I want to further train the Greek model of spaCy el_core_news_lg for the NER task with the training configuration file that is introduced in spaCy v3.
spacy train command throws the error:
[E923] It looks like there is no proper sample data to initialize the Model of component 'tok2vec'. To check your input data paths and annotation, run: python -m spacy debug data config.cfg
When I run spacy debug data as the error suggests, I get the error:
[E930] Received invalid get_examples callback in Tok2Vec.initialize. Expected function that returns an iterable of Example objects but got: []
Any ideas?
Related
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 am trying to train spaCy models using just the python -m spacy train command line tool without writing any code of my own.
I have a training set of documents to which I have added OIL_COMPANY entity spans. I used gold.docs_to_json to create training files in the JSON-serializable format.
I can train starting from an empty model. However, if I try to extend the existing en_core_web_lg model I see the following error.
KeyError: "[E022] Could not find a transition with the name 'B-OIL_COMPANY' in the NER model."
So I need to be able to tell the command line tool to add OIL_COMPANY to an existing list of NER labels. The discussion in Training an additional entity type shows how to do this in code by calling add_label on the NER pipeline, but I don't see any command line option that does this.
Is it possible to extend an existing NER model to new entities with just the command line training tools, or do I have to write code?
Ines answered this for me on the Prodigy support forum.
I think what's happening here is that the spacy train command expects
the base model you want to update to already have all labels added
that you want to train. (It processes the data as a stream, so it's
not going to compile all labels upfront and silently add them on the
fly.) So if you want to update an existing pretrained model and add a
new label, you should be able to just add the label and save out the
base model:
ner = nlp.get_pipe("ner") ner.add_label("YOUR_LABEL")
nlp.to_disk("./base-model")
This isn't quite writing no code but it's pretty close.
See this link for the CLI in spaCy.
Train a model. Expects data in spaCy’s JSON format. On each epoch, a model will be saved out to the directory. Accuracy scores and model details will be added to a meta.json to allow packaging the model using the package command.
python -m spacy train [lang] [output_path] [train_path] [dev_path]
[--base-model] [--pipeline] [--vectors] [--n-iter] [--n-early-stopping]
[--n-examples] [--use-gpu] [--version] [--meta-path] [--init-tok2vec]
[--parser-multitasks] [--entity-multitasks] [--gold-preproc] [--noise-level]
[--orth-variant-level] [--learn-tokens] [--textcat-arch] [--textcat-multilabel]
[--textcat-positive-label] [--verbose]
I have a trained sklearn SVM model in .pkl format and a Keras .h5 model. Can I load these models using tensorflow.js on a browser?
I do most of my coding in python and not sure how to work with tensorflow.js
My model saving code looks like this
from sklearn.externals import joblib
joblib.dump(svc,'model.pkl')
model = joblib.load('model.pkl')
prediction = model.predict(X_test)
#------------------------------------------------------------------
from keras.models import load_model
model.save('model.h5')
model = load_model('my_model.h5')
In order to deploy your model with tensorflow-js, you need to use the tensorflowjs_converter, so you also need to install the tensorflowjs dependency.
You can do that in python via pip install tensorflowjs.
Next, you convert your trained model via this operation, according to your custom names: tensorflowjs_converter --input_format=keras /tmp/model.h5 /tmp/tfjs_model, where the last path is the output path of the conversion result.
Note that, after the conversion you will get a model.json (architecture of your model) and a list of N shards (weights split in N shards).
Then, in JavaScript, you need to us the function tf.loadLayersModel(MODEL_URL), where MODEL_URL is the url pointing to your model.json. Ensure that, at the same location with the model.json, the shards are also located.
Since this is an asynchronous operation(you do not want your web-page to get blocked while your model is loading), you need to use the JavaScript await keyword; hence await tf.loadLayersModel(MODEL_URL)
Please have a look at the following link to see an example: https://www.tensorflow.org/js/guide/conversion
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.
I have trained a model using keras and saved to json file and .h5 weight file. Then I need to put this run on intel Neural Compute stick. So I converted these two model and weight files to .meta file for tensorflow using my repo: https://github.com/anuragcp/to_ncs_graph.git
Prediction working without NCS. On creating graph from this meta file using mvNCCompile command it make an error:
[Error 5] Toolkit Error: Stage Details Not Supported: VarHandleOp
I Have checked on both ncsdk v1 & v2 - same result.
Any idea how to solve this?