Skip to content

attention_unet

AttentionUNet

Attention based UNet implementation in TensorFlow.

Parameters:

Name Type Description Default
input_size Tuple[int, int, int]

The size of the input tensor (height, width, channels).

 (128, 128, 1)

Raises:

Type Description
ValueError

Length of input_size is not 3.
ValueError

input_size[0] or input_size[1] is not a multiple of 16.

Returns:

Type Description
Model

A TensorFlow Attention UNet model.
Source code in fastestimator/fastestimator/architecture/tensorflow/attention_unet.py 
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
def AttentionUNet(input_size: Tuple[int, int, int] = (128, 128, 1)) -> tf.keras.Model:
    """Attention based UNet implementation in TensorFlow.

    Args:
        input_size: The size of the input tensor (height, width, channels).

    Raises:
        ValueError: Length of `input_size` is not 3.
        ValueError: `input_size`[0] or `input_size`[1] is not a multiple of 16.

    Returns:
        A TensorFlow Attention UNet model.
    """
    _check_input_size(input_size)
    conv_config = {'activation': 'relu', 'padding': 'same', 'kernel_initializer': 'he_normal'}
    up_config = {'size': (2, 2), 'interpolation': 'bilinear'}
    inputs = Input(input_size)
    conv1 = Conv2D(64, 3, **conv_config)(inputs)
    conv1 = Conv2D(64, 3, **conv_config)(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(128, 3, **conv_config)(pool1)
    conv2 = Conv2D(128, 3, **conv_config)(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(256, 3, **conv_config)(pool2)
    conv3 = Conv2D(256, 3, **conv_config)(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(512, 3, **conv_config)(pool3)
    conv4 = Conv2D(512, 3, **conv_config)(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, **conv_config)(pool4)
    conv5 = Conv2D(1024, 3, **conv_config)(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 3, **conv_config)(UpSampling2D(**up_config)(drop5))
    drop4 = attention_block(512, decoder_input=up6, encoder_input=drop4)
    merge6 = concatenate([drop4, up6], axis=-1)
    conv6 = Conv2D(512, 3, **conv_config)(merge6)
    conv6 = Conv2D(512, 3, **conv_config)(conv6)

    up7 = Conv2D(256, 3, **conv_config)(UpSampling2D(**up_config)(conv6))
    conv3 = attention_block(256, decoder_input=up7, encoder_input=conv3)
    merge7 = concatenate([conv3, up7], axis=-1)
    conv7 = Conv2D(256, 3, **conv_config)(merge7)
    conv7 = Conv2D(256, 3, **conv_config)(conv7)

    up8 = Conv2D(128, 3, **conv_config)(UpSampling2D(**up_config)(conv7))
    conv2 = attention_block(128, decoder_input=up8, encoder_input=conv2)
    merge8 = concatenate([conv2, up8], axis=-1)
    conv8 = Conv2D(128, 3, **conv_config)(merge8)
    conv8 = Conv2D(128, 3, **conv_config)(conv8)

    up9 = Conv2D(64, 3, **conv_config)(UpSampling2D(**up_config)(conv8))
    conv1 = attention_block(64, decoder_input=up9, encoder_input=conv1)
    merge9 = concatenate([conv1, up9], axis=-1)
    conv9 = Conv2D(64, 3, **conv_config)(merge9)
    conv9 = Conv2D(64, 3, **conv_config)(conv9)
    conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
    model = Model(inputs=inputs, outputs=conv10)
    return model

attention_block

An attention unit for Attention Unet.

Parameters:

Name Type Description Default
n_filters int

How many filters for the convolution layer.

 required
decoder_input Tensor

Input tensor in the decoder section.

 required
encoder_input Tensor

Input tensor in the encoder section.

 required
Return

Output Keras tensor.

Source code in fastestimator/fastestimator/architecture/tensorflow/attention_unet.py 
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
def attention_block(n_filters: int, decoder_input: tf.Tensor, encoder_input: tf.Tensor) -> tf.Tensor:
    """An attention unit for Attention Unet.

    Args:
        n_filters: How many filters for the convolution layer.
        decoder_input: Input tensor in the decoder section.
        encoder_input: Input tensor in the encoder section.

    Return:
        Output Keras tensor.
    """
    c1 = Conv2D(n_filters, kernel_size=1)(decoder_input)
    c1 = BatchNormalization()(c1)
    x1 = Conv2D(n_filters, kernel_size=1)(encoder_input)
    x1 = BatchNormalization()(x1)
    att = ReLU()(x1 + c1)
    att = Conv2D(1, kernel_size=1)(att)
    att = BatchNormalization()(att)
    att = tf.sigmoid(att)
    return encoder_input * att