1 | /** |
2 | * Copyright (c) Glow Contributors. See CONTRIBUTORS file. |
3 | * |
4 | * Licensed under the Apache License, Version 2.0 (the "License"); |
5 | * you may not use this file except in compliance with the License. |
6 | * You may obtain a copy of the License at |
7 | * |
8 | * http://www.apache.org/licenses/LICENSE-2.0 |
9 | * |
10 | * Unless required by applicable law or agreed to in writing, software |
11 | * distributed under the License is distributed on an "AS IS" BASIS, |
12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
13 | * See the License for the specific language governing permissions and |
14 | * limitations under the License. |
15 | */ |
16 | #include "ImporterTestUtils.h" |
17 | #include "glow/ExecutionEngine/ExecutionEngine.h" |
18 | #include "glow/Graph/Graph.h" |
19 | #include "glow/Graph/Nodes.h" |
20 | #include "glow/Graph/PlaceholderBindings.h" |
21 | #include "glow/Importer/ONNXModelLoader.h" |
22 | #include "llvm/Support/FileSystem.h" |
23 | #include "gtest/gtest.h" |
24 | |
25 | #ifndef GLOW_DATA_PATH |
26 | #define GLOW_DATA_PATH |
27 | #endif |
28 | |
29 | using namespace glow; |
30 | |
31 | #include <fstream> |
32 | using namespace std; |
33 | |
34 | class OnnxImporterTest : public ::testing::Test { |
35 | protected: |
36 | // By default constant folding at load time is enabled in general, but we do |
37 | // many tests here loading Constants, so keep it false during these tests by |
38 | // default. |
39 | void SetUp() override { glow::setConstantFoldLoaderOpsFlag(false); } |
40 | void TearDown() override { glow::setConstantFoldLoaderOpsFlag(true); } |
41 | }; |
42 | |
43 | /// Loads onnxtxt model file \p filename and \returns ModelProto object. |
44 | Expected<ONNX_NAMESPACE::ModelProto> loadProto(const std::string &filename) { |
45 | std::ifstream ff(filename, std::ios::in | std::ios::binary); |
46 | RETURN_ERR_IF_NOT(ff, |
47 | strFormat("Can't find the model or network files for %s." , |
48 | filename.c_str()), |
49 | ErrorValue::ErrorCode::MODEL_LOADER_INVALID_PROTOBUF); |
50 | if (filename.find(".onnxtxt" ) != std::string::npos) { |
51 | std::string str((std::istreambuf_iterator<char>(ff)), |
52 | std::istreambuf_iterator<char>()); |
53 | ONNX_NAMESPACE::ModelProto MP; |
54 | bool parseNet = google::protobuf::TextFormat::ParseFromString(str, &MP); |
55 | RETURN_ERR_IF_NOT(parseNet, "Failed to parse ModelProto" , |
56 | ErrorValue::ErrorCode::MODEL_LOADER_INVALID_PROTOBUF); |
57 | return MP; |
58 | } |
59 | return MAKE_ERR("Can't load proto file" ); |
60 | } |
61 | |
62 | /// Saves ModelProto object \p model as onnxtxt model file \p filename |
63 | /// and \returns true if successful. |
64 | Expected<bool> saveProto(const std::string &filename, |
65 | ONNX_NAMESPACE::ModelProto &model) { |
66 | std::ofstream ff(filename, std::ios::out); |
67 | RETURN_ERR_IF_NOT(ff, "Can't write the proto file." , |
68 | ErrorValue::ErrorCode::RUNTIME_ERROR); |
69 | if (filename.find(".onnxtxt" ) != std::string::npos) { |
70 | std::string onnx_message = model.DebugString(); |
71 | ff << onnx_message; |
72 | ff.close(); |
73 | return true; |
74 | } |
75 | ff.close(); |
76 | return false; |
77 | } |
78 | |
79 | /// Replaces placeholders with names \p tensorNames in model proto object \p |
80 | /// model with initializers of same name and values specified in input tensor |
81 | /// array \p tensors and \returns true if successful. |
82 | Expected<bool> |
83 | replacePlaceholderWithConstant(ONNX_NAMESPACE::ModelProto &model, |
84 | llvm::ArrayRef<const char *> tensorNames, |
85 | llvm::ArrayRef<Tensor *> tensors) { |
86 | ONNX_NAMESPACE::NodeProto np; |
87 | ONNX_NAMESPACE::GraphProto *gp = model.mutable_graph(); |
88 | RETURN_ERR_IF_NOT(gp, "Can't get mutable graph." , |
89 | ErrorValue::ErrorCode::RUNTIME_ERROR); |
90 | for (size_t i = 0; i < tensorNames.size(); i++) { |
91 | for (int j = 0; j < gp->input_size(); j++) { |
92 | ONNX_NAMESPACE::ValueInfoProto *valueInfo = gp->mutable_input(j); |
93 | const std::string &inputName = valueInfo->name(); |
94 | if (inputName != tensorNames[i]) { |
95 | continue; |
96 | } |
97 | std::string newName = "dummy_input" + std::to_string(i); |
98 | valueInfo->set_name(newName); |
99 | auto RH = tensors[i]->getHandle<>(); |
100 | ONNX_NAMESPACE::TensorProto *tp = gp->add_initializer(); |
101 | tp->set_name(tensorNames[i]); |
102 | for (size_t k = 0; k < tensors[i]->dims().size(); k++) { |
103 | tp->add_dims(tensors[i]->dims()[k]); |
104 | } |
105 | switch (RH.getElementType()) { |
106 | case ElemKind::FloatTy: |
107 | tp->set_data_type(ONNX_NAMESPACE::TensorProto::FLOAT); |
108 | for (size_t k = 0; k < tensors[i]->size(); k++) { |
109 | tp->add_float_data(RH.raw(k)); |
110 | } |
111 | break; |
112 | case ElemKind::Int64ITy: |
113 | tp->set_data_type(ONNX_NAMESPACE::TensorProto::INT64); |
114 | for (size_t k = 0; k < tensors[i]->size(); k++) { |
115 | tp->add_int64_data(RH.raw(k)); |
116 | } |
117 | break; |
118 | case ElemKind::Int32ITy: |
119 | tp->set_data_type(ONNX_NAMESPACE::TensorProto::INT32); |
120 | for (size_t k = 0; k < tensors[i]->size(); k++) { |
121 | tp->add_int32_data(RH.raw(k)); |
122 | } |
123 | break; |
124 | default: |
125 | std::cout << "Unsupported datatype" ; |
126 | return false; |
127 | } |
128 | } |
129 | } |
130 | gp->clear_input(); |
131 | return true; |
132 | } |
133 | |
134 | /// Performs constant folding test on the given model file \p NetFilename |
135 | /// with single output and then checking against expected values |
136 | /// \p expectedValues and \returns true if the test completes without error. |
137 | Error checkConstFoldLegalName(std::string NetFilename, |
138 | std::vector<float> expectedValues) { |
139 | Tensor T(glow::ElemKind::FloatTy, {3, 2}); |
140 | T.getHandle<float>() = expectedValues; |
141 | ONNX_NAMESPACE::ModelProto modelDef; |
142 | ASSIGN_VALUE_OR_RETURN_ERR(modelDef, loadProto(NetFilename)); |
143 | setConstantFoldLoaderOpsFlag(true); |
144 | |
145 | // It is expected that loading will fold the whole graph and output |
146 | // nodes will become constants during the loading process. |
147 | ExecutionEngine EE; |
148 | Module &mod = EE.getModule(); |
149 | Function *F = mod.createFunction("temp" ); |
150 | ONNXModelLoader onnxLD(NetFilename, {}, {}, *F); |
151 | |
152 | setConstantFoldLoaderOpsFlag(false); |
153 | |
154 | // The folded output tensors are expected to be constants and should |
155 | // match the expected values. |
156 | NodeValue NV; |
157 | ASSIGN_VALUE_OR_RETURN_ERR( |
158 | NV, onnxLD.getNodeValueByName(modelDef.graph().output(0).name())); |
159 | auto *constOut = llvm::dyn_cast<Constant>(NV.getNode()); |
160 | RETURN_ERR_IF_NOT(constOut, "Failed cast to Constant" ); |
161 | EXPECT_TRUE(T.isEqual(constOut->getPayload())); |
162 | return Error::success(); |
163 | } |
164 | |
165 | /// Performs constant folding test on the given model file \p NetFilename |
166 | /// by replacing input tensors with name \p tensorNames, and values \p tensors |
167 | /// and then checking against expected output expectedTensors. \returns true |
168 | /// if the test completes without error. |
169 | Error checkConstFoldedOutput(std::string NetFilename, |
170 | llvm::ArrayRef<const char *> tensorNames, |
171 | llvm::ArrayRef<Tensor *> tensors, |
172 | llvm::ArrayRef<Tensor *> expectedTensors) { |
173 | ONNX_NAMESPACE::ModelProto modelDef; |
174 | llvm::SmallVector<char, 64> resultPath; |
175 | llvm::sys::fs::createTemporaryFile("dummy" , "onnxtxt" , resultPath); |
176 | std::string netFilename(resultPath.begin(), resultPath.end()); |
177 | |
178 | ASSIGN_VALUE_OR_RETURN_ERR(modelDef, loadProto(NetFilename)); |
179 | // Replace placeholders in the original onnx model with constants. |
180 | RETURN_IF_ERR(replacePlaceholderWithConstant(modelDef, tensorNames, tensors) |
181 | .takeError()); |
182 | RETURN_IF_ERR(saveProto(netFilename, modelDef).takeError()); |
183 | setConstantFoldLoaderOpsFlag(true); |
184 | |
185 | // It is expected that loading will fold the whole graph and output |
186 | // nodes will become constants during the loading process. |
187 | ExecutionEngine EE; |
188 | Module &mod = EE.getModule(); |
189 | Function *F = mod.createFunction("temp" ); |
190 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
191 | setConstantFoldLoaderOpsFlag(false); |
192 | |
193 | // The folded output tensors are expected to be constants and should |
194 | // match the expectedTensors passed in. |
195 | for (int i = 0; i < modelDef.graph().output_size(); i++) { |
196 | NodeValue NV; |
197 | ASSIGN_VALUE_OR_RETURN_ERR( |
198 | NV, onnxLD.getNodeValueByName(modelDef.graph().output(i).name())); |
199 | auto *constOut = llvm::dyn_cast<Constant>(NV.getNode()); |
200 | RETURN_ERR_IF_NOT(constOut, "Failed cast to Constant" ); |
201 | EXPECT_TRUE(expectedTensors[i]->isEqual(constOut->getPayload())); |
202 | } |
203 | return Error::success(); |
204 | } |
205 | |
206 | static void importReduceL2Test(const std::string &netFilename, |
207 | llvm::ArrayRef<float> inputValues, |
208 | llvm::ArrayRef<dim_t> inputShape, |
209 | llvm::ArrayRef<dim_t> outputShape, |
210 | llvm::ArrayRef<float> expectedValues) { |
211 | float delta = 1e-08; |
212 | ExecutionEngine EE{}; |
213 | auto &mod = EE.getModule(); |
214 | Function *F = mod.createFunction("main" ); |
215 | PlaceholderBindings bindings; |
216 | Placeholder *graphOutputVar; |
217 | |
218 | // Load the .onnxtxt model. |
219 | Type inputType(ElemKind::FloatTy, inputShape); |
220 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&inputType}, *F); |
221 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
222 | auto PH = mod.getPlaceholderByNameSlow("input" ); |
223 | auto *inTensor = bindings.allocate(PH); |
224 | inTensor->getHandle() = inputValues; |
225 | EE.compile(CompilationMode::Infer); |
226 | bindings.allocate(mod.getPlaceholders()); |
227 | EE.run(bindings); |
228 | auto result = bindings.get(graphOutputVar)->getHandle(); |
229 | ASSERT_TRUE(result.dims() == (llvm::ArrayRef<dim_t>)outputShape); |
230 | for (size_t i = 0; i < result.getType().size(); i++) { |
231 | EXPECT_NEAR(result.raw(i), expectedValues[i], delta); |
232 | } |
233 | } |
234 | |
235 | /// Test the utility function that gets the inputs name and glow types |
236 | /// from updated graph proto |
237 | |
238 | TEST_F(OnnxImporterTest, getInputNamesAndTypes) { |
239 | // Set onnx-define-symbol if present in model |
240 | std::string inputSymbol = "batch_size,5" ; |
241 | setOnnxDefineSymbol({inputSymbol}); |
242 | |
243 | std::string netFilename( |
244 | GLOW_DATA_PATH |
245 | "tests/models/onnxModels/getInputsOnnxDefineSample.onnxtxt" ); |
246 | |
247 | bool isError = false; |
248 | |
249 | std::vector<std::string> names; |
250 | std::vector<Type> types; |
251 | |
252 | std::vector<std::string> expectedNames = {"input" }; |
253 | std::vector<std::vector<dim_t>> expectedDims = {{5, 3, 224, 224}}; |
254 | |
255 | isError = ERR_TO_BOOL( |
256 | ONNXModelLoader::getInputsNamesAndTypes(names, types, netFilename)); |
257 | |
258 | EXPECT_FALSE(isError); |
259 | |
260 | for (size_t i = 0; i < expectedNames.size(); i++) { |
261 | EXPECT_TRUE(expectedNames[i] == names[i]); |
262 | std::vector<dim_t> dims = types[i].dims(); |
263 | for (size_t j = 0; j < expectedDims[i].size(); j++) { |
264 | EXPECT_EQ(expectedDims[i][j], dims[j]); |
265 | } |
266 | } |
267 | } |
268 | |
269 | /// Test the utility function which wraps a negative axis. |
270 | TEST_F(OnnxImporterTest, getPositiveAxis) { |
271 | int axisPos; |
272 | ASSIGN_VALUE_OR_FAIL_TEST(axisPos, getPositiveAxis<int>(-3, 3)); |
273 | EXPECT_EQ(axisPos, 0); |
274 | ASSIGN_VALUE_OR_FAIL_TEST(axisPos, getPositiveAxis<int>(-2, 3)); |
275 | EXPECT_EQ(axisPos, 1); |
276 | ASSIGN_VALUE_OR_FAIL_TEST(axisPos, getPositiveAxis<int>(-1, 3)); |
277 | EXPECT_EQ(axisPos, 2); |
278 | ASSIGN_VALUE_OR_FAIL_TEST(axisPos, getPositiveAxis<int>(0, 3)); |
279 | EXPECT_EQ(axisPos, 0); |
280 | ASSIGN_VALUE_OR_FAIL_TEST(axisPos, getPositiveAxis<int>(1, 3)); |
281 | EXPECT_EQ(axisPos, 1); |
282 | ASSIGN_VALUE_OR_FAIL_TEST(axisPos, getPositiveAxis<int>(2, 3)); |
283 | EXPECT_EQ(axisPos, 2); |
284 | } |
285 | |
286 | /// Test loading reduceL2 op from an ONNX model |
287 | /// with axes = []. |
288 | TEST_F(OnnxImporterTest, reduceL2NoAxis) { |
289 | std::vector<float> inputValues = {1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2}; |
290 | std::vector<dim_t> inputShape = {2, 3, 2}; |
291 | std::vector<dim_t> outputShape = {1, 1, 1}; |
292 | std::vector<float> expectedValues = {5.477226}; |
293 | std::string netFilename(GLOW_DATA_PATH |
294 | "tests/models/onnxModels/ReduceL2NoAxis.onnxtxt" ); |
295 | importReduceL2Test(netFilename, inputValues, inputShape, outputShape, |
296 | expectedValues); |
297 | } |
298 | |
299 | /// Test loading reduceL2 op from an ONNX model |
300 | /// with negative axis values. |
301 | TEST_F(OnnxImporterTest, reduceL2NegAxis) { |
302 | std::vector<float> inputValues = {1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2}; |
303 | std::vector<dim_t> inputShape = {2, 3, 2}; |
304 | std::vector<dim_t> outputShape = {2, 1, 1}; |
305 | std::vector<float> expectedValues = {3.8729835, 3.8729835}; |
306 | std::string netFilename(GLOW_DATA_PATH |
307 | "tests/models/onnxModels/ReduceL2NegAxis.onnxtxt" ); |
308 | importReduceL2Test(netFilename, inputValues, inputShape, outputShape, |
309 | expectedValues); |
310 | } |
311 | |
312 | /// Test loading reduceL2 op from an ONNX model |
313 | /// with keepdims = True. |
314 | TEST_F(OnnxImporterTest, reduceL2KeepDims) { |
315 | std::vector<float> inputValues = {1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2}; |
316 | std::vector<dim_t> inputShape = {2, 3, 2}; |
317 | std::vector<dim_t> outputShape = {2, 1, 1}; |
318 | std::vector<float> expectedValues = {3.8729835, 3.8729835}; |
319 | std::string netFilename(GLOW_DATA_PATH |
320 | "tests/models/onnxModels/ReduceL2KeepDims.onnxtxt" ); |
321 | importReduceL2Test(netFilename, inputValues, inputShape, outputShape, |
322 | expectedValues); |
323 | } |
324 | |
325 | /// Test loading reduceL2 op from an ONNX model |
326 | /// with keepdims = False. |
327 | TEST_F(OnnxImporterTest, reduceL2NoKeepDims) { |
328 | std::vector<float> inputValues = {1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2}; |
329 | std::vector<dim_t> inputShape = {2, 3, 2}; |
330 | std::vector<dim_t> outputShape = {2}; |
331 | std::vector<float> expectedValues = {3.8729835, 3.8729835}; |
332 | std::string netFilename(GLOW_DATA_PATH |
333 | "tests/models/onnxModels/ReduceL2NoKeepDims.onnxtxt" ); |
334 | importReduceL2Test(netFilename, inputValues, inputShape, outputShape, |
335 | expectedValues); |
336 | } |
337 | |
338 | /// Test loading constant+relu ops with numeric input names from an ONNX model. |
339 | TEST_F(OnnxImporterTest, reluConstFoldLegalName) { |
340 | std::string NetFilename(GLOW_DATA_PATH |
341 | "tests/models/onnxModels/constRelu.onnxtxt" ); |
342 | FAIL_TEST_IF_ERR( |
343 | checkConstFoldLegalName(NetFilename, {1.0, 0.0, 0.0, 1.0, 1.0, 1.0})); |
344 | } |
345 | |
346 | template <class OpType> |
347 | static void |
348 | importArithMultiBroadcastTest(std::string fileName, |
349 | llvm::ArrayRef<dim_t> inputShape, bool multi, |
350 | bool leftBroadcast, bool rightBroadcast, |
351 | const std::function<float(float, float)> &op) { |
352 | ExecutionEngine EE{}; |
353 | auto &mod = EE.getModule(); |
354 | Function *F = mod.createFunction("main" ); |
355 | |
356 | std::string NetFilename = |
357 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName; |
358 | PlaceholderBindings bindings; |
359 | Placeholder *graphOutputVar; |
360 | // Destroy the loader after the graph is loaded since the following execution |
361 | // will not depend on anyting from the loader. |
362 | Tensor data; |
363 | getNCHWData(&data, inputShape[0], inputShape[1], inputShape[2], |
364 | inputShape[3]); |
365 | { |
366 | ONNXModelLoader onnxLD(NetFilename, {"data" }, {&data.getType()}, *F); |
367 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
368 | bindings.allocate(mod.getPlaceholders()); |
369 | updateInputPlaceholdersByName(bindings, &mod, {"data" }, {&data}); |
370 | } |
371 | // ONNX importer loads an arithmetic node and inserts: |
372 | // Check the graph structure |
373 | auto *saveNode = getSaveNodeFromDest(graphOutputVar); |
374 | auto *node = saveNode->getInput().getNode(); |
375 | auto *opNode = llvm::dyn_cast<OpType>(node); |
376 | EXPECT_NE(nullptr, opNode); |
377 | |
378 | BroadcastNode *leftBN = |
379 | llvm::dyn_cast<BroadcastNode>(opNode->getLHS().getNode()); |
380 | BroadcastNode *rightBN = |
381 | llvm::dyn_cast<BroadcastNode>(opNode->getRHS().getNode()); |
382 | EXPECT_NE(leftBroadcast, leftBN == nullptr); |
383 | EXPECT_NE(rightBroadcast, rightBN == nullptr); |
384 | |
385 | // Compile&run the graph, and check the output |
386 | EE.compile(CompilationMode::Infer); |
387 | EE.run(bindings); |
388 | auto result = bindings.get(graphOutputVar)->getHandle(); |
389 | std::vector<dim_t> expectedDims = {1, 3, 4, 2}; |
390 | std::vector<float> expectedValues; |
391 | |
392 | if (multi) { |
393 | expectedValues = {op(0, 2), op(1, 2), op(0, 2), op(1, 2), op(0, 2), |
394 | op(1, 2), op(0, 2), op(1, 2), op(2, 2), op(3, 2), |
395 | op(2, 2), op(3, 2), op(2, 2), op(3, 2), op(2, 2), |
396 | op(3, 2), op(4, 2), op(5, 2), op(4, 2), op(5, 2), |
397 | op(4, 2), op(5, 2), op(4, 2), op(5, 2)}; |
398 | } else { |
399 | expectedValues = {op(0, 2), op(1, 2), op(2, 2), op(3, 2), op(4, 2), |
400 | op(5, 2), op(6, 2), op(7, 2), op(8, 2), op(9, 2), |
401 | op(10, 2), op(11, 2), op(12, 2), op(13, 2), op(14, 2), |
402 | op(15, 2), op(16, 2), op(17, 2), op(18, 2), op(19, 2), |
403 | op(20, 2), op(21, 2), op(22, 2), op(23, 2)}; |
404 | } |
405 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
406 | for (size_t i = 0; i < result.getType().size(); i++) { |
407 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
408 | } |
409 | // Constant Folding Test. |
410 | FAIL_TEST_IF_ERR(checkConstFoldedOutput(NetFilename, {"data" }, {&data}, |
411 | {bindings.get(graphOutputVar)})); |
412 | } |
413 | |
414 | static void importExpandTest(const std::string &netFilename, |
415 | llvm::ArrayRef<float> inputValues, |
416 | llvm::ArrayRef<dim_t> inputShape, |
417 | llvm::ArrayRef<dim_t> outputShape, |
418 | llvm::ArrayRef<float> expectedValues) { |
419 | float delta = 1e-08; |
420 | ExecutionEngine EE{}; |
421 | auto &mod = EE.getModule(); |
422 | Function *F = mod.createFunction("main" ); |
423 | PlaceholderBindings bindings; |
424 | Placeholder *graphOutputVar; |
425 | // Load the .onnxtxt model. |
426 | Type inputType(ElemKind::FloatTy, inputShape); |
427 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&inputType}, *F); |
428 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
429 | auto *PH = mod.getPlaceholderByNameSlow("x" ); |
430 | auto *inTensor = bindings.allocate(PH); |
431 | inTensor->getHandle() = inputValues; |
432 | EE.compile(CompilationMode::Infer); |
433 | bindings.allocate(mod.getPlaceholders()); |
434 | EE.run(bindings); |
435 | auto result = bindings.get(graphOutputVar)->getHandle(); |
436 | ASSERT_TRUE(result.dims() == (llvm::ArrayRef<dim_t>)outputShape); |
437 | for (size_t i = 0; i < result.getType().size(); i++) { |
438 | EXPECT_NEAR(result.raw(i), expectedValues[i], delta); |
439 | } |
440 | } |
441 | |
442 | /// Import maxPool1D |
443 | static void importMaxPool1DTest(std::string &netFilename, |
444 | llvm::ArrayRef<float> inputValues, |
445 | llvm::ArrayRef<dim_t> inputShape, |
446 | llvm::ArrayRef<dim_t> outputShape, |
447 | llvm::ArrayRef<float> expectedValues) { |
448 | float delta = 1e-08; |
449 | ExecutionEngine EE{}; |
450 | auto &mod = EE.getModule(); |
451 | Function *F = mod.createFunction("main" ); |
452 | PlaceholderBindings bindings; |
453 | Placeholder *graphOutputVar; |
454 | |
455 | Type input_type(ElemKind::FloatTy, inputShape); |
456 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&input_type}, *F); |
457 | |
458 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
459 | |
460 | auto PH = mod.getPlaceholderByNameSlow("x" ); |
461 | auto *inTensor = bindings.allocate(PH); |
462 | inTensor->getHandle() = inputValues; |
463 | |
464 | EE.compile(CompilationMode::Infer); |
465 | bindings.allocate(mod.getPlaceholders()); |
466 | EE.run(bindings); |
467 | |
468 | auto result = bindings.get(graphOutputVar)->getHandle(); |
469 | ASSERT_TRUE(result.dims() == (llvm::ArrayRef<dim_t>)outputShape); |
470 | for (size_t i = 0; i < result.getType().size(); i++) { |
471 | EXPECT_NEAR(result.raw(i), expectedValues[i], delta); |
472 | } |
473 | } |
474 | |
475 | /// Test loading expand op from an ONNX model |
476 | /// with different output shape. |
477 | TEST_F(OnnxImporterTest, expandDiffShape) { |
478 | std::vector<float> inputValues = {1, 2, 3}; |
479 | std::vector<dim_t> inputShape = {3, 1}; |
480 | std::vector<dim_t> outputShape = {2, 3, 6}; |
481 | std::vector<float> expectedValues = { |
482 | 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, |
483 | 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, |
484 | }; |
485 | std::string netFilename( |
486 | GLOW_DATA_PATH "tests/models/onnxModels/expandnodeDiffShape.onnxtxt" ); |
487 | importExpandTest(netFilename, inputValues, inputShape, outputShape, |
488 | expectedValues); |
489 | } |
490 | |
491 | /// Test loading expand op from an ONNX model |
492 | /// with same output shape. |
493 | TEST_F(OnnxImporterTest, expandSameShape) { |
494 | std::vector<float> inputValues = {1, 2, 3}; |
495 | std::vector<dim_t> inputShape = {3, 1}; |
496 | std::vector<dim_t> outputShape = {3, 4}; |
497 | std::vector<float> expectedValues = { |
498 | 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, |
499 | }; |
500 | std::string netFilename( |
501 | GLOW_DATA_PATH "tests/models/onnxModels/expandnodeSameShape.onnxtxt" ); |
502 | importExpandTest(netFilename, inputValues, inputShape, outputShape, |
503 | expectedValues); |
504 | } |
505 | |
506 | /// Test loading maxPool1D op from an ONNX model |
507 | /// with different output shape. |
508 | TEST_F(OnnxImporterTest, maxPool1D) { |
509 | std::vector<float> inputValues = { |
510 | 1.4206449, 0.54408556, 1.3318906, 0.771925, 0.9450552, |
511 | 0.08600737, 0.30009857, 1.4206449, 0.54408556, 1.3318906, |
512 | 0.771925, 0.9450552, 0.08600737, 0.30009857}; |
513 | |
514 | std::vector<dim_t> inputShape = {1, 2, 7}; |
515 | std::vector<dim_t> outputShape = {1, 2, 2}; |
516 | std::vector<float> expectedValues = { |
517 | 1.4206449, |
518 | 0.9450552, |
519 | 1.4206449, |
520 | 0.9450552, |
521 | }; |
522 | std::string netFilename(GLOW_DATA_PATH |
523 | "tests/models/onnxModels/maxPool1D.onnxtxt" ); |
524 | importMaxPool1DTest(netFilename, inputValues, inputShape, outputShape, |
525 | expectedValues); |
526 | } |
527 | |
528 | /// Test loading LeakyRelu op from an ONNX model. |
529 | TEST_F(OnnxImporterTest, leakyRelu) { |
530 | ExecutionEngine EE{}; |
531 | auto &mod = EE.getModule(); |
532 | Function *F = mod.createFunction("main" ); |
533 | |
534 | std::string netFilename(GLOW_DATA_PATH |
535 | "tests/models/onnxModels/leakyRelu.onnxtxt" ); |
536 | |
537 | PlaceholderBindings bindings; |
538 | Placeholder *output; |
539 | { |
540 | Tensor x(ElemKind::FloatTy, {7}); |
541 | x.getHandle() = {0, -1, -2, -3, 4, 5, 6}; |
542 | |
543 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
544 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
545 | } |
546 | |
547 | auto *save = getSaveNodeFromDest(output); |
548 | LeakyReluNode *LR = llvm::dyn_cast<LeakyReluNode>(save->getInput().getNode()); |
549 | ASSERT_TRUE(LR); |
550 | EXPECT_FLOAT_EQ(LR->getAlpha(), 0.100000001); |
551 | } |
552 | |
553 | /// Test Loading LeakyRelu op from an ONNX model with default alpha. |
554 | TEST_F(OnnxImporterTest, leakyReluDefault) { |
555 | ExecutionEngine EE{}; |
556 | auto &mod = EE.getModule(); |
557 | Function *F = mod.createFunction("main" ); |
558 | |
559 | std::string netFilename(GLOW_DATA_PATH |
560 | "tests/models/onnxModels/leakyReluDefault.onnxtxt" ); |
561 | |
562 | PlaceholderBindings bindings; |
563 | Placeholder *output; |
564 | { |
565 | Tensor x(ElemKind::FloatTy, {7}); |
566 | x.getHandle() = {0, -1, -2, -3, 4, 5, 6}; |
567 | |
568 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
569 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
570 | } |
571 | |
572 | auto *save = getSaveNodeFromDest(output); |
573 | LeakyReluNode *LR = llvm::dyn_cast<LeakyReluNode>(save->getInput().getNode()); |
574 | ASSERT_TRUE(LR); |
575 | EXPECT_FLOAT_EQ(LR->getAlpha(), 0.01); |
576 | } |
577 | |
578 | TEST_F(OnnxImporterTest, importAddMultiBroadcastOp7) { |
579 | importArithMultiBroadcastTest<AddNode>( |
580 | "addMultiBroadcastOp7.onnxtxt" , {1, 3, 1, 2}, /* multi */ true, |
581 | /* leftBroadcast */ true, /* rightBroadcast */ true, |
582 | [](float a, float b) { return a + b; }); |
583 | } |
584 | |
585 | TEST_F(OnnxImporterTest, importAddUniBroadcastOp6NoAxis) { |
586 | importArithMultiBroadcastTest<AddNode>( |
587 | "addUniBroadcastOp6NoAxis.onnxtxt" , {1, 3, 4, 2}, /* multi */ false, |
588 | /* leftBroadcast */ false, /* rightBroadcast */ true, |
589 | [](float a, float b) { return a + b; }); |
590 | } |
591 | |
592 | TEST_F(OnnxImporterTest, importAddUniBroadcastOp6Axis) { |
593 | importArithMultiBroadcastTest<AddNode>( |
594 | "addUniBroadcastOp6Axis.onnxtxt" , {1, 3, 4, 2}, /* multi */ false, |
595 | /* leftBroadcast */ false, /* rightBroadcast */ true, |
596 | [](float a, float b) { return a + b; }); |
597 | } |
598 | |
599 | TEST_F(OnnxImporterTest, importSubMultiBroadcastOp7) { |
600 | importArithMultiBroadcastTest<SubNode>( |
601 | "subMultiBroadcastOp7.onnxtxt" , {1, 3, 1, 2}, /* multi */ true, |
602 | /* leftBroadcast */ true, /* rightBroadcast */ true, |
603 | [](float a, float b) { return a - b; }); |
604 | } |
605 | |
606 | TEST_F(OnnxImporterTest, importSubUniBroadcastOp6NoAxis) { |
607 | importArithMultiBroadcastTest<SubNode>( |
608 | "subUniBroadcastOp6NoAxis.onnxtxt" , {1, 3, 4, 2}, /* multi */ false, |
609 | /* leftBroadcast */ false, /* rightBroadcast */ true, |
610 | [](float a, float b) { return a - b; }); |
611 | } |
612 | |
613 | TEST_F(OnnxImporterTest, importSubUniBroadcastOp6Axis) { |
614 | importArithMultiBroadcastTest<SubNode>( |
615 | "subUniBroadcastOp6Axis.onnxtxt" , {1, 3, 4, 2}, /* multi */ false, |
616 | /* leftBroadcast */ false, /* rightBroadcast */ true, |
617 | [](float a, float b) { return a - b; }); |
618 | } |
619 | |
620 | TEST_F(OnnxImporterTest, importMulMultiBroadcastOp7) { |
621 | importArithMultiBroadcastTest<MulNode>( |
622 | "mulMultiBroadcastOp7.onnxtxt" , {1, 3, 1, 2}, /* multi */ true, |
623 | /* leftBroadcast */ true, /* rightBroadcast */ true, |
624 | [](float a, float b) { return a * b; }); |
625 | } |
626 | |
627 | TEST_F(OnnxImporterTest, importMulUniBroadcastOp6NoAxis) { |
628 | importArithMultiBroadcastTest<MulNode>( |
629 | "mulUniBroadcastOp6NoAxis.onnxtxt" , {1, 3, 4, 2}, /* multi */ false, |
630 | /* leftBroadcast */ false, /* rightBroadcast */ true, |
631 | [](float a, float b) { return a * b; }); |
632 | } |
633 | |
634 | TEST_F(OnnxImporterTest, importMulUniBroadcastOp6Axis) { |
635 | importArithMultiBroadcastTest<MulNode>( |
636 | "mulUniBroadcastOp6Axis.onnxtxt" , {1, 3, 4, 2}, /* multi */ false, |
637 | /* leftBroadcast */ false, /* rightBroadcast */ true, |
638 | [](float a, float b) { return a * b; }); |
639 | } |
640 | |
641 | TEST_F(OnnxImporterTest, importDivMultiBroadcastOp7) { |
642 | importArithMultiBroadcastTest<DivNode>( |
643 | "divMultiBroadcastOp7.onnxtxt" , {1, 3, 1, 2}, /* multi */ true, |
644 | /* leftBroadcast */ true, /* rightBroadcast */ true, |
645 | [](float a, float b) { return a / b; }); |
646 | } |
647 | |
648 | TEST_F(OnnxImporterTest, importDivUniBroadcastOp6NoAxis) { |
649 | importArithMultiBroadcastTest<DivNode>( |
650 | "divUniBroadcastOp6NoAxis.onnxtxt" , {1, 3, 4, 2}, /* multi */ false, |
651 | /* leftBroadcast */ false, /* rightBroadcast */ true, |
652 | [](float a, float b) { return a / b; }); |
653 | } |
654 | |
655 | TEST_F(OnnxImporterTest, importDivUniBroadcastOp6Axis) { |
656 | importArithMultiBroadcastTest<DivNode>( |
657 | "divUniBroadcastOp6Axis.onnxtxt" , {1, 3, 4, 2}, /* multi */ false, |
658 | /* leftBroadcast */ false, /* rightBroadcast */ true, |
659 | [](float a, float b) { return a / b; }); |
660 | } |
661 | |
662 | TEST_F(OnnxImporterTest, importPowMultiBroadcastOp7) { |
663 | importArithMultiBroadcastTest<PowNode>( |
664 | "powMultiBroadcastOp7.onnxtxt" , {1, 3, 1, 2}, /* multi */ true, |
665 | /* leftBroadcast */ true, /* rightBroadcast */ true, |
666 | [](float a, float b) { return std::pow(a, b); }); |
667 | } |
668 | |
669 | /// This tests reproduces issue #2135. |
670 | TEST_F(OnnxImporterTest, importUniBroadcastMultiOutput) { |
671 | ExecutionEngine EE{}; |
672 | auto &mod = EE.getModule(); |
673 | Function *F = mod.createFunction("main" ); |
674 | |
675 | std::string NetFilename = std::string( |
676 | GLOW_DATA_PATH "tests/models/onnxModels/UniBroadcastIssue2135.onnxtxt" ); |
677 | Tensor data(ElemKind::FloatTy, {20}); |
678 | ONNXModelLoader onnxLD(NetFilename, {"data" }, {&data.getType()}, *F); |
679 | (void)onnxLD; |
680 | } |
681 | |
682 | /// Test Onnx QuantizeLinear and DequantizeLinear together. |
683 | TEST_F(OnnxImporterTest, quantizeLinearDequantizeLinear) { |
684 | ExecutionEngine EE{}; |
685 | auto &mod = EE.getModule(); |
686 | Function *F = mod.createFunction("main" ); |
687 | std::string fileName = "QuantizeLinearDequantizeLinear.onnxtxt" ; |
688 | std::string NetFilename = |
689 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName; |
690 | PlaceholderBindings bindings; |
691 | Placeholder *graphOutputVar; |
692 | std::vector<dim_t> inputShape{6}; |
693 | Type input_type(ElemKind::FloatTy, inputShape); |
694 | std::string inputName = "x" ; |
695 | ONNXModelLoader onnxLD(NetFilename, {inputName.c_str()}, {&input_type}, *F); |
696 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
697 | auto *PH = mod.getPlaceholderByNameSlow(inputName); |
698 | auto *inTensor = bindings.allocate(PH); |
699 | inTensor->getHandle().randomize(-1.0, 1.0, mod.getPRNG()); |
700 | // Compile&run the graph, and check the output |
701 | EE.compile(CompilationMode::Infer); |
702 | bindings.allocate(mod.getPlaceholders()); |
703 | EE.run(bindings); |
704 | auto result = bindings.get(graphOutputVar)->getHandle(); |
705 | auto inHandle = inTensor->getHandle(); |
706 | for (size_t i = 0; i < result.getType().size(); i++) { |
707 | EXPECT_NEAR(result.raw(i), inHandle.raw(i), 1e-05); |
708 | } |
709 | } |
710 | |
711 | /// Test loading of Elementwise Unary Ops floating point. |
712 | static void testEltwiseUnaryOpFloat(std::string fileName, |
713 | llvm::ArrayRef<dim_t> inputShape, |
714 | std::string input_name, float delta, |
715 | const std::function<float(float)> &op) { |
716 | ExecutionEngine EE{}; |
717 | auto &mod = EE.getModule(); |
718 | Function *F = mod.createFunction("main" ); |
719 | std::string NetFilename = |
720 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName; |
721 | PlaceholderBindings bindings; |
722 | Placeholder *graphOutputVar; |
723 | Type input_type(ElemKind::FloatTy, inputShape); |
724 | ONNXModelLoader onnxLD(NetFilename, {input_name.c_str()}, {&input_type}, *F); |
725 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
726 | auto PH = mod.getPlaceholderByNameSlow(input_name); |
