From 91d1a7a2ebeac8add29a3924001a86fece1651e4 Mon Sep 17 00:00:00 2001 From: qxygxt Date: Tue, 31 Oct 2023 10:02:54 +0800 Subject: [PATCH 1/2] modified: core/testcasecontroller/metrics/metrics.py new file: docs/proposals/algorithms/lifelong-learning/Implementation of a Class Incremental Learning Algorithm Evaluation System based on Ianvs.md new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_1.png new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_10.png new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_11.png new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_2.png new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_3.png new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_4.png new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_5.png new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_7.png new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_8.png new file: docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_9.png new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/README.md new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/benchmarkingjob.yaml new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/accuracy.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/basemodel.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/__init__.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/custom_transforms.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/custom_transforms_rgb.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/__init__.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/citylostfound.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/cityrand.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/cityscapes.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/e1.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/mapillary.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/target.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/temp.txt new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/xrlab.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/utils.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/eval.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/models/erfnet.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/models/replicate.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/models/util.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/mypath.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/predict.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/run_server.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/sedna_evaluate.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/sedna_predict.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/sedna_train.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/test.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/train.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/__init__.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/args.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/calculate_weights.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/iouEval.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/loss.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/lr_scheduler.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/metrics.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/saver.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/summaries.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/basemodel.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/task_allocation_by_domain.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/task_definition_by_domain.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/test_algorithm.yaml new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py new file: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml Signed-off-by: qxygxt xingyu.q@outlook.com Signed-off-by: qxygxt --- core/testcasecontroller/metrics/metrics.py | 58 ++- ...orithm Evaluation System based on Ianvs.md | 270 +++++++++++++ .../images/OSPP_MDIL-SS_1.png | Bin 0 -> 2176021 bytes .../images/OSPP_MDIL-SS_10.png | Bin 0 -> 69665 bytes .../images/OSPP_MDIL-SS_11.png | Bin 0 -> 545632 bytes .../images/OSPP_MDIL-SS_2.png | Bin 0 -> 2344539 bytes .../images/OSPP_MDIL-SS_3.png | Bin 0 -> 1036638 bytes .../images/OSPP_MDIL-SS_4.png | Bin 0 -> 9940 bytes .../images/OSPP_MDIL-SS_5.png | Bin 0 -> 12126 bytes .../images/OSPP_MDIL-SS_7.png | Bin 0 -> 74742 bytes .../images/OSPP_MDIL-SS_8.png | Bin 0 -> 128444 bytes .../images/OSPP_MDIL-SS_9.png | Bin 0 -> 209266 bytes .../lifelong_learning_bench/README.md | 112 ++++++ .../benchmarkingjob.yaml | 72 ++++ .../testalgorithms/erfnet/ERFNet/accuracy.py | 38 ++ 