-// Copyright (C) 2018 Intel Corporation
+// Copyright (C) 2018-2019 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "tests_common.hpp"
#include "../test_graph.hpp"
#include <ext_list.hpp>
+#include <ie_builders.hpp>
using namespace ::testing;
using namespace std;
MKLDNNGraphTestClass graph;
graph.CreateGraph(net_reader.getNetwork());
- // TODO: WA for ttps://jira01.devtools.intel.com/browse/CVS-10715
- bool isAvx512 = false;
-
size_t reorders_num = 0;
auto& nodes = graph.getNodes();
for (auto &node : nodes) {
if (node->getType() == MKLDNNPlugin::Reorder) {
reorders_num++;
- if (!isAvx512 && node->getChildEdgeAt(0)->getMemory().GetFormat() == memory::nChw16c)
- isAvx512 = true;
- if (!isAvx512)
ASSERT_EQ(MKLDNNPlugin::Output, node->getChildEdgeAt(0)->getChild()->getType());
}
}
- if (!isAvx512)
- ASSERT_EQ(reorders_num, 1);
- else
- ASSERT_EQ(reorders_num, 3);
+
+ ASSERT_EQ(reorders_num, 1);
}
TEST_F(MKLDNNGraphStructureTests, TestFailedPartDPN92) {
net_reader.SetWeights(weights_ptr);
-
MKLDNNGraphTestClass graph;
graph.CreateGraph(net_reader.getNetwork());
const auto& nodes = graph.getNodes();
ASSERT_EQ(nodes.size(), 12);
- ASSERT_EQ(nodes[0].get()->getType(), MKLDNNPlugin::Type::Input);
- ASSERT_EQ(nodes[1].get()->getType(), MKLDNNPlugin::Type::Split);
- ASSERT_EQ(nodes[2].get()->getType(), MKLDNNPlugin::Type::Reorder);
- ASSERT_EQ(nodes[3].get()->getType(), MKLDNNPlugin::Type::Reshape);
- ASSERT_EQ(nodes[4].get()->getType(), MKLDNNPlugin::Type::Output);
- ASSERT_EQ(nodes[5].get()->getType(), MKLDNNPlugin::Type::Reorder);
- ASSERT_EQ(nodes[6].get()->getType(), MKLDNNPlugin::Type::Reshape);
- ASSERT_EQ(nodes[7].get()->getType(), MKLDNNPlugin::Type::Output);
- ASSERT_EQ(nodes[8].get()->getType(), MKLDNNPlugin::Type::Reorder);
- ASSERT_EQ(nodes[9].get()->getType(), MKLDNNPlugin::Type::Output);
- ASSERT_EQ(nodes[10].get()->getType(), MKLDNNPlugin::Type::Reshape);
- ASSERT_EQ(nodes[11].get()->getType(), MKLDNNPlugin::Type::Output);
+ ASSERT_EQ(nodes[0]->getType(), MKLDNNPlugin::Type::Input);
+ ASSERT_EQ(nodes[1]->getType(), MKLDNNPlugin::Type::Split);
+ ASSERT_EQ(nodes[2]->getType(), MKLDNNPlugin::Type::Reorder);
+ ASSERT_EQ(nodes[3]->getType(), MKLDNNPlugin::Type::Reshape);
+ ASSERT_EQ(nodes[4]->getType(), MKLDNNPlugin::Type::Output);
+ ASSERT_EQ(nodes[5]->getType(), MKLDNNPlugin::Type::Reorder);
+ ASSERT_EQ(nodes[6]->getType(), MKLDNNPlugin::Type::Reshape);
+ ASSERT_EQ(nodes[7]->getType(), MKLDNNPlugin::Type::Output);
+ ASSERT_EQ(nodes[8]->getType(), MKLDNNPlugin::Type::Reorder);
+ ASSERT_EQ(nodes[9]->getType(), MKLDNNPlugin::Type::Reshape);
+ ASSERT_EQ(nodes[10]->getType(), MKLDNNPlugin::Type::Output);
+ ASSERT_EQ(nodes[11]->getType(), MKLDNNPlugin::Type::Output);
InferenceEngine::OutputsDataMap outputs = reader.getNetwork().getOutputsInfo();
std::vector<std::pair<std::string, InferenceEngine::DataPtr>> outputItems = {
compare(*outputBlobs[i], *expectedOutputBlobs[i]);
}
}
+
+TEST_F(MKLDNNGraphStructureTests, TestCreateGraphWithMultipleData_2) {
