GlobalGenerator¶

Inheritance Diagram

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)[source]¶

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.

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)[source]¶

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)[source]¶

Call of the Pix2Pix HD model.

Parameters:	inputs – input tensor(s). training – If True training phase.
Returns:	`Tuple` – Generated images.