I have trained a model using the Tensorflow 2 Object Detection API on a custom dataset with the model_main_tf2.py script, exported it with export_inference_graph.py and evaluated it with model_main_tf2.py by setting the checkpoint_dir flag.
I would like to see the PR-curve for my exported model. However, I can't seem to find a way to do this. I found an answer here but I don't understand how this would be integrated in the TF Object Detection API.
Edit: I've run tensorboard and it shows me the standard coco-metrics (mAP, mAP#.5IOU, mAP#.75IOU.... etc). However, clicking "PR-curve" in the top right corner makes the board empty, as in seen in the picture below.
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What I'm trying to do
I'm trying to learn TensorFlow object recognition and as usual with new things, I scoured the web for tutorials. I don't want to involve any third party cloud service or web development framework, I want to learn to do it with just native JavaScript, Python, and the TensorFlow library.
What I have so far
So far, I've followed a TensorFlow object detection tutorial (accompanied by a 5+ hour video) to the point where I've trained a model in Tensorflow (python) and want to convert it to run in a browser via TensorflowJS. I've also tried other tutorials and haven't seemed to find one that explains how to do this without a third party cloud / tool and React.
I know in order to use this model with tensorflow.js my goal is to get files like:
group1-shard1of2.bin
group1-shard2of2.bin
labels.json
model.json
I've gotten to the point where I created my TFRecord files and started training:
py Tensorflow\models\research\object_detection\model_main_tf2.py --model_dir=Tensorflow\workspace\models\my_ssd_mobnet --pipeline_config_path=Tensorflow\workspace\models\my_ssd_mobnet\pipeline.config --num_train_steps=100
It seems after training the model, I'm left with:
files named checkpoint, ckpt-1.data-00000-of-00001, ckpt-1.index, pipeline.config
the pre-trained model (which I believe isn't the file that changes during training, right?) ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8
I'm sure it's not hard to get from this step to the files I need, but I honestly browsed a lot of documentation and tutorials and google and didn't see an example of doing it without some third party cloud service. Maybe it's in the documentation, I'm missing something obvious.
The project directory structure looks like this:
Where I've looked for an answer
For some reason, frustratingly, every single tutorial I've found (including the one linked above) for using a pre-trained Tensorflow model for object detection via TensorFlowJS has required the use of IBM Cloud and ReactJS. Maybe they're all copying from some tutorial they found and now all the tutorials include this, I don't know. What I do know is I'm building an Electron.js desktop app and object detection shouldn't require network connectivity assuming the compute is happening on the user's device. To clarify: I'm creating an app where the user trains the model, so it's not just a matter of one time conversion. I want to be able to train with Python Tensorflow and convert the model to run on JavaScript Tensorflow without any cloud API.
So I stopped looking for tutorials and tried looking directly at the documentation at https://github.com/tensorflow/tfjs.
When you get to the section about importing pre-trained models, it says:
Importing pre-trained models
We support porting pre-trained models from:
TensorFlow SavedModel
Keras
So I followed that link to Tensorflow SavedModel, which brings us to a project called tfjs-converter. That repo says:
This repository has been archived in favor of tensorflow/tfjs.
This repo will remain around for some time to keep history but all
future PRs should be sent to tensorflow/tfjs inside the tfjs-core
folder.
All history and contributions have been preserved in the monorepo.
Which sounds a bit like a circular reference to me, considering it's directing me to the page that just told me to go here. So at this point you're wondering well is this whole library deprecated, will it work or what? I look around in this repo anyway, into: https://github.com/tensorflow/tfjs-converter/tree/master/tfjs-converter
It says:
A 2-step process to import your model:
A python pip package to convert a TensorFlow SavedModel or TensorFlow Hub module to a web friendly format. If you already have a converted model, or are using an already hosted model (e.g. MobileNet), skip this step.
JavaScript API, for loading and running inference.
