ArmNN
 22.05
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DepthwiseConvolution2D.cpp File Reference
#include "ParserFlatbuffersFixture.hpp"

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Functions

 TEST_SUITE ("TensorflowLiteParser_DepthwiseConvolution2D")
 

Function Documentation

◆ TEST_SUITE()

TEST_SUITE ( "TensorflowLiteParser_DepthwiseConvolution2D"  )

Definition at line 9 of file DepthwiseConvolution2D.cpp.

References ParserFlatbuffersFixture::SetupSingleInputSingleOutput(), and TEST_CASE_FIXTURE().

10 {
11 struct DepthwiseConvolution2dFixture : public ParserFlatbuffersFixture
12 {
13  explicit DepthwiseConvolution2dFixture(const std::string& inputShape,
14  const std::string& outputShape,
15  const std::string& filterShape,
16  const std::string& filterData,
17  const std::string& strides,
18  const std::string& paddingType,
19  const std::string biasShape = "",
20  const std::string biasData = "")
21  {
22  std::string inputTensors = "[ 0, 2 ]";
23  std::string biasTensor = "";
24  std::string biasBuffer = "";
25  if (biasShape.size() > 0 && biasData.size() > 0)
26  {
27  inputTensors = "[ 0, 2, 3 ]";
28  biasTensor = R"(
29  {
30  "shape": )" + biasShape + R"( ,
31  "type": "INT32",
32  "buffer": 3,
33  "name": "biasTensor",
34  "quantization": {
35  "min": [ 0.0 ],
36  "max": [ 255.0 ],
37  "scale": [ 1.0 ],
38  "zero_point": [ 0 ],
39  }
40  } )";
41  biasBuffer = R"(
42  { "data": )" + biasData + R"(, }, )";
43  }
44  m_JsonString = R"(
45  {
46  "version": 3,
47  "operator_codes": [ { "builtin_code": "DEPTHWISE_CONV_2D" } ],
48  "subgraphs": [ {
49  "tensors": [
50  {
51  "shape": )" + inputShape + R"(,
52  "type": "UINT8",
53  "buffer": 0,
54  "name": "inputTensor",
55  "quantization": {
56  "min": [ 0.0 ],
57  "max": [ 255.0 ],
58  "scale": [ 1.0 ],
59  "zero_point": [ 0 ],
60  }
61  },
62  {
63  "shape": )" + outputShape + R"(,
64  "type": "UINT8",
65  "buffer": 1,
66  "name": "outputTensor",
67  "quantization": {
68  "min": [ 0.0 ],
69  "max": [ 511.0 ],
70  "scale": [ 2.0 ],
71  "zero_point": [ 0 ],
72  }
73  },
74  {
75  "shape": )" + filterShape + R"(,
76  "type": "UINT8",
77  "buffer": 2,
78  "name": "filterTensor",
79  "quantization": {
80  "min": [ 0.0 ],
81  "max": [ 255.0 ],
82  "scale": [ 1.0 ],
83  "zero_point": [ 0 ],
84  }
85  }, )" + biasTensor + R"(
86  ],
87  "inputs": [ 0 ],
88  "outputs": [ 1 ],
89  "operators": [
90  {
91  "opcode_index": 0,
92  "inputs": )" + inputTensors + R"(,
93  "outputs": [ 1 ],
94  "builtin_options_type": "DepthwiseConv2DOptions",
95  "builtin_options": {
96  "padding": ")" + paddingType + R"(",
97  "stride_w": )" + strides+ R"(,
98  "stride_h": )" + strides+ R"(,
99  "depth_multiplier": 1,
100  "fused_activation_function": "NONE"
101  },
102  "custom_options_format": "FLEXBUFFERS"
103  }
104  ],
105  } ],
106  "buffers" : [
107  { },
108  { },
109  { "data": )" + filterData + R"(, }, )"
110  + biasBuffer + R"(
111  ]
112  }
113  )";
114  SetupSingleInputSingleOutput("inputTensor", "outputTensor");
115  }
116 };
117 
118 struct DepthwiseConvolution2dSameFixture : DepthwiseConvolution2dFixture
119 {
120  DepthwiseConvolution2dSameFixture()
121  : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape
122  "[ 1, 3, 3, 1 ]", // outputShape
123  "[ 1, 3, 3, 1 ]", // filterShape
124  "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData
125  "1", // stride w and h
126  "SAME") // padding type
127  {}
128 };
129 
130 TEST_CASE_FIXTURE(DepthwiseConvolution2dSameFixture, "ParseDepthwiseConv2DSame")
131 {
132  RunTest<4, armnn::DataType::QAsymmU8>(
133  0,
134  { 0, 1, 2,
135  3, 4, 5,
136  6, 7, 8 },
137  // the expected values were generated using the example python implementation at
138  // https://eli.thegreenplace.net/2018/depthwise-separable-convolutions-for-machine-learning/
139  // divide the expected values by the output scale, as it is not 1.0
140  { 14/2, 35/2, 38/2,
141  57/2, 120/2, 111/2,
142  110/2, 197/2, 158/2 });
143 }
144 
145 struct DepthwiseConvolution2dValidFixture : DepthwiseConvolution2dFixture
146 {
147  DepthwiseConvolution2dValidFixture ()
148  : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape
149  "[ 1, 1, 1, 1 ]", // outputShape
150  "[ 1, 3, 3, 1 ]", // filterShape
151  "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData
152  "1", // stride w and h
153  "VALID") // padding type
154  {}
155 };
156 
157 TEST_CASE_FIXTURE(DepthwiseConvolution2dValidFixture, "ParseDepthwiseConv2DValid")
158 {
159  RunTest<4, armnn::DataType::QAsymmU8>(
160  0,
161  { 0, 1, 2,
162  3, 4, 5,
163  6, 7, 8 },
164  // divide the expected values by the output scale, as it is not 1.0
165  { 120/2 });
166 }
167 
168 struct DepthwiseConvolution2dSameBiasFixture : DepthwiseConvolution2dFixture
