// Copyright (C) 2018-2019 Intel Corporation // SPDX-License-Identifier: Apache-2.0 // #include <gtest/gtest.h> #include <gmock/gmock-spec-builders.h> #include "mkldnn_plugin/mkldnn_graph.h" #include "test_graph.hpp" #include "single_layer_common.hpp" #include <mkldnn_plugin/mkldnn_extension_utils.h> #include "tests_common.hpp" using namespace ::testing; using namespace std; using namespace mkldnn; struct interp_test_params { struct { size_t n; size_t c; size_t h; size_t w; } in; struct { size_t h; size_t w; } out; int pad_beg; int pad_end; size_t num_prim_desc; int selectedType; std::vector<std::function<void(MKLDNNPlugin::PrimitiveDescInfo)>> comp; }; void interpolate(const int N, const int C, const float *src, const int x1, const int y1, const int IH_pad, const int IW_pad, const int IH, const int IW, float *dst, const int x2, const int y2, const int OH_pad, const int OW_pad, const int OH, const int OW) { if (IH_pad == OH_pad && IW_pad == OW_pad) { for (int i = 0; i < N * C * OH * OW; i++) { dst[i] = src[i]; } return; } const float rh = (OH_pad > 1) ? static_cast<float>(IH_pad - 1) / (OH_pad - 1) : 0.0f; const float rw = (OW_pad > 1) ? static_cast<float>(IW_pad - 1) / (OW_pad - 1) : 0.0f; const int block_size = 1; // Align channel number to block size to deal with channels padding in IE with multiple blobs int CB = (C + block_size - 1) & (-block_size); int CH = (C + block_size - 1) / block_size; for (int n = 0; n < N; n++) { for (int cb = 0; cb < CH; ++cb) { for (int h = 0; h < OH_pad; ++h) { const float *psrc = src + n * CB * IH * IW; float fh = rh * h; int ih0 = static_cast<int>(fh); int ih1 = (ih0 < IH_pad - 1) ? ih0 + 1 : ih0; float h_lambda0 = fh - ih0; float h_lambda1 = 1.0f - h_lambda0; for (int w = 0; w < OW_pad; ++w) { float fw = rw * w; int iw0 = static_cast<int>(fw); int iw1 = (iw0 < IW_pad - 1) ? iw0 + 1 : iw0; float w_lambda0 = fw - iw0; float w_lambda1 = 1.0f - w_lambda0; const float *psrc00 = psrc + cb * block_size * IW * IH + (y1 + ih0) * IW * block_size + (x1 + iw0) * block_size; const float *psrc01 = psrc + cb * block_size * IW * IH + (y1 + ih0) * IW * block_size + (x1 + iw1) * block_size; const float *psrc10 = psrc + cb * block_size * IW * IH + (y1 + ih1) * IW * block_size + (x1 + iw0) * block_size; const float *psrc11 = psrc + cb * block_size * IW * IH + (y1 + ih1) * IW * block_size + (x1 + iw1) * block_size; float *pdst = dst + n * CB * OH * OW + cb * block_size * OW * OH + (y2 + h) * OW * block_size + (x2 + w) * block_size; for (int c = 0; c < block_size; ++c) { pdst[c] = h_lambda1 * (w_lambda1 * psrc00[c] + w_lambda0 * psrc01[c]) + h_lambda0 * (w_lambda1 * psrc10[c] + w_lambda0 * psrc11[c]); } } } } } } template <typename data_t> void ref_interp(const InferenceEngine::TBlob<data_t> &src, InferenceEngine::TBlob<data_t> &dst, interp_test_params prm) { int IB = static_cast<int>(src.getTensorDesc().getDims()[0]); int IC = static_cast<int>(src.getTensorDesc().getDims()[1]); int IH = static_cast<int>(src.getTensorDesc().getDims()[2]); int IW = static_cast<int>(src.getTensorDesc().getDims()[3]); int OH = static_cast<int>(dst.getTensorDesc().getDims()[2]); int OW = static_cast<int>(dst.getTensorDesc().getDims()[3]); int IH_pad = IH + prm.pad_beg + prm.pad_end; int IW_pad = IW + prm.pad_beg + prm.pad_end; const data_t *src_data = src.readOnly(); data_t *dst_data = dst.data(); interpolate(IB, IC, src_data, -prm.pad_beg, -prm.pad_beg, IH_pad, IW_pad, IH, IW, dst_data, 0, 0, OH, OW, OH, OW); } class MKLDNNCPUExtInterpTests: public TestsCommon, public WithParamInterface<interp_test_params> { std::string model_t = R"V0G0N( <Net Name="Convolution_Only" version="2" precision="FP32" batch="1"> <layers> <layer