And basically says to create a venv and do:
pip install tensorflowjs
tensorflowjs_converter \
--input_format=tf_saved_model \
--output_format=tfjs_graph_model \
--signature_name=serving_default \
--saved_model_tags=serve \
/mobilenet/saved_model \
/mobilenet/web_model
But wait, are the checkpoint files I have a "TensorFlow SavedModel"? This doesn't seem clear, the documentation doesn't explain. So I google it, find the documentation, and it says:
You can save and load a model in the SavedModel format using the
following APIs:
Low-level tf.saved_model API. This document describes how to use this
API in detail. Save: tf.saved_model.save(model, path_to_dir)
The linked syntax extrapolates somewhat:
tf.saved_model.save(
obj, export_dir, signatures=None, options=None
)
with an example:
class Adder(tf.Module):
#tf.function(input_signature=[tf.TensorSpec(shape=[], dtype=tf.float32)])
def add(self, x):
return x + x
model = Adder()
tf.saved_model.save(model, '/tmp/adder')
But so far, this isn't familiar at all. I don't understand how to take the results of my training process so far (the checkpoints) to load it into a variable model so I can pass it to this function.
This passage seems important:
Variables must be tracked by assigning them to an attribute of a
tracked object or to an attribute of obj directly. TensorFlow objects
(e.g. layers from tf.keras.layers, optimizers from tf.train) track
their variables automatically. This is the same tracking scheme that
tf.train.Checkpoint uses, and an exported Checkpoint object may be
restored as a training checkpoint by pointing
tf.train.Checkpoint.restore to the SavedModel's "variables/"
subdirectory.
And it might be the answer, but I'm not really clear on what it means as far as being "restored", or where I go from there, if that's even the right step to take. All of this is very confusing to someone learning TF which is why I looked for a tutorial that does it, but again, I can't seem to find one without third party cloud services / React.
Please help me connect the dots.
You can convert your model to TensorFlowJS format without any cloud services. I have laid out the steps below.
I'm sure it's not hard to get from this step to the files I need.
The checkpoints you see are in tf.train.Checkpoint format (relevant source code that creates these checkpoints in the object detection model code). This is different from the SavedModel and Keras formats.
We will go through these steps:
Checkpoint (current) --> SavedModel --> TensorFlowJS
Converting from tf.train.Checkpoint to SavedModel
Please see the script models/research/object_detection/export_inference_graph.py to convert the Checkpoint files to SavedModel.
The code below is taken from the docs of that script. Please adjust the paths to your specific project. --input_type should remain as image_tensor.
python export_inference_graph.py \
--input_type image_tensor \
--pipeline_config_path path/to/ssd_inception_v2.config \
--trained_checkpoint_prefix path/to/model.ckpt \
--output_directory path/to/exported_model_directory
In the output directory, you should see a savedmodel directory. We will use this in the next step.
Converting SavedModel to TensorFlowJS
Follow the instructions at https://github.com/tensorflow/tfjs/tree/master/tfjs-converter, specifically paying attention to the "TensorFlow SavedModel example". The example conversion code is copied below. Please modify the input and output paths for your project. The --signature_name and --saved_model_tags might have to be changed, but hopefully not.
tensorflowjs_converter \
--input_format=tf_saved_model \
--output_format=tfjs_graph_model \
--signature_name=serving_default \
--saved_model_tags=serve \
/mobilenet/saved_model \
/mobilenet/web_model
Using the TensorFlowJS model
I know in order to use this model with tensorflow.js my goal is to get files like:
group1-shard1of2.bin
group1-shard2of2.bin
labels.json
model.json
The steps above should create these files for you, though I don't think labels.json will be created. I am not sure what that file should contain. TensorFlowJS will use model.json to construct the inference graph, and it will load the weights from the .bin files.
Because we converted a TensorFlow SavedModel to a TensorFlowJS model, we will need to load the JS model with tf.loadGraphModel(). See the tfjs converter page for more information.
Note that for TensorFlowJS, there is a difference between a TensorFlow SavedModel and a Keras SavedModel. Here, we are dealing with a TensorFlow SavedModel.
