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FFmpeg/libavfilter/dnn/Makefile

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libavfilter/dnn: move dnn files from libavfilter to libavfilter/dnn it is expected that there will be more files to support native mode, so put all the dnn codes under libavfilter/dnn The main change of this patch is to move the file location, see below: modified: libavfilter/Makefile new file: libavfilter/dnn/Makefile renamed: libavfilter/dnn_backend_native.c -> libavfilter/dnn/dnn_backend_native.c renamed: libavfilter/dnn_backend_native.h -> libavfilter/dnn/dnn_backend_native.h renamed: libavfilter/dnn_backend_tf.c -> libavfilter/dnn/dnn_backend_tf.c renamed: libavfilter/dnn_backend_tf.h -> libavfilter/dnn/dnn_backend_tf.h renamed: libavfilter/dnn_interface.c -> libavfilter/dnn/dnn_interface.c Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2019-07-16 07:55:45 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_interface.o
dnn: change dnn interface to replace DNNData* with AVFrame* Currently, every filter needs to provide code to transfer data from AVFrame* to model input (DNNData*), and also from model output (DNNData*) to AVFrame*. Actually, such transfer can be implemented within DNN module, and so filter can focus on its own business logic. DNN module also exports the function pointer pre_proc and post_proc in struct DNNModel, just in case that a filter has its special logic to transfer data between AVFrame* and DNNData*. The default implementation within DNN module is used if the filter does not set pre/post_proc.
2020-08-28 06:51:44 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_io_proc.o
dnn/queue: add queue and safe_queue support 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>
2020-11-09 08:09:13 +02:00
OBJS-$(CONFIG_DNN) += dnn/queue.o
OBJS-$(CONFIG_DNN) += dnn/safe_queue.o
libavfilter/dnn: move dnn files from libavfilter to libavfilter/dnn it is expected that there will be more files to support native mode, so put all the dnn codes under libavfilter/dnn The main change of this patch is to move the file location, see below: modified: libavfilter/Makefile new file: libavfilter/dnn/Makefile renamed: libavfilter/dnn_backend_native.c -> libavfilter/dnn/dnn_backend_native.c renamed: libavfilter/dnn_backend_native.h -> libavfilter/dnn/dnn_backend_native.h renamed: libavfilter/dnn_backend_tf.c -> libavfilter/dnn/dnn_backend_tf.c renamed: libavfilter/dnn_backend_tf.h -> libavfilter/dnn/dnn_backend_tf.h renamed: libavfilter/dnn_interface.c -> libavfilter/dnn/dnn_interface.c Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2019-07-16 07:55:45 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native.o
avfilter/dnn: unify the layer execution function in native mode Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2019-10-09 16:08:11 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layers.o
dnn/native: add native support for avg_pool 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>
2020-08-09 18:33:13 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_avgpool.o
dnn/native: add native support for dense Signed-off-by: Mingyu Yin <mingyu.yin@intel.com>
2020-09-22 09:11:09 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_dense.o
dnn: add layer pad which is equivalent to tf.pad the reason to add this layer first is that vf_sr uses it in its tensorflow model, and the next plan is to update the python script to convert tf.pad into native model. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2019-07-29 03:56:33 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_pad.o
libavfilter/dnn: separate conv2d layer from dnn_backend_native.c to a new file the logic is that one layer in one separated source file to make the source files simple for maintaining. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2019-09-05 08:00:28 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_conv2d.o
libavfilter/dnn: separate depth_to_space layer from dnn_backend_native.c to a new file the logic is that one layer in one separated source file to make the source files simple for maintaining. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2019-09-05 08:00:46 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_depth2space.o
libavfilter/dnn: add layer maximum for native mode. The reason to add this layer is that it is used by srcnn in vf_sr. This layer is currently ignored in native mode. After this patch, we can add multiple outputs support for native mode. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2019-09-20 05:55:48 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_maximum.o
