[FFmpeg-cvslog] dnn/native: add native support for 'add'
Guo, Yejun
git at videolan.org
Wed Apr 22 09:41:39 EEST 2020
ffmpeg | branch: master | Guo, Yejun <yejun.guo at intel.com> | Fri Apr 10 21:35:11 2020 +0800| [6aa7e07e7caed7997e40cee8b203ec56b12d7300] | committer: Guo, Yejun
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 at intel.com>
> http://git.videolan.org/gitweb.cgi/ffmpeg.git/?a=commit;h=6aa7e07e7caed7997e40cee8b203ec56b12d7300
---
libavfilter/dnn/dnn_backend_native_layer_mathbinary.c | 13 +++++++++++++
libavfilter/dnn/dnn_backend_native_layer_mathbinary.h | 1 +
tools/python/convert_from_tensorflow.py | 15 +++++++--------
tools/python/convert_header.py | 2 +-
4 files changed, 22 insertions(+), 9 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
index 3b8bab82bc..3fe337f730 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
@@ -107,6 +107,19 @@ int dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_ope
}
}
return 0;
+ case DMBO_ADD:
+ 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 6b684d1165..3c5bc6b2e1 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
@@ -32,6 +32,7 @@
typedef enum {
DMBO_SUB = 0,
+ DMBO_ADD = 1,
DMBO_COUNT
} DNNMathBinaryOperation;
diff --git a/tools/python/convert_from_tensorflow.py b/tools/python/convert_from_tensorflow.py
index 2485f16cd6..9a495c0a9e 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}
+ self.mathbin2code = {'Sub':0, 'Add':1}
self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
self.name_operand_dict = {}
@@ -255,8 +255,7 @@ class TFConverter:
np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
- def dump_sub_to_file(self, node, f):
- assert(node.op == 'Sub')
+ def dump_mathbinary_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]]
@@ -264,15 +263,13 @@ class TFConverter:
np.array([self.op2code['MathBinary'], self.mathbin2code[node.op]], dtype=np.uint32).tofile(f)
if i0_node.op == 'Const':
scalar = i0_node.attr['value'].tensor.float_val[0]
- assert(i0_node.name.find('sub/x'))
- np.array([1], dtype=np.uint32).tofile(f)
+ np.array([1], dtype=np.uint32).tofile(f) # broadcast: 1
np.array([scalar], dtype=np.float32).tofile(f)
- np.array([0], dtype=np.uint32).tofile(f)
+ np.array([0], dtype=np.uint32).tofile(f) # broadcast: 0
input_operand_index = self.add_operand(i1_node.name, Operand.IOTYPE_INPUT)
np.array([input_operand_index], dtype=np.uint32).tofile(f)
elif i1_node.op == 'Const':
scalar = i1_node.attr['value'].tensor.float_val[0]
- assert(i1_node.name.find('sub/y'))
np.array([0], 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)
@@ -309,7 +306,9 @@ class TFConverter:
elif node.op == 'Maximum':
self.dump_maximum_to_file(node, f)
elif node.op == 'Sub':
- self.dump_sub_to_file(node, f)
+ self.dump_mathbinary_to_file(node, f)
+ elif node.op == 'Add':
+ 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 6576fca7a1..70270225f1 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 = 1
+minor = 2
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