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-rw-r--r--libavfilter/dnn/dnn_backend_native_layer_conv2d.c107
-rw-r--r--tests/dnn/dnn-layer-conv2d-test.c14
2 files changed, 108 insertions, 13 deletions
diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
index d079795bf8..777a54db43 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
@@ -19,10 +19,27 @@
*/
#include "libavutil/avassert.h"
+#include "libavutil/thread.h"
+#include "libavutil/cpu.h"
#include "dnn_backend_native_layer_conv2d.h"
#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
+//struct to pass parameters
+typedef struct thread_common_param{
+ DnnOperand *operands;
+ const int32_t *input_operand_indexes;
+ int32_t output_operand_index;
+ const void *parameters;
+ NativeContext *ctx;
+ int thread_num;
+} thread_common_param;
+
+typedef struct thread_param{
+ thread_common_param *thread_common_param;
+ int thread_index;
+} thread_param;
+
int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
ConvolutionalParams *conv_params;
@@ -88,17 +105,20 @@ int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int fil
return dnn_size;
}
-int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters, NativeContext *ctx)
+static void * dnn_execute_layer_conv2d_thread(void *threadarg)
{
+ //pass parameters
+ thread_param *thread_param = (struct thread_param *)threadarg;
+ thread_common_param *thread_common_param = thread_param->thread_common_param;
+ DnnOperand *operands = thread_common_param->operands;
float *output;
- int32_t input_operand_index = input_operand_indexes[0];
+ int32_t input_operand_index = thread_common_param->input_operand_indexes[0];
int number = operands[input_operand_index].dims[0];
int height = operands[input_operand_index].dims[1];
int width = operands[input_operand_index].dims[2];
int channel = operands[input_operand_index].dims[3];
const float *input = operands[input_operand_index].data;
- const ConvolutionalParams *conv_params = (const ConvolutionalParams *)parameters;
+ const ConvolutionalParams *conv_params = (const ConvolutionalParams *)(thread_common_param->parameters);
int radius = conv_params->kernel_size >> 1;
int src_linesize = width * conv_params->input_num;
@@ -106,7 +126,11 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
int filter_size = conv_params->kernel_size * filter_linesize;
int pad_size = (conv_params->padding_method == VALID) ? (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;
- DnnOperand *output_operand = &operands[output_operand_index];
+ int thread_stride = (height - pad_size * 2) / thread_common_param->thread_num;
+ int thread_start = thread_stride * thread_param->thread_index + pad_size;
+ int thread_end = (thread_param->thread_index == thread_common_param->thread_num - 1) ? (height - pad_size) : (thread_start + thread_stride);
+
+ DnnOperand *output_operand = &operands[thread_common_param->output_operand_index];
output_operand->dims[0] = number;
output_operand->dims[1] = height - pad_size * 2;
output_operand->dims[2] = width - pad_size * 2;
@@ -114,19 +138,21 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
output_operand->data_type = operands[input_operand_index].data_type;
output_operand->length = calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
- av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ av_log(thread_common_param->ctx, AV_LOG_ERROR, "The output data length overflow\n");
+ return (void *)DNN_ERROR;
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
- av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ av_log(thread_common_param->ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
+ return (void *)DNN_ERROR;
}
+
output = output_operand->data;
+ output += (conv_params->output_num) * (width - 2 * pad_size) * (thread_start - pad_size);
av_assert0(channel == conv_params->input_num);
