2019-07-16 07:55:45 +02:00
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OBJS-$(CONFIG_DNN) += dnn/dnn_interface.o
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native.o
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2019-10-09 16:08:11 +02:00
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layers.o
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2019-07-29 03:56:33 +02:00
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_pad.o
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2019-09-05 08:00:28 +02:00
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_conv2d.o
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2019-09-05 08:00:46 +02:00
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_depth2space.o
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2019-09-20 05:55:48 +02:00
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_maximum.o
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2020-03-20 14:55:38 +02:00
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathbinary.o
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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
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathunary.o
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2019-07-16 07:55:45 +02:00
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DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn/dnn_backend_tf.o
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2020-05-25 09:38:09 +02:00
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DNN-OBJS-$(CONFIG_LIBOPENVINO) += dnn/dnn_backend_openvino.o
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2019-07-16 07:55:45 +02:00
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OBJS-$(CONFIG_DNN) += $(DNN-OBJS-yes)
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