add default parameter value to runtime::reference::fake_quantize
This commit is contained in:
Patryk Elszkowski
parent
2e9c6fd752
commit
9d2c00d967
@ -195,106 +195,72 @@ namespace ngraph
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}
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}
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} // namespace fake_quantize_details
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} // namespace fake_quantize_details
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namespace v1
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{
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template <typename T>
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void fake_quantize(const T* const arg,
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const T* const in_low,
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const T* const in_high,
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const T* const out_low,
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const T* const out_high,
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T* const out,
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const Shape& arg_shape,
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const Shape& in_low_shape,
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const Shape& in_high_shape,
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const Shape& out_low_shape,
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const Shape& out_high_shape,
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size_t levels,
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const op::AutoBroadcastSpec& broadcast)
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{
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using namespace fake_quantize_details;
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const FeRound round_mode(FE_TONEAREST);
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if (shape_size(in_low_shape) == 1 && shape_size(in_high_shape) == 1 &&
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shape_size(out_low_shape) == 1 && shape_size(out_high_shape) == 1)
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{
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const size_t arg_size = shape_size(arg_shape);
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const auto q = [=](const T& a) {
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return quantize(a, *in_low, *in_high, *out_low, *out_high, levels);
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};
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for (size_t i = 0; i < arg_size; ++i)
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{
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out[i] = q(arg[i]);
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}
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}
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else
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{
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NGRAPH_CHECK(in_low_shape.size() <= arg_shape.size() &&
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in_high_shape.size() <= arg_shape.size() &&
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out_low_shape.size() <= arg_shape.size() &&
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out_high_shape.size() <= arg_shape.size(),
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"Tensors with inout\\output ranges should have rank less or "
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"equal to data tensor rank equal to ",
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arg_shape.size());
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const QuantizationBound<T> in_low_bound(
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in_low, in_low_shape, arg_shape, broadcast);
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const QuantizationBound<T> in_high_bound(
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in_high, in_high_shape, arg_shape, broadcast);
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const QuantizationBound<T> out_low_bound(
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out_low, out_low_shape, arg_shape, broadcast);
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const QuantizationBound<T> out_high_bound(
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out_high, out_high_shape, arg_shape, broadcast);
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std::vector<size_t> current_dim(arg_shape.size(), 0);
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const auto arg_shape_size = shape_size(arg_shape);
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for (size_t index = 0; index < arg_shape_size; ++index)
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{
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const T in_low_val = in_low_bound.get_value(current_dim, index);
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const T in_high_val = in_high_bound.get_value(current_dim, index);
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const T out_low_val = out_low_bound.get_value(current_dim, index);
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const T out_high_val = out_high_bound.get_value(current_dim, index);
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out[index] = quantize(arg[index],
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in_low_val,
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in_high_val,
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out_low_val,
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out_high_val,
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levels);
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increment_current_dim(current_dim, arg_shape);
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}
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}
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}
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} // namespace v1
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template <typename T>
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template <typename T>
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void fake_quantize(const T* const arg,
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void
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const T* const in_low,
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fake_quantize(const T* const arg,
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const T* const in_high,
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const T* const in_low,
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const T* const out_low,
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const T* const in_high,
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const T* const out_high,
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const T* const out_low,
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T* const out,
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const T* const out_high,
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const Shape& arg_shape,
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T* const out,
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const Shape& in_low_shape,
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const Shape& arg_shape,
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const Shape& in_high_shape,
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const Shape& in_low_shape,
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const Shape& out_low_shape,
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const Shape& in_high_shape,
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const Shape& out_high_shape,
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const Shape& out_low_shape,
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size_t levels)
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const Shape& out_high_shape,
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size_t levels,
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const op::AutoBroadcastSpec& broadcast = op::AutoBroadcastType::NUMPY)
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{
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{
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v1::fake_quantize(arg,
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using namespace fake_quantize_details;
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in_low,
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in_high,
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const FeRound round_mode(FE_TONEAREST);
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out_low,
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out_high,
