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authorGuo, Yejun <yejun.guo@intel.com>2020-04-11 13:46:47 +0800
committerGuo, Yejun <yejun.guo@intel.com>2020-04-22 13:15:00 +0800
commit8ce9d88f930cecd55eb73ea5e8ce749090002aa8 (patch)
treecce50fdf0ab0a55b4e2bcc78ccfb37bac79f3693
parent265b5bd324496d6a342506ffa6157df5f2a85353 (diff)
downloadffmpeg-8ce9d88f930cecd55eb73ea5e8ce749090002aa8.tar.gz
dnn/native: add native support for divide
it can be tested with 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 = 2 / x z2 = 1 / z1 z3 = z2 / 0.25 + 0.3 z4 = z3 - x * 1.5 - 0.3 y = tf.identity(z4, 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@intel.com>
-rw-r--r--libavfilter/dnn/dnn_backend_native_layer_mathbinary.c17
-rw-r--r--libavfilter/dnn/dnn_backend_native_layer_mathbinary.h1
-rw-r--r--tools/python/convert_from_tensorflow.py5
-rw-r--r--tools/python/convert_header.py2
4 files changed, 22 insertions, 3 deletions
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
index 222941e952..c32a042788 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
@@ -133,6 +133,23 @@ int dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_ope
}
}
return 0;
+ case DMBO_REALDIV:
+ if (params->input0_broadcast) {
+ for (int i = 0; i < dims_count; ++i) {
+ dst[i] = params->v / src[i];
+ }
+ } else if (params->input1_broadcast) {
+ for (int i = 0; i < dims_count; ++i) {
+ dst[i] = src[i] / params->v;
+ }
+ } 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 d58b48c747..2ffbb66eeb 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
@@ -34,6 +34,7 @@ typedef enum {
DMBO_SUB = 0,
DMBO_ADD = 1,
DMBO_MUL = 2,
+ DMBO_REALDIV = 3,
DMBO_COUNT
} DNNMathBinaryOperation;
diff --git a/tools/python/convert_from_tensorflow.py b/tools/python/convert_from_tensorflow.py
index dc3b4e381d..a0fdad25b7 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, 'Mul':2}
+ self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3}
self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
self.name_operand_dict = {}
@@ -311,7 +311,8 @@ class TFConverter:
self.dump_mathbinary_to_file(node, f)
elif node.op == 'Mul':
self.dump_mathbinary_to_file(node, f)
-
+ elif node.op == 'RealDiv':
+ self.dump_mathbinary_to_file(node, f)
def dump_operands_to_file(self, f):
operands = sorted(self.name_operand_dict.values())
diff --git a/tools/python/convert_header.py b/tools/python/convert_header.py
index 87899fe72c..75d1ce803c 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 = 3
+minor = 4