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spec: an InputSpec object.
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Returns:
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a tf.TensorShape object
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"""
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if spec.ndim is None and spec.shape is None:
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return tf.TensorShape(None)
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elif spec.shape is not None:
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return tf.TensorShape(spec.shape)
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else:
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shape = [None] * spec.ndim
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for a in spec.axes:
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shape[a] = spec.axes[a] # Assume that axes is defined
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return tf.TensorShape(shape)
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def assert_input_compatibility(input_spec, inputs, layer_name):
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"""Checks compatibility between the layer and provided inputs.
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This checks that the tensor(s) `inputs` verify the input assumptions
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of a layer (if any). If not, a clear and actional exception gets raised.
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Args:
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input_spec: An InputSpec instance, list of InputSpec instances, a nested
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structure of InputSpec instances, or None.
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inputs: Input tensor, list of input tensors, or a nested structure of
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input tensors.
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layer_name: String, name of the layer (for error message formatting).
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Raises:
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ValueError: in case of mismatch between
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the provided inputs and the expectations of the layer.
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"""
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if not input_spec:
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return
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input_spec = tf.nest.flatten(input_spec)
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if isinstance(inputs, dict):
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# Flatten `inputs` by reference order if input spec names are provided
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names = [spec.name for spec in input_spec]
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if all(names):
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list_inputs = []
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for name in names:
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if name not in inputs:
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raise ValueError(
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f'Missing data for input "{name}". '
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"You passed a data dictionary with keys "
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f"{list(inputs.keys())}. "
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f"Expected the following keys: {names}"
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)
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list_inputs.append(inputs[name])
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inputs = list_inputs
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inputs = tf.nest.flatten(inputs)
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for x in inputs:
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# Having a shape/dtype is the only commonality of the various
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# tensor-like objects that may be passed. The most common kind of
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# invalid type we are guarding for is a Layer instance (Functional API),
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# which does not have a `shape` attribute.
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if not hasattr(x, "shape"):
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raise TypeError(
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f"Inputs to a layer should be tensors. Got '{x}' "
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f"(of type {type(x)}) as input for layer '{layer_name}'."
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)
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if len(inputs) != len(input_spec):
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raise ValueError(
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f'Layer "{layer_name}" expects {len(input_spec)} input(s),'
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f" but it received {len(inputs)} input tensors. "
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f"Inputs received: {inputs}"
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)
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for input_index, (x, spec) in enumerate(zip(inputs, input_spec)):
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if spec is None:
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continue
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shape = tf.TensorShape(x.shape)
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if shape.rank is None:
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return
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# Check ndim.
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if spec.ndim is not None and not spec.allow_last_axis_squeeze:
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ndim = shape.rank
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if ndim != spec.ndim:
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raise ValueError(
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f'Input {input_index} of layer "{layer_name}" '
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"is incompatible with the layer: "
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f"expected ndim={spec.ndim}, found ndim={ndim}. "
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f"Full shape received: {tuple(shape)}"
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)
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if spec.max_ndim is not None:
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ndim = x.shape.rank
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if ndim is not None and ndim > spec.max_ndim:
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raise ValueError(
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f'Input {input_index} of layer "{layer_name}" '
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"is incompatible with the layer: "
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f"expected max_ndim={spec.max_ndim}, "
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f"found ndim={ndim}"
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)
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if spec.min_ndim is not None:
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ndim = x.shape.rank
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if ndim is not None and ndim < spec.min_ndim:
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raise ValueError(
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f'Input {input_index} of layer "{layer_name}" '
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"is incompatible with the layer: "
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f"expected min_ndim={spec.min_ndim}, "
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f"found ndim={ndim}. "
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f"Full shape received: {tuple(shape)}"
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)
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# Check dtype.
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if spec.dtype is not None:
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if x.dtype.name != spec.dtype:
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raise ValueError(
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f'Input {input_index} of layer "{layer_name}" '
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"is incompatible with the layer: "
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f"expected dtype={spec.dtype}, "
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f"found dtype={x.dtype}"
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)
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# Check specific shape axes.
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shape_as_list = shape.as_list()
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if spec.axes:
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for axis, value in spec.axes.items():
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if hasattr(value, "value"):
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value = value.value
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if value is not None and shape_as_list[int(axis)] not in {
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value,
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None,
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}:
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raise ValueError(
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f'Input {input_index} of layer "{layer_name}" is '
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f"incompatible with the layer: expected axis {axis} "
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f"of input shape to have value {value}, "
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"but received input with "
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f"shape {display_shape(x.shape)}"
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)
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# Check shape.
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if spec.shape is not None and shape.rank is not None:
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spec_shape = spec.shape
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if spec.allow_last_axis_squeeze:
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if shape_as_list and shape_as_list[-1] == 1:
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shape_as_list = shape_as_list[:-1]
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if spec_shape and spec_shape[-1] == 1:
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spec_shape = spec_shape[:-1]
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for spec_dim, dim in zip(spec_shape, shape_as_list):
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if spec_dim is not None and dim is not None:
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if spec_dim != dim:
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raise ValueError(
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f'Input {input_index} of layer "{layer_name}" is '
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"incompatible with the layer: "
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f"expected shape={spec.shape}, "
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f"found shape={display_shape(x.shape)}"
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)
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def display_shape(shape):
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return str(tuple(shape.as_list()))
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def to_tensor_spec(input_spec, default_dtype=None):
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"""Converts a Keras InputSpec object to a TensorSpec."""
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default_dtype = default_dtype or backend.floatx()
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if isinstance(input_spec, InputSpec):
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dtype = input_spec.dtype or default_dtype
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return tf.TensorSpec(to_tensor_shape(input_spec), dtype)
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return tf.TensorSpec(None, default_dtype)
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