169 {
170  DepthwiseConvolution2dSameBiasFixture()
171  : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape
172  "[ 1, 3, 3, 1 ]", // outputShape
173  "[ 1, 3, 3, 1 ]", // filterShape
174  "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData
175  "1", // stride w and h
176  "SAME", // padding type
177  "[ 1 ]", // biasShape
178  "[ 10, 0, 0, 0 ]") // biasData
179  {}
180 };
181 
182 TEST_CASE_FIXTURE(DepthwiseConvolution2dSameBiasFixture, "ParseDepthwiseConv2DSameBias")
183 {
184  RunTest<4, armnn::DataType::QAsymmU8>(
185  0,
186  { 0, 1, 2,
187  3, 4, 5,
188  6, 7, 8 },
189  // divide the expected values by the output scale, as it is not 1.0
190  { ( 14+10)/2, ( 35+10)/2, ( 38+10)/2,
191  ( 57+10)/2, (120+10)/2, (111+10)/2,
192  (110+10)/2, (197+10)/2, (158+10)/2 });
193 }
194 
195 struct DynamicDepthwiseConvolution2dSameBiasFixture : DepthwiseConvolution2dFixture
196 {
197  DynamicDepthwiseConvolution2dSameBiasFixture()
198  : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape
199  "[ ]", // outputShape
200  "[ 1, 3, 3, 1 ]", // filterShape
201  "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData
202  "1", // stride w and h
203  "SAME", // padding type
204  "[ 1 ]", // biasShape
205  "[ 10, 0, 0, 0 ]") // biasData
206  {}
207 };
208 
209 TEST_CASE_FIXTURE(DynamicDepthwiseConvolution2dSameBiasFixture, "ParseDynamicDepthwiseConv2DSameBias")
210 {
211  RunTest<4, armnn::DataType::QAsymmU8, armnn::DataType::QAsymmU8>(0,
212  { { "inputTensor", { 0, 1, 2,
213  3, 4, 5,
214  6, 7, 8 } } },
215  { { "outputTensor", { ( 14+10)/2, ( 35+10)/2, ( 38+10)/2,
216  ( 57+10)/2, (120+10)/2, (111+10)/2,
217  (110+10)/2, (197+10)/2, (158+10)/2 } } },
218  true);
219 }
220 
221 struct DepthwiseConvolution2dFixture2 : public ParserFlatbuffersFixture
222 {
223  explicit DepthwiseConvolution2dFixture2(const std::string& inputShape,
224  const std::string& outputShape,
225  const std::string& filterShape,
226  const std::string& filterData,
227  const std::string& strides,
228  const std::string& paddingType,
229  const std::string biasShape = "",
230  const std::string biasData = "",
231  const std::string filter_quant_min = "[ 0.0 ]",
232  const std::string filter_quant_max = "[ 255.0 ]",
233  const std::string filter_quant_scale = "[ 1.0 ]",
234  const std::string filter_quant_zero_point = "[ 0 ]",
235  const std::string filter_quant_axis = "",
236  const std::string output_scale = "[ 1.0 ]")
237  {
238  std::string inputTensors = "[ 0, 2 ]";
239  std::string biasTensor = "";
240  std::string biasBuffer = "";
241  if (biasShape.size() > 0 && biasData.size() > 0)
242  {
243  inputTensors = "[ 0, 2, 3 ]";
244  biasTensor = R"(
245  {
246  "shape": )" + biasShape + R"( ,
247  "type": "INT32",
248  "buffer": 3,
249  "name": "biasTensor",
250  "quantization": {
251  "min": [ 0.0 ],
252  "max": [ 255.0 ],
253  "scale": [ 1.0 ],
254  "zero_point": [ 0 ],
255  }
256  } )";
257  biasBuffer = R"(
258  { "data": )" + biasData + R"(, }, )";
259  }
260 
261  std::string filter_qantization =
262  R"(
263  "min": )" + filter_quant_min + R"(,
264  "max": )" + filter_quant_max + R"(,
265  "scale": )" + filter_quant_scale + R"(,
266  "zero_point": )" + filter_quant_zero_point;
267  // A given quantization axis indicates if per channel quantization is used for filters
268  if (filter_quant_axis.size() > 0)
269  {
270  filter_qantization +=
271  R"(,
272  "quantized_dimension": )" + filter_quant_axis;
273  }
274  m_JsonString = R"(
275  {
276  "version": 3,
277  "operator_codes": [ { "builtin_code": "DEPTHWISE_CONV_2D" } ],
278  "subgraphs": [ {
279  "tensors": [
280  {
281  "shape": )" + inputShape + R"(,
282  "type": "INT8",
283  "buffer": 0,
284  "name": "inputTensor",
285  "quantization": {
286  "min": [ 0.0 ],
287  "max": [ 255.0 ],
288  "scale": [ 1.0 ],
289  "zero_point": [ 0 ],
290  }
291  },
292  {
293  "shape": )" + outputShape + R"(,
294  "type": "INT8",
295  "buffer": 1,
296  "name": "outputTensor",
297  "quantization": {
298  "min": [ 0.0 ],
299  "max": [ 511.0 ],
300  "scale": )" + output_scale + R"(,
301  "zero_point": [ 0 ],
302  }
303  },
304  {
305  "shape": )" + filterShape + R"(,
306  "type": "INT8",
307  "buffer": 2,
308  "name": "filterTensor",
309  "quantization": {)" + filter_qantization + R"(
310  }
311  }, )" + biasTensor + R"(
312  ],
313  "inputs": [ 0 ],
314  "outputs": [ 1 ],
315  "operators": [
316  {
317  "opcode_index": 0,
318  "inputs": )" + inputTensors + R"(,
319  "outputs": [ 1 ],
320  "builtin_options_type": "DepthwiseConv2DOptions",
321  "builtin_options": {
322  "padding": ")" + paddingType + R"(",
323  "stride_w": )" + strides+ R"(,
324  "stride_h": )" + strides+ R"(,
325  "depth_multiplier": 1,