+ std::string model = R"V0G0N(
+<net name="net" version="2" batch="1">
+ <layers>
+ <layer name="data" type="Input" precision="FP32" id="0">
+ <output>
+ <port id="0">
+ <dim>1</dim>
+ <dim>2</dim>
+ <dim>8</dim>
+ <dim>8</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="1" name="split" precision="FP32" type="Split">
+ <data axis="1"/>
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ <dim>2</dim>
+ <dim>8</dim>
+ <dim>8</dim>
+ </port>
+ </input>
+ <output>
+ <port id="1">
+ <dim>1</dim>
+ <dim>1</dim>
+ <dim>8</dim>
+ <dim>8</dim>
+ </port>
+ <port id="2">
+ <dim>1</dim>
+ <dim>1</dim>
+ <dim>8</dim>
+ <dim>8</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="2" name="power" precision="FP32" type="Power">
+ <data power="1" scale="-1.0" shift="0.0"/>
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ <dim>1</dim>
+ <dim>8</dim>
+ <dim>8</dim>
+ </port>
+ </input>
+ <output>
+ <port id="1">
+ <dim>1</dim>
+ <dim>1</dim>
+ <dim>8</dim>
+ <dim>8</dim>
+ </port>
+ </output>
+ </layer>
+ </layers>
+ <edges>
+ <edge from-layer="0" from-port="0" to-layer="1" to-port="0"/>
+ <edge from-layer="1" from-port="1" to-layer="2" to-port="0"/>
+ </edges>
+</net>
+)V0G0N";
+ using namespace InferenceEngine;
+
+ const size_t H = 8;
+ const size_t W = 8;
+ const size_t imgSz = H * W;
+ const float channel1Value = 1.0;
+ const float channel2Value = 2.0;
+
+ const auto weights = std::make_shared<TBlob<uint8_t>>(Precision::U8, Layout::C, SizeVector{0});
+
+ InferenceEngine::CNNNetReader reader;
+ reader.ReadNetwork(model.data(), model.size());
+ reader.SetWeights(weights);
+
+ auto net = reader.getNetwork();
+ net.addOutput("split", 0);
+
+ MKLDNNGraphTestClass graph;
+ graph.CreateGraph(net);
+
+ auto inBlob = make_shared_blob<float>(Precision::FP32, SizeVector{1, 2, H, W});
+ auto outBlob1 = make_shared_blob<float>(Precision::FP32, SizeVector{1, 1, H, W});
+ auto outBlob2 = make_shared_blob<float>(Precision::FP32, SizeVector{1, 1, H, W});
+ auto outBlob3 = make_shared_blob<float>(Precision::FP32, SizeVector{1, 1, H, W});
+
+ inBlob->allocate();
+ outBlob1->allocate();
+ outBlob2->allocate();
+ outBlob3->allocate();
+
+ auto in_ptr = inBlob->buffer().as<float*>();
+ for (int i = 0; i < imgSz; i++) {
+ in_ptr[i] = channel1Value;
+ in_ptr[i + imgSz] = channel2Value;
+ }
+
+ BlobMap inputBlobMap = { {"data" , inBlob } },
+ outputBlobMap = { {"split.0", outBlob1},
+ {"split.1", outBlob2},
+ {"power" , outBlob3} };
+
+ graph.Infer(inputBlobMap, outputBlobMap);
+
+ auto out_check = [] ( Blob::Ptr blob, float val) {
+ auto size = blob->size();
+ auto ptr = blob->buffer().as<float*>();
+ bool res = true;
+ for (int i = 0; i < size; i++)
+ res &= ( std::abs( ptr[i] - val ) < 0.00001f );
+ return res;
+ };
+
+ EXPECT_TRUE(out_check(outBlob1, 1));
+ EXPECT_TRUE(out_check(outBlob2, 2));
+ EXPECT_TRUE(out_check(outBlob3, -1));
+}
+
+TEST_F(MKLDNNGraphStructureTests, TestCreateGraphAllDataToConcat) {
+ using namespace InferenceEngine;
+ // Build the network.