727 | auto *inTensor = bindings.allocate(PH); |
728 | inTensor->getHandle().randomize(-10.0, 10.0, mod.getPRNG()); |
729 | // Compile&run the graph, and check the output |
730 | EE.compile(CompilationMode::Infer); |
731 | bindings.allocate(mod.getPlaceholders()); |
732 | EE.run(bindings); |
733 | auto result = bindings.get(graphOutputVar)->getHandle(); |
734 | auto inHandle = inTensor->getHandle(); |
735 | ASSERT_TRUE(result.dims() == inputShape); |
736 | for (size_t i = 0; i < result.getType().size(); i++) { |
737 | EXPECT_NEAR(result.raw(i), op(inHandle.raw(i)), delta); |
738 | } |
739 | } |
740 | |
741 | TEST_F(OnnxImporterTest, importExp) { |
742 | testEltwiseUnaryOpFloat("exp.onnxtxt" , {1, 2, 4, 3}, "data" , 0.002, |
743 | [](float a) { return std::exp(a); }); |
744 | } |
745 | |
746 | TEST(onnx, importNeg) { |
747 | testEltwiseUnaryOpFloat("neg.onnxtxt" , {1, 2, 4, 3}, "data" , 0.000, |
748 | [](float a) { return -a; }); |
749 | } |
750 | |
751 | TEST(onnx, importCeil) { |
752 | testEltwiseUnaryOpFloat("ceil.onnxtxt" , {1, 2, 4, 3}, "data" , 0.000, |
753 | [](float a) { return std::ceil(a); }); |
754 | } |
755 | |
756 | TEST(onnx, importFloor) { |
757 | testEltwiseUnaryOpFloat("floor.onnxtxt" , {1, 2, 4, 3}, "data" , 0.000, |
758 | [](float a) { return std::floor(a); }); |
759 | } |
760 | |
761 | TEST_F(OnnxImporterTest, importSin) { |
762 | testEltwiseUnaryOpFloat("Sin.onnxtxt" , {2, 3, 1}, "X" , 0.002, |
763 | [](float a) { return std::sin(a); }); |
764 | } |
765 | |
766 | TEST_F(OnnxImporterTest, importCos) { |
767 | testEltwiseUnaryOpFloat("Cos.onnxtxt" , {2, 3, 1}, "X" , 0.002, |
768 | [](float a) { return std::cos(a); }); |
769 | } |
770 | |
771 | TEST_F(OnnxImporterTest, importErf) { |
772 | testEltwiseUnaryOpFloat("Erf.onnxtxt" , {1, 3, 4, 5}, "input" , 0.002, |
773 | [](float a) { return std::erf(a); }); |
774 | } |
775 | |
776 | TEST(onnx, importAbs) { |
777 | testEltwiseUnaryOpFloat("abs.onnxtxt" , {1, 2, 3, 2}, "input" , 0.002, |
778 | [](float a) { return std::abs(a); }); |
779 | } |
780 | |
781 | // Tests log node for random positive values. |
782 | static void testImportLog(std::string fileName, |
783 | llvm::ArrayRef<dim_t> inputShape, |
784 | std::string input_name, float delta, |
785 | const std::function<float(float)> &op) { |
786 | |
787 | ExecutionEngine EE{}; |
788 | auto &mod = EE.getModule(); |
789 | Function *F = mod.createFunction("main" ); |
790 | std::string NetFilename = |
791 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName; |
792 | PlaceholderBindings bindings; |
793 | Placeholder *graphOutputVar; |
794 | Type input_type(ElemKind::FloatTy, inputShape); |
795 | ONNXModelLoader onnxLD(NetFilename, {input_name.c_str()}, {&input_type}, *F); |
796 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
797 | auto PH = mod.getPlaceholderByNameSlow(input_name); |
798 | auto *inTensor = bindings.allocate(PH); |
799 | |
800 | inTensor->getHandle().randomize(0, 500.0, mod.getPRNG()); |
801 | // Compile&run the graph, and check the output |
802 | EE.compile(CompilationMode::Infer); |
803 | bindings.allocate(mod.getPlaceholders()); |
804 | EE.run(bindings); |
805 | auto result = bindings.get(graphOutputVar)->getHandle(); |
806 | auto inHandle = inTensor->getHandle(); |
807 | ASSERT_TRUE(result.dims() == inputShape); |
808 | for (size_t i = 0; i < result.getType().size(); i++) { |
809 | EXPECT_NEAR(result.raw(i), op(inHandle.raw(i)), delta); |
810 | } |
811 | } |
812 | |
813 | /// Test loading of Elemenntwise Trigonometric Ops |
814 | /// Extendable for other ops in future |
815 | static void |
816 | testEltwiseTrigonometricOpFloat(std::string fileName, |
817 | llvm::ArrayRef<dim_t> inputShape, |
818 | std::string input_name, float delta, |
819 | const std::function<float(float)> &op) { |
820 | ExecutionEngine EE{}; |
821 | auto &mod = EE.getModule(); |
822 | Function *F = mod.createFunction("main" ); |
823 | std::string NetFilename = |
824 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName; |
825 | PlaceholderBindings bindings; |
826 | Placeholder *graphOutputVar; |
827 | Type input_type(ElemKind::FloatTy, inputShape); |
828 | ONNXModelLoader onnxLD(NetFilename, {input_name.c_str()}, {&input_type}, *F); |
829 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
830 | auto PH = mod.getPlaceholderByNameSlow(input_name); |
831 | auto *inTensor = bindings.allocate(PH); |
832 | |
833 | // Range of Asin/Acos is -1 to 1 |
834 | inTensor->getHandle().randomize(-1.0, 1.0, mod.getPRNG()); |
835 | // Compile&run the graph, and check the output |
836 | EE.compile(CompilationMode::Infer); |
837 | bindings.allocate(mod.getPlaceholders()); |
838 | EE.run(bindings); |
839 | auto result = bindings.get(graphOutputVar)->getHandle(); |
840 | auto inHandle = inTensor->getHandle(); |
841 | ASSERT_TRUE(result.dims() == inputShape); |
842 | for (size_t i = 0; i < result.getType().size(); i++) { |
843 | EXPECT_NEAR(result.raw(i), op(inHandle.raw(i)), delta); |
844 | } |
845 | } |
846 | |
847 | TEST_F(OnnxImporterTest, importAsin) { |
848 | testEltwiseTrigonometricOpFloat("Asin.onnxtxt" , {1, 3, 4, 5}, "input" , 0.002, |
849 | [](float a) { return std::asin(a); }); |
850 | } |
851 | |
852 | TEST_F(OnnxImporterTest, importAcos) { |
853 | testEltwiseTrigonometricOpFloat("Acos.onnxtxt" , {1, 3, 4, 5}, "input" , 0.002, |
854 | [](float a) { return std::acos(a); }); |
855 | } |
856 | |
857 | TEST_F(OnnxImporterTest, importAtan) { |
858 | testEltwiseTrigonometricOpFloat("Atan.onnxtxt" , {1, 3, 4, 5}, "input" , 0.002, |
859 | [](float a) { return std::atan(a); }); |
860 | } |
861 | |
862 | TEST_F(OnnxImporterTest, importLog) { |
863 | testImportLog("log.onnxtxt" , {1, 2, 3, 2}, "data" , 0.002, |
864 | [](float a) { return std::log(a); }); |
865 | } |
866 | |
867 | static void testImportPRelu(std::string filename, |
868 | llvm::ArrayRef<dim_t> inputShape, |
869 | std::vector<float> expectedSlope) { |
870 | ExecutionEngine EE{}; |
871 | auto &mod = EE.getModule(); |
872 | Function *F = mod.createFunction("main" ); |
873 | |
874 | std::string NetFileName = |
875 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + filename; |
876 | |
877 | PlaceholderBindings bindings; |
878 | Placeholder *graphOutputVar; |
879 | // Destroy the loader after the graph is loaded since the following execution |
880 | // will not depend on anyting from the loader. |
881 | Tensor data(ElemKind::FloatTy, inputShape); |
882 | data.getHandle().randomize(-4.0, 4.0, mod.getPRNG()); |
883 | { |
884 | ONNXModelLoader onnxLoader(NetFileName, {"data" }, {&data.getType()}, *F); |
885 | graphOutputVar = EXIT_ON_ERR(onnxLoader.getSingleOutput()); |
886 | bindings.allocate(mod.getPlaceholders()); |
887 | updateInputPlaceholdersByName(bindings, &mod, {"data" }, {&data}); |
888 | } |
889 | |
890 | // Compile&run the graph, and check the output. |
891 | EE.compile(CompilationMode::Infer); |
892 | EE.run(bindings); |
893 | auto dataH = |
894 | bindings.get(bindings.getPlaceholderByNameSlow("data" ))->getHandle(); |
895 | auto result = bindings.get(graphOutputVar)->getHandle(); |
896 | std::vector<dim_t> expectedDims = {inputShape[0], inputShape[1], |
897 | inputShape[2], inputShape[3]}; |
898 | |
899 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
900 | for (size_t i = 0; i < dataH.size(); i++) { |
901 | float expectedVal = expectedSlope[i] * std::min<float>(0, dataH.raw(i)) + |
902 | std::max<float>(0, dataH.raw(i)); |
903 | EXPECT_FLOAT_EQ(result.raw(i), expectedVal); |
904 | } |
905 | |
906 | // Constant Folding Test. |
907 | FAIL_TEST_IF_ERR(checkConstFoldedOutput(NetFileName, {"data" }, {&data}, |
908 | {bindings.get(graphOutputVar)})); |
909 | } |
910 | |
911 | TEST_F(OnnxImporterTest, importPreluSlopeHasSameShape) { |
912 | // The expected slope values correspond to the pre-broadcast |
913 | // initializer values in the model file. |
914 | std::vector<float> expectedSlope = {1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, |
915 | 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0}; |
916 | testImportPRelu("preluSlopeHasSameShape.onnxtxt" , {1, 4, 2, 2}, |
917 | expectedSlope); |
918 | } |
919 | |
920 | TEST_F(OnnxImporterTest, importPReluBroadcastSlope) { |
921 | // The expected slope values correspond to the pre-broadcast |
922 | // initializer values in the model file. |
923 | std::vector<float> expectedSlope = {1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, |
924 | 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0}; |
925 | testImportPRelu("preluBroadcastSlope.onnxtxt" , {1, 4, 2, 2}, expectedSlope); |
926 | } |
927 | |
928 | /// Expects failure to load PRelu in case of invalid slope shape. |
929 | TEST_F(OnnxImporterTest, importPReluInvalidBroadcastSlope) { |
930 | ExecutionEngine EE{}; |
931 | auto &mod = EE.getModule(); |
932 | Function *F = mod.createFunction("main" ); |
933 | |
934 | std::string NetFileName = |
935 | std::string(GLOW_DATA_PATH |
936 | "tests/models/onnxModels/preluInvalidBroadcastSlope.onnxtxt" ); |
937 | |
938 | // Destroy the loader after the graph is loaded since the following execution |
939 | // will not depend on anyting from the loader. |
940 | { |
941 | Tensor data(ElemKind::FloatTy, {1, 4, 2, 2}); |
942 | EXPECT_DEATH(ONNXModelLoader(NetFileName, {"data" }, {&data.getType()}, *F), |
943 | "" ); |
944 | } |
945 | } |
946 | |
947 | /// Test loading HardSigmoid op from an ONNX model. |
948 | TEST_F(OnnxImporterTest, hardsigmoid) { |
949 | ExecutionEngine EE{}; |
950 | auto &mod = EE.getModule(); |
951 | Function *F = mod.createFunction("main" ); |
952 | |
953 | std::string netFilename(GLOW_DATA_PATH |
954 | "tests/models/onnxModels/hardsigmoid.onnxtxt" ); |
955 | |
956 | PlaceholderBindings bindings; |
957 | Placeholder *output; |
958 | { |
959 | Tensor x(ElemKind::FloatTy, {5}); |
960 | x.getHandle() = {-3, -1, 0, 1, 3}; |
961 | |
962 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&x.getType()}, *F); |
963 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
964 | } |
965 | |
966 | auto *save = getSaveNodeFromDest(output); |
967 | ClipNode *LR = llvm::dyn_cast<ClipNode>(save->getInput().getNode()); |
968 | ASSERT_TRUE(LR); |
969 | |
970 | // check beta |
971 | AddNode *addBeta = llvm::dyn_cast<AddNode>(LR->getInput()); |
972 | ASSERT_TRUE(addBeta); |
973 | SplatNode *betaSplat = llvm::dyn_cast<SplatNode>(addBeta->getRHS()); |
974 | ASSERT_TRUE(betaSplat); |
975 | EXPECT_FLOAT_EQ(betaSplat->getValue(), 0.500000001); |
976 | |
977 | // check alpha |
978 | MulNode *mulAlpha = llvm::dyn_cast<MulNode>(addBeta->getLHS()); |
979 | ASSERT_TRUE(mulAlpha); |
980 | SplatNode *alphaSplat = llvm::dyn_cast<SplatNode>(mulAlpha->getLHS()); |
981 | ASSERT_TRUE(alphaSplat); |
982 | EXPECT_FLOAT_EQ(alphaSplat->getValue(), 0.16666667); |
983 | } |
984 | |
985 | /// Helper method to run the Conv operator test cases. |
986 | /// \p filename contains the model .onnxtxt. |
987 | /// \p expectedDims: output Tensor dimensions. |
988 | /// \p expectedValues : output Tensor values expected. |
989 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
990 | /// strides is {1, 1}, group is 1. Pads can vary. |
991 | static void convTestHelper(std::string &filename, |
992 | llvm::ArrayRef<dim_t> expectedDims, |
993 | llvm::ArrayRef<float> expectedValues) { |
994 | |
995 | ExecutionEngine EE{}; |
996 | auto &mod = EE.getModule(); |
997 | Function *F = mod.createFunction("main" ); |
998 | |
999 | std::string NetFilename = |
1000 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + filename; |
1001 | |
1002 | PlaceholderBindings bindings; |
1003 | Placeholder *graphOutputVar; |
1004 | // Destroy the loader after the graph is loaded since the following execution |
1005 | // will not depend on anyting from the loader. |
1006 | { |
1007 | Tensor data; |
1008 | getNCHWData(&data, 1, 1, 3, 3); |
1009 | ONNXModelLoader onnxLD(NetFilename, {"data" }, {&data.getType()}, *F); |
1010 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1011 | bindings.allocate(mod.getPlaceholders()); |
1012 | updateInputPlaceholdersByName(bindings, &mod, {"data" }, {&data}); |
1013 | } |
1014 | |
1015 | // ONNX importer loads a conv node and converts it to 4 ops: |
1016 | // Transpose (input) -> Conv -> Transpose |
1017 | // Transpose (filter) -> |
1018 | // A save node is added in the network as well. Therefore there are 5 nodes: |
1019 | // Transpose (input) -> Conv -> Transpose -> Save |
1020 | // Transpose (filter) -> |
1021 | // Note that in case the convolution filter is a constant tensor, the filter |
1022 | // transpose node will be later optimized out by the optimizer. |
1023 | EXPECT_EQ(F->getNodes().size(), 5); |
1024 | EXPECT_EQ(mod.getPlaceholders().size(), 2); |
1025 | EXPECT_EQ(mod.getConstants().size(), 2); |
1026 | |
1027 | auto *saveNode = getSaveNodeFromDest(graphOutputVar); |
1028 | auto *node = saveNode->getInput().getNode(); |
1029 | |
1030 | EXPECT_TRUE(node->getKind() == Kinded::Kind::TransposeNodeKind); |
1031 | auto *convNode = llvm::dyn_cast<TransposeNode>(node)->getInput().getNode(); |
1032 | |
1033 | EXPECT_TRUE(convNode->getKind() == Kinded::Kind::ConvolutionNodeKind); |
1034 | auto *tInNode = |
1035 | llvm::dyn_cast<ConvolutionNode>(convNode)->getInput().getNode(); |
1036 | auto *tFilterNode = |
1037 | llvm::dyn_cast<ConvolutionNode>(convNode)->getFilter().getNode(); |
1038 | EXPECT_TRUE(tInNode->getKind() == Kinded::Kind::TransposeNodeKind); |
1039 | EXPECT_TRUE(tFilterNode->getKind() == Kinded::Kind::TransposeNodeKind); |
1040 | |
1041 | EE.compile(CompilationMode::Infer); |
1042 | EE.run(bindings); |
1043 | auto result = bindings.get(graphOutputVar)->getHandle(); |
1044 | EXPECT_TRUE(result.dims() == expectedDims); |
1045 | for (size_t i = 0, e = expectedValues.size(); i < e; i++) { |
1046 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
1047 | } |
1048 | } |
1049 | |
1050 | /// Helper method to run the Conv operator test cases. |
1051 | /// \p filename contains the model .onnxtxt. |
1052 | /// \p expectedDims: output Tensor dimensions. |
1053 | /// \p expectedValues : output Tensor values expected. |
1054 | /// The input is N*C*H*W (1*1*2*3*3), the kernels is {2, 3, 3}, |
1055 | /// strides is {1, 1, 1}, group is 1. Pads can vary. |
1056 | static void conv3DTestHelper(std::string &filename, |
1057 | llvm::ArrayRef<dim_t> inputDims, |
1058 | llvm::ArrayRef<dim_t> expectedDims, |
1059 | llvm::ArrayRef<float> expectedValues) { |
1060 | |
1061 | ExecutionEngine EE{}; |
1062 | auto &mod = EE.getModule(); |
1063 | Function *F = mod.createFunction("main" ); |
1064 | |
1065 | std::string NetFilename = |
1066 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + filename; |
1067 | |
1068 | PlaceholderBindings bindings; |
1069 | Placeholder *graphOutputVar; |
1070 | // Destroy the loader after the graph is loaded since the following execution |
1071 | // will not depend on anyting from the loader. |
1072 | { |
1073 | Tensor data; |
1074 | getNCTHWData(&data, inputDims[0], inputDims[1], inputDims[2], inputDims[3], |
1075 | inputDims[4]); |
1076 | ONNXModelLoader onnxLD(NetFilename, {"data" }, {&data.getType()}, *F); |
1077 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1078 | bindings.allocate(mod.getPlaceholders()); |
1079 | updateInputPlaceholdersByName(bindings, &mod, {"data" }, {&data}); |
1080 | } |
1081 | |
1082 | // ONNX importer loads a conv node and converts it to 4 ops: |
1083 | // Transpose (input) -> Conv -> Transpose |
1084 | // Transpose (filter) -> |
1085 | // A save node is added in the network as well. Therefore there are 5 nodes: |
1086 | // Transpose (input) -> Conv -> Transpose -> Save |
1087 | // Transpose (filter) -> |
1088 | // Note that in case the convolution filter is a constant tensor, the filter |
1089 | // transpose node will be later optimized out by the optimizer. |
1090 | EXPECT_EQ(F->getNodes().size(), 5); |
1091 | EXPECT_EQ(mod.getPlaceholders().size(), 2); |
1092 | EXPECT_EQ(mod.getConstants().size(), 2); |
1093 | |
1094 | auto *saveNode = getSaveNodeFromDest(graphOutputVar); |
1095 | auto *node = saveNode->getInput().getNode(); |
1096 | |
1097 | EXPECT_TRUE(node->getKind() == Kinded::Kind::TransposeNodeKind); |
1098 | auto *convNode = llvm::dyn_cast<TransposeNode>(node)->getInput().getNode(); |
1099 | |
1100 | EXPECT_TRUE(convNode->getKind() == Kinded::Kind::Convolution3DNodeKind); |
1101 | auto *tInNode = |
1102 | llvm::dyn_cast<Convolution3DNode>(convNode)->getInput().getNode(); |
1103 | auto *tFilterNode = |
1104 | llvm::dyn_cast<Convolution3DNode>(convNode)->getFilter().getNode(); |
1105 | EXPECT_TRUE(tInNode->getKind() == Kinded::Kind::TransposeNodeKind); |
1106 | EXPECT_TRUE(tFilterNode->getKind() == Kinded::Kind::TransposeNodeKind); |
1107 | |
1108 | EE.compile(CompilationMode::Infer); |
1109 | EE.run(bindings); |
1110 | auto result = bindings.get(graphOutputVar)->getHandle(); |
1111 | EXPECT_TRUE(result.dims() == expectedDims); |
1112 | for (size_t i = 0, e = expectedValues.size(); i < e; i++) { |
1113 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
1114 | } |
1115 | } |
1116 | |
1117 | /// Test loading conv op from a ONNX model. |
1118 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
1119 | /// strides is {1, 1}, pads is {1, 1, 1, 1}, group is 1. |
1120 | TEST_F(OnnxImporterTest, importConv) { |
1121 | std::string filename("simpleConv.onnxtxt" ); |
1122 | std::vector<dim_t> expectedDims = {1, 1, 4, 4}; |
1123 | std::vector<float> expectedValues = {2, 3, 5, 4, 5, 10, 14, 9, |
1124 | 11, 22, 26, 15, 8, 15, 17, 10}; |
1125 | convTestHelper(filename, expectedDims, expectedValues); |
1126 | } |
1127 | |
1128 | /// Test loading conv op from a ONNX model. |
1129 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
1130 | /// strides is {1, 1}, pads is {1, 1, 1, 1}, group is 1, dilation is {1, 2}. |
1131 | TEST_F(OnnxImporterTest, importConvNonSquareDilation) { |
1132 | std::string filename("simpleConvNonSquareDilation.onnxtxt" ); |
1133 | std::vector<dim_t> expectedDims = {1, 1, 4, 3}; |
1134 | std::vector<float> expectedValues = {3, 4, 3, 7, 12, 7, 13, 24, 13, 9, 16, 9}; |
1135 | convTestHelper(filename, expectedDims, expectedValues); |
1136 | } |
1137 | |
1138 | /// Test loading conv op from a ONNX model. |
1139 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
1140 | /// strides is {1, 1}, auto_pad VALID (i.e. no padding), group is 1. |
1141 | TEST_F(OnnxImporterTest, importConvAutoPadValid) { |
1142 | std::string filename("simpleConvAutoPadValid.onnxtxt" ); |
1143 | std::vector<dim_t> expectedDims = {1, 1, 2, 2}; |
1144 | std::vector<float> expectedValues = {10, 14, 22, 26}; |
1145 | convTestHelper(filename, expectedDims, expectedValues); |
1146 | } |
1147 | |
1148 | /// Test loading conv op from a ONNX model. |
1149 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
1150 | /// strides is {1, 1}, auto_pad SAME_UPPER, group is 1. |
1151 | TEST_F(OnnxImporterTest, importConvAutoPadSameUpper) { |
1152 | std::string filename("simpleConvAutoPadSameUpper.onnxtxt" ); |
1153 | std::vector<dim_t> expectedDims = {1, 1, 3, 3}; |
1154 | std::vector<float> expectedValues = {10, 14, 9, 22, 26, 15, 15, 17, 10}; |
1155 | convTestHelper(filename, expectedDims, expectedValues); |
1156 | } |
1157 | |
1158 | /// Test loading conv op from a ONNX model. |
1159 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
1160 | /// strides is {1, 1}, auto_pad SAME_LOWER, group is 1. |
1161 | TEST_F(OnnxImporterTest, importConvAutoPadSameLower) { |
1162 | std::string filename("simpleConvAutoPadSameLower.onnxtxt" ); |
1163 | std::vector<dim_t> expectedDims = {1, 1, 3, 3}; |
1164 | std::vector<float> expectedValues = {2, 3, 5, 5, 10, 14, 11, 22, 26}; |
1165 | convTestHelper(filename, expectedDims, expectedValues); |
1166 | } |
1167 | |
1168 | /// Test loading conv 3D op from a ONNX model. |
1169 | /// The input is N*C*T*H*W (1*1*2*3*3), the kernels is {2, 3, 3}, |
1170 | /// strides is {1, 1, 1}, pads is {1, 1, 1, 1, 1, 1}, group is 1. |
1171 | TEST_F(OnnxImporterTest, importConv3D) { |
1172 | std::string filename("simpleConv3D.onnxtxt" ); |
1173 | std::vector<dim_t> inputDims = {1, 1, 2, 3, 3}; |
1174 | std::vector<dim_t> expectedDims = {1, 1, 3, 3, 3}; |
1175 | std::vector<float> expectedValues = { |
1176 | 3.0, 6.0, 6.0, 10.0, 16.0, 14.0, 12.0, 18.0, 15.0, |
1177 | 26.25, 39.0, 32.25, 47.0, 68.0, 55.0, 44.25, 63.0, 50.25, |
1178 | 23.25, 33.0, 26.25, 37.0, 52.0, 41.0, 32.25, 45.0, 35.25}; |
1179 | conv3DTestHelper(filename, inputDims, expectedDims, expectedValues); |
1180 | } |
1181 | |
1182 | /// Test loading conv 3D op from a ONNX model. |
1183 | /// The input is N*C*T*H*W (1*1*2*3*3), the kernels is {2, 3, 3}, |
1184 | /// strides is {1, 1, 1}, pads is {1, 1, 1, 1, 1, 1}, group is 1. |
1185 | /// Dilation is {1, 2, 1}. |
1186 | // TEST_F(OnnxImporterTest, importConv3DNonSquareDilation) { |
1187 | // std::string filename("simpleConv3D.onnxtxt"); |
1188 | // std::vector<dim_t> inputDims = {1, 1, 2, 3, 3}; |
1189 | // std::vector<dim_t> expectedDims = {1, 1, 3, 1, 3}; |
1190 | // std::vector<float> expectedValues = { |
1191 | // 5.0, 8.0, 7.0, 23.5, 34.0, 27.5, 18.5, 26.0, 20.5 |
1192 | // }; |
1193 | // conv3DTestHelper(filename, inputDims, expectedDims, expectedValues); |
1194 | //} |
1195 | |
1196 | /// Test loading conv 3D op from a ONNX model. |
1197 | /// The input is N*C*T*H*W (1*1*2*3*3), the kernels is {2, 3, 3}, |
1198 | /// strides is {1, 1, 1}, auto_pad VALID (i.e. no padding), group is 1. |
1199 | TEST_F(OnnxImporterTest, importConv3DAutoPadValid) { |
1200 | std::string filename("simpleConv3DAutoPadValid.onnxtxt" ); |
1201 | std::vector<dim_t> inputDims = {1, 1, 2, 3, 3}; |
1202 | std::vector<dim_t> expectedDims = {1, 1, 1, 1, 1}; |
1203 | std::vector<float> expectedValues = {68.0}; |
1204 | conv3DTestHelper(filename, inputDims, expectedDims, expectedValues); |
1205 | } |
1206 | |
1207 | /// Test loading conv 3D op from a ONNX model. |
1208 | /// The input is N*C*T*H*W (1*1*2*3*3), the kernels is {2, 3, 3}, |
1209 | /// strides is {1, 2, 2}, auto_pad SAME_LOWER, group is 1. |
1210 | TEST_F(OnnxImporterTest, importConv3DAutoPadSameLower) { |
1211 | std::string filename("simpleConv3DAutoPadSameLower.onnxtxt" ); |
1212 | std::vector<dim_t> inputDims = {1, 1, 2, 3, 3}; |
1213 | std::vector<dim_t> expectedDims = {1, 1, 2, 2, 2}; |
1214 | std::vector<float> expectedValues = {3.0, 6.0, 12.0, 15.0, |
1215 | 26.25, 32.25, 44.25, 50.25}; |
1216 | conv3DTestHelper(filename, inputDims, expectedDims, expectedValues); |
1217 | } |
1218 | |
1219 | /// Test loading conv 3D op from a ONNX model. |
1220 | /// The input is N*C*T*H*W (1*1*2*3*3), the kernels is {2, 3, 3}, |
1221 | /// strides is {1, 2, 2}, auto_pad SAME_UPPER, group is 1. |
1222 | TEST_F(OnnxImporterTest, importConv3DAutoPadSameUpper) { |
1223 | std::string filename("simpleConv3DAutoPadSameUpper.onnxtxt" ); |
1224 | std::vector<dim_t> inputDims = {1, 1, 2, 3, 3}; |
1225 | std::vector<dim_t> expectedDims = {1, 1, 2, 2, 2}; |
1226 | std::vector<float> expectedValues = {26.25, 32.25, 44.25, 50.25, |
1227 | 23.25, 26.25, 32.25, 35.25}; |
1228 | conv3DTestHelper(filename, inputDims, expectedDims, expectedValues); |
1229 | } |
1230 | |
1231 | /// Test loading conv 3D op with non-cubic pads from a ONNX model. |
1232 | /// The input is N*C*T*H*W (1*1*3*3*3), kernels is {1, 1, 1}, |
1233 | /// strides is {1, 1, 1}, pads is {1, 2, 3, 3, 1, 2}, group is 1. |
1234 | /// Filter is 1.0 so that output equals input + padding |
1235 | TEST_F(OnnxImporterTest, importConv3DNonCubicPads) { |
1236 | std::string filename("simpleConv3DNonCubicPads.onnxtxt" ); |
1237 | std::vector<dim_t> inputDims = {1, 1, 3, 3, 3}; |
1238 | std::vector<dim_t> expectedDims = {1, 1, 7, 6, 8}; |
1239 | std::vector<float> expectedValues = { |
1240 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1241 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1242 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1243 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1244 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1245 | |
1246 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1247 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1248 | 1.00, 2.00, 0.00, 0.00, 0.00, 0.00, 0.00, 3.00, 4.00, 5.00, |
1249 | 0.00, 0.00, 0.00, 0.00, 0.00, 6.00, 7.00, 8.00, 0.00, 0.00, |
1250 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1251 | |
1252 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1253 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 9.00, |
1254 | 10.00, 11.00, 0.00, 0.00, 0.00, 0.00, 0.00, 12.00, 13.00, 14.00, |
1255 | 0.00, 0.00, 0.00, 0.00, 0.00, 15.00, 16.00, 17.00, 0.00, 0.00, |
1256 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1257 | |
1258 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1259 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 18.00, |
1260 | 19.00, 20.00, 0.00, 0.00, 0.00, 0.00, 0.00, 21.00, 22.00, 23.00, |
1261 | 0.00, 0.00, 0.00, 0.00, 0.00, 24.00, 25.00, 26.00, 0.00, 0.00, |
1262 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1263 | |
1264 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1265 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1266 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1267 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1268 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1269 | |
1270 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1271 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1272 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1273 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1274 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1275 | |
1276 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1277 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1278 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1279 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, |
1280 | 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00}; |
1281 | conv3DTestHelper(filename, inputDims, expectedDims, expectedValues); |
1282 | } |
1283 | |
1284 | /// Import conv1D |
1285 | static void importConv1DTest(std::string &netFilename, |
1286 | llvm::ArrayRef<float> inputXValues, |
1287 | llvm::ArrayRef<dim_t> inputXShape, |
1288 | llvm::ArrayRef<float> inputWValues, |
1289 | llvm::ArrayRef<dim_t> inputWShape, |
1290 | llvm::ArrayRef<dim_t> outputShape, |
1291 | llvm::ArrayRef<float> expectedValues) { |
1292 | float delta = 1e-07; |
1293 | ExecutionEngine EE{}; |
1294 | auto &mod = EE.getModule(); |
1295 | Function *F = mod.createFunction("main" ); |
1296 | PlaceholderBindings bindings; |
1297 | Placeholder *graphOutputVar; |
1298 | |
1299 | Type input_type_x(ElemKind::FloatTy, inputXShape); |
1300 | Type input_type_w(ElemKind::FloatTy, inputWShape); |
1301 | ONNXModelLoader onnxLD(netFilename, {"x" , "w" }, |
1302 | {&input_type_x, &input_type_w}, *F); |
1303 | |
1304 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1305 | |
1306 | auto PHX = mod.getPlaceholderByNameSlow("x" ); |
1307 | auto *inTensorX = bindings.allocate(PHX); |
1308 | inTensorX->getHandle() = inputXValues; |
1309 | |
1310 | auto PHW = mod.getPlaceholderByNameSlow("w" ); |
1311 | auto *inTensorW = bindings.allocate(PHW); |
1312 | inTensorW->getHandle() = inputWValues; |
1313 | |
1314 | EE.compile(CompilationMode::Infer); |
1315 | bindings.allocate(mod.getPlaceholders()); |
1316 | EE.run(bindings); |
1317 | |
1318 | auto result = bindings.get(graphOutputVar)->getHandle(); |
1319 | ASSERT_TRUE(result.dims() == (llvm::ArrayRef<dim_t>)outputShape); |
1320 | for (size_t i = 0; i < result.getType().size(); i++) { |
1321 | EXPECT_NEAR(result.raw(i), expectedValues[i], delta); |
1322 | } |
1323 | } |
1324 | |
1325 | /// Test Conv1D |
1326 | TEST_F(OnnxImporterTest, conv1D) { |
1327 | std::vector<float> inputXValues = { |
1328 | 1.4206449, -0.54408556, -1.3318906, 0.771925, 0.9450552, 0.08600737, |
1329 | 0.30009857, -0.36060193, -0.33999684, -0.9809143, -1.0172559, -0.4921318, |
1330 | -1.0513021, 1.8671927, -0.842103, -0.8903683}; |
1331 | std::vector<float> inputWValues = {0.16575365, -0.42219377, 0.55620337, |
1332 | -0.5700942, -1.1148645, -0.33808824}; |
1333 | std::vector<dim_t> inputXShape = {1, 2, 8}; |
1334 | std::vector<dim_t> inputWShape = {3, 2, 1}; |
1335 | std::vector<dim_t> outputShape = {1, 3, 8}; |
1336 | std::vector<float> expectedValues = { |
1337 | 0.3790216, 0.32395172, 0.20871338, 0.33572435, 0.6004995, -0.7740611, |
1338 | 0.40527308, 0.31613684, 0.9839977, 0.25659135, -0.16087033, 0.7099088, |
1339 | 1.1249841, -1.0166382, 0.6469939, 0.30702582, -1.4688776, 0.9382173, |
1340 | 1.8287997, -0.6942077, -0.69817555, -0.7271625, -0.04986412, 0.7030453}; |
1341 | std::string netFilename(GLOW_DATA_PATH |
1342 | "tests/models/onnxModels/conv1D.onnxtxt" ); |
1343 | importConv1DTest(netFilename, inputXValues, inputXShape, inputWValues, |
1344 | inputWShape, outputShape, expectedValues); |
1345 | } |
1346 | |
1347 | /// Test to ensure error handling for missing bias |
1348 | /// input is handled correctly. Remaining input is |
1349 | /// still sane to make sure it only fails for the |
1350 | /// intended case. |
1351 | TEST_F(OnnxImporterTest, importConvBiasFail) { |
1352 | ExecutionEngine EE{}; |
1353 | auto &mod = EE.getModule(); |
1354 | Function *F = mod.createFunction("main" ); |
1355 | |
1356 | std::string NetFilename(GLOW_DATA_PATH |
1357 | "tests/models/onnxModels/simpleConvBiasFail.onnxtxt" ); |
1358 | |
1359 | // Destroy the loader after the graph is loaded since the following execution |
1360 | // will not depend on anyting from the loader. |
1361 | { |
1362 | Tensor data; |
1363 | getNCHWData(&data, 1, 1, 3, 3); |
1364 | |
1365 | EXPECT_DEATH(ONNXModelLoader(NetFilename, {"data" }, {&data.getType()}, *F), |
1366 | "" ); |
1367 | } |
1368 | } |
1369 | |
1370 | /// Helper method to run the ConvTranspose operator test cases. |
1371 | /// \p filename contains the model .onnxtxt. |
1372 | /// \p expectedDims: output Tensor dimensions. |
1373 | /// \p expectedValues : output Tensor values expected. |
1374 | /// The input is N*C*H*W (1*1*2*2), the kernels is {3, 3}, |
1375 | /// strides is {1, 1}, group is 1. Pads can vary. |
1376 | static void convTransposeTestHelper(std::string &filename, |
1377 | llvm::ArrayRef<dim_t> expectedDims, |
1378 | llvm::ArrayRef<float> expectedValues) { |
1379 | |
1380 | ExecutionEngine EE{}; |
1381 | auto &mod = EE.getModule(); |
1382 | Function *F = mod.createFunction("main" ); |
1383 | |
1384 | std::string NetFilename = |
1385 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + filename; |
1386 | |
1387 | PlaceholderBindings bindings; |
1388 | Placeholder *graphOutputVar; |
1389 | // Destroy the loader after the graph is loaded since the following execution |
1390 | // will not depend on anyting from the loader. |
1391 | { |
1392 | Tensor data(ElemKind::FloatTy, {1, 1, 2, 2}); |
1393 | data.getHandle() = {2., 3., 4., 5.}; |
1394 | |
1395 | ONNXModelLoader onnxLD(NetFilename, {"data" }, {&data.getType()}, *F); |
1396 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1397 | bindings.allocate(mod.getPlaceholders()); |
1398 | updateInputPlaceholdersByName(bindings, &mod, {"data" }, {&data}); |
1399 | } |
1400 | |
1401 | // ONNX importer loads a ConvTranspose node and converts it to 4 ops: |
1402 | // Transpose (input) -> Conv -> Transpose |
1403 | // Transpose (filter) -> |
1404 | // A save node is added in the network as well. Therefore there are 5 nodes: |
1405 | // Transpose (input) -> Conv -> Transpose -> Save |
