From 705e1d04245de363119979165712cc34973dfb80 Mon Sep 17 00:00:00 2001 From: AdamMiltonBarker Date: Fri, 20 Aug 2021 18:01:22 +0200 Subject: [PATCH 1/3] 1.0.0 --- LICENSE | 2 +- README.md | 156 +- assets/images/all-idb.jpg | Bin 0 -> 79011 bytes assets/images/bug-report.jpg | Bin 0 -> 79504 bytes assets/images/feature-proposals.jpg | Bin 0 -> 85738 bytes assets/images/feature-request.jpg | Bin 0 -> 83660 bytes assets/images/fork.jpg | Bin 0 -> 323331 bytes assets/images/nvidia-jetson-nano-devkit.jpg | Bin 0 -> 645428 bytes assets/images/project-banner.jpg | Bin 0 -> 95367 bytes assets/images/repo-issues.jpg | Bin 0 -> 59208 bytes classifier.py | 159 ++ configuration/config.json | 64 + docs/img/plots/accuracy.png | Bin 0 -> 23806 bytes docs/img/plots/auc.png | Bin 0 -> 27717 bytes docs/img/plots/confusion-matrix.png | Bin 0 -> 8904 bytes docs/img/plots/loss.png | Bin 0 -> 28618 bytes docs/img/plots/precision.png | Bin 0 -> 32401 bytes docs/img/plots/recall.png | Bin 0 -> 31856 bytes 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create mode 100644 modules/__init__.py create mode 100644 modules/augmentation.py create mode 100644 modules/data.py create mode 100644 modules/engine.py create mode 100644 modules/helpers.py create mode 100644 modules/model.py create mode 100644 modules/server.py create mode 100644 notebooks/classifier.ipynb create mode 100644 scripts/docker.txt create mode 100644 scripts/install.sh create mode 100644 scripts/oneapi.sh diff --git a/LICENSE b/LICENSE index 1453ee5..1f7fb6f 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2021 Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project +Copyright (c) 2021 Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index 18f913e..e5ef455 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,154 @@ -# ALL-Jetson-Nano-Classifier -An Acute Lymphoblastic Leukemia classifier developed for the NVIDIA Jetson Nano. Jetson AI Certification project by Adam Milton-Barker. +# Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project +## Acute Lymphoblastic Leukemia Jetson Nano Classifier + +![Acute Lymphoblastic Leukemia Jetson Nano Classifier](assets/images/project-banner.jpg) + +[![CURRENT RELEASE](https://img.shields.io/badge/CURRENT%20RELEASE-1.0.0-blue.svg)](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/tree/1.0.0) [![DEV BRANCH](https://img.shields.io/badge/DEV%20BRANCH-develop-blue.svg)](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/tree/develop) [![Contributions Welcome!](https://img.shields.io/badge/Contributions-Welcome-lightgrey.svg)](CONTRIBUTING.md) [![Issues](https://img.shields.io/badge/Issues-Welcome-lightgrey.svg)](issues) + +[![PEP8](https://img.shields.io/badge/code%20style-pep8-orange.svg)](https://www.python.org/dev/peps/pep-0008/) [![Documentation Status](https://readthedocs.org/projects/acute-lymphoblastic-leukemia-pytorch-segmentation-2021/badge/?version=latest)](https://acute-lymphoblastic-leukemia-pytorch-segmentation-2021.readthedocs.io/en/latest/?badge=latest) + [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5068/badge)](https://bestpractices.coreinfrastructure.org/projects/5068) [![DOI](https://zenodo.org/badge/383255630.svg)](https://zenodo.org/badge/latestdoi/383255630) + + + [![LICENSE](https://img.shields.io/badge/LICENSE-MIT-blue.svg)](LICENSE) + +  + +# Table Of Contents + +- [Introduction](#introduction) +- [NVIDIA Jetson Nano](#nvidia-jetson-nano) +- [TFRT](#tfrt) +- [ONNX](#onnx) +- [TensorRT](#tensorrt) +- [Motivation](#motivation) +- [DISCLAIMER](#disclaimer) +- [Acute Lymphoblastic Leukemia](#acute-lymphoblastic-leukemia) +- [ALL IDB](#all-idb) +- [Getting Started](#getting-started) +- [Quick Start](#quick-start) +- [Contributing](#contributing) + - [Contributors](#contributors) +- [Versioning](#versioning) +- [License](#license) +- [Bugs/Issues](#bugs-issues) + +  + +# Introduction + +The **Acute Lymphoblastic Leukemia Jetson Nano Classifier** is a Convolutional Neural Network (CNN) developed using [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi.html) and [Intel® Optimization for Tensorflow*](https://software.intel.com/content/www/us/en/develop/articles/getting-started-with-intel-optimization-of-pytorch.html) to accelerate the training process, and [TensorRT](https://developer.nvidia.com/tensorrt) for high performance inference on the [NVIDIA® Jetson Nano™](https://developer.nvidia.com/embedded/jetson-nano-developer-kit). + +In this project you will do the following. + +- Train a custom CNN for Acute Lymphoblastic Leukemia on your development machine. +- Convert the Tensorflow SavedModel to TFRT format +- Convert the Tensorflow SavedModel to ONNX format +- Convert the Tensorflow ONNX model to TensorRT format +- Test the CNN on your development machine. +- Download or create a custom docker container for your NVIDIA Jetson Nano. +- Run the CNN on your NVIDIA Jetson Nano using Tensorflow. +- Run the CNN on your NVIDIA Jetson Nano using TFRT. +- Run the CNN on your NVIDIA Jetson Nano using TensorRT. + +  + +# NVIDIA Jetson Nano + +![NVIDIA Jetson Nano Devkit](assets/images/nvidia-jetson-nano-devkit.jpg) + +The NVIDIA® Jetson Nano™ is a small, powerful computer that is capable of running multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. + +  + +# TFRT + +"TFRT is a new TensorFlow runtime. It aims to provide a unified, extensible infrastructure layer with best-in-class performance across a wide variety of domain specific hardware. It provides efficient use of multithreaded host CPUs, supports fully asynchronous programming models, and focuses on low-level efficiency." + +[Source](https://github.com/tensorflow/runtime) + +  + +# ONNX + +"ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers." + +[Source](https://onnx.ai/) + +  + +TensorRT + +"NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications." + +[Source](https://developer.nvidia.com/tensorrt) + +  + +# Motivation + +The motivation behind this project was to test the capabilities of the NVIDIA® Jetson Nano™ for the classification of Acute Lymphoblastic Leukemia on the edge. This project is also a submission for the [NVIDIA Jetson AI Specialist Certification](https://developer.nvidia.com/embedded/learn/jetson-ai-certification-programs). + +  + +# DISCLAIMER + +_This project should be used for research purposes only. The purpose of the project is to show the potential of Artificial Intelligence for medical support systems such as diagnostic systems._ + +_Although the model is accurate and shows good results both on paper and in real world testing, it is trained on a small amount of data and needs to be trained on larger datasets to really evaluate it's accuracy._ + +_Developers that have contributed to this repository have experience in using Artificial Intelligence for detecting certain types of cancer. They are not doctors, medical or cancer experts._ + +  + +# Acute Lymphoblastic Leukemia +[Acute lymphoblastic leukemia (ALL)](https://www.leukemiaairesearch.com/research/leukemia), also known as Acute Lymphocytic Leukemia, is a cancer that affects the lymphoid blood cell lineage. It is the most common leukemia in children, and it accounts for 10-20% of acute leukemias in adults. The prognosis for both adult and especially childhood ALL has improved substantially since the 1970s. The 5- year survival is approximately 95% in children. In adults, the 5-year survival varies between 25% and 75%, with more favorable results in younger than in older patients. + +For more information about Acute Lymphoblastic Leukemia please visit our [Leukemia Information Page](https://www.leukemiaairesearch.com/research/leukemia) + +  + +# ALL-IDB + +![Acute Lymphoblastic Leukemia Jetson Nano Classifier](assets/images/all-idb.jpg) + +You need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on [this page](https://homes.di.unimi.it/scotti/all/#download) as well as information on how to contribute back to the project [here](https://homes.di.unimi.it/scotti/all/results.php). If you are not able to obtain a copy of the dataset please feel free to try this tutorial on your own dataset, we would be very happy to find additional AML & ALL datasets. + +  + +# Getting Started + +To get started follow the project [documentation](docs/index.md). + +  + +# Quick Start + +- [Ubuntu Installation Guide](docs/installation/ubuntu.md) +- [Notebook Usage Guide](docs/usage/notebooks.md) +- [NVIDIA Jetson Nano Installation Guide](docs/installation/jetson.md) +- [NVIDIA Jetson Nano Usage Guide](docs/usage/jetson.md) + +  + +# Contributing +Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss encourages and welcomes code contributions, bug fixes and enhancements from the Github community. + +Please read the [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") document for a full guide to forking our repositories and submitting your pull requests. You will also find our code of conduct in the [Code of Conduct](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CODE-OF-CONDUCT.md) document. + +## Contributors +- [Adam Milton-Barker](https://www.leukemiaairesearch.com/association/volunteers/adam-milton-barker "Adam Milton-Barker") - [Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss](https://www.leukemiaresearchassociation.ai "Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss") President/Founder & Lead Developer, Sabadell, Spain + +  + +# Versioning +We use [SemVer](https://semver.org/) for versioning. + +  + +# License +This project is licensed under the **MIT License** - see the [LICENSE](LICENSE "LICENSE") file for details. + +  + +# Bugs/Issues +We use the [repo issues](issues "repo issues") to track bugs and general requests related to using this project. 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b/classifier.py @@ -0,0 +1,159 @@ +#!/usr/bin/env python3 +""" ALL Jetson Nano Classifier + +An Acute Lymphoblastic Leukemia classifier developed for the NVIDIA Jetson Nano. +Jetson AI Certification project by Adam Milton-Barker. + +MIT License + +Copyright (c) 2021 Asociación de Investigacion en Inteligencia Artificial +Para la Leucemia Peter Moss + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +import sys + +from abc import ABC, abstractmethod + +from modules.AbstractClassifier import AbstractClassifier + +from modules.helpers import helpers +from modules.model import model +from modules.server import server + + +class classifier(AbstractClassifier): + """ ALL Jetson Nano Classifier + + Represents a AI classifier that processes data + using the ALL Jetson Nano Classifier model. + """ + + def train(self): + """ Creates & trains the model. """ + + self.model.prepare_data() + self.model.prepare_network() + self.model.train() + self.model.evaluate() + + def init_model(self): + """ Initializes the model class """ + + self.model = model(self.helpers) + + def load_model(self): + """ Loads the trained model """ + + self.model.load() + + def load_model_tfrt(self): + """ Loads the trained TFRT model """ + + self.model.load_tfrt() + + def inference(self): + """ Classifies test data locally """ + + self.load_model() + self.model.test() + + def server(self): + """ Starts the API server """ + + self.load_model() + self.server = server(self.helpers, self.model, + self.model_type) + self.server.start() + + def inference_http(self): + """ Classifies test data via HTTP requests """ + + self.model.test_http() + + def inference_tfrt(self): + """ Classifies test data via HTTP requests """ + + self.load_model_tfrt() + self.model.test_tfrt() + + def init_engine(self): + """ Initizializes the engine class """ + + from modules.engine import engine + + self.engine = engine(self.helpers) + + def inference_tensorrt(self): + """ Classifies test data via HTTP requests """ + + self.engine.load_engine() + self.engine.test() + + def signal_handler(self, signal, frame): + self.helpers.logger.info("Disconnecting") + sys.exit(1) + + +classifier = classifier() + + +def main(): + + if len(sys.argv) < 2: + print("You must provide an argument") + exit() + elif sys.argv[1] not in classifier.helpers.confs["agent"]["params"]: + print("Mode not supported! server, train or inference") + exit() + + mode = sys.argv[1] + + if mode == "train": + classifier.init_model() + classifier.train() + + elif mode == "classify": + classifier.init_model() + classifier.inference() + + elif mode == "server": + classifier.init_model() + classifier.server() + + elif mode == "classify_http": + classifier.init_model() + classifier.inference_http() + + elif mode == "classify_tfrt": + classifier.init_model() + classifier.inference_tfrt() + + elif mode == "classify_tensorrt": + classifier.init_engine() + classifier.inference_tensorrt() + + +if __name__ == "__main__": + main() diff --git a/configuration/config.json b/configuration/config.json new file mode 100644 index 0000000..6e34045 --- /dev/null +++ b/configuration/config.json @@ -0,0 +1,64 @@ +{ + "agent": { + "cores": 8, + "port": 1234, + "params": [ + "train", + "classify", + "server", + "classify_http", + "classify_tfrt", + "classify_tensorrt" + ] + }, + "data": { + "dim": 100, + "file_type": ".jpg", + "labels": [0, 1], + "rotations": 10, + "seed": 2, + "split": 0.255, + "test": "model/data/test", + "test_data": [ + "Im006_1.jpg", + "Im020_1.jpg", + "Im024_1.jpg", + "Im026_1.jpg", + "Im028_1.jpg", + "Im031_1.jpg", + "Im035_0.jpg", + "Im041_0.jpg", + "Im047_0.jpg", + "Im053_1.jpg", + "Im057_1.jpg", + "Im060_1.jpg", + "Im063_1.jpg", + "Im069_0.jpg", + "Im074_0.jpg", + "Im088_0.jpg", + "Im095_0.jpg", + "Im099_0.jpg", + "Im101_0.jpg", + "Im106_0.jpg" + ], + "train_dir": "model/data/train", + "valid_types": [ + ".jpg" + ] + }, + "model": { + "model": "model/all_jetson_nano.json", + "weights": "model/all_jetson_nano.h5", + "saved": "model/saved", + "tfrt": "model/tfrt", + "onnx": "model/all_jetson_nano.onnx", + "tensorrt": "model/all_jetson_nano.plan" + }, + "train": { + "batch": 100, + "decay_adam": 1e-6, + "epochs": 150, + "learning_rate_adam": 1e-4, + "val_steps": 10 + } +} \ No newline at end of file diff --git a/docs/img/plots/accuracy.png b/docs/img/plots/accuracy.png new file mode 100644 index 0000000000000000000000000000000000000000..924f6900cbff6e79df27fea40f2b6701c8cd3ac4 GIT binary patch literal 23806 zcmdSBg;!MH8$NnSML-lpq@+V)_)sE^gmiazmvl2Ah?I1vlG4&Klp@mIF@TZ-3`qCP z@9@3%uDkA^a9N85oHOT~ecrwI8_)YZM5(FB5;@$@lww+5We zJ>NUIdOF!#(0W_Dd)T|W@ItsDJRG!ko}TZ8xVZk`^AJ~eTP~)UuL=M_3&=~q)$+~R zhd&5R(QfNIViBc#;vmWL<&^#*tBk}GjM{>|52QC1GBV4lUNzNvYIBgE1`;yr>QBi? 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new file mode 100644 index 0000000..cafad79 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,61 @@ +# Documentation + +![ALL Jetson Nano](img/project-banner.jpg) + +# Welcome + +Welcome to the [ALL Jetson Nano](https://github.com/AMLResearchProject/ALL-Jetson-Nano) official documentation. + +  + +#DISCLAIMER + +_This project should be used for research purposes only. The purpose of the project is to show the potential of Artificial Intelligence for medical support systems such as diagnostic systems._ + +_Although the model is accurate and shows good results both on paper and in real world testing, it is trained on a small amount of data and needs to be trained on larger datasets to really evaluate it's accuracy._ + +_Developers that have contributed to this repository have experience in using Artificial Intelligence for detecting certain types of cancer. They are not doctors, medical or cancer experts._ + +  + +# Installation + +Use the following installation guides to set up your project.: + +- [Ubuntu Installation Guide](installation/ubuntu.md) +- [NVDIA Jetson Nano Installation Guide](installation/jetson.md) + +  + +# Usage + +Use the following usage guides to to train your classifier and use your classifier on Arduino: + +- [Python Usage Guide](usage/python.md) +- [Jupyter Notebooks Usage Guide](usage/notebooks.md) +- [NVDIA Jetson Nano Usage Guide](usage/jetson.md) + +  + +# Contributing +Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss encourages and welcomes code contributions, bug fixes and enhancements from the Github community. + +Please read the [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") document for a full guide to forking our repositories and submitting your pull requests. You will also find our code of conduct in the [Code of Conduct](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CODE-OF-CONDUCT.md) document. + +## Contributors +- [Adam Milton-Barker](https://www.leukemiaairesearch.com/association/volunteers/adam-milton-barker "Adam Milton-Barker") - [Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss](https://www.leukemiaresearchassociation.ai "Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss") President/Founder & Lead Developer, Sabadell, Spain + +  + +# Versioning +We use [SemVer](https://semver.org/) for versioning. + +  + +# License +This project is licensed under the **MIT License** - see the [LICENSE](https://github.com/AMLResearchProject/ALL-Arduino-Nano-33-BLE-Sense-Classifier/blob/main/LICENSE "LICENSE") file for details. + +  + +# Bugs/Issues +We use the [repo issues](https://github.com/AMLResearchProject/ALL-Jetson-Nano/issues "repo issues") to track bugs and general requests related to using this project. See [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") for more info on how to submit bugs, feature requests and proposals. \ No newline at end of file diff --git a/docs/installation/jetson.md b/docs/installation/jetson.md new file mode 100644 index 0000000..3067605 --- /dev/null +++ b/docs/installation/jetson.md @@ -0,0 +1,103 @@ +# Jetson Nano Installation + +![ALL Jetson Nano Classifier](../img/project-banner.jpg) + +# Introduction +This guide will guide you through the installation process for the **ALL Jetson Nano Classifier** on your Jetson Nano device. + +  + +# Operating System +This project requires the [Jetson Nano Developer Kit SD Card Image](https://developer.nvidia.com/jetson-nano-sd-card-image). For information on how to set up your Jetson Nano prior to starting this tutorial visit [Getting Started with Jetson Nano Developer Kit](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit) + +  + +# Hardware +The following hardware is required for this project: + +- [NVIDIA Jetson Nano Developer Kit](https://developer.nvidia.com/embedded/jetson-nano-developer-kit) + +  + +# Software +We have prepared a Docker image for this project to allow easy installation on the NVIDIA Jetson Nano. To download the image visit the [petermossleukemiaairesearch/all-jetson-nano](https://hub.docker.com/r/petermossleukemiaairesearch/all-jetson-nano) repository. + +  + +# Prerequisites +You will need to ensure you have followed the provided guides below: + +- [Ubuntu Installation Guide](../installation/ubuntu.md) +- [Python Usage Guide](../usage/python.md) or [Jupyter Notebooks Usage Guide](../usage/notebooks.md) + +  + +# Setup + +The first thing you need to do is transfer the trained model and data from your development/training machine to your home directory on the Jetson Nano. The remainder of this tutorial assumes you have copied the entire ALL-Jetson-Nano-Classifier directory to your home directory on the Jetson Nano. + +In your home directory on your Jetson Nano, ensure that you have completed this step. + +``` bash +ls +``` + +You should see the following output: + +``` +ALL-Jetson-Nano-Classifier +``` + +You should now ensure that you have the following: + +- The test data is in the model/data/test directory +- The model/saved directory is not empty and contains the saved_model.pb file, assets and variables directories. +- The model/tfrt directory is not empty and contains the saved_model.pb file, assets and variables directories. +- The all_jetson_nano.json file is in the model directory +- The all_jetson_nano.h5 file is in the model directory +- The all_jetson_nano.onnx file is in the model directory + +  + +# Docker Image + +Now you are ready to use the ALL Jetson Nano Classifier docker image on your Jetson Nano. + +Open terminal on your Jetson Nano or login using ssh and use the following command to download the image, start the container and mount the ALL-Jetson-Nano-Classifier directory as a volume in the container. Ensure you change YourUser with the user you are logged into the nano on. + +``` bash +sudo docker run -it --name all-jetson-nano --rm --runtime nvidia --network host -v /home/YourUser/ALL-Jetson-Nano-Classifier:/ALL-Jetson-Nano-Classifier petermossleukemiaairesearch/all-jetson-nano:v1 +``` + +If you want to create your own Docker image, steps have been provided in the [scripts/docker.txt](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/blob/main/scripts/docker.txt) file. + +  + +# Continue + +Now you are ready to use the ALL Jetson Nano Classifier. Head over to the [Jetson Nano Usage Guide](../usage/jetson.md) for instructions on how to use your model with on the Jetson Nano. + +  + +# Contributing +Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss encourages and welcomes code contributions, bug fixes and enhancements from the Github community. + +Please read the [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") document for a full guide to forking our repositories and submitting your pull requests. You will also find our code of conduct in the [Code of Conduct](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CODE-OF-CONDUCT.md) document. + +## Contributors +- [Adam Milton-Barker](https://www.leukemiaairesearch.com/association/volunteers/adam-milton-barker "Adam Milton-Barker") - [Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss](https://www.leukemiaresearchassociation.ai "Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss") President/Founder & Lead Developer, Sabadell, Spain + +  + +# Versioning +We use [SemVer](https://semver.org/) for versioning. + +  + +# License +This project is licensed under the **MIT License** - see the [LICENSE](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/blob/main/LICENSE "LICENSE") file for details. + +  + +# Bugs/Issues +We use the [repo issues](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/issues "repo issues") to track bugs and general requests related to using this project. See [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") for more info on how to submit bugs, feature requests and proposals. \ No newline at end of file diff --git a/docs/installation/ubuntu.md b/docs/installation/ubuntu.md new file mode 100644 index 0000000..c83b6c9 --- /dev/null +++ b/docs/installation/ubuntu.md @@ -0,0 +1,135 @@ +# Ubuntu Installation + +![ALL Jetson Nano Classifier](../img/project-banner.jpg) + +# Introduction +This guide will take you through the installation process for the **ALL Jetson Nano Classifier** trainer. + +  + +# Operating System +This project supports the following operating system(s), but may work as described on other OS. + +- [Ubuntu 20.04](https://releases.ubuntu.com/20.04/) + +  + +# Software +This project uses the following libraries. + +- Conda +- Intel® oneAPI AI Analytics Toolkit +- Jupyter Notebooks +- NBConda +- Mlxtend +- Pillow +- Opencv +- Scipy +- Scikit Image +- Scikit Learn + +  + +# Clone the repository + +Clone the [ALL Jetson Nano Classifier](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier " ALL Jetson Nano Classifier") repository from the [Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project](https://github.com/AMLResearchProject "Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project") Github Organization. + +To clone the repository and install the project, make sure you have Git installed. Now navigate to the directory you would like to clone the project to and then use the following command. + +``` bash + git clone https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier.git +``` + +This will clone the ALL Jetson Nano Classifier repository. + +``` bash + ls +``` + +Using the ls command in your home directory should show you the following. + +``` bash + ALL-Jetson-Nano-Classifier +``` + +Navigate to the **ALL-Jetson-Nano-Classifier** directory, this is your project root directory for this tutorial. + +## Developer forks + +Developers from the Github community that would like to contribute to the development of this project should first create a fork, and clone that repository. For detailed information please view the research project [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") guide. You should pull the latest code from the development branch. + +``` bash + git clone -b "develop" https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier.git +``` + +The **-b "develop"** parameter ensures you get the code from the latest develop branch. + +  + +# Installation + +First you will install the [Intel® oneAPI Basekit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/base-toolkit.html), [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html) and the Intel oneAPI GPU drivers. + +**WARNING:** This script assumes you have not already installed the