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>
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>
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>
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>
and also change CONV to DLT_CONV2D for better description
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
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>
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>
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>
the info can be saved in dnn operand object without regenerating again and again,
and it is also needed for layer split/merge, and for memory reuse.
to make things step by step, this patch just focuses on c code,
the change within python script will be added later.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
since tf.pad is enabled, the conv2d(valid) changes back to its original behavior.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
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>
Xu Jun
Ting Fu
Guo, Yejun
Reimar Döffinger
Wu Zhiwen