1406 | // Transpose (filter) -> |
1407 | // Note that in case the convolution filter is a constant tensor, the filter |
1408 | // transpose node will be later optimized out by the optimizer. |
1409 | EXPECT_EQ(F->getNodes().size(), 5); |
1410 | EXPECT_EQ(mod.getPlaceholders().size(), 2); |
1411 | EXPECT_EQ(mod.getConstants().size(), 2); |
1412 | |
1413 | auto *saveNode = getSaveNodeFromDest(graphOutputVar); |
1414 | auto *node = saveNode->getInput().getNode(); |
1415 | |
1416 | EXPECT_TRUE(node->getKind() == Kinded::Kind::TransposeNodeKind); |
1417 | auto *convTrNode = llvm::dyn_cast<TransposeNode>(node)->getInput().getNode(); |
1418 | |
1419 | EXPECT_TRUE(convTrNode->getKind() == Kinded::Kind::ConvTransposeNodeKind); |
1420 | auto *tInNode = |
1421 | llvm::dyn_cast<ConvTransposeNode>(convTrNode)->getInput().getNode(); |
1422 | auto *tFilterNode = |
1423 | llvm::dyn_cast<ConvTransposeNode>(convTrNode)->getFilter().getNode(); |
1424 | EXPECT_TRUE(tInNode->getKind() == Kinded::Kind::TransposeNodeKind); |
1425 | EXPECT_TRUE(tFilterNode->getKind() == Kinded::Kind::TransposeNodeKind); |
1426 | |
1427 | EE.compile(CompilationMode::Infer); |
1428 | EE.run(bindings); |
1429 | |
1430 | EXPECT_EQ(F->getNodes().size(), 4); |
1431 | EXPECT_EQ(mod.getPlaceholders().size(), 2); |
1432 | EXPECT_EQ(mod.getConstants().size(), 2); |
1433 | |
1434 | auto result = bindings.get(graphOutputVar)->getHandle(); |
1435 | EXPECT_TRUE(result.dims() == expectedDims); |
1436 | for (dim_t i = 0, e = expectedValues.size(); i < e; i++) { |
1437 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
1438 | } |
1439 | } |
1440 | |
1441 | /// Test loading ConvTranspose op from a ONNX model, no pads. |
1442 | TEST_F(OnnxImporterTest, importConvTranspose) { |
1443 | std::string filename("simpleConvTranspose.onnxtxt" ); |
1444 | std::vector<dim_t> expectedDims = {1, 1, 4, 4}; |
1445 | std::vector<float> expectedValues = {5, 13, 18, 13, 19, 50, 64, 42, |
1446 | 37, 92, 106, 66, 33, 77, 86, 51}; |
1447 | convTransposeTestHelper(filename, expectedDims, expectedValues); |
1448 | } |
1449 | |
1450 | /// Test loading ConvTranspose op from a ONNX model, symmetric pads. |
1451 | TEST_F(OnnxImporterTest, importConvTransposePads) { |
1452 | std::string filename("simpleConvTransposePads.onnxtxt" ); |
1453 | std::vector<dim_t> expectedDims = {1, 1, 3, 3}; |
1454 | std::vector<float> expectedValues = {14., 19., 14., 51., 65., |
1455 | 43., 93., 107., 67.}; |
1456 | convTransposeTestHelper(filename, expectedDims, expectedValues); |
1457 | } |
1458 | |
1459 | /// Test loading ConvTranspose op from a ONNX model, auto_pad=VALID |
1460 | TEST_F(OnnxImporterTest, importConvTransposeAutoPadValid) { |
1461 | std::string filename("simpleConvTransposeAutoPadValid.onnxtxt" ); |
1462 | std::vector<dim_t> expectedDims = {1, 1, 4, 4}; |
1463 | std::vector<float> expectedValues = {4, 12, 17, 12, 18, 49, 63, 41, |
1464 | 36, 91, 105, 65, 32, 76, 85, 50}; |
1465 | convTransposeTestHelper(filename, expectedDims, expectedValues); |
1466 | } |
1467 | |
1468 | /// Test loading ConvTranspose op from a ONNX model, auto_pad=SAME_UPPER |
1469 | TEST_F(OnnxImporterTest, importConvTransposeAutoPadSameUpper) { |
1470 | std::string filename("simpleConvTransposeAutoPadSameUpper.onnxtxt" ); |
1471 | std::vector<dim_t> expectedDims = {1, 1, 2, 2}; |
1472 | std::vector<float> expectedValues = {49., 63., 91., 105.}; |
1473 | convTransposeTestHelper(filename, expectedDims, expectedValues); |
1474 | } |
1475 | |
1476 | /// Test loading ConvTranspose op from a ONNX model, auto_pad=SAME_LOWER |
1477 | TEST_F(OnnxImporterTest, importConvTransposeAutoPadSameLower) { |
1478 | std::string filename("simpleConvTransposeAutoPadSameLower.onnxtxt" ); |
1479 | std::vector<dim_t> expectedDims = {1, 1, 2, 2}; |
1480 | std::vector<float> expectedValues = {49., 63., 91., 105.}; |
1481 | convTransposeTestHelper(filename, expectedDims, expectedValues); |
1482 | } |
1483 | |
1484 | /// Test loading ConvTranspose op, explicit output_shape, auto_pad=SAME_UPPER. |
1485 | TEST_F(OnnxImporterTest, importConvTransposeOutputShapeSameUpper) { |
1486 | std::string filename("simpleConvTransposeOutShapeSameUpper.onnxtxt" ); |
1487 | std::vector<dim_t> expectedDims = {1, 1, 4, 4}; |
1488 | std::vector<float> expectedValues = {4, 12, 17, 12, 18, 49, 63, 41, |
1489 | 36, 91, 105, 65, 32, 76, 85, 50}; |
1490 | convTransposeTestHelper(filename, expectedDims, expectedValues); |
1491 | } |
1492 | |
1493 | /// Test loading deconv op, explicit output_shape, auto_pad=SAME_LOWER. |
1494 | TEST_F(OnnxImporterTest, importConvTransposeOutputShapeSameLower) { |
1495 | std::string filename("simpleConvTransposeOutShapeSameLower.onnxtxt" ); |
1496 | std::vector<dim_t> expectedDims = {1, 1, 4, 4}; |
1497 | std::vector<float> expectedValues = {4, 12, 17, 12, 18, 49, 63, 41, |
1498 | 36, 91, 105, 65, 32, 76, 85, 50}; |
1499 | convTransposeTestHelper(filename, expectedDims, expectedValues); |
1500 | } |
1501 | |
1502 | /// Test loading ConvTranspose op, explicit output_shape, auto_pad not set. |
1503 | TEST_F(OnnxImporterTest, importConvTransposeOutputShape) { |
1504 | std::string filename("simpleConvTransposeOutShape.onnxtxt" ); |
1505 | std::vector<dim_t> expectedDims = {1, 1, 4, 4}; |
1506 | std::vector<float> expectedValues = {4, 12, 17, 12, 18, 49, 63, 41, |
1507 | 36, 91, 105, 65, 32, 76, 85, 50}; |
1508 | convTransposeTestHelper(filename, expectedDims, expectedValues); |
1509 | } |
1510 | |
1511 | /// Helper method to run the Range operator test cases. |
1512 | /// \p filename contains the model .onnxtxt. |
1513 | /// \p expectedDims: output Tensor dimensions. |
1514 | /// \p expectedValues : output Tensor values expected. |
1515 | template <typename T> |
1516 | static void rangeTestHelper(std::string &filename, |
1517 | llvm::ArrayRef<dim_t> expectedDims, |
1518 | llvm::ArrayRef<T> expectedValues) { |
1519 | ExecutionEngine EE{}; |
1520 | auto &mod = EE.getModule(); |
1521 | Function *F = mod.createFunction("main" ); |
1522 | |
1523 | std::string NetFilename = |
1524 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + filename; |
1525 | |
1526 | PlaceholderBindings bindings; |
1527 | Placeholder *output; |
1528 | { |
1529 | ONNXModelLoader onnxLD(NetFilename, {}, {}, *F); |
1530 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1531 | bindings.allocate(mod.getPlaceholders()); |
1532 | updateInputPlaceholdersByName(bindings, &mod, {}, {}); |
1533 | } |
1534 | auto *res = bindings.get(output); |
1535 | EE.compile(CompilationMode::Infer); |
1536 | EE.run(bindings); |
1537 | auto result = res->getHandle<T>(); |
1538 | EXPECT_TRUE(result.dims() == expectedDims); |
1539 | for (dim_t i = 0, e = expectedValues.size(); i < e; i++) { |
1540 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
1541 | } |
1542 | } |
1543 | |
1544 | /// Test loading Range with int32 datatype. |
1545 | TEST(onnx, importRangeInt32) { |
1546 | std::string filename("RangeInt32.onnxtxt" ); |
1547 | std::vector<dim_t> expectedDims = {2}; |
1548 | std::vector<int32_t> expectedValues = {10, 7}; |
1549 | rangeTestHelper<int32_t>(filename, expectedDims, expectedValues); |
1550 | } |
1551 | |
1552 | /// Test loading Range with float datatype. |
1553 | TEST(onnx, importRangeFloat) { |
1554 | std::string filename("RangeFloat.onnxtxt" ); |
1555 | std::vector<dim_t> expectedDims = {5}; |
1556 | std::vector<float> expectedValues = {0.0, 1.0, 2.0, 3.0, 4.0}; |
1557 | rangeTestHelper<float>(filename, expectedDims, expectedValues); |
1558 | } |
1559 | |
1560 | /// Test loading ConvTranspose, implicit kernel, multi-channel input/output, |
1561 | /// asymmetric kernel and pads. |
1562 | TEST(onnx, importDeconvAsymmetric) { |
1563 | |
1564 | ExecutionEngine EE{}; |
1565 | auto &mod = EE.getModule(); |
1566 | Function *F = mod.createFunction("main" ); |
1567 | |
1568 | std::string NetFilename = std::string( |
1569 | GLOW_DATA_PATH "tests/models/onnxModels/convTransposeAsymmetric.onnxtxt" ); |
1570 | |
1571 | PlaceholderBindings bindings; |
1572 | Placeholder *output; |
1573 | { |
1574 | Tensor input(ElemKind::FloatTy, {1, 3, 4, 4}); |
1575 | for (dim_t i = 0; i < 3 * 4 * 4; i++) { |
1576 | input.getHandle().raw(i) = i; |
1577 | } |
1578 | Tensor filter(ElemKind::FloatTy, {3, 2, 3, 2}); |
1579 | for (dim_t i = 0; i < 3 * 2 * 3 * 2; i++) { |
1580 | filter.getHandle().raw(i) = i * 2; |
1581 | } |
1582 | ONNXModelLoader onnxLD(NetFilename, {"X" , "W" }, |
1583 | {&input.getType(), &filter.getType()}, *F); |
1584 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1585 | bindings.allocate(mod.getPlaceholders()); |
1586 | updateInputPlaceholdersByName(bindings, &mod, {"X" , "W" }, |
1587 | {&input, &filter}); |
1588 | } |
1589 | auto *res = bindings.get(output); |
1590 | EE.compile(CompilationMode::Infer); |
1591 | EE.run(bindings); |
1592 | |
1593 | auto result = res->getHandle(); |
1594 | |
1595 | EXPECT_TRUE(result.dims() == llvm::ArrayRef<dim_t>({1, 2, 5, 3})); |
1596 | |
1597 | std::vector<float> expected = { |
1598 | 2095.1, 2065.1, 2173.1, 4705.1, 4633.1, 4873.1, 7879.1, 7753.1, |
1599 | 8149.1, 8959.1, 8761.1, 9229.1, 6697.1, 6553.1, 6889.1, 2708.2, |
1600 | 2714.2, 2822.2, 6074.2, 6074.2, 6314.2, 10148.2, 10130.2, 10526.2, |
1601 | 11660.2, 11570.2, 12038.2, 8642.2, 8570.2, 8906.2}; |
1602 | |
1603 | for (dim_t i = 0, e = expected.size(); i < e; i++) { |
1604 | EXPECT_FLOAT_EQ(result.raw(i), expected[i]); |
1605 | } |
1606 | } |
1607 | |
1608 | // ConvTranspose test with Group>1 |
1609 | TEST(onnx, importDeconvGrouped) { |
1610 | |
1611 | ExecutionEngine EE{}; |
1612 | auto &mod = EE.getModule(); |
1613 | Function *F = mod.createFunction("main" ); |
1614 | |
1615 | std::string NetFilename = std::string( |
1616 | GLOW_DATA_PATH "tests/models/onnxModels/convTransposeGroup.onnxtxt" ); |
1617 | |
1618 | PlaceholderBindings bindings; |
1619 | Placeholder *output; |
1620 | { |
1621 | Tensor input(ElemKind::FloatTy, {1, 2, 3, 3}); |
1622 | for (dim_t i = 0; i < 2 * 3 * 3; i++) { |
1623 | input.getHandle().raw(i) = i; |
1624 | } |
1625 | Tensor filter(ElemKind::FloatTy, {2, 1, 2, 2}); |
1626 | for (dim_t i = 0; i < 2 * 2 * 2; i++) { |
1627 | filter.getHandle().raw(i) = i * 2; |
1628 | } |
1629 | ONNXModelLoader onnxLD(NetFilename, {"X" , "W" }, |
1630 | {&input.getType(), &filter.getType()}, *F); |
1631 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1632 | bindings.allocate(mod.getPlaceholders()); |
1633 | updateInputPlaceholdersByName(bindings, &mod, {"X" , "W" }, |
1634 | {&input, &filter}); |
1635 | } |
1636 | auto *res = bindings.get(output); |
1637 | EE.compile(CompilationMode::Infer); |
1638 | EE.run(bindings); |
1639 | |
1640 | auto result = res->getHandle(); |
1641 | |
1642 | EXPECT_TRUE(result.dims() == llvm::ArrayRef<dim_t>({1, 2, 6, 6})); |
1643 | |
1644 | std::vector<float> expected = { |
1645 | 0, 0, 0, 2, 0, 4, 0, 0, 4, 6, 8, 12, 0, 6, 0, |
1646 | 8, 0, 10, 12, 18, 16, 24, 20, 30, 0, 12, 0, 14, 0, 16, |
1647 | 24, 36, 28, 42, 32, 48, 72, 90, 80, 100, 88, 110, 108, 126, 120, |
1648 | 140, 132, 154, 96, 120, 104, 130, 112, 140, 144, 168, 156, 182, 168, 196, |
1649 | 120, 150, 128, 160, 136, 170, 180, 210, 192, 224, 204, 238}; |
1650 | |
1651 | for (dim_t i = 0, e = expected.size(); i < e; i++) { |
1652 | EXPECT_FLOAT_EQ(result.raw(i), expected[i]); |
1653 | } |
1654 | } |
1655 | |
1656 | /// Helper method to run the AveragePool operator test cases. |
1657 | /// \p filename contains the model .onnxtxt. |
1658 | /// \p expectedDims: output Tensor dimensions. |
1659 | /// \p expectedValues : output Tensor values expected. |
1660 | /// \p global: GlobalAveragePool if true, AveragePool if false. |
1661 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
1662 | /// strides is {1, 1}, group is 1. Pads can vary in filename. |
1663 | static void averagePoolTestHelper(std::string &filename, |
1664 | llvm::ArrayRef<dim_t> expectedDims, |
1665 | llvm::ArrayRef<float> expectedValues) { |
1666 | |
1667 | ExecutionEngine EE{}; |
1668 | auto &mod = EE.getModule(); |
1669 | Function *F = mod.createFunction("main" ); |
1670 | |
1671 | std::string NetFilename = |
1672 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + filename; |
1673 | |
1674 | PlaceholderBindings bindings; |
1675 | Placeholder *graphOutputVar; |
1676 | // Destroy the loader after the graph is loaded since the following execution |
1677 | // will not depend on anyting from the loader. |
1678 | Tensor data; |
1679 | getNCHWData(&data, 1, 1, 3, 3); |
1680 | { |
1681 | ONNXModelLoader onnxLD(NetFilename, {"x" }, {&data.getType()}, *F); |
1682 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1683 | bindings.allocate(mod.getPlaceholders()); |
1684 | updateInputPlaceholdersByName(bindings, &mod, {"x" }, {&data}); |
1685 | } |
1686 | |
1687 | // ONNX importer loads a AveragePool node and converts it to 4 ops: |
1688 | // Transpose (input) -> AveragePool -> Transpose -> Save |
1689 | EXPECT_EQ(F->getNodes().size(), 4); |
1690 | EXPECT_EQ(mod.getPlaceholders().size(), 2); |
1691 | |
1692 | auto *saveNode = getSaveNodeFromDest(graphOutputVar); |
1693 | auto *node = saveNode->getInput().getNode(); |
1694 | |
1695 | EXPECT_TRUE(node->getKind() == Kinded::Kind::TransposeNodeKind); |
1696 | auto *poolNode = llvm::dyn_cast<TransposeNode>(node)->getInput().getNode(); |
1697 | |
1698 | EXPECT_TRUE(poolNode->getKind() == Kinded::Kind::AvgPoolNodeKind); |
1699 | auto *tInNode = llvm::dyn_cast<AvgPoolNode>(poolNode)->getInput().getNode(); |
1700 | |
1701 | EXPECT_TRUE(tInNode->getKind() == Kinded::Kind::TransposeNodeKind); |
1702 | |
1703 | EE.compile(CompilationMode::Infer); |
1704 | EE.run(bindings); |
1705 | auto result = bindings.get(graphOutputVar)->getHandle(); |
1706 | EXPECT_TRUE(result.dims() == expectedDims); |
1707 | for (size_t i = 0, e = expectedValues.size(); i < e; i++) { |
1708 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
1709 | } |
1710 | |
1711 | // Constant Folding Test. |
1712 | FAIL_TEST_IF_ERR(checkConstFoldedOutput(NetFilename, {"x" }, {&data}, |
1713 | {bindings.get(graphOutputVar)})); |
1714 | } |
1715 | |
1716 | /// Test loading AveragePool op from a ONNX model. |
1717 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
1718 | /// strides is {1, 1}, pads is auto_pad VALID (no padding), group is 1. |
1719 | TEST_F(OnnxImporterTest, importAveragePool2DAutoPadValid) { |
1720 | std::string filename("averagePool2DAutoPadValid.onnxtxt" ); |
1721 | std::vector<dim_t> expectedDims = {1, 1, 2, 2}; |
1722 | std::vector<float> expectedValues = {2, 3, 5, 6}; |
1723 | averagePoolTestHelper(filename, expectedDims, expectedValues); |
1724 | } |
1725 | |
1726 | /// Test loading AveragePool op from a ONNX model. |
1727 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
1728 | /// strides is {1, 1}, pads is auto_pad SAME_UPPER, group is 1. |
1729 | TEST_F(OnnxImporterTest, importAveragePool2DAutoPadSameUpper) { |
1730 | std::string filename("averagePool2DAutoPadSameUpper.onnxtxt" ); |
1731 | std::vector<dim_t> expectedDims = {1, 1, 3, 3}; |
1732 | std::vector<float> expectedValues = {2, 3, 1.75, 5, 6, 3.25, 3.25, 3.75, 2}; |
1733 | averagePoolTestHelper(filename, expectedDims, expectedValues); |
1734 | } |
1735 | |
1736 | /// Test loading AveragePool op from a ONNX model. |
1737 | /// The input is N*C*H*W (1*1*3*3), the kernels is {2, 2}, |
1738 | /// strides is {1, 1}, pads is auto_pad SAME_LOWER, group is 1. |
1739 | TEST_F(OnnxImporterTest, importAveragePool2DAutoPadSameLower) { |
1740 | std::string filename("averagePool2DAutoPadSameLower.onnxtxt" ); |
1741 | std::vector<dim_t> expectedDims = {1, 1, 3, 3}; |
1742 | std::vector<float> expectedValues = {0, 0.25, 0.75, 0.75, 2, 3, 2.25, 5, 6}; |
1743 | averagePoolTestHelper(filename, expectedDims, expectedValues); |
1744 | } |
1745 | |
1746 | /// Test loading AveragePool op from a ONNX model. |
1747 | /// The input is N*C*H*W (1*1*3*3), the kernels is {3, 3}, |
1748 | /// strides is {2, 2}, pads is {1, 1, 1, 1}, |
1749 | /// countIncludePads is false. |
1750 | TEST_F(OnnxImporterTest, importAveragePool2DCountExcludePads) { |
1751 | std::string filename("averagePool2DCountExcludePads.onnxtxt" ); |
1752 | std::vector<dim_t> expectedDims = {1, 1, 2, 2}; |
1753 | std::vector<float> expectedValues = {2, 3, 5, 6}; |
1754 | averagePoolTestHelper(filename, expectedDims, expectedValues); |
1755 | } |
1756 | |
1757 | TEST_F(OnnxImporterTest, importAveragePool3D) { |
1758 | ExecutionEngine EE{}; |
1759 | auto &mod = EE.getModule(); |
1760 | Function *F = mod.createFunction("main" ); |
1761 | |
1762 | std::string NetFilename(GLOW_DATA_PATH |
1763 | "tests/models/onnxModels/averagePool3D.onnxtxt" ); |
1764 | |
1765 | // Destroy the loader after the graph is loaded since the following execution |
1766 | // will not depend on anyting from the loader. |
1767 | { |
1768 | Tensor data(ElemKind::FloatTy, {1, 3, 32, 32, 32}); |
1769 | EXPECT_DEATH(ONNXModelLoader(NetFilename, {"x" }, {&data.getType()}, *F), |
1770 | "" ); |
1771 | } |
1772 | } |
1773 | |
1774 | static void testReductionOps(std::string modelName, |
1775 | const std::vector<dim_t> &expectedDims, |
1776 | const std::vector<float> &expectedValues) { |
1777 | ExecutionEngine EE{}; |
1778 | auto &mod = EE.getModule(); |
1779 | Function *F = mod.createFunction("main" ); |
1780 | |
1781 | // Input. |
1782 | Tensor x(ElemKind::FloatTy, {2, 2, 2, 2}); |
1783 | x.getHandle() = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; |
1784 | |
1785 | // Load model. |
1786 | std::string netFilename = |
1787 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + modelName; |
1788 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
1789 | Placeholder *output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1790 | |
1791 | // Allocate placeholders. |
1792 | PlaceholderBindings bindings; |
1793 | bindings.allocate(mod.getPlaceholders()); |
1794 | updateInputPlaceholdersByName(bindings, &mod, {"x" }, {&x}); |
1795 | |
1796 | auto *res = bindings.get(output); |
1797 | EE.compile(CompilationMode::Infer); |
1798 | EE.run(bindings); |
1799 | |
1800 | // Compare results. |
1801 | auto result = res->getHandle(); |
1802 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
1803 | for (dim_t i = 0; i < result.size(); i++) { |
1804 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
1805 | } |
1806 | |
1807 | // Constant Folding Test. |
1808 | FAIL_TEST_IF_ERR( |
1809 | checkConstFoldedOutput(netFilename, {"x" }, {&x}, {bindings.get(output)})); |
1810 | } |
1811 | |
1812 | /// Test loading ReduceMean op from a ONNX model. |
1813 | /// Input shape is 4D, one dimension is reduced, and output shape is 3D. |
1814 | TEST_F(OnnxImporterTest, reduceMean4Dto3D) { |
1815 | testReductionOps("reduceMean4Dto3D.onnxtxt" , {2, 2, 2}, |
1816 | {1.5, 3.5, 5.5, 7.5, 9.5, 11.5, 13.5, 15.5}); |
1817 | } |
1818 | |
1819 | /// Test loading ReduceMean op from a ONNX model. |
1820 | /// Input shape is 4D, one dimension is reduced, and output shape stays 4D. |
1821 | TEST_F(OnnxImporterTest, reduceMean4Dto4D) { |
1822 | testReductionOps("reduceMean4Dto4D.onnxtxt" , {2, 2, 2, 1}, |
1823 | {1.5, 3.5, 5.5, 7.5, 9.5, 11.5, 13.5, 15.5}); |
1824 | } |
1825 | |
1826 | /// Test loading ReduceSum op from a ONNX model. |
1827 | /// Input shape is 4D, one dimension is reduced, and output shape is 4D. |
1828 | TEST_F(OnnxImporterTest, reduceSum4D) { |
1829 | testReductionOps("reduceSum4D.onnxtxt" , {2, 2, 2, 1}, |
1830 | {3, 7, 11, 15, 19, 23, 27, 31}); |
1831 | } |
1832 | |
1833 | /// Test loading ReduceMean op from a ONNX model. |
1834 | /// Input shape is 4D, two dimensions are reduced, targeting ReduceMean |
1835 | /// optimization using AvgPool. Output shape is 4D. |
1836 | TEST_F(OnnxImporterTest, reduceMean2AvgPoolKeepDims) { |
1837 | testReductionOps("reduceMean2AvgPool.onnxtxt" , {2, 2, 1, 1}, |
1838 | {2.5, 6.5, 10.5, 14.5}); |
1839 | } |
1840 | |
1841 | /// Test loading ReduceSumSquare op from a ONNX model. |
1842 | /// Input shape is 4D, one dimension is reduced, and output shape is 4D. |
1843 | TEST_F(OnnxImporterTest, reduceSumSquare4D) { |
1844 | ExecutionEngine EE{}; |
1845 | auto &mod = EE.getModule(); |
1846 | Function *F = mod.createFunction("main" ); |
1847 | |
1848 | std::string netFilename(GLOW_DATA_PATH |
1849 | "tests/models/onnxModels/reduceSumSquare4D.onnxtxt" ); |
1850 | |
1851 | PlaceholderBindings bindings; |
1852 | Placeholder *output; |
1853 | Tensor x(ElemKind::FloatTy, {2, 2, 2, 2}); |
1854 | x.getHandle() = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; |
1855 | |
1856 | { |
1857 | |
1858 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
1859 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1860 | bindings.allocate(mod.getPlaceholders()); |
1861 | |
1862 | updateInputPlaceholdersByName(bindings, &mod, {"x" }, {&x}); |
1863 | } |
1864 | |
1865 | auto *res = bindings.get(output); |
1866 | EE.compile(CompilationMode::Infer); |
1867 | EE.run(bindings); |
1868 | auto result = res->getHandle(); |
1869 | std::vector<dim_t> expectedDims = {2, 2, 2, 1}; |
1870 | std::vector<float> expectedValues = {5, 25, 61, 113, 181, 265, 365, 481}; |
1871 | |
1872 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
1873 | for (size_t i = 0; i < 8; i++) { |
1874 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
1875 | } |
1876 | // Constant Folding Test. |
1877 | FAIL_TEST_IF_ERR( |
1878 | checkConstFoldedOutput(netFilename, {"x" }, {&x}, {bindings.get(output)})); |
1879 | } |
1880 | |
1881 | /// Test loading ReduceMean op from a ONNX model. |
1882 | /// Input shape is 4D, two dimensions are reduced, targeting ReduceMean |
1883 | /// optimization using AvgPool. Output shape is 2D. |
1884 | TEST_F(OnnxImporterTest, reduceMean2AvgPoolNoKeepDims) { |
1885 | testReductionOps("reduceMean2AvgPoolNoKeep.onnxtxt" , {2, 2}, |
1886 | {2.5, 6.5, 10.5, 14.5}); |
1887 | } |
1888 | |
1889 | /// Test loading ReduceMax op from a ONNX model. |
1890 | /// Input shape is 4D, two dimensions are reduced,Output shape is 4D. |
1891 | TEST_F(OnnxImporterTest, reduceMaxKeepDims) { |
1892 | testReductionOps("reduceMax.onnxtxt" , {2, 2, 1, 1}, {4, 8, 12, 16}); |
1893 | } |
1894 | |
1895 | /// Test loading ReduceMax op from a ONNX model. |
1896 | /// Input shape is 4D, two dimensions are reduced, targeting ReduceMean |
1897 | /// optimization using AvgPool. Output shape is 2D. |
1898 | TEST_F(OnnxImporterTest, reduceMaxNoKeepDims) { |
1899 | testReductionOps("reduceMaxNoKeep.onnxtxt" , {2, 2}, {4, 8, 12, 16}); |
1900 | } |
1901 | |
1902 | /// Test loading ReduceMax op from a ONNX model. |
1903 | /// Input shape is 4D, two dimensions are reduced,Output shape is 4D. |
1904 | TEST_F(OnnxImporterTest, reduceMaxKeepDimsDefaultAxis) { |
1905 | testReductionOps("reduceMaxDefaultAxis.onnxtxt" , {1, 1, 1, 1}, {16}); |
1906 | } |
1907 | |
1908 | /// Test loading ReduceMin op from a ONNX model. |
1909 | /// Input shape is 4D, two dimensions are reduced,Output shape is 4D. |
1910 | TEST_F(OnnxImporterTest, reduceMinKeepDims) { |
1911 | testReductionOps("reduceMin.onnxtxt" , {2, 2, 1, 1}, {1, 5, 9, 13}); |
1912 | } |
1913 | |
1914 | /// Test loading ReduceMin op from a ONNX model. |
1915 | /// Input shape is 4D, two dimensions are reduced, targeting ReduceMean |
1916 | /// optimization using AvgPool. Output shape is 2D. |
1917 | TEST_F(OnnxImporterTest, reduceMinNoKeepDims) { |
1918 | testReductionOps("reduceMinNoKeep.onnxtxt" , {2, 2}, {1, 5, 9, 13}); |
1919 | } |
1920 | |
1921 | /// Test loading ReduceMin op from a ONNX model. |
1922 | /// Input shape is 4D, two dimensions are reduced,Output shape is 4D. |
1923 | TEST_F(OnnxImporterTest, reduceMinKeepDimsDefaultAxis) { |
1924 | testReductionOps("reduceMinDefaultAxis.onnxtxt" , {1, 1, 1, 1}, {1}); |
1925 | } |
1926 | |
1927 | /// Test loading ReduceProd op from a ONNX model. |
1928 | /// Input shape is 4D, one dimension is reduced, and output shape is 4D |
1929 | TEST_F(OnnxImporterTest, reduceProd4D) { |
1930 | testReductionOps("reduceProd.onnxtxt" , {2, 2, 2, 1}, |
1931 | {2, 12, 30, 56, 90, 132, 182, 240}); |
1932 | } |
1933 | |
1934 | static void testDepthToSpace(std::string &filename, |
1935 | const std::vector<dim_t> &expectedDims, |
1936 | const std::vector<float> &expectedValues) { |
1937 | ExecutionEngine EE{}; |
1938 | auto &mod = EE.getModule(); |
1939 | Function *F = mod.createFunction("main" ); |
1940 | |
1941 | std::string netFilename = |
1942 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + filename; |
1943 | |
1944 | PlaceholderBindings bindings; |
1945 | Placeholder *output; |
1946 | { |
1947 | // NCHW |
1948 | Tensor x(ElemKind::FloatTy, {1, 8, 2, 3}); |
1949 | x.getHandle() = {0., 1., 2., 3., 4., 5., 9., 10., 11., 12., |
1950 | 13., 14., 18., 19., 20., 21., 22., 23., 27., 28., |
1951 | 29., 30., 31., 32., 36., 37., 38., 39., 40., 41., |
1952 | 45., 46., 47., 48., 49., 50., 54., 55., 56., 57., |
1953 | 58., 59., 63., 64., 65., 66., 67., 68.}; |
1954 | |
1955 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
1956 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
1957 | bindings.allocate(mod.getPlaceholders()); |
1958 | updateInputPlaceholdersByName(bindings, &mod, {"x" }, {&x}); |
1959 | } |
1960 | |
1961 | auto *res = bindings.get(output); |
1962 | EE.compile(CompilationMode::Infer); |
1963 | EE.run(bindings); |
1964 | |
1965 | auto result = res->getHandle(); |
1966 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
1967 | for (size_t i = 0; i < result.size(); i++) { |
1968 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
1969 | } |
1970 | } |
1971 | |
1972 | /// Test loading DepthToSpace with mode=CRD from an ONNX model. |
1973 | TEST_F(OnnxImporterTest, depthToSpaceCRD) { |
1974 | std::string filename("depthToSpace_crd.onnxtxt" ); |
1975 | std::vector<dim_t> expectedDims = {1, 2, 4, 6}; |
1976 | std::vector<float> expectedValues = { |
1977 | 0, 9, 1, 10, 2, 11, 18, 27, 19, 28, 20, 29, 3, 12, 4, 13, |
1978 | 5, 14, 21, 30, 22, 31, 23, 32, 36, 45, 37, 46, 38, 47, 54, 63, |
1979 | 55, 64, 56, 65, 39, 48, 40, 49, 41, 50, 57, 66, 58, 67, 59, 68}; |
1980 | testDepthToSpace(filename, expectedDims, expectedValues); |
1981 | } |
1982 | |
1983 | /// Test loading DepthToSpace with default mode(DCR) from an ONNX model. |
1984 | TEST_F(OnnxImporterTest, depthToSpaceDCR) { |
1985 | std::string filename("depthToSpace.onnxtxt" ); |
1986 | std::vector<dim_t> expectedDims = {1, 2, 4, 6}; |
1987 | std::vector<float> expectedValues = { |
1988 | 0, 18, 1, 19, 2, 20, 36, 54, 37, 55, 38, 56, 3, 21, 4, 22, |
1989 | 5, 23, 39, 57, 40, 58, 41, 59, 9, 27, 10, 28, 11, 29, 45, 63, |
1990 | 46, 64, 47, 65, 12, 30, 13, 31, 14, 32, 48, 66, 49, 67, 50, 68, |
1991 | }; |
1992 | testDepthToSpace(filename, expectedDims, expectedValues); |
1993 | } |
1994 | |
1995 | /// Test loading SpaceToDepth op from an ONNX model. |
1996 | TEST_F(OnnxImporterTest, spaceToDepth) { |
1997 | ExecutionEngine EE{}; |
1998 | auto &mod = EE.getModule(); |
1999 | Function *F = mod.createFunction("main" ); |
2000 | |
2001 | std::string netFilename(GLOW_DATA_PATH |
2002 | "tests/models/onnxModels/spaceToDepth.onnxtxt" ); |
2003 | |
2004 | PlaceholderBindings bindings; |
2005 | Placeholder *output; |
2006 | { |
2007 | Tensor x(ElemKind::FloatTy, {1, 2, 4, 4}); |
2008 | x.zero(); |
2009 | |
2010 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
2011 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2012 | } |
2013 | |
2014 | auto *save = getSaveNodeFromDest(output); |
2015 | TransposeNode *TRN = |
2016 | llvm::dyn_cast<TransposeNode>(save->getInput().getNode()); |
2017 | ASSERT_TRUE(TRN); |
2018 | SpaceToDepthNode *STDN = |
2019 | llvm::dyn_cast<SpaceToDepthNode>(TRN->getInput().getNode()); |
2020 | ASSERT_TRUE(STDN); |
2021 | unsigned blockSize = STDN->getBlockSize(); |
2022 | EXPECT_EQ(blockSize, 2); |
2023 | } |
2024 | |
2025 | /// Test loading clip op from an ONNX model. |
2026 | /// Test with arg min = 20.0 max = 60.0 |
2027 | TEST_F(OnnxImporterTest, importClip) { |
2028 | ExecutionEngine EE{}; |
2029 | auto &mod = EE.getModule(); |
2030 | Function *F = mod.createFunction("main" ); |
2031 | |
2032 | std::string netFilename(GLOW_DATA_PATH |
2033 | "tests/models/onnxModels/clip.onnxtxt" ); |
2034 | |
2035 | PlaceholderBindings bindings; |
2036 | Placeholder *output; |
2037 | Tensor x(ElemKind::FloatTy, {3, 3}); |
2038 | x.getHandle() = {1, 2, 3, 40, 5, 6, 7, 8, 90}; |
2039 | |
2040 | { |
2041 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
2042 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2043 | bindings.allocate(mod.getPlaceholders()); |
2044 | |
2045 | updateInputPlaceholdersByName(bindings, &mod, {"x" }, {&x}); |
2046 | } |
2047 | |
2048 | auto *res = bindings.get(output); |
2049 | EE.compile(CompilationMode::Infer); |
2050 | EE.run(bindings); |
2051 | |
2052 | auto result = res->getHandle(); |
2053 | std::vector<dim_t> expectedDims = {3, 3}; |
2054 | std::vector<float> expectedValues = {20, 20, 20, 40, 20, 20, 20, 20, 60}; |
2055 | |
2056 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
2057 | for (size_t i = 0; i < 3 * 3; i++) { |
2058 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
2059 | } |
2060 | |
2061 | // Constant Folding Test. |
2062 | FAIL_TEST_IF_ERR( |
2063 | checkConstFoldedOutput(netFilename, {"x" }, {&x}, {bindings.get(output)})); |
2064 | } |
2065 | |
2066 | /// Test loading MatMul op from an ONNX model with dimension equal to 3 |
2067 | TEST_F(OnnxImporterTest, importMatMul) { |
2068 | ExecutionEngine EE{}; |
2069 | auto &mod = EE.getModule(); |
2070 | Function *F = mod.createFunction("main" ); |
2071 | std::string netFilename(GLOW_DATA_PATH |
2072 | "tests/models/onnxModels/matmul.onnxtxt" ); |
2073 | |
2074 | PlaceholderBindings bindings; |
2075 | Placeholder *output; |
2076 | Tensor inputs_0(ElemKind::FloatTy, {20, 40, 7}); |
2077 | Tensor inputs_1(ElemKind::FloatTy, {20, 7, 40}); |
2078 | auto data_0 = inputs_0.getHandle(); |
2079 | auto data_1 = inputs_1.getHandle(); |
2080 | // Fill inputs with random positive values. |
2081 | data_0.randomize(0.0, 5.0, mod.getPRNG()); |
2082 | data_1.randomize(1.0, 2.0, mod.getPRNG()); |
2083 | { |
2084 | ONNXModelLoader onnxLD(netFilename, {"inputs_0" , "inputs_1" }, |
2085 | {&inputs_0.getType(), &inputs_1.getType()}, *F); |
2086 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2087 | bindings.allocate(mod.getPlaceholders()); |
2088 | updateInputPlaceholdersByName(bindings, &mod, {"inputs_0" , "inputs_1" }, |
2089 | {&inputs_0, &inputs_1}); |
2090 | } |
2091 | auto *res = bindings.get(output); |
2092 | EE.compile(CompilationMode::Infer); |
2093 | EE.run(bindings); |
2094 | |
2095 | auto result = res->getHandle(); |
2096 | std::vector<dim_t> expectedDims = {20, 40, 40}; |
2097 | EXPECT_EQ(result.dims().vec(), expectedDims); |
2098 | } |
2099 | |
2100 | /// Test loading BatchMatMul op from an ONNX model. |
2101 | TEST_F(OnnxImporterTest, importBatchMatMul) { |
2102 | ExecutionEngine EE{}; |
2103 | auto &mod = EE.getModule(); |
2104 | Function *F = mod.createFunction("main" ); |
2105 | std::string netFilename(GLOW_DATA_PATH |
2106 | "tests/models/onnxModels/batch_matmul.onnxtxt" ); |
2107 | |
2108 | PlaceholderBindings bindings; |
2109 | Placeholder *output; |
2110 | Tensor inputs_0(ElemKind::FloatTy, {20, 40, 7}); |
2111 | Tensor inputs_1(ElemKind::FloatTy, {20, 7, 40}); |
2112 | auto data_0 = inputs_0.getHandle(); |
2113 | auto data_1 = inputs_1.getHandle(); |
2114 | // Fill inputs with random positive values. |
2115 | data_0.randomize(0.0, 5.0, mod.getPRNG()); |
