[FFmpeg-devel] [PATCH V2] dnn/native: add log error message
Ting Fu
ting.fu at intel.com
Mon Aug 17 08:35:18 EEST 2020
Signed-off-by: Ting Fu <ting.fu at intel.com>
---
V2:
Fix the issue in V1: make fate failed
libavfilter/dnn/dnn_backend_native.c | 74 +++++++++++++++----
libavfilter/dnn/dnn_backend_native.h | 5 ++
.../dnn/dnn_backend_native_layer_avgpool.c | 2 +-
.../dnn/dnn_backend_native_layer_avgpool.h | 2 +-
.../dnn/dnn_backend_native_layer_conv2d.c | 2 +-
.../dnn/dnn_backend_native_layer_conv2d.h | 2 +-
.../dnn_backend_native_layer_depth2space.c | 2 +-
.../dnn_backend_native_layer_depth2space.h | 2 +-
.../dnn/dnn_backend_native_layer_mathbinary.c | 10 ++-
.../dnn/dnn_backend_native_layer_mathbinary.h | 2 +-
.../dnn/dnn_backend_native_layer_mathunary.c | 10 ++-
.../dnn/dnn_backend_native_layer_mathunary.h | 2 +-
.../dnn/dnn_backend_native_layer_maximum.c | 2 +-
.../dnn/dnn_backend_native_layer_maximum.h | 2 +-
.../dnn/dnn_backend_native_layer_pad.c | 2 +-
.../dnn/dnn_backend_native_layer_pad.h | 2 +-
libavfilter/dnn/dnn_backend_native_layers.h | 2 +-
tests/dnn/dnn-layer-avgpool-test.c | 4 +-
tests/dnn/dnn-layer-conv2d-test.c | 4 +-
tests/dnn/dnn-layer-depth2space-test.c | 2 +-
tests/dnn/dnn-layer-mathbinary-test.c | 6 +-
tests/dnn/dnn-layer-mathunary-test.c | 2 +-
tests/dnn/dnn-layer-maximum-test.c | 2 +-
tests/dnn/dnn-layer-pad-test.c | 6 +-
24 files changed, 103 insertions(+), 48 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_native.c b/libavfilter/dnn/dnn_backend_native.c
index adc652a2c4..6ddffa54af 100644
--- a/libavfilter/dnn/dnn_backend_native.c
+++ b/libavfilter/dnn/dnn_backend_native.c
@@ -28,15 +28,30 @@
#include "dnn_backend_native_layer_conv2d.h"
#include "dnn_backend_native_layers.h"
+static const AVClass dnn_native_class = {
+ .class_name = "dnn_native",
+ .item_name = av_default_item_name,
+ .option = NULL,
+ .version = LIBAVUTIL_VERSION_INT,
+ .category = AV_CLASS_CATEGORY_FILTER,
+};
+
+NetworkContext network_ctx = {
+ .class = &dnn_native_class,
+};
+
static DNNReturnType get_input_native(void *model, DNNData *input, const char *input_name)
{
ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
+ NetworkContext *ctx = network->log_ctx;
for (int i = 0; i < network->operands_num; ++i) {
DnnOperand *oprd = &network->operands[i];
if (strcmp(oprd->name, input_name) == 0) {
- if (oprd->type != DOT_INPUT)
+ if (oprd->type != DOT_INPUT) {
+ av_log(ctx, AV_LOG_ERROR, "Found \"%s\" in model, but it is not input node\n", input_name);
return DNN_ERROR;
+ }
input->dt = oprd->data_type;
av_assert0(oprd->dims[0] == 1);
input->height = oprd->dims[1];
@@ -47,30 +62,37 @@ static DNNReturnType get_input_native(void *model, DNNData *input, const char *i
}
// do not find the input operand
+ av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
return DNN_ERROR;
}
static DNNReturnType set_input_output_native(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output)
{
ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
+ NetworkContext *ctx = network->log_ctx;
DnnOperand *oprd = NULL;
- if (network->layers_num <= 0 || network->operands_num <= 0)
+ if (network->layers_num <= 0 || network->operands_num <= 0) {
+ av_log(ctx, AV_LOG_ERROR, "No operands or layers in model\n");
return DNN_ERROR;
+ }
/* inputs */
for (int i = 0; i < network->operands_num; ++i) {
oprd = &network->operands[i];
if (strcmp(oprd->name, input_name) == 0) {
- if (oprd->type != DOT_INPUT)
+ if (oprd->type != DOT_INPUT) {
+ av_log(ctx, AV_LOG_ERROR, "Found \"%s\" in model, but it is not input node\n", input_name);
return DNN_ERROR;
+ }
break;
}
oprd = NULL;
}
-
- if (!oprd)
+ if (!oprd) {
