nihui
/
ncnn
Not watched
1
0
Fork 0
Code
Releases 46 Wiki evaluate Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain HPC
Browse Source

split tests (#4354)

tags/20221128
nihui GitHub 3 years ago
parent
aed05aa851
commit
057b5bb515
No known key found for this signature in database GPG Key ID: 4AEE18F83AFDEB23
18 changed files with 2585 additions and 982 deletions
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. +13
    -2
      tests/CMakeLists.txt
  2. +1
    -1
      tests/test_binaryop.cpp
  3. +431
    -0
      tests/test_binaryop_1.cpp
  4. +431
    -0
      tests/test_binaryop_2.cpp
  5. +2
    -303
      tests/test_convolution.cpp
  6. +136
    -0
      tests/test_convolution_1.cpp
  7. +108
    -0
      tests/test_convolution_2.cpp
  8. +288
    -0
      tests/test_convolution_3.cpp
  9. +1
    -214
      tests/test_convolutiondepthwise.cpp
  10. +236
    -0
      tests/test_convolutiondepthwise_1.cpp
  11. +1
    -454
      tests/test_crop.cpp
  12. +377
    -0
      tests/test_crop_1.cpp
  13. +122
    -0
      tests/test_crop_2.cpp
  14. +2
    -8
      tests/test_deformableconv2d.cpp
  15. +120
    -0
      tests/test_deformableconv2d_1.cpp
  16. +120
    -0
      tests/test_deformableconv2d_2.cpp
  17. +120
    -0
      tests/test_deformableconv2d_3.cpp
  18. +76
    -0
      tests/test_deformableconv2d_4.cpp

+ 13
- 2
tests/CMakeLists.txt View File

@@ -19,7 +19,18 @@ macro(ncnn_add_layer_test class)


# enable if WITH_LAYER_xxx option ON # enable if WITH_LAYER_xxx option ON
if(${WITH_LAYER_${name}}) if(${WITH_LAYER_${name}})
ncnn_add_test(${name})
file(GLOB test_${name}_SRCS "test_${name}.cpp" "test_${name}_*.cpp" LIST_DIRECTORIES FALSE)

foreach(test_file ${test_${name}_SRCS})
get_filename_component(test_filename ${test_file} NAME_WE)
add_executable(${test_filename} ${test_file})
target_link_libraries(${test_filename} PRIVATE ncnn)

add_test(NAME ${test_filename} COMMAND ${CMAKE_COMMAND} -DTEST_EXECUTABLE=$<TARGET_FILE:${test_filename}> -P ${CMAKE_CURRENT_SOURCE_DIR}/../cmake/run_test.cmake)

# add test to a virtual project group
set_property(TARGET ${test_filename} PROPERTY FOLDER "tests")
endforeach()
endif() endif()
endmacro() endmacro()


@@ -77,7 +88,7 @@ ncnn_add_layer_test(DeconvolutionDepthWise)
ncnn_add_layer_test(DeconvolutionDepthWise1D) ncnn_add_layer_test(DeconvolutionDepthWise1D)
ncnn_add_layer_test(DeconvolutionDepthWise3D) ncnn_add_layer_test(DeconvolutionDepthWise3D)
ncnn_add_layer_test(DeepCopy) ncnn_add_layer_test(DeepCopy)
# ncnn_add_layer_test(DeformableConv2D) too slow :(
ncnn_add_layer_test(DeformableConv2D)
ncnn_add_layer_test(Dequantize) ncnn_add_layer_test(Dequantize)
ncnn_add_layer_test(Dropout) ncnn_add_layer_test(Dropout)
ncnn_add_layer_test(Einsum) ncnn_add_layer_test(Einsum)


+ 1
- 1
tests/test_binaryop.cpp View File

@@ -382,7 +382,7 @@ int main()
{ {
SRAND(7767517); SRAND(7767517);


for (op_type = 0; op_type < OP_TYPE_MAX; op_type++)
for (op_type = 0; op_type < 3; op_type++)
{ {
int ret = 0 int ret = 0
|| test_binaryop_1() || test_binaryop_1()


+ 431
- 0
tests/test_binaryop_1.cpp View File

@@ -0,0 +1,431 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/binaryop.h"
#include "testutil.h"

#define OP_TYPE_MAX 9

static int op_type = 0;

static int test_binaryop(const ncnn::Mat& _a, const ncnn::Mat& _b)
{
ncnn::Mat a = _a;
ncnn::Mat b = _b;
if (op_type == 6)
{
// value must be positive for pow
Randomize(a, 0.001f, 2.f);
Randomize(b, 0.001f, 2.f);
}
if (op_type == 3 || op_type == 8)
{
// value must be positive for pow
Randomize(a, 0.1f, 10.f);
Randomize(b, 0.1f, 10.f);
}

ncnn::ParamDict pd;
pd.set(0, op_type);
pd.set(1, 0); // with_scalar
pd.set(2, 0.f); // b

std::vector<ncnn::Mat> weights(0);

std::vector<ncnn::Mat> ab(2);
ab[0] = a;
ab[1] = b;

int ret = test_layer<ncnn::BinaryOp>("BinaryOp", pd, weights, ab);
if (ret != 0)
{
fprintf(stderr, "test_binaryop failed a.dims=%d a=(%d %d %d %d) b.dims=%d b=(%d %d %d %d) op_type=%d\n", a.dims, a.w, a.h, a.d, a.c, b.dims, b.w, b.h, b.d, b.c, op_type);
}

return ret;
}

static int test_binaryop(const ncnn::Mat& _a, float b)
{
ncnn::Mat a = _a;
if (op_type == 6)
{
// value must be positive for pow
Randomize(a, 0.001f, 2.f);
b = RandomFloat(0.001f, 2.f);
}

ncnn::ParamDict pd;
pd.set(0, op_type);
pd.set(1, 1); // with_scalar
pd.set(2, b); // b

std::vector<ncnn::Mat> weights(0);

int ret = test_layer<ncnn::BinaryOp>("BinaryOp", pd, weights, a);
if (ret != 0)
{
fprintf(stderr, "test_binaryop failed a.dims=%d a=(%d %d %d %d) b=%f op_type=%d\n", a.dims, a.w, a.h, a.d, a.c, b, op_type);
}

return ret;
}

// https://github.com/Tencent/ncnn/wiki/binaryop-broadcasting

static int test_binaryop_1()
{
return 0
|| test_binaryop(RandomMat(1), 1.f);
}

static int test_binaryop_2()
{
return 0
|| test_binaryop(RandomMat(1), RandomMat(1))
|| test_binaryop(RandomMat(1), RandomMat(4))
|| test_binaryop(RandomMat(1), RandomMat(16));
}

static int test_binaryop_3()
{
return 0
|| test_binaryop(RandomMat(1), RandomMat(11, 3))
|| test_binaryop(RandomMat(1), RandomMat(11, 4))
|| test_binaryop(RandomMat(1), RandomMat(11, 16));
}

static int test_binaryop_4()
{
return 0
|| test_binaryop(RandomMat(1), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(1), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(1), RandomMat(11, 6, 16));
}

static int test_binaryop_5()
{
return 0
|| test_binaryop(RandomMat(2), 1.f)
|| test_binaryop(RandomMat(4), 1.f)
|| test_binaryop(RandomMat(16), 1.f);
}

static int test_binaryop_6()
{
return 0
|| test_binaryop(RandomMat(2), RandomMat(1))
|| test_binaryop(RandomMat(4), RandomMat(1))
|| test_binaryop(RandomMat(16), RandomMat(1));
}

static int test_binaryop_7()
{
return 0
|| test_binaryop(RandomMat(2), RandomMat(2))
|| test_binaryop(RandomMat(4), RandomMat(4))
|| test_binaryop(RandomMat(16), RandomMat(16));
}

static int test_binaryop_8()
{
return 0
|| test_binaryop(RandomMat(3), RandomMat(11, 3))
|| test_binaryop(RandomMat(4), RandomMat(11, 4))
|| test_binaryop(RandomMat(16), RandomMat(11, 16));
}

static int test_binaryop_9()
{
return 0
|| test_binaryop(RandomMat(2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(16), RandomMat(11, 6, 16));
}

static int test_binaryop_10()
{
return 0
|| test_binaryop(RandomMat(11, 3), 1.f)
|| test_binaryop(RandomMat(11, 4), 1.f)
|| test_binaryop(RandomMat(11, 16), 1.f);
}

static int test_binaryop_11()
{
return 0
|| test_binaryop(RandomMat(11, 3), RandomMat(1))
|| test_binaryop(RandomMat(11, 4), RandomMat(1))
|| test_binaryop(RandomMat(11, 16), RandomMat(1));
}

static int test_binaryop_12()
{
return 0
|| test_binaryop(RandomMat(11, 3), RandomMat(3))
|| test_binaryop(RandomMat(11, 4), RandomMat(4))
|| test_binaryop(RandomMat(11, 16), RandomMat(16));
}

static int test_binaryop_13()
{
return 0
|| test_binaryop(RandomMat(11, 3), RandomMat(11, 3))
|| test_binaryop(RandomMat(11, 4), RandomMat(11, 4))
|| test_binaryop(RandomMat(11, 16), RandomMat(11, 16));
}

static int test_binaryop_14()
{
return 0
|| test_binaryop(RandomMat(6, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(6, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(6, 16), RandomMat(11, 6, 16));
}

static int test_binaryop_15()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), 1.f)
|| test_binaryop(RandomMat(11, 6, 4), 1.f)
|| test_binaryop(RandomMat(11, 6, 16), 1.f);
}

static int test_binaryop_16()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(1))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(1))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(1));
}

static int test_binaryop_17()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(16));
}

static int test_binaryop_18()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(6, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(6, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(6, 16));
}

static int test_binaryop_19()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(11, 6, 16));
}

static int test_binaryop_20()
{
return 0
|| test_binaryop(RandomMat(1), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(1), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(1), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_21()
{
return 0
|| test_binaryop(RandomMat(2), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(4), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(16), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_22()
{
return 0
|| test_binaryop(RandomMat(4, 2), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(4, 4), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(4, 16), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_23()
{
return 0
|| test_binaryop(RandomMat(3, 4, 2), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(3, 4, 4), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(3, 4, 16), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_24()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), 1.f)
|| test_binaryop(RandomMat(11, 3, 4, 4), 1.f)
|| test_binaryop(RandomMat(11, 3, 4, 16), 1.f);
}

static int test_binaryop_25()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(1))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(1))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(1));
}

static int test_binaryop_26()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(2))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(4))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(16));
}

static int test_binaryop_27()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(4, 2))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(4, 4))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(4, 16));
}

static int test_binaryop_28()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(3, 4, 2))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(3, 4, 4))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(3, 4, 16));
}

static int test_binaryop_29()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_s1()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(1, 1, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(1, 1, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(1, 1, 16));
}

static int test_binaryop_s2()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(11, 6, 1))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(11, 6, 1))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(11, 6, 1));
}

static int test_binaryop_s3()
{
return 0
|| test_binaryop(RandomMat(1, 1, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(1, 1, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(1, 1, 16), RandomMat(11, 6, 16));
}

static int test_binaryop_s4()
{
return 0
|| test_binaryop(RandomMat(11, 6, 1), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(11, 6, 1), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(11, 6, 1), RandomMat(11, 6, 16));
}

static int test_binaryop_s5()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(1, 6, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(1, 6, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(1, 6, 16));
}

static int test_binaryop_s6()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(11, 1, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(11, 1, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(11, 1, 16));
}

static int test_binaryop_s7()
{
return 0
|| test_binaryop(RandomMat(1, 6, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(1, 6, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(1, 6, 16), RandomMat(11, 6, 16));
}

static int test_binaryop_s8()
{
return 0
|| test_binaryop(RandomMat(11, 1, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(11, 1, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(11, 1, 16), RandomMat(11, 6, 16));
}

int main()
{
SRAND(7767517);

for (op_type = 3; op_type < 6; op_type++)
{
int ret = 0
|| test_binaryop_1()
|| test_binaryop_2()
|| test_binaryop_3()
|| test_binaryop_4()
|| test_binaryop_5()
|| test_binaryop_6()
|| test_binaryop_7()
|| test_binaryop_8()
|| test_binaryop_9()
|| test_binaryop_10()
|| test_binaryop_11()
|| test_binaryop_12()
|| test_binaryop_13()
|| test_binaryop_14()
|| test_binaryop_15()
|| test_binaryop_16()
|| test_binaryop_17()
|| test_binaryop_18()
|| test_binaryop_19()
|| test_binaryop_20()
|| test_binaryop_21()
|| test_binaryop_22()
|| test_binaryop_23()
|| test_binaryop_24()
|| test_binaryop_25()
|| test_binaryop_26()
|| test_binaryop_27()
|| test_binaryop_28()
|| test_binaryop_29()
|| test_binaryop_s1()
|| test_binaryop_s2()
|| test_binaryop_s3()
|| test_binaryop_s4()
|| test_binaryop_s5()
|| test_binaryop_s6()
|| test_binaryop_s7()
|| test_binaryop_s8();

if (ret != 0)
return ret;
}

return 0;
}

+ 431
- 0
tests/test_binaryop_2.cpp View File

@@ -0,0 +1,431 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/binaryop.h"
#include "testutil.h"

