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.