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c0cdeea0ee
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.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')
please uncomment the part you want to test
x_sinh_1 = tf.sinh(x)
x_out = tf.divide(x_sinh_1, 1.176) # sinh(1.0)
x_cosh_1 = tf.cosh(x)
x_out = tf.divide(x_cosh_1, 1.55) # cosh(1.0)
x_tanh_1 = tf.tanh(x)
x__out = tf.divide(x_tanh_1, 0.77) # tanh(1.0)
x_asinh_1 = tf.asinh(x)
x_out = tf.divide(x_asinh_1, 0.89) # asinh(1.0/1.1)
x_acosh_1 = tf.add(x, 1.1)
x_acosh_2 = tf.acosh(x_acosh_1) # accept (1, inf)
x_out = tf.divide(x_acosh_2, 1.4) # acosh(2.1)
x_atanh_1 = tf.divide(x, 1.1)
x_atanh_2 = tf.atanh(x_atanh_1) # accept (-1, 1)
x_out = tf.divide(x_atanh_2, 1.55) # atanhh(1.0/1.1)
y = tf.identity(x_out, name='dnn_out') #please only preserve the x_out you want to test
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@intel.com>
58 lines
1.7 KiB
C
58 lines
1.7 KiB
C
/*
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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_MATHUNARY_H
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#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHUNARY_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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DMUO_ABS = 0,
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DMUO_SIN = 1,
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DMUO_COS = 2,
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DMUO_TAN = 3,
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DMUO_ASIN = 4,
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DMUO_ACOS = 5,
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DMUO_ATAN = 6,
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DMUO_SINH = 7,
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DMUO_COSH = 8,
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DMUO_TANH = 9,
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DMUO_ASINH = 10,
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DMUO_ACOSH = 11,
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DMUO_ATANH = 12,
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DMUO_COUNT
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} DNNMathUnaryOperation;
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typedef struct DnnLayerMathUnaryParams{
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DNNMathUnaryOperation un_op;
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} DnnLayerMathUnaryParams;
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int dnn_load_layer_math_unary(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
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int dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_operand_indexes,
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int32_t output_operand_index, const void *parameters);
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#endif
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