forked from mindspore-Ecosystem/mindspore
multi input multi dimension
This commit is contained in:
zhaozhenlong
parent
411f13bde9
commit
f4f37a9639
|
|
@ -39,7 +39,7 @@
|
|||
#define MSVALID(left, x, right) (MSMIN((MSMAX(left, x)), right))
|
||||
|
||||
#define DIMENSION_4D 4
|
||||
|
||||
#define DIMENSION_6D 6
|
||||
#define kInputIndex 0
|
||||
#define kWeightIndex 1
|
||||
#define kBiasIndex 2
|
||||
|
|
|
|||
|
|
@ -17,11 +17,11 @@
|
|||
#include "nnacl/strided_slice.h"
|
||||
#include "nnacl/errorcode.h"
|
||||
|
||||
void PadStridedSliceParameterTo4D(StridedSliceParameter *param) {
|
||||
int32_t begins[DIMENSION_4D];
|
||||
int32_t ends[DIMENSION_4D];
|
||||
int32_t strides[DIMENSION_4D];
|
||||
int32_t input_shape[DIMENSION_4D];
|
||||
void PadStridedSliceParameterTo6D(StridedSliceParameter *param) {
|
||||
int32_t begins[DIMENSION_6D];
|
||||
int32_t ends[DIMENSION_6D];
|
||||
int32_t strides[DIMENSION_6D];
|
||||
int32_t input_shape[DIMENSION_6D];
|
||||
int32_t i;
|
||||
for (i = 0; i < param->num_axes_; ++i) {
|
||||
begins[i] = param->begins_[i];
|
||||
|
|
@ -37,7 +37,7 @@ void PadStridedSliceParameterTo4D(StridedSliceParameter *param) {
|
|||
}
|
||||
|
||||
int32_t real_index = param->in_shape_length_ - 1;
|
||||
for (i = DIMENSION_4D - 1; i >= 0; --i) {
|
||||
for (i = DIMENSION_6D - 1; i >= 0; --i) {
|
||||
if (real_index >= 0) {
|
||||
param->begins_[i] = begins[real_index];
|
||||
param->ends_[i] = ends[real_index];
|
||||
|
|
@ -50,13 +50,13 @@ void PadStridedSliceParameterTo4D(StridedSliceParameter *param) {
|
|||
param->in_shape_[i] = 1;
|
||||
}
|
||||
}
|
||||
param->num_axes_ = DIMENSION_4D;
|
||||
param->in_shape_length_ = DIMENSION_4D;
|
||||
param->num_axes_ = DIMENSION_6D;
|
||||
param->in_shape_length_ = DIMENSION_6D;
|
||||
}
|
||||
|
||||
void ChangeNegToPositive(StridedSliceParameter *param) {
|
||||
int i;
|
||||
for (i = 0; i < DIMENSION_4D; ++i) {
|
||||
for (i = 0; i < DIMENSION_6D; ++i) {
|
||||
if (param->begins_[i] < 0) {
|
||||
param->begins_[i] += param->in_shape_[i];
|
||||
}
|
||||
|
|
@ -72,7 +72,7 @@ int DoStridedSlice(const void *in_data, void *out_data, StridedSliceParameter *p
|
|||
if (in_data == NULL || out_data == NULL || param == NULL) {
|
||||
return NNACL_NULL_PTR;
|
||||
}
|
||||
if (param->num_axes_ > DIMENSION_4D) {
|
||||
if (param->num_axes_ > DIMENSION_6D) {
|
||||
return NNACL_PARAM_INVALID;
|
||||
}
|
||||
|
||||
|
|
@ -81,28 +81,35 @@ int DoStridedSlice(const void *in_data, void *out_data, StridedSliceParameter *p
|
|||
int *strides = param->strides_;
|
||||
