UNet

Inheritance Diagram

Inheritance diagram of ashpy.models.convolutional.unet.UNet

class ashpy.models.convolutional.unet.UNet(input_res, min_res, kernel_size, initial_filters, filters_cap, channels, use_dropout_encoder=True, use_dropout_decoder=True, dropout_prob=0.3, encoder_non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>, decoder_non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.ReLU'>, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, last_activation=<function tanh>, use_attention=False)[source]

Bases: ashpy.models.convolutional.interfaces.Conv2DInterface

UNet Architecture.

Used in Image-to-Image Translation with Conditional Adversarial Nets [1].

Examples

  • Direct Usage:

    x = tf.ones((1, 512, 512, 3))
    u_net = UNet(input_res = 512,
                 min_res=4,
                 kernel_size=4,
                 initial_filters=64,
                 filters_cap=512,
                 channels=3)
    y = u_net(x)
    print(y.shape)
    print(len(u_net.trainable_variables)>0)
    
    (1, 512, 512, 3)
    True
    
[1]Image-to-Image Translation with Conditional Adversarial Nets https://arxiv.org/abs/1611.04076

Methods

__init__(input_res, min_res, kernel_size, …) Initialize the UNet.
call(inputs[, training]) Forward pass of the UNet model.
get_decoder_block(filters[, use_bn, …]) Return a block to be used in the decoder part of the UNET.
get_encoder_block(filters[, use_bn, …]) Return a block to be used in the encoder part of the UNET.

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, channels, use_dropout_encoder=True, use_dropout_decoder=True, dropout_prob=0.3, encoder_non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>, decoder_non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.ReLU'>, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, last_activation=<function tanh>, use_attention=False)[source]

Initialize the UNet.

Parameters:
  • input_res (int) – input resolution.
  • min_res (int) – minimum resolution reached after decode.
  • kernel_size (int) – kernel size used in the network.
  • initial_filters (int) – number of filter of the initial convolution.
  • filters_cap (int) – maximum number of filters.
  • channels (int) – number of output channels.
  • use_dropout_encoder (bool) – whether to use dropout in the encoder module.
  • use_dropout_decoder (bool) – whether to use dropout in the decoder module.
  • dropout_prob (float) – probability of dropout.
  • encoder_non_linearity (Type[Layer]) – non linearity of encoder.
  • decoder_non_linearity (Type[Layer]) – non linearity of decoder.
  • last_activation (<module 'tensorflow.python.keras.api._v2.keras.activations' from '/home/docs/checkouts/readthedocs.org/user_builds/ashpy/envs/latest/lib/python3.7/site-packages/tensorflow/python/keras/api/_v2/keras/activations/__init__.py'>) – last activation function, tanh or softmax (for semantic images).
  • use_attention (bool) – whether to use attention.
call(inputs, training=False)[source]

Forward pass of the UNet model.

get_decoder_block(filters, use_bn=True, use_dropout=False, use_attention=False)[source]

Return a block to be used in the decoder part of the UNET.

Parameters:
  • filters – number of filters
  • use_bn – whether to use batch normalization
  • use_dropout – whether to use dropout
  • use_attention – whether to use attention
Returns:

A block to be used in the decoder part

get_encoder_block(filters, use_bn=True, use_attention=False)[source]

Return a block to be used in the encoder part of the UNET.

Parameters:
  • filters – number of filters.
  • use_bn – whether to use batch normalization.
  • use_attention – whether to use attention.
Returns:

A block to be used in the encoder part.