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* libavfilter/dnn: add batch mode for async executionGuo, Yejun2021-01-15
| | | | | | | | the default number of batch_size is 1 Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* Mark some pointers as constAndreas Rheinhardt2021-01-01
| | | | | Reviewed-by: Lynne <dev@lynne.ee> Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
* dnn/queue: add error check and cleanupGuo, Yejun2020-12-31
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: fix issue when pthread is not supportedGuo, Yejun2020-12-31
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: fix redefining typedefs and also refine naming with correct prefixGuo, Yejun2020-12-31
| | | | | | | The prefix for symbols not exported from the library and not local to one translation unit is ff_ (or FF for types). Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_interface: change from 'void *userdata' to 'AVFilterContext *filter_ctx'Guo, Yejun2020-12-29
| | | | | | | | | | 'void *' is too flexible, since we can derive info from AVFilterContext*, so we just unify the interface with this data structure. Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: add async execution support for openvino backendGuo, Yejun2020-12-29
| | | | | | Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_interface: add interface to support async executionGuo, Yejun2020-12-29
| | | | | | Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_openvino.c: refine code for error handleGuo, Yejun2020-12-29
| | | | | | Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_openvino.c: separate function execute_model_ovGuo, Yejun2020-12-29
| | | | | | | | | function fill_model_input_ov and infer_completion_callback are extracted, it will help the async execution for reuse. Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn/queue: add queue and safe_queue supportXie, Lin2020-12-29
| | | | | | Signed-off-by: Xie, Lin <lin.xie@intel.com> Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: add NV12 pixel format supportTing Fu2020-12-22
| | | | | Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* lavfi/dnn: check the return value from sws_getContextJun Zhao2020-12-12
| | | | | | | sws_getContext may be return NULL, and it's will be dereferenced, so add the check. Signed-off-by: Jun Zhao <barryjzhao@tencent.com>
* lavfi/dnn: used the format name in debug messageJun Zhao2020-12-12
| | | | | | Used the format name in debug message. Signed-off-by: Jun Zhao <barryjzhao@tencent.com>
* dnn_backend_tf.c: add option sess_config for tf backendGuo, Yejun2020-10-19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | TensorFlow C library accepts config for session options to set different parameters for the inference. This patch exports this interface. The config is a serialized tensorflow.ConfigProto proto, so we need two steps to use it: 1. generate the serialized proto with python (see script example below) the output looks like: 0xab...cd where 0xcd is the least significant byte and 0xab is the most significant byte. 2. pass the python script output into ffmpeg with dnn_processing=options=sess_config=0xab...cd The following script is an example to specify one GPU. If the system contains 3 GPU cards, the visible_device_list could be '0', '1', '2', '0,1' etc. '0' does not mean physical GPU card 0, we need to try and see. And we can also add more opitions here to generate more serialized proto. script example to generate serialized proto which specifies one GPU: import tensorflow as tf gpu_options = tf.GPUOptions(visible_device_list='0') config = tf.ConfigProto(gpu_options=gpu_options) s = config.SerializeToString() b = ''.join("%02x" % int(ord(b)) for b in s[::-1]) print('0x%s' % b)
* libavfilter/dnn/dnn_backend{openvino, tf}: check memory alloc non-NULLChris Miceli2020-10-14
| | | | | These previously would not check that the return value was non-null meaning it was susceptible to a sigsegv. This checks those values.
* libavfilter/dnn_backend_native: check mem allocationChris Miceli2020-10-14
| | | | check that frame allocations return non-null.
* dnn/native: add native support for denseMingyu Yin2020-09-29
| | | | Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
* dnn: add a new interface DNNModel.get_outputGuo, Yejun2020-09-21
| | | | | | | | | | for some cases (for example, super resolution), the DNN model changes the frame size which impacts the filter behavior, so the filter needs to know the out frame size at very beginning. Currently, the filter reuses DNNModule.execute_model to query the out frame size, it is not clear from interface perspective, so add a new explict interface DNNModel.get_output for such query.
