connectInput(layer, ptr_inputs, num_data_bytes_in, 0, 0);
if (gnaFlags->sw_fp32) {
+ IE_ASSERT(quantized == nullptr);
gnamem->readonly().push_value(ptr_weights, power.scale, num_rows_out, 64);
gnamem->readonly().push_value(ptr_biases, power.offset, num_rows_out, 64);
} else {
+ IE_ASSERT(quantized != nullptr);
auto quantizedScale = FLOAT_TO_INT16(std::min(quantized->_weights_quant.scale * power.scale,
static_cast<float>(INT16_MAX)));
auto quantizedOffset = FLOAT_TO_INT32(std::min(quantized->_dst_quant.scale * power.offset,
protected:
std::string _pluginName = "GNA";
- Config config;
+ Config config {};
std::shared_ptr<GNAPluginNS::backend::AMIntelDNN> dnn;
std::shared_ptr<GNAPluginNS::GNAFlags> gnaFlags;
std::shared_ptr<GNAPluginNS::gna_memory_type> gnamem;
#include <cstdint>
typedef struct {
- double slope;
+ double slope {};
uint64_t slope_scale = 0;
- uint32_t slope_scale_index;
+ uint32_t slope_scale_index {};
} pwl_gna_slope_scale_t;
pwl_gna_slope_scale_t gna_slope(const double slope, const double in_scale, const double out_scale);
};
auto getNthChild = [](CNNLayerPtr l, int N) {
auto first = getInputTo(l->outData.front()).begin();
+ auto last = getInputTo(l->outData.front()).end();
+ IE_ASSERT(first != last);
+ IE_ASSERT(N <= std::distance(first, last));
std::advance(first, N);
return first->second;
};
for (size_t k = 0; k != totalSplits; k++) {
auto eltwiseRaw = std::make_shared<EltwiseLayer>(
LayerParams{l->name + "/eltwise/" + std::to_string(k), "Eltwise", Precision::FP32});
+ IE_ASSERT(eltwiseRaw != nullptr);
eltwiseRaw->_operation = masterEltwise->_operation;
eltwiseRaw->coeff = masterEltwise->coeff;
auto eltwise = quantized ? InferenceEngine::injectData<QuantizedLayerParams>(eltwiseRaw) : eltwiseRaw;