Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge remote-tracking branch 'upstream/3.4' into merge-3.4

Browse Source
pull/11938/head
Alexander Alekhin 6 years ago
parent 2da96be217 82c7ab0231
commit fa66c6b797
9 changed files with 205 additions and 115 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 6
      modules/core/include/opencv2/core/mat.hpp
  2. 169
      modules/dnn/src/opencl/conv_layer_spatial.cl
  3. 18
      modules/dnn/test/test_backends.cpp
  4. 10
      modules/dnn/test/test_caffe_importer.cpp
  5. 2
      modules/dnn/test/test_googlenet.cpp
  6. 48
      modules/dnn/test/test_halide_layers.cpp
  7. 61
      modules/dnn/test/test_precomp.hpp
  8. 2
      modules/dnn/test/test_tf_importer.cpp
  9. 4
      modules/dnn/test/test_torch_importer.cpp

6
modules/core/include/opencv2/core/mat.hpp
Unescape View File

@ -1318,7 +1318,7 @@ public:
/** @brief Returns a zero array of the specified size and type.
The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant
array as a function parameter, part of a matrix expression, or as a matrix initializer. :
array as a function parameter, part of a matrix expression, or as a matrix initializer:
@code
Mat A;
A = Mat::zeros(3, 3, CV_32F);
@ -1354,6 +1354,8 @@ public:
The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it
just remembers the scale factor (3 in this case) and use it when actually invoking the matrix
initializer.
@note In case of multi-channels type, only the first channel will be initialized with 1's, the
others will be set to 0's.
@param rows Number of rows.
@param cols Number of columns.
@param type Created matrix type.
@ -1381,6 +1383,8 @@ public:
// make a 4x4 diagonal matrix with 0.1's on the diagonal.
Mat A = Mat::eye(4, 4, CV_32F)*0.1;
@endcode
@note In case of multi-channels type, identity matrix will be initialized only for the first channel,
the others will be set to 0's
@param rows Number of rows.
@param cols Number of columns.
@param type Created matrix type.

