It can be tested with the model generated with below python script: import tensorflow as tf import os import numpy as np import imageio from tensorflow.python.framework import graph_util name = 'ceil' pb_file_path = os.getcwd() if not os.path.exists(pb_file_path+'/{}_savemodel/'.format(name)): os.mkdir(pb_file_path+'/{}_savemodel/'.format(name)) with tf.Session(graph=tf.Graph()) as sess: in_img = imageio.imread('detection.jpg') in_img = in_img.astype(np.float32) in_data = in_img[np.newaxis, :] input_x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in') y = tf.math.ceil( input_x, name='dnn_out') sess.run(tf.global_variables_initializer()) constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out']) with tf.gfile.FastGFile(pb_file_path+'/{}_savemodel/model.pb'.format(name), mode='wb') as f: f.write(constant_graph.SerializeToString()) print("model.pb generated, please in ffmpeg path use\n \n \ python tools/python/convert.py ceil_savemodel/model.pb --outdir=ceil_savemodel/ \n \n \ to generate model.model\n") output = sess.run(y, feed_dict={ input_x: in_data}) imageio.imsave("out.jpg", np.squeeze(output)) print("To verify, please ffmpeg path use\n \n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model=ceil_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 ceil_savemodel/tensorflow_out.md5\n \n \ to generate output result of tensorflow model\n") print("To verify, please ffmpeg path use\n \n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model=ceil_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 ceil_savemodel/native_out.md5\n \n \ to generate output result of native model\n")
Unified · Split
| @@ -130,6 +130,10 @@ int dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_oper · 129 unmodified lines | |||
| 130 | for (int i = 0; i < dims_count; ++i) | 130 | for (int i = 0; i < dims_count; ++i) |
| 131 | dst[i] = atanh(src[i]); | 131 | dst[i] = atanh(src[i]); |
| 132 | return 0; | 132 | return 0; |
| 133 | case DMUO_CEIL: | ||
| 134 | for (int i = 0; i < dims_count; ++i) | ||
| 135 | dst[i] = ceil(src[i]); | ||
| 136 | return 0; | ||
| 133 | default: | 137 | default: |
| 134 | return -1; | 138 | return -1; |
| 135 | } | 139 | } |
| @@ -43,6 +43,7 @@ typedef enum { · 42 unmodified lines | |||
| 43 | DMUO_ASINH = 10, | 43 | DMUO_ASINH = 10, |
| 44 | DMUO_ACOSH = 11, | 44 | DMUO_ACOSH = 11, |
| 45 | DMUO_ATANH = 12, | 45 | DMUO_ATANH = 12, |
| 46 | DMUO_CEIL = 13, | ||
| 46 | DMUO_COUNT | 47 | DMUO_COUNT |
| 47 | } DNNMathUnaryOperation; | 48 | } DNNMathUnaryOperation; |
| 48 | 49 | ||
| @@ -56,6 +56,8 @@ static float get_expected(float f, DNNMathUnaryOperation op) · 55 unmodified lines | |||
| 56 | return acosh(f); | 56 | return acosh(f); |
| 57 | case DMUO_ATANH: | 57 | case DMUO_ATANH: |
| 58 | return atanh(f); | 58 | return atanh(f); |
| 59 | case DMUO_CEIL: | ||
| 60 | return ceil(f); | ||
| 59 | default: | 61 | default: |
| 60 | av_assert0(!"not supported yet"); | 62 | av_assert0(!"not supported yet"); |
| 61 | return 0.f; | 63 | return 0.f; |
| @@ -128,5 +130,7 @@ int main(int agrc, char **argv) · 66 unmodified lines | |||
| 128 | return 1; | 130 | return 1; |
| 129 | if (test(DMUO_ATANH)) | 131 | if (test(DMUO_ATANH)) |
| 130 | return 1; | 132 | return 1; |
| 133 | if (test(DMUO_CEIL)) | ||
| 134 | return 1; | ||
| 131 | return 0; | 135 | return 0; |
| 132 | } | 136 | } |
| @@ -72,7 +72,9 @@ class TFConverter: · 71 unmodified lines | |||
| 72 | self.conv2d_scopename_inputname_dict = {} | 72 | self.conv2d_scopename_inputname_dict = {} |
| 73 | self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5, 'MathUnary':6} | 73 | self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5, 'MathUnary':6} |
| 74 | self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4} | 74 | self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4} |
| 75 | self.mathun2code = {'Abs':0, 'Sin':1, 'Cos':2, 'Tan':3, 'Asin':4, 'Acos':5, 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10, 'Acosh':11, 'Atanh':12} | 75 | self.mathun2code = {'Abs':0, 'Sin':1, 'Cos':2, 'Tan':3, 'Asin':4, |
| 76 | 'Acos':5, 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10, | ||
| 77 | 'Acosh':11, 'Atanh':12, 'Ceil':13} | ||
| 76 | self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2} | 78 | self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2} |
| 77 | self.name_operand_dict = {} | 79 | self.name_operand_dict = {} |
| 78 | 80 | ||
| @@ -23,4 +23,4 @@ str = 'FFMPEGDNNNATIVE' · 22 unmodified lines | |||
| 23 | major = 1 | 23 | major = 1 |
| 24 | 24 | ||
| 25 | # increase minor when we don't have to re-convert the model file | 25 | # increase minor when we don't have to re-convert the model file |
| 26 | minor = 18 | 26 | minor = 19 |