Rebase dev on main

libs/architectures/architectures/__init__.py

@@ -6,4 +6,5 @@
     SupervisedSpectrogramDomainResNet,
     SupervisedTimeDomainResNet,
     SupervisedTimeSpectrogramResNet,
+    SupervisedHeterodyneTimeDomainResNet,
 )

libs/architectures/architectures/supervised.py

@@ -281,3 +281,43 @@ def forward(self, X, X_spec):
         time_domain_output = self.time_domain_resnet(X)
         spec_domain_output = self.spectrogram_resnet(X_spec)
         return time_domain_output, spec_domain_output
+
+
+class SupervisedHeterodyneTimeDomainResNet(SupervisedArchitecture):
+    """
+    Time Domain ResNet that processes a Heterodyned timeseries.
+
+    Args:
+        num_chirp_masses (int):
+            Number of chirp masses used to define the input channel
+            dimension (in_channels = num_ifos x num_chirp_masses).
+    """
+
+    def __init__(
+        self,
+        num_ifos: int,
+        num_chirp_masses: int,
+        layers: list[int],
+        kernel_size: int = 3,
+        zero_init_residual: bool = False,
+        groups: int = 1,
+        width_per_group: int = 64,
+        stride_type: Optional[list[Literal["stride", "dilation"]]] = None,
+        norm_layer: Optional[NormLayer] = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.time_domain_resnet = ResNet1D(
+            in_channels=num_ifos * num_chirp_masses,
+            layers=layers,
+            classes=1,
+            kernel_size=kernel_size,
+            zero_init_residual=zero_init_residual,
+            groups=groups,
+            width_per_group=width_per_group,
+            stride_type=stride_type,
+            norm_layer=norm_layer,
+        )
+
+    def forward(self, X):
+        return self.time_domain_resnet(X)

libs/utils/pyproject.toml

@@ -9,7 +9,7 @@ dependencies = [
     "h5py~=3.6",
     "numpy>=2",
     "s3fs>=2024,<2025",
-    "ml4gw>=0.7.10",
+    "ml4gw>=0.8.0",
     "astropy>=6.0.1",
 ]
 

libs/utils/tests/test_preprocessing.py

@@ -7,6 +7,10 @@
     PsdEstimator,
     TimeSpectrogramPreprocessor,
 )
+from utils.augmentation import HeterodyneAugmentor
+from ml4gw.transforms import Heterodyne
+import torch
+import pytest
 
 
 class TestBackgroundSnapshotter:
@@ -247,6 +251,59 @@ def simple_augmentor(x):
         kernels_aug = whitener_aug(x)
         assert torch.allclose(kernels_aug, kernels * 2)
 
+    def test_with_heterodyne_augmentor(self):
+        heterodyne_augmentor = HeterodyneAugmentor(
+            sample_rate=self.sample_rate,
+            kernel_length=self.kernel_length,
+            chirp_mass_low=1.0,
+            chirp_mass_high=2.5,
+            num_chirp_masses=10,
+            chirp_mass_spacing="log",
+            keep_last_n_seconds=None,
+        )
+
+        whitener = BatchWhitener(
+            kernel_length=self.kernel_length,
+            sample_rate=self.sample_rate,
+            inference_sampling_rate=self.inference_sampling_rate,
+            batch_size=self.batch_size,
+            fduration=self.fduration,
+            fftlength=self.fftlength,
+        )
+
+        whitener_aug = BatchWhitener(
+            kernel_length=self.kernel_length,
+            sample_rate=self.sample_rate,
+            inference_sampling_rate=self.inference_sampling_rate,
+            batch_size=self.batch_size,
+            fduration=self.fduration,
+            fftlength=self.fftlength,
+            augmentor=heterodyne_augmentor,
+        )
+
+        channels = 2
+        total_samples = int(
+            (
+                (self.batch_size - 1) * whitener.stride_size
+                + whitener.kernel_size
+            )
+            * 2
+        )
+        x = torch.randn(channels, total_samples)
+
+        heterodyne = Heterodyne(
+            sample_rate=self.sample_rate,
+            kernel_length=self.kernel_length,
+            chirp_mass=heterodyne_augmentor.chirp_mass_grid,
+            return_type="time",
+        )
+
+        kernels_heterodyned = heterodyne(whitener(x))
+        _B, _C, _M, _T = kernels_heterodyned.shape
+        kernels = kernels_heterodyned.reshape(_B, _C * _M, _T)
+        kernels_aug = whitener_aug(x)
+        assert torch.allclose(kernels_aug, kernels)
+
 