#define OP_TYPE_MAX 9

static int op_type = 0;

static int test_binaryop(const ncnn::Mat& _a, const ncnn::Mat& _b)
{
ncnn::Mat a = _a;
ncnn::Mat b = _b;
if (op_type == 6)
{
// value must be positive for pow
Randomize(a, 0.001f, 2.f);
Randomize(b, 0.001f, 2.f);
}
if (op_type == 3 || op_type == 8)
{
// value must be positive for pow
Randomize(a, 0.1f, 10.f);
Randomize(b, 0.1f, 10.f);
}

ncnn::ParamDict pd;
pd.set(0, op_type);
pd.set(1, 0); // with_scalar
pd.set(2, 0.f); // b

std::vector<ncnn::Mat> weights(0);

std::vector<ncnn::Mat> ab(2);
ab[0] = a;
ab[1] = b;

int ret = test_layer<ncnn::BinaryOp>("BinaryOp", pd, weights, ab);
if (ret != 0)
{
fprintf(stderr, "test_binaryop failed a.dims=%d a=(%d %d %d %d) b.dims=%d b=(%d %d %d %d) op_type=%d\n", a.dims, a.w, a.h, a.d, a.c, b.dims, b.w, b.h, b.d, b.c, op_type);
}

return ret;
}

static int test_binaryop(const ncnn::Mat& _a, float b)
{
ncnn::Mat a = _a;
if (op_type == 6)
{
// value must be positive for pow
Randomize(a, 0.001f, 2.f);
b = RandomFloat(0.001f, 2.f);
}

ncnn::ParamDict pd;
pd.set(0, op_type);
pd.set(1, 1); // with_scalar
pd.set(2, b); // b

std::vector<ncnn::Mat> weights(0);

int ret = test_layer<ncnn::BinaryOp>("BinaryOp", pd, weights, a);
if (ret != 0)
{
fprintf(stderr, "test_binaryop failed a.dims=%d a=(%d %d %d %d) b=%f op_type=%d\n", a.dims, a.w, a.h, a.d, a.c, b, op_type);
}

return ret;
}

// https://github.com/Tencent/ncnn/wiki/binaryop-broadcasting

static int test_binaryop_1()
{
return 0
|| test_binaryop(RandomMat(1), 1.f);
}

static int test_binaryop_2()
{
return 0
|| test_binaryop(RandomMat(1), RandomMat(1))
|| test_binaryop(RandomMat(1), RandomMat(4))
|| test_binaryop(RandomMat(1), RandomMat(16));
}

static int test_binaryop_3()
{
return 0
|| test_binaryop(RandomMat(1), RandomMat(11, 3))
|| test_binaryop(RandomMat(1), RandomMat(11, 4))
|| test_binaryop(RandomMat(1), RandomMat(11, 16));
}

static int test_binaryop_4()
{
return 0
|| test_binaryop(RandomMat(1), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(1), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(1), RandomMat(11, 6, 16));
}

static int test_binaryop_5()
{
return 0
|| test_binaryop(RandomMat(2), 1.f)
|| test_binaryop(RandomMat(4), 1.f)
|| test_binaryop(RandomMat(16), 1.f);
}

static int test_binaryop_6()
{
return 0
|| test_binaryop(RandomMat(2), RandomMat(1))
|| test_binaryop(RandomMat(4), RandomMat(1))
|| test_binaryop(RandomMat(16), RandomMat(1));
}

static int test_binaryop_7()
{
return 0
|| test_binaryop(RandomMat(2), RandomMat(2))
|| test_binaryop(RandomMat(4), RandomMat(4))
|| test_binaryop(RandomMat(16), RandomMat(16));
}

static int test_binaryop_8()
{
return 0
|| test_binaryop(RandomMat(3), RandomMat(11, 3))
|| test_binaryop(RandomMat(4), RandomMat(11, 4))
|| test_binaryop(RandomMat(16), RandomMat(11, 16));
}

static int test_binaryop_9()
{
return 0
|| test_binaryop(RandomMat(2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(16), RandomMat(11, 6, 16));
}

static int test_binaryop_10()
{
return 0
|| test_binaryop(RandomMat(11, 3), 1.f)
|| test_binaryop(RandomMat(11, 4), 1.f)
|| test_binaryop(RandomMat(11, 16), 1.f);
}

static int test_binaryop_11()
{
return 0
|| test_binaryop(RandomMat(11, 3), RandomMat(1))
|| test_binaryop(RandomMat(11, 4), RandomMat(1))
|| test_binaryop(RandomMat(11, 16), RandomMat(1));
}

static int test_binaryop_12()
{
return 0
|| test_binaryop(RandomMat(11, 3), RandomMat(3))
|| test_binaryop(RandomMat(11, 4), RandomMat(4))
|| test_binaryop(RandomMat(11, 16), RandomMat(16));
}

static int test_binaryop_13()
{
return 0
|| test_binaryop(RandomMat(11, 3), RandomMat(11, 3))
|| test_binaryop(RandomMat(11, 4), RandomMat(11, 4))
|| test_binaryop(RandomMat(11, 16), RandomMat(11, 16));
}

static int test_binaryop_14()
{
return 0
|| test_binaryop(RandomMat(6, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(6, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(6, 16), RandomMat(11, 6, 16));
}

static int test_binaryop_15()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), 1.f)
|| test_binaryop(RandomMat(11, 6, 4), 1.f)
|| test_binaryop(RandomMat(11, 6, 16), 1.f);
}

static int test_binaryop_16()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(1))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(1))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(1));
}

static int test_binaryop_17()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(16));
}

static int test_binaryop_18()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(6, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(6, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(6, 16));
}

static int test_binaryop_19()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(11, 6, 16));
}

static int test_binaryop_20()
{
return 0
|| test_binaryop(RandomMat(1), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(1), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(1), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_21()
{
return 0
|| test_binaryop(RandomMat(2), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(4), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(16), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_22()
{
return 0
|| test_binaryop(RandomMat(4, 2), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(4, 4), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(4, 16), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_23()
{
return 0
|| test_binaryop(RandomMat(3, 4, 2), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(3, 4, 4), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(3, 4, 16), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_24()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), 1.f)
|| test_binaryop(RandomMat(11, 3, 4, 4), 1.f)
|| test_binaryop(RandomMat(11, 3, 4, 16), 1.f);
}

static int test_binaryop_25()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(1))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(1))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(1));
}

static int test_binaryop_26()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(2))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(4))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(16));
}

static int test_binaryop_27()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(4, 2))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(4, 4))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(4, 16));
}

static int test_binaryop_28()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(3, 4, 2))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(3, 4, 4))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(3, 4, 16));
}

static int test_binaryop_29()
{
return 0
|| test_binaryop(RandomMat(11, 3, 4, 2), RandomMat(11, 3, 4, 2))
|| test_binaryop(RandomMat(11, 3, 4, 4), RandomMat(11, 3, 4, 4))
|| test_binaryop(RandomMat(11, 3, 4, 16), RandomMat(11, 3, 4, 16));
}

static int test_binaryop_s1()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(1, 1, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(1, 1, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(1, 1, 16));
}

static int test_binaryop_s2()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(11, 6, 1))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(11, 6, 1))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(11, 6, 1));
}

static int test_binaryop_s3()
{
return 0
|| test_binaryop(RandomMat(1, 1, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(1, 1, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(1, 1, 16), RandomMat(11, 6, 16));
}

static int test_binaryop_s4()
{
return 0
|| test_binaryop(RandomMat(11, 6, 1), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(11, 6, 1), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(11, 6, 1), RandomMat(11, 6, 16));
}

static int test_binaryop_s5()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(1, 6, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(1, 6, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(1, 6, 16));
}

static int test_binaryop_s6()
{
return 0
|| test_binaryop(RandomMat(11, 6, 2), RandomMat(11, 1, 2))
|| test_binaryop(RandomMat(11, 6, 4), RandomMat(11, 1, 4))
|| test_binaryop(RandomMat(11, 6, 16), RandomMat(11, 1, 16));
}

static int test_binaryop_s7()
{
return 0
|| test_binaryop(RandomMat(1, 6, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(1, 6, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(1, 6, 16), RandomMat(11, 6, 16));
}

static int test_binaryop_s8()
{
return 0
|| test_binaryop(RandomMat(11, 1, 2), RandomMat(11, 6, 2))
|| test_binaryop(RandomMat(11, 1, 4), RandomMat(11, 6, 4))
|| test_binaryop(RandomMat(11, 1, 16), RandomMat(11, 6, 16));
}

int main()
{
SRAND(7767517);

for (op_type = 6; op_type < OP_TYPE_MAX; op_type++)
{
int ret = 0
|| test_binaryop_1()
|| test_binaryop_2()
|| test_binaryop_3()
|| test_binaryop_4()
|| test_binaryop_5()
|| test_binaryop_6()
|| test_binaryop_7()
|| test_binaryop_8()
|| test_binaryop_9()
|| test_binaryop_10()
|| test_binaryop_11()
|| test_binaryop_12()
|| test_binaryop_13()
|| test_binaryop_14()
|| test_binaryop_15()
|| test_binaryop_16()
|| test_binaryop_17()
|| test_binaryop_18()
|| test_binaryop_19()
|| test_binaryop_20()
|| test_binaryop_21()
|| test_binaryop_22()
|| test_binaryop_23()
|| test_binaryop_24()
|| test_binaryop_25()
|| test_binaryop_26()
|| test_binaryop_27()
|| test_binaryop_28()
|| test_binaryop_29()
|| test_binaryop_s1()
|| test_binaryop_s2()
|| test_binaryop_s3()
|| test_binaryop_s4()
|| test_binaryop_s5()
|| test_binaryop_s6()
|| test_binaryop_s7()
|| test_binaryop_s8();

if (ret != 0)
return ret;
}

return 0;
}

+ 2
- 303
tests/test_convolution.cpp View File

@@ -82,7 +82,7 @@ static int test_convolution_0()
{7, 2, 1, -233}, {7, 2, 1, -233},
}; };


for (int i = 0; i < 16; i++)
for (int i = 0; i < 12; i++)
{ {
const int k = kdsp[i][0]; const int k = kdsp[i][0];
const int d = kdsp[i][1]; const int d = kdsp[i][1];
@@ -125,313 +125,12 @@ static int test_convolution_0()
return -1; return -1;
} }


return 0
|| test_convolution(7, 5, 1, 4, 3, 1, 1, 1, 1)
|| test_convolution(14, 5, 1, 4, 3, 1, 2, 1, 1)
|| test_convolution(11, 5, 2, 12, 2, 2, 2, 1, 1)
|| test_convolution(15, 11, 4, 4, 3, 1, 1, 1, 1)
|| test_convolution(15, 11, 8, 8, 3, 1, 1, 1, 1)
|| test_convolution(11, 11, 8, 16, 3, 1, 1, 1, 1)
|| test_convolution(13, 16, 16, 24, 3, 1, 1, 1, 1)
|| test_convolution(20, 19, 24, 24, 3, 1, 1, 1, 1)
|| test_convolution(8, 8, 16, 24, 3, 1, 1, 1, 0)
|| test_convolution(4, 8, 16, 24, 3, 1, 1, 1, 1)
|| test_convolution(4, 20, 16, 24, 3, 1, 1, 1, 0)
|| test_convolution(6, 7, 64, 64, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 24, 32, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 24, 32, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 24, 32, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 24, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 32, 24, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 24, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 28, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 32, 28, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 28, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 26, 32, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 26, 32, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 26, 32, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 26, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 32, 26, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 26, 3, 1, 2, 0, 1)
|| test_convolution(30, 30, 32, 26, 3, 1, 1, 1, 0)
|| test_convolution(12, 18, 8, 16, 3, 1, 1, 1, 1)
|| test_convolution(42, 18, 32, 160, 3, 1, 1, 1, 1)
|| test_convolution(12, 18, 32, 160, 3, 1, 1, 1, 1)
|| test_convolution(12, 18, 4, 12, 3, 1, 1, 1, 1)
|| test_convolution(42, 18, 28, 140, 3, 1, 1, 1, 1)
|| test_convolution(12, 18, 28, 140, 3, 1, 1, 1, 1);
}

static int test_convolution_vec(int w, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
ncnn::Mat a = RandomMat(w);

ncnn::ParamDict pd;
pd.set(0, outch); // num_output
pd.set(1, kernel); // kernel_w
pd.set(2, dilation); // dilation_w
pd.set(3, stride); // stride_w
pd.set(4, pad); // pad_w
pd.set(5, bias); // bias_term
pd.set(6, outch * w * kernel * kernel);

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * w * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);

int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a);
if (ret != 0)
{
fprintf(stderr, "test_convolution_vec failed w=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolution_2()
{
return 0
|| test_convolution_vec(1, 1, 1, 1, 1, 0, 1)
|| test_convolution_vec(11, 12, 1, 1, 1, 0, 0)
|| test_convolution_vec(20, 15, 1, 1, 1, 0, 1)
|| test_convolution_vec(12, 20, 1, 1, 1, 0, 0)
|| test_convolution_vec(3, 24, 1, 1, 1, 0, 1)
|| test_convolution_vec(24, 5, 1, 1, 1, 0, 0)
|| test_convolution_vec(32, 24, 1, 1, 1, 0, 1)
|| test_convolution_vec(12, 32, 1, 1, 1, 0, 0)
|| test_convolution_vec(64, 20, 1, 1, 1, 0, 1)
|| test_convolution_vec(64, 128, 1, 1, 1, 0, 0);
}

static int test_convolution_dynamic(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, 0);
pd.set(1, 0);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, 0);
pd.set(19, 1); // dynamic weight

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> as(bias ? 3 : 2);
as[0] = a;
as[1] = RandomMat(kernel, kernel, c, outch);
if (bias)
as[2] = RandomMat(outch);

std::vector<ncnn::Mat> weights(0);

int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, as);
if (ret != 0)
{
fprintf(stderr, "test_convolution_dynamic failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolution_3()
{
static const int kdsp[7][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, -233},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, -234},
};

for (int i = 0; i < 7; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolution_dynamic(11, 10, 1, 1, k, d, s, p, 1)
|| test_convolution_dynamic(11, 10, 4, 13, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 13, 4, k, d, s, p, 1)
|| test_convolution_dynamic(11, 10, 12, 12, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 8, 12, k, d, s, p, 1)
|| test_convolution_dynamic(11, 10, 8, 13, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 13, 8, k, d, s, p, 1)
|| test_convolution_dynamic(11, 10, 12, 16, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 15, 15, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 16, 16, k, d, s, p, 0);

if (ret != 0)
return -1;
}

return 0; return 0;
} }


#if NCNN_INT8
static int test_convolution_int8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, bool requant = false)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch * c * kernel * kernel);
pd.set(8, requant ? 101 : 1); // int8_scale_term

