I want to convert rcmalli_vggface_tf_vgg16.h5 pre-trained model to js format to use in a tensorflow-js project.
to convert this file I tried different ways but yet I could not to solve the problem.
I'm using converters.convert_tf_saved_model method to load and then convert it to a json file.
converters.convert_tf_saved_model('rcmalli_vggface_tf_vgg16.h5','web_model')
But every time following error is shown:
SavedModel file does not exist at: rcmalli_vggface_tf_vgg16.h5
While I am sure the h5 file is next to the file that is running the program.
I try full path address but same error occured. I do not know what is problem
You are converting a keras saved_model but the model is a keras layers_model because it is stored in h5 file.
I find this way the simplest to convert the model. And it did it in about 2 seconds.
Go to TensorflowJS converter Github
Follow installation instructions
Download the model you point to from the Github repo.
The model is written in the HDF5 file format
By the way, this is the oldest of the models, why not to download a new one ?
Also, this is a huge model, unless runs on the server it is of no use for a browser (produces 50 shard files of 4MB.)
Perform the conversion*
tensorflowjs_converter --input_format=keras rcmalli_vggface_tf_vgg16.h5 ./converted
The output will then be a layers model, and this has to be loaded with the tf.loadLayers API.
For example, use const model = await tf.loadGraphModel('path/to/model.json');
Note*: ./converted is the output directory, be sure not to overwrite your own stuff.
Related
I am trying to generate the custom tensor flow model (tf/tflite file) which i wanted to use for my mobile application.
I have gone through few machine learning and tensor flow blogs, from there I started to generate a simple ML model.
https://www.datacamp.com/community/tutorials/tensorflow-tutorial
https://www.edureka.co/blog/tensorflow-object-detection-tutorial/
https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc
https://www.youtube.com/watch?v=ICY4Lvhyobk
All these are really nice and they guided me to do the below steps,
i)Install all necessary tools (TensorFlow,Python,Jupyter,etc).
ii)Load the Training and testing Data.
iii)Run the tensor flow session for train and evaluate the results.
iv)Steps to increase the accuracy
But i am not able to generate the .tf/.tflite files.
I tried the following code, but that generates an empty file.
converter = tf.contrib.lite.TFLiteConverter.from_session(sess,[],[])
model = converter.convert()
file = open( 'model.tflite' , 'wb' )
file.write( model )
I have checked few answers in stackoverflow and according to my understanding in-order to generate the .tf files we need to create the pb files, freezing the pb file and then generating the .tf files.
But how can we achieve this?
Tensorflow provides Tflite converter to convert saved model to Tflite model.For more details find here.
tf.lite.TFLiteConverter.from_saved_model() (recommended): Converts a SavedModel.
tf.lite.TFLiteConverter.from_keras_model(): Converts a Keras model.
tf.lite.TFLiteConverter.from_concrete_functions(): Converts concrete functions.
I've beeen trying out the Tensorflow 2 alpha and I have been trying to freeze and export a model to a .pb graphdef file.
In Tensorflow 1 I could do something like this:
# Freeze the graph.
frozen_graph_def = tf.graph_util.convert_variables_to_constants(
sess,
sess.graph_def,
output_node_names)
# Save the frozen graph to .pb file.
with open('model.pb', 'wb') as f:
f.write(frozen_graph_def.SerializeToString())
However this doesn't seem possible anymore as convert_variables_to_constants is removed and use of sessions is discouraged.
I looked and found there is the freeze graph util
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py that works with SavedModel exports.
Is there some way to do it within Python still or I am meant to switch and use this tool now?
I have also faced this same problem while migrating from tensorflow1.x to tensoflow2.0 beta.
This problem can be solved by 2 methods:
1st is to go to the tensflow2.0 docs search for the methods you have used and change the syntax for each line &
To use google's tf_ugrade_v2 script
tf_upgrade_v2 --infile your_tf1_script_file --outfile converted_tf2_file
You try above command to change your tensorflow1.x script to tensorflow2.0, it will solve all your problem.
Also, you can rename the method (Manual step by refering documentation)
Rename 'tf.graph_util.convert_variables_to_constants' to 'tf.compat.v1.graph_util.convert_variables_to_constants'
The measure problem is that in tensorflow2.0 is that many syntax and function has changed try referring the tensoflow2.0 docs or use the google's tf_upgrade_v2 script
Not sure if you've seen this Tensorflow 2.0 issue, but this response seems to be a work-around:
https://github.com/tensorflow/tensorflow/issues/29253#issuecomment-530782763
Note: this hasn't worked for my nlp model but maybe it will work for you. The suggested work-around is to use model.save_weights('weights.h5') while in TF 2.0 environment. Then create new environment with TF 1.14 and do all following steps in TF 1.14 env. Build your model model = create_model() and use model.load_weights('weights.h5') to load weights back into your model. Then save entire model with model.save('final_model.h5'). If you manage to have success with the above steps, then follow the rest of the steps in the link to use freeze_graph.