- for (int y = pad_size; y < height - pad_size; ++y) {
+ for (int y = thread_start; y < thread_end; ++y) {
for (int x = pad_size; x < width - pad_size; ++x) {
for (int n_filter = 0; n_filter < conv_params->output_num; ++n_filter) {
if (conv_params->has_bias)
@@ -174,5 +200,64 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
output += conv_params->output_num;
}
}
- return 0;
+ return (void *)DNN_SUCCESS;
+}
+
+
+int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
+ int32_t output_operand_index, const void *parameters, NativeContext *ctx)
+{
+ int thread_num = (ctx->options.conv2d_threads <= 0 || ctx->options.conv2d_threads > av_cpu_count())
+ ? (av_cpu_count() + 1) : (ctx->options.conv2d_threads);
+#if HAVE_PTHREAD_CANCEL
+ pthread_t *thread_id = av_malloc(thread_num * sizeof(pthread_t));
+#endif
+ thread_param **thread_param = av_malloc(thread_num * sizeof(*thread_param));
+ void *res;
+ int error_flag = DNN_SUCCESS;
+
+ //struct used to pass parameters
+ thread_common_param thread_common_param;
+ thread_common_param.operands = operands;
+ thread_common_param.input_operand_indexes = input_operand_indexes;
+ thread_common_param.output_operand_index = output_operand_index;
+ thread_common_param.parameters = parameters;
+ thread_common_param.ctx = ctx;
+#if HAVE_PTHREAD_CANCEL
+ thread_common_param.thread_num = thread_num;
+
+ //create threads
+ for (int i = 0; i < thread_num; i++){
+ thread_param[i] = av_malloc(sizeof(thread_param));
+ thread_param[i]->thread_common_param = &thread_common_param;
+ thread_param[i]->thread_index = i;
+ pthread_create(&thread_id[i], NULL, dnn_execute_layer_conv2d_thread, (void *)thread_param[i]);
+ }
+
+ //join threads, res gets function return
+ for (int i = 0; i < thread_num; i++){
+ pthread_join(thread_id[i], &res);
+ if ((int)res != DNN_SUCCESS)
+ error_flag = (int)res;
+ }
+
+ //release memory
+ av_free(thread_id);
+
+ for (int i = 0; i < thread_num; i++){
+ av_free(thread_param[i]);
+ }
+#else
+ thread_common_param.thread_num = 1;
+ thread_param[0] = av_malloc(sizeof(thread_param));
+ thread_param[0]->thread_common_param = &thread_common_param;
+ thread_param[0]->thread_index = 0;
+ res = dnn_execute_layer_conv2d_thread((void *)thread_param[0]);
+ if ((int)res != DNN_SUCCESS)
+ error_flag = (int)res;
+ av_free(thread_param[0]);
+#endif
+
+ av_free(thread_param);
+ return error_flag;
}
diff --git a/tests/dnn/dnn-layer-conv2d-test.c b/tests/dnn/dnn-layer-conv2d-test.c
index 836839cc64..378a05eafc 100644
--- a/tests/dnn/dnn-layer-conv2d-test.c
+++ b/tests/dnn/dnn-layer-conv2d-test.c
@@ -25,6 +25,8 @@
#define EPSON 0.00001
+extern const AVClass dnn_native_class;
+
static int test_with_same_dilate(void)
{
// the input data and expected data are generated with below python code.
@@ -96,6 +98,10 @@ static int test_with_same_dilate(void)
};
float bias[2] = { -1.6574852, -0.72915393 };
+ NativeContext ctx;
+ ctx.class = &dnn_native_class;
+ ctx.options.conv2d_threads = 1;
+
params.activation = TANH;
params.has_bias = 1;
params.biases = bias;
@@ -114,7 +120,7 @@ static int test_with_same_dilate(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_conv2d(operands, input_indexes, 1, &params, NULL);
+ dnn_execute_layer_conv2d(operands, input_indexes, 1, &params, &ctx);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
@@ -196,6 +202,10 @@ static int test_with_valid(void)
};
float bias[2] = { -0.4773722, -0.19620377 };
+ NativeContext ctx;
+ ctx.class = &dnn_native_class;
+ ctx.options.conv2d_threads = 1;
+
params.activation = TANH;
params.has_bias = 1;
params.biases = bias;
@@ -214,7 +224,7 @@ static int test_with_valid(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_conv2d(operands, input_indexes, 1, &params, NULL);
+ dnn_execute_layer_conv2d(operands, input_indexes, 1, &params, &ctx);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-07 14:29:56 +0000