def hr_3_2_16(inputs):
x = [inputs]
x = clayers.HighResolutionModule(filters=[16], blocks=[2], name='HR_0')(x)
x = clayers.HighResolutionModule(filters=[16, 32],
blocks=[2, 2],
name='HR_1')(x)
x = clayers.HighResolutionModule(filters=[16, 32, 64],
blocks=[2, 2, 2],
name='HR_2')(x)
x = clayers.HighResolutionFusion(filters=[32], name='Fusion_0')(x)
outputs = layers.Activation('linear', dtype='float32')(x[0])
return outputs
def hr_2_2_0(inputs):
# WRONG NAME
# Should be hr_3_2_0
x = [inputs]
x = clayers.HighResolutionModule(filters=[8], blocks=[2], name='HR_0')(x)
x = clayers.HighResolutionModule(filters=[8, 16],
blocks=[2, 2],
name='HR_1')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32],
blocks=[2, 2, 2],
name='HR_2')(x)
x = clayers.HighResolutionFusion(filters=[8], name='Fusion_0')(x)
x = layers.Conv2D(1, 1, padding='same', name='Final_conv')(x[0])
x = tf.squeeze(x, axis=-1)
outputs = layers.Activation('linear', dtype='float32')(x)
return outputs
def hr_5_3_8(inputs):
x = [inputs]
x = clayers.HighResolutionModule(filters=[8], blocks=[3], name='HR_0')(x)
x = clayers.HighResolutionModule(filters=[8, 16],
blocks=[3, 3],
name='HR_1')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32],
blocks=[3, 3, 3],
name='HR_2')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32, 64],
blocks=[3, 3, 3, 3],
name='HR_3')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32, 64],
blocks=[3, 3, 3, 3],
name='HR_4')(x)
outputs = clayers.HighResolutionFusion(filters=[8], name='Fusion_0')(x)[0]
return outputs
def hr_5_3_0(inputs):
x = [inputs]
x = clayers.HighResolutionModule(filters=[8], blocks=[3], name='HR_0')(x)
x = clayers.HighResolutionModule(filters=[8, 16],
blocks=[3, 3],
name='HR_1')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32],
blocks=[3, 3, 3],
name='HR_2')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32, 64],
blocks=[3, 3, 3, 3],
name='HR_3')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32, 64],
blocks=[3, 3, 3, 3],
name='HR_4')(x)
x = clayers.HighResolutionFusion(filters=[8], name='Fusion_0')(x)
x = layers.Conv2D(1, 1, padding='same', name='Final_conv')(x[0])
x = tf.squeeze(x, axis=-1)
outputs = layers.Activation('linear', dtype='float32')(x)
return outputs
def hr_5_3_0(inputs):
x = [inputs]
x = clayers.HighResolutionModule(filters=[8], blocks=[3], name='HR_0')(x)
x = clayers.HighResolutionModule(filters=[8, 16],
blocks=[3, 3],
name='HR_1')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32],
blocks=[3, 3, 3],
name='HR_2')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32, 64],
blocks=[3, 3, 3, 3],
name='HR_3')(x)
x = clayers.HighResolutionModule(filters=[8, 16, 32, 64],
blocks=[3, 3, 3, 3],
name='HR_4')(x)
x = clayers.HighResolutionFusion(filters=[8], name='Fusion_0')(x)
x = layers.Conv2D(8, 2, strides=2, padding='same',
name='Down_sample_0')(x[0])
x = layers.Conv2D(8, 2, strides=2, padding='same', name='Down_sample_1')(x)
x = layers.Conv2D(8, 2, strides=2, padding='same', name='Down_sample_2')(x)
x = layers.Flatten()(x)
outputs = layers.Activation('linear', dtype='float32')(x)
return outputs