File size: 6,324 Bytes
25ade36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | /* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/reference/cumsum.h"
#include "edge-impulse-sdk/tensorflow/lite/c/common.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/quantization_util.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/types.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/kernel_util.h"
#include "edge-impulse-sdk/tensorflow/lite/micro/kernels/kernel_util.h"
#include "edge-impulse-sdk/tensorflow/lite/micro/micro_log.h"
namespace tflite {
namespace {
constexpr int kInputTensor = 0;
constexpr int kAxisTensor = 1;
constexpr int kOutputTensor = 0;
constexpr int kCumSumIntegerShift = 20;
// only used with INT8 tensors
struct OpData {
int32_t output_activation_min;
int32_t output_activation_max;
int32_t input_offset;
int32_t output_offset;
int32_t input_multiplier;
int32_t output_multiplier;
int input_shift;
int output_shift;
int left_shift;
};
TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
MicroContext* micro_context = GetMicroContext(context);
TfLiteTensor* input =
micro_context->AllocateTempInputTensor(node, kInputTensor);
TfLiteTensor* axis =
micro_context->AllocateTempInputTensor(node, kAxisTensor);
TF_LITE_ENSURE(context,
input->type == kTfLiteFloat32 || input->type == kTfLiteInt8);
TF_LITE_ENSURE_EQ(context, axis->type, kTfLiteInt32);
TF_LITE_ENSURE_EQ(context, NumElements(axis), 1);
TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
TfLiteTensor* output =
micro_context->AllocateTempOutputTensor(node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, input->type, output->type);
TF_LITE_ENSURE(context, HaveSameShapes(input, output));
if (output->type == kTfLiteInt8) {
node->user_data =
context->AllocatePersistentBuffer(context, sizeof(OpData));
OpData* data = static_cast<OpData*>(node->user_data);
// 8bit -> 8bit general quantized path, with general rescalings
data->input_offset = -input->params.zero_point;
data->output_offset = output->params.zero_point;
data->left_shift = kCumSumIntegerShift;
const double twice_max_input_scale =
2 * static_cast<double>(input->params.scale);
const double real_input_multiplier =
static_cast<double>(input->params.scale) / twice_max_input_scale;
const double real_output_multiplier =
twice_max_input_scale /
((1 << data->left_shift) * static_cast<double>(output->params.scale));
QuantizeMultiplierSmallerThanOneExp(
real_input_multiplier, &data->input_multiplier, &data->input_shift);
QuantizeMultiplierSmallerThanOneExp(
real_output_multiplier, &data->output_multiplier, &data->output_shift);
TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
context, kTfLiteActNone, output, &data->output_activation_min,
&data->output_activation_max));
}
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(axis);
micro_context->DeallocateTempTfLiteTensor(output);
return kTfLiteOk;
}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
return CalculateOpData(context, node);
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, kInputTensor);
const TfLiteEvalTensor* axis_tensor =
tflite::micro::GetEvalInput(context, node, kAxisTensor);
TfLiteEvalTensor* output =
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
auto* cs_params = static_cast<TfLiteCumsumParams*>(node->builtin_data);
auto input_shape = tflite::micro::GetTensorShape(input);
int32_t axis = *tflite::micro::GetTensorData<int32_t>(axis_tensor);
if (axis < 0) axis += input_shape.DimensionsCount();
if (axis < 0 || axis >= input_shape.DimensionsCount()) {
MicroPrintf("CUMSUM Invalid axis: %d", axis);
return kTfLiteError;
}
switch (input->type) {
case kTfLiteFloat32: {
reference_ops::CumSum(tflite::micro::GetTensorData<float>(input),
input_shape, axis, cs_params->exclusive,
cs_params->reverse,
tflite::micro::GetTensorData<float>(output));
return kTfLiteOk;
} break;
case kTfLiteInt8: {
auto* data = static_cast<OpData*>(node->user_data);
ArithmeticParams params;
params.left_shift = data->left_shift;
params.input1_offset = data->input_offset;
params.input1_multiplier = data->input_multiplier;
params.input1_shift = data->input_shift;
params.output_offset = data->output_offset;
params.output_multiplier = data->output_multiplier;
params.output_shift = data->output_shift;
SetActivationParams(data->output_activation_min,
data->output_activation_max, ¶ms);
reference_ops::CumSum(params, tflite::micro::GetTensorData<int8_t>(input),
input_shape, axis, cs_params->exclusive,
cs_params->reverse,
tflite::micro::GetTensorData<int8_t>(output));
return kTfLiteOk;
} break;
default: {
MicroPrintf("CUMSUM only supports FLOAT32 and INT8, got %s.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
}
return kTfLiteError;
}
} // namespace
TfLiteRegistration Register_CUMSUM() {
return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
}
} // namespace tflite
|