2116 | data_1.randomize(1.0, 2.0, mod.getPRNG()); |
2117 | { |
2118 | ONNXModelLoader onnxLD(netFilename, {"inputs_0" , "inputs_1" }, |
2119 | {&inputs_0.getType(), &inputs_1.getType()}, *F); |
2120 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2121 | bindings.allocate(mod.getPlaceholders()); |
2122 | updateInputPlaceholdersByName(bindings, &mod, {"inputs_0" , "inputs_1" }, |
2123 | {&inputs_0, &inputs_1}); |
2124 | } |
2125 | auto *res = bindings.get(output); |
2126 | EE.compile(CompilationMode::Infer); |
2127 | EE.run(bindings); |
2128 | |
2129 | auto result = res->getHandle(); |
2130 | std::vector<dim_t> expectedDims = {20, 7, 7}; |
2131 | EXPECT_EQ(result.dims().vec(), expectedDims); |
2132 | |
2133 | // High level check on the content of the graph. |
2134 | // We have 2 transpose, 20 * (matmul, 2 slices, 2 reshapes), 1 concat, 1 |
2135 | // reshape, 1 save. |
2136 | EXPECT_EQ(F->getNodes().size(), 2 + 20 * 5 + 3); |
2137 | // With have 2 inputs and one outputs. |
2138 | EXPECT_EQ(mod.getPlaceholders().size(), 3); |
2139 | // Check that the graph has the expected shape, |
2140 | // starting from the output. |
2141 | // Batched matmul with broadcasted RHS are lowered |
2142 | // to a regular matmul, where LHS is reshaped from |
2143 | // a 3D tensor to a flattened matrix. |
2144 | auto *saveNode = getSaveNodeFromDest(output); |
2145 | auto *reshapeResult = |
2146 | llvm::dyn_cast<ReshapeNode>(saveNode->getInput().getNode()); |
2147 | ASSERT_TRUE(reshapeResult); |
2148 | auto *concat = |
2149 | llvm::dyn_cast<ConcatNode>(reshapeResult->getInput().getNode()); |
2150 | ASSERT_TRUE(concat); |
2151 | for (size_t i = 0; i < 20; ++i) { |
2152 | auto *matmulI = |
2153 | llvm::dyn_cast<MatMulNode>(concat->getNthInput(i).getNode()); |
2154 | ASSERT_TRUE(matmulI); |
2155 | for (size_t j = 0; j < 2; ++j) { |
2156 | auto *reshape0 = |
2157 | llvm::dyn_cast<ReshapeNode>(matmulI->getNthInput(j).getNode()); |
2158 | ASSERT_TRUE(reshape0); |
2159 | auto *slice0 = llvm::dyn_cast<SliceNode>(reshape0->getInput().getNode()); |
2160 | ASSERT_TRUE(slice0); |
2161 | } |
2162 | } |
2163 | // Constant Folding Test. |
2164 | FAIL_TEST_IF_ERR(checkConstFoldedOutput(netFilename, {"inputs_0" , "inputs_1" }, |
2165 | {&inputs_0, &inputs_1}, |
2166 | {bindings.get(output)})); |
2167 | } |
2168 | |
2169 | /// Test loading BatchBoxCox op from an ONNX model. |
2170 | TEST_F(OnnxImporterTest, importBatchBoxCox) { |
2171 | ExecutionEngine EE{}; |
2172 | auto &mod = EE.getModule(); |
2173 | Function *F = mod.createFunction("main" ); |
2174 | |
2175 | std::string netFilename(GLOW_DATA_PATH |
2176 | "tests/models/onnxModels/batchBoxCox.onnxtxt" ); |
2177 | |
2178 | PlaceholderBindings bindings; |
2179 | Placeholder *output; |
2180 | |
2181 | // Make input tensors. |
2182 | const dim_t kRows = 3; |
2183 | const dim_t kCols = 3; |
2184 | Tensor data(ElemKind::FloatTy, {kRows, kCols}); |
2185 | Tensor lambda1(ElemKind::FloatTy, {kCols}); |
2186 | Tensor lambda2(ElemKind::FloatTy, {kCols}); |
2187 | auto dataH = data.getHandle(); |
2188 | auto lambda1H = lambda1.getHandle(); |
2189 | auto lambda2H = lambda2.getHandle(); |
2190 | |
2191 | // Fill inputs with random positive values. |
2192 | dataH.randomize(0.0, 5.0, mod.getPRNG()); |
2193 | lambda1H.randomize(1.0, 2.0, mod.getPRNG()); |
2194 | lambda2H.randomize(1.0, 2.0, mod.getPRNG()); |
2195 | |
2196 | // Zero out every other element to lambda1 to test that case of the transform. |
2197 | for (dim_t i = 0; i < kCols; i += 2) { |
2198 | lambda1H.at({i}) = 0; |
2199 | } |
2200 | |
2201 | { |
2202 | ONNXModelLoader onnxLD( |
2203 | netFilename, {"data" , "lambda1" , "lambda2" }, |
2204 | {&data.getType(), &lambda1.getType(), &lambda2.getType()}, *F); |
2205 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2206 | bindings.allocate(mod.getPlaceholders()); |
2207 | |
2208 | updateInputPlaceholdersByName(bindings, &mod, |
2209 | {"data" , "lambda1" , "lambda2" }, |
2210 | {&data, &lambda1, &lambda2}); |
2211 | } |
2212 | |
2213 | auto *res = bindings.get(output); |
2214 | EE.compile(CompilationMode::Infer); |
2215 | EE.run(bindings); |
2216 | |
2217 | auto result = res->getHandle(); |
2218 | |
2219 | // Output should have the same dims as the inputs. |
2220 | EXPECT_TRUE(result.dims().vec() == data.dims().vec()); |
2221 | |
2222 | // Compute elementwise Box-Cox transform and compare with corresponding |
2223 | // element of result. |
2224 | for (dim_t i = 0; i < kRows; ++i) { |
2225 | for (dim_t j = 0; j < kCols; ++j) { |
2226 | float d = dataH.at({i, j}); |
2227 | float l1 = lambda1H.at({j}); |
2228 | float l2 = lambda2H.at({j}); |
2229 | |
2230 | float tmp = std::max(d + l2, 1e-6f); |
2231 | float y = 0; |
2232 | |
2233 | if (l1 == 0) { |
2234 | // Clip argument to log and pow at 1e-6 to avoid saturation. |
2235 | y = std::log(tmp); |
2236 | } else { |
2237 | y = (std::pow(tmp, l1) - 1) / l1; |
2238 | } |
2239 | |
2240 | EXPECT_FLOAT_EQ(y, result.at({i, j})); |
2241 | } |
2242 | } |
2243 | |
2244 | // Constant Folding Test. |
2245 | FAIL_TEST_IF_ERR(checkConstFoldedOutput( |
2246 | netFilename, {"data" , "lambda1" , "lambda2" }, {&data, &lambda1, &lambda2}, |
2247 | {bindings.get(output)})); |
2248 | } |
2249 | |
2250 | /// Test loading DotProduct op from an ONNX model. |
2251 | TEST_F(OnnxImporterTest, importDotProduct) { |
2252 | ExecutionEngine EE{}; |
2253 | auto &mod = EE.getModule(); |
2254 | Function *F = mod.createFunction("main" ); |
2255 | |
2256 | std::string netFilename(GLOW_DATA_PATH |
2257 | "tests/models/onnxModels/dot_product.onnxtxt" ); |
2258 | |
2259 | Placeholder *output; |
2260 | { |
2261 | Tensor x(ElemKind::FloatTy, {3, 3}); |
2262 | Tensor y(ElemKind::FloatTy, {3, 3}); |
2263 | |
2264 | ONNXModelLoader onnxLD(netFilename, {"x" , "y" }, |
2265 | {&x.getType(), &y.getType()}, *F); |
2266 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2267 | } |
2268 | |
2269 | // Just verify the structure. |
2270 | // SaveNode + MulNode + BatchedReduceAddNode. |
2271 | ASSERT_EQ(3, F->getNodes().size()); |
2272 | auto *saveNode = getSaveNodeFromDest(output); |
2273 | auto *saveInput = saveNode->getInput().getNode(); |
2274 | ASSERT_TRUE(llvm::isa<BatchedReduceAddNode>(saveInput)); |
2275 | |
2276 | auto *batchedReduceAdd = llvm::cast<BatchedReduceAddNode>(saveInput); |
2277 | ASSERT_TRUE(llvm::isa<MulNode>(batchedReduceAdd->getBatch())); |
2278 | } |
2279 | |
2280 | /// Test loading Sum with more than 2 inputs |
2281 | TEST_F(OnnxImporterTest, importSumN) { |
2282 | ExecutionEngine EE{}; |
2283 | auto &mod = EE.getModule(); |
2284 | Function *F = mod.createFunction("main" ); |
2285 | std::string netFilename(GLOW_DATA_PATH |
2286 | "tests/models/onnxModels/sumN.onnxtxt" ); |
2287 | |
2288 | PlaceholderBindings bindings; |
2289 | Placeholder *output; |
2290 | Tensor i0(ElemKind::FloatTy, {3}); |
2291 | i0.getHandle() = {1, 2, 3}; |
2292 | Tensor i1(ElemKind::FloatTy, {3}); |
2293 | i1.getHandle() = {4, 5, 6}; |
2294 | Tensor i2(ElemKind::FloatTy, {3}); |
2295 | i2.getHandle() = {7, 8, 9}; |
2296 | { |
2297 | |
2298 | ONNXModelLoader onnxLD(netFilename, {"i0" , "i1" , "i2" }, |
2299 | {&i0.getType(), &i1.getType(), &i2.getType()}, *F); |
2300 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2301 | |
2302 | bindings.allocate(mod.getPlaceholders()); |
2303 | updateInputPlaceholdersByName(bindings, &mod, {"i0" , "i1" , "i2" }, |
2304 | {&i0, &i1, &i2}); |
2305 | } |
2306 | |
2307 | auto *res = bindings.get(output); |
2308 | EE.compile(CompilationMode::Infer); |
2309 | EE.run(bindings); |
2310 | |
2311 | auto result = res->getHandle(); |
2312 | std::vector<dim_t> expectedDims = {3}; |
2313 | std::vector<float> expectedValues = {12, 15, 18}; |
2314 | |
2315 | EXPECT_EQ(result.dims().vec(), expectedDims); |
2316 | for (size_t i = 0; i < 3; i++) { |
2317 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
2318 | } |
2319 | |
2320 | // Verify the structure |
2321 | // Reshape x 3 -> Concat -> batchedReduceAdd -> Save |
2322 | ASSERT_EQ(6, F->getNodes().size()); |
2323 | auto *saveNode = getSaveNodeFromDest(output); |
2324 | auto *batchedReduceAdd = |
2325 | llvm::dyn_cast<BatchedReduceAddNode>(saveNode->getInput().getNode()); |
2326 | ASSERT_TRUE(batchedReduceAdd); |
2327 | auto *concat = |
2328 | llvm::dyn_cast<ConcatNode>(batchedReduceAdd->getBatch().getNode()); |
2329 | ASSERT_TRUE(concat); |
2330 | for (size_t i = 0; i < 3; ++i) { |
2331 | auto *reshape = |
2332 | llvm::dyn_cast<ReshapeNode>(concat->getNthInput(i).getNode()); |
2333 | ASSERT_TRUE(reshape); |
2334 | } |
2335 | |
2336 | // Constant Folding Test. |
2337 | FAIL_TEST_IF_ERR(checkConstFoldedOutput(netFilename, {"i0" , "i1" , "i2" }, |
2338 | {&i0, &i1, &i2}, |
2339 | {bindings.get(output)})); |
2340 | } |
2341 | |
2342 | /// Test loading Sum with one input and one output |
2343 | TEST_F(OnnxImporterTest, importSum1) { |
2344 | ExecutionEngine EE{}; |
2345 | auto &mod = EE.getModule(); |
2346 | Function *F = mod.createFunction("main" ); |
2347 | std::string netFilename(GLOW_DATA_PATH |
2348 | "tests/models/onnxModels/sum1.onnxtxt" ); |
2349 | |
2350 | PlaceholderBindings bindings; |
2351 | Placeholder *output; |
2352 | Tensor x(ElemKind::FloatTy, {3}); |
2353 | x.getHandle() = {1, 2, 3}; |
2354 | |
2355 | { |
2356 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
2357 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2358 | |
2359 | bindings.allocate(mod.getPlaceholders()); |
2360 | updateInputPlaceholdersByName(bindings, &mod, {"x" }, {&x}); |
2361 | } |
2362 | |
2363 | auto *res = bindings.get(output); |
2364 | EE.compile(CompilationMode::Infer); |
2365 | EE.run(bindings); |
2366 | |
2367 | auto result = res->getHandle(); |
2368 | std::vector<dim_t> expectedDims = {3}; |
2369 | std::vector<float> expectedValues = {1, 2, 3}; |
2370 | |
2371 | EXPECT_EQ(result.dims().vec(), expectedDims); |
2372 | for (size_t i = 0; i < 3; i++) { |
2373 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
2374 | } |
2375 | |
2376 | // Verify structure: input -> Save -> output |
2377 | ASSERT_EQ(mod.getPlaceholders().size(), 2); |
2378 | ASSERT_EQ(F->getNodes().size(), 1); |
2379 | auto *save = getSaveNodeFromDest(output); |
2380 | ASSERT_TRUE(llvm::isa<Placeholder>(save->getInput().getNode())); |
2381 | |
2382 | // Constant Folding Test. |
2383 | FAIL_TEST_IF_ERR( |
2384 | checkConstFoldedOutput(netFilename, {"x" }, {&x}, {bindings.get(output)})); |
2385 | } |
2386 | |
2387 | /// Test loading LengthsToRanges from an ONNX model. |
2388 | TEST_F(OnnxImporterTest, importLengthsToRanges) { |
2389 | ExecutionEngine EE; |
2390 | auto &mod = EE.getModule(); |
2391 | auto *F = mod.createFunction("main" ); |
2392 | std::string netFilename(GLOW_DATA_PATH |
2393 | "tests/models/onnxModels/lengths_to_ranges.onnxtxt" ); |
2394 | Placeholder *output; |
2395 | { |
2396 | Tensor lengths(ElemKind::Int32ITy, {4}); |
2397 | ONNXModelLoader onnxLD(netFilename, {"lengths" }, {&lengths.getType()}, *F); |
2398 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2399 | } |
2400 | // Verify structure: PH -> LengthsToRanges -> Save -> PH. |
2401 | ASSERT_EQ(mod.getPlaceholders().size(), 2); |
2402 | ASSERT_EQ(F->getNodes().size(), 2); |
2403 | auto *save = getSaveNodeFromDest(output); |
2404 | auto *LTR = llvm::dyn_cast<LengthsToRangesNode>(save->getInput().getNode()); |
2405 | ASSERT_TRUE(LTR); |
2406 | ASSERT_TRUE(llvm::isa<Placeholder>(LTR->getLengths())); |
2407 | } |
2408 | |
2409 | /// Test loading ReplaceNaN op from an ONNX model. |
2410 | /// Test with arg value = 1.0. |
2411 | TEST_F(OnnxImporterTest, importReplaceNaN) { |
2412 | ExecutionEngine EE{}; |
2413 | auto &mod = EE.getModule(); |
2414 | Function *F = mod.createFunction("main" ); |
2415 | |
2416 | std::string netFilename(GLOW_DATA_PATH |
2417 | "tests/models/onnxModels/replaceNaN.onnxtxt" ); |
2418 | |
2419 | PlaceholderBindings bindings; |
2420 | Placeholder *output; |
2421 | Tensor x(ElemKind::FloatTy, {3, 3}); |
2422 | |
2423 | { |
2424 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
2425 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2426 | bindings.allocate(mod.getPlaceholders()); |
2427 | updateInputPlaceholdersByName(bindings, &mod, {"x" }, {&x}); |
2428 | } |
2429 | |
2430 | // Verify structure: Input -> ReplaceNaN -> Save. |
2431 | EXPECT_EQ(F->getNodes().size(), 2); |
2432 | auto *saveNode = getSaveNodeFromDest(output); |
2433 | auto *replaceNaNNode = |
2434 | llvm::dyn_cast<ReplaceNaNNode>(saveNode->getInput().getNode()); |
2435 | EXPECT_EQ(replaceNaNNode->getValue(), 1.0f); |
2436 | auto *inputNode = |
2437 | llvm::dyn_cast<Placeholder>(replaceNaNNode->getInput().getNode()); |
2438 | ASSERT_EQ(inputNode, mod.getPlaceholderByNameSlow("x" )); |
2439 | |
2440 | // We have one input and one output. |
2441 | EXPECT_EQ(mod.getPlaceholders().size(), 2); |
2442 | } |
2443 | |
2444 | /// Test loading SparseToDense op from an ONNX model. |
2445 | TEST_F(OnnxImporterTest, importSparseToDense) { |
2446 | ExecutionEngine EE{}; |
2447 | auto &mod = EE.getModule(); |
2448 | Function *F = mod.createFunction("main" ); |
2449 | |
2450 | std::string netFilename(GLOW_DATA_PATH |
2451 | "tests/models/onnxModels/sparseToDense.onnxtxt" ); |
2452 | |
2453 | PlaceholderBindings bindings; |
2454 | Placeholder *output; |
2455 | |
2456 | // Create inputs. |
2457 | constexpr dim_t kNumIndices = 5; |
2458 | constexpr dim_t kMaxIndex = 20; |
2459 | constexpr dim_t kRows = 10; |
2460 | constexpr dim_t kCols = 5; |
2461 | Tensor indices(ElemKind::Int64ITy, {kNumIndices}); |
2462 | Tensor values(ElemKind::FloatTy, {kNumIndices, kRows, kCols}); |
2463 | Tensor dataToInferDim(ElemKind::FloatTy, {kMaxIndex, kRows, kCols}); |
2464 | |
2465 | // Load model. |
2466 | { |
2467 | ONNXModelLoader onnxLD( |
2468 | netFilename, {"indices" , "values" , "dataToInferDim" }, |
2469 | {&indices.getType(), &values.getType(), &dataToInferDim.getType()}, *F); |
2470 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2471 | } |
2472 | |
2473 | // Verify structure: Inputs -> Splat + Reshape -> ScatterData -> Save. |
2474 | ASSERT_EQ(mod.getPlaceholders().size(), 4); |
2475 | ASSERT_EQ(F->getNodes().size(), 4); |
2476 | |
2477 | auto *save = getSaveNodeFromDest(output); |
2478 | auto *out = save->getPlaceholder(); |
2479 | EXPECT_TRUE(out->dims().vec() == dataToInferDim.dims().vec()); |
2480 | |
2481 | auto *STD = llvm::dyn_cast<ScatterDataNode>(save->getInput().getNode()); |
2482 | ASSERT_TRUE(STD); |
2483 | auto *reshapeNode = llvm::dyn_cast<ReshapeNode>(STD->getIndices().getNode()); |
2484 | ASSERT_TRUE(reshapeNode); |
2485 | auto *idx = llvm::dyn_cast<Placeholder>(reshapeNode->getInput().getNode()); |
2486 | EXPECT_EQ(idx, mod.getPlaceholderByNameSlow("indices" )); |
2487 | auto *vals = llvm::dyn_cast<Placeholder>(STD->getSlices().getNode()); |
2488 | EXPECT_EQ(vals, mod.getPlaceholderByNameSlow("values" )); |
2489 | } |
2490 | |
2491 | /// Test loading SparseLengthsSum from an ONNX model. |
2492 | TEST_F(OnnxImporterTest, importSparseLengthsSum) { |
2493 | ExecutionEngine EE; |
2494 | auto &mod = EE.getModule(); |
2495 | auto *F = mod.createFunction("main" ); |
2496 | std::string netFilename(GLOW_DATA_PATH |
2497 | "tests/models/onnxModels/sparseLengthsSum.onnxtxt" ); |
2498 | Placeholder *output; |
2499 | { |
2500 | Tensor data(ElemKind::FloatTy, {2, 1}); |
2501 | Tensor indices(ElemKind::Int64ITy, {2}); |
2502 | Tensor lengths(ElemKind::Int32ITy, {2}); |
2503 | ONNXModelLoader onnxLD( |
2504 | netFilename, {"data" , "indices" , "lengths" }, |
2505 | {&data.getType(), &indices.getType(), &lengths.getType()}, *F); |
2506 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2507 | } |
2508 | // Verify structure: PH, PH -> SparseLengthsSum -> Save -> PH. |
2509 | // PH -> Splat / |
2510 | ASSERT_EQ(mod.getPlaceholders().size(), 4); |
2511 | ASSERT_EQ(F->getNodes().size(), 2); |
2512 | auto *save = getSaveNodeFromDest(output); |
2513 | auto *LS = llvm::dyn_cast<SparseLengthsSumNode>(save->getInput().getNode()); |
2514 | ASSERT_TRUE(LS); |
2515 | ASSERT_TRUE(llvm::isa<Placeholder>(LS->getData())); |
2516 | ASSERT_TRUE(llvm::isa<Placeholder>(LS->getIndices())); |
2517 | ASSERT_TRUE(llvm::isa<Placeholder>(LS->getLengths())); |
2518 | } |
2519 | |
2520 | /// Test loading LengthsSum from an ONNX model. |
2521 | TEST_F(OnnxImporterTest, importLengthsSum) { |
2522 | ExecutionEngine EE; |
2523 | auto &mod = EE.getModule(); |
2524 | auto *F = mod.createFunction("main" ); |
2525 | std::string netFilename(GLOW_DATA_PATH |
2526 | "tests/models/onnxModels/lengths_sum.onnxtxt" ); |
2527 | Placeholder *output; |
2528 | { |
2529 | Tensor data(ElemKind::FloatTy, {10, 2, 3}); |
2530 | Tensor lengths(ElemKind::Int32ITy, {5}); |
2531 | ONNXModelLoader onnxLD(netFilename, {"data" , "lengths" }, |
2532 | {&data.getType(), &lengths.getType()}, *F); |
2533 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2534 | } |
2535 | // Verify structure: PH, PH -> LengthsSum -> Save -> PH. |
2536 | ASSERT_EQ(mod.getPlaceholders().size(), 3); |
2537 | ASSERT_EQ(F->getNodes().size(), 2); |
2538 | auto *save = getSaveNodeFromDest(output); |
2539 | auto *LS = llvm::dyn_cast<LengthsSumNode>(save->getInput().getNode()); |
2540 | ASSERT_TRUE(LS); |
2541 | ASSERT_TRUE(llvm::isa<Placeholder>(LS->getData())); |
2542 | ASSERT_TRUE(llvm::isa<Placeholder>(LS->getLengths())); |
2543 | } |
2544 | |
2545 | /// Test loading CumSum from an ONNX model. |
2546 | TEST_F(OnnxImporterTest, importCumSum) { |
2547 | ExecutionEngine EE; |
2548 | auto &mod = EE.getModule(); |
2549 | auto *F = mod.createFunction("main" ); |
2550 | std::string netFilename(GLOW_DATA_PATH |
2551 | "tests/models/onnxModels/cumsum.onnxtxt" ); |
2552 | Placeholder *output; |
2553 | { |
2554 | Tensor lengths(ElemKind::FloatTy, {10}); |
2555 | lengths.getHandle() = {10, 9, 8, 7, 6, 5, 4, 3, 2, 1}; |
2556 | ONNXModelLoader onnxLD(netFilename, {"lengths" }, {&lengths.getType()}, *F); |
2557 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2558 | } |
2559 | // Verify structure: PH -> CumSum -> Save -> PH. |
2560 | ASSERT_EQ(mod.getPlaceholders().size(), 2); |
2561 | ASSERT_EQ(F->getNodes().size(), 2); |
2562 | auto *save = getSaveNodeFromDest(output); |
2563 | auto *CS = llvm::dyn_cast<CumSumNode>(save->getInput().getNode()); |
2564 | ASSERT_TRUE(CS); |
2565 | ASSERT_TRUE(llvm::isa<Placeholder>(CS->getInput())); |
2566 | ASSERT_FALSE(CS->getExclusive()); |
2567 | ASSERT_TRUE(CS->getReverse()); |
2568 | } |
2569 | |
2570 | /// Test loading a FCTransposed node: I * W + B, where I is need to be flatten. |
2571 | TEST_F(OnnxImporterTest, FCTransposedWithFlatten) { |
2572 | ExecutionEngine EE{}; |
2573 | auto &mod = EE.getModule(); |
2574 | Function *F = mod.createFunction("main" ); |
2575 | |
2576 | std::string netFilename(GLOW_DATA_PATH |
2577 | "tests/models/onnxModels/FCTransposed.onnxtxt" ); |
2578 | |
2579 | Placeholder *output; |
2580 | |
2581 | { |
2582 | Tensor data(ElemKind::FloatTy, {2, 1, 3}); |
2583 | data.getHandle() = {1, 2, 3, 4, 5, 6}; |
2584 | ONNXModelLoader onnxLD(netFilename, {"data" }, {&data.getType()}, *F); |
2585 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2586 | } |
2587 | |
2588 | // High level check on the content of the graph. We have 1 reshape, 1 FC, |
2589 | // and 1 save. |
2590 | EXPECT_EQ(F->getNodes().size(), 3); |
2591 | auto *saveNode = getSaveNodeFromDest(output); |
2592 | auto *fcNode = |
2593 | llvm::dyn_cast<FullyConnectedNode>(saveNode->getInput().getNode()); |
2594 | ASSERT_TRUE(fcNode); |
2595 | auto *reshape = llvm::dyn_cast<ReshapeNode>(fcNode->getInput()); |
2596 | ASSERT_TRUE(reshape); |
2597 | } |
2598 | |
2599 | /// Test loading Constant from an ONNX model. |
2600 | TEST_F(OnnxImporterTest, constant) { |
2601 | ExecutionEngine EE; |
2602 | auto &mod = EE.getModule(); |
2603 | auto *F = mod.createFunction("main" ); |
2604 | std::string netFilename(GLOW_DATA_PATH |
2605 | "tests/models/onnxModels/constant.onnxtxt" ); |
2606 | Placeholder *output; |
2607 | { |
2608 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
2609 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2610 | EXPECT_NE(output, nullptr); |
2611 | } |
2612 | // Constant -> Save -> PH |
2613 | ASSERT_EQ(mod.getPlaceholders().size(), 1); |
2614 | ASSERT_EQ(F->getNodes().size(), 1); |
2615 | } |
2616 | |
2617 | /// Test loading of testConstantOfShape. |
2618 | template <class ElemType> |
2619 | static void testConstantOfShape(std::string fileName, ElemType ref) { |
2620 | ExecutionEngine EE; |
2621 | auto &mod = EE.getModule(); |
2622 | auto *F = mod.createFunction("main" ); |
2623 | PlaceholderBindings bindings; |
2624 | |
2625 | std::string netFilename = |
2626 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName; |
2627 | Placeholder *output; |
2628 | { |
2629 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
2630 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2631 | EXPECT_NE(output, nullptr); |
2632 | } |
2633 | // ConstantOfShape -> Save -> PH |
2634 | ASSERT_EQ(mod.getPlaceholders().size(), 1); |
2635 | ASSERT_EQ(F->getNodes().size(), 2); |
2636 | |
2637 | EE.compile(CompilationMode::Infer); |
2638 | bindings.allocate(mod.getPlaceholders()); |
2639 | EE.run(bindings); |
2640 | |
2641 | auto result = bindings.get(output)->getHandle<ElemType>(); |
2642 | for (size_t i = 0; i < result.getType().size(); i++) { |
2643 | ElemType val = result.raw(i); |
2644 | EXPECT_EQ(val, ref); |
2645 | } |
2646 | } |
2647 | |
2648 | /// Test loading of testConstantOfShape. |
2649 | template <class ElemType> |
2650 | static void testConstantOfShapeFailure(std::string fileName) { |
2651 | ExecutionEngine EE; |
2652 | auto &mod = EE.getModule(); |
2653 | auto *F = mod.createFunction("main" ); |
2654 | std::string netFilename = |
2655 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName; |
2656 | ASSERT_DEATH(ONNXModelLoader(netFilename, {}, {}, *F), "losses" ); |
2657 | } |
2658 | |
2659 | TEST_F(OnnxImporterTest, importConstantOfShapeFloat) { |
2660 | testConstantOfShape<float>("constantOfShape.onnxtxt" , 1.0F); |
2661 | } |
2662 | |
2663 | TEST_F(OnnxImporterTest, importConstantOfShapeInt32) { |
2664 | testConstantOfShape<int32_t>("constantOfShapeInt32.onnxtxt" , 65535); |
2665 | } |
2666 | |
2667 | TEST_F(OnnxImporterTest, importConstantOfShapeInt64) { |
2668 | testConstantOfShape<int64_t>("constantOfShapeInt64.onnxtxt" , 16777216LL); |
2669 | } |
2670 | |
2671 | TEST_F(OnnxImporterTest, importConstantOfShapeInt64LossFailure) { |
2672 | testConstantOfShapeFailure<int64_t>("constantOfShapeInt64Fail.onnxtxt" ); |
2673 | } |
2674 | |
2675 | TEST_F(OnnxImporterTest, importConstantOfShapeInt32LossFailure) { |
2676 | testConstantOfShapeFailure<int32_t>("constantOfShapeInt32Fail.onnxtxt" ); |
2677 | } |
2678 | |
2679 | /// Test loading ExpandDims from an ONNX model. |
2680 | TEST_F(OnnxImporterTest, expandDims) { |
2681 | ExecutionEngine EE; |
2682 | auto &mod = EE.getModule(); |
2683 | auto *F = mod.createFunction("main" ); |
2684 | std::string netFilename(GLOW_DATA_PATH |
2685 | "tests/models/onnxModels/expandDims.onnxtxt" ); |
2686 | Placeholder *output; |
2687 | { |
2688 | Tensor x(ElemKind::FloatTy, {2, 2}); |
2689 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
2690 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2691 | } |
2692 | |
2693 | // Verify structure: PH -> Reshape -> Save -> PH. |
2694 | ASSERT_EQ(mod.getPlaceholders().size(), 2); |
2695 | ASSERT_EQ(F->getNodes().size(), 2); |
2696 | auto *save = getSaveNodeFromDest(output); |
2697 | auto *reshape = llvm::dyn_cast<ReshapeNode>(save->getInput().getNode()); |
2698 | ASSERT_TRUE(reshape); |
2699 | EXPECT_TRUE(reshape->getDims().equals({1, 2, 2, 1})); |
2700 | } |
2701 | |
2702 | /// Helper method to run the gather operator test cases. |
2703 | /// \p filename contains the model .onnxtxt. |
2704 | /// \p dataShape: data Tensor dimensions. |
2705 | /// \p indicesShape: indices Tensor dimensions |
2706 | /// \p expectedValues : output Tensor values expected. |
2707 | template <class OpType> |
2708 | static void gatherTestHelper(llvm::StringRef fileName, |
2709 | llvm::ArrayRef<dim_t> dataShape, |
2710 | llvm::ArrayRef<dim_t> indicesShape, |
2711 | llvm::ArrayRef<dim_t> expectedDims) { |
2712 | ExecutionEngine EE{}; |
2713 | auto &mod = EE.getModule(); |
2714 | Function *F = mod.createFunction("main" ); |
2715 | std::string netFilename = |
2716 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName.str(); |
2717 | Placeholder *output; |
2718 | Tensor data(ElemKind::FloatTy, dataShape); |
2719 | Tensor indices(ElemKind::Int32ITy, indicesShape); |
2720 | |
2721 | { |
2722 | ONNXModelLoader onnxLD(netFilename, {"data" , "indices" }, |
2723 | {&data.getType(), &indices.getType()}, *F); |
2724 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2725 | } |
2726 | |
2727 | // Verify structure: PH/PH -> Gather/GatherND -> Save -> PH. |
2728 | auto *saveNode = getSaveNodeFromDest(output); |
2729 | auto *node = saveNode->getInput().getNode(); |
2730 | auto *nodeGather = llvm::dyn_cast<OpType>(node); |
2731 | ASSERT_TRUE(nodeGather); |
2732 | EXPECT_TRUE(nodeGather->getResult().dims().equals({expectedDims})); |
2733 | } |
2734 | |
2735 | /// Test loading gather op from a ONNX model. |
2736 | TEST_F(OnnxImporterTest, importGather) { |
2737 | std::string filename("gather.onnxtxt" ); |
2738 | std::vector<dim_t> dataShape = {3, 2}; |
2739 | std::vector<dim_t> indicesShape = {2, 4}; |
2740 | std::vector<dim_t> expectedDims = {2, 4, 2}; |
2741 | gatherTestHelper<GatherNode>(filename, dataShape, indicesShape, expectedDims); |
2742 | } |
2743 | |
2744 | /// Test loading gatherND op from a ONNX model. |
2745 | TEST_F(OnnxImporterTest, importGatherND) { |
2746 | std::string filename("gatherND.onnxtxt" ); |
2747 | std::vector<dim_t> dataShape = {2, 2, 2}; |
2748 | std::vector<dim_t> indicesShape = {2, 2}; |
2749 | std::vector<dim_t> expectedDims = {2, 2}; |
2750 | gatherTestHelper<GatherNDNode>(filename, dataShape, indicesShape, |
2751 | expectedDims); |
2752 | } |
2753 | |
2754 | /// Test loading ScatterND from an ONNX model. |
2755 | // Simplified test |
2756 | TEST_F(OnnxImporterTest, scatterND) { |
2757 | ExecutionEngine EE; |
2758 | auto &mod = EE.getModule(); |
2759 | std::string netFilename(GLOW_DATA_PATH |
2760 | "tests/models/onnxModels/scatterND.onnxtxt" ); |
2761 | auto *F = mod.createFunction("main" ); |
2762 | Placeholder *output; |
2763 | Tensor data(ElemKind::FloatTy, {8}); |
2764 | Tensor indices(ElemKind::Int64ITy, {4, 1}); |
2765 | Tensor updates(ElemKind::FloatTy, {4}); |
2766 | |
2767 | ONNXModelLoader onnxLD( |
2768 | netFilename, {"data" , "indices" , "updates" }, |
2769 | {&data.getType(), &indices.getType(), &updates.getType()}, *F); |
2770 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2771 | |
2772 | // Verify structure: PH/PH/PH -> ScatterND -> Save -> PH. |
2773 | ASSERT_EQ(mod.getPlaceholders().size(), 4); |
2774 | ASSERT_EQ(F->getNodes().size(), 2); |
2775 | auto *save = getSaveNodeFromDest(output); |
2776 | auto *scatter = llvm::dyn_cast<ScatterDataNode>(save->getInput().getNode()); |
2777 | ASSERT_TRUE(scatter); |
2778 | EXPECT_TRUE(scatter->getResult().dims().equals({8})); |
2779 | } |
2780 | |
2781 | /// Test loading ScatterND from an ONNX model. |
2782 | // multi-dim test |
2783 | TEST_F(OnnxImporterTest, mscatterND) { |
2784 | ExecutionEngine EE; |
2785 | auto &mod = EE.getModule(); |
2786 | std::string netFilename(GLOW_DATA_PATH |
2787 | "tests/models/onnxModels/mscatterND.onnxtxt" ); |
2788 | auto *F = mod.createFunction("main" ); |
2789 | Placeholder *output; |
2790 | Tensor data(ElemKind::FloatTy, {4, 4, 4}); |
2791 | Tensor indices(ElemKind::Int64ITy, {2, 1}); |
2792 | Tensor updates(ElemKind::FloatTy, {2, 4, 4}); |
2793 | |
2794 | ONNXModelLoader onnxLD( |
2795 | netFilename, {"data" , "indices" , "updates" }, |
2796 | {&data.getType(), &indices.getType(), &updates.getType()}, *F); |
2797 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2798 | |
2799 | // Verify structure: PH/PH/PH -> ScatterND -> Save -> PH. |
2800 | ASSERT_EQ(mod.getPlaceholders().size(), 4); |
2801 | ASSERT_EQ(F->getNodes().size(), 2); |
2802 | auto *save = getSaveNodeFromDest(output); |
2803 | auto *scatter = llvm::dyn_cast<ScatterDataNode>(save->getInput().getNode()); |
2804 | ASSERT_TRUE(scatter); |
2805 | EXPECT_TRUE(scatter->getResult().dims().equals({4, 4, 4})); |
2806 | } |
2807 | |
2808 | /// Test loading GatherRanges from an ONNX model. |
2809 | TEST_F(OnnxImporterTest, gatherRanges) { |
2810 | ExecutionEngine EE; |
2811 | auto &mod = EE.getModule(); |
2812 | std::string netFilename(GLOW_DATA_PATH |
2813 | "tests/models/onnxModels/gatherranges.onnxtxt" ); |
2814 | auto *F = mod.createFunction("main" ); |
2815 | Placeholder *output; |
2816 | Tensor data(ElemKind::FloatTy, {6}); |
2817 | Tensor ranges(ElemKind::Int32ITy, {2, 2, 2}); |
2818 | |
2819 | { |
2820 | ONNXModelLoader onnxLD(netFilename, {"data" , "ranges" }, |
2821 | {&data.getType(), &ranges.getType()}, *F); |
2822 | output = EXIT_ON_ERR(onnxLD.getOutputByName("output" )); |
2823 | } |
2824 | |
2825 | // Verify structure: PH/PH -> GatherRanges -> Save -> PH/PH. |
2826 | ASSERT_EQ(mod.getPlaceholders().size(), 4); |
2827 | ASSERT_EQ(F->getNodes().size(), 3); |
2828 | auto *save = getSaveNodeFromDest(output); |
2829 | auto *gatherRanges = |
2830 | llvm::dyn_cast<GatherRangesNode>(save->getInput().getNode()); |
2831 | ASSERT_TRUE(gatherRanges); |
2832 | EXPECT_TRUE(gatherRanges->getOutput().dims().equals({5})); |
2833 | EXPECT_TRUE(gatherRanges->getLengths().dims().equals({2})); |
2834 | } |
2835 | |
2836 | /// Test loading Gather ops with constant folding from an ONNX model. |
2837 | TEST_F(OnnxImporterTest, gatherOpConstantFoldingAndReshape) { |
2838 | // This test verifies that Gather gets constant-folded, so that the argument |
2839 | // of the reshape becomes constant. |
2840 | ExecutionEngine EE; |
2841 | auto &mod = EE.getModule(); |
2842 | std::string netFilename( |
2843 | GLOW_DATA_PATH "tests/models/onnxModels/gatherConstantFolding.onnxtxt" ); |
2844 | PlaceholderBindings bindings; |
2845 | auto *F = mod.createFunction("main" ); |
2846 | Placeholder *output; |
2847 | Tensor data(ElemKind::FloatTy, {1, 2, 4, 3}); |
2848 | setConstantFoldLoaderOpsFlag(true); |
2849 | { |
2850 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&data.getType()}, *F); |
2851 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2852 | EXPECT_EQ(mod.getPlaceholders().size(), 2); |
2853 | bindings.allocate(mod.getPlaceholders()); |
2854 | } |
2855 | EE.compile(CompilationMode::Infer); |
2856 | EE.run(bindings); |
2857 | setConstantFoldLoaderOpsFlag(false); |
2858 | |
2859 | auto result = bindings.get(output)->getHandle(); |
2860 | std::vector<dim_t> expectedDims = {1, 4, 3, 2}; |
2861 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
2862 | } |
2863 | |
2864 | static void importSliceTest(std::string fileName, const char *inputName, |
2865 | llvm::ArrayRef<dim_t> inputShape, |
2866 | llvm::ArrayRef<dim_t> starts, |
2867 | llvm::ArrayRef<dim_t> outputShape, |
2868 | bool expectLoadError = false) { |
2869 | ExecutionEngine EE{}; |
2870 | auto &mod = EE.getModule(); |
2871 | Function *F = mod.createFunction("main" ); |
2872 | |
2873 | std::string NetFilename = |
2874 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName; |
2875 | PlaceholderBindings bindings; |
2876 | Placeholder *graphOutputVar; |
2877 | // Destroy the loader after the graph is loaded since the following execution |
