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During training and\r\nevaluation, we use TensorFlow ops for the full pipeline, including the\r\nnormalization step of the preprocessing.  This pipeline reads in images\r\ninto a float32 format prior to running normalization and the rest of the\r\npipeline.  Therefore, the normalization ops will correctly yield float32\r\noutputs.  However, when the normalization function is called with NumPy\r\ninputs, such as during our prediction script, the normalization function\r\nincorrectly yields float64 ouputs for ImageNet-style normalization.\r\nThis is due to the ImageNet means literal array being implicitly\r\nconverted to a float64 array instead of a float32 array.  Once that\r\nhappens, the mean subtraction op will yield a float64 output, regardless\r\nof the input datatype.  Testing reveals that this causes significant\r\ndifferences in the output of the normalization function, as well as the\r\ndownstream predictions of a full model.\r\n\r\nThis updates the normalization function to explicitly create the\r\nImageNet means as a float32 tensor.  Testing confirms that no change is\r\nexperienced for TensorFlow inputs, while NumPy inputs now yield results\r\nequivalent to that with TensorFlow 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