#include "OnnxParser.hpp"
#include <armnn/Descriptors.hpp>
-#include <armnn/Utils.hpp>
#include <VerificationHelpers.hpp>
#include <boost/format.hpp>
{ "BatchNormalization", &OnnxParser::ParseBatchNormalization},
{ "GlobalAveragePool", &OnnxParser::ParseGlobalAveragePool},
{ "AveragePool", &OnnxParser::ParseAveragePool },
+ { "Clip", &OnnxParser::ParseClip },
{ "Constant", &OnnxParser::ParseConstant },
{ "MaxPool", &OnnxParser::ParseMaxPool },
{ "Reshape", &OnnxParser::ParseReshape },
void OnnxParser::ParseActivation(const onnx::NodeProto& node, const armnn::ActivationFunction func)
{
- CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1);
+ CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1, 3);
CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1);
VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT));
ActivationDescriptor desc;
desc.m_Function = func;
+ if (func == ActivationFunction::BoundedReLu)
+ {
+ desc.m_A = node.input(2).empty() ? std::numeric_limits<float>::max() : std::stof(node.input(2));
+ desc.m_B = node.input(1).empty() ? std::numeric_limits<float>::lowest() : std::stof(node.input(1));
+ }
+
IConnectableLayer* const layer = m_Network->AddActivationLayer(desc, node.name().c_str());
BOOST_ASSERT(layer != nullptr);
RegisterOutputSlots(layer, {node.output(0)});
}
+void OnnxParser::ParseClip(const onnx::NodeProto& node)
+{
+ ParseActivation(node, ActivationFunction::BoundedReLu);
+}
+
void OnnxParser::ParseSigmoid(const onnx::NodeProto& node)
{
ParseActivation(node, ActivationFunction::Sigmoid);
--- /dev/null
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <boost/test/unit_test.hpp>
+#include "armnnOnnxParser/IOnnxParser.hpp"
+#include "ParserPrototxtFixture.hpp"
+
+BOOST_AUTO_TEST_SUITE(OnnxParser)
+
+struct ClipMainFixture : public armnnUtils::ParserPrototxtFixture<armnnOnnxParser::IOnnxParser>
+{
+ ClipMainFixture(std::string min, std::string max)
+ {
+ m_Prototext = R"(
+ ir_version: 3
+ producer_name: "CNTK"
+ producer_version: "2.5.1"
+ domain: "ai.cntk"
+ model_version: 1
+ graph {
+ name: "CNTKGraph"
+ input {
+ name: "Input"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 5
+ }
+ }
+ }
+ }
+ }
+ node {
+ input: "Input"
+ input:")" + min + R"("
+ input:")" + max + R"("
+ output: "Output"
+ name: "ActivationLayer"
+ op_type: "Clip"
+ }
+ output {
+ name: "Output"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 5
+ }
+ }
+ }
+ }
+ }
+ }
+ opset_import {
+ version: 7
+ })";
+ Setup();
+ }
+};
+
+struct ClipFixture : ClipMainFixture
+{
+ ClipFixture() : ClipMainFixture("2", "3.5") {}
+};
+
+BOOST_FIXTURE_TEST_CASE(ValidClipTest, ClipFixture)
+{
+ RunTest<1>({{"Input", { -1.5f, 1.25f, 3.5f, 8.0, 2.5}}},
+ {{ "Output", { 2.0f, 2.0f, 3.5f, 3.5, 2.5}}});
+}
+
+struct ClipNoMaxInputFixture : ClipMainFixture
+{
+ ClipNoMaxInputFixture() : ClipMainFixture("0", std::string()) {}
+};
+
+BOOST_FIXTURE_TEST_CASE(ValidNoMaxInputClipTest, ClipNoMaxInputFixture)
+{
+ RunTest<1>({{"Input", { -1.5f, -5.25f, -0.5f, 8.0f, std::numeric_limits<float>::max() }}},
+ {{ "Output", { 0.0f, 0.0f, 0.0f, 8.0f, std::numeric_limits<float>::max() }}});
+}
+
+struct ClipNoMinInputFixture : ClipMainFixture
+{
+ ClipNoMinInputFixture() : ClipMainFixture(std::string(), "6") {}
+};
+
+BOOST_FIXTURE_TEST_CASE(ValidNoMinInputClipTest, ClipNoMinInputFixture)
+{
+ RunTest<1>({{"Input", { std::numeric_limits<float>::lowest(), -5.25f, -0.5f, 8.0f, 200.0f }}},
+ {{ "Output", { std::numeric_limits<float>::lowest(), -5.25f, -0.5f, 6.0f, 6.0f }}});
+}
+
+struct ClipNoInputFixture : ClipMainFixture
+{
+ ClipNoInputFixture() : ClipMainFixture(std::string(), std::string()) {}
+};
+
+BOOST_FIXTURE_TEST_CASE(ValidNoInputClipTest, ClipNoInputFixture)
+{
+ RunTest<1>({{"Input", { std::numeric_limits<float>::lowest(), -1.25f, 3.5f, 8.0f,
+ std::numeric_limits<float>::max()}}},
+ {{ "Output", { std::numeric_limits<float>::lowest(), -1.25f, 3.5f, 8.0f,
+ std::numeric_limits<float>::max()}}});
+}
+
+BOOST_AUTO_TEST_SUITE_END()