oneAPI Basekit. + +**WARNING:** This script assumes you have not already installed the oneAPI AI Analytics Toolkit. + +**WARNING:** This script assumes you have an Intel GPU. + +**WARNING:** This script assumes you have already installed the Intel GPU drivers. + +**HINT:** If any of the above are not relevant to you, please comment out the relevant sections below before running this installation script. + +From the project root run the following command in terminal: + +``` bash + sh scripts/oneapi.sh +``` + +You are now ready to install the ALL Jetson Nano Classifier trainer. All software requirements are included in **scripts/install.sh**. You can run this file on your machine from the project root in terminal. + +Before you begin make sure you have activated the **all-jetson-nano** Conda environment. + +``` bash + conda activate all-jetson-nano +``` + +Then use the following command to install the required software: + +``` bash + sh scripts/install.sh +``` + +  + +# Continue + +Choose one of the following usage guides to train your model: + +- [Python Usage Guide](../usage/python.md) +- [Jupyter Notebook Usage Guide](../usage/notebooks.md) + +  + +# Contributing +Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss encourages and welcomes code contributions, bug fixes and enhancements from the Github community. + +Please read the [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") document for a full guide to forking our repositories and submitting your pull requests. You will also find our code of conduct in the [Code of Conduct](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CODE-OF-CONDUCT.md) document. + +## Contributors +- [Adam Milton-Barker](https://www.leukemiaairesearch.com/association/volunteers/adam-milton-barker "Adam Milton-Barker") - [Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss](https://www.leukemiaresearchassociation.ai "Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss") President/Founder & Lead Developer, Sabadell, Spain + +  + +# Versioning +We use [SemVer](https://semver.org/) for versioning. + +  + +# License +This project is licensed under the **MIT License** - see the [LICENSE](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/blob/main/LICENSE "LICENSE") file for details. + +  + +# Bugs/Issues +We use the [repo issues](https://github.com/AMLResearchProject/ALL-Jetson-Nano/issues "repo issues") to track bugs and general requests related to using this project. See [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") for more info on how to submit bugs, feature requests and proposals. \ No newline at end of file diff --git a/docs/usage/jetson.md b/docs/usage/jetson.md new file mode 100644 index 0000000..a8aa2a8 --- /dev/null +++ b/docs/usage/jetson.md @@ -0,0 +1,274 @@ +# Jetson Nano Usage + +![ALL Jetson Nano Classifier](../img/project-banner.jpg) + +# Introduction +This guide will take you through the using the **ALL Jetson Nano Classifier** to detect Acute Lymphoblastic Leukemia. + +  + +# Installation +You will need to ensure you have followed the provided guides below: + +- [Ubuntu Installation Guide](../installation/ubuntu.md) +- [NVIDIA Jetson Nano Installation Guide](../installation/jetson.md) + +  + +# Training +Before you can start to use this tutorial you must have already trained your classifier, to do so use one of the following guides: + +- [Python Usage Guide](../usage/python.md). +- [Jupyter Notebooks Usage Guide](../usage/notebooks.md) + +  + +# Docker Container +During the [NVIDIA Jetson Nano Installation](../installation/jetson.md) you download the ALL Jetson Nano Classifier Docker Image and started it. You should make sure you are inside the Docker container before you continue this tutorial. + +# Running The Tensorflow Model + +Now you are ready to run the Tensorflow model. You will use the 20 images that were removed from the training data in the previous tutorials. + +To run the classifier in use the following command: + +``` +python3 classifier.py classify +``` + +You should see the following which shows you the network architecture: + +``` +2021-08-19 21:21:53,645 - Classifier - INFO - Model loaded +Model: "AllJetsonNano" +_________________________________________________________________ +Layer (type) Output Shape Param # +================================================================= +average_pooling2d (AveragePo (None, 50, 50, 3) 0 +_________________________________________________________________ +conv2d (Conv2D) (None, 46, 46, 30) 2280 +_________________________________________________________________ +depthwise_conv2d (DepthwiseC (None, 17, 17, 30) 27030 +_________________________________________________________________ +flatten (Flatten) (None, 8670) 0 +_________________________________________________________________ +dense (Dense) (None, 2) 17342 +_________________________________________________________________ +softmax (Activation) (None, 2) 0 +================================================================= +Total params: 46,652 +Trainable params: 46,652 +Non-trainable params: 0 +_________________________________________________________________ +``` + +Finally the application will start processing the test images and the results will be displayed in the console. The first classification will take longer due to loading the required dynamic libraries. + +``` +2021-08-19 21:21:54,042 - Classifier - INFO - Loaded test image model/data/test/Im041_0.jpg +2021-08-19 21:21:54.781830: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10 +2021-08-19 21:21:56.736191: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8 +2021-08-19 21:22:04,620 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 10.574294567108154 seconds. +2021-08-19 21:22:05,944 - Classifier - INFO - Loaded test image model/data/test/Im028_1.jpg +2021-08-19 21:22:06,134 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.18299126625061035 seconds. +2021-08-19 21:22:07,205 - Classifier - INFO - Loaded test image model/data/test/Im053_1.jpg +2021-08-19 21:22:07,382 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.16392922401428223 seconds. +2021-08-19 21:22:07,852 - Classifier - INFO - Loaded test image model/data/test/Im047_0.jpg +2021-08-19 21:22:08,032 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.16694879531860352 seconds. +2021-08-19 21:22:08,107 - Classifier - INFO - Loaded test image model/data/test/Im026_1.jpg +2021-08-19 21:22:08,271 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.1616969108581543 seconds. +2021-08-19 21:22:08,807 - Classifier - INFO - Loaded test image model/data/test/Im106_0.jpg +2021-08-19 21:22:08,995 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.17544174194335938 seconds. +2021-08-19 21:22:09,192 - Classifier - INFO - Loaded test image model/data/test/Im088_0.jpg +2021-08-19 21:22:09,371 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.16634845733642578 seconds. +2021-08-19 21:22:09,806 - Classifier - INFO - Loaded test image model/data/test/Im074_0.jpg +2021-08-19 21:22:09,980 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.16352033615112305 seconds. +2021-08-19 21:22:10,259 - Classifier - INFO - Loaded test image model/data/test/Im057_1.jpg +2021-08-19 21:22:10,441 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.17132163047790527 seconds. +2021-08-19 21:22:10,650 - Classifier - INFO - Loaded test image model/data/test/Im101_0.jpg +2021-08-19 21:22:10,822 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.1612401008605957 seconds. +2021-08-19 21:22:10,886 - Classifier - INFO - Loaded test image model/data/test/Im024_1.jpg +2021-08-19 21:22:11,051 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.16300010681152344 seconds. +2021-08-19 21:22:11,115 - Classifier - INFO - Loaded test image model/data/test/Im006_1.jpg +2021-08-19 21:22:11,278 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.16106534004211426 seconds. +2021-08-19 21:22:11,500 - Classifier - INFO - Loaded test image model/data/test/Im035_0.jpg +2021-08-19 21:22:11,684 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.17350363731384277 seconds. +2021-08-19 21:22:11,748 - Classifier - INFO - Loaded test image model/data/test/Im031_1.jpg +2021-08-19 21:22:11,912 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.16186809539794922 seconds. +2021-08-19 21:22:12,151 - Classifier - INFO - Loaded test image model/data/test/Im069_0.jpg +2021-08-19 21:22:12,322 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.16080045700073242 seconds. +2021-08-19 21:22:12,839 - Classifier - INFO - Loaded test image model/data/test/Im063_1.jpg +2021-08-19 21:22:13,020 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.17079424858093262 seconds. +2021-08-19 21:22:13,434 - Classifier - INFO - Loaded test image model/data/test/Im060_1.jpg +2021-08-19 21:22:14,971 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 1.5258052349090576 seconds. +2021-08-19 21:22:15,034 - Classifier - INFO - Loaded test image model/data/test/Im020_1.jpg +2021-08-19 21:22:15,199 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.16217803955078125 seconds. +2021-08-19 21:22:16,319 - Classifier - INFO - Loaded test image model/data/test/Im095_0.jpg +2021-08-19 21:22:16,492 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.1624619960784912 seconds. +2021-08-19 21:22:18,036 - Classifier - INFO - Loaded test image model/data/test/Im099_0.jpg +2021-08-19 21:22:18,223 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.17438983917236328 seconds. +2021-08-19 21:22:18,224 - Classifier - INFO - Images Classified: 20 +2021-08-19 21:22:18,224 - Classifier - INFO - True Positives: 10 +2021-08-19 21:22:18,224 - Classifier - INFO - False Positives: 0 +2021-08-19 21:22:18,225 - Classifier - INFO - True Negatives: 10 +2021-08-19 21:22:18,225 - Classifier - INFO - False Negatives: 0 +2021-08-19 21:22:18,225 - Classifier - INFO - Total Time Taken: 15.103600025177002 +``` + +  + +# Running The TFRT Model + +Now you will run the TFRT model: + +``` bash +python3 classifier.py classify_tfrt +``` + +You will see the following output: + +``` bash +2021-08-19 21:23:17.557074: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1428] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 375 MB memory) -> physical GPU (device: 0, name: NVIDIA Tegra X1, pci bus id: 0000:00:00.0, compute capability: 5.3) +2021-08-19 21:23:22,277 - Classifier - INFO - Loaded test image model/data/test/Im041_0.jpg +2021-08-19 21:23:22.403990: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10 +2021-08-19 21:23:24.297541: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8 +2021-08-19 21:23:29,946 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 7.66000771522522 seconds. +2021-08-19 21:23:31,574 - Classifier - INFO - Loaded test image model/data/test/Im028_1.jpg +2021-08-19 21:23:31,589 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.007614850997924805 seconds. +2021-08-19 21:23:33,488 - Classifier - INFO - Loaded test image model/data/test/Im053_1.jpg +2021-08-19 21:23:33,506 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.006653547286987305 seconds. +2021-08-19 21:23:33,825 - Classifier - INFO - Loaded test image model/data/test/Im047_0.jpg +2021-08-19 21:23:33,844 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.006745576858520508 seconds. +2021-08-19 21:23:33,915 - Classifier - INFO - Loaded test image model/data/test/Im026_1.jpg +2021-08-19 21:23:33,924 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0067136287689208984 seconds. +2021-08-19 21:23:34,398 - Classifier - INFO - Loaded test image model/data/test/Im106_0.jpg +2021-08-19 21:23:34,417 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.006788492202758789 seconds. +2021-08-19 21:23:34,708 - Classifier - INFO - Loaded test image model/data/test/Im088_0.jpg +2021-08-19 21:23:34,726 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.006501197814941406 seconds. +2021-08-19 21:23:34,928 - Classifier - INFO - Loaded test image model/data/test/Im074_0.jpg +2021-08-19 21:23:34,946 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.006418704986572266 seconds. +2021-08-19 21:23:35,234 - Classifier - INFO - Loaded test image model/data/test/Im057_1.jpg +2021-08-19 21:23:35,251 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.006302595138549805 seconds. +2021-08-19 21:23:35,519 - Classifier - INFO - Loaded test image model/data/test/Im101_0.jpg +2021-08-19 21:23:35,535 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.006386280059814453 seconds. +2021-08-19 21:23:35,599 - Classifier - INFO - Loaded test image model/data/test/Im024_1.jpg +2021-08-19 21:23:35,607 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0062999725341796875 seconds. +2021-08-19 21:23:35,669 - Classifier - INFO - Loaded test image model/data/test/Im006_1.jpg +2021-08-19 21:23:35,677 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.00655674934387207 seconds. +2021-08-19 21:23:35,983 - Classifier - INFO - Loaded test image model/data/test/Im035_0.jpg +2021-08-19 21:23:36,000 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.006490468978881836 seconds. +2021-08-19 21:23:36,066 - Classifier - INFO - Loaded test image model/data/test/Im031_1.jpg +2021-08-19 21:23:36,075 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.006487846374511719 seconds. +2021-08-19 21:23:36,384 - Classifier - INFO - Loaded test image model/data/test/Im069_0.jpg +2021-08-19 21:23:36,401 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.006212949752807617 seconds. +2021-08-19 21:23:36,629 - Classifier - INFO - Loaded test image model/data/test/Im063_1.jpg +2021-08-19 21:23:36,647 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.006224155426025391 seconds. +2021-08-19 21:23:36,874 - Classifier - INFO - Loaded test image model/data/test/Im060_1.jpg +2021-08-19 21:23:36,892 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.006279706954956055 seconds. +2021-08-19 21:23:36,954 - Classifier - INFO - Loaded test image model/data/test/Im020_1.jpg +2021-08-19 21:23:36,962 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.006190299987792969 seconds. +2021-08-19 21:23:37,256 - Classifier - INFO - Loaded test image model/data/test/Im095_0.jpg +2021-08-19 21:23:37,276 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.006523609161376953 seconds. +2021-08-19 21:23:37,482 - Classifier - INFO - Loaded test image model/data/test/Im099_0.jpg +2021-08-19 21:23:37,499 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.00617218017578125 seconds. +2021-08-19 21:23:37,500 - Classifier - INFO - Images Classified: 20 +2021-08-19 21:23:37,500 - Classifier - INFO - True Positives: 10 +2021-08-19 21:23:37,500 - Classifier - INFO - False Positives: 0 +2021-08-19 21:23:37,501 - Classifier - INFO - True Negatives: 10 +2021-08-19 21:23:37,501 - Classifier - INFO - False Negatives: 0 +2021-08-19 21:23:37,501 - Classifier - INFO - Total Time Taken: 7.7835705280303955 +``` + +  + +# Running The TensorRT Model + +Now you will run the TensorRT model: + +``` bash +python3 classifier.py classify_tensorrt +``` + +You will see the following output: + +``` bash +2021-08-19 21:25:47,794 - Classifier - INFO - Loaded test image model/data/test/Im041_0.jpg +2021-08-19 21:25:57,528 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 9.695297002792358 seconds. +2021-08-19 21:25:58,770 - Classifier - INFO - Loaded test image model/data/test/Im028_1.jpg +2021-08-19 21:25:58,891 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.05570673942565918 seconds. +2021-08-19 21:25:59,799 - Classifier - INFO - Loaded test image model/data/test/Im053_1.jpg +2021-08-19 21:25:59,825 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.01838541030883789 seconds. +2021-08-19 21:26:00,039 - Classifier - INFO - Loaded test image model/data/test/Im047_0.jpg +2021-08-19 21:26:00,064 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.017393112182617188 seconds. +2021-08-19 21:26:00,174 - Classifier - INFO - Loaded test image model/data/test/Im026_1.jpg +2021-08-19 21:26:00,192 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.016171693801879883 seconds. +2021-08-19 21:26:06,638 - Classifier - INFO - Loaded test image model/data/test/Im106_0.jpg +2021-08-19 21:26:06,665 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.017331838607788086 seconds. +2021-08-19 21:26:07,826 - Classifier - INFO - Loaded test image model/data/test/Im088_0.jpg +2021-08-19 21:26:07,854 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.018476009368896484 seconds. +2021-08-19 21:26:16,488 - Classifier - INFO - Loaded test image model/data/test/Im074_0.jpg +2021-08-19 21:26:16,516 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.017465829849243164 seconds. +2021-08-19 21:26:24,054 - Classifier - INFO - Loaded test image model/data/test/Im057_1.jpg +2021-08-19 21:26:24,082 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.01739645004272461 seconds. +2021-08-19 21:26:26,183 - Classifier - INFO - Loaded test image model/data/test/Im101_0.jpg +2021-08-19 21:26:26,208 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.014073848724365234 seconds. +2021-08-19 21:26:26,272 - Classifier - INFO - Loaded test image model/data/test/Im024_1.jpg +2021-08-19 21:26:26,292 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.018465757369995117 seconds. +2021-08-19 21:26:26,355 - Classifier - INFO - Loaded test image model/data/test/Im006_1.jpg +2021-08-19 21:26:26,375 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.018265962600708008 seconds. +2021-08-19 21:26:28,109 - Classifier - INFO - Loaded test image model/data/test/Im035_0.jpg +2021-08-19 21:26:28,137 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.017232894897460938 seconds. +2021-08-19 21:26:28,208 - Classifier - INFO - Loaded test image model/data/test/Im031_1.jpg +2021-08-19 21:26:28,223 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.013829946517944336 seconds. +2021-08-19 21:26:29,144 - Classifier - INFO - Loaded test image model/data/test/Im069_0.jpg +2021-08-19 21:26:29,172 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.01685953140258789 seconds. +2021-08-19 21:26:30,215 - Classifier - INFO - Loaded test image model/data/test/Im063_1.jpg +2021-08-19 21:26:30,243 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.018213987350463867 seconds. +2021-08-19 21:26:31,108 - Classifier - INFO - Loaded test image model/data/test/Im060_1.jpg +2021-08-19 21:26:31,136 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.016917705535888672 seconds. +2021-08-19 21:26:31,199 - Classifier - INFO - Loaded test image model/data/test/Im020_1.jpg +2021-08-19 21:26:31,216 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.015204191207885742 seconds. +2021-08-19 21:26:33,055 - Classifier - INFO - Loaded test image model/data/test/Im095_0.jpg +2021-08-19 21:26:33,083 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.01775836944580078 seconds. +2021-08-19 21:26:34,294 - Classifier - INFO - Loaded test image model/data/test/Im099_0.jpg +2021-08-19 21:26:34,322 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.017148733139038086 seconds. +2021-08-19 21:26:34,322 - Classifier - INFO - Images Classified: 20 +2021-08-19 21:26:34,322 - Classifier - INFO - True Positives: 10 +2021-08-19 21:26:34,323 - Classifier - INFO - False Positives: 0 +2021-08-19 21:26:34,323 - Classifier - INFO - True Negatives: 10 +2021-08-19 21:26:34,323 - Classifier - INFO - False Negatives: 0 +2021-08-19 21:26:34,324 - Classifier - INFO - Total Time Taken: 10.057595014572144 +``` + +  + +# Conclusion + +Here we can see that optimizing our Tensorflow model using TFRT improves our classification by around 8 seconds which is a considerable improvement. When comparing the performance of the TFRT model with the TensorRT model however, we see an increase in classification time of around 3 seconds, this is something that should be investigated. + +This completes our tutorial for now. You have created a Tensorflow model for Acute Lymphoblastic Leukemia classification, converted it to TFRT and ONNX formats, and converted the ONNX model to TensorRT format, then ran each model on your desktop/laptop device and the NVIDIA Jetson Nano. + +  + +# Contributing +Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss encourages and welcomes code contributions, bug fixes and enhancements from the Github community. + +Please read the [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") document for a full guide to forking our repositories and submitting your pull requests. You will also find our code of conduct in the [Code of Conduct](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CODE-OF-CONDUCT.md) document. + +## Contributors +- [Adam Milton-Barker](https://www.leukemiaairesearch.com/association/volunteers/adam-milton-barker "Adam Milton-Barker") - [Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss](https://www.leukemiaresearchassociation.ai "Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss") President/Founder & Lead Developer, Sabadell, Spain + +  + +# Versioning +We use [SemVer](https://semver.org/) for versioning. + +  + +# License +This project is licensed under the **MIT License** - see the [LICENSE](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/blob/main/LICENSE "LICENSE") file for details. + +  + +# Bugs/Issues +We use the [repo issues](https://github.com/AMLResearchProject/ALL-Jetson-Nano/issues "repo issues") to track bugs and general requests related to using this project. See [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") for more info on how to submit bugs, feature requests and proposals. \ No newline at end of file diff --git a/docs/usage/notebooks.md b/docs/usage/notebooks.md new file mode 100644 index 0000000..6bcdcb5 --- /dev/null +++ b/docs/usage/notebooks.md @@ -0,0 +1,107 @@ +# Notebooks Usage + +![ALL Jetson Nano Classifier](../img/project-banner.jpg) + +# Introduction +This guide will take you through using the **ALL Jetson Nano Classifier** Jupyter Notebook to train and test your classifier. + +  + +# Installation +First you need to install the required software for training the model. Below are the available installation guides: + +- [Ubuntu installation guide](../installation/ubuntu.md). + +  + +# Network Architecture +We will build a Convolutional Neural Network with the following architecture: + +- Average pooling layer +- Conv layer +- Depthwise conv layer +- Flatten layer +- Fully connected layer +- Softmax layer + +  + +# Data +You need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on [this page](https://homes.di.unimi.it/scotti/all/#download) as well as information on how to contribute back to the project [here](https://homes.di.unimi.it/scotti/all/results.php). + +_If you are not able to obtain a copy of the dataset please feel free to try this tutorial on your own dataset._ + +Once you have your data you need to add it to the project filesystem. You will notice the data folder in the Model directory, **model/data**, inside you have **train** & **test**. Add all of the images from the ALL_IDB1 dataset to the **model/data/train** folder. + +## Data Augmentation + +We will create an augmented dataset based on the [Leukemia Blood Cell Image Classification Using Convolutional Neural Network](http://www.ijcte.org/vol10/1198-H0012.pdf "Leukemia Blood Cell Image Classification Using Convolutional Neural Network") by T. T. P. Thanh, Caleb Vununu, Sukhrob Atoev, Suk-Hwan Lee, and Ki-Ryong Kwon. + +## Application testing data + +In the data processing stage, ten negative images and ten positive images are removed from the dataset and moved to the **model/data/test/** directory. This data is not seen by the network during the training process, and is used to test the performance of the model. + +To ensure your model gets the same results, you should use the same test images. You can also try with your own image selection, however results may vary. + +  + +# Start Jupyter Notebooks +Now you need to start Jupyter Notebooks. In your project root execute the following command, replacing the IP and port as desired. + +``` bash +jupyter notebook --ip YourIP --port 8888 +``` + +  + +# Open The Training Notebook + +Navigate to the URL provided when starting Jupyter Notebooks and you should be in the project root. Now navigate to **notebooks/classifier.ipynb**. With everything set up you can now begin training. Run the Jupyter Notebook and wait for it to finish. + +  + +# Preparing For NVIDIA Jetson Nano + +After training the model was converted to ONNX format ready to be converted to TensorRT format for usage on the NVIDIA Jetson Nano. + +  + +# Conclusion + +Here we trained a deep learning model for Acute Lymphoblastic Leukemia detection utilizing Intel® Optimization for Tensorflow* from the Intel® oneAPI AI Analytics Toolkit to optimize and accelarate training. We introduced a 6 layer deep learning model and applied data augmentation to increase the training data. + +We trained our model with a target of 150 epochs and used early stopping to avoid