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Use PyTorch instead of NumPy #10

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cfalas merged 5 commits from torch into main 2025-01-30 13:58:26 +02:00
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pyproject.toml
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@ -15,6 +15,7 @@ dependencies = [
"h5py>=3.12.1",
"pyyaml>=6.0.2",
"pydantic>=2.10.6",
"torch"
]
[project.scripts]

223
uv.lock
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{ name = "scipy" },
{ name = "scipy-stubs" },
{ name = "torch" },
{ name = "typer" },
]
@ -842,6 +1064,7 @@ requires-dist = [
{ name = "pyyaml", specifier = ">=6.0.2" },
{ name = "scipy" },
{ name = "scipy-stubs" },
{ name = "torch" },
{ name = "typer" },
]

7
where_fi/cli/__init__.py
View File

@ -3,6 +3,7 @@ import multiprocessing as mp
import numpy as np
import numpy.typing as npt
import torch
import typer
from .. import visualise
@ -13,6 +14,7 @@ from . import file, globals
app = typer.Typer(callback=globals.main)
logger = logging.getLogger(__name__)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@app.command()
@ -42,11 +44,12 @@ def heatmap() -> None:
webapp = mp.Process(target=visualise.start, args=(webapp_queue,))
webapp.start()
def callback(antenna_data: npt.NDArray[np.complex128]) -> None:
def callback(antenna_data: npt.NDArray[np.complex64]) -> None:
logger.info(f"Got final CSI data with shape {antenna_data.shape}")
processed = preprocessor.preprocess(antenna_data)
processed_tensor = torch.tensor(processed, device=device)
# visualise.add_data(all_data, processed)
aoa.update(processed)
aoa.update(processed_tensor)
if not webapp_queue.full():
webapp_queue.put(aoa)

6
where_fi/collection/csi_frame.py
View File

@ -93,14 +93,14 @@ class CSIHeader:
class CSI:
@staticmethod
def parseCsiData(data: bytes, header: CSIHeader) -> npt.NDArray[np.complex128]:
csi_matrix: npt.NDArray[np.complex128] = np.zeros(
def parseCsiData(data: bytes, header: CSIHeader) -> npt.NDArray[np.complex64]:
csi_matrix: npt.NDArray[np.complex64] = np.zeros(
(
header.num_subcarriers,
header.num_rx,
header.num_tx,
),
dtype=np.complex128,
dtype=np.complex64,
)
pos = 0
for j in range(header.num_rx):

2
where_fi/collection/protocols.py
View File

@ -3,7 +3,7 @@ from typing import Callable, Protocol
import numpy as np
import numpy.typing as npt
CSICallback = Callable[[npt.NDArray[np.complex128]], None]
CSICallback = Callable[[npt.NDArray[np.complex64]], None]
class CSIProducer(Protocol):