+ Builder::Network netBuilder("");
+
+ // First input layer
+ idx_t inpId = netBuilder.addLayer(InferenceEngine::Builder::InputLayer("input").setPort(InferenceEngine::Port({1, 1, 4, 5})));
+
+ std::vector<size_t> weightsSize = {1, 1, 1, 1}; // OIHW
+ auto weights = make_shared_blob<float>(Precision::FP32, InferenceEngine::Layout::OIHW, weightsSize);
+ weights->allocate();
+
+ std::vector<float> twos(1, 2);
+ weights->set(twos);
+ idx_t weightsId = netBuilder.addLayer({}, Builder::ConstLayer("weights").setData(weights));
+
+ // Convolution layer
+ idx_t firstConvId = netBuilder.addLayer({{inpId}, {weightsId}}, Builder::ConvolutionLayer("conv").setKernel({1, 1})
+ .setStrides({1, 1}).setDilation({1, 1}).setPaddingsBegin({0, 0}).setPaddingsEnd({0, 0}).setGroup(1).setOutDepth(1));
+
+ weights = make_shared_blob<float>(Precision::FP32, InferenceEngine::Layout::OIHW, weightsSize);
+ weights->allocate();
+
+ std::vector<float> threes(1, 3);
+ weights->set(threes);
+
+ weightsId = netBuilder.addLayer({}, Builder::ConstLayer("weights").setData(weights));
+ // Convolution layer
+ idx_t secondConvId = netBuilder.addLayer({{inpId}, {weightsId}}, Builder::ConvolutionLayer("conv").setKernel({1, 1})
+ .setStrides({1, 1}).setDilation({1, 1}).setPaddingsBegin({0, 0}).setPaddingsEnd({0, 0}).setGroup(1).setOutDepth(1));
+
+ // Concat layer
+ idx_t concatId = netBuilder.addLayer({{inpId}, {firstConvId}, {secondConvId}},
+ InferenceEngine::Builder::ConcatLayer("concat").setAxis(1).setInputPorts(std::vector<InferenceEngine::Port>(3)));
+
+ // Output layer
+ InferenceEngine::Builder::OutputLayer outLayer("output");
+ netBuilder.addLayer({concatId}, outLayer);
+
+ auto cnn = CNNNetwork(Builder::convertToICNNNetwork(netBuilder.build()));
+
+ // Load the network
+ std::vector<size_t> inpSize = {5, 4, 1, 1};
+ std::vector<size_t> outSize = {5, 4, 3, 1};
+
+ InferenceEngine::BlobMap inputBlobs;
+ InferenceEngine::BlobMap outputBlobs;
+
+ std::vector<float> inpData(4*5, 1);
+ std::vector<float> outData(3*4*5, 1);
+ for (int i = 0; i < 4*5; ++i)
+ {
+ inpData[i] = i;
+ }
+
+ inputBlobs["input"] = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32, inpSize, &inpData[0]);
+ outputBlobs["concat"] = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32, outSize, &outData[0]);
+
+
+ MKLDNNGraphTestClass graph;
+ graph.CreateGraph(cnn);
+ graph.Infer(inputBlobs, outputBlobs);
+
+ std::vector<float> refDst = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38,
+ 0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57};
+
+ InferenceEngine::TBlob<float>::Ptr dstOut = InferenceEngine::make_shared_blob<float>(outputBlobs["concat"]->getTensorDesc(), refDst.data());
+
+ compare(*outputBlobs["concat"], *dstOut);
+}
+
+TEST_F(MKLDNNGraphStructureTests, TestCreateGraphAllDataFromInputToConcat) {
+ using namespace InferenceEngine;
+ // Build the network.