+ av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
return DNN_ERROR;
+ }
oprd->dims[0] = 1;
oprd->dims[1] = input->height;
@@ -79,11 +101,15 @@ static DNNReturnType set_input_output_native(void *model, DNNData *input, const
av_freep(&oprd->data);
oprd->length = calculate_operand_data_length(oprd);
- if (oprd->length <= 0)
+ if (oprd->length <= 0) {
+ av_log(ctx, AV_LOG_ERROR, "The input data length overflow\n");
return DNN_ERROR;
+ }
oprd->data = av_malloc(oprd->length);
- if (!oprd->data)
+ if (!oprd->data) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to malloc memory for input data\n");
return DNN_ERROR;
+ }
input->data = oprd->data;
@@ -91,8 +117,10 @@ static DNNReturnType set_input_output_native(void *model, DNNData *input, const
network->nb_output = 0;
av_freep(&network->output_indexes);
network->output_indexes = av_mallocz_array(nb_output, sizeof(*network->output_indexes));
- if (!network->output_indexes)
+ if (!network->output_indexes) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to malloc memory for output\n");
return DNN_ERROR;
+ }
for (uint32_t i = 0; i < nb_output; ++i) {
const char *output_name = output_names[i];
@@ -105,8 +133,10 @@ static DNNReturnType set_input_output_native(void *model, DNNData *input, const
}
}
- if (network->nb_output != nb_output)
+ if (network->nb_output != nb_output) {
+ av_log(ctx, AV_LOG_ERROR, "Output(s) name are not all set correctly\n");
return DNN_ERROR;
+ }
return DNN_SUCCESS;
}
@@ -128,6 +158,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *optio
int32_t layer;
DNNLayerType layer_type;
+
if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
return NULL;
}
@@ -171,6 +202,8 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *optio
if (!network){
goto fail;
}
+
+ network->log_ctx = &network_ctx;
model->model = (void *)network;
avio_seek(model_file_context, file_size - 8, SEEK_SET);
@@ -258,20 +291,29 @@ fail:
DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
{
ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
+ NetworkContext *ctx = network->log_ctx;
int32_t layer;
uint32_t nb = FFMIN(nb_output, network->nb_output);
- if (network->layers_num <= 0 || network->operands_num <= 0)
+ if (network->layers_num <= 0 || network->operands_num <= 0) {
+ av_log(ctx, AV_LOG_ERROR, "Error network layer number: %d\n", network->layers_num);
return DNN_ERROR;
- if (!network->operands[0].data)
+ }
+ if (!network->operands[0].data) {
+ av_log(ctx, AV_LOG_ERROR, "Empty network input data\n");
return DNN_ERROR;
+ }
- for (layer = 0; layer < network->layers_num; ++layer){
+ for (layer = 0; layer < network->layers_num; ++layer) {
DNNLayerType layer_type = network->layers[layer].type;
- layer_funcs[layer_type].pf_exec(network->operands,
- network->layers[layer].input_operand_indexes,
- network->layers[layer].output_operand_index,
- network->layers[layer].params);
+ if (layer_funcs[layer_type].pf_exec(network->operands,
+ network->layers[layer].input_operand_indexes,
+ network->layers[layer].output_operand_index,
+ network->layers[layer].params,
+ network->log_ctx) == DNN_ERROR) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to execuet network operands\n");
+ return DNN_ERROR;
+ }
}
for (uint32_t i = 0; i < nb; ++i) {
diff --git a/libavfilter/dnn/dnn_backend_native.h b/libavfilter/dnn/dnn_backend_native.h
index b455e44992..954a4187d1 100644
--- a/libavfilter/dnn/dnn_backend_native.h
+++ b/libavfilter/dnn/dnn_backend_native.h
@@ -106,8 +106,13 @@ typedef struct InputParams{
int height, width, channels;
} InputParams;
+typedef struct NetworkContext {
+ const AVClass *class;
+} NetworkContext;
+
// Represents simple feed-forward convolutional network.