overfitting. The model trained for 37 epochs resulting in a fairly good fit, accuracy/precision/recall and AUC are satisfying. In addition the model reacts well during testing classifying each of the twenty unseen test images correctly. + +We see that the TFRT model performs considerably better than the Tensorflow model, improving classification time by 0.35 seconds. + +  + +# Continue + +- To test with the Python scripts continue to the [Python usage documentation](python.md) +- To begin installation on your Jetson Nano continue to the [Jetson Nano installation documentation](../installation/jetson.md) + +  + +# Contributing +Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss encourages and welcomes code contributions, bug fixes and enhancements from the Github community. + +Please read the [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") document for a full guide to forking our repositories and submitting your pull requests. You will also find our code of conduct in the [Code of Conduct](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CODE-OF-CONDUCT.md) document. + +## Contributors +- [Adam Milton-Barker](https://www.leukemiaairesearch.com/association/volunteers/adam-milton-barker "Adam Milton-Barker") - [Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss](https://www.leukemiaresearchassociation.ai "Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss") President/Founder & Lead Developer, Sabadell, Spain + +  + +# Versioning +We use [SemVer](https://semver.org/) for versioning. + +  + +# License +This project is licensed under the **MIT License** - see the [LICENSE](https://github.com/AMLResearchProject/ALL-Arduino-Nano-33-BLE-Sense-Classifier/blob/main/LICENSE "LICENSE") file for details. + +  + +# Bugs/Issues +We use the [repo issues](https://github.com/AMLResearchProject/ALL-Jetson-Nano/issues "repo issues") to track bugs and general requests related to using this project. See [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") for more info on how to submit bugs, feature requests and proposals. \ No newline at end of file diff --git a/docs/usage/python.md b/docs/usage/python.md new file mode 100644 index 0000000..52835d8 --- /dev/null +++ b/docs/usage/python.md @@ -0,0 +1,466 @@ +# Python Usage + +![ALL Jetson Nano Classifier](../img/project-banner.jpg) + +# Introduction +This guide will take you through the using the **ALL Jetson Nano Classifier** Python trainer to train and test your classifier. + +  + +# Installation +First you need to install the required software for training the model. Below are the available installation guides: + +- [Ubuntu installation guide](../installation/ubuntu.md). + +  + +# Network Architecture +We will build a Convolutional Neural Network with the following architecture: + +- Average pooling layer +- Conv layer +- Depthwise conv layer +- Flatten layer +- Fully connected layer +- Softmax layer + +  + +# Data +You need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on [this page](https://homes.di.unimi.it/scotti/all/#download) as well as information on how to contribute back to the project [here](https://homes.di.unimi.it/scotti/all/results.php). + +_If you are not able to obtain a copy of the dataset please feel free to try this tutorial on your own dataset._ + +Once you have your data you need to add it to the project filesystem. You will notice the data folder in the Model directory, **model/data**, inside you have **train** & **test**. Add all of the images from the ALL_IDB1 dataset to the **model/data/train** folder. + +## Data Augmentation + +We will create an augmented dataset based on the [Leukemia Blood Cell Image Classification Using Convolutional Neural Network](http://www.ijcte.org/vol10/1198-H0012.pdf "Leukemia Blood Cell Image Classification Using Convolutional Neural Network") by T. T. P. Thanh, Caleb Vununu, Sukhrob Atoev, Suk-Hwan Lee, and Ki-Ryong Kwon. + +## Application testing data + +In the data processing stage, ten negative images and ten positive images are removed from the dataset and moved to the **model/data/test/** directory. This data is not seen by the network during the training process, and is used to test the performance of the model. + +To ensure your model gets the same results, you should use the same test images. You can also try with your own image selection, however results may vary. + +To specify which test images to use modify the [configuration/config.json](../configuration/config.json) file as shown below: + +``` json +"test_data": [ + "Im006_1.jpg", + "Im020_1.jpg", + "Im024_1.jpg", + "Im026_1.jpg", + "Im028_1.jpg", + "Im031_1.jpg", + "Im035_0.jpg", + "Im041_0.jpg", + "Im047_0.jpg", + "Im053_1.jpg", + "Im057_1.jpg", + "Im060_1.jpg", + "Im063_1.jpg", + "Im069_0.jpg", + "Im074_0.jpg", + "Im088_0.jpg", + "Im095_0.jpg", + "Im099_0.jpg", + "Im101_0.jpg", + "Im106_0.jpg" +], +``` + +  + +# Configuration + +All configuration can be found in the **configuration/config.json** file. + +``` json +{ + "agent": { + "cores": 8, + "port": 1234, + "params": [ + "train", + "classify", + "server", + "classify_http", + "classify_tfrt", + "classify_tensorrt" + ] + }, + "data": { + "dim": 100, + "file_type": ".jpg", + "labels": [0, 1], + "rotations": 10, + "seed": 2, + "split": 0.255, + "test": "model/data/test", + "test_data": [ + "Im006_1.jpg", + "Im020_1.jpg", + "Im024_1.jpg", + "Im026_1.jpg", + "Im028_1.jpg", + "Im031_1.jpg", + "Im035_0.jpg", + "Im041_0.jpg", + "Im047_0.jpg", + "Im053_1.jpg", + "Im057_1.jpg", + "Im060_1.jpg", + "Im063_1.jpg", + "Im069_0.jpg", + "Im074_0.jpg", + "Im088_0.jpg", + "Im095_0.jpg", + "Im099_0.jpg", + "Im101_0.jpg", + "Im106_0.jpg" + ], + "train_dir": "model/data/train", + "valid_types": [ + ".jpg" + ] + }, + "model": { + "model": "model/all_jetson_nano.json", + "weights": "model/all_jetson_nano.h5", + "saved": "model/saved", + "tfrt": "model/tfrt", + "onnx": "model/all_jetson_nano.onnx", + "tensorrt": "model/all_jetson_nano.plan" + }, + "train": { + "batch": 100, + "decay_adam": 1e-6, + "epochs": 150, + "learning_rate_adam": 1e-4, + "val_steps": 10 + } +} +``` + +You should update the following values: + +- **agent->cores** Should represent the amount of cores your CPU has. +- **agent->port** Should be the port you will run your test server on. + +You can modify the values in the train object as required, however to ensure you achieve the same results you can leave them as they are. + +# Training +Now you are ready to train your model. + +## Metrics +We can use metrics to measure the effectiveness of our model. In this network you will use the following metrics: + +``` +tf.keras.metrics.BinaryAccuracy(name='accuracy'), +tf.keras.metrics.Precision(name='precision'), +tf.keras.metrics.Recall(name='recall'), +tf.keras.metrics.AUC(name='auc') +``` + +These metrics will be displayed and plotted once our model is trained. A useful tutorial while working on the metrics was the [Classification on imbalanced data](https://www.tensorflow.org/tutorials/structured_data/imbalanced_data) tutorial on Tensorflow's website. + +## Start Training +Ensuring you have completed all previous steps, you can start training using the following command. + +``` bash +python classifier.py train +``` + +This tells the application to start training the model. + +## Training Data +First the training and validation data will be prepared. + +``` bash +2021-08-19 19:27:18,023 - Classifier - INFO - Augmented data size: 1584 +2021-08-19 19:27:18,024 - Classifier - INFO - Negative data size: 882 +2021-08-19 19:27:18,024 - Classifier - INFO - Positive data size: 702 +2021-08-19 19:27:18,024 - Classifier - INFO - Augmented data shape: (1584, 100, 100, 3) +2021-08-19 19:27:18,024 - Classifier - INFO - Labels shape: (1584, 2) +2021-08-19 19:27:18,083 - Classifier - INFO - Training data: (1180, 100, 100, 3) +2021-08-19 19:27:18,083 - Classifier - INFO - Training labels: (1180, 2) +2021-08-19 19:27:18,083 - Classifier - INFO - Validation data: (404, 100, 100, 3) +2021-08-19 19:27:18,083 - Classifier - INFO - Validation labels: (404, 2) +``` + +### Model Summary + +Before the model begins training, you will be shown the model summary. + +``` bash +Model: "AllJetsonNano" +_________________________________________________________________ +Layer (type) Output Shape Param # +================================================================= +average_pooling2d (AveragePo (None, 50, 50, 3) 0 +_________________________________________________________________ +conv2d (Conv2D) (None, 46, 46, 30) 2280 +_________________________________________________________________ +depthwise_conv2d (DepthwiseC (None, 17, 17, 30) 27030 +_________________________________________________________________ +flatten (Flatten) (None, 8670) 0 +_________________________________________________________________ +dense (Dense) (None, 2) 17342 +_________________________________________________________________ +softmax (Activation) (None, 2) 0 +================================================================= +Total params: 46,652 +Trainable params: 46,652 +Non-trainable params: 0 +_________________________________________________________________ +2021-08-19 19:27:18,162 - Classifier - INFO - Network initialization complete. +2021-08-19 19:27:18,162 - Classifier - INFO - Using Adam Optimizer. +2021-08-19 19:27:18,162 - Classifier - INFO - Using Early Stopping. +``` + +## Training Results +Below are the training results for 28 epochs. + +![Accuracy](../img/plots/accuracy.png) + +_Fig 1. Accuracy_ + +![Loss](../img/plots/loss.png) + +_Fig 2. Loss_ + +![Precision](../img/plots/precision.png) + +_Fig 3. Precision_ + +![Recall](../img/plots/recall.png) + +_Fig 4. Recall_ + +![AUC](../img/plots/auc.png) + +_Fig 5. AUC_ + +![Confusion Matrix](../img/plots/confusion-matrix.png) + +_Fig 6. Confusion Matrix_ + + +``` bash +2021-08-19 19:32:06,527 - Classifier - INFO - Metrics: acc 0.9158415794372559 +2021-08-19 19:32:06,527 - Classifier - INFO - Metrics: precision 0.9158415794372559 +2021-08-19 19:32:06,527 - Classifier - INFO - Metrics: recall 0.9158415794372559 +2021-08-19 19:32:06,527 - Classifier - INFO - Metrics: auc 0.984254002571106 +2021-08-19 19:32:07,191 - Classifier - INFO - Confusion Matrix: [[210 10] [ 24 160]] +2021-08-19 19:32:07,307 - Classifier - INFO - True Positives: 160(39.603960396039604%) +2021-08-19 19:32:07,307 - Classifier - INFO - False Positives: 10(2.4752475247524752%) +2021-08-19 19:32:07,307 - Classifier - INFO - True Negatives: 210(51.98019801980198%) +2021-08-19 19:32:07,307 - Classifier - INFO - False Negatives: 24(5.9405940594059405%) +2021-08-19 19:32:07,307 - Classifier - INFO - Specificity: 0.9545454545454546 +2021-08-19 19:32:07,307 - Classifier - INFO - Misclassification: 34(8.415841584158416%) +``` + +## Metrics Overview +| Accuracy | Recall | Precision | AUC/ROC | +| ---------- | ---------- | ---------- | ---------- | +| 0.9158415794372559 | 0.9158415794372559 | 0.9158415794372559 | 0.984254002571106 | + +## Figures Of Merit +| Figures of merit | Amount/Value | Percentage | +| -------------------- | ----- | ---------- | +| True Positives | 160 | 39.603960396039604% | +| False Positives | 10 | 2.4752475247524752% | +| True Negatives | 210 | 51.98019801980198% | +| False Negatives | 24 | 5.9405940594059405% | +| Misclassification | 34 | 8.415841584158416% | +| Sensitivity / Recall | 0.9158415794372559 | 92% | +| Specificity | 0.9545454545454546 | 96% | + +  + +# Testing Tensorflow Model + +Now you will test the Intel Optimized Tensorflow model on your training machine. You will use the 20 images that were removed from the training data in the previous part of this tutorial. + +To run the classifier in test mode use the following command: + +``` +python3 classifier.py classify +``` + +You should see the following which shows you the network architecture: + +``` +Model: "AllJetsonNano" +_________________________________________________________________ +Layer (type) Output Shape Param # +================================================================= +average_pooling2d (AveragePo (None, 50, 50, 3) 0 +_________________________________________________________________ +conv2d (Conv2D) (None, 46, 46, 30) 2280 +_________________________________________________________________ +depthwise_conv2d (DepthwiseC (None, 17, 17, 30) 27030 +_________________________________________________________________ +flatten (Flatten) (None, 8670) 0 +_________________________________________________________________ +dense (Dense) (None, 2) 17342 +_________________________________________________________________ +softmax (Activation) (None, 2) 0 +================================================================= +Total params: 46,652 +Trainable params: 46,652 +Non-trainable params: 0 +_________________________________________________________________ +``` + +Finally the application will start processing the test images and the results will be displayed in the console. + +``` +2021-08-19 20:02:29,901 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04636073112487793 seconds. +2021-08-19 20:02:29,901 - Classifier - INFO - Loaded test image model/data/test/Im047_0.jpg +2021-08-19 20:02:29,937 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.03577733039855957 seconds. +2021-08-19 20:02:29,938 - Classifier - INFO - Loaded test image model/data/test/Im026_1.jpg +2021-08-19 20:02:29,984 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04593682289123535 seconds. +2021-08-19 20:02:29,985 - Classifier - INFO - Loaded test image model/data/test/Im088_0.jpg +2021-08-19 20:02:30,021 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.036516427993774414 seconds. +2021-08-19 20:02:30,022 - Classifier - INFO - Loaded test image model/data/test/Im095_0.jpg +2021-08-19 20:02:30,069 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.04674267768859863 seconds. +2021-08-19 20:02:30,070 - Classifier - INFO - Loaded test image model/data/test/Im106_0.jpg +2021-08-19 20:02:30,117 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.046518564224243164 seconds. +2021-08-19 20:02:30,118 - Classifier - INFO - Loaded test image model/data/test/Im024_1.jpg +2021-08-19 20:02:30,164 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04618191719055176 seconds. +2021-08-19 20:02:30,165 - Classifier - INFO - Loaded test image model/data/test/Im020_1.jpg +2021-08-19 20:02:30,213 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04806184768676758 seconds. +2021-08-19 20:02:30,214 - Classifier - INFO - Loaded test image model/data/test/Im057_1.jpg +2021-08-19 20:02:30,276 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.062065839767456055 seconds. +2021-08-19 20:02:30,277 - Classifier - INFO - Loaded test image model/data/test/Im063_1.jpg +2021-08-19 20:02:30,328 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.051260948181152344 seconds. +2021-08-19 20:02:30,329 - Classifier - INFO - Loaded test image model/data/test/Im069_0.jpg +2021-08-19 20:02:30,373 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.04395794868469238 seconds. +2021-08-19 20:02:30,373 - Classifier - INFO - Loaded test image model/data/test/Im101_0.jpg +2021-08-19 20:02:30,409 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.03528141975402832 seconds. +2021-08-19 20:02:30,409 - Classifier - INFO - Loaded test image model/data/test/Im060_1.jpg +2021-08-19 20:02:30,445 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0352783203125 seconds. +2021-08-19 20:02:30,445 - Classifier - INFO - Loaded test image model/data/test/Im031_1.jpg +2021-08-19 20:02:30,481 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.035286664962768555 seconds. +2021-08-19 20:02:30,481 - Classifier - INFO - Loaded test image model/data/test/Im006_1.jpg +2021-08-19 20:02:30,529 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04689455032348633 seconds. +2021-08-19 20:02:30,529 - Classifier - INFO - Loaded test image model/data/test/Im099_0.jpg +2021-08-19 20:02:30,576 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.046888113021850586 seconds. +2021-08-19 20:02:30,577 - Classifier - INFO - Loaded test image model/data/test/Im035_0.jpg +2021-08-19 20:02:30,624 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.04698944091796875 seconds. +2021-08-19 20:02:30,625 - Classifier - INFO - Loaded test image model/data/test/Im028_1.jpg +2021-08-19 20:02:30,673 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04763507843017578 seconds. +2021-08-19 20:02:30,673 - Classifier - INFO - Loaded test image model/data/test/Im074_0.jpg +2021-08-19 20:02:30,732 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.058196306228637695 seconds. +2021-08-19 20:02:30,732 - Classifier - INFO - Images Classified: 20 +2021-08-19 20:02:30,732 - Classifier - INFO - True Positives: 10 +2021-08-19 20:02:30,732 - Classifier - INFO - False Positives: 0 +2021-08-19 20:02:30,732 - Classifier - INFO - True Negatives: 10 +2021-08-19 20:02:30,732 - Classifier - INFO - False Negatives: 0 +2021-08-19 20:02:30,732 - Classifier - INFO - Total Time Taken: 1.1011667251586914 +``` + +# Testing TFRT Model + +Now you will test the TFRT model on your training machine: + +``` bash +python3 classifier.py classify_tfrt +``` + +You will see the following output: + +``` bash +2021-08-19 20:05:40,637 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.016054153442382812 seconds. +2021-08-19 20:05:40,638 - Classifier - INFO - Loaded test image model/data/test/Im047_0.jpg +2021-08-19 20:05:40,639 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.0012242794036865234 seconds. +2021-08-19 20:05:40,640 - Classifier - INFO - Loaded test image model/data/test/Im026_1.jpg +2021-08-19 20:05:40,641 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0011212825775146484 seconds. +2021-08-19 20:05:40,642 - Classifier - INFO - Loaded test image model/data/test/Im088_0.jpg +2021-08-19 20:05:40,648 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.00597381591796875 seconds. +2021-08-19 20:05:40,649 - Classifier - INFO - Loaded test image model/data/test/Im095_0.jpg +2021-08-19 20:05:40,651 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.0020668506622314453 seconds. +2021-08-19 20:05:40,651 - Classifier - INFO - Loaded test image model/data/test/Im106_0.jpg +2021-08-19 20:05:40,654 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.0019850730895996094 seconds. +2021-08-19 20:05:40,654 - Classifier - INFO - Loaded test image model/data/test/Im024_1.jpg +2021-08-19 20:05:40,656 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0019817352294921875 seconds. +2021-08-19 20:05:40,657 - Classifier - INFO - Loaded test image model/data/test/Im020_1.jpg +2021-08-19 20:05:40,665 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.007786750793457031 seconds. +2021-08-19 20:05:40,665 - Classifier - INFO - Loaded test image model/data/test/Im057_1.jpg +2021-08-19 20:05:40,667 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0019440650939941406 seconds. +2021-08-19 20:05:40,668 - Classifier - INFO - Loaded test image model/data/test/Im063_1.jpg +2021-08-19 20:05:40,670 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0018777847290039062 seconds. +2021-08-19 20:05:40,670 - Classifier - INFO - Loaded test image model/data/test/Im069_0.jpg +2021-08-19 20:05:40,672 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.0018739700317382812 seconds. +2021-08-19 20:05:40,673 - Classifier - INFO - Loaded test image model/data/test/Im101_0.jpg +2021-08-19 20:05:40,674 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.001840829849243164 seconds. +2021-08-19 20:05:40,675 - Classifier - INFO - Loaded test image model/data/test/Im060_1.jpg +2021-08-19 20:05:40,677 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0018241405487060547 seconds. +2021-08-19 20:05:40,677 - Classifier - INFO - Loaded test image model/data/test/Im031_1.jpg +2021-08-19 20:05:40,679 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0018105506896972656 seconds. +2021-08-19 20:05:40,680 - Classifier - INFO - Loaded test image model/data/test/Im006_1.jpg +2021-08-19 20:05:40,681 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0018205642700195312 seconds. +2021-08-19 20:05:40,682 - Classifier - INFO - Loaded test image model/data/test/Im099_0.jpg +2021-08-19 20:05:40,684 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.0018069744110107422 seconds. +2021-08-19 20:05:40,684 - Classifier - INFO - Loaded test image model/data/test/Im035_0.jpg +2021-08-19 20:05:40,686 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.00182342529296875 seconds. +2021-08-19 20:05:40,687 - Classifier - INFO - Loaded test image model/data/test/Im028_1.jpg +2021-08-19 20:05:40,688 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0018129348754882812 seconds. +2021-08-19 20:05:40,689 - Classifier - INFO - Loaded test image model/data/test/Im074_0.jpg +2021-08-19 20:05:40,691 - Classifier - INFO - Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.001878976821899414 seconds. +2021-08-19 20:05:40,691 - Classifier - INFO - Images Classified: 20 +2021-08-19 20:05:40,691 - Classifier - INFO - True Positives: 10 +2021-08-19 20:05:40,691 - Classifier - INFO - False Positives: 0 +2021-08-19 20:05:40,691 - Classifier - INFO - True Negatives: 10 +2021-08-19 20:05:40,691 - Classifier - INFO - False Negatives: 0 +2021-08-19 20:05:40,691 - Classifier - INFO - Total Time Taken: 0.11129593849182129 +``` + +  + +# Preparing For NVIDIA Jetson Nano + +After training the model was converted to ONNX format ready to be converted to TensorRT format for usage on the NVIDIA Jetson Nano. + +  + +# Conclusion + +Here we trained a deep learning model for Acute Lymphoblastic Leukemia detection utilizing Intel® Optimization for Tensorflow* from the Intel® oneAPI AI Analytics Toolkit to optimize and accelarate training. We introduced a 6 layer deep learning model and applied data augmentation to increase the training data. + +We trained our model with a target of 150 epochs and used early stopping to avoid overfitting. The model trained for 37 epochs resulting in a fairly good fit, accuracy/precision/recall and AUC are satisfying. In addition the model reacts well during testing classifying each of the twenty unseen test images correctly. + +We see that the TFRT model performs considerably better than the Tensorflow model, improving classification time by 0.98 seconds. + +  + +# Continue + +Now you are ready to set up your NVIDIA Jetson Nano. Head over to the [NVIDIA Jetson Nano Installation Guide](../installation/jetson.md) to prepare your Jetson. + +  + +# Contributing +Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss encourages and welcomes code contributions, bug fixes and enhancements from the Github community. + +Please read the [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") document for a full guide to forking our repositories and submitting your pull requests. You will also find our code of conduct in the [Code of Conduct](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CODE-OF-CONDUCT.md) document. + +## Contributors +- [Adam Milton-Barker](https://www.leukemiaairesearch.com/association/volunteers/adam-milton-barker "Adam Milton-Barker") - [Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss](https://www.leukemiaresearchassociation.ai "Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss") President/Founder & Lead Developer, Sabadell, Spain + +  + +# Versioning +We use [SemVer](https://semver.org/) for versioning. + +  + +# License +This project is licensed under the **MIT License** - see the [LICENSE](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/blob/main/LICENSE "LICENSE") file for details. + +  + +# Bugs/Issues +We use the [repo issues](https://github.com/AMLResearchProject/ALL-Jetson-Nano/issues "repo issues") to track bugs and general requests related to using this project. See [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md "CONTRIBUTING") for more info on how to submit bugs, feature requests and proposals. \ No newline at end of file diff --git a/logs/.gitkeep b/logs/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..9bd3bf7 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,2 @@ +site_name: ALL Jetson Nano Classifier +site_url: https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier diff --git a/model/all_jetson_nano.h5 b/model/all_jetson_nano.h5 new file mode 100644 index 0000000000000000000000000000000000000000..bbc780e138749d7a95728b60d17fdd6e0c811866 GIT binary patch literal 205104 zcmeFZ2V51+vM)+9fFd9$2q-A1hyf$Wnkobp#Du6IDx#u-Sy52T0wPf)qXfxG1p}hO znkr0)8Nr;zoDp-@xBP^&@AvJy?>^_=bM8CuZRhu6)%0{tclDp@n(pfA4z(X_r>df> zBKPxZ+Eh+SPW_MDhMynxSI_o}e>`ePH{?&Pzmcz}A@ww0;n$74Tw}SPc{=sa)$7YQ z{8gNsSN)4lV{C2ZWSkA!j^W}djc;kBc8mRkH zkHzjYJ*IdqTsUv`f|$7{dt z<>dY_2)`cvk>6a-zP_9mf0Q#FZ?OeKP&H#qUy{0Wg33g{P&l2UXjlgdNek1T3f!_%HM&LIBzY+M2z;6V8Bk&u6|1JoObr?1H zr;onDKN3~%w`lMK{MC#8==2M?opFimrwXf$3ar-mhaot~zlAb=lYH+OQ%dby2)T`6(7hnH|g@<~+|19#) z)-S9Lf7R50^M6*ozsvdm1_FQeN7lRB!5{h8{@Gk^ ze7!$kRo3_a-XHmM{oR8b8SY+xWq+UT+)9`>P)Ry9@poWBi4q!oS&pkGB7702=zam;V2n?