I am trying convert my tensorflow segmentation model to openvino with quantization. I convert my .pb model to intermediate representation with openvino model optimizer. But how quantize model. In official documentation write that to do it with DL workbench. But in workbench i have only detection and classification dataset.
Can i convert my model to int8 without dataset or can i create dataset to segmentation?
The overall flow for converting a model from FP32 to INT8 is:
Select an FP32 model
Select an appropriate dataset
Run a baseline inference
Configure INT8 calibration settings
Configure inference settings for a calibrated model
View INT8 calibration
View inference results
Compare the calibrated model with the original FP32 model
Only some convolution models in the FP32 format can be quantized to INT8. If your model is incompatible, you will receive an error message.
The second stage of creating a configuration is adding a sample dataset. You can import a dataset, automatically generate a test dataset consisting of Gaussian distributed noise, or select a previously uploaded dataset.
You can find more details in the below link:
http://docs.openvinotoolkit.org/latest/_docs_Workbench_DG_Select_Datasets.html
You can find additional information about low precision inference in OpenVINO here:
General approach: https://docs.openvino.ai/latest/openvino_docs_IE_DG_Int8Inference.html
Post-Training Optimisation Tool (POT) with default algorithm: https://docs.openvino.ai/latest/pot_docs_LowPrecisionOptimizationGuide.html#doxid-pot-docs-low-precision-optimization-guide
Let me know if you still have questions.
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I have a pretrained tensor flow model; I converted it to tflite to make inferences on Rpi4. Now, I want to test the model on the same original test data saved in a (.csv) file. How can I do that using tflite. Interpreter?
I have tried testing the tflite model to get approximate results to the original one.
I want to retrain an existing object detection model with a new image dataset and quantize it for Intel Movidius. Is there any working procedure to do this?
I have successfully retrained the model but failing to quantize it. I have followed the following tutorial Retrain SSD MobileNet
The Movidius devices only support FP16 models, and to convert a caffe version of SSD Mobilenet then you supply the "--data_type FP16"to the model optimizer (mo.py)
The openvino model zoo has a mobilenet-ssd model,also using caffe, and the associated yaml file has the following parameters
model_optimizer_args:
--input_shape=[1,3,300,300]
--input=data
--mean_values=data[127.5,127.5,127.5]
--scale_values=data[127.5]
--output=detection_out
--input_model=$dl_dir/mobilenet-ssd.caffemodel
--input_proto=$dl_dir/mobilenet-ssd.prototxt.
Note that your input shape and the mean and scale values will likely be different so change those to match your retrained model.
There's also a demo file shipped with openvino that can be used with your converted model. See the associated mode.lst file for all the supported architectures. https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos/object_detection_demo/python
I have trained a CNN model with Keras for semantic segmentation of craneal images and saved the weights and this trained model.
Now, I want to put it into production on a microprocessor. The pipeline of the process in the micro involves reading an image from a sensor and using it as input for the CNN model (U-Net). Then, the resulted binary image is used as a mask for an area of interest from which a variable is measured. Finally, a number is given as a result.
So, is it possible to load a trained model on a microprocessor? And if so, how?
Which features should have the microprocessor in order to work with CNN models?
Thanks in advance!
I'm training tensorflow slim based models for image classification on a custom dataset. Before I invest a lot of time training such huge a dataset, I wanted to know whether or not can I convert all the models available in the slim model zoo to tflite format.
Also, I know that I can convert my custom slim-model to a frozen graph. It is the step after this which I'm worried about i.e, conversion to .tflite from my custom trained .pb model.
Is this supported ? or is there anyone who is facing conversion problems that has not yet been resolved ?
Thanks.
Many Slim models can be converted to TFLite, but it isn't a guarantee since some models might have ops not supported by TFLite.
What you could do, is try and convert your model to TensorFlow Lite using TFLiteConverter in Python before training. If the conversion succeeds, then you can train your TF model and convert it once again.
I have a model that is trained in Keras with tensor flow backend. The weights are in .h5 format. I am interested in applying quantization feature part of tensorflow (https://www.tensorflow.org/api_docs/python/tf/quantization). So far, I have managed to convert the weights from .h5 format to tensor flow .pb format using the tool available online (https://github.com/amir-abdi/keras_to_tensorflow/). There are a couple of issues with this and the main concern is I don’t see a reduction in my model size post quantization. Also, I need to re-convert the .pb weights to .h5 format to test it with my infrastructure.
Is there a known best method for performing tensorflow
quantization within Keras?
Is there an easy way to convert weights format from .pb to .h5?
Thanks