326  "fused_activation_function": "NONE"
327  },
328  "custom_options_format": "FLEXBUFFERS"
329  }
330  ],
331  } ],
332  "buffers" : [
333  { },
334  { },
335  { "data": )" + filterData + R"(, }, )"
336  + biasBuffer + R"(
337  ]
338  }
339  )";
340  SetupSingleInputSingleOutput("inputTensor", "outputTensor");
341  }
342 };
343 
344 
345 // No quantization meaning scale=1.0 and offset=0.0 and tensor quantization
346 struct DepthwiseConvolution2dNoQuantFixture : DepthwiseConvolution2dFixture2
347 {
348  DepthwiseConvolution2dNoQuantFixture()
349  : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
350  "[ 1, 3, 3, 3 ]", // outputShape
351  "[ 1, 3, 3, 3 ]", // filterShape
352  "[ 9,8,7, 6,5,4, 3,2,1, "
353  "9,8,7, 6,5,4, 3,2,1, "
354  "9,8,7, 6,5,4, 3,2,1 ]", // filterData
355  "1", // stride w and h
356  "SAME", // padding type
357  "", // bias shape
358  "" // bias data
359  )
360  {}
361 };
362 
363 // No quantization meaning scale=1.0 and offset=0.0 and tensor quantization
364 TEST_CASE_FIXTURE(DepthwiseConvolution2dNoQuantFixture, "ParseDepthwiseConv2DNoQuant")
365 {
366  RunTest<4, armnn::DataType::QAsymmS8>(
367  0,
368  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
369  { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,
370  36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});
371 }
372 
373 // Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0
374 struct DepthwiseConvolution2dNoChannelQuantFixture : DepthwiseConvolution2dFixture2
375 {
376  DepthwiseConvolution2dNoChannelQuantFixture()
377  : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
378  "[ 1, 3, 3, 3 ]", // outputShape
379  "[ 1, 3, 3, 3 ]", // filterShape
380  "[ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]", //filterData
381  "1", // stride w and h
382  "SAME", // padding type
383  "", // bias shape
384  "", // bias data
385  "[ 0.0 ]", // filter quantization min values
386  "[ 255.0 ]", // filter quantization max values
387  "[ 1.0, 1.0, 1.0]", // filter quantization scales
388  "[ 0, 0, 0]", // filter quantization zero-points
389  "3" // filter quantized axis
390  // (in case of per channel quantization)
391  )
392  {}
393 };
394 
395 // Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0
396 TEST_CASE_FIXTURE(DepthwiseConvolution2dNoChannelQuantFixture, "ParseDepthwiseConv2DFilterNoChannelQuant")
397 {
398  RunTest<4, armnn::DataType::QAsymmS8>(
399  0,
400  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
401  { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,
402  36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});
403 }
404 
405 // Uses per channel quantization on weights but all scales are set to the same value
406 struct DepthwiseConvolution2dWeightsPerChannelQuantFixture : DepthwiseConvolution2dFixture2
407 {
408  DepthwiseConvolution2dWeightsPerChannelQuantFixture()
409  : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
410  "[ 1, 3, 3, 3 ]", // outputShape
411  "[ 1, 3, 3, 3 ]", // filterShape
412  // filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]
413  // quantized per channel with q_dim=3
414  "[36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, "
415  "20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 8, 4]",
416  "1", // stride w and h
417  "SAME", // padding type
418  "", // bias shape
419  "", // bias data
420  "[ 0.0 ]", // filter quantization min values
421  "[ 255.0 ]", // filter quantization max values
422  "[ 0.25, 0.25, 0.25]", // filter quantization scales
423  "[ 0, 0, 0]", // filter quantization zero-points
424  "3" // filter quantized axis
425  // (in case of per channel quantization)
426  )
427  {}
428 };
429 
430 // Weights are per channel quantized but all scales are set to the same value
431 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuantFixture,
432  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant")
433 {
434  RunTest<4, armnn::DataType::QAsymmS8>(
435  0,
436  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
437  { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,
438  36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});
439 }
440 
441 // Uses per channel quantization on weights all scales are different in this test
442 struct DepthwiseConvolution2dWeightsPerChannelQuant1Fixture : DepthwiseConvolution2dFixture2
443 {
444  DepthwiseConvolution2dWeightsPerChannelQuant1Fixture()
445  : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
446  "[ 1, 3, 3, 3 ]", // outputShape
447  "[ 1, 3, 3, 3 ]", // filterShape
448  // filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]
449  // quantized per channel with q_dim=3
450  "[36, 40, 70, 24, 25, 40, 12, 10, 10, 36, 40, 70, 24, "
451  "25, 40, 12, 10, 10, 36, 40, 70, 24, 25, 40, 12, 10, 10]",
