I am trying to check if my .onnx model is correct, and need to run inference to verify the output for the same.
I know we can run validation on .mlmodel using coremltools in Python - basically load the model and input and get the prediction. I am trying to do a similar thing for the .onnx model.
I found the MXNet framework but I can't seem to understand how to import the model - I just have the .onnx file and MXNet requires some extra input besides the onnx model.
Is there any other simple way to do this in Python? I am guessing this is a common problem but can't seem to find any relevant libraries/frameworks to do this as easily as coremltools for .mlmodel.
I do not wish to convert .onnx to another type of model (like say PyTorch) as I want to check the .onnx model as is, not worrying if the conversion was correct. Just need a way to load the model and input, run inference and print the output.
This is my first time encountering these formats, so any help or insight would be appreciated.
Thanks!
I figured out a way to do this using Caffe2 - just posting in case someone in the future tries to do the same thing.
The main code snippet is:
import onnx
import caffe2.python.onnx.backend
from caffe2.python import core, workspace
import numpy as np
# make input Numpy array of correct dimensions and type as required by the model
modelFile = onnx.load('model.onnx')
output = caffe2.python.onnx.backend.run_model(modelFile, inputArray.astype(np.float32))
Also it is important to note that the input to run_model can only be a numpy array or a string. The output will be an object of the Backend.Outputs type. I was able to extract the output numpy array from it.
I was able to execute inference on the CPU, and hence did not need the Caffe2 installation with GPU (requiring CUDA and CDNN).
Related
I have a model in onnx format, and I want to run it in fastai learner. possibly something like this
learn = learn.load('model.onnx')
another way is to convert back to pth format, but I dont see any proper library on this task. I need your help in either one of this approach. Thanks.
There is no native solution and some people are currently working on it : https://github.com/ENOT-AutoDL/onnx2torch
Also to be clear, a .pth checkpoint , usually only contains the parameters such as weights, biases... not the operations like conv2d, batchnorm2d, pooling. An onnx model, in another hand, contains both operations and parameters that's why you can infer them. If, from an onnx, you only need the weights & biases in order to load a state into a torch model already implemented, it might be quite easy, if you want to automatically build a torch model from an onnx, that's the hard part.
I read several discussions about this and still cannot make it work for my case
Have a classification model trained using Google Tables.
Exported the model and download the directory with cli.
My goal is to get a better understanding of the model trained by google, study it, understand its decisions. And later try to prune it to improve performance.
I'm using this code, just to start:
import tensorflow as tf
from tensorflow import keras
import struct2tensor
location = "model_dir"
model = tf.saved_model.load(location)
model.summary()
I get this error:
AttributeError: 'AutoTrackable' object has no attribute 'summary'
the variable model is of type:
<tensorflow.python.training.tracking.tracking.AutoTrackable at 0x7fa8eaa7ed30>
And I stuck there, don't know how to continue. Using Python 3.8 and the last version of those libraries. Any idea of how can I proceed?
Thanks!
The proper method to load your model depends on your file formatting.
You can see in the Tensorflow documentation that "The object returned by tf.saved_model.load is not a Keras object (i.e. doesn't have .fit, .predict, etc. methods)" and "Use tf.keras.models.load_model to restore the Keras model".
I'm not sure if you want to use the keras module or not, but since you have imported it I assume you do. In that case I would recommend checking this other Stackoverflow thread where it is explained how to use the tf.keras.models.load_model method depending if your model is saved as .pb or .h5.
If the model is saved as .pb you should use it with the string pointing to the directory where the model is saved, as you did in your code snippet but in this case using the keras method:
model = tf.keras.models.load_model('model_dir')
If instead it's saved as .h5 you should use it specifying it:
model = tf.keras.models.load_model('my_model_in_h5.h5')
I'm new to this topic, so forgive me my lack of knowledge. There is a very good model called inception resnet v2 that basically works like this, the input is an image and outputs a list of predictions with their positions and bounded rectangles. I find this very useful, and I thought of using the already worked model in order to recognize things that it now can't (for example if a human is wearing a mask or not). Yes, I wanted to add a new recognition class to the model.
import tensorflow as tf
import tensorflow_hub as hub
mod = hub.load("https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1")
mod is an object of type
tensorflow.python.training.tracking.tracking.AutoTrackable, reading the documentation (that was only available on the source code was a bit hard to understand without context)
and I tried to inspect some of it's properties in order to see if I could figure it out by myself.
And well, I didn't. How can I see the network, the layers, the weights? the fit methods, Is it's all abstracted away?. Can I convert it to keras? I want to experiment with it, see if I can modify it, and see if I could export the model to another representation, for example pytorch.
I wanted to do this because I thought it'd be better to modify an already working model instead of creating one from scratch. Also because I'm not good at training models myself.
I've run into this issue too. Tensorflow hub guide says:
This error frequently arises when loading models in TF1 Hub format with the hub.load() API in TF2. Adding the correct signature should fix this problem.
mod = hub.load(handle).signatures['default']
As an example, you can see this notebook.
You can dir the loaded model asset to see what's defined on it
m = hub.load(handle)
dir(model)
As mentioned in the other answer, you can also look at the signatures with print(m.signatures)
Hub models are SavedModel assets and do not have a keras .fit method on them. If you want to train the model from scratch, you'll need to go to the source code.
Some models have more extensive exported interfaces including access to individual layers, but this model does not.
I'm trying to train and test a CNN model for classification and every time I run the code in testing I get different accuracy results.
How can I get the same result every time? is there any possible solution in python TensorFlow for this problem?
Try this:
import numpy as np
import tensorflow as tf
np.random.seed(1)
tf.set_random_seed(1)
The value you set the seed to is not important as long as you keep it fixed.
Make sure you also fix the seed for any third party library. Randomness can also caused by GPU-libraries, if you don't use GPU's don't worry about it.
Edit: Assuming you use TensorFlow as backend. For PyTorch (works for CPU and GPU btw) use:
import torch
torch.manual_seed(1)
I am new with machine learning and want to do following implementation
Want to create a custom .mlmodel with input of "xls or csv or nsdata of this files" and output should be double or array.
Pythone file should be able to read input data because i am going to use train_data from this input.
Pythone will do some calculation on this input data and provide prediction on this (i will do this calculation using sklearn,LinearRegression)
Can any one please help me how i can do this ?
You can use python to train your model with SKLearn as you suggested. This is a good post on getting started with that (make sure you use Sklearn and not Statsmodels).
https://towardsdatascience.com/simple-and-multiple-linear-regression-in-python-c928425168f9
When you have trained your model, you can convert it using Apple's coremltools:
https://github.com/apple/coremltools
When you've converted it you can add your .mlmodel file to your xcode project. You'll then need to write some code to get all of the your model inputs collected from your app and pass them as inputs to the model.
Good luck!