_Vu9=XnaQ z?wlZcY}$`|9D?cc>#_Kq+G5DsSZ>>V0bZJ?PyUw3Pu06}a8VLJEo#P@Ymx+Me_K8j zkcp;0y0bk*;WKY<{Fok&C%&)c9@F;H<)_y`snrV*T-Ra3##jn`vk~+k<>SfpWOnH= z5jXk_;WwtP(8ny3Or|Abn^PTm`X+4*$=|}Ip>gr^LP6 zbrk1v;(I6Vr{F@-TjF?9w*+p}awL5B>W0yaTxpkK3^^`q!}I2?;_S9Z=td5p)2TPY zn3IN>Va>uB_Y5fRmWOR;uELwaIXLr12(Ga9#U;a%Sl1{Gv-GaTtb}bYuJNX|O7FJ**itn$;$y!`;x){Nmdbv|Fz5(&p>D#!v z`$J)z#bmzr$b#373*y;lyYK?*iJ0Ox6s>z|ql-s0MvtA1zHMwUXo(KmHqSz7uMA8I zje`ES2f>?u%h5L@09)QTiEr1eXZ4lxl%(69R&N>0Cw-JywO^)KI5Gg@AGzU>y9zkS zth=OpMkvl6vK744U2uw491p&=4GkTg@KczGo*$-(o*U9wJ}U*yJ~V~wFQM#jwTVmB z7huRZcN{RJBg^%fijeqB+>?{V?Z=3ave=C2-7)Iz6^tv+4dsZGDLm$z0{SVZ;`7N$ zR__lhST!=YMy2sh1Wn&zXjIvOHYfO#&6bB0(WaEC!+L)CHj~O4D?qbpA(%RBDY`t2 z#P3duT(U*;i%UHof3RiW*II6z`_vihf zAZf>zgRX*oXbXNn{ubuGjAUoUAZpj@gZLrP3$v%Rgw;xh9Od;0bgxW;cX_^OSS=Ms z==8#aOZDj1jC0WF!aC@b6@el97URti8_-Fn|5J?PP%E?r<`l*7)j^#xM(z^KH_PRn zq9-o*-+@6|Q}CO+H_yzN#mn#!4j=nYQfrir2fVY{U`{^6w#|GrQGq3c<57Ec8&p}j z7~O_0!SOr#;>qp#XtAh?@T&he4103|%Y4FlLRJlpiX8<#Y?JxGgeBlyl!0#}Z24`w z-YoIljAz<7va^{hpLUG}_nmp@9TSF+KApzcp>w$dUlwLPbwcmZbSOFdP?)P#$hnCl z+5T2%-ddr;4qZ}si;pF4TAW60Pj1J)6Pj8j7^qs^9kh|pPut0}N;7HaipSIz3ix{L zRH4XxG-jW-!@z(A=E7$J4Pz!F+6T46IODfx*YJFx~cxaJuR^cI%%cnQ*H=YJNS; 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You can find the application form and information about getting access to the dataset on [this page](https://homes.di.unimi.it/scotti/all/#download) as well as information on how to contribute back to the project [here](https://homes.di.unimi.it/scotti/all/results.php). If you are not able to obtain a copy of the dataset please feel free to try this tutorial on your own dataset, we would be very happy to find additional AML & ALL datasets. \ No newline at end of file diff --git a/model/data/test/README.md b/model/data/test/README.md new file mode 100644 index 0000000..3aaf9cd --- /dev/null +++ b/model/data/test/README.md @@ -0,0 +1,3 @@ +# Gain access to ALL-IDB + +You you need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on [this page](https://homes.di.unimi.it/scotti/all/#download) as well as information on how to contribute back to the project [here](https://homes.di.unimi.it/scotti/all/results.php). If you are not able to obtain a copy of the dataset please feel free to try this tutorial on your own dataset, we would be very happy to find additional AML & ALL datasets. \ No newline at end of file diff --git a/model/data/train/README.md b/model/data/train/README.md new file mode 100644 index 0000000..3aaf9cd --- /dev/null +++ b/model/data/train/README.md @@ -0,0 +1,3 @@ +# Gain access to ALL-IDB + +You you need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on [this page](https://homes.di.unimi.it/scotti/all/#download) as well as information on how to contribute back to the project [here](https://homes.di.unimi.it/scotti/all/results.php). 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z2`dU45*>RDli7ixQDefggW~90aZd3K1JAav(#Xm{DPYyT*glY^Jx)x9Y2#DpC?LFf&0kqr!{s=c zxTs-LXaLrJ9Hv`-UpOeLC`C+!Z~{xjd(J@LGb#K4rl}8eSxjFgd(4+Ll_EDPkW5@~ zUkGgCrx`iR%w(hp!~v4V2@~bO8hz`z_!+eM5d~6GYDr?eA^!4$LE#553Rq14zCM1R zUxwd^km^5R)w|#Ry2+)$pOTuKSV>4n1Gow@{*h+MCC{H(0xT%;v?e%#t%(odG|?Sp zrOKtjpOczcl$V*89$%DLl8R3!s2$P*w#vUG^%MgTtYl_j_{Yw`0Hhcg7JL;>1ksEj p;=rec8yFaw3mB#e!Nv6b-loqM;#eWX4-&!;!hbh(tCYIm1^`vxl{^3d literal 0 HcmV?d00001 diff --git a/modules/AbstractClassifier.py b/modules/AbstractClassifier.py new file mode 100644 index 0000000..095ede0 --- /dev/null +++ b/modules/AbstractClassifier.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python3 +""" Abstract class representing an AI Classifier. + +Represents an AI Classifier. AI Classifiers process data using AI +models. Based on HIAS AI Agents for future compatibility with +the HIAS Network. + +MIT License + +Copyright (c) 2021 Asociación de Investigacion en Inteligencia Artificial +Para la Leucemia Peter Moss + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +from abc import ABC, abstractmethod + +from modules.helpers import helpers +from modules.model import model + + +class AbstractClassifier(ABC): + """ Abstract class representing an AI Classifier. + + Represents an AI Classifier. AI Classifiers process data using AI + models. Based on HIAS AI Agents for future compatibility with + the HIAS Network. + """ + + def __init__(self): + """ Initializes the AbstractClassifier object. """ + super().__init__() + + self.helpers = helpers("Classifier") + self.confs = self.helpers.confs + self.model_type = None + + self.helpers.logger.info("Classifier initialization complete.") + + @abstractmethod + def init_model(self): + """ Loads the model class """ + pass + + @abstractmethod + def train(self): + """ Creates & trains the model. """ + pass + + @abstractmethod + def load_model(self): + """ Loads the AI model """ + pass + + @abstractmethod + def inference(self): + """ Loads model and classifies test data """ + pass + + @abstractmethod + def server(self): + """ Loads the API server """ + pass + + @abstractmethod + def inference_http(self): + """ Classifies test data via HTTP requests """ + pass \ No newline at end of file diff --git a/modules/AbstractData.py b/modules/AbstractData.py new file mode 100644 index 0000000..a4a427e --- /dev/null +++ b/modules/AbstractData.py @@ -0,0 +1,115 @@ +#!/usr/bin/env python3 +""" AI Model Data Abstract Class. + +Provides the AI Model with the required required data +processing functionality. + +MIT License + +Copyright (c) 2021 Asociación de Investigacion en Inteligencia Artificial +Para la Leucemia Peter Moss + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +import cv2 +import pathlib +import random +import os + +from numpy.random import seed + +from abc import ABC, abstractmethod + + +class AbstractData(ABC): + """ AI Model Data Abstract Class. + + Provides the AI Model with the required required data + processing functionality. + """ + + def __init__(self, helpers): + "Initializes the AbstractData object." + super().__init__() + + self.helpers = helpers + self.confs = self.helpers.confs + + self.seed = self.confs["data"]["seed"] + self.dim = self.confs["data"]["dim"] + + seed(self.seed) + random.seed(self.seed) + + self.data = [] + self.labels = [] + + self.helpers.logger.info("Data class initialization complete.") + + def remove_testing(self): + """ Removes the testing images from the dataset. """ + + for img in self.confs["data"]["test_data"]: + original = "model/data/train/"+img + destination = "model/data/test/"+img + + if not os.path.isfile(original): + self.helpers.logger.error("Original " + destination + " does not exist, please ensure all data is in the model/data/train directory") + exit() + + pathlib.Path(original).rename(destination) + self.helpers.logger.info(original + " moved to " + destination) + cv2.imwrite(destination, cv2.resize(cv2.imread(destination), + (self.dim, self.dim))) + self.helpers.logger.info("Resized " + destination) + + @abstractmethod + def process(self): + """ Processes the images. """ + pass + + @abstractmethod + def encode_labels(self): + """ One Hot Encodes the labels. """ + pass + + @abstractmethod + def convert_data(self): + """ Converts the training data to a numpy array. """ + pass + + @abstractmethod + def shuffle(self): + """ Shuffles the data and labels. """ + pass + + @abstractmethod + def get_split(self): + """ Splits the data and labels creating training and validation datasets. """ + pass + + @abstractmethod + def resize(self, path, dim): + """ Resizes an image to the provided dimensions (dim). """ + pass diff --git a/modules/AbstractModel.py b/modules/AbstractModel.py new file mode 100644 index 0000000..2054c9c --- /dev/null +++ b/modules/AbstractModel.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python3 +""" AI Model abstract class. + +Provides the AI Model functionality. + +MIT License + +Copyright (c) 2021 Asociación de Investigacion en Inteligencia Artificial +Para la Leucemia Peter Moss + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +import os +import random + +from numpy.random import seed + +from abc import ABC, abstractmethod + +from modules.data import data + +class AbstractModel(ABC): + """ AI Model abstract class """ + + def __init__(self, helpers): + """ Initializes the AbstractModel object. """ + super().__init__() + + self.helpers = helpers + self.confs = self.helpers.confs + + os.environ["KMP_BLOCKTIME"] = "1" + os.environ["KMP_SETTINGS"] = "1" + os.environ["KMP_AFFINITY"] = "granularity=fine,verbose,compact,1,0" + os.environ["OMP_NUM_THREADS"] = str( + self.confs["agent"]["cores"]) + + self.data = data(self.helpers) + + self.data_labels = self.confs["data"]["labels"] + + self.testing_dir = self.confs["data"]["test"] + self.valid = self.confs["data"]["valid_types"] + self.seed = self.confs["data"]["seed"] + + random.seed(self.seed) + seed(self.seed) + + self.weights_file_path = self.confs["model"]["weights"] + self.json_model_path = self.confs["model"]["model"] + self.saved_model_path = self.confs["model"]["saved"] + self.tfrt_model_path = self.confs["model"]["tfrt"] + self.onnx_model_path = self.confs["model"]["onnx"] + self.tensorrt_model_path = self.confs["model"]["tensorrt"] + + self.helpers.logger.info("Model class initialization complete.") + + @abstractmethod + def prepare_data(self): + """ Prepares the model data """ + pass + + @abstractmethod + def prepare_network(self): + """ Builds the network """ + pass + + @abstractmethod + def train(self): + """ Trains the model """ + pass + + @abstractmethod + def save_model_as_json(self): + """ Saves the model as JSON """ + pass + + @abstractmethod + def save_weights(self): + """ Saves the model weights """ + pass + + @abstractmethod + def evaluate(self): + """ Evaluates the model """ + pass + + @abstractmethod + def plot_accuracy(self): + """ Plots the accuracy. """ + pass + + @abstractmethod + def plot_loss(self): + """ Plots the loss. """ + pass + + @abstractmethod + def plot_auc(self): + """ Plots the AUC curve. """ + pass + + @abstractmethod + def plot_precision(self): + """ Plots the precision. """ + pass + + @abstractmethod + def plot_recall(self): + """ Plots the recall. """ + pass + + @abstractmethod + def confusion_matrix(self): + """ Prints/displays the confusion matrix. """ + pass + + @abstractmethod + def figures_of_merit(self): + """ Calculates/prints the figures of merit. """ + pass + + @abstractmethod + def predictions(self): + """ Makes predictions on the train & test sets. """ + pass + + @abstractmethod + def predict(self, img): + """ Gets a prediction for an image. """ + pass + + @abstractmethod + def reshape(self, img): + """ Reshapes an image. """ + pass + + @abstractmethod + def test(self): + """Local test mode + + Loops through the test directory and classifies the images. + """ + pass + + @abstractmethod + def http_request(self): + """ Sends image to the inference API endpoint. """ + pass + + @abstractmethod + def test_http(self): + """Server test mode + + Loops through the test directory and sends the images to the classification server. + """ + pass \ No newline at end of file diff --git a/modules/AbstractServer.py b/modules/AbstractServer.py new file mode 100644 index 0000000..b0f326e --- /dev/null +++ b/modules/AbstractServer.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 +""" Server/API abstract class. + +Abstract class for the classifier server/API. + +MIT License + +Copyright (c) 2021 Asociación de Investigacion en Inteligencia Artificial +Para la Leucemia Peter Moss + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +from abc import ABC, abstractmethod + +class AbstractServer(ABC): + """ Server/API abstract class. + + Abstract class for the classifier server/API. + """ + + def __init__(self, helpers, model, model_type): + "Initializes the AbstractServer object." + super().__init__() + + self.helpers = helpers + self.confs = self.helpers.confs + + self.model = model + self.model_type = model_type + + self.helpers.logger.info("Server initialization complete.") + + + @abstractmethod + def predict(self, req): + """ Classifies an image sent via HTTP. """ + pass + + @abstractmethod + def start(self, img_path): + """ Sends image to the inference API endpoint. """ + pass diff --git a/modules/__init__.py b/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/modules/augmentation.py b/modules/augmentation.py new file mode 100644 index 0000000..8764d28 --- /dev/null +++ b/modules/augmentation.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +""" Data Augmentation Class. + +Provides data augmentation methods. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +import cv2 +import random + +import numpy as np + +from numpy.random import seed +from scipy import ndimage +from skimage import transform as tm + +class augmentation(): + """ HIAS AI Model Data Augmentation Class + + Provides data augmentation methods. + """ + + def __init__(self, helpers): + """ Initializes the class. """ + + self.helpers = helpers + + self.seed = self.helpers.confs["data"]["seed"] + seed(self.seed) + + self.helpers.logger.info("Augmentation class initialization complete.") + + def grayscale(self, data): + """ Creates a grayscale copy. """ + + gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY) + return np.dstack([gray, gray, gray]).astype(np.float32)/255. + + def equalize_hist(self, data): + """ Creates a histogram equalized copy. """ + + img_to_yuv = cv2.cvtColor(data, cv2.COLOR_BGR2YUV) + img_to_yuv[:, :, 0] = cv2.equalizeHist(img_to_yuv[:, :, 0]) + hist_equalization_result = cv2.cvtColor(img_to_yuv, cv2.COLOR_YUV2BGR) + return hist_equalization_result.astype(np.float32)/255. + + def reflection(self, data): + """ Creates a reflected copy. """ + + return cv2.flip(data, 0).astype(np.float32)/255., cv2.flip(data, 1).astype(np.float32)/255. + + def gaussian(self, data): + """ Creates a gaussian blurred copy. """ + + return ndimage.gaussian_filter(data, sigma=5.11).astype(np.float32)/255. + + def translate(self, data): + """ Creates transformed copy. """ + + cols, rows, chs = data.shape + + return cv2.warpAffine(data, np.float32([[1, 0, 84], [0, 1, 56]]), (rows, cols), + borderMode=cv2.BORDER_CONSTANT, borderValue=(144, 159, 162)).astype(np.float32)/255. + + def rotation(self, data): + """ Creates a rotated copy. """ + + cols, rows, chs = data.shape + + rand_deg = random.randint(-180, 180) + matrix = cv2.getRotationMatrix2D((cols/2, rows/2), rand_deg, 0.70) + rotated = cv2.warpAffine(data, matrix, (rows, cols), borderMode=cv2.BORDER_CONSTANT, + borderValue=(144, 159, 162)) + + return rotated.astype(np.float32)/255. + + def shear(self, data): + """ Creates a histogram equalized copy. """ + + at = tm.AffineTransform(shear=0.5) + return tm.warp(data, inverse_map=at) diff --git a/modules/data.py b/modules/data.py new file mode 100644 index 0000000..c6f96d8 --- /dev/null +++ b/modules/data.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +""" AI Model Data Class. + +Provides the AI Model with the required required data +processing functionality. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +import cv2 +import os +import pathlib + +import numpy as np + +from numpy.random import seed +from PIL import Image +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import OneHotEncoder +from sklearn.utils import shuffle + +from modules.AbstractData import AbstractData +from modules.augmentation import augmentation + +class data(AbstractData): + """ AI Model Data Class. + + Provides the AI Model with the required required data + processing functionality. + """ + + def process(self): + """ Processes the images. """ + + aug = augmentation(self.helpers) + + data_dir = pathlib.Path(self.confs["data"]["train_dir"]) + data = list(data_dir.glob( + '*' + self.confs["data"]["file_type"])) + + count = 0 + neg_count = 0 + pos_count = 0 + + augmented_data = [] + self.labels = [] + temp = [] + + for rimage in data: + fpath = str(rimage) + fname = os.path.basename(rimage) + label = 0 if "_0" in fname else 1 + + # Resize Image + image = self.resize(fpath, self.dim) + + if image.shape[2] == 1: + image = np.dstack( + [image, image, image]) + + temp.append(image.astype(np.float32)/255.) + + self.data.append(image.astype(np.float32)/255.) + self.labels.append(label) + + # Grayscale + self.data.append(aug.grayscale(image)) + self.labels.append(label) + + # Histogram Equalization + self.data.append(aug.equalize_hist(image)) + self.labels.append(label) + + # Reflection + horizontal, vertical = aug.reflection(image) + self.data.append(horizontal) + self.labels.append(label) + self.data.append(vertical) + self.labels.append(label) + + # Gaussian Blur + self.data.append(aug.gaussian(image)) + self.labels.append(label) + + # Translation + self.data.append(aug.translate(image)) + self.labels.append(label) + + # Shear + self.data.append(aug.shear(image)) + self.labels.append(label) + + # Rotation + for i in range(0, self.helpers.confs["data"]["rotations"]): + self.data.append(aug.rotation(image)) + self.labels.append(label) + if "_0" in fname: + neg_count += 1 + else: + pos_count += 1 + count += 1 + + if "_0" in fname: + neg_count += 8 + else: + pos_count += 8 + count += 8 + + self.shuffle() + self.convert_data() + self.encode_labels() + + self.helpers.logger.info("Augmented data size: " + str(count)) + self.helpers.logger.info("Negative data size: " + str(neg_count)) + self.helpers.logger.info("Positive data size: " + str(pos_count)) + self.helpers.logger.info("Augmented data shape: " + str(self.data.shape)) + self.helpers.logger.info("Labels shape: " + str(self.labels.shape)) + + self.X_train_arr = np.asarray(temp) + + self.get_split() + + def convert_data(self): + """ Converts the training data to a numpy array. """ + + self.data = np.array(self.data) + + def encode_labels(self): + """ One Hot Encodes the labels. """ + + encoder = OneHotEncoder(categories='auto') + + self.labels = np.reshape(self.labels, (-1, 1)) + self.labels = encoder.fit_transform(self.labels).toarray() + + def shuffle(self): + """ Shuffles the data and labels. """ + + self.data, self.labels = shuffle( + self.data, self.labels, random_state=self.seed) + + def get_split(self): + """ Splits the data and labels creating training and validation datasets. """ + + self.X_train, self.X_test, self.y_train, self.y_test = train_test_split( + self.data, self.labels, test_size=self.helpers.confs["data"]["split"], + random_state=self.seed) + + self.helpers.logger.info("Training data: " + str(self.X_train.shape)) + self.helpers.logger.info("Training labels: " + str(self.y_train.shape)) + self.helpers.logger.info("Validation data: " + str(self.X_test.shape)) + self.helpers.logger.info("Validation labels: " + str(self.y_test.shape)) + + def resize(self, path, dim): + """ Resizes an image to the provided dimensions (dim). """ + + return cv2.resize(cv2.imread(path), (dim, dim)) diff --git a/modules/engine.py b/modules/engine.py new file mode 100644 index 0000000..3d22c6f --- /dev/null +++ b/modules/engine.py @@ -0,0 +1,199 @@ +""" TensorRT engine + +Provides the TensorRT engine functionality. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +Reference: https://github.com/jkjung-avt/keras_imagenet/ +""" + +import cv2 +import numpy as np +import os.path +import tensorrt as trt +import time + +TRT_LOGGER = trt.Logger() +EXPLICIT_BATCH = [1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)] + + +class engine(): + """ TensorRT engine """ + + def __init__(self, helpers): + """ Initializes the TensorRT engine class. """ + + self.helpers = helpers + self.confs = helpers.confs + self.onnx_model_path = self.confs["model"]["onnx"] + self.tensorrt_model_path = self.confs["model"]["tensorrt"] + self.testing_dir = self.confs["data"]["test"] + self.valid = self.confs["data"]["valid_types"] + self.labels = self.confs["data"]["labels"] + + if not os.path.isfile(self.tensorrt_model_path): + self.save_engine(self.build_engine()) + self.helpers.logger.info("TensorRT model generated.") + + self.helpers.logger.info("Engine class initialization complete.") + + def build_engine(self): + """ Builds the TensorRT engine. """ + + with trt.Builder(TRT_LOGGER) as builder, builder.create_network(*EXPLICIT_BATCH) \ + as network, trt.OnnxParser(network, TRT_LOGGER) as parser: + builder.max_workspace_size = 1 << 30 + builder.max_batch_size = 1 + builder.fp16_mode = False + with open(self.onnx_model_path, 'rb') as model: + if not parser.parse(model.read()): + self.helpers.logger.info("ERROR: Failed to parse the ONNX file.") + for error in range(parser.num_errors): + self.helpers.logger.info(parser.get_error(error)) + return None + shape = list(network.get_input(0).shape) + shape[0] = 1 + network.get_input(0).shape = shape + return builder.build_cuda_engine(network) + + self.helpers.logger.info("Engine build complete.") + + def save_engine(self, engine): + """ Saves the TensorRT engine. """ + + with open(self.tensorrt_model_path, 'wb') as f: + f.write(engine.serialize()) + + self.helpers.logger.info("Engine save complete.") + + def load_engine(self): + """ Loads the TensorRT engine. """ + + with open(self.tensorrt_model_path, 'rb') as f: + engine_data = f.read() + self.engine = trt.Runtime(TRT_LOGGER).deserialize_cuda_engine(engine_data) + + self.helpers.logger.info("Engine load complete.") + + def init_trt_buffers(self, cuda): + """ Initialize host buffers and cuda buffers for the engine.""" + + size = trt.volume((1, 100, 100, 3)) * self.engine.max_batch_size + host_input = cuda.pagelocked_empty(size, np.float32) + cuda_input = cuda.mem_alloc(host_input.nbytes) + size = trt.volume((1, 2)) * self.engine.max_batch_size + host_output = cuda.pagelocked_empty(size, np.float32) + cuda_output = cuda.mem_alloc(host_output.nbytes) + return host_input, cuda_input, host_output, cuda_output + + self.helpers.logger.info("Engine buffers initialized.") + + def predict(self, img): + """ Inference the image with TensorRT engine.""" + + import pycuda.autoinit + import pycuda.driver as cuda + + with open(self.tensorrt_model_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime: + engine = runtime.deserialize_cuda_engine(f.read()) + + host_input, cuda_input, host_output, cuda_output = self.init_trt_buffers( + cuda) + stream = cuda.Stream() + + context = self.engine.create_execution_context() + context.set_binding_shape(0, (1, 100, 100, 3)) + + np.copyto(host_input, img.ravel()) + cuda.memcpy_htod_async(cuda_input, host_input, stream) + + context.execute_async_v2(bindings=[int(cuda_input), int(cuda_output)], + stream_handle=stream.handle) + + cuda.memcpy_dtoh_async(host_output, cuda_output, stream) + stream.synchronize() + + return host_output + + def reshape(self, img): + """ Reshapes an image. """ + + dx, dy, dz = img.shape + input_data = img.reshape((-1, dx, dy, dz)) + input_data = input_data / 255.0 + + return input_data + + def test(self): + """TensorRT test mode + + Loops through the test directory and classifies the images + using the TensorRT model. + """ + + files = 0 + tp = 0 + fp = 0 + tn = 0 + fn = 0 + totaltime = 0 + + for testFile in os.listdir(self.testing_dir): + if os.path.splitext(testFile)[1] in self.valid: + files += 1 + fileName = self.testing_dir + "/" + testFile + + img = cv2.imread(fileName).astype(np.float32) + self.helpers.logger.info("Loaded test image " + fileName) + + img = cv2.resize(img, (100,100)) + img = self.reshape(img) + + start = time.time() + predictions = self.predict(img) + predictions = predictions.argsort()[::-1] + prediction = self.labels[predictions[0]] + end = time.time() + benchmark = end - start + totaltime += benchmark + + msg = "" + if prediction == 1 and "_1." in testFile: + tp += 1 + msg = "Acute Lymphoblastic Leukemia correctly detected (True Positive) in " + str(benchmark) + " seconds." + elif prediction == 1 and "_0." in testFile: + fp += 1 + msg = "Acute Lymphoblastic Leukemia incorrectly detected (False Positive) in " + str(benchmark) + " seconds." + elif prediction == 0 and "_0." in testFile: + tn += 1 + msg = "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in " + str(benchmark) + " seconds." + elif prediction == 0 and "_1." in testFile: + fn += 1 + msg = "Acute Lymphoblastic Leukemia incorrectly not detected (False Negative) in " + str(benchmark) + " seconds." + self.helpers.logger.info(msg) + + self.helpers.logger.info("Images Classified: " + str(files)) + self.helpers.logger.info("True Positives: " + str(tp)) + self.helpers.logger.info("False Positives: " + str(fp)) + self.helpers.logger.info("True Negatives: " + str(tn)) + self.helpers.logger.info("False Negatives: " + str(fn)) + self.helpers.logger.info("Total Time Taken: " + str(totaltime)) \ No newline at end of file diff --git a/modules/helpers.py b/modules/helpers.py new file mode 100644 index 0000000..b3a2db0 --- /dev/null +++ b/modules/helpers.py @@ -0,0 +1,99 @@ +""" Helpers file. + +Configuration and logging functions. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +import logging +import logging.handlers as handlers +import json +import os +import socket +import sys +import time + +from datetime import datetime + + +class helpers(): + """ Helper Class + + Configuration and logging functions. + """ + + def __init__(self, ltype, log=True): + """ Initializes the Helpers Class. """ + + # Loads system configs + self.confs = {} + self.load_confs() + + # Sets system logging + self.logger = logging.getLogger(ltype) + self.logger.setLevel(logging.INFO) + + formatter = logging.Formatter( + '%(asctime)s - %(name)s - %(levelname)s - %(message)s') + + allLogHandler = handlers.TimedRotatingFileHandler( + os.path.dirname(os.path.abspath(__file__)) + '/../logs/all.log', when='H', interval=1, backupCount=0) + allLogHandler.setLevel(logging.INFO) + allLogHandler.setFormatter(formatter) + + errorLogHandler = handlers.TimedRotatingFileHandler( + os.path.dirname(os.path.abspath(__file__)) + '/../logs/error.log', when='H', interval=1, backupCount=0) + errorLogHandler.setLevel(logging.ERROR) + errorLogHandler.setFormatter(formatter) + + warningLogHandler = handlers.TimedRotatingFileHandler( + os.path.dirname(os.path.abspath(__file__)) + '/../logs/warning.log', when='H', interval=1, backupCount=0) + warningLogHandler.setLevel(logging.WARNING) + warningLogHandler.setFormatter(formatter) + + consoleHandler = logging.StreamHandler(sys.stdout) + consoleHandler.setFormatter(formatter) + + self.logger.addHandler(allLogHandler) + self.logger.addHandler(errorLogHandler) + self.logger.addHandler(warningLogHandler) + self.logger.addHandler(consoleHandler) + + if log is True: + self.logger.info("Helpers class initialization complete.") + + def load_confs(self): + """ Load the configuration. """ + + with open(os.path.dirname(os.path.abspath(__file__)) + '/../configuration/config.json') as confs: + self.confs = json.loads(confs.read()) + + def get_ip_addr(self): + """ Load the configuration. """ + + s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + s.connect(("8.8.8.8", 80)) + ipaddr = s.getsockname()[0] + s.close() + + return ipaddr \ No newline at end of file diff --git a/modules/model.py b/modules/model.py new file mode 100644 index 0000000..fd81816 --- /dev/null +++ b/modules/model.py @@ -0,0 +1,515 @@ +#!/usr/bin/env python3 +""" AI Model class. + +Provides the AI Model functionality. + +MIT License + +Copyright (c) 2021 Asociación de Investigacion en Inteligencia Artificial +Para la Leucemia Peter Moss + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +import cv2 +import json +import os +import pathlib +import requests +import time + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +from PIL import Image +from sklearn.metrics import confusion_matrix +from tensorflow.keras import layers, models +from tensorflow.python.compiler.tensorrt import trt_convert as trt +from mlxtend.plotting import plot_confusion_matrix + +from modules.AbstractModel import AbstractModel + +plt.switch_backend('Agg') + + +class model(AbstractModel): + """ AI Model class """ + + def prepare_data(self): + """ Creates/sorts dataset. """ + + self.data.remove_testing() + self.data.process() + + self.helpers.logger.info("Data preperation complete.") + + def prepare_network(self): + """ Builds the network. """ + + self.tf_model = tf.keras.models.Sequential([ + tf.keras.layers.InputLayer(input_shape=(self.data.X_train.shape[1:])), + tf.keras.layers.AveragePooling2D( + pool_size=(2, 2), strides=None, padding='valid'), + tf.keras.layers.Conv2D(30, (5, 5), strides=1, + padding="valid", activation='relu'), + tf.keras.layers.DepthwiseConv2D(30, (1, 1), + padding="valid", activation='relu'), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(2), + tf.keras.layers.Activation('softmax', name='softmax') + ], + "AllJetsonNano") + self.tf_model.summary() + + self.helpers.logger.info("Network initialization complete.") + + def train(self): + """ Trains the model + + Compiles and fits the model. + """ + + self.helpers.logger.info("Using Adam Optimizer.") + optimizer = tf.keras.optimizers.Adam(learning_rate=self.confs["train"]["learning_rate_adam"], + decay = self.confs["train"]["decay_adam"]) + + self.helpers.logger.info("Using Early Stopping.") + callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', + patience=3, + verbose=0, + mode='auto', + restore_best_weights=True) + + self.tf_model.compile(optimizer=optimizer, + loss='binary_crossentropy', + metrics=[tf.keras.metrics.BinaryAccuracy(name='acc'), + tf.keras.metrics.Precision(name='precision'), + tf.keras.metrics.Recall(name='recall'), + tf.keras.metrics.AUC(name='auc') ]) + + self.history = self.tf_model.fit(self.data.X_train, self.data.y_train, + validation_data=(self.data.X_test, self.data.y_test), + validation_steps=self.confs["train"]["val_steps"], + epochs=self.confs["train"]["epochs"], callbacks=[callback]) + + print(self.history) + print("") + + self.save_model_as_json() + self.save_weights() + self.convert_to_tfrt() + self.convert_to_onnx() + + def save_model_as_json(self): + """ Saves the model as JSON """ + + with open(self.json_model_path, "w") as file: + file.write(self.tf_model.to_json()) + + self.tf_model.save(self.saved_model_path) + + self.helpers.logger.info("Model JSON saved " + self.json_model_path) + + def save_weights(self): + """ Saves the model weights """ + + self.tf_model.save_weights(self.weights_file_path) + self.helpers.logger.info("Weights saved " + self.weights_file_path) + + def convert_to_tfrt(self): + """ Converts the model to TFRT format """ + + converter = trt.TrtGraphConverterV2( + input_saved_model_dir=self.saved_model_path) + converter.convert() + converter.save(self.tfrt_model_path) + + def convert_to_onnx(self): + """ Converts the model to ONNX format """ + + os.system('python3 -m tf2onnx.convert --saved-model ' \ + + self.saved_model_path \ + + ' --output ' + self.onnx_model_path \ + + ' --tag serve --signature_def serving_default') + + def predictions(self): + """ Gets a prediction for an image. """ + + self.train_preds = self.tf_model.predict(self.data.X_train) + self.test_preds = self.tf_model.predict(self.data.X_test) + + def evaluate(self): + """ Evaluates the model """ + + self.predictions() + + metrics = self.tf_model.evaluate( + self.data.X_test, self.data.y_test, verbose=0) + for name, value in zip(self.tf_model.metrics_names, metrics): + self.helpers.logger.info("Metrics: " + name + " " + str(value)) + print() + + self.plot_accuracy() + self.plot_loss() + self.plot_auc() + self.plot_precision() + self.plot_recall() + self.confusion_matrix() + self.figures_of_merit() + + def plot_accuracy(self): + """ Plots the accuracy. """ + + plt.plot(self.history.history['acc']) + plt.plot(self.history.history['val_acc']) + plt.title('Model Accuracy') + plt.ylabel('Accuracy') + plt.xlabel('Epoch') + plt.ylim((0, 1)) + plt.legend(['Train', 'Validate'], loc='upper left') + plt.savefig('model/plots/accuracy.png') + plt.show() + plt.clf() + + def plot_loss(self): + """ Plots the loss. """ + + plt.plot(self.history.history['loss']) + plt.plot(self.history.history['val_loss']) + plt.title('Model Loss') + plt.ylabel('loss') + plt.xlabel('Epoch') + plt.legend(['Train', 'Validate'], loc='upper left') + plt.savefig('model/plots/loss.png') + plt.show() + plt.clf() + + def plot_auc(self): + """ Plots the AUC. """ + + plt.plot(self.history.history['auc']) + plt.plot(self.history.history['val_auc']) + plt.title('Model AUC') + plt.ylabel('AUC') + plt.xlabel('Epoch') + plt.legend(['Train', 'Validate'], loc='upper left') + plt.savefig('model/plots/auc.png') + plt.show() + plt.clf() + + def plot_precision(self): + """ Plots the precision. """ + + plt.plot(self.history.history['precision']) + plt.plot(self.history.history['val_precision']) + plt.title('Model Precision') + plt.ylabel('Precision') + plt.xlabel('Epoch') + plt.legend(['Train', 'Validate'], loc='upper left') + plt.savefig('model/plots/precision.png') + plt.show() + plt.clf() + + def plot_recall(self): + """ Plots the recall. """ + + plt.plot(self.history.history['recall']) + plt.plot(self.history.history['val_recall']) + plt.title('Model Recall') + plt.ylabel('Recall') + plt.xlabel('Epoch') + plt.legend(['Train', 'Validate'], loc='upper left') + plt.savefig('model/plots/recall.png') + plt.show() + plt.clf() + + def confusion_matrix(self): + """ Plots the confusion matrix. """ + + self.matrix = confusion_matrix(self.data.y_test.argmax(axis=1), + self.test_preds.argmax(axis=1)) + + self.helpers.logger.info("Confusion Matrix: " + str(self.matrix)) + print("") + + plot_confusion_matrix(conf_mat=self.matrix) + plt.savefig('model/plots/confusion-matrix.png') + plt.show() + plt.clf() + + def figures_of_merit(self): + """ Calculates/prints the figures of merit. + + https://homes.di.unimi.it/scotti/all/ + """ + + test_len = len(self.data.X_test) + + TP = self.matrix[1][1] + TN = self.matrix[0][0] + FP = self.matrix[0][1] + FN = self.matrix[1][0] + + TPP = (TP * 100)/test_len + FPP = (FP * 100)/test_len + FNP = (FN * 100)/test_len + TNP = (TN * 100)/test_len + + specificity = TN/(TN+FP) + + misc = FP + FN + miscp = (misc * 100)/test_len + + self.helpers.logger.info( + "True Positives: " + str(TP) + "(" + str(TPP) + "%)") + self.helpers.logger.info( + "False Positives: " + str(FP) + "(" + str(FPP) + "%)") + self.helpers.logger.info( + "True Negatives: " + str(TN) + "(" + str(TNP) + "%)") + self.helpers.logger.info( + "False Negatives: " + str(FN) + "(" + str(FNP) + "%)") + + self.helpers.logger.info("Specificity: " + str(specificity)) + self.helpers.logger.info("Misclassification: " + + str(misc) + "(" + str(miscp) + "%)") + + def load(self): + """ Loads the model """ + + with open(self.json_model_path) as file: + m_json = file.read() + + self.tf_model = tf.keras.models.model_from_json(m_json) + self.tf_model.load_weights(self.weights_file_path) + + self.helpers.logger.info("Model loaded ") + + self.tf_model.summary() + + def predict(self, img): + """ Gets a prediction for an image. """ + + predictions = self.tf_model.predict(img) + prediction = np.argmax(predictions, axis=-1) + + return prediction + + def reshape(self, img): + """ Reshapes an image. """ + + dx, dy, dz = img.shape + input_data = img.reshape((-1, dx, dy, dz)) + input_data = input_data / 255.0 + + return input_data + + def test(self): + """ Test mode + + Loops through the test directory and classifies the images. + """ + + files = 0 + tp = 0 + fp = 0 + tn = 0 + fn = 0 + totaltime = 0 + + for testFile in os.listdir(self.testing_dir): + if os.path.splitext(testFile)[1] in self.valid: + files += 1 + fileName = self.testing_dir + "/" + testFile + + img = cv2.imread(fileName).astype(np.float32) + self.helpers.logger.info("Loaded test image " + fileName) + + img = cv2.resize(img, (self.data.dim, self.data.dim)) + img = self.reshape(img) + + start = time.time() + prediction = self.predict(img) + end = time.time() + benchmark = end - start + totaltime += benchmark + + msg = "" + if prediction == 1 and "_1." in testFile: + tp += 1 + msg = "Acute Lymphoblastic Leukemia correctly detected (True Positive) in " + str(benchmark) + " seconds." + elif prediction == 1 and "_0." in testFile: + fp += 1 + msg = "Acute Lymphoblastic Leukemia incorrectly detected (False Positive) in " + str(benchmark) + " seconds." + elif prediction == 0 and "_0." in testFile: + tn += 1 + msg = "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in " + str(benchmark) + " seconds." + elif prediction == 0 and "_1." in testFile: + fn += 1 + msg = "Acute Lymphoblastic Leukemia incorrectly not detected (False Negative) in " + str(benchmark) + " seconds." + self.helpers.logger.info(msg) + + self.helpers.logger.info("Images Classified: " + str(files)) + self.helpers.logger.info("True Positives: " + str(tp)) + self.helpers.logger.info("False Positives: " + str(fp)) + self.helpers.logger.info("True Negatives: " + str(tn)) + self.helpers.logger.info("False Negatives: " + str(fn)) + self.helpers.logger.info("Total Time Taken: " + str(totaltime)) + + def http_request(self, img_path): + """ Sends image to the inference API endpoint. """ + + self.helpers.logger.info("Sending request for: " + img_path) + + _, img_encoded = cv2.imencode('.jpg', cv2.imread(img_path)) + response = requests.post(self.addr, data=img_encoded.tostring(), headers=self.headers) + response = json.loads(response.text) + + return response + + def test_http(self): + """Server test mode + + Loops through the test directory and sends the images to the + classification server. + """ + + totaltime = 0 + files = 0 + + tp = 0 + fp = 0 + tn = 0 + fn = 0 + + self.addr = "http://" + self.helpers.get_ip_addr() + \ + ':'+str(self.helpers.confs["agent"]["port"]) + '/Inference' + self.headers = {'content-type': 'image/jpeg'} + + for testFile in os.listdir(self.testing_dir): + if os.path.splitext(testFile)[1] in self.valid: + + start = time.time() + prediction = self.http_request(self.testing_dir + "/" + testFile) + end = time.time() + benchmark = end - start + totaltime += benchmark + + msg = "" + status = "" + outcome = "" + + if prediction["Diagnosis"] == "Positive" and "_1." in testFile: + tp += 1 + status = "correctly" + outcome = "(True Positive)" + elif prediction["Diagnosis"] == "Positive" and "_0." in testFile: + fp += 1 + status = "incorrectly" + outcome = "(False Positive)" + elif prediction["Diagnosis"] == "Negative" and "_0." in testFile: + tn += 1 + status = "correctly" + outcome = "(True Negative)" + elif prediction["Diagnosis"] == "Negative" and "_1." in testFile: + fn += 1 + status = "incorrectly" + outcome = "(False Negative)" + + files += 1 + self.helpers.logger.info("Acute Lymphoblastic Leukemia " + status + + " detected " + outcome + " in " + str(benchmark) + " seconds.") + + self.helpers.logger.info("Images Classified: " + str(files)) + self.helpers.logger.info("True Positives: " + str(tp)) + self.helpers.logger.info("False Positives: " + str(fp)) + self.helpers.logger.info("True Negatives: " + str(tn)) + self.helpers.logger.info("False Negatives: " + str(fn)) + self.helpers.logger.info("Total Time Taken: " + str(totaltime)) + + def load_tfrt(self): + """ Loads the tfrt model """ + + self.tfrt_model = tf.saved_model.load(self.tfrt_model_path) + + def predict_tfrt(self, img): + """ Gets a prediction for an image. """ + + inference = self.tfrt_model.signatures["serving_default"] + prediction = inference(tf.constant(img, dtype=float))['softmax'] + prediction = self.data_labels[int(tf.argmax(prediction, axis=1))] + + return prediction + + def test_tfrt(self): + """TFRT test mode + + Loops through the test directory and classifies the images + usin the TFRT model. + """ + + files = 0 + tp = 0 + fp = 0 + tn = 0 + fn = 0 + totaltime = 0 + + for testFile in os.listdir(self.testing_dir): + if os.path.splitext(testFile)[1] in self.valid: + files += 1 + fileName = self.testing_dir + "/" + testFile + + img = cv2.imread(fileName).astype(np.float32) + self.helpers.logger.info("Loaded test image " + fileName) + + img = cv2.resize(img, (self.data.dim, self.data.dim)) + img = self.reshape(img) + + start = time.time() + prediction = self.predict_tfrt(img) + end = time.time() + benchmark = end - start + totaltime += benchmark + + msg = "" + if prediction == 1 and "_1." in testFile: + tp += 1 + msg = "Acute Lymphoblastic Leukemia correctly detected (True Positive) in " + str(benchmark) + " seconds." + elif prediction == 1 and "_0." in testFile: + fp += 1 + msg = "Acute Lymphoblastic Leukemia incorrectly detected (False Positive) in " + str(benchmark) + " seconds." + elif prediction == 0 and "_0." in testFile: + tn += 1 + msg = "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in " + str(benchmark) + " seconds." + elif prediction == 0 and "_1." in testFile: + fn += 1 + msg = "Acute Lymphoblastic Leukemia incorrectly not detected (False Negative) in " + str(benchmark) + " seconds." + self.helpers.logger.info(msg) + + self.helpers.logger.info("Images Classified: " + str(files)) + self.helpers.logger.info("True Positives: " + str(tp)) + self.helpers.logger.info("False Positives: " + str(fp)) + self.helpers.logger.info("True Negatives: " + str(tn)) + self.helpers.logger.info("False Negatives: " + str(fn)) + self.helpers.logger.info("Total Time Taken: " + str(totaltime)) \ No newline at end of file diff --git a/modules/server.py b/modules/server.py new file mode 100644 index 0000000..b38cb17 --- /dev/null +++ b/modules/server.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python3 +""" Server/API class. + +Class for the classifier server/API. + +MIT License + +Copyright (c) 2021 Asociación de Investigacion en Inteligencia Artificial +Para la Leucemia Peter Moss + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files(the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and / or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Contributors: +- Adam Milton-Barker + +""" + +import cv2 +import json +import jsonpickle +import os +import requests +import time + +import numpy as np + +from io import BytesIO +from PIL import Image +from flask import Flask, request, Response + +from modules.AbstractServer import AbstractServer + +class server(AbstractServer): + """ Server/API class. + + Class for the classifier server/API. + """ + + def predict(self, req): + """ Classifies an image sent via HTTP. """ + + img = np.fromstring(req.data, np.uint8) + img = cv2.imdecode(img, cv2.IMREAD_COLOR) + + img = cv2.resize(img, (self.model.data.dim, + self.model.data.dim)) + img = self.model.reshape(img) + + return self.model.predict(img) + + def start(self): + """ Starts the server. """ + + app = Flask("AllJetsonNano") + + @app.route('/Inference', methods=['POST']) + def Inference(): + """ Responds to HTTP POST requests. """ + + prediction = self.predict(request) + + if prediction == 1: + message = "Acute Lymphoblastic Leukemia detected!" + diagnosis = "Positive" + elif prediction == 0: + message = "Acute Lymphoblastic Leukemia not detected!" + diagnosis = "Negative" + + resp = jsonpickle.encode({ + 'Response': 'OK', + 'Message': message, + 'Diagnosis': diagnosis + }) + + return Response(response=resp, status=200, mimetype="application/json") + + app.run(host=self.helpers.get_ip_addr(), port=self.helpers.confs["agent"]["port"]) \ No newline at end of file diff --git a/notebooks/classifier.ipynb b/notebooks/classifier.ipynb new file mode 100644 index 0000000..2c71689 --- /dev/null +++ b/notebooks/classifier.ipynb @@ -0,0 +1,1942 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project\n", + "## Acute Lymphoblastic Leukemia Jetson Nano Classifier\n", + "\n", + "![Acute Lymphoblastic Leukemia Jetson Nano Classifier](../assets/images/project-banner.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "\n", + "The **Acute Lymphoblastic Leukemia Jetson Nano Classifier** is a Convolutional Neural Network (CNN) developed using [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi.html) and [Intel® Optimization for Tensorflow*](https://software.intel.com/content/www/us/en/develop/articles/getting-started-with-intel-optimization-of-pytorch.html) to accelerate the training process, and [TensorRT](https://developer.nvidia.com/tensorrt) for high performance inference on the [NVIDIA® Jetson Nano™](https://developer.nvidia.com/embedded/jetson-nano-developer-kit)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DISCLAIMER\n", + "\n", + "_This project should be used for research purposes only. The purpose of the project is to show the potential of Artificial Intelligence for medical support systems such as diagnostic systems._\n", + "\n", + "_Although the model is accurate and shows good results both on paper and in real world testing, it is trained on a small amount of data and needs to be trained on larger datasets to really evaluate it's accuracy._\n", + "\n", + "_Developers that have contributed to this repository have experience in using Artificial Intelligence for detecting certain types of cancer. They are not doctors, medical or cancer experts._" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Software\n", + "\n", + "To use this project you will need to install the following software:\n", + "\n", + "- Conda\n", + "- Intel® oneAPI AI Analytics Toolkit\n", + "- Jupyter Notebooks\n", + "- NBConda \n", + "- Mlxtend\n", + "- Pillow\n", + "- Opencv\n", + "- Scipy\n", + "- Scikit Image\n", + "- Scikit Learn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Installation\n", + "\n", + "Before you can begin this tutorial you need to follow the [installation guide](https://github.com/AMLResearchProject/ALL-jetson-Nano/blob/main/docs/installation/ubuntu.md) to install the relevant software and setup your environment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "import os\n", + "import pathlib\n", + "import random\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "\n", + "from numpy.random import seed\n", + "from PIL import Image\n", + "from scipy import ndimage\n", + "from skimage import transform as tm\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.utils import shuffle\n", + "from tensorflow.python.compiler.tensorrt import trt_convert as trt\n", + "from mlxtend.plotting import plot_confusion_matrix\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set Global Variables" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_labels = [0, 1]\n", + "decay = 1e-6\n", + "dim = 100\n", + "epochs = 150\n", + "lr = 1e-4\n", + "rotations = 10\n", + "seed_amount = 2\n", + "test_split = 0.255\n", + "val_steps = 10\n", + "\n", + "valid_files = [\n", + " \".jpg\"\n", + "]\n", + "\n", + "modelDir = \"../model/\"\n", + "dataDir = modelDir + \"data/\"\n", + "testDataDir = dataDir + \"test/\"\n", + "trainDataDir = dataDir + \"train/\"\n", + "\n", + "model_path = modelDir + \"all_jetson_nano.json\"\n", + "weights_path = modelDir + \"all_jetson_nano.h5\"\n", + "\n", + "saved_model_dir = modelDir + \"saved\"\n", + "tfrt_model_dir = modelDir + \"tfrt\"\n", + "\n", + "testData = [\n", + " \"Im006_1.jpg\",\n", + " \"Im020_1.jpg\",\n", + " \"Im024_1.jpg\",\n", + " \"Im026_1.jpg\",\n", + " \"Im028_1.jpg\",\n", + " \"Im031_1.jpg\",\n", + " \"Im035_0.jpg\",\n", + " \"Im041_0.jpg\",\n", + " \"Im047_0.jpg\",\n", + " \"Im053_1.jpg\",\n", + " \"Im057_1.jpg\",\n", + " \"Im060_1.jpg\",\n", + " \"Im063_1.jpg\",\n", + " \"Im069_0.jpg\",\n", + " \"Im074_0.jpg\",\n", + " \"Im088_0.jpg\",\n", + " \"Im095_0.jpg\",\n", + " \"Im099_0.jpg\",\n", + " \"Im101_0.jpg\",\n", + " \"Im106_0.jpg\"\n", + "]\n", + "\n", + "seed(seed_amount)\n", + "random.seed(seed_amount)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data\n", + "\n", + "## ALL-IDB\n", + "You need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on [this page](https://homes.di.unimi.it/scotti/all/#download) as well as information on how to contribute back to the project [here](https://homes.di.unimi.it/scotti/all/results.php). If you are not able to obtain a copy of the dataset please feel free to try this tutorial on your own dataset, we would be very happy to find additional AML & ALL datasets.\n", + "\n", + "## ALL_IDB1\n", + "In this project, [ALL-IDB1](https://homes.di.unimi.it/scotti/all/#datasets) is used, one of the datsets from the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. We will use data augmentation to increase the amount of training and testing data we have." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preprocessing \n", + "\n", + "In this part of the notebook we will create the methods used for processing the data ready for training." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing Data\n", + "\n", + "First we need to remove the test data that we will use for testing the classifier later from the training data folder. We remove these images before the training so that the classifier does not learn them during training. The following cell provides the function to move the test data to the **model/data/test** folder: " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def remove_testing():\n", + " \"\"\" Removes the testing images from the dataset. \"\"\"\n", + "\n", + " for img in testData:\n", + " original = trainDataDir+img\n", + " destination = testDataDir+img\n", + " pathlib.Path(original).rename(destination)\n", + " print(original + \" moved to \" + destination)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Augmentation\n", + "\n", + "We will create an augmented dataset based on the [Leukemia Blood Cell Image Classification Using Convolutional Neural Network](http://www.ijcte.org/vol10/1198-H0012.pdf \"Leukemia Blood Cell Image Classification Using Convolutional Neural Network\") by T. T. P. Thanh, Caleb Vununu, Sukhrob Atoev, Suk-Hwan Lee, and Ki-Ryong Kwon." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def resize(path, dim):\n", + " \"\"\" Resizes an image to the provided dimensions (dim). \"\"\"\n", + "\n", + " return cv2.resize(cv2.imread(path), (dim, dim))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def grayscale(data):\n", + " \"\"\" Creates a grayscale copy. \"\"\"\n", + "\n", + " gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)\n", + " return np.dstack([gray, gray, gray]).astype(np.float32)/255." