inference-engine/tests/unit/engines/mkldnn/graph/layers/internal/graph_crop_test.cpp

   1 // Copyright (C) 2018-2019 Intel Corporation
   2 // SPDX-License-Identifier: Apache-2.0
   3 //
   4
   5 #include <gtest/gtest.h>
   6 #include <gmock/gmock-spec-builders.h>
   7 #include "mkldnn_plugin/mkldnn_graph.h"
   8
   9 #include "test_graph.hpp"
  10
  11 #include "single_layer_common.hpp"
  12 #include <mkldnn_plugin/mkldnn_extension_utils.h>
  13 #include <inference_engine/cnn_network_impl.hpp>
  14 #include "tests_common.hpp"
  15
  16
  17 using namespace ::testing;
  18 using namespace std;
  19 using namespace mkldnn;
  20
  21 struct crop_test_params {
  22     struct {
  23         size_t n;
  24         size_t c;
  25         size_t h;
  26         size_t w;
  27     } in;
  28
  29     std::vector<int> axis;
  30     std::vector<int> offsets;
  31     std::vector<int> dims;
  32
  33     size_t num_prim_desc;
  34
  35     MKLDNNPlugin::impl_desc_type selectedType;
  36
  37     std::vector<std::function<void(MKLDNNPlugin::PrimitiveDescInfo)>> comp;
  38 };
  39
  40
  41
  42 template <typename data_t>
  43 void ref_crop(InferenceEngine::TBlob<data_t> &src, InferenceEngine::TBlob<data_t> &dst, crop_test_params prm) {
  44     data_t *dst_ptr = dst.data();
  45
  46     std::vector<int> offsets(4);
  47     for (size_t i = 0; i < prm.offsets.size(); i++) {
  48         offsets[prm.axis[i]] = prm.offsets[i];
  49     }
  50     int OFFSET_N = offsets.at(0);
  51     int OFFSET_C = offsets.at(1);
  52     int OFFSET_H = offsets.at(2);
  53     int OFFSET_W = offsets.at(3);
  54
  55     const int ON = dst.dims().at(3);
  56     const int OC = dst.dims().at(2);
  57     const int OH = dst.dims().at(1);
  58     const int OW = dst.dims().at(0);
  59
  60     const int _IN = src.dims().at(0);
  61     const int IC = src.dims().at(1);
  62     const int IH = src.dims().at(2);
  63     const int IW = src.dims().at(3);
  64
  65     auto dst_off = [=](int n, int c, int h, int w) {
  66         return (n * OW * OH * OC + c * OW * OH + h * OW + w);
  67     };
  68     auto src_off = [=](int n, int c, int h, int w) {
  69         return (n * IW * IH * IC + c * IW * IH + h * IW + w);
  70     };
  71
  72     ASSERT_GE(_IN - OFFSET_N, ON);
  73     ASSERT_GE(IC - OFFSET_C, OC);
  74     ASSERT_GE(IH - OFFSET_H, OH);
  75     ASSERT_GE(IW - OFFSET_W, OW);
  76
  77     data_t* src_ptr = src.data();
  78     for (int n = 0; n < ON; ++n) {
  79         for (int c = 0; c < OC; ++c) {
  80             for (int h = 0; h < OH; ++h) {
  81                 for (int w = 0; w < OW; ++w) {
  82                     dst_ptr[dst_off(n, c, h, w)] = src_ptr[src_off(n + OFFSET_N, c + OFFSET_C,
  83                                                                    h + OFFSET_H, w + OFFSET_W)];
  84                 }
  85             }
  86         }
  87     }
  88 }
  89
  90 class MKLDNNGraphCropTests: public TestsCommon,
  91                                      public WithParamInterface<crop_test_params> {
  92     std::string model_t = R"V0G0N(
  93 <Net Name="Crop_Only" version="2" precision="FP32" batch="1">
  94     <layers>
  95         <layer name="in1" type="Input" precision="FP32" id="0">
  96             <output>
  97                 <port id="0">
  98                     <dim>_IN_</dim>
  99                     <dim>_IC_</dim>
 100                     <dim>_IH_</dim>
 101                     <dim>_IW_</dim>
 102                 </port>
 103             </output>
 104         </layer>
 105         <layer name="crop" id="1" type="Crop" precision="FP32">
 106             <data axis="_AXC_" offset="_OFC_" dim="_DIMC_" />