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 5 : 4);
weights[0] = RandomMat(outch * c * kernel * kernel);

ncnn::Mat weight_scales = scales_mat(weights[0], outch, c * kernel * kernel, c * kernel * kernel);
ncnn::Mat input_scales = scales_mat(a, 1, w * h * c, a.cstep);
ncnn::Mat top_scales = requant ? scales_mat(a, 1, w * h * c, a.cstep) : ncnn::Mat();
if (bias)
{
weights[1] = RandomMat(outch);
weights[2] = weight_scales;
weights[3] = input_scales;
weights[4] = top_scales;
}
else
{
weights[1] = weight_scales;
weights[2] = input_scales;
weights[3] = top_scales;
}

int flag = TEST_LAYER_DISABLE_GPU_TESTING;
int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a, requant ? 1.0f : 0.001f, 0, flag);
if (ret != 0)
{
fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d requant=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, requant, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolution_1()
{
static const int kdsp[16][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, -233},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{4, 1, 1, 2},
{4, 1, 2, -233},
{4, 2, 1, -234},
{5, 1, 1, -234},
{5, 1, 2, 2},
{5, 2, 2, 2},
{7, 1, 1, 3},
{7, 1, 2, 3},
{7, 2, 1, -233},
};

for (int i = 0; i < 16; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolution_int8(9, 7, 1, 1, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 2, 2, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 3, 3, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 4, 4, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 7, 7, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 8, 8, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 15, 15, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 16, 15, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 15, 16, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 16, 16, k, d, s, p, 1);

if (ret != 0)
return -1;
}
for (int i = 0; i < 16; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolution_int8(9, 7, 1, 1, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 1, 1, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 2, 2, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 3, 3, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 4, 4, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 7, 7, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 8, 8, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 15, 15, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 16, 15, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 15, 16, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 16, 16, k, d, s, p, 1, true);

if (ret != 0)
return -1;
}

return 0
|| test_convolution_int8(11, 11, 8, 16, 3, 1, 1, 1, 1)
|| test_convolution_int8(13, 16, 16, 24, 3, 1, 1, 1, 1)
|| test_convolution_int8(8, 8, 16, 24, 3, 1, 1, 1, 0)
|| test_convolution_int8(4, 8, 16, 24, 3, 1, 1, 1, 1)
|| test_convolution_int8(4, 20, 16, 24, 3, 1, 1, 1, 0)
|| test_convolution_int8(6, 7, 64, 64, 3, 1, 2, 0, 1)
|| test_convolution_int8(25, 33, 16, 15, 3, 1, 1, 1, 0)
|| test_convolution_int8(7, 7, 15, 12, 3, 1, 1, 1, 0);
}
#endif // NCNN_INT8

int main() int main()
{ {
SRAND(7767517); SRAND(7767517);


#if NCNN_INT8
return 0
|| test_convolution_0()
|| test_convolution_1()
|| test_convolution_2()
|| test_convolution_3();
#else
return 0
|| test_convolution_0()
|| test_convolution_2()
|| test_convolution_3();
#endif
return test_convolution_0();
} }

+ 136
- 0
tests/test_convolution_1.cpp View File

@@ -0,0 +1,136 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/convolution.h"
#include "testutil.h"

static int test_convolution(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch * c * kernel * kernel);

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * c * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);

float epsilon = 0.001;
// larget epsilon for winograd optimization
if (kernel == 3 && dilation == 1 && stride == 1 && c >= 16 && outch >= 16)
{
Randomize(a, -1, 1);
if (c >= 64 || outch >= 64)
Randomize(weights[0], -0.3, 0.3);
else
Randomize(weights[0], -1, 1);
epsilon = 0.002;
}

int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a, epsilon);
if (ret != 0)
{
fprintf(stderr, "test_convolution failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolution_0()
{
static const int kdsp[16][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, -233},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{4, 1, 1, 2},
{4, 1, 2, -233},
{4, 2, 1, -234},
{5, 1, 1, -234},
{5, 1, 2, 2},
{5, 2, 2, 2},
{7, 1, 1, 3},
{7, 1, 2, 3},
{7, 2, 1, -233},
};

for (int i = 12; i < 16; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolution(9, 7, 1, 1, k, d, s, p, 1)
|| test_convolution(9, 7, 4, 13, k, d, s, p, 0)
|| test_convolution(9, 7, 13, 4, k, d, s, p, 1)
|| test_convolution(9, 7, 12, 12, k, d, s, p, 0)
|| test_convolution(9, 7, 8, 12, k, d, s, p, 1)
|| test_convolution(9, 7, 8, 13, k, d, s, p, 0)
|| test_convolution(9, 7, 13, 8, k, d, s, p, 1)
|| test_convolution(9, 7, 12, 16, k, d, s, p, 0)
|| test_convolution(9, 7, 15, 15, k, d, s, p, 0)
|| test_convolution(9, 7, 16, 16, k, d, s, p, 0)
|| test_convolution(18, 17, 1, 1, k, d, s, p, 1)
|| test_convolution(18, 17, 4, 13, k, d, s, p, 0)
|| test_convolution(18, 17, 13, 4, k, d, s, p, 1)
|| test_convolution(18, 17, 12, 12, k, d, s, p, 0)
|| test_convolution(18, 17, 8, 12, k, d, s, p, 1)
|| test_convolution(18, 17, 8, 13, k, d, s, p, 0)
|| test_convolution(18, 17, 13, 8, k, d, s, p, 1)
|| test_convolution(18, 17, 12, 16, k, d, s, p, 0)
|| test_convolution(18, 17, 15, 15, k, d, s, p, 0)
|| test_convolution(18, 17, 16, 16, k, d, s, p, 0)
|| test_convolution(25, 33, 1, 1, k, d, s, p, 1)
|| test_convolution(25, 33, 4, 13, k, d, s, p, 0)
|| test_convolution(25, 33, 13, 4, k, d, s, p, 1)
|| test_convolution(25, 33, 12, 12, k, d, s, p, 0)
|| test_convolution(25, 33, 8, 12, k, d, s, p, 1)
|| test_convolution(25, 33, 8, 13, k, d, s, p, 0)
|| test_convolution(25, 33, 13, 8, k, d, s, p, 1)
|| test_convolution(25, 33, 12, 16, k, d, s, p, 0)
|| test_convolution(25, 33, 15, 15, k, d, s, p, 0)
|| test_convolution(25, 33, 16, 16, k, d, s, p, 0);

if (ret != 0)
return -1;
}

return 0;
}

int main()
{
SRAND(7767517);

return test_convolution_0();
}

+ 108
- 0
tests/test_convolution_2.cpp View File

@@ -0,0 +1,108 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/convolution.h"
#include "testutil.h"

static int test_convolution(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch * c * kernel * kernel);

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * c * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);

float epsilon = 0.001;
// larget epsilon for winograd optimization
if (kernel == 3 && dilation == 1 && stride == 1 && c >= 16 && outch >= 16)
{
Randomize(a, -1, 1);
if (c >= 64 || outch >= 64)
Randomize(weights[0], -0.3, 0.3);
else
Randomize(weights[0], -1, 1);
epsilon = 0.002;
}

int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a, epsilon);
if (ret != 0)
{
fprintf(stderr, "test_convolution failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolution_0()
{
return 0
|| test_convolution(7, 5, 1, 4, 3, 1, 1, 1, 1)
|| test_convolution(14, 5, 1, 4, 3, 1, 2, 1, 1)
|| test_convolution(11, 5, 2, 12, 2, 2, 2, 1, 1)
|| test_convolution(15, 11, 4, 4, 3, 1, 1, 1, 1)
|| test_convolution(15, 11, 8, 8, 3, 1, 1, 1, 1)
|| test_convolution(11, 11, 8, 16, 3, 1, 1, 1, 1)
|| test_convolution(13, 16, 16, 24, 3, 1, 1, 1, 1)
|| test_convolution(20, 19, 24, 24, 3, 1, 1, 1, 1)
|| test_convolution(8, 8, 16, 24, 3, 1, 1, 1, 0)
|| test_convolution(4, 8, 16, 24, 3, 1, 1, 1, 1)
|| test_convolution(4, 20, 16, 24, 3, 1, 1, 1, 0)
|| test_convolution(6, 7, 64, 64, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 24, 32, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 24, 32, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 24, 32, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 24, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 32, 24, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 24, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 28, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 32, 28, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 28, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 26, 32, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 26, 32, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 26, 32, 3, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 26, 1, 1, 1, 0, 0)
|| test_convolution(15, 17, 32, 26, 1, 1, 2, 0, 1)
|| test_convolution(15, 17, 32, 26, 3, 1, 2, 0, 1)
|| test_convolution(30, 30, 32, 26, 3, 1, 1, 1, 0)
|| test_convolution(12, 18, 8, 16, 3, 1, 1, 1, 1)
|| test_convolution(42, 18, 32, 160, 3, 1, 1, 1, 1)
|| test_convolution(12, 18, 32, 160, 3, 1, 1, 1, 1)
|| test_convolution(12, 18, 4, 12, 3, 1, 1, 1, 1)
|| test_convolution(42, 18, 28, 140, 3, 1, 1, 1, 1)
|| test_convolution(12, 18, 28, 140, 3, 1, 1, 1, 1);
}

int main()
{
SRAND(7767517);

return test_convolution_0();
}

+ 288
- 0
tests/test_convolution_3.cpp View File

@@ -0,0 +1,288 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/convolution.h"
#include "testutil.h"

static int test_convolution_vec(int w, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
ncnn::Mat a = RandomMat(w);

ncnn::ParamDict pd;
pd.set(0, outch); // num_output
pd.set(1, kernel); // kernel_w
pd.set(2, dilation); // dilation_w
pd.set(3, stride); // stride_w
pd.set(4, pad); // pad_w
pd.set(5, bias); // bias_term
pd.set(6, outch * w * kernel * kernel);

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * w * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);

int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a);
if (ret != 0)
{
fprintf(stderr, "test_convolution_vec failed w=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolution_2()
{
return 0
|| test_convolution_vec(1, 1, 1, 1, 1, 0, 1)
|| test_convolution_vec(11, 12, 1, 1, 1, 0, 0)
|| test_convolution_vec(20, 15, 1, 1, 1, 0, 1)
|| test_convolution_vec(12, 20, 1, 1, 1, 0, 0)
|| test_convolution_vec(3, 24, 1, 1, 1, 0, 1)
|| test_convolution_vec(24, 5, 1, 1, 1, 0, 0)
|| test_convolution_vec(32, 24, 1, 1, 1, 0, 1)
|| test_convolution_vec(12, 32, 1, 1, 1, 0, 0)
|| test_convolution_vec(64, 20, 1, 1, 1, 0, 1)
|| test_convolution_vec(64, 128, 1, 1, 1, 0, 0);
}

static int test_convolution_dynamic(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, 0);
pd.set(1, 0);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, 0);
pd.set(19, 1); // dynamic weight

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> as(bias ? 3 : 2);
as[0] = a;
as[1] = RandomMat(kernel, kernel, c, outch);
if (bias)
as[2] = RandomMat(outch);

std::vector<ncnn::Mat> weights(0);

int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, as);
if (ret != 0)
{
fprintf(stderr, "test_convolution_dynamic failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolution_3()
{
static const int kdsp[7][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, -233},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, -234},
};

for (int i = 0; i < 7; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolution_dynamic(11, 10, 1, 1, k, d, s, p, 1)
|| test_convolution_dynamic(11, 10, 4, 13, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 13, 4, k, d, s, p, 1)
|| test_convolution_dynamic(11, 10, 12, 12, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 8, 12, k, d, s, p, 1)
|| test_convolution_dynamic(11, 10, 8, 13, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 13, 8, k, d, s, p, 1)
|| test_convolution_dynamic(11, 10, 12, 16, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 15, 15, k, d, s, p, 0)
|| test_convolution_dynamic(11, 10, 16, 16, k, d, s, p, 0);

if (ret != 0)
return -1;
}

return 0;
}

#if NCNN_INT8
static int test_convolution_int8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, bool requant = false)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch * c * kernel * kernel);
pd.set(8, requant ? 101 : 1); // int8_scale_term

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 5 : 4);
weights[0] = RandomMat(outch * c * kernel * kernel);

ncnn::Mat weight_scales = scales_mat(weights[0], outch, c * kernel * kernel, c * kernel * kernel);
ncnn::Mat input_scales = scales_mat(a, 1, w * h * c, a.cstep);
ncnn::Mat top_scales = requant ? scales_mat(a, 1, w * h * c, a.cstep) : ncnn::Mat();
if (bias)
{
weights[1] = RandomMat(outch);
weights[2] = weight_scales;
weights[3] = input_scales;
weights[4] = top_scales;
}
else
{
weights[1] = weight_scales;
weights[2] = input_scales;
weights[3] = top_scales;
}

int flag = TEST_LAYER_DISABLE_GPU_TESTING;
int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a, requant ? 1.0f : 0.001f, 0, flag);
if (ret != 0)
{
fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d requant=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, requant, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolution_1()
{
static const int kdsp[16][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, -233},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{4, 1, 1, 2},
{4, 1, 2, -233},
{4, 2, 1, -234},
{5, 1, 1, -234},
{5, 1, 2, 2},
{5, 2, 2, 2},
{7, 1, 1, 3},
{7, 1, 2, 3},
{7, 2, 1, -233},
};

for (int i = 0; i < 16; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolution_int8(9, 7, 1, 1, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 2, 2, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 3, 3, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 4, 4, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 7, 7, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 8, 8, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 15, 15, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 16, 15, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 15, 16, k, d, s, p, 1)
|| test_convolution_int8(9, 7, 16, 16, k, d, s, p, 1);

if (ret != 0)
return -1;
}
for (int i = 0; i < 16; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolution_int8(9, 7, 1, 1, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 1, 1, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 2, 2, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 3, 3, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 4, 4, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 7, 7, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 8, 8, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 15, 15, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 16, 15, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 15, 16, k, d, s, p, 1, true)
|| test_convolution_int8(9, 7, 16, 16, k, d, s, p, 1, true);

if (ret != 0)
return -1;
}

return 0
|| test_convolution_int8(11, 11, 8, 16, 3, 1, 1, 1, 1)
|| test_convolution_int8(13, 16, 16, 24, 3, 1, 1, 1, 1)
|| test_convolution_int8(8, 8, 16, 24, 3, 1, 1, 1, 0)
|| test_convolution_int8(4, 8, 16, 24, 3, 1, 1, 1, 1)
|| test_convolution_int8(4, 20, 16, 24, 3, 1, 1, 1, 0)
|| test_convolution_int8(6, 7, 64, 64, 3, 1, 2, 0, 1)
|| test_convolution_int8(25, 33, 16, 15, 3, 1, 1, 1, 0)
|| test_convolution_int8(7, 7, 15, 12, 3, 1, 1, 1, 0);
}
#endif // NCNN_INT8

int main()
{
SRAND(7767517);

