From b59cc067322456aac357e838151cf6dc12cd8a15 Mon Sep 17 00:00:00 2001 From: Deep Chatterjee Date: Mon, 2 Feb 2026 11:20:16 -0500 Subject: [PATCH 1/7] amplfi bns hlv config (#421) * amplfi bns hlv config * update ml4gw and amplfi versions (cherry picked from commit eb0ccc6636e1a7ceda948635597a1f656994babc) --- projects/online/config.yaml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/projects/online/config.yaml b/projects/online/config.yaml index acecb9b29..261981c26 100644 --- a/projects/online/config.yaml +++ b/projects/online/config.yaml @@ -29,11 +29,15 @@ amplfi_hlv_architecture: transforms: 15 randperm: true embedding_net: - class_path: amplfi.train.architectures.embeddings.MultiModalPsd + class_path: amplfi.train.architectures.embeddings.MultiModalPsdEmbeddingWithDecimator init_args: num_ifos: 3 time_context_dim: 48 freq_context_dim: 48 + strain_sample_rate: ${sample_rate} + decimator_schedule: [[0, 10, 256], [10, 28, 512], [28, 30, 2048]] + time_kernel_size: 5 + freq_kernel_size: 3 time_layers: [5, 3, 3] freq_layers: [5, 3, 3] norm_layer: From 9a9a5abca829b5aaaea0bff0da1ec8ef52962067 Mon Sep 17 00:00:00 2001 From: Deep Chatterjee Date: Mon, 15 Jun 2026 14:10:01 -0400 Subject: [PATCH 2/7] Sync main and bns-o3-mdc to the Decimator days (#463) * amplfi bns hlv config (#421) * amplfi bns hlv config * update ml4gw and amplfi versions (cherry picked from commit eb0ccc6636e1a7ceda948635597a1f656994babc) * change config params and add correct preprocessor (cherry picked from commit 3ecc50659102a8f733cbd142d94900f9a1f741a4) * change parameters in online project's config and update preprocessor (cherry picked from commit 42484f59c4bbd0d3cd4565c71c14b97afbc0475e) * Fix issues with whitened being a tuple (cherry picked from commit 8c609c1c1a61bb037bf25fde59576be746ecfca1) * Align decimator API between amplfi and aframe (cherry picked from commit 5871638e061495ab6df7a8e86127ef782d111cd4) * Update configs (cherry picked from commit fc0ab624f20287855c7b0ba4ef3ba5b2df4c38cd) * set ml4gw>=0.8.0, update lock file --------- Co-authored-by: Bhavya Gupta Co-authored-by: William Benoit --- projects/online/config.yaml | 12 +- projects/online/uv.lock | 4494 +++++++++++++++++++++++++++++++++++ 2 files changed, 4502 insertions(+), 4 deletions(-) create mode 100644 projects/online/uv.lock diff --git a/projects/online/config.yaml b/projects/online/config.yaml index 261981c26..3f88e28fe 100644 --- a/projects/online/config.yaml +++ b/projects/online/config.yaml @@ -51,17 +51,21 @@ channels: ["H1:GDS-CALIB_STRAIN_CLEAN_INJ1_O4Replay", "L1:GDS-CALIB_STRAIN_CLEAN state_channels: ["H1:GDS-CALIB_STATE_VECTOR", "L1:GDS-CALIB_STATE_VECTOR", "V1:DQ_ANALYSIS_STATE_VECTOR"] data_source: "frames" sample_rate: 2048 -astro_event_rate: 31 -kernel_length: 1.5 +astro_event_rate: 89 +kernel_length: 20 online_inference_rate: 512 -offline_inference_rate: 4 +offline_inference_rate: 16 +schedule: [[0, 16, 512], [16, 20, 2048]] +split: True +q: 45.6 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"https://files.pythonhosted.org/packages/b6/1e/2b3e8837b6aca634a088618cc6f62ae0cdde992f4a4b2ec1a9e1b8153c4c/zuko-1.4.1-py3-none-any.whl", hash = "sha256:0ca82dd9a701f44802f54d36291249397d857f52232a733f270b33508540f091", size = 44520, upload-time = "2025-06-03T08:57:26.399Z" }, +] From 2c33e7ab86144003398d7180853c9bc3bb5a445a Mon Sep 17 00:00:00 2001 From: Bhavya Gupta Date: Wed, 5 Aug 2026 22:38:16 -0500 Subject: [PATCH 3/7] Add heterodyne-topk functionality --- .../architectures/architectures/supervised.py | 6 +- libs/utils/tests/test_preprocessing.py | 79 +++++++++++++++++++ libs/utils/utils/augmentation.py | 41 +++++++++- projects/export/export_heterodyne.yaml | 7 +- projects/online/config.yaml | 12 +-- projects/online/online/main.py | 33 ++++++++ projects/train/configs/time_heterodyne.yaml | 39 ++++----- projects/train/train/cli.py | 13 ++- .../train/data/supervised/time_domain.py | 43 ++++++++-- 9 files changed, 232 insertions(+), 41 deletions(-) diff --git a/libs/architectures/architectures/supervised.py b/libs/architectures/architectures/supervised.py index 9f745fddd..3c5105a3b 100644 --- a/libs/architectures/architectures/supervised.py +++ b/libs/architectures/architectures/supervised.py @@ -302,13 +302,17 @@ def __init__( zero_init_residual: bool = False, groups: int = 1, width_per_group: int = 64, + top_k: int | None = None, stride_type: list[Literal["stride", "dilation"]] | None = None, norm_layer: NormLayer | None = None, **kwargs, ) -> None: super().