Create frozen graph from pretrained model - python-2.7

Question

Hi I am newbie to tensorflow. My aim is to convert .pb file to .tflite from pretrain model for my understanding. I have download mobilenet_v1_1.0_224 Model. Below is structure for model
mobilenet_v1_1.0_224.ckpt.data-00000-of-00001 - 66312kb
mobilenet_v1_1.0_224.ckpt.index - 20kb
mobilenet_v1_1.0_224.ckpt.meta - 3308kb
mobilenet_v1_1.0_224.tflite - 16505kb
mobilenet_v1_1.0_224_eval.pbtxt - 520kb
mobilenet_v1_1.0_224_frozen.pb - 16685kb
I know model already has .tflite file, but for my understanding I am trying to convert it.
My First Step : Creating frozen Graph file
import tensorflow as tf
imported_meta = tf.train.import_meta_graph(base_dir + model_folder_name + meta_file,clear_devices=True)
graph_ = tf.get_default_graph()
with tf.Session() as sess:
#saver = tf.train.import_meta_graph(base_dir + model_folder_name + meta_file, clear_devices=True)
imported_meta.restore(sess, base_dir + model_folder_name + checkpoint)
graph_def = sess.graph.as_graph_def()
output_graph_def = graph_util.convert_variables_to_constants(sess, graph_def, ['MobilenetV1/Predictions/Reshape_1'])
with tf.gfile.GFile(base_dir + model_folder_name + './my_frozen.pb', "wb") as f:
f.write(output_graph_def.SerializeToString())
I have successfully created my_frozen.pb - 16590 kb . But original file size is 16,685kb, which is clearly visible in folder structure above. So this is my first question why file size is different, Am I following some wrong path.
My Second Step : Creating tflite file using bazel command
bazel run --config=opt tensorflow/contrib/lite/toco:toco -- --input_file=/path_to_folder/my_frozen.pb --output_file=/path_to_folder/model.tflite --inference_type=FLOAT --input_shape=1,224,224,3 --input_array=input --output_array=MobilenetV1/Predictions/Reshape_1
This commands give me model.tflite - 0 kb.
Trackback for bazel Command
INFO: Analysed target //tensorflow/contrib/lite/toco:toco (0 packages loaded).
INFO: Found 1 target...
Target //tensorflow/contrib/lite/toco:toco up-to-date:
bazel-bin/tensorflow/contrib/lite/toco/toco
INFO: Elapsed time: 0.369s, Critical Path: 0.01s
INFO: Build completed successfully, 1 total action
INFO: Running command line: bazel-bin/tensorflow/contrib/lite/toco/toco '--input_file=/home/ubuntu/DEEP_LEARNING/Prashant/TensorflowBasic/mobilenet_v1_1.0_224/frozengraph.pb' '--output_file=/home/ubuntu/DEEP_LEARNING/Prashant/TensorflowBasic/mobilenet_v1_1.0_224/float_model.tflite' '--inference_type=FLOAT' '--input_shape=1,224,224,3' '--input_array=input' '--output_array=MobilenetV1/Predictions/Reshape_1'
2018-04-12 16:36:16.190375: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1265] Converting unsupported operation: FIFOQueueV2
2018-04-12 16:36:16.190707: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1265] Converting unsupported operation: QueueDequeueManyV2
2018-04-12 16:36:16.202293: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before Removing unused ops: 290 operators, 462 arrays (0 quantized)
2018-04-12 16:36:16.211322: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before general graph transformations: 290 operators, 462 arrays (0 quantized)
2018-04-12 16:36:16.211756: F tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_normalization.cc:86] Check failed: mean_shape.dims() == multiplier_shape.dims()
Python Version - 2.7.6
Tensorflow Version - 1.5.0
Thanks In advance :)

Answer 1

The error Check failed: mean_shape.dims() == multiplier_shape.dims()
was an issue with resolution of batch norm and has been resolved in:
https://github.com/tensorflow/tensorflow/commit/460a8b6a5df176412c0d261d91eccdc32e9d39f1#diff-49ed2a40acc30ff6d11b7b326fbe56bc

Answer 2

In my case the error occurred using tensorflow v1.7
Solution was to use tensorflow v1.15 (nightly)
toco --graph_def_file=/path_to_folder/my_frozen.pb \
--input_format=TENSORFLOW_GRAPHDEF \
--output_file=/path_to_folder/my_output_model.tflite \
--input_shape=1,224,224,3 \
--input_arrays=input \
--output_format=TFLITE \
--output_arrays=MobilenetV1/Predictions/Reshape_1 \
--inference-type=FLOAT