float real_multiplier = 0.0;
int32_t output_multiplier = 0;
int32_t output_shift = 0;
- GetQuantizedConvolutionMultipler(_inputShape, _kernelShape, _biasShape, _outputShape,
- &real_multiplier);
+ GetQuantizedConvolutionMultiplier(_inputShape, _kernelShape, _biasShape, _outputShape,
+ &real_multiplier);
QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
nnfw::cker::ConvParams op_params;
float real_multiplier = 0.0;
int32_t output_multiplier = 0;
int32_t output_shift = 0;
- GetQuantizedConvolutionMultipler(_inputShape, _kernelShape, _biasShape, _outputShape,
- &real_multiplier);
+ GetQuantizedConvolutionMultiplier(_inputShape, _kernelShape, _biasShape, _outputShape,
+ &real_multiplier);
QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
nnfw::cker::DepthwiseConvParams op_params;
int32_t output_shift = 0;
int32_t output_activation_min = 0;
int32_t output_activation_max = 0;
- GetQuantizedConvolutionMultipler(_inputShape, _weightsShape, _biasShape, _outputShape,
- &real_multiplier);
+ GetQuantizedConvolutionMultiplier(_inputShape, _weightsShape, _biasShape, _outputShape,
+ &real_multiplier);
QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
CalculateActivationRangeUint8(_activation, _outputShape, &output_activation_min,
&output_activation_max);
*quantized_multiplier = static_cast<int32_t>(q_fixed);
}
-void GetQuantizedConvolutionMultipler(const Shape &inputShape, const Shape &filterShape,
- const Shape &biasShape, const Shape &outputShape,
- float *multiplier)
+void GetQuantizedConvolutionMultiplier(const Shape &inputShape, const Shape &filterShape,
+ const Shape &biasShape, const Shape &outputShape,
+ float *multiplier)
{
const float input_product_scale = inputShape.scale * filterShape.scale;
const float bias_scale = biasShape.scale;
void QuantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift);
-void GetQuantizedConvolutionMultipler(const Shape &inputShape, const Shape &filterShape,
- const Shape &biasShape, const Shape &outputShape,
- float *multiplier);
+void GetQuantizedConvolutionMultiplier(const Shape &inputShape, const Shape &filterShape,
+ const Shape &biasShape, const Shape &outputShape,
+ float *multiplier);
void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier,
int *left_shift);
const ::internal::tflite::operand::Index hstride_index{node.param().hstride_index};
const ::internal::tflite::operand::Index padding_index{node.param().padding_index};
- const ::internal::tflite::operand::Index multipler_index{node.param().multipler_index};
+ const ::internal::tflite::operand::Index multiplier_index{node.param().multiplier_index};
const ::internal::tflite::operand::Index activation_index{node.param().activation_index};
const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature();
const auto ker_shape = _ctx.at(ker_index).shape().asFeature();
const auto bias_size = _ctx.at(bias_index).shape().asVector();
- auto multiplier = _ctx.at(multipler_index).asScalar<int>();
+ auto multiplier = _ctx.at(multiplier_index).asScalar<int>();
assert(ker_shape.C == bias_size);
assert(ker_shape.C == ifm_shape.C * multiplier);
const ::internal::tflite::operand::Index padding_top_index{node.param().padding_top_index};
const ::internal::tflite::operand::Index padding_bottom_index{node.param().padding_bottom_index};
- const ::internal::tflite::operand::Index multipler_index{node.param().multipler_index};
+ const ::internal::tflite::operand::Index multiplier_index{node.param().multiplier_index};
const ::internal::tflite::operand::Index activation_index{node.param().activation_index};
const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature();
const auto ker_shape = _ctx.at(ker_index).shape().asFeature();
const auto bias_size = _ctx.at(bias_index).shape().asVector();
- auto multiplier = _ctx.at(multipler_index).asScalar<int>();
+ auto multiplier = _ctx.at(multiplier_index).asScalar<int>();
assert(ker_shape.C == bias_size);
assert(ker_shape.C == ifm_shape.C * multiplier);
// 6 -> Padding_bottom index
// 7 -> Stride (width) Index
// 8 -> Stride (height) INdex
- // 9 -> Depthwise Multipler
+ // 9 -> Depthwise Multiplier
// 10 -> Activation Index
ifm_index = inputs[0];
ker_index = inputs[1];
padding_bottom_index = inputs[6];
hstride_index = inputs[7];
vstride_index = inputs[8];
- multipler_index = inputs[9];
+ multiplier_index = inputs[9];
activation_index = inputs[10];
}
// 3 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index
// 4 -> Stride (width) Index
// 5 -> Stride (height) INdex
- // 6 -> Depthwise Multipler
+ // 6 -> Depthwise Multiplier
// 7 -> Activation Index
ifm_index = inputs[0];
ker_index = inputs[1];
padding_index = inputs[3];
hstride_index = inputs[4];
vstride_index = inputs[5];
- multipler_index = inputs[6];
+ multiplier_index = inputs[6];
activation_index = inputs[7];
}
int32_t padding_top_index; /**< Index of padding top */
int32_t padding_bottom_index; /**< Index of padding bottom */
- int32_t multipler_index; /**< Index of multipler */
+ int32_t multiplier_index; /**< Index of multipler */
int32_t activation_index; /**< Index of activation */
/**
* @brief Construct as default
int32_t vstride_index; /**< Index of vertical stride */
int32_t padding_index; /**< Index of padding */
- int32_t multipler_index; /**< Index of multipler */
+ int32_t multiplier_index; /**< Index of multipler */
int32_t activation_index; /**< Index of activation */
/**
* @brief Construct as default