量化规范
训练后量化 将浮点模型转换为定点模型。这种转换技术可以缩减模型大小，降低 CPU 和硬件加速器延迟，同时不会降低准确度。
如 ESP32-S3 芯片，存储空间相对有限，在 240 MHz 的情况下每秒乘加累计运算次数 (MACs) 仅达 75 亿次。在这样的芯片上必须要用量化后的模型做推理。您可使用我们提供的 量化工具包 量化浮点模型，或根据 convert.py 使用说明 中的步骤部署定点模型。
全整数量化
模型中的所有数据都需要量化为 8 位或 16 位整数。所有数据包括 - 常量：权重、偏差和激活函数 - 变量：张量，如中间层（激活函数）的输入和输出
8 位或 16 位量化使用以下公式近似表示浮点值：
real\_value = int\_value * 2^{\ exponent}
有符号整数
8 位量化的 int_value 代表一个 int8 的有符号数, 其范围是 [-128, 127]。16 位量化的 int_value 代表一个 int16 的有符号数, 其范围是 [-32768, 32767]。
对称
所有量化后的数据都是对称的，也就是说没有零点（偏差），因此可以节省将零点与其他值相乘的运算时间。
粒度
按张量（又名按层）量化的意思是每个完整张量只有一个指数位，该张量内的所有值都按照该指数位量化。
按通道量化的意思是卷积核的每个通道对应着不同的指数位。
与按张量量化相比，按通道量化通常对于部分模型来说精确度会更高，但也会更耗时间。您可使用 量化工具包 中的 评估器 模拟在芯片上进行量化推理的性能，之后再决定使用哪种量化形式。
16 位量化为确保更快的运算速度，目前仅支持按张量量化。8 位量化支持按张量和按通道两种形式，可让您在性能和速度之间折中。
量化算子规范
以下是 API 的量化要求：
Add2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Input 1:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
AvgPool2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Concat
Input ...:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Inputs and output must have the same exponent
Conv2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Input 1 (Weight):
data_type : int8 / int16
range : [-127, 127] / [-32767, 32767]
granularity: {per-channel / per-tensor for int8} / {per-tensor for int16}
Input 2 (Bias):
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: exponent = output_exponent
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
DepthwiseConv2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Input 1 (Weight):
data_type : int8 / int16
range : [-127, 127] / [-32767, 32767]
granularity: {per-channel / per-tensor for int8} / {per-tensor for int16}
Input 2 (Bias):
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: exponent = output_exponent
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
ExpandDims
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
Flatten
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
FullyConnected
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Input 1 (Weight):
data_type : int8 / int16
range : [-127, 127] / [-32767, 32767]
granularity: {per-channel / per-tensor for int8} / {per-tensor for int16}
Input 2 (Bias):
data_type : int8 / int16
range : {[-32768, 32767] for int8 per-channel / [-128, 127] for int8 per-tensor} / {[-32768, 32767] for int16}
granularity: {per-channel / per-tensor for int8} / {per-tensor for int16}
restriction: {exponent = input_exponent + weight_exponent + 4 for per-channel / exponent = output_exponent for per-tensor}
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
GlobalAveragePool2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
GlobalMaxPool2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
LeakyReLU
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Input 1 (Alpha):
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
Max2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
MaxPool2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
Min2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
Mul2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Input 1:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
PReLU
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Input 1 (Alpha):
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
ReLU
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
Reshape
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
Squeeze
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent
Sub2D
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Input 1:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Transpose
Input 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
Output 0:
data_type : int8 / int16
range : [-128, 127] / [-32768, 32767]
granularity: per-tensor
restriction: Input and output must have the same exponent