GlobalGenerator

Inheritance Diagram

Inheritance diagram of ashpy.models.convolutional.pix2pixhd.GlobalGenerator

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.