2878 | // will not depend on anyting from the loader. |
2879 | Tensor data; |
2880 | getNCHWData(&data, inputShape[0], inputShape[1], inputShape[2], |
2881 | inputShape[3]); |
2882 | { |
2883 | if (expectLoadError) { |
2884 | Error err = Error::empty(); |
2885 | ONNXModelLoader(NetFilename, {inputName}, {&data.getType()}, *F, &err); |
2886 | EXPECT_TRUE(ERR_TO_BOOL(std::move(err))); |
2887 | return; |
2888 | } |
2889 | ONNXModelLoader onnxLD(NetFilename, {inputName}, {&data.getType()}, *F); |
2890 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
2891 | bindings.allocate(mod.getPlaceholders()); |
2892 | updateInputPlaceholdersByName(bindings, &mod, {inputName}, {&data}); |
2893 | } |
2894 | |
2895 | // ONNX importer loads an Slice operator and adds to the IR: |
2896 | // - a Slice node |
2897 | |
2898 | // Check the graph structure. |
2899 | auto *saveNode = getSaveNodeFromDest(graphOutputVar); |
2900 | auto *node = saveNode->getInput().getNode(); |
2901 | auto *sliceNode = llvm::dyn_cast<SliceNode>(node); |
2902 | EXPECT_NE(nullptr, sliceNode); |
2903 | |
2904 | // Compile&run the graph, and check the output. |
2905 | EE.compile(CompilationMode::Infer); |
2906 | EE.run(bindings); |
2907 | auto result = bindings.get(graphOutputVar)->getHandle(); |
2908 | EXPECT_TRUE(result.dims().vec() == outputShape.vec()); |
2909 | dim_t wSliceSize = inputShape[3]; |
2910 | dim_t hSliceSize = inputShape[2] * wSliceSize; |
2911 | dim_t cSliceSize = inputShape[1] * hSliceSize; |
2912 | dim_t indexOutput = 0; |
2913 | for (dim_t n = 0; n < outputShape[0]; n++) { |
2914 | for (dim_t c = 0; c < outputShape[1]; c++) { |
2915 | for (dim_t h = 0; h < outputShape[2]; h++) { |
2916 | for (dim_t w = 0; w < outputShape[3]; w++) { |
2917 | dim_t indexInput = (starts[0] + n) * cSliceSize + |
2918 | (starts[1] + c) * hSliceSize + |
2919 | (starts[2] + h) * wSliceSize + (starts[3] + w); |
2920 | EXPECT_FLOAT_EQ(result.raw(indexOutput++), indexInput); |
2921 | } |
2922 | } |
2923 | } |
2924 | } |
2925 | |
2926 | // Constant Folding Test. |
2927 | FAIL_TEST_IF_ERR(checkConstFoldedOutput(NetFilename, {inputName}, {&data}, |
2928 | {bindings.get(graphOutputVar)})); |
2929 | } |
2930 | |
2931 | TEST_F(OnnxImporterTest, importSliceDynamicNoAxes) { |
2932 | importSliceTest("sliceDynamic.onnxtxt" , "data" , {2, 3, 3, 3} /* input */, |
2933 | {0, 1, 1, 1} /* starts */, /* ends: {2, 2, 3, 3} */ |
2934 | {2, 1, 2, 2} /* output */); |
2935 | } |
2936 | |
2937 | TEST_F(OnnxImporterTest, importSliceAxesFull) { |
2938 | importSliceTest("sliceAxesFull.onnxtxt" , "data" , {2, 3, 3, 3} /* input */, |
2939 | {0, 1, 1, 2} /* starts */, /* ends: {1, 2, 3, 3} */ |
2940 | {1, 1, 2, 1} /* output */); |
2941 | } |
2942 | |
2943 | TEST_F(OnnxImporterTest, importSliceAxesAnyOrder) { |
2944 | importSliceTest("sliceAxesAnyOrder.onnxtxt" , "data" , {2, 3, 3, 3} /* input */, |
2945 | {1, 2, 0, 2} /* starts */, /* ends: {2, 3, 1, 3} */ |
2946 | {1, 1, 1, 1} /* output */); |
2947 | } |
2948 | |
2949 | TEST_F(OnnxImporterTest, importSliceAxesOverwrite) { |
2950 | importSliceTest("sliceAxesOverwrite.onnxtxt" , "data" , |
2951 | {2, 3, 3, 3} /* input */, |
2952 | {0, 1, 1, 2} /* starts */, /* ends: {1, 2, 3, 3} */ |
2953 | {1, 1, 2, 1} /* output */); |
2954 | } |
2955 | |
2956 | TEST_F(OnnxImporterTest, importSliceAxesPartial) { |
2957 | importSliceTest("sliceAxesPartial.onnxtxt" , "data" , {2, 3, 3, 3} /* input */, |
2958 | {0, 1, 1, 0} /* starts */, /* ends: {2, 2, 3, 3} */ |
2959 | {2, 1, 2, 3} /* output */); |
2960 | } |
2961 | |
2962 | TEST_F(OnnxImporterTest, importSliceNoAxes) { |
2963 | importSliceTest("sliceNoAxes.onnxtxt" , "data" , {2, 3, 3, 3} /* input */, |
2964 | {0, 1, 1, 1} /* starts */, /* ends: {2, 2, 3, 3} */ |
2965 | {2, 1, 2, 2} /* output */); |
2966 | } |
2967 | |
2968 | TEST_F(OnnxImporterTest, importSliceInvalidAxes) { |
2969 | importSliceTest("sliceInvalidAxes.onnxtxt" , "data" , {2, 3, 3, 3} /* input */, |
2970 | {0, 1, 1, 1} /* starts */, /* ends: {2, 2, 3, 3} */ |
2971 | {2, 1, 2, 2} /* output */, true); |
2972 | } |
2973 | |
2974 | TEST_F(OnnxImporterTest, importSliceWithStep) { |
2975 | importSliceTest("sliceWithStep.onnxtxt" , "data" , {2, 3, 3, 3} /* input */, |
2976 | {0, 1, 1, 1} /* starts */, /* ends: {2, 2, 3, 3} */ |
2977 | {2, 1, 2, 2} /* output */); |
2978 | } |
2979 | |
2980 | TEST_F(OnnxImporterTest, importSliceWithUnsupportedStep) { |
2981 | importSliceTest("sliceWithUnsupportedStep.onnxtxt" , "data" , |
2982 | {2, 3, 3, 3} /* input */, |
2983 | {0, 1, 1, 1} /* starts */, /* ends: {2, 2, 3, 3} */ |
2984 | {2, 1, 2, 2} /* output */, true); |
2985 | } |
2986 | |
2987 | static void importCast(llvm::StringRef fileName, llvm::StringRef inputName, |
2988 | llvm::ArrayRef<dim_t> inputShape, ElemKind outputKind) { |
2989 | ExecutionEngine EE{}; |
2990 | auto &mod = EE.getModule(); |
2991 | Function *F = mod.createFunction("main" ); |
2992 | |
2993 | std::string NetFilename = |
2994 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName.str(); |
2995 | PlaceholderBindings bindings; |
2996 | Placeholder *graphOutputVar; |
2997 | { |
2998 | Tensor data; |
2999 | getNCHWData(&data, inputShape[0], inputShape[1], inputShape[2], |
3000 | inputShape[3]); |
3001 | ONNXModelLoader onnxLD(NetFilename, {inputName.str().c_str()}, |
3002 | {&data.getType()}, *F); |
3003 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
3004 | bindings.allocate(mod.getPlaceholders()); |
3005 | updateInputPlaceholdersByName(bindings, &mod, {inputName}, {&data}); |
3006 | } |
3007 | |
3008 | // ONNX importer loads a Cast operator and adds to the IR: |
3009 | // - a ConvertTo node |
3010 | |
3011 | // Check the graph structure. |
3012 | auto *saveNode = getSaveNodeFromDest(graphOutputVar); |
3013 | auto *node = saveNode->getInput().getNode(); |
3014 | auto *castNode = llvm::dyn_cast<ConvertToNode>(node); |
3015 | ASSERT_NE(nullptr, castNode); |
3016 | |
3017 | // Check node output type. |
3018 | ASSERT_EQ(castNode->getResult().getType()->getElementType(), outputKind); |
3019 | } |
3020 | |
3021 | TEST_F(OnnxImporterTest, importCastToFloat) { |
3022 | importCast("castToFloat.onnxtxt" , "data" , {1, 2, 2, 2}, ElemKind::FloatTy); |
3023 | } |
3024 | TEST_F(OnnxImporterTest, importCastToFloat16) { |
3025 | importCast("castToFloat16.onnxtxt" , "data" , {1, 2, 2, 2}, |
3026 | ElemKind::Float16Ty); |
3027 | } |
3028 | TEST_F(OnnxImporterTest, importCastToInt32) { |
3029 | importCast("castToInt32.onnxtxt" , "data" , {1, 2, 2, 2}, ElemKind::Int32ITy); |
3030 | } |
3031 | TEST_F(OnnxImporterTest, importCastToInt64) { |
3032 | importCast("castToInt64.onnxtxt" , "data" , {1, 2, 2, 2}, ElemKind::Int64ITy); |
3033 | } |
3034 | TEST(onnx, importCastToBool) { |
3035 | importCast("castToBool.onnxtxt" , "data" , {1, 2, 2, 2}, ElemKind::BoolTy); |
3036 | } |
3037 | |
3038 | TEST_F(OnnxImporterTest, cast_32_64) { |
3039 | ExecutionEngine EE{}; |
3040 | auto &mod = EE.getModule(); |
3041 | Function *F = mod.createFunction("main" ); |
3042 | |
3043 | std::string netFilename(GLOW_DATA_PATH |
3044 | "tests/models/onnxModels/castInt-32-64.onnxtxt" ); |
3045 | PlaceholderBindings bindings; |
3046 | Placeholder *graphOutputVar; |
3047 | std::vector<float> init(1 * 2 * 4 * 3); |
3048 | std::vector<float> expectedOut(1 * 2 * 4 * 3); |
3049 | for (size_t i = 0; i < init.size(); i++) { |
3050 | const float value = i * 12.345678f; |
3051 | init[i] = value; |
3052 | expectedOut[i] = int32_t(value); |
3053 | } |
3054 | { |
3055 | Tensor data(ElemKind::FloatTy, {1, 2, 4, 3}); |
3056 | data.getHandle() = init; |
3057 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&data.getType()}, *F); |
3058 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
3059 | bindings.allocate(mod.getPlaceholders()); |
3060 | updateInputPlaceholdersByName(bindings, &mod, {"input" }, {&data}); |
3061 | } |
3062 | |
3063 | EE.compile(CompilationMode::Infer); |
3064 | EE.run(bindings); |
3065 | // Make sure that the optimizer did not eliminate float->int casts. They are |
3066 | // not NOOP. Conversions int32 -> int64 -> int32 are always NOOP, so they can |
3067 | // be optimized away. |
3068 | EXPECT_EQ(F->getNodes().size(), 3); |
3069 | auto result = bindings.get(graphOutputVar)->getHandle(); |
3070 | std::vector<dim_t> expectedDims = {1, 2, 4, 3}; |
3071 | |
3072 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
3073 | for (size_t i = 0; i < expectedOut.size(); i++) { |
3074 | EXPECT_EQ(result.raw(i), expectedOut[i]); |
3075 | } |
3076 | } |
3077 | |
3078 | static void importPad(std::string fileName, const char *inputName, |
3079 | llvm::ArrayRef<dim_t> inputShape, |
3080 | llvm::ArrayRef<sdim_t> starts, |
3081 | llvm::ArrayRef<sdim_t> ends, PaddingMode mode, |
3082 | float value, bool testOutput, |
3083 | bool expectLoadError = false) { |
3084 | ExecutionEngine EE{}; |
3085 | auto &mod = EE.getModule(); |
3086 | Function *F = mod.createFunction("main" ); |
3087 | |
3088 | std::string NetFilename = |
3089 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + fileName; |
3090 | PlaceholderBindings bindings; |
3091 | Placeholder *graphOutputVar; |
3092 | // Destroy the loader after the graph is loaded since the following execution |
3093 | // will not depend on anyting from the loader. |
3094 | { |
3095 | Tensor data; |
3096 | getNCHWData(&data, inputShape[0], inputShape[1], inputShape[2], |
3097 | inputShape[3]); |
3098 | if (expectLoadError) { |
3099 | Error err = Error::empty(); |
3100 | ONNXModelLoader(NetFilename, {inputName}, {&data.getType()}, *F, &err); |
3101 | EXPECT_TRUE(ERR_TO_BOOL(std::move(err))); |
3102 | return; |
3103 | } |
3104 | ONNXModelLoader onnxLD(NetFilename, {inputName}, {&data.getType()}, *F); |
3105 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
3106 | bindings.allocate(mod.getPlaceholders()); |
3107 | updateInputPlaceholdersByName(bindings, &mod, {inputName}, {&data}); |
3108 | } |
3109 | |
3110 | // ONNX importer loads a Pad operator and adds to the IR: |
3111 | // - a Pad node |
3112 | |
3113 | // Check the graph structure. |
3114 | auto *saveNode = getSaveNodeFromDest(graphOutputVar); |
3115 | auto *node = saveNode->getInput().getNode(); |
3116 | auto *padNode = llvm::dyn_cast<PadNode>(node); |
3117 | EXPECT_NE(nullptr, padNode); |
3118 | |
3119 | // Check Pad node properties. |
3120 | assert(padNode->getMode() == mode); |
3121 | if (mode == PaddingMode::CONSTANT) { |
3122 | EXPECT_EQ(value, padNode->getValue()); |
3123 | } |
3124 | // Check the Pad node output shape. |
3125 | std::vector<dim_t> expectedOutputShape(inputShape.size()); |
3126 | for (unsigned int i = 0; i < inputShape.size(); i++) { |
3127 | expectedOutputShape[i] = |
3128 | size_t(ssize_t(inputShape[i]) + starts[i] + ends[i]); |
3129 | } |
3130 | EXPECT_TRUE(padNode->getResult().dims().vec() == expectedOutputShape); |
3131 | |
3132 | // Currently, only constant with positive pads is supported at lowering. |
3133 | // We just consider this test case. |
3134 | if (testOutput && mode == PaddingMode::CONSTANT) { |
3135 | // Compile&run the graph, and check the output. |
3136 | EE.compile(CompilationMode::Infer); |
3137 | EE.run(bindings); |
3138 | auto result = bindings.get(graphOutputVar)->getHandle(); |
3139 | EXPECT_TRUE(result.dims().vec() == expectedOutputShape); |
3140 | size_t indexOutput = 0; |
3141 | size_t indexinput = 0; |
3142 | for (size_t n = 0; n < expectedOutputShape[0]; n++) { |
3143 | for (size_t c = 0; c < expectedOutputShape[1]; c++) { |
3144 | for (size_t h = 0; h < expectedOutputShape[2]; h++) { |
3145 | for (size_t w = 0; w < expectedOutputShape[3]; w++) { |
3146 | float expectedValue = value; |
3147 | if ((n >= size_t(starts[0])) && |
3148 | (n < (expectedOutputShape[0] - size_t(ends[0]))) && |
3149 | (c >= size_t(starts[1])) && |
3150 | (c < (expectedOutputShape[1] - size_t(ends[1]))) && |
3151 | (h >= size_t(starts[2])) && |
3152 | (h < (expectedOutputShape[2] - size_t(ends[2]))) && |
3153 | (w >= size_t(starts[3])) && |
3154 | (w < (expectedOutputShape[3] - size_t(ends[3])))) { |
3155 | // This is the way 'getNCHWData' initializes data. |
3156 | expectedValue = indexinput++; |
3157 | } |
3158 | EXPECT_FLOAT_EQ(result.raw(indexOutput++), expectedValue); |
3159 | } |
3160 | } |
3161 | } |
3162 | } |
3163 | } |
3164 | } |
3165 | |
3166 | TEST_F(OnnxImporterTest, importPadDefault) { |
3167 | importPad("padDefault.onnxtxt" , "data" , {4, 6, 5, 7} /* input */, |
3168 | {1, 2, -2, 0} /* starts */, {0, -2, 1, 2} /* ends */, |
3169 | PaddingMode::CONSTANT, 0.f, false); |
3170 | } |
3171 | |
3172 | TEST_F(OnnxImporterTest, importPadDefaultInputPads) { |
3173 | // This test Pad in opset v11 where "pads" is passed through the 2nd input. |
3174 | importPad("padDefaultInputPad.onnxtxt" , "data" , {4, 6, 5, 7} /* input */, |
3175 | {1, 2, -2, 0} /* starts */, {0, -2, 1, 2} /* ends */, |
3176 | PaddingMode::CONSTANT, 0.f, false); |
3177 | } |
3178 | |
3179 | TEST_F(OnnxImporterTest, importPadConstant) { |
3180 | importPad("padConstant.onnxtxt" , "data" , {4, 6, 5, 7} /* input */, |
3181 | {1, 2, -2, 0} /* starts */, {0, -2, 1, 2} /* ends */, |
3182 | PaddingMode::CONSTANT, 2.55f, false); |
3183 | } |
3184 | |
3185 | TEST_F(OnnxImporterTest, importPadConstantInput) { |
3186 | // This tests Pad in opset v11 where "pads" is passed through the 2nd input |
3187 | // and "value" through the 3rd input. |
3188 | importPad("padConstantInput.onnxtxt" , "data" , {4, 6, 5, 7} /* input */, |
3189 | {1, 2, -2, 0} /* starts */, {0, -2, 1, 2} /* ends */, |
3190 | PaddingMode::CONSTANT, 2.55f, false); |
3191 | } |
3192 | |
3193 | TEST_F(OnnxImporterTest, importPadReflect) { |
3194 | // Note: PaddingMode::REFLECT is not yet supported, so we assert death when |
3195 | // loading the model. |
3196 | importPad("padReflect.onnxtxt" , "data" , {4, 6, 5, 7} /* input */, |
3197 | {1, 2, -2, 0} /* starts */, {0, -2, 1, 2} /* ends */, |
3198 | PaddingMode::REFLECT, 0.f /* any */, false, |
3199 | /* expectLoadError */ true); |
3200 | } |
3201 | |
3202 | TEST_F(OnnxImporterTest, importPadEdge) { |
3203 | // Note: PaddingMode::EDGE is not yet supported, so we assert death when |
3204 | // loading the model. |
3205 | importPad("padEdge.onnxtxt" , "data" , {4, 6, 5, 7} /* input */, |
3206 | {1, 2, -2, 0} /* starts */, {0, -2, 1, 2} /* ends */, |
3207 | PaddingMode::EDGE, 0.f /* any */, false, |
3208 | /* expectLoadError */ true); |
3209 | } |
3210 | |
3211 | TEST_F(OnnxImporterTest, importPadConstantPositive) { |
3212 | importPad("padConstantPositive.onnxtxt" , "data" , {4, 6, 5, 7} /* input */, |
3213 | {1, 2, 3, 4} /* starts */, {0, 3, 1, 2} /* ends */, |
3214 | PaddingMode::CONSTANT, 2.55f, true); |
3215 | } |
3216 | |
3217 | TEST_F(OnnxImporterTest, instNorm) { |
3218 | ExecutionEngine EE; |
3219 | auto &mod = EE.getModule(); |
3220 | std::string netFilename(GLOW_DATA_PATH |
3221 | "tests/models/onnxModels/instNorm.onnxtxt" ); |
3222 | auto *F = mod.createFunction("main" ); |
3223 | Placeholder *output; |
3224 | Tensor inputTensor(ElemKind::FloatTy, {1, 3, 10, 10}); |
3225 | { |
3226 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&inputTensor.getType()}, |
3227 | *F); |
3228 | output = EXIT_ON_ERR(onnxLD.getOutputByName("output" )); |
3229 | auto inputs = onnxLD.getInputVarsMapping(); |
3230 | EXPECT_EQ(inputs.size(), 1); |
3231 | EXPECT_TRUE(inputTensor.getType().isEqual(inputs["input" ]->getType())); |
3232 | } |
3233 | |
3234 | // Check the graph structure. |
3235 | auto *saveNode = getSaveNodeFromDest(output); |
3236 | auto *inNode = |
3237 | llvm::dyn_cast<InstanceNormalizationNode>(saveNode->getInput().getNode()); |
3238 | EXPECT_NE(nullptr, inNode); |
3239 | } |
3240 | |
3241 | /// Test loading BatchNorm with all optional outputs declared, but not used in |
3242 | /// the model. Glow supports only the first mandatory output, but declaring |
3243 | /// optional outputs while not using them in the model should not make the |
3244 | /// import fail. |
3245 | TEST_F(OnnxImporterTest, batchNormPR2304) { |
3246 | ExecutionEngine EE; |
3247 | auto &mod = EE.getModule(); |
3248 | std::string netFilename(GLOW_DATA_PATH |
3249 | "tests/models/onnxModels/batchNormPR2304.onnxtxt" ); |
3250 | auto *F = mod.createFunction("main" ); |
3251 | Placeholder *output; |
3252 | Tensor inputTensor(ElemKind::FloatTy, {1, 2, 10, 10}); |
3253 | { |
3254 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&inputTensor.getType()}, |
3255 | *F); |
3256 | output = EXIT_ON_ERR(onnxLD.getOutputByName("output" )); |
3257 | } |
3258 | |
3259 | // Check the graph structure. |
3260 | auto *saveNode = getSaveNodeFromDest(output); |
3261 | auto *trNode = llvm::dyn_cast<TransposeNode>(saveNode->getInput().getNode()); |
3262 | EXPECT_NE(nullptr, trNode); |
3263 | auto *bnNode = |
3264 | llvm::dyn_cast<BatchNormalizationNode>(trNode->getInput().getNode()); |
3265 | EXPECT_NE(nullptr, bnNode); |
3266 | } |
3267 | |
3268 | /// Test constructor for auto loading inputs case. |
3269 | TEST_F(OnnxImporterTest, autoLoadInputs) { |
3270 | ExecutionEngine EE; |
3271 | auto &mod = EE.getModule(); |
3272 | std::string netFilename(GLOW_DATA_PATH |
3273 | "tests/models/onnxModels/batchNormPR2304.onnxtxt" ); |
3274 | auto *F = mod.createFunction("main" ); |
3275 | Tensor inputTensor(ElemKind::FloatTy, {1, 2, 10, 10}); |
3276 | llvm::StringRef inputName = "input" ; |
3277 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
3278 | auto inputs = onnxLD.getInputVarsMapping(); |
3279 | EXPECT_EQ(inputs.size(), 1); |
3280 | EXPECT_TRUE(inputTensor.getType().isEqual(inputs[inputName]->getType())); |
3281 | } |
3282 | |
3283 | TEST_F(OnnxImporterTest, shape) { |
3284 | ExecutionEngine EE{}; |
3285 | auto &mod = EE.getModule(); |
3286 | Function *F = mod.createFunction("main" ); |
3287 | |
3288 | std::string netFilename(GLOW_DATA_PATH |
3289 | "tests/models/onnxModels/shape.onnxtxt" ); |
3290 | |
3291 | PlaceholderBindings bindings; |
3292 | Placeholder *output; |
3293 | Tensor x(ElemKind::FloatTy, {2, 2, 2, 2}); |
3294 | x.getHandle() = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; |
3295 | |
3296 | { |
3297 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&x.getType()}, *F); |
3298 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
3299 | bindings.allocate(mod.getPlaceholders()); |
3300 | updateInputPlaceholdersByName(bindings, &mod, {"input" }, {&x}); |
3301 | } |
3302 | |
3303 | auto *res = bindings.get(output); |
3304 | EE.compile(CompilationMode::Infer); |
3305 | EE.run(bindings); |
3306 | |
3307 | auto result = res->getHandle<int64_t>(); |
3308 | std::vector<dim_t> expectedDims = {1}; |
3309 | std::vector<int64_t> expectedValues = {4}; |
3310 | |
3311 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
3312 | for (size_t i = 0; i < expectedValues.size(); i++) { |
3313 | EXPECT_EQ(result.raw(i), expectedValues[i]); |
3314 | } |
3315 | |
3316 | // Constant Folding Test. |
3317 | FAIL_TEST_IF_ERR(checkConstFoldedOutput(netFilename, {"input" }, {&x}, |
3318 | {bindings.get(output)})); |
3319 | } |
3320 | |
3321 | TEST_F(OnnxImporterTest, tile) { |
3322 | ExecutionEngine EE; |
3323 | auto &mod = EE.getModule(); |
3324 | Function *F = mod.createFunction("main" ); |
3325 | |
3326 | std::string netFilename(GLOW_DATA_PATH |
3327 | "tests/models/onnxModels/tile.onnxtxt" ); |
3328 | |
3329 | PlaceholderBindings bindings; |
3330 | Placeholder *output; |
3331 | { |
3332 | Tensor x(ElemKind::FloatTy, {1, 2, 2, 1}); |
3333 | x.getHandle() = {1., 2., 3., 4.}; |
3334 | |
3335 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&x.getType()}, *F); |
3336 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
3337 | bindings.allocate(mod.getPlaceholders()); |
3338 | updateInputPlaceholdersByName(bindings, &mod, {"input" }, {&x}); |
3339 | } |
3340 | |
3341 | auto *res = bindings.get(output); |
3342 | EE.compile(CompilationMode::Infer); |
3343 | EE.run(bindings); |
3344 | |
3345 | auto result = res->getHandle(); |
3346 | std::vector<dim_t> expectedDims = {1, 4, 4, 3}; |
3347 | std::vector<float> expectedValues = { |
3348 | 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, |
3349 | 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, |
3350 | 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, |
3351 | 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, |
3352 | }; |
3353 | |
3354 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
3355 | for (size_t i = 0; i < expectedValues.size(); i++) { |
3356 | EXPECT_EQ(result.raw(i), expectedValues[i]); |
3357 | } |
3358 | } |
3359 | |
3360 | static void importPowTest(const std::string &netFilename, Tensor &x, Tensor &y, |
3361 | std::vector<dim_t> &expectedDims, |
3362 | std::vector<float> &expectedValues) { |
3363 | ExecutionEngine EE{}; |
3364 | auto &mod = EE.getModule(); |
3365 | Function *F = mod.createFunction("main" ); |
3366 | |
3367 | PlaceholderBindings bindings; |
3368 | Placeholder *output; |
3369 | |
3370 | ONNXModelLoader onnxLD(netFilename, {"base" , "exp" }, |
3371 | {&x.getType(), &y.getType()}, *F); |
3372 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
3373 | bindings.allocate(mod.getPlaceholders()); |
3374 | updateInputPlaceholdersByName(bindings, &mod, {"base" }, {&x}); |
3375 | updateInputPlaceholdersByName(bindings, &mod, {"exp" }, {&y}); |
3376 | |
3377 | auto *outputT = bindings.get(output); |
3378 | |
3379 | EE.compile(CompilationMode::Infer); |
3380 | EE.run(bindings); |
3381 | |
3382 | auto outputH = outputT->getHandle(); |
3383 | |
3384 | EXPECT_TRUE(outputH.dims().vec() == expectedDims); |
3385 | for (size_t i = 0; i < expectedValues.size(); i++) { |
3386 | EXPECT_EQ(outputH.raw(i), expectedValues[i]); |
3387 | } |
3388 | } |
3389 | |
3390 | TEST_F(OnnxImporterTest, pow_scalar_broadcast) { |
3391 | Tensor x(ElemKind::FloatTy, {2, 3}); |
3392 | x.getHandle() = {1, 2, 3, 4, 5, 6}; |
3393 | |
3394 | Tensor y(ElemKind::FloatTy, {1}); |
3395 | y.getHandle() = { |
3396 | 3, |
3397 | }; |
3398 | |
3399 | std::string netFilename( |
3400 | GLOW_DATA_PATH "tests/models/onnxModels/pow_scalar_broadcast.onnxtxt" ); |
3401 | |
3402 | std::vector<dim_t> expectedDims = {2, 3}; |
3403 | std::vector<float> expectedValues = { |
3404 | 1., 8., 27., 64., 125, 216., |
3405 | }; |
3406 | |
3407 | importPowTest(netFilename, x, y, expectedDims, expectedValues); |
3408 | } |
3409 | |
3410 | TEST_F(OnnxImporterTest, pow_vector_broadcast) { |
3411 | Tensor x(ElemKind::FloatTy, {2, 3}); |
3412 | x.getHandle() = {1, 2, 3, 4, 5, 6}; |
3413 | |
3414 | Tensor y(ElemKind::FloatTy, {3}); |
3415 | y.getHandle() = { |
3416 | 1, |
3417 | 2, |
3418 | 3, |
3419 | }; |
3420 | |
3421 | std::string netFilename( |
3422 | GLOW_DATA_PATH "tests/models/onnxModels/pow_array_broadcast.onnxtxt" ); |
3423 | |
3424 | std::vector<dim_t> expectedDims = {2, 3}; |
3425 | std::vector<float> expectedValues = { |
3426 | 1., 4., 27., 4., 25, 216., |
3427 | }; |
3428 | |
3429 | importPowTest(netFilename, x, y, expectedDims, expectedValues); |
3430 | } |
3431 | |
3432 | TEST_F(OnnxImporterTest, pow_element_wise) { |
3433 | Tensor x(ElemKind::FloatTy, {3}); |
3434 | x.getHandle() = {1, 2, 3}; |
3435 | |
3436 | Tensor y(ElemKind::FloatTy, {3}); |
3437 | y.getHandle() = {4, 5, 6}; |
3438 | |
3439 | std::string netFilename(GLOW_DATA_PATH |
3440 | "tests/models/onnxModels/pow_element_wise.onnxtxt" ); |
3441 | |
3442 | std::vector<dim_t> expectedDims = {3}; |
3443 | std::vector<float> expectedValues = { |
3444 | 1., |
3445 | 32., |
3446 | 729., |
3447 | }; |
3448 | |
3449 | importPowTest(netFilename, x, y, expectedDims, expectedValues); |
3450 | } |
3451 | |
3452 | TEST_F(OnnxImporterTest, topK) { |
3453 | ExecutionEngine EE{}; |
3454 | auto &mod = EE.getModule(); |
3455 | Function *F = mod.createFunction("main" ); |
3456 | |
3457 | std::string netFilename(GLOW_DATA_PATH |
3458 | "tests/models/onnxModels/TopK.onnxtxt" ); |
3459 | |
3460 | PlaceholderBindings bindings; |
3461 | Placeholder *output; |
3462 | Placeholder *index; |
3463 | Tensor x(ElemKind::FloatTy, {1, 3, 4}); |
3464 | x.getHandle() = {1., 2., 3., 4., 8., 7., 7., 7., 11., 12., 11., 10.}; |
3465 | |
3466 | { |
3467 | ONNXModelLoader onnxLD(netFilename, {"scores" }, {&x.getType()}, *F); |
3468 | output = EXIT_ON_ERR(onnxLD.getOutputByName("topscores" )); |
3469 | index = EXIT_ON_ERR(onnxLD.getOutputByName("topindices" )); |
3470 | bindings.allocate(mod.getPlaceholders()); |
3471 | updateInputPlaceholdersByName(bindings, &mod, {"scores" }, {&x}); |
3472 | } |
3473 | |
3474 | auto *outputT = bindings.get(output); |
3475 | auto *indexT = bindings.get(index); |
3476 | |
3477 | EE.compile(CompilationMode::Infer); |
3478 | EE.run(bindings); |
3479 | |
3480 | auto outputH = outputT->getHandle(); |
3481 | auto indexH = indexT->getHandle<int64_t>(); |
3482 | std::vector<dim_t> expectedDims = {1, 3, 2}; |
3483 | std::vector<float> expectedValues = { |
3484 | 4., 3., 8., 7., 12, 11., |
3485 | }; |
3486 | std::vector<int64_t> expectedIndices = {3, 2, 0, 1, 1, 0}; |
3487 | |
3488 | EXPECT_TRUE(outputH.dims().vec() == expectedDims); |
3489 | for (size_t i = 0; i < expectedValues.size(); i++) { |
3490 | EXPECT_EQ(outputH.raw(i), expectedValues[i]); |
3491 | } |
3492 | |
3493 | EXPECT_TRUE(indexH.dims().vec() == expectedDims); |
3494 | for (size_t i = 0; i < expectedIndices.size(); i++) { |
3495 | EXPECT_EQ(indexH.raw(i), expectedIndices[i]); |
3496 | } |
3497 | |
3498 | // Constant Folding Test. |
3499 | FAIL_TEST_IF_ERR( |
3500 | checkConstFoldedOutput(netFilename, {"scores" }, {&x}, {outputT, indexT})); |
3501 | } |
3502 | |
3503 | void testArgMinMax(llvm::StringRef filename, bool isMin, |
3504 | const std::vector<dim_t> &expectedDims) { |
3505 | ExecutionEngine EE; |
3506 | auto &mod = EE.getModule(); |
3507 | Function *F = mod.createFunction("main" ); |
3508 | |
3509 | std::string netFilename = std::string(GLOW_DATA_PATH) + filename.str(); |
3510 | |
3511 | PlaceholderBindings bindings; |
3512 | Placeholder *PH; |
3513 | std::vector<dim_t> inDims = {2, 3, 4, 5}; |
3514 | { |
3515 | Tensor inT(ElemKind::FloatTy, inDims); |
3516 | |
3517 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&inT.getType()}, *F); |
3518 | PH = EXIT_ON_ERR(onnxLD.getOutputByName("scores" )); |
3519 | bindings.allocate(mod.getPlaceholders()); |
3520 | updateInputPlaceholdersByName(bindings, &mod, {"input" }, {&inT}); |
3521 | } |
3522 | |
3523 | EE.compile(CompilationMode::Infer); |
3524 | EE.run(bindings); |
3525 | |
3526 | auto output = bindings.get(PH)->getHandle<int64_t>(); |
3527 | EXPECT_TRUE(output.dims().vec() == expectedDims); |
3528 | |
3529 | auto *save = getSaveNodeFromDest(PH); |
3530 | if (isMin) { |
3531 | EXPECT_TRUE(llvm::isa<ArgMinNode>(save->getInput())); |
3532 | } else { |
3533 | EXPECT_TRUE(llvm::isa<ArgMaxNode>(save->getInput())); |
3534 | } |
3535 | } |
3536 | |
3537 | TEST_F(OnnxImporterTest, argMaxKeepDim) { |
3538 | testArgMinMax("tests/models/onnxModels/ArgMaxKeepDim.onnxtxt" , false, |
3539 | {2, 3, 1, 5}); |
3540 | } |
3541 | |
3542 | TEST_F(OnnxImporterTest, argMaxNoKeepDim) { |
3543 | testArgMinMax("tests/models/onnxModels/ArgMaxNoKeepDim.onnxtxt" , false, |
3544 | {2, 4, 5}); |
3545 | } |
3546 | |
3547 | TEST_F(OnnxImporterTest, argMaxDefault) { |
3548 | testArgMinMax("tests/models/onnxModels/ArgMaxDefault.onnxtxt" , false, |
3549 | {1, 3, 4, 5}); |
3550 | } |
3551 | |
3552 | TEST_F(OnnxImporterTest, argMinKeepDim) { |
3553 | testArgMinMax("tests/models/onnxModels/ArgMinKeepDim.onnxtxt" , true, |
3554 | {2, 3, 1, 5}); |
3555 | } |
3556 | |
3557 | TEST_F(OnnxImporterTest, argMinNoKeepDim) { |
3558 | testArgMinMax("tests/models/onnxModels/ArgMinNoKeepDim.onnxtxt" , true, |
3559 | {2, 4, 5}); |
3560 | } |
3561 | |
3562 | TEST_F(OnnxImporterTest, argMinDefault) { |
3563 | testArgMinMax("tests/models/onnxModels/ArgMinDefault.onnxtxt" , true, |
3564 | {1, 3, 4, 5}); |
3565 | } |
3566 | |
3567 | TEST_F(OnnxImporterTest, importMaxPoolWithArgmax) { |
3568 | ExecutionEngine EE; |
3569 | auto &mod = EE.getModule(); |
3570 | std::string netFilename(GLOW_DATA_PATH |
3571 | "tests/models/onnxModels/maxPoolWithArgmax.onnxtxt" ); |
3572 | auto *F = mod.createFunction("main" ); |
3573 | PlaceholderBindings bindings; |
3574 | Placeholder *resultPH, *indicesPH; |
3575 | Tensor inputTensor(ElemKind::FloatTy, {1, 3, 4, 4}); |
3576 | |
3577 | // Execute the following scenario for MaxPool with Argmax output: |
3578 | // Input: |
3579 | // [[[[ 0. 47. 35. 23.] |
3580 | // [11. 58. 46. 34.] |
3581 | // [22. 10. 57. 45.] |
3582 | // [33. 21. 9. 56.]] |
3583 | // |
3584 | // [[44. 32. 20. 8.] |
3585 | // [55. 43. 31. 19.] |
3586 | // [ 7. 54. 42. 30.] |
3587 | // [18. 6. 53. 41.]] |
3588 | // |
3589 | // [[29. 17. 5. 52.] |
3590 | // [40. 28. 16. 4.] |
3591 | // [51. 39. 27. 15.] |
3592 | // [ 3. 50. 38. 26.]]]] |
3593 | // |
3594 | // Result: |
3595 | // [[[[58. 46.] |
3596 | // [33. 57.]] |
3597 | // |
3598 | // [[55. 31.] |
3599 | // [54. 53.]] |
3600 | // |
3601 | // [[40. 52.] |
3602 | // [51. 38.]]]] |
3603 | // |
3604 | // Argmax: |
3605 | // [[[[15 18] |
3606 | // [36 30]] |
3607 | // |
3608 | // [[13 19] |
3609 | // [28 43]] |
3610 | // |
3611 | // [[14 11] |
3612 | // [26 44]]]] |
3613 | inputTensor.getHandle() = { |
3614 | 0.0, 47.0, 35.0, 23.0, 11.0, 58.0, 46.0, 34.0, 22.0, 10.0, 57.0, 45.0, |
3615 | 33.0, 21.0, 9.0, 56.0, 44.0, 32.0, 20.0, 8.0, 55.0, 43.0, 31.0, 19.0, |
3616 | 7.0, 54.0, 42.0, 30.0, 18.0, 6.0, 53.0, 41.0, 29.0, 17.0, 5.0, 52.0, |
3617 | 40.0, 28.0, 16.0, 4.0, 51.0, 39.0, 27.0, 15.0, 3.0, 50.0, 38.0, 26.0}; |
3618 | |
3619 | { |
3620 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&inputTensor.getType()}, |
3621 | *F); |
3622 | resultPH = EXIT_ON_ERR(onnxLD.getOutputByName("result" )); |
3623 | indicesPH = EXIT_ON_ERR(onnxLD.getOutputByName("indices" )); |
3624 | bindings.allocate(mod.getPlaceholders()); |
3625 | updateInputPlaceholdersByName(bindings, &mod, {"input" }, {&inputTensor}); |
3626 | } |
3627 | |
3628 | EE.compile(CompilationMode::Infer); |
3629 | EE.run(bindings); |
3630 | |
3631 | auto result = bindings.get(resultPH)->getHandle(); |
3632 | auto indices = bindings.get(indicesPH)->getHandle<int64_t>(); |
3633 | std::vector<dim_t> expectedDims = {1, 3, 2, 2}; |
3634 | |
3635 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
3636 | EXPECT_TRUE(indices.dims().vec() == expectedDims); |
3637 | |
3638 | std::vector<float> expectedResult = {58.0, 46.0, 33.0, 57.0, 55.0, 31.0, |
3639 | 54.0, 53.0, 40.0, 52.0, 51.0, 38.0}; |
3640 | std::vector<int64_t> expectedIndices = {15, 18, 36, 30, 13, 19, |
3641 | 28, 43, 14, 11, 26, 44}; |
3642 | |
3643 | for (size_t i = 0; i < expectedResult.size(); i++) { |
3644 | EXPECT_EQ(result.raw(i), expectedResult[i]); |
3645 | EXPECT_EQ(indices.raw(i), expectedIndices[i]); |
3646 | } |
3647 | } |
3648 | |
3649 | TEST_F(OnnxImporterTest, importMean) { |
3650 | ExecutionEngine EE; |
3651 | auto &mod = EE.getModule(); |
3652 | std::string netFilename(GLOW_DATA_PATH |
3653 | "tests/models/onnxModels/Mean.onnxtxt" ); |
3654 | auto *F = mod.createFunction("main" ); |
3655 | PlaceholderBindings bindings; |
3656 | Placeholder *resultPH; |
3657 | Tensor T0(ElemKind::FloatTy, {2, 3, 2}); |
3658 | Tensor T1(ElemKind::FloatTy, {2, 3, 2}); |
3659 | Tensor T2(ElemKind::FloatTy, {2, 3, 2}); |
3660 | T0.getHandle() = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}; |
3661 | T1.getHandle() = {11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0}; |
3662 | T2.getHandle() = {2.5, 1, 2.5, 1, 2.5, 1, 2.5, 1, 2.5, 1, 0, 1}; |
3663 | { |
3664 | ONNXModelLoader onnxLD(netFilename, {"T0" , "T1" , "T2" }, |
