Function residual_bottleneck

src/models/blocks.py:80–117

residual convolution bottleneck block Args: inputs: input to the bottleneck residual block filters: number of filters kernel_size: kernel size used in convolution temp: used in convolution relu: if relu is active or not. Returns: out: out

(inputs,
                        filters=64,
                        kernel_size=(3, 3),
                        strides=(1, 1),
                        temp=3,
                        relu=False)

    return out

def residual_bottleneck(inputs,
                        filters=64,
                        kernel_size=(3, 3),
                        strides=(1, 1),
                        temp=3,
                        relu=False):
    """
    residual convolution bottleneck block
    Args:
        inputs: input to the bottleneck residual block
        filters: number of filters
        kernel_size: kernel size used in convolution
        temp: used in convolution
        relu: if relu is active or not. 
    Returns:
        out: output from residual convolution bottleneck block
    """
    tchannel = tf.keras.backend.int_shape(inputs)[-1] * temp
    out = conv_block(inputs,
                   conv_type="conv",
                   filters=tchannel,
                   kernel_size=(1, 1))
    out = tf.keras.layers.DepthwiseConv2D(kernel_size=kernel_size,
                                          depth_multiplier=1,
                                          strides=strides,
                                          padding="same")(out)
    out = tf.keras.layers.BatchNormalization()(out)
    out = tf.keras.activations.relu(out)
    out = conv_block(out,
                     conv_type="conv",
                     filters=filters,
                     kernel_size=(1, 1),
                     padding="same",
                     relu=False
    )
    if relu:
        out = tf.keras.layers.add([out, inputs])
    return out

def bottleneck_block(inputs,
                     filters=64,