452  "1", // stride w and h
453  "SAME", // padding type
454  "", // bias shape
455  "", // bias data
456  "[ 0.0 ]", // filter quantization min values
457  "[ 255.0 ]", // filter quantization max values
458  "[ 0.25, 0.2, 0.1]", // filter quantization scales
459  "[ 0, 0, 0]", // filter quantization zero-points
460  "3" // filter quantized axis
461  // (in case of per channel quantization)
462  )
463  {}
464 };
465 
466 // Uses per channel quantization on weights all scales are different in this test
467 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant1Fixture,
468  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant1")
469 {
470  RunTest<4, armnn::DataType::QAsymmS8>(
471  0,
472  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
473  { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,
474  36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});
475 }
476 
477 
478 // Uses per channel quantization on weights all scales are different in this test
479 // Uses different shape for weights and input compared to the other tests above
480 struct DepthwiseConvolution2dWeightsPerChannelQuant2Fixture : DepthwiseConvolution2dFixture2
481 {
482  DepthwiseConvolution2dWeightsPerChannelQuant2Fixture()
483  : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape
484  "[ 1, 4, 4, 4 ]", // outputShape
485  "[ 1, 2, 2, 4 ]", // filterShape
486  // filterData is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ]
487  // quantized per channel with q_dim=3
488  "[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]",
489  "1", // stride w and h
490  "SAME", // padding type
491  "", // bias shape
492  "", // bias data
493  "[ 0.0 ]", // filter quantization min values
494  "[ 255.0 ]", // filter quantization max values
495  "[ 0.25, 0.2, 0.1, 0.3]", // filter quantization scales
496  "[ 0, 0, 0, 0]", // filter quantization zero-points
497  "3" // filter quantized axis
498  // (in case of per channel quantization)
499  )
500  {}
501 };
502 
503 // Uses per channel quantization on weights all scales are different in this test
504 // Uses different shape for weights and input compared to the other tests above
505 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant2Fixture,
506  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant2")
507 {
508  RunTest<4, armnn::DataType::QAsymmS8>(
509  0,
510  { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
511  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
512  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
513  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1},
514  { 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13,
515  21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13,
516  21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13,
517  14, 12, 10, 8, 14, 12, 10, 8, 14, 12, 10, 8, 9, 8, 7, 6});
518 }
519 
520 // Test for depthwise_multiplier different to one (M > 1)
521 struct DepthwiseConvolution2dWeightsPerChannelQuant4Fixture : DepthwiseConvolution2dFixture2
522 {
523  DepthwiseConvolution2dWeightsPerChannelQuant4Fixture()
524  : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape
525  "[ 1, 4, 4, 16 ]", // outputShape
526  "[ 1, 2, 2, 16 ]", // filterShape
527  // filter data is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3,
528  // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3,
529  // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3,
530  // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ]
531  // quantized per channel with q_dim=3
532  "[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, "
533  "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, "
534  "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, "
535  "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]",
536  "1", // stride w and h
537  "SAME", // padding type
538  "", // bias shape
539  "", // bias data
540  "[ 0.0 ]", // filter quantization min values
541  "[ 255.0 ]", // filter quantization max values
542  "[ 0.25, 0.2, 0.1, 0.3,"
543  "0.25, 0.2, 0.1, 0.3,"
544  "0.25, 0.2, 0.1, 0.3,"
545  "0.25, 0.2, 0.1, 0.3]", // filter quantization scales
546  "[ 0, 0, 0, 0]", // filter quantization zero-points
547  "3" // filter quantized axis
548  // (in case of per channel quantization)
549  )
550  {}
551 };
552 
553 // Test for depthwise_multiplier different to one (M > 1)
554 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4Fixture,
555  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4")
556 {
557  RunTest<4, armnn::DataType::QAsymmS8>(
558  0,
559  { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
560  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
561  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