 class TestMultiModalPreprocessor:
     """Test suite for MultiModalPreprocessor module."""

libs/utils/utils/augmentation.py

@@ -0,0 +1,135 @@
+import math
+import torch
+from torch import Tensor
+from typing import Literal
+
+from ml4gw.transforms import Heterodyne
+
+
+class HeterodyneAugmentor(torch.nn.Module):
+    """
+    Apply a heterodyne transform over a grid of chirp masses to a batch
+    of time-series data.
+
+    Args:
+        sample_rate (float): Input sampling rate in Hz.
+        kernel_length (float): Length of output kernels in seconds.
+        chirp_mass_low (float):
+            Lower bound of chirp mass range (in solar masses).
+        chirp_mass_high (float):
+            Upper bound of chirp mass range (in solar masses).
+        num_chirp_masses (int):
+            Number of chirp mass samples to generate.
+        chirp_mass_spacing (Literal["linear", "log"]):
+            Spacing of chirp mass grid. Use "linear" for evenly spaced
+            values or "log" for logarithmic spacing.
+        keep_last_n_seconds (float):
+            If provided, only the last `n` seconds of the kernel_length are
+            returned. Otherwise, the full kernel_length is returned.
+    Shape:
+        Input: (batch_size, channels, time)
+        Output: (batch_size, channels * num_chirp_masses, time_out)
+
+        where `time_out = time` unless `keep_last_n_seconds` is set.
+
+    Note:
+        The output shape of the `BatchWhitener` is
+        (batch_size, channels, kernel_size).
+        The `HeterodyneAugmentor` changes the output shape to
+        (batch_size, channels * num_chirp_masses, time_out)
+        where time_out is determined by the `keep_last_n_seconds` parameter.
+
+    Example:
+        >>> augmentor = HeterodyneAugmentor(
+        ...     sample_rate=2048, kernel_length=8, chirp_mass_low=1.0,
+        ...     chirp_mass_high=2.5, num_chirp_masses=100,
+        ...     chirp_mass_spacing="log", keep_last_n_seconds=4.0,
+        ... )
+        >>> x = torch.randn(8, 2, 16384)  # (batch, channels, time)
+        >>> y = augmentor(x)
+        >>> # y: (8, 2 * 100, 8192) since we keep the last 4 seconds at 2048 Hz
+    """
+
+    def __init__(
+        self,
+        sample_rate: float,
+        kernel_length: float,
+        chirp_mass_low: float = 1.0,
+        chirp_mass_high: float = 2.5,
+        num_chirp_masses: int = 100,
+        chirp_mass_spacing: Literal["linear", "log"] = "log",
+        keep_last_n_seconds: float = None,
+    ):
+        super().__init__()
+        self.sample_rate = sample_rate
+        self.kernel_length = kernel_length
+        self.keep_last_n_seconds = keep_last_n_seconds
+        self.num_chirp_masses = num_chirp_masses
+        self.keep_last_n_seconds = keep_last_n_seconds
+
+        self.chirp_mass_grid = self._create_chirp_mass_grid(
+            chirp_mass_low,
+            chirp_mass_high,
+            num_chirp_masses,
+            chirp_mass_spacing,
+        )
+
+        if self.keep_last_n_seconds is not None:
+            self.keep_last_n_samples = int(
+                self.keep_last_n_seconds * sample_rate
+            )
+
+        self.heterodyne_transform = Heterodyne(
+            sample_rate=sample_rate,
+            kernel_length=kernel_length,
+            chirp_mass=self.chirp_mass_grid,
+            return_type="time",
+        )
+
+    def _create_chirp_mass_grid(
+        self,
+        chirp_mass_low: float,
+        chirp_mass_high: float,
+        num_chirp_masses: int,
+        chirp_mass_spacing: Literal["linear", "log"],
+    ) -> torch.Tensor:
+        if chirp_mass_spacing == "linear":
+            return torch.linspace(
+                chirp_mass_low, chirp_mass_high, num_chirp_masses
+            )
+        elif chirp_mass_spacing == "log":
+            return torch.logspace(
+                math.log10(chirp_mass_low),
+                math.log10(chirp_mass_high),
+                num_chirp_masses,
+            )
+        else:
+            raise ValueError(
+                f"Invalid chirp mass spacing: {chirp_mass_spacing}"
+            )
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Args:
+            x (Tensor): Input data of shape (batch, channels, time).
+
+        Returns:
+            Tensor: Output data of shape
+                (batch, channels * num_chirp_masses, time_out),
+                where `time_out` is either the same as input time
+                or determined by `keep_last_n_seconds`.
+        """
+        _B, _C, _T = x.shape
+        x_heterodyned = torch.empty((_B, _C * self.num_chirp_masses, _T))
+        # Heterodyne the whitened timeseries
+        x = self.heterodyne_transform(x)
+        # Reshaping x from (batch_size, channels, num_chirp_mass, kernel_size)
+        # to (batch_size, channels x num_chirp_mass, kernel_size)
+        x = x.reshape(_B, _C * self.num_chirp_masses, _T)
+        x_heterodyned[:, :, :] = x
+        # Returning the desired length of heterodyned strain in the
+        # time dimension
+        if self.keep_last_n_seconds is not None:
+            return x_heterodyned[..., -self.keep_last_n_samples :]
+        else:
+            return x_heterodyned