169
modules/dnn/src/opencl/conv_layer_spatial.cl
Unescape View File

@ -467,7 +467,7 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
int saved_y = curr_y;
#endif
const __global Dtype *src0_read = src0
+ aligned_input_size * global_z // batch offset
+ aligned_input_size * global_z // batch offset
+ (curr_y - INPUT_PAD_H) * ROW_PITCH // y offset
+ (curr_x - INPUT_PAD_W); // x offset
@ -502,15 +502,23 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_W == 0 && INPUT_PAD_H == 0 && DILATION_X == 1 && DILATION_Y == 1 && INPUT_PAD_BOTTOM == 0 && INPUT_PAD_RIGHT == 0
#if KERNEL_WIDTH == 3
Dtype_t blockA00 = vload3(0, src0_read);
Dtype* pblockA00 = (Dtype*)(&blockA00);
#else
Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read )[ 0 ];
Dtype* pblockA00 = (Dtype*)(&blockA00);
#endif
#else
Dtype_t blockA00;
Dtype* pblockA00 = (Dtype*)(&blockA00);
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + pos * DILATION_X >= INPUT_PAD_W && curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
if (curr_y >= INPUT_PAD_H &&
curr_y < input_height + INPUT_PAD_H &&
curr_x + pos * DILATION_X >= INPUT_PAD_W &&
curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read[pos * DILATION_X];
else
pblockA00[pos] = 0;
@ -564,17 +572,18 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
//while( ++patch_row < 1 ); //debug
while( ++patch_row < KERNEL_HEIGHT );
src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y); // reset to start of next slice of patch
// reset to start of next slice of patch
src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y);
}
//while ( ++patch_depth < 1 ); //debug
while ( ++patch_depth < INPUT_DEPTH );
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
int out_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M ) / output_width + OUT_PADDING_HEIGHT) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT; // x offset
+ ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT; // x offset
__global Dtype *out = dst + out_offset;
#if APPLY_BIAS
@ -621,7 +630,7 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
int saved_y = curr_y;
#endif
const __global Dtype *src0_read = src0
+ aligned_input_size * global_z // batch offset
+ aligned_input_size * global_z // batch offset
+ (curr_y - INPUT_PAD_H) * ROW_PITCH // y offset
+ (curr_x - INPUT_PAD_W); // x offset
@ -653,7 +662,10 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + pos * DILATION_X >= INPUT_PAD_W && curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
if (curr_y >= INPUT_PAD_H &&
curr_y < input_height + INPUT_PAD_H &&
curr_x + pos * DILATION_X >= INPUT_PAD_W &&
curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read[pos * DILATION_X];
else
pblockA00[pos] = 0;
@ -730,17 +742,18 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
//while( ++patch_row < 1 ); //debug
while( ++patch_row < KERNEL_HEIGHT );
src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
// reset to start of next slice of patch
src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
}
//while ( ++patch_depth < 1 ); //debug
while ( ++patch_depth < INPUT_DEPTH );
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
int out_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M ) / output_width + OUT_PADDING_HEIGHT) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT; // x offset
+ ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT; // x offset
__global Dtype *out = dst + out_offset;
#if APPLY_BIAS
Dtype bias[4];
@ -849,11 +862,11 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
int saved_y1 = curr_y1;
#endif
const __global Dtype *src0_read0 = src0
+ aligned_input_size * global_z // batch offset
+ aligned_input_size * global_z // batch offset
+ (curr_y0 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x0 - INPUT_PAD_W; // x offset
const __global Dtype *src0_read1 = src0
+ aligned_input_size * global_z // batch offset
+ aligned_input_size * global_z // batch offset
+ (curr_y1 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x1 - INPUT_PAD_W; // x offset
@ -883,17 +896,38 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
// ...
const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_H == 0 && INPUT_PAD_W == 0 && DILATION_X == 1 && DILATION_Y == 1 && INPUT_PAD_BOTTOM == 0 && INPUT_PAD_RIGHT == 0
Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read0 )[ 0 ]; src0_read0 += ROW_PITCH;
Dtype_t blockA01 = ( (const __global Dtype_t*)src0_read1 )[ 0 ]; src0_read1 += ROW_PITCH;
#if KERNEL_WIDTH == 3
Dtype_t blockA00 = vload3(0, src0_read0); src0_read0 += ROW_PITCH;
Dtype_t blockA01 = vload3(0, src0_read1); src0_read1 += ROW_PITCH;
Dtype* pblockA00 = (Dtype*)(&blockA00);
Dtype* pblockA01 = (Dtype*)(&blockA01);
