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>
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>
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>
There are lots of files that don't need it: The number of object
files that actually need it went down from 2011 to 884 here.
Keep it for external users in order to not cause breakages.
Also improve the other headers a bit while just at it.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
PyTorch is an open source machine learning framework that accelerates
the path from research prototyping to production deployment. Official
website: https://pytorch.org/. We call the C++ library of PyTorch as
LibTorch, the same below.
To build FFmpeg with LibTorch, please take following steps as
reference:
1. download LibTorch C++ library in
https://pytorch.org/get-started/locally/,
please select C++/Java for language, and other options as your need.
Please download cxx11 ABI version:
(libtorch-cxx11-abi-shared-with-deps-*.zip).
2. unzip the file to your own dir, with command
unzip libtorch-shared-with-deps-latest.zip -d your_dir
3. export libtorch_root/libtorch/include and
libtorch_root/libtorch/include/torch/csrc/api/include to $PATH
export libtorch_root/libtorch/lib/ to $LD_LIBRARY_PATH
4. config FFmpeg with ../configure --enable-libtorch \
--extra-cflag=-I/libtorch_root/libtorch/include \
--extra-cflag=-I/libtorch_root/libtorch/include/torch/csrc/api/include \
--extra-ldflags=-L/libtorch_root/libtorch/lib/
5. make
To run FFmpeg DNN inference with LibTorch backend:
./ffmpeg -i input.jpg -vf \
dnn_processing=dnn_backend=torch:model=LibTorch_model.pt -y output.jpg
The LibTorch_model.pt can be generated by Python with torch.jit.script()
api. https://pytorch.org/tutorials/advanced/cpp_export.html. This is
pytorch official guide about how to convert and load torchscript model.
Please note, torch.jit.trace() is not recommanded, since it does
not support ambiguous input size.
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
Makes it robust against adding fields before it, which will be useful in
following commits.
Majority of the patch generated by the following Coccinelle script:
@@
typedef AVOption;
identifier arr_name;
initializer list il;
initializer list[8] il1;
expression tail;
@@
AVOption arr_name[] = { il, { il1,
- tail
+ .unit = tail
}, ... };
with some manual changes, as the script:
* has trouble with options defined inside macros
* sometimes does not handle options under an #else branch
* sometimes swallows whitespace
For detect and classify output, width and height make no sence, so
change width, height to dims to represent the shape of tensor. Use
layout and dims to get width, height and channel.
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
Now when using openvino backend, user doesn't need to set input/output
names in command line. Model ports will be automatically detected.
For example:
ffmpeg -i input.png -vf \
dnn_detect=dnn_backend=openvino:model=model.xml:input=image:\
output=detection_out -y output.png
can be simplified to:
ffmpeg -i input.png -vf dnn_detect=dnn_backend=openvino:model=model.xml\
-y output.png
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
Add dynamic outputs support. Some models don't have fixed output size.
Its size changes according to result. Now openvino can run these kinds of
models.
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
Add input pad to get model input resolution. Detection models always
have fixed input size. And the output coordinators are based on the
input resolution, so we need to get input size to map coordinators to
our real output frames.
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
Add multiple output support to openvino backend. You can use '&' to
split different output when you set output name using command line.
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
Add yolo support. Yolo model doesn't output final result. It outputs
candidate boxes, so we need post-process to remove overlap boxes to
get final results. Also, the box's coordinators relate to cell and
anchors, so we need these information to calculate boxes as well.
Model detail please refer to: https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v2-tf
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
Dnn models has different data preprocess requirements. Scale and mean
parameters are added to preprocess input data.
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Dnn models have different input layout (NCHW or NHWC), so a
"layout" option is added
Use openvino's API to do layout conversion for input data. Use swscale
to do layout conversion for output data as openvino doesn't have
similiar C API for output.
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
When ov_model_const_input_by_name/ov_model_const_output_by_name
failed, input_port/output_port can be wild pointer.
Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
OpenVINO API 2.0 was released in March 2022, which introduced new
features.
This commit implements current OpenVINO features with new 2.0 APIs. And
will add other features in API 2.0.
Please add installation path, which include openvino.pc, to
PKG_CONFIG_PATH mannually for new OpenVINO libs config.
Signed-off-by: Ting Fu <ting.fu@intel.com>
Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
This also fixed a warning: implicit conversion from enumeration
type 'TF_DataType' (aka 'enum TF_DataType') to different
enumeration type 'DNNDataType'.
Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
Native backend will be removed in following commits, so change the
dnn interface and modify the error message in it first.
Signed-off-by: Ting Fu <ting.fu@intel.com>
Bugfix: The OpenVino DNN backend in the 'async' mode sets
'task->inference_done' to 'complete' prior to data copy from
OpenVino output buffer to task's output frame.
This order causes task destroy in ff_dnn_get_result_common()
prior to model output processing.
Signed-off-by: Rafik Saliev <rafik.f.saliev@intel.com>
Dump all input/output names to OVModel struct. In case other funcs use
them for reporting errors or locating issues.
Signed-off-by: Ting Fu <ting.fu@intel.com>