The Javascript code to run the model is probably out of scope for this answer, but I would recommend reading this TensorFlowJS tutorial. I have included a representative javascript portion below.
import * as tf from '#tensorflow/tfjs';
import {loadGraphModel} from '#tensorflow/tfjs-converter';
const MODEL_URL = 'model_directory/model.json';
const model = await loadGraphModel(MODEL_URL);
const cat = document.getElementById('cat');
model.execute(tf.browser.fromPixels(cat));
Extra notes
... Which sounds a bit like a circular reference to me,
The TensorFlowJS ecosystem has been consolidated in the tensorflow/tfjs GitHub repository. The tfjs-converter documentation lives there now. You can create a pull request to https://github.com/tensorflow/tfjs to fix the SavedModel link to point to the tensorflow/tfjs repository.
I am trying to use an object detection model, that was created using the TF Object Detection API, in TensorFlow.js.
I converted the model using:
tensorflowjs_converter --input_format=tf_saved_model inference_graph/saved_model inference_graph/web_model
It gets converted without any problems and loads in my javascript code.
Now I am a bit unsure about what my next steps should be.
I have to translate the Python into JavaScript, but certain areas I am unsure about.
With the object detection API in python, there are many steps; (1)preprocessing the image, such as convert to RGB, numpy array reshape, expand dimensions (I have an idea of how I would approach it) and (2) the run inference for single image function, I am not sure how I would go about it in tensorflow.js.
I tried to find some general information about deploying an object detection model in tensorflow.js, but I could not find much, except with pre-trained models.
Any information about this topic would be great!
Thanks!
as mentioned by #edkeveked you will need to perform similar input processing and output processing in JavaScript as is being done in Python. i can't say exactly what you will need to do since i am not familiar with the model. however, you can find an example using a specific object detection model here:
https://github.com/vabarbosa/tfjs-model-playground/blob/master/object-detector/demo/object-detector.js
see also
https://medium.com/codait/bring-machine-learning-to-the-browser-with-tensorflow-js-part-iii-62d2b09b10a3
You would need to replicate the same process in javascript before giving it to the model. In js, the image use by default the RGB channel, so there is no need to make that conversion again.
I am trying to understand what I need from any pre-trained model used in the API regardless of any additional code found on the Tensorflow object detection API.
For example: ssd_mobilenet_v1_coco_2017_11_17, depending on what I have understood: it is a model that is already trained to detect objects (there is a classification to know the category of the object + Regression to bound the objects with rectangles and those rectangles are actually the x,y,w,h coordinates on the object).
How do we benefit from the regression output of that model (x,y,w,h coordinates) to use them in another model?
Let's assume we want to print out just the coordinates x,y,w,h of a detected object on an image without any need of the code of Tensorflow object detection API, how can we do that?
Certainly you can use the pretrained model provided in tensorflow object detection model zoo without installing object detection api. The alternative solution is to use opencv.
Opencv has provided both c++ and python api to call .pb models generated by tensorflow. Here is a nice tutorial.
I used Object Detection API from Tensorflow to train my model. The eval script also worked fine.
However, I want to get the coordinates of the bounding box in eval images. Is that possible to do so in eval script? How can I do it?
I used this script.
A few examples (how many is set by num_visualizations in the config file) are shown on TensorBoard.
If you want more than that, I suggest to export the model and use inference.
See the TF OD tutorial notebook for how to use an exported model for inference and visualizing results.
You can refer to the following link.
Although this does not have any relation with the eval.py script, it gets the job done. If you specify a directory consisting of the test images, you can get the bounding box coordinates of all the detected objects using the model you have trained.
I have used the TensorFlow object detection API to train the SSD Inception model from scratch. The evaluation script shows that the model has learned something and now I want to use the model.
I have looked at the object detection ipynb that can feed single images to a trained model. However, this is for SSD with MobileNet. I have used the following line (after loading the meta graph) to print the tensor names of the TensorFlow model I trained.
print([str(op.name) for op in tf.get_default_graph().get_operations()] )
But it does not contain the same input or output tensor names as in the ipynb. I have also searched through the code, but many functions point toward each other and it is difficult to find what I am looking for.
How can I find the tensor names I need? Or is there another method I do not know about?
To use the graph, you need to freeze/export it, using this provided script. The resulting .pb file will contain the nodes you need. I don't know why it's organized like that, but it is.