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TFLu: Update RELU #39868

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merged 2 commits into from
Jul 16, 2020
Merged

TFLu: Update RELU #39868

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43d8a82
TFLu: Update RELU
mansnils May 25, 2020
8af3689
TFlu: Fix implicit conversion error in relu op
mansnils Jul 2, 2020
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48 changes: 38 additions & 10 deletions tensorflow/lite/micro/kernels/activations.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@ limitations under the License.
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/micro_utils.h"
#include "tensorflow/lite/kernels/internal/types.h"

namespace tflite {
namespace ops {
Expand All @@ -31,14 +32,43 @@ constexpr int kInputTensor = 0;
constexpr int kOutputTensor = 0;

template <typename Q>
inline void ReluQuantized(int32_t lower, const RuntimeShape& input_shape,
const Q* input_data, const RuntimeShape& output_shape,
Q* output_data) {
inline void ReluQuantized(const TfLiteTensor* input, TfLiteTensor* output,
const Q* input_data, Q* output_data) {
ReluParams params;
float act_min = 0.0;
float act_max = std::numeric_limits<float>::infinity();
double real_multiplier = static_cast<double>(input->params.scale / output->params.scale);

const RuntimeShape input_shape = GetTensorShape(input);
const RuntimeShape output_shape = GetTensorShape(output);

QuantizeMultiplier(real_multiplier, &params.output_multiplier,
&params.output_shift);

params.quantized_activation_min =
std::max(static_cast<int32_t>(std::numeric_limits<Q>::min()),
output->params.zero_point +
static_cast<int32>(roundf(act_min / output->params.scale)));
params.quantized_activation_max =
act_max == std::numeric_limits<float>::infinity()
? static_cast<int32_t>(std::numeric_limits<Q>::max())
: std::min(
static_cast<int32_t>(std::numeric_limits<Q>::max()),
output->params.zero_point +
static_cast<int32>(roundf(act_max / output->params.scale)));
params.input_offset = input->params.zero_point;
params.output_offset = output->params.zero_point;

const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const Q val = input_data[i];
const Q clamped = val < lower ? lower : val;
output_data[i] = clamped;
const int32 val = static_cast<int32_t>(input_data[i]);
int32 clamped = params.output_offset +
MultiplyByQuantizedMultiplier(val - params.input_offset,
params.output_multiplier,
params.output_shift);
clamped = std::max(params.quantized_activation_min, clamped);
clamped = std::min(params.quantized_activation_max, clamped);
output_data[i] = static_cast<Q>(clamped);
}
}

Expand Down Expand Up @@ -93,16 +123,14 @@ TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) {
return kTfLiteOk;
}
case kTfLiteInt8: {
ReluQuantized<int8_t>(input->params.zero_point, GetTensorShape(input),
ReluQuantized<int8_t>(input, output,
GetTensorData<int8_t>(input),
GetTensorShape(output),
GetTensorData<int8_t>(output));
return kTfLiteOk;
}
case kTfLiteUInt8: {
ReluQuantized<uint8_t>(input->params.zero_point, GetTensorShape(input),
ReluQuantized<uint8_t>(input, output,
GetTensorData<uint8_t>(input),
GetTensorShape(output),
GetTensorData<uint8_t>(output));
return kTfLiteOk;
}
Expand Down