I'm a newbie in python. I have model.ckpt file. I want to convert that model to core ML model .mlmodel to using in iOS project. I have spent a lots of time to research but I don't know how to do it. Someone told me use coremltools but I could not find tutorial how to do that. This code bellow is not work.
coreml_model = coremltools.converters.keras.convert('./model.ckpt',
input_names='image',
image_input_names='image',
output_names='output',
class_labels=['1', '2'],
image_scale=1/255)
coreml_model.save('abc.mlmodel')
From the article:
convert_tf_keras_model
# Tested with TensorFlow 2.6.2
import tensorflow as tf
import coremltools as ct
tf_keras_model = tf.keras.Sequential(
[
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax),
]
)
# Pass in `tf.keras.Model` to the Unified Conversion API
mlmodel = ct.convert(tf_keras_model, convert_to="mlprogram")
# or save the keras model in SavedModel directory format and then convert
tf_keras_model.save('tf_keras_model')
mlmodel = ct.convert('tf_keras_model', convert_to="mlprogram")
# or load the model from a SavedModel and then convert
tf_keras_model = tf.keras.models.load_model('tf_keras_model')
mlmodel = ct.convert(tf_keras_model, convert_to="mlprogram")
# or save the keras model in HDF5 format and then convert
tf_keras_model.save('tf_keras_model.h5')
mlmodel = ct.convert('tf_keras_model.h5', convert_to="mlprogram")
To load tensorflow saved models, check this
Related
I am trying to convert an ONNX model with a dynamic input shape to TensorFlow format using the onnx_tf package. I am using TensorFlow 2.11.0, ONNX 1.13.0, and onnx_tf 1.10.0. The input to the model contains 3 arrays: the data with dynamic shape (1, None) and 2 arrays with fixed shapes (2,1,64). When I run the following code, I get on the last line a "ValueError: Cannot take the length of shape with unknown rank" :
# Imports
import onnx
from onnx import version_converter
import os
from onnx_tf.backend import prepare
# load onnx model
model_dir = r'model\vad'
model_name = 'vad.onnx'
onnx_model = onnx.load(os.path.join(model_dir,'onnx', model_name)) # load onnx model
# Export to Tensorflow
os.makedirs(os.path.join(model_dir ,'tensorflow'), exist_ok=True)
tf_rep = prepare(onnx_model) # prepare tf representation
tf_rep.export_graph(os.path.join(model_dir,'tensorflow','vad')) # export the model
What could be causing this error and how can I fix it?
I'm working with neuronal networks and I need to use a Raspberry Pi v2.
When I want to install tensorflow 2.X it fails, and I just can install tensorflow 1.14. For this reason I found the tflite library, that theoretically helps me, with a lite version of tf.
Here an image that shows I can't install it.
First of all, I convert my keras model (model.h5) into .tflite model.
# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
# Save the model.
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
Since here, all OK. The problem is when I want to use this model. With tensorflow I know how to do it,
from tensorflow import keras
def importModel(myPath):
file = open(myPath+'model/model.json', 'r')
model_json = file.read(); file.close()
model = keras.models.model_from_json(model_json)
model.load_weights(myPath+'model/model.h5')
return model, scaler
But I really don't understand how to do it with tflite, can somebody help me, please??
You can find this in the official documentation
import numpy as np
import tensorflow as tf
# Load the TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="converted_model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test the model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# The function `get_tensor()` returns a copy of the tensor data.
# Use `tensor()` in order to get a pointer to the tensor.
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
And if you have trouble to install TensorFlow 2.x on your raspberry it's maybe because you are not using the latest version of Python3
I want to quantization-aware train with my keras model. I have tried like below. I'm using tensorflow 1.14.0
train_graph = tf.Graph()
train_sess = tf.compat.v1.Session(graph=train_graph)
tf.compat.v1.keras.backend.set_session(train_sess)
with train_graph.as_default():
tf.keras.backend.set_learning_phase(1)
model = my_keras_model()
tf.contrib.quantize.create_training_graph(input_graph = train_graph, quant_delay=5)
train_sess.run(tf.global_variables_initializer())
model.compile(...)
model.fit_generator(...)
saver = tf.compat.v1.train.Saver()
saver.save(train_sess, checkpoint_path)
It works without errors.