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def equalize_hist(data):\n", + " \"\"\" Creates a histogram equalized copy. \"\"\"\n", + "\n", + " img_to_yuv = cv2.cvtColor(data, cv2.COLOR_BGR2YUV)\n", + " img_to_yuv[:, :, 0] = cv2.equalizeHist(img_to_yuv[:, :, 0])\n", + " hist_equalization_result = cv2.cvtColor(img_to_yuv, cv2.COLOR_YUV2BGR)\n", + " return hist_equalization_result.astype(np.float32)/255." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def reflection(data):\n", + " \"\"\" Creates a reflected copy. \"\"\"\n", + "\n", + " return cv2.flip(data, 0).astype(np.float32)/255., cv2.flip(data, 1).astype(np.float32)/255." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def gaussian(data):\n", + " \"\"\" Creates a gaussian blurred copy. \"\"\"\n", + "\n", + " return ndimage.gaussian_filter(data, sigma=5.11).astype(np.float32)/255." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def translate(data):\n", + " \"\"\" Creates transformed copy. \"\"\"\n", + "\n", + " cols, rows, chs = data.shape\n", + "\n", + " return cv2.warpAffine(data, np.float32([[1, 0, 84], [0, 1, 56]]), (rows, cols),\n", + " borderMode=cv2.BORDER_CONSTANT, borderValue=(144, 159, 162)).astype(np.float32)/255." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def rotation(data):\n", + " \"\"\" Creates a rotated copy. \"\"\"\n", + "\n", + " cols, rows, chs = data.shape\n", + "\n", + " rand_deg = random.randint(-180, 180)\n", + " matrix = cv2.getRotationMatrix2D((cols/2, rows/2), rand_deg, 0.70)\n", + " rotated = cv2.warpAffine(data, matrix, (rows, cols), borderMode=cv2.BORDER_CONSTANT,\n", + " borderValue=(144, 159, 162))\n", + "\n", + " return rotated.astype(np.float32)/255." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def shear(data):\n", + " \"\"\" Creates a sheared copy. \"\"\"\n", + "\n", + " at = tm.AffineTransform(shear=0.5)\n", + " return tm.warp(data, inverse_map=at)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def preprocess():\n", + " \"\"\" Preprocesses the data. \"\"\"\n", + " \n", + " # Read all data from training dir\n", + " data_dir = pathlib.Path(trainDataDir)\n", + " train_data = list(data_dir.glob('*.jpg'))\n", + "\n", + " count = 0\n", + " neg_count = 0\n", + " pos_count = 0\n", + "\n", + " data = []\n", + " labels = []\n", + " temp = []\n", + "\n", + " # Loop through training data\n", + " for rimage in train_data:\n", + " fpath = str(rimage)\n", + " fname = os.path.basename(rimage)\n", + " label = 0 if \"_0\" in fname else 1\n", + "\n", + " # Resize image\n", + " image = resize(fpath, dim)\n", + "\n", + " if image.shape[2] == 1:\n", + " image = np.dstack(\n", + " [image, image, image])\n", + "\n", + " temp.append(image.astype(np.float32)/255.)\n", + "\n", + " data.append(image.astype(np.float32)/255.)\n", + " labels.append(label)\n", + "\n", + " # Grayscale\n", + " data.append(grayscale(image))\n", + " labels.append(label)\n", + "\n", + " # Histogram Equalization\n", + " data.append(equalize_hist(image))\n", + " labels.append(label)\n", + " \n", + " # Reflection\n", + " horizontal, vertical = reflection(image)\n", + " data.append(horizontal)\n", + " labels.append(label)\n", + "\n", + " data.append(vertical)\n", + " labels.append(label)\n", + "\n", + " # Gaussian Blur\n", + " data.append(gaussian(image))\n", + " labels.append(label)\n", + "\n", + " # Tranlsation\n", + " data.append(translate(image))\n", + " labels.append(label)\n", + "\n", + " # Shear\n", + " data.append(shear(image))\n", + " labels.append(label)\n", + " \n", + " # Rotation\n", + " for i in range(0, rotations):\n", + " data.append(rotation(image))\n", + " labels.append(label)\n", + " if \"_0\" in fname:\n", + " neg_count += 1\n", + " else:\n", + " pos_count += 1\n", + " count +=1\n", + "\n", + " if \"_0\" in fname:\n", + " neg_count += 8\n", + " else:\n", + " pos_count += 8\n", + " count +=8\n", + "\n", + " train_arr = np.asarray(temp)\n", + "\n", + " print(\"Augmented data size: \" + str(count))\n", + " print(\"Negative data size: \" + str(neg_count))\n", + " print(\"Positive data size: \" + str(pos_count))\n", + " \n", + " return data, labels, train_arr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def shuffle_data(data, labels):\n", + " \"\"\" Shuffles the data and labels. \"\"\"\n", + "\n", + " data, labels = shuffle(\n", + " data, labels, random_state=seed_amount)\n", + " \n", + " return data, labels" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_data(data):\n", + " \"\"\" Converts the training data to a numpy array. \"\"\"\n", + "\n", + " data = np.array(data)\n", + " print(\"Augmented data shape: \" + str(data.shape))\n", + " \n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def encode_labels(labels):\n", + " \"\"\" One Hot Encodes the labels. \"\"\"\n", + "\n", + " encoder = OneHotEncoder(categories='auto')\n", + "\n", + " labels = np.reshape(labels, (-1, 1))\n", + " labels = encoder.fit_transform(labels).toarray()\n", + " print(\"Labels shape: \" + str(labels.shape))\n", + " \n", + " return labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Split " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def get_split(data, labels):\n", + " \"\"\" Splits the data and labels creating training and validation datasets. \"\"\"\n", + "\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " data, labels, test_size=test_split, random_state=seed_amount)\n", + "\n", + " print(\"Training data: \" + str(X_train.shape))\n", + " print(\"Training labels: \" + str(y_train.shape))\n", + " print(\"Validation data: \" + str(X_test.shape))\n", + " print(\"Validation labels: \" + str(y_test.shape))\n", + " \n", + " return X_train, X_test, y_train, y_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Process & Prepare Data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../model/data/train/Im006_1.jpg moved to ../model/data/test/Im006_1.jpg\n", + "../model/data/train/Im020_1.jpg moved to ../model/data/test/Im020_1.jpg\n", + "../model/data/train/Im024_1.jpg moved to ../model/data/test/Im024_1.jpg\n", + "../model/data/train/Im026_1.jpg moved to ../model/data/test/Im026_1.jpg\n", + "../model/data/train/Im028_1.jpg moved to ../model/data/test/Im028_1.jpg\n", + "../model/data/train/Im031_1.jpg moved to ../model/data/test/Im031_1.jpg\n", + "../model/data/train/Im035_0.jpg moved to ../model/data/test/Im035_0.jpg\n", + "../model/data/train/Im041_0.jpg moved to ../model/data/test/Im041_0.jpg\n", + "../model/data/train/Im047_0.jpg moved to ../model/data/test/Im047_0.jpg\n", + "../model/data/train/Im053_1.jpg moved to ../model/data/test/Im053_1.jpg\n", + "../model/data/train/Im057_1.jpg moved to ../model/data/test/Im057_1.jpg\n", + "../model/data/train/Im060_1.jpg moved to ../model/data/test/Im060_1.jpg\n", + "../model/data/train/Im063_1.jpg moved to ../model/data/test/Im063_1.jpg\n", + "../model/data/train/Im069_0.jpg moved to ../model/data/test/Im069_0.jpg\n", + "../model/data/train/Im074_0.jpg moved to ../model/data/test/Im074_0.jpg\n", + "../model/data/train/Im088_0.jpg moved to ../model/data/test/Im088_0.jpg\n", + "../model/data/train/Im095_0.jpg moved to ../model/data/test/Im095_0.jpg\n", + "../model/data/train/Im099_0.jpg moved to ../model/data/test/Im099_0.jpg\n", + "../model/data/train/Im101_0.jpg moved to ../model/data/test/Im101_0.jpg\n", + "../model/data/train/Im106_0.jpg moved to ../model/data/test/Im106_0.jpg\n" + ] + } + ], + "source": [ + "remove_testing()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Augmented data size: 1584\n", + "Negative data size: 882\n", + "Positive data size: 702\n", + "Augmented data shape: (1584, 100, 100, 3)\n", + "Labels shape: (1584, 2)\n", + "Training data: (1180, 100, 100, 3)\n", + "Training labels: (1180, 2)\n", + "Validation data: (404, 100, 100, 3)\n", + "Validation labels: (404, 2)\n" + ] + } + ], + "source": [ + "data, labels, train_arr = preprocess()\n", + "data, labels = shuffle_data(data, labels)\n", + "data = convert_data(data)\n", + "labels = encode_labels(labels)\n", + "X_train, X_test, y_train, y_test = get_split(data, labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Visualization " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y = np.argmax(labels, axis=-1)\n", + "fig, ax = plt.subplots(4,5, figsize=(30,7))\n", + "for i in range(0,20):\n", + " ax[i//5, i%5].imshow(data[i])\n", + " if y[i]==1:\n", + " ax[i//5, i%5].set_title(\"Non-ALL\")\n", + " else:\n", + " ax[i//5, i%5].set_title(\"ALL\")\n", + "plt.show()\n", + "plt.clf()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Network\n", + "## Build Network " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"AllJetsonNano\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "average_pooling2d (AveragePo (None, 50, 50, 3) 0 \n", + "_________________________________________________________________\n", + "conv2d (Conv2D) (None, 46, 46, 30) 2280 \n", + "_________________________________________________________________\n", + "depthwise_conv2d (DepthwiseC (None, 17, 17, 30) 27030 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 8670) 0 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 2) 17342 \n", + "_________________________________________________________________\n", + "softmax (Activation) (None, 2) 0 \n", + "=================================================================\n", + "Total params: 46,652\n", + "Trainable params: 46,652\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "def build_network():\n", + " \"\"\" Builds the network. \"\"\"\n", + "\n", + " tf_model = tf.keras.models.Sequential([\n", + " tf.keras.layers.InputLayer(input_shape=(X_train.shape[1:])),\n", + " tf.keras.layers.AveragePooling2D(\n", + " pool_size=(2, 2), strides=None, padding='valid'),\n", + " tf.keras.layers.Conv2D(30, (5, 5), strides=1,\n", + " padding=\"valid\", activation='relu'),\n", + " tf.keras.layers.DepthwiseConv2D(30, (1, 1),\n", + " padding=\"valid\", activation='relu'),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(2),\n", + " tf.keras.layers.Activation('softmax', name='softmax')\n", + " ],\n", + " \"AllJetsonNano\")\n", + " tf_model.summary()\n", + " \n", + " return tf_model\n", + " \n", + "tf_model = build_network()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model\n", + "## Train Model " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n", + "37/37 [==============================] - 12s 328ms/step - loss: 0.6876 - acc: 0.5805 - precision: 0.5805 - recall: 0.5805 - auc: 0.5681 - val_loss: 0.6801 - val_acc: 0.5520 - val_precision: 0.5520 - val_recall: 0.5520 - val_auc: 0.6858\n", + "Epoch 2/150\n", + "37/37 [==============================] - 11s 303ms/step - loss: 0.6554 - acc: 0.6339 - precision: 0.6339 - recall: 0.6339 - auc: 0.6838 - val_loss: 0.6389 - val_acc: 0.7871 - val_precision: 0.7871 - val_recall: 0.7871 - val_auc: 0.8182\n", + "Epoch 3/150\n", + "37/37 [==============================] - 12s 316ms/step - loss: 0.5953 - acc: 0.7415 - precision: 0.7415 - recall: 0.7415 - auc: 0.7845 - val_loss: 0.5801 - val_acc: 0.7921 - val_precision: 0.7921 - val_recall: 0.7921 - val_auc: 0.8459\n", + "Epoch 4/150\n", + "37/37 [==============================] - 11s 297ms/step - loss: 0.5442 - acc: 0.7763 - precision: 0.7763 - recall: 0.7763 - auc: 0.8200 - val_loss: 0.5215 - val_acc: 0.7550 - val_precision: 0.7550 - val_recall: 0.7550 - val_auc: 0.8338\n", + "Epoch 5/150\n", + "37/37 [==============================] - 11s 310ms/step - loss: 0.4732 - acc: 0.7907 - precision: 0.7907 - recall: 0.7907 - auc: 0.8692 - val_loss: 0.4630 - val_acc: 0.7574 - val_precision: 0.7574 - val_recall: 0.7574 - val_auc: 0.8765\n", + "Epoch 6/150\n", + "37/37 [==============================] - 11s 300ms/step - loss: 0.5042 - acc: 0.7686 - precision: 0.7686 - recall: 0.7686 - auc: 0.8339 - val_loss: 0.4508 - val_acc: 0.7772 - val_precision: 0.7772 - val_recall: 0.7772 - val_auc: 0.8851\n", + "Epoch 7/150\n", + "37/37 [==============================] - 11s 304ms/step - loss: 0.4262 - acc: 0.8059 - precision: 0.8059 - recall: 0.8059 - auc: 0.8944 - val_loss: 0.4284 - val_acc: 0.7970 - val_precision: 0.7970 - val_recall: 0.7970 - val_auc: 0.8967\n", + "Epoch 8/150\n", + "37/37 [==============================] - 11s 310ms/step - loss: 0.3992 - acc: 0.8314 - precision: 0.8314 - recall: 0.8314 - auc: 0.9096 - val_loss: 0.4098 - val_acc: 0.7946 - val_precision: 0.7946 - val_recall: 0.7946 - val_auc: 0.8990\n", + "Epoch 9/150\n", + "37/37 [==============================] - 11s 307ms/step - loss: 0.3765 - acc: 0.8441 - precision: 0.8441 - recall: 0.8441 - auc: 0.9185 - val_loss: 0.3808 - val_acc: 0.8416 - val_precision: 0.8416 - val_recall: 0.8416 - val_auc: 0.9126\n", + "Epoch 10/150\n", + "37/37 [==============================] - 11s 304ms/step - loss: 0.3538 - acc: 0.8559 - precision: 0.8559 - recall: 0.8559 - auc: 0.9287 - val_loss: 0.3638 - val_acc: 0.8540 - val_precision: 0.8540 - val_recall: 0.8540 - val_auc: 0.9270\n", + "Epoch 11/150\n", + "37/37 [==============================] - 11s 304ms/step - loss: 0.3506 - acc: 0.8619 - precision: 0.8619 - recall: 0.8619 - auc: 0.9274 - val_loss: 0.3584 - val_acc: 0.8441 - val_precision: 0.8441 - val_recall: 0.8441 - val_auc: 0.9194\n", + "Epoch 12/150\n", + "37/37 [==============================] - 11s 307ms/step - loss: 0.3171 - acc: 0.8763 - precision: 0.8763 - recall: 0.8763 - auc: 0.9427 - val_loss: 0.3203 - val_acc: 0.8787 - val_precision: 0.8787 - val_recall: 0.8787 - val_auc: 0.9395\n", + "Epoch 13/150\n", + "37/37 [==============================] - 11s 304ms/step - loss: 0.2973 - acc: 0.8932 - precision: 0.8932 - recall: 0.8932 - auc: 0.9478 - val_loss: 0.3042 - val_acc: 0.8886 - val_precision: 0.8886 - val_recall: 0.8886 - val_auc: 0.9460\n", + "Epoch 14/150\n", + "37/37 [==============================] - 11s 307ms/step - loss: 0.2831 - acc: 0.9034 - precision: 0.9034 - recall: 0.9034 - auc: 0.9546 - val_loss: 0.2863 - val_acc: 0.8911 - val_precision: 0.8911 - val_recall: 0.8911 - val_auc: 0.9558\n", + "Epoch 15/150\n", + "37/37 [==============================] - 11s 309ms/step - loss: 0.2782 - acc: 0.8975 - precision: 0.8975 - recall: 0.8975 - auc: 0.9549 - val_loss: 0.3080 - val_acc: 0.8564 - val_precision: 0.8564 - val_recall: 0.8564 - val_auc: 0.9464\n", + "Epoch 16/150\n", + "37/37 [==============================] - 12s 316ms/step - loss: 0.2837 - acc: 0.8975 - precision: 0.8975 - recall: 0.8975 - auc: 0.9530 - val_loss: 0.2790 - val_acc: 0.8960 - val_precision: 0.8960 - val_recall: 0.8960 - val_auc: 0.9513\n", + "Epoch 17/150\n", + "37/37 [==============================] - 12s 319ms/step - loss: 0.2760 - acc: 0.9017 - precision: 0.9017 - recall: 0.9017 - auc: 0.9553 - val_loss: 0.2686 - val_acc: 0.9183 - val_precision: 0.9183 - val_recall: 0.9183 - val_auc: 0.9607\n", + "Epoch 18/150\n", + "37/37 [==============================] - 11s 305ms/step - loss: 0.2458 - acc: 0.9161 - precision: 0.9161 - recall: 0.9161 - auc: 0.9659 - val_loss: 0.2514 - val_acc: 0.9257 - val_precision: 0.9257 - val_recall: 0.9257 - val_auc: 0.9670\n", + "Epoch 19/150\n", + "37/37 [==============================] - 11s 307ms/step - loss: 0.2331 - acc: 0.9297 - precision: 0.9297 - recall: 0.9297 - auc: 0.9664 - val_loss: 0.2576 - val_acc: 0.9035 - val_precision: 0.9035 - val_recall: 0.9035 - val_auc: 0.9669\n", + "Epoch 20/150\n", + "37/37 [==============================] - 12s 319ms/step - loss: 0.2486 - acc: 0.9076 - precision: 0.9076 - recall: 0.9076 - auc: 0.9639 - val_loss: 0.2483 - val_acc: 0.9035 - val_precision: 0.9035 - val_recall: 0.9035 - val_auc: 0.9619\n", + "Epoch 21/150\n", + "37/37 [==============================] - 12s 316ms/step - loss: 0.2215 - acc: 0.9203 - precision: 0.9203 - recall: 0.9203 - auc: 0.9725 - val_loss: 0.2495 - val_acc: 0.9035 - val_precision: 0.9035 - val_recall: 0.9035 - val_auc: 0.9617\n", + "Epoch 22/150\n", + "37/37 [==============================] - 11s 304ms/step - loss: 0.2089 - acc: 0.9356 - precision: 0.9356 - recall: 0.9356 - auc: 0.9743 - val_loss: 0.2374 - val_acc: 0.8985 - val_precision: 0.8985 - val_recall: 0.8985 - val_auc: 0.9692\n", + "Epoch 23/150\n", + "37/37 [==============================] - 12s 313ms/step - loss: 0.2039 - acc: 0.9305 - precision: 0.9305 - recall: 0.9305 - auc: 0.9767 - val_loss: 0.2129 - val_acc: 0.9158 - val_precision: 0.9158 - val_recall: 0.9158 - val_auc: 0.9771\n", + "Epoch 24/150\n", + "37/37 [==============================] - 12s 315ms/step - loss: 0.1933 - acc: 0.9288 - precision: 0.9288 - recall: 0.9288 - auc: 0.9804 - val_loss: 0.2149 - val_acc: 0.9183 - val_precision: 0.9183 - val_recall: 0.9183 - val_auc: 0.9723\n", + "Epoch 25/150\n", + "37/37 [==============================] - 11s 305ms/step - loss: 0.2041 - acc: 0.9271 - precision: 0.9271 - recall: 0.9271 - auc: 0.9760 - val_loss: 0.2382 - val_acc: 0.9183 - val_precision: 0.9183 - val_recall: 0.9183 - val_auc: 0.9660\n", + "Epoch 26/150\n", + "37/37 [==============================] - 12s 319ms/step - loss: 0.1835 - acc: 0.9398 - precision: 0.9398 - recall: 0.9398 - auc: 0.9815 - val_loss: 0.1999 - val_acc: 0.9307 - val_precision: 0.9307 - val_recall: 0.9307 - val_auc: 0.9758\n", + "Epoch 27/150\n", + "37/37 [==============================] - 11s 310ms/step - loss: 0.1708 - acc: 0.9508 - precision: 0.9508 - recall: 0.9508 - auc: 0.9838 - val_loss: 0.1906 - val_acc: 0.9233 - val_precision: 0.9233 - val_recall: 0.9233 - val_auc: 0.9815\n", + "Epoch 28/150\n", + "37/37 [==============================] - 12s 314ms/step - loss: 0.1974 - acc: 0.9246 - precision: 0.9246 - recall: 0.9246 - auc: 0.9778 - val_loss: 0.2021 - val_acc: 0.9233 - val_precision: 0.9233 - val_recall: 0.9233 - val_auc: 0.9758\n", + "Epoch 29/150\n", + "37/37 [==============================] - 11s 309ms/step - loss: 0.1645 - acc: 0.9483 - precision: 0.9483 - recall: 0.9483 - auc: 0.9859 - val_loss: 0.1858 - val_acc: 0.9257 - val_precision: 0.9257 - val_recall: 0.9257 - val_auc: 0.9828\n", + "Epoch 30/150\n", + "37/37 [==============================] - 11s 309ms/step - loss: 0.1685 - acc: 0.9390 - precision: 0.9390 - recall: 0.9390 - auc: 0.9846 - val_loss: 0.1777 - val_acc: 0.9158 - val_precision: 0.9158 - val_recall: 0.9158 - val_auc: 0.9845\n", + "Epoch 31/150\n", + "37/37 [==============================] - 12s 317ms/step - loss: 0.1486 - acc: 0.9517 - precision: 0.9517 - recall: 0.9517 - auc: 0.9897 - val_loss: 0.1740 - val_acc: 0.9332 - val_precision: 0.9332 - val_recall: 0.9332 - val_auc: 0.9826\n", + "Epoch 32/150\n", + "37/37 [==============================] - 11s 310ms/step - loss: 0.1522 - acc: 0.9441 - precision: 0.9441 - recall: 0.9441 - auc: 0.9884 - val_loss: 0.1675 - val_acc: 0.9356 - val_precision: 0.9356 - val_recall: 0.9356 - val_auc: 0.9856\n", + "Epoch 33/150\n", + "37/37 [==============================] - 11s 303ms/step - loss: 0.1522 - acc: 0.9441 - precision: 0.9441 - recall: 0.9441 - auc: 0.9878 - val_loss: 0.2041 - val_acc: 0.9010 - val_precision: 0.9010 - val_recall: 0.9010 - val_auc: 0.9791\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 34/150\n", + "37/37 [==============================] - 11s 309ms/step - loss: 0.1555 - acc: 0.9525 - precision: 0.9525 - recall: 0.9525 - auc: 0.9881 - val_loss: 0.1636 - val_acc: 0.9356 - val_precision: 0.9356 - val_recall: 0.9356 - val_auc: 0.9866\n", + "Epoch 35/150\n", + "37/37 [==============================] - 12s 319ms/step - loss: 0.1398 - acc: 0.9534 - precision: 0.9534 - recall: 0.9534 - auc: 0.9901 - val_loss: 0.1790 - val_acc: 0.9282 - val_precision: 0.9282 - val_recall: 0.9282 - val_auc: 0.9810\n", + "Epoch 36/150\n", + "37/37 [==============================] - 12s 313ms/step - loss: 0.1504 - acc: 0.9508 - precision: 0.9508 - recall: 0.9508 - auc: 0.9880 - val_loss: 0.1608 - val_acc: 0.9406 - val_precision: 0.9406 - val_recall: 0.9406 - val_auc: 0.9862\n", + "Epoch 37/150\n", + "37/37 [==============================] - 12s 314ms/step - loss: 0.1576 - acc: 0.9458 - precision: 0.9458 - recall: 0.9458 - auc: 0.9865 - val_loss: 0.1669 - val_acc: 0.9406 - val_precision: 0.9406 - val_recall: 0.9406 - val_auc: 0.9854\n", + "Epoch 38/150\n", + "37/37 [==============================] - 11s 310ms/step - loss: 0.1324 - acc: 0.9517 - precision: 0.9517 - recall: 0.9517 - auc: 0.9918 - val_loss: 0.1528 - val_acc: 0.9406 - val_precision: 0.9406 - val_recall: 0.9406 - val_auc: 0.9876\n", + "Epoch 39/150\n", + "37/37 [==============================] - 12s 313ms/step - loss: 0.1457 - acc: 0.9458 - precision: 0.9458 - recall: 0.9458 - auc: 0.9891 - val_loss: 0.2202 - val_acc: 0.9109 - val_precision: 0.9109 - val_recall: 0.9109 - val_auc: 0.9718\n", + "Epoch 40/150\n", + "37/37 [==============================] - 12s 314ms/step - loss: 0.1542 - acc: 0.9441 - precision: 0.9441 - recall: 0.9441 - auc: 0.9870 - val_loss: 0.1482 - val_acc: 0.9406 - val_precision: 0.9406 - val_recall: 0.9406 - val_auc: 0.9887\n", + "Epoch 41/150\n", + "37/37 [==============================] - 12s 323ms/step - loss: 0.1382 - acc: 0.9441 - precision: 0.9441 - recall: 0.9441 - auc: 0.9904 - val_loss: 0.1505 - val_acc: 0.9332 - val_precision: 0.9332 - val_recall: 0.9332 - val_auc: 0.9891\n", + "Epoch 42/150\n", + "37/37 [==============================] - 12s 317ms/step - loss: 0.1287 - acc: 0.9525 - precision: 0.9525 - recall: 0.9525 - auc: 0.9914 - val_loss: 0.1444 - val_acc: 0.9406 - val_precision: 0.9406 - val_recall: 0.9406 - val_auc: 0.9900\n", + "Epoch 43/150\n", + "37/37 [==============================] - 12s 317ms/step - loss: 0.1207 - acc: 0.9551 - precision: 0.9551 - recall: 0.9551 - auc: 0.9934 - val_loss: 0.1401 - val_acc: 0.9431 - val_precision: 0.9431 - val_recall: 0.9431 - val_auc: 0.9895\n", + "Epoch 44/150\n", + "37/37 [==============================] - 11s 308ms/step - loss: 0.1247 - acc: 0.9534 - precision: 0.9534 - recall: 0.9534 - auc: 0.9928 - val_loss: 0.1528 - val_acc: 0.9307 - val_precision: 0.9307 - val_recall: 0.9307 - val_auc: 0.9889\n", + "Epoch 45/150\n", + "37/37 [==============================] - 12s 324ms/step - loss: 0.1114 - acc: 0.9576 - precision: 0.9576 - recall: 0.9576 - auc: 0.9943 - val_loss: 0.1347 - val_acc: 0.9381 - val_precision: 0.9381 - val_recall: 0.9381 - val_auc: 0.9918\n", + "Epoch 46/150\n", + "37/37 [==============================] - 11s 304ms/step - loss: 0.1145 - acc: 0.9542 - precision: 0.9542 - recall: 0.9542 - auc: 0.9939 - val_loss: 0.1581 - val_acc: 0.9332 - val_precision: 0.9332 - val_recall: 0.9332 - val_auc: 0.9852\n", + "Epoch 47/150\n", + "37/37 [==============================] - 12s 314ms/step - loss: 0.1097 - acc: 0.9593 - precision: 0.9593 - recall: 0.9593 - auc: 0.9944 - val_loss: 0.1463 - val_acc: 0.9381 - val_precision: 0.9381 - val_recall: 0.9381 - val_auc: 0.9874\n", + "Epoch 48/150\n", + "37/37 [==============================] - 12s 313ms/step - loss: 0.1036 - acc: 0.9627 - precision: 0.9627 - recall: 0.9627 - auc: 0.9952 - val_loss: 0.1325 - val_acc: 0.9381 - val_precision: 0.9381 - val_recall: 0.9381 - val_auc: 0.9902\n", + "Epoch 49/150\n", + "37/37 [==============================] - 12s 317ms/step - loss: 0.1015 - acc: 0.9602 - precision: 0.9602 - recall: 0.9602 - auc: 0.9957 - val_loss: 0.1419 - val_acc: 0.9332 - val_precision: 0.9332 - val_recall: 0.9332 - val_auc: 0.9880\n", + "Epoch 50/150\n", + "37/37 [==============================] - 12s 314ms/step - loss: 0.1009 - acc: 0.9610 - precision: 0.9610 - recall: 0.9610 - auc: 0.9958 - val_loss: 0.1533 - val_acc: 0.9332 - val_precision: 0.9332 - val_recall: 0.9332 - val_auc: 0.9881\n", + "Epoch 51/150\n", + "37/37 [==============================] - 12s 320ms/step - loss: 0.1038 - acc: 0.9576 - precision: 0.9576 - recall: 0.9576 - auc: 0.9948 - val_loss: 0.1690 - val_acc: 0.9406 - val_precision: 0.9406 - val_recall: 0.9406 - val_auc: 0.9841\n" + ] + } + ], + "source": [ + "def train(X_train, y_train, X_test, y_test):\n", + " \"\"\" Trains the model. \"\"\"\n", + " \n", + " callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', \n", + " patience=3, \n", + " verbose=0, \n", + " mode='auto', \n", + " restore_best_weights=True)\n", + " optimizer = tf.keras.optimizers.Adam(learning_rate=lr, decay = decay)\n", + "\n", + " tf_model.compile(optimizer=optimizer, loss='binary_crossentropy',\n", + " metrics=[tf.keras.metrics.BinaryAccuracy(name='acc'),\n", + " tf.keras.metrics.Precision(name='precision'),\n", + " tf.keras.metrics.Recall(name='recall'),\n", + " tf.keras.metrics.AUC(name='auc') ])\n", + "\n", + " history = tf_model.fit(X_train, y_train,\n", + " validation_data=(X_test, y_test),\n", + " validation_steps=val_steps,\n", + " epochs=epochs, callbacks=[callback])\n", + " return tf_model, history\n", + "\n", + "tf_model, history = train(X_train, y_train, X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save Model " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/hias/.conda/envs/all-jetson-nano/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "If using Keras pass *_constraint arguments to layers.