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 = 'floor' 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.floor(input_x*255)/255 y = tf.identity(y_, 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 {}_savemodel/model.pb --outdir={}_savemodel/ \n \nto generate model.model\n".format(name,name)) 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={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow -f framemd5 {}_savemodel/tensorflow_out.md5\n \ or\n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow {}_savemodel/out_tensorflow.jpg\n \nto generate output result of tensorflow model\n".format(name, name, name, name)) print("To verify, please ffmpeg path use\n \n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native -f framemd5 {}_savemodel/native_out.md5\n \ or \n \ ./ffmpeg -i detection.jpg -vf format=rgb24,dnn_processing=model={}_savemodel/model.model:input=dnn_in:output=dnn_out:dnn_backend=native {}_savemodel/out_native.jpg\n \nto generate output result of native model\n".format(name, name, name, name))
Showing the changes to tools/python/convert_from_tensorflow.py (1 of 5 files). Show all
Unified · Split
| @@ -74,7 +74,7 @@ class TFConverter: · 73 unmodified lines | |||
| 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, | 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, | 76 | 'Acos':5, 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10, |
| 77 | 'Acosh':11, 'Atanh':12, 'Ceil':13} | 77 | 'Acosh':11, 'Atanh':12, 'Ceil':13, 'Floor':14} |
| 78 | self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2} | 78 | self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2} |
| 79 | self.name_operand_dict = {} | 79 | self.name_operand_dict = {} |
| 80 | 80 | ||