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Code Issues Releases Activity

dnn_backend_native_layer_mathunary: add abs support

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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>
This commit is contained in:
Ting Fu
2020-05-25 22:46:26 +08:00
committed by Guo, Yejun
parent b6d6597bef
commit f73cc61bf5
7 changed files with 145 additions and 2 deletions
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16
tools/python/convert_from_tensorflow.py
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@ -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):