This patch removes all occurences of DNNReturnType from the DNN module.
This commit replaces DNN_SUCCESS by 0 (essentially the same), so the
functions with DNNReturnType now return 0 in case of success, the negative
values otherwise.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit returns specific error codes from the execution
functions in the Native Backend layers instead of DNN_ERROR.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
It can't; these are just remnants of commit
3c7cad69f2 which let the worker threads
do the reallocation.
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
If an error happens when preparing the output data buffer, an already
allocated array would leak. Fix this by postponing its allocation.
Fixes Coverity issue #1473531.
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
Also fixes a memleak in single-threaded mode when an error happens
in preparing the output data buffer; and also removes an unchecked
allocation.
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
Before patch, fate test for dnn may fail in some Windows environment
while succeed in my Linux. The bug was caused by a wrong loop boundary.
After patch, fate test succeed in my windows mingw 64-bit.
Signed-off-by: Xu Jun <xujunzz@sjtu.edu.cn>
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Before patch, memory was allocated in each thread functions,
which may cause more than one time of memory allocation and
cause crash.
After patch, memory is allocated in the main thread once,
an index was parsed into thread functions. Bug fixed.
Signed-off-by: Xu Jun <xujunzz@sjtu.edu.cn>
Found via ASAN with the dnn-layer-conv2d FATE-test.
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
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>
Unify all error return as DNN_ERROR, in order to cease model executing
when return error in ff_dnn_execute_model_native layer_func.pf_exec
Signed-off-by: Ting Fu <ting.fu@intel.com>
We should not silently allocate an incorrect sized buffer.
Fixes trac issue #8718.
Signed-off-by: Reimar Döffinger <Reimar.Doeffinger@gmx.de>
Reviewed-by: Michael Niedermayer <michael@niedermayer.cc>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
so, we can make a filter more general to accept different network
models, by adding a data type convertion after getting data from network.
After we add dt field into struct DNNData, it becomes the same as
DNNInputData, so merge them with one struct: DNNData.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
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>
the logic is that one layer in one separated source file to make
the source files simple for maintaining.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>