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examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/task_allocation_by_domain.py create mode 100644 examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/task_definition_by_domain.py create mode 100644 examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/test_algorithm.yaml create mode 100644 examples/class_increment_semantic_segmentation/lifelong_learning_bench/testenv/accuracy.py create mode 100644 examples/class_increment_semantic_segmentation/lifelong_learning_bench/testenv/testenv.yaml diff --git a/core/testcasecontroller/metrics/metrics.py b/core/testcasecontroller/metrics/metrics.py index 112dc691..b03ce7ce 100644 --- a/core/testcasecontroller/metrics/metrics.py +++ b/core/testcasecontroller/metrics/metrics.py @@ -49,41 +49,65 @@ def samples_transfer_ratio_func(system_metric_info: dict): def compute(key, matrix): """ - compute BWT and FWT + Compute BWT and FWT scores for a given matrix. """ # pylint: disable=C0103 + print(f"compute function: key={key}, matrix={matrix}, type(matrix)={type(matrix)}") + length = len(matrix) accuracy = 0.0 BWT_score = 0.0 FWT_score = 0.0 flag = True - for i in range(length): - if len(matrix[i]) != length-1: + + if key == 'all': + for i in range(length-1, 0, -1): + sum_before_i = sum(item['accuracy'] for item in matrix[i][:i]) + sum_after_i = sum(item['accuracy'] for item in matrix[i][-(length - i - 1):]) + if i == 0: + seen_class_accuracy = 0.0 + else: + seen_class_accuracy = sum_before_i / i + if length - 1 - i == 0: + unseen_class_accuracy = 0.0 + else: + unseen_class_accuracy = sum_after_i / (length - 1 - i) + print(f"round {i} : unseen class accuracy is {unseen_class_accuracy}, seen class accuracy is {seen_class_accuracy}") + + for row in matrix: + if not isinstance(row, list) or len(row) != length-1: flag = False break - if flag is False: + + if not flag: BWT_score = np.nan FWT_score = np.nan return BWT_score, FWT_score for i in range(length-1): - accuracy += matrix[length-1][i]['accuracy'] - BWT_score += matrix[length-1][i]['accuracy'] - matrix[i+1][i]['accuracy'] - for i in range(0,length-1): - FWT_score += matrix[i][i]['accuracy'] - matrix[0][i]['accuracy'] - accuracy = accuracy/(length) - BWT_score = BWT_score/(length-1) - FWT_score = FWT_score/(length-1) - #print(f"{key} accuracy: ", accuracy) - print(f"{key} BWT_score: ", BWT_score) - print(f"{key} FWT_score: ", FWT_score) + for j in range(length-1): + if 'accuracy' in matrix[i+1][j] and 'accuracy' in matrix[i][j]: + accuracy += matrix[i+1][j]['accuracy'] + BWT_score += matrix[i+1][j]['accuracy'] - matrix[i][j]['accuracy'] + + for i in range(0, length-1): + if 'accuracy' in matrix[i][i] and 'accuracy' in matrix[0][i]: + FWT_score += matrix[i][i]['accuracy'] - matrix[0][i]['accuracy'] + + accuracy = accuracy / ((length-1) * (length-1)) + BWT_score = BWT_score / ((length-1) * (length-1)) + FWT_score = FWT_score / (length-1) + + print(f"{key} BWT_score: {BWT_score}") + print(f"{key} FWT_score: {FWT_score}") + my_matrix = [] for i in range(length-1): my_matrix.append([]) - for i in range(length-1): for j in range(length-1): - my_matrix[i].append(matrix[i+1][j]['accuracy']) - #self.draw_picture(key,my_matrix) + if 'accuracy' in matrix[i+1][j]: + my_matrix[i].append(matrix[i+1][j]['accuracy']) + return my_matrix, BWT_score, FWT_score def bwt_func(system_metric_info: dict): diff --git a/docs/proposals/algorithms/lifelong-learning/Implementation of a Class Incremental Learning Algorithm Evaluation System based on Ianvs.md b/docs/proposals/algorithms/lifelong-learning/Implementation of a Class Incremental Learning Algorithm Evaluation System based on Ianvs.md new file mode 100644 index 00000000..b516515a --- /dev/null +++ b/docs/proposals/algorithms/lifelong-learning/Implementation of a Class Incremental Learning Algorithm Evaluation System based on Ianvs.md @@ -0,0 +1,270 @@ + + +- [Implementation of a Class Incremental Learning Algorithm Evaluation System based on Ianvs](#implementation-of-a-class-incremental-learning-algorithm-evaluation-system-based-on-ianvs) + - [Motivation](#motivation) + - [Background](#background) + - [Goals](#goals) + - [Proposal](#proposal) + - [Design Details](#design-details) + - [Overall Design](#overall-design) + - [Datasets](#datasets) + - [File-level Design](#file-level-design) + - [Test Environment](#test-environment) + - [Test Algorithm](#test-algorithm) + - [Test Report](#test-report) + - [Roadmap](#roadmap) + - [Phase 1 July 1st - August 15th](#phase-1-july-1st---august-15th) + - [Phase 2 August 16th - September 30th](#phase-2-august-16th---september-30th) + + + +# Implementation of a Class Incremental Learning Algorithm Evaluation System based on Ianvs + +## 1 Motivation + +### 1.1 Background +Currently, lifelong learning is facing a challenge: new classes may appear when models are trained on a new data domain ( for example, in the figure below, three classes in red are new classes in `Domain 2` ), which makes it difficult for models to maintain generalization ability and results in a severe performance drop. + +
