libavfilter/dnn/dnn_backend_native_layer_conv2d.c - manifest_repos/ffmpeg - Git at Google

 /*
  * Copyright (c) 2018 Sergey Lavrushkin
  *
  * This file is part of FFmpeg.
  *
  * FFmpeg is free software; you can redistribute it and/or
  * modify it under the terms of the GNU Lesser General Public
  * License as published by the Free Software Foundation; either
  * version 2.1 of the License, or (at your option) any later version.
  *
  * FFmpeg is distributed in the hope that it will be useful,
  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  * Lesser General Public License for more details.
  *
  * You should have received a copy of the GNU Lesser General Public
  * License along with FFmpeg; if not, write to the Free Software
  * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
  */

 #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;
     float *output_data;
 } thread_common_param;

 typedef struct thread_param{
     thread_common_param *thread_common_param;
     int thread_start, thread_end;
 } thread_param;

 int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
 {
     ConvolutionalParams *conv_params;
     int kernel_size;
     int dnn_size = 0;
     conv_params = av_malloc(sizeof(*conv_params));
     if (!conv_params)
         return 0;

     conv_params->dilation = (int32_t)avio_rl32(model_file_context);
     conv_params->padding_method = (int32_t)avio_rl32(model_file_context);
     conv_params->activation = (int32_t)avio_rl32(model_file_context);
     conv_params->input_num = (int32_t)avio_rl32(model_file_context);
     conv_params->output_num = (int32_t)avio_rl32(model_file_context);
     conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
     conv_params->has_bias = (int32_t)avio_rl32(model_file_context);
     dnn_size += 28;

     kernel_size = conv_params->input_num * conv_params->output_num *
                       conv_params->kernel_size * conv_params->kernel_size;
     dnn_size += kernel_size * 4;
     if (conv_params->has_bias)
         dnn_size += conv_params->output_num * 4;

     if (dnn_size > file_size || conv_params->input_num <= 0 ||
         conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
         av_freep(&conv_params);
         return 0;
     }

     conv_params->kernel = av_malloc(kernel_size * sizeof(float));
     if (!conv_params->kernel) {
         av_freep(&conv_params);
         return 0;
     }
     for (int i = 0; i < kernel_size; ++i) {
         conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
     }

     conv_params->biases = NULL;
     if (conv_params->has_bias) {
         conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
         if (!conv_params->biases){
             av_freep(&conv_params->kernel);
             av_freep(&conv_params);
             return 0;
         }
         for (int i = 0; i < conv_params->output_num; ++i){
             conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
         }
     }

     layer->params = conv_params;

     layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
     layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
     dnn_size += 8;

     if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
         return 0;
     }

     return dnn_size;
 }

 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;
     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);

     int radius = conv_params->kernel_size >> 1;
     int src_linesize = width * conv_params->input_num;
     int filter_linesize = conv_params->kernel_size * 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;

     float *output = thread_common_param->output_data;
     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_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[n_filter] = conv_params->biases[n_filter];
                 else
                     output[n_filter] = 0.f;

                 for (int ch = 0; ch < conv_params->input_num; ++ch) {
                     for (int kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y) {
                         for (int kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x) {
                             float input_pel;
                             if (conv_params->padding_method == SAME_CLAMP_TO_EDGE) {
                                 int y_pos = CLAMP_TO_EDGE(y + (kernel_y - radius) * conv_params->dilation, height);
                                 int x_pos = CLAMP_TO_EDGE(x + (kernel_x - radius) * conv_params->dilation, width);
                                 input_pel = input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
                             } else {
                                 int y_pos = y + (kernel_y - radius) * conv_params->dilation;
                                 int x_pos = x + (kernel_x - radius) * conv_params->dilation;
                                 input_pel = (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) ? 0.0 :
                                                    input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
                             }


                             output[n_filter] += input_pel * conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
                                                                                 kernel_x * conv_params->input_num + ch];
                         }
                     }
                 }
                 switch (conv_params->activation){
                 case RELU:
                     output[n_filter] = FFMAX(output[n_filter], 0.0);
                     break;
                 case TANH:
                     output[n_filter] = 2.0f  / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
                     break;
                 case SIGMOID:
                     output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
                     break;
                 case NONE:
                     break;
                 case LEAKY_RELU:
                     output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
                 }
             }
             output += conv_params->output_num;
         }
     }
     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));
     int thread_stride;
 #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);
     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];

     output_operand->dims[0] = operands[input_operand_indexes[0]].dims[0];
     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);
     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_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_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);
         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], NULL);
     }

     //release memory
     av_free(thread_id);

     for (int i = 0; i < thread_num; i++){
         av_free(thread_param[i]);
     }
 #else
     thread_param[0] = av_malloc(sizeof(**thread_param));
     thread_param[0]->thread_common_param = &thread_common_param;
     thread_param[0]->thread_start = pad_size;
     thread_param[0]->thread_end = height - pad_size;
     dnn_execute_layer_conv2d_thread((void *)thread_param[0]);
     av_free(thread_param[0]);
 #endif

     av_free(thread_param);
     return DNN_SUCCESS;
 }
	/*
	* Copyright (c) 2018 Sergey Lavrushkin
	*
	* This file is part of FFmpeg.
	*
	* FFmpeg is free software; you can redistribute it and/or
	* modify it under the terms of the GNU Lesser General Public
	* License as published by the Free Software Foundation; either
	* version 2.1 of the License, or (at your option) any later version.
	*
	* FFmpeg is distributed in the hope that it will be useful,
	* but WITHOUT ANY WARRANTY; without even the implied warranty of
	* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
	* Lesser General Public License for more details.
	*
	* You should have received a copy of the GNU Lesser General Public
	* License along with FFmpeg; if not, write to the Free Software
	* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
	*/