562  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1},
563  { 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
564  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
565  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
566  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
567  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
568  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
569  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
570  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
571  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
572  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
573  36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
574  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
575  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
576  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
577  18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
578  9, 8, 7, 6, 5, 4, 3, 2, 1, 9, 8, 7, 6, 5, 4, 3});
579 }
580 
581 
582 struct DepthwiseConvolution2dWeightsPerChannelQuant6Fixture : DepthwiseConvolution2dFixture2
583 {
584  DepthwiseConvolution2dWeightsPerChannelQuant6Fixture()
585  : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape
586  "[ 1, 4, 4, 16 ]", // outputShape
587  "[ 1, 2, 2, 16 ]", // filterShape
588  // filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4,
589  // 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1,
590  // 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3,
591  // 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0]
592  // quantized per channel with q_dim=3
593  "[12,20,10, 3, 4,15,30, 6, 4,20,30,12, 4,10,20,12,"
594  " 8, 0,30, 3, 0,10,40, 9,16,15, 0, 3,12,20,40, 3,"
595  " 12,15,20, 0, 0, 0,10, 9,12,10,40,12,12, 5,10, 9,"
596  " 4, 0, 0, 6,12, 0,10, 3,16,10,20, 3, 8,15,20, 0]",
597  "1", // stride w and h
598  "SAME", // padding type
599  "", // bias shape
600  "", // bias data
601  "[ 0.0 ]", // filter quantization min values
602  "[ 255.0 ]", // filter quantization max values
603  "[ 0.25, 0.2, 0.1, 0.333333333,"
604  "0.25, 0.2, 0.1, 0.333333333,"
605  "0.25, 0.2, 0.1, 0.333333333,"
606  "0.25, 0.2, 0.1, 0.333333333]", // filter quantization scales
607  "[ 0, 0, 0, 0]", // filter quantization zero-points
608  "3" // filter quantized axis
609  // (in case of per channel quantization)
610  )
611  {}
612 };
613 
614 
615 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant6Fixture,
616  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant6")
617 {
618  RunTest<4, armnn::DataType::QAsymmS8>(
619  0,
620  { 1,0,1,2,0,4,4,0,2,1,2,0,1,3,3,0,
621  1,2,2,3,3,4,1,1,2,4,1,3,4,2,0,2,
622  0,3,1,3,4,3,2,0,1,2,3,3,0,2,4,2,
623  1,2,1,4,3,4,1,3,1,0,2,3,1,3,2,0},
624  { 9, 7, 3, 7,12, 8,22,22,27,22,13,17,13,10, 9,17,
625  15, 9,12, 6,16,14,24,27,19,26,18,23, 9,10, 7, 3,
626  18,14, 9,11, 7, 9,21,25,17,19,10,15,13, 9, 7, 9,
627  15,16, 9, 1, 3, 9,11,12, 3,12, 9,12, 6, 2, 2, 6,
628  13, 4,10,12,11,14,28,28,17,17,14,15,15,13,13,22,
629  26,24,17, 7,10,20,33,31,23,17,17,16,16,23,20, 7,
630  17,11,16, 6,10,16,24,22,26,18,23,20,22,23,21,23,
631  12,16, 4, 4, 2, 6, 8,10,12, 8,16,16, 8, 6, 6,14,
632  14, 3,14,10,15,15,27,25,16,14, 9,11,21,19,16,24,
633  24,25,13, 7, 3,13,21,24,25,23,14,17,24,24,21,12,
634  7, 7, 3, 3,11,10,17,13,33,32,21,26,18,17,17,23,
635  3, 3, 2, 0, 2, 6, 9,13,10,20,20,24, 2, 4, 4, 8,
636  9, 4,10, 4, 2,14,22,16, 5, 7, 3, 5,13,20,20,19,
637  11,12, 6, 4, 4,12,12, 8, 9,10, 3, 6,12,18,18,15,
638  5, 4, 4, 2, 0, 6,12, 9,10,14, 6,10, 3, 6, 6,12,
639  3, 4, 1, 1, 3, 9, 9, 6, 2, 8, 6, 8, 0, 0, 0, 0});
640 }
641 
642 
643 struct DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture : DepthwiseConvolution2dFixture2
644 {
645  DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture()
646  : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
647  "[ 1, 3, 3, 3 ]", // outputShape
648  "[ 1, 3, 3, 3 ]", // filterShape
649  // filterData is [ 1,4,0,2,4,3,1,0,1,
650  // 3,0,4,0,1,3,4,2,4,
651  // 3,0,3,4,4,0,3,4,2]
652  // quantized per channel with q_dim=3
653  "[ 4,20, 0, 8,20,30, 4, 0,10,12,"
654  " 0,40, 0, 5,30,16,10,40,12, 0,"
655  "30,16,20, 0,12,20,20]",
656  "1", // stride w and h
657  "SAME", // padding type
658  "", // bias shape
659  "", // bias data
660  "[ 0.0 ]", // filter quantization min values
661  "[ 255.0 ]", // filter quantization max values
662  "[ 0.25, 0.2, 0.1]", // filter quantization scales
663  "[ 0, 0, 0]", // filter quantization zero-points
664  "3" // filter quantized axis
665  // (in case of per channel quantization)
666  )
667  {}
668 };
669 
670 
671 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture,
672  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant1_1")
673 {
