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 in the common DNN backend functions.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
Switch to returning specific error codes or DNN_GENERIC_ERROR
when an error is encountered.
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 TensorFlow C API errors, currently
DNN_GENERIC_ERROR is returned.
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 commit returns specific error codes from the functions in the
dnn_io_proc instead of DNN_ERROR.
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>
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>
Remove async flag from filter's perspective after the unification
of async and sync modes in the DNN backend.
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>
This commit adds the case handling if the asynchronous execution
of a request fails by checking the exit status of the thread when
joining before starting another execution. On failure, it does the
cleanup as well.
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>
Since requests are running in parallel, there is inconsistency in
the status of the execution. To resolve it, we avoid using mutex
as it would result in single TF_Session running at a time. So add
TF_Status to the TFRequestItem
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This patch adds error handling for cases where the execute_model_tf
fails, clears the used memory in the TFRequestItem and finally pushes
it back to the request queue.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit enables async execution in the TensorFlow backend
and adds function to flush extra frames.
The async execution mechanism executes the TFInferRequests on
a separate thread which is joined before the next execution of
same TFRequestItem/while freeing the model.
The following is the comparison of this mechanism with the existing
sync mechanism on TensorFlow C API 2.5 CPU variant.
Async Mode: 4m32.846s
Sync Mode: 5m17.582s
The above was performed on super resolution filter using SRCNN model.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit adds a function for execution of TFInferRequest and documentation
for functions related to TFInferRequest.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit adds an async execution mechanism for common use
in the TensorFlow and Native backends.
This commit also adds the documentation of typedefs and functions in
the async module for common use in DNN backends.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
The reasons for including them don't exist any longer: ff_tlog() has
been moved to libavutil/internal.h and FF_QSCALE_TYPE_* has been moved
to qp_table.h.
Reviewed-by: Nicolas George <george@nsup.org>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.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 adds handling for cases where an error may occur, clearing
the allocated memory resources.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit rearranges the existing code to create a separate function
for the completion callback in execute_model_tf.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit rearranges the existing code to create separate function
for filling request with execution data.
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>
This commit introduces a typedef TFInferRequest to store
execution parameters for a single call to the TensorFlow C API.
This typedef is used in the TFRequestItem.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit uses the common TaskItem and InferenceItem typedefs
for execution in TensorFlow backend.
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>
These properties have values either 0 or 1, so using uint8_t
is a better option as compared to int.
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>