#else
Dtype_t blockA00 = { (Dtype)0.f };
Dtype_t blockA01 = { (Dtype)0.f };
Dtype* pblockA00 = (Dtype*)(&blockA00);
Dtype* pblockA01 = (Dtype*)(&blockA01);
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_x0 + pos < input_width)
pblockA00[pos] = src0_read0[pos];
if (curr_x1 + pos < input_width)
pblockA01[pos] = src0_read1[pos];
})
src0_read0 += ROW_PITCH;
src0_read1 += ROW_PITCH;
#endif
#else
Dtype_t blockA00;
Dtype* pblockA00 = (Dtype*)(&blockA00);
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y0 >= INPUT_PAD_H && curr_y0 < input_height + INPUT_PAD_H && curr_x0 + pos * DILATION_X >= INPUT_PAD_W && curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
if (curr_y0 >= INPUT_PAD_H &&
curr_y0 < input_height + INPUT_PAD_H &&
curr_x0 + pos * DILATION_X >= INPUT_PAD_W &&
curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read0[pos * DILATION_X];
else
pblockA00[pos] = 0;
@ -904,7 +938,10 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y1 >= INPUT_PAD_H && curr_y1 < input_height + INPUT_PAD_H && curr_x1 + pos * DILATION_X >= INPUT_PAD_W && curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
if (curr_y1 >= INPUT_PAD_H &&
curr_y1 < input_height + INPUT_PAD_H &&
curr_x1 + pos * DILATION_X >= INPUT_PAD_W &&
curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA01[pos] = src0_read1[pos * DILATION_X];
else
pblockA01[pos] = 0;
@ -972,7 +1009,8 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
curr_y0 = saved_y0;
curr_y1 = saved_y1;
#endif
src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
// reset to start of next slice of patch
src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
src0_read1 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
}
//while ( ++patch_depth < 1 ); //debug
@ -980,14 +1018,14 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out0_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
int out0_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 0 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT; // x offset
int out1_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT; // x offset
int out1_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 1 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT; // x offset
+ ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT; // x offset
#if APPLY_BIAS
Dtype bias[4];
@ -1049,11 +1087,11 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
int saved_y1 = curr_y1;
#endif
const __global Dtype *src0_read0 = src0
+ aligned_input_size * global_z // batch offset
+ aligned_input_size * global_z // batch offset
+ (curr_y0 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x0 - INPUT_PAD_W; // x offset
const __global Dtype *src0_read1 = src0
+ aligned_input_size * global_z // batch offset
+ aligned_input_size * global_z // batch offset
+ (curr_y1 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x1 - INPUT_PAD_W; // x offset
@ -1084,7 +1122,10 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y0 >= INPUT_PAD_H && curr_y0 < input_height + INPUT_PAD_H && curr_x0 + pos * DILATION_X >= INPUT_PAD_W && curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
if (curr_y0 >= INPUT_PAD_H &&
curr_y0 < input_height + INPUT_PAD_H &&
curr_x0 + pos * DILATION_X >= INPUT_PAD_W &&
curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read0[pos * DILATION_X];
else
pblockA00[pos] = 0;
@ -1095,7 +1136,10 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y1 >= INPUT_PAD_H && curr_y1 < input_height + INPUT_PAD_H && curr_x1 + pos * DILATION_X >= INPUT_PAD_W && curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
if (curr_y1 >= INPUT_PAD_H &&
curr_y1 < input_height + INPUT_PAD_H &&
curr_x1 + pos * DILATION_X >= INPUT_PAD_W &&
curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA01[pos] = src0_read1[pos * DILATION_X];
else
pblockA01[pos] = 0;
@ -1185,7 +1229,8 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
curr_y0 = saved_y0;
curr_y1 = saved_y1;
#endif
src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
// reset to start of next slice of patch
src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
src0_read1 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
}
//while ( ++patch_depth < 1 ); //debug
@ -1193,14 +1238,14 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out0_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
int out0_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 0 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT; // x offset
int out1_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT; // x offset
int out1_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 1 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT; // x offset
+ ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT; // x offset
__global Dtype *out1 = dst + out1_offset;
#if APPLY_BIAS
@ -1352,9 +1397,9 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
int saved_y = curr_y;
#endif
const __global Dtype *src0_read = src0
+ aligned_input_size * global_z // batch offset
+ aligned_input_size * global_z // batch offset
+ (curr_y - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x - INPUT_PAD_W; // x offset
+ curr_x - INPUT_PAD_W; // x offset
const __global Dtype *src0_read_orig = src0_read;
// Src1 (filter) is directly used as btile.
@ -1409,15 +1454,23 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_W == 0 && INPUT_PAD_H == 0 && DILATION_X == 1 && DILATION_Y == 1 && INPUT_PAD_BOTTOM == 0 && INPUT_PAD_RIGHT == 0
#if KERNEL_WIDTH == 3
Dtype_t blockA00 = vload3(0, src0_read);
Dtype* pblockA00 = (Dtype*)(&blockA00);
#else
Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read )[ 0 ];
Dtype* pblockA00 = (Dtype*)(&blockA00);
#endif
#else
Dtype_t blockA00;
Dtype* pblockA00 = (Dtype*)(&blockA00);
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + pos * DILATION_X >= INPUT_PAD_W && curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
if (curr_y >= INPUT_PAD_H &&
curr_y < input_height + INPUT_PAD_H &&
curr_x + pos * DILATION_X >= INPUT_PAD_W &&
curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read[pos * DILATION_X];
else
pblockA00[pos] = 0;
@ -1463,17 +1516,18 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
//while( ++patch_row < 1 ); //debug
while( ++patch_row < KERNEL_HEIGHT );
src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
// reset to start of next slice of patch
src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
}
//while ( ++patch_depth < 1 ); //debug
while ( ++patch_depth < INPUT_DEPTH );
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
int out_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M ) / output_width + OUT_PADDING_HEIGHT) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT; // x offset
+ ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT; // x offset
__global Dtype *out = dst + out_offset;
#if APPLY_BIAS
@ -1556,11 +1610,11 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
int saved_y1 = curr_y1;
#endif
const __global Dtype *src0_read0 = src0
+ aligned_input_size * global_z // batch offset
+ aligned_input_size * global_z // batch offset
+ (curr_y0 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x0 - INPUT_PAD_W; // x offset
const __global Dtype *src0_read1 = src0
+ aligned_input_size * global_z // batch offset
+ aligned_input_size * global_z // batch offset
+ (curr_y1 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x1 - INPUT_PAD_W; // x offset
@ -1600,7 +1654,10 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y0 >= INPUT_PAD_H && curr_y0 < input_height + INPUT_PAD_H && curr_x0 + pos * DILATION_X >= INPUT_PAD_W && curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
if (curr_y0 >= INPUT_PAD_H &&
curr_y0 < input_height + INPUT_PAD_H &&
curr_x0 + pos * DILATION_X >= INPUT_PAD_W &&
curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read0[pos * DILATION_X];
else
pblockA00[pos] = 0;
@ -1611,7 +1668,10 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y1 >= INPUT_PAD_H && curr_y1 < input_height + INPUT_PAD_H && curr_x1 + pos * DILATION_X >= INPUT_PAD_W && curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
if (curr_y1 >= INPUT_PAD_H &&
curr_y1 < input_height + INPUT_PAD_H &&
curr_x1 + pos * DILATION_X >= INPUT_PAD_W &&
curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA01[pos] = src0_read1[pos * DILATION_X];
else
pblockA01[pos] = 0;
@ -1667,7 +1727,8 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
curr_y0 = saved_y0;
curr_y1 = saved_y1;
#endif
src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y); // reset to start of next slice of patch
// reset to start of next slice of patch
src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y);
src0_read1 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y);
}
//while ( ++patch_depth < 1 ); //debug
@ -1675,14 +1736,14 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out0_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
int out0_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 0 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT; // x offset
int out1_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT; // x offset
int out1_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 1 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT; // x offset
+ ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT; // x offset
#if APPLY_BIAS
Dtype bias[2];