674  RunTest<4, armnn::DataType::QAsymmS8>(
675  0,
676  { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
677  { 11,11, 9,17,11,16,10, 5,10,
678  14,15,13,21,19,20,13,13,13,
679  7, 7,11,11,11,15, 6, 9,10});
680 }
681 
682 // Same with input different to 1
683 struct DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture : DepthwiseConvolution2dFixture2
684 {
685  DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture()
686  : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
687  "[ 1, 3, 3, 3 ]", // outputShape
688  "[ 1, 3, 3, 3 ]", // filterShape
689  // filterData is [ 1,4,0,2,4,3,1,0,1,
690  // 3,0,4,0,1,3,4,2,4,
691  // 3,0,3,4,4,0,3,4,2]
692  // quantized per channel with q_dim=3
693  "[ 4,20, 0, 8,20,30, 4, 0,10,12,"
694  " 0,40, 0, 5,30,16,10,40,12, 0,"
695  "30,16,20, 0,12,20,20]",
696  "1", // stride w and h
697  "SAME", // padding type
698  "", // bias shape
699  "", // bias data
700  "[ 0.0 ]", // filter quantization min values
701  "[ 255.0 ]", // filter quantization max values
702  "[ 0.25, 0.2, 0.1]", // filter quantization scales
703  "[ 0, 0, 0]", // filter quantization zero-points
704  "3" // filter quantized axis
705  // (in case of per channel quantization)
706  )
707  {}
708 };
709 
710 
711 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture,
712  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant1_2")
713 {
714  RunTest<4, armnn::DataType::QAsymmS8>(
715  0,
716  { 3,2,0,0,4,3,0,1,2,
717  0,1,3,0,4,2,2,2,3,
718  2,4,3,2,0,4,3,4,0},
719  { 0,30,16,15,30,32, 8, 9,24,
720  20,33,28,34,48,50,18,38,35,
721  8, 8,36,20,28,33,10,28,25});
722 }
723 
724 
725 struct DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture : DepthwiseConvolution2dFixture2
726 {
727  DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture()
728  : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape
729  "[ 1, 4, 4, 16 ]", // outputShape
730  "[ 1, 2, 2, 16 ]", // filterShape
731  // filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4,
732  // 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1,
733  // 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3,
734  // 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0 ]
735  // quantized per channel with q_dim=3
736  "[12,20,10, 3, 4,15,30, 6, 4,20,30,13, 4,10,20,13,"
737  " 8, 0,30, 3, 0,10,40,10,16,15, 0, 3,12,20,40, 3,"
738  " 12,15,20, 0, 0, 0,10,10,12,10,40,13,12, 5,10,10,"
739  " 4, 0, 0, 6,12, 0,10, 3,16,10,20, 3, 8,15,20, 0]",
740  "1", // stride w and h
741  "SAME", // padding type
742  "", // bias shape
743  "", // bias data
744  "[ 0.0 ]", // filter quantization min values
745  "[ 255.0 ]", // filter quantization max values
746  "[ 0.25, 0.2, 0.1, 0.3,"
747  "0.25, 0.2, 0.1, 0.3,"
748  "0.25, 0.2, 0.1, 0.3,"
749  "0.25, 0.2, 0.1, 0.3]", // filter quantization scales
750  "[ 0, 0, 0, 0]", // filter quantization zero-points
751  "3" // filter quantized axis
752  // (in case of per channel quantization)
753  )
754  {}
755 };
756 
757 
758 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture,
759  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_1")
760 {
761  RunTest<4, armnn::DataType::QAsymmS8>(
762  0,
763  { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
764  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
765  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
766  1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1},
767  { 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,
768  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,
769  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,
770  6, 7, 3, 1, 1, 3, 4, 5, 4, 6, 7, 8, 4, 3, 3, 7,
771  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,
772  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,
773  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,
774  6, 7, 3, 1, 1, 3, 4, 5, 4, 6, 7, 8, 4, 3, 3, 7,
775  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,
776  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,
777  9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8,
778  6, 7, 3, 1, 1, 3, 4, 5, 4, 6, 7, 8, 4, 3, 3, 7,
779  5, 4, 4, 2, 1, 5, 7, 5, 5, 7, 3, 5, 4, 6, 6, 5,
780  5, 4, 4, 2, 1, 5, 7, 5, 5, 7, 3, 5, 4, 6, 6, 5,
781  5, 4, 4, 2, 1, 5, 7, 5, 5, 7, 3, 5, 4, 6, 6, 5,
782  3, 4, 1, 1, 1, 3, 3, 2, 1, 4, 3, 4, 1, 2, 2, 4});
783 }
784 
785 
786 
787 struct DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture : DepthwiseConvolution2dFixture2
788 {
789  DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture()
790  : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape
791  "[ 1, 4, 4, 16 ]", // outputShape
792  "[ 1, 2, 2, 16 ]", // filterShape
793  // filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4,