+ Builder::Network netBuilder("");
+
+ // First input layer
+ idx_t inpId = netBuilder.addLayer(InferenceEngine::Builder::InputLayer("input").setPort(InferenceEngine::Port({1, 1, 4, 5})));
+
+ // Concat layer
+ idx_t concatId = netBuilder.addLayer({{inpId}, {inpId}, {inpId}},
+ InferenceEngine::Builder::ConcatLayer("concat").setAxis(1).setInputPorts(std::vector<InferenceEngine::Port>(3)));
+
+ // Output layer
+ InferenceEngine::Builder::OutputLayer outLayer("output");
+ netBuilder.addLayer({concatId}, outLayer);
+
+ auto cnn = CNNNetwork(Builder::convertToICNNNetwork(netBuilder.build()));
+
+ // Load the network
+ std::vector<size_t> inpSize = {5, 4, 1, 1};
+ std::vector<size_t> outSize = {5, 4, 3, 1};
+
+ InferenceEngine::BlobMap inputBlobs;
+ InferenceEngine::BlobMap outputBlobs;
+
+ std::vector<float> inpData(4*5, 1);
+ std::vector<float> outData(3*4*5, 1);
+ for (int i = 0; i < 4*5; ++i)
+ {
+ inpData[i] = i;
+ }
+
+ inputBlobs["input"] = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32, inpSize, &inpData[0]);
+ outputBlobs["concat"] = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32, outSize, &outData[0]);
+
+
+ MKLDNNGraphTestClass graph;
+ graph.CreateGraph(cnn);
+ graph.Infer(inputBlobs, outputBlobs);
+
+ std::vector<float> refDst = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,};
+
+ InferenceEngine::TBlob<float>::Ptr dstOut = InferenceEngine::make_shared_blob<float>(outputBlobs["concat"]->getTensorDesc(), refDst.data());
+
+ compare(*outputBlobs["concat"], *dstOut);
+}
+
+
+TEST_F(MKLDNNGraphStructureTests, TestCheckIncorrectScaleShift) {
+ std::string model = R"V0G0N(
+<net name="net" version="2" batch="1">
+ <layers>
+ <layer name="data" type="Input" precision="FP32" id="0">
+ <output>
+ <port id="0">
+ <dim>1</dim>
+ <dim>1000</dim>
+ <dim>16</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="1" name="test" precision="FP32" type="ScaleShift">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ <dim>1000</dim>
+ <dim>16</dim>
+ </port>
+ </input>
+ <output>
+ <port id="3">
+ <dim>1</dim>
+ <dim>100</dim>
+ <dim>16</dim>
+ </port>
+ </output>
+ <blobs>
+ <weights offset="0" size="64"/>
+ <biases offset="0" size="64"/>
+ </blobs>
+ </layer>
+ </layers>
+ <edges>
+ <edge from-layer="0" from-port="0" to-layer="1" to-port="0"/>
+ </edges>
+</net>
+)V0G0N";
+ using namespace InferenceEngine;
+ const auto weights = std::make_shared<TBlob<uint8_t>>(Precision::U8, Layout::C, SizeVector{64});
+
+ InferenceEngine::CNNNetReader reader;
+ reader.ReadNetwork(model.data(), model.size());
+ reader.SetWeights(weights);
+
+ MKLDNNGraphTestClass graph;
+ ASSERT_THROW(graph.CreateGraph(reader.getNetwork()), InferenceEngine::details::InferenceEngineException);
+}
\ No newline at end of file
projects / platform / upstream / dldt.git / blobdiff