18
modules/dnn/test/test_backends.cpp
Unescape View File

@ -278,19 +278,19 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf);
}
const tuple<DNNBackend, DNNTarget> testCases[] = {
const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_HALIDE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases));

10
modules/dnn/test/test_caffe_importer.cpp
Unescape View File

@ -82,7 +82,7 @@ TEST(Test_Caffe, read_googlenet)
ASSERT_FALSE(net.empty());
}
typedef testing::TestWithParam<tuple<bool, DNNTarget> > Reproducibility_AlexNet;
typedef testing::TestWithParam<tuple<bool, Target> > Reproducibility_AlexNet;
TEST_P(Reproducibility_AlexNet, Accuracy)
{
bool readFromMemory = get<0>(GetParam());
@ -179,7 +179,7 @@ TEST(Reproducibility_SSD, Accuracy)
normAssertDetections(ref, out);
}
typedef testing::TestWithParam<DNNTarget> Reproducibility_MobileNet_SSD;
typedef testing::TestWithParam<Target> Reproducibility_MobileNet_SSD;
TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
{
const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
@ -234,7 +234,7 @@ TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD,
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
typedef testing::TestWithParam<DNNTarget> Reproducibility_ResNet50;
typedef testing::TestWithParam<Target> Reproducibility_ResNet50;
TEST_P(Reproducibility_ResNet50, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
@ -270,7 +270,7 @@ TEST_P(Reproducibility_ResNet50, Accuracy)
INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_ResNet50,
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
typedef testing::TestWithParam<DNNTarget> Reproducibility_SqueezeNet_v1_1;
typedef testing::TestWithParam<Target> Reproducibility_SqueezeNet_v1_1;
TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
{
Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
@ -413,7 +413,7 @@ TEST(Test_Caffe, multiple_inputs)
normAssert(out, first_image + second_image);
}
typedef testing::TestWithParam<tuple<std::string, DNNTarget> > opencv_face_detector;
typedef testing::TestWithParam<tuple<std::string, Target> > opencv_face_detector;
TEST_P(opencv_face_detector, Accuracy)
{
std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false);

2
modules/dnn/test/test_googlenet.cpp
Unescape View File

@ -52,7 +52,7 @@ static std::string _tf(TString filename)
return (getOpenCVExtraDir() + "/dnn/") + filename;
}
typedef testing::TestWithParam<DNNTarget> Reproducibility_GoogLeNet;
typedef testing::TestWithParam<Target> Reproducibility_GoogLeNet;
TEST_P(Reproducibility_GoogLeNet, Batching)
{
Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),

