Tutorial for UWB Occupancy Detection

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 *

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 `

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’

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

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

Load the model

Model zoo: - LSTM - AutoEncoder

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

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)
)

Model train

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,
)

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

Model inference

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)

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])

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.

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)

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