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