MultiScaleDiscriminator

Inheritance Diagram

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

class ashpy.models.convolutional.discriminators.MultiScaleDiscriminator(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, n_discriminators=1)[source]

Bases: ashpy.models.gans.Discriminator

Multi-Scale discriminator. This discriminator architecture is composed by multiple discriminators working at different scales. Each discriminator is a ashpy.models.convolutional.discriminators.PatchDiscriminator.

Examples

x = tf.ones((1, 256, 256, 3))

# instantiate the PathDiscriminator
multiScaleDiscriminator = MultiScaleDiscriminator(input_res=256,
                                                  min_res=16,
                                                  kernel_size=5,
                                                  initial_filters=64,
                                                  filters_cap=512,
                                                  n_discriminators=3
                                                  )

# evaluate passing x
outputs = multiScaleDiscriminator(x)

# the output shape is
# the same as the input shape
print(len(outputs))
for output in outputs:
    print(output.shape)
3
(1, 12, 12, 1)
(1, 12, 12, 1)
(1, 12, 12, 1)

Methods

__init__(input_res, min_res, kernel_size, …)

Multi Scale Discriminator.

build_discriminator(input_res)

Build a single discriminator using parameters defined in this object :param input_res: input resolution of the discriminator

call(inputs[, training, return_features])

type inputs

Union[List[~T], Tensor]

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, n_discriminators=1)[source]

Multi Scale Discriminator. Different generator for different scales of the input image.

Used by Pix2PixHD 1 .

Parameters
  • input_res (int) – input resolution

  • min_res (int) – minimum resolution reached by the discriminators

  • kernel_size (int) – kernel size of discriminators

  • initial_filters (int) – number of initial filters in the first layer of the discriminators

  • filters_cap (int) – maximum number of filters in the discriminators

  • use_dropout (bool) – whether to use dropout

  • dropout_prob (float) – probability of dropout

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

  • normalization_layer (tf.keras.layers.Layer) – normalization used by the discriminators

  • use_attention (bool) – whether to use attention

  • n_discriminators (int) – Number of discriminators

1

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs https://arxiv.org/abs/1711.11585

build_discriminator(input_res)[source]

Build a single discriminator using parameters defined in this object :param input_res: input resolution of the discriminator

Return type

Discriminator

Returns

A Discriminator (PatchDiscriminator).

call(inputs, training=True, return_features=False)[source]
Parameters
  • inputs (tf.Tensor) – input tensor

  • training (bool) – whether is training or not

  • return_features (bool) – whether to return features or not

Return type

Union[List[Tensor], Tuple[List[Tensor], List[Tensor]]]

Returns

([tf.Tensor]) –

A List of Tensors containing the

value of D_i for each input

([tf.Tensor]): A List of features for each discriminator if

return_features