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unet

UNet

A standard 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)
output_channel int

The number of output channels.

 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 UNet model.
Source code in fastestimator/fastestimator/architecture/tensorflow/unet.py 
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def UNet(input_size: Tuple[int, int, int] = (128, 128, 1), output_channel: int = 1) -> tf.keras.Model:
    """A standard UNet implementation in TensorFlow

    Args:
        input_size: The size of the input tensor (height, width, channels).
        output_channel: The number of output 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 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))
    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))
    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))
    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))
    merge9 = concatenate([conv1, up9], axis=-1)
    conv9 = Conv2D(64, 3, **conv_config)(merge9)
    conv9 = Conv2D(64, 3, **conv_config)(conv9)
    conv10 = Conv2D(output_channel, 1, activation='sigmoid')(conv9)
    model = Model(inputs=inputs, outputs=conv10)
    return model