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>
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>
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>
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>
Switch to returning specific error codes or DNN_GENERIC_ERROR
when an error is encountered. For OpenVINO API errors, currently
DNN_GENERIC_ERROR is returned.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This patch renames the InferenceItem to LastLevelTaskItem in the
three backends to avoid confusion among the meanings of these structs.
The following are the renames done in this patch:
1. extract_inference_from_task -> extract_lltask_from_task
2. InferenceItem -> LastLevelTaskItem
3. inference_queue -> lltask_queue
4. inference -> lltask
5. inference_count -> lltask_count
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit unifies the async and sync mode from the DNN filters'
perspective. As of this commit, the Native backend only supports
synchronous execution mode.
Now the user can switch between async and sync mode by using the
'async' option in the backend_configs. The values can be 1 for
async and 0 for sync mode of execution.
This commit affects the following filters:
1. vf_dnn_classify
2. vf_dnn_detect
3. vf_dnn_processing
4. vf_sr
5. vf_derain
This commit also updates the filters vf_dnn_detect and vf_dnn_classify
to send only the input frame and send NULL as output frame instead of
input frame to the DNN backends.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
The frame allocation and filling the TaskItem with execution
parameters is common in the three backends. This commit shifts
this logic to dnn_backend_common.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
It is not used here at all; instead, add it where it is used without
including it or any of the arch-specific CPU headers.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
In cases where the execution inside the function execute_model_ov fails,
the OVRequestItem must be pushed back to the request_queue before returning
the error. In case pushing back fails, release the allocated memory.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit uses TFRequestItem and the existing sync execution
mechanism to use request-based execution. It will help in adding
async functionality to the TensorFlow backend later.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
In cases where the execution inside the function execute_model_ov fails,
push the RequestItem back to the request_queue before returning the error.
In case pushing back fails, release the allocated memory.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
Fix memory leak for RequestItem upon error while pushing to the
request_queue in the completion callback.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
Convert output_name to char **output_names in TaskItem and use it as
a pointer to array of output names in the DNN backend.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
Extract TaskItem and InferenceItem from OpenVino backend and convert
ov_model to void in TaskItem.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit corrects the type of pointer of elements from the
inference queue in ff_dnn_free_model_ov.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
Different function type of model requires different parameters, for
example, object detection detects lots of objects (cat/dog/...) in
the frame, and classifcation needs to know which object (cat or dog)
it is going to classify.
The current interface needs to add a new function with more parameters
to support new requirement, with this change, we can just add a new
struct (for example DNNExecClassifyParams) based on DNNExecBaseParams,
and so we can continue to use the current interface execute_model just
with params changed.
There's one task item for one function call from dnn interface,
there's one request item for one call to openvino. For classify,
one task might need multiple inference for classification on every
bounding box, so add InferenceItem.
