1 // Copyright (C) 2018-2019 Intel Corporation
2 // SPDX-License-Identifier: Apache-2.0
5 #include <gtest/gtest.h>
6 #include <gmock/gmock-spec-builders.h>
7 #include "mkldnn_plugin/mkldnn_graph.h"
9 #include "test_graph.hpp"
11 #include "single_layer_common.hpp"
12 #include <mkldnn_plugin/mkldnn_extension_utils.h>
13 #include <extension/ext_list.hpp>
14 #include "tests_common.hpp"
17 using namespace ::testing;
19 using namespace mkldnn;
21 struct fill_test_params {
22 std::string precision;
23 InferenceEngine::SizeVector out_shape;
26 std::vector<std::function<void(MKLDNNPlugin::PrimitiveDescInfo)>> comp;
29 class MKLDNNCPUExtFillTests : public TestsCommon, public WithParamInterface<fill_test_params> {
30 std::string model_t = R"V0G0N(
31 <net Name="Fill_net" version="2" precision="_IIDXP_" batch="1">
33 <layer name="dims" type="Input" precision="I32" id="1">
40 <layer name="value" type="Input" precision="_IIDXP_" id="2">
47 <layer name="output" id="2" type="Fill" precision="_IIDXP_">
65 <edge from-layer="1" from-port="1" to-layer="2" to-port="1"/>
66 <edge from-layer="2" from-port="2" to-layer="2" to-port="2"/>
71 std::string getModel(fill_test_params p) {
72 std::string model = model_t;
73 std::string out_shape;
75 REPLACE_WITH_STR(model, "_IIDXP_", p.precision);
76 for (size_t i = 0; i < p.out_shape.size(); i++) {
78 out_shape += std::to_string(p.out_shape[i]) + "</dim>\n";
80 REPLACE_WITH_STR(model, "_OUT_", out_shape);
81 REPLACE_WITH_NUM(model, "_DIM_SIZE_", p.out_shape.size());
87 virtual void TearDown() {
90 virtual void SetUp() {
93 fill_test_params p = ::testing::WithParamInterface<fill_test_params>::GetParam();
94 std::string model = getModel(p);
96 InferenceEngine::CNNNetReader net_reader;
97 ASSERT_NO_THROW(net_reader.ReadNetwork(model.data(), model.length()));
99 InferenceEngine::Extension cpuExt(make_so_name("cpu_extension"));
100 MKLDNNPlugin::MKLDNNExtensionManager::Ptr extMgr(new MKLDNNPlugin::MKLDNNExtensionManager());
101 extMgr->AddExtension(InferenceEngine::IExtensionPtr(&cpuExt, [](InferenceEngine::IExtension*){}));
103 MKLDNNGraphTestClass graph;
104 graph.CreateGraph(net_reader.getNetwork(), extMgr);
107 InferenceEngine::OutputsDataMap out;
108 out = net_reader.getNetwork().getOutputsInfo();
109 InferenceEngine::BlobMap outputBlobs;
112 InferenceEngine::Blob::Ptr dims;
113 InferenceEngine::SizeVector vector_dim(1, p.out_shape.size());
114 dims = InferenceEngine::make_shared_blob<int32_t>({ InferenceEngine::Precision::I32, vector_dim, InferenceEngine::TensorDesc::getLayoutByDims(vector_dim) });
116 for (size_t i = 0; i < p.out_shape.size(); i++) {
117 static_cast<int32_t*>(dims->buffer())[i] = static_cast<int32_t>(p.out_shape[i]);
119 auto * srcPtr = dynamic_cast<InferenceEngine::TBlob<int32_t>*>(dims.get());
120 if (srcPtr == nullptr)
121 FAIL() << "Cannot cast blob to TBlob<int32_t>.";
123 InferenceEngine::BlobMap srcs;
124 InferenceEngine::Blob::Ptr value_scalar;
125 InferenceEngine::SizeVector value_scalar_dim(1, 1);
