[FFmpeg-devel] [PATCH 1/2] dnn/native: add native support for avg_pool
Guo, Yejun
yejun.guo at intel.com
Mon Jul 20 08:45:37 EEST 2020
> -----Original Message-----
> From: ffmpeg-devel <ffmpeg-devel-bounces at ffmpeg.org> On Behalf Of Ting Fu
> Sent: 2020年7月17日 23:23
> To: ffmpeg-devel at ffmpeg.org
> Subject: [FFmpeg-devel] [PATCH 1/2] dnn/native: add native support for
> avg_pool
>
> It can be tested with the model generated with below python script:
>
> import tensorflow as tf
> import numpy as np
> import imageio
>
> in_img = imageio.imread('input_odd.jpg')
> in_img = in_img.astype(np.float32)/255.0
> in_data = in_img[np.newaxis, :]
>
> x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
> x_pool = tf.nn.avg_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
> #please alter the params as needed
> y = tf.identity(x_pool, name='dnn_out')
>
> sess=tf.Session()
> sess.run(tf.global_variables_initializer())
>
> graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def,
> ['dnn_out'])
> tf.train.write_graph(graph_def, '.', 'image_process.pb', as_text=False)
>
> print("image_process.pb generated, please use \
> path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")
>
> output = sess.run(y, feed_dict={x: in_data})
> imageio.imsave("out.jpg", np.squeeze(output))
>
> Signed-off-by: Ting Fu <ting.fu at intel.com>
> ---
> libavfilter/dnn/Makefile | 1 +
> libavfilter/dnn/dnn_backend_native.h | 2 +
> .../dnn/dnn_backend_native_layer_avgpool.c | 136 ++++++++++++++++++
> .../dnn/dnn_backend_native_layer_avgpool.h | 35 +++++
> .../dnn/dnn_backend_native_layer_conv2d.h | 3 +-
> libavfilter/dnn/dnn_backend_native_layers.c | 2 +
> tools/python/convert_from_tensorflow.py | 31 +++-
> 7 files changed, 207 insertions(+), 3 deletions(-)
> create mode 100644 libavfilter/dnn/dnn_backend_native_layer_avgpool.c
> create mode 100644 libavfilter/dnn/dnn_backend_native_layer_avgpool.h
>
> diff --git a/libavfilter/dnn/Makefile b/libavfilter/dnn/Makefile
> index d90137ec42..e0957073ee 100644
> --- a/libavfilter/dnn/Makefile
> +++ b/libavfilter/dnn/Makefile
> @@ -1,6 +1,7 @@
> OBJS-$(CONFIG_DNN) +=
> dnn/dnn_interface.o
> OBJS-$(CONFIG_DNN) +=
> dnn/dnn_backend_native.o
> OBJS-$(CONFIG_DNN) +=
> dnn/dnn_backend_native_layers.o
> +OBJS-$(CONFIG_DNN) +=
> dnn/dnn_backend_native_layer_avgpool.o
> OBJS-$(CONFIG_DNN) +=
> dnn/dnn_backend_native_layer_pad.o
> OBJS-$(CONFIG_DNN) +=
> dnn/dnn_backend_native_layer_conv2d.o
> OBJS-$(CONFIG_DNN) +=
> dnn/dnn_backend_native_layer_depth2space.o
> diff --git a/libavfilter/dnn/dnn_backend_native.h
> b/libavfilter/dnn/dnn_backend_native.h
> index 62191ffe88..26e9a33387 100644
> --- a/libavfilter/dnn/dnn_backend_native.h
> +++ b/libavfilter/dnn/dnn_backend_native.h
> @@ -43,10 +43,12 @@ typedef enum {
> DLT_MAXIMUM = 4,
> DLT_MATH_BINARY = 5,
> DLT_MATH_UNARY = 6,
> + DLT_AVG_POOL = 7,
> DLT_COUNT
> } DNNLayerType;
>
> typedef enum {DOT_INPUT = 1, DOT_OUTPUT = 2, DOT_INTERMEDIATE =
> DOT_INPUT | DOT_OUTPUT} DNNOperandType;
> +typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNPaddingParam;
>
> typedef struct Layer{
> DNNLayerType type;
> diff --git a/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
> b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
> new file mode 100644
> index 0000000000..f5a3f4a0dc
> --- /dev/null
> +++ b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
> @@ -0,0 +1,136 @@
> +/*
> + * Copyright (c) 2020
> + *
> + * 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
> + */
> +
> +/**
> + * @file
> + * DNN native backend implementation.