__init__() + + num_channels = top_k if top_k is not None else num_chirp_masses + self.time_domain_resnet = ResNet1D( - in_channels=num_ifos * num_chirp_masses, + in_channels=num_ifos * num_channels, layers=layers, classes=1, kernel_size=kernel_size, diff --git a/libs/utils/tests/test_preprocessing.py b/libs/utils/tests/test_preprocessing.py index a7544fb1f..9cc3b6c68 100644 --- a/libs/utils/tests/test_preprocessing.py +++ b/libs/utils/tests/test_preprocessing.py @@ -1,5 +1,6 @@ import pytest import torch +import torch.nn.functional as F from ml4gw.transforms import Heterodyne from utils.augmentation import HeterodyneAugmentor from utils.preprocessing import ( @@ -302,6 +303,84 @@ def test_with_heterodyne_augmentor(self): kernels_aug = whitener_aug(x) assert torch.allclose(kernels_aug, kernels) + def test_with_heterodyne_augmentor_top_k(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=1.5, + top_k=5, + ) + + 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_aug.stride_size + + whitener_aug.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 = whitener_aug(x) + + kernels_heterodyned = heterodyne(whitener(x)) + _B, _C, _M, _T = kernels_heterodyned.shape + avgpool = F.avg_pool1d( + torch.abs(kernels_heterodyned.reshape(_B, _C * _M, _T)), + 31, + stride=1, + padding=15, + ).reshape(_B, _C, _M, _T) + avgpool_snr = torch.sqrt( + (avgpool[..., -int(1.5 * self.sample_rate) :] ** 2).sum(dim=1) + ) + vals = torch.max(avgpool_snr, dim=-1).values + idx = torch.topk(vals, k=5, dim=-1).indices + idx_expand = idx[:, None, :, None].expand(-1, _C, -1, _T) + kernels_heterodyned = torch.gather( + kernels_heterodyned, dim=2, index=idx_expand + ) + kernels_heterodyned = kernels_heterodyned.reshape(_B, _C * 5, _T) + kernels_heterodyned = kernels_heterodyned[ + ..., -int(1.5 * self.sample_rate) : + ] + kernels = kernels_heterodyned + + kernels_aug = whitener_aug(x) + assert torch.allclose(kernels_aug, kernels) + class TestMultiModalPreprocessor: """Test suite for MultiModalPreprocessor module.""" diff --git a/libs/utils/utils/augmentation.py b/libs/utils/utils/augmentation.py index bd13a1e34..5f3d70e4b 100644 --- a/libs/utils/utils/augmentation.py +++ b/libs/utils/utils/augmentation.py @@ -2,6 +2,7 @@ from typing import Literal import torch +import torch.nn.functional as F from ml4gw.transforms import Heterodyne from torch import Tensor @@ -26,6 +27,10 @@ class HeterodyneAugmentor(torch.nn.Module): 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. + top_k (int): + If provided, only the top `k` chirp mass channels are chosen + for the heterodyned timeseries. Otherwise, all chirp mass + channels are returned. Shape: Input: (batch_size, channels, time) Output: (batch_size, channels * num_chirp_masses, time_out) @@ -59,11 +64,13 @@ def __init__( num_chirp_masses: int = 100, chirp_mass_spacing: Literal["linear", "log"] = "log", keep_last_n_seconds: float = None, + top_k: int = None, ): super().__init__() self.sample_rate = sample_rate self.kernel_length = kernel_length self.keep_last_n_seconds = keep_last_n_seconds + self.top_k = top_k self.num_chirp_masses = num_chirp_masses self.keep_last_n_seconds = keep_last_n_seconds @@ -120,12 +127,38 @@ def forward(self, x: Tensor) -> Tensor: or determined by `keep_last_n_seconds`. """ _B, _C, _T = x.shape - x_heterodyned = torch.empty((_B, _C * self.num_chirp_masses, _T)) + if self.top_k is not None: + x_heterodyned = torch.empty((_B, _C * self.top_k, _T)) + else: + 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) + # + if self.top_k is not None: + avgpool = F.avg_pool1d( + torch.abs(x.reshape(_B, _C * self.num_chirp_masses, _T)), + 31, + stride=1, + padding=15, + ).reshape(_B, _C, self.num_chirp_masses, _T) + if self.keep_last_n_seconds is not None: + avgpool_snr = torch.sqrt( + (avgpool[..., -self.keep_last_n_samples :] ** 2).sum(dim=1) + ) + else: + avgpool_snr = torch.sqrt((avgpool**2).sum(dim=1)) + vals = torch.max(avgpool_snr, dim=-1).values + idx = torch.topk(vals, k=self.top_k, dim=-1).indices + idx_expand = idx[:, None, :, None].expand(-1, _C, -1, _T) + x = torch.gather(x, dim=2, index=idx_expand) + # Reshaping x from (batch_size, channels, top_k, kernel_size) + # to (batch_size, channels x top_k, kernel_size) + x = x.reshape(_B, _C * self.top_k, _T) + else: + # 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 diff --git a/projects/export/export_heterodyne.yaml b/projects/export/export_heterodyne.yaml index 1e3414634..fd4fcfcf5 100644 --- a/projects/export/export_heterodyne.yaml +++ b/projects/export/export_heterodyne.yaml @@ -9,7 +9,7 @@ weights: batch_file: repository_directory: num_ifos: 2 -kernel_length: 4.0 +kernel_length: 8.0 inference_sampling_rate: 4 sample_rate: 2048 batch_size: 128 @@ -18,7 +18,7 @@ psd_length: 64 preprocessor: class_path: utils.preprocessing.BatchWhitener init_args: - kernel_length: 4.0 + kernel_length: 8.0 sample_rate: 2048 inference_sampling_rate: 4 batch_size: 128 @@ -28,12 +28,13 @@ preprocessor: class_path: utils.augmentation.HeterodyneAugmentor init_args: sample_rate: 2048 - kernel_length: 4.0 + kernel_length: 8.0 chirp_mass_low: 1.0 chirp_mass_high: 2.5 num_chirp_masses: 100 chirp_mass_spacing: "log" keep_last_n_seconds: null + top_k: 10 highpass: 32.0 streams_per_gpu: 12 # num_outputs: diff --git a/projects/online/config.yaml b/projects/online/config.yaml index 3f88e28fe..7e9e2523b 100644 --- a/projects/online/config.yaml +++ b/projects/online/config.yaml @@ -52,13 +52,15 @@ state_channels: ["H1:GDS-CALIB_STATE_VECTOR", "L1:GDS-CALIB_STATE_VECTOR", "V1:D data_source: "frames" sample_rate: 2048 astro_event_rate: 89 -kernel_length: 20 +kernel_length: 8 online_inference_rate: 512 offline_inference_rate: 16 -schedule: [[0, 16, 512], [16, 20, 2048]] -split: True -q: 45.6 -spectrogram: [64, 128] +chirp_mass_low: 1.0 +chirp_mass_high: 2.5 +num_chirp_masses: 100 +chirp_mass_spacing: "log" +keep_last_n_seconds: 1.5 +top_k: 10 inference_params: ["chirp_mass", "mass_ratio", "luminosity_distance", "phic", "inclination", "dec", "psi", "phi"] psd_length: 64 nside: 64 diff --git a/projects/online/online/main.py b/projects/online/online/main.py index 3a71ea166..fd848fdbd 100644 --- a/projects/online/online/main.py +++ b/projects/online/online/main.py @@ -16,6 +16,7 @@ from ledger.events import EventSet from ml4gw.transforms import ChannelWiseScaler, SpectralDensity, Whiten from torch.multiprocessing import Array, Process, Queue +from utils.augmentation import HeterodyneAugmentor from utils.preprocessing import BatchWhitener from online.dataloading import ( @@ -376,6 +377,10 @@ def main( kernel_length: float, online_inference_rate: float, offline_inference_rate: float, + chirp_mass_low: float, + chirp_mass_high: float, + num_chirp_masses: int, + chirp_mass_spacing: Literal["linear", "log"], psd_length: float, amplfi_psd_length: float, aframe_right_pad: float, @@ -386,6 +391,8 @@ def main( integration_window_length: float, astro_event_rate: float, data_source: Literal["frames", "arrakis"] = "frames", + keep_last_n_seconds: float = None, + top_k: int = None, state_channels: list[str] | None = None, fftlength: float | None = None, highpass: float | None = None, @@ -450,6 +457,22 @@ def main( offline_inference_rate: Rate at which inference was performed offline when establishing the background and foreground distributions + chirp_mass_low: + Lower bound of chirp mass range (in solar masses). + chirp_mass_high: + Upper bound of chirp mass range (in solar masses). + num_chirp_masses: + Number of chirp mass samples to generate. + chirp_mass_spacing: + Spacing of chirp mass grid. Use "linear" for evenly spaced + values or "log" for logarithmic spacing. + keep_last_n_seconds: + If provided, only the last `n` seconds of the kernel_length are + returned. Otherwise, the full kernel_length is returned. + top_k: + If provided, only the top `k` chirp mass channels are chosen + for the heterodyned timeseries. Otherwise, all chirp mass + channels are returned. psd_length: Length of PSD estimation window in seconds for PSD used to whiten aframe data @@ -842,6 +865,16 @@ def main( batch_size=int(update_size * online_inference_rate), fduration=fduration, fftlength=fftlength, + augmentor=HeterodyneAugmentor( + sample_rate=sample_rate, + kernel_length=kernel_length, + chirp_mass_low=chirp_mass_low, + chirp_mass_high=chirp_mass_high, + num_chirp_masses=num_chirp_masses, + chirp_mass_spacing=chirp_mass_spacing, + keep_last_n_seconds=keep_last_n_seconds, + top_k=top_k, + ), highpass=highpass, lowpass=lowpass, ).to(device) diff --git a/projects/train/configs/time_heterodyne.yaml b/projects/train/configs/time_heterodyne.yaml index 928ad26c5..2692dcfc8 100644 --- a/projects/train/configs/time_heterodyne.yaml +++ b/projects/train/configs/time_heterodyne.yaml @@ -13,7 +13,7 @@ model: class_path: architectures.supervised.SupervisedHeterodyneTimeDomainResNet init_args: layers: [3, 4, 6, 3] - kernel_size: 25 + kernel_size: 3 norm_layer: class_path: ml4gw.nn.norm.GroupNorm1D init_args: @@ -37,12 +37,12 @@ data: waveforms_dir: ifos: [H1,L1] sample_rate: 2048 - batches_per_epoch: 100 + batches_per_epoch: 200 num_files_per_batch: 10 - chunk_size: 1000 + chunk_size: 5000 chunks_per_epoch: 20 # preprocessing args - batch_size: 100 + batch_size: 2000 kernel_length: 8 psd_length: 20 fduration: 2 @@ -57,25 +57,26 @@ data: chirp_mass_high: 2.5 num_chirp_masses: 100 chirp_mass_spacing: "log" - keep_last_n_seconds: null + keep_last_n_seconds: 1.5 + top_k: 10 # highpass: # lowpass: fftlength: 2 - snr_sampler: - class_path: ml4gw.distributions.PowerLaw - init_args: - minimum: 8 - maximum: 100 - index: -3 - # curriculum learning for snr sampler # snr_sampler: - # class_path: train.augmentations.SnrSampler - # init_args: - # max_min_snr: 30 - # min_min_snr: 8 - # max_snr: 100 - # alpha: -3 - # decay_steps: 600 + # class_path: ml4gw.distributions.PowerLaw + # init_args: + # minimum: 8 + # maximum: 100 + # index: -3 + # curriculum learning for snr sampler + snr_sampler: + class_path: train.augmentations.SnrSampler + init_args: + max_min_snr: 30 + min_min_snr: 8 + max_snr: 100 + alpha: -3 + decay_steps: 600 waveform_sampler: class_path: train.data.waveforms.WaveformLoader init_args: diff --git a/projects/train/train/cli.py b/projects/train/train/cli.py index e0dfd1b2e..5fdf4080c 100644 --- a/projects/train/train/cli.py +++ b/projects/train/train/cli.py @@ -57,8 +57,9 @@ def add_arguments_to_parser(self, parser): except Exception: pass - # TODO: This is a workaround for linking