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[ONNX][Code refactor] ONNX GroupNormalization - Optimize getting inputs

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* Optimize getting inputs and use more const

* Fix typo
This commit is contained in:
Katarzyna Mitrus 2023-10-31 08:19:11 +01:00 committed by GitHub
parent b13cb8ce12
commit 1384471849
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1 changed files with 22 additions and 18 deletions
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src/frontends/onnx/frontend/src/op/group_normalization.cpp
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@ -12,30 +12,34 @@ namespace onnx_import {
namespace op { namespace op {
namespace set_1 { namespace set_1 {
OutputVector group_normalization(const Node& node) { OutputVector group_normalization(const Node& node) {
const auto data = node.get_ng_inputs().at(0); // Shape [N, C, ...] const auto inputs = node.get_ng_inputs();
auto scale = node.get_ng_inputs().at(1); // Shape [num_groups] OPENVINO_ASSERT(inputs.size() == 3);
auto bias = node.get_ng_inputs().at(2); // Shape [num_groups]
auto eps = node.get_attribute_value<float>("epsilon", 1e-05f); const auto& data = inputs[0]; // Shape [N, C, ...]
auto num_groups = node.get_attribute_value<int64_t>("num_groups"); const auto& scale = inputs[1]; // Shape [num_groups]
const auto& bias = inputs[2]; // Shape [num_groups]
auto zero = default_opset::Constant::create(element::i64, Shape{1}, {0}); const auto eps = node.get_attribute_value<float>("epsilon", 1e-05f);
auto one = default_opset::Constant::create(element::i64, Shape{1}, {1}); const auto num_groups = node.get_attribute_value<int64_t>("num_groups");
auto c_dim = std::make_shared<default_opset::Gather>(std::make_shared<default_opset::ShapeOf>(data), one, zero);
auto g_dim = default_opset::Constant::create(element::i64, Shape{1}, {num_groups});
auto c_g_div = std::make_shared<default_opset::Divide>(c_dim, g_dim); const auto zero = default_opset::Constant::create(element::i64, Shape{1}, {0});
const auto one = default_opset::Constant::create(element::i64, Shape{1}, {1});
const auto c_dim =
std::make_shared<default_opset::Gather>(std::make_shared<default_opset::ShapeOf>(data), one, zero);
const auto g_dim = default_opset::Constant::create(element::i64, Shape{1}, {num_groups});
const auto c_g_div = std::make_shared<default_opset::Divide>(c_dim, g_dim);
// Adjust scale and bias shape, [G] -> [G, C/G] -> [C] // Adjust scale and bias shape, [G] -> [G, C/G] -> [C]
scale = std::make_shared<default_opset::Unsqueeze>(scale, one); const auto scale_unsq = std::make_shared<default_opset::Unsqueeze>(scale, one);
auto broadcast_scale = const auto broadcast_scale =
std::make_shared<default_opset::Broadcast>(scale, c_g_div, ov::op::BroadcastType::BIDIRECTIONAL); std::make_shared<default_opset::Broadcast>(scale_unsq, c_g_div, ov::op::BroadcastType::BIDIRECTIONAL);
auto c_scale = std::make_shared<default_opset::Reshape>(broadcast_scale, c_dim, false); const auto c_scale = std::make_shared<default_opset::Reshape>(broadcast_scale, c_dim, false);
bias = std::make_shared<default_opset::Unsqueeze>(bias, one); const auto bias_unsq = std::make_shared<default_opset::Unsqueeze>(bias, one);
auto broadcast_bias = const auto broadcast_bias =
std::make_shared<default_opset::Broadcast>(bias, c_g_div, ov::op::BroadcastType::BIDIRECTIONAL); std::make_shared<default_opset::Broadcast>(bias_unsq, c_g_div, ov::op::BroadcastType::BIDIRECTIONAL);
auto c_bias = std::make_shared<default_opset::Reshape>(broadcast_bias, c_dim, false); const auto c_bias = std::make_shared<default_opset::Reshape>(broadcast_bias, c_dim, false);
return {std::make_shared<default_opset::GroupNormalization>(data, c_scale, c_bias, num_groups, eps)}; return {std::make_shared<default_opset::GroupNormalization>(data, c_scale, c_bias, num_groups, eps)};
} }