diff mbox series

[FFmpeg-devel,v4] dnn_backend_native_layer_mathunary: add ceil support

Message ID 20200731074124.30876-1-mingyu.yin@intel.com
State Accepted
Headers show
Series [FFmpeg-devel,v4] dnn_backend_native_layer_mathunary: add ceil support
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Commit Message

Mingyu Yin July 31, 2020, 7:41 a.m. UTC
It can be tested with the model generated with below python script:

import tensorflow as tf
import os
import numpy as np
import imageio
from tensorflow.python.framework import graph_util
name = 'ceil'

pb_file_path = os.getcwd()
if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)):
    os.mkdir(pb_file_path+'/{}_savemodel/'.format(name))

with tf.Session(graph=tf.Graph()) as sess:
    in_img = imageio.imread('detection.jpg')
    in_img = in_img.astype(np.float32)
    in_data = in_img[np.newaxis, :]
    input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
    y = tf.math.ceil( input_x, name='dnn_out')
    sess.run(tf.global_variables_initializer())
    constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])

    with tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name), mode='wb') as f:
        f.write(constant_graph.SerializeToString())

    print("model.pb generated, please in ffmpeg path use\n \n \
    python tools/python/convert.py ceil_savemodel/model.pb --outdir=ceil_savemodel/ \n \n \
    to generate model.model\n")

    output = sess.run(y, feed_dict={ input_x: in_data})
    imageio.imsave("out.jpg", np.squeeze(output))

    print("To verify, please ffmpeg path use\n \n \
    ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model=ceil_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 ceil_savemodel/tensorflow_out.md5\n \n \
    to generate output result of tensorflow model\n")

    print("To verify, please ffmpeg path use\n \n \
    ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model=ceil_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 ceil_savemodel/native_out.md5\n \n \
    to generate output result of native model\n")

Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
---
 libavfilter/dnn/dnn_backend_native_layer_mathunary.c | 4 ++++
 libavfilter/dnn/dnn_backend_native_layer_mathunary.h | 1 +
 tests/dnn/dnn-layer-mathunary-test.c                 | 4 ++++
 tools/python/convert_from_tensorflow.py              | 4 +++-
 tools/python/convert_header.py                       | 2 +-
 5 files changed, 13 insertions(+), 2 deletions(-)

Comments

Guo, Yejun Aug. 3, 2020, 3:13 a.m. UTC | #1
> -----Original Message-----
> From: ffmpeg-devel <ffmpeg-devel-bounces@ffmpeg.org> On Behalf Of Mingyu
> Yin
> Sent: 2020年7月31日 15:41
> To: ffmpeg-devel@ffmpeg.org
> Subject: [FFmpeg-devel] [v4] dnn_backend_native_layer_mathunary: add ceil
> support
> 
> It can be tested with the model generated with below python script:
> 
> import tensorflow as tf
> import os
> import numpy as np
> import imageio
> from tensorflow.python.framework import graph_util name = 'ceil'
> 
> pb_file_path = os.getcwd()
> if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)):
>     os.mkdir(pb_file_path+'/{}_savemodel/'.format(name))
> 
> with tf.Session(graph=tf.Graph()) as sess:
>     in_img = imageio.imread('detection.jpg')
>     in_img = in_img.astype(np.float32)
>     in_data = in_img[np.newaxis, :]
>     input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3],
> name='dnn_in')
>     y = tf.math.ceil( input_x, name='dnn_out')
>     sess.run(tf.global_variables_initializer())
>     constant_graph = graph_util.convert_variables_to_constants(sess,
> sess.graph_def, ['dnn_out'])
> 
>     with
> tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name),
> mode='wb') as f:
>         f.write(constant_graph.SerializeToString())
> 
>     print("model.pb generated, please in ffmpeg path use\n \n \
>     python tools/python/convert.py ceil_savemodel/model.pb
> --outdir=ceil_savemodel/ \n \n \
>     to generate model.model\n")
> 
>     output = sess.run(y, feed_dict={ input_x: in_data})
>     imageio.imsave("out.jpg", np.squeeze(output))
> 
>     print("To verify, please ffmpeg path use\n \n \
>     ./ffmpeg -i detection.jpg -vf
> format=rgb24,dnn_processing=model=ceil_savemodel/model.pb:input=dnn_in:
> output=dnn_out:dnn_backend=tensorflow -f framemd5
> ceil_savemodel/tensorflow_out.md5\n \n \
>     to generate output result of tensorflow model\n")
> 
>     print("To verify, please ffmpeg path use\n \n \
>     ./ffmpeg -i detection.jpg -vf
> format=rgb24,dnn_processing=model=ceil_savemodel/model.model:input=dnn
> _in:output=dnn_out:dnn_backend=native -f framemd5
> ceil_savemodel/native_out.md5\n \n \
>     to generate output result of native model\n")

