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authorTing Fu <ting.fu@intel.com>2020-06-06 20:12:50 +0800
committerGuo Yejun <yejun.guo@intel.com>2020-06-11 11:10:51 +0800
commit22d0860c132af041c75de54bfee611cdd9e57822 (patch)
tree3f1e4223bb4daf25b74435185b5f6da1f099188e /libavutil/libm.h
parentdd3fe3e77ca1868f54fb8fac72ae2942a5c29f9c (diff)
downloadffmpeg-22d0860c132af041c75de54bfee611cdd9e57822.tar.gz
dnn_backend_native_layer_mathunary: add tan support
It can be tested with the model 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.multiply(x, 0.78) x2 = tf.tan(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>
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