I am training the maskrcnn inception v2 model on the Tensorflow version for further work with OpenVino. After training the model, I freeze the model using a script in object_detection_API directory:
python exporter_main_v2.py \
--trained_checkpoint_dir training
--output_directory inference_graph
--pipeline_config_path training/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.config
After this script, I get the saved model and pipeline files, which should be used in OpenVInO in the future
The following error occurs when uploading the received files to model optimizer:
Model Optimizer version:
2020-08-20 11:37:05.425293: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
[ FRAMEWORK ERROR ] Cannot load input model: TensorFlow cannot read the model file: "C:\Users\Anna\Downloads\inference_graph\inference_graph\saved_model\saved_model.pb" is incorrect TensorFlow model file.
The file should contain one of the following TensorFlow graphs:
frozen graph in text or binary format
inference graph for freezing with checkpoint (--input_checkpoint) in text or binary format
meta graph
Make sure that --input_model_is_text is provided for a model in text format. By default, a model is interpreted in binary format. Framework error details: Error parsing message.
For more information please refer to Model Optimizer FAQ (https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html), question #43.
I teach the model by following the example from the link article, using my own dataset: https://gilberttanner.com/blog/train-a-mask-r-cnn-model-with-the-tensorflow-object-detection-api
On gpu, the model starts and works, but I need to get the converted model for OpenVINO
Run the mo_tf.py script with a path to the SavedModel directory:
python3 mo_tf.py --saved_model_dir <SAVED_MODEL_DIRECTORY>
Related
I have downloaded a pre-trained PoseNet model for Tensorflow.js (tfjs) from Google, so its a json file.
However, I want to use it on Android, so I need the .tflite model. Although someone has 'ported' a similar model from tfjs to tflite here, I have no idea what model (there are many variants of PoseNet) they converted. I want to do the steps myself. Also, I don't want to run some arbitrary code someone uploaded into a file in stackOverflow:
Caution: Be careful with untrusted code—TensorFlow models are code. See Using TensorFlow Securely for details. Tensorflow docs
Does anyone know any convenient ways to do this?
You can find out what tfjs format you have by looking in the json file. It often says "graph-model". The difference between them are here.
From tfjs graph model to SavedModel (more common)
Use tfjs-to-tf by Patrick Levin.
import tfjs_graph_converter.api as tfjs
tfjs.graph_model_to_saved_model(
"savedmodel/posenet/mobilenet/float/050/model-stride16.json",
"realsavedmodel"
)
# Code below taken from https://www.tensorflow.org/lite/convert/python_api
converter = tf.lite.TFLiteConverter.from_saved_model("realsavedmodel")
tflite_model = converter.convert()
# Save the TF Lite model.
with tf.io.gfile.GFile('model.tflite', 'wb') as f:
f.write(tflite_model)
From tfjs layers model to SavedModel
Note: This will only work for layers model format, not graph model format as in the question. I've written the difference between them here.
Install and use tensorflowjs-convert to convert the .json file into a Keras HDF5 file (from another SO thread).
On mac, you'll face issues running pyenv (fix) and on Z-shell, pyenv won't load correctly (fix). Also, once pyenv is running, use python -m pip install tensorflowjs instead of pip install tensorflowjs, because pyenv did not change python used by pip for me.
Once you've followed the tensorflowjs_converter guide, run tensorflowjs_converter to verify it works with no errors, and should just warn you about Missing input_path argument. Then:
tensorflowjs_converter --input_format=tfjs_layers_model --output_format=keras tfjs_model.json hdf5_keras_model.hdf5
Convert the Keras HDF5 file into a SavedModel (standard Tensorflow model file) or directly into .tflite file using the TFLiteConverter. The following runs in a Python file:
# Convert the model.
model = tf.keras.models.load_model('hdf5_keras_model.hdf5')
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
# Save the TF Lite model.
with tf.io.gfile.GFile('model.tflite', 'wb') as f:
f.write(tflite_model)
or to save to a SavedModel:
# Convert the model.
model = tf.keras.models.load_model('hdf5_keras_model.hdf5')
tf.keras.models.save_model(
model, filepath, overwrite=True, include_optimizer=True, save_format=None,
signatures=None, options=None
)
I trained an ANN model as saved it as .h5 file.Then I converted the model into tensorflow model and got 'savedmodel.pb' and 'variables' folder.
Then I used model optimiser openvino to generate IR files using:
python3 mo_tf.py --input_model saved_model.pb
But I get the following error:
[ FRAMEWORK ERROR ] Error parsing message
TensorFlow cannot read the model file: "/home/user/Downloads/OpenVino/dldt-2019/model-optimizer/saved_model.pb" is incorrect TensorFlow model file
Can openVINO be used to convert ANN models in the first place?
Unfortunately, OpenVINO does not support Keras model format.
I guess you got an error from model optimizer because your model is not frozen.
There are a lot of scripts which can be used to convert a Keras model to a frozen Tensorflow graph. I can recommend this one for example.
Hope it will help.
I'm trying to determine if this Tensorflow Hub model can be converted to TFLITE format (and eventually compiled for the TPU/Coral Board), by doing something like this.
converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model("./")
tflite_model = converter.convert()
However, I need to specify the model tag-sets and this command gives no results (in both TF 1.13.1 and 2.0):
% saved_model_cli show --dir .
The given SavedModel contains the following tag-sets:
The saved_model.pb file is in this directory, and Netron is too unwieldy - given the size of the model it barely opens - so it's difficult to inspect. The model can be opened for inference: detector = hub.load(module_url).signatures['default'] so perhaps I can show the model summary from the detector object (?).
Any ideas how I can determine the model structure?
Any insight into the practicality of converting this model to TFLITE and then compiling for the TPU would be appreciated.
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.
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.
TensorFlow Object Detection API
Using the TensorFlow Object Detection API to retrain MobileNet on my own DataSet. The issue occurs as I try to run my inference graph that has been both frozen and quantized.
System:
Ubuntu 16.04,
TensorFlow 1.2 (from source, CPU only),
Bazel 0.4.5
Issue:
Use provided frozen_graph.pb from model zoo.
Quantize to 8-bit using
bazel-bin/tensorflow/tools/graph_transforms/transform_graph.
Run inference
This works, however,
Re-train and produce my own frozen_graph.pb using object_detection/export_inference_graph.py
Quantize to 8-bit using bazel-bin/tensorflow/tools/graph_transforms/transform_graph
Run inference <-- Produces error
Does NOT work, and the error I'm getting during the attempt to run the graph is:
File
"/home/unibap/TensorFlow/tensorflow-python2-sse4.2/local/lib/python2.7/site-packages/tensorflow/python/client/session.py",
line 1298, in _do_call
raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: The node
'Preprocessor/map/while/ResizeImage/ResizeBilinear/eightbit' has
inputs from different frames. The input
'Preprocessor/map/while/ResizeImage/size' is in frame
'Preprocessor/map/while/Preprocessor/map/while/'. The input
'Preprocessor/map/while/ResizeImage/ResizeBilinear_eightbit/Preprocessor/map/while/ResizeImage/ExpandDims/quantize'
is in frame ''.
Since I can quantize and run the provided frozen_graph.pb the issue has to be with the export tool? Which export tool was used to create the frozen_graph.pb that are in the model zoo? Or how was the export tool called?
PS:
Quote from comments in export_inference_graph.pb, assuring me that it should produce a frozen graph if checkpoint is provided.
"Optionally, one can freeze the graph by converting the weights in the provided
checkpoint as graph constants thereby eliminating the need to use a checkpoint
file during inference."
Best