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* tests/dnn/dnn-layer-mathunary-test: add unit test for expWenlong Ding2021-03-24
| | | | Signed-off-by: Wenlong Ding <wenlong.ding@intel.com>
* tests/dnn: enable unit test denseGuo, Yejun2021-01-28
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* tests/dnn: fix build issue after function name changedGuo, Yejun2021-01-22
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* dnn-layer-conv2d-test.c: remove dependency of dnn_native_classGuo, Yejun2021-01-22
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* dnn/native: add native support for denseMingyu Yin2020-09-29
| | | | Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
* dnn_backend_native_layer_conv2d.c:Add mutithread functionXu Jun2020-09-09
| | | | | | | | | | | | | | | | | | | | | | Use pthread to multithread dnn_execute_layer_conv2d. Can be tested with command "./ffmpeg_g -i input.png -vf \ format=yuvj420p,dnn_processing=dnn_backend=native:model= \ espcn.model:input=x:output=y:options=conv2d_threads=23 \ -y sr_native.jpg -benchmark" before patch: utime=11.238s stime=0.005s rtime=11.248s after patch: utime=20.817s stime=0.047s rtime=1.051s on my 3900X 12c24t @4.2GHz About the increase of utime, it's because that CPU HyperThreading technology makes logical cores twice of physical cores while cpu's counting performance improves less than double. And utime sums all cpu's logical cores' runtime. As a result, using threads num near cpu's logical core's number will double utime, while reduce rtime less than half for HyperThreading CPUs. Signed-off-by: Xu Jun <xujunzz@sjtu.edu.cn> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn/native: add log error messageTing Fu2020-08-25
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn_backend_native_layer_mathbinary: add floormod supportMingyu Yin2020-08-24
| | | | Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
* dnn_backend_native_layer_mathunary: add round supportMingyu Yin2020-08-12
| | | | | Signed-off-by: Mingyu Yin <mingyu.yin@intel.com> Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
* FATE/dnn: add unit test for dnn avgpool layerTing Fu2020-08-10
| | | | | | | 'make fate-dnn-layer-avgpool' to run the test Signed-off-by: Ting Fu <ting.fu@intel.com> Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_native_layer_mathunary: add floor supportMingyu Yin2020-08-07
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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>
* dnn_backend_native_layer_mathunary: add ceil supportMingyu Yin2020-08-04
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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>
* tests/dnn/mathunary: fix the issue of NANTing Fu2020-07-09
| | | | | | | 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>
* dnn-layer-math-unary-test: add unit test for atanhTing Fu2020-07-06
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn-layer-math-unary-test: add unit test for acoshTing Fu2020-07-06
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn-layer-math-unary-test: add unit test for asinhTing Fu2020-07-06
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn-layer-math-unary-test: add unit test for tanhTing Fu2020-07-06
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn-layer-math-unary-test: add unit test for coshTing Fu2020-07-06
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn-layer-math-unary-test: add unit test for sinhTing Fu2020-07-06
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn-layer-math-unary-test: add unit test for atanTing Fu2020-06-25
| | | | | Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo Yejun <yejun.guo@intel.com>
* dnn-layer-math-unary-test: add unit test for acosTing Fu2020-06-25
| | | | | Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo Yejun <yejun.guo@intel.com>
* dnn-layer-math-unary-test: add unit test for asinTing Fu2020-06-25
| | | | | Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo Yejun <yejun.guo@intel.com>
* dnn-layer-mathunary-test: add unit test for tanTing Fu2020-06-11
| | | | | Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo Yejun <yejun.guo@intel.com>
* dnn-layer-mathunary-test: add unit test for cosTing Fu2020-06-11
| | | | | Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo Yejun <yejun.guo@intel.com>
* dnn-layer-mathunary-test: add unit test for sinTing Fu2020-06-11
| | | | | Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo Yejun <yejun.guo@intel.com>
* dnn-layer-mathunary-test: add unit test for absTing Fu2020-05-28
| | | | | Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn-layer-mathbinary-test: add unit test for minimumGuo, Yejun2020-05-08
