.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "uwb/uwb_localization_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_localization_tutorial.py>`
to download the full example code.
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_uwb_uwb_localization_tutorial.py:
Tutorial for UWB Localization
=======
.. GENERATED FROM PYTHON SOURCE LINES 5-15
.. 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.localization import *
from pysensing.uwb.inference.predict import *
from pysensing.uwb.inference.embedding import *
.. GENERATED FROM PYTHON SOURCE LINES 16-36
Download Data from Cloud Storage
-----------------------------------
Open the following linke in your browser to download Localization datasets:
[Download Pedestrian_Tracking Dataset](https://pysensing.oss-ap-southeast-1.aliyuncs.com/data/uwb/Pedestrian_Tracking.zip) \
[...]()
Unzip the downloaded file and move to your data folder. For HAR, the data folder should look like this:
```
|---data
|------|---localization
|------|------|---Pedestrian_Tracking
|------|------|------|---processed_data
|------|------|------|------|---AnchorPos.mat
|------|------|------|------|---Bg_CIR_VAR.mat
|------|------|------|------|---Dyn_CIR_VAR.mat
|------|------|------|---raw_data
......
```
.. GENERATED FROM PYTHON SOURCE LINES 38-52
Load the data
-----------------------------------
Human action recognition dataset:
Human Tracking Dataset
- UWB size : n x 1 x 500 x 2
Dataset name choices are:
- 'pedestrian_tracking_mod1_CIR'
- 'pedestrian_tracking_mod2_CIR'
- 'pedestrian_tracking_mod3_CIR'
- 'pedestrian_tracking_mod1_Var'
- 'pedestrian_tracking_mod2_Var'
- 'pedestrian_tracking_mod3_Var'
.. GENERATED FROM PYTHON SOURCE LINES 52-62
.. code-block:: Python
root = './data'
train_loader, val_loader, test_loader = load_localization_dataset(root, 'pedestrian_tracking_mod1_CIR')
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 pedestrian tracking CIR dataset in mod 1...
torch.Size([64, 1, 500, 2])
torch.Size([64, 1])
.. GENERATED FROM PYTHON SOURCE LINES 63-67
Load the model
-----------------------------------
Model zoo:
ResNet
.. GENERATED FROM PYTHON SOURCE LINES 67-71
.. code-block:: Python
model = load_localization_model(dataset_name = 'human_tracking', model_name = 'resnet')
print(model)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
human_tracking_resnet(
(convblock1): Sequential(
(0): Conv2d(1, 8, kernel_size=(10, 1), stride=(1, 1), padding=(4, 0))
(1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(maxpool1): MaxPool2d(kernel_size=(10, 1), stride=(5, 1), padding=0, dilation=1, ceil_mode=False)
(convblock2): Sequential(
(0): Conv2d(8, 16, kernel_size=(4, 2), stride=(1, 1), padding=(1, 0))
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(maxpool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
(block1): Block(
(conv1): Conv2d(16, 32, kernel_size=(4, 1), stride=(2, 1), padding=(1, 0), bias=False)
(batch_norm1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(32, 32, kernel_size=(4, 1), stride=(1, 1), padding=(1, 0), bias=False)
(batch_norm2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU()
(res): Sequential(
(0): Conv2d(16, 32, kernel_size=(1, 1), stride=(2, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block2): Block(
(conv1): Conv2d(32, 64, kernel_size=(4, 1), stride=(2, 1), padding=(1, 0), bias=False)
(batch_norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(4, 1), stride=(1, 1), padding=(1, 0), bias=False)
(batch_norm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU()
(res): Sequential(
(0): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block3): Block(
(conv1): Conv2d(64, 128, kernel_size=(4, 1), stride=(2, 1), padding=(1, 0), bias=False)
(batch_norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(4, 1), stride=(1, 1), padding=(1, 0), bias=False)
(batch_norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU()
(res): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(maxpool3): MaxPool2d(kernel_size=(7, 1), stride=(7, 1), padding=0, dilation=1, ceil_mode=False)
(fc): Sequential(
(0): Linear(in_features=128, out_features=10, bias=True)
(1): Linear(in_features=10, out_features=1, bias=True)
)
)
.. GENERATED FROM PYTHON SOURCE LINES 72-74
Model train
-----------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 74-87
.. code-block:: Python
criterion = nn.CrossEntropyLoss()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
localization_training(
root = root,
dataset_name='pedestrian_tracking_mod1_CIR',
model_name='resnet',
num_epochs=1,
learning_rate=0.001,
save_weights=True,
)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Loading pedestrian tracking CIR dataset in mod 1...
Epoch:1,MAE Loss:26.150909321
.. GENERATED FROM PYTHON SOURCE LINES 88-90
Model inference
-----------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 90-103
.. code-block:: Python
localization_predictor = predictor(
task='localization',
dataset_name='human_tracking',
model_name='resnet',
pt_weights = './human_tracking_weights.pth'
)
for data in test_loader:
x, y = data
break
outputs = localization_predictor.predict(x)
print("output shape:", outputs.shape)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Pretrained weights loaded.
output shape: torch.Size([256, 1])
.. GENERATED FROM PYTHON SOURCE LINES 104-107
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 107-114
.. code-block:: Python
model = load_localization_model(dataset_name = 'human_tracking', model_name = 'resnet')
model = load_pretrain_weights(model, dataset_name = 'human_tracking', model_name = 'CIR_model', device=device)
uwb_embedding = localization_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, 128])
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 28.170 seconds)
.. _sphx_glr_download_uwb_uwb_localization_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_localization_tutorial.ipynb <uwb_localization_tutorial.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: uwb_localization_tutorial.py <uwb_localization_tutorial.py>`
.. only:: html
.. rst-class:: sphx-glr-signature
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