typedef struct ConvolutionalNetwork{
+ NetworkContext *log_ctx;
Layer *layers;
int32_t layers_num;
DnnOperand *operands;
diff --git a/libavfilter/dnn/dnn_backend_native_layer_avgpool.c b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
index d745c35b4a..98d670f965 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
@@ -56,7 +56,7 @@ int dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int f
}
int dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters)
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx)
{
float *output;
int height_end, width_end, height_radius, width_radius, output_height, output_width, kernel_area;
diff --git a/libavfilter/dnn/dnn_backend_native_layer_avgpool.h b/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
index 8e31ddb7c8..7c7623803a 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
@@ -35,6 +35,6 @@ typedef struct AvgPoolParams{
int dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters);
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx);
#endif
diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
index a2202e4073..1b898813a9 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
@@ -89,7 +89,7 @@ int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int fil
}
int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters)
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx)
{
float *output;
int32_t input_operand_index = input_operand_indexes[0];
diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.h b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
index b240b7ef6b..8cee63e558 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
@@ -37,5 +37,5 @@ typedef struct ConvolutionalParams{
int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters);
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx);
#endif
diff --git a/libavfilter/dnn/dnn_backend_native_layer_depth2space.c b/libavfilter/dnn/dnn_backend_native_layer_depth2space.c
index 2c8bddf23d..54fee37406 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_depth2space.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_depth2space.c
@@ -50,7 +50,7 @@ int dnn_load_layer_depth2space(Layer *layer, AVIOContext *model_file_context, in
}
int dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters)
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx)
{
float *output;
const DepthToSpaceParams *params = (const DepthToSpaceParams *)parameters;
diff --git a/libavfilter/dnn/dnn_backend_native_layer_depth2space.h b/libavfilter/dnn/dnn_backend_native_layer_depth2space.h
index b2901e0141..6a6f52f722 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_depth2space.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_depth2space.h
@@ -36,6 +36,6 @@ typedef struct DepthToSpaceParams{
int dnn_load_layer_depth2space(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters);
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx);
#endif
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
index dd42c329a9..413120d0bd 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
@@ -77,7 +77,7 @@ int dnn_load_layer_math_binary(Layer *layer, AVIOContext *model_file_context, in
}
int dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters)
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx)
{
const DnnOperand *input = &operands[input_operand_indexes[0]];
DnnOperand *output = &operands[output_operand_index];
@@ -91,11 +91,15 @@ int dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_ope
output->data_type = input->data_type;
output->length = calculate_operand_data_length(output);
- if (output->length <= 0)
+ if (output->length <= 0) {
+ av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return DNN_ERROR;
+ }
output->data = av_realloc(output->data, output->length);
- if (!output->data)
+ if (!output->data) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return DNN_ERROR;
+ }
dims_count = calculate_operand_dims_count(output);
src = input->data;
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
index 0acf3b0ea0..b2a0105fe6 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
@@ -48,6 +48,6 @@ typedef struct DnnLayerMathBinaryParams{
int dnn_load_layer_math_binary(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters);
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx);
#endif