+MDIL-SS +
+ +Many algorithms have been proposed to solve the class increment problem in domain shift scenario. However, such algorithms lack a unified testing environment, which is not conducive to comparing algorithms. In some cases, new algorithms are only tested on certain datasets, which is not rigorous. + +In this context, it is necessary to develop an algorithm evaluation system that provides standardized testing for class-incremental learning algorithms, which is increasingly widely used in the industry, and evaluates the effectiveness of these algorithms. + +[KubeEdge-Ianvs](https://github.com/kubeedge/ianvs) is a distributed collaborative AI benchmarking project which can perform benchmarks with respect to several types of paradigms (e.g. single-task learning, incremental learning, etc.). This project aims to leverage the benchmarking capabilities of Ianvs to develop an evaluation system for class-incremental learning algorithms, in order to fulfill the benchmarking requirements specific to this type of algorithm. + +### 1.2 Goals + +This project aims to build a benchmarking for class-incremental learning in domain shift scenario on KubeEdge-Ianvs, which includes: + - Reproduce the Multi-Domain Incremental Learning for Semantic Segmentation (MDIL-SS) algorithm proposed in the [WACV2022 paper](https://github.com/prachigarg23/MDIL-SS). + - Use three datasets (including Cityscapes, SYNTHIA, and the Cloud-Robotic dataset provided by KubeEdge SIG AI) to conduct benchmarking tests and generate a comprehensive test report (including rankings, time, algorithm name, dataset, and test metrics, among other details). + +## 2 Proposal + +`Implementation of a Class Incremental Learning Algorithm Evaluation System based on Ianvs` taking MDIL-SS algorithm as an example, aims to test the performance of class-incremental learning models following benchmarking standards, to make the development more efficient and productive. + +The scope of the system includes + +- A test case for class-incremental learning semantic segmentation algorithms, in which a test report can be successfully generated following instructions. +- Easy to expand, allowing users to seamlessly integrate existing algorithms into the system for testing. + +Targeting users include + +- Beginners: Familiarize with distributed synergy AI and lifelong learning, among other concepts. +- Developers: Quickly integrate class-increment algorithms into Ianvs and test the performance for further optimization. + + +## 3 Design Details + +### 3.1 Overall Design + + +First, let's introduce the training process for lifelong learning: +- When the model enters a data domain (e.g., the Cloud-Robotic dataset) for training, the first step is `Unseen Task Detection`. This is done to assess the value of the domain data for updating the model. We consider that samples of unknown tasks have learning value, while samples from known tasks are not worth annotating and using for model training, meaning they provide no value for model updating. +- Once samples from unknown tasks are detected, in order to utilize these samples for model updating, we need to label them (through manual labelling or assisted labelling algorithms). +- Finally, we utilize samples with `Unseen Task Processing`, which means updating the model using labelled samples from unseen tasks. +- In summary, the workflow is `Unseen Task Detection -> Labeling -> Unseen Task Processing`. + +Next, let's outline the class-incremental learning algorithm evaluating process for this project, as shown in the figure below: + +![MDIL-SS](images/OSPP_MDIL-SS_11.png) +- In the first round, an ERFNet model initially enters the Synthia data domain for learning. As this is the model's first encounter with this data domain, all samples are considered as unseen task samples. We manually label all of these samples and utilize them for the model's first update, meaning that the model is trained using the Synthia dataset. +- During the testing phase of the first round, some of the testing samples are seen by the model, which is Synthia, while the other samples are unseen. +- Before the start of the second round of training, we manually detect and label all Cityscapes samples. Since unseen task detection and labeling are crucial for training effectiveness, we employ the most reliable method, which is manual way. +- In the second round, the model is updated using the already labeled Cityscapes samples, meaning