GlobalGenerator

Inheritance Diagram

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class ashpy.models.convolutional.pix2pixhd.GlobalGenerator(input_res=512, min_res=64, initial_filters=64, filters_cap=512, channels=3, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.ReLU'>, num_resnet_blocks=9, kernel_size_resnet=3, kernel_size_front_back=7, num_internal_resnet_blocks=2)

Bases: ashpy.models.convolutional.interfaces.Conv2DInterface

Global Generator from pix2pixHD paper:

• G1^F: Convolutional frontend (downsampling)

• G1^R: ResNet Block

• G1^B: Convolutional backend (upsampling)

Methods

__init__([input_res, min_res, …])	Global Generator from Pix2PixHD
call(inputs[, training])	Call of the Pix2Pix HD model :param inputs: input tensor(s) :param training: If True training phase

Attributes

activity_regularizer	Optional regularizer function for the output of this layer.
dtype
dynamic
inbound_nodes	Deprecated, do NOT use! Only for compatibility with external Keras.
input	Retrieves the input tensor(s) of a layer.
input_mask	Retrieves the input mask tensor(s) of a layer.
input_shape	Retrieves the input shape(s) of a layer.
input_spec	Gets the network’s input specs.
layers
losses	Losses which are associated with this Layer.
metrics	Returns the model’s metrics added using compile, add_metric APIs.
metrics_names	Returns the model’s display labels for all outputs.
name	Returns the name of this module as passed or determined in the ctor.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables
non_trainable_weights
outbound_nodes	Deprecated, do NOT use! Only for compatibility with external Keras.
output	Retrieves the output tensor(s) of a layer.
output_mask	Retrieves the output mask tensor(s) of a layer.
output_shape	Retrieves the output shape(s) of a layer.
run_eagerly	Settable attribute indicating whether the model should run eagerly.
sample_weights
state_updates	Returns the updates from all layers that are stateful.
stateful
submodules	Sequence of all sub-modules.
trainable
trainable_variables	Sequence of variables owned by this module and it’s submodules.
trainable_weights
updates
variables	Returns the list of all layer variables/weights.
weights	Returns the list of all layer variables/weights.

__init__(input_res=512, min_res=64, initial_filters=64, filters_cap=512, channels=3, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.ReLU'>, num_resnet_blocks=9, kernel_size_resnet=3, kernel_size_front_back=7, num_internal_resnet_blocks=2)

Global Generator from Pix2PixHD

Parameters

• input_res (int) – Input Resolution

• min_res (int) – Minimum resolution reached by the downsampling

• initial_filters (int) – number of initial filters

• filters_cap (int) – maximum number of filters

• channels (int) – output channels

• normalization_layer (tf.keras.layers.Layer) – normalization layer used by the global generator, can be Instance Norm, Layer Norm, Batch Norm

• non_linearity (tf.keras.layers.Layer) – non linearity used in the global generator

• num_resnet_blocks (int) – number of resnet blocks

• kernel_size_resnet (int) – kernel size used in resnets conv layers

• kernel_size_front_back (int) – kernel size used by the convolutional frontend and backend

• num_internal_resnet_blocks (int) – number of blocks used by internal resnet

call(inputs, training=True)

Call of the Pix2Pix HD model :param inputs: input tensor(s) :param training: If True training phase

Returns

Tuple – Generated images.