794  // 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1,
795  // 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3,
796  // 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0 ]
797  // quantized per channel with q_dim=3
798  "[12,20,10, 3, 4,15,30, 6, 4,20,30,13, 4,10,20,13,"
799  " 8, 0,30, 3, 0,10,40,10,16,15, 0, 3,12,20,40, 3,"
800  " 12,15,20, 0, 0, 0,10,10,12,10,40,13,12, 5,10,10,"
801  " 4, 0, 0, 6,12, 0,10, 3,16,10,20, 3, 8,15,20, 0]",
802  "1", // stride w and h
803  "SAME", // padding type
804  "", // bias shape
805  "", // bias data
806  "[ 0.0 ]", // filter quantization min values
807  "[ 255.0 ]", // filter quantization max values
808  "[ 0.25, 0.2, 0.1, 0.3,"
809  "0.25, 0.2, 0.1, 0.3,"
810  "0.25, 0.2, 0.1, 0.3,"
811  "0.25, 0.2, 0.1, 0.3]", // filter quantization scales
812  "[ 0, 0, 0, 0]", // filter quantization zero-points
813  "3" // filter quantized axis
814  // (in case of per channel quantization)
815  )
816  {}
817 };
818 
819 
820 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture,
821  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_2")
822 {
823  RunTest<4, armnn::DataType::QAsymmS8>(
824  0,
825  { 3,3,3,4, 4,4,0,0, 0,3,4,3, 0,2,2,3,
826  3,0,3,0, 0,3,2,1, 4,1,2,2, 0,0,0,4,
827  3,2,2,2, 2,1,0,4, 4,3,2,4, 3,2,0,0,
828  4,1,4,4, 1,0,4,3, 3,2,0,3, 1,1,0,2},
829  { 26,21,21, 7,12,17,28,21,20,22,25,26, 6,11,10,16,
830  16,16, 4,12, 7,18,28,27,30,20,12,14,16,19,17, 6,
831  12,12, 8, 0, 3,13,18,15,18,26,20,26,26,32,28,21,
832  0, 0, 0, 0, 2, 6, 6, 4, 2, 8, 6, 8,15,10,10,24,
833  20,21, 9, 7, 3, 6,15,16,17,22,17,22,17,18,14, 7,
834  18, 6,16,12,12,11,17,15,18,18,10,12,27,26,22,18,
835  27,28,12,10, 7, 3, 8,13, 8,12,14,16,26,24,24,24,
836  9, 9, 6, 0, 0, 0, 2, 6, 0, 0, 0, 0, 4, 8, 8,16,
837  26,24,17, 7, 2, 8,11,10,30,24,30,28,32,33,30,24,
838  20,11,16,12, 7, 9,17,13,20,14,16,18,31,36,33,29,
839  28,25,19, 9, 6,13,20,19, 2, 8, 6, 8,17,17,15,25,
840  12,15, 5, 3, 2, 6, 7, 7, 0, 0, 0, 0, 6, 2, 2, 6,
841  14,16, 7, 5, 1, 3, 3, 2,20,28,12,20,13,20,20,19,
842  9, 4,10, 4, 0, 4, 8, 6, 4,16,12,16,12,18,18,15,
843  11,12, 6, 4, 2, 8,10, 7, 0, 0, 0, 0, 9,14,14,14,
844  3, 4, 1, 1, 1, 3, 3, 2, 0, 0, 0, 0, 2, 4, 4, 8});
845 }
846 
847 
848 struct DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture : DepthwiseConvolution2dFixture2
849 {
850  DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture()
851  : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape
852  "[ 1, 4, 4, 16 ]", // outputShape
853  "[ 1, 2, 2, 16 ]", // filterShape
854  // filter data is [ 1, 4, 9, 16, 25, 36,
855  // 49, 64, 81, 100, 121, 144,
856  // 169, 196, 225, 256, 17, 36,
857  // 57, 80, 105, 132, 161, 192,
858  // 225, 260, 297, 336, 377, 420,
859  // 465, 512, 33, 68, 105, 144,
860  // 185, 228, 273, 320, 369, 420,
861  // 473, 528, 585, 644, 705, 768,
862  // 49, 100, 153, 208, 265, 324,
863  // 385, 448, 513, 580, 649, 720,
864  // 793, 868, 945,1024 ]
865  // quantized per channel with q_dim=3
866  "[ 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15,16,"
867  " 17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,"
868  " 33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,"
869  "49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64]",
870  "1", // stride w and h
871  "SAME", // padding type
872  "", // bias shape
873  "", // bias data
874  "[ 0.0 ]", // filter quantization min values
875  "[ 255.0 ]", // filter quantization max values
876  "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,15,16]", // filter quantization scales
877  "[ 0, 0, 0, 0]", // filter quantization zero-points
878  "3", // filter quantized axis
879  // (in case of per channel quantization)
880  "[ 100.0 ]" // output scale
881  )
882  {}
883 };
884 
885 // Test for depthwise_multiplier different to one (M > 1)
886 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture,
887  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_5")
888 {
889  RunTest<4, armnn::DataType::QAsymmS8>(
890  0,
891  { 1,1,1,2,2,2,1,2,1,2,2,1,2,2,1,1,1,1,1,1,1,2,2,2,
892  1,2,2,2,1,1,1,2,1,1,1,1,2,1,2,1,2,1,1,2,1,2,1,1,
893  1,2,2,1,2,2,1,1,2,1,2,1,1,2,1,2},
894  { 1, 2, 3, 5, 9,11,14,16,17,19,21,24,32,36,39,43,
895  1, 2, 3, 4,11,14,17,20,22,26,29,33,34,38,42,46,
896  1, 2, 3, 5, 8,11,13,16,16,18,21,24,33,36,39,43,
897  0, 0, 1, 1, 2, 3, 3, 4, 4, 5, 5, 6,13,14,16,17,
898  1, 3, 4, 6, 6, 8,10,12,19,22,24,27,23,25,28,30,
899  1, 3, 5, 8, 7, 8,10,12,18,21,24,27,32,36,39,43,
900  1, 2, 4, 5, 8,10,13,15,12,14,16,18,30,33,37,40,
901  0, 0, 1, 1, 3, 4, 5, 7, 4, 5, 5, 6, 9,10,11,12,
902  1, 3, 5, 7,10,12,15,17,17,20,23,25,19,21,23,25,
903  2, 4, 6, 8, 7, 9,11,13,17,20,23,25,23,25,28,30,
904  1, 2, 4, 6, 9,11,14,16,15,17,20,22,28,31,35,38,
905  0, 0, 1, 1, 4, 5, 6, 7, 4, 5, 5, 6,13,14,16,17,