dnn_backend_native_layer_mathbinary: add sub support more math binary operations will be added here Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
2020-03-20 14:55:38 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathbinary.o
dnn_backend_native_layer_mathunary: add abs support more math unary operations will be added here It can be tested with the model file generated with below python scripy: import tensorflow as tf import numpy as np import imageio in_img = imageio.imread('input.jpeg') in_img = in_img.astype(np.float32)/255.0 in_data = in_img[np.newaxis, :] x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in') x1 = tf.subtract(x, 0.5) x2 = tf.abs(x1) y = tf.identity(x2, name='dnn_out') sess=tf.Session() sess.run(tf.global_variables_initializer()) graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out']) tf.train.write_graph(graph_def, '.', 'image_process.pb', as_text=False) print("image_process.pb generated, please use \ path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n") output = sess.run(y, feed_dict={x: in_data}) imageio.imsave("out.jpg", np.squeeze(output)) Signed-off-by: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
2020-05-25 16:46:26 +02:00
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathunary.o
libavfilter/dnn: move dnn files from libavfilter to libavfilter/dnn it is expected that there will be more files to support native mode, so put all the dnn codes under libavfilter/dnn The main change of this patch is to move the file location, see below: modified: libavfilter/Makefile new file: libavfilter/dnn/Makefile renamed: libavfilter/dnn_backend_native.c -> libavfilter/dnn/dnn_backend_native.c renamed: libavfilter/dnn_backend_native.h -> libavfilter/dnn/dnn_backend_native.h renamed: libavfilter/dnn_backend_tf.c -> libavfilter/dnn/dnn_backend_tf.c renamed: libavfilter/dnn_backend_tf.h -> libavfilter/dnn/dnn_backend_tf.h renamed: libavfilter/dnn_interface.c -> libavfilter/dnn/dnn_interface.c Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2019-07-16 07:55:45 +02:00
DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn/dnn_backend_tf.o
dnn: add openvino as one of dnn backend OpenVINO is a Deep Learning Deployment Toolkit at https://github.com/openvinotoolkit/openvino, it supports CPU, GPU and heterogeneous plugins to accelerate deep learning inferencing. Please refer to https://github.com/openvinotoolkit/openvino/blob/master/build-instruction.md to build openvino (c library is built at the same time). Please add option -DENABLE_MKL_DNN=ON for cmake to enable CPU path. The header files and libraries are installed to /usr/local/deployment_tools/inference_engine/ with default options on my system. To build FFmpeg with openvion, take my system as an example, run with: $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/deployment_tools/inference_engine/lib/intel64/:/usr/local/deployment_tools/inference_engine/external/tbb/lib/ $ ../ffmpeg/configure --enable-libopenvino --extra-cflags=-I/usr/local/deployment_tools/inference_engine/include/ --extra-ldflags=-L/usr/local/deployment_tools/inference_engine/lib/intel64 $ make Here are the features provided by OpenVINO inference engine: - support more DNN model formats It supports TensorFlow, Caffe, ONNX, MXNet and Kaldi by converting them into OpenVINO format with a python script. And torth model can be first converted into ONNX and then to OpenVINO format. see the script at https://github.com/openvinotoolkit/openvino/tree/master/model-optimizer/mo.py which also does some optimization at model level. - optimize at inference stage It optimizes for X86 CPUs with SSE, AVX etc. It also optimizes based on OpenCL for Intel GPUs. (only Intel GPU supported becuase Intel OpenCL extension is used for optimization) Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2020-05-25 09:38:09 +02:00
DNN-OBJS-$(CONFIG_LIBOPENVINO) += dnn/dnn_backend_openvino.o
libavfilter/dnn: move dnn files from libavfilter to libavfilter/dnn it is expected that there will be more files to support native mode, so put all the dnn codes under libavfilter/dnn The main change of this patch is to move the file location, see below: modified: libavfilter/Makefile new file: libavfilter/dnn/Makefile renamed: libavfilter/dnn_backend_native.c -> libavfilter/dnn/dnn_backend_native.c renamed: libavfilter/dnn_backend_native.h -> libavfilter/dnn/dnn_backend_native.h renamed: libavfilter/dnn_backend_tf.c -> libavfilter/dnn/dnn_backend_tf.c renamed: libavfilter/dnn_backend_tf.h -> libavfilter/dnn/dnn_backend_tf.h renamed: libavfilter/dnn_interface.c -> libavfilter/dnn/dnn_interface.c Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
2019-07-16 07:55:45 +02:00
OBJS-$(CONFIG_DNN) += $(DNN-OBJS-yes)
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