int *in_shape = param->in_shape_;
|
||||
|
||||
if (param->num_axes_ < DIMENSION_4D) {
|
||||
PadStridedSliceParameterTo4D(param);
|
||||
if (param->num_axes_ < DIMENSION_6D) {
|
||||
PadStridedSliceParameterTo6D(param);
|
||||
}
|
||||
ChangeNegToPositive(param);
|
||||
|
||||
size_t dim_offset[DIMENSION_4D - 1];
|
||||
dim_offset[2] = in_shape[3];
|
||||
dim_offset[1] = dim_offset[2] * in_shape[2];
|
||||
dim_offset[0] = dim_offset[1] * in_shape[1];
|
||||
size_t dim_offset[DIMENSION_6D - 1];
|
||||
dim_offset[4] = in_shape[5];
|
||||
dim_offset[3] = in_shape[4] * dim_offset[4];
|
||||
dim_offset[2] = in_shape[3] * dim_offset[3];
|
||||
dim_offset[1] = in_shape[2] * dim_offset[2];
|
||||
dim_offset[0] = in_shape[1] * dim_offset[1];
|
||||
size_t out_offset = 0;
|
||||
int32_t dim0, dim1, dim2, dim3;
|
||||
int32_t dim0, dim1, dim2, dim3, dim4, dim5;
|
||||
for (dim0 = begins[0]; LoopContinue(strides[0], dim0, ends[0]); dim0 += strides[0]) {
|
||||
for (dim1 = begins[1]; LoopContinue(strides[1], dim1, ends[1]); dim1 += strides[1]) {
|
||||
for (dim2 = begins[2]; LoopContinue(strides[2], dim2, ends[2]); dim2 += strides[2]) {
|
||||
for (dim3 = begins[3]; LoopContinue(strides[3], dim3, ends[3]); dim3 += strides[3]) {
|
||||
int32_t in_offset = dim0 * dim_offset[0] + dim1 * dim_offset[1] + dim2 * dim_offset[2] + dim3;
|
||||
if (param->data_type == kDataTypeFloat) {
|
||||
*((float *)out_data + out_offset) = *((float *)in_data + in_offset);
|
||||
} else {
|
||||
*((int8_t *)out_data + out_offset) = *((int8_t *)in_data + in_offset);
|
||||
for (dim4 = begins[4]; LoopContinue(strides[4], dim4, ends[4]); dim4 += strides[4]) {
|
||||
for (dim5 = begins[5]; LoopContinue(strides[5], dim5, ends[5]); dim5 += strides[5]) {
|
||||
int32_t in_offset = dim0 * dim_offset[0] + dim1 * dim_offset[1] + dim2 * dim_offset[2] +
|
||||
dim3 * dim_offset[3] + dim4 * dim_offset[4] + dim5;
|
||||
if (param->data_type == kDataTypeFloat) {
|
||||
*((float *)out_data + out_offset) = *((float *)in_data + in_offset);
|
||||
} else {
|
||||
*((int8_t *)out_data + out_offset) = *((int8_t *)in_data + in_offset);
|
||||
}
|
||||
out_offset++;
|
||||
}
|
||||
}
|
||||
out_offset++;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -29,7 +29,12 @@ using mindspore::lite::RET_OK;
|
|||
using mindspore::schema::PrimitiveType_StridedSlice;
|
||||
|
||||
namespace mindspore::kernel {
|
||||
|
||||
namespace {
|
||||
constexpr size_t kMultiInputsSize = 4;
|
||||
constexpr size_t kBeginsIndex = 1;
|
||||
constexpr size_t kEndsIndex = 2;
|
||||
constexpr size_t kStridesInex = 3;
