original_docstring: Same as mobilenet_v2_fpn except with separable convolution in FPN.
docstring: Same as mobilenet_v2_fpn except with separable convolution in FPN.
docstring_tokens: Same as mobilenet_v2_fpn except with separable convolution in FPN .
code: def mobilenet_v2_fpn_sep_conv(channel_means, channel_stds, bgr_ordering):
    """Same as mobilenet_v2_fpn except with separable convolution in FPN."""
  
    # Setting batchnorm_training to True, which will use the correct
    # BatchNormalization layer strategy based on the current Keras learning phase.
    # TODO(yuhuic): expriment with True vs. False to understand it's effect in
    # practice.
    network = mobilenetv2.mobilenet_v2(batchnorm_training=True, include_top=False)
    return CenterNetMobileNetV2FPNFeatureExtractor(
        network,
        channel_means=channel_means,
        channel_stds=channel_stds,
        bgr_ordering=bgr_ordering,
        fpn_separable_conv=True)
code_tokens: def mobilenet_v2_fpn_sep_conv ( channel_means , channel_stds , bgr_ordering ) : network = mobilenetv2 . mobilenet_v2 ( batchnorm_training = True , include_top = False ) return CenterNetMobileNetV2FPNFeatureExtractor ( network , channel_means = channel_means , channel_stds = channel_stds , bgr_ordering = bgr_ordering , fpn_separable_conv = True )
short_docstring: Same as mobilenet_v2_fpn except with separable convolution in FPN.
short_docstring_tokens: Same as mobilenet_v2_fpn except with separable convolution in FPN .
comment: """Same as mobilenet_v2_fpn except with separable convolution in FPN."""$SEP$# Setting batchnorm_training to True, which will use the correct$SEP$# BatchNormalization layer strategy based on the current Keras learning phase.$SEP$# TODO(yuhuic): expriment with True vs. False to understand it's effect in$SEP$# practice.