it is trained with the Cityscapes dataset. +- Similarly, before commencing the final round of training, we manually detect and label all Cloud-Robotic samples. These samples are then used to update the model in the third round, where the model is trained on the Cloud-Robotic dataset. +- In each round, we conduct testing using the same three datasets for lifelong learning metric, and the final test report will demonstrate the model's lifelong learning capabilities. +- Please note that the lifelong learning model will undergo training and testing across three successive data domains, namely Cityscapes, SYNTHIA, and Cloud-Robotics, comprising a total of three rounds of training and testing. As the model shifts among data domains, class also changes, signifying class increments. The following diagram illustrates the differences of classes among these three data domains. + +
+MDIL-SS +
+ + +In this project, we have maintained a relatively default setup for Unseen Task Detection and Labelling. Our primary focus lies on the `Unseen Task Processing`, which corresponds to the red-boxed section in the ianvs lifelong learning architecture: + +![MDIL-SS](images/OSPP_MDIL-SS_9.png ) + +The architecture diagram for the project is as follows: + +![MDIL-SS](images/OSPP_MDIL-SS_8.png) + +All in all, we use the three class-different datasets to conduct training (i.e., Unseen Task Processing) and testing, and the core concern is to test the ability of the algorithm to update the model using labelled unseen samples (i.e., evaluating `Unseen Task Processing` ability). + +### 3.2 Datasets + +This project will use three datasets, namely **Cityscapes**, **SYNTHIA**, and KubeEdge SIG AI's **Cloud-Robotics** dataset (**CS**, **SYN**, **CR**). + +Ianvs has already provides [Cityscapes and SYNTHIA datasets](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/algorithms/lifelong-learning/Additional-documentation/curb_detetion_datasets.md). The following two images are examples from them respectively. + +| CS Example | SYN Example | +| :----------------------------------------------------------: | :----------------------------------------------------------: | +| ![MDIL-SS](images/OSPP_MDIL-SS_1.png) |![MDIL-SS](images/OSPP_MDIL-SS_2.png) | + +In addition, this project utilizes the CR dataset from KubeEdge. + +| CR Example | +| :----------------------------------------------------------: | +| ![MDIL-SS](images/OSPP_MDIL-SS_3.png) | + +The following code is an excerpt from the `train-index-mix.txt` file. The first column represents the path to the original image, and the second column represents the corresponding label image path. + +```txt +rgb/train/20220420_garden/00480.png gtFine/train/20220420_garden/00480_TrainIds.png +rgb/train/20220420_garden/00481.png gtFine/train/20220420_garden/00481_TrainIds.png +rgb/train/20220420_garden/00483.png gtFine/train/20220420_garden/00483_TrainIds.png +``` + +The following code snippet is an excerpt from the `test-index.txt` file, which follows a similar format to the training set. + +```txt +rgb/test/20220420_garden/01357.png gtFine/test/20220420_garden/01357_TrainIds.png +rgb/test/20220420_garden/01362.png gtFine/test/20220420_garden/01362_TrainIds.png +rgb/test/20220420_garden/01386.png gtFine/test/20220420_garden/01386_TrainIds.png +rgb/test/20220420_garden/01387.png gtFine/test/20220420_garden/01387_TrainIds.png +``` + +As shown in the table below, this dataset contains 7 groups and 30 classes. + +| Group | Classes | +| :----------: | :----------------------------------------------------------: | +| flat | road · sidewalk · ramp · runway | +| human | person · rider | +| vehicle | car · truck · bus · train · motorcycle · bicycle | +| construction | building · wall · fence · stair · curb · flowerbed · door | +| object | pole · traffic sign · traffic light · CCTV camera · Manhole · hydrant · belt · dustbin | +| nature | vegetation · terrain | +| sky | sky | + +More detail about CR dataset please refer to [this link](https://github.com/kubeedge/ianvs/blob/main/docs/proposals/scenarios/Cloud-Robotics/Cloud-Robotics_zh.md). + +### 3.3 File-level Design + +The development consists of two main parts, which are **test environment (test env)** and **test algorithms**. + +Test environment can be understood as an exam paper, which specifies the dataset, evaluation metrics, and the number of increments used for testing. It is used to evaluate the performance of the "students". And test algorithms can be seen as the students who will take the exam. + +
+MDIL-SS +
