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....@intel.com>
---
libavfilter/dnn/dnn_backend_native_layer_mathbinary.c | 13 +++++++++++++
libavfilter/dnn/dnn_backend_native_layer_mathbinary.h | 1 +
tools/python/convert_from_tensorflow.py | 4 +++-
tools/python/convert_header.py | 2 +-
4 files changed, 18 insertions(+), 2 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
index 3fe337f..222941e 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
@@ -120,6 +120,19 @@ int dnn_execute_layer_math_binary(DnnOperand *operands,
const int32_t *input_ope
}
}
return 0;
+ case DMBO_MUL:
+ if (params->input0_broadcast || params->input1_broadcast) {
+ for (int i = 0; i < dims_count; ++i) {
+ dst[i] = params->v * src[i];
+ }
+ } else {
+ const DnnOperand *input1 = &operands[input_operand_indexes[1]];
+ const float *src1 = input1->data;
+ for (int i = 0; i < dims_count; ++i) {
+ dst[i] = src[i] * src1[i];
+ }
+ }
+ return 0;
default:
return -1;
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
index 3c5bc6b..d58b48c 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
@@ -33,6 +33,7 @@
typedef enum {
DMBO_SUB = 0,
DMBO_ADD = 1,
+ DMBO_MUL = 2,
DMBO_COUNT
} DNNMathBinaryOperation;
diff --git a/tools/python/convert_from_tensorflow.py
b/tools/python/convert_from_tensorflow.py
index 9a495c0..dc3b4e3 100644
--- a/tools/python/convert_from_tensorflow.py
+++ b/tools/python/convert_from_tensorflow.py
@@ -71,7 +71,7 @@ class TFConverter:
self.conv2d_scope_names = set()
self.conv2d_scopename_inputname_dict = {}
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3,
'Maximum':4, 'MathBinary':5}
- self.mathbin2code = {'Sub':0, 'Add':1}
+ self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2}
self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
self.name_operand_dict = {}
@@ -309,6 +309,8 @@ class TFConverter:
self.dump_mathbinary_to_file(node, f)
elif node.op == 'Add':
self.dump_mathbinary_to_file(node, f)
+ elif node.op == 'Mul':
+ self.dump_mathbinary_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 7027022..87899fe 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 = 2
+minor = 3
--
2.7.4
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