Tflite api


  • How to convert .pb and .h5 into .tflite file using python API
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  • On-Device Deep Learning: PyTorch Mobile and TensorFlow Lite
  • How to convert .pb and .h5 into .tflite file using python API
  • Build issue when including TFLite C API in Android app that uses C++ and Java
  • How to convert .pb and .h5 into .tflite file using python API

    It is possible to detect this at the time of model authoring to avoid having to re-construct the model to be compliant with TFLite. Operator Versioning PyTorch Mobile has no known notion of operator versions, so one needs to ensure that they are running a model only on the PyTorch Mobile runtime the model was built for.

    TFLite has experimental support for operator versioning , which understands three types of compatibility semantics. TFLite supports adding structured metadata to the model. This includes: Model information - Overall description of the model as well as items such as license terms. See ModelMetadata. Input information - Description of the inputs and pre-processing required such as normalization. Output information - Description of the output and post-processing required such as mapping to labels.

    A code snippet of the specific API defined on the Module class is shown below. The updated model is used in lite interpreter for mobile applications.

    Args: f: a string containing a file name. PyTorch also provides a way to benchmark its models for different platforms.

    There are some limitations regarding what kind of subgraphs can be supported, though. It is not clear what level of sparse operator support PyTorch Mobile offers, even though PyTorch itself supports sparse tensors. The PyTorch provided pre-trained models need to be re-saved in the lite-interpreter format before using on mobile platforms that is a trivial operation.

    Both provide numerous high-quality implementations of various features that are important to quickly and efficiently run your ML models on mobile platforms.

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    We start off by giving a brief overview of quantization in deep neural networks, followed by explaining different approaches to quantization and discussing the advantages and disadvantages of using each approach. Finally, as a use case example, we will examine the performance of different quantization approaches on the Coral Edge TPU. Quantization in neural networks: the concept Quantization, in general, refers to the process of reducing the number of bits that represent a number.

    Deep neural networks usually have tens or hundreds of millions of weights, represented by high-precision numerical values. Working with these numbers requires significant computational power, bandwidth, and memory.

    However, model quantization optimizes deep learning models by representing model parameters with low-precision data types, such as int8 and float16, without incurring a significant accuracy loss. Storing model parameters with low-precision data types not only saves bandwidth and storage but also results in faster calculations.

    Quantization brings efficiency to neural networks Quantization improves the overall efficiency in several ways. It saves the maximum possible memory space by converting parameters to 8-bits or bits instead of the standard bit representation format.

    Quantized neural networks consume less memory bandwidth. Moreover, quantizing neural networks results in 2x to 4x speedup during inference. Faster arithmetics could be another benefit of quantizing neural networks in some cases, depending on different factors such as the hardware architecture. As an example, 8-bit addition is almost 2x faster than bit addition on an Intel Core i7 processor. These benefits make quantization valuable, especially for edge devices that have modest compute and memory but are required to perform AI tasks in real-time.

    Quantizing neural networks is a win-win By reducing the number of bits that represent a parameter, some information is lost.

    However, this loss of information incurs little to no degradation in the accuracy of neural networks for two main reasons: This reduction in the number of bits acts like adding some noise to the network. Since a well-trained neural network is noise-robust, i.

    There are millions of weight and activation parameters in a neural network that are distributed in a relatively small range of values. Since these numbers are densely spread, quantizing them does not result in losing too much precision. To give you a better understanding of quantization, we next provide a brief explanation of how numbers are represented in a computer.

    Computer representation of numbers Computers have limited memory to store numbers. There are only discrete possibilities to represent the continuous spectrum of real numbers in the representation system of a computer. The limited memory only allows a fixed amount of values to be stored and represented in a computer, which can be determined based on the number of bits and bytes the computer representation system works with.

    Therefore, representing real numbers in a computer involves an approximation and a potential loss of significant digits. There are two main approaches to store and represent real numbers in modern computers: 1. Floating-point representation The floating-point representation of numbers consists of a mantissa and an exponent. In this representation system, the position of the decimal point is specified by the exponent value. Thus, this system can represent both very small values and very large numbers.