However, size of saved model(h5 and ckpt) is completely same as the model without quantization.
Is it the right way? How I can check whether it is quantized well?
Or, is there better way to quantize?
When you finish the quantization-aware-training and save your model to disk, it is actually not already quantized. In other words, it is "prepared" for quantization, but the weights are still float32. You have to further convert your model to TFLite for it to actually be quantized. You can do so with the following piece of code:
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
quantized_tflite_model = converter.convert()
This will quantize your model with int8 weights and uint8 activations.
Have a look at the official example for further reference.
I trained a Pipeline model, which uses CountVectorizer, TfidfTransformer, OneVsRestClassifier and also a GridSearchCV.
Now I want to save it into a tflite file, to use it on my Android app.
For a Sequential model (where my tflite file was created successfully), I did:
sequential_model = Sequential()
...
# train and fit the model
...
h5_file = "h5_model.h5"
tflite_file = "tflite_model.tflite"
sequential_model.save(h5_file)
converter = tf.lite.TFLiteConverter.from_keras_model_file(h5_file)
tflite_model = converter.convert()
open(tflite_file, "wb").write(tflite_model)
All good to save Sequential model into a tflite file.
Well, Pipeline has no attribute "save", unlike a Sequential model, so I tried saving the Pipeline model with joblib and then with pickle, but none of them worked.
Let's say that pipeline_model is my trained model (the one described in the first sentence).
pb_file = 'pipeline_model.pb'
# I also tried with other extensions, like h5, hdf5, sav, pkl
joblib.dump(pipeline_model, filename)
# or with pickle equivalent and pkl extension
# pickle.dump(pipeline_model, open(pb_file, 'wb'))
Now the pb file is created and I want to create a tflite one. Since it's not a Keras model, I can't use from_keras_model_file, so I tried instead with from_saved_model.
pb_file = 'pipeline_model.pb'
tflite_file = "tflite_model.tflite"
converter = tf.lite.TFLiteConverter.from_saved_model(pb_file)
tflite_model = converter.convert()
open(tflite_file, "wb").write(tflite_model)
It generates the error on line of converter = ...:
OSError: SavedModel file does not exist at: pb_file.pb/{saved_model.pbtxt|saved_model.pb}
I tried running it on Kaggle, Colab, PyCharm IDE, with both versions of tensorflow (1 and 2), with different file extensions and nothing seems to work.
I also noticed that TFLiteConverter contains the methods from_frozen_graph and from_session, but these two requires an extra parameter, so I don't think these could be the solution.
So, how can I obtain my tflite file from the trained Pipeline model? Please, if you find any solution, tell me the library versions that you used, since there could be a different behaviour on different libs.
I've freezed my model and got .pb file. Then I've quantize my model using tocoConverter on Linux, as it's not supported on Windows. I've got quantized_model.tflite. I can load it and get predictions on Linux, but I have issues to make it on Windows, as my project requires.
I've tried to load it using tf.contrib.lite.Interpreter using this code:
import numpy as np
import tensorflow as tf
# Load TFLite model and allocate tensors.
interpreter=tf.contrib.lite.Interpreter(model_path="quantized_model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test model on random input data.
input_shape = input_details[0]['shape']
# change the following line to feed into your own data.
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'],input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
*ImportError: No module named 'tensorflow.contrib.lite.python.Interpreter*
But it failed with "No module named 'tensorflow.contrib.lite.python.interpreter" error. I always get this errors on Windows, when trying to use something from tf.contrib.lite. Maybe there is a way to load this on Windows? Or can you advice alternative options to quantize a model on Windows?
toco is currently not supported on Windows build for cmake. This is what I remember reading somewhere.