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathunary.c b/libavfilter/dnn/dnn_backend_native_layer_mathunary.c
index 58ee0e9d3d..4758d147f2 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathunary.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathunary.c
@@ -53,7 +53,7 @@ int dnn_load_layer_math_unary(Layer *layer, AVIOContext *model_file_context, int
}
int dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters)
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx)
{
const DnnOperand *input = &operands[input_operand_indexes[0]];
DnnOperand *output = &operands[output_operand_index];
@@ -67,11 +67,15 @@ int dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_oper
output->data_type = input->data_type;
output->length = calculate_operand_data_length(output);
- if (output->length <= 0)
+ if (output->length <= 0) {
+ av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return DNN_ERROR;
+ }
output->data = av_realloc(output->data, output->length);
- if (!output->data)
+ if (!output->data) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return DNN_ERROR;
+ }
dims_count = calculate_operand_dims_count(output);
src = input->data;
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathunary.h b/libavfilter/dnn/dnn_backend_native_layer_mathunary.h
index d6a61effd5..ba16c06912 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathunary.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathunary.h
@@ -55,6 +55,6 @@ typedef struct DnnLayerMathUnaryParams{
int dnn_load_layer_math_unary(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters);
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx);
#endif
diff --git a/libavfilter/dnn/dnn_backend_native_layer_maximum.c b/libavfilter/dnn/dnn_backend_native_layer_maximum.c
index cdddfdd87b..98323abff3 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_maximum.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_maximum.c
@@ -50,7 +50,7 @@ int dnn_load_layer_maximum(Layer *layer, AVIOContext *model_file_context, int fi
}
int dnn_execute_layer_maximum(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters)
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx)
{
const DnnOperand *input = &operands[input_operand_indexes[0]];
DnnOperand *output = &operands[output_operand_index];
diff --git a/libavfilter/dnn/dnn_backend_native_layer_maximum.h b/libavfilter/dnn/dnn_backend_native_layer_maximum.h
index c049c63fd8..b8c25da958 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_maximum.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_maximum.h
@@ -39,6 +39,6 @@ typedef struct DnnLayerMaximumParams{
int dnn_load_layer_maximum(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int dnn_execute_layer_maximum(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters);
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx);
#endif
diff --git a/libavfilter/dnn/dnn_backend_native_layer_pad.c b/libavfilter/dnn/dnn_backend_native_layer_pad.c
index feaab001e8..01d1edcc6e 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_pad.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_pad.c
@@ -76,7 +76,7 @@ static int after_get_buddy(int given, int border, LayerPadModeParam mode)
}
int dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters)
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx)
{
int32_t before_paddings;
int32_t after_paddings;
diff --git a/libavfilter/dnn/dnn_backend_native_layer_pad.h b/libavfilter/dnn/dnn_backend_native_layer_pad.h
index 18e05bdd5c..bf84924a68 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_pad.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_pad.h
@@ -38,6 +38,6 @@ typedef struct LayerPadParams{
int dnn_load_layer_pad(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters);
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx);
#endif
diff --git a/libavfilter/dnn/dnn_backend_native_layers.h b/libavfilter/dnn/dnn_backend_native_layers.h
index b696e9c6fa..f6dcdc6ecb 100644
--- a/libavfilter/dnn/dnn_backend_native_layers.h
+++ b/libavfilter/dnn/dnn_backend_native_layers.h
@@ -25,7 +25,7 @@
#include "dnn_backend_native.h"
typedef int (*LAYER_EXEC_FUNC)(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters);
+ int32_t output_operand_index, const void *parameters, NetworkContext *ctx);
typedef int (*LAYER_LOAD_FUNC)(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