126 std::pair<std::string, InferenceEngine::DataPtr> item = *out.begin();
127 if (p.precision == "I32") {
128 value_scalar = InferenceEngine::make_shared_blob<int32_t>({ InferenceEngine::Precision::I32, value_scalar_dim, InferenceEngine::TensorDesc::getLayoutByDims(value_scalar_dim) });
129 value_scalar->allocate();
130 static_cast<int32_t*>(value_scalar->buffer())[0] = static_cast<int32_t>(p.value);
131 auto * value_scalarPtr = dynamic_cast<InferenceEngine::TBlob<int32_t>*>(value_scalar.get());
132 if (value_scalarPtr == nullptr)
133 FAIL() << "Cannot cast blob to TBlob<int32_t>.";
135 srcs.insert(std::pair<std::string, InferenceEngine::Blob::Ptr>("dims", dims));
136 srcs.insert(std::pair<std::string, InferenceEngine::Blob::Ptr>("value", value_scalar));
139 InferenceEngine::TBlob<int32_t>::Ptr output;
140 output = InferenceEngine::make_shared_blob<int32_t>(item.second->getTensorDesc());
142 outputBlobs[item.first] = output;
145 InferenceEngine::TBlob<int32_t> dst_ref(item.second->getTensorDesc());
147 std::fill_n(static_cast<int32_t*>(dst_ref.data()), dst_ref.size(), static_cast<int32_t>(p.value));
150 graph.Infer(srcs, outputBlobs);
151 for (int i = 0; i < dst_ref.size(); i++) {
152 if(dst_ref.data()[i] != (*output).data()[i])
153 FAIL() << "The difference between res_ptr[i] and ref_ptr[i]";
155 } else if (p.precision == "FP32") {
156 value_scalar = InferenceEngine::make_shared_blob<float>({ InferenceEngine::Precision::FP32, value_scalar_dim, InferenceEngine::TensorDesc::getLayoutByDims(value_scalar_dim) });
157 value_scalar->allocate();
158 static_cast<float*>(value_scalar->buffer())[0] = p.value;
159 auto * value_scalarPtr = dynamic_cast<InferenceEngine::TBlob<float>*>(value_scalar.get());
160 if (value_scalarPtr == nullptr)
161 FAIL() << "Cannot cast blob to TBlob<float>.";
163 srcs.insert(std::pair<std::string, InferenceEngine::Blob::Ptr>("dims", dims));
164 srcs.insert(std::pair<std::string, InferenceEngine::Blob::Ptr>("value", value_scalar));
167 InferenceEngine::TBlob<float>::Ptr output;
168 output = InferenceEngine::make_shared_blob<float>(item.second->getTensorDesc());
170 outputBlobs[item.first] = output;
173 InferenceEngine::TBlob<float> dst_ref(item.second->getTensorDesc());
175 std::fill_n(static_cast<float*>(dst_ref.data()), dst_ref.size(), p.value);
178 graph.Infer(srcs, outputBlobs);
179 compare(*output, dst_ref);
183 } catch (const InferenceEngine::details::InferenceEngineException &e) {
189 TEST_P(MKLDNNCPUExtFillTests, TestsFill) {}
191 INSTANTIATE_TEST_CASE_P(
192 TestsFill, MKLDNNCPUExtFillTests,
194 // Params: precision, value, out_shape
195 fill_test_params{ "I32", { 1 }, 1.f },
196 fill_test_params{ "I32", { 1, 3, 1 }, 1.f },
197 fill_test_params{ "I32", { 2, 3, 6 }, -1.f },
198 fill_test_params{"FP32", { 2, 3, 6 }, -1.f },
199 fill_test_params{"FP32", { 1 }, 1.f },
200 fill_test_params{"FP32", { 1, 3, 1, 2 }, .5f },
201 fill_test_params{"FP32", { 4, 3, 2, 5, 4, 2 }, .25f }