unet¶
UNET implementations.
Functions
Functional UNET Implementation. |
Classes
Semantic UNet. |
|
UNet Architecture. |
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ashpy.models.convolutional.unet.
FUNet
(input_res, min_res, kernel_size, initial_filters, filters_cap, channels, input_channels=3, 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'>, last_activation=<function tanh>, use_attention=False)[source]¶ Functional UNET Implementation.
-
class
ashpy.models.convolutional.unet.
SUNet
(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'>, use_attention=False)[source]¶ Bases:
ashpy.models.convolutional.unet.UNet
Semantic UNet.
-
__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'>, use_attention=False)[source]¶ Build the Semantic UNet model.
-
-
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
-
__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/v0.2.0/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.
-
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.