Source code for ashpy.models.convolutional.encoders

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"""Collection of Encoders (i.e., GANs' Discriminators) models."""
from typing import Tuple, Type, Union

from ashpy.models.convolutional.interfaces import Conv2DInterface
from tensorflow import keras  # pylint: disable=no-name-in-module

__ALL__ = ["Encoder", "FCNNEncoder"]


[docs]class Encoder(Conv2DInterface): """ Primitive Model for all encoder (i.e., convolution) based architecture. Notes: Default to DCGAN Discriminator architecture. Examples: * Direct Usage: .. testcode:: dummy_generator = Encoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=4, filters_cap=128, output_shape=1, ) * Subclassing .. testcode:: class DummyDiscriminator(Encoder): def call(self, inputs, training=True): print("Dummy Discriminator!") # build the model using # self._layers and inputs return inputs dummy_discriminator = DummyDiscriminator( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=16, filters_cap=128, output_shape=1, ) dummy_discriminator(tf.zeros((1,28,28,3))) .. testoutput:: Dummy Discriminator! """
[docs] def __init__( self, layer_spec_input_res: Union[int, Tuple[int, int]], layer_spec_target_res: Union[int, Tuple[int, int]], kernel_size: Union[int, Tuple[int, int]], initial_filters: int, filters_cap: int, output_shape: int, use_dropout: bool = True, dropout_prob: float = 0.3, non_linearity: Type[keras.layers.Activation] = keras.layers.LeakyReLU, ): """ Instantiate the :py:class:`Decoder`. Args: layer_spec_input_res (:obj:`tuple` of (:obj:`int`, :obj:`int`)): Shape of the input tensors. layer_spec_target_res: (:obj:`tuple` of (:obj:`int`, :obj:`int`)): Shape of tensor desired as output of :func:`_get_layer_spec`. kernel_size (int): Kernel used by the convolution layers. initial_filters (int): Numbers of filters to used as a base value. filters_cap (int): Cap filters to a set amount, in the case of an Encoder is a ceil value AKA the max amount of filters. output_shape (int): Amount of units of the last :py:obj:`tf.keras.layers.Dense`. Returns: :py:obj:`None` Raises: ValueError: If `filters_cap` < `initial_filters` """ super().__init__() if filters_cap < initial_filters: raise ValueError( "`filters_cap` < `initial_filters`. " "When decoding ``filters_cap`` is a ceil value AKA the maximum " "amount of filters." ) filters = self._get_layer_spec( initial_filters, filters_cap, layer_spec_input_res, layer_spec_target_res ) self.model_layers = [] # layer specification self.use_dropout = use_dropout self.dropout_prob = dropout_prob self.non_linearity = non_linearity self.kernel_size = kernel_size # Assembling Model for layer_filters in filters: self._add_building_block(layer_filters) self._add_final_block(output_shape)
[docs] def _add_building_block(self, filters): """ Construct the core of the :py:obj:`tf.keras.Model`. The layers specified here get added to the :py:obj:`tf.keras.Model` multiple times consuming the hyper-parameters generated in the :func:`_get_layer_spec`. Args: filters (int): Number of filters to use for this iteration of the Building Block. """ self.model_layers.extend( [ keras.layers.Conv2D( filters, self.kernel_size, strides=(2, 2), padding="same" ), self.non_linearity(), ] ) if self.use_dropout: self.model_layers.append(keras.layers.Dropout(self.dropout_prob))
[docs] def _add_final_block(self, output_shape): """ Prepare the results of :func:`_add_building_block` for the final output. Args: output_shape (int): Amount of units of the last :py:obj:`tf.keras.layers.Dense` """ self.model_layers.extend( [keras.layers.Flatten(), keras.layers.Dense(output_shape)] )
[docs]class FCNNEncoder(Encoder): """Fully Convolutional Encoder. Output a 1x1xencoding_size vector. The output neurons are linear. Examples: * Direct Usage: .. testcode:: dummy_generator = FCNNEncoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=4, filters_cap=128, encoding_dimension=100, ) print(dummy_generator(tf.zeros((1, 64, 64, 3))).shape) .. testoutput:: (1, 1, 1, 100) """
[docs] def __init__( self, layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension, ): """ Instantiate the :py:class:`FCNNDecoder`. Args: layer_spec_input_res (:obj:`tuple` of (:obj:`int`, :obj:`int`)): Shape of the input tensors. layer_spec_target_res: (:obj:`tuple` of (:obj:`int`, :obj:`int`)): Shape of tensor desired as output of :func:`_get_layer_spec`. kernel_size (int): Kernel used by the convolution layers. initial_filters (int): Numbers of filters to used as a base value. filters_cap (int): Cap filters to a set amount, in the case of an Encoder is a ceil value AKA the max amount of filters. encoding_dimension (int): encoding dimension. Returns: :py:obj:`None` Raises: ValueError: If `filters_cap` < `initial_filters` """ self._layer_spec_target_res = layer_spec_target_res self._encoding_dimension = encoding_dimension super().__init__( layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, 0, )
[docs] def _add_final_block(self, output_shape): self.model_layers.append( keras.layers.Conv2D( self._encoding_dimension, self._layer_spec_target_res, strides=(1, 1), padding="valid", ) )