|
||||
} // namespace
|
||||
int StridedSliceCPUKernel::Init() {
|
||||
if (!InferShapeDone()) {
|
||||
return RET_OK;
|
||||
|
|
@ -52,6 +57,39 @@ int StridedSliceCPUKernel::ReSize() {
|
|||
return RET_OK;
|
||||
}
|
||||
|
||||
int StridedSliceCPUKernel::HandleMultiInputs() {
|
||||
if (in_tensors_.size() != kMultiInputsSize) {
|
||||
MS_LOG(ERROR) << "Inputs size should be " << kMultiInputsSize << ", got " << in_tensors_.size();
|
||||
return RET_ERROR;
|
||||
}
|
||||
auto param = reinterpret_cast<StridedSliceParameter *>(op_parameter_);
|
||||
if (param == nullptr) {
|
||||
MS_LOG(ERROR) << "StridedSliceParamater cast nullptr";
|
||||
return RET_ERROR;
|
||||
}
|
||||
auto begins = in_tensors_.at(kBeginsIndex);
|
||||
MS_ASSERT(begins != nullptr);
|
||||
int axis_num = begins->ElementsNum();
|
||||
if (axis_num > DIMENSION_6D) {
|
||||
MS_LOG(ERROR) << "StridedSlice supports max dimension " << DIMENSION_6D << ", input begins dim is " << axis_num;
|
||||
return RET_ERROR;
|
||||
}
|
||||
memcpy(param->begins_, begins->MutableData(), axis_num * sizeof(int));
|
||||
|
||||
auto ends = in_tensors_.at(kEndsIndex);
|
||||
MS_ASSERT(ends != nullptr);
|
||||
MS_ASSERT(axis_num == ends->ElementsNum());
|
||||
memcpy(param->ends_, ends->MutableData(), axis_num * sizeof(int));
|
||||
|
||||
auto strides = in_tensors_.at(kStridesInex);
|
||||
MS_ASSERT(strides != nullptr);
|
||||
MS_ASSERT(axis_num == strides->ElementsNum());
|
||||
memcpy(param->strides_, strides->MutableData(), axis_num * sizeof(int));
|
||||
|
||||
param->num_axes_ = axis_num;
|
||||
return RET_OK;
|
||||
}
|
||||
|
||||
int StridedSliceCPUKernel::Run() {
|
||||
auto ret = Prepare();
|
||||
if (ret != RET_OK) {
|
||||
|
|
@ -63,7 +101,12 @@ int StridedSliceCPUKernel::Run() {
|
|||
auto output = out_tensors_.at(0);
|
||||
MS_ASSERT(input);
|
||||
MS_ASSERT(output);
|
||||
|
||||
if (in_tensors().size() == kMultiInputsSize) {
|
||||
ret = HandleMultiInputs();
|
||||
if (ret != RET_OK) {
|
||||
return ret;
|
||||
}
|
||||
}
|
||||
ret = DoStridedSlice(input->MutableData(), output->MutableData(),
|
||||
reinterpret_cast<StridedSliceParameter *>(op_parameter_));
|
||||
if (ret != RET_OK) {
|
||||
|
|
|
|||
|
|
@ -33,6 +33,9 @@ class StridedSliceCPUKernel : public LiteKernel {
|
|||
int Init() override;
|
||||
int ReSize() override;
|
||||
int Run() override;
|
||||
|
||||
private:
|
||||
int HandleMultiInputs();
|
||||
};
|
||||
} // namespace mindspore::kernel
|
||||
|
||||
|
|
|
|||
|
|
@ -431,4 +431,153 @@ TEST_F(TestStridedSliceFp32, StridedSlice8) {
|
|||
