tf.raw_ops.UniformQuantizedConvolutionHybrid

Perform hybrid quantized convolution of float Tensor lhs and quantized Tensor rhs.

tf.raw_ops.UniformQuantizedConvolutionHybrid(
    lhs,
    rhs,
    rhs_scales,
    rhs_zero_points,
    Tout,
    padding,
    rhs_quantization_min_val,
    rhs_quantization_max_val,
    window_strides=[],
    explicit_padding=[],
    lhs_dilation=[],
    rhs_dilation=[],
    batch_group_count=1,
    feature_group_count=1,
    dimension_numbers='',
    rhs_quantization_axis=-1,
    name=None
)

Given float lhs and quantized rhs, internally performs quantization on lhs, and then performs quantized convolution on quantized lhs and rhs.

The internal quantization on lhs is a quantization to Trhs, dynamic range, per-batch (per-axis along axis dimension_numbers.input_batch_dimension), asymmetric, and not narrow range (the range is [Trhs_MIN, Trhs_MAX]).

lhs and rhs must be Tensors of same rank, and meet following shape conditions.

• lhs_feature % feature_group_count == 0
• lhs_feature % rhs_input_feature == 0
• lhs_feature / feature_group_count == rhs_input_feature
• rhs_output_feature % feature_group_count == 0
• lhs_batch % batch_group_count == 0
• rhs_output_feature % batch_group_count == 0

rhs must be quantized Tensor, where its data value is quantized using the formula: quantized_data = clip(original_data / scale + zero_point, quantization_min_val, quantization_max_val).