I have read through some of the PFA documentation and understand that PFA model can be imported and used in Production deployments (I have followed along a few examples on GitHub). However, it is not clear to me how the PFA model is exported/generated. Is it possible to export Python scikit-learn model as a PFA model? Is it possible to export Tensorflow model built in Python as a PFA model? Would you please be able to provide guidelines on the export process?
There is no compatibility at the moment for that, you can check it out in their wiki in github https://github.com/opendatagroup/hadrian/wiki
It is possible to translate some SKLearn models to PFA code using the SKompiler library:
from skompiler import skompile
skompile(model.predict).to('pfa/yaml')
Related
I am using spacy version==2.2.4 for name entity recognition and wishes to use the same version for testing custom spacy relation extraction pipeline. But unfortunately, I am facing the below issue while running custom relation extraction model with the above spacy version.
ModuleNotFoundError: No module named 'thinc.types'
I have used spacy github link to train the custom relation extraction pipeline. For training, I have used spacy==3.1.4.
Now, I need to connect two different models whereas Name entity recognition is trained on spacy version 2 whereas spacy relation extraction model works fine with spacy version 3.
I did some debugging and here are my results
I read in spacy github issue 7219 that to use the relation extraction model with spaCy v2, use spacy-transformers==0.6.2. I did exactly the same but no success.There is pypi link about spacy transformers which says that spacy transformers requires spacy>=3.0
I did not stopped researching there and went to another spacy github issue 7910 which says use the thinc version 8.0.3. This version is not compatible with spacy==2.2.4
I am facing the issue to use spaCy v2 for testing custom spaCy relation extraction pipeline. If it is not possible then one of the solution would be to use the same spacy version on both end. I could easily implement this but there is another challenge which comes in between i.e also using neuralcoref in between which cannot be installed with spaCy v3. So any solution to this problem would help in solving that.
I am also thinking about using different environments for (NER + Coreference) and (Relation Extraction). Does this sounds a good solution.
I have a Deep Learning Code for Object Detection. What I did is that I ran the code on Google Colab and then Exported the model to use it locally. Now to run the model I have to again install whole Tensorflow package which is quite heavy for my system.
I want to ask if there is a way to download and run only specific parts of Tensorflow Library?
I am using Tensorflow at only 2 places in my code and I have to install whole Tensorflow library for it.
This is where I am loading the model.
detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL)
This is where I am using Tensorflow 2nd time.
input_tensor = tf.convert_to_tensor(image_rgb)
These are the only 2 functions required to me from the Tensorflow Library and not the whole library... Thanks in anticipation.
Though I'm not entirely sure on the library as a whole, there is a Lite version of Tensorflow (I guess they realised 430MB is a bit much too).
Information regarding this can be found here:
https://www.tensorflow.org/lite/
A guide here seems to detail how to pick and choose parts of the Lite library and although not used myself, I should expect some degree of compatibility between the two...
https://www.tensorflow.org/lite/guide/reduce_binary_size
I understand that I have to visualize my model I have to follow to steps: 1) Preprocessing the pre-trained model (lets assume it's called my_model.h5) and 2.) creation of the interactive model.
Further I have created a json file of my model as mentioned within the instructions (Model Preprocessing):https://tensorspace.org/html/docs/preKeras.html
I have node.js installed and I installed tensorspace via npm install tensorspace. However I'm not able to recall the API of tensorspace. Does anyone now if I missed something out?
I am trying to convert .onnx model to .pb format by using onnx-tf package, however, after invoking onnx_tf.backend.prepare function, the python kernel crashes. I am using the code below:
import onnx
from onnx_tf.backend import prepare
import tensorflow
onnx_model = onnx.load(<path-to-model>) # load onnx model
tf_rep = prepare(onnx_model) # <------ That's where python crashes
tf_rep.export_graph(<output-path>) # export the model
I have double-checked package version dependencies, as wrong dependencies caused different errors while loading the .onnx model, and those are as follows:
onnx==1.7.0
onnx-tf==1.6.0
tensorflow==2.2.0
tensorflow-addons==0.10.0
torch==1.6.0+cu101
However, the dependencies seem to be correct, according to Github pages.
There were large changes between ONNX 1.6 and 1.7 that caused many dependency issues. Try with Onnx 1.6, which may be the source of your issue. Where did you find 1.7 mentioned as the required version?
Additionally, you mention the Python kernel crashing? Are you running in a Jupyter notebook?
I've found this package to be a lot more unstable in Juypter notebook, and not print out all of the relevant errors. If you could test your system in a standalone Python script you may get more information.
Importing onnx and onnx-tf related libraries before tf and pytorch should help. The problem is probably caused by a version clash since both onnx and onnx-tf use tf.
Can you install onnx-tf of this commit 7d8fa7d88fab469253d75e5e11cf9cdcb90104c4
Between order of imports and this commit the issue was solved for me
I'm working on an object detection project. I followed the instruction from Github.
But I used a different model.
I run this command
python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_inception_v2_coco.config
The error is
ValueError: ssd_inception_v2 is not supported. See `model_builder.py` for features extractors compatible with different versions of Tensorflow
I don't know why. I tried to change the model version but still error.
Please guide me. How to solve it?
I already knew how to solve it. I used model which is support Tensorflow 1., but I build my program with Tensorflow 2.. So, I changed the to use a model which support tensorflow 2.*
For those asking where to find v2 version of ssd_mobilenet_v1_coco trained models please visit and download appropriate model:
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md