#if NCNN_INT8
return 0
|| test_convolution_1()
|| test_convolution_2()
|| test_convolution_3();
#else
return 0
|| test_convolution_2()
|| test_convolution_3();
#endif
}

+ 1
- 214
tests/test_convolutiondepthwise.cpp View File

@@ -125,222 +125,9 @@ static int test_convolutiondepthwise_0()
return 0; return 0;
} }


static int test_convolutiondepthwise_dynamic(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, 0);
pd.set(1, 0);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, 0);
pd.set(7, group);
pd.set(19, 1); // dynamic weight

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> as(bias ? 3 : 2);
as[0] = a;
as[1] = RandomMat(kernel, kernel, c / group, outch);
if (bias)
as[2] = RandomMat(outch);

std::vector<ncnn::Mat> weights(0);

int ret = test_layer<ncnn::ConvolutionDepthWise>("ConvolutionDepthWise", pd, weights, as);
if (ret != 0)
{
fprintf(stderr, "test_convolutiondepthwise_dynamic failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolutiondepthwise_2()
{
static const int kdsp[7][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, -233},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, -234},
};

for (int i = 0; i < 7; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolutiondepthwise_dynamic(11, 10, 1, 1, k, d, s, p, 1, 1)
|| test_convolutiondepthwise_dynamic(11, 10, 2, 2, k, d, s, p, 0, 1)
|| test_convolutiondepthwise_dynamic(11, 10, 2, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise_dynamic(11, 10, 3, 3, k, d, s, p, 0, 3)
|| test_convolutiondepthwise_dynamic(11, 10, 4, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise_dynamic(11, 10, 4, 4, k, d, s, p, 0, 4)
|| test_convolutiondepthwise_dynamic(11, 10, 7, 7, k, d, s, p, 1, 7)
|| test_convolutiondepthwise_dynamic(11, 10, 8, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise_dynamic(11, 10, 8, 8, k, d, s, p, 1, 8)
|| test_convolutiondepthwise_dynamic(11, 10, 12, 12, k, d, s, p, 0, 4)
|| test_convolutiondepthwise_dynamic(11, 10, 15, 15, k, d, s, p, 1, 15)
|| test_convolutiondepthwise_dynamic(11, 10, 16, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise_dynamic(11, 10, 16, 16, k, d, s, p, 1, 16);

if (ret != 0)
return -1;
}

return 0;
}

#if NCNN_INT8
static int test_convolutiondepthwise_int8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group, bool requant = false)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch / group * c / group * kernel * kernel * group);
pd.set(7, group);
pd.set(8, requant ? 101 : 1); // int8_scale_term

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 5 : 4);
weights[0] = RandomMat(outch / group * c / group * kernel * kernel * group);
ncnn::Mat weight_scales = scales_mat(weights[0], group, c * kernel * kernel / group, c * kernel * kernel / group);
ncnn::Mat input_scales = scales_mat(a, 1, w * h * c, a.cstep);
ncnn::Mat top_scales = requant ? scales_mat(a, 1, w * h * c, a.cstep) : ncnn::Mat();
if (bias)
{
weights[1] = RandomMat(outch);
weights[2] = weight_scales;
weights[3] = input_scales;
weights[4] = top_scales;
}
else
{
weights[1] = weight_scales;
weights[2] = input_scales;
weights[3] = top_scales;
}

int flag = TEST_LAYER_DISABLE_GPU_TESTING;
int ret = test_layer<ncnn::ConvolutionDepthWise>("ConvolutionDepthWise", pd, weights, a, requant ? 1.0f : 0.001f, 0, flag);
if (ret != 0)
{
fprintf(stderr, "test_convolutiondepthwise_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d requant=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, group, requant, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolutiondepthwise_1()
{
static const int kdsp[16][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, -233},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{4, 1, 1, 2},
{4, 1, 2, -233},
{4, 2, 1, -234},
{5, 1, 1, -234},
{5, 1, 2, 2},
{5, 2, 2, 2},
{7, 1, 1, 3},
{7, 1, 2, 3},
{7, 2, 1, -233},
};

for (int i = 0; i < 16; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolutiondepthwise_int8(15, 7, 1, 1, k, d, s, p, 1, 1)
|| test_convolutiondepthwise_int8(15, 7, 2, 2, k, d, s, p, 0, 1)
|| test_convolutiondepthwise_int8(15, 7, 2, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise_int8(15, 7, 3, 3, k, d, s, p, 0, 3)
|| test_convolutiondepthwise_int8(15, 7, 4, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise_int8(15, 7, 4, 4, k, d, s, p, 0, 4)
|| test_convolutiondepthwise_int8(15, 7, 7, 7, k, d, s, p, 1, 7)
|| test_convolutiondepthwise_int8(15, 7, 8, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise_int8(15, 7, 8, 8, k, d, s, p, 1, 8)
|| test_convolutiondepthwise_int8(15, 7, 12, 12, k, d, s, p, 0, 4)
|| test_convolutiondepthwise_int8(15, 7, 15, 15, k, d, s, p, 1, 15)
|| test_convolutiondepthwise_int8(15, 7, 16, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise_int8(15, 7, 16, 16, k, d, s, p, 1, 16);

if (ret != 0)
return -1;
}

for (int i = 0; i < 16; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolutiondepthwise_int8(9, 7, 1, 1, k, d, s, p, 1, 1, true)
|| test_convolutiondepthwise_int8(9, 7, 2, 2, k, d, s, p, 0, 1, true)
|| test_convolutiondepthwise_int8(9, 7, 2, 2, k, d, s, p, 1, 2, true)
|| test_convolutiondepthwise_int8(9, 7, 3, 3, k, d, s, p, 0, 3, true)
|| test_convolutiondepthwise_int8(9, 7, 4, 2, k, d, s, p, 1, 2, true)
|| test_convolutiondepthwise_int8(9, 7, 4, 4, k, d, s, p, 0, 4, true)
|| test_convolutiondepthwise_int8(9, 7, 7, 7, k, d, s, p, 1, 7, true)
|| test_convolutiondepthwise_int8(9, 7, 8, 8, k, d, s, p, 0, 2, true)
|| test_convolutiondepthwise_int8(9, 7, 8, 8, k, d, s, p, 1, 8, true)
|| test_convolutiondepthwise_int8(9, 7, 12, 12, k, d, s, p, 0, 4, true)
|| test_convolutiondepthwise_int8(9, 7, 15, 15, k, d, s, p, 1, 15, true)
|| test_convolutiondepthwise_int8(9, 7, 16, 8, k, d, s, p, 0, 2, true)
|| test_convolutiondepthwise_int8(9, 7, 16, 16, k, d, s, p, 1, 16, true);

if (ret != 0)
return -1;
}

return 0;
}
#endif // NCNN_INT8

int main() int main()
{ {
SRAND(7767517); SRAND(7767517);


#if NCNN_INT8
return test_convolutiondepthwise_0() || test_convolutiondepthwise_1() || test_convolutiondepthwise_2();
#else
return test_convolutiondepthwise_0() || test_convolutiondepthwise_2();
#endif
return test_convolutiondepthwise_0();
} }

+ 236
- 0
tests/test_convolutiondepthwise_1.cpp View File

@@ -0,0 +1,236 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/convolutiondepthwise.h"
#include "testutil.h"

static int test_convolutiondepthwise_dynamic(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, 0);
pd.set(1, 0);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, 0);
pd.set(7, group);
pd.set(19, 1); // dynamic weight

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> as(bias ? 3 : 2);
as[0] = a;
as[1] = RandomMat(kernel, kernel, c / group, outch);
if (bias)
as[2] = RandomMat(outch);

std::vector<ncnn::Mat> weights(0);

int ret = test_layer<ncnn::ConvolutionDepthWise>("ConvolutionDepthWise", pd, weights, as);
if (ret != 0)
{
fprintf(stderr, "test_convolutiondepthwise_dynamic failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolutiondepthwise_2()
{
static const int kdsp[7][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, -233},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, -234},
};

for (int i = 0; i < 7; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolutiondepthwise_dynamic(11, 10, 1, 1, k, d, s, p, 1, 1)
|| test_convolutiondepthwise_dynamic(11, 10, 2, 2, k, d, s, p, 0, 1)
|| test_convolutiondepthwise_dynamic(11, 10, 2, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise_dynamic(11, 10, 3, 3, k, d, s, p, 0, 3)
|| test_convolutiondepthwise_dynamic(11, 10, 4, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise_dynamic(11, 10, 4, 4, k, d, s, p, 0, 4)
|| test_convolutiondepthwise_dynamic(11, 10, 7, 7, k, d, s, p, 1, 7)
|| test_convolutiondepthwise_dynamic(11, 10, 8, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise_dynamic(11, 10, 8, 8, k, d, s, p, 1, 8)
|| test_convolutiondepthwise_dynamic(11, 10, 12, 12, k, d, s, p, 0, 4)
|| test_convolutiondepthwise_dynamic(11, 10, 15, 15, k, d, s, p, 1, 15)
|| test_convolutiondepthwise_dynamic(11, 10, 16, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise_dynamic(11, 10, 16, 16, k, d, s, p, 1, 16);

if (ret != 0)
return -1;
}

return 0;
}

#if NCNN_INT8
static int test_convolutiondepthwise_int8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group, bool requant = false)
{
ncnn::Mat a = RandomMat(w, h, c);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch / group * c / group * kernel * kernel * group);
pd.set(7, group);
pd.set(8, requant ? 101 : 1); // int8_scale_term

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 5 : 4);
weights[0] = RandomMat(outch / group * c / group * kernel * kernel * group);
ncnn::Mat weight_scales = scales_mat(weights[0], group, c * kernel * kernel / group, c * kernel * kernel / group);
ncnn::Mat input_scales = scales_mat(a, 1, w * h * c, a.cstep);
ncnn::Mat top_scales = requant ? scales_mat(a, 1, w * h * c, a.cstep) : ncnn::Mat();
if (bias)
{
weights[1] = RandomMat(outch);
weights[2] = weight_scales;
weights[3] = input_scales;
weights[4] = top_scales;
}
else
{
weights[1] = weight_scales;
weights[2] = input_scales;
weights[3] = top_scales;
}

int flag = TEST_LAYER_DISABLE_GPU_TESTING;
int ret = test_layer<ncnn::ConvolutionDepthWise>("ConvolutionDepthWise", pd, weights, a, requant ? 1.0f : 0.001f, 0, flag);
if (ret != 0)
{
fprintf(stderr, "test_convolutiondepthwise_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d requant=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, group, requant, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_convolutiondepthwise_1()
{
static const int kdsp[16][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, -233},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{4, 1, 1, 2},
{4, 1, 2, -233},
{4, 2, 1, -234},
{5, 1, 1, -234},
{5, 1, 2, 2},
{5, 2, 2, 2},
{7, 1, 1, 3},
{7, 1, 2, 3},
{7, 2, 1, -233},
};

for (int i = 0; i < 16; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolutiondepthwise_int8(15, 7, 1, 1, k, d, s, p, 1, 1)
|| test_convolutiondepthwise_int8(15, 7, 2, 2, k, d, s, p, 0, 1)
|| test_convolutiondepthwise_int8(15, 7, 2, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise_int8(15, 7, 3, 3, k, d, s, p, 0, 3)
|| test_convolutiondepthwise_int8(15, 7, 4, 2, k, d, s, p, 1, 2)
|| test_convolutiondepthwise_int8(15, 7, 4, 4, k, d, s, p, 0, 4)
|| test_convolutiondepthwise_int8(15, 7, 7, 7, k, d, s, p, 1, 7)
|| test_convolutiondepthwise_int8(15, 7, 8, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise_int8(15, 7, 8, 8, k, d, s, p, 1, 8)
|| test_convolutiondepthwise_int8(15, 7, 12, 12, k, d, s, p, 0, 4)
|| test_convolutiondepthwise_int8(15, 7, 15, 15, k, d, s, p, 1, 15)
|| test_convolutiondepthwise_int8(15, 7, 16, 8, k, d, s, p, 0, 2)
|| test_convolutiondepthwise_int8(15, 7, 16, 16, k, d, s, p, 1, 16);

if (ret != 0)
return -1;
}

for (int i = 0; i < 16; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_convolutiondepthwise_int8(9, 7, 1, 1, k, d, s, p, 1, 1, true)
|| test_convolutiondepthwise_int8(9, 7, 2, 2, k, d, s, p, 0, 1, true)
|| test_convolutiondepthwise_int8(9, 7, 2, 2, k, d, s, p, 1, 2, true)
|| test_convolutiondepthwise_int8(9, 7, 3, 3, k, d, s, p, 0, 3, true)
|| test_convolutiondepthwise_int8(9, 7, 4, 2, k, d, s, p, 1, 2, true)
|| test_convolutiondepthwise_int8(9, 7, 4, 4, k, d, s, p, 0, 4, true)
|| test_convolutiondepthwise_int8(9, 7, 7, 7, k, d, s, p, 1, 7, true)
|| test_convolutiondepthwise_int8(9, 7, 8, 8, k, d, s, p, 0, 2, true)
|| test_convolutiondepthwise_int8(9, 7, 8, 8, k, d, s, p, 1, 8, true)
|| test_convolutiondepthwise_int8(9, 7, 12, 12, k, d, s, p, 0, 4, true)
|| test_convolutiondepthwise_int8(9, 7, 15, 15, k, d, s, p, 1, 15, true)
|| test_convolutiondepthwise_int8(9, 7, 16, 8, k, d, s, p, 0, 2, true)
|| test_convolutiondepthwise_int8(9, 7, 16, 16, k, d, s, p, 1, 16, true);

if (ret != 0)
return -1;
}

return 0;
}
#endif // NCNN_INT8

int main()
{
SRAND(7767517);

