dnn/native: add native support for minimum

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')
x1 = tf.minimum(0.7, x)
x2 = tf.maximum(x1, 0.4)
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: Guo, Yejun <yejun.guo@intel.com>

libavfilter/dnn/dnn_backend_native_layer_mathbinary.c

@@ -150,6 +150,19 @@ int dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_ope
             }
         }
         return 0;
+    case DMBO_MINIMUM:
+        if (params->input0_broadcast || params->input1_broadcast) {
+            for (int i = 0; i < dims_count; ++i) {
+                dst[i] = FFMIN(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] = FFMIN(src[i], src1[i]);
+            }
+        }
+        return 0;
     default:
         return -1;
     }

libavfilter/dnn/dnn_backend_native_layer_mathbinary.h

@@ -35,6 +35,7 @@ typedef enum {
     DMBO_ADD = 1,
     DMBO_MUL = 2,
     DMBO_REALDIV = 3,
+    DMBO_MINIMUM = 4,
     DMBO_COUNT
 } DNNMathBinaryOperation;
 

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, 'RealDiv':3}
+        self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4}
         self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
         self.name_operand_dict = {}
 
@@ -305,15 +305,10 @@ class TFConverter:
                 self.dump_mirrorpad_to_file(node, f)
             elif node.op == 'Maximum':
                 self.dump_maximum_to_file(node, f)
-            elif node.op == 'Sub':
-                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)
-            elif node.op == 'RealDiv':
+            elif node.op in self.mathbin2code:
                 self.dump_mathbinary_to_file(node, f)
 
+
     def dump_operands_to_file(self, f):
             operands = sorted(self.name_operand_dict.values())
             for operand in operands:

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 = 4
+minor = 5