    Fixed-point representation In this representation format, the position of the decimal point is fixed. The numbers share the exponent, and they vary in the mantissa portion only. Figure 1. Floating-point and fixed-point representation of numbers image source.

    The amount of memory required for the fixed-point format is much less than the floating-point format since the exponent is shared between different numbers in the former. However, the floating-point representation system can represent a wider range of numbers compared to the fixed-point format. The precision of computer numbers The precision of a representation system depends on the number of values it can represent precisely, which is 2b, where b is the number of bits.

    In this system, only values are represented precisely. The rest of the numbers are rounded to the nearest number of these values. Thus, the more bits we can use, the more precise our numbers will be. It is worth mentioning that the 8-bit representation system in the previous example is not limited to representing integer values from 1 to This system can represent pieces of information in any arbitrary range of numbers.

    How to quantize numbers in a representation system To determine the representable numbers in a representation system with b bits, we subtract the minimum value from the maximum one to calculate r, the range of values. Then, we divide r by 2b to find u, the smallest unit in this format. However, when quantizing neural networks, it is critical to represent the 0 value precisely without any approximation error , as explained in this paper. Figure 2. Quantizing numbers in a representation system image source.

    In the next section, we will explain how we can calculate the range of parameters in a neural network in order to quantize them. How to quantize neural networks Quantization is to change the current representation format of numbers to another lower precision format by reducing the amount of the representing bits. In machine learning, we use the floating-point format to represent numbers. By applying quantization, we can change the representation to the fixed-point format and down-sample these values.

    In most cases, we convert the bit floating-point to the 8-bit fixed-point format, which gives almost 4x reduction in memory utilization. There are at least two sets of numerical parameters in each neural network; the set of weights, that are constant numbers in inference learned by the network during the training phase, and the set of activations, which are the output values of activation functions in each layer.

    By quantizing neural networks, we mean quantizing these two sets of parameters. As we saw in the previous section, to quantize each set of parameters, we need to know the range of values each set holds and then quantize each number within that range to a representable value in our representation system.

    While finding the range of weights is straight-forward, calculating the range of activations can be challenging. As we will see in the following sections, each quantization approach deals with this challenge in its own way. Most of the quantization techniques are applied to inference but not training. The reason is that in each backpropagation step of the training phase, parameters are updated with changes that are too small to be tracked by a low-precision data-type. Therefore, we train a neural network with high-precision numbers and then quantize the weight values.

    Types of neural network quantization There are two common approaches to neural network quantization: 1 post-training quantization, and 2 quantization-aware training. We will next explain each method in more detail and discuss the advantages and disadvantages of each technique. Post-training quantization The post-training quantization approach is the most commonly used form of quantization.

    In this approach, quantization takes place only after the model has finished training. To perform post-training quantization, we first need to know the range of each parameter, i. Finding the range of weights is straight-forward since weights remain constant after training has been finished.

    However, the range of activations is challenging to determine because activation values vary based on the input tensor.

    Thus, we need to estimate the range of activations. To do so, we provide a dataset that represents the inference data to the quantization engine the module that performs quantization. The quantization engine calculates all the activations for each data point in the representative dataset and estimates the range of activations.

    After calculating the range of both parameters, the quantization engine converts all the values within those ranges to lower bit numbers. The main advantage of using this technique is that it does not require any model training or fine-tuning. You can apply 8-bit quantization on any existing pre-trained floating-point model without using many resources.

    However, this approach comes at the cost of losing some accuracy because the pre-trained network was trained regardless of the fact that the parameters will be quantized to 8-bit values after training has been finished, and quantization adds some noise to the input of the model at inference time. Quantization-aware training As we explained in the previous section, in the post-processing quantization approach, training was in floating-point precision regardless of the fact that the parameters will be quantized to lower bit values.

    This difference of precision that originates from quantizing weights and activations enters some error to the network that propagates through the network by multiplications and additions. In quantization-aware training, however, we attempt to artificially enter this quantization error into the model during training to make the model robust to this error.

    Note that similar to post-training quantization, in quantization-aware training, backpropagation is still performed on floating-point weights to capture the small changes.