#if NCNN_INT8
return test_convolutiondepthwise_1() || test_convolutiondepthwise_2();
#else
return test_convolutiondepthwise_2();
#endif
}

+ 1
- 454
tests/test_crop.cpp View File

@@ -42,112 +42,6 @@ static int test_crop(const ncnn::Mat& a, int woffset, int hoffset, int doffset,
return ret; return ret;
} }


static ncnn::Mat IntArrayMat(int a0)
{
ncnn::Mat m(1);
int* p = m;
p[0] = a0;
return m;
}

static ncnn::Mat IntArrayMat(int a0, int a1)
{
ncnn::Mat m(2);
int* p = m;
p[0] = a0;
p[1] = a1;
return m;
}

static ncnn::Mat IntArrayMat(int a0, int a1, int a2)
{
ncnn::Mat m(3);
int* p = m;
p[0] = a0;
p[1] = a1;
p[2] = a2;
return m;
}

static ncnn::Mat IntArrayMat(int a0, int a1, int a2, int a3)
{
ncnn::Mat m(4);
int* p = m;
p[0] = a0;
p[1] = a1;
p[2] = a2;
p[3] = a3;
return m;
}

static void print_int_array(const ncnn::Mat& a)
{
const int* pa = a;

fprintf(stderr, "[");
for (int i = 0; i < a.w; i++)
{
fprintf(stderr, " %d", pa[i]);
}
fprintf(stderr, " ]");
}

static int test_crop(const ncnn::Mat& a, const ncnn::Mat& starts, const ncnn::Mat& ends, const ncnn::Mat& axes)
{
ncnn::ParamDict pd;
pd.set(9, starts); // starts
pd.set(10, ends); // ends
pd.set(11, axes); // axes

std::vector<ncnn::Mat> weights(0);

int ret = test_layer<ncnn::Crop>("Crop", pd, weights, a);
if (ret != 0)
{
fprintf(stderr, "test_crop failed a.dims=%d a=(%d %d %d %d)", a.dims, a.w, a.h, a.d, a.c);
fprintf(stderr, " starts=");
print_int_array(starts);
fprintf(stderr, " ends=");
print_int_array(ends);
fprintf(stderr, " axes=");
print_int_array(axes);
fprintf(stderr, "\n");
}

return ret;
}

static int test_crop(const ncnn::Mat& a, int woffset, int hoffset, int doffset, int coffset, const ncnn::Mat& ref)
{
ncnn::ParamDict pd;
pd.set(0, woffset);
pd.set(1, hoffset);
pd.set(13, doffset);
pd.set(2, coffset);
pd.set(3, 0); // outw
pd.set(4, 0); // outh
pd.set(14, 0); // outd
pd.set(5, 0); // outc
pd.set(6, 0); // woffset2
pd.set(7, 0); // hoffset2
pd.set(15, 0); // doffset2
pd.set(8, 0); // coffset2

std::vector<ncnn::Mat> weights(0);

std::vector<ncnn::Mat> ab(2);
ab[0] = a;
ab[1] = ref;

int ret = test_layer<ncnn::Crop>("Crop", pd, weights, ab);
if (ret != 0)
{
fprintf(stderr, "test_crop failed a.dims=%d a=(%d %d %d %d) woffset=%d hoffset=%d doffset=%d coffset=%d ref.dims=%d ref=(%d %d %d %d)\n", a.dims, a.w, a.h, a.d, a.c, woffset, hoffset, doffset, coffset, ref.dims, ref.w, ref.h, ref.d, ref.c);
}

return ret;
}

static int test_crop_0(const ncnn::Mat& a) static int test_crop_0(const ncnn::Mat& a)
{ {
return 0 return 0
@@ -161,30 +55,6 @@ static int test_crop_0(const ncnn::Mat& a)
|| test_crop(a, 16, 0, 0, 0, -233, 0, 0, 0, 7, 0, 0, 0); || test_crop(a, 16, 0, 0, 0, -233, 0, 0, 0, 7, 0, 0, 0);
} }


static int test_crop_1(const ncnn::Mat& a)
{
return 0
|| test_crop(a, IntArrayMat(12), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(16), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(11), IntArrayMat(11 + 16), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(12 + 7), IntArrayMat(-1))
|| test_crop(a, IntArrayMat(16), IntArrayMat(16 + 12), ncnn::Mat())
|| test_crop(a, IntArrayMat(11), IntArrayMat(-7 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(-12 + 1), IntArrayMat(-1))
|| test_crop(a, IntArrayMat(16), IntArrayMat(-16 + 1), ncnn::Mat());
}

static int test_crop_2(const ncnn::Mat& a)
{
return 0
|| test_crop(a, 0, 0, 0, 0, a)
|| test_crop(a, 0, 0, 0, 0, ncnn::Mat(27))

|| test_crop(a, 11, 0, 0, 0, ncnn::Mat(7))
|| test_crop(a, 12, 0, 0, 0, ncnn::Mat(12))
|| test_crop(a, 16, 0, 0, 0, ncnn::Mat(16));
}

static int test_crop_3(const ncnn::Mat& a) static int test_crop_3(const ncnn::Mat& a)
{ {
return 0 return 0
@@ -220,52 +90,6 @@ static int test_crop_3(const ncnn::Mat& a)
|| test_crop(a, 4, 8, 0, 0, -233, -233, 0, 0, 6, 12, 0, 0); || test_crop(a, 4, 8, 0, 0, -233, -233, 0, 0, 6, 12, 0, 0);
} }


static int test_crop_4(const ncnn::Mat& a)
{
return 0
|| test_crop(a, IntArrayMat(12), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-233), IntArrayMat(1))
|| test_crop(a, IntArrayMat(5, 11), IntArrayMat(-233, -233), IntArrayMat(0, 1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(11 + 16), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(12 + 7), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(8 + 12), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(5), IntArrayMat(8), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(9), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(12), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(11 + 7, 11), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(12 + 12, 12), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(8 + 16, 10), IntArrayMat(0, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(-16 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(-7 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-12 + 1), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(5), IntArrayMat(-5 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-6 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-4 + 1), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-12 + 1, -6 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-16 + 1, -5 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-7 + 1, -4 + 1), IntArrayMat(-2, -1));
}

static int test_crop_5(const ncnn::Mat& a)
{
return 0
|| test_crop(a, 0, 0, 0, 0, a)

|| test_crop(a, 0, 12, 0, 0, ncnn::Mat(8, 7))
|| test_crop(a, 5, 0, 0, 0, ncnn::Mat(7, 27))

|| test_crop(a, 5, 11, 0, 0, ncnn::Mat(5, 12))
|| test_crop(a, 6, 12, 0, 0, ncnn::Mat(4, 16))
|| test_crop(a, 4, 8, 0, 0, ncnn::Mat(6, 7));
}

static int test_crop_6(const ncnn::Mat& a) static int test_crop_6(const ncnn::Mat& a)
{ {
return 0 return 0
@@ -338,94 +162,6 @@ static int test_crop_6(const ncnn::Mat& a)
|| test_crop(a, 4, 4, 0, 8, -233, -233, 0, -233, 6, 2, 0, 12); || test_crop(a, 4, 4, 0, 8, -233, -233, 0, -233, 6, 2, 0, 12);
} }


static int test_crop_7(const ncnn::Mat& a)
{
return 0
|| test_crop(a, IntArrayMat(11), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(5), IntArrayMat(-233), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-233), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-233), IntArrayMat(-1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-233, -233), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-233, -233), IntArrayMat(0, -1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-233, -233), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(6, 6), IntArrayMat(-233, -233), IntArrayMat(1, -1))
|| test_crop(a, IntArrayMat(11, 5, 5), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 4), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(11 + 7), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(12 + 12), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(8 + 16), IntArrayMat(0))

|| test_crop(a, IntArrayMat(5), IntArrayMat(13), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(12), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(11), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(5), IntArrayMat(12), IntArrayMat(2))
|| test_crop(a, IntArrayMat(6), IntArrayMat(11), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(13), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(11 + 7, 11), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(12 + 16, 12), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(8 + 12, 13), IntArrayMat(0, -2))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(11 + 16, 13), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(12 + 12, 11), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(8 + 7, 12), IntArrayMat(0, -1))

|| test_crop(a, IntArrayMat(5, 4), IntArrayMat(12, 12), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(6, 3), IntArrayMat(13, 13), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(4, 2), IntArrayMat(11, 11), IntArrayMat(-2, -1))

|| test_crop(a, IntArrayMat(11, 5, 2), IntArrayMat(11 + 7, 11, 11), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(12, 6, 4), IntArrayMat(12 + 16, 12, 12), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 3), IntArrayMat(8 + 12, 13, 13), IntArrayMat(-3, -2, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(-7 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(-12 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-16 + 1), IntArrayMat(-3))

|| test_crop(a, IntArrayMat(5), IntArrayMat(-6 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-5 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-4 + 1), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(5), IntArrayMat(-5 + 1), IntArrayMat(2))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-4 + 1), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-6 + 1), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-7 + 1, -4 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-12 + 1, -6 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-16 + 1, -5 + 1), IntArrayMat(-3, -2))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-12 + 1, -6 + 1), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-16 + 1, -5 + 1), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-7 + 1, -4 + 1), IntArrayMat(-3, -1))

|| test_crop(a, IntArrayMat(5, 2), IntArrayMat(-5 + 1, -5 + 1), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(6, 4), IntArrayMat(-4 + 1, -4 + 1), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(4, 3), IntArrayMat(-6 + 1, -6 + 1), IntArrayMat(-2, -1))

|| test_crop(a, IntArrayMat(11, 5, 4), IntArrayMat(-7 + 1, -5 + 1, -5 + 1), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(12, 6, 3), IntArrayMat(-12 + 1, -6 + 1, -6 + 1), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 2), IntArrayMat(-16 + 1, -4 + 1, -4 + 1), IntArrayMat(-3, -2, -1));
}

static int test_crop_8(const ncnn::Mat& a)
{
return 0
|| test_crop(a, 0, 0, 0, 0, a)

|| test_crop(a, 0, 5, 0, 0, ncnn::Mat(6, 6))
|| test_crop(a, 6, 0, 0, 0, ncnn::Mat(8, 8))
|| test_crop(a, 5, 2, 0, 0, ncnn::Mat(6, 3))
|| test_crop(a, 6, 3, 0, 0, ncnn::Mat(8, 4))
|| test_crop(a, 4, 4, 0, 0, ncnn::Mat(7, 5))

|| test_crop(a, 5, 3, 0, 11, ncnn::Mat(7, 3, 7))
|| test_crop(a, 6, 4, 0, 12, ncnn::Mat(6, 4, 12))
|| test_crop(a, 4, 2, 0, 8, ncnn::Mat(5, 5, 16));
}

static int test_crop_9(const ncnn::Mat& a) static int test_crop_9(const ncnn::Mat& a)
{ {
return 0 return 0
@@ -524,171 +260,6 @@ static int test_crop_9(const ncnn::Mat& a)
|| test_crop(a, 3, 3, 3, 8, -233, -233, -233, -233, 3, 3, 3, 12); || test_crop(a, 3, 3, 3, 8, -233, -233, -233, -233, 3, 3, 3, 12);
} }


static int test_crop_10(const ncnn::Mat& a)
{
return 0
|| test_crop(a, IntArrayMat(11), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-233), IntArrayMat(1))
|| test_crop(a, IntArrayMat(5), IntArrayMat(-233), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-233), IntArrayMat(-2))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-233), IntArrayMat(3))
|| test_crop(a, IntArrayMat(5), IntArrayMat(-233), IntArrayMat(-1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-233, -233), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-233, -233), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-233, -233), IntArrayMat(-4, -2))
|| test_crop(a, IntArrayMat(4, 4), IntArrayMat(-233, -233), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-233, -233), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(5, 5), IntArrayMat(-233, -233), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(4, 4), IntArrayMat(-233, -233), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(12, 6, 6), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(11, 5, 5), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 4), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(12, 6, 6), IntArrayMat(-233, -233, -233), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(11, 5, 5), IntArrayMat(-233, -233, -233), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(4, 4, 4), IntArrayMat(-233, -233, -233), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(6, 6, 6), IntArrayMat(-233, -233, -233), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(11, 5, 5, 5), IntArrayMat(-233, -233, -233, -233), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(8, 4, 4, 4), IntArrayMat(-233, -233, -233, -233), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(12, 6, 6, 6), IntArrayMat(-233, -233, -233, -233), IntArrayMat(-4, -3, -2, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(11 + 16), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(12 + 7), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(8 + 12), IntArrayMat(-4))

|| test_crop(a, IntArrayMat(5), IntArrayMat(11), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(13), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(12), IntArrayMat(-3))

|| test_crop(a, IntArrayMat(3), IntArrayMat(12), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(13), IntArrayMat(2))
|| test_crop(a, IntArrayMat(5), IntArrayMat(11), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(1), IntArrayMat(8), IntArrayMat(3))
|| test_crop(a, IntArrayMat(2), IntArrayMat(7), IntArrayMat(3))
|| test_crop(a, IntArrayMat(3), IntArrayMat(6), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(11 + 7, 11), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(12 + 12, 12), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(8 + 16, 13), IntArrayMat(-4, -3))

|| test_crop(a, IntArrayMat(11, 4), IntArrayMat(11 + 12, 13), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(12, 3), IntArrayMat(12 + 16, 11), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(8, 2), IntArrayMat(8 + 7, 12), IntArrayMat(-4, -2))

|| test_crop(a, IntArrayMat(11, 1), IntArrayMat(11 + 16, 5), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(12, 2), IntArrayMat(12 + 7, 6), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(8, 3), IntArrayMat(8 + 12, 7), IntArrayMat(-4, -1))

