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.
Related
What I tried so far:
pre-train a model using unsupervised method in PyTorch, and save off the checkpoint file (using torch.save(state, filename))
convert the checkpoint file to onnx format (using torch.onnx.export)
convert the onnx to tensorflow saved model (using onnx-tf)
trying to load the variables in saved_model folder as checkpoint in my tensorflow training code (using tf.train.init_from_checkpoint) for fine-tuning
But now I am getting stuck at step 4 because I notice that variables.index and variables.data#1 files are basically empty (probably because of this: https://github.com/onnx/onnx-tensorflow/issues/994)
Also, specifically, if I try to use tf.train.NewCheckpointReader to load the files and call ckpt_reader.get_variable_to_shape_map(), _CHECKPOINTABLE_OBJECT_GRAPH is empty
Any suggestions/experience are appreciated :-)
I have retrained some tensorflow2.0 model, it's working as 1 class object detector, prepared with object detection api v2 (https://tensorflow-object-detection-api-tutorial.readthedocs.io/).
After that I have converted it to onnx (tf2onnx.convert) and tested - got the same inference results.
I have tested all pretrained models (downloaded from tf model zoo https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md):
ssd_mobilenet_v2_320x320_coco17_tpu-8
ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8
ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8
ssd_resnet50_v1_fpn_640x640_coco17_tpu-8
I have retrained it by using some small batch of data.
The problem is with using it with gstreamer/deepstream. As I have seen, gstreamer consumes the onnx model, or model after converting it to TensorRT. (If I will provide onnx - model is also converted to TensorRT of course, but it's done by gstreamer right before running)
I was also trying to same pipeline with train->convert to onnx->convert to trt (or just provide onnx model to gstreamer). Same issue.
Error:
ERROR: [TRT]: [graph.cpp::computeInputExecutionUses::519] Error Code
9: Internal Error ((Unnamed Layer* 747) [Recurrence]: IRecurrenceLayer
cannot be used to compute a shape tensor)
TensorRT Version: 8.2.1.8
tf2onnx Version: 1.9.3
Is there any chance to get some help?
Or maybe I should skip the onnx model and just convert it from tensorflow to tensorRT engine? Is it possible?
Of course I can upload the model if it would help.
BR!
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>
I have a Problem converting Keras Models into Layers API format models to use with tensorflowjs
I use the command:
$ tensorflowjs_converter --input_format keras kerasModels/vgg16_weights_tf_dim_ordering_tf_kernels.h5 convertedModels/
I get an error "KeyError: Can't open attribute (can't locate attribute 'keras version')"
Here is an image of the error log:
I assume you are trying to convert the model downloaded from here, which is possibly outdated now.
You can download the VGG16 model fresh from keras-applications using the following python script:
from keras.applications.vgg16 import VGG16
model = VGG16(include_top=True, weights='imagenet')
model.save("VGG16.h5")
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.