    In this method, extra nodes that are responsible for simulating the quantization effect will be added. These nodes quantize the weights to lower precision and convert them back to the floating-point in each forward pass and are deactivated during backpropagation.

    This approach will add quantization noise to the model during training while performing backpropagation in floating-point format. Since these nodes quantize weights and activations during training, calculating the ranges of weights and activations is automatic during training. Therefore, there is no need to provide a representative dataset to estimate the range of parameters.

    Figure 3. Quantization-aware training method image source. Quantization-aware training gives less accuracy drop compared to post-training quantization and allows us to recover most of the accuracy loss introduced by quantization. Moreover, it does not require a representative dataset to estimate the range of activations.

    The main disadvantage of quantization-aware training is that it requires retraining of the model. Here you can see benchmarks of various models with and without quantization.

    Model quantization with TensorFlow So far, we have described the purpose behind quantization and reviewed different quantization approaches. In this section, we will dive deep into the TensorFlow Object Detection API and explain how to perform post-training quantization and quantization-aware training.

    You can quickly train an object detector in three steps: STEP 1: Change the format of your training dataset to tfrecord format. STEP 3: Customize a config file according to your model architecture. Figure 4. This tool provides developers with a large number of pre-trained models that are trained on different datasets such as COCO.

    Therefore, you do not need to start from scratch to train a new model; you can simply retrain the pre-trained models for your specific needs. You can find the available config files here. Note that TensorFlow 1. You can build the Docker container from source or pull the container from Docker Hub.

    See the instructions below to run the container. To perform quantization or inference, you need to export these trained checkpoints to a protobuf file by freezing its computational graph.

    The precision of computer numbers The precision of a representation system depends on the number of values it can represent precisely, which is 2b, where b is the number of bits. In this system, only values are represented precisely. The rest of the numbers are rounded to the nearest number of these values. Thus, the more bits we can use, the more precise our numbers will be.

    It is worth mentioning that the 8-bit representation system in the previous example is not limited to representing integer values from 1 to This system can represent pieces of information in any arbitrary range of numbers. How to quantize numbers in a representation system To determine the representable numbers in a representation system with b bits, we subtract the minimum value from the maximum one to calculate r, the range of values.

    Then, we divide r by 2b to find u, the smallest unit in this format. However, when quantizing neural networks, it is critical to represent the 0 value precisely without any approximation erroras explained in this paper. Figure 2. Quantizing numbers in a representation system image source. In the next section, we will explain how we can calculate the range of parameters in a neural network in order to quantize them.

    How to quantize neural networks Quantization is to change the current representation format of numbers to another lower precision format by reducing the amount of the representing bits.

    In machine learning, we use the floating-point format to represent numbers. By applying quantization, we can change the representation to the fixed-point format and down-sample these values. In most cases, we convert the bit floating-point to the 8-bit fixed-point format, which gives almost 4x reduction in memory utilization.

    There are at least two sets of numerical parameters in each neural network; the set of weights, that are constant numbers in inference learned by the network during the training phase, and the set of activations, which are the output values of activation functions in each layer.

    By quantizing neural networks, we mean quantizing these two sets of parameters.

    On-Device Deep Learning: PyTorch Mobile and TensorFlow Lite

    As we saw in the previous section, to quantize each set of parameters, we need to know the range of values each set holds and then quantize each number within that range to a representable value in our representation system.

    While finding the range of weights is straight-forward, calculating the range of activations can be challenging. As we will see in the following sections, each quantization approach deals with this challenge in its own way. Most of the quantization techniques are applied to inference but not training.

    The reason is that in each backpropagation step of the training phase, parameters are updated with changes that are too small to be tracked by a low-precision data-type. Therefore, we train a neural network with high-precision numbers and then quantize the weight values.

    Types of neural network quantization There are two common approaches to neural network quantization: 1 post-training quantization, and 2 quantization-aware training. We will next explain each method in more detail and discuss the advantages and disadvantages of each technique.

