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20 Commits

Author SHA1 Message Date
Wenlong Ding
16e5014649 tests/dnn/dnn-layer-mathunary-test: add unit test for exp 
Signed-off-by: Wenlong Ding <wenlong.ding@intel.com>
 2021-03-24 13:57:19 +08:00
Guo, Yejun
07a18ff477 tests/dnn: fix build issue after function name changed 2021-01-22 19:28:29 +08:00
Ting Fu
c8ba0daf8d dnn/native: add log error message 
Signed-off-by: Ting Fu <ting.fu@intel.com>
 2020-08-25 13:03:46 +08:00
Mingyu Yin
4ed6bca4ae dnn_backend_native_layer_mathunary: add round support 
Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
 2020-08-12 10:30:46 +08:00
Mingyu Yin
fab00b0ae0 dnn_backend_native_layer_mathunary: add floor 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 = 'floor'

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.floor(input_x*255)/255
    y = tf.identity(y_, 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 {}_savemodel/model.pb --outdir={}_savemodel/ \n \nto generate model.model\n".format(name,name))

    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={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 {}_savemodel/tensorflow_out.md5\n  \
    or\n \
    ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow {}_savemodel/out_tensorflow.jpg\n \nto generate output result of tensorflow model\n".format(name, name, name, name))

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

Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
 2020-08-07 10:34:22 +08:00
Mingyu Yin
9fbdd5454b 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")

Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
 2020-08-04 19:56:54 +08:00
Ting Fu
dbf66b9e0e tests/dnn/mathunary: fix the issue of NAN 
When one of output[i] & expected_output is NAN, the unit test will always pass.

Signed-off-by: Ting Fu <ting.fu@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
 2020-07-09 09:34:44 +08:00
Ting Fu
57ea0483af dnn-layer-math-unary-test: add unit test for atanh 
Signed-off-by: Ting Fu <ting.fu@intel.com>
 2020-07-06 12:45:14 +08:00
Ting Fu
52d2e16665 dnn-layer-math-unary-test: add unit test for acosh 
Signed-off-by: Ting Fu <ting.fu@intel.com>
 2020-07-06 12:45:14 +08:00
Ting Fu
33374bdbd8 dnn-layer-math-unary-test: add unit test for asinh 
Signed-off-by: Ting Fu <ting.fu@intel.com>
 2020-07-06 12:45:14 +08:00
Ting Fu
0de5043060 dnn-layer-math-unary-test: add unit test for tanh 
Signed-off-by: Ting Fu <ting.fu@intel.com>
 2020-07-06 12:45:14 +08:00
Ting Fu
c5de77e33c dnn-layer-math-unary-test: add unit test for cosh 
Signed-off-by: Ting Fu <ting.fu@intel.com>
 2020-07-06 12:45:14 +08:00
Ting Fu
85c0608c6b dnn-layer-math-unary-test: add unit test for sinh 
Signed-off-by: Ting Fu <ting.fu@intel.com>
 2020-07-06 12:45:14 +08:00
Ting Fu
24d1781cbd dnn-layer-math-unary-test: add unit test for atan 
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo Yejun <yejun.guo@intel.com>
 2020-06-25 08:41:50 +08:00
Ting Fu
130c600144 dnn-layer-math-unary-test: add unit test for acos 
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo Yejun <yejun.guo@intel.com>
 2020-06-25 08:41:50 +08:00
Ting Fu
057f6ee7f4 dnn-layer-math-unary-test: add unit test for asin 
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo Yejun <yejun.guo@intel.com>
 2020-06-25 08:41:50 +08:00
Ting Fu
3ac2f7ccd7 dnn-layer-mathunary-test: add unit test for tan 
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo Yejun <yejun.guo@intel.com>
 2020-06-11 11:10:51 +08:00
Ting Fu
dd3fe3e77c dnn-layer-mathunary-test: add unit test for cos 
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo Yejun <yejun.guo@intel.com>
 2020-06-11 11:10:51 +08:00
Ting Fu
3f7c5a375b dnn-layer-mathunary-test: add unit test for sin 
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo Yejun <yejun.guo@intel.com>
 2020-06-11 11:10:51 +08:00
Ting Fu
c51b46e5dd dnn-layer-mathunary-test: add unit test for abs 
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
 2020-05-28 11:04:21 +08:00