org.bytedeco.javacpp

Class tensorflow.QuantizeAndDequantizeV2

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.tensorflow.QuantizeAndDequantizeV2

All Implemented Interfaces:

AutoCloseable

Enclosing class:

tensorflow

@Namespace(value="tensorflow::ops")
 @NoOffset
public static class tensorflow.QuantizeAndDequantizeV2
extends Pointer

Quantizes then dequantizes a tensor. This op simulates the precision loss from the quantized forward pass by: 1. Quantizing the tensor to fixed point numbers, which should match the target quantization method when it is used in inference. 2. Dequantizing it back to floating point numbers for the following ops, most likely matmul. There are different ways to quantize. This version uses only scaling, so 0.0 maps to 0. From the specified 'num_bits' in the quantized output type, it determines minimum and maximum representable quantized values. e.g. * [-128, 127] for signed, num_bits = 8, or * [0, 255] for unsigned, num_bits = 8. If range_given == False, the initial input_min, input_max will be determined automatically as the minimum and maximum values in the input tensor, otherwise the specified values of input_min, input_max are used. Note: If the input_min, input_max are specified, they do not need to equal the actual minimum and maximum values in the tensor. e.g. in some cases it may be beneficial to specify these values such that the low probability extremes of the input distribution are clipped. This op determines the maximum scale_factor that would map the initial [input_min, input_max] range to a range that lies within the representable quantized range. It determines the scale from one of input_min and input_max, then updates the other one to maximize the respresentable range. e.g. * if the output is signed, num_bits = 8, [input_min, input_max] = [-10.0, 5.0]: it would use a scale_factor of -128 / -10.0 = 12.8 In this case, it would update input_max to be 127 / 12.8 = 9.921875 * if the output is signed, num_bits = 8, [input_min, input_max] = [-10.0, 10.0]: it would use a scale_factor of 127 / 10.0 = 12.7 In this case, it would update input_min to be 128.0 / 12.7 = -10.07874 * if the output is unsigned, input_min is forced to be 0, and only the specified input_max is used. After determining the scale_factor and updating the input range, it applies the following to each value in the 'input' tensor. output = round(clamp(value, input_min, input_max) * scale_factor) / scale_factor. Arguments: * scope: A Scope object * input: Tensor to quantize and then dequantize. * input_min: If range_given == True, this specifies the minimum input value that needs to be represented, otherwise it is determined from the min value of the input tensor. * input_max: If range_given == True, this specifies the maximum input value that needs to be represented, otherwise it is determined from the max value of the input tensor. Optional attributes (see Attrs): * signed_input: Whether the quantization is signed or unsigned. (actually this parameter should have been called signed_output) * num_bits: The bitwidth of the quantization. * range_given: Whether the range is given or should be determined from the input tensor. Returns: * Output: The output tensor.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	tensorflow.QuantizeAndDequantizeV2.Attrs Optional attribute setters for QuantizeAndDequantizeV2

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
QuantizeAndDequantizeV2(Pointer p) Pointer cast constructor.
QuantizeAndDequantizeV2(tensorflow.Scope scope, tensorflow.Input input, tensorflow.Input input_min, tensorflow.Input input_max)
QuantizeAndDequantizeV2(tensorflow.Scope scope, tensorflow.Input input, tensorflow.Input input_min, tensorflow.Input input_max, tensorflow.QuantizeAndDequantizeV2.Attrs attrs)

Method Summary

Modifier and Type	Method and Description
tensorflow.Input	asInput()
tensorflow.Output	asOutput()
tensorflow.Node	node()
static tensorflow.QuantizeAndDequantizeV2.Attrs	NumBits(long x)
tensorflow.Operation	operation()
tensorflow.QuantizeAndDequantizeV2	operation(tensorflow.Operation operation)
tensorflow.Output	output()
tensorflow.QuantizeAndDequantizeV2	output(tensorflow.Output output)
static tensorflow.QuantizeAndDequantizeV2.Attrs	RangeGiven(boolean x)
static tensorflow.QuantizeAndDequantizeV2.Attrs	SignedInput(boolean x)

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, position, put, realloc, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

QuantizeAndDequantizeV2

public QuantizeAndDequantizeV2(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

QuantizeAndDequantizeV2

public QuantizeAndDequantizeV2(@Const @ByRef
                               tensorflow.Scope scope,
                               @ByVal
                               tensorflow.Input input,
                               @ByVal
                               tensorflow.Input input_min,
                               @ByVal
                               tensorflow.Input input_max)

QuantizeAndDequantizeV2

public QuantizeAndDequantizeV2(@Const @ByRef
                               tensorflow.Scope scope,
                               @ByVal
                               tensorflow.Input input,
                               @ByVal
                               tensorflow.Input input_min,
                               @ByVal
                               tensorflow.Input input_max,
                               @Const @ByRef
                               tensorflow.QuantizeAndDequantizeV2.Attrs attrs)

Method Detail

asOutput

@ByVal
 @Name(value="operator tensorflow::Output")
public tensorflow.Output asOutput()

asInput

@ByVal
 @Name(value="operator tensorflow::Input")
public tensorflow.Input asInput()

node

public tensorflow.Node node()

SignedInput

@ByVal
public static tensorflow.QuantizeAndDequantizeV2.Attrs SignedInput(@Cast(value="bool")
                                                                           boolean x)

NumBits

@ByVal
public static tensorflow.QuantizeAndDequantizeV2.Attrs NumBits(@Cast(value="tensorflow::int64")
                                                                       long x)

RangeGiven

@ByVal
public static tensorflow.QuantizeAndDequantizeV2.Attrs RangeGiven(@Cast(value="bool")
                                                                          boolean x)

operation

@ByRef
public tensorflow.Operation operation()

operation

public tensorflow.QuantizeAndDequantizeV2 operation(tensorflow.Operation operation)

output

@ByRef
public tensorflow.Output output()

output

public tensorflow.QuantizeAndDequantizeV2 output(tensorflow.Output output)