name="in1" type="Input" precision="FP32" id="0"> <output> <port id="0"> <dim>_IN_</dim> <dim>_IC_</dim> <dim>_IH_</dim> <dim>_IW_</dim> </port> </output> </layer> <layer name="interp1" id="1" type="Interp" precision="FP32"> <data pad_beg="_PB_" pad_end="_PE_" height="_OH_" width="_OW_"/> <input> <port id="1"> <dim>_IN_</dim> <dim>_IC_</dim> <dim>_IH_</dim> <dim>_IW_</dim> </port> </input> <output> <port id="2"> <dim>_IN_</dim> <dim>_IC_</dim> <dim>_OH_</dim> <dim>_OW_</dim> </port> </output> </layer> </layers> <edges> <edge from-layer="0" from-port="0" to-layer="1" to-port="1"/> </edges> </Net> )V0G0N"; std::string getModel(interp_test_params p) { std::string model = model_t; REPLACE_WITH_NUM(model, "_IW_", p.in.w); REPLACE_WITH_NUM(model, "_IH_", p.in.h); REPLACE_WITH_NUM(model, "_IC_", p.in.c); REPLACE_WITH_NUM(model, "_IN_", p.in.n); REPLACE_WITH_NUM(model, "_OH_", p.out.h); REPLACE_WITH_NUM(model, "_OW_", p.out.w); REPLACE_WITH_NUM(model, "_PB_", p.pad_beg); REPLACE_WITH_NUM(model, "_PE_", p.pad_end); return model; } protected: virtual void TearDown() { } virtual void SetUp() { try { TestsCommon::SetUp(); interp_test_params p = ::testing::WithParamInterface<interp_test_params>::GetParam(); std::string model = getModel(p); InferenceEngine::CNNNetReader net_reader; ASSERT_NO_THROW(net_reader.ReadNetwork(model.data(), model.length())); InferenceEngine::Extension cpuExt(make_so_name("cpu_extension")); MKLDNNPlugin::MKLDNNExtensionManager::Ptr extMgr(new MKLDNNPlugin::MKLDNNExtensionManager()); extMgr->AddExtension(InferenceEngine::IExtensionPtr(&cpuExt, [](InferenceEngine::IExtension*){})); MKLDNNGraphTestClass graph; graph.CreateGraph(net_reader.getNetwork(), extMgr); auto& nodes = graph.getNodes(); nodes = graph.getNodes(); for (auto &node : nodes) { if (node->getName() == "interp1") { ASSERT_LE(p.num_prim_desc, node->getSupportedPrimitiveDescriptors().size()); for (size_t j = 0; j < p.num_prim_desc && j < p.comp.size(); j++) { p.comp.at(j)(node->getSupportedPrimitiveDescriptors().at(j)); } ASSERT_NE(nullptr, node->getSelectedPrimitiveDescriptor()); ASSERT_EQ(p.selectedType, node->getSelectedPrimitiveDescriptor()->getImplementationType() & p.selectedType); } } ASSERT_LE(4, nodes.size()); InferenceEngine::SizeVector dims_src = {p.in.n, p.in.c, p.in.h, p.in.w}; InferenceEngine::Blob::Ptr src = InferenceEngine::make_shared_blob<float>({InferenceEngine::Precision::FP32, dims_src, InferenceEngine::NCHW}); src->allocate(); fill_data(src->buffer(), src->size()); auto * srcPtr = dynamic_cast<InferenceEngine::TBlob<float>*>(src.get()); if (srcPtr == nullptr) FAIL() << "Cannot cast blob to TBlob<float>."; InferenceEngine::BlobMap srcs; srcs.insert(std::pair<std::string, InferenceEngine::Blob::Ptr>("in1", src)); InferenceEngine::OutputsDataMap out; out = net_reader.getNetwork().getOutputsInfo(); InferenceEngine::BlobMap outputBlobs; std::pair<std::string, InferenceEngine::DataPtr> item = *out.begin(); InferenceEngine::TBlob<float>::Ptr output; output = InferenceEngine::make_shared_blob<float>(item.second->getTensorDesc()); output->allocate(); outputBlobs[item.first] = output; graph.Infer(srcs, outputBlobs); InferenceEngine::TBlob<float> dst_ref(item.second->getTensorDesc()); dst_ref.allocate(); ref_interp(*srcPtr, dst_ref, p); compare(*output, dst_ref); } catch (const InferenceEngine::details::InferenceEngineException &e) { FAIL() << e.what(); } } }; TEST_P(MKLDNNCPUExtInterpTests, TestsInterp) {} INSTANTIATE_TEST_CASE_P( TestsInterp, MKLDNNCPUExtInterpTests, ::testing::Values( interp_test_params{{1, 256, 1, 1}, {33, 65}, 0, 0, 1, MKLDNNPlugin::impl_desc_type::unknown }, interp_test_params{{1, 2, 33, 65}, {33, 65}, 0, 0, 1, MKLDNNPlugin::impl_desc_type::unknown }));