> + */
> +
> +#include "libavutil/avassert.h"
> +#include "dnn_backend_native_layer_avgpool.h"
> +
> +int dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context,
> int file_size, int operands_num)
> +{
> + AvgPoolParams *avgpool_params;
> + int dnn_size = 0;
> + avgpool_params = av_malloc(sizeof(*avgpool_params));
> + if(!avgpool_params)
> + return 0;
> +
> + avgpool_params->strides = (int32_t)avio_rl32(model_file_context);
> + avgpool_params->padding_method =
> (int32_t)avio_rl32(model_file_context);
> + avgpool_params->in_channels = (int32_t)avio_rl32(model_file_context);
> + avgpool_params->out_channels = (int32_t)avio_rl32(model_file_context);
> + avgpool_params->kernel_size = (int32_t)avio_rl32(model_file_context);
> + dnn_size += 20;
> +
> + if (dnn_size > file_size || avgpool_params->in_channels <= 0 ||
> + avgpool_params->out_channels <= 0 ||
> avgpool_params->kernel_size <= 0 ||
> + avgpool_params->strides <=0){
> + av_freep(&avgpool_params);
> + return 0;
> + }
> +
> + layer->params = avgpool_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;
> +}
> +
> +int dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t
> *input_operand_indexes,
> + int32_t output_operand_index, const void
> *parameters)
> +{
> + float *output;
> + int height_end, width_end, height_radius, width_radius, output_height,
> output_width, kernel_area;
> + int32_t input_operand_index = 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];
the input channel should come from here, not in AvgPoolParams.
And so as output channel.
> + const float *input = operands[input_operand_index].data;
> + const AvgPoolParams *avgpool_params = (const AvgPoolParams
> *)parameters;
> +
> + float kernel_strides = avgpool_params->strides;
why float?
> + int src_linesize = width * avgpool_params->in_channels;
> + DnnOperand *output_operand = &operands[output_operand_index];
> +
> + if (avgpool_params->padding_method == SAME) {
> + height_end = height;
> + width_end = width;
> + height_radius = (avgpool_params->kernel_size - ((height - 1) % (int)
> kernel_strides + 1));
don't need the first '(' and last ')'.
why we need to consider kernel_strides here?
> + width_radius = (avgpool_params->kernel_size - ((width - 1) % (int)
> kernel_strides + 1));
same as above.
> + height_radius = height_radius < 0 ? 0 : height_radius >> 1;
> + width_radius = width_radius < 0 ? 0 : width_radius >> 1;
> + output_height = ceil(height / kernel_strides);
> + output_width = ceil(width / kernel_strides);
> + } else {
> + height_end = height - avgpool_params->kernel_size + 1;
> + width_end = width - avgpool_params->kernel_size + 1;
> + height_radius = 0;
> + width_radius = 0;
> + output_height = ceil((height - avgpool_params->kernel_size + 1) /
> kernel_strides);
> + output_width = ceil((width - avgpool_params->kernel_size + 1) /
> kernel_strides);
> + }
> +
> + output_operand->dims[0] = number;
> + output_operand->dims[1] = output_height;
> + output_operand->dims[2] = output_width;
> + output_operand->dims[3] = avgpool_params->out_channels;
> + output_operand->data_type =
> operands[input_operand_index].data_type;
> + output_operand->length =
> calculate_operand_data_length(output_operand);
> + output_operand->data = av_realloc(output_operand->data,
> output_operand->length);
> + if (!output_operand->data)
> + return -1;
> + output = output_operand->data;
> +
> + av_assert0(channel == avgpool_params->in_channels);
> +
> + for (int y = 0; y < height_end; y += kernel_strides) {
> + for (int x = 0; x < width_end; x += kernel_strides) {
> + for (int n_filter = 0; n_filter < avgpool_params->out_channels;
> ++n_filter) {
[]
better to use n_channel, instead of n_filter.
> + output[n_filter] = 0.0;
> + kernel_area = 0;
> + for (int kernel_y = 0; kernel_y <
> avgpool_params->kernel_size; ++kernel_y) {
> + for (int kernel_x = 0; kernel_x <
> avgpool_params->kernel_size; ++kernel_x) {
> + float input_pel;
> + int y_pos = y + (kernel_y - height_radius);
> + int x_pos = x + (kernel_x - width_radius);
> + if (x_pos < 0 || x_pos >= width || y_pos < 0 ||
> y_pos >= height) {
> + input_pel = 0.0;
> + } else {
> + kernel_area++;
> + input_pel = input[y_pos * src_linesize +
> x_pos * avgpool_params->in_channels + n_filter];
> + }
> + output[n_filter] += input_pel;
> + }
> + }
> + output[n_filter] /= kernel_area;
> + }
> + output += avgpool_params->out_channels;
> + }
> + }
> +
> + return 0;
> +}
> diff --git a/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
> b/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
> new file mode 100644
> index 0000000000..0b37a8f64b
> --- /dev/null
> +++ b/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
> @@ -0,0 +1,35 @@
> +/*
> + * 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
> + */
> +
> +#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_AVGPOOL_H
> +#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_AVGPOOL_H
