export_darkflow.py

from darkflow.net.build import TFNet
import cv2

import tensorflow as tf
import os
import sys
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import signature_def_utils
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.saved_model import utils
from tensorflow.python.util import compat

tf.app.flags.DEFINE_integer('model_version', 1, 'version number of the model.')
FLAGS = tf.app.flags.FLAGS

options = {	"model": "/path/to/yolo.cfg", 
			"load": "/path/to/yolo.weights", 
			"threshold": 0.25, 
			"gpu": 0.8}

tfnet = TFNet(options)

imgcv = cv2.imread("/path/to/dog.jpg")
result = tfnet.return_predict(imgcv)
for res in result:
	print("%s (confidence: %s)" % (res['label'], str(res['confidence'])))
	print("    topleft(%s, %s), botright(%s, %s)" % 
			(res['topleft']['x'], res['topleft']['y'], 
			res['bottomright']['x'], res['bottomright']['y']))

export_path_base = "darkflow_yolo"
export_path = os.path.join(
	compat.as_bytes(export_path_base),
	compat.as_bytes(str(FLAGS.model_version)))
print 'Exporting trained model to', export_path
builder = saved_model_builder.SavedModelBuilder(export_path)

tensor_info_x = tf.saved_model.utils.build_tensor_info(tfnet.inp)
tensor_info_y = tf.saved_model.utils.build_tensor_info(tfnet.out)

prediction_signature = tf.saved_model.signature_def_utils.build_signature_def(
	inputs={'input': tensor_info_x},
	outputs={'output': tensor_info_y},
	method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)

legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
	tfnet.sess, [tf.saved_model.tag_constants.SERVING],
	signature_def_map={
		'predict_images':
			prediction_signature,
	},
	legacy_init_op=legacy_init_op)

builder.save()

print('Done exporting!')

Here tfnet.out gives a tensor.

tensor_info_y = tf.saved_model.utils.build_tensor_info(tfnet.out)

can you help with output layer such that i will get dictionary/list format same as in any classification model.
I want the return to be same as from return_predict().
please help!

Hello @gauravgola96
Have you found the response about you question ?