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/* |
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* Copyright (c) 2020 |
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* |
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* This file is part of FFmpeg. |
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* |
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* FFmpeg is free software; you can redistribute it and/or |
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* modify it under the terms of the GNU Lesser General Public |
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* License as published by the Free Software Foundation; either |
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* version 2.1 of the License, or (at your option) any later version. |
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* |
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* FFmpeg is distributed in the hope that it will be useful, |
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* but WITHOUT ANY WARRANTY; without even the implied warranty of |
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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* Lesser General Public License for more details. |
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* |
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* You should have received a copy of the GNU Lesser General Public |
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* License along with FFmpeg; if not, write to the Free Software |
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA |
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*/ |
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/** |
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* @file |
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* DNN native backend implementation. |
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*/ |
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#include "libavutil/avassert.h" |
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#include "dnn_backend_native_layer_avgpool.h" |
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int ff_dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num) |
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{ |
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AvgPoolParams *avgpool_params; |
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int dnn_size = 0; |
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avgpool_params = av_malloc(sizeof(*avgpool_params)); |
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if(!avgpool_params) |
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return 0; |
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avgpool_params->strides = (int32_t)avio_rl32(model_file_context); |
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avgpool_params->padding_method = (int32_t)avio_rl32(model_file_context); |
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avgpool_params->kernel_size = (int32_t)avio_rl32(model_file_context); |
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dnn_size += 12; |
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if (dnn_size > file_size || avgpool_params->kernel_size <= 0 || avgpool_params->strides <=0){ |
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av_freep(&avgpool_params); |
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return 0; |
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} |
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layer->params = avgpool_params; |
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layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context); |
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layer->output_operand_index = (int32_t)avio_rl32(model_file_context); |
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dnn_size += 8; |
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if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) { |
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return 0; |
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} |
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return dnn_size; |
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} |
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int ff_dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes, |
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int32_t output_operand_index, const void *parameters, NativeContext *ctx) |
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{ |
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float *output; |
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int height_end, width_end, height_radius, width_radius, output_height, output_width, kernel_area; |
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int32_t input_operand_index = input_operand_indexes[0]; |
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int number = operands[input_operand_index].dims[0]; |
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int height = operands[input_operand_index].dims[1]; |
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int width = operands[input_operand_index].dims[2]; |
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int channel = operands[input_operand_index].dims[3]; |
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const float *input = operands[input_operand_index].data; |
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const AvgPoolParams *avgpool_params = parameters; |
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int kernel_strides = avgpool_params->strides; |
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int src_linesize = width * channel; |
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DnnOperand *output_operand = &operands[output_operand_index]; |
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/** |
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* When padding_method = SAME, the tensorflow will only padding the hald number of 0 pxiels |
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* except the remainders. |
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* Eg: assuming the input height = 1080, the strides = 11, so the remainders = 1080 % 11 = 2 |
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* and if ksize = 5: it will fill (5 - 2) >> 1 = 1 line before the first line of input image, |
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* and 5 - 2 - 1 = 2 lines after the last line of input image. |
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* and if ksize = 7: it will fill (7 - 2) >> 1 = 2 lines before the first line of input image, |
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* and 7 - 2 - 2 = 3 lines after the last line of input image. |
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*/ |
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✓✓ |
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if (avgpool_params->padding_method == SAME) { |
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height_end = height; |
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width_end = width; |
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height_radius = avgpool_params->kernel_size - ((height - 1) % kernel_strides + 1); |
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width_radius = avgpool_params->kernel_size - ((width - 1) % kernel_strides + 1); |
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✓✗ |
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height_radius = height_radius < 0 ? 0 : height_radius >> 1; |
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✓✗ |
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width_radius = width_radius < 0 ? 0 : width_radius >> 1; |
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output_height = ceil(height / (kernel_strides * 1.0)); |
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output_width = ceil(width / (kernel_strides * 1.0)); |
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} else { |
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✗✓ |
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av_assert0(avgpool_params->padding_method == VALID); |
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height_end = height - avgpool_params->kernel_size + 1; |
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width_end = width - avgpool_params->kernel_size + 1; |
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height_radius = 0; |
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width_radius = 0; |
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output_height = ceil((height - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0)); |
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output_width = ceil((width - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0)); |
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} |
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output_operand->dims[0] = number; |
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output_operand->dims[1] = output_height; |
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output_operand->dims[2] = output_width; |
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// not support pooling in channel dimension now |
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output_operand->dims[3] = channel; |
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output_operand->data_type = operands[input_operand_index].data_type; |
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output_operand->length = ff_calculate_operand_data_length(output_operand); |
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✗✓ |
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if (output_operand->length <= 0) { |
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av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n"); |
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return DNN_ERROR; |
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} |
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output_operand->data = av_realloc(output_operand->data, output_operand->length); |
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✗✓ |
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if (!output_operand->data) { |
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av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n"); |
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return DNN_ERROR; |
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} |
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output = output_operand->data; |
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✓✓ |
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for (int y = 0; y < height_end; y += kernel_strides) { |
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✓✓ |
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for (int x = 0; x < width_end; x += kernel_strides) { |
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✓✓ |
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for (int n_channel = 0; n_channel < channel; ++n_channel) { |
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output[n_channel] = 0.0; |
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kernel_area = 0; |
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✓✓ |
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for (int kernel_y = 0; kernel_y < avgpool_params->kernel_size; ++kernel_y) { |
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✓✓ |
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for (int kernel_x = 0; kernel_x < avgpool_params->kernel_size; ++kernel_x) { |
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float input_pel; |
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int y_pos = y + (kernel_y - height_radius); |
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int x_pos = x + (kernel_x - width_radius); |
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✓✗✓✓
✓✗✓✓
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if (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) { |
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input_pel = 0.0; |
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} else { |
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kernel_area++; |
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input_pel = input[y_pos * src_linesize + x_pos * channel + n_channel]; |
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} |
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output[n_channel] += input_pel; |
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} |
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} |
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output[n_channel] /= kernel_area; |
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} |
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output += channel; |
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} |
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} |
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return 0; |
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} |