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Add Int8 -> Int8 requantize operation. 

PiperOrigin-RevId: 313800318
Change-Id: I04650f59f8551b482648a7468fb1b2773a64b415
This commit is contained in:
A. Unique TensorFlower 2020-05-29 10:01:29 -07:00 committed by TensorFlower Gardener
parent a475c198ec
commit 57749ce64f
2 changed files with 68 additions and 14 deletions
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25
tensorflow/lite/micro/kernels/quantize.cc
View File

@ -63,12 +63,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE(context, affine_quantization->scale); TF_LITE_ENSURE(context, affine_quantization->scale);
TF_LITE_ENSURE(context, affine_quantization->scale->size == 1); TF_LITE_ENSURE(context, affine_quantization->scale->size == 1);
TF_LITE_ENSURE(context, TF_LITE_ENSURE(context, input->type == kTfLiteFloat32 ||
input->type == kTfLiteFloat32 || input->type == kTfLiteInt16); input->type == kTfLiteInt16 ||
input->type == kTfLiteInt8);
TF_LITE_ENSURE(context, TF_LITE_ENSURE(context,
output->type == kTfLiteUInt8 || output->type == kTfLiteInt8); output->type == kTfLiteUInt8 || output->type == kTfLiteInt8);
if (input->type == kTfLiteInt16 && output->type == kTfLiteInt8) { if ((input->type == kTfLiteInt16 || input->type == kTfLiteInt8) &&
output->type == kTfLiteInt8) {
double effective_scale = double effective_scale =
static_cast<double>(input->params.scale / output->params.scale); static_cast<double>(input->params.scale / output->params.scale);
@ -122,6 +124,23 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteTypeGetName(output->type)); TfLiteTypeGetName(output->type));
return kTfLiteError; return kTfLiteError;
} }
} else if (input->type == kTfLiteInt8) {
// Int8 to Int8 requantization, required if the input and output tensors
// have different scales and/or zero points.
size_t size = ElementCount(*input->dims);
switch (output->type) {
case kTfLiteInt8:
reference_ops::Requantize(
GetTensorData<int8_t>(input), size, data->output_multiplier,
data->output_shift, input->params.zero_point,
output->params.zero_point, GetTensorData<int8_t>(output));
break;
default:
TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
TfLiteTypeGetName(input->type),
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
} else { } else {
TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
TfLiteTypeGetName(input->type), TfLiteTypeGetName(input->type),

49
tensorflow/lite/micro/kernels/quantize_test.cc
View File

@ -110,13 +110,13 @@ void TestQuantizeFloat(const int* input_dims_data, const float* input_data,
scale, zero_point, output_dims_count, output_data); scale, zero_point, output_dims_count, output_data);
} }
template <typename T> template <typename InputType, typename OutputType>
void TestQuantizeInt16(const int* input_dims_data, const float* input_data, void TestRequantize(const int* input_dims_data, const float* input_data,
int16_t* input_quantized, const float input_scale, InputType* input_quantized, const float input_scale,
const int input_zero_point, const int* output_dims_data, const int input_zero_point, const int* output_dims_data,
const float* golden, T* golden_quantized, const float* golden, OutputType* golden_quantized,
const float output_scale, const int output_zero_point, const float output_scale, const int output_zero_point,
T* output_data) { OutputType* output_data) {
TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data); TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data); TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data);
const int output_dims_count = ElementCount(*output_dims); const int output_dims_count = ElementCount(*output_dims);
@ -212,10 +212,45 @@ TF_LITE_MICRO_TEST(QuantizeOpTestInt16toInt8) {
const float output_scale = 0.5; const float output_scale = 0.5;
const int output_zero_point = 0; const int output_zero_point = 0;
int8_t output_quantized[length]; int8_t output_quantized[length];
int8_t values_quantized[length];
int16_t input_quantized[length]; int16_t input_quantized[length];
tflite::testing::TestQuantizeInt16(dims, values, input_quantized, input_scale, tflite::testing::TestRequantize(dims, values, input_quantized, input_scale,
input_zero_point, dims, values, input_zero_point, dims, values,
output_quantized, output_scale, values_quantized, output_scale,
output_zero_point, output_quantized);
}
TF_LITE_MICRO_TEST(QuantizeOpTestInt8toInt8) {
const int length = 10;
const int dims[] = {2, 2, 5};
const float values[] = {-64, -62, -60, -58, -56, 54, 56, 58, 60, 62};
const float input_scale = 2.f;
const int input_zero_point = 0;
const float output_scale = 0.5;
const int output_zero_point = 32;
int8_t output_quantized[length];
int8_t values_quantized[length];
int8_t input_quantized[length];
tflite::testing::TestRequantize(dims, values, input_quantized, input_scale,
input_zero_point, dims, values,
values_quantized, output_scale,
output_zero_point, output_quantized);
}
TF_LITE_MICRO_TEST(QuantizeOpTestInt8toInt8NoZeroPoint) {
const int length = 10;
const int dims[] = {2, 2, 5};
const float values[] = {-32, -31, -30, -29, -28, 27, 28, 29, 30, 31};
const float input_scale = 1.f;
const int input_zero_point = 0;
const float output_scale = 0.5;
const int output_zero_point = 0;
int8_t output_quantized[length];
int8_t values_quantized[length];
int8_t input_quantized[length];
tflite::testing::TestRequantize(dims, values, input_quantized, input_scale,
input_zero_point, dims, values,
values_quantized, output_scale,
output_zero_point, output_quantized); output_zero_point, output_quantized);
} }