num_chirp_masses between - # the model and the architecture for in_channels. + # TODO: This is a workaround for linking num_chirp_masses and + # top_k channel argument between the model and the architecture + # for in_channels. try: parser.link_arguments( "data.init_args.num_chirp_masses", @@ -67,6 +68,14 @@ def add_arguments_to_parser(self, parser): except Exception: pass + try: + parser.link_arguments( + "data.init_args.top_k", + "model.init_args.arch.init_args.top_k", + ) + except Exception: + pass + parser.link_arguments( "data.init_args.sample_rate", "data.init_args.waveform_sampler.init_args.sample_rate", diff --git a/projects/train/train/data/supervised/time_domain.py b/projects/train/train/data/supervised/time_domain.py index 50076ab24..d0eccf516 100644 --- a/projects/train/train/data/supervised/time_domain.py +++ b/projects/train/train/data/supervised/time_domain.py @@ -2,6 +2,7 @@ from typing import Literal import torch +import torch.nn.functional as F from ml4gw.transforms import Heterodyne from train.data.supervised.supervised import SupervisedAframeDataset @@ -46,6 +47,10 @@ class HeterodyneTimeDomainSupervisedAframeDataset(SupervisedAframeDataset): 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. + top_k (int): + If provided, only the top `k` chirp mass channels are chosen + for the heterodyned timeseries. Otherwise, all chirp mass + channels are returned. """ def __init__( @@ -55,6 +60,7 @@ def __init__( num_chirp_masses: int = 100, chirp_mass_spacing: Literal["linear", "log"] = "log", keep_last_n_seconds: float = None, + top_k: int = None, *args, **kwargs, ): @@ -68,7 +74,7 @@ def __init__( ) self.keep_last_n_seconds = keep_last_n_seconds - + self.top_k = top_k if self.keep_last_n_seconds is not None: self.keep_last_n_samples = int( self.keep_last_n_seconds * self.hparams.sample_rate @@ -105,21 +111,45 @@ def _create_chirp_mass_grid( f"Invalid chirp mass spacing: {chirp_mass_spacing}" ) + def _select_top_k(self, X): + B, C, M, T = X.shape + # If no top-k requested, flatten the chirp-mass dimension + if self.top_k is None: + return X.reshape(B, C * M, T) + _X = [] + for b in range(B): + x = X[b] + avgpool = F.avg_pool1d( + torch.abs(x.reshape(C * M, T)), + kernel_size=31, + stride=1, + padding=15, + ).reshape(C, M, T) + if self.keep_last_n_seconds is not None: + avgpool_snr = torch.sqrt( + (avgpool[..., -self.keep_last_n_samples :] ** 2).sum(dim=0) + ) + else: + avgpool_snr = torch.sqrt((avgpool**2).sum(dim=0)) + vals = avgpool_snr.max(dim=-1).values + idx = torch.topk(vals, k=self.top_k).indices + idx = idx[None, :, None].expand(C, -1, T) + _X.append(torch.gather(x, dim=1, index=idx)) + return torch.stack(_X).reshape(B, C * self.top_k, T) + def build_val_batches(self, background, signals): X_bg, X_inj, psds = super().build_val_batches(background, signals) X_bg = self.whitener(X_bg, psds) X_bg = self.heterodyne_transform(X_bg) - _B_bg, _C_bg, _M_bg, _T_bg = X_bg.shape - X_bg = X_bg.view(_B_bg, _C_bg * _M_bg, _T_bg) + X_bg = self._select_top_k(X_bg) # whiten each view of injections X_fg = [] for inj in X_inj: inj = self.whitener(inj, psds) inj = self.heterodyne_transform(inj) + inj = self._select_top_k(inj) X_fg.append(inj) X_fg = torch.stack(X_fg) - _V_fg, _B_fg, _C_fg, _M_fg, _T_fg = X_fg.shape - X_fg = X_fg.view(_V_fg, _B_fg, _C_fg * _M_fg, _T_fg) if self.keep_last_n_seconds is not None: return X_bg[..., -self.keep_last_n_samples :], X_fg[ @@ -132,8 +162,7 @@ def inject(self, X, waveforms=None): X, y, psds = super().inject(X, waveforms) X = self.whitener(X, psds) X = self.heterodyne_transform(X) - _B, _C, _M, _T = X.shape - X = X.view(_B, _C * _M, _T) + X = self._select_top_k(X) if self.keep_last_n_seconds is not None: return X[..., -self.keep_last_n_samples :], y From f065270a2bd14e47dabd93621aa51e38d9ae3d3c Mon Sep 17 00:00:00 2001 From: Bhavya Gupta Date: Sat, 15 Aug 2026 23:46:21 -0500 Subject: [PATCH 4/7] implement stand alone module for choosing top k channels --- libs/utils/utils/augmentation.py | 113 ++++++++++++++---- .../train/data/supervised/time_domain.py | 51 +++----- 2 files changed, 105 insertions(+), 59 deletions(-) diff --git a/libs/utils/utils/augmentation.py b/libs/utils/utils/augmentation.py index 5f3d70e4b..88ffdc594 100644 --- a/libs/utils/utils/augmentation.py +++ b/libs/utils/utils/augmentation.py @@ -7,6 +7,78 @@ from torch import Tensor +class SelectTopK(torch.nn.Module): + """ + Select top `k` chirp mass channels are chosen from `M` heterodyned + timeseries. The input is expected to have shape `(B, C, M, T)`, + where `B` is the batch size, `C` is the number of detector channels, + `M` is the number of chirp mass channels, and `T` is the number of + time samples. + + Args: + kernel_size (int): + Size of the running average (average pooling) window used to + smooth the absolute value of the input before computing the + selection statistic. + stride (int): + Stride of the average pooling operation. + padding (int): + Zero-padding applied to both sides of the timeseries before + the average pooling operation. + keep_last_n_samples (int, optional): + If provided, only the final `n` samples are used when + computing the statistic used to select the top k chirp mass + channels. If `None`, all samples are used. + top_k (int): + Number of chirp mass channels to retain. If `None`, all + chirp mass channels are retained and the `M` dimension is + flattened into the channel dimension. + """ + + def __init__( + self, + top_k: int, + kernel_size: int = 31, + stride: int = 1, + padding: int = 15, + keep_last_n_samples: int | None = None, + ): + super().