upload an example about the scence based unknown task regconition alg…

…orithm

update algorithms
delete annotated codes
refer the origin reproduced p
add class documentation
upload Readme
Modify folder name
add an example
modify readme

Signed-off-by: Frank-lilinjie <lilinjie@bupt.edu.cn>

examples/scene-based-unknown-task-recognition/lifelong_learning_bench/Readme.md

@@ -0,0 +1,100 @@
+# Quick Start about Unknown task recognition
+
+Welcome to Ianvs! Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, in order to facilitate more efficient and effective development. Quick start helps you to test your algorithm on Ianvs with a simple example of industrial defect detection. You can reduce manual procedures to just a few steps so that you can build and start your distributed synergy AI solution development within minutes.
+
+Before using Ianvs, you might want to have the device ready:
+
+- One machine is all you need, i.e., a laptop or a virtual machine is sufficient and a cluster is not necessary
+- 2 CPUs or more
+- 4GB+ free memory depends on the algorithm and simulation setting
+- 10GB+ free disk space
+- Internet connection for GitHub and pip, etc
+- Python 3.6+ installed
+
+In this example, we are using the Linux platform with Python 3.7.1. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different.
+
+## Step 1. Ianvs Preparation
+
+First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git` as:
+
+```shell
+mkdir /ianvs
+cd /ianvs #One might use another path preferred
+
+mkdir project
+cd project
+git clone https://github.com/kubeedge/ianvs.git   
+```
+
+Then, we install third-party dependencies for ianvs.
+
+```shell
+sudo apt-get update
+sudo apt-get install libgl1-mesa-glx -y
+python -m pip install --upgrade pip
+
+cd ianvs 
+python -m pip install ./examples/resources/third_party/*
+python -m pip install -r requirements.txt
+```
+
+We are now ready to install Ianvs.
+
+```shell
+python setup.py install 
+```
+
+## Step 2. Dataset and Model Preparation
+
+Datasets and models can be large. To avoid over-size projects in the GitHub repository of Ianvs, the Ianvs code base does not include origin datasets. Then developers do not need to download non-necessary datasets for a quick start.
+
+```shell
+cd /ianvs #One might use another path preferred
+mkdir dataset   
+cd dataset
+wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/ianvs/curb-detection/curb-detection.zip
+unzip dataset.zip
+```
+
+The URL address of this dataset then should be filled in the configuration file `testenv.yaml`. In this quick start, we have done that for you and interested readers can refer to [testenv.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details.
+
+```shell
+cd /ianvs/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet
+mkdir results
+```
+
+Put the model to results.Download [model](https://pan.baidu.com/s/18MA8Gaw7ptpipfLD6Hz6SA) *access code*: 37ff.
+
+The related algorithm is also ready for this quick start.
+
+```shell
+export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/RFNet
+export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/
+```
+
+The URL address of this algorithm then should be filled in the configuration file `algorithm.yaml`. In this quick start, we have done that for you and interested readers can refer to [algorithm.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details.
+
+## Step 3. Ianvs Execution and Presentation
+
+We are now ready to run the ianvs for benchmarking.
+
+```shell
+cd /ianvs/project
+ianvs -f examples/scene-based-unknown-task-recognition/lifelong_learning_bench/benchmarkingjob.yaml
+```
+
+Finally, the user can check the result of benchmarking on the console and also in the output path( e.g. `/ianvs/lifelong_learning_bench/workspace`) defined in the benchmarking config file ( e.g. `benchmarkingjob.yaml`). In this quick start, we have done all configurations for you and the interested readers can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details.
+
+The final output might look like this:
+
+| rank | algorithm               | accuracy            | samples_transfer_ratio | paradigm         | basemodel | task_definition        | task_allocation        | unseen_sample_recognition      | basemodel-learning_rate | task_definition-origins | task_allocation-origins | unseen_sample_recognition-model_path                         | time                | url                                                          |
+| ---- | ----------------------- | ------------------- | ---------------------- | ---------------- | --------- | ---------------------- | ---------------------- | ------------------------------ | ----------------------- | ----------------------- | ----------------------- | :----------------------------------------------------------- | ------------------- | ------------------------------------------------------------ |
+| 1    | rfnet_lifelong_learning | 0.30090234155994056 | 0.4535                 | lifelonglearning | BaseModel | TaskDefinitionByOrigin | TaskAllocationByOrigin | UnseenSampleRecognitionByScene | 0.0001                  | ['real', 'sim']         | ['real', 'sim']         | /examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/results/Epochofprose17.pth | 2022-10-25 14:50:01 | /ianvs/lifelong_learning_bench/workspace/benchmarkingjob/rfnet_lifelong_learning/1dfff552-542f-11ed-b875-b07b25dd6922 |
+
+This ends the quick start experiment.
+
+# What is next
+
+If any problems happen, the user can refer to [the issue page on Github](https://github.com/kubeedge/ianvs/issues) for help and are also welcome to raise any new issue.
+
+Enjoy your journey on Ianvs!

examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml → examples/scene-based-unknown-task-recognition/lifelong_learning_bench/benchmarkingjob.yaml

@@ -6,7 +6,7 @@ benchmarkingjob:
 
   # the url address of test environment configuration file; string type;
   # the file format supports yaml/yml;
-  testenv: "./examples/curb-detection/lifelong_learning_bench/testenv/testenv.yaml"
+  testenv: "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testenv/testenv.yaml"
 