|| test_crop(a, IntArrayMat(3, 3), IntArrayMat(13, 4), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(4, 2), IntArrayMat(12, 3), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(5, 1), IntArrayMat(11, 2), IntArrayMat(-3, -2))

|| test_crop(a, IntArrayMat(5, 5), IntArrayMat(11, 8), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(4, 6), IntArrayMat(12, 9), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(3, 4), IntArrayMat(13, 7), IntArrayMat(-3, -1))

|| test_crop(a, IntArrayMat(2, 3), IntArrayMat(12, 9), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(3, 2), IntArrayMat(11, 7), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(4, 1), IntArrayMat(10, 8), IntArrayMat(-2, -1))

|| test_crop(a, IntArrayMat(11, 2, 2), IntArrayMat(11 + 6, 9, 9), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(12, 3, 3), IntArrayMat(12 + 1, 10, 10), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 4), IntArrayMat(8 + 3, 11, 11), IntArrayMat(-4, -3, -2))

|| test_crop(a, IntArrayMat(11, 4, 4), IntArrayMat(11 + 12, 12, 12), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(12, 5, 5), IntArrayMat(12 + 8, 11, 11), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(8, 6, 6), IntArrayMat(8 + 4, 13, 13), IntArrayMat(-4, -3, -1))

|| test_crop(a, IntArrayMat(11, 1, 4), IntArrayMat(11 + 5, 12, 12), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(12, 3, 3), IntArrayMat(12 + 3, 11, 11), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(8, 2, 5), IntArrayMat(8 + 2, 10, 10), IntArrayMat(-4, -2, -1))

|| test_crop(a, IntArrayMat(1, 1, 1), IntArrayMat(7, 7, 7), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(2, 2, 2), IntArrayMat(8, 9, 10), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(3, 3, 3), IntArrayMat(11, 12, 13), IntArrayMat(-3, -2, -1))

|| test_crop(a, IntArrayMat(11, 2, 3, 6), IntArrayMat(11 + 11, 10, 12, 11), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(12, 3, 4, 5), IntArrayMat(12 + 12, 9, 11, 13), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(8, 4, 5, 4), IntArrayMat(8 + 8, 8, 10, 12), IntArrayMat(-4, -3, -2, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(-7 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(-12 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-16 + 1), IntArrayMat(-4))

|| test_crop(a, IntArrayMat(5), IntArrayMat(-6 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-5 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-4 + 1), IntArrayMat(-3))

|| test_crop(a, IntArrayMat(4), IntArrayMat(-4 + 1), IntArrayMat(2))
|| test_crop(a, IntArrayMat(5), IntArrayMat(-5 + 1), IntArrayMat(2))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-6 + 1), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(1), IntArrayMat(-5 + 1), IntArrayMat(3))
|| test_crop(a, IntArrayMat(2), IntArrayMat(-4 + 1), IntArrayMat(3))
|| test_crop(a, IntArrayMat(3), IntArrayMat(-3 + 1), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 3), IntArrayMat(-7 + 1, -3 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 4), IntArrayMat(-12 + 1, -4 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 5), IntArrayMat(-16 + 1, -5 + 1), IntArrayMat(-4, -3))

|| test_crop(a, IntArrayMat(11, 1), IntArrayMat(-12 + 1, -5 + 1), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(12, 2), IntArrayMat(-16 + 1, -4 + 1), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(8, 3), IntArrayMat(-7 + 1, -6 + 1), IntArrayMat(-4, -2))

|| test_crop(a, IntArrayMat(11, 3), IntArrayMat(-12 + 1, -2 + 1), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(12, 4), IntArrayMat(-16 + 1, -3 + 1), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(8, 5), IntArrayMat(-7 + 1, -4 + 1), IntArrayMat(-4, -1))

|| test_crop(a, IntArrayMat(2, 3), IntArrayMat(-4 + 1, -2 + 1), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(3, 4), IntArrayMat(-2 + 1, -3 + 1), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(4, 5), IntArrayMat(-3 + 1, -4 + 1), IntArrayMat(-3, -2))

|| test_crop(a, IntArrayMat(3, 2), IntArrayMat(-2 + 1, -4 + 1), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(4, 3), IntArrayMat(-3 + 1, -2 + 1), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(5, 4), IntArrayMat(-4 + 1, -3 + 1), IntArrayMat(-3, -1))

|| test_crop(a, IntArrayMat(2, 3), IntArrayMat(-4 + 1, -6 + 1), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(1, 2), IntArrayMat(-5 + 1, -5 + 1), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(3, 1), IntArrayMat(-6 + 1, -4 + 1), IntArrayMat(-2, -1))

|| test_crop(a, IntArrayMat(11, 3, 3), IntArrayMat(-7 + 1, -3 + 1, -4 + 1), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(12, 4, 4), IntArrayMat(-12 + 1, -4 + 1, -3 + 1), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 5, 5), IntArrayMat(-16 + 1, -5 + 1, -5 + 1), IntArrayMat(-4, -3, -2))

|| test_crop(a, IntArrayMat(11, 2, 2), IntArrayMat(-7 + 1, -5 + 1, -4 + 1), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(12, 1, 1), IntArrayMat(-12 + 1, -6 + 1, -5 + 1), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(8, 3, 3), IntArrayMat(-16 + 1, -4 + 1, -6 + 1), IntArrayMat(-4, -3, -1))

|| test_crop(a, IntArrayMat(11, 2, 5), IntArrayMat(-7 + 1, -2 + 1, -5 + 1), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(12, 3, 3), IntArrayMat(-12 + 1, -3 + 1, -4 + 1), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(8, 4, 4), IntArrayMat(-16 + 1, -4 + 1, -3 + 1), IntArrayMat(-4, -2, -1))

|| test_crop(a, IntArrayMat(1, 3, 3), IntArrayMat(-3 + 1, -6 + 1, -4 + 1), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(2, 2, 2), IntArrayMat(-4 + 1, -4 + 1, -5 + 1), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(3, 1, 1), IntArrayMat(-5 + 1, -5 + 1, -6 + 1), IntArrayMat(-3, -2, -1))

|| test_crop(a, IntArrayMat(11, 3, 4, 4), IntArrayMat(-7 + 1, -3 + 1, -2 + 1, -4 + 1), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(12, 4, 5, 3), IntArrayMat(-12 + 1, -4 + 1, -3 + 1, -5 + 1), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(8, 5, 6, 2), IntArrayMat(-16 + 1, -5 + 1, -4 + 1, -3 + 1), IntArrayMat(-4, -3, -2, -1));
}

static int test_crop_11(const ncnn::Mat& a)
{
return 0
|| test_crop(a, 0, 0, 0, 0, a)

|| test_crop(a, 0, 5, 0, 0, ncnn::Mat(6, 6, 6))
|| test_crop(a, 6, 0, 0, 0, ncnn::Mat(8, 8, 8))
|| test_crop(a, 5, 5, 5, 0, ncnn::Mat(6, 6, 6))
|| test_crop(a, 6, 6, 6, 0, ncnn::Mat(8, 8, 8))
|| test_crop(a, 4, 4, 4, 0, ncnn::Mat(5, 5, 5))

|| test_crop(a, 3, 3, 3, 11, ncnn::Mat(3, 3, 3, 7))
|| test_crop(a, 4, 4, 4, 12, ncnn::Mat(6, 6, 6, 12))
|| test_crop(a, 5, 5, 5, 8, ncnn::Mat(8, 8, 8, 16));
}

int main() int main()
{ {
SRAND(776757); SRAND(776757);
@@ -697,37 +268,13 @@ int main()
|| test_crop_0(RandomMat(112)) || test_crop_0(RandomMat(112))
|| test_crop_0(RandomMat(126)) || test_crop_0(RandomMat(126))
|| test_crop_0(RandomMat(127)) || test_crop_0(RandomMat(127))
|| test_crop_1(RandomMat(112))
|| test_crop_1(RandomMat(126))
|| test_crop_1(RandomMat(127))
|| test_crop_2(RandomMat(112))
|| test_crop_2(RandomMat(126))
|| test_crop_2(RandomMat(127))
|| test_crop_3(RandomMat(20, 48)) || test_crop_3(RandomMat(20, 48))
|| test_crop_3(RandomMat(15, 36)) || test_crop_3(RandomMat(15, 36))
|| test_crop_3(RandomMat(16, 33)) || test_crop_3(RandomMat(16, 33))
|| test_crop_4(RandomMat(20, 48))
|| test_crop_4(RandomMat(15, 36))
|| test_crop_4(RandomMat(16, 33))
|| test_crop_5(RandomMat(20, 48))
|| test_crop_5(RandomMat(15, 36))
|| test_crop_5(RandomMat(16, 33))
|| test_crop_6(RandomMat(20, 20, 48)) || test_crop_6(RandomMat(20, 20, 48))
|| test_crop_6(RandomMat(15, 15, 36)) || test_crop_6(RandomMat(15, 15, 36))
|| test_crop_6(RandomMat(16, 16, 33)) || test_crop_6(RandomMat(16, 16, 33))
|| test_crop_7(RandomMat(20, 20, 48))
|| test_crop_7(RandomMat(15, 15, 36))
|| test_crop_7(RandomMat(16, 16, 33))
|| test_crop_8(RandomMat(20, 20, 48))
|| test_crop_8(RandomMat(15, 15, 36))
|| test_crop_8(RandomMat(16, 16, 33))
|| test_crop_9(RandomMat(20, 20, 20, 48)) || test_crop_9(RandomMat(20, 20, 20, 48))
|| test_crop_9(RandomMat(15, 15, 15, 36)) || test_crop_9(RandomMat(15, 15, 15, 36))
|| test_crop_9(RandomMat(16, 16, 16, 33))
|| test_crop_10(RandomMat(20, 20, 20, 48))
|| test_crop_10(RandomMat(15, 15, 15, 36))
|| test_crop_10(RandomMat(16, 16, 16, 33))
|| test_crop_11(RandomMat(20, 20, 20, 48))
|| test_crop_11(RandomMat(15, 15, 15, 36))
|| test_crop_11(RandomMat(16, 16, 16, 33));
|| test_crop_9(RandomMat(16, 16, 16, 33));
} }

+ 377
- 0
tests/test_crop_1.cpp View File

@@ -0,0 +1,377 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/crop.h"
#include "testutil.h"

static ncnn::Mat IntArrayMat(int a0)
{
ncnn::Mat m(1);
int* p = m;
p[0] = a0;
return m;
}

static ncnn::Mat IntArrayMat(int a0, int a1)
{
ncnn::Mat m(2);
int* p = m;
p[0] = a0;
p[1] = a1;
return m;
}

static ncnn::Mat IntArrayMat(int a0, int a1, int a2)
{
ncnn::Mat m(3);
int* p = m;
p[0] = a0;
p[1] = a1;
p[2] = a2;
return m;
}

static ncnn::Mat IntArrayMat(int a0, int a1, int a2, int a3)
{
ncnn::Mat m(4);
int* p = m;
p[0] = a0;
p[1] = a1;
p[2] = a2;
p[3] = a3;
return m;
}

static void print_int_array(const ncnn::Mat& a)
{
const int* pa = a;

fprintf(stderr, "[");
for (int i = 0; i < a.w; i++)
{
fprintf(stderr, " %d", pa[i]);
}
fprintf(stderr, " ]");
}

static int test_crop(const ncnn::Mat& a, const ncnn::Mat& starts, const ncnn::Mat& ends, const ncnn::Mat& axes)
{
ncnn::ParamDict pd;
pd.set(9, starts); // starts
pd.set(10, ends); // ends
pd.set(11, axes); // axes

std::vector<ncnn::Mat> weights(0);

int ret = test_layer<ncnn::Crop>("Crop", pd, weights, a);
if (ret != 0)
{
fprintf(stderr, "test_crop failed a.dims=%d a=(%d %d %d %d)", a.dims, a.w, a.h, a.d, a.c);
fprintf(stderr, " starts=");
print_int_array(starts);
fprintf(stderr, " ends=");
print_int_array(ends);
fprintf(stderr, " axes=");
print_int_array(axes);
fprintf(stderr, "\n");
}

return ret;
}

static int test_crop_1(const ncnn::Mat& a)
{
return 0
|| test_crop(a, IntArrayMat(12), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(16), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(11), IntArrayMat(11 + 16), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(12 + 7), IntArrayMat(-1))
|| test_crop(a, IntArrayMat(16), IntArrayMat(16 + 12), ncnn::Mat())
|| test_crop(a, IntArrayMat(11), IntArrayMat(-7 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(-12 + 1), IntArrayMat(-1))
|| test_crop(a, IntArrayMat(16), IntArrayMat(-16 + 1), ncnn::Mat());
}

static int test_crop_4(const ncnn::Mat& a)
{
return 0
|| test_crop(a, IntArrayMat(12), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-233), IntArrayMat(1))
|| test_crop(a, IntArrayMat(5, 11), IntArrayMat(-233, -233), IntArrayMat(0, 1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(11 + 16), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(12 + 7), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(8 + 12), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(5), IntArrayMat(8), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(9), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(12), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(11 + 7, 11), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(12 + 12, 12), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(8 + 16, 10), IntArrayMat(0, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(-16 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(-7 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-12 + 1), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(5), IntArrayMat(-5 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-6 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-4 + 1), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-12 + 1, -6 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-16 + 1, -5 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-7 + 1, -4 + 1), IntArrayMat(-2, -1));
}

static int test_crop_7(const ncnn::Mat& a)
{
return 0
|| test_crop(a, IntArrayMat(11), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(5), IntArrayMat(-233), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-233), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-233), IntArrayMat(-1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-233, -233), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-233, -233), IntArrayMat(0, -1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-233, -233), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(6, 6), IntArrayMat(-233, -233), IntArrayMat(1, -1))
|| test_crop(a, IntArrayMat(11, 5, 5), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 4), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(11 + 7), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(12 + 12), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(8 + 16), IntArrayMat(0))