I am using some implementation for creating a face recognition which uses this file:
"facenet.load_model("20170512-110547/20170512-110547.pb")"
What is the use of this file? I am not sure how it works.
console log :
Model filename: 20170512-110547/20170512-110547.pb
distance = 0.72212267
Github link of the actual owner of the code
https://github.com/arunmandal53/facematch
pb stands for protobuf. In TensorFlow, the protbuf file contains the graph definition as well as the weights of the model. Thus, a pb file is all you need to be able to run a given trained model.
Given a pb file, you can load it as follow.
def load_pb(path_to_pb):
with tf.gfile.GFile(path_to_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def, name='')
return graph
Once you have loaded the graph, you can basically do anything. For instance, you can retrieve tensors of interest with
input = graph.get_tensor_by_name('input:0')
output = graph.get_tensor_by_name('output:0')
and use regular TensorFlow routine like:
sess.run(output, feed_dict={input: some_data})
Explanation
The .pb format is the protocol buffer (protobuf) format, and in Tensorflow, this format is used to hold models. Protobufs are a general way to store data by Google that is much nicer to transport, as it compacts the data more efficiently and enforces a structure to the data. When used in TensorFlow, it's called a SavedModel protocol buffer, which is the default format when saving Keras/ Tensorflow 2.0 models. More information about this format can be found here and here.
For example, the following code (specifically, m.save), will create a folder called my_new_model, and save in it, the saved_model.pb, an assets/ folder, and a variables/ folder.
# first download a SavedModel from TFHub.dev, a website with models
m = tf.keras.Sequential([
hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4")
])
m.build([None, 224, 224, 3]) # Batch input shape.
m.save("my_new_model") # defaults to save as SavedModel in tensorflow 2
In some places, you may also see .h5 models, which was the default format for TF 1.X. source
Extra information: In TensorFlow Lite, the library for running models on mobile and IoT devices, instead of protocol buffers, flatbuffers are used. This is what the TensorFlow Lite Converter converts into (.tflite format). This is another Google format which is also very efficient: it allows access to any part of the message without deserialization (unlike json, xml). For devices with less memory (RAM), it makes more sense to load what you need from the model file, instead of loading the entire thing into memory to deserialize it.
Loading SavedModels in TensorFlow 2
I noticed BiBi's answer to show loading models was popular, and there is a shorter way to do this in TF2:
import tensorflow as tf
model_path = "/path/to/directory/inception_v1_224_quant_20181026"
model = tf.saved_model.load(model_path)
Note,
the directory (i.e. inception_v1_224_quant_20181026) has to have a saved_model.pb or saved_model.pbtxt, otherwise the code will crash. You cannot specify the .pb path, specify the directory.
you might get TypeError: 'AutoTrackable' object is not callable for older models, fix here.
If you load a TF1 model, I found that I don't get any errors, but the loaded file doesn't behave as expected. (e.g. it doesn't have any functions on it, like predict)
I am using toco to optimize a frozen model (.pb).
How do I read the .tflite file in python - something similar to tf.gfile.GFile('frozen.pb', 'rb')?
.tflite file is flatbuffer format, as far as I know, there are two ways to parse info from .tflite file:
1.Parse by flatc and json. Tensorflow has implemented the parse function in visualize.py, which is in tensorflow/contrib/lite/tools, you can refer to it for parsing.
2.Parse by pure python. Flatbuffer format file has a schema, which can generate code for different programming language(link:https://google.github.io/flatbuffers/flatbuffers_guide_tutorial.html), you will get a series of python file, and you can use following code to parse the .tflite file:
from Model import Model
buf = open('you-tflite-file', 'rb').read()
buf = bytearray(buf)
model = Model.getRootAsModel(buf, 0)
Now you can get information from the model object.
The point isn't to read it in Python -- it's for Android and iOS where there are C++ libraries to read it (with a Java Wrapper for Android)
Now I'm in front of the problem about inception v-3 and checkpoint data.
I have been tackling with updating inception-v3's checkpoint data by my images, reading the git page below and succeeded to make new checkpoint data.
https://github.com/tensorflow/models/tree/master/inception
I thought at first just by little change of the code, I can use those checkpoint data to recognise new image datas like the below url.
https://www.tensorflow.org/versions/master/tutorials/image_recognition/index.html
I thought at first that "classify.py" or something reads the new check point datas and just by "python classify.py -image something.png", the program recognises the image data. But It doesn't....
I really need a help.
thanks.
To have input .pb file, during training, import also tf.train.write_graph(sess.graph.as_graph_def(), 'path_to_folder', 'input_graph.pb',False)
If you have downloaded the inception v3 source code, in the inception_train.py, add the line I wrote above, under
saver.save(sess, checkpoint_path, global_step=step). (Where you save the checkpoint/s)
Hope this helps!
To use your checkpoints and model in something like the label_image example, you'll need to run the tensorflow/python/tools/freeze_graph script to convert your variables into constants stored inside the GraphDef. That's how we created the graph file used in that sample code, for example.