LocalEnhancer

Inheritance Diagram

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

class ashpy.models.convolutional.pix2pixhd.LocalEnhancer(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_global=9, num_resnet_blocks_local=3, kernel_size_resnet=3, kernel_size_front_back=7, num_internal_resnet_blocks=2)[source]

Bases: tensorflow.python.keras.engine.training.Model

Local Enhancer module of the Pix2PixHD architecture.

Example

# instantiate the model
model = LocalEnhancer()

# call the model passing inputs
inputs = tf.ones((1, 512, 512, 3))
output = model(inputs)

# the output shape is
# the same as the input shape
print(output.shape)
(1, 512, 512, 3)

Methods

__init__([input_res, min_res, …])

Build the LocalEnhancer module of the Pix2PixHD architecture.

call(inputs[, training])

LocalEnhancer call.

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_global=9, num_resnet_blocks_local=3, kernel_size_resnet=3, kernel_size_front_back=7, num_internal_resnet_blocks=2)[source]

Build the LocalEnhancer module of the Pix2PixHD architecture.

See Pix2PixHD 2 for more details.

Parameters
  • input_res (int) – input resolution

  • min_res (int) – minimum resolution reached by the global generator

  • initial_filters (int) – number of initial filters

  • filters_cap (int) – maximum number of filters

  • channels (int) – number of channels

  • normalization_layer (tf.keras.layers.Layer) – layer of normalization

  • Instance Normalization or BatchNormalization or LayerNormalization) ((e.g.) –

  • non_linearity (tf.keras.layers.Layer) – non linearity used in Pix2Pix HD

  • num_resnet_blocks_global (int) – number of residual blocks used in the global generator

  • num_resnet_blocks_local (int) – number of residual blocks used in the local generator

  • kernel_size_resnet (int) – kernel size used in resnets

  • kernel_size_front_back (int) – kernel size used for the front and back convolution

  • num_internal_resnet_blocks (int) – number of internal blocks of the resnet

2

https://arxiv.org/abs/1711.11585

call(inputs, training=False)[source]

LocalEnhancer call. :param inputs: Input Tensors :type inputs: tf.Tensor :param training: Whether it is training phase or not :type training: bool

Returns

(tf.Tensor) –

Image of size (input_res, input_res, channels)

as specified in the init call