|| test_crop(a, IntArrayMat(5), IntArrayMat(13), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(12), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(11), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(5), IntArrayMat(12), IntArrayMat(2))
|| test_crop(a, IntArrayMat(6), IntArrayMat(11), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(13), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(11 + 7, 11), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(12 + 16, 12), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(8 + 12, 13), IntArrayMat(0, -2))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(11 + 16, 13), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(12 + 12, 11), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(8 + 7, 12), IntArrayMat(0, -1))

|| test_crop(a, IntArrayMat(5, 4), IntArrayMat(12, 12), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(6, 3), IntArrayMat(13, 13), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(4, 2), IntArrayMat(11, 11), IntArrayMat(-2, -1))

|| test_crop(a, IntArrayMat(11, 5, 2), IntArrayMat(11 + 7, 11, 11), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(12, 6, 4), IntArrayMat(12 + 16, 12, 12), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 3), IntArrayMat(8 + 12, 13, 13), IntArrayMat(-3, -2, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(-7 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(-12 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-16 + 1), IntArrayMat(-3))

|| test_crop(a, IntArrayMat(5), IntArrayMat(-6 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-5 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-4 + 1), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(5), IntArrayMat(-5 + 1), IntArrayMat(2))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-4 + 1), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-6 + 1), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-7 + 1, -4 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-12 + 1, -6 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-16 + 1, -5 + 1), IntArrayMat(-3, -2))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-12 + 1, -6 + 1), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-16 + 1, -5 + 1), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-7 + 1, -4 + 1), IntArrayMat(-3, -1))

|| test_crop(a, IntArrayMat(5, 2), IntArrayMat(-5 + 1, -5 + 1), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(6, 4), IntArrayMat(-4 + 1, -4 + 1), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(4, 3), IntArrayMat(-6 + 1, -6 + 1), IntArrayMat(-2, -1))

|| test_crop(a, IntArrayMat(11, 5, 4), IntArrayMat(-7 + 1, -5 + 1, -5 + 1), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(12, 6, 3), IntArrayMat(-12 + 1, -6 + 1, -6 + 1), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 2), IntArrayMat(-16 + 1, -4 + 1, -4 + 1), IntArrayMat(-3, -2, -1));
}

static int test_crop_10(const ncnn::Mat& a)
{
return 0
|| test_crop(a, IntArrayMat(11), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-233), IntArrayMat(0))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-233), IntArrayMat(1))
|| test_crop(a, IntArrayMat(5), IntArrayMat(-233), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-233), IntArrayMat(-2))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-233), IntArrayMat(3))
|| test_crop(a, IntArrayMat(5), IntArrayMat(-233), IntArrayMat(-1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(-233, -233), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-233, -233), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(-233, -233), IntArrayMat(-4, -2))
|| test_crop(a, IntArrayMat(4, 4), IntArrayMat(-233, -233), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(-233, -233), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(5, 5), IntArrayMat(-233, -233), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(4, 4), IntArrayMat(-233, -233), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(12, 6, 6), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(11, 5, 5), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 4), IntArrayMat(-233, -233, -233), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(12, 6, 6), IntArrayMat(-233, -233, -233), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(11, 5, 5), IntArrayMat(-233, -233, -233), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(4, 4, 4), IntArrayMat(-233, -233, -233), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(6, 6, 6), IntArrayMat(-233, -233, -233), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(11, 5, 5, 5), IntArrayMat(-233, -233, -233, -233), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(8, 4, 4, 4), IntArrayMat(-233, -233, -233, -233), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(12, 6, 6, 6), IntArrayMat(-233, -233, -233, -233), IntArrayMat(-4, -3, -2, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(11 + 16), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(12 + 7), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(8 + 12), IntArrayMat(-4))

|| test_crop(a, IntArrayMat(5), IntArrayMat(11), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(13), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(12), IntArrayMat(-3))

|| test_crop(a, IntArrayMat(3), IntArrayMat(12), IntArrayMat(2))
|| test_crop(a, IntArrayMat(4), IntArrayMat(13), IntArrayMat(2))
|| test_crop(a, IntArrayMat(5), IntArrayMat(11), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(1), IntArrayMat(8), IntArrayMat(3))
|| test_crop(a, IntArrayMat(2), IntArrayMat(7), IntArrayMat(3))
|| test_crop(a, IntArrayMat(3), IntArrayMat(6), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 5), IntArrayMat(11 + 7, 11), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 6), IntArrayMat(12 + 12, 12), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 4), IntArrayMat(8 + 16, 13), IntArrayMat(-4, -3))

|| test_crop(a, IntArrayMat(11, 4), IntArrayMat(11 + 12, 13), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(12, 3), IntArrayMat(12 + 16, 11), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(8, 2), IntArrayMat(8 + 7, 12), IntArrayMat(-4, -2))

|| test_crop(a, IntArrayMat(11, 1), IntArrayMat(11 + 16, 5), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(12, 2), IntArrayMat(12 + 7, 6), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(8, 3), IntArrayMat(8 + 12, 7), IntArrayMat(-4, -1))

|| test_crop(a, IntArrayMat(3, 3), IntArrayMat(13, 4), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(4, 2), IntArrayMat(12, 3), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(5, 1), IntArrayMat(11, 2), IntArrayMat(-3, -2))

|| test_crop(a, IntArrayMat(5, 5), IntArrayMat(11, 8), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(4, 6), IntArrayMat(12, 9), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(3, 4), IntArrayMat(13, 7), IntArrayMat(-3, -1))

|| test_crop(a, IntArrayMat(2, 3), IntArrayMat(12, 9), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(3, 2), IntArrayMat(11, 7), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(4, 1), IntArrayMat(10, 8), IntArrayMat(-2, -1))

|| test_crop(a, IntArrayMat(11, 2, 2), IntArrayMat(11 + 6, 9, 9), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(12, 3, 3), IntArrayMat(12 + 1, 10, 10), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 4, 4), IntArrayMat(8 + 3, 11, 11), IntArrayMat(-4, -3, -2))

|| test_crop(a, IntArrayMat(11, 4, 4), IntArrayMat(11 + 12, 12, 12), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(12, 5, 5), IntArrayMat(12 + 8, 11, 11), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(8, 6, 6), IntArrayMat(8 + 4, 13, 13), IntArrayMat(-4, -3, -1))

|| test_crop(a, IntArrayMat(11, 1, 4), IntArrayMat(11 + 5, 12, 12), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(12, 3, 3), IntArrayMat(12 + 3, 11, 11), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(8, 2, 5), IntArrayMat(8 + 2, 10, 10), IntArrayMat(-4, -2, -1))

|| test_crop(a, IntArrayMat(1, 1, 1), IntArrayMat(7, 7, 7), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(2, 2, 2), IntArrayMat(8, 9, 10), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(3, 3, 3), IntArrayMat(11, 12, 13), IntArrayMat(-3, -2, -1))

|| test_crop(a, IntArrayMat(11, 2, 3, 6), IntArrayMat(11 + 11, 10, 12, 11), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(12, 3, 4, 5), IntArrayMat(12 + 12, 9, 11, 13), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(8, 4, 5, 4), IntArrayMat(8 + 8, 8, 10, 12), IntArrayMat(-4, -3, -2, -1))

|| test_crop(a, IntArrayMat(11), IntArrayMat(-7 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(12), IntArrayMat(-12 + 1), IntArrayMat(0))
|| test_crop(a, IntArrayMat(8), IntArrayMat(-16 + 1), IntArrayMat(-4))

|| test_crop(a, IntArrayMat(5), IntArrayMat(-6 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-5 + 1), IntArrayMat(1))
|| test_crop(a, IntArrayMat(4), IntArrayMat(-4 + 1), IntArrayMat(-3))

|| test_crop(a, IntArrayMat(4), IntArrayMat(-4 + 1), IntArrayMat(2))
|| test_crop(a, IntArrayMat(5), IntArrayMat(-5 + 1), IntArrayMat(2))
|| test_crop(a, IntArrayMat(6), IntArrayMat(-6 + 1), IntArrayMat(-2))

|| test_crop(a, IntArrayMat(1), IntArrayMat(-5 + 1), IntArrayMat(3))
|| test_crop(a, IntArrayMat(2), IntArrayMat(-4 + 1), IntArrayMat(3))
|| test_crop(a, IntArrayMat(3), IntArrayMat(-3 + 1), IntArrayMat(-1))

|| test_crop(a, IntArrayMat(11, 3), IntArrayMat(-7 + 1, -3 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(12, 4), IntArrayMat(-12 + 1, -4 + 1), IntArrayMat(0, 1))
|| test_crop(a, IntArrayMat(8, 5), IntArrayMat(-16 + 1, -5 + 1), IntArrayMat(-4, -3))

|| test_crop(a, IntArrayMat(11, 1), IntArrayMat(-12 + 1, -5 + 1), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(12, 2), IntArrayMat(-16 + 1, -4 + 1), IntArrayMat(0, 2))
|| test_crop(a, IntArrayMat(8, 3), IntArrayMat(-7 + 1, -6 + 1), IntArrayMat(-4, -2))

|| test_crop(a, IntArrayMat(11, 3), IntArrayMat(-12 + 1, -2 + 1), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(12, 4), IntArrayMat(-16 + 1, -3 + 1), IntArrayMat(0, 3))
|| test_crop(a, IntArrayMat(8, 5), IntArrayMat(-7 + 1, -4 + 1), IntArrayMat(-4, -1))

|| test_crop(a, IntArrayMat(2, 3), IntArrayMat(-4 + 1, -2 + 1), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(3, 4), IntArrayMat(-2 + 1, -3 + 1), IntArrayMat(1, 2))
|| test_crop(a, IntArrayMat(4, 5), IntArrayMat(-3 + 1, -4 + 1), IntArrayMat(-3, -2))

|| test_crop(a, IntArrayMat(3, 2), IntArrayMat(-2 + 1, -4 + 1), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(4, 3), IntArrayMat(-3 + 1, -2 + 1), IntArrayMat(1, 3))
|| test_crop(a, IntArrayMat(5, 4), IntArrayMat(-4 + 1, -3 + 1), IntArrayMat(-3, -1))

|| test_crop(a, IntArrayMat(2, 3), IntArrayMat(-4 + 1, -6 + 1), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(1, 2), IntArrayMat(-5 + 1, -5 + 1), IntArrayMat(2, 3))
|| test_crop(a, IntArrayMat(3, 1), IntArrayMat(-6 + 1, -4 + 1), IntArrayMat(-2, -1))

|| test_crop(a, IntArrayMat(11, 3, 3), IntArrayMat(-7 + 1, -3 + 1, -4 + 1), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(12, 4, 4), IntArrayMat(-12 + 1, -4 + 1, -3 + 1), IntArrayMat(0, 1, 2))
|| test_crop(a, IntArrayMat(8, 5, 5), IntArrayMat(-16 + 1, -5 + 1, -5 + 1), IntArrayMat(-4, -3, -2))

|| test_crop(a, IntArrayMat(11, 2, 2), IntArrayMat(-7 + 1, -5 + 1, -4 + 1), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(12, 1, 1), IntArrayMat(-12 + 1, -6 + 1, -5 + 1), IntArrayMat(0, 1, 3))
|| test_crop(a, IntArrayMat(8, 3, 3), IntArrayMat(-16 + 1, -4 + 1, -6 + 1), IntArrayMat(-4, -3, -1))

|| test_crop(a, IntArrayMat(11, 2, 5), IntArrayMat(-7 + 1, -2 + 1, -5 + 1), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(12, 3, 3), IntArrayMat(-12 + 1, -3 + 1, -4 + 1), IntArrayMat(0, 2, 3))
|| test_crop(a, IntArrayMat(8, 4, 4), IntArrayMat(-16 + 1, -4 + 1, -3 + 1), IntArrayMat(-4, -2, -1))

|| test_crop(a, IntArrayMat(1, 3, 3), IntArrayMat(-3 + 1, -6 + 1, -4 + 1), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(2, 2, 2), IntArrayMat(-4 + 1, -4 + 1, -5 + 1), IntArrayMat(1, 2, 3))
|| test_crop(a, IntArrayMat(3, 1, 1), IntArrayMat(-5 + 1, -5 + 1, -6 + 1), IntArrayMat(-3, -2, -1))

|| test_crop(a, IntArrayMat(11, 3, 4, 4), IntArrayMat(-7 + 1, -3 + 1, -2 + 1, -4 + 1), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(12, 4, 5, 3), IntArrayMat(-12 + 1, -4 + 1, -3 + 1, -5 + 1), IntArrayMat(0, 1, 2, 3))
|| test_crop(a, IntArrayMat(8, 5, 6, 2), IntArrayMat(-16 + 1, -5 + 1, -4 + 1, -3 + 1), IntArrayMat(-4, -3, -2, -1));
}

int main()
{
SRAND(776757);

return 0
|| test_crop_1(RandomMat(112))
|| test_crop_1(RandomMat(126))
|| test_crop_1(RandomMat(127))
|| test_crop_4(RandomMat(20, 48))
|| test_crop_4(RandomMat(15, 36))
|| test_crop_4(RandomMat(16, 33))
|| test_crop_7(RandomMat(20, 20, 48))
|| test_crop_7(RandomMat(15, 15, 36))
|| test_crop_7(RandomMat(16, 16, 33))
|| test_crop_10(RandomMat(20, 20, 20, 48))
|| test_crop_10(RandomMat(15, 15, 15, 36))
|| test_crop_10(RandomMat(16, 16, 16, 33));
}