output_tensor.SetData(nullptr);
|
||||
}
|
||||
|
||||
// 5d input, multi inputs
|
||||
TEST_F(TestStridedSliceFp32, StridedSlice9) {
|
||||
// prepare stage
|
||||
auto strided_slice_param = new StridedSliceParameter();
|
||||
std::vector<int> in_shape{2, 1, 10, 7, 7};
|
||||
std::vector<int> begins{1};
|
||||
std::vector<int> ends{2};
|
||||
std::vector<int> strides{1};
|
||||
int length = 1;
|
||||
int in_shape_length = 5;
|
||||
InitStridedSliceParam(strided_slice_param, in_shape, begins, ends, strides, length, in_shape_length);
|
||||
|
||||
float input_data[980] = {
|
||||
0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0,
|
||||
16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0,
|
||||
32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0,
|
||||
48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0,
|
||||
64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0,
|
||||
80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0,
|
||||
96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0,
|
||||
112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0,
|
||||
128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0, 139.0, 140.0, 141.0, 142.0, 143.0,
|
||||
144.0, 145.0, 146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0, 157.0, 158.0, 159.0,
|
||||
160.0, 161.0, 162.0, 163.0, 164.0, 165.0, 166.0, 167.0, 168.0, 169.0, 170.0, 171.0, 172.0, 173.0, 174.0, 175.0,
|
||||
176.0, 177.0, 178.0, 179.0, 180.0, 181.0, 182.0, 183.0, 184.0, 185.0, 186.0, 187.0, 188.0, 189.0, 190.0, 191.0,
|
||||
192.0, 193.0, 194.0, 195.0, 196.0, 197.0, 198.0, 199.0, 200.0, 201.0, 202.0, 203.0, 204.0, 205.0, 206.0, 207.0,
|
||||
208.0, 209.0, 210.0, 211.0, 212.0, 213.0, 214.0, 215.0, 216.0, 217.0, 218.0, 219.0, 220.0, 221.0, 222.0, 223.0,
|
||||
224.0, 225.0, 226.0, 227.0, 228.0, 229.0, 230.0, 231.0, 232.0, 233.0, 234.0, 235.0, 236.0, 237.0, 238.0, 239.0,
|
||||
240.0, 241.0, 242.0, 243.0, 244.0, 245.0, 246.0, 247.0, 248.0, 249.0, 250.0, 251.0, 252.0, 253.0, 254.0, 255.0,
|
||||
256.0, 257.0, 258.0, 259.0, 260.0, 261.0, 262.0, 263.0, 264.0, 265.0, 266.0, 267.0, 268.0, 269.0, 270.0, 271.0,
|
||||
272.0, 273.0, 274.0, 275.0, 276.0, 277.0, 278.0, 279.0, 280.0, 281.0, 282.0, 283.0, 284.0, 285.0, 286.0, 287.0,
|
||||
288.0, 289.0, 290.0, 291.0, 292.0, 293.0, 294.0, 295.0, 296.0, 297.0, 298.0, 299.0, 300.0, 301.0, 302.0, 303.0,
|
||||
304.0, 305.0, 306.0, 307.0, 308.0, 309.0, 310.0, 311.0, 312.0, 313.0, 314.0, 315.0, 316.0, 317.0, 318.0, 319.0,
|
||||
320.0, 321.0, 322.0, 323.0, 324.0, 325.0, 326.0, 327.0, 328.0, 329.0, 330.0, 331.0, 332.0, 333.0, 334.0, 335.0,