* dnn: put DNNModel.set_input and DNNModule.execute_model togetherGuo, Yejun2020-09-21
| | | | | | | | | | | | | | suppose we have a detect and classify filter in the future, the detect filter generates some bounding boxes (BBox) as AVFrame sidedata, and the classify filter executes DNN model for each BBox. For each BBox, we need to crop the AVFrame, copy data to DNN model input and do the model execution. So we have to save the in_frame at DNNModel.set_input and use it at DNNModule.execute_model, such saving is not feasible when we support async execute_model. This patch sets the in_frame as execution_model parameter, and so all the information are put together within the same function for each inference. It also makes easy to support BBox async inference.
* dnn: change dnn interface to replace DNNData* with AVFrame*Guo, Yejun2020-09-21
| | | | | | | | | | | | Currently, every filter needs to provide code to transfer data from AVFrame* to model input (DNNData*), and also from model output (DNNData*) to AVFrame*. Actually, such transfer can be implemented within DNN module, and so filter can focus on its own business logic. DNN module also exports the function pointer pre_proc and post_proc in struct DNNModel, just in case that a filter has its special logic to transfer data between AVFrame* and DNNData*. The default implementation within DNN module is used if the filter does not set pre/post_proc.
* dnn: add userdata for load model parameterGuo, Yejun2020-09-21
| | | | the userdata will be used for the interaction between AVFrame and DNNData
* dnn_backend_native_layer_conv2d.c: fix bug of loop boundary in single thread ↵Xu Jun2020-09-20
| | | | | | | | | | | mode. Before patch, fate test for dnn may fail in some Windows environment while succeed in my Linux. The bug was caused by a wrong loop boundary. After patch, fate test succeed in my windows mingw 64-bit. Signed-off-by: Xu Jun <xujunzz@sjtu.edu.cn> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_native_layer_conv2d.c: refine code.Xu Jun2020-09-17
| | | | | | | Move thread area allocate out of thread function into main thread. Signed-off-by: Xu Jun <xujunzz@sjtu.edu.cn>
* dnn_backend_native_layer_conv2d.c: fix memory allocation bug in multithread ↵Xu Jun2020-09-17
| | | | | | | | | | | | | function. Before patch, memory was allocated in each thread functions, which may cause more than one time of memory allocation and cause crash. After patch, memory is allocated in the main thread once, an index was parsed into thread functions. Bug fixed. Signed-off-by: Xu Jun <xujunzz@sjtu.edu.cn>
* dnn/openvino: add input/output name infoTing Fu2020-09-12
| | | | | | | show all input/output names when the input or output name not correct Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn/openvino: support run inference via GPUTing Fu2020-09-12
| | | | | | | | | | | | | for enabling OpenVINO GPU please: 1. install required OpenCL drivers, see: https://github.com/intel/compute-runtime/releases/tag/19.41.14441 2. build OpenVINO c lib with GPU enabled: use cmake config with: -DENABLE_CLDNN=ON 3. then make, and include the OpenVINO c lib in environment variables detailed steps please refer: https://github.com/openvinotoolkit/openvino/blob/master/build-instruction.md inference model with GPU please add: optioins=device=GPU Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn/dnn_backend_native_layer_conv2d: Fix allocation sizeAndreas Rheinhardt2020-09-09
| | | | | | | Found via ASAN with the dnn-layer-conv2d FATE-test. Reviewed-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
* dnn_backend_native_layer_conv2d.c:Add mutithread functionXu Jun2020-09-09
| | | | | | | | | | | | | | | | | | | | | | Use pthread to multithread dnn_execute_layer_conv2d. Can be tested with command "./ffmpeg_g -i input.png -vf \ format=yuvj420p,dnn_processing=dnn_backend=native:model= \ espcn.model:input=x:output=y:options=conv2d_threads=23 \ -y sr_native.jpg -benchmark" before patch: utime=11.238s stime=0.005s rtime=11.248s after patch: utime=20.817s stime=0.047s rtime=1.051s on my 3900X 12c24t @4.2GHz About the increase of utime, it's because that CPU HyperThreading technology makes logical cores twice of physical cores while cpu's counting performance improves less than double. And utime sums all cpu's logical cores' runtime. As a result, using threads num near cpu's logical core's number will double utime, while reduce rtime less than half for HyperThreading CPUs. Signed-off-by: Xu Jun <xujunzz@sjtu.edu.cn> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_native.c: parse options in native backendXu Jun2020-09-09
| | | | | Signed-off-by: Xu Jun <xujunzz@sjtu.edu.cn> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn/tensorflow: add log error messageTing Fu2020-08-31