	#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;
	float *output_data;
	} thread_common_param;

	typedef struct thread_param{
	thread_common_param *thread_common_param;
	int thread_start, thread_end;
	} thread_param;

	int dnn_load_layer_conv2d(Layer layer, AVIOContext model_file_context, int file_size, int operands_num)
	{
	ConvolutionalParams *conv_params;
	int kernel_size;
	int dnn_size = 0;
	conv_params = av_malloc(sizeof(*conv_params));
	if (!conv_params)
	return 0;

	conv_params->dilation = (int32_t)avio_rl32(model_file_context);
	conv_params->padding_method = (int32_t)avio_rl32(model_file_context);
	conv_params->activation = (int32_t)avio_rl32(model_file_context);
	conv_params->input_num = (int32_t)avio_rl32(model_file_context);
	conv_params->output_num = (int32_t)avio_rl32(model_file_context);
	conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
	conv_params->has_bias = (int32_t)avio_rl32(model_file_context);
	dnn_size += 28;

	kernel_size = conv_params->input_num * conv_params->output_num *
	conv_params->kernel_size * conv_params->kernel_size;
	dnn_size += kernel_size * 4;
	if (conv_params->has_bias)
	dnn_size += conv_params->output_num * 4;

	if (dnn_size > file_size \|\| conv_params->input_num <= 0 \|\|
	conv_params->output_num <= 0 \|\| conv_params->kernel_size <= 0){
	av_freep(&conv_params);
	return 0;
	}

	conv_params->kernel = av_malloc(kernel_size * sizeof(float));
	if (!conv_params->kernel) {
	av_freep(&conv_params);
	return 0;
	}
	for (int i = 0; i < kernel_size; ++i) {
	conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
	}

	conv_params->biases = NULL;
	if (conv_params->has_bias) {
	conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
	if (!conv_params->biases){
	av_freep(&conv_params->kernel);
	av_freep(&conv_params);
	return 0;
	}
	for (int i = 0; i < conv_params->output_num; ++i){
	conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
	}
	}

	layer->params = conv_params;

	layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
	layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
	dnn_size += 8;

	if (layer->input_operand_indexes[0] >= operands_num \|\| layer->output_operand_index >= operands_num) {
	return 0;
	}

	return dnn_size;
	}

	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;
	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);

	int radius = conv_params->kernel_size >> 1;
	int src_linesize = width * conv_params->input_num;
	int filter_linesize = conv_params->kernel_size * 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;

	float *output = thread_common_param->output_data;
	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_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[n_filter] = conv_params->biases[n_filter];
	else
	output[n_filter] = 0.f;

	for (int ch = 0; ch < conv_params->input_num; ++ch) {
	for (int kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y) {
	for (int kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x) {
	float input_pel;
	if (conv_params->padding_method == SAME_CLAMP_TO_EDGE) {
	int y_pos = CLAMP_TO_EDGE(y + (kernel_y - radius) * conv_params->dilation, height);
	int x_pos = CLAMP_TO_EDGE(x + (kernel_x - radius) * conv_params->dilation, width);
	input_pel = input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
	} else {
	int y_pos = y + (kernel_y - radius) * conv_params->dilation;
	int x_pos = x + (kernel_x - radius) * conv_params->dilation;
	input_pel = (x_pos < 0 \|\| x_pos >= width \|\| y_pos < 0 \|\| y_pos >= height) ? 0.0 :
	input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
	}


	output[n_filter] += input_pel * conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
	kernel_x * conv_params->input_num + ch];
	}
	}
	}
	switch (conv_params->activation){
	case RELU:
	output[n_filter] = FFMAX(output[n_filter], 0.0);
	break;
	case TANH:
	output[n_filter] = 2.0f / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
	break;
	case SIGMOID:
	output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
	break;
	case NONE:
	break;
	case LEAKY_RELU:
	output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
	}
	}
	output += conv_params->output_num;
	}
	}
	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));
	int thread_stride;
	#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);
	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];

	output_operand->dims[0] = operands[input_operand_indexes[0]].dims[0];
	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);
	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_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_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);
	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], NULL);
	}

	//release memory
	av_free(thread_id);

	for (int i = 0; i < thread_num; i++){
	av_free(thread_param[i]);
	}
	#else
	thread_param[0] = av_malloc(sizeof(**thread_param));
	thread_param[0]->thread_common_param = &thread_common_param;
	thread_param[0]->thread_start = pad_size;
	thread_param[0]->thread_end = height - pad_size;
	dnn_execute_layer_conv2d_thread((void *)thread_param[0]);
	av_free(thread_param[0]);
	#endif

	av_free(thread_param);
	return DNN_SUCCESS;
	}