+ +In addition, `benchmarkingjob.yaml` is used for integrating the configuration of test env and test algorithms, and is a necessary ianvs configuration file. + +For test env, the development work mainly focuses on the implementation of `mIoU.py`. And for test algorithms, development is concentrated on `basemodel.py`, as shown in the picture below. + +![MDIL-SS](images/OSPP_MDIL-SS_5.png) + +#### 3.3.1 Test Environment + +The following code is the `testenv.yaml` file designed for this project. + +As a configuration file for test env, it contains the 3 aspects, which are the dataset and the number of increments, model validation logic, and model evaluation metrics. + +```yaml +# testenv.yaml + +testenv: + + # 1 + dataset: + train_url: "/home/QXY/ianvs/dataset/mdil-ss-dataset/train_data/index.txt" + test_url: "/home/QXY/ianvs/dataset/mdil-ss-dataset/test_data/index.txt" + using: "CS SYN CR" + incremental_rounds: 3 + + # 2 + model_eval: + model_metric: + name: "mIoU" + url: "/home/QXY/ianvs/examples/mdil-ss/testenv/mIoU.py" + threshold: 0 + operator: ">=" + + # 3 + metrics: + - name: "mIoU" + url: "/home/QXY/ianvs/examples/mdil-ss/testenv/mIoU.py" + - name: "BWT" + - name: "FWT" +``` + +After each round of lifelong learning, the model will be evaluated on the validation set. In this project, **mIoU** (mean Intersection over Union) is used as the evaluation metric. If the model achieves an mIoU greater than the specified threshold on the validation set, the model will be updated. + +**BWT** (Backward Transfer) and **FWT** (Forward Transfer) are two important concepts in the field of lifelong learning. BWT refers to the impact of previously learned knowledge on the learning of the current task, while FWT refers to the impact of the current task on the learning of future tasks. Along with mIoU, they serve as testing metrics to assess the lifelong learning capability of the model in semantic segmentation. Functions related to BWT and FWT have already been implemented in [Ianvs repository](https://github.com/kubeedge/ianvs/blob/main/core/testcasecontroller/metrics/metrics.py). + +#### 3.3.2 Test Algorithm + +The following code is the `mdil-ss_algorithm.yaml` file designed for this project. + +```yaml +# mdil-ss_algorithm.yaml + +algorithm: + paradigm_type: "incrementallearning" + + incremental_learning_data_setting: + train_ratio: 0.8 + splitting_method: "default" + + modules: + - type: "basemodel" + + # 1 + name: "ERFNet" + url: "/home/QXY/ianvs/examples/mdil-ss/testalgorithms/mdil-ss/basemodel.py" + + # 2 + hyperparameters: + - learning_rate: + values: + - 0.01 + - 0.0001 + - epochs: + values: + - 5 + - 10 + - batch_size: + values: + - 10 + - 20 +``` + +First, `basemodel.py`, which involves encapsulating various functional components of the model, including its architecture, layers, and operations, which is the focus of development. + +Second, **hyperparameters** setting for the model is also defined in this yaml file. In addition, the evaluation system can perform tests with multiple combinations of hyperparameters at once by configuring multiple hyperparameters in `mdil-ss_algorithm.yaml`. + +#### 3.3.3 Test Report + +The test report is designed as follows, which contains the ranking, algorithm name, three metrics, dataset name, base model, three hyperparameters, and time. + +| Rank | Algorithm | mIoU_Overall | BWT | FWT | Paradigm | Round | Dataset | Basemodel | Learning_rate | Epoch | Batch_size | Time | +| :-------: | :-------: | :------: | :-----: | :-----: | :----------------: | :-----: | :--------------: | :---------: | :-------------: | :-----: | :----------: | :-------------------: | +| 1 | MDIL-SS | 0.8734 | 0.075 | 0.021 | Lifelonglearning | 3 | CS SYN CR | ERFNet | 0.0001 | 1 | 10 | 2023-05-28 17:05:15 | + +## 4 Roadmap + +### 4.1 Phase 1 (July 1st - August 15th) + +- Engage in discussions with the project mentor and the community to finalize the development details. + +- Further refine the workflow of the MDIL-SS testing task, including the relationships between different components and modules. + +- Develop the test environment, including datasets and model metrics. + +- Begin the development of the base model encapsulation for the test algorithms. + +### 4.2 Phase 2 (August 16th - September 30th) + +- Summarize the progress of Phase 1 and generate relevant documentation. + +- Complete the remaining development tasks, including models, test reports, etc. + +- Generate initial algorithm evaluation reports. + +- Engage in discussions with the project mentor and the community to further supplement and improve the project. + +- Organize the project code and related documentation, and merge them into the Ianvs repository. + +- Upon merging into the repository, explore new research areas and produce additional outcomes based on this project. diff --git a/docs/proposals/algorithms/lifelong-learning/images/OSPP_MDIL-SS_1.png 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