+ 122
- 0
tests/test_crop_2.cpp View File

@@ -0,0 +1,122 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/crop.h"
#include "testutil.h"

static int test_crop(const ncnn::Mat& a, int woffset, int hoffset, int doffset, int coffset, const ncnn::Mat& ref)
{
ncnn::ParamDict pd;
pd.set(0, woffset);
pd.set(1, hoffset);
pd.set(13, doffset);
pd.set(2, coffset);
pd.set(3, 0); // outw
pd.set(4, 0); // outh
pd.set(14, 0); // outd
pd.set(5, 0); // outc
pd.set(6, 0); // woffset2
pd.set(7, 0); // hoffset2
pd.set(15, 0); // doffset2
pd.set(8, 0); // coffset2

std::vector<ncnn::Mat> weights(0);

std::vector<ncnn::Mat> ab(2);
ab[0] = a;
ab[1] = ref;

int ret = test_layer<ncnn::Crop>("Crop", pd, weights, ab);
if (ret != 0)
{
fprintf(stderr, "test_crop failed a.dims=%d a=(%d %d %d %d) woffset=%d hoffset=%d doffset=%d coffset=%d ref.dims=%d ref=(%d %d %d %d)\n", a.dims, a.w, a.h, a.d, a.c, woffset, hoffset, doffset, coffset, ref.dims, ref.w, ref.h, ref.d, ref.c);
}

return ret;
}

static int test_crop_2(const ncnn::Mat& a)
{
return 0
|| test_crop(a, 0, 0, 0, 0, a)
|| test_crop(a, 0, 0, 0, 0, ncnn::Mat(27))

|| test_crop(a, 11, 0, 0, 0, ncnn::Mat(7))
|| test_crop(a, 12, 0, 0, 0, ncnn::Mat(12))
|| test_crop(a, 16, 0, 0, 0, ncnn::Mat(16));
}

static int test_crop_5(const ncnn::Mat& a)
{
return 0
|| test_crop(a, 0, 0, 0, 0, a)

|| test_crop(a, 0, 12, 0, 0, ncnn::Mat(8, 7))
|| test_crop(a, 5, 0, 0, 0, ncnn::Mat(7, 27))

|| test_crop(a, 5, 11, 0, 0, ncnn::Mat(5, 12))
|| test_crop(a, 6, 12, 0, 0, ncnn::Mat(4, 16))
|| test_crop(a, 4, 8, 0, 0, ncnn::Mat(6, 7));
}

static int test_crop_8(const ncnn::Mat& a)
{
return 0
|| test_crop(a, 0, 0, 0, 0, a)

|| test_crop(a, 0, 5, 0, 0, ncnn::Mat(6, 6))
|| test_crop(a, 6, 0, 0, 0, ncnn::Mat(8, 8))
|| test_crop(a, 5, 2, 0, 0, ncnn::Mat(6, 3))
|| test_crop(a, 6, 3, 0, 0, ncnn::Mat(8, 4))
|| test_crop(a, 4, 4, 0, 0, ncnn::Mat(7, 5))

|| test_crop(a, 5, 3, 0, 11, ncnn::Mat(7, 3, 7))
|| test_crop(a, 6, 4, 0, 12, ncnn::Mat(6, 4, 12))
|| test_crop(a, 4, 2, 0, 8, ncnn::Mat(5, 5, 16));
}

static int test_crop_11(const ncnn::Mat& a)
{
return 0
|| test_crop(a, 0, 0, 0, 0, a)

|| test_crop(a, 0, 5, 0, 0, ncnn::Mat(6, 6, 6))
|| test_crop(a, 6, 0, 0, 0, ncnn::Mat(8, 8, 8))
|| test_crop(a, 5, 5, 5, 0, ncnn::Mat(6, 6, 6))
|| test_crop(a, 6, 6, 6, 0, ncnn::Mat(8, 8, 8))
|| test_crop(a, 4, 4, 4, 0, ncnn::Mat(5, 5, 5))

|| test_crop(a, 3, 3, 3, 11, ncnn::Mat(3, 3, 3, 7))
|| test_crop(a, 4, 4, 4, 12, ncnn::Mat(6, 6, 6, 12))
|| test_crop(a, 5, 5, 5, 8, ncnn::Mat(8, 8, 8, 16));
}

int main()
{
SRAND(776757);

return 0
|| test_crop_2(RandomMat(112))
|| test_crop_2(RandomMat(126))
|| test_crop_2(RandomMat(127))
|| test_crop_5(RandomMat(20, 48))
|| test_crop_5(RandomMat(15, 36))
|| test_crop_5(RandomMat(16, 33))
|| test_crop_8(RandomMat(20, 20, 48))
|| test_crop_8(RandomMat(15, 15, 36))
|| test_crop_8(RandomMat(16, 16, 33))
|| test_crop_11(RandomMat(20, 20, 20, 48))
|| test_crop_11(RandomMat(15, 15, 15, 36))
|| test_crop_11(RandomMat(16, 16, 16, 33));
}

+ 2
- 8
tests/test_deformableconv2d.cpp View File

@@ -59,7 +59,7 @@ static int test_deformableconv2d(int w, int h, int c, int outch, int kernel, int


static int test_deformableconv2d_0() static int test_deformableconv2d_0()
{ {
static const int kdsp[16][4] = {
static const int kdsp[10][4] = {
{1, 1, 1, 0}, {1, 1, 1, 0},
{1, 1, 2, 0}, {1, 1, 2, 0},
{2, 1, 1, 1}, {2, 1, 1, 1},
@@ -67,18 +67,12 @@ static int test_deformableconv2d_0()
{3, 1, 1, 1}, {3, 1, 1, 1},
{3, 1, 2, 1}, {3, 1, 2, 1},
{3, 2, 1, 1}, {3, 2, 1, 1},
{4, 1, 1, 0},
{4, 1, 2, 1}, {4, 1, 2, 1},
{4, 2, 1, 1},
{5, 1, 1, 2},
{5, 1, 2, 2}, {5, 1, 2, 2},
{5, 2, 2, 2}, {5, 2, 2, 2},
{7, 1, 1, 3},
{7, 1, 2, 3},
{7, 2, 1, 3},
}; };


for (int i = 0; i < 16; i++)
for (int i = 0; i < 4; i++)
{ {
const int k = kdsp[i][0]; const int k = kdsp[i][0];
const int d = kdsp[i][1]; const int d = kdsp[i][1];


+ 120
- 0
tests/test_deformableconv2d_1.cpp View File

@@ -0,0 +1,120 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/deformableconv2d.h"
#include "testutil.h"

static int test_deformableconv2d(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
const int kernel_extent_w = dilation * (kernel - 1) + 1;
const int kernel_extent_h = dilation * (kernel - 1) + 1;
const int out_w = (w + pad + pad - kernel_extent_w) / stride + 1;
const int out_h = (h + pad + pad - kernel_extent_h) / stride + 1;
std::vector<ncnn::Mat> a(3);
a[0] = RandomMat(w, h, c);
a[1] = RandomMat(out_w, out_h, kernel * kernel * 2);
a[2] = RandomMat(out_w, out_h, kernel * kernel);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch * c * kernel * kernel);

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * c * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);

float epsilon = 0.001;
int ret = test_layer<ncnn::DeformableConv2D>("DeformableConv2D", pd, weights, a, 1, epsilon);
if (ret != 0)
{
fprintf(stderr, "test_deformableconv2d failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_deformableconv2d_0()
{
static const int kdsp[10][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, 0},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{4, 1, 2, 1},
{5, 1, 2, 2},
{5, 2, 2, 2},
};

for (int i = 4; i < 6; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_deformableconv2d(9, 7, 1, 1, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 4, 13, k, d, s, p, 0)
|| test_deformableconv2d(9, 7, 13, 4, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 4, 8, k, d, s, p, 0)
|| test_deformableconv2d(9, 7, 8, 4, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 8, 13, k, d, s, p, 0)
|| test_deformableconv2d(9, 7, 13, 8, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 16, 16, k, d, s, p, 0)
|| test_deformableconv2d(16, 16, 1 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 1 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 1 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 1 * 3, 16 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 16 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 16 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 16 * 3, k, d, s, p, 1);

if (ret != 0)
return -1;
}

return 0;
}

int main()
{
SRAND(7767517);

return test_deformableconv2d_0();
}

+ 120
- 0
tests/test_deformableconv2d_2.cpp View File

@@ -0,0 +1,120 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/deformableconv2d.h"
#include "testutil.h"

static int test_deformableconv2d(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
const int kernel_extent_w = dilation * (kernel - 1) + 1;
const int kernel_extent_h = dilation * (kernel - 1) + 1;
const int out_w = (w + pad + pad - kernel_extent_w) / stride + 1;
const int out_h = (h + pad + pad - kernel_extent_h) / stride + 1;
std::vector<ncnn::Mat> a(3);
a[0] = RandomMat(w, h, c);
a[1] = RandomMat(out_w, out_h, kernel * kernel * 2);
a[2] = RandomMat(out_w, out_h, kernel * kernel);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch * c * kernel * kernel);

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * c * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);

float epsilon = 0.001;
int ret = test_layer<ncnn::DeformableConv2D>("DeformableConv2D", pd, weights, a, 1, epsilon);
if (ret != 0)
{
fprintf(stderr, "test_deformableconv2d failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_deformableconv2d_0()
{
static const int kdsp[10][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, 0},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{4, 1, 2, 1},
{5, 1, 2, 2},
{5, 2, 2, 2},
};

for (int i = 6; i < 8; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_deformableconv2d(9, 7, 1, 1, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 4, 13, k, d, s, p, 0)
|| test_deformableconv2d(9, 7, 13, 4, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 4, 8, k, d, s, p, 0)
|| test_deformableconv2d(9, 7, 8, 4, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 8, 13, k, d, s, p, 0)
|| test_deformableconv2d(9, 7, 13, 8, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 16, 16, k, d, s, p, 0)
|| test_deformableconv2d(16, 16, 1 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 1 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 1 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 1 * 3, 16 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 16 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 16 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 16 * 3, k, d, s, p, 1);

if (ret != 0)
return -1;
}

return 0;
}

int main()
{
SRAND(7767517);

return test_deformableconv2d_0();
}

+ 120
- 0
tests/test_deformableconv2d_3.cpp View File

@@ -0,0 +1,120 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/deformableconv2d.h"
#include "testutil.h"

static int test_deformableconv2d(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
const int kernel_extent_w = dilation * (kernel - 1) + 1;
const int kernel_extent_h = dilation * (kernel - 1) + 1;
const int out_w = (w + pad + pad - kernel_extent_w) / stride + 1;
const int out_h = (h + pad + pad - kernel_extent_h) / stride + 1;
std::vector<ncnn::Mat> a(3);
a[0] = RandomMat(w, h, c);
a[1] = RandomMat(out_w, out_h, kernel * kernel * 2);
a[2] = RandomMat(out_w, out_h, kernel * kernel);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch * c * kernel * kernel);

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * c * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);

float epsilon = 0.001;
int ret = test_layer<ncnn::DeformableConv2D>("DeformableConv2D", pd, weights, a, 1, epsilon);
if (ret != 0)
{
fprintf(stderr, "test_deformableconv2d failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_deformableconv2d_0()
{
static const int kdsp[10][4] = {
{1, 1, 1, 0},
{1, 1, 2, 0},
{2, 1, 1, 1},
{2, 1, 2, 0},
{3, 1, 1, 1},
{3, 1, 2, 1},
{3, 2, 1, 1},
{4, 1, 2, 1},
{5, 1, 2, 2},
{5, 2, 2, 2},
};

for (int i = 8; i < 10; i++)
{
const int k = kdsp[i][0];
const int d = kdsp[i][1];
const int s = kdsp[i][2];
const int p = kdsp[i][3];

int ret = 0
|| test_deformableconv2d(9, 7, 1, 1, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 4, 13, k, d, s, p, 0)
|| test_deformableconv2d(9, 7, 13, 4, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 4, 8, k, d, s, p, 0)
|| test_deformableconv2d(9, 7, 8, 4, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 8, 13, k, d, s, p, 0)
|| test_deformableconv2d(9, 7, 13, 8, k, d, s, p, 1)
|| test_deformableconv2d(9, 7, 16, 16, k, d, s, p, 0)
|| test_deformableconv2d(16, 16, 1 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 1 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 1 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 1 * 3, 16 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 4 * 3, 16 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 8 * 3, 16 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 1 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 4 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 8 * 3, k, d, s, p, 1)
|| test_deformableconv2d(16, 16, 16 * 3, 16 * 3, k, d, s, p, 1);

if (ret != 0)
return -1;
}

return 0;
}

int main()
{
SRAND(7767517);

return test_deformableconv2d_0();
}

+ 76
- 0
tests/test_deformableconv2d_4.cpp View File

@@ -0,0 +1,76 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "layer/deformableconv2d.h"
#include "testutil.h"

static int test_deformableconv2d(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
{
const int kernel_extent_w = dilation * (kernel - 1) + 1;
const int kernel_extent_h = dilation * (kernel - 1) + 1;
const int out_w = (w + pad + pad - kernel_extent_w) / stride + 1;
const int out_h = (h + pad + pad - kernel_extent_h) / stride + 1;
std::vector<ncnn::Mat> a(3);
a[0] = RandomMat(w, h, c);
a[1] = RandomMat(out_w, out_h, kernel * kernel * 2);
a[2] = RandomMat(out_w, out_h, kernel * kernel);

ncnn::ParamDict pd;
pd.set(0, outch);
pd.set(1, kernel);
pd.set(2, dilation);
pd.set(3, stride);
pd.set(4, pad);
pd.set(5, bias);
pd.set(6, outch * c * kernel * kernel);

int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
ncnn::Mat activation_params(2);
activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha
activation_params[1] = RandomFloat(0, 1); // beta
pd.set(9, activation_type);
pd.set(10, activation_params);

std::vector<ncnn::Mat> weights(bias ? 2 : 1);
weights[0] = RandomMat(outch * c * kernel * kernel);
if (bias)
weights[1] = RandomMat(outch);

float epsilon = 0.001;
int ret = test_layer<ncnn::DeformableConv2D>("DeformableConv2D", pd, weights, a, 1, epsilon);
if (ret != 0)
{
fprintf(stderr, "test_deformableconv2d failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_deformableconv2d_0()
{
return 0
|| test_deformableconv2d(7, 5, 24, 32, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 32, 24, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 28, 32, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 32, 28, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 26, 32, 4, 2, 2, 2, 1)
|| test_deformableconv2d(7, 5, 32, 26, 4, 2, 2, 2, 1);
}

int main()
{
SRAND(7767517);

return test_deformableconv2d_0();
}

Write Preview
Loading…
Cancel
Save