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if (shape_size(in_low_shape) == 1 && shape_size(in_high_shape) == 1 &&
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out,
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shape_size(out_low_shape) == 1 && shape_size(out_high_shape) == 1)
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arg_shape,
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{
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in_low_shape,
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const size_t arg_size = shape_size(arg_shape);
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in_high_shape,
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const auto q = [=](const T& a) {
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out_low_shape,
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return quantize(a, *in_low, *in_high, *out_low, *out_high, levels);
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out_high_shape,
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};
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levels,
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for (size_t i = 0; i < arg_size; ++i)
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op::AutoBroadcastType::NUMPY);
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{
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out[i] = q(arg[i]);
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}
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}
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else
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{
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NGRAPH_CHECK(in_low_shape.size() <= arg_shape.size() &&
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in_high_shape.size() <= arg_shape.size() &&
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out_low_shape.size() <= arg_shape.size() &&
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out_high_shape.size() <= arg_shape.size(),
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"Tensors with inout\\output ranges should have rank less or "
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"equal to data tensor rank equal to ",
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arg_shape.size());
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const QuantizationBound<T> in_low_bound(
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in_low, in_low_shape, arg_shape, broadcast);
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const QuantizationBound<T> in_high_bound(
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in_high, in_high_shape, arg_shape, broadcast);
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const QuantizationBound<T> out_low_bound(
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out_low, out_low_shape, arg_shape, broadcast);
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const QuantizationBound<T> out_high_bound(
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out_high, out_high_shape, arg_shape, broadcast);
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std::vector<size_t> current_dim(arg_shape.size(), 0);
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const auto arg_shape_size = shape_size(arg_shape);
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for (size_t index = 0; index < arg_shape_size; ++index)
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{
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const T in_low_val = in_low_bound.get_value(current_dim, index);
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const T in_high_val = in_high_bound.get_value(current_dim, index);
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const T out_low_val = out_low_bound.get_value(current_dim, index);
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const T out_high_val = out_high_bound.get_value(current_dim, index);
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out[index] = quantize(
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arg[index], in_low_val, in_high_val, out_low_val, out_high_val, levels);
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increment_current_dim(current_dim, arg_shape);
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}
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}
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}
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}
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} // namespace reference
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} // namespace reference
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} // namespace runtime
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} // namespace runtime
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@ -1173,7 +1173,7 @@ namespace
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info.selected_outputs_shape,
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info.selected_outputs_shape,
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selected_indices.data(),
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selected_indices.data(),
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info.selected_indices_shape,
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info.selected_indices_shape,
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valid_outputs.data());
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valid_outputs.data());
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void* pscores = nullptr;
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void* pscores = nullptr;
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void* pselected_num = nullptr;
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void* pselected_num = nullptr;
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@ -2383,19 +2383,19 @@ namespace
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const HostTensorVector& inputs)
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const HostTensorVector& inputs)
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{
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{
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using T = typename element_type_traits<ET>::value_type;
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using T = typename element_type_traits<ET>::value_type;
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runtime::reference::v1::fake_quantize<T>(inputs[0]->get_data_ptr<const T>(),
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runtime::reference::fake_quantize<T>(inputs[0]->get_data_ptr<const T>(),
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inputs[1]->get_data_ptr<const T>(),
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inputs[1]->get_data_ptr<const T>(),
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inputs[2]->get_data_ptr<const T>(),
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inputs[2]->get_data_ptr<const T>(),
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inputs[3]->get_data_ptr<const T>(),
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inputs[3]->get_data_ptr<const T>(),
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inputs[4]->get_data_ptr<const T>(),
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inputs[4]->get_data_ptr<const T>(),
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outputs[0]->get_data_ptr<T>(),
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outputs[0]->get_data_ptr<T>(),
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op->get_input_shape(0),
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op->get_input_shape(0),
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op->get_input_shape(1),
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op->get_input_shape(1),
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op->get_input_shape(2),
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op->get_input_shape(2),
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op->get_input_shape(3),
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op->get_input_shape(3),
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op->get_input_shape(4),
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op->get_input_shape(4),
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op->get_levels(),
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op->get_levels(),
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op->get_auto_broadcast());
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op->get_auto_broadcast());
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return true;
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return true;
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}
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}
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