48
modules/dnn/test/test_halide_layers.cpp
Unescape View File

@ -41,21 +41,21 @@ static void test(LayerParams& params, Mat& input, int backendId, int targetId)
test(input, net, backendId, targetId);
}
static testing::internal::ParamGenerator<tuple<DNNBackend, DNNTarget> > dnnBackendsAndTargetsWithHalide()
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsWithHalide()
{
static const tuple<DNNBackend, DNNTarget> testCases[] = {
static const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_HALIDE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
return testing::ValuesIn(testCases);
}
@ -89,7 +89,7 @@ TEST_P(Test_Halide_layers, Padding)
////////////////////////////////////////////////////////////////////////////////
// Convolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool, tuple<DNNBackend, DNNTarget> > > Convolution;
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool, tuple<Backend, Target> > > Convolution;
TEST_P(Convolution, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
@ -154,7 +154,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Convolution, Combine(
////////////////////////////////////////////////////////////////////////////////
// Deconvolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool, tuple<DNNBackend, DNNTarget> > > Deconvolution;
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool, tuple<Backend, Target> > > Deconvolution;
TEST_P(Deconvolution, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
@ -220,7 +220,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Deconvolution, Combine(
////////////////////////////////////////////////////////////////////////////////
// LRN
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string, tuple<DNNBackend, DNNTarget> > > LRN;
typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string, tuple<Backend, Target> > > LRN;
TEST_P(LRN, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
@ -265,7 +265,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, LRN, Combine(
////////////////////////////////////////////////////////////////////////////////
// Average pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size, tuple<DNNBackend, DNNTarget> > > AvePooling;
typedef TestWithParam<tuple<int, Size, Size, Size, tuple<Backend, Target> > > AvePooling;
TEST_P(AvePooling, Accuracy)
{
int inChannels = get<0>(GetParam());
@ -305,7 +305,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, AvePooling, Combine(
////////////////////////////////////////////////////////////////////////////////
// Maximum pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size, Size, tuple<DNNBackend, DNNTarget> > > MaxPooling;
typedef TestWithParam<tuple<int, Size, Size, Size, Size, tuple<Backend, Target> > > MaxPooling;
TEST_P(MaxPooling, Accuracy)
{
int inChannels = get<0>(GetParam());
@ -344,7 +344,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, MaxPooling, Combine(
////////////////////////////////////////////////////////////////////////////////
// Fully-connected
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, int, bool, tuple<DNNBackend, DNNTarget> > > FullyConnected;
typedef TestWithParam<tuple<int, Size, int, bool, tuple<Backend, Target> > > FullyConnected;
TEST_P(FullyConnected, Accuracy)
{
int inChannels = get<0>(GetParam());
@ -387,7 +387,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, FullyConnected, Combine(
////////////////////////////////////////////////////////////////////////////////
// SoftMax
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, tuple<DNNBackend, DNNTarget> > > SoftMax;
typedef TestWithParam<tuple<int, tuple<Backend, Target> > > SoftMax;
TEST_P(SoftMax, Accuracy)
{
int inChannels = get<0>(GetParam());
@ -476,7 +476,7 @@ void testInPlaceActivation(LayerParams& lp, int backendId, int targetId)
test(input, net, backendId, targetId);
}
typedef TestWithParam<tuple<bool, bool, float, tuple<DNNBackend, DNNTarget> > > BatchNorm;
typedef TestWithParam<tuple<bool, bool, float, tuple<Backend, Target> > > BatchNorm;
TEST_P(BatchNorm, Accuracy)
{
bool hasWeights = get<0>(GetParam());
@ -511,7 +511,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, BatchNorm, Combine(
dnnBackendsAndTargetsWithHalide()
));
typedef TestWithParam<tuple<float, tuple<DNNBackend, DNNTarget> > > ReLU;
typedef TestWithParam<tuple<float, tuple<Backend, Target> > > ReLU;
TEST_P(ReLU, Accuracy)
{
float negativeSlope = get<0>(GetParam());
@ -530,7 +530,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Combine(
dnnBackendsAndTargetsWithHalide()
));
typedef TestWithParam<tuple<std::string, tuple<DNNBackend, DNNTarget> > > NoParamActivation;
typedef TestWithParam<tuple<std::string, tuple<Backend, Target> > > NoParamActivation;
TEST_P(NoParamActivation, Accuracy)
{
int backendId = get<0>(get<1>(GetParam()));
@ -546,7 +546,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Combine(
dnnBackendsAndTargetsWithHalide()
));
typedef TestWithParam<tuple<Vec3f, tuple<DNNBackend, DNNTarget> > > Power;
typedef TestWithParam<tuple<Vec3f, tuple<Backend, Target> > > Power;
TEST_P(Power, Accuracy)
{
float power = get<0>(GetParam())[0];
@ -582,7 +582,7 @@ TEST_P(Test_Halide_layers, ChannelsPReLU)
testInPlaceActivation(lp, backend, target);
}
typedef TestWithParam<tuple<bool, tuple<DNNBackend, DNNTarget> > > Scale;
typedef TestWithParam<tuple<bool, tuple<Backend, Target> > > Scale;
TEST_P(Scale, Accuracy)
{
bool hasBias = get<0>(GetParam());
@ -616,7 +616,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Scale, Combine(
// `--- conv ----^ ^ ^
// `---- ... ------' '
// `-----------------'
typedef TestWithParam<tuple<Vec3i, Vec3i, tuple<DNNBackend, DNNTarget> > > Concat;
typedef TestWithParam<tuple<Vec3i, Vec3i, tuple<Backend, Target> > > Concat;
TEST_P(Concat, Accuracy)
{
Vec3i inSize = get<0>(GetParam());
@ -682,7 +682,7 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Concat, Combine(
// `--- conv ----^ ^ ^
// `---- ... ------' '
// `-----------------'
typedef TestWithParam<tuple<Vec3i, std::string, int, bool, tuple<DNNBackend, DNNTarget> > > Eltwise;
typedef TestWithParam<tuple<Vec3i, std::string, int, bool, tuple<Backend, Target> > > Eltwise;
TEST_P(Eltwise, Accuracy)
{
Vec3i inSize = get<0>(GetParam());