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn/openvino: add log error messageTing Fu2020-08-31
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn/native: add log error messageTing Fu2020-08-25
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn/native: unify error return to DNN_ERRORTing Fu2020-08-25
| | | | | | | Unify all error return as DNN_ERROR, in order to cease model executing when return error in ff_dnn_execute_model_native layer_func.pf_exec Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn: move output name from DNNModel.set_input_output to DNNModule.execute_modelGuo, Yejun2020-08-25
| | | | | | | | | | | currently, output is set both at DNNModel.set_input_output and DNNModule.execute_model, it makes sense that the output name is provided at model inference time so all the output info is set at a single place. and so DNNModel.set_input_output is renamed to DNNModel.set_input Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_native_layer_mathbinary: add floormod supportMingyu Yin2020-08-24
| | | | Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
* dnn_backend_native_layer_mathbinary: change to function pointerMingyu Yin2020-08-24
| | | | Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
* dnn_backend_native_layer_avgpool: Fix invalid assignment, use av_assertAndreas Rheinhardt2020-08-21
| | | | | | | | | | | | | | | | | | | | | | | | dnn_execute_layer_avg_pool() contains the following line: assert(avgpool_params->padding_method = VALID); This statement contains an assignment where obviously a comparison was intended. Furthermore, *avgpool_params is const, so that the attempted assignment leads to a compilation failure if asserts are enabled (i.e. if DEBUG is defined which leads libavutil/internal.h to not define NDEBUG). Moreover, the enumeration constant VALID actually has the value 0, so that the assert would be triggered if a compiler compiles this with asserts enabled. Finally, the statement uses assert() directly instead of av_assert*(). All these errors have been fixed. Thanks to ubitux for providing a FATE-box [1] where DEBUG is defined. [1]: http://fate.ffmpeg.org/history.cgi?slot=x86_64-archlinux-gcc-ddebug Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com> Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
* dnn/native: rename struct ConvolutionalNetwork to NativeModelTing Fu2020-08-21
| | | | | Signed-off-by: Ting Fu <ting.fu@intel.com> Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_tf.c: fix build issue for tensorflow backendGuo, Yejun2020-08-14
| | | | Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn: add backend options when load the modelGuo, Yejun2020-08-12
| | | | | | | different backend might need different options for a better performance, so, add the parameter into dnn interface, as a preparation. Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_native_layer_mathunary: add round supportMingyu Yin2020-08-12
| | | | | Signed-off-by: Mingyu Yin <mingyu.yin@intel.com> Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
* dnn/native: add native support for avg_poolTing Fu2020-08-10
| | | | | | | Not support pooling strides in channel dimension yet. Signed-off-by: Ting Fu <ting.fu@intel.com> Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_native_layer_mathunary: add floor supportMingyu Yin2020-08-07
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | It can be tested with the model generated with below python script: import tensorflow as tf import os import numpy as np import imageio from tensorflow.python.framework import graph_util name = 'floor' pb_file_path = os.getcwd() if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)): os.mkdir(pb_file_path+'/{}_savemodel/'.format(name)) with tf.Session(graph=tf.Graph()) as sess: in_img = imageio.imread('detection.jpg') in_img = in_img.astype(np.float32) in_data = in_img[np.newaxis, :] input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in') y_ = tf.math.floor(input_x*255)/255 y = tf.identity(y_, name='dnn_out') sess.run(tf.global_variables_initializer()) constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out']) with tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name), mode='wb') as f: f.write(constant_graph.SerializeToString()) print("model.pb generated, please in ffmpeg path use\n \n \ python tools/python/convert.py {}_savemodel/model.pb --outdir={}_savemodel/ \n \nto generate model.model\n".format(name,name)) output = sess.run(y, feed_dict={ input_x: in_data}) imageio.imsave("out.jpg", np.squeeze(output)) print("To verify, please ffmpeg path use\n \n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 {}_savemodel/tensorflow_out.md5\n \ or\n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow {}_savemodel/out_tensorflow.jpg\n \nto generate output result of tensorflow model\n".format(name, name, name, name)) print("To verify, please ffmpeg path use\n \n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 {}_savemodel/native_out.md5\n \ or \n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native {}_savemodel/out_native.jpg\n \nto generate output result of native model\n".format(name, name, name, name)) Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
