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>
duplicate ff_hex_to_data() function from avformat and rename it to
hex_to_data() as static function.
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Limin Wang <lance.lmwang@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.
please use tools/python/tf_sess_config.py to get the sess_config after that.
note the byte order of session config is in normal order.
bump the MICRO version for the config change.
Signed-off-by: Limin Wang <lance.lmwang@gmail.com>
Shubhanshu Saxena
Andreas Rheinhardt
Guo Yejun
Limin Wang
Ting Fu