MultiScaleDiscriminator¶

Inheritance Diagram

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

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.
`call`(inputs[, training, return_features])	Forward pass of the Multi Scale Discriminator.

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.

Parameters:	input_res – input resolution of the discriminator.
Return type:	`Encoder`
Returns:	A Discriminator (PatchDiscriminator).

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

Forward pass of the Multi Scale Discriminator.

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.