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"""
Data Augmentation Example
Demonstrates how to create a custom data augmentation function
following the architecture design pattern.
"""
import torch
from typing import Dict
from src.data_module.augmentation import register_augmentation
@register_augmentation("time_shift")
def time_shift(signal: torch.Tensor, max_shift: int = 10) -> torch.Tensor:
"""Randomly shift signal in time.
Args:
signal: Input signal tensor of shape (channels, time_steps)
max_shift: Maximum number of steps to shift
Returns:
Shifted signal tensor
"""
shift = torch.randint(-max_shift, max_shift + 1, (1,)).item()
return torch.roll(signal, shifts=shift, dims=-1)
@register_augmentation("amplitude_scale")
def amplitude_scale(
signal: torch.Tensor,
min_scale: float = 0.8,
max_scale: float = 1.2
) -> torch.Tensor:
"""Randomly scale signal amplitude.
Args:
signal: Input signal tensor of shape (channels, time_steps)
min_scale: Minimum scaling factor
max_scale: Maximum scaling factor
Returns:
Scaled signal tensor
"""
scale = torch.empty(1).uniform_(min_scale, max_scale).item()
return signal * scale
@register_augmentation("gaussian_noise")
def add_gaussian_noise(
signal: torch.Tensor,
mean: float = 0.0,
std: float = 0.1
) -> torch.Tensor:
"""Add Gaussian noise to signal.
Args:
signal: Input signal tensor of shape (channels, time_steps)
mean: Mean of Gaussian noise
std: Standard deviation of Gaussian noise
Returns:
Signal with added noise
"""
noise = torch.randn_like(signal) * std + mean
return signal + noise
# Example: Composed augmentation
@register_augmentation("composed")
def composed_augmentation(signal: torch.Tensor, cfg) -> torch.Tensor:
"""Apply multiple augmentations in sequence.
Args:
signal: Input signal tensor
cfg: Configuration object with augmentation parameters
Returns:
Augmented signal tensor
"""
# Apply each augmentation based on config
if cfg.augmentation.time_shift:
signal = time_shift(signal, cfg.augmentation.max_shift)
if cfg.augmentation.amplitude_scale:
signal = amplitude_scale(
signal,
cfg.augmentation.min_scale,
cfg.augmentation.max_scale
)
if cfg.augmentation.gaussian_noise:
signal = add_gaussian_noise(
signal,
cfg.augmentation.noise_mean,
cfg.augmentation.noise_std
)
return signal
# Usage in dataset class
class AugmentedDataset:
"""Example dataset with augmentation support."""
def __init__(self, cfg):
self.cfg = cfg
self.augmentation_fn = AugmentationFactory(cfg.augmentation.name)
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
# Load signal
signal = self.load_signal(idx)
# Apply augmentation (training mode only)
if self.training and self.augmentation_fn:
signal = self.augmentation_fn(signal, self.cfg)
return {"signal": signal, "label": self.labels[idx]}
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