* dnn_backend_native_layer_mathunary: add ceil supportMingyu Yin2020-08-04
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | It can be tested with the model generated with below python script: import tensorflow as tf import os import numpy as np import imageio from tensorflow.python.framework import graph_util name = 'ceil' pb_file_path = os.getcwd() if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)): os.mkdir(pb_file_path+'/{}_savemodel/'.format(name)) with tf.Session(graph=tf.Graph()) as sess: in_img = imageio.imread('detection.jpg') in_img = in_img.astype(np.float32) in_data = in_img[np.newaxis, :] input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in') y = tf.math.ceil( input_x, name='dnn_out') sess.run(tf.global_variables_initializer()) constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out']) with tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name), mode='wb') as f: f.write(constant_graph.SerializeToString()) print("model.pb generated, please in ffmpeg path use\n \n \ python tools/python/convert.py ceil_savemodel/model.pb --outdir=ceil_savemodel/ \n \n \ to generate model.model\n") output = sess.run(y, feed_dict={ input_x: in_data}) imageio.imsave("out.jpg", np.squeeze(output)) print("To verify, please ffmpeg path use\n \n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model=ceil_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 ceil_savemodel/tensorflow_out.md5\n \n \ to generate output result of tensorflow model\n") print("To verify, please ffmpeg path use\n \n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model=ceil_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 ceil_savemodel/native_out.md5\n \n \ to generate output result of native model\n") Signed-off-by: Mingyu Yin <mingyu.yin@intel.com> Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_native: Add overflow check for length calculation.Reimar Döffinger2020-07-06
| | | | | | | | | We should not silently allocate an incorrect sized buffer. Fixes trac issue #8718. Signed-off-by: Reimar Döffinger <Reimar.Doeffinger@gmx.de> Reviewed-by: Michael Niedermayer <michael@niedermayer.cc> Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
* dnn_backend_native_layer_mathunary: add atanh supportTing Fu2020-07-06
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | It can be tested with the model generated with below python script: import tensorflow as tf import numpy as np import imageio in_img = imageio.imread('input.jpeg') in_img = in_img.astype(np.float32)/255.0 in_data = in_img[np.newaxis, :] x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in') please uncomment the part you want to test x_sinh_1 = tf.sinh(x) x_out = tf.divide(x_sinh_1, 1.176) # sinh(1.0) x_cosh_1 = tf.cosh(x) x_out = tf.divide(x_cosh_1, 1.55) # cosh(1.0) x_tanh_1 = tf.tanh(x) x__out = tf.divide(x_tanh_1, 0.77) # tanh(1.0) x_asinh_1 = tf.asinh(x) x_out = tf.divide(x_asinh_1, 0.89) # asinh(1.0/1.1) x_acosh_1 = tf.add(x, 1.1) x_acosh_2 = tf.acosh(x_acosh_1) # accept (1, inf) x_out = tf.divide(x_acosh_2, 1.4) # acosh(2.1) x_atanh_1 = tf.divide(x, 1.1) x_atanh_2 = tf.atanh(x_atanh_1) # accept (-1, 1) x_out = tf.divide(x_atanh_2, 1.55) # atanhh(1.0/1.1) y = tf.identity(x_out, name='dnn_out') #please only preserve the x_out you want to test sess=tf.Session() sess.run(tf.global_variables_initializer()) graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out']) tf.train.write_graph(graph_def, '.', 'image_process.pb', as_text=False) print("image_process.pb generated, please use \ path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n") output = sess.run(y, feed_dict={x: in_data}) imageio.imsave("out.jpg", np.squeeze(output)) Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn_backend_native_layer_mathunary: add acosh supportTing Fu2020-07-06
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn_backend_native_layer_mathunary: add asinh supportTing Fu2020-07-06
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
* dnn_backend_native_layer_mathunary: add tanh supportTing Fu2020-07-06
| | | | Signed-off-by: Ting Fu <ting.fu@intel.com>
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