Module delta.extensions.layers.simple

Simple helpful layers.

# Copyright © 2020, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration.
# All rights reserved.
#
# The DELTA (Deep Earth Learning, Tools, and Analysis) platform is
# 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.

"""
Simple helpful layers.
"""

import tensorflow as tf
import tensorflow.keras.layers #pylint: disable=no-name-in-module
import tensorflow.keras.backend as K #pylint: disable=no-name-in-module

from delta.config.extensions import register_layer

class RepeatedGlobalAveragePooling2D(tensorflow.keras.layers.Layer):
    """
    Global average pooling in 2D for fully convolutional networks.

    Takes the global average over the entire input, and repeats
    it to return a tensor the same size as the input.
    """
    def compute_output_shape(self, input_shape): # pylint: disable=no-self-use
        return input_shape

    def call(self, inputs, **_): # pylint: disable=no-self-use
        ones = tf.fill(tf.shape(inputs)[:-1], 1.0)
        ones = tf.expand_dims(ones, -1)
        mean = K.mean(inputs, axis=[1, 2])
        mean = tf.expand_dims(mean, 1)
        mean = tf.expand_dims(mean, 1)
        return mean * ones

class ReflectionPadding2D(tensorflow.keras.layers.Layer):
    """
    Add reflected padding of the given size surrounding the input.
    """
    def __init__(self, padding=(1, 1), **kwargs):
        super().__init__(**kwargs)
        self.padding = tuple(padding)

    def get_config(self):
        config = super().get_config()
        config.update({'padding': self.padding})
        return config

    def call(self, inputs, **_):
        w_pad,h_pad = self.padding
        return tf.pad(inputs, [[0,0], [h_pad,h_pad], [w_pad,w_pad], [0,0] ], 'REFLECT')

register_layer('RepeatedGlobalAveragePooling2D', RepeatedGlobalAveragePooling2D)
register_layer('ReflectionPadding2D', ReflectionPadding2D)

Classes

class ReflectionPadding2D (padding=(1, 1), **kwargs)

Add reflected padding of the given size surrounding the input.

Expand source code

class ReflectionPadding2D(tensorflow.keras.layers.Layer):
    """
    Add reflected padding of the given size surrounding the input.
    """
    def __init__(self, padding=(1, 1), **kwargs):
        super().__init__(**kwargs)
        self.padding = tuple(padding)

    def get_config(self):
        config = super().get_config()
        config.update({'padding': self.padding})
        return config

    def call(self, inputs, **_):
        w_pad,h_pad = self.padding
        return tf.pad(inputs, [[0,0], [h_pad,h_pad], [w_pad,w_pad], [0,0] ], 'REFLECT')

Ancestors

• keras.engine.base_layer.Layer
• tensorflow.python.module.module.Module
• tensorflow.python.training.tracking.autotrackable.AutoTrackable
• tensorflow.python.training.tracking.base.Trackable
• keras.utils.version_utils.LayerVersionSelector

Methods

def call(self, inputs, **_)

This is where the layer's logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state in __init__(), or the build() method that is called automatically before call() executes the first time.

Args

inputs

Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument. - NumPy array or Python scalar values in inputs get cast as tensors. - Keras mask metadata is only collected from inputs. - Layers are built (build(input_shape) method) using shape info from inputs only. - input_spec compatibility is only checked against inputs. - Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually. - The SavedModel input specification is generated using inputs only. - Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args

Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.

**kwargs

Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference. - mask: Boolean input mask. If the layer's call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns

A tensor or list/tuple of tensors.

Expand source code

def call(self, inputs, **_):
    w_pad,h_pad = self.padding
    return tf.pad(inputs, [[0,0], [h_pad,h_pad], [w_pad,w_pad], [0,0] ], 'REFLECT')

def get_config(self)

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.

Returns

Python dictionary.

Expand source code

def get_config(self):
    config = super().get_config()
    config.update({'padding': self.padding})
    return config

class RepeatedGlobalAveragePooling2D (trainable=True, name=None, dtype=None, dynamic=False, **kwargs)

Global average pooling in 2D for fully convolutional networks.

Takes the global average over the entire input, and repeats it to return a tensor the same size as the input.

Expand source code

class RepeatedGlobalAveragePooling2D(tensorflow.keras.layers.Layer):
    """
    Global average pooling in 2D for fully convolutional networks.

    Takes the global average over the entire input, and repeats
    it to return a tensor the same size as the input.
    """
    def compute_output_shape(self, input_shape): # pylint: disable=no-self-use
        return input_shape

    def call(self, inputs, **_): # pylint: disable=no-self-use
        ones = tf.fill(tf.shape(inputs)[:-1], 1.0)
        ones = tf.expand_dims(ones, -1)
        mean = K.mean(inputs, axis=[1, 2])
        mean = tf.expand_dims(mean, 1)
        mean = tf.expand_dims(mean, 1)
        return mean * ones

Ancestors

• keras.engine.base_layer.Layer
• tensorflow.python.module.module.Module
• tensorflow.python.training.tracking.autotrackable.AutoTrackable
• tensorflow.python.training.tracking.base.Trackable
• keras.utils.version_utils.LayerVersionSelector

Methods

def call(self, inputs, **_)

This is where the layer's logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state in __init__(), or the build() method that is called automatically before call() executes the first time.

Args

inputs

Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument. - NumPy array or Python scalar values in inputs get cast as tensors. - Keras mask metadata is only collected from inputs. - Layers are built (build(input_shape) method) using shape info from inputs only. - input_spec compatibility is only checked against inputs. - Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually. - The SavedModel input specification is generated using inputs only. - Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args

Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.

**kwargs

Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference. - mask: Boolean input mask. If the layer's call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns

A tensor or list/tuple of tensors.

Expand source code

def call(self, inputs, **_): # pylint: disable=no-self-use
    ones = tf.fill(tf.shape(inputs)[:-1], 1.0)
    ones = tf.expand_dims(ones, -1)
    mean = K.mean(inputs, axis=[1, 2])
    mean = tf.expand_dims(mean, 1)
    mean = tf.expand_dims(mean, 1)
    return mean * ones

def compute_output_shape(self, input_shape)

Computes the output shape of the layer.

This method will cause the layer's state to be built, if that has not happened before. This requires that the layer will later be used with inputs that match the input shape provided here.

Args

input_shape

Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns

An input shape tuple.

Expand source code

def compute_output_shape(self, input_shape): # pylint: disable=no-self-use
    return input_shape