.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "uwb/uwb_occupancy_detection_tutorial.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_uwb_uwb_occupancy_detection_tutorial.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_uwb_uwb_occupancy_detection_tutorial.py:


Tutorial for UWB Occupancy Detection
=======

.. GENERATED FROM PYTHON SOURCE LINES 5-16

.. code-block:: Python


    import torch
    import torch.nn as nn
    import os

    from pysensing.uwb.datasets.get_dataloader import *
    from pysensing.uwb.models.get_model import *
    from pysensing.uwb.training.occupancy_detection import *
    from pysensing.uwb.inference.predict import *
    from pysensing.uwb.inference.embedding import *








.. GENERATED FROM PYTHON SOURCE LINES 17-33

Download Data from Cloud Storage
-----------------------------------

Open the following link in your browser to download HAR datasets:

[Download nlos_human_detection Dataset](https://pysensing.oss-ap-southeast-1.aliyuncs.com/data/uwb/nlos_human_detection.zip) \
[...]()

Unzip the downloaded file and move to your data folder. For HAR, the data folder should look like this:
```
|---data 
|------|---occupancy_detection 
|------|------|---nlos_human_detection 
|------|------|------|---dynamic
|------|------|------|---static
```

.. GENERATED FROM PYTHON SOURCE LINES 35-45

Load the data
-----------------------------------
Occupancy Detection dataset: 

NLOS Huaman Detection Dataset
- UWB size : n x 256

Dataset name choices are:
- 'nlos_human_detection_raw_dynamic'
- 'nlos_human_detection_raw_static'

.. GENERATED FROM PYTHON SOURCE LINES 45-55

.. code-block:: Python


    root = './data' 
    train_loader, val_loader = load_occupancy_detection_dataset(root, datasetname='nlos_human_detection_raw_dynamic')
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    for data in train_loader:
        x, y = data
        print(x.size())
        print(y.size())
        break





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Loading nlos_human_detection time domain dynamic dataset...
    torch.Size([64, 256])
    torch.Size([64])




.. GENERATED FROM PYTHON SOURCE LINES 56-61

Load the model
-----------------------------------
Model zoo:
- LSTM
- AutoEncoder

.. GENERATED FROM PYTHON SOURCE LINES 61-65

.. code-block:: Python


    model = load_occupancy_detection_model(dataset_name = 'nlos_human_detection', model_name = 'autoencoder')
    print(model)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    occupancy_detection_autoencoder(
      (encoder): Sequential(
        (0): Linear(in_features=256, out_features=64, bias=True)
        (1): ReLU()
        (2): Linear(in_features=64, out_features=32, bias=True)
        (3): ReLU()
        (4): Linear(in_features=32, out_features=16, bias=True)
      )
      (decoder): Sequential(
        (0): Linear(in_features=16, out_features=32, bias=True)
        (1): ReLU()
        (2): Linear(in_features=32, out_features=64, bias=True)
      )
      (fc): Linear(in_features=64, out_features=2, bias=True)
    )




.. GENERATED FROM PYTHON SOURCE LINES 66-68

Model train
-----------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 68-81

.. code-block:: Python


    criterion = nn.CrossEntropyLoss()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    nlos_human_detection_training(
        root = root,
        dataset_name='nlos_human_detection_raw_dynamic',
        model_name='autoencoder',
        num_epochs=1,
        learning_rate=0.001,
        save_weights=True,
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Loading nlos_human_detection time domain dynamic dataset...
    Epoch:1, Accuracy:0.7926,Loss:0.394943418




.. GENERATED FROM PYTHON SOURCE LINES 82-84

Model inference
-----------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 84-97

.. code-block:: Python


    occupancy_detector = predictor(
        task='occupancy_detection',
        dataset_name='nlos_human_detection',
        model_name='autoencoder',
        pt_weights='./nlos_human_detection_weights.pth'
        )
    for data in val_loader:
        x, y = data
        break
    outputs = occupancy_detector.predict(x)
    print("output:", outputs)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Pretrained weights loaded.
    output: tensor([1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
            1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,
            1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,
            1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0,
            1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0,
            1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0,
            1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1,
            0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0,
            1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,
            0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1,
            0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1])




.. GENERATED FROM PYTHON SOURCE LINES 98-101

Generate embedding
-----------------------------------
- noted that the `model_name` variable defined in `load_model` function represents the model structure name, and in `load_pretrain_weights` function represents the model structure and pretrain dataset name.

.. GENERATED FROM PYTHON SOURCE LINES 101-108

.. code-block:: Python


    model = load_occupancy_detection_model(dataset_name = 'nlos_human_detection', model_name = 'autoencoder')
    model = load_pretrain_weights(model, dataset_name = 'nlos_human_detection_static', model_name = 'autoencoder',device=device)
    uwb_embedding = occupancy_detection_uwb_embedding(x, model, device)
    print('uwb_embedding shape: ', uwb_embedding.shape)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    uwb_embedding shape:  torch.Size([256, 64])





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.766 seconds)


.. _sphx_glr_download_uwb_uwb_occupancy_detection_tutorial.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: uwb_occupancy_detection_tutorial.ipynb <uwb_occupancy_detection_tutorial.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: uwb_occupancy_detection_tutorial.py <uwb_occupancy_detection_tutorial.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_