3665 | {&T0.getType(), &T1.getType(), &T2.getType()}, *F); |
3666 | resultPH = EXIT_ON_ERR(onnxLD.getOutputByName("Y" )); |
3667 | bindings.allocate(mod.getPlaceholders()); |
3668 | updateInputPlaceholdersByName(bindings, &mod, {"T0" , "T1" , "T2" }, |
3669 | {&T0, &T1, &T2}); |
3670 | } |
3671 | EE.compile(CompilationMode::Infer); |
3672 | EE.run(bindings); |
3673 | auto result = bindings.get(resultPH)->getHandle(); |
3674 | std::vector<dim_t> expectedDims = {2, 3, 2}; |
3675 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
3676 | std::vector<float> expectedResult = {4.5, 4, 4.5, 4, 4.5, 4, |
3677 | 4.5, 4, 4.5, 4, 11.0 / 3, 4}; |
3678 | for (size_t i = 0; i < expectedResult.size(); i++) { |
3679 | EXPECT_EQ(result.raw(i), expectedResult[i]); |
3680 | } |
3681 | } |
3682 | |
3683 | TEST_F(OnnxImporterTest, importMeanBroadcast) { |
3684 | ExecutionEngine EE; |
3685 | auto &mod = EE.getModule(); |
3686 | std::string netFilename(GLOW_DATA_PATH |
3687 | "tests/models/onnxModels/Mean_broadcast.onnxtxt" ); |
3688 | auto *F = mod.createFunction("main" ); |
3689 | PlaceholderBindings bindings; |
3690 | Placeholder *resultPH; |
3691 | Tensor T0(ElemKind::FloatTy, {1, 2, 1}); |
3692 | Tensor T1(ElemKind::FloatTy, {3}); |
3693 | Tensor T2(ElemKind::FloatTy, {1, 2, 3}); |
3694 | T0.getHandle() = {0, 1}; |
3695 | T1.getHandle() = {11, 10, 9}; |
3696 | T2.getHandle() = {5, 4, 3, 2, 1, 0}; |
3697 | |
3698 | { |
3699 | ONNXModelLoader onnxLD(netFilename, {"T0" , "T1" , "T2" }, |
3700 | {&T0.getType(), &T1.getType(), &T2.getType()}, *F); |
3701 | resultPH = EXIT_ON_ERR(onnxLD.getOutputByName("Y" )); |
3702 | bindings.allocate(mod.getPlaceholders()); |
3703 | updateInputPlaceholdersByName(bindings, &mod, {"T0" , "T1" , "T2" }, |
3704 | {&T0, &T1, &T2}); |
3705 | } |
3706 | EE.compile(CompilationMode::Infer); |
3707 | EE.run(bindings); |
3708 | auto result = bindings.get(resultPH)->getHandle(); |
3709 | std::vector<dim_t> expectedDims = {1, 2, 3}; |
3710 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
3711 | std::vector<float> expectedResult = {16.0 / 3, 14.0 / 3, 4.0, |
3712 | 14.0 / 3, 4.0, 10.0 / 3}; |
3713 | for (size_t i = 0; i < expectedResult.size(); i++) { |
3714 | EXPECT_EQ(result.raw(i), expectedResult[i]); |
3715 | } |
3716 | } |
3717 | |
3718 | TEST_F(OnnxImporterTest, importWhere) { |
3719 | ExecutionEngine EE{}; |
3720 | auto &mod = EE.getModule(); |
3721 | Function *F = mod.createFunction("main" ); |
3722 | |
3723 | std::string netFilename(GLOW_DATA_PATH |
3724 | "tests/models/onnxModels/Where.onnxtxt" ); |
3725 | |
3726 | Placeholder *out = nullptr; |
3727 | { |
3728 | Tensor condition(ElemKind::BoolTy, {1, 1, 4}); |
3729 | Tensor X(ElemKind::FloatTy, {1, 4, 1}); |
3730 | Tensor Y(ElemKind::FloatTy, {4, 1, 1}); |
3731 | |
3732 | condition.zero(); |
3733 | X.zero(); |
3734 | Y.zero(); |
3735 | |
3736 | ONNXModelLoader onnxLD(netFilename, {"Condition" , "X" , "Y" }, |
3737 | {&condition.getType(), &X.getType(), &Y.getType()}, |
3738 | *F); |
3739 | out = EXIT_ON_ERR(onnxLD.getOutputByName("Out" )); |
3740 | } |
3741 | |
3742 | auto *save = getSaveNodeFromDest(out); |
3743 | |
3744 | SelectNode *WHR = llvm::dyn_cast<SelectNode>(save->getInput().getNode()); |
3745 | |
3746 | ASSERT_TRUE(WHR); |
3747 | EXPECT_EQ(WHR->getResult().dims()[0], 4); |
3748 | EXPECT_EQ(WHR->getResult().dims()[1], 4); |
3749 | EXPECT_EQ(WHR->getResult().dims()[2], 4); |
3750 | } |
3751 | |
3752 | TEST_F(OnnxImporterTest, importLess) { |
3753 | ExecutionEngine EE{}; |
3754 | auto &mod = EE.getModule(); |
3755 | Function *F = mod.createFunction("main" ); |
3756 | |
3757 | std::string netFilename(GLOW_DATA_PATH |
3758 | "tests/models/onnxModels/Less.onnxtxt" ); |
3759 | |
3760 | Placeholder *out = nullptr; |
3761 | { |
3762 | Tensor X(ElemKind::FloatTy, {1, 4, 1}); |
3763 | Tensor Y(ElemKind::FloatTy, {4, 1, 1}); |
3764 | X.zero(); |
3765 | Y.zero(); |
3766 | |
3767 | ONNXModelLoader onnxLD(netFilename, {"X" , "Y" }, |
3768 | {&X.getType(), &Y.getType()}, *F); |
3769 | out = EXIT_ON_ERR(onnxLD.getOutputByName("Out" )); |
3770 | } |
3771 | |
3772 | auto *save = getSaveNodeFromDest(out); |
3773 | |
3774 | CmpLTNode *CMPLT = llvm::dyn_cast<CmpLTNode>(save->getInput().getNode()); |
3775 | |
3776 | ASSERT_TRUE(CMPLT); |
3777 | ASSERT_EQ(CMPLT->getResult().dims().size(), 3); |
3778 | EXPECT_EQ(CMPLT->getResult().dims()[0], 4); |
3779 | EXPECT_EQ(CMPLT->getResult().dims()[1], 4); |
3780 | EXPECT_EQ(CMPLT->getResult().dims()[2], 1); |
3781 | } |
3782 | |
3783 | TEST_F(OnnxImporterTest, importLessEqual) { |
3784 | ExecutionEngine EE{}; |
3785 | auto &mod = EE.getModule(); |
3786 | Function *F = mod.createFunction("main" ); |
3787 | |
3788 | std::string netFilename(GLOW_DATA_PATH |
3789 | "tests/models/onnxModels/CmpLTE.onnxtxt" ); |
3790 | |
3791 | Placeholder *out = nullptr; |
3792 | { |
3793 | Tensor X(ElemKind::FloatTy, {1, 4, 1}); |
3794 | Tensor Y(ElemKind::FloatTy, {4, 1, 1}); |
3795 | X.zero(); |
3796 | Y.zero(); |
3797 | |
3798 | ONNXModelLoader onnxLD(netFilename, {"X" , "Y" }, |
3799 | {&X.getType(), &Y.getType()}, *F); |
3800 | out = EXIT_ON_ERR(onnxLD.getOutputByName("Out" )); |
3801 | } |
3802 | |
3803 | auto *save = getSaveNodeFromDest(out); |
3804 | |
3805 | CmpLTENode *CMPLTE = llvm::dyn_cast<CmpLTENode>(save->getInput().getNode()); |
3806 | |
3807 | ASSERT_TRUE(CMPLTE); |
3808 | ASSERT_EQ(CMPLTE->getResult().dims().size(), 3); |
3809 | EXPECT_EQ(CMPLTE->getResult().dims()[0], 4); |
3810 | EXPECT_EQ(CMPLTE->getResult().dims()[1], 4); |
3811 | EXPECT_EQ(CMPLTE->getResult().dims()[2], 1); |
3812 | } |
3813 | |
3814 | TEST_F(OnnxImporterTest, importEqual) { |
3815 | ExecutionEngine EE{}; |
3816 | auto &mod = EE.getModule(); |
3817 | Function *F = mod.createFunction("main" ); |
3818 | |
3819 | std::string netFilename(GLOW_DATA_PATH |
3820 | "tests/models/onnxModels/Equal.onnxtxt" ); |
3821 | |
3822 | Placeholder *out = nullptr; |
3823 | { |
3824 | Tensor X(ElemKind::FloatTy, {1, 4, 1}); |
3825 | Tensor Y(ElemKind::FloatTy, {4, 1, 1}); |
3826 | X.zero(); |
3827 | Y.zero(); |
3828 | |
3829 | ONNXModelLoader onnxLD(netFilename, {"X" , "Y" }, |
3830 | {&X.getType(), &Y.getType()}, *F); |
3831 | out = EXIT_ON_ERR(onnxLD.getOutputByName("Out" )); |
3832 | } |
3833 | |
3834 | auto *save = getSaveNodeFromDest(out); |
3835 | |
3836 | CmpEQNode *CMPEQ = llvm::dyn_cast<CmpEQNode>(save->getInput().getNode()); |
3837 | |
3838 | ASSERT_TRUE(CMPEQ); |
3839 | ASSERT_EQ(CMPEQ->getResult().dims().size(), 3); |
3840 | EXPECT_EQ(CMPEQ->getResult().dims()[0], 4); |
3841 | EXPECT_EQ(CMPEQ->getResult().dims()[1], 4); |
3842 | EXPECT_EQ(CMPEQ->getResult().dims()[2], 1); |
3843 | } |
3844 | |
3845 | static void importLogical(const std::string &netFilename, |
3846 | llvm::ArrayRef<bool> LHS, llvm::ArrayRef<bool> RHS, |
3847 | llvm::ArrayRef<dim_t> LHSShape, |
3848 | llvm::ArrayRef<dim_t> RHSShape, |
3849 | llvm::ArrayRef<dim_t> outputShape, |
3850 | llvm::ArrayRef<bool> expectedValues) { |
3851 | ExecutionEngine EE{}; |
3852 | auto &mod = EE.getModule(); |
3853 | Function *F = mod.createFunction("main" ); |
3854 | |
3855 | // Load the .onnxtxt model. |
3856 | Type LHSType(ElemKind::BoolTy, LHSShape); |
3857 | Type RHSType(ElemKind::BoolTy, RHSShape); |
3858 | ONNXModelLoader onnxLD(netFilename, {"LHS" , "RHS" }, {&LHSType, &RHSType}, *F); |
3859 | |
3860 | // Get placeholder bindings |
3861 | PlaceholderBindings bindings; |
3862 | Placeholder *graphOutputVar; |
3863 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
3864 | auto *LHSPH = mod.getPlaceholderByNameSlow("LHS" ); |
3865 | auto *LHSTensor = bindings.allocate(LHSPH); |
3866 | LHSTensor->getHandle<bool>() = LHS; |
3867 | auto *RHSPH = mod.getPlaceholderByNameSlow("RHS" ); |
3868 | auto *RHSTensor = bindings.allocate(RHSPH); |
3869 | RHSTensor->getHandle<bool>() = RHS; |
3870 | |
3871 | // Compile and run graph |
3872 | EE.compile(CompilationMode::Infer); |
3873 | bindings.allocate(mod.getPlaceholders()); |
3874 | EE.run(bindings); |
3875 | auto result = bindings.get(graphOutputVar)->getHandle<bool>(); |
3876 | |
3877 | // Validate results |
3878 | ASSERT_TRUE(result.dims() == (llvm::ArrayRef<dim_t>)outputShape); |
3879 | for (size_t i = 0; i < result.getType().size(); i++) { |
3880 | EXPECT_EQ(result.raw(i), (bool)expectedValues[i]); |
3881 | } |
3882 | } |
3883 | |
3884 | /// Test "and" operation of dimensions 4 |
3885 | TEST_F(OnnxImporterTest, importLogicAnd) { |
3886 | llvm::SmallVector<bool, 12> LHS = {true, true, false, false, true, true, |
3887 | false, false, false, false, true, true}; |
3888 | llvm::SmallVector<bool, 12> RHS = {true, true, false, true, false, true, |
3889 | false, true, true, true, true, true}; |
3890 | std::vector<dim_t> LHSShape = {1, 2, 3, 2}; |
3891 | std::vector<dim_t> RHSShape = {1, 2, 3, 2}; |
3892 | std::vector<dim_t> outputShape = {1, 2, 3, 2}; |
3893 | llvm::SmallVector<bool, 12> expectedValues = {true, true, false, false, |
3894 | false, true, false, false, |
3895 | false, false, true, true}; |
3896 | std::string netFilename(GLOW_DATA_PATH |
3897 | "tests/models/onnxModels/logicalAnd.onnxtxt" ); |
3898 | importLogical(netFilename, LHS, RHS, LHSShape, RHSShape, outputShape, |
3899 | expectedValues); |
3900 | } |
3901 | |
3902 | /// Test "broadcast and" of dimensions 4 and 2 |
3903 | TEST_F(OnnxImporterTest, importLogicBcastAnd) { |
3904 | llvm::SmallVector<bool, 12> LHS = {true, true, false, false, true, true, |
3905 | false, false, false, false, true, true}; |
3906 | llvm::SmallVector<bool, 6> RHS = {false, true, true, true, true, false}; |
3907 | std::vector<dim_t> LHSShape = {1, 2, 3, 2}; |
3908 | std::vector<dim_t> RHSShape = {3, 2}; |
3909 | std::vector<dim_t> outputShape = {1, 2, 3, 2}; |
3910 | llvm::SmallVector<bool, 12> expectedValues = {false, true, false, false, |
3911 | true, false, false, false, |
3912 | false, false, true, false}; |
3913 | std::string netFilename(GLOW_DATA_PATH |
3914 | "tests/models/onnxModels/logicalAndBcast.onnxtxt" ); |
3915 | importLogical(netFilename, LHS, RHS, LHSShape, RHSShape, outputShape, |
3916 | expectedValues); |
3917 | } |
3918 | |
3919 | /// Test "or" operation of dimensions 4 |
3920 | TEST_F(OnnxImporterTest, importLogicOr) { |
3921 | llvm::SmallVector<bool, 12> LHS = {true, true, false, false, true, true, |
3922 | false, false, false, false, true, true}; |
3923 | llvm::SmallVector<bool, 12> RHS = {true, true, false, true, false, true, |
3924 | false, true, true, true, true, true}; |
3925 | std::vector<dim_t> LHSShape = {1, 2, 3, 2}; |
3926 | std::vector<dim_t> RHSShape = {1, 2, 3, 2}; |
3927 | std::vector<dim_t> outputShape = {1, 2, 3, 2}; |
3928 | llvm::SmallVector<bool, 12> expectedValues = { |
3929 | true, true, false, true, true, true, false, true, true, true, true, true}; |
3930 | std::string netFilename(GLOW_DATA_PATH |
3931 | "tests/models/onnxModels/logicalOr.onnxtxt" ); |
3932 | importLogical(netFilename, LHS, RHS, LHSShape, RHSShape, outputShape, |
3933 | expectedValues); |
3934 | } |
3935 | |
3936 | /// Test "broadcast or" of dimensions 4 and 2 |
3937 | TEST_F(OnnxImporterTest, importLogicBcastOr) { |
3938 | llvm::SmallVector<bool, 12> LHS = {true, true, false, false, true, true, |
3939 | false, false, false, false, true, true}; |
3940 | llvm::SmallVector<bool, 6> RHS = {false, true, true, true, true, false}; |
3941 | std::vector<dim_t> LHSShape = {1, 2, 3, 2}; |
3942 | std::vector<dim_t> RHSShape = {3, 2}; |
3943 | std::vector<dim_t> outputShape = {1, 2, 3, 2}; |
3944 | llvm::SmallVector<bool, 12> expectedValues = { |
3945 | true, true, true, true, true, true, false, true, true, true, true, true}; |
3946 | std::string netFilename(GLOW_DATA_PATH |
3947 | "tests/models/onnxModels/logicalOrBcast.onnxtxt" ); |
3948 | importLogical(netFilename, LHS, RHS, LHSShape, RHSShape, outputShape, |
3949 | expectedValues); |
3950 | } |
3951 | |
3952 | /// Test "xor" operation of dimensions 4 |
3953 | TEST_F(OnnxImporterTest, importLogicXor) { |
3954 | llvm::SmallVector<bool, 12> LHS = {true, true, false, false, true, true, |
3955 | false, false, false, false, true, true}; |
3956 | llvm::SmallVector<bool, 12> RHS = {true, true, false, true, false, true, |
3957 | false, true, true, true, true, true}; |
3958 | std::vector<dim_t> LHSShape = {1, 2, 3, 2}; |
3959 | std::vector<dim_t> RHSShape = {1, 2, 3, 2}; |
3960 | std::vector<dim_t> outputShape = {1, 2, 3, 2}; |
3961 | llvm::SmallVector<bool, 12> expectedValues = {false, false, false, true, |
3962 | true, false, false, true, |
3963 | true, true, false, false}; |
3964 | std::string netFilename(GLOW_DATA_PATH |
3965 | "tests/models/onnxModels/logicalXor.onnxtxt" ); |
3966 | importLogical(netFilename, LHS, RHS, LHSShape, RHSShape, outputShape, |
3967 | expectedValues); |
3968 | } |
3969 | |
3970 | /// Test "broadcast xor" of dimensions 4 and 2 |
3971 | TEST_F(OnnxImporterTest, importLogicBcastXor) { |
3972 | llvm::SmallVector<bool, 12> LHS = {true, true, false, false, true, true, |
3973 | false, false, false, false, true, true}; |
3974 | llvm::SmallVector<bool, 6> RHS = {false, true, true, true, true, false}; |
3975 | std::vector<dim_t> LHSShape = {1, 2, 3, 2}; |
3976 | std::vector<dim_t> RHSShape = {3, 2}; |
3977 | std::vector<dim_t> outputShape = {1, 2, 3, 2}; |
3978 | llvm::SmallVector<bool, 12> expectedValues = {true, false, true, true, |
3979 | false, true, false, true, |
3980 | true, true, false, true}; |
3981 | std::string netFilename(GLOW_DATA_PATH |
3982 | "tests/models/onnxModels/logicalXorBcast.onnxtxt" ); |
3983 | importLogical(netFilename, LHS, RHS, LHSShape, RHSShape, outputShape, |
3984 | expectedValues); |
3985 | } |
3986 | |
3987 | /// Test not operation |
3988 | TEST_F(OnnxImporterTest, importNot) { |
3989 | llvm::SmallVector<bool, 12> X = {true, true, false, false, true, true, |
3990 | false, false, false, false, true, true}; |
3991 | std::vector<dim_t> XShape = {1, 2, 3, 2}; |
3992 | std::vector<dim_t> YShape = {1, 2, 3, 2}; |
3993 | llvm::SmallVector<bool, 12> expectedValues = {false, false, true, true, |
3994 | false, false, true, true, |
3995 | true, true, false, false}; |
3996 | std::string netFilename(GLOW_DATA_PATH |
3997 | "tests/models/onnxModels/logicalNot.onnxtxt" ); |
3998 | |
3999 | ExecutionEngine EE{}; |
4000 | auto &mod = EE.getModule(); |
4001 | Function *F = mod.createFunction("main" ); |
4002 | PlaceholderBindings bindings; |
4003 | Placeholder *graphOutputVar; |
4004 | |
4005 | // Load the .onnxtxt model. |
4006 | Type XType(ElemKind::BoolTy, XShape); |
4007 | ONNXModelLoader onnxLD(netFilename, {"X" }, {&XType}, *F); |
4008 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
4009 | auto *XPH = mod.getPlaceholderByNameSlow("X" ); |
4010 | auto *XTensor = bindings.allocate(XPH); |
4011 | XTensor->getHandle<bool>() = X; |
4012 | |
4013 | // Compile and run the graph |
4014 | EE.compile(CompilationMode::Infer); |
4015 | bindings.allocate(mod.getPlaceholders()); |
4016 | EE.run(bindings); |
4017 | |
4018 | // Validate results |
4019 | auto result = bindings.get(graphOutputVar)->getHandle<bool>(); |
4020 | ASSERT_TRUE(result.dims() == (llvm::ArrayRef<dim_t>)YShape); |
4021 | for (size_t i = 0; i < result.getType().size(); i++) { |
4022 | EXPECT_EQ(result.raw(i), (bool)expectedValues[i]); |
4023 | } |
4024 | } |
4025 | |
4026 | /// Test loading NonZero from a ONNX model. |
4027 | static void testNonZero(llvm::StringRef name, |
4028 | const std::vector<dim_t> &expectedDims, |
4029 | const std::vector<int64_t> &expVals) { |
4030 | ExecutionEngine EE{}; |
4031 | auto &mod = EE.getModule(); |
4032 | Function *F = mod.createFunction("main" ); |
4033 | |
4034 | PlaceholderBindings bindings; |
4035 | Placeholder *out = nullptr; |
4036 | |
4037 | std::string netFilename(GLOW_DATA_PATH |
4038 | "tests/models/onnxModels/NonZero.onnxtxt" ); |
4039 | { |
4040 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
4041 | out = EXIT_ON_ERR(onnxLD.getOutputByName(name)); |
4042 | EXPECT_NE(out, nullptr); |
4043 | } |
4044 | |
4045 | // Constant -> NonZero -> PH (x2 for 3 models inside the file) |
4046 | ASSERT_EQ(mod.getPlaceholders().size(), 3); |
4047 | ASSERT_EQ(F->getNodes().size(), 3); |
4048 | |
4049 | EE.compile(CompilationMode::Infer); |
4050 | bindings.allocate(mod.getPlaceholders()); |
4051 | EE.run(bindings); |
4052 | |
4053 | auto result = bindings.get(out)->getHandle<int64_t>(); |
4054 | |
4055 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
4056 | for (size_t i = 0; i < expVals.size(); i++) { |
4057 | EXPECT_EQ(result.raw(i), expVals[i]); |
4058 | } |
4059 | } |
4060 | |
4061 | /// Test loading NonZero using constant int32_t tensor initializer. |
4062 | TEST_F(OnnxImporterTest, importNonZeroI32) { |
4063 | std::vector<int64_t> expVals = { |
4064 | 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, |
4065 | 3, 3, 3, 3, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, |
4066 | 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 0, 1, 2, 0, 0, 1, |
4067 | 2, 0, 1, 1, 0, 1, 1, 2, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
4068 | 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, |
4069 | 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0}; |
4070 | testNonZero("out_i32" , {5, 29}, expVals); |
4071 | } |
4072 | |
4073 | /// Test loading NonZero using constant float tensor initializer. |
4074 | TEST_F(OnnxImporterTest, importNonZeroF) { |
4075 | std::vector<int64_t> expVals = {0, 1, 3, 4, 6, 8, 10, |
4076 | 12, 14, 16, 18, 19, 21, 22}; |
4077 | testNonZero("out_f" , {1, 14}, expVals); |
4078 | } |
4079 | |
4080 | /// Test loading NonZero using constant float tensor initializer. |
4081 | TEST_F(OnnxImporterTest, importNonZeroI64) { |
4082 | std::vector<int64_t> expVals = {0, 1, 3, 4, 6, 8, 10, |
4083 | 12, 14, 16, 18, 19, 21, 22}; |
4084 | testNonZero("out_i64" , {1, 14}, expVals); |
4085 | } |
4086 | |
4087 | /// Test loading NMS using initializer nodes op from an ONNX model. |
4088 | TEST_F(OnnxImporterTest, importNMSInitializer) { |
4089 | ExecutionEngine EE{}; |
4090 | auto &mod = EE.getModule(); |
4091 | Function *F = mod.createFunction("main" ); |
4092 | |
4093 | std::string netFilename(GLOW_DATA_PATH |
4094 | "tests/models/onnxModels/NonMaxSuppression.onnxtxt" ); |
4095 | |
4096 | PlaceholderBindings bindings; |
4097 | Placeholder *output; |
4098 | { |
4099 | Tensor boxes(ElemKind::FloatTy, {8, 4}); |
4100 | boxes.zero(); |
4101 | |
4102 | Tensor scores(ElemKind::FloatTy, {8}); |
4103 | scores.zero(); |
4104 | |
4105 | ONNXModelLoader onnxLD(netFilename, {"boxes" , "scores" }, |
4106 | {&boxes.getType(), &scores.getType()}, *F); |
4107 | output = EXIT_ON_ERR(onnxLD.getOutputByName("indices" )); |
4108 | } |
4109 | |
4110 | auto *save = getSaveNodeFromDest(output); |
4111 | NonMaxSuppressionNode *NMS = |
4112 | llvm::dyn_cast<NonMaxSuppressionNode>(save->getInput().getNode()); |
4113 | ASSERT_TRUE(NMS); |
4114 | EXPECT_EQ(NMS->dims(0)[0], 3); |
4115 | EXPECT_EQ(NMS->getCenterPointBox(), 0); |
4116 | } |
4117 | |
4118 | /// Test loading NMS using optional parameters from an ONNX model. |
4119 | TEST_F(OnnxImporterTest, importNMSInitOptionalParams) { |
4120 | ExecutionEngine EE{}; |
4121 | auto &mod = EE.getModule(); |
4122 | Function *F = mod.createFunction("main" ); |
4123 | |
4124 | std::string netFilename( |
4125 | GLOW_DATA_PATH |
4126 | "tests/models/onnxModels/NonMaxSuppressionOptionalParams.onnxtxt" ); |
4127 | |
4128 | PlaceholderBindings bindings; |
4129 | Placeholder *output; |
4130 | { |
4131 | Tensor boxes(ElemKind::FloatTy, {8, 4}); |
4132 | boxes.zero(); |
4133 | |
4134 | Tensor scores(ElemKind::FloatTy, {8}); |
4135 | scores.zero(); |
4136 | |
4137 | ONNXModelLoader onnxLD(netFilename, {"boxes" , "scores" }, |
4138 | {&boxes.getType(), &scores.getType()}, *F); |
4139 | output = EXIT_ON_ERR(onnxLD.getOutputByName("indices" )); |
4140 | } |
4141 | |
4142 | auto *save = getSaveNodeFromDest(output); |
4143 | NonMaxSuppressionNode *NMS = |
4144 | llvm::dyn_cast<NonMaxSuppressionNode>(save->getInput().getNode()); |
4145 | ASSERT_TRUE(NMS); |
4146 | EXPECT_EQ(NMS->dims(0)[0], 3); |
4147 | EXPECT_EQ(NMS->getCenterPointBox(), 0); |
4148 | EXPECT_EQ(NMS->getMaxOutputBoxesPerClass(), 3); |
4149 | EXPECT_EQ(NMS->getIouThreshold(), 0); |
4150 | EXPECT_EQ(NMS->getScoreThreshold(), 0); |
4151 | } |
4152 | |
4153 | /// Test loading NMS using Constant Tensors op from an ONNX model. |
4154 | TEST_F(OnnxImporterTest, importNMSConstTensor) { |
4155 | ExecutionEngine EE{}; |
4156 | auto &mod = EE.getModule(); |
4157 | Function *F = mod.createFunction("main" ); |
4158 | |
4159 | std::string netFilename( |
4160 | GLOW_DATA_PATH "tests/models/onnxModels/NonMaxSuppressionSSD.onnxtxt" ); |
4161 | |
4162 | PlaceholderBindings bindings; |
4163 | Placeholder *output; |
4164 | { |
4165 | Tensor boxes(ElemKind::FloatTy, {8, 4}); |
4166 | boxes.zero(); |
4167 | |
4168 | Tensor scores(ElemKind::FloatTy, {8}); |
4169 | scores.zero(); |
4170 | |
4171 | ONNXModelLoader onnxLD(netFilename, {"boxes" , "scores" }, |
4172 | {&boxes.getType(), &scores.getType()}, *F); |
4173 | output = EXIT_ON_ERR(onnxLD.getOutputByName("indices" )); |
4174 | } |
4175 | |
4176 | auto *save = getSaveNodeFromDest(output); |
4177 | NonMaxSuppressionNode *NMS = |
4178 | llvm::dyn_cast<NonMaxSuppressionNode>(save->getInput().getNode()); |
4179 | ASSERT_TRUE(NMS); |
4180 | EXPECT_EQ(NMS->dims(0)[0], 3); |
4181 | EXPECT_EQ(NMS->getCenterPointBox(), 1); |
4182 | } |
4183 | |
4184 | /// Test loading ONNX NMS using Constant Tensors op from an ONNX model. |
4185 | TEST_F(OnnxImporterTest, importNMSONNXConstTensor) { |
4186 | ExecutionEngine EE{}; |
4187 | auto &mod = EE.getModule(); |
4188 | Function *F = mod.createFunction("main" ); |
4189 | |
4190 | std::string netFilename( |
4191 | GLOW_DATA_PATH |
4192 | "tests/models/onnxModels/NonMaxSuppressionSSD_ONNX.onnxtxt" ); |
4193 | |
4194 | PlaceholderBindings bindings; |
4195 | Placeholder *output; |
4196 | { |
4197 | Tensor boxes(ElemKind::FloatTy, {1, 8, 4}); |
4198 | boxes.zero(); |
4199 | |
4200 | Tensor scores(ElemKind::FloatTy, {1, 1, 8}); |
4201 | scores.zero(); |
4202 | |
4203 | ONNXModelLoader onnxLD(netFilename, {"boxes" , "scores" }, |
4204 | {&boxes.getType(), &scores.getType()}, *F); |
4205 | output = EXIT_ON_ERR(onnxLD.getOutputByName("indices" )); |
4206 | } |
4207 | |
4208 | auto *save = getSaveNodeFromDest(output); |
4209 | NonMaxSuppressionNode *NMS = |
4210 | llvm::dyn_cast<NonMaxSuppressionNode>(save->getInput().getNode()); |
4211 | ASSERT_TRUE(NMS); |
4212 | EXPECT_EQ(NMS->dims(0)[0], 3); |
4213 | EXPECT_EQ(NMS->dims(0)[1], 3); |
4214 | EXPECT_EQ(NMS->getCenterPointBox(), 1); |
4215 | } |
4216 | |
4217 | /// Test loading and inference of ONNX ROIAlign of onnx example |
4218 | TEST(onnx, ROIAlign_onnx) { |
4219 | ExecutionEngine EE{}; |
4220 | auto &mod = EE.getModule(); |
4221 | Function *F = mod.createFunction("main" ); |
4222 | std::string netFilename(GLOW_DATA_PATH |
4223 | "tests/models/onnxModels/ROIAlign_onnx.onnxtxt" ); |
4224 | PlaceholderBindings bindings; |
4225 | Placeholder *output; |
4226 | Tensor featureMap(ElemKind::FloatTy, {1, 1, 10, 10}); |
4227 | Tensor boxes(ElemKind::FloatTy, {3, 4}); |
4228 | Tensor batchedIndices(ElemKind::Int64ITy, { |
4229 | 3, |
4230 | }); |
4231 | |
4232 | featureMap.getHandle() = { |
4233 | 0.2764, 0.7150, 0.1958, 0.3416, 0.4638, 0.0259, 0.2963, 0.6518, 0.4856, |
4234 | 0.7250, 0.9637, 0.0895, 0.2919, 0.6753, 0.0234, 0.6132, 0.8085, 0.5324, |
4235 | 0.8992, 0.4467, 0.3265, 0.8479, 0.9698, 0.2471, 0.9336, 0.1878, 0.4766, |
4236 | 0.4308, 0.3400, 0.2162, 0.0206, 0.1720, 0.2155, 0.4394, 0.0653, 0.3406, |
4237 | 0.7724, 0.3921, 0.2541, 0.5799, 0.4062, 0.2194, 0.4473, 0.4687, 0.7109, |
4238 | 0.9327, 0.9815, 0.6320, 0.1728, 0.6119, 0.3097, 0.1283, 0.4984, 0.5068, |
4239 | 0.4279, 0.0173, 0.4388, 0.0430, 0.4671, 0.7119, 0.1011, 0.8477, 0.4726, |
4240 | 0.1777, 0.9923, 0.4042, 0.1869, 0.7795, 0.9946, 0.9689, 0.1366, 0.3671, |
4241 | 0.7011, 0.6234, 0.9867, 0.5585, 0.6985, 0.5609, 0.8788, 0.9928, 0.5697, |
4242 | 0.8511, 0.6711, 0.9406, 0.8751, 0.7496, 0.1650, 0.1049, 0.1559, 0.2514, |
4243 | 0.7012, 0.4056, 0.7879, 0.3461, 0.0415, 0.2998, 0.5094, 0.3727, 0.5482, |
4244 | 0.0502}; |
4245 | |
4246 | boxes.getHandle() = {0, 0, 9, 9, 0, 5, 4, 9, 5, 5, 9, 9}; |
4247 | |
4248 | batchedIndices.getHandle<int64_t>() = {0, 0, 0}; |
4249 | std::vector<float> expectedResult = { |
4250 | 0.4664, 0.4466, 0.3405, 0.5688, 0.6068, 0.3714, 0.4296, 0.3835, 0.5562, |
4251 | 0.351, 0.2768, 0.4883, 0.5222, 0.5528, 0.4171, 0.4713, 0.4844, 0.6904, |
4252 | 0.492, 0.8774, 0.6239, 0.7125, 0.6289, 0.3355, 0.3495, |
4253 | |
4254 | 0.3022, 0.4305, 0.4696, 0.3978, 0.5423, 0.3656, 0.705, 0.5165, 0.3172, |
4255 | 0.7015, 0.2912, 0.5059, 0.6476, 0.6235, 0.8299, 0.5916, 0.7389, 0.7048, |
4256 | 0.8372, 0.8893, 0.6227, 0.6153, 0.7097, 0.6154, 0.4585, |
4257 | |
4258 | 0.2384, 0.3379, 0.3717, 0.61, 0.7601, 0.3767, 0.3785, 0.7147, 0.9243, |
4259 | 0.9727, 0.5749, 0.5826, 0.5709, 0.7619, 0.877, 0.5355, 0.2566, 0.2141, |
4260 | 0.2796, 0.36, 0.4365, 0.3504, 0.2887, 0.3661, 0.2349, |
4261 | }; |
4262 | |
4263 | ONNXModelLoader onnxLD( |
4264 | netFilename, {"featureMap" , "boxes" , "batchIndices" }, |
4265 | {&featureMap.getType(), &boxes.getType(), &batchedIndices.getType()}, *F); |
4266 | |
4267 | bindings.allocate(mod.getPlaceholders()); |
4268 | updateInputPlaceholdersByName(bindings, &mod, |
4269 | {"featureMap" , "boxes" , "batchIndices" }, |
4270 | {&featureMap, &boxes, &batchedIndices}); |
4271 | output = EXIT_ON_ERR(onnxLD.getOutputByName("result" )); |
4272 | EE.compile(CompilationMode::Infer); |
4273 | EE.run(bindings); |
4274 | auto resultH = bindings.get(output)->getHandle<float>(); |
4275 | std::vector<dim_t> outputShape = {3, 1, 5, 5}; |
4276 | float delta = 1e-03; |
4277 | ASSERT_TRUE(resultH.dims() == (llvm::ArrayRef<dim_t>)outputShape); |
4278 | for (size_t i = 0; i < resultH.getType().size(); i++) { |
4279 | EXPECT_NEAR(resultH.raw(i), expectedResult[i], delta); |
4280 | } |
4281 | } |
4282 | |
4283 | /// Test loading and inference of ONNX MatMul operator with |
4284 | /// 4D inputs. |
4285 | TEST(onnx, MatMul4D) { |
4286 | ExecutionEngine EE{}; |
4287 | auto &mod = EE.getModule(); |
4288 | Function *F = mod.createFunction("main" ); |
4289 | std::string netFilename(GLOW_DATA_PATH |
4290 | "tests/models/onnxModels/MatMul4D.onnxtxt" ); |
4291 | PlaceholderBindings bindings; |
4292 | Placeholder *output; |
4293 | Placeholder *refOutput; |
4294 | |
4295 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
4296 | output = EXIT_ON_ERR(onnxLD.getOutputByName("Y" )); |
4297 | refOutput = EXIT_ON_ERR(onnxLD.getOutputByName("Yref" )); |
4298 | |
4299 | EE.compile(CompilationMode::Infer); |
4300 | bindings.allocate(mod.getPlaceholders()); |
4301 | EE.run(bindings); |
4302 | auto resultH = bindings.get(output)->getHandle(); |
4303 | auto refYH = bindings.get(refOutput)->getHandle(); |
4304 | std::vector<dim_t> outputShape = {1, 2, 3, 3}; |
4305 | float delta = 1e-03; |
4306 | ASSERT_TRUE(resultH.dims() == (llvm::ArrayRef<dim_t>)outputShape); |
4307 | for (size_t i = 0; i < resultH.getType().size(); i++) { |
4308 | EXPECT_NEAR(resultH.raw(i), refYH.raw(i), delta); |
4309 | } |
4310 | } |
4311 | |
4312 | TEST_F(OnnxImporterTest, importDimParamExplicit) { |
4313 | ExecutionEngine EE; |
4314 | auto &mod = EE.getModule(); |
4315 | std::string netFilename(GLOW_DATA_PATH |
4316 | "tests/models/onnxModels/dimParam.onnxtxt" ); |
4317 | auto *F = mod.createFunction("main" ); |
4318 | |
4319 | // Import ONNX model with explicit input information. |
4320 | { |
4321 | Tensor inputTensor(ElemKind::FloatTy, {1, 2}); |
4322 | setOnnxDefineSymbol({"ONNXUndefinedSymbol,1" }); |
4323 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&inputTensor.getType()}, |
4324 | *F); |
4325 | setOnnxDefineSymbol({}); |
4326 | } |
4327 | |
4328 | // Validate placeholder sizes. |
4329 | Placeholder *inputPH, *outputPH; |
4330 | inputPH = mod.getPlaceholderByNameSlow("input" ); |
4331 | outputPH = mod.getPlaceholderByNameSlow("output" ); |
4332 | EXPECT_TRUE(inputPH); |
4333 | EXPECT_TRUE(outputPH); |
4334 | EXPECT_EQ(inputPH->dims()[0], 1); |
4335 | EXPECT_EQ(inputPH->dims()[1], 2); |
4336 | EXPECT_EQ(outputPH->dims()[0], 1); |
4337 | EXPECT_EQ(outputPH->dims()[1], 2); |
4338 | } |
4339 | |
4340 | TEST_F(OnnxImporterTest, importDimParamImplicit) { |
4341 | ExecutionEngine EE; |
4342 | auto &mod = EE.getModule(); |
4343 | std::string netFilename(GLOW_DATA_PATH |
4344 | "tests/models/onnxModels/dimParam.onnxtxt" ); |
4345 | auto *F = mod.createFunction("main" ); |
4346 | |
4347 | // Import ONNX model with implicit input information. |
4348 | { |
4349 | setOnnxDefineSymbol({"ONNXUndefinedSymbol,1" }); |
4350 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
4351 | setOnnxDefineSymbol({}); |
4352 | } |
4353 | |
4354 | // Validate placeholder sizes. |
4355 | Placeholder *inputPH, *outputPH; |
4356 | inputPH = mod.getPlaceholderByNameSlow("input" ); |
4357 | outputPH = mod.getPlaceholderByNameSlow("output" ); |
4358 | EXPECT_TRUE(inputPH); |
4359 | EXPECT_TRUE(outputPH); |
4360 | EXPECT_EQ(inputPH->dims()[0], 1); |
4361 | EXPECT_EQ(inputPH->dims()[1], 2); |
4362 | EXPECT_EQ(outputPH->dims()[0], 1); |
4363 | EXPECT_EQ(outputPH->dims()[1], 2); |
4364 | } |
4365 | |
4366 | static void importUnary(const std::string &netFilename, |
4367 | llvm::ArrayRef<float> input, |
4368 | llvm::ArrayRef<dim_t> inputShape, |
4369 | llvm::ArrayRef<dim_t> outputShape, |
4370 | llvm::ArrayRef<float> expectedValues) { |
4371 | |
4372 | float delta = 1e-08; |
4373 | ExecutionEngine EE{}; |
4374 | auto &mod = EE.getModule(); |
4375 | Function *F = mod.createFunction("main" ); |
4376 | PlaceholderBindings bindings; |
4377 | Placeholder *graphOutputVar; |
4378 | // Load the .onnxtxt model |
4379 | Type inputType(ElemKind::FloatTy, inputShape); |
4380 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&inputType}, *F); |
4381 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
4382 | auto inputPH = mod.getPlaceholderByNameSlow("input" ); |
4383 | auto *inputTensor = bindings.allocate(inputPH); |
4384 | inputTensor->getHandle<float>() = input; |
4385 | EE.compile(CompilationMode::Infer); |
4386 | bindings.allocate(mod.getPlaceholders()); |
4387 | EE.run(bindings); |
4388 | auto result = bindings.get(graphOutputVar)->getHandle<float>(); |
4389 | ASSERT_TRUE(result.dims() == (llvm::ArrayRef<dim_t>)outputShape); |
4390 | for (size_t i = 0; i < result.getType().size(); i++) { |
4391 | EXPECT_NEAR(result.raw(i), (float)expectedValues[i], delta); |
4392 | } |
4393 | } |
4394 | |
4395 | TEST(onnx, importSign) { |
4396 | std::vector<float> input = {-1, -2, 0, -2, 1, 2, 1, 2, -10, 0, 0, -2}; |
4397 | std::vector<dim_t> inputShape = {1, 2, 3, 2}; |
4398 | std::vector<dim_t> outputShape = {1, 2, 3, 2}; |
4399 | std::vector<float> expectedValues = {-1, -1, 0, -1, 1, 1, 1, 1, -1, 0, 0, -1}; |
4400 | std::string netFilename(GLOW_DATA_PATH |
4401 | "tests/models/onnxModels/sign.onnxtxt" ); |
4402 | importUnary(netFilename, input, inputShape, outputShape, expectedValues); |
4403 | } |
4404 | |
4405 | static void |
4406 | testLoop(std::string &filename, const std::vector<dim_t> &expected_v_finalDims, |
4407 | const std::vector<dim_t> &expected_scan_output_finalDims, |
4408 | const std::vector<float> &expected_v_finalValues, |
4409 | const std::vector<float> &expectedscan_output_finalValues) { |
4410 | ExecutionEngine EE; |
4411 | auto &mod = EE.getModule(); |
4412 | auto *F = mod.createFunction("main" ); |