|
||||
336.0, 337.0, 338.0, 339.0, 340.0, 341.0, 342.0, 343.0, 344.0, 345.0, 346.0, 347.0, 348.0, 349.0, 350.0, 351.0,
|
||||
352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0, 360.0, 361.0, 362.0, 363.0, 364.0, 365.0, 366.0, 367.0,
|
||||
368.0, 369.0, 370.0, 371.0, 372.0, 373.0, 374.0, 375.0, 376.0, 377.0, 378.0, 379.0, 380.0, 381.0, 382.0, 383.0,
|
||||
384.0, 385.0, 386.0, 387.0, 388.0, 389.0, 390.0, 391.0, 392.0, 393.0, 394.0, 395.0, 396.0, 397.0, 398.0, 399.0,
|
||||
400.0, 401.0, 402.0, 403.0, 404.0, 405.0, 406.0, 407.0, 408.0, 409.0, 410.0, 411.0, 412.0, 413.0, 414.0, 415.0,
|
||||
416.0, 417.0, 418.0, 419.0, 420.0, 421.0, 422.0, 423.0, 424.0, 425.0, 426.0, 427.0, 428.0, 429.0, 430.0, 431.0,
|
||||
432.0, 433.0, 434.0, 435.0, 436.0, 437.0, 438.0, 439.0, 440.0, 441.0, 442.0, 443.0, 444.0, 445.0, 446.0, 447.0,
|
||||
448.0, 449.0, 450.0, 451.0, 452.0, 453.0, 454.0, 455.0, 456.0, 457.0, 458.0, 459.0, 460.0, 461.0, 462.0, 463.0,
|
||||
464.0, 465.0, 466.0, 467.0, 468.0, 469.0, 470.0, 471.0, 472.0, 473.0, 474.0, 475.0, 476.0, 477.0, 478.0, 479.0,
|
||||
480.0, 481.0, 482.0, 483.0, 484.0, 485.0, 486.0, 487.0, 488.0, 489.0, 490.0, 491.0, 492.0, 493.0, 494.0, 495.0,
|
||||
496.0, 497.0, 498.0, 499.0, 500.0, 501.0, 502.0, 503.0, 504.0, 505.0, 506.0, 507.0, 508.0, 509.0, 510.0, 511.0,
|
||||
512.0, 513.0, 514.0, 515.0, 516.0, 517.0, 518.0, 519.0, 520.0, 521.0, 522.0, 523.0, 524.0, 525.0, 526.0, 527.0,
|
||||
528.0, 529.0, 530.0, 531.0, 532.0, 533.0, 534.0, 535.0, 536.0, 537.0, 538.0, 539.0, 540.0, 541.0, 542.0, 543.0,
|
||||
544.0, 545.0, 546.0, 547.0, 548.0, 549.0, 550.0, 551.0, 552.0, 553.0, 554.0, 555.0, 556.0, 557.0, 558.0, 559.0,
|
||||
560.0, 561.0, 562.0, 563.0, 564.0, 565.0, 566.0, 567.0, 568.0, 569.0, 570.0, 571.0, 572.0, 573.0, 574.0, 575.0,
|
||||
576.0, 577.0, 578.0, 579.0, 580.0, 581.0, 582.0, 583.0, 584.0, 585.0, 586.0, 587.0, 588.0, 589.0, 590.0, 591.0,
|
||||
592.0, 593.0, 594.0, 595.0, 596.0, 597.0, 598.0, 599.0, 600.0, 601.0, 602.0, 603.0, 604.0, 605.0, 606.0, 607.0,
|
||||
608.0, 609.0, 610.0, 611.0, 612.0, 613.0, 614.0, 615.0, 616.0, 617.0, 618.0, 619.0, 620.0, 621.0, 622.0, 623.0,
|
||||
624.0, 625.0, 626.0, 627.0, 628.0, 629.0, 630.0, 631.0, 632.0, 633.0, 634.0, 635.0, 636.0, 637.0, 638.0, 639.0,
|
||||
640.0, 641.0, 642.0, 643.0, 644.0, 645.0, 646.0, 647.0, 648.0, 649.0, 650.0, 651.0, 652.0, 653.0, 654.0, 655.0,
|
||||
656.0, 657.0, 658.0, 659.0, 660.0, 661.0, 662.0, 663.0, 664.0, 665.0, 666.0, 667.0, 668.0, 669.0, 670.0, 671.0,
|
||||
