LocalEnhancer¶
Inheritance Diagram
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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])LocalEnhancer call.
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.
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__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 normalizationInstance Normalization or BatchNormalization or LayerNormalization) ((e.g.) –
non_linearity (
tf.keras.layers.Layer
) – non linearity used in Pix2Pix HDnum_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
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