projects/export/export_heterodyne.yaml

@@ -0,0 +1,43 @@
+# commented args (i.e. comments with colon at end 
+# to illustrate a value needs to be filled out)
+# represent values filled out
+# by the export task at run time. To build a functional
+# standalone config, add these in yourself.
+
+logfile:
+weights:
+batch_file:
+repository_directory:
+num_ifos: 2
+kernel_length: 4.0
+inference_sampling_rate: 4
+sample_rate: 2048
+batch_size: 128
+fduration: 2
+psd_length: 64
+preprocessor:
+  class_path: utils.preprocessing.BatchWhitener
+  init_args:
+    kernel_length: 4.0
+    sample_rate: 2048
+    inference_sampling_rate: 4
+    batch_size: 128
+    fduration: 2
+    fftlength: 2
+    augmentor:
+      class_path: utils.augmentation.HeterodyneAugmentor
+      init_args:
+        sample_rate: 2048
+        kernel_length: 4.0
+        chirp_mass_low: 1.0
+        chirp_mass_high: 2.5 
+        num_chirp_masses: 100
+        chirp_mass_spacing: "log" 
+        keep_last_n_seconds: null
+    highpass: 32.0
+streams_per_gpu: 12
+# num_outputs:
+aframe_instances: 1
+platform: TENSORRT
+clean: true
+verbose: true

projects/export/pyproject.toml

@@ -6,7 +6,7 @@ authors = [{ name = "Ethan Jacob Marx", email = "ethan.marx@ligo.org" }]
 requires-python = ">=3.11,<3.14"
 license = "MIT"
 dependencies = [
-    "ml4gw>=0.7.7",
+    "ml4gw>=0.8.0",
     "boto3~=1.30",
     "fsspec[s3]>=2024,<2025",
     "ml4gw-hermes[torch]",