672.0, 673.0, 674.0, 675.0, 676.0, 677.0, 678.0, 679.0, 680.0, 681.0, 682.0, 683.0, 684.0, 685.0, 686.0, 687.0,
|
||||
688.0, 689.0, 690.0, 691.0, 692.0, 693.0, 694.0, 695.0, 696.0, 697.0, 698.0, 699.0, 700.0, 701.0, 702.0, 703.0,
|
||||
704.0, 705.0, 706.0, 707.0, 708.0, 709.0, 710.0, 711.0, 712.0, 713.0, 714.0, 715.0, 716.0, 717.0, 718.0, 719.0,
|
||||
720.0, 721.0, 722.0, 723.0, 724.0, 725.0, 726.0, 727.0, 728.0, 729.0, 730.0, 731.0, 732.0, 733.0, 734.0, 735.0,
|
||||
736.0, 737.0, 738.0, 739.0, 740.0, 741.0, 742.0, 743.0, 744.0, 745.0, 746.0, 747.0, 748.0, 749.0, 750.0, 751.0,
|
||||
752.0, 753.0, 754.0, 755.0, 756.0, 757.0, 758.0, 759.0, 760.0, 761.0, 762.0, 763.0, 764.0, 765.0, 766.0, 767.0,
|
||||
768.0, 769.0, 770.0, 771.0, 772.0, 773.0, 774.0, 775.0, 776.0, 777.0, 778.0, 779.0, 780.0, 781.0, 782.0, 783.0,
|
||||
784.0, 785.0, 786.0, 787.0, 788.0, 789.0, 790.0, 791.0, 792.0, 793.0, 794.0, 795.0, 796.0, 797.0, 798.0, 799.0,
|
||||
800.0, 801.0, 802.0, 803.0, 804.0, 805.0, 806.0, 807.0, 808.0, 809.0, 810.0, 811.0, 812.0, 813.0, 814.0, 815.0,
|
||||
816.0, 817.0, 818.0, 819.0, 820.0, 821.0, 822.0, 823.0, 824.0, 825.0, 826.0, 827.0, 828.0, 829.0, 830.0, 831.0,
|
||||
832.0, 833.0, 834.0, 835.0, 836.0, 837.0, 838.0, 839.0, 840.0, 841.0, 842.0, 843.0, 844.0, 845.0, 846.0, 847.0,
|
||||
848.0, 849.0, 850.0, 851.0, 852.0, 853.0, 854.0, 855.0, 856.0, 857.0, 858.0, 859.0, 860.0, 861.0, 862.0, 863.0,
|
||||
864.0, 865.0, 866.0, 867.0, 868.0, 869.0, 870.0, 871.0, 872.0, 873.0, 874.0, 875.0, 876.0, 877.0, 878.0, 879.0,
|
||||
880.0, 881.0, 882.0, 883.0, 884.0, 885.0, 886.0, 887.0, 888.0, 889.0, 890.0, 891.0, 892.0, 893.0, 894.0, 895.0,
|
||||
896.0, 897.0, 898.0, 899.0, 900.0, 901.0, 902.0, 903.0, 904.0, 905.0, 906.0, 907.0, 908.0, 909.0, 910.0, 911.0,
|
||||
912.0, 913.0, 914.0, 915.0, 916.0, 917.0, 918.0, 919.0, 920.0, 921.0, 922.0, 923.0, 924.0, 925.0, 926.0, 927.0,
|
||||
928.0, 929.0, 930.0, 931.0, 932.0, 933.0, 934.0, 935.0, 936.0, 937.0, 938.0, 939.0, 940.0, 941.0, 942.0, 943.0,
|
||||
944.0, 945.0, 946.0, 947.0, 948.0, 949.0, 950.0, 951.0, 952.0, 953.0, 954.0, 955.0, 956.0, 957.0, 958.0, 959.0,
|
||||
960.0, 961.0, 962.0, 963.0, 964.0, 965.0, 966.0, 967.0, 968.0, 969.0, 970.0, 971.0, 972.0, 973.0, 974.0, 975.0,
|
||||
976.0, 977.0, 978.0, 979.0};
|
||||
|
||||
float correct[490] = {
|
||||
490.0, 491.0, 492.0, 493.0, 494.0, 495.0, 496.0, 497.0, 498.0, 499.0, 500.0, 501.0, 502.0, 503.0, 504.0, 505.0,
|
||||
506.0, 507.0, 508.0, 509.0, 510.0, 511.0, 512.0, 513.0, 514.0, 515.0, 516.0, 517.0, 518.0, 519.0, 520.0, 521.0,
|
||||