> +
> +#include "dnn_backend_native.h"
> +
> +typedef struct AvgPoolParams{
> + int32_t strides, in_channels, out_channels, kernel_size;
> + DNNPaddingParam padding_method;
> +} AvgPoolParams;
> +
> +int dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context,
> int file_size, int operands_num);
> +int dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t
> *input_operand_indexes,
> + int32_t output_operand_index, const void
> *parameters);
> +
> +#endif
> diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
> b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
> index eeb15fdf01..b240b7ef6b 100644
> --- a/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
> +++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
> @@ -24,12 +24,11 @@
> #include "dnn_backend_native.h"
>
> typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU}
> DNNActivationFunc;
> -typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE}
> DNNConvPaddingParam;
>
> typedef struct ConvolutionalParams{
> int32_t input_num, output_num, kernel_size;
> DNNActivationFunc activation;
> - DNNConvPaddingParam padding_method;
> + DNNPaddingParam padding_method;
> int32_t dilation;
> int32_t has_bias;
> float *kernel;
> diff --git a/libavfilter/dnn/dnn_backend_native_layers.c
> b/libavfilter/dnn/dnn_backend_native_layers.c
> index 70f9a5f958..4f42f62abb 100644
> --- a/libavfilter/dnn/dnn_backend_native_layers.c
> +++ b/libavfilter/dnn/dnn_backend_native_layers.c
> @@ -26,6 +26,7 @@
> #include "dnn_backend_native_layer_maximum.h"
> #include "dnn_backend_native_layer_mathbinary.h"
> #include "dnn_backend_native_layer_mathunary.h"
> +#include "dnn_backend_native_layer_avgpool.h"
>
> LayerFunc layer_funcs[DLT_COUNT] = {
> {NULL, NULL},
> @@ -35,4 +36,5 @@ LayerFunc layer_funcs[DLT_COUNT] = {
> {dnn_execute_layer_maximum, dnn_load_layer_maximum},
> {dnn_execute_layer_math_binary, dnn_load_layer_math_binary},
> {dnn_execute_layer_math_unary, dnn_load_layer_math_unary},
> + {dnn_execute_layer_avg_pool, dnn_load_layer_avg_pool},
> };
> diff --git a/tools/python/convert_from_tensorflow.py
> b/tools/python/convert_from_tensorflow.py
> index 85db7bf710..975381e720 100644
> --- a/tools/python/convert_from_tensorflow.py
> +++ b/tools/python/convert_from_tensorflow.py
> @@ -67,10 +67,12 @@ class TFConverter:
> self.edges = {}
> self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'None':3,
> 'LeakyRelu':4}
> self.conv_paddings = {'VALID':0, 'SAME':1}
> + self.pool_paddings = {'VALID':0, 'SAME':1}
> self.converted_nodes = set()
> self.conv2d_scope_names = set()
> self.conv2d_scopename_inputname_dict = {}
> - self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3,
> 'Maximum':4, 'MathBinary':5, 'MathUnary':6}
> + self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3,
> 'Maximum':4,
> + 'MathBinary':5, 'MathUnary':6, 'AvgPool':7}
> self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3,
> 'Minimum':4}
> self.mathun2code = {'Abs':0, 'Sin':1, 'Cos':2, 'Tan':3, 'Asin':4, 'Acos':5,
> 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10, 'Acosh':11, 'Atanh':12}
> self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
> @@ -298,6 +300,31 @@ class TFConverter:
> np.array([output_operand_index],dtype=np.uint32).tofile(f)
>
>
> + def dump_avg_pool_to_file(self, node, f):
> + assert(node.op == 'AvgPool')
> + self.layer_number = self.layer_number + 1
> + self.converted_nodes.add(node.name)
> + node0 = self.name_node_dict[node.input[0]]
> + strides = node.attr['strides']
> + assert(strides.list.i[1]==strides.list.i[2])
> + strides = strides.list.i[1]
> + filter_node = node.attr['ksize']
> + input_name = node.input[0]
[]
we can save strides[4] and ksize[4] in .model file, and do part support in .c file.
> +
> + filter_height = filter_node.list.i[1]
> + filter_width = filter_node.list.i[2]
> +
> + in_channels = node0.attr['shape'].shape.dim[3].size
> + out_channels = in_channels
> + padding = node.attr['padding'].s.decode("utf-8")
> + np.array([self.op2code[node.op], strides, self.pool_paddings[padding],
> in_channels, out_channels,
> + filter_height],dtype=np.uint32).tofile(f)
> +
> + input_operand_index = self.add_operand(input_name,
> Operand.IOTYPE_INPUT)
> + output_operand_index = self.add_operand(node.name,
> Operand.IOTYPE_OUTPUT)
> + np.array([input_operand_index,
> output_operand_index],dtype=np.uint32).tofile(f)
> +
> +
> def dump_layers_to_file(self, f):
> for node in self.nodes:
> if node.name in self.converted_nodes:
> @@ -311,6 +338,8 @@ class TFConverter:
>
> if node.op == 'Conv2D':
> self.dump_simple_conv2d_to_file(node, f)
> + if node.op == 'AvgPool':
> + self.dump_avg_pool_to_file(node, f)
> elif node.op == 'DepthToSpace':
> self.dump_depth2space_to_file(node, f)
> elif node.op == 'MirrorPad':
> --
> 2.17.1
>
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