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]) Call the LocalEnhancer 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_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 High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs [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]High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs https://arxiv.org/abs/1711.11585
call(inputs, training=False)[source]

Call the LocalEnhancer model.

Parameters:
  • inputs (tf.Tensor) – Input Tensors.
  • training (bool) – Whether it is training phase or not.
Returns:

(tf.Tensor) –

Image of size (input_res, input_res, channels)

as specified in the init call.