522.0, 523.0, 524.0, 525.0, 526.0, 527.0, 528.0, 529.0, 530.0, 531.0, 532.0, 533.0, 534.0, 535.0, 536.0, 537.0,
|
||||
538.0, 539.0, 540.0, 541.0, 542.0, 543.0, 544.0, 545.0, 546.0, 547.0, 548.0, 549.0, 550.0, 551.0, 552.0, 553.0,
|
||||
554.0, 555.0, 556.0, 557.0, 558.0, 559.0, 560.0, 561.0, 562.0, 563.0, 564.0, 565.0, 566.0, 567.0, 568.0, 569.0,
|
||||
570.0, 571.0, 572.0, 573.0, 574.0, 575.0, 576.0, 577.0, 578.0, 579.0, 580.0, 581.0, 582.0, 583.0, 584.0, 585.0,
|
||||
586.0, 587.0, 588.0, 589.0, 590.0, 591.0, 592.0, 593.0, 594.0, 595.0, 596.0, 597.0, 598.0, 599.0, 600.0, 601.0,
|
||||
602.0, 603.0, 604.0, 605.0, 606.0, 607.0, 608.0, 609.0, 610.0, 611.0, 612.0, 613.0, 614.0, 615.0, 616.0, 617.0,
|
||||
618.0, 619.0, 620.0, 621.0, 622.0, 623.0, 624.0, 625.0, 626.0, 627.0, 628.0, 629.0, 630.0, 631.0, 632.0, 633.0,
|
||||
634.0, 635.0, 636.0, 637.0, 638.0, 639.0, 640.0, 641.0, 642.0, 643.0, 644.0, 645.0, 646.0, 647.0, 648.0, 649.0,
|
||||
650.0, 651.0, 652.0, 653.0, 654.0, 655.0, 656.0, 657.0, 658.0, 659.0, 660.0, 661.0, 662.0, 663.0, 664.0, 665.0,
|
||||
666.0, 667.0, 668.0, 669.0, 670.0, 671.0, 672.0, 673.0, 674.0, 675.0, 676.0, 677.0, 678.0, 679.0, 680.0, 681.0,
|
||||
682.0, 683.0, 684.0, 685.0, 686.0, 687.0, 688.0, 689.0, 690.0, 691.0, 692.0, 693.0, 694.0, 695.0, 696.0, 697.0,
|
||||
698.0, 699.0, 700.0, 701.0, 702.0, 703.0, 704.0, 705.0, 706.0, 707.0, 708.0, 709.0, 710.0, 711.0, 712.0, 713.0,
|
||||
714.0, 715.0, 716.0, 717.0, 718.0, 719.0, 720.0, 721.0, 722.0, 723.0, 724.0, 725.0, 726.0, 727.0, 728.0, 729.0,
|
||||
730.0, 731.0, 732.0, 733.0, 734.0, 735.0, 736.0, 737.0, 738.0, 739.0, 740.0, 741.0, 742.0, 743.0, 744.0, 745.0,
|
||||
746.0, 747.0, 748.0, 749.0, 750.0, 751.0, 752.0, 753.0, 754.0, 755.0, 756.0, 757.0, 758.0, 759.0, 760.0, 761.0,
|
||||
762.0, 763.0, 764.0, 765.0, 766.0, 767.0, 768.0, 769.0, 770.0, 771.0, 772.0, 773.0, 774.0, 775.0, 776.0, 777.0,
|
||||
778.0, 779.0, 780.0, 781.0, 782.0, 783.0, 784.0, 785.0, 786.0, 787.0, 788.0, 789.0, 790.0, 791.0, 792.0, 793.0,
|
||||
794.0, 795.0, 796.0, 797.0, 798.0, 799.0, 800.0, 801.0, 802.0, 803.0, 804.0, 805.0, 806.0, 807.0, 808.0, 809.0,
|
||||
810.0, 811.0, 812.0, 813.0, 814.0, 815.0, 816.0, 817.0, 818.0, 819.0, 820.0, 821.0, 822.0, 823.0, 824.0, 825.0,
|
||||
826.0, 827.0, 828.0, 829.0, 830.0, 831.0, 832.0, 833.0, 834.0, 835.0, 836.0, 837.0, 838.0, 839.0, 840.0, 841.0,
|
||||
842.0, 843.0, 844.0, 845.0, 846.0, 847.0, 848.0, 849.0, 850.0, 851.0, 852.0, 853.0, 854.0, 855.0, 856.0, 857.0,
|
||||
858.0, 859.0, 860.0, 861.0, 862.0, 863.0, 864.0, 865.0, 866.0, 867.0, 868.0, 869.0, 870.0, 871.0, 872.0, 873.0,