   # the configuration of test object
   test_object:
@@ -19,7 +19,7 @@ benchmarkingjob:
       - name: "rfnet_lifelong_learning"
         # the url address of test algorithm configuration file; string type;
         # the file format supports yaml/yml
-        url: "./examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml"
+        url: "./examples/scene-based-unknown-task-recognition//lifelong_learning_bench/testalgorithms/rfnet/rfnet_algorithm.yaml"
 
   # the configuration of ranking leaderboard
   rank:

examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/_init_.py

@@ -0,0 +1 @@
+from models.wide_resnet_embedding import *

examples/scene-based-unknown-task-recognition/lifelong_learning_bench/testalgorithms/rfnet/models/utils.py

@@ -0,0 +1,149 @@
+import os
+import shutil
+import time
+import pprint
+import torch
+import numpy as np
+import torch.nn as nn
+#  --- functional helper ---
+def category_mean(data, label, label_max):
+    '''compute mean for each category'''
+    one_hot_label = one_hot(label, label_max)
+    class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15
+    one_hot_label = one_hot_label / class_num
+    return torch.mm(data.view(1, -1), one_hot_label).squeeze(0)
+
+def category_mean1(data, label, label_max):
+    '''compute mean for each category
+       only return centers for given categories'''
+    labelset = torch.unique(label, sorted=True)
+    one_hot_label = one_hot(label, label_max)
+    class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15
+    one_hot_label = one_hot_label / class_num
+    output = torch.mm(data.view(1, -1), one_hot_label).squeeze(0)
+    return output[labelset]
+
+def category_mean2(data, label, label_max):
+    '''compute mean for each category, based on a matrix'''
+    one_hot_label = one_hot(label, label_max)
+    data = torch.gather(data, 1, label.unsqueeze(1))
+    class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15
+    one_hot_label = one_hot_label / class_num
+    return torch.mm(data.view(1, -1), one_hot_label).squeeze(0)
+
+def category_mean3(data, label, label_max):
+    '''compute mean for each category, each row corresponds to an elements'''
+    one_hot_label = one_hot(label, label_max)
+    class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15
+    one_hot_label = one_hot_label / class_num
+    return torch.mm(one_hot_label.t(), data)
+
+def category_mean4(data, label, label_max):
+    '''compute mean for each category, each row corresponds to an elements
+       only return centers for given categories'''
+    labelset = torch.unique(label, sorted=True)
+    one_hot_label = one_hot(label, label_max)
+    class_num = torch.sum(one_hot_label, 0, keepdim=True) + 1e-15
+    one_hot_label = one_hot_label / class_num
+    pre_center = torch.mm(one_hot_label.t(), data)
+    return pre_center[labelset, :]
+
+def one_hot(indices, depth):
+    """
+    Returns a one-hot tensor.
+    This is a PyTorch equivalent of Tensorflow's tf.one_hot.
+
+    Parameters:
+      indices:  a (n_batch, m) Tensor or (m) Tensor.
+      depth: a scalar. Represents the depth of the one hot dimension.
+    Returns: a (n_batch, m, depth) Tensor or (m, depth) Tensor.
+    """
+
+    encoded_indicies = torch.zeros(indices.size() + torch.Size([depth]))
+    if indices.is_cuda:
+        encoded_indicies = encoded_indicies.cuda()    
+    index = indices.view(indices.size()+torch.Size([1]))
+    encoded_indicies = encoded_indicies.scatter_(1,index,1)
+
+    return encoded_indicies
+
+def set_gpu(x):
+    os.environ['CUDA_VISIBLE_DEVICES'] = x
+    print('using gpu:', x)
+
+
+def ensure_path(path, remove=True):
+    if os.path.exists(path):
+        if remove:
+            if input('{} exists, remove? ([y]/n)'.format(path)) != 'n':
+                shutil.rmtree(path)
+                os.mkdir(path)
+    else:
+        os.mkdir(path)
+
+class Averager():
+
+    def __init__(self):
+        self.n = 0
+        self.v = 0
+
+    def add(self, x):
+        self.v = (self.v * self.n + x) / (self.n + 1)
+        self.n += 1
+
+    def item(self):
+        return self.v
+
+
+def count_acc(logits, label):
+    pred = torch.argmax(logits, dim=1)
+    if torch.cuda.is_available():
+        return (pred == label).type(torch.cuda.FloatTensor).mean().item()
+    else:
+        return (pred == label).type(torch.FloatTensor).mean().item()
+
+def euclidean_metric(a, b):
+    n = a.shape[0]
+    m = b.shape[0]
+    a = a.unsqueeze(1).expand(n, m, -1)
+    b = b.unsqueeze(0).expand(n, m, -1)
+    logits = -((a - b)**2).sum(dim=2)
+    return logits
+
+class Timer():
+
+    def __init__(self):
+        self.o = time.time()
+
+    def measure(self, p=1):
+        x = (time.time() - self.o) / p
+        x = int(x)
+        if x >= 3600:
+            return '{:.1f}h'.format(x / 3600)
+        if x >= 60:
+            return '{}m'.format(round(x / 60))
+        return '{}s'.format(x)
+
+_utils_pp = pprint.PrettyPrinter()
+def pprint(x):
+    _utils_pp.pprint(x)
+
+def compute_confidence_interval(data):
+    """
+    Compute 95% confidence interval
+    :param data: An array of mean accuracy (or mAP) across a number of sampled episodes.
+    :return: the 95% confidence interval for this data.
+    """
+    a = 1.0 * np.array(data)
+    m = np.mean(a)
+    std = np.std(a)
+    pm = 1.96 * (std / np.sqrt(len(a)))
+    return m, pm
+
+
+class Identity(nn.Module):
+    def __init__(self):
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return x