2020-03-20 12:55:38 +00:00
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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 inference functions interface for native backend.
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*/
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#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHBINARY_H
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#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHBINARY_H
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#include "libavformat/avio.h"
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#include "dnn_backend_native.h"
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typedef enum {
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DMBO_SUB = 0,
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dnn/native: add native support for 'add'
It can be tested with the model file generated with below python script:
import tensorflow as tf
import numpy as np
import imageio
in_img = imageio.imread('input.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')
z1 = 0.039 + x
z2 = x + 0.042
z3 = z1 + z2
z4 = z3 - 0.381
z5 = z4 - x
y = tf.math.maximum(z5, 0.0, 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: Guo, Yejun <yejun.guo@intel.com>
2020-04-10 13:35:11 +00:00
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DMBO_ADD = 1,
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dnn/native: add native support for 'mul'
it can be tested with model file generated from above python script:
import tensorflow as tf
import numpy as np
import imageio
in_img = imageio.imread('input.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')
z1 = 0.5 + 0.3 * x
z2 = z1 * 4
z3 = z2 - x - 2.0
y = tf.identity(z3, 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: Guo, Yejun <yejun.guo@intel.com>
2020-04-11 05:22:24 +00:00
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DMBO_MUL = 2,
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dnn/native: add native support for divide
it can be tested with model file generated with below python script:
import tensorflow as tf
import numpy as np
import imageio
in_img = imageio.imread('input.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')
z1 = 2 / x
z2 = 1 / z1
z3 = z2 / 0.25 + 0.3
z4 = z3 - x * 1.5 - 0.3
y = tf.identity(z4, 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: Guo, Yejun <yejun.guo@intel.com>
2020-04-11 05:46:47 +00:00
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DMBO_REALDIV = 3,
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dnn/native: add native support for minimum
it can be tested with model file generated with below python script:
import tensorflow as tf
import numpy as np
import imageio
in_img = imageio.imread('input.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')
x1 = tf.minimum(0.7, x)
x2 = tf.maximum(x1, 0.4)
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: Guo, Yejun <yejun.guo@intel.com>
2020-04-26 07:46:38 +00:00
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DMBO_MINIMUM = 4,
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2020-08-23 15:12:13 +00:00
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DMBO_FLOORMOD = 5,
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2020-03-20 12:55:38 +00:00
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DMBO_COUNT
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} DNNMathBinaryOperation;
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typedef struct DnnLayerMathBinaryParams{
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DNNMathBinaryOperation bin_op;
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int input0_broadcast;
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int input1_broadcast;
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float v;
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} DnnLayerMathBinaryParams;
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2020-06-10 05:36:11 +00:00
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int dnn_load_layer_math_binary(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
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2020-03-20 12:55:38 +00:00
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int dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_operand_indexes,
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2020-08-25 03:47:50 +00:00
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int32_t output_operand_index, const void *parameters, NativeContext *ctx);
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2020-03-20 12:55:38 +00:00
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#endif
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