__init__() + + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.keep_last_n_samples = keep_last_n_samples + self.top_k = top_k + + def forward(self, X): + B, C, M, T = X.shape + + avgpool = torch.stack( + [ + F.avg_pool1d( + torch.abs(x.reshape(C * M, T)), + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding, + ) + for x in X + ] + ).reshape(B, C, M, T) + + if self.keep_last_n_samples is not None: + avgpool_snr = torch.sqrt( + (avgpool[..., -self.keep_last_n_samples :] ** 2).sum(dim=1) + ) + else: + avgpool_snr = torch.sqrt((avgpool**2).sum(dim=1)) + + vals = avgpool_snr.max(dim=-1).values + idx = torch.topk(vals, k=self.top_k, dim=-1).indices + idx = idx[:, None, :, None].expand(B, C, self.top_k, T) + return torch.gather(X, dim=2, index=idx).reshape(B, C * self.top_k, T) + + class HeterodyneAugmentor(torch.nn.Module): """ Apply a heterodyne transform over a grid of chirp masses to a batch @@ -28,9 +100,9 @@ class HeterodyneAugmentor(torch.nn.Module): If provided, only the last `n` seconds of the kernel_length are returned. Otherwise, the full kernel_length is returned. top_k (int): - If provided, only the top `k` chirp mass channels are chosen - for the heterodyned timeseries. Otherwise, all chirp mass - channels are returned. + If provided, the top `k` chirp mass channels are selected based on + their absolute amplitude, corresponding to the `k` matching chirp + masses with. If `None`, all chirp mass channels are returned. Shape: Input: (batch_size, channels, time) Output: (batch_size, channels * num_chirp_masses, time_out) @@ -63,13 +135,12 @@ def __init__( chirp_mass_high: float = 2.5, num_chirp_masses: int = 100, chirp_mass_spacing: Literal["linear", "log"] = "log", - keep_last_n_seconds: float = None, - top_k: int = None, + keep_last_n_seconds: float | None = None, + top_k: int | None = None, ): super().__init__() self.sample_rate = sample_rate self.kernel_length = kernel_length - self.keep_last_n_seconds = keep_last_n_seconds self.top_k = top_k self.num_chirp_masses = num_chirp_masses self.keep_last_n_seconds = keep_last_n_seconds @@ -85,6 +156,8 @@ def __init__( self.keep_last_n_samples = int( self.keep_last_n_seconds * sample_rate ) + else: + self.keep_last_n_samples = None self.heterodyne_transform = Heterodyne( sample_rate=sample_rate, @@ -93,6 +166,12 @@ def __init__( return_type="time", ) + if self.top_k is not None: + self.select_top_k = SelectTopK( + keep_last_n_samples=self.keep_last_n_samples, + top_k=self.top_k, + ) + def _create_chirp_mass_grid( self, chirp_mass_low: float, @@ -133,27 +212,9 @@ def forward(self, x: Tensor) -> Tensor: x_heterodyned = torch.empty((_B, _C * self.num_chirp_masses, _T)) # Heterodyne the whitened timeseries x = self.heterodyne_transform(x) - # if self.top_k is not None: - avgpool = F.avg_pool1d( - torch.abs(x.reshape(_B, _C * self.num_chirp_masses, _T)), - 31, - stride=1, - padding=15, - ).reshape(_B, _C, self.num_chirp_masses, _T) - if self.keep_last_n_seconds is not None: - avgpool_snr = torch.sqrt( - (avgpool[..., -self.keep_last_n_samples :] ** 2).sum(dim=1) - ) - else: - avgpool_snr = torch.sqrt((avgpool**2).sum(dim=1)) - vals = torch.max(avgpool_snr, dim=-1).values - idx = torch.topk(vals, k=self.top_k, dim=-1).indices - idx_expand = idx[:, None, :, None].expand(-1, _C, -1, _T) - x = torch.gather(x, dim=2, index=idx_expand) - # Reshaping x from (batch_size, channels, top_k, kernel_size) - # to (batch_size, channels x top_k, kernel_size) - x = x.reshape(_B, _C * self.top_k, _T) + # Select the top k chirp mass channels + x = self.select_top_k(x) else: # Reshaping x from # (batch_size, channels, num_chirp_mass, kernel_size) diff --git a/projects/train/train/data/supervised/time_domain.py b/projects/train/train/data/supervised/time_domain.py index d0eccf516..f40318fc9 100644 --- a/projects/train/train/data/supervised/time_domain.py +++ b/projects/train/train/data/supervised/time_domain.py @@ -2,8 +2,8 @@ from typing import Literal import torch -import torch.nn.functional as F from ml4gw.transforms import Heterodyne +from utils.augmentation import SelectTopK from train.data.supervised.supervised import SupervisedAframeDataset @@ -48,9 +48,9 @@ class HeterodyneTimeDomainSupervisedAframeDataset(SupervisedAframeDataset): If provided, only the last `n` seconds of the kernel_length are returned. Otherwise, the full kernel_length is returned. top_k (int): - If provided, only the