\n", + "INFO:tensorflow:Assets written to: ../model/saved/assets\n" + ] + } + ], + "source": [ + "def save_model_as_json(tf_model):\n", + " \"\"\" Saves the model as JSON & SavedModel format\"\"\"\n", + "\n", + " with open(model_path, \"w\") as file:\n", + " file.write(tf_model.to_json())\n", + " \n", + " tf_model.save(saved_model_dir)\n", + "\n", + "save_model_as_json(tf_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save Weights" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weights saved ../model/all_jetson_nano.h5\n" + ] + } + ], + "source": [ + "def save_weights(tf_model):\n", + " \"\"\" Saves the model weights \"\"\"\n", + "\n", + " tf_model.save_weights(weights_path)\n", + " print(\"Weights saved \" + weights_path)\n", + " \n", + "save_weights(tf_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate Model " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metrics: loss 0.1324760615825653\n", + "Metrics: acc 0.9381188154220581\n", + "Metrics: precision 0.9381188154220581\n", + "Metrics: recall 0.9381188154220581\n", + "Metrics: auc 0.9902154207229614\n" + ] + } + ], + "source": [ + "def evaluate(tf_model, X_test, y_test):\n", + " \"\"\" Evaluates the model \"\"\"\n", + "\n", + " metrics = tf_model.evaluate(\n", + " X_test, y_test, verbose=0)\n", + " for name, value in zip(tf_model.metrics_names, metrics):\n", + " print(\"Metrics: \" + name + \" \" + str(value))\n", + " \n", + "evaluate(tf_model, X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def predictions(tf_model, X_train, X_test):\n", + " \"\"\" Gets predictions. \"\"\"\n", + "\n", + " train_preds = tf_model.predict(X_train)\n", + " test_preds = tf_model.predict(X_test)\n", + " \n", + " return train_preds, test_preds\n", + " \n", + "train_preds, test_preds = predictions(tf_model, X_train, X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Metrics \n", + "\n", + "### Accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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O7DWR5W+hAW+KSBv8nel3s+17ke1Qvi6hAudYvxvu2dd2LsvfxPdjBqu6Qux3P+wxw35Mbduwd7jGrvGc9N0etWsp+v2tmHbieM26dt69UYeUnXri3s36m98CaWLTin/XTq4RnGnJ3VfUqFHj+PbmzZvl5ZdfthqQExISZPTo0aWOhvZsXNaGZ3UaQY2WMg9ssQ2KMQxaKpy+dLtMWbTFihf/a3QPq1GyohwpOCb//mG7TJyXYoVMhnVrKDee19iK369DXH3DnhxZjBDKJz/vKvXzETBIVq8S08vEM2adh/DF12v2SkFRkQxrGyMP1FomDTa9K7IApTjYY6ndqqQRjYbhAOpcJt3UQ/40c5U8//VGOJOjsnz7AasGqw5gTM9kcWXvsEMZ+gevpViDOoU+LU7sSxFKnsveEPnub7ZB7DgMVYIGJUvCUXEiH45FzOkmkXHzyy7lK1/92XYCg1FaLiq0S6G/4J1+mmyMH5zP4YN4t5YiV/yvFd6RiBP/titMS3SqGD7NzpM6nlEw+OEw6p6owfzsbvv7u24qrFSkbegHoFZ8NAcGe4kIHINVMm8+EI0PKGiFVbBL5TmdRX4HR3Lxk/b3twxGfuOXIrVQYu98w4nfzLO0rt+1lvatGgCUs9euKbhL+PrvtiK9qPTfec4euxah90rfZP923ui96na086JpFbQrBp0jCGSys7OtEdBxcXGyZw+q+3PmyODBg/2dLf+QBQO81V31XWBXfWMRxrhmmkzdVsuKg2tXxr4ta8vGvTlyzavfyz+Gd7Hi5OWhYZbPVu2SF+ZssnpaaPhkwuVtrUZKRWPvnjH6/Wj03IYugBqKUIehYZtslPC1AVX3c47vF1hxed3WWspdnYrl1vA5KKF+iPfIs0ttlz1j/6HrO618D0Zmim1Ez+liG2igzZEvwx7clZwje5blyTUhedKm1mGJng8jO/dQyXBCi0EinVBybnsFHKRHfFxL5F/cj9LtStsQqmH2DHF4MgL5eAPG94PR6Jr21clGV1kOY7sCJeR+fxTpjXCJ0uceu5aQusx+n4Nb7by0vOTsDZO+zzWvi8z6PRzVLSI3wOGEmobalG9FPkIto0FPO+/qBDzRgkLry2ydDbH1RAY9JnLBn1BqyLL3yyIEv0USHKCqva6ndYaogY+rb6vVxWd+Hx9AR1CFdO/e3QoDaYNw8+bNpW9fVD2dhBqWhc/bJT53PDcmyaryHqrTXQq+nygx7wyR1MKx0rP1CLlnUEuri6H2TLlz+gq5C2GV31/YXB66rE2JLoruGsBnK3ehBrHV6uPdsUGcPH9dZzgSj1J0KWiDYe3ig7YBD9XQChoVC6Gj2uCL41Gw2t62M3cf4uPzbOOlxrH3nSL1Op04j1CG9a67lttGVMMO2nBp0LJjG9izOiGFEgUDFJ1Yzy7Baw1AS5fRKI2m/iTyK0rLs8bhrxQl37ZDUOq/1i4JayldHYuWljsMK780Wgcx7WG4fsaNIrPvtw2w5/XbfxD58iHbwA9CiMkTLWU3xb9RVWXT5QY7DPOfB2yHMAxOcyfeecZoO8+jPjyzGsfporWKcN90yaxOsPuoQ6ny7qMay9bS384f7ZKuClXfzNhWMmXxNnnr+20SWZAp7yVOkXZ5MKA9b7XDFKbKr4Oa/jZ7nRWT79OitvxzZDfLiOsI0Ok/bpdpS7ZbXRo7oGF/fP8WcgVqDqfs031wG4wgQiKb55x8LhxGKDqx9NJvaKQditH4eGz53UHPCoSerO/r15kia2fZoRl1I71ut0uzJuRUIbSBdP6zqLX8DxzVXfaxLMShJw+wwz63f3d696ssFv+fyLcI07QbatcMNW6uNRcNb5FKBT2e2H2U+JEdMGYzERM+ihLgdW9ZRlRDLVPhAN5YtEBy0WB6Zef6ct9FfaVlEkrY3/0/ke9fsuOx179jVdu1e+TT13SyBiI9isbWof9cLAPa1pFZiPEfRm1gQJtkGXdBczkfTkK7+JWLlta10XPB83boZuCjdrzXs1ReFaXRU6FOqMn5ttQpatfImqg91Du5F9ApuRCl/r1oKP3mMbvXivYMmoGeMwVooxrzH/84AUXDUUfQy2/xi2jDaGQ3INMJVDmsETiU9adRI9A+4j+gf7oOn9dRlBWeNEsbxzT+/NXD+CNvaMV88xJby9sovf9r4W9WG8BlHerKHy9pbd23BGs+QYPhPXZM+IIHSvTG0Ni/1gzUmejIVO1yqXPCHEc/U6ed3Wjr3YioDY2zcb/09XYpdPDf7bw5AW1ofeMSuz2mMRpfN8IBjJxh95zxJ/rvZM3HtnNK8Fy0kFQmrBGQ00Zj7v/dkGb1qtHRpzofi6JOQMMv7ikCOjWMt3rTeFOUf0TCvn5IkjbPlN8S+sir8Q/LmvfS0TC73erGORAl+AcuaWN9vlQ09KJ9tj8YZfdo8UCbeh92t3DpmnZlrWsXEmZ3g9SufVoK3o+eSSvRMBnfGAbwAxhAhzXUq4McMV1kykDbCWhNyN9OQNEaXKfr/J0LR8PGYlJi+LxOcvUxjP9/Vu+2etDoXDJj+zZFyb2e7EN3x1WpmbJyR6Z8sGwn4vKIsZdCpOTL+xFPS/eQFHm58HcyMe06aYQeeDp/y8A2deRS1AJ6NClnMI0bNd53/2R3pzwdtI932no7tKTSOPuaj2zHoH3f+z8cGKEff6C9i0ah9L19MXoG4bsgBNAREAuddfHJz36V3SmrpHFYljzUyCV96h6TZtF5EqIDlVYUoAT5F7mic7vjI0A3o3fOml1Z1twrnvRc9z/SdkeK/Hr+S3JVj+vlrsToMx/Jqz1z4srvMnoSer3G/D1LmdpFUNsGGH9GCKaXLUIMdAQOR0NAH8yeI0d+niHPhiyRBpGmm6POZqDSkanaeHo40x6if/u31kAl7b6pffPd/fOPs3kuGoffR5fKu6TTZQG0DLX2jCGElEpgTIVZzRkwYIA1OMyTl156Se66y3TTKwWdoVTRaSZ0vqGy7us906o3+py8vLzTzLFYo0c3f/K0pD7bXW5ZNVJuD/1Ckpp1ErkKvWnGfClyD577MEI/j8MxPLBO5EY0Ku5Psft8a7fG0sjFtZ/eZY+2vOiJ088TIcQv0BFUAiNHjpQZM2AoPdB9PX4q6tevLx99hPj1GXImjqAgJ12Ks3dLq9X/kKMhUbKl1xMS+uBGiRyDvurdb7IHEOkQeu1H7+6K2exC9EF/1h6OvwA9bUrr+fH5H+wQzLVTSh/BSggJSOgIKgEt0esU00ePHrX2t23bZpX0u3btai1YoyOKO3XqZE0n7Y1eqyONFZ2aesSIEdK5c2drimrdd3PnnXfqWgzSoUMHeeIJu7T9yiuvWM/RWUxVyjfffCPnn3++9czhw4dLbq5OomWTX3hM0jL2S2h2KppzI2R678+l1V9+kOZXPFCxicPOQ22gK3rxLECf9nWflzynUxRs+sqew0Xj84SQakPwtRF8NUFk76+Ve0+dPuDyUkrBBl0M/txzz5Wvv/5arr76aqs2oIZcZxGdNWuWNbdQRkaG9O7dW6666qoyBzy9/vrr1voDq1evtqTG3M0zzzxjTV997Ngxy7no+XvvvVdefPFFmTdvnrWGgT7j6aeflm+//daa7O65556zzv/lkcesOeJz8g5LC9klRSEREhabLKPOP82BSZrvK14USd+AEJFO4dvS7tmjMyt+/Yg9SZZO7UsIqVawRuCD8JA7LKTdMR955BGrhH/xxRfLrl27ZN++fWXeY+HChdaMpIp+RuVm5syZlmPo1q2brF271pq+2pulS5dax3UOI62NTJv2tqzf/Jts2JcjmXlHpXlImoSFFEtYUgtrVtMzQkM+OkmYToI2Y6S98MfHt9vHr5lUJTMlEkIql+CrEZRTcvcluhKZLiLjXn1Mjfa0adMkPT1dVqxYIeHh4dK0adNSp532pLTawtatW+WFF16QZcuWSWJioowZM6bEfazl89D7J+dIvvTrP0heeH2qNe2CHtclBhOjw+Wc4jQJPYrPJDY/+/i9zp54/b9Fpl0h8lpvkbz99v7pdvMkhAQELL5VEtoLSHv53HrrrccbiXWlMV1iUp2Ahm+2b99e7j0uvPBCmT59urW9Zs0aK/zjnr5aQz3x8fFWjUKXsFR0ndaYGjVl65bNkpW2XZq3aiM/Lv1Btm/9zVqGMBn2PiRnjzSMyIUTOCgSC0MdXUndKBufZ09/rE6gG2ox7a+qnPsSQqqc4KsR+BF1AMOGDTseIho1apQMHTrUauTVUE3btm3L/bw2CI8dO9YKCen12u6gdOnSxQoJaUOxTl99/vl9JOvQETmwZ5uMG3Gl3IznnFMnSeZ9NFnefeUpmXDnLVYNQWP6T//1EekQ38buR68zO1YmPW6x59pn4zAh1RpOOldN0DCPtZYragcxBQckXnKttttjEXESGltHXLrk3ZFMe6pinVxM0XnsdQUkHZ2rc+7oAhtnMOkcIaT6w0nnqjFFcAA6S6fOtZ9QmC4NXFlS5AqR4pgkCamZLGG6dqsbXd9UpWueqkNQ6TTLtZqXcAKEEOIJQ0MBSmFRkTX98/7cfGtpxPiwQkmGEyiOSpQQXRxdJ1ArC60B6LiA01lUnBDiWILGEVg9ZCqygHQ1eI90lP51YXOtDdSMDJOGidFSM2cr+v/g5zqVE6jgMwghJKgcQVRUlOzfv98a2FWdnYEaaF10RWsCcVHhUjcuUqIj8BPlHRApOGTPo18JTkC/K/3OCCEkaBxBw4YNJTU11eqzX11RA60O4HBBkbXQS3h0uGzT1ykustf7VQeQuQ8x/7SzfpY6Af3OCCEkaByB9tNv1qyZv7NxxmQdLpA73lkuP209IE8MbS9ju3u8i07dsPQ1kTv+K9Kgvf8ySQgJWoLCEVRn0rKPyM1Tf5Lf0nPl5RFd5equDTxOrhf5cZLdX7/BiXmHCCGk2owsRrx+MLQRSoEmlHI+HvoCWgWthQJoJZOqWRVs2OtLZMeBPHnzll4lnYA26H75kLUIDOf2J4RUyxoBjLp2XH8VugRKhZbh2OeIhXvOlnY3tA7HhuJcMrbVaUzHfr6v8hUoHER7wKgpS+VIYZG8f0dv6dIooeQFaz8R2bbInu0zpgLr+xJCSADWCHR+hBQY9S3GsOu8C1d7XaP9GGNddlcfXbIL3WOk0Id5CgiKiorlTx+ukpDcvfLBFWHSpWYWegWdWHtAjuaKzHnMnr6hxxj/ZZQQ4gh82UagcY6dHvtaKzjP65qJ0OdmddxY6AY4jZPWQYSfGIdEJY0bowtlVaALnWfvEklsemKVrkrijUW/Sf3N02Vy5PsS9rnHbKQR8IU1ku3RwDn4Sq5/myOCCSHV2hGUZj29RzJdBq2EBkEtoLkw+ovgDLJLfKi4eDISlU7gVvmjobJhdPesRuPsWpF9iFylQRmbrHV9ZdDjIhc+eMqun3PW7pXJC7fIha2T5d5BrSQkpHTnsWrDJmnx3e9lXPjPUtzsIpFet9njBA6hW+ihDHt+f93ufINII3vSOUIIqa6OQGsAjTz2G5qSvyfaOPx3GFI17tqgvBWpTtH5kw/zVZK9a0Qm9cOG8S/xyHIddNNsfZntFOY9g7dARabZBSd9VLM9b2OavDh3k6zZlW1N/fzSt5tl1c5MeemGbhIfE17i+pxf/yONPr5T2obkyZGLn5WoPndyIRdCSFA7gmVQKxh37RSPGIuMgG70umYHhGKxLMJ1OkdyG2iLD/N0MtpFU53A8GkizQeKRHs02uosnpMHiHyMUvv4xcfn7lEHsOS3/fLCNxvllx2Z0qhWtPzv8C7o9VNfZizbKU99sVaGTlwsk0b3kPb10esnP0+Kv3lcYpe/IRuKG0vItR9J684s7RNCgtwRwFgWwrjfg805kPYgmopj2kXUWtQW25OQ/A2ahmO6yLDGUh7GccRHqpAs9UWg1aWI0dcoeS4SzRbDEad/4yJ7OcabZkluQbHc+e4KWbQ5Q86Jj5Jnf9dJhvdsKOGhdrv76N5NLOOv1wx7/Xt5bVCYDFr7qLgyNsqUwiESPfhJGd1Z/R0hhDhgQBmM+pdIvvQ6pg7Ava2hIlhgP5KVak/d7O0E3NTrKDLkH2jS/oMULXhe/pR6iVUbePzK9lkLdnEAABKmSURBVDLqvMYSFX7y9M7dGyfK7Hv6yjdTHpV+86dJZlii3FvwF4ltf6lM7Nvaxy9ECCGnB0cWZ6JjU/wp5t3pdpPItu/FteA5yckPkUeGXC+39WtWrnNJnjVeRuUskvWJA2Tk3pESX7uufHFtp2o9KR4hJDihI9AaQW3tsFQOMN7ftZwgjVcukn9Fvy41O99c9rVrPhaZ/UeRY+hxdNVEaddttLy+5YDVjqAzihJCSKDhbEegnZWyUCNojgbhckhJy5X7Pt4k/Ws9KhMPPSiuT+5Ax9dn0c0z3ZbV5RNp+gaRzd+INOgpMmzycQdzfguEngghJEBxtiPQNX7zc0USPHu5njwz6Lh3lqMtIEQeHTNMXNsQ2vkU3T7/5dWdNDTS7lXUf4I97kBXCSOEkGqAsx2Btg8oZbQRHCsqlvtn/GJNCvfeHb2lfkK0SFf0gI2rDyeSJVLDLAdZIwk9jNBNlPF/Qkg1xNmOQNsHynEE/zd3k8zbmC5/u6ajnNvMY+K3U4SSCCGkOuHTaagDHm0fUHQJSC90fYCJ81JkRK9GMhrdRAkhJFihIwiLskM7XizcZC97ec+gluzySQgJapztCNxjCEqJ7eugsca1YqRhYowfMkYIIVWHsx2BthGU0j5QeKxIlm7ZL31bstsnIST4cbgj0BrByV1H1+zOlpwjhdKnxckhI0IICTac6wgKj4rk7ivVESz5zZ73jgPBCCFOwLmOwN11tJTBZEtS9kvberGSVDOyijNFCCFVDx2BVxvB0cJjsmzbAdYGCCGOwcGOwD2GoGSN4OftmXAGRdKX7QOEEIfg8NAQuo3GNTipfUCXGz63ucdIYkIICWKcXSOIrScSFnHS+IHODRM4ZTQhxDE41xGUsiBN7tFCa+F5jh8ghDiJEGcPJivZPrBs6wEpLCrm+AFCiKNwpiMoKip1VPH3KRkSERYiPZok+iljhBBS9TjTEeRliBw7KpJQclbR79E+0KNxYqkL0hNCSLDiTEdQyoI0Bw7ly/o92WwfIIQ4Dmc6glLGEPyA2oDSpyXnFyKEOAuHO4KGJcYP1IwMk84N4v2UKUII8Q8OdQSp9hrD0Qklxg+c16yWhIU68yshhDgXZ1o9rzEEuzMPy9aMQ5xfiBDiSEKcuw6BZ1jIbh/oy/YBQogDcbAjONFQvCQlQ2rXiJA2dWP9mClCCPEPznMER3NFDh88XiMoLi62agS9W9SWEJ1tjhBCHEaIcxeksQeTpaTlyt7sI5x2mhDiWJzrCEyNYNKCLRIZFiIXt6vjx0wRQoj/cKAj2GGnaCPYvC9HZv2SKrf0aSp14qL8my9CCPETzqwRhIRZaxG8OHeTxESEyfj+LfydK0II8RshjhxDEFdfVu/Oka/W7JXbL2gmtdBjiBBCnEqIL2/ucrkGQxuhFGhCGdcMgFZCa6EFvsyP5zoE/5izURJjwuW2fs18/khCCHGkI4BR17mcX4Uuh9pDI3Gsvdc1OsfDa9BV6MbZAelwX+XHcwxBemgdWbQ5Q+4e2FJio8J9/khCCHFqjeBcKAUGfguUj+0Z0NVe19wIfYLzVgsu0jQf5kfkWKEUZ++WBfsipR4ah0f3buLTxxFCiNMdQQPITPNpkWqOedIaSkTNYD60Arq5tBvh+DhouSo9Pf3Mc5SzR1zFx2R5Zk2596JWXICGEEJ87AhKG6Zb7LWP7jvSA7oCugx6HMa+9UkfKi6eDPVUJScnn3GGisyCNIU1G8jwniWXqSSEEKdySkcAw3wPdCaL+GoNwHN1eLW8u0u55msY+ENQBrYXQl3O4FkV4pc1a6z08n69JJzTTRNCSIVrBPWgZXAGM00voIpOyLMMaoXLm0HaP3ME9LnXNZ9BF+B8GBSD7fOg9RW8/2lRcKxIVqxaZW0P7NXNF48ghJDgdAQoqT+GpBX0JjQG2gyj/SzU4hSfK0RyDzTHGPeZOKZdRMerzDV6/GtoNfQT9AaO2cX2SmbWL7sk5vAeyY9MlJComr54BCGEVEs0Rn9KYJyLYbz3YlOlBl5DRR/h2Fyc+nM5n/sSyZdexyZ57f8DicqnDO1cXzJ/zJfwMHuyOUIIIRV0BDD29yK5BdIY/hvQQzDeBTiutYnNUJmOIJCIjgiVaH2FeE4nQQghp1sjSIKGwfhv9zyI/SI4gysr8PnAoLjYXpCm+QB/54QQQqpdY7GGdg64d2D8Y6HzPGL81YMjmSL5uSIJnh2ZCCGEVMQRvA7Bgh7nkDlWvTBjCDzXKiaEEFKxGoFLG4s9Q0IVbWQO5AVpCCGEVNwRbNEGYyjc6D49VoHPBRa6NGUftHvXau7vnBBCSEBRkZK99vl/BdLxBFoz+A4a58tM+YR6HW0RQgg5PUdgZgTVUcGEEEIcOo5AF/O9DdL1AqI8HMStPswXIYSQAGoj+LeZb0hnB9UVxLS1NceXmSKEEBJYjqAlSv+PI9UZQt82U0Z38m22CCGEBJIjKDBpJsJE2toaDzX1XZYIIYQEWq+hyWY9gsfMNNI6dafWEAghhAS7IzATy2UjJHTQLBrDTviEEOKk0JAZRaxrChBCCHFwG8Fc1AwehBpBtdzyec4IIYQETBuBe7zA3R7HdIQxw0SEEOKQkcXNqiIjhBBCAndk8c1lOIh3Kj87hBBCAjE01MtjW6eYuAj6GaIjIIQQh4SG/uBVQ4g3004QQghxSK8hb/KgVpWdEUIIIYHbRvAFkmIPx9EemunLTBFCCAmsNoIXPLYLoe0IF5l1HwkhhDjBEeyA9sD4HzE1hGioKfa3+TZrhBBCAqWN4ENIp5pwc8wcI4QQ4hBHEIbSf757x2xH+C5LhBBCAs0RpCMUdJV7B9tXI8nwXZYIIYQEWhvBeGg6HMBEs68NxaWONiaEEBKcA8p+Q9IbjkAXpHFhn+sVE0KIk0JDcADPQglwALnqBHS1MujpqsgcIYSQwGgjuBwOINO9Y1YrG+K7LBFCCAk0RxCKGkCke0fHESA5vk8IIST4G4vfhb6DA3jL7I+F3vZdlgghhARaY/HzcAKrsXkx5IK+hpr4OmOEEEICa/bRvWZ08bVmPYL1FfkQHMhgaCOUAk0o57pe0DHougrmhxBCiK9rBDDKrZGMgEZC+6EP9DBqCAMrcmN8PhTJq9AlZuzBMhz7HJ9fV8p1z0FzzugNCCGE+KxGsMGU/ofCePeD/mnmGaoo50Ip+NwWMy3FDEhHJXujC998DKWdxr0JIYRUgSO41oSE5qHUPgW6yLQRVJQG0E6P/VRz7Di4p+7/DppU3o1w3ThouSo9Pf00skAIIeSMHQFK8bOgG7DZFpoP/RGqC2P8OnTpqW5chtNwL3Dj5iXoYTyn3JoGzk+GeqqSk5Mr8GhCCCGV2WvoEJLpKjiAWkiHQ9rw+80pPqo1gEYe+w2h3V7X9IRm4L66nQQNwXYhnvlpxbJPCCGkKsYRHAcG+gCSfxmdimVQKxj2Zkh3mYbnG73up+cscN00JLPpBAghJIAdwekAg14I436P6Q2kPYOm4thaHBtvzpfbLkAIIaSaOwJj7L9E8qXXsVIdAI6P8WVeCCGEnN2AMkIIIUEKHQEhhDgcOgJCCHE4dASEEOJw6AgIIcTh0BEQQojDoSMghBCHQ0dACCEOh46AEEIcDh0BIYQ4HDoCQghxOHQEhBDicOgICCHE4dAREEKIw6EjIIQQh0NHQAghDoeOgBBCHA4dASGEOBw6AkIIcTh0BIQQ4nDoCAghxOHQERBCiMOhIyCEEIdDR0AIIQ6HjoAQQhwOHQEhhDgcOgJCCHE4dASEEOJw6AgIIcTh0BEQQojDoSMghBCHQ0dACCEOh46AEEIcDh0BIYQ4HJ86ApfLNRjaCKVAE0o5PwpabbQE6uLL/BBCCKlCRwCjHorkVehyqD00Esc09WQr1L+4uLgz0r9Bk32VH0IIIVVfIzgXSoGR3wLlY3sGdLXnBTi+BDpodpdCDX2YH0IIIVXsCBpAOz32U82xsrgN+qq0E6hJjIOWq9LT0ysxi4QQQnzpCFylHCsu9UKXa6BxBA+Xdh61hslQT1VycnIlZpEQQkiYD78CrQE08tjXsM/uUpyAtg+8AV0OQ7+fPwkhhARPjWAZ1AqGvhkUge0R0OeeF+B4YySfQDfBCWzyYV4IIYRUdY0Ahr0Qhv4ebM6BtAfRVBxbi2PjzflJSP4K1YZew3E9XKjhH1/liRBCyMm4YHhPPhrA9OzZs3j58uX+zgYhhFQrUNheUVZBmyOLCSHE4dAREEKIw6EjIIQQh0NHQAghDoeOgBBCHA4dASGEOBw6AkIIcTh0BIQQ4nDoCAghxOHQERBCiMMJ8XcGCCGE+Bc6AkIIcTh0BIQQ4nDoCAghxOHQERBCiMOhIyCEEIdDR0AIIQ6HjoAQQhwOHQEhhDgcOgJCCHE4dASEEOJw6AgIIcTh0BEQQojDoSMghBCHQ0dACCEOh46AEEIcDh0BIYQ4HDoCQghxOHQEhBDicOgICCHE4dAREEKIw6EjIIQQh0NHQAghDoeOgBBCHA4dASGEOByfOgKXyzUY2gilQBNKOa+8Ys6vhrr7Mj+EEEKq0BHAqIcieRW6HGoPjcQxTT3Rc62MxkGv+yo/hBBCqr5GcC6UUlxcvAXKx/YM6Gqva3T/HZxXlmI7Ac7iHB/miRBCiBdhXvuVSQNop8d+KnReBa7RY3s8L4Jz0NqCSsnVcNMZ5ikJyjjDz1ZX+M7OgL+zM0g6CxvWxB+OwFXKseIzuEZQW5iMZPJZZ8jlWo579Tzb+1Qn+M7OgL+zM3D5yIb5MjSkpftGHvsNod1ncA0hhBAf4ktHsAxqBQ/WDIrA9gjoc69rdP9mnFd6YzsL3q5EWIgQQohv8VloCAa9EMb9HmzOgbQH0VQcW4tj4835SUi+hIZAKVAeNNZX+TGcdXipGsJ3dgb8nZ3BZF/c1AWD7Iv7EkIIqSZwZDEhhDgcOgJCCHE4jnEEp5ruIhjAe02F0qA1HsdqQXOhzSZN9GceKxu8TyNoHrQe0jao+4L5vfEeUdBP0Crzvk8F8/t6gncKhX6BZjvhnV0u1zboV2gltNyX7+wIR1DB6S6CgWnQYK9j6vS+Q1uQTuPxndkPJgqhP+H92iHVnmd3m982WN/7KDQI79UFaVdICzi9g/h9PVEnv95j3wnvPBDv19Vj7IBP3tkRjqCC011Ue/BuC5Ec8Dqs7/m22db0mirNlI/R7sbQz2Y7xxiKBsH63mY6llyzG25UHKzv6wbOTscYXQG94XE4qN+5DHzyzk5xBGVNZeEE6rrHZpi0jp/z40tj0RRJN+jHYH5vreFquACbadBcvF9Qv6/hJejPUJHHsWB/52LoG/zWK6BxvnxnX04xEUhUaCoLUn3BH0pNJB9D9+MPJBv7/s6Sz8D7HUPSFe+YgHQW0o7+zpMvwftdiSQN760GcYC/81OF9MU778Y7q7HX9oANvnqQU2oETp7KYp97RleTaikyqMB7hRsnMB1/OJ845b3xrplI5pt2oWB+377QVdp4asK6g7D9bpC/s6gTMKm+1ywT4vbJOzvFEVRkuotgRd/zFrOt6Wd+zEul47KL/m9C6/EH82KwvzdeN9nUBHQ7GsnF0IZgfV8Fv+tfoIZQU/O3+19sjw7md3a5XDWgWPc2kkuhNb56Z8eMLMaXOcTEGd3TXTzj5yz54h3fRzLATFW7D3oC+hSaCTWGdkDD8e7eDcrV+Z37IVkE/eoRP34E+jEY3xvv29k0EoaagtxMvNf/w/Hauh1s7+sN3lP/fT+Id7symN/Z5XI1N7UAdwj/PbVZvnpnxzgCQgghzg4NEUIIKQM6AkIIcTh0BIQQ4nDoCAghxOHQERBCiMOhIyDEC3TRO2ZmfHSr0iYzw72aes4OS0gg4JQpJgg5HQ7rjI/8yohTYI2AkApi5od/zqwHoGppjjeBvoNWm7SxOV4XmmXWDlD1MbfSSeOmmPUEvjEjhAnxG3QEhJxMtFdo6AaPc9moLeicLxPNSHUx2+/guI76nQ69Yo5rusCsHdAdWmuO61zyr+J4B6Q6X9C1/BGIP+HIYkK8/yhcrlwY6ZqlHN9mFoXZYia624vt2tjOwPY52C4wx3WNhCRsp2Nb58g56nGPpmbq6FZm/2Ek4dh/mj8E8ResERByenjOyVLW/CynmrfluGMAOqU02+qIX6EjIOT0cIeJNP3BbC8xs2Iqo6DFZluXErzTlPy1XSCOXzYJRFgSIaSMNgKP/a8RunF3IY3EuR9NIWqkOXYvNBXHH0Kq4aCxHmvsTsbx20zJX52CtboUIYEE2wgIqegfi91G0BNOQdsECAkaGBoihBCHwxoBIYQ4HNYICCHE4dAREEKIw6EjIIQQh0NHQAghDoeOgBBCHM7/B5XyBFRXqRwTAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_accuracy(history):\n", + " \"\"\" Plots the accuracy. \"\"\"\n", + "\n", + " plt.plot(history.history['acc'])\n", + " plt.plot(history.history['val_acc'])\n", + " plt.title('Model Accuracy')\n", + " plt.ylabel('Accuracy')\n", + " plt.xlabel('Epoch')\n", + " plt.ylim((0, 1))\n", + " plt.legend(['Train', 'Validate'], loc='upper left')\n", + " plt.savefig('../model/plots/accuracy.png')\n", + " plt.show()\n", + " plt.clf()\n", + " \n", + "plot_accuracy(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_loss(history):\n", + " \"\"\" Plots the loss. \"\"\"\n", + "\n", + " plt.plot(history.history['loss'])\n", + " plt.plot(history.history['val_loss'])\n", + " plt.title('Model Loss')\n", + " plt.ylabel('loss')\n", + " plt.xlabel('Epoch')\n", + " plt.legend(['Train', 'Validate'], loc='upper left')\n", + " plt.savefig('../model/plots/loss.png')\n", + " plt.show()\n", + " plt.clf()\n", + " \n", + "plot_loss(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Precision" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_precision(history):\n", + " \"\"\" Plots the precision. \"\"\"\n", + "\n", + " plt.plot(history.history['precision'])\n", + " plt.plot(history.history['val_precision'])\n", + " plt.title('Model Precision')\n", + " plt.ylabel('Precision')\n", + " plt.xlabel('Epoch')\n", + " plt.legend(['Train', 'Validate'], loc='upper left')\n", + " plt.savefig('../model/plots/precision.png')\n", + " plt.show()\n", + " plt.clf()\n", + " \n", + "plot_precision(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### AUC" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_auc(history):\n", + " \"\"\" Plots the AUC. \"\"\"\n", + "\n", + " plt.plot(history.history['auc'])\n", + " plt.plot(history.history['val_auc'])\n", + " plt.title('Model AUC')\n", + " plt.ylabel('AUC')\n", + " plt.xlabel('Epoch')\n", + " plt.legend(['Train', 'Validate'], loc='upper left')\n", + " plt.savefig('../model/plots/auc.png')\n", + " plt.show()\n", + " plt.clf()\n", + " \n", + "plot_auc(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Recall" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_recall(history):\n", + " \"\"\" Plots the recall. \"\"\"\n", + "\n", + " plt.plot(history.history['recall'])\n", + " plt.plot(history.history['val_recall'])\n", + " plt.title('Model Recall')\n", + " plt.ylabel('Recall')\n", + " plt.xlabel('Epoch')\n", + " plt.legend(['Train', 'Validate'], loc='upper left')\n", + " plt.savefig('../model/plots/recall.png')\n", + " plt.show()\n", + " plt.clf()\n", + " \n", + "plot_recall(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: [[220 0]\n", + " [ 25 159]]\n", + "\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def confusion_matrix_plot(y_test, test_preds):\n", + " \"\"\" Plots the confusion matrix. \"\"\"\n", + " \n", + " matrix = confusion_matrix(y_test.argmax(axis=1),\n", + " test_preds.argmax(axis=1))\n", + "\n", + " print(\"Confusion Matrix: \" + str(matrix))\n", + " print(\"\")\n", + "\n", + " plot_confusion_matrix(conf_mat=matrix)\n", + " plt.savefig('../model/plots/confusion-matrix.png')\n", + " plt.show()\n", + " plt.clf()\n", + " \n", + " return matrix\n", + " \n", + "matrix = confusion_matrix_plot(y_test, test_preds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Figures Of Merit" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True Positives: 159(39.35643564356435%)\n", + "False Positives: 0(0.0%)\n", + "True Negatives: 220(54.45544554455446%)\n", + "False Negatives: 25(6.188118811881188%)\n", + "Specificity: 1.0\n", + "Misclassification: 25(6.188118811881188%)\n" + ] + } + ], + "source": [ + "def figures_of_merit(X_test, matrix):\n", + " \"\"\" Calculates/prints the figures of merit.\n", + "\n", + " https://homes.di.unimi.it/scotti/all/\n", + " \"\"\"\n", + "\n", + " test_len = len(X_test)\n", + "\n", + " TP = matrix[1][1]\n", + " TN = matrix[0][0]\n", + " FP = matrix[0][1]\n", + " FN = matrix[1][0]\n", + "\n", + " TPP = (TP * 100)/test_len\n", + " FPP = (FP * 100)/test_len\n", + " FNP = (FN * 100)/test_len\n", + " TNP = (TN * 100)/test_len\n", + "\n", + " specificity = TN/(TN+FP)\n", + "\n", + " misc = FP + FN\n", + " miscp = (misc * 100)/test_len\n", + "\n", + " print(\"True Positives: \" + str(TP) + \"(\" + str(TPP) + \"%)\")\n", + " print(\"False Positives: \" + str(FP) + \"(\" + str(FPP) + \"%)\")\n", + " print(\"True Negatives: \" + str(TN) + \"(\" + str(TNP) + \"%)\")\n", + " print(\"False Negatives: \" + str(FN) + \"(\" + str(FNP) + \"%)\")\n", + "\n", + " print(\"Specificity: \" + str(specificity))\n", + " print(\"Misclassification: \" + str(misc) + \"(\" + str(miscp) + \"%)\")\n", + "\n", + "figures_of_merit(X_test, matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test Tensorflow Model" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"AllJetsonNano\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "average_pooling2d (AveragePo (None, 50, 50, 3) 0 \n", + "_________________________________________________________________\n", + "conv2d (Conv2D) (None, 46, 46, 30) 2280 \n", + "_________________________________________________________________\n", + "depthwise_conv2d (DepthwiseC (None, 17, 17, 30) 27030 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 8670) 0 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 2) 17342 \n", + "_________________________________________________________________\n", + "softmax (Activation) (None, 2) 0 \n", + "=================================================================\n", + "Total params: 46,652\n", + "Trainable params: 46,652\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "def load():\n", + " \"\"\" Loads the model \"\"\"\n", + "\n", + " with open(model_path) as file:\n", + " m_json = file.read()\n", + "\n", + " model = tf.keras.models.model_from_json(m_json)\n", + " model.load_weights(weights_path)\n", + "\n", + " model.summary()\n", + " \n", + " return model\n", + "\n", + "model = load()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def reshape(img):\n", + " \"\"\" Reshapes an image. \"\"\"\n", + "\n", + " dx, dy, dz = img.shape\n", + " input_data = img.reshape((-1, dx, dy, dz))\n", + " input_data = input_data / 255.0\n", + "\n", + " return input_data" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(model, img):\n", + " \"\"\" Gets a prediction for an image. \"\"\"\n", + "\n", + " preds = model.predict(img)\n", + " pred = np.argmax(preds, axis=-1)\n", + "\n", + " return pred" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded test image ../model/data/test/Im041_0.jpg\n", + "Resized test image ../model/data/test/Im041_0.jpg\n", + "Reshaped test image ../model/data/test/Im041_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.1119682788848877 seconds.\n", + "Loaded test image ../model/data/test/Im053_1.jpg\n", + "Resized test image ../model/data/test/Im053_1.jpg\n", + "Reshaped test image ../model/data/test/Im053_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.05084347724914551 seconds.\n", + "Loaded test image ../model/data/test/Im047_0.jpg\n", + "Resized test image ../model/data/test/Im047_0.jpg\n", + "Reshaped test image ../model/data/test/Im047_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.049056053161621094 seconds.\n", + "Loaded test image ../model/data/test/Im026_1.jpg\n", + "Resized test image ../model/data/test/Im026_1.jpg\n", + "Reshaped test image ../model/data/test/Im026_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.08868265151977539 seconds.\n", + "Loaded test image ../model/data/test/Im088_0.jpg\n", + "Resized test image ../model/data/test/Im088_0.jpg\n", + "Reshaped test image ../model/data/test/Im088_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.04874563217163086 seconds.\n", + "Loaded test image ../model/data/test/Im095_0.jpg\n", + "Resized test image ../model/data/test/Im095_0.jpg\n", + "Reshaped test image ../model/data/test/Im095_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.03998160362243652 seconds.\n", + "Loaded test image ../model/data/test/Im106_0.jpg\n", + "Resized test image ../model/data/test/Im106_0.jpg\n", + "Reshaped test image ../model/data/test/Im106_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.03571462631225586 seconds.\n", + "Loaded test image ../model/data/test/Im024_1.jpg\n", + "Resized test image ../model/data/test/Im024_1.jpg\n", + "Reshaped test image ../model/data/test/Im024_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.06783843040466309 seconds.\n", + "Loaded test image ../model/data/test/Im020_1.jpg\n", + "Resized test image ../model/data/test/Im020_1.jpg\n", + "Reshaped test image ../model/data/test/Im020_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.05595731735229492 seconds.\n", + "Loaded test image ../model/data/test/Im057_1.jpg\n", + "Resized test image ../model/data/test/Im057_1.jpg\n", + "Reshaped test image ../model/data/test/Im057_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.03931689262390137 seconds.\n", + "Loaded test image ../model/data/test/Im063_1.jpg\n", + "Resized test image ../model/data/test/Im063_1.jpg\n", + "Reshaped test image ../model/data/test/Im063_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.042519569396972656 seconds.\n", + "Loaded test image ../model/data/test/Im069_0.jpg\n", + "Resized test image ../model/data/test/Im069_0.jpg\n", + "Reshaped test image ../model/data/test/Im069_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.044512271881103516 seconds.\n", + "Loaded test image ../model/data/test/Im101_0.jpg\n", + "Resized test image ../model/data/test/Im101_0.jpg\n", + "Reshaped test image ../model/data/test/Im101_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.05829262733459473 seconds.\n", + "Loaded test image ../model/data/test/Im060_1.jpg\n", + "Resized test image ../model/data/test/Im060_1.jpg\n", + "Reshaped test image ../model/data/test/Im060_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04625654220581055 seconds.\n", + "Loaded test image ../model/data/test/Im031_1.jpg\n", + "Resized test image ../model/data/test/Im031_1.jpg\n", + "Reshaped test image ../model/data/test/Im031_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.058502912521362305 seconds.\n", + "Loaded test image ../model/data/test/Im006_1.jpg\n", + "Resized test image ../model/data/test/Im006_1.jpg\n", + "Reshaped test image ../model/data/test/Im006_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0796513557434082 seconds.\n", + "Loaded test image ../model/data/test/Im099_0.jpg\n", + "Resized test image ../model/data/test/Im099_0.jpg\n", + "Reshaped test image ../model/data/test/Im099_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.03330731391906738 seconds.