4413 | |
4414 | std::string netFilename = |
4415 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + filename; |
4416 | |
4417 | PlaceholderBindings bindings; |
4418 | Placeholder *v_final; |
4419 | Placeholder *scan_output_final; |
4420 | |
4421 | Tensor init_i(ElemKind::FloatTy, {1}); |
4422 | init_i.getHandle() = {0}; |
4423 | Tensor inc(ElemKind::FloatTy, {1}); |
4424 | inc.getHandle() = {1}; |
4425 | |
4426 | { |
4427 | ONNXModelLoader onnxLD(netFilename, {"init_i" , "inc" }, |
4428 | {&init_i.getType(), &inc.getType()}, *F); |
4429 | |
4430 | v_final = EXIT_ON_ERR(onnxLD.getOutputByName("v_final" )); |
4431 | scan_output_final = |
4432 | EXIT_ON_ERR(onnxLD.getOutputByName("scan_output_final" )); |
4433 | |
4434 | bindings.allocate(mod.getPlaceholders()); |
4435 | updateInputPlaceholdersByName(bindings, &mod, {"init_i" , "inc" }, |
4436 | {&init_i, &inc}); |
4437 | } |
4438 | |
4439 | auto *v_finalT = bindings.get(v_final); |
4440 | auto *scan_output_finalT = bindings.get(scan_output_final); |
4441 | |
4442 | EE.compile(CompilationMode::Infer); |
4443 | EE.run(bindings); |
4444 | |
4445 | auto v_finalH = v_finalT->getHandle(); |
4446 | auto scan_output_finalH = scan_output_finalT->getHandle(); |
4447 | |
4448 | EXPECT_EQ(v_finalH.dims().vec(), expected_v_finalDims); |
4449 | EXPECT_EQ(scan_output_finalH.dims().vec(), expected_scan_output_finalDims); |
4450 | for (size_t i = 0; i < expected_v_finalValues.size(); i++) { |
4451 | EXPECT_FLOAT_EQ(v_finalH.raw(i), expected_v_finalValues[i]); |
4452 | } |
4453 | for (size_t i = 0; i < expectedscan_output_finalValues.size(); i++) { |
4454 | EXPECT_FLOAT_EQ(scan_output_finalH.raw(i), |
4455 | expectedscan_output_finalValues[i]); |
4456 | } |
4457 | } |
4458 | |
4459 | TEST_F(OnnxImporterTest, importLoopStatic) { |
4460 | // In this loop, cond is not changed in the loop body. |
4461 | // |
4462 | // input (trip_count, cond) |
4463 | // |
4464 | // int max_trip_count = 10; |
4465 | // cond = true; |
4466 | // init_i = 0; |
4467 | // for (i=0; i< max_trip_count && cond; ++i){ |
4468 | // scan_output[i] = init_i; |
4469 | // inti_i = init_i + inc; |
4470 | // } |
4471 | std::string filename("loop_static.onnxtxt" ); |
4472 | std::vector<dim_t> expected_v_finalDims = {1}; |
4473 | std::vector<dim_t> expected_scan_output_finalDims = {10, 1}; |
4474 | std::vector<float> expected_v_finalValues = {10.}; |
4475 | std::vector<float> expectedscan_output_finalValues = {0., 1., 2., 3., 4., |
4476 | 5., 6., 7., 8., 9.}; |
4477 | testLoop(filename, expected_v_finalDims, expected_scan_output_finalDims, |
4478 | expected_v_finalValues, expectedscan_output_finalValues); |
4479 | } |
4480 | |
4481 | TEST_F(OnnxImporterTest, importLoopNoIteration) { |
4482 | // The loop should be zero iteration. |
4483 | // |
4484 | // input (trip_count, 0) |
4485 | // |
4486 | // int max_trip_count = 10; |
4487 | // cond = false; |
4488 | // init_i = 0; |
4489 | // for (i=0; i < max_trip_count && cond; ++i) { |
4490 | // scan_output[i] = init_i; |
4491 | // inti_i = init_i + inc; |
4492 | // } |
4493 | std::string filename("loop_no_iteration.onnxtxt" ); |
4494 | std::vector<dim_t> expected_v_finalDims = {1}; |
4495 | std::vector<dim_t> expected_scan_output_finalDims = {1, 1}; |
4496 | std::vector<float> expected_v_finalValues = {0.}; |
4497 | std::vector<float> expectedscan_output_finalValues = {0.}; |
4498 | testLoop(filename, expected_v_finalDims, expected_scan_output_finalDims, |
4499 | expected_v_finalValues, expectedscan_output_finalValues); |
4500 | } |
4501 | |
4502 | TEST(onnx, importLoopCond) { |
4503 | // In this loop, cond is updated in the loop body, but it should be folded |
4504 | // into a Constant during loading time. |
4505 | // The loop should exit by cond. |
4506 | // |
4507 | // input(trip_count, cond) : |
4508 | // |
4509 | // int max_trip_count = 9223372036854775807; |
4510 | // int reduce_i = 20; |
4511 | // for (i=0; i < max_trip_count && cond; ++i) { |
4512 | // scan_output[i] = reduce_i; |
4513 | // reduce_i = reduce_i - 1; |
4514 | // cond = (bool)(reduce_i - 1); |
4515 | // } |
4516 | std::string filename("loop_cond.onnxtxt" ); |
4517 | std::vector<dim_t> expected_v_finalDims = {1}; |
4518 | std::vector<dim_t> expected_scan_output_finalDims = {20, 1}; |
4519 | std::vector<float> expected_v_finalValues = {0.}; |
4520 | std::vector<float> expectedscan_output_finalValues = { |
4521 | 20., 19., 18., 17., 16., 15., 14., 13., 12., 11., |
4522 | 10., 9., 8., 7., 6., 5., 4., 3., 2., 1.}; |
4523 | testLoop(filename, expected_v_finalDims, expected_scan_output_finalDims, |
4524 | expected_v_finalValues, expectedscan_output_finalValues); |
4525 | } |
4526 | |
4527 | TEST(onnx, importLoopTripCount) { |
4528 | // The loop should exit by trip_count. |
4529 | // |
4530 | // input(trip_count, cond) : |
4531 | // |
4532 | // int max_trip_count = 20; |
4533 | // int reduce_i = 20; |
4534 | // for (i=0; i < max_trip_count && cond; ++i) { |
4535 | // scan_output[i] = reduce_i; |
4536 | // reduce_i = reduce_i - 1; |
4537 | // cond = (bool)(reduce_i - 1); |
4538 | // } |
4539 | std::string filename("loop_tripcount.onnxtxt" ); |
4540 | std::vector<dim_t> expected_v_finalDims = {1}; |
4541 | std::vector<dim_t> expected_scan_output_finalDims = {20, 1}; |
4542 | std::vector<float> expected_v_finalValues = {0.0}; |
4543 | std::vector<float> expectedscan_output_finalValues = { |
4544 | 20., 19., 18., 17., 16., 15., 14., 13., 12., 11., |
4545 | 10., 9., 8., 7., 6., 5., 4., 3., 2., 1.}; |
4546 | testLoop(filename, expected_v_finalDims, expected_scan_output_finalDims, |
4547 | expected_v_finalValues, expectedscan_output_finalValues); |
4548 | } |
4549 | |
4550 | TEST(onnx, importLoopEmptyTripCount) { |
4551 | // The loop should ignore trip-count, so exit by cond. |
4552 | // |
4553 | // input ("", 1) |
4554 | // |
4555 | // int reduce_i = 10; |
4556 | // bool cond = true; |
4557 | // for (int i = 0; cond; ++i) { |
4558 | // scan_output[i] = reduce_i; |
4559 | // reduce_i = reduce_i - 1; |
4560 | // cond = (bool)reduce_i; |
4561 | // } |
4562 | std::string filename("loop_empty_tripcount.onnxtxt" ); |
4563 | std::vector<dim_t> expected_v_finalDims = {1}; |
4564 | std::vector<dim_t> expected_scan_output_finalDims = {10, 1}; |
4565 | std::vector<float> expected_v_finalValues = {0.}; |
4566 | std::vector<float> expectedscan_output_finalValues = {10., 9., 8., 7., 6., |
4567 | 5., 4., 3., 2., 1.}; |
4568 | testLoop(filename, expected_v_finalDims, expected_scan_output_finalDims, |
4569 | expected_v_finalValues, expectedscan_output_finalValues); |
4570 | } |
4571 | |
4572 | TEST(onnx, importLoopEmptyCond) { |
4573 | // The loop should ignore cond, so exit by trip_count. |
4574 | // |
4575 | // input(trip_count, "") : |
4576 | // |
4577 | // int max_trip_count = 7; |
4578 | // int reduce_i = 5; |
4579 | // for (i=0; i < max_trip_count; ++i) { |
4580 | // scan_output[i] = reduce_i; |
4581 | // reduce_i = reduce_i - 1; |
4582 | // cond = (bool)(reduce_i - 1); // ignored |
4583 | // } |
4584 | std::string filename("loop_emptycond.onnxtxt" ); |
4585 | std::vector<dim_t> expected_v_finalDims = {1}; |
4586 | std::vector<dim_t> expected_scan_output_finalDims = {7, 1}; |
4587 | std::vector<float> expected_v_finalValues = {-2.0}; |
4588 | std::vector<float> expectedscan_output_finalValues = {5., 4., 3., 2., |
4589 | 1., 0., -1.}; |
4590 | testLoop(filename, expected_v_finalDims, expected_scan_output_finalDims, |
4591 | expected_v_finalValues, expectedscan_output_finalValues); |
4592 | } |
4593 | |
4594 | TEST(onnx, importLoopWithoutN) { |
4595 | // The loop should exit by trip_count. |
4596 | // |
4597 | // input(trip_count, cond) : |
4598 | // bool cond = true; |
4599 | // int max_trip_count = 10; |
4600 | // for (i=0; i < max_trip_count && cond; ++i) { |
4601 | // scan_output[i] = i; |
4602 | // } |
4603 | std::string filename("loop_withoutN.onnxtxt" ); |
4604 | std::vector<dim_t> expected_v_finalDims = {1}; |
4605 | std::vector<dim_t> expected_scan_output_finalDims = {10, 1}; |
4606 | std::vector<float> expected_v_finalValues = {0.0}; |
4607 | std::vector<float> expectedscan_output_finalValues = {0., 1., 2., 3., 4., |
4608 | 5., 6., 7., 8., 9.}; |
4609 | testLoop(filename, expected_v_finalDims, expected_scan_output_finalDims, |
4610 | expected_v_finalValues, expectedscan_output_finalValues); |
4611 | } |
4612 | |
4613 | /// Test loading RNN from a ONNX model. The ONNX model already computes |
4614 | /// the error compared to a PyTorch reference implementation. |
4615 | static void importRNN(std::string fileName) { |
4616 | ExecutionEngine EE; |
4617 | auto &mod = EE.getModule(); |
4618 | Function *F = mod.createFunction("main" ); |
4619 | |
4620 | PlaceholderBindings bindings; |
4621 | { |
4622 | ONNXModelLoader onnxLD(fileName, {}, {}, *F); |
4623 | bindings.allocate(mod.getPlaceholders()); |
4624 | } |
4625 | |
4626 | // Compile and run. |
4627 | EE.compile(CompilationMode::Infer); |
4628 | EE.run(bindings); |
4629 | |
4630 | // Verify RNN error. |
4631 | Placeholder *Y_err_ph = mod.getPlaceholderByNameSlow("Y_err" ); |
4632 | EXPECT_TRUE(Y_err_ph); |
4633 | auto err = bindings.get(Y_err_ph)->getHandle(); |
4634 | for (size_t idx = 0; idx < Y_err_ph->getType()->size(); idx++) { |
4635 | EXPECT_TRUE(std::abs(err.raw(idx)) < 1e-6); |
4636 | } |
4637 | } |
4638 | |
4639 | TEST_F(OnnxImporterTest, importRNNForward) { |
4640 | importRNN(GLOW_DATA_PATH "tests/models/onnxModels/rnnForward.onnxtxt" ); |
4641 | } |
4642 | |
4643 | TEST_F(OnnxImporterTest, importRNNReverse) { |
4644 | importRNN(GLOW_DATA_PATH "tests/models/onnxModels/rnnReverse.onnxtxt" ); |
4645 | } |
4646 | |
4647 | TEST_F(OnnxImporterTest, importRNNBidirectional) { |
4648 | importRNN(GLOW_DATA_PATH "tests/models/onnxModels/rnnBidirectional.onnxtxt" ); |
4649 | } |
4650 | |
4651 | TEST_F(OnnxImporterTest, importRNNForwardNoBias) { |
4652 | importRNN(GLOW_DATA_PATH "tests/models/onnxModels/rnnForwardNoBias.onnxtxt" ); |
4653 | } |
4654 | |
4655 | TEST_F(OnnxImporterTest, importRNNForwardNoState) { |
4656 | importRNN(GLOW_DATA_PATH "tests/models/onnxModels/rnnForwardNoState.onnxtxt" ); |
4657 | } |
4658 | |
4659 | /// Test loading GRU from a ONNX model. The ONNX model already computes |
4660 | /// the error compared to a PyTorch reference implementation. |
4661 | static void importGRU(std::string fileName) { |
4662 | ExecutionEngine EE; |
4663 | auto &mod = EE.getModule(); |
4664 | Function *F = mod.createFunction("main" ); |
4665 | |
4666 | PlaceholderBindings bindings; |
4667 | { |
4668 | ONNXModelLoader onnxLD(fileName, {}, {}, *F); |
4669 | bindings.allocate(mod.getPlaceholders()); |
4670 | } |
4671 | |
4672 | // Compile and run. |
4673 | EE.compile(CompilationMode::Infer); |
4674 | EE.run(bindings); |
4675 | |
4676 | // Verify GRU error. |
4677 | Placeholder *Y_err_ph = mod.getPlaceholderByNameSlow("Y_err" ); |
4678 | EXPECT_TRUE(Y_err_ph); |
4679 | auto err = bindings.get(Y_err_ph)->getHandle(); |
4680 | for (size_t idx = 0; idx < Y_err_ph->getType()->size(); idx++) { |
4681 | EXPECT_TRUE(std::abs(err.raw(idx)) < 1e-6); |
4682 | } |
4683 | } |
4684 | |
4685 | TEST_F(OnnxImporterTest, importGRUForward) { |
4686 | importGRU(GLOW_DATA_PATH "tests/models/onnxModels/gruForward.onnxtxt" ); |
4687 | } |
4688 | |
4689 | TEST_F(OnnxImporterTest, importGRUReverse) { |
4690 | importGRU(GLOW_DATA_PATH "tests/models/onnxModels/gruReverse.onnxtxt" ); |
4691 | } |
4692 | |
4693 | TEST_F(OnnxImporterTest, importGRUBidirectional) { |
4694 | importGRU(GLOW_DATA_PATH "tests/models/onnxModels/gruBidirectional.onnxtxt" ); |
4695 | } |
4696 | |
4697 | TEST_F(OnnxImporterTest, importGRUForwardNoBias) { |
4698 | importGRU(GLOW_DATA_PATH "tests/models/onnxModels/gruForwardNoBias.onnxtxt" ); |
4699 | } |
4700 | |
4701 | TEST_F(OnnxImporterTest, importGRUForwardNoState) { |
4702 | importGRU(GLOW_DATA_PATH "tests/models/onnxModels/gruForwardNoState.onnxtxt" ); |
4703 | } |
4704 | |
4705 | TEST_F(OnnxImporterTest, importGRUForwardLinearBeforeReset) { |
4706 | importGRU(GLOW_DATA_PATH |
4707 | "tests/models/onnxModels/gruForwardLinearBeforeReset.onnxtxt" ); |
4708 | } |
4709 | |
4710 | /// Test loading LSTM from a ONNX model. The ONNX model already computes |
4711 | /// the error compared to a PyTorch reference implementation. |
4712 | static void importLSTM(std::string fileName) { |
4713 | ExecutionEngine EE; |
4714 | auto &mod = EE.getModule(); |
4715 | Function *F = mod.createFunction("main" ); |
4716 | |
4717 | PlaceholderBindings bindings; |
4718 | { |
4719 | ONNXModelLoader onnxLD(fileName, {}, {}, *F); |
4720 | bindings.allocate(mod.getPlaceholders()); |
4721 | } |
4722 | |
4723 | // Compile and run. |
4724 | EE.compile(CompilationMode::Infer); |
4725 | EE.run(bindings); |
4726 | |
4727 | // Verify LSTM error. |
4728 | Placeholder *Y_err_ph = mod.getPlaceholderByNameSlow("Y_err" ); |
4729 | EXPECT_TRUE(Y_err_ph); |
4730 | auto err = bindings.get(Y_err_ph)->getHandle(); |
4731 | for (size_t idx = 0; idx < Y_err_ph->getType()->size(); idx++) { |
4732 | EXPECT_TRUE(std::abs(err.raw(idx)) < 1e-6); |
4733 | } |
4734 | } |
4735 | |
4736 | TEST_F(OnnxImporterTest, importLSTMForward) { |
4737 | importLSTM(GLOW_DATA_PATH "tests/models/onnxModels/lstmForward.onnxtxt" ); |
4738 | } |
4739 | |
4740 | TEST_F(OnnxImporterTest, importLSTMReverse) { |
4741 | importLSTM(GLOW_DATA_PATH "tests/models/onnxModels/lstmReverse.onnxtxt" ); |
4742 | } |
4743 | |
4744 | TEST_F(OnnxImporterTest, importLSTMBidirectional) { |
4745 | importLSTM(GLOW_DATA_PATH |
4746 | "tests/models/onnxModels/lstmBidirectional.onnxtxt" ); |
4747 | } |
4748 | |
4749 | TEST_F(OnnxImporterTest, importLSTMForwardNoBias) { |
4750 | importLSTM(GLOW_DATA_PATH |
4751 | "tests/models/onnxModels/lstmForwardNoBias.onnxtxt" ); |
4752 | } |
4753 | |
4754 | TEST_F(OnnxImporterTest, importLSTMForwardNoState) { |
4755 | importLSTM(GLOW_DATA_PATH |
4756 | "tests/models/onnxModels/lstmForwardNoState.onnxtxt" ); |
4757 | } |
4758 | |
4759 | TEST_F(OnnxImporterTest, importLSTMForwardWithPeephole) { |
4760 | importLSTM(GLOW_DATA_PATH |
4761 | "tests/models/onnxModels/lstmForwardWithPeephole.onnxtxt" ); |
4762 | } |
4763 | |
4764 | TEST_F(OnnxImporterTest, importLSTMForwardInputForget) { |
4765 | importLSTM(GLOW_DATA_PATH |
4766 | "tests/models/onnxModels/lstmForwardInputForget.onnxtxt" ); |
4767 | } |
4768 | |
4769 | /// Test loading Flip from a ONNX model. The ONNX model already computes |
4770 | /// the error. |
4771 | static void importFlip(std::string fileName) { |
4772 | ExecutionEngine EE; |
4773 | auto &mod = EE.getModule(); |
4774 | Function *F = mod.createFunction("main" ); |
4775 | |
4776 | PlaceholderBindings bindings; |
4777 | { |
4778 | ONNXModelLoader onnxLD(fileName, {}, {}, *F); |
4779 | bindings.allocate(mod.getPlaceholders()); |
4780 | } |
4781 | |
4782 | // Compile and run. |
4783 | EE.compile(CompilationMode::Infer); |
4784 | EE.run(bindings); |
4785 | |
4786 | // Verify error. |
4787 | Placeholder *Y_err_ph = mod.getPlaceholderByNameSlow("Y_err" ); |
4788 | EXPECT_TRUE(Y_err_ph); |
4789 | auto err = bindings.get(Y_err_ph)->getHandle(); |
4790 | for (size_t idx = 0; idx < Y_err_ph->getType()->size(); idx++) { |
4791 | EXPECT_EQ(err.raw(idx), 0); |
4792 | } |
4793 | } |
4794 | |
4795 | TEST_F(OnnxImporterTest, importFlipWithAxis) { |
4796 | importFlip(GLOW_DATA_PATH "tests/models/onnxModels/flipWithAxis.onnxtxt" ); |
4797 | } |
4798 | |
4799 | TEST_F(OnnxImporterTest, importFlipNoAxis) { |
4800 | importFlip(GLOW_DATA_PATH "tests/models/onnxModels/flipNoAxis.onnxtxt" ); |
4801 | } |
4802 | |
4803 | /// Test loading FRWQSparseLengthsWeightedSum from an ONNX model. |
4804 | TEST_F(OnnxImporterTest, importFRWQSLWS) { |
4805 | ExecutionEngine EE; |
4806 | auto &mod = EE.getModule(); |
4807 | auto *F = mod.createFunction("main" ); |
4808 | std::string netFilename(GLOW_DATA_PATH |
4809 | "tests/models/onnxModels/fusedSLWS.onnxtxt" ); |
4810 | Placeholder *output; |
4811 | { |
4812 | Tensor weights(ElemKind::FloatTy, {8}); |
4813 | Tensor indices(ElemKind::Int64ITy, {8}); |
4814 | Tensor lengths(ElemKind::Int32ITy, {5}); |
4815 | ONNXModelLoader onnxLD( |
4816 | netFilename, {"weights" , "indices" , "lengths" }, |
4817 | {&weights.getType(), &indices.getType(), &lengths.getType()}, *F); |
4818 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
4819 | } |
4820 | |
4821 | // Verify structure: {Constant, PH, PH, PH} -> FRWQSLWS -> Save -> PH. |
4822 | EXPECT_EQ(mod.getPlaceholders().size(), 4); |
4823 | // FRWQSLWS, Save nodes |
4824 | EXPECT_EQ(F->getNodes().size(), 2); |
4825 | auto *save = getSaveNodeFromDest(output); |
4826 | auto *FRWQSLWS = |
4827 | llvm::dyn_cast<FusedRowwiseQuantizedSparseLengthsWeightedSumNode>( |
4828 | save->getInput().getNode()); |
4829 | ASSERT_TRUE(FRWQSLWS); |
4830 | auto *data = llvm::dyn_cast<Constant>(FRWQSLWS->getData()); |
4831 | ASSERT_TRUE(data); |
4832 | EXPECT_EQ(data->dims().vec(), std::vector<dim_t>({3, 10})); |
4833 | EXPECT_EQ(data->getType()->getElementType(), ElemKind::UInt8FusedQTy); |
4834 | auto *weights = llvm::dyn_cast<Placeholder>(FRWQSLWS->getWeights()); |
4835 | ASSERT_TRUE(weights); |
4836 | EXPECT_EQ(weights->dims().vec(), std::vector<dim_t>({8})); |
4837 | EXPECT_EQ(weights->getType()->getElementType(), ElemKind::FloatTy); |
4838 | auto *indices = llvm::dyn_cast<Placeholder>(FRWQSLWS->getIndices()); |
4839 | ASSERT_TRUE(indices); |
4840 | EXPECT_EQ(indices->dims().vec(), std::vector<dim_t>({8})); |
4841 | EXPECT_EQ(indices->getType()->getElementType(), ElemKind::Int64ITy); |
4842 | auto *lengths = llvm::dyn_cast<Placeholder>(FRWQSLWS->getLengths()); |
4843 | ASSERT_TRUE(lengths); |
4844 | EXPECT_EQ(lengths->dims().vec(), std::vector<dim_t>({5})); |
4845 | EXPECT_EQ(lengths->getType()->getElementType(), ElemKind::Int32ITy); |
4846 | } |
4847 | |
4848 | /// Test loading AudioSpectrogram from an ONNX model. The ONNX model already |
4849 | /// computes the error compared to a TensorFlow reference implementation. |
4850 | static void importAudioSpectrogram(std::string fileName) { |
4851 | ExecutionEngine EE; |
4852 | auto &mod = EE.getModule(); |
4853 | Function *F = mod.createFunction("main" ); |
4854 | |
4855 | PlaceholderBindings bindings; |
4856 | { |
4857 | ONNXModelLoader onnxLD(fileName, {}, {}, *F); |
4858 | bindings.allocate(mod.getPlaceholders()); |
4859 | } |
4860 | |
4861 | // Compile and run. |
4862 | EE.compile(CompilationMode::Infer); |
4863 | EE.run(bindings); |
4864 | |
4865 | // Verify error. |
4866 | Placeholder *errPH = mod.getPlaceholderByNameSlow("spectrogram_err" ); |
4867 | EXPECT_TRUE(errPH); |
4868 | auto errH = bindings.get(errPH)->getHandle(); |
4869 | auto fftLen = (errPH->getType()->dims()[1] - 1) * 2; |
4870 | for (size_t idx = 0; idx < errPH->getType()->size(); idx++) { |
4871 | float errVal = std::abs(errH.raw(idx)) / (float)(fftLen); |
4872 | EXPECT_TRUE(errVal < 1e-5); |
4873 | } |
4874 | } |
4875 | |
4876 | TEST_F(OnnxImporterTest, importAudioSpectrogramOneWindow) { |
4877 | importAudioSpectrogram( |
4878 | GLOW_DATA_PATH |
4879 | "tests/models/onnxModels/audioSpectrogramOneWindow.onnxtxt" ); |
4880 | } |
4881 | |
4882 | TEST_F(OnnxImporterTest, importAudioSpectrogramTwoWindow) { |
4883 | importAudioSpectrogram( |
4884 | GLOW_DATA_PATH |
4885 | "tests/models/onnxModels/audioSpectrogramTwoWindow.onnxtxt" ); |
4886 | } |
4887 | |
4888 | TEST_F(OnnxImporterTest, importAudioSpectrogramNonSquared) { |
4889 | importAudioSpectrogram( |
4890 | GLOW_DATA_PATH |
4891 | "tests/models/onnxModels/audioSpectrogramNonSquared.onnxtxt" ); |
4892 | } |
4893 | |
4894 | /// Test loading MFCC from an ONNX model. The ONNX model already computes |
4895 | /// the error compared to a TensorFlow reference implementation. |
4896 | static void importMFCC(std::string fileName) { |
4897 | ExecutionEngine EE; |
4898 | auto &mod = EE.getModule(); |
4899 | Function *F = mod.createFunction("main" ); |
4900 | |
4901 | PlaceholderBindings bindings; |
4902 | { |
4903 | ONNXModelLoader onnxLD(fileName, {}, {}, *F); |
4904 | bindings.allocate(mod.getPlaceholders()); |
4905 | } |
4906 | |
4907 | // Compile and run. |
4908 | EE.compile(CompilationMode::Infer); |
4909 | EE.run(bindings); |
4910 | |
4911 | // Verify error. |
4912 | Placeholder *errPH = mod.getPlaceholderByNameSlow("coefficients_err" ); |
4913 | EXPECT_TRUE(errPH); |
4914 | auto errH = bindings.get(errPH)->getHandle(); |
4915 | for (size_t idx = 0; idx < errPH->getType()->size(); idx++) { |
4916 | EXPECT_TRUE(std::abs(errH.raw(idx)) < 1e-5); |
4917 | } |
4918 | } |
4919 | |
4920 | TEST_F(OnnxImporterTest, importMFCCOneWindow) { |
4921 | importMFCC(GLOW_DATA_PATH "tests/models/onnxModels/mfccOneWindow.onnxtxt" ); |
4922 | } |
4923 | |
4924 | TEST_F(OnnxImporterTest, importMFCCTwoWindow) { |
4925 | importMFCC(GLOW_DATA_PATH "tests/models/onnxModels/mfccTwoWindow.onnxtxt" ); |
4926 | } |
4927 | |
4928 | /// Test loading a custom ONNX Glow quantized TopK. |
4929 | TEST_F(OnnxImporterTest, CustomGlowTopKQuantized) { |
4930 | ExecutionEngine EE; |
4931 | auto &mod = EE.getModule(); |
4932 | auto *F = mod.createFunction("main" ); |
4933 | std::string netFilename( |
4934 | GLOW_DATA_PATH |
4935 | "tests/models/onnxModels/glow_custom_op_topk_quantized.onnxtxt" ); |
4936 | Placeholder *valuesPH, *indicesPH; |
4937 | { |
4938 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
4939 | valuesPH = EXIT_ON_ERR(onnxLD.getOutputByName("save_values" )); |
4940 | indicesPH = EXIT_ON_ERR(onnxLD.getOutputByName("save_indices" )); |
4941 | } |
4942 | |
4943 | // Verify structure: PH -> TopK -> Save -> PH. |
4944 | // | |
4945 | // v |
4946 | // Save -> PH |
4947 | EXPECT_EQ(mod.getPlaceholders().size(), 3); |
4948 | // TopK, Save nodes |
4949 | EXPECT_EQ(F->getNodes().size(), 3); |
4950 | |
4951 | auto *values = getSaveNodeFromDest(valuesPH); |
4952 | ASSERT_TRUE(values); |
4953 | EXPECT_EQ(values->getInput().getType()->getElementType(), ElemKind::Int8QTy); |
4954 | EXPECT_EQ(values->getInput().getType()->getScale(), 1.2f); |
4955 | EXPECT_EQ(values->getInput().getType()->getOffset(), 5); |
4956 | EXPECT_EQ(values->getInput().dims().vec(), std::vector<dim_t>({3, 1, 3})); |
4957 | |
4958 | auto *indices = getSaveNodeFromDest(indicesPH); |
4959 | ASSERT_TRUE(indices); |
4960 | EXPECT_EQ(indices->getInput().getType()->getElementType(), |
4961 | ElemKind::Int64ITy); |
4962 | EXPECT_EQ(indices->getInput().dims().vec(), std::vector<dim_t>({3, 1, 3})); |
4963 | |
4964 | EXPECT_EQ(indices->getInput().getNode(), values->getInput().getNode()); |
4965 | |
4966 | auto *TKN = llvm::dyn_cast<TopKNode>(indices->getInput()); |
4967 | ASSERT_TRUE(TKN); |
4968 | EXPECT_EQ(TKN->getK(), 3); |
4969 | |
4970 | auto *input = llvm::dyn_cast<Placeholder>(TKN->getInput()); |
4971 | ASSERT_TRUE(input); |
4972 | EXPECT_EQ(input->dims().vec(), std::vector<dim_t>({3, 1, 5})); |
4973 | EXPECT_EQ(input->getType()->getElementType(), ElemKind::Int8QTy); |
4974 | EXPECT_EQ(input->getType()->getScale(), 1.2f); |
4975 | EXPECT_EQ(input->getType()->getOffset(), 5); |
4976 | } |
4977 | |
4978 | /// Test loading a custom ONNX Glow ChannelwiseQuantizedGroupConvolution. |
4979 | TEST_F(OnnxImporterTest, CustomGlowChannelwiseQuantizedGroupConvolution) { |
4980 | ExecutionEngine EE; |
4981 | auto &mod = EE.getModule(); |
4982 | auto *F = mod.createFunction("main" ); |
4983 | std::string netFilename( |
4984 | GLOW_DATA_PATH "tests/models/onnxModels/" |
4985 | "glow_custom_op_channelwise_quantized_group_conv.onnxtxt" ); |
4986 | Placeholder *outputPH; |
4987 | { |
4988 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
4989 | outputPH = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
4990 | } |
4991 | |
4992 | // Verify structure: |
4993 | // {(PH -> Quantize), Constant, Constant, Constant, Constant} -> |
4994 | // ChannelwiseQuantizedConvolution -> Save -> PH. |
4995 | EXPECT_EQ(mod.getPlaceholders().size(), 2); |
4996 | EXPECT_EQ(mod.getConstants().size(), 6); |
4997 | // ChannelwiseQuantizedConvolution, Save, Quantize, Dequantize |
4998 | EXPECT_EQ(F->getNodes().size(), 4); |
4999 | |
5000 | auto *save = getSaveNodeFromDest(outputPH); |
5001 | ASSERT_TRUE(save); |
5002 | |
5003 | auto *DQN = llvm::dyn_cast<DequantizeNode>(save->getInput()); |
5004 | ASSERT_TRUE(DQN); |
5005 | EXPECT_EQ(DQN->getInput().getType()->getElementType(), ElemKind::Int8QTy); |
5006 | EXPECT_EQ(DQN->getInput().getType()->getScale(), 1.0f); |
5007 | EXPECT_EQ(DQN->getInput().getType()->getOffset(), 0); |
5008 | EXPECT_EQ(DQN->getInput().dims().vec(), std::vector<dim_t>({1, 1, 3, 4})); |
5009 | |
5010 | auto *CN = |
5011 | llvm::dyn_cast<ChannelwiseQuantizedConvolutionNode>(DQN->getInput()); |
5012 | ASSERT_TRUE(CN); |
5013 | EXPECT_EQ(CN->getKernels().vec(), std::vector<unsigned_t>({2, 1})); |
5014 | EXPECT_EQ(CN->getStrides().vec(), std::vector<unsigned_t>({1, 1})); |
5015 | EXPECT_EQ(CN->getPads().vec(), std::vector<unsigned_t>({0, 0, 0, 0})); |
5016 | EXPECT_EQ(CN->getGroup(), 2); |
5017 | EXPECT_EQ(CN->getDilation().vec(), std::vector<unsigned_t>({1, 1})); |
5018 | |
5019 | auto *QN = llvm::dyn_cast<QuantizeNode>(CN->getInput()); |
5020 | ASSERT_TRUE(QN); |
5021 | EXPECT_EQ(QN->getResult().getType()->getElementType(), ElemKind::Int8QTy); |
5022 | EXPECT_EQ(QN->getResult().getType()->getScale(), 1.0f); |
5023 | EXPECT_EQ(QN->getResult().getType()->getOffset(), 0); |
5024 | EXPECT_EQ(QN->getResult().dims().vec(), std::vector<dim_t>({1, 2, 3, 2})); |
5025 | EXPECT_TRUE(llvm::isa<Placeholder>(QN->getInput())); |
5026 | |
5027 | auto *filter = llvm::dyn_cast<Constant>(CN->getFilter()); |
5028 | ASSERT_TRUE(filter); |
5029 | EXPECT_EQ(filter->getOutput().getType()->getElementType(), ElemKind::Int8QTy); |
5030 | EXPECT_EQ(filter->getOutput().dims().vec(), std::vector<dim_t>({4, 2, 1, 1})); |
5031 | |
5032 | auto *bias = llvm::dyn_cast<Constant>(CN->getBias()); |
5033 | ASSERT_TRUE(bias); |
5034 | EXPECT_EQ(bias->getOutput().getType()->getElementType(), ElemKind::Int32QTy); |
5035 | EXPECT_EQ(bias->getOutput().dims().vec(), std::vector<dim_t>({4})); |
5036 | |
5037 | auto *filterScales = llvm::dyn_cast<Constant>(CN->getFilterScales()); |
5038 | ASSERT_TRUE(filterScales); |
5039 | EXPECT_EQ(filterScales->getOutput().getType()->getElementType(), |
5040 | ElemKind::FloatTy); |
5041 | EXPECT_EQ(filterScales->getOutput().dims().vec(), std::vector<dim_t>({4})); |
5042 | |
5043 | auto *filterOffsets = llvm::dyn_cast<Constant>(CN->getFilterOffsets()); |
5044 | ASSERT_TRUE(filterOffsets); |
5045 | EXPECT_EQ(filterOffsets->getOutput().getType()->getElementType(), |
5046 | ElemKind::Int32ITy); |
5047 | EXPECT_EQ(filterOffsets->getOutput().dims().vec(), std::vector<dim_t>({4})); |
5048 | |
5049 | auto *biasScales = llvm::dyn_cast<Constant>(CN->getBiasScales()); |
5050 | ASSERT_TRUE(biasScales); |
5051 | EXPECT_EQ(biasScales->getOutput().getType()->getElementType(), |
5052 | ElemKind::FloatTy); |
5053 | EXPECT_EQ(biasScales->getOutput().dims().vec(), std::vector<dim_t>({4})); |
5054 | |
5055 | auto *biasOffsets = llvm::dyn_cast<Constant>(CN->getBiasOffsets()); |
5056 | ASSERT_TRUE(biasOffsets); |
5057 | EXPECT_EQ(biasOffsets->getOutput().getType()->getElementType(), |
5058 | ElemKind::Int32ITy); |
5059 | EXPECT_EQ(biasOffsets->getOutput().dims().vec(), std::vector<dim_t>({4})); |
5060 | } |
5061 | |
5062 | /// Upsample Test Helper |
5063 | static void importUpsampleTest(std::string &netFilename) { |
5064 | ExecutionEngine EE; |
5065 | auto &mod = EE.getModule(); |
5066 | auto *F = mod.createFunction("main" ); |
5067 | PlaceholderBindings bindings; |
5068 | Placeholder *resultPH; |
5069 | Tensor inputTensor(ElemKind::FloatTy, {1, 1, 2, 2}); |
5070 | |
5071 | inputTensor.getHandle() = {1, 2, 3, 4}; |
5072 | |
5073 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&inputTensor.getType()}, *F); |
5074 | resultPH = EXIT_ON_ERR(onnxLD.getOutputByName("Y" )); |
5075 | bindings.allocate(mod.getPlaceholders()); |
5076 | updateInputPlaceholdersByName(bindings, &mod, {"input" }, {&inputTensor}); |
5077 | |
5078 | EE.compile(CompilationMode::Infer); |
5079 | EE.run(bindings); |
5080 | |
5081 | auto result = bindings.get(resultPH)->getHandle(); |
5082 | std::vector<dim_t> expectedDims = {1, 1, 4, 6}; |
5083 | |
5084 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
5085 | |
5086 | std::vector<float> expectedResult = {1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, |
5087 | 3, 3, 3, 4, 4, 4, 3, 3, 3, 4, 4, 4}; |
5088 | |
5089 | for (dim_t i = 0; i < expectedResult.size(); i++) { |
5090 | EXPECT_EQ(result.raw(i), expectedResult[i]); |
5091 | } |
5092 | } |
5093 | |
5094 | TEST_F(OnnxImporterTest, importUpsampleOpset7) { |
5095 | std::string netFilename(GLOW_DATA_PATH |
5096 | "tests/models/onnxModels/upsampleOpset7.onnxtxt" ); |
5097 | importUpsampleTest(netFilename); |
5098 | } |
5099 | |
5100 | TEST_F(OnnxImporterTest, importUpsampleOpset9) { |
5101 | std::string netFilename(GLOW_DATA_PATH |
5102 | "tests/models/onnxModels/upsampleOpset9.onnxtxt" ); |
5103 | importUpsampleTest(netFilename); |
5104 | } |
5105 | |
5106 | static void testIf(std::string filename, float inputVal, float outputVal) { |
5107 | ExecutionEngine EE{}; |
5108 | auto &mod = EE.getModule(); |
5109 | Function *F = mod.createFunction("main" ); |
5110 | |
5111 | std::string netFilename = std::string(GLOW_DATA_PATH) + filename; |
5112 | |
5113 | PlaceholderBindings bindings; |
5114 | Placeholder *output; |
5115 | { |
5116 | Tensor x(ElemKind::FloatTy, {1}); |
5117 | x.getHandle() = {inputVal}; |
5118 | |
5119 | ONNXModelLoader onnxLD(netFilename, {"input" }, {&x.getType()}, *F); |
5120 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5121 | bindings.allocate(mod.getPlaceholders()); |
5122 | updateInputPlaceholdersByName(bindings, &mod, {"input" }, {&x}); |
5123 | } |
5124 | |
5125 | auto *res = bindings.get(output); |
5126 | EE.compile(CompilationMode::Infer); |
5127 | EE.run(bindings); |
5128 | |
5129 | auto result = res->getHandle(); |
5130 | |
5131 | std::vector<float> expectedValues = {outputVal}; |
5132 | for (size_t i = 0; i < expectedValues.size(); i++) { |
5133 | EXPECT_EQ(result.raw(i), expectedValues[i]); |
5134 | } |
5135 | } |
5136 | |
5137 | TEST(onnx, testIfConstantTrue) { |
5138 | testIf("tests/models/onnxModels/if_true.onnxtxt" , 3.0f, 6.0f); |
5139 | } |
5140 | |
5141 | TEST(onnx, testIfConstantFalse) { |
5142 | testIf("tests/models/onnxModels/if_false.onnxtxt" , 3.0f, 9.0f); |
5143 | } |
5144 | |
5145 | /// ResizeNearest Test Helper |
5146 | static void importResizeNearest(std::string filename) { |
5147 | ExecutionEngine EE; |
5148 | auto &mod = EE.getModule(); |
5149 | Function *F = mod.createFunction("main" ); |
5150 | |
5151 | std::string netFilename(filename); |
5152 | |
5153 | PlaceholderBindings bindings; |
5154 | Placeholder *output; |
5155 | Tensor in(ElemKind::FloatTy, {2, 2, 2, 2}); |
5156 | in.getHandle() = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; |
5157 | { |
5158 | ONNXModelLoader onnxLD(netFilename, {"in" }, {&in.getType()}, *F); |
5159 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5160 | |
5161 | bindings.allocate(mod.getPlaceholders()); |
5162 | updateInputPlaceholdersByName(bindings, &mod, {"in" }, {&in}); |
5163 | } |
5164 | |
5165 | auto *res = bindings.get(output); |
5166 | EE.compile(CompilationMode::Infer); |
5167 | EE.run(bindings); |
5168 | ASSERT_EQ(2, F->getNodes().size()); |
5169 | |
5170 | auto *saveNode = getSaveNodeFromDest(output); |
5171 | auto *RN = llvm::dyn_cast<ResizeNearestNode>(saveNode->getInput()); |