906  0, 0, 1, 1, 2, 3, 4, 5, 3, 4, 5, 6, 5, 6, 6, 7,
907  0, 0, 1, 1, 1, 2, 2, 3, 5, 6, 7, 8, 5, 6, 6, 7,
908  0, 0, 0, 1, 2, 3, 3, 4, 3, 4, 5, 6, 9,10,11,12,
909  0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 3, 3, 4, 5});
910 }
911 
912 
913 struct DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture : DepthwiseConvolution2dFixture2
914 {
915  DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture()
916  : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape
917  "[ 1, 4, 4, 16 ]", // outputShape
918  "[ 1, 2, 2, 16 ]", // filterShape
919  // filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4,
920  // 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1,
921  // 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3,
922  // 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0 ]
923  // quantized per channel with q_dim=3
924  "[12,20,10, 3, 2,24, 9,10, 5,16,30,12, 3,10, 4,32,"
925  " 8, 0,30, 3, 0,16,12,15,20,12, 0, 3, 9,20, 8, 8,"
926  " 12,15,20, 0, 0, 0, 3,15,15, 8,40,12, 9, 5, 2,24,"
927  " 4, 0, 0, 6, 6, 0, 3, 5,20, 8,20, 3, 6,15, 4, 0]",
928  "1", // stride w and h
929  "SAME", // padding type
930  "", // bias shape
931  "", // bias data
932  "[ 0.0 ]", // filter quantization min values
933  "[ 255.0 ]", // filter quantization max values
934  "[0.25, 0.2, 0.1, 0.3333333333, "
935  "0.5, 0.125, 0.33333333, 0.2, "
936  "0.2, 0.25, 0.1, 0.333333333, "
937  "0.3333333333, 0.2, 0.5, 0.125]", // filter quantization scales
938  "[ 0, 0, 0, 0]", // filter quantization zero-points
939  "3" // filter quantized axis
940  // (in case of per channel quantization)
941  )
942  {}
943 };
944 
945 // Test for depthwise_multiplier different to one (M > 1)
946 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture,
947  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_3_1")
948 {
949  RunTest<4, armnn::DataType::QAsymmS8>(
950  0,
951  { 3,3,3,4, 4,4,0,0, 0,3,4,3, 0,2,2,3,
952  3,0,3,0, 0,3,2,1, 4,1,2,2, 0,0,0,4,
953  3,2,2,2, 2,1,0,4, 4,3,2,4, 3,2,0,0,
954  4,1,4,4, 1,0,4,3, 3,2,0,3, 1,1,0,2},
955  { 26,21,21, 7,12,17,28,21,20,22,25,26, 6,11,10,16,
956  16,16, 4,12, 7,18,28,27,30,20,12,14,16,19,17, 6,
957  12,12, 8, 0, 3,13,18,15,18,26,20,26,26,32,28,21,
958  0, 0, 0, 0, 2, 6, 6, 4, 2, 8, 6, 8,15,10,10,24,
959  20,21, 9, 7, 3, 6,15,16,17,22,17,22,17,18,14, 7,
960  18, 6,16,12,12,11,17,15,18,18,10,12,27,26,22,18,
961  27,28,12,10, 7, 3, 8,13, 8,12,14,16,26,24,24,24,
962  9, 9, 6, 0, 0, 0, 2, 6, 0, 0, 0, 0, 4, 8, 8,16,
963  26,24,17, 7, 2, 8,11,10,30,24,30,28,32,33,30,24,
964  20,11,16,12, 7, 9,17,13,20,14,16,18,31,36,33,29,
965  28,25,19, 9, 6,13,20,19, 2, 8, 6, 8,17,17,15,25,
966  12,15, 5, 3, 2, 6, 7, 7, 0, 0, 0, 0, 6, 2, 2, 6,
967  14,16, 7, 5, 1, 3, 3, 2,20,28,12,20,13,20,20,19,
968  9, 4,10, 4, 0, 4, 8, 6, 4,16,12,16,12,18,18,15,
969  11,12, 6, 4, 2, 8,10, 7, 0, 0, 0, 0, 9,14,14,14,
970  3, 4, 1, 1, 1, 3, 3, 2, 0, 0, 0, 0, 2, 4, 4, 8});
971 }
972 
973 struct DepthwiseConvolution2dWeightsPerChannelQuant4_3_2Fixture : DepthwiseConvolution2dFixture2
974 {
975  DepthwiseConvolution2dWeightsPerChannelQuant4_3_2Fixture()
976  : DepthwiseConvolution2dFixture2("[ 1, 2, 2, 2 ]", // inputShape
977  "[ 1, 2, 2, 4 ]", // outputShape
978  "[ 1, 3, 3, 4 ]", // filterShape
979  // filter data is [ 0,1,2,3,4,5,6,7,8,
980  // 0,1,2,3,4,5,6,7,8,
981  // 0,1,2,3,4,5,6,7,8,
982  // 0,1,2,3,4,5,6,7,8 ]
983  // quantized per channel with q_dim=3
984  "[0, 5,20, 9,16,25,60,21,32,"
985  " 0,10, 6,12,20,50,18,28,40,"
986  " 0, 3, 8,15,40,15,24,35,80,"
987  " 0, 4,10,30,12,20,30,70,24]",
988  "1", // stride w and h
989  "SAME", // padding type
990  "", // bias shape
991  "", // bias data
992  "[ 0.0 ]", // filter quantization min values
993  "[ 255.0 ]", // filter quantization max values
994  "[0.25, 0.2, 0.1, 0.3333333333]", // filter quantization scales
995  "[ 0, 0, 0, 0]", // filter quantization zero-points
996  "3" // filter quantized axis
997  // (in case of per channel quantization)
998  )
999  {}
1000 };
1001 
1002 // An easy test with M > 1 for debugging
1003 TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_3_2Fixture,
1004  "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_3_2")
1005 {
1006  RunTest<4, armnn::DataType::QAsymmS8>(
1007  0,
1008  { 0,1,2,3,4,5,6,7},
1009  { 38,50,76,92,44,56,66,37,56,50,37,53,62,74,45,61});
1010 }
1011 
1012 } // end of TEST_SUITE("TensorflowLiteParser_DepthwiseConvolution2D")
TEST_CASE_FIXTURE
TEST_CASE_FIXTURE(ClContextControlFixture, "CopyBetweenNeonAndGpu")
Definition: MemCopyTests.cpp:89
ParserFlatbuffersFixture::SetupSingleInputSingleOutput
void SetupSingleInputSingleOutput(const std::string &inputName, const std::string &outputName)
Definition: ParserFlatbuffersFixture.hpp:152