top `k` chirp mass channels are chosen - for the heterodyned timeseries. Otherwise, all chirp mass - channels are returned. + If provided, the top `k` chirp mass channels are selected based on + their absolute amplitude, corresponding to the `k` matching chirp + masses with. If `None`, all chirp mass channels are returned. """ def __init__( @@ -79,6 +79,14 @@ def __init__( self.keep_last_n_samples = int( self.keep_last_n_seconds * self.hparams.sample_rate ) + else: + self.keep_last_n_samples = None + + if self.top_k is not None: + self.select_top_k = SelectTopK( + keep_last_n_samples=self.keep_last_n_samples, + top_k=self.top_k, + ) def build_transforms(self, *args, **kwargs): super().build_transforms(*args, **kwargs) @@ -111,43 +119,19 @@ def _create_chirp_mass_grid( f"Invalid chirp mass spacing: {chirp_mass_spacing}" ) - def _select_top_k(self, X): - B, C, M, T = X.shape - # If no top-k requested, flatten the chirp-mass dimension - if self.top_k is None: - return X.reshape(B, C * M, T) - _X = [] - for b in range(B): - x = X[b] - avgpool = F.avg_pool1d( - torch.abs(x.reshape(C * M, T)), - kernel_size=31, - stride=1, - padding=15, - ).reshape(C, M, T) - if self.keep_last_n_seconds is not None: - avgpool_snr = torch.sqrt( - (avgpool[..., -self.keep_last_n_samples :] ** 2).sum(dim=0) - ) - else: - avgpool_snr = torch.sqrt((avgpool**2).sum(dim=0)) - vals = avgpool_snr.max(dim=-1).values - idx = torch.topk(vals, k=self.top_k).indices - idx = idx[None, :, None].expand(C, -1, T) - _X.append(torch.gather(x, dim=1, index=idx)) - return torch.stack(_X).reshape(B, C * self.top_k, T) - def build_val_batches(self, background, signals): X_bg, X_inj, psds = super().build_val_batches(background, signals) X_bg = self.whitener(X_bg, psds) X_bg = self.heterodyne_transform(X_bg) - X_bg = self._select_top_k(X_bg) + if self.top_k is not None: + X_bg = self.select_top_k(X_bg) # whiten each view of injections X_fg = [] for inj in X_inj: inj = self.whitener(inj, psds) inj = self.heterodyne_transform(inj) - inj = self._select_top_k(inj) + if self.top_k is not None: + inj = self.select_top_k(inj) X_fg.append(inj) X_fg = torch.stack(X_fg) @@ -162,7 +146,8 @@ def inject(self, X, waveforms=None): X, y, psds = super().inject(X, waveforms) X = self.whitener(X, psds) X = self.heterodyne_transform(X) - X = self._select_top_k(X) + if self.top_k is not None: + X = self.select_top_k(X) if self.keep_last_n_seconds is not None: return X[..., -self.keep_last_n_samples :], y From 09eefa4b0e23a94964a238d0d447939a14479c2b Mon Sep 17 00:00:00 2001 From: Bhavya Gupta Date: Sun, 16 Aug 2026 20:43:29 -0500 Subject: [PATCH 5/7] convert top k from a class to function --- libs/utils/utils/augmentation.py | 101 ++++++++---------- .../train/data/supervised/time_domain.py | 22 ++-- 2 files changed, 57 insertions(+), 66 deletions(-) diff --git a/libs/utils/utils/augmentation.py b/libs/utils/utils/augmentation.py index 88ffdc594..4507fe6d9 100644 --- a/libs/utils/utils/augmentation.py +++ b/libs/utils/utils/augmentation.py @@ -7,15 +7,24 @@ from torch import Tensor -class SelectTopK(torch.nn.Module): +def select_top_k( + X: torch.Tensor, + top_k: int, + kernel_size: int = 31, + stride: int = 1, + padding: int = 15, + keep_last_n_samples: int | None = None, +) -> torch.Tensor: """ - Select top `k` chirp mass channels are chosen from `M` heterodyned - timeseries. The input is expected to have shape `(B, C, M, T)`, - where `B` is the batch size, `C` is the number of detector channels, - `M` is the number of chirp mass channels, and `T` is the number of - time samples. + Select top `k` chirp mass channels from `M` heterodyned timeseries. Args: + X (Tensor): + Input tensor of shape (B, C, M, T) where B is the batch + size, C is the number of channels, M is the number of chirp + mass channels, and T is the number of time samples. + top_k (int): + Number of chirp mass channels to retain. kernel_size (int): Size of the running average (average pooling) window used to smooth the absolute value of the input before computing the @@ -29,54 +38,38 @@ class SelectTopK(torch.nn.Module): If provided, only the final `n` samples are used when computing the statistic used to select the top k chirp mass channels. If `None`, all samples are used. - top_k (int): - Number of chirp mass channels to retain. If `None`, all - chirp mass channels are retained and the `M` dimension is - flattened into the channel dimension. - """ - def __init__( - self, - top_k: int, - kernel_size: int = 31, - stride: int = 1, - padding: int = 15, - keep_last_n_samples: int | None = None, - ): - super().