Using model optimizer for tensorflow slim models

Question

I am aiming to inference tensorflow slim model with Intel OpenVINO optimizer. Using open vino docs and slides for inference and tf slim docs for training model.

It's a multi-class classification problem. I have trained tf slim mobilnet_v2 model from scratch (using sript train_image_classifier.py). Evaluation of trained model on test set gives relatively good results to begin with (using script eval_image_classifier.py):

eval/Accuracy[0.8017]eval/Recall_5[0.9993]

However, single .ckpt file is not saved (even though at the end of train_image_classifier.py run there is a message like "model.ckpt is saved to checkpoint_dir"), there are 3 files (.ckpt-180000.data-00000-of-00001, .ckpt-180000.index, .ckpt-180000.meta) instead.

OpenVINO model optimizer requires a single checkpoint file.

According to docs I call mo_tf.py with following params:

python mo_tf.py --input_model D:/model/mobilenet_v2_224.pb --input_checkpoint D:/model/model.ckpt-180000 -b 1

It gives the error (same if pass --input_checkpoint D:/model/model.ckpt):

[ ERROR ]  The value for command line parameter "input_checkpoint" must be existing file/directory,  but "D:/model/model.ckpt-180000" does not exist.

Error message is clear, there are not such files on disk. But as I know most tf utilities convert .ckpt-????.meta to .ckpt under the hood.

Trying to call:

python mo_tf.py --input_model D:/model/mobilenet_v2_224.pb --input_meta_graph D:/model/model.ckpt-180000.meta -b 1

Causes:

[ ERROR ]  Unknown configuration of input model parameters

It doesn't matter for me in which way I will transfer graph to OpenVINO intermediate representation, just need to reach that result.

Thanks a lot.

EDIT

I managed to run OpenVINO model optimizer on frozen graph of tf slim model. However I still have no idea why had my previous attempts (based on docs) failed.

Answer 1

you can try converting the model to frozen format (.pb) and then convert the model using OpenVINO.

.ckpt-meta has the metagraph. The computation graph structure without variable values. the one you can observe in tensorboard.

.ckpt-data has the variable values,without the skeleton or structure. to restore a model we need both meta and data files.

.pb file saves the whole graph (meta+data)

As per the documentation of OpenVINO:

When a network is defined in Python* code, you have to create an inference graph file. Usually, graphs are built in a form that allows model training. That means that all trainable parameters are represented as variables in the graph. To use the graph with the Model Optimizer, it should be frozen. https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow

the OpenVINO optimizes the model by converting the weighted graph passed in frozen form.