I tried to change the command line to generate a .jpg file instead of md5,
the result is that the image is all white.

Suggest to change the python script, for example: y = ceil(x*100)/100
diff mbox series

Patch

diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathunary.c b/libavfilter/dnn/dnn_backend_native_layer_mathunary.c
index c5f0f7adec..a62f6ba6f0 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathunary.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathunary.c
@@ -130,6 +130,10 @@  int dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_oper
         for (int i = 0; i < dims_count; ++i)
             dst[i] = atanh(src[i]);
         return 0;
+    case DMUO_CEIL:
+        for (int i = 0; i < dims_count; ++i)
+            dst[i] = ceil(src[i]);
+        return 0;
     default:
         return -1;
     }
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathunary.h b/libavfilter/dnn/dnn_backend_native_layer_mathunary.h
index 8076356ba4..82b2d7f4ab 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathunary.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathunary.h
@@ -43,6 +43,7 @@  typedef enum {
     DMUO_ASINH = 10,
     DMUO_ACOSH = 11,
     DMUO_ATANH = 12,
+    DMUO_CEIL = 13,
     DMUO_COUNT
 } DNNMathUnaryOperation;
 
diff --git a/tests/dnn/dnn-layer-mathunary-test.c b/tests/dnn/dnn-layer-mathunary-test.c
index 5afc5c157e..7da3a206ed 100644
--- a/tests/dnn/dnn-layer-mathunary-test.c
+++ b/tests/dnn/dnn-layer-mathunary-test.c
@@ -56,6 +56,8 @@  static float get_expected(float f, DNNMathUnaryOperation op)
         return acosh(f);
     case DMUO_ATANH:
         return atanh(f);
+    case DMUO_CEIL:
+        return ceil(f);
     default:
         av_assert0(!"not supported yet");
         return 0.f;
@@ -128,5 +130,7 @@  int main(int agrc, char **argv)
         return 1;
     if (test(DMUO_ATANH))
         return 1;
+    if (test(DMUO_CEIL))
+        return 1;
     return 0;
 }
diff --git a/tools/python/convert_from_tensorflow.py b/tools/python/convert_from_tensorflow.py
index 85db7bf710..64b7551314 100644
--- a/tools/python/convert_from_tensorflow.py
+++ b/tools/python/convert_from_tensorflow.py
@@ -72,7 +72,9 @@  class TFConverter:
         self.conv2d_scopename_inputname_dict = {}
         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.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.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, 'Ceil':13}
         self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
         self.name_operand_dict = {}
 
diff --git a/tools/python/convert_header.py b/tools/python/convert_header.py
index 9851d84144..62f1d342f3 100644
--- a/tools/python/convert_header.py
+++ b/tools/python/convert_header.py
@@ -23,4 +23,4 @@  str = 'FFMPEGDNNNATIVE'
 major = 1
 
 # increase minor when we don't have to re-convert the model file
-minor = 18
+minor = 19