 107             <input>
 108                 <port id="1">
 109                     <dim>_IN_</dim>
 110                     <dim>_IC_</dim>
 111                     <dim>_IH_</dim>
 112                     <dim>_IW_</dim>
 113                 </port>
 114             </input>
 115             <output>
 116                 <port id="2">
 117                     <dim>_DN_</dim>
 118                     <dim>_DC_</dim>
 119                     <dim>_DH_</dim>
 120                     <dim>_DW_</dim>
 121                 </port>
 122             </output>
 123         </layer>
 124     </layers>
 125     <edges>
 126         <edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
 127     </edges>
 128 </Net>
 129 )V0G0N";
 130
 131 protected:
 132     std::string getModel(crop_test_params p) {
 133         std::string model = model_t;
 134
 135         std::string axis, offset, dim;
 136         std::vector<size_t> outDims = {p.in.n, p.in.c, p.in.h, p.in.w};
 137         for (size_t i = 0; i < p.offsets.size(); i++) {
 138             if (!axis.empty())
 139                 axis += ",";
 140             axis += std::to_string(p.axis[i]);
 141             if (!offset.empty())
 142                 offset += ",";
 143             offset += std::to_string(p.offsets[i]);
 144             if (!dim.empty())
 145                 dim += ",";
 146             dim += std::to_string(p.dims[i]);
 147             outDims[p.axis[i]] = p.dims[i];
 148         }
 149
 150         REPLACE_WITH_NUM(model, "_IW_", p.in.w);
 151         REPLACE_WITH_NUM(model, "_IH_", p.in.h);
 152         REPLACE_WITH_NUM(model, "_IC_", p.in.c);
 153         REPLACE_WITH_NUM(model, "_IN_", p.in.n);
 154         REPLACE_WITH_NUM(model, "_DN_", outDims[0]);
 155         REPLACE_WITH_NUM(model, "_DC_", outDims[1]);
 156         REPLACE_WITH_NUM(model, "_DH_", outDims[2]);
 157         REPLACE_WITH_NUM(model, "_DW_", outDims[3]);
 158         REPLACE_WITH_STR(model, "_AXC_", axis);
 159         REPLACE_WITH_STR(model, "_OFC_", offset);
 160         REPLACE_WITH_STR(model, "_DIMC_", dim);
 161         return model;
 162     }
 163
 164     virtual void TearDown() {
 165     }
 166
 167     virtual void SetUp() {
 168         try {
 169             TestsCommon::SetUp();
 170             crop_test_params p = ::testing::WithParamInterface<crop_test_params>::GetParam();
 171             std::string model = getModel(p);
 172
 173             InferenceEngine::CNNNetReader net_reader;
 174             ASSERT_NO_THROW(net_reader.ReadNetwork(model.data(), model.length()));
 175
 176             MKLDNNGraphTestClass graph;
 177             graph.CreateGraph(net_reader.getNetwork());
 178
 179             auto& nodes = graph.getNodes();
 180             for (int i = 0; i < nodes.size(); i++) {
 181                 if (nodes[i]->getType() == MKLDNNPlugin::Crop) {
 182                     ASSERT_EQ(p.num_prim_desc, nodes[i]->getSupportedPrimitiveDescriptors().size());
 183                     for (size_t j = 0; j < p.num_prim_desc && j < p.comp.size(); j++) {
 184                         p.comp.at(j)(nodes[i]->getSupportedPrimitiveDescriptors().at(j));
 185                     }
 186                     ASSERT_NE(nullptr, nodes[i]->getSelectedPrimitiveDescriptor());
 187                     ASSERT_EQ(p.selectedType, nodes[i]->getSelectedPrimitiveDescriptor()->getImplementationType());
 188                 }
 189             }
 190
 191             InferenceEngine::SizeVector dims_src = {p.in.n, p.in.c, p.in.h, p.in.w};
 192
 193             InferenceEngine::Blob::Ptr src = InferenceEngine::make_shared_blob<float, const InferenceEngine::SizeVector>(InferenceEngine::Precision::FP32, InferenceEngine::NCHW,  dims_src);
