I have been trying to find where to collect the output from Sagemaker Debugger, coming from my own dataset. I do not know where to find the raw outputs. Is it possible to get the raw outputs of your dataset from Debugger?
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I am trying to debug this project : https://github.com/VisualComputingInstitute/TrackR-CNN
This is a MaskRCNN based project and I want to visualize the data flow among various functions in network/FasterRCNN.py(https://github.com/VisualComputingInstitute/TrackR-CNN/blob/master/network/FasterRCNN.py)
mainly rpn_head(), fastrcnn_head(). I tried it with py_func and pdb but was not successful. SEssion.run() is created inside core/Engine.py(https://github.com/VisualComputingInstitute/TrackR-CNN/blob/master/core/Engine.py).
Is there any way to see the image manipulation during the training(i.e. rpn values, reid_dim, etc)?
Thanks.
I am trying to modify a tensorflow project so that it becomes compatible with TPU.
For this, I started with the code explained on this site.
Here COCO dataset is downloaded and first its features are extracted using InceptionV3 model.
I wanted to modify this code so that it supports TPU.
For this, I added the mandatory code for TPU as per this link.
Withe TPU strategy scope, I created the InceptionV3 model using keras library and loaded model with ImageNet weights as per existing code.
Now, since TPU needs data to be stored on Google Cloud storage, I created a tf records file using tf.Example with the help of this link.
Now, I tried to create this file in several ways so that it will have the data that TPU will find through TFRecordDataset.
At first I directly added image data and image path to the file and uploaded it to GCP bucket but while reading this data, I realized that this image data is not useful as it does not contain shape/size information which it will need and I had not resized it to the required dimension before storage. This file size became 2.5GB which was okay.
Then I thought lets only keep image path at cloud, so I created another tf records file with only image path, then I thought that this may not be an optimized code as TPU will have to open the image individually resize it to 299,299 and then feed to model and it will be better if I have image data through .map() function inside TFRecordDataset, so I again tried, this time by using this link, by storing R, G and B along with image path inside tf records file.
However, now I see that the size of tf records file is abnormally large, like some 40-45GB and ultimately, I stopped the execution as my memory was getting filled up on Google Colab TPU.
The original size of COCO dataset is not that large. It almost like 13GB.. and from that the dataset is being created with only first 30,000 records. so 40GB looks weird number.
May I know what is the problem with this way of feature storage? Is there any better way to store image data in TF records file and then extract through TFRecordDataset.
I think the COCO dataset processed as TFRecords should be around 24-25 GB on GCS. Note that TFRecords aren't meant to act as a form of compression, they represent data as protobufs so it can be optimally loaded into TensorFlow programs.
You might have more success if you refer to: https://cloud.google.com/tpu/docs/coco-setup (corresponding script can be found here) for converting COCO (or a subset) into TFRecords.
Furthermore, we have implemented detection models for COCO using TF2/Keras optimized for GPU/TPU here which you might find useful for optimal input pipelines. An example tutorial can be found here. Thanks!
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.
Does any one know of high level api if exist in java for directly reading tfrecords and feeding to a tensorflow savedModel . Python api allows both example.proto (tfrecords) and tensors to be fed to tf model for inference. The only api i have seen in java is of creating raw tensors, is there a way similar to python sdk where i can directly feed tfrecords (example.proto_ to a saved model bundle in java as well.
I just came across same scenario and I used TFRecordIO from Java Apache Beam to read records. For example,
pipeline
.apply(TFRecordIO.read().from(dataPath))
.apply(ParDo.of(new ModelEvaluationFn()));
Inside ModelEvaluationFn I do the scoring using savedModel. With Java Apache Beam, you can run locally, on GCP Dataflow, Spark, Flink, etc. But if you are using Spark directly, there's spark-tensorflow-connector.
Another thing I came across is how to parse the tfrecords in Java because I need get the label value and group by using some column values to get breakdown scores. org.tensorlfow/proto package can help you do that. Here are examples: example1, example2. Essentially, it's Example.parseFrom(byte[]).
I would like to get all the scalar summary data in Tensorflow summary.
TensorBoard web GUI only allows me to download 1,000 records of the summary. Is there any way I can get every single scalar data that I've put in the summary?
If you have access to the data you can load it up in python, as an alternative to downloading via tensorboard. See How to export tensor board data? how to do this.