64
where_fi/processing/aoa.py
View File

@ -3,21 +3,25 @@ from datetime import datetime
import numpy as np
import numpy.typing as npt
import torch
from ..config import config
logger = logging.getLogger(__name__)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_default_device(device)
class AoA:
def __init__(self) -> None:
self.historical_autocorr = np.array([])
self.historical_autocorr = torch.tensor([], dtype=torch.complex64)
self.N_subcarriers = -1
self.N_rx = -1
self.timestamp = datetime.now()
pass
def smooth(self, data: npt.NDArray[np.complex128]) -> npt.NDArray[np.complex128]:
def smooth(self, data: torch.Tensor) -> torch.Tensor:
assert len(data.shape) == 3
M = data.shape[0] # Number of subcarriers
@ -32,32 +36,32 @@ class AoA:
# This only works with 1 TX antenna (i.e. no MIMO) - see #4 for more details
assert T == 1, "The current implementation only supports 1 TX antenna"
H_n = np.zeros((N, M // 2, M // 2 + 1), dtype=np.complex128)
H_n = torch.zeros((N, M // 2, M // 2 + 1), dtype=torch.complex64)
for i in range(N):
for j in range(M // 2):
H_n[i, j] = data[j : j + M // 2 + 1, i, 0]
H_sm_rows = [np.hstack(H_n[i : i + N // 2 + 1]) for i in range(N // 2)]
H_sm = np.vstack(H_sm_rows)
H_sm_rows = [torch.hstack(list(H_n[i : i + N // 2 + 1])) for i in range(N // 2)]
H_sm = torch.vstack(H_sm_rows)
logger.debug(f"Smoothed: {H_sm.shape}")
return H_sm
def update(self, data: npt.NDArray[np.complex128]) -> None:
def update(self, data: torch.Tensor) -> None:
self.timestamp = datetime.now()
H_sm = self.smooth(data)
auto_corr = np.matmul(H_sm, np.conj(H_sm).T)
auto_corr = H_sm @ torch.conj(H_sm).T
# This matrix is by definition Hermitian.
# Therefore, all of its eigenvectors are orthogonal.
if self.historical_autocorr.size == 0:
self.historical_autocorr = np.expand_dims(auto_corr, 0)
if len(self.historical_autocorr.shape) <= 1:
self.historical_autocorr = torch.unsqueeze(auto_corr, 0)
else:
self.historical_autocorr = np.append(
self.historical_autocorr, np.expand_dims(auto_corr, 0), axis=0
self.historical_autocorr = torch.cat(
(self.historical_autocorr, torch.unsqueeze(auto_corr, 0))
)
WINDOW_SIZE = config.music.window_size
@ -65,18 +69,20 @@ class AoA:
self.historical_autocorr = self.historical_autocorr[-WINDOW_SIZE:]
# Is the moving average also Hermitian?
R = np.mean(self.historical_autocorr, axis=0)
R = torch.mean(self.historical_autocorr, dim=0)
# The smallest eigenvectors span the noise subspace,
# and the largest span the signal subspace.
eigvals, eigvecs = np.linalg.eigh(R)
self.E_n = eigvecs[:, np.abs(eigvals) < config.music.eigval_threshold]
eigvals, eigvecs = torch.linalg.eigh(R)
self.E_n = (
eigvecs[:, torch.abs(eigvals) < config.music.eigval_threshold].cpu().numpy()
)
def steering_vector(
self, theta: float, tof: float
) -> npt.NDArray[np.complexfloating]:
omega_t: npt.NDArray[np.complex128] = np.exp(-2j * np.pi * config.delta_f * tof)
phi_theta: npt.NDArray[np.complex128] = np.exp(
omega_t: npt.NDArray[np.complex64] = np.exp(-2j * np.pi * config.delta_f * tof)
phi_theta: npt.NDArray[np.complex64] = np.exp(
2j
* np.pi
* config.central_freq_hz
@ -88,10 +94,12 @@ class AoA:
omega_t = np.expand_dims(omega_t, axis=-1)
phi_theta = np.expand_dims(phi_theta, axis=-1)
antenna_v = omega_t ** np.arange(self.N_subcarriers // 2)
phis = phi_theta ** np.arange(self.N_rx // 2)
antenna_v = omega_t ** torch.arange(self.N_subcarriers // 2)
phis = phi_theta ** torch.arange(self.N_rx // 2)
antenna_v = np.expand_dims(antenna_v, axis=-1)
steering = antenna_v * phis
print(antenna_v.shape, phis.shape)
steering = antenna_v[0] * phis
print(steering.shape)
return steering.T.reshape(-1)
def evaluate(self, theta: float, tof: float) -> float:
@ -101,23 +109,25 @@ class AoA:
except Exception as e:
logger.exception(e)
return 0
assert isinstance(self.E_n, torch.Tensor)
E_n = self.E_n
E_n_H = np.conj(E_n).T
c = 1 / (0.001 + (steering_h @ E_n @ E_n_H @ steering))
return np.abs(c.real)
return np.abs(c.real).item()
def test_smoothing() -> None:
row, col = np.indices((4, 2))
row, col = np.indices((6, 4))
data = row + 1j * col
data = np.expand_dims(data, axis=2)
np.set_printoptions(linewidth=200)
print(data.shape)
aoa = AoA()
aoa.N_subcarriers = 6
aoa.N_rx = 4
smoothed = aoa.smooth(data)
H_0 = np.array([[0 + 0j, 0 + 1j, 0 + 2j], [0 + 1j, 0 + 2j, 0 + 3j]])
H_01 = np.vstack([H_0, H_0 + 1])
H_12 = np.vstack([H_0 + 1, H_0 + 2])
expected = np.hstack([H_01, H_12])
print(expected)
assert np.allclose(smoothed, expected)
print(smoothed)
def test_steering_vector() -> None:

10
where_fi/processing/preprocess.py
View File

@ -15,9 +15,9 @@ np.seterr(invalid="ignore")
class Preprocessor:
def __init__(self) -> None:
self.prev_entries: Queue[npt.NDArray[np.complex128]] = Queue(maxsize=100)
self.short_term_avg = np.zeros((1,), dtype=np.complex128)
self.long_term_avg = np.zeros((1,), dtype=np.complex128)
self.prev_entries: Queue[npt.NDArray[np.complex64]] = Queue(maxsize=100)
self.short_term_avg = np.zeros((1,), dtype=np.complex64)
self.long_term_avg = np.zeros((1,), dtype=np.complex64)
self.filter = butter(
5,
config.preprocessing.bandpass.bounds,
@ -26,7 +26,7 @@ class Preprocessor:
output="sos",
)
def preprocess(self, h: npt.NDArray[np.complex128]) -> npt.NDArray[np.complex128]:
def preprocess(self, h: npt.NDArray[np.complex64]) -> npt.NDArray[np.complex64]:
# CSI data is not available for pilot subcarriers.
h_hat = np.where(
np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)),
@ -46,7 +46,7 @@ class Preprocessor:
# Assume that all csi matrices will have the same shape
if self.long_term_avg.shape != h_hat.shape:
self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex128)
self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex64)
self.long_term_avg = (
self.long_term_avg * (1 - config.preprocessing.moving_average_alpha)