 194             src->allocate();
 195             fill_data(src->buffer(), src->size());
 196
 197             InferenceEngine::TBlob<float>* srcPtr = dynamic_cast<InferenceEngine::TBlob<float>*>(src.get());
 198
 199             if (srcPtr == nullptr)
 200                 FAIL() << "Cannot cast blob to TBlob<float>.";
 201
 202             InferenceEngine::BlobMap srcs;
 203             srcs.insert(std::pair<std::string, InferenceEngine::Blob::Ptr>("in1", src));
 204
 205             InferenceEngine::OutputsDataMap out;
 206             out = net_reader.getNetwork().getOutputsInfo();
 207             InferenceEngine::BlobMap outputBlobs;
 208
 209             std::pair<std::string, InferenceEngine::DataPtr> item = *out.begin();
 210
 211             InferenceEngine::TBlob<float>::Ptr output;
 212             output = InferenceEngine::make_shared_blob<float>(item.second->getTensorDesc());
 213             output->allocate();
 214             outputBlobs[item.first] = output;
 215
 216             graph.Infer(srcs, outputBlobs);
 217
 218             InferenceEngine::TBlob<float> dst_ref(item.second->getTensorDesc());
 219             dst_ref.allocate();
 220
 221             ref_crop(*srcPtr, dst_ref, p);
 222
 223             compare(*output, dst_ref);
 224         } catch (const InferenceEngine::details::InferenceEngineException &e) {
 225             FAIL() << e.what();
 226         }
 227     }
 228 };
 229
 230 TEST_P(MKLDNNGraphCropTests, TestCrop) {}
 231
 232
 233 INSTANTIATE_TEST_CASE_P(
 234         TestCrop, MKLDNNGraphCropTests,
 235         ::testing::Values(
 236                 crop_test_params{{1, 5, 32, 32}, {1, 2, 3}, {2, 5, 4}, {2, 23, 23}, 1, MKLDNNPlugin::impl_desc_type::unknown, {
 237                         [](MKLDNNPlugin::PrimitiveDescInfo impl) {
 238                             ASSERT_EQ(MKLDNNPlugin::impl_desc_type::unknown, impl.getImplementationType());
 239                             ASSERT_EQ(1, impl.getConfig().inConfs.size());
 240                             ASSERT_EQ(1, impl.getConfig().outConfs.size());
 241                             ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().inConfs.at(0).desc.getLayout());
 242                             ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().outConfs.at(0).desc.getLayout());
 243                         }}},
 244                 crop_test_params{{3, 8, 32, 32}, {0, 1, 2, 3}, {1, 0, 20, 20}, {2, 8, 5, 5}, 2, MKLDNNPlugin::impl_desc_type::unknown, {
 245                         [](MKLDNNPlugin::PrimitiveDescInfo impl) {
 246                             ASSERT_EQ(MKLDNNPlugin::impl_desc_type::unknown, impl.getImplementationType());
 247                             ASSERT_EQ(1, impl.getConfig().inConfs.size());
 248                             ASSERT_EQ(1, impl.getConfig().outConfs.size());
 249                             ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().inConfs.at(0).desc.getLayout());
 250                             ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().outConfs.at(0).desc.getLayout());
 251                         }} },
 252                 crop_test_params{{1, 5, 32, 32}, {3}, {10}, {20}, 1, MKLDNNPlugin::impl_desc_type::unknown },
 253                 crop_test_params{{1, 5, 32, 20}, {2, 3}, {30, 10}, {2, 10}, 1, MKLDNNPlugin::impl_desc_type::unknown }));
 254
 255 class MKLDNNGraphDynBatchCropTests: public MKLDNNGraphCropTests {
 256 protected:
 257
 258     virtual void SetUp() {
 259         try {
 260             TestsCommon::SetUp();
