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 discriminatorsnormalization_layer (
tf.keras.layers.Layer
) – normalization used by the discriminatorsuse_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
- Returns
A Discriminator (PatchDiscriminator).
-