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>

2 files changed, 16 insertions, 2 deletions

diff --git a/tools/python/convert_from_tensorflow.py b/tools/python/convert_from_tensorflow.py
index 1c20891fcc..8c0a9be7be 100644
--- a/tools/python/convert_from_tensorflow.py
+++ b/tools/python/convert_from_tensorflow.py
@@ -70,8 +70,9 @@ class TFConverter:
         self.converted_nodes = set()
         self.conv2d_scope_names = set()
         self.conv2d_scopename_inputname_dict = {}
-        self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5}
+        self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5, 'MathUnary':6}
         self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4}
+        self.mathun2code  = {'Abs':0}
         self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
         self.name_operand_dict = {}
 
@@ -286,6 +287,17 @@ class TFConverter:
         np.array([output_operand_index], dtype=np.uint32).tofile(f)
 
 
+    def dump_mathunary_to_file(self, node, f):
+        self.layer_number = self.layer_number + 1
+        self.converted_nodes.add(node.name)
+        i0_node = self.name_node_dict[node.input[0]]
+        np.array([self.op2code['MathUnary'], self.mathun2code[node.op]], dtype=np.uint32).tofile(f)
+        input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT)
+        np.array([input_operand_index], dtype=np.uint32).tofile(f)
+        output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
+        np.array([output_operand_index],dtype=np.uint32).tofile(f)
+
+
     def dump_layers_to_file(self, f):
         for node in self.nodes:
             if node.name in self.converted_nodes:
@@ -307,6 +319,8 @@ class TFConverter:
                 self.dump_maximum_to_file(node, f)
             elif node.op in self.mathbin2code:
                 self.dump_mathbinary_to_file(node, f)
+            elif node.op in self.mathun2code:
+                self.dump_mathunary_to_file(node, f)
 
 
     def dump_operands_to_file(self, f):
diff --git a/tools/python/convert_header.py b/tools/python/convert_header.py
index e692a5e217..ad4491729a 100644
--- a/tools/python/convert_header.py
+++ b/tools/python/convert_header.py
@@ -23,4 +23,4 @@ str = 'FFMPEGDNNNATIVE'
 major = 1
 
 # increase minor when we don't have to re-convert the model file
-minor = 5
+minor = 6