5172 | ASSERT_TRUE(RN); |
5173 | |
5174 | auto result = res->getHandle(); |
5175 | std::vector<dim_t> expectedDims = {2, 2, 4, 4}; |
5176 | EXPECT_EQ(result.dims().vec(), expectedDims); |
5177 | |
5178 | std::vector<float> expectedValues = { |
5179 | 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, |
5180 | 4.0, 3.0, 3.0, 4.0, 4.0, 5.0, 5.0, 6.0, 6.0, 5.0, 5.0, |
5181 | 6.0, 6.0, 7.0, 7.0, 8.0, 8.0, 7.0, 7.0, 8.0, 8.0, 9.0, |
5182 | 9.0, 10.0, 10.0, 9.0, 9.0, 10.0, 10.0, 11.0, 11.0, 12.0, 12.0, |
5183 | 11.0, 11.0, 12.0, 12.0, 13.0, 13.0, 14.0, 14.0, 13.0, 13.0, 14.0, |
5184 | 14.0, 15.0, 15.0, 16.0, 16.0, 15.0, 15.0, 16.0, 16.0}; |
5185 | |
5186 | for (dim_t i = 0; i < 64; i++) { |
5187 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
5188 | } |
5189 | |
5190 | // Constant Folding Test. |
5191 | FAIL_TEST_IF_ERR(checkConstFoldedOutput(netFilename, {"in" }, {&in}, |
5192 | {bindings.get(output)})); |
5193 | } |
5194 | |
5195 | /// Test ONNX Resize mode=nearest. |
5196 | TEST(onnx, importResizeNearest) { |
5197 | std::string netFilename(GLOW_DATA_PATH |
5198 | "tests/models/onnxModels/resizeNearest.onnxtxt" ); |
5199 | importResizeNearest(netFilename); |
5200 | } |
5201 | |
5202 | /// Test ONNX Resize V11 mode=nearest that is compatible with V10 spec |
5203 | TEST(onnx, importResizeNearestV11compat) { |
5204 | std::string netFilename( |
5205 | GLOW_DATA_PATH "tests/models/onnxModels/resizeNearestV11compat.onnxtxt" ); |
5206 | importResizeNearest(netFilename); |
5207 | } |
5208 | |
5209 | /// Test ONNX Resize V11 mode=nearest that is compatible with V10 spec |
5210 | /// except that scales are inferred from sizes input. |
5211 | TEST(onnx, importResizeNearestV11compat_sizes) { |
5212 | std::string netFilename( |
5213 | GLOW_DATA_PATH |
5214 | "tests/models/onnxModels/resizeNearestV11compat_sizes.onnxtxt" ); |
5215 | importResizeNearest(netFilename); |
5216 | } |
5217 | |
5218 | static void importResizeBilinear(std::string filename) { |
5219 | ExecutionEngine EE; |
5220 | auto &mod = EE.getModule(); |
5221 | Function *F = mod.createFunction("main" ); |
5222 | std::string netFilename(filename); |
5223 | |
5224 | PlaceholderBindings bindings; |
5225 | Placeholder *output; |
5226 | Tensor in(ElemKind::FloatTy, {1, 1, 2, 2}); |
5227 | in.getHandle() = {1, 2, 3, 4}; |
5228 | { |
5229 | ONNXModelLoader onnxLD(netFilename, {"in" }, {&in.getType()}, *F); |
5230 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5231 | |
5232 | bindings.allocate(mod.getPlaceholders()); |
5233 | updateInputPlaceholdersByName(bindings, &mod, {"in" }, {&in}); |
5234 | } |
5235 | |
5236 | auto *res = bindings.get(output); |
5237 | EE.compile(CompilationMode::Infer); |
5238 | EE.run(bindings); |
5239 | ASSERT_EQ(4, F->getNodes().size()); |
5240 | |
5241 | auto *saveNode = getSaveNodeFromDest(output); |
5242 | auto *TR = llvm::dyn_cast<ReshapeNode>(saveNode->getInput().getNode()); |
5243 | ASSERT_TRUE(TR); |
5244 | auto *RN = llvm::dyn_cast<ResizeBilinearNode>(TR->getInput()); |
5245 | ASSERT_TRUE(RN); |
5246 | |
5247 | auto result = res->getHandle(); |
5248 | std::vector<dim_t> expectedDims = {1, 1, 4, 4}; |
5249 | EXPECT_EQ(result.dims().vec(), expectedDims); |
5250 | |
5251 | std::vector<float> expectedValues = {1.0, 1.5, 2.0, 2.0, 2.0, 2.5, 3.0, 3.0, |
5252 | 3.0, 3.5, 4.0, 4.0, 3.0, 3.5, 4.0, 4.0}; |
5253 | |
5254 | for (dim_t i = 0; i < 16; i++) { |
5255 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
5256 | } |
5257 | |
5258 | // Constant Folding Test. |
5259 | FAIL_TEST_IF_ERR(checkConstFoldedOutput(netFilename, {"in" }, {&in}, |
5260 | {bindings.get(output)})); |
5261 | } |
5262 | |
5263 | TEST_F(OnnxImporterTest, importBoolFromInt) { |
5264 | ExecutionEngine EE; |
5265 | auto &mod = EE.getModule(); |
5266 | std::string netFilename(GLOW_DATA_PATH |
5267 | "tests/models/onnxModels/bool_from_int.onnxtxt" ); |
5268 | auto *F = mod.createFunction("main" ); |
5269 | PlaceholderBindings bindings; |
5270 | Placeholder *output; |
5271 | { |
5272 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
5273 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5274 | ASSERT_TRUE(output); |
5275 | } |
5276 | |
5277 | EE.compile(CompilationMode::Infer); |
5278 | bindings.allocate(mod.getPlaceholders()); |
5279 | EE.run(bindings); |
5280 | |
5281 | std::vector<bool> expectedOut = {true, false, true}; |
5282 | auto result = bindings.get(output)->getHandle<bool>(); |
5283 | for (size_t i = 0; i < result.getType().size(); i++) |
5284 | EXPECT_EQ(result.raw(i), expectedOut[i]); |
5285 | } |
5286 | |
5287 | /// ResizeNearest Test Helper. |
5288 | TEST(onnx, importResizeBilinear) { |
5289 | std::string netFilename(GLOW_DATA_PATH |
5290 | "tests/models/onnxModels/resizeBilinear.onnxtxt" ); |
5291 | importResizeBilinear(netFilename); |
5292 | } |
5293 | |
5294 | /// Test ONNX Resize V11 mode=nearest that is compatible with V10 spec |
5295 | TEST(onnx, importResizeBilinearV11compat) { |
5296 | std::string netFilename( |
5297 | GLOW_DATA_PATH "tests/models/onnxModels/resizeBilinearV11compat.onnxtxt" ); |
5298 | importResizeBilinear(netFilename); |
5299 | } |
5300 | |
5301 | /// Test ONNX Resize V11 mode=bilinear that is compatible with V10 spec |
5302 | /// except that scales are inferred from sizes input. |
5303 | TEST(onnx, importResizeBilinearV11compat_sizes) { |
5304 | std::string netFilename( |
5305 | GLOW_DATA_PATH |
5306 | "tests/models/onnxModels/resizeBilinearV11compat_sizes.onnxtxt" ); |
5307 | importResizeBilinear(netFilename); |
5308 | } |
5309 | |
5310 | /// Test loading a custom ONNX Glow net with NodeOpts. |
5311 | TEST_F(OnnxImporterTest, CustomGlowWithNodeOpts) { |
5312 | ExecutionEngine EE; |
5313 | auto &mod = EE.getModule(); |
5314 | auto *F = mod.createFunction("main" ); |
5315 | std::string netFilename( |
5316 | GLOW_DATA_PATH |
5317 | "tests/models/onnxModels/glow_custom_op_node_opts.onnxtxt" ); |
5318 | Placeholder *outputPH; |
5319 | BackendSpecificNodeInfo funNodeInfo; |
5320 | { |
5321 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F, /* errPtr */ nullptr, |
5322 | /* zipMode */ false, &funNodeInfo); |
5323 | outputPH = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5324 | } |
5325 | |
5326 | auto itF = funNodeInfo.find(F); |
5327 | ASSERT_NE(itF, funNodeInfo.end()); |
5328 | auto &nodeInfo = itF->second; |
5329 | |
5330 | SaveNode *save = getSaveNodeFromDest(outputPH); |
5331 | ASSERT_TRUE(save); |
5332 | // Verify that there are no options specified for the Save. |
5333 | EXPECT_EQ(nodeInfo.find(save), nodeInfo.end()); |
5334 | |
5335 | // Verify that the options for the MatMul are loaded correctly. |
5336 | MatMulNode *MN = llvm::dyn_cast<MatMulNode>(save->getInput()); |
5337 | auto itMN = nodeInfo.find(MN); |
5338 | ASSERT_NE(itMN, nodeInfo.end()); |
5339 | llvm::StringMap<std::vector<std::string>> &opts = itMN->second; |
5340 | |
5341 | // attribute { |
5342 | // name: "NodeOpt_BackendA_Option1" |
5343 | // strings: "1" |
5344 | // strings: "2" |
5345 | // type: STRINGS |
5346 | // } |
5347 | auto itOpt1 = opts.find("BackendA_Option1" ); |
5348 | ASSERT_NE(itOpt1, opts.end()); |
5349 | EXPECT_EQ(itOpt1->second.size(), 2); |
5350 | EXPECT_EQ(itOpt1->second[0], "1" ); |
5351 | EXPECT_EQ(itOpt1->second[1], "2" ); |
5352 | |
5353 | // attribute { |
5354 | // name: "NodeOpt_BackendA_Option2" |
5355 | // strings: "3" |
5356 | // type: STRINGS |
5357 | // } |
5358 | auto itOpt2 = opts.find("BackendA_Option2" ); |
5359 | ASSERT_NE(itOpt2, opts.end()); |
5360 | EXPECT_EQ(itOpt2->second.size(), 1); |
5361 | EXPECT_EQ(itOpt2->second[0], "3" ); |
5362 | |
5363 | // attribute { |
5364 | // name: "NodeOpt_BackendB_Option3" |
5365 | // strings: "4" |
5366 | // strings: "5" |
5367 | // type: STRINGS |
5368 | // } |
5369 | auto itOpt3 = opts.find("BackendB_Option3" ); |
5370 | ASSERT_NE(itOpt3, opts.end()); |
5371 | EXPECT_EQ(itOpt3->second.size(), 2); |
5372 | EXPECT_EQ(itOpt3->second[0], "4" ); |
5373 | EXPECT_EQ(itOpt3->second[1], "5" ); |
5374 | } |
5375 | |
5376 | /// Test loading a custom ONNX Glow net with serialized strides. |
5377 | TEST_F(OnnxImporterTest, CustomGlowWithStrides) { |
5378 | ExecutionEngine EE; |
5379 | auto &mod = EE.getModule(); |
5380 | auto *F = mod.createFunction("main" ); |
5381 | std::string netFilename( |
5382 | GLOW_DATA_PATH |
5383 | "tests/models/onnxModels/glow_custom_with_strides.onnxtxt" ); |
5384 | { |
5385 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F, /* errPtr */ nullptr, |
5386 | /* zipMode */ false); |
5387 | EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5388 | } |
5389 | |
5390 | // Find MatMul node. |
5391 | auto *MN = llvm::cast<MatMulNode>(F->getNodeByName("MM" )); |
5392 | |
5393 | // The MatMul node should have a custom stride[0] equal to 96. |
5394 | ASSERT_EQ(MN->getResult().getType()->strides()[0], 96); |
5395 | // LHS should have a custom stride[0] equal to 31. |
5396 | ASSERT_EQ(MN->getLHS().getType()->strides()[0], 31); |
5397 | } |
5398 | |
5399 | static bool vecContainsVal(const std::vector<runtime::DeviceIDTy> &vec, |
5400 | runtime::DeviceIDTy val) { |
5401 | return std::find(vec.begin(), vec.end(), val) != vec.end(); |
5402 | } |
5403 | |
5404 | /// Test loading a custom ONNX Glow net that has been already partitioned, |
5405 | /// turned into a DAG, and then exported. |
5406 | TEST_F(OnnxImporterTest, CustomGlowDAGMultiOp) { |
5407 | ExecutionEngine EE("Interpreter" , /* deviceMemory (16GB) */ 0x400000000, |
5408 | /* ignoreUserDeviceConfig */ false, /* numDevices */ 3); |
5409 | auto &mod = EE.getModule(); |
5410 | std::string netFilename( |
5411 | GLOW_DATA_PATH |
5412 | "tests/models/onnxModels/glow_custom_dag_multi_op.onnxtxt" ); |
5413 | |
5414 | Placeholder *outputPH; |
5415 | Tensor *resultPartitionedT; |
5416 | PlaceholderBindings bindingsU; |
5417 | PlaceholderBindings bindingsP; |
5418 | |
5419 | runtime::PrePartitionedConfig PPC; |
5420 | Tensor mmIn0T(ElemKind::FloatTy, {10, 10}); |
5421 | Tensor mmIn1T(ElemKind::FloatTy, {10, 10}); |
5422 | Tensor addInT(ElemKind::FloatTy, {10, 10}); |
5423 | mmIn0T.getHandle().randomize(-3.0, 3.0, mod.getPRNG()); |
5424 | mmIn1T.getHandle().randomize(-3.0, 3.0, mod.getPRNG()); |
5425 | addInT.getHandle().randomize(-3.0, 3.0, mod.getPRNG()); |
5426 | Placeholder *mmIn0P = nullptr, *mmIn1P = nullptr, *addInP = nullptr; |
5427 | { |
5428 | ONNXModelLoader onnxLD(netFilename, {}, {}, mod, "main" , &PPC, |
5429 | /* errPtr */ nullptr, /* zipMode */ false); |
5430 | outputPH = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5431 | NodeValue mmIn0NV; |
5432 | ASSIGN_VALUE_OR_FAIL_TEST(mmIn0NV, onnxLD.getNodeValueByName("mm0_in" )); |
5433 | mmIn0P = llvm::dyn_cast<Placeholder>(mmIn0NV); |
5434 | NodeValue mmIn1NV; |
5435 | ASSIGN_VALUE_OR_FAIL_TEST(mmIn1NV, onnxLD.getNodeValueByName("mm1_in" )); |
5436 | mmIn1P = llvm::dyn_cast<Placeholder>(mmIn1NV); |
5437 | NodeValue addInNV; |
5438 | ASSIGN_VALUE_OR_FAIL_TEST(addInNV, onnxLD.getNodeValueByName("add_in" )); |
5439 | addInP = llvm::dyn_cast<Placeholder>(addInNV); |
5440 | } |
5441 | |
5442 | { |
5443 | ASSERT_TRUE(mmIn0P); |
5444 | ASSERT_TRUE(mmIn1P); |
5445 | ASSERT_TRUE(addInP); |
5446 | |
5447 | ASSERT_EQ(mod.getFunctions().size(), 3); |
5448 | Function *P0 = nullptr, *P1 = nullptr, *P2 = nullptr; |
5449 | for (size_t i = 0, e = PPC.funcs.size(); i < e; i++) { |
5450 | // Find the expected Function, and check that the logical device IDs were |
5451 | // correctly loaded. |
5452 | Function *F = PPC.funcs[i]; |
5453 | if (F->getName() == "main_p0" ) { |
5454 | P0 = F; |
5455 | ASSERT_EQ(PPC.logicalIDs[i].size(), 1); |
5456 | EXPECT_TRUE(vecContainsVal(PPC.logicalIDs[i], 2)); |
5457 | EXPECT_EQ(PPC.backendSpecificOpts[i].size(), 0); |
5458 | } else if (F->getName() == "main_p1" ) { |
5459 | P1 = F; |
5460 | ASSERT_EQ(PPC.logicalIDs[i].size(), 2); |
5461 | EXPECT_TRUE(vecContainsVal(PPC.logicalIDs[i], 0)); |
5462 | EXPECT_TRUE(vecContainsVal(PPC.logicalIDs[i], 1)); |
5463 | EXPECT_EQ(PPC.backendSpecificOpts[i].size(), 0); |
5464 | } else if (F->getName() == "main_p2" ) { |
5465 | P2 = F; |
5466 | ASSERT_EQ(PPC.logicalIDs[i].size(), 1); |
5467 | EXPECT_TRUE(vecContainsVal(PPC.logicalIDs[i], 2)); |
5468 | EXPECT_EQ(PPC.backendSpecificOpts[i].size(), 3); |
5469 | ASSERT_TRUE(PPC.backendSpecificOpts[i].count("BackendA_opt1" )); |
5470 | EXPECT_EQ(PPC.backendSpecificOpts[i].at("BackendA_opt1" ), "val1" ); |
5471 | ASSERT_TRUE(PPC.backendSpecificOpts[i].count("BackendA_opt2" )); |
5472 | EXPECT_EQ(PPC.backendSpecificOpts[i].at("BackendA_opt2" ), "val2" ); |
5473 | ASSERT_TRUE(PPC.backendSpecificOpts[i].count("BackendB_opt3" )); |
5474 | EXPECT_EQ(PPC.backendSpecificOpts[i].at("BackendB_opt3" ), "val3" ); |
5475 | } else { |
5476 | FAIL() << "Unknown Function found." ; |
5477 | } |
5478 | |
5479 | // Check that the function was also found in the module. |
5480 | auto &modFuns = mod.getFunctions(); |
5481 | ASSERT_NE(std::find(modFuns.begin(), modFuns.end(), F), modFuns.end()); |
5482 | } |
5483 | ASSERT_TRUE(P0); |
5484 | ASSERT_TRUE(P1); |
5485 | ASSERT_TRUE(P2); |
5486 | |
5487 | // Verify P0: |
5488 | auto *finalSave = getSaveNodeFromDest(outputPH); |
5489 | ASSERT_TRUE(finalSave); |
5490 | EXPECT_EQ(finalSave->getParent(), P0); |
5491 | SubNode *sub = llvm::dyn_cast<SubNode>(finalSave->getInput()); |
5492 | ASSERT_TRUE(sub); |
5493 | Placeholder *intermedAddOut = llvm::dyn_cast<Placeholder>(sub->getRHS()); |
5494 | ASSERT_TRUE(intermedAddOut); |
5495 | MulNode *mul = llvm::dyn_cast<MulNode>(sub->getLHS()); |
5496 | ASSERT_TRUE(mul); |
5497 | Placeholder *intermedMMOut = llvm::dyn_cast<Placeholder>(mul->getRHS()); |
5498 | ASSERT_TRUE(intermedMMOut); |
5499 | Placeholder *mmIn0 = llvm::dyn_cast<Placeholder>(mul->getLHS()); |
5500 | ASSERT_TRUE(mmIn0); |
5501 | |
5502 | // Verify P2: |
5503 | Node *userFromP2 = nullptr; |
5504 | for (auto &U : intermedAddOut->getUsers()) { |
5505 | if (U.getUser()->getParent() == P2) { |
5506 | ASSERT_FALSE(userFromP2); |
5507 | userFromP2 = U.getUser(); |
5508 | } |
5509 | } |
5510 | ASSERT_TRUE(userFromP2); |
5511 | SaveNode *saveIntermedP2Out = llvm::dyn_cast<SaveNode>(userFromP2); |
5512 | ASSERT_TRUE(saveIntermedP2Out); |
5513 | AddNode *add = llvm::dyn_cast<AddNode>(saveIntermedP2Out->getInput()); |
5514 | ASSERT_TRUE(add); |
5515 | Placeholder *addIn = llvm::dyn_cast<Placeholder>(add->getRHS()); |
5516 | ASSERT_TRUE(addIn); |
5517 | EXPECT_EQ(add->getLHS().getNode(), intermedMMOut); |
5518 | |
5519 | // Verify P1: |
5520 | Node *userFromP1 = nullptr; |
5521 | for (auto &U : intermedMMOut->getUsers()) { |
5522 | if (U.getUser()->getParent() == P1) { |
5523 | ASSERT_FALSE(userFromP1); |
5524 | userFromP1 = U.getUser(); |
5525 | } |
5526 | } |
5527 | ASSERT_TRUE(userFromP1); |
5528 | SaveNode *saveIntermedP1Out = llvm::dyn_cast<SaveNode>(userFromP1); |
5529 | ASSERT_TRUE(saveIntermedP1Out); |
5530 | MatMulNode *matMul = |
5531 | llvm::dyn_cast<MatMulNode>(saveIntermedP1Out->getInput()); |
5532 | ASSERT_TRUE(matMul); |
5533 | EXPECT_EQ(matMul->getLHS().getNode(), mmIn0); |
5534 | Placeholder *matMulIn = llvm::dyn_cast<Placeholder>(matMul->getRHS()); |
5535 | ASSERT_TRUE(matMulIn); |
5536 | |
5537 | // Now that we've verifed the shape of the Module, run it and keep around |
5538 | // the pointer to the result. |
5539 | CompilationContext cctx; |
5540 | cctx.prepartitionedConfig = &PPC; |
5541 | EE.compile(cctx); |
5542 | bindingsP.insert(mmIn0P, mmIn0T.getUnowned()); |
5543 | bindingsP.insert(mmIn1P, mmIn1T.getUnowned()); |
5544 | bindingsP.insert(addInP, addInT.getUnowned()); |
5545 | bindingsP.allocate(mod.getPlaceholders()); |
5546 | EE.run(bindingsP); |
5547 | |
5548 | resultPartitionedT = bindingsP.get(outputPH); |
5549 | } |
5550 | |
5551 | // Now that we have the model result from pre-partitioned execution, execute |
5552 | // the model ignoring the pre-partitioning and bitwise compare results. |
5553 | EE.setBackendName(EE.getBackendName()); |
5554 | |
5555 | Module &modU = EE.getModule(); |
5556 | { |
5557 | Function *F = modU.createFunction("main" ); |
5558 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
5559 | outputPH = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5560 | NodeValue mmIn0NV; |
5561 | ASSIGN_VALUE_OR_FAIL_TEST(mmIn0NV, onnxLD.getNodeValueByName("mm0_in" )); |
5562 | mmIn0P = llvm::dyn_cast<Placeholder>(mmIn0NV); |
5563 | NodeValue mmIn1NV; |
5564 | ASSIGN_VALUE_OR_FAIL_TEST(mmIn1NV, onnxLD.getNodeValueByName("mm1_in" )); |
5565 | mmIn1P = llvm::dyn_cast<Placeholder>(mmIn1NV); |
5566 | NodeValue addInNV; |
5567 | ASSIGN_VALUE_OR_FAIL_TEST(addInNV, onnxLD.getNodeValueByName("add_in" )); |
5568 | addInP = llvm::dyn_cast<Placeholder>(addInNV); |
5569 | } |
5570 | |
5571 | Tensor *resultUnpartitonedT; |
5572 | |
5573 | { |
5574 | ASSERT_TRUE(mmIn0P); |
5575 | ASSERT_TRUE(mmIn1P); |
5576 | ASSERT_TRUE(addInP); |
5577 | ASSERT_EQ(modU.getFunctions().size(), 1); |
5578 | |
5579 | EE.compile(CompilationMode::Infer); |
5580 | bindingsU.insert(mmIn0P, mmIn0T.getUnowned()); |
5581 | bindingsU.insert(mmIn1P, mmIn1T.getUnowned()); |
5582 | bindingsU.insert(addInP, addInT.getUnowned()); |
5583 | bindingsU.allocate(modU.getPlaceholders()); |
5584 | EE.run(bindingsU); |
5585 | |
5586 | resultUnpartitonedT = bindingsU.get(outputPH); |
5587 | } |
5588 | |
5589 | EXPECT_TRUE(resultPartitionedT->isBitwiseEqual(*resultUnpartitonedT, |
5590 | /* verbose */ true)); |
5591 | } |
5592 | |
5593 | /// Utility function to test ONNX Gemm import. |
5594 | static void importGemm(std::string filename, bool hasC, bool batchedC, |
5595 | bool transA, bool transB) { |
5596 | ExecutionEngine EE; |
5597 | auto &mod = EE.getModule(); |
5598 | Function *F = mod.createFunction("main" ); |
5599 | std::string netFilename(filename); |
5600 | |
5601 | PlaceholderBindings bindings; |
5602 | Placeholder *output; |
5603 | |
5604 | Tensor tensorA; |
5605 | if (transA) { |
5606 | tensorA = Tensor(ElemKind::FloatTy, {3, 2}); |
5607 | tensorA.getHandle() = {1, 4, 2, 5, 3, 6}; |
5608 | } else { |
5609 | tensorA = Tensor(ElemKind::FloatTy, {2, 3}); |
5610 | tensorA.getHandle() = {1, 2, 3, 4, 5, 6}; |
5611 | } |
5612 | |
5613 | Tensor tensorB; |
5614 | if (transB) { |
5615 | tensorB = Tensor(ElemKind::FloatTy, {4, 3}); |
5616 | tensorB.getHandle() = {1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4}; |
5617 | } else { |
5618 | tensorB = Tensor(ElemKind::FloatTy, {3, 4}); |
5619 | tensorB.getHandle() = {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4}; |
5620 | } |
5621 | |
5622 | Tensor tensorC; |
5623 | if (batchedC) { |
5624 | tensorC = Tensor(ElemKind::FloatTy, {2, 4}); |
5625 | tensorC.getHandle() = {1, 2, 3, 4, 1, 2, 3, 4}; |
5626 | } else { |
5627 | tensorC = Tensor(ElemKind::FloatTy, {4}); |
5628 | tensorC.getHandle() = {1, 2, 3, 4}; |
5629 | } |
5630 | |
5631 | { |
5632 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
5633 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5634 | bindings.allocate(mod.getPlaceholders()); |
5635 | if (hasC) { |
5636 | updateInputPlaceholdersByName(bindings, &mod, {"A" , "B" , "C" }, |
5637 | {&tensorA, &tensorB, &tensorC}); |
5638 | } else { |
5639 | updateInputPlaceholdersByName(bindings, &mod, {"A" , "B" }, |
5640 | {&tensorA, &tensorB}); |
5641 | } |
5642 | } |
5643 | |
5644 | auto *saveNode = getSaveNodeFromDest(output); |
5645 | auto *GN = llvm::dyn_cast<GemmNode>(saveNode->getInput().getNode()); |
5646 | ASSERT_TRUE(GN); |
5647 | |
5648 | auto *res = bindings.get(output); |
5649 | EE.compile(CompilationMode::Infer); |
5650 | EE.run(bindings); |
5651 | |
5652 | // Check output size. |
5653 | auto result = res->getHandle(); |
5654 | std::vector<dim_t> expectedDims = {2, 4}; |
5655 | EXPECT_EQ(result.dims().vec(), expectedDims); |
5656 | |
5657 | // Check output values. |
5658 | std::vector<float> expectedValues(8); |
5659 | if (hasC) { |
5660 | expectedValues = {7.0, 14.0, 21.0, 28.0, 16.0, 32.0, 48.0, 64.0}; |
5661 | } else { |
5662 | expectedValues = {6.0, 12.0, 18.0, 24.0, 15.0, 30.0, 45.0, 60.0}; |
5663 | } |
5664 | for (dim_t i = 0; i < 8; i++) { |
5665 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
5666 | } |
5667 | } |
5668 | |
5669 | /// Test ONNX Gemm. |
5670 | TEST_F(OnnxImporterTest, importGemmNoC) { |
5671 | std::string netFilename(GLOW_DATA_PATH |
5672 | "tests/models/onnxModels/gemmNoC.onnxtxt" ); |
5673 | importGemm(netFilename, /* hasC */ false, /* batchedC */ false, |
5674 | /* transA */ false, /* transB */ false); |
5675 | } |
5676 | |
5677 | TEST_F(OnnxImporterTest, importGemmSingleC) { |
5678 | std::string netFilename(GLOW_DATA_PATH |
5679 | "tests/models/onnxModels/gemmSingleC.onnxtxt" ); |
5680 | importGemm(netFilename, /* hasC */ true, /* batchedC */ false, |
5681 | /* transA */ false, /* transB */ false); |
5682 | } |
5683 | |
5684 | TEST_F(OnnxImporterTest, importGemmBatchedC) { |
5685 | std::string netFilename(GLOW_DATA_PATH |
5686 | "tests/models/onnxModels/gemmBatchedC.onnxtxt" ); |
5687 | importGemm(netFilename, /* hasC */ true, /* batchedC */ true, |
5688 | /* transA */ false, /* transB */ false); |
5689 | } |
5690 | |
5691 | TEST_F(OnnxImporterTest, importGemmTransA) { |
5692 | std::string netFilename(GLOW_DATA_PATH |
5693 | "tests/models/onnxModels/gemmTransA.onnxtxt" ); |
5694 | importGemm(netFilename, /* hasC */ true, /* batchedC */ false, |
5695 | /* transA */ true, /* transB */ false); |
5696 | } |
5697 | |
5698 | TEST_F(OnnxImporterTest, importGemmTransB) { |
5699 | std::string netFilename(GLOW_DATA_PATH |
5700 | "tests/models/onnxModels/gemmTransB.onnxtxt" ); |
5701 | importGemm(netFilename, /* hasC */ true, /* batchedC */ false, |
5702 | /* transA */ false, /* transB */ true); |
5703 | } |
5704 | |
5705 | TEST(onnx, importTransposeNullPerm) { |
5706 | ExecutionEngine EE; |
5707 | auto &mod = EE.getModule(); |
5708 | std::string netFilename( |
5709 | GLOW_DATA_PATH "tests/models/onnxModels/transpose_null_perm.onnxtxt" ); |
5710 | auto *F = mod.createFunction("main" ); |
5711 | PlaceholderBindings bindings; |
5712 | Placeholder *output_0; |
5713 | |
5714 | Tensor input_0(ElemKind::Int32ITy, {1, 2, 3, 4}); |
5715 | input_0.getHandle<int32_t>() = {1, 2, 3, 6, 4, 5, 6, 3, 1, 2, 3, 6, |
5716 | 4, 5, 6, 3, 7, 8, 9, 2, 3, 5, 7, 1}; |
5717 | { |
5718 | ONNXModelLoader onnxLD(netFilename, {"X1" }, {&input_0.getType()}, *F); |
5719 | |
5720 | output_0 = EXIT_ON_ERR(onnxLD.getOutputByName("output0" )); |
5721 | |
5722 | bindings.allocate(mod.getPlaceholders()); |
5723 | updateInputPlaceholdersByName(bindings, &mod, {"X1" }, {&input_0}); |
5724 | } |
5725 | |
5726 | EE.compile(CompilationMode::Infer); |
5727 | EE.run(bindings); |
5728 | |
5729 | std::vector<dim_t> expectedDims = {4, 3, 2, 1}; |
5730 | std::vector<int32_t> expectedValues = {1, 4, 4, 7, 1, 3, 2, 5, 5, 8, 2, 5, |
5731 | 3, 6, 6, 9, 3, 7, 6, 3, 3, 2, 6, 1}; |
5732 | |
5733 | auto result = bindings.get(output_0)->getHandle<int32_t>(); |
5734 | |
5735 | EXPECT_EQ(result.dims().vec(), expectedDims); |
5736 | for (dim_t i = 0; i < 24; i++) { |
5737 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
5738 | } |
5739 | } |
5740 | |
5741 | TEST(onnx, importNames) { |
5742 | ExecutionEngine EE{}; |
5743 | auto &mod = EE.getModule(); |
5744 | Function *F = mod.createFunction("main" ); |
5745 | std::string NetFilename(GLOW_DATA_PATH |
5746 | "tests/models/onnxModels/legalizeNames.onnxtxt" ); |
5747 | |
5748 | PlaceholderBindings bindings; |
5749 | Placeholder *graphOutputVar; |
5750 | Type input_type(ElemKind::FloatTy, {1, 2, 4, 3}); |
5751 | ONNXModelLoader onnxLD(NetFilename, {"data" }, {&input_type}, *F); |
5752 | graphOutputVar = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5753 | auto PH = mod.getPlaceholderByNameSlow("data" ); |
5754 | auto *inTensor = bindings.allocate(PH); |
5755 | inTensor->getHandle().randomize(-10.0, 10.0, mod.getPRNG()); |
5756 | // Compile&run the graph, and check the output |
5757 | EE.compile(CompilationMode::Infer); |
5758 | vector<std::string> origNames = {"a__1" , "a__1" , "a__3__3" , "a__2" , |
5759 | "a__1_" , "a__b" , "a" }; |
5760 | auto *currNode = (Node *)getSaveNodeFromDest(graphOutputVar); |
5761 | for (size_t i = 0; i < origNames.size(); i++) { |
5762 | auto *prevNode = currNode->getNthInput(0).getNode(); |
5763 | // Make sure original names are retained in the legalized names. |
5764 | EXPECT_EQ(prevNode->getName().find(origNames[i]), 0); |
5765 | currNode = prevNode; |
5766 | } |
5767 | } |
5768 | |
5769 | TEST(onnx, importClipDefaultMin) { |
5770 | // Test loading Clip in opset v11 format where min(default) and max(2) are |
5771 | // passed as inputs. |
5772 | ExecutionEngine EE; |
5773 | auto &mod = EE.getModule(); |
5774 | std::string netFilename(GLOW_DATA_PATH |
5775 | "tests/models/onnxModels/clip_default.onnxtxt" ); |
5776 | auto *F = mod.createFunction("main" ); |
5777 | PlaceholderBindings bindings; |
5778 | Placeholder *output_0; |
5779 | |
5780 | Tensor X(ElemKind::FloatTy, {1, 2, 2, 2}); |
5781 | X.getHandle() = {-3, -2, -1, 0, 1, 2, 3, 4}; |
5782 | |
5783 | { |
5784 | ONNXModelLoader onnxLD(netFilename, {"X" }, {&X.getType()}, *F); |
5785 | output_0 = EXIT_ON_ERR(onnxLD.getOutputByName("output0" )); |
5786 | bindings.allocate(mod.getPlaceholders()); |
5787 | updateInputPlaceholdersByName(bindings, &mod, {"X" }, {&X}); |
5788 | } |
5789 | |
5790 | EE.compile(CompilationMode::Infer); |
5791 | EE.run(bindings); |
5792 | |
5793 | std::vector<dim_t> expectedDims = {1, 2, 2, 2}; |
5794 | std::vector<float> expectedValues = {-3, -2, -1, 0, 1, 2, 2, 2}; |
5795 | auto result = bindings.get(output_0)->getHandle(); |
5796 | EXPECT_EQ(result.dims().vec(), expectedDims); |
5797 | |
5798 | for (size_t i = 0; i < 8; i++) { |
5799 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
5800 | } |
5801 | } |
5802 | |
5803 | TEST(onnx, importClipV11) { |
5804 | // Test loading Clip in opset v11 format where min(-2) and max(2) are passed |
5805 | // as inputs. |
5806 | ExecutionEngine EE; |
5807 | auto &mod = EE.getModule(); |
5808 | std::string netFilename(GLOW_DATA_PATH |
5809 | "tests/models/onnxModels/clipv11.onnxtxt" ); |
5810 | auto *F = mod.createFunction("main" ); |
5811 | PlaceholderBindings bindings; |
5812 | Placeholder *output_0; |
5813 | |
5814 | Tensor X(ElemKind::FloatTy, {1, 2, 2, 2}); |
5815 | X.getHandle() = {-3, -2, -1, 0, 1, 2, 3, 4}; |
5816 | |
5817 | { |
5818 | ONNXModelLoader onnxLD(netFilename, {"X" }, {&X.getType()}, *F); |
5819 | output_0 = EXIT_ON_ERR(onnxLD.getOutputByName("output0" )); |
5820 | bindings.allocate(mod.getPlaceholders()); |
5821 | updateInputPlaceholdersByName(bindings, &mod, {"X" }, {&X}); |
5822 | } |
5823 | |
5824 | EE.compile(CompilationMode::Infer); |
5825 | EE.run(bindings); |
5826 | |
5827 | std::vector<dim_t> expectedDims = {1, 2, 2, 2}; |
5828 | std::vector<float> expectedValues = {-2, -2, -1, 0, 1, 2, 2, 2}; |
5829 | auto result = bindings.get(output_0)->getHandle(); |
5830 | EXPECT_EQ(result.dims().vec(), expectedDims); |
5831 | |
5832 | for (size_t i = 0; i < 8; i++) { |
5833 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
5834 | } |
5835 | } |
5836 | |
5837 | // Utility function to test ONNX Softmax |
5838 | static void testSoftmax(const std::string &modelName, |
5839 | const std::vector<dim_t> &expectedDims, |
5840 | const std::vector<float> &expectedValues) { |
5841 | ExecutionEngine EE{}; |
5842 | auto &mod = EE.getModule(); |
5843 | Function *F = mod.createFunction("main" ); |
5844 | |
5845 | // Input. |
5846 | Tensor x(ElemKind::FloatTy, {2, 2, 2, 2}); |
5847 | x.getHandle() = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, |
5848 | 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0}; |
5849 | |
5850 | // Load model. |
5851 | std::string netFilename = |
5852 | std::string(GLOW_DATA_PATH "tests/models/onnxModels/" ) + modelName; |
5853 | ONNXModelLoader onnxLD(netFilename, {"x" }, {&x.getType()}, *F); |
5854 | Placeholder *output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5855 | |
5856 | // Allocate placeholders. |
5857 | PlaceholderBindings bindings; |
5858 | bindings.allocate(mod.getPlaceholders()); |
5859 | updateInputPlaceholdersByName(bindings, &mod, {"x" }, {&x}); |
5860 | |
5861 | auto *res = bindings.get(output); |
5862 | EE.compile(CompilationMode::Infer); |
5863 | EE.run(bindings); |
5864 | |
5865 | // Compare results. |
5866 | auto result = res->getHandle(); |
5867 | EXPECT_TRUE(result.dims().vec() == expectedDims); |
5868 | for (dim_t i = 0; i < result.size(); i++) { |
5869 | EXPECT_FLOAT_EQ(result.raw(i), expectedValues[i]); |
5870 | } |
5871 | } |
5872 | |
5873 | /// Test loading Softmax from a ONNX model. |
5874 | TEST_F(OnnxImporterTest, softmax) { |
5875 | testSoftmax("softmax11.onnxtxt" , {2, 2, 2, 2}, |
5876 | {5.7661277e-04, 1.5673960e-03, 4.2606238e-03, 1.1581578e-02, |
5877 | 3.1481992e-02, 8.5576929e-02, 2.3262219e-01, 6.3233274e-01, |
5878 | 5.7661277e-04, 1.5673960e-03, 4.2606238e-03, 1.1581578e-02, |
5879 | 3.1481992e-02, 8.5576929e-02, 2.3262219e-01, 6.3233274e-01}); |
5880 | } |
5881 | /// Test loading Softmax opset13 from a ONNX model. |
5882 | TEST_F(OnnxImporterTest, softmax13) { |
5883 | testSoftmax("softmax13.onnxtxt" , {2, 2, 2, 2}, |
5884 | {0.11920292, 0.11920292, 0.880797, 0.880797, 0.11920292, |
5885 | 0.11920292, 0.880797, 0.880797, 0.11920292, 0.11920292, 0.880797, |
5886 | 0.880797, 0.11920292, 0.11920292, 0.880797, 0.880797}); |
5887 | } |
5888 | |
5889 | /// Test loading Conv model with auto_pad=NOTSET from an ONNX model. |
5890 | TEST_F(OnnxImporterTest, importConvPadNotset) { |
5891 | ExecutionEngine EE; |
5892 | auto &mod = EE.getModule(); |
5893 | auto *F = mod.createFunction("main" ); |
5894 | std::string netFilename(GLOW_DATA_PATH |
5895 | "tests/models/onnxModels/convPadNotset.onnxtxt" ); |
5896 | Placeholder *output; |
5897 | { |
5898 | ONNXModelLoader onnxLD(netFilename, {}, {}, *F); |
5899 | output = EXIT_ON_ERR(onnxLD.getSingleOutput()); |
5900 | } |
5901 | ASSERT_EQ(mod.getPlaceholders().size(), 2); |
5902 | // Each Conv2D is loaded as 4 operations: input Transpose, filter Transpose, |
5903 | // Conv2D node and output Transpose. |
5904 | ASSERT_EQ(F->getNodes().size(), 11); |
5905 | auto *save = getSaveNodeFromDest(output); |
5906 | ASSERT_TRUE(save); |
5907 | auto *trans1 = llvm::dyn_cast<TransposeNode>(save->getInput().getNode()); |
5908 | ASSERT_TRUE(trans1); |
5909 | auto *trans2 = llvm::dyn_cast<TransposeNode>(trans1->getInput().getNode()); |
5910 | ASSERT_TRUE(trans2); |
5911 | auto *conv1 = llvm::dyn_cast<ConvolutionNode>(trans2->getInput().getNode()); |
5912 | ASSERT_TRUE(conv1); |
5913 | auto *trans3 = llvm::dyn_cast<TransposeNode>(conv1->getInput().getNode()); |
5914 | ASSERT_TRUE(trans3); |
5915 | auto *trans4 = llvm::dyn_cast<TransposeNode>(trans3->getInput().getNode()); |
5916 | ASSERT_TRUE(trans4); |
5917 | auto *conv2 = llvm::dyn_cast<ConvolutionNode>(trans4->getInput().getNode()); |
5918 | ASSERT_TRUE(conv2); |
5919 | EXPECT_EQ(conv2->getPads().vec(), std::vector<unsigned_t>({1, 1, 1, 1})); |
5920 | EXPECT_EQ(conv1->getPads().vec(), std::vector<unsigned_t>({0, 0, 0, 0})); |
5921 | } |
5922 | |
5923 | /// Test loading LogSoftmax opset13 from a ONNX model. |
5924 | TEST_F(OnnxImporterTest, logsoftmax) { |
5925 | testSoftmax("logsoftmax.onnxtxt" , {2, 2, 2, 2}, |
5926 | {-2.1269281, -2.1269281, -0.12692806, -0.12692806, -2.1269281, |
5927 | -2.1269281, -0.12692806, -0.12692806, -2.1269281, -2.1269281, |
5928 | -0.12692806, -0.12692806, -2.1269281, -2.1269281, -0.12692806, |
5929 | -0.12692806}); |
5930 | } |
5931 | |