typedef struct LayerFunc {
diff --git a/tests/dnn/dnn-layer-avgpool-test.c b/tests/dnn/dnn-layer-avgpool-test.c
index d7c33a0e88..0e6be8ba57 100644
--- a/tests/dnn/dnn-layer-avgpool-test.c
+++ b/tests/dnn/dnn-layer-avgpool-test.c
@@ -91,7 +91,7 @@ static int test_with_same(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_avg_pool(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_avg_pool(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); ++i) {
@@ -171,7 +171,7 @@ static int test_with_valid(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_avg_pool(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_avg_pool(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); ++i) {
diff --git a/tests/dnn/dnn-layer-conv2d-test.c b/tests/dnn/dnn-layer-conv2d-test.c
index 2da01e5372..836839cc64 100644
--- a/tests/dnn/dnn-layer-conv2d-test.c
+++ b/tests/dnn/dnn-layer-conv2d-test.c
@@ -114,7 +114,7 @@ static int test_with_same_dilate(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
@@ -214,7 +214,7 @@ static int test_with_valid(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
diff --git a/tests/dnn/dnn-layer-depth2space-test.c b/tests/dnn/dnn-layer-depth2space-test.c
index 5225ec7b7a..2c641884c1 100644
--- a/tests/dnn/dnn-layer-depth2space-test.c
+++ b/tests/dnn/dnn-layer-depth2space-test.c
@@ -81,7 +81,7 @@ static int test(void)
input_indexes[0] = 0;
params.block_size = 2;
- dnn_execute_layer_depth2space(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_depth2space(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
diff --git a/tests/dnn/dnn-layer-mathbinary-test.c b/tests/dnn/dnn-layer-mathbinary-test.c
index e7f8f8557c..187a2b9bf2 100644
--- a/tests/dnn/dnn-layer-mathbinary-test.c
+++ b/tests/dnn/dnn-layer-mathbinary-test.c
@@ -69,7 +69,7 @@ static int test_broadcast_input0(DNNMathBinaryOperation op)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_math_binary(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_math_binary(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(input) / sizeof(float); i++) {
@@ -109,7 +109,7 @@ static int test_broadcast_input1(DNNMathBinaryOperation op)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_math_binary(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_math_binary(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(input) / sizeof(float); i++) {
@@ -157,7 +157,7 @@ static int test_no_broadcast(DNNMathBinaryOperation op)
input_indexes[0] = 0;
input_indexes[1] = 1;
- dnn_execute_layer_math_binary(operands, input_indexes, 2, ¶ms);
+ dnn_execute_layer_math_binary(operands, input_indexes, 2, ¶ms, NULL);
output = operands[2].data;
for (int i = 0; i < sizeof(input0) / sizeof(float); i++) {
diff --git a/tests/dnn/dnn-layer-mathunary-test.c b/tests/dnn/dnn-layer-mathunary-test.c
index e9235120f3..ce14c41311 100644
--- a/tests/dnn/dnn-layer-mathunary-test.c
+++ b/tests/dnn/dnn-layer-mathunary-test.c
@@ -87,7 +87,7 @@ static int test(DNNMathUnaryOperation op)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_math_unary(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_math_unary(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(input) / sizeof(float); ++i) {
diff --git a/tests/dnn/dnn-layer-maximum-test.c b/tests/dnn/dnn-layer-maximum-test.c
index 06daf64481..c982670591 100644
--- a/tests/dnn/dnn-layer-maximum-test.c
+++ b/tests/dnn/dnn-layer-maximum-test.c
@@ -45,7 +45,7 @@ static int test(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_maximum(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_maximum(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(input) / sizeof(float); i++) {
diff --git a/tests/dnn/dnn-layer-pad-test.c b/tests/dnn/dnn-layer-pad-test.c
index ea8c824d1e..6a72adb3ae 100644
--- a/tests/dnn/dnn-layer-pad-test.c
+++ b/tests/dnn/dnn-layer-pad-test.c
@@ -79,7 +79,7 @@ static int test_with_mode_symmetric(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
@@ -144,7 +144,7 @@ static int test_with_mode_reflect(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
@@ -210,7 +210,7 @@ static int test_with_mode_constant(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms);
+ dnn_execute_layer_pad(operands, input_indexes, 1, ¶ms, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
--
2.17.1
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