__init__() + Returns: + Tensor: + Output tensor of shape (B, C * top_k, T) containing the + selected top k chirp mass channels for each batch and channel. + """ - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.keep_last_n_samples = keep_last_n_samples - self.top_k = top_k + B, C, M, T = X.shape - def forward(self, X): - B, C, M, T = X.shape - - avgpool = torch.stack( - [ - F.avg_pool1d( - torch.abs(x.reshape(C * M, T)), - kernel_size=self.kernel_size, - stride=self.stride, - padding=self.padding, - ) - for x in X - ] - ).reshape(B, C, M, T) - - if self.keep_last_n_samples is not None: - avgpool_snr = torch.sqrt( - (avgpool[..., -self.keep_last_n_samples :] ** 2).sum(dim=1) + avgpool = torch.stack( + [ + F.avg_pool1d( + torch.abs(x.reshape(C * M, T)), + kernel_size=kernel_size, + stride=stride, + padding=padding, ) - else: - avgpool_snr = torch.sqrt((avgpool**2).sum(dim=1)) + for x in X + ] + ).reshape(B, C, M, T) + + if keep_last_n_samples is not None: + avgpool_snr = torch.sqrt( + (avgpool[..., -keep_last_n_samples:] ** 2).sum(dim=1) + ) + else: + avgpool_snr = torch.sqrt((avgpool**2).sum(dim=1)) - vals = avgpool_snr.max(dim=-1).values - idx = torch.topk(vals, k=self.top_k, dim=-1).indices - idx = idx[:, None, :, None].expand(B, C, self.top_k, T) - return torch.gather(X, dim=2, index=idx).reshape(B, C * self.top_k, T) + vals = avgpool_snr.max(dim=-1).values + idx = torch.topk(vals, k=top_k, dim=-1).indices + idx = idx[:, None, :, None].expand(B, C, top_k, T) + return torch.gather(X, dim=2, index=idx).reshape(B, C * top_k, T) class HeterodyneAugmentor(torch.nn.Module): @@ -166,12 +159,6 @@ def __init__( return_type="time", ) - if self.top_k is not None: - self.select_top_k = SelectTopK( - keep_last_n_samples=self.keep_last_n_samples, - top_k=self.top_k, - ) - def _create_chirp_mass_grid( self, chirp_mass_low: float, @@ -214,7 +201,9 @@ def forward(self, x: Tensor) -> Tensor: x = self.heterodyne_transform(x) if self.top_k is not None: # Select the top k chirp mass channels - x = self.select_top_k(x) + x = select_top_k( + x, self.top_k, keep_last_n_samples=self.keep_last_n_samples + ) else: # Reshaping x from # (batch_size, channels, num_chirp_mass, kernel_size) diff --git a/projects/train/train/data/supervised/time_domain.py b/projects/train/train/data/supervised/time_domain.py index f40318fc9..a2b823f6b 100644 --- a/projects/train/train/data/supervised/time_domain.py +++ b/projects/train/train/data/supervised/time_domain.py @@ -3,7 +3,7 @@ import torch from ml4gw.transforms import Heterodyne -from utils.augmentation import SelectTopK +from utils.augmentation import select_top_k from train.data.supervised.supervised import SupervisedAframeDataset @@ -82,12 +82,6 @@ def __init__( else: self.keep_last_n_samples = None - if self.top_k is not None: - self.select_top_k = SelectTopK( - keep_last_n_samples=self.keep_last_n_samples, - top_k=self.top_k, - ) - def build_transforms(self, *args, **kwargs): super().build_transforms(*args, **kwargs) self.heterodyne_transform = Heterodyne( @@ -124,14 +118,20 @@ def build_val_batches(self, background, signals): X_bg = self.whitener(X_bg, psds) X_bg = self.heterodyne_transform(X_bg) if self.top_k is not None: - X_bg = self.select_top_k(X_bg) + X_bg = select_top_k( + X_bg, self.top_k, keep_last_n_samples=self.keep_last_n_samples + ) # whiten each view of injections X_fg = [] for inj in X_inj: inj = self.whitener(inj, psds) inj = self.heterodyne_transform(inj) if self.top_k is not None: - inj = self.select_top_k(inj) + inj = select_top_k( + inj, + self.top_k, + keep_last_n_samples=self.keep_last_n_samples, + ) X_fg.append(inj) X_fg = torch.stack(X_fg) @@ -147,7 +147,9 @@ def inject(self, X, waveforms=None): X = self.whitener(X, psds) X = self.heterodyne_transform(X) if self.top_k is not None: - X = self.select_top_k(X) + X = select_top_k( + X, self.top_k, keep_last_n_samples=self.keep_last_n_samples + ) if self.keep_last_n_seconds is not None: return X[..., -self.keep_last_n_samples :], y From 62a793252f9f76fd5a7e3579951ab9e1899ee216 Mon Sep 17 00:00:00 2001 From: Bhavya Gupta Date: Sat, 3 Oct 2026 19:56:00 -0500 Subject: [PATCH 6/7] attempt to rebase bns model commits from `bns-o3-mdc` to `dev` --- projects/online/config.yaml | 30 +++++++++++++------------- projects/online/online/cli.py | 21 ++++++++++++++++++ projects/online/online/main.py | 39 ++++++---------------------------- 3 files changed, 42 insertions(+), 48 deletions(-) diff --git a/projects/online/config.yaml b/projects/online/config.yaml index 7e9e2523b..d10ac9696 100644 --- a/projects/online/config.yaml +++ b/projects/online/config.yaml @@ -29,15 +29,11 @@ amplfi_hlv_architecture: transforms: 15 randperm: true embedding_net: - class_path: amplfi.train.architectures.embeddings.MultiModalPsdEmbeddingWithDecimator + class_path: amplfi.train.architectures.embeddings.MultiModalPsd init_args: num_ifos: 3 time_context_dim: 48 freq_context_dim: 48 - strain_sample_rate: ${sample_rate} - decimator_schedule: [[0, 10, 256], [10, 28, 512], [28, 30, 2048]] - time_kernel_size: 5 - freq_kernel_size: 3 time_layers: [5, 3, 3] freq_layers: [5, 3, 3] norm_layer: @@ -51,23 +47,17 @@ channels: ["H1:GDS-CALIB_STRAIN_CLEAN_INJ1_O4Replay", "L1:GDS-CALIB_STRAIN_CLEAN state_channels: ["H1:GDS-CALIB_STATE_VECTOR", "L1:GDS-CALIB_STATE_VECTOR", "V1:DQ_ANALYSIS_STATE_VECTOR"] data_source: "frames" sample_rate: 2048 -astro_event_rate: 89 -kernel_length: 8 +astro_event_rate: 31 +kernel_length: 1.5 online_inference_rate: 512 -offline_inference_rate: 16 -chirp_mass_low: 1.0 -chirp_mass_high: 2.5 -num_chirp_masses: 100 -chirp_mass_spacing: "log" -keep_last_n_seconds: 1.5 -top_k: 10 +offline_inference_rate: 4 inference_params: ["chirp_mass", "mass_ratio", "luminosity_distance", "phic", "inclination", "dec", "psi", "phi"] psd_length: 64 nside: 64 min_samples_per_pix: 5 amplfi_psd_length: 64 aframe_right_pad: 0.0 -fduration: 2 +fduration: 1 amplfi_fduration: 2 integration_window_length: 1.5 amplfi_kernel_length: 4 @@ -88,3 +78,13 @@ output_buffer_length: 8 device: "cuda" verbose: true matmul_precision: "high" +# optional augmentor arguments +# augmentor: +# class_path: utils.augmentation.HeterodyneAugmentor +# init_args: +# chirp_mass_low: 1.0 +# chirp_mass_high: 2.5 +# num_chirp_masses: 100 +# chirp_mass_spacing: "log" +# keep_last_n_seconds: 1.5 +# top_k: 10 diff --git a/projects/online/online/cli.py b/projects/online/online/cli.py index f0cb60f8f..ce22e9fe8 100644 --- a/projects/online/online/cli.py +++ b/projects/online/online/cli.py @@ -23,6 +23,27 @@ def build_parser(): apply_on="parse", ) + # TODO: This is a workaround for linking sample_rate and + # kernel_length argument between the augmentor and + # the global parameters in the config. + try: + parser.link_arguments( + "sample_rate", + "augmentor.init_args.sample_rate", + apply_on="parse", + ) + except Exception: + pass + + try: + parser.link_arguments( + "kernel_length", + "augmentor.init_args.kernel_length", + apply_on="parse", + ) + except Exception: + pass + return parser diff --git a/projects/online/online/main.py b/projects/online/online/main.py index fd848fdbd..93ca4d95c 100644 --- a/projects/online/online/main.py +++ b/projects/online/online/main.py @@ -16,7 +16,6 @@ from ledger.events import EventSet from ml4gw.transforms import ChannelWiseScaler, SpectralDensity, Whiten from torch.multiprocessing import Array, Process, Queue -from utils.augmentation import HeterodyneAugmentor from utils.preprocessing import BatchWhitener from online.dataloading import ( @@ -377,10 +376,6 @@ def main( kernel_length: float, online_inference_rate: float, offline_inference_rate: float, - chirp_mass_low: float, - chirp_mass_high: float, - num_chirp_masses: int, - chirp_mass_spacing: Literal["linear", "log"], psd_length: float, amplfi_psd_length: float, aframe_right_pad: float, @@ -391,8 +386,7 @@ def main( integration_window_length: float, astro_event_rate: float, data_source: Literal["frames", "arrakis"] = "frames", - keep_last_n_seconds: float = None, - top_k: int = None, + augmentor: torch.nn.Module | None = None, state_channels: list[str] | None = None, fftlength: float | None = None, highpass: float | None = None, @@ -457,22 +451,10 @@ def main( offline_inference_rate: Rate at which inference was performed offline when establishing the background and foreground distributions - chirp_mass_low: - Lower bound of chirp mass range (in solar masses). - chirp_mass_high: - Upper bound of chirp mass range (in solar masses). - num_chirp_masses: - Number of chirp mass samples to generate. - chirp_mass_spacing: - Spacing of chirp mass grid. Use "linear" for evenly spaced - values or "log" for logarithmic spacing. - keep_last_n_seconds: - If provided, only the last `n` seconds of the kernel_length are - returned. Otherwise, the full kernel_length is returned. - top_k: - If provided, only the top `k` chirp mass channels are chosen - for the heterodyned timeseries. Otherwise, all chirp mass - channels are returned. + augmentor: + Optional augmentor module that performs augmentation + of the input data that need to be analyzed by Aframe. + If not provided, no augmentation will be performed. psd_length: Length of PSD estimation window in seconds for PSD used to whiten aframe data @@ -865,16 +847,7 @@ def main( batch_size=int(update_size * online_inference_rate), fduration=fduration, fftlength=fftlength, - augmentor=HeterodyneAugmentor( - sample_rate=sample_rate, - kernel_length=kernel_length, - chirp_mass_low=chirp_mass_low, - chirp_mass_high=chirp_mass_high, - num_chirp_masses=num_chirp_masses, - chirp_mass_spacing=chirp_mass_spacing, - keep_last_n_seconds=keep_last_n_seconds, - top_k=top_k, - ), + augmentor=augmentor, highpass=highpass, lowpass=lowpass, ).to(device) From 2e89597734a8837d49749189b7971b47254bbf0e Mon Sep 17 00:00:00 2001 From: Bhavya Gupta Date: Sat, 3 Oct 2026 20:27:50 -0500 Subject: [PATCH 7/7] remove online project's `uv.locl` file --- projects/online/uv.lock | 4494 --------------------------------------- 1 file changed, 4494 deletions(-) delete mode 100644 projects/online/uv.lock diff --git a/projects/online/uv.lock b/projects/online/uv.lock deleted file mode 100644 index 11aebd3f8..000000000 --- a/projects/online/uv.lock +++ /dev/null @@ -1,4494 +0,0 @@ -version = 1 -revision = 3 -requires-python = ">=3.11, <3.13" -resolution-markers = [ - 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