 261             crop_test_params p = ::testing::WithParamInterface<crop_test_params>::GetParam();
 262             std::string model = getModel(p);
 263             size_t MB = p.in.n;
 264             if (MB < 2)
 265                 MB = 2;
 266
 267             InferenceEngine::CNNNetReader net_reader;
 268             ASSERT_NO_THROW(net_reader.ReadNetwork(model.data(), model.length()));
 269             InferenceEngine::CNNNetwork network = net_reader.getNetwork();
 270             network.setBatchSize(MB);
 271
 272             MKLDNNGraphTestClass graph;
 273             graph.setProperty({{InferenceEngine::PluginConfigParams::KEY_DYN_BATCH_ENABLED, InferenceEngine::PluginConfigParams::YES}});
 274             graph.CreateGraph(network);
 275
 276             InferenceEngine::SizeVector dims_src = {MB, p.in.c, p.in.h, p.in.w};
 277             InferenceEngine::Blob::Ptr src = InferenceEngine::make_shared_blob<float, const InferenceEngine::SizeVector>(InferenceEngine::Precision::FP32, InferenceEngine::NCHW, dims_src);
 278             InferenceEngine::TBlob<float>* srcPtr = dynamic_cast<InferenceEngine::TBlob<float>*>(src.get());
 279             if (srcPtr == nullptr)
 280                 FAIL() << "Cannot cast blob to TBlob<float>.";
 281
 282             src->allocate();
 283             fill_data(src->buffer(), src->size());
 284
 285             InferenceEngine::BlobMap srcs;
 286             srcs.insert(std::pair<std::string, InferenceEngine::Blob::Ptr>("in1", src));
 287
 288             InferenceEngine::OutputsDataMap out;
 289             out = net_reader.getNetwork().getOutputsInfo();
 290             InferenceEngine::BlobMap outputBlobs;
 291
 292             std::pair<std::string, InferenceEngine::DataPtr> item = *out.begin();
 293
 294             InferenceEngine::TBlob<float>::Ptr output;
 295             output = InferenceEngine::make_shared_blob<float>(item.second->getTensorDesc());
 296             output->allocate();
 297             outputBlobs[item.first] = output;
 298
 299             auto checkCrop = [](const MKLDNNPlugin::MKLDNNNodePtr& node) {
 300                 return node->getType() == MKLDNNPlugin::Crop;
 301             };
 302
 303             graph.checkDynBatch(srcs, outputBlobs, MB, MB, checkCrop);
 304             graph.checkDynBatch(srcs, outputBlobs, 1, MB, checkCrop);
 305         } catch (const InferenceEngine::details::InferenceEngineException &e) {
 306             FAIL() << e.what();
 307         }
 308     }
 309 };
 310
 311 TEST_P(MKLDNNGraphDynBatchCropTests, TestsDynBatchCrop) {}
 312
 313 INSTANTIATE_TEST_CASE_P(
 314         TestsDynBatchCrop, MKLDNNGraphDynBatchCropTests,
 315         ::testing::Values(
 316                 crop_test_params{{1, 5, 32, 32}, {1, 2, 3}, {2, 5, 4}, {2, 23, 23}, 1, MKLDNNPlugin::impl_desc_type::unknown, {
 317                         [](MKLDNNPlugin::PrimitiveDescInfo impl) {
 318                             ASSERT_EQ(MKLDNNPlugin::impl_desc_type::unknown, impl.getImplementationType());
 319                             ASSERT_EQ(1, impl.getConfig().inConfs.size());
 320                             ASSERT_EQ(1, impl.getConfig().outConfs.size());
 321                             ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().inConfs.at(0).desc.getLayout());
 322                             ASSERT_EQ(InferenceEngine::Layout::NCHW, impl.getConfig().outConfs.at(0).desc.getLayout());
 323                         }}},
 324                 crop_test_params{{1, 5, 32, 32}, {3}, {10}, {20}, 1, MKLDNNPlugin::impl_desc_type::unknown },
 325                 crop_test_params{{1, 5, 32, 20}, {2, 3}, {30, 10}, {2, 10}, 1, MKLDNNPlugin::impl_desc_type::unknown }));