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Code Issues Releases Activity

dnn_backend_native_layer_conv2d.c: fix memory allocation bug in multithread function.

Browse Source 
Before patch, memory was allocated in each thread functions,
which may cause more than one time of memory allocation and
cause crash.

After patch, memory is allocated in the main thread once,
an index was parsed into thread functions. Bug fixed.

Signed-off-by: Xu Jun <xujunzz@sjtu.edu.cn>
This commit is contained in:
Xu Jun 2020-09-16 18:07:17 +08:00 committed by Guo, Yejun
parent a265e6604e
commit 8e67ae2cb4
1 changed files with 26 additions and 31 deletions
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57
libavfilter/dnn/dnn_backend_native_layer_conv2d.c
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@ -32,6 +32,7 @@ typedef struct thread_common_param{
int32_t output_operand_index;
const void *parameters;
NativeContext *ctx;
float *output_data;
int thread_num;
} thread_common_param;
@ -111,9 +112,7 @@ static void * dnn_execute_layer_conv2d_thread(void *threadarg)
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 = 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];
@ -130,24 +129,7 @@ static void * dnn_execute_layer_conv2d_thread(void *threadarg)
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;
output_operand->dims[3] = conv_params->output_num;
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(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(thread_common_param->ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return (void *)DNN_ERROR;
}
output = output_operand->data;
float *output = thread_common_param->output_data;
output += (conv_params->output_num) * (width - 2 * pad_size) * (thread_start - pad_size);
av_assert0(channel == conv_params->input_num);
@ -213,16 +195,33 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
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;
const ConvolutionalParams *conv_params = (const ConvolutionalParams *)(parameters);
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];
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[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);
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) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return DNN_ERROR;
}
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.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;
@ -236,9 +235,7 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
//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;
pthread_join(thread_id[i], NULL);
}
//release memory
@ -252,12 +249,10 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
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;
dnn_execute_layer_conv2d_thread((void *)thread_param[0]);
av_free(thread_param[0]);
#endif
av_free(thread_param);
return error_flag;
return DNN_SUCCESS;
}