    Post-training quantization The post-training quantization approach is the most commonly used form of quantization. In this approach, quantization takes place only after the model has finished training. To perform post-training quantization, we first need to know the range of each parameter, i. Finding the range of weights is straight-forward since weights remain constant after training has been finished. However, the range of activations is challenging to determine because activation values vary based on the input tensor.

    Thus, we need to estimate the range of activations. To do so, we provide a dataset that represents the inference data to the quantization engine the module that performs quantization. The quantization engine calculates all the activations for each data point in the representative dataset and estimates the range of activations. After calculating the range of both parameters, the quantization engine converts all the values within those ranges to lower bit numbers.

    The main advantage of using this technique is that it does not require any model training or fine-tuning. You can apply 8-bit quantization on any existing pre-trained floating-point model without using many resources. However, this approach comes at the cost of losing some accuracy because the pre-trained network was trained regardless of the fact that the parameters will be quantized to 8-bit values after training has been finished, and quantization adds some noise to the input of the model at inference time.

    Quantization-aware training As we explained in the previous section, in the post-processing quantization approach, training was in floating-point precision regardless of the fact that the parameters will be quantized to lower bit values. This difference of precision that originates from quantizing weights and activations enters some error to the network that propagates through the network by multiplications and additions. In quantization-aware training, however, we attempt to artificially enter this quantization error into the model during training to make the model robust to this error.

    Note that similar to post-training quantization, in quantization-aware training, backpropagation is still performed on floating-point weights to capture the small changes. In this method, extra nodes that are responsible for simulating the quantization effect will be added. These nodes quantize the weights to lower precision and convert them back to the floating-point in each forward pass and are deactivated during backpropagation.

    This approach will add quantization noise to the model during training while performing backpropagation in floating-point format. Since these nodes quantize weights and activations during training, calculating the ranges of weights and activations is automatic during training. Therefore, there is no need to provide a representative dataset to estimate the range of parameters.

    Figure 3. Quantization-aware training method image source. Quantization-aware training gives less accuracy drop compared to post-training quantization and allows us to recover most of the accuracy loss introduced by quantization.

    Moreover, it does not require a representative dataset to estimate the range of activations. The main disadvantage of quantization-aware training is that it requires retraining of the model. Here you can see benchmarks of various models with and without quantization. Model quantization with TensorFlow So far, we have described the purpose behind quantization and reviewed different quantization approaches.

    How to convert .pb and .h5 into .tflite file using python API

    In this section, we will dive deep into the TensorFlow Object Detection API and explain how to perform post-training quantization and quantization-aware training.

    You can quickly train an object detector in three steps: STEP 1: Change the format of your training dataset to tfrecord format. STEP 3: Customize a config file according to your model architecture. Figure 4. This tool provides developers with a large number of pre-trained models that are trained on different datasets such as COCO.

    Therefore, you do not need to start from scratch to train a new model; you can simply retrain the pre-trained models for your specific needs. You can find the available config files here. Note that TensorFlow 1. You can build the Docker container from source or pull the container from Docker Hub.

    See the instructions below to run the container. To perform quantization or inference, you need to export these trained checkpoints to a protobuf file by freezing its computational graph. We will use this file in the next steps to perform quantization. Post-training quantization with TFlite Converter As described earlier, post-training quantization allows you to convert a model trained with floating-point numbers to a quantized model.

    You can apply post-training quantization using TFlite Converter to convert a TensorFlow model into a TensorFlow Lite model that is suitable for on-device inference. A code snippet of the specific API defined on the Module class is shown below. The updated model is used in lite interpreter for mobile applications. Args: f: a string containing a file name.

    Build issue when including TFLite C API in Android app that uses C++ and Java

    PyTorch also provides a way to benchmark its models for different platforms. There are some limitations regarding what kind of subgraphs can be supported, though. It is not clear what level of sparse operator support PyTorch Mobile offers, even though PyTorch itself supports sparse tensors. The PyTorch provided pre-trained models need to be re-saved in the lite-interpreter format before using on mobile platforms that is a trivial operation.

    Both provide numerous high-quality implementations of various features that are important to quickly and efficiently run your ML models on mobile platforms.


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