#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
//struct to pass parameters
-typedef struct thread_common_param{
+typedef struct ThreadCommonParam{
DnnOperand *operands;
const int32_t *input_operand_indexes;
int32_t output_operand_index;
const void *parameters;
NativeContext *ctx;
float *output_data;
- int thread_num;
-} thread_common_param;
+} ThreadCommonParam;
-typedef struct thread_param{
- thread_common_param *thread_common_param;
- int thread_index;
-} thread_param;
+typedef struct ThreadParam{
+ ThreadCommonParam *thread_common_param;
+ int thread_start, thread_end;
+#if HAVE_PTHREAD_CANCEL
+ pthread_t thread;
+#endif
+} ThreadParam;
-int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
+int ff_dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
ConvolutionalParams *conv_params;
int kernel_size;
return 0;
}
- conv_params->kernel = av_malloc(kernel_size * sizeof(float));
+ conv_params->kernel = av_malloc_array(kernel_size, sizeof(*conv_params->kernel));
if (!conv_params->kernel) {
av_freep(&conv_params);
return 0;
conv_params->biases = NULL;
if (conv_params->has_bias) {
- conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
+ conv_params->biases = av_malloc_array(conv_params->output_num, sizeof(*conv_params->biases));
if (!conv_params->biases){
av_freep(&conv_params->kernel);
av_freep(&conv_params);
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;
+ ThreadParam *thread_param = threadarg;
+ ThreadCommonParam *thread_common_param = thread_param->thread_common_param;
DnnOperand *operands = thread_common_param->operands;
int32_t input_operand_index = thread_common_param->input_operand_indexes[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 *)(thread_common_param->parameters);
+ const ConvolutionalParams *conv_params = thread_common_param->parameters;
int radius = conv_params->kernel_size >> 1;
int src_linesize = width * conv_params->input_num;
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;
- 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);
-
float *output = thread_common_param->output_data;
- output += (conv_params->output_num) * (width - 2 * pad_size) * (thread_start - pad_size);
+ output += (conv_params->output_num) * (width - 2 * pad_size) * (thread_param->thread_start - pad_size);
av_assert0(channel == conv_params->input_num);
- for (int y = thread_start; y < thread_end; ++y) {
+ for (int y = thread_param->thread_start; y < thread_param->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)
output += conv_params->output_num;
}
}
- return (void *)DNN_SUCCESS;
+ return NULL;
}
-int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters, NativeContext *ctx)
+int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
+ int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
+#if HAVE_PTHREAD_CANCEL
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));
+ int ret = DNN_SUCCESS, thread_stride;
+ ThreadParam *thread_param;
+#else
+ ThreadParam thread_param = { 0 };
#endif
- thread_param **thread_param = av_malloc(thread_num * sizeof(*thread_param));
- thread_common_param thread_common_param;
- const ConvolutionalParams *conv_params = (const ConvolutionalParams *)(parameters);
+ ThreadCommonParam thread_common_param;
+ const ConvolutionalParams *conv_params = parameters;
+ int height = operands[input_operand_indexes[0]].dims[1];
+ int width = operands[input_operand_indexes[0]].dims[2];
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];
+ void *tmp;
output_operand->dims[0] = operands[input_operand_indexes[0]].dims[0];
- output_operand->dims[1] = operands[input_operand_indexes[0]].dims[1] - pad_size * 2;
- output_operand->dims[2] = operands[input_operand_indexes[0]].dims[2] - pad_size * 2;
+ output_operand->dims[1] = height - pad_size * 2;
+ output_operand->dims[2] = width - pad_size * 2;
output_operand->dims[3] = conv_params->output_num;
output_operand->data_type = operands[input_operand_indexes[0]].data_type;
- output_operand->length = calculate_operand_data_length(output_operand);
+ output_operand->length = ff_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;
}
- output_operand->data = av_realloc(output_operand->data, output_operand->length);
- if (!output_operand->data) {
+ tmp = av_realloc(output_operand->data, output_operand->length);
+ if (!tmp) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return DNN_ERROR;
}
+ output_operand->data = tmp;
thread_common_param.output_data = output_operand->data;
thread_common_param.operands = operands;
thread_common_param.input_operand_indexes = input_operand_indexes;
thread_common_param.ctx = ctx;
#if HAVE_PTHREAD_CANCEL
- thread_common_param.thread_num = thread_num;
-
+ thread_param = av_malloc_array(thread_num, sizeof(*thread_param));
+ if (!thread_param)
+ return DNN_ERROR;
+ thread_stride = (height - pad_size * 2) / 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]);
+ thread_param[i].thread_common_param = &thread_common_param;
+ thread_param[i].thread_start = thread_stride * i + pad_size;
+ thread_param[i].thread_end = (i == thread_num - 1) ? (height - pad_size) : (thread_param[i].thread_start + thread_stride);
+ if (pthread_create(&thread_param[i].thread, NULL,
+ dnn_execute_layer_conv2d_thread, &thread_param[i])) {
+ thread_num = i;
+ ret = DNN_ERROR;
+ break;
+ }
}
- //join threads, res gets function return
for (int i = 0; i < thread_num; i++){
- pthread_join(thread_id[i], NULL);
+ pthread_join(thread_param[i].thread, NULL);
}
//release memory
- av_free(thread_id);
+ av_freep(&thread_param);
- for (int i = 0; i < thread_num; i++){
- av_free(thread_param[i]);
- }
+ return ret;
#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;
- dnn_execute_layer_conv2d_thread((void *)thread_param[0]);
- av_free(thread_param[0]);
-#endif
+ thread_param.thread_common_param = &thread_common_param;
+ thread_param.thread_start = pad_size;
+ thread_param.thread_end = height - pad_size;
+ dnn_execute_layer_conv2d_thread(&thread_param);
- av_free(thread_param);
return DNN_SUCCESS;
+#endif
}
projects / ffmpeg / blobdiff