Commit f73cc61b authored by Ting Fu's avatar Ting Fu Committed by Guo, Yejun

dnn_backend_native_layer_mathunary: add abs support

more math unary operations will be added here

It can be tested with the model file generated with below python scripy:

import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpeg')
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')
x1 = tf.subtract(x, 0.5)
x2 = tf.abs(x1)
y = tf.identity(x2, 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: 's avatarTing Fu <ting.fu@intel.com>
Signed-off-by: 's avatarGuo, Yejun <yejun.guo@intel.com>
parent b6d6597b
...@@ -6,6 +6,7 @@ OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_con ...@@ -6,6 +6,7 @@ OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_con
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_depth2space.o OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_depth2space.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_maximum.o OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_maximum.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathbinary.o OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathbinary.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathunary.o
DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn/dnn_backend_tf.o DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn/dnn_backend_tf.o
......
...@@ -42,6 +42,7 @@ typedef enum { ...@@ -42,6 +42,7 @@ typedef enum {
DLT_MIRROR_PAD = 3, DLT_MIRROR_PAD = 3,
DLT_MAXIMUM = 4, DLT_MAXIMUM = 4,
DLT_MATH_BINARY = 5, DLT_MATH_BINARY = 5,
DLT_MATH_UNARY = 6,
DLT_COUNT DLT_COUNT
} DNNLayerType; } DNNLayerType;
......
/*
* 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 "dnn_backend_native.h"
#include "libavutil/avassert.h"
#include "dnn_backend_native_layer_mathunary.h"
int dnn_load_layer_math_unary(Layer *layer, AVIOContext *model_file_context, int file_size)
{
DnnLayerMathUnaryParams *params;
int dnn_size = 0;
params = av_malloc(sizeof(*params));
if(!params)
return 0;
params->un_op = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
layer->params = 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;
return dnn_size;
}
int dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters)
{
const DnnOperand *input = &operands[input_operand_indexes[0]];
DnnOperand *output = &operands[output_operand_index];
const DnnLayerMathUnaryParams *params = (const DnnLayerMathUnaryParams *)parameters;
int dims_count;
const float *src;
float *dst;
for (int i = 0; i < 4; ++i)
output->dims[i] = input->dims[i];
output->data_type = input->data_type;
output->length = calculate_operand_data_length(output);
output->data = av_realloc(output->data, output->length);
if (!output->data)
return DNN_ERROR;
dims_count = calculate_operand_dims_count(output);
src = input->data;
dst = output->data;
switch (params->un_op) {
case DMUO_ABS:
for (int i = 0; i < dims_count; ++i)
dst[i] = FFABS(src[i]);
return 0;
default:
return -1;
}
}
/*
* 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 inference functions interface for native backend.
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHUNARY_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHUNARY_H
#include "libavformat/avio.h"
#include "dnn_backend_native.h"
typedef enum {
DMUO_ABS = 0,
DMUO_COUNT
} DNNMathUnaryOperation;
typedef struct DnnLayerMathUnaryParams{
DNNMathUnaryOperation un_op;
} DnnLayerMathUnaryParams;
int dnn_load_layer_math_unary(Layer *layer, AVIOContext *model_file_context, int file_size);
int dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters);
#endif
...@@ -25,6 +25,7 @@ ...@@ -25,6 +25,7 @@
#include "dnn_backend_native_layer_depth2space.h" #include "dnn_backend_native_layer_depth2space.h"
#include "dnn_backend_native_layer_maximum.h" #include "dnn_backend_native_layer_maximum.h"
#include "dnn_backend_native_layer_mathbinary.h" #include "dnn_backend_native_layer_mathbinary.h"
#include "dnn_backend_native_layer_mathunary.h"
LayerFunc layer_funcs[DLT_COUNT] = { LayerFunc layer_funcs[DLT_COUNT] = {
{NULL, NULL}, {NULL, NULL},
...@@ -33,4 +34,5 @@ LayerFunc layer_funcs[DLT_COUNT] = { ...@@ -33,4 +34,5 @@ LayerFunc layer_funcs[DLT_COUNT] = {
{dnn_execute_layer_pad, dnn_load_layer_pad}, {dnn_execute_layer_pad, dnn_load_layer_pad},
{dnn_execute_layer_maximum, dnn_load_layer_maximum}, {dnn_execute_layer_maximum, dnn_load_layer_maximum},
{dnn_execute_layer_math_binary, dnn_load_layer_math_binary}, {dnn_execute_layer_math_binary, dnn_load_layer_math_binary},
{dnn_execute_layer_math_unary, dnn_load_layer_math_unary},
}; };
...@@ -70,8 +70,9 @@ class TFConverter: ...@@ -70,8 +70,9 @@ class TFConverter:
self.converted_nodes = set() self.converted_nodes = set()
self.conv2d_scope_names = set() self.conv2d_scope_names = set()
self.conv2d_scopename_inputname_dict = {} self.conv2d_scopename_inputname_dict = {}
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5} self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5, 'MathUnary':6}
self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4} self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4}
self.mathun2code = {'Abs':0}
self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2} self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
self.name_operand_dict = {} self.name_operand_dict = {}
...@@ -286,6 +287,17 @@ class TFConverter: ...@@ -286,6 +287,17 @@ class TFConverter:
np.array([output_operand_index], dtype=np.uint32).tofile(f) np.array([output_operand_index], dtype=np.uint32).tofile(f)
def dump_mathunary_to_file(self, node, f):
self.layer_number = self.layer_number + 1
self.converted_nodes.add(node.name)
i0_node = self.name_node_dict[node.input[0]]
np.array([self.op2code['MathUnary'], self.mathun2code[node.op]], dtype=np.uint32).tofile(f)
input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT)
np.array([input_operand_index], dtype=np.uint32).tofile(f)
output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
np.array([output_operand_index],dtype=np.uint32).tofile(f)
def dump_layers_to_file(self, f): def dump_layers_to_file(self, f):
for node in self.nodes: for node in self.nodes:
if node.name in self.converted_nodes: if node.name in self.converted_nodes:
...@@ -307,6 +319,8 @@ class TFConverter: ...@@ -307,6 +319,8 @@ class TFConverter:
self.dump_maximum_to_file(node, f) self.dump_maximum_to_file(node, f)
elif node.op in self.mathbin2code: elif node.op in self.mathbin2code:
self.dump_mathbinary_to_file(node, f) self.dump_mathbinary_to_file(node, f)
elif node.op in self.mathun2code:
self.dump_mathunary_to_file(node, f)
def dump_operands_to_file(self, f): def dump_operands_to_file(self, f):
......
...@@ -23,4 +23,4 @@ str = 'FFMPEGDNNNATIVE' ...@@ -23,4 +23,4 @@ str = 'FFMPEGDNNNATIVE'
major = 1 major = 1
# increase minor when we don't have to re-convert the model file # increase minor when we don't have to re-convert the model file
minor = 5 minor = 6
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