61
modules/dnn/test/test_precomp.hpp
Unescape View File

@ -49,15 +49,41 @@
#include "opencv2/dnn.hpp"
#include "test_common.hpp"
namespace opencv_test { namespace {
using namespace cv::dnn;
namespace cv {
namespace dnn {
CV__DNN_EXPERIMENTAL_NS_BEGIN
static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
{
switch (v) {
case DNN_BACKEND_DEFAULT: *os << "DNN_BACKEND_DEFAULT"; return;
case DNN_BACKEND_HALIDE: *os << "DNN_BACKEND_HALIDE"; return;
case DNN_BACKEND_INFERENCE_ENGINE: *os << "DNN_BACKEND_INFERENCE_ENGINE"; return;
case DNN_BACKEND_OPENCV: *os << "DNN_BACKEND_OPENCV"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << v << ")";
}
static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
{
switch (v) {
case DNN_TARGET_CPU: *os << "DNN_TARGET_CPU"; return;
case DNN_TARGET_OPENCL: *os << "DNN_TARGET_OPENCL"; return;
case DNN_TARGET_OPENCL_FP16: *os << "DNN_TARGET_OPENCL_FP16"; return;
case DNN_TARGET_MYRIAD: *os << "DNN_TARGET_MYRIAD"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_TARGET_UNKNOWN(" << v << ")";
}
CV_ENUM(DNNBackend, DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE, DNN_BACKEND_OPENCV)
CV_ENUM(DNNTarget, DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD)
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace
static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
namespace opencv_test {
using namespace cv::dnn;
static testing::internal::ParamGenerator<Target> availableDnnTargets()
{
static std::vector<DNNTarget> targets;
static std::vector<Target> targets;
if (targets.empty())
{
targets.push_back(DNN_TARGET_CPU);
@ -69,23 +95,23 @@ static testing::internal::ParamGenerator<DNNTarget> availableDnnTargets()
return testing::ValuesIn(targets);
}
static testing::internal::ParamGenerator<tuple<DNNBackend, DNNTarget> > dnnBackendsAndTargets()
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargets()
{
static const tuple<DNNBackend, DNNTarget> testCases[] = {
static const tuple<Backend, Target> testCases[] = {
#ifdef HAVE_INF_ENGINE
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple<Backend, Target>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_CPU),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple<Backend, Target>(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
return testing::ValuesIn(testCases);
}
class DNNTestLayer : public TestWithParam <tuple<DNNBackend, DNNTarget> >
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
public:
dnn::Backend backend;
@ -156,6 +182,5 @@ protected:
}
};
}}
} // namespace
#endif

2
modules/dnn/test/test_tf_importer.cpp
Unescape View File

@ -243,7 +243,7 @@ TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
runTensorFlowNet("l2_normalize_3d");
}
typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_nets;
typedef testing::TestWithParam<Target> Test_TensorFlow_nets;
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{

4
modules/dnn/test/test_torch_importer.cpp
Unescape View File

@ -100,7 +100,7 @@ static void runTorchNet(String prefix, int targetId = DNN_TARGET_CPU, String out
}
}
typedef testing::TestWithParam<DNNTarget> Test_Torch_layers;
typedef testing::TestWithParam<Target> Test_Torch_layers;
TEST_P(Test_Torch_layers, run_convolution)
{
@ -208,7 +208,7 @@ TEST_P(Test_Torch_layers, net_non_spatial)
INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_layers, availableDnnTargets());
typedef testing::TestWithParam<DNNTarget> Test_Torch_nets;
typedef testing::TestWithParam<Target> Test_Torch_nets;
TEST_P(Test_Torch_nets, OpenFace_accuracy)
{

Write Preview
Loading…
Cancel
Save