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn-layer-mathbinary-test: Fix tests for cases with extra intermediate precisionMartin Storsjö2020-04-24
| | | | | | | | | | | | | | | | | | | | | This fixes tests on 32 bit x86 mingw with clang, which uses x87 fpu by default. In this setup, while the get_expected function is declared to return float, the compiler is (especially given the optimization flags set) free to keep the intermediate values (in this case, the return value from the inlined function) in higher precision. This results in the situation where 7.28 (which actually, as a float, ends up as 7.2800002098), multiplied by 100, is 728.000000 when really forced into a 32 bit float, but 728.000021 when kept with higher intermediate precision. For the multiplication case, a more suitable epsilon would e.g. be 2*FLT_EPSILON*fabs(expected_output), but just increase the current hardcoded threshold for now. Signed-off-by: Martin Storsjö <martin@martin.st>
* dnn-layer-mathbinary-test: add unit test for divideGuo, Yejun2020-04-22
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn-layer-mathbinary-test: add unit test for 'mul'Guo, Yejun2020-04-22
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn-layer-mathbinary-test: add unit test for addGuo, Yejun2020-04-22
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn-layer-mathbinary-test: add unit test for subtractionGuo, Yejun2020-04-07
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: add tf.nn.conv2d support for native modelGuo, Yejun2019-10-30
| | | | | | | | | | | | | Unlike other tf.*.conv2d layers, tf.nn.conv2d does not create many nodes (within a scope) in the graph, it just acts like other layers. tf.nn.conv2d only creates one node in the graph, and no internal nodes such as 'kernel' are created. The format of native model file is also changed, a flag named has_bias is added, so change the version number. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* FATE/dnn: add .gitignoreZhao Zhili2019-10-23
| | | | Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
* avfilter/dnn: unify the layer execution function in native modeGuo, Yejun2019-10-15
| | | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* FATE/dnn: fix stack buffer overflowZhao Zhili2019-10-04
| | | | Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* FATE/dnn: add unit test for layer maximumGuo, Yejun2019-09-20
| | | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* FATE/dnn: add unit test for dnn depth_to_space layerGuo, Yejun2019-09-19
| | | | | | | 'make fate-dnn-layer-depth2space' to run the test Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* FATE/dnn: add unit test for dnn conv2d layerGuo, Yejun2019-09-19
| | | | | | | 'make fate-dnn-layer-conv2d' to run the test Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* dnn: introduce dnn operand (in c code) to hold operand infos within networkGuo, Yejun2019-08-30
| | | | | | | | | | | the info can be saved in dnn operand object without regenerating again and again, and it is also needed for layer split/merge, and for memory reuse. to make things step by step, this patch just focuses on c code, the change within python script will be added later. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* FATE/dnn: let fate/dnn tests depend on ffmpeg static librariesGuo, Yejun2019-08-19
| | | | | | | | | | | | | | | | | | | | | | | | | | | background: DNN (deep neural network) is a sub module of libavfilter, and FATE/dnn is unit test for the DNN module, one unit test for one dnn layer. The unit tests are not based on the APIs exported by libavfilter, they just directly call into the functions within DNN submodule. There is an issue when run the following command: build$ ../ffmpeg/configure --disable-static --enable-shared make make fate-dnn-layer-pad And part of error message: tests/dnn/dnn-layer-pad-test.o: In function `test_with_mode_symmetric': /work/media/ffmpeg/build/src/tests/dnn/dnn-layer-pad-test.c:73: undefined reference to `dnn_execute_layer_pad' The root cause is that function dnn_execute_layer_pad is a LOCAL symbol in libavfilter.so, and so the linker could not find it when build dnn-layer-pad-test. To check it, just run: readelf -s libavfilter/libavfilter.so | grep dnn So, add dependency in fate/dnn Makefile with ffmpeg static libraries. This is the same method used in fate/checkasm Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
* fate: add unit test for dnn-layer-padGuo, Yejun2019-07-29
'make fate-dnn-layer-pad' to run the test Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
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