|
||||
874.0, 875.0, 876.0, 877.0, 878.0, 879.0, 880.0, 881.0, 882.0, 883.0, 884.0, 885.0, 886.0, 887.0, 888.0, 889.0,
|
||||
890.0, 891.0, 892.0, 893.0, 894.0, 895.0, 896.0, 897.0, 898.0, 899.0, 900.0, 901.0, 902.0, 903.0, 904.0, 905.0,
|
||||
906.0, 907.0, 908.0, 909.0, 910.0, 911.0, 912.0, 913.0, 914.0, 915.0, 916.0, 917.0, 918.0, 919.0, 920.0, 921.0,
|
||||
922.0, 923.0, 924.0, 925.0, 926.0, 927.0, 928.0, 929.0, 930.0, 931.0, 932.0, 933.0, 934.0, 935.0, 936.0, 937.0,
|
||||
938.0, 939.0, 940.0, 941.0, 942.0, 943.0, 944.0, 945.0, 946.0, 947.0, 948.0, 949.0, 950.0, 951.0, 952.0, 953.0,
|
||||
954.0, 955.0, 956.0, 957.0, 958.0, 959.0, 960.0, 961.0, 962.0, 963.0, 964.0, 965.0, 966.0, 967.0, 968.0, 969.0,
|
||||
970.0, 971.0, 972.0, 973.0, 974.0, 975.0, 976.0, 977.0, 978.0, 979.0};
|
||||
float output_data[490];
|
||||
std::vector<int> output_shape = {1, 1, 10, 7, 7};
|
||||
|
||||
lite::Tensor input_tensor;
|
||||
input_tensor.SetData(input_data);
|
||||
input_tensor.set_shape(in_shape);
|
||||
lite::Tensor begins_tensor;
|
||||
begins_tensor.SetData(begins.data());
|
||||
begins_tensor.set_shape({1});
|
||||
lite::Tensor ends_tensor;
|
||||
ends_tensor.SetData(ends.data());
|
||||
ends_tensor.set_shape({1});
|
||||
lite::Tensor strides_tensor;
|
||||
strides_tensor.SetData(strides.data());
|
||||
strides_tensor.set_shape({1});
|
||||
|
||||
std::vector<lite::Tensor *> inputs_tensor{&input_tensor, &begins_tensor, &ends_tensor, &strides_tensor};
|
||||
|
||||
std::vector<lite::Tensor *> outputs_tensor;
|
||||
lite::Tensor output_tensor;
|
||||
outputs_tensor.push_back(&output_tensor);
|
||||
output_tensor.SetData(output_data);
|
||||
output_tensor.set_data_type(input_tensor.data_type());
|
||||
output_tensor.set_shape(output_shape);
|
||||
|
||||
lite::Context *ctx = new lite::Context();
|
||||
strided_slice_param->op_parameter_.type_ = schema::PrimitiveType_StridedSlice;
|
||||
kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_StridedSlice};
|
||||
auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
|
||||
ASSERT_NE(creator, nullptr);
|
||||
kernel::LiteKernel *kernel =
|
||||
creator(inputs_tensor, outputs_tensor, reinterpret_cast<OpParameter *>(strided_slice_param), ctx, desc, nullptr);
|
||||
ASSERT_NE(kernel, nullptr);
|
||||
kernel->Run();
|
||||
delete ctx;
|
||||
|
||||
CompareOutputData(output_data, correct, 490, 0.000001);
|
||||
input_tensor.SetData(nullptr);
|
||||
output_tensor.SetData(nullptr);
|
||||
}
|
||||
|
||||
} // namespace mindspore
|
||||
|
|
|
|||
Loading…
Reference in New Issue