FFmpeg

Commit Graph

Author	SHA1	Message	Date
Mingyu Yin	fab00b0ae0	dnn_backend_native_layer_mathunary: add floor support 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>	5 years ago
Mingyu Yin	9fbdd5454b	dnn_backend_native_layer_mathunary: add ceil support 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>	5 years ago
Ting Fu	c0cdeea0ee	dnn_backend_native_layer_mathunary: add atanh support 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>	5 years ago
Ting Fu	cd2e3a864d	dnn_backend_native_layer_mathunary: add acosh support Signed-off-by: Ting Fu <ting.fu@intel.com>	5 years ago
Ting Fu	9d14b38d9d	dnn_backend_native_layer_mathunary: add asinh support Signed-off-by: Ting Fu <ting.fu@intel.com>	5 years ago
Ting Fu	ea71e731f4	dnn_backend_native_layer_mathunary: add tanh support Signed-off-by: Ting Fu <ting.fu@intel.com>	5 years ago
Ting Fu	62fc7e3035	dnn_backend_native_layer_mathunary: add cosh support Signed-off-by: Ting Fu <ting.fu@intel.com>	5 years ago
Ting Fu	91b4037101	dnn_backend_native_layer_mathunary: add sinh support Signed-off-by: Ting Fu <ting.fu@intel.com>	5 years ago
Ting Fu	13f5613e68	dnn_backend_native_layer_mathunary: add atan support 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') x1 = tf.atan(x) x2 = tf.divide(x1, 3.1416/4) # pi/4 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>	5 years ago
Ting Fu	461485feac	dnn_backend_native_layer_mathunary: add acos support 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') x1 = tf.acos(x) x2 = tf.divide(x1, 3.1416/2) # pi/2 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>	5 years ago
Ting Fu	486c0c419d	dnn_backend_native_layer_mathunary: add asin support 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') x1 = tf.asin(x) x2 = tf.divide(x1, 3.1416/2) # pi/2 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>	5 years ago
Ting Fu	22d0860c13	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>	5 years ago
Ting Fu	88fb494f42	dnn_backend_native_layer_mathunary: add cos 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, 1.5) x2 = tf.cos(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>	5 years ago
Ting Fu	0b6d3f0d83	dnn_backend_native_layer_mathunary: add sin support 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.multiply(x, 3.14) x2 = tf.sin(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>	5 years ago
Ting Fu	f73cc61bf5	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>	5 years ago
Guo, Yejun	71e28c5422	dnn/native: add native support for minimum it can be tested with model file generated with below python script: import tensorflow as tf import numpy as np import imageio in_img = imageio.imread('input.jpg') 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.minimum(0.7, x) x2 = tf.maximum(x1, 0.4) 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: Guo, Yejun <yejun.guo@intel.com>	5 years ago
Guo, Yejun	8ce9d88f93	dnn/native: add native support for divide it can be tested with model file generated with below python script: import tensorflow as tf import numpy as np import imageio in_img = imageio.imread('input.jpg') 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') z1 = 2 / x z2 = 1 / z1 z3 = z2 / 0.25 + 0.3 z4 = z3 - x * 1.5 - 0.3 y = tf.identity(z4, 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: Guo, Yejun <yejun.guo@intel.com>	5 years ago
Guo, Yejun	ef79408e97	dnn/native: add native support for 'mul' it can be tested with model file generated from above python script: import tensorflow as tf import numpy as np import imageio in_img = imageio.imread('input.jpg') 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') z1 = 0.5 + 0.3 * x z2 = z1 * 4 z3 = z2 - x - 2.0 y = tf.identity(z3, 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: Guo, Yejun <yejun.guo@intel.com>	5 years ago
Guo, Yejun	6aa7e07e7c	dnn/native: add native support for 'add' It can be tested with the model file generated with below python script: import tensorflow as tf import numpy as np import imageio in_img = imageio.imread('input.jpg') 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') z1 = 0.039 + x z2 = x + 0.042 z3 = z1 + z2 z4 = z3 - 0.381 z5 = z4 - x y = tf.math.maximum(z5, 0.0, 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: Guo, Yejun <yejun.guo@intel.com>	5 years ago
Guo, Yejun	ffa1561608	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>	5 years ago
Guo, Yejun	dff39ea9f0	dnn: add tf.nn.conv2d support for native model Unlike other tf.*.conv2d layers, tf.nn.conv2d does not create many nodes (within a scope) in the graph, it just acts like other layers. tf.nn.conv2d only creates one node in the graph, and no internal nodes such as 'kernel' are created. The format of native model file is also changed, a flag named has_bias is added, so change the version number. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>	6 years ago
Guo, Yejun	b2683c66b2	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>	6 years ago
Guo, Yejun	022f50d3fe	libavfilter/dnn: add header into native model file Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>	6 years ago

23 Commits (fab00b0ae077207a9aa1fa2cea34beb6ed860452)