PatchDiscriminator

Inheritance Diagram

Inheritance diagram of ashpy.models.convolutional.discriminators.PatchDiscriminator

class ashpy.models.convolutional.discriminators.PatchDiscriminator(input_res, min_res, kernel_size, initial_filters, filters_cap, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, use_attention=False)[source]

Bases: ashpy.models.convolutional.encoders.Encoder

Pix2Pix discriminator.

The last layer is an image in which each pixels is the probability of being fake or real.

Examples

x = tf.ones((1, 64, 64, 3))

# instantiate the PathDiscriminator
patchDiscriminator = PatchDiscriminator(input_res=64,
                                        min_res=16,
                                        kernel_size=5,
                                        initial_filters=64,
                                        filters_cap=512,
                                        )

# evaluate passing x
output = patchDiscriminator(x)

# the output shape is the same as the input shape
print(output.shape)
(1, 12, 12, 1)

Methods

__init__(input_res, min_res, kernel_size, …)

Patch Discriminator used by pix2pix.

call(inputs[, training, return_features])

Forward pass of the PatchDiscriminator.

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, min_res, kernel_size, initial_filters, filters_cap, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, use_attention=False)[source]

Patch Discriminator used by pix2pix.

When min_res=1 this is the same as a standard fully convolutional discriminator.

Parameters
  • input_res (int) – Input Resolution.

  • min_res (int) – Minimum Resolution reached by the discriminator.

  • kernel_size (int) – Kernel Size used in Conv Layer.

  • initial_filters (int) – number of filters in the first convolutional layer.

  • filters_cap (int) – Maximum number of filters.

  • use_dropout (bool) – whether to use dropout.

  • dropout_prob (float) – probability of dropout.

  • non_linearity (tf.keras.layers.Layer) – non linearity used in the model.

  • normalization_layer (tf.keras.layers.Layer) – normalization layer used in the model.

  • use_attention (bool) – whether to use attention.

_add_building_block(filters, use_bn=False)[source]

Construct the core of the tf.keras.Model.

The layers specified here get added to the tf.keras.Model multiple times consuming the hyper-parameters generated in the _get_layer_spec().

Parameters

filters (int) – Number of filters to use for this iteration of the Building Block.

_add_final_block(output_shape)[source]

Prepare the results of _add_building_block() for the final output.

Parameters

output_shape (int) – Amount of units of the last tf.keras.layers.Dense

call(inputs, training=False, return_features=False)[source]

Forward pass of the PatchDiscriminator.