\n", + "Loaded test image ../model/data/test/Im035_0.jpg\n", + "Resized test image ../model/data/test/Im035_0.jpg\n", + "Reshaped test image ../model/data/test/Im035_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.04484081268310547 seconds.\n", + "Loaded test image ../model/data/test/Im028_1.jpg\n", + "Resized test image ../model/data/test/Im028_1.jpg\n", + "Reshaped test image ../model/data/test/Im028_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0916593074798584 seconds.\n", + "Loaded test image ../model/data/test/Im074_0.jpg\n", + "Resized test image ../model/data/test/Im074_0.jpg\n", + "Reshaped test image ../model/data/test/Im074_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.027399778366088867 seconds.\n", + "Images Classified: 20\n", + "True Positives: 10\n", + "False Positives: 0\n", + "True Negatives: 10\n", + "False Negatives: 0\n", + "Total Time Taken: 1.1150474548339844\n" + ] + } + ], + "source": [ + "def test(model):\n", + " \"\"\" Test mode\n", + "\n", + " Loops through the test directory and classifies the images.\n", + " \"\"\"\n", + "\n", + " files = 0\n", + " tp = 0\n", + " fp = 0\n", + " tn = 0\n", + " fn = 0\n", + " totaltime = 0\n", + "\n", + " for testFile in os.listdir(testDataDir):\n", + " if os.path.splitext(testFile)[1] in valid_files:\n", + " files += 1\n", + " fileName = testDataDir + testFile\n", + "\n", + " img = cv2.imread(fileName).astype(np.float32)\n", + " print(\"Loaded test image \" + fileName)\n", + " img = cv2.resize(img, (dim, dim))\n", + " print(\"Resized test image \" + fileName)\n", + " img = reshape(img)\n", + " print(\"Reshaped test image \" + fileName)\n", + "\n", + " start = time.time()\n", + " prediction = predict(model, img)\n", + " end = time.time()\n", + " benchmark = end - start\n", + " totaltime += benchmark\n", + "\n", + " msg = \"\"\n", + " if prediction == 1 and \"_1.\" in testFile:\n", + " tp += 1\n", + " msg = \"Acute Lymphoblastic Leukemia correctly detected (True Positive) in \" + str(benchmark) + \" seconds.\"\n", + " elif prediction == 1 and \"_0.\" in testFile:\n", + " fp += 1\n", + " msg = \"Acute Lymphoblastic Leukemia incorrectly detected (False Positive) in \" + str(benchmark) + \" seconds.\"\n", + " elif prediction == 0 and \"_0.\" in testFile:\n", + " tn += 1\n", + " msg = \"Acute Lymphoblastic Leukemia correctly not detected (True Negative) in \" + str(benchmark) + \" seconds.\"\n", + " elif prediction == 0 and \"_1.\" in testFile:\n", + " fn += 1\n", + " msg = \"Acute Lymphoblastic Leukemia incorrectly not detected (False Negative) in \" + str(benchmark) + \" seconds.\"\n", + " print(msg)\n", + "\n", + " print(\"Images Classified: \" + str(files))\n", + " print(\"True Positives: \" + str(tp))\n", + " print(\"False Positives: \" + str(fp))\n", + " print(\"True Negatives: \" + str(tn))\n", + " print(\"False Negatives: \" + str(fn))\n", + " print(\"Total Time Taken: \" + str(totaltime))\n", + " \n", + "test(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert To TFRT" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Linked TensorRT version: (0, 0, 0)\n", + "INFO:tensorflow:Loaded TensorRT version: (0, 0, 0)\n", + "INFO:tensorflow:Assets written to: ../model/tfrt/assets\n" + ] + } + ], + "source": [ + "def convert_to_tfrt():\n", + " \"\"\" Converts the model to TFRT format \"\"\"\n", + " \n", + " converter = trt.TrtGraphConverterV2(input_saved_model_dir=saved_model_dir)\n", + " converter.convert()\n", + " converter.save(tfrt_model_dir)\n", + " \n", + "convert_to_tfrt()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test TFRT Model" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def load_tfrt():\n", + " \"\"\" Loads the tfrt model \"\"\"\n", + "\n", + " tfrt_model = tf.saved_model.load(saved_model_dir)\n", + " \n", + " return tfrt_model\n", + "\n", + "model_tfrt = load_tfrt()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def predict_tfrt(model, img):\n", + " \"\"\" Gets a prediction for an image. \"\"\"\n", + "\n", + " inference = model.signatures[\"serving_default\"]\n", + " prediction = inference(tf.constant(img,dtype=float))['softmax']\n", + " prediction = data_labels[int(tf.argmax(prediction, axis=1))]\n", + " \n", + " return prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded test image ../model/data/test/Im041_0.jpg\n", + "Resized test image ../model/data/test/Im041_0.jpg\n", + "Reshaped test image ../model/data/test/Im041_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.0625162124633789 seconds.\n", + "Loaded test image ../model/data/test/Im053_1.jpg\n", + "Resized test image ../model/data/test/Im053_1.jpg\n", + "Reshaped test image ../model/data/test/Im053_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04581952095031738 seconds.\n", + "Loaded test image ../model/data/test/Im047_0.jpg\n", + "Resized test image ../model/data/test/Im047_0.jpg\n", + "Reshaped test image ../model/data/test/Im047_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.023001670837402344 seconds.\n", + "Loaded test image ../model/data/test/Im026_1.jpg\n", + "Resized test image ../model/data/test/Im026_1.jpg\n", + "Reshaped test image ../model/data/test/Im026_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.028900623321533203 seconds.\n", + "Loaded test image ../model/data/test/Im088_0.jpg\n", + "Resized test image ../model/data/test/Im088_0.jpg\n", + "Reshaped test image ../model/data/test/Im088_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.04416775703430176 seconds.\n", + "Loaded test image ../model/data/test/Im095_0.jpg\n", + "Resized test image ../model/data/test/Im095_0.jpg\n", + "Reshaped test image ../model/data/test/Im095_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.0542142391204834 seconds.\n", + "Loaded test image ../model/data/test/Im106_0.jpg\n", + "Resized test image ../model/data/test/Im106_0.jpg\n", + "Reshaped test image ../model/data/test/Im106_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.05998969078063965 seconds.\n", + "Loaded test image ../model/data/test/Im024_1.jpg\n", + "Resized test image ../model/data/test/Im024_1.jpg\n", + "Reshaped test image ../model/data/test/Im024_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.061249494552612305 seconds.\n", + "Loaded test image ../model/data/test/Im020_1.jpg\n", + "Resized test image ../model/data/test/Im020_1.jpg\n", + "Reshaped test image ../model/data/test/Im020_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.03109431266784668 seconds.\n", + "Loaded test image ../model/data/test/Im057_1.jpg\n", + "Resized test image ../model/data/test/Im057_1.jpg\n", + "Reshaped test image ../model/data/test/Im057_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.03261303901672363 seconds.\n", + "Loaded test image ../model/data/test/Im063_1.jpg\n", + "Resized test image ../model/data/test/Im063_1.jpg\n", + "Reshaped test image ../model/data/test/Im063_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04342508316040039 seconds.\n", + "Loaded test image ../model/data/test/Im069_0.jpg\n", + "Resized test image ../model/data/test/Im069_0.jpg\n", + "Reshaped test image ../model/data/test/Im069_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.06667351722717285 seconds.\n", + "Loaded test image ../model/data/test/Im101_0.jpg\n", + "Resized test image ../model/data/test/Im101_0.jpg\n", + "Reshaped test image ../model/data/test/Im101_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.03921627998352051 seconds.\n", + "Loaded test image ../model/data/test/Im060_1.jpg\n", + "Resized test image ../model/data/test/Im060_1.jpg\n", + "Reshaped test image ../model/data/test/Im060_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.0022323131561279297 seconds.\n", + "Loaded test image ../model/data/test/Im031_1.jpg\n", + "Resized test image ../model/data/test/Im031_1.jpg\n", + "Reshaped test image ../model/data/test/Im031_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.02861309051513672 seconds.\n", + "Loaded test image ../model/data/test/Im006_1.jpg\n", + "Resized test image ../model/data/test/Im006_1.jpg\n", + "Reshaped test image ../model/data/test/Im006_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.025559425354003906 seconds.\n", + "Loaded test image ../model/data/test/Im099_0.jpg\n", + "Resized test image ../model/data/test/Im099_0.jpg\n", + "Reshaped test image ../model/data/test/Im099_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.021227359771728516 seconds.\n", + "Loaded test image ../model/data/test/Im035_0.jpg\n", + "Resized test image ../model/data/test/Im035_0.jpg\n", + "Reshaped test image ../model/data/test/Im035_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.01686549186706543 seconds.\n", + "Loaded test image ../model/data/test/Im028_1.jpg\n", + "Resized test image ../model/data/test/Im028_1.jpg\n", + "Reshaped test image ../model/data/test/Im028_1.jpg\n", + "Acute Lymphoblastic Leukemia correctly detected (True Positive) in 0.04134345054626465 seconds.\n", + "Loaded test image ../model/data/test/Im074_0.jpg\n", + "Resized test image ../model/data/test/Im074_0.jpg\n", + "Reshaped test image ../model/data/test/Im074_0.jpg\n", + "Acute Lymphoblastic Leukemia correctly not detected (True Negative) in 0.04427742958068848 seconds.\n", + "Images Classified: 20\n", + "True Positives: 10\n", + "False Positives: 0\n", + "True Negatives: 10\n", + "False Negatives: 0\n", + "Total Time Taken: 0.7730000019073486\n" + ] + } + ], + "source": [ + "def test_tfrt(model):\n", + " \"\"\" Test mode\n", + "\n", + " Loops through the test directory and classifies the images.\n", + " \"\"\"\n", + "\n", + " files = 0\n", + " tp = 0\n", + " fp = 0\n", + " tn = 0\n", + " fn = 0\n", + " totaltime = 0\n", + "\n", + " for testFile in os.listdir(testDataDir):\n", + " if os.path.splitext(testFile)[1] in valid_files:\n", + " files += 1\n", + " fileName = testDataDir + testFile\n", + "\n", + " img = cv2.imread(fileName).astype(np.float32)\n", + " print(\"Loaded test image \" + fileName)\n", + " img = cv2.resize(img, (dim, dim))\n", + " print(\"Resized test image \" + fileName)\n", + " img = reshape(img)\n", + " print(\"Reshaped test image \" + fileName)\n", + "\n", + " start = time.time()\n", + " prediction = predict_tfrt(model, img)\n", + " end = time.time()\n", + " benchmark = end - start\n", + " totaltime += benchmark\n", + "\n", + " msg = \"\"\n", + " if prediction == 1 and \"_1.\" in testFile:\n", + " tp += 1\n", + " msg = \"Acute Lymphoblastic Leukemia correctly detected (True Positive) in \" + str(benchmark) + \" seconds.\"\n", + " elif prediction == 1 and \"_0.\" in testFile:\n", + " fp += 1\n", + " msg = \"Acute Lymphoblastic Leukemia incorrectly detected (False Positive) in \" + str(benchmark) + \" seconds.\"\n", + " elif prediction == 0 and \"_0.\" in testFile:\n", + " tn += 1\n", + " msg = \"Acute Lymphoblastic Leukemia correctly not detected (True Negative) in \" + str(benchmark) + \" seconds.\"\n", + " elif prediction == 0 and \"_1.\" in testFile:\n", + " fn += 1\n", + " msg = \"Acute Lymphoblastic Leukemia incorrectly not detected (False Negative) in \" + str(benchmark) + \" seconds.\"\n", + " print(msg)\n", + "\n", + " print(\"Images Classified: \" + str(files))\n", + " print(\"True Positives: \" + str(tp))\n", + " print(\"False Positives: \" + str(fp))\n", + " print(\"True Negatives: \" + str(tn))\n", + " print(\"False Negatives: \" + str(fn))\n", + " print(\"Total Time Taken: \" + str(totaltime))\n", + " \n", + "test_tfrt(model_tfrt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convert to ONNX" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/hias/.conda/envs/all-jetson-nano/lib/python3.7/runpy.py:125: RuntimeWarning: 'tf2onnx.convert' found in sys.modules after import of package 'tf2onnx', but prior to execution of 'tf2onnx.convert'; this may result in unpredictable behaviour\n", + " warn(RuntimeWarning(msg))\n", + "2021-08-19 15:48:43,123 - INFO - Signatures found in model: [serving_default].\n", + "2021-08-19 15:48:43,123 - INFO - Output names: ['softmax']\n", + "WARNING:tensorflow:From /home/hias/.conda/envs/all-jetson-nano/lib/python3.7/site-packages/tf2onnx/tf_loader.py:557: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", + "2021-08-19 15:48:43,164 - WARNING - From /home/hias/.conda/envs/all-jetson-nano/lib/python3.7/site-packages/tf2onnx/tf_loader.py:557: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", + "2021-08-19 15:48:43,176 - INFO - Using tensorflow=2.2.0, onnx=1.6.0, tf2onnx=1.8.4/None\n", + "2021-08-19 15:48:43,177 - INFO - Using opset \n", + "2021-08-19 15:48:43,182 - INFO - Computed 0 values for constant folding\n", + "2021-08-19 15:48:43,211 - INFO - Optimizing ONNX model\n", + "2021-08-19 15:48:43,235 - INFO - After optimization: Add -1 (2->1), Cast -1 (1->0), Const -1 (8->7), Identity -5 (5->0), Reshape -1 (2->1), Transpose -5 (7->2)\n", + "2021-08-19 15:48:43,237 - INFO - \n", + "2021-08-19 15:48:43,237 - INFO - Successfully converted TensorFlow model ../model/saved to ONNX\n", + "2021-08-19 15:48:43,237 - INFO - Model inputs: ['input_1:0']\n", + "2021-08-19 15:48:43,237 - INFO - Model outputs: ['softmax']\n", + "2021-08-19 15:48:43,237 - INFO - ONNX model is saved at ../model/all_jetson_nano.onnx\n" + ] + } + ], + "source": [ + "def convert_to_onnx():\n", + " \"\"\" Converts the model to ONNX format \"\"\"\n", + " \n", + " !python3 -m tf2onnx.convert --saved-model ../model/saved --output ../model/all_jetson_nano.onnx --tag serve --signature_def serving_default\n", + " \n", + "convert_to_onnx()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusion \n", + "\n", + "Here we trained a deep learning model for Acute Lymphoblastic Leukemia detection utilizing Intel® Optimization for Tensorflow* from the Intel® oneAPI AI Analytics Toolkit to optimize and accelarate training. We introduced a 6 layer deep learning model and applied data augmentation to increase the training data. \n", + "\n", + "We trained our model with a target of 150 epochs and used early stopping to avoid overfitting. The model trained for 37 epochs resulting in a fairly good fit, accuracy/precision/recall and AUC are satisfying. In addition the model reacts well during testing classifying each of the twenty unseen test images correctly. \n", + "\n", + "We see that the TFRT model performs considerably better than the Tensorflow model, improving classification time by 0.35 seconds.\n", + "\n", + "- To test with the Python scripts continue to the [Python usage documentation](https://github.com/AMLResearchProject/ALL-Jetson-Nano/blob/main/docs/usage/python.md)\n", + "- To begin installation on your Jetson Nano continue to the [Jetson Nano installation documentation](https://github.com/AMLResearchProject/ALL-Jetson-Nano/blob/main/docs/installation/jetson.md)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Contributing\n", + "Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss encourages and welcomes code contributions, bug fixes and enhancements from the Github community.\n", + "\n", + "Please read the [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md \"CONTRIBUTING\") document for a full guide to forking our repositories and submitting your pull requests. You will also find our code of conduct in the [Code of Conduct](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CODE-OF-CONDUCT.md) document.\n", + "\n", + "## Contributors\n", + "- [Adam Milton-Barker](https://www.leukemiaairesearch.com/association/volunteers/adam-milton-barker \"Adam Milton-Barker\") - [Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss](https://www.leukemiaresearchassociation.ai \"Asociación de Investigacion en Inteligencia Artificial Para la Leucemia Peter Moss\") President/Founder & Lead Developer, Sabadell, Spain\n", + "\n", + " \n", + "\n", + "# Versioning\n", + "We use [SemVer](https://semver.org/) for versioning.\n", + "\n", + " \n", + "\n", + "# License\n", + "This project is licensed under the **MIT License** - see the [LICENSE](https://github.com/AMLResearchProject/ALL-Arduino-Nano-33-BLE-Sense-Classifier/blob/main/LICENSE \"LICENSE\") file for details.\n", + "\n", + " \n", + "\n", + "# Bugs/Issues\n", + "We use the [repo issues](https://github.com/AMLResearchProject/ALL-Jetson-Nano/issues \"repo issues\") to track bugs and general requests related to using this project. See [CONTRIBUTING](https://github.com/AMLResearchProject/Contributing-Guide/blob/main/CONTRIBUTING.md \"CONTRIBUTING\") for more info on how to submit bugs, feature requests and proposals." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-all-jetson-nano] *", + "language": "python", + "name": "conda-env-.conda-all-jetson-nano-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/docker.txt b/scripts/docker.txt new file mode 100644 index 0000000..8ab1d52 --- /dev/null +++ b/scripts/docker.txt @@ -0,0 +1,46 @@ + +sudo apt install docker.io + +sudo apt-cache show nvidia-jetpack +sudo nvpmodel -m 0 +sudo jetson_clocks + +sudo docker pull nvcr.io/nvidia/l4t-tensorflow:r32.5.0-tf2.3-py3 +sudo docker run -it --name all-jetson-nano-base --rm --runtime nvidia --network host -v /home/jetson-nano/ALL-Jetson-Nano-Classifier:/ALL-Jetson-Nano-Classifier nvcr.io/nvidia/l4t-tensorflow:r32.5.0-tf2.3-py3 + +apt update && apt -y install cmake libprotobuf-dev protobuf-compiler +pip3 install scipy +pip3 install scikit-build +pip3 install scikit-learnx +pip3 install opencv-python +pip3 install pandas +pip3 install mlxtend +apt install nano + +sed -i -e 's/# en_US.UTF-8 UTF-8/en_US.UTF-8 UTF-8/' /etc/locale.gen && \ + dpkg-reconfigure --frontend=noninteractive locales && \ + update-locale LANG=en_US.UTF-8 + +echo 'export LANG=en_US.UTF-8' >> ~/.bashrc +echo 'export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libgomp.so.1' >> ~/.bashrc +source ~/.bashrc + +export LANG=en_US.UTF-8 +pip3 install jsonpickle +pip3 install flask +pip3 install tf2onnx +pip3 install onnx +pip3 install pycuda + +apt purge cmake && \ +wget https://github.com/Kitware/CMake/releases/download/v3.13.4/cmake-3.13.4.tar.gz && \ +tar zxvf cmake-3.13.4.tar.gz && \ +cd cmake-3.13.4 && \ +./bootstrap && \ +make && \ +make install && \ +cd ../.. + +docker commit -m "Your Message" -a "Your Name" all-jetson-nano YourDockerHubUser/all-jetson-nano:v1 + +sudo docker run -it --name all-jetson-nano --rm --runtime nvidia --network host -v /home/YourUser/ALL-Jetson-Nano-Classifier:/ALL-Jetson-Nano-Classifier YourDockerHubUser/all-jetson-nano:v1 \ No newline at end of file diff --git a/scripts/install.sh b/scripts/install.sh new file mode 100644 index 0000000..ec748f8 --- /dev/null +++ b/scripts/install.sh @@ -0,0 +1,31 @@ +#!/bin/bash + +FMSG="Acute Lymphoblastic Leukemia Jetson Nano Classifier trainer installation terminated!" + +printf -- 'This script will install the Acute Lymphoblastic Leukemia Jetson Nano Classifier trainer on your machine.\n'; +printf -- '\033[33m WARNING: This script assumes Ubuntu 20.04. \033[0m\n'; +printf -- '\033[33m WARNING: This script assumes you have already run scripts/oenapi.sh. \033[0m\n'; +printf -- '\033[33m WARNING: This script assumes you have already activated the all-jetson-nano environment. \033[0m\n'; +printf -- '\033[33m WARNING: This is an inteteractive installation, please follow instructions provided. \033[0m\n'; + +read -p "Proceed (y/n)? " proceed +if [ "$proceed" = "Y" -o "$proceed" = "y" ]; then + printf -- 'Installing the Acute Lymphoblastic Leukemia Jetson Nano Classifier trainer on your machine.\n'; + conda activate all-jetson-nano + conda install jupyter + conda install nb_conda + conda install -c conda-forge mlxtend + conda install matplotlib + conda install Pillow + conda install opencv + conda install scipy + conda install scikit-learn + conda install scikit-image + conda install -c conda-forge onnx + conda install protobuf=3.9 -c conda-forge + conda install -c conda-forge tf2onnx + printf -- '\033[32m SUCCESS: Acute Lymphoblastic Leukemia Jetson Nano Classifier trainer installed! \033[0m\n'; +else + echo $FMSG; + exit 1 +fi \ No newline at end of file diff --git a/scripts/oneapi.sh b/scripts/oneapi.sh new file mode 100644 index 0000000..2b6fca7 --- /dev/null +++ b/scripts/oneapi.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +FMSG="Acute Lymphoblastic Leukemia Jetson Nano Classifier oneAPI installation terminated!" + +printf -- 'This script will install oneAPI on your machine.\n'; +printf -- '\033[33m WARNING: This script assumes Ubuntu 20.04. \033[0m\n'; +printf -- '\033[33m WARNING: This script assumes you have not already installed the oneAPI Basekit. \033[0m\n'; +printf -- '\033[33m WARNING: This script assumes you have not already installed the oneAPI AI Analytics Toolkit. \033[0m\n'; +printf -- '\033[33m WARNING: If any of the above are not relevant to you, please comment out the relevant sections below before running this installation script. \033[0m\n'; +printf -- '\033[33m WARNING: This is an inteteractive installation, please follow instructions provided. \033[0m\n'; + +read -p "Proceed (y/n)? " proceed +if [ "$proceed" = "Y" -o "$proceed" = "y" ]; then + printf -- 'Installing oneAPI on your machine.\n'; + # Comment out the following if you have already installed oneAPI Basekit + wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB -O - | sudo apt-key add - + echo "deb https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list + sudo apt update + sudo apt install intel-basekit + sudo apt -y install cmake pkg-config build-essential + echo 'source /opt/intel/oneapi/setvars.sh' >> ~/.bashrc + source ~/.bashrc + # Comment out the following if you have already installed oneAPI AI Analytics + sudo apt install intel-aikit + # The following will create a Conda env with Intel Optimized TensorFlow + conda create -n all-jetson-nano -c intel intel-aikit-tensorflow + # Comment out the following if you have already installed the Intel GPU drivers + # or do not have an Intel GPU on your training device + sudo apt-get install -y gpg-agent wget + wget -qO - https://repositories.intel.com/graphics/intel-graphics.key | + sudo apt-key add - + sudo apt-add-repository \ + 'deb [arch=amd64] https://repositories.intel.com/graphics/ubuntu focal main' + sudo apt-get update + sudo apt-get install \ + intel-opencl-icd \ + intel-level-zero-gpu level-zero \ + intel-media-va-driver-non-free libmfx1 + stat -c "%G" /dev/dri/render* + groups ${USER} + sudo gpasswd -a ${USER} render + newgrp render + sudo usermod -a -G video ${USER} +else + echo $FMSG; + exit 1 +fi \ No newline at end of file From 35d494948548e2cd6b860f97263fc2ecc1fee419 Mon Sep 17 00:00:00 2001 From: AdamMiltonBarker Date: Fri, 20 Aug 2021 18:06:05 +0200 Subject: [PATCH 2/3] Update README.md --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index e5ef455..1e649d7 100644 --- a/README.md +++ b/README.md @@ -5,8 +5,7 @@ [![CURRENT RELEASE](https://img.shields.io/badge/CURRENT%20RELEASE-1.0.0-blue.svg)](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/tree/1.0.0) [![DEV BRANCH](https://img.shields.io/badge/DEV%20BRANCH-develop-blue.svg)](https://github.com/AMLResearchProject/ALL-Jetson-Nano-Classifier/tree/develop) [![Contributions Welcome!](https://img.shields.io/badge/Contributions-Welcome-lightgrey.svg)](CONTRIBUTING.md) [![Issues](https://img.shields.io/badge/Issues-Welcome-lightgrey.svg)](issues) -[![PEP8](https://img.shields.io/badge/code%20style-pep8-orange.svg)](https://www.python.org/dev/peps/pep-0008/) [![Documentation Status](https://readthedocs.org/projects/acute-lymphoblastic-leukemia-pytorch-segmentation-2021/badge/?version=latest)](https://acute-lymphoblastic-leukemia-pytorch-segmentation-2021.readthedocs.io/en/latest/?badge=latest) - [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5068/badge)](https://bestpractices.coreinfrastructure.org/projects/5068) [![DOI](https://zenodo.org/badge/383255630.svg)](https://zenodo.org/badge/latestdoi/383255630) +[![PEP8](https://img.shields.io/badge/code%20style-pep8-orange.svg)](https://www.python.org/dev/peps/pep-0008/) [![Documentation Status](https://readthedocs.org/projects/all-jetson-nano-classifier/badge/?version=latest)](https://all-jetson-nano-classifier.readthedocs.io/en/latest/?badge=latest) [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5068/badge)](https://bestpractices.coreinfrastructure.org/projects/5068) [![DOI](https://zenodo.org/badge/383255630.svg)](https://zenodo.org/badge/latestdoi/383255630) [![LICENSE](https://img.shields.io/badge/LICENSE-MIT-blue.svg)](LICENSE) From b8bb1307fc09d3c19ca5fcb247a4b01a01f3cb98 Mon Sep 17 00:00:00 2001 From: AdamMiltonBarker Date: Fri, 20 Aug 2021 18:08:21 +0200 Subject: [PATCH 3/3] Update codeql-analysis.yml --- .github/workflows/codeql-analysis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml index edf7308..5f8e9b0 100644 --- a/.github/workflows/codeql-analysis.yml +++ b/.github/workflows/codeql-analysis.yml @@ -32,7 +32,7 @@ jobs: strategy: fail-fast: false matrix: - language: [ ] + language: [ 'python' ] # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ] # Learn more: # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed

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