Due to hysterical raisins, most RISC-V Linux distributions target a
RV64GC baseline excluding the Bit-manipulation ISA extensions, most
notably:
- Zba: address generation extension and
- Zbb: basic bit manipulation extension.
Most CPUs that would make sense to run FFmpeg on support Zba and Zbb
(including the current FATE runner), so it makes sense to optimise for
them. In fact a large chunk of existing assembler optimisations relies
on Zba and/or Zbb.
Since we cannot patch shared library code, the next best thing is to
carry a flag initialised at load-time and check it on need basis.
This results in 3 instructions overhead on isolated use, e.g.:
1: AUIPC rd, %pcrel_hi(ff_rv_zbb_supported)
LBU rd, %pcrel_lo(1b)(rd)
BEQZ rd, non_Zbb_fallback_code
// Zbb code here
The C compiler will typically load the flag ahead of time to reducing
latency, and can also keep it around if Zbb is used multiple times in a
single optimisation scope. For this to work, the flag symbol must be
hidden; otherwise the optimisation degrades with a GOT look-up to
support interposition:
1: AUIPC rd, GOT_OFFSET_HI
LD rd, GOT_OFFSET_LO(rd)
LBU rd, (rd)
BEQZ rd, non_Zbb_fallback_code
// Zbb code here
This patch adds code to provision the flag in libraries using bit
manipulation functions from libavutil: byte-swap, bit-weight and
counting leading or trailing zeroes.
Add xpu device support to libtorch backend.
To enable xpu support you need to add
"-Wl,--no-as-needed -lintel-ext-pt-gpu -Wl,--as-needed" to
"--extra-libs" when configure ffmpeg.
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Actually, the jaccard distance is defined as D = 1 - intersect / union.
Additionally, the distance value is compared against a constant that
must be between 0 and 1, which is not the case here. Both facts together
has led to the fact, that the function always returned a matching course
signature. To leave the constant intact and to avoid floating point
computation, this commit multiplies with 1 << 16 making the constant
effectively 9000 / (1<<16) =~ 0.14.
Reported-by: Sachin Tilloo <sachin.tilloo@gmail.com>
Reviewed-by: Sachin Tilloo <sachin.tilloo@gmail.com>
Tested-by: Sachin Tilloo <sachin.tilloo@gmail.com>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
The result might not fit into 32bit if an image has gigantic
dimensions and one of the planes has a dominant value
(particularly so if said value is big).
Fixes Coverity issues #1598399, #1598401, #1598402, #1598403, #1598404.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
For code such as 'model->model = ov_model' is confusing. We can
just drop the member variable and use cast to get the subclass.
Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
Reviewed-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
It will be freed again by ff_dnn_uninit.
Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
Reviewed-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
It will be freed again by ff_dnn_uninit.
Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
Reviewed-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
The earlier code distinguished between a partial reset
(yae_clear()) and a complete reset (yae_release_buffers()
which also releases the buffers); this separation existed
to avoid allocations, as buffers were reallocated on reconfigs.
Yet it is pointless since a5704659e3,
so simply use yae_release_buffers() everywhere.
Reviewed-by: Pavel Koshevoy <pkoshevoy@gmail.com>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
Should fix many Coverity false positives, namely #1457947-#1457994
as well as #1461195-#146210.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
This patch trying to resolve mulitiple issues related to parameter
configuration:
Firstly, each DNN filters duplicate DNN_COMMON_OPTIONS, which should
be the common options of backend.
Secondly, backend options are hidden behind the scene. It's a
AV_OPT_TYPE_STRING backend_configs for user, and parsed by each
backend. We don't know each backend support what kind of options
from the help message.
Third, DNN backends duplicate DNN_BACKEND_COMMON_OPTIONS.
Last but not the least, pass backend options via AV_OPT_TYPE_STRING
makes it hard to pass AV_OPT_TYPE_BINARY to backend, if not impossible.
This patch puts backend common options and each backend options inside
DnnContext to reduce code duplication, make options user friendly, and
easy to extend for future usecase.
For example,
./ffmpeg -h filter=dnn_processing
dnn_processing AVOptions:
dnn_backend <int> ..FV....... DNN backend (from INT_MIN to INT_MAX) (default tensorflow)
tensorflow 1 ..FV....... tensorflow backend flag
openvino 2 ..FV....... openvino backend flag
torch 3 ..FV....... torch backend flag
dnn_base AVOptions:
model <string> ..F........ path to model file
input <string> ..F........ input name of the model
output <string> ..F........ output name of the model
backend_configs <string> ..F.......P backend configs (deprecated)
options <string> ..F.......P backend configs (deprecated)
nireq <int> ..F........ number of request (from 0 to INT_MAX) (default 0)
async <boolean> ..F........ use DNN async inference (default true)
device <string> ..F........ device to run model
dnn_tensorflow AVOptions:
sess_config <string> ..F........ config for SessionOptions
dnn_openvino AVOptions:
batch_size <int> ..F........ batch size per request (from 1 to 1000) (default 1)
input_resizable <boolean> ..F........ can input be resizable or not (default false)
layout <int> ..F........ input layout of model (from 0 to 2) (default none)
none 0 ..F........ none
nchw 1 ..F........ nchw
nhwc 2 ..F........ nhwc
scale <float> ..F........ Add scale preprocess operation. Divide each element of input by specified value. (from INT_MIN to INT_MAX) (default 0)
mean <float> ..F........ Add mean preprocess operation. Subtract specified value from each element of input. (from INT_MIN to INT_MAX) (default 0)
dnn_th AVOptions:
optimize <int> ..F........ turn on graph executor optimization (from 0 to 1) (default 0)
Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
Reviewed-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
Michael Niedermayer
Rémi Denis-Courmont
Wenbin Chen
Gerion Entrup
Gyan Doshi
Andreas Rheinhardt
Zhao Zhili
LuMingYin
Haihao Xiang
Andrea Mastroberti
Marton Balint
J. Dekker
PoorvaGaikar