IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Reverted GetConvData helpers

Browse Source
This commit is contained in:
Mikhail Ryzhov 2023-03-23 10:15:42 +01:00
parent d78f7e7fc6
commit 8b81867d18
2 changed files with 57 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

48
src/plugins/intel_gna/src/transformations/utils/transformation_helper.cpp
View File

@ -18,6 +18,54 @@ namespace intel_gna {
namespace pass {
namespace helper {
void GetConvData(std::shared_ptr<ngraph::opset7::Convolution> conv, ConvData& conv_data) {
OPENVINO_ASSERT(conv);
conv_data.output_height = conv->get_output_shape(0)[2];
conv_data.output_width = conv->get_output_shape(0)[3];
conv_data.input_channel_count = conv->input_value(0).get_shape()[1];
conv_data.input_height = conv->input_value(0).get_shape()[2];
conv_data.input_width = conv->input_value(0).get_shape()[3];
conv_data.filter_count = conv->input_value(1).get_shape()[0];
conv_data.filter_channel_count = conv->input_value(1).get_shape()[1];
conv_data.filter_height = conv->input_value(1).get_shape()[2];
conv_data.filter_width = conv->input_value(1).get_shape()[3];
conv_data.filter_dilation_height = conv->get_dilations()[0];
conv_data.filter_dilation_width = conv->get_dilations()[1];
conv_data.filter_stride_height = conv->get_strides()[0];
conv_data.filter_stride_width = conv->get_strides()[1];
conv_data.output_channel_count = conv_data.filter_count;
conv_data.pads_begin_height = conv->get_pads_begin()[0];
conv_data.pads_begin_width = conv->get_pads_begin()[1];
conv_data.pads_end_height = conv->get_pads_end()[0];
conv_data.pads_end_width = conv->get_pads_end()[1];
conv_data.padding_type = conv->get_auto_pad();
conv_data.element_type = conv->get_element_type();
}
void GetConvData(std::shared_ptr<ov::intel_gna::op::GNAConvolution> conv, ConvData& conv_data) {
OPENVINO_ASSERT(conv);
conv_data.output_height = conv->get_output_shape(0)[2];
conv_data.output_width = conv->get_output_shape(0)[3];
conv_data.input_channel_count = conv->input_value(0).get_shape()[3];
conv_data.input_height = conv->input_value(0).get_shape()[1];
conv_data.input_width = conv->input_value(0).get_shape()[2];
conv_data.filter_count = conv->input_value(1).get_shape()[0];
conv_data.filter_channel_count = conv->input_value(1).get_shape()[3];
conv_data.filter_height = conv->input_value(1).get_shape()[1];
conv_data.filter_width = conv->input_value(1).get_shape()[2];
conv_data.filter_dilation_height = conv->get_dilations()[0];
conv_data.filter_dilation_width = conv->get_dilations()[1];
conv_data.filter_stride_height = conv->get_strides()[0];
conv_data.filter_stride_width = conv->get_strides()[1];
conv_data.output_channel_count = conv_data.filter_count;
conv_data.pads_begin_height = conv->get_pads_begin()[0];
conv_data.pads_begin_width = conv->get_pads_begin()[1];
conv_data.pads_end_height = conv->get_pads_end()[0];
conv_data.pads_end_width = conv->get_pads_end()[1];
conv_data.padding_type = conv->get_auto_pad();
conv_data.element_type = conv->get_element_type();
}
std::function<bool(ngraph::Output<ngraph::Node>)> consumers_and_rank(const size_t expected_count,
const ngraph::Dimension& expected_rank) {
return [=](ngraph::Output<ngraph::Node> output) -> bool {

33
src/plugins/intel_gna/src/transformations/utils/transformation_helper.hpp
View File

@ -42,30 +42,15 @@ struct ConvData {
* @param conv_data convolution data structure to put data into
* @return void
*/
template <class T>
void GetConvData(const T& conv, ConvData& conv_data) {
OPENVINO_ASSERT(conv);
conv_data.output_height = conv->get_output_shape(0)[2];
conv_data.output_width = conv->get_output_shape(0)[3];
conv_data.input_channel_count = conv->input_value(0).get_shape()[1];
conv_data.input_height = conv->input_value(0).get_shape()[2];
conv_data.input_width = conv->input_value(0).get_shape()[3];
conv_data.filter_count = conv->input_value(1).get_shape()[0];
conv_data.filter_channel_count = conv->input_value(1).get_shape()[1];
conv_data.filter_height = conv->input_value(1).get_shape()[2];
conv_data.filter_width = conv->input_value(1).get_shape()[3];
conv_data.filter_dilation_height = conv->get_dilations()[0];
conv_data.filter_dilation_width = conv->get_dilations()[1];
conv_data.filter_stride_height = conv->get_strides()[0];
conv_data.filter_stride_width = conv->get_strides()[1];
conv_data.output_channel_count = conv_data.filter_count;
conv_data.pads_begin_height = conv->get_pads_begin()[0];
conv_data.pads_begin_width = conv->get_pads_begin()[1];
conv_data.pads_end_height = conv->get_pads_end()[0];
conv_data.pads_end_width = conv->get_pads_end()[1];
conv_data.padding_type = conv->get_auto_pad();
conv_data.element_type = conv->get_element_type();
}
void GetConvData(std::shared_ptr<ngraph::opset7::Convolution> conv, ConvData& conv_data);
/**
* @brief gets all convolution related data into a struct for further processing
* @param conv GNA custom convolution node to get data of
* @param conv_data convolution data structure to put data into
* @return void
*/
void GetConvData(std::shared_ptr<ov::intel_gna::op::GNAConvolution> conv, ConvData& conv_data);
/**
* @brief ngraph matcher predicate fusing existing predicates for consumers count and rank of a layer