{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# [`ml4gw`](https://github.com/ML4GW/ml4gw) Tutorial\n", "\n", "This tutorial has two parts:\n", "1. An overview of many of the features of `ml4gw`, with demonstrations\n", "2. An example of training a model using these features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Requirements:** This notebook requires a number of packages besides `ml4gw` to run completely.\n", "Install with:\n", "\n", "```bash\n", "pip install \"ml4gw>=0.7.6\" \"gwpy>=3.0\" \"h5py>=3.12\" \"torchmetrics>=1.6\" \"lightning>=2.4.0\" \"rich>=10.2.2,<14.0\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Overview\n", "\n", "We'll go through this as though our goal is to build a binary black hole detection model, \n", "with some excursions to look at other features. Much of this is similar to how the [Aframe](https://www.github.com/ML4GW/aframe) algorithm works.\n", "The development of `ml4gw` was guided by what was needed for Aframe,\n", "which makes BBH detection a good test case.\n", "\n", "Goals of this tutorial:\n", "- Introduce and demonstrate how to interact with many of the features of `ml4gw`\n", "- Explain why these tools are useful for doing machine learning in gravitational wave physics\n", "- Present areas where it may be possible to contribute to `ml4gw`" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams.update(\n", " {\n", " \"text.usetex\": True,\n", " \"font.family\": \"Computer Modern\",\n", " \"font.size\": 16,\n", " \"figure.dpi\": 100,\n", " }\n", ")\n", "\n", "# Most of this notebook can be run on CPU in a reasonable amount of time.\n", "# The example training at the end cannot be.\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Waveform Generation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll start by generating some BBH waveforms. Currently, `ml4gw` has implemented the following [CBC waveforms](https://github.com/ML4GW/ml4gw/tree/main/ml4gw/waveforms/cbc): TaylorF2, IMRPhenomD, and IMRPhenomPv2. We'll use IMRPhenomD for our example. These are all frequency-domain waveforms, and so return a frequency-series of gravitational-wave strain. We provide the [`TimeDomainCBCWaveformGenerator`](https://github.com/ML4GW/ml4gw/blob/main/ml4gw/waveforms/generator.py) class for producing time-domain signals; however, we'll start with frequency-domain.\n", "\n", "Additionally, sine-gaussian and ringdown (damped cosinusoidal) waveforms are [available](https://github.com/ML4GW/ml4gw/tree/main/ml4gw/waveforms/adhoc).\n", "\n", "These modules allow simultaneous generation of batches of waveforms from a set of parameters." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# Desired duration of time-domain waveform\n", "waveform_duration = 8\n", "# Sample rate of all the data we'll be using today\n", "sample_rate = 2048\n", "\n", "# Define minimum, maximum, and reference frequencies\n", "f_min = 20\n", "f_max = 1024\n", "f_ref = 20\n", "\n", "nyquist = sample_rate / 2\n", "num_samples = int(waveform_duration * sample_rate)\n", "num_freqs = num_samples // 2 + 1\n", "\n", "# Create an array of frequency values at which to generate our waveform\n", "# At the moment, only frequency-domain approximants have been implemented\n", "frequencies = torch.linspace(0, nyquist, num_freqs).to(device)\n", "freq_mask = (frequencies >= f_min) * (frequencies < f_max).to(device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameter sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate waveforms, we need sets of parameters. For creating a training dataset, we'd like to randomly sample these parameters from some probability distributions.\n", "\n", "PyTorch has its own [probability distributions](https://pytorch.org/docs/stable/distributions.html), but there are some distributions that they haven't yet implemented (at least at time of writing), so we implemented them [ourselves](https://github.com/ML4GW/ml4gw/blob/main/ml4gw/distributions.py)." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "from ml4gw.distributions import PowerLaw, Sine, Cosine, DeltaFunction\n", "from torch.distributions import Uniform\n", "\n", "# On CPU, keep the number of waveforms around 100. On GPU, you can go higher,\n", "# subject to memory constraints.\n", "num_waveforms = 500\n", "\n", "# Create a dictionary of parameter distributions\n", "# This is not intended to be an astrophysically\n", "# meaningful distribution\n", "param_dict = {\n", " \"chirp_mass\": PowerLaw(10, 100, -2.35),\n", " \"mass_ratio\": Uniform(0.125, 0.999),\n", " \"chi1\": Uniform(-0.999, 0.999),\n", " \"chi2\": Uniform(-0.999, 0.999),\n", " \"distance\": PowerLaw(100, 1000, 2),\n", " \"phic\": DeltaFunction(0),\n", " \"inclination\": Sine(),\n", "}\n", "\n", "# And then sample from each of those distributions\n", "params = {\n", " k: v.sample((num_waveforms,)).to(device) for k, v in param_dict.items()\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generation in the frequency domain" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([500, 8032]) torch.Size([500, 8032])\n" ] } ], "source": [ "from ml4gw.waveforms import IMRPhenomD\n", "\n", "approximant = IMRPhenomD().to(device)\n", "\n", "# Calling the approximant with the frequency array, reference frequency, and waveform parameters\n", "# returns the cross and plus polarizations\n", "hc_f, hp_f = approximant(f=frequencies[freq_mask], f_ref=f_ref, **params)\n", "print(hc_f.shape, hp_f.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have the plus and cross polarizations 500 BBH waveforms in the frequency domain. We can plot one of them, just to take a look:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Note that I have to move data to CPU to plot\n", "# In an actual training setup, you wouldn't be moving data between devices so much\n", "plt.plot(frequencies[freq_mask].cpu(), torch.abs(hp_f[0]).cpu())\n", "plt.xscale(\"log\")\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"Frequency (Hz)\")\n", "plt.ylabel(\"Strain\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Time-domain waveforms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll now generate waveforms in the time domain using the `TimeDomainCBCWaveformGenerator`. This `Module` uses the `approximant` to generate a waveform `duration` seconds long with the given `sample_rate`. The coalescence point of the signal is placed `right_pad` seconds from the right edge of the window. For conditioning the frequency-domain waveforms, the parameter dictionary is required to contain the `mass_1`, `mass_2`, `s1z`, and `s2z` keys. We'll use a conversion function from [`ml4gw.waveforms.conversion`](https://github.com/ML4GW/ml4gw/blob/main/ml4gw/waveforms/conversion.py) to add these keys.\n", "\n", "Because we're using IMRPhenomD and have aligned spins `s1z = chi1` and `s2z = chi2`. If we had non-aligned spins, we could use the `bilby_spins_to_lalsim` conversion function to compute the cartesian spin components." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([500, 16384]) torch.Size([500, 16384])\n" ] } ], "source": [ "from ml4gw.waveforms.generator import TimeDomainCBCWaveformGenerator\n", "from ml4gw.waveforms.conversion import chirp_mass_and_mass_ratio_to_components\n", "\n", "waveform_generator = TimeDomainCBCWaveformGenerator(\n", " approximant=approximant,\n", " sample_rate=sample_rate,\n", " f_min=f_min,\n", " duration=waveform_duration,\n", " right_pad=0.5,\n", " f_ref=f_ref,\n", ").to(device)\n", "\n", "params[\"mass_1\"], params[\"mass_2\"] = chirp_mass_and_mass_ratio_to_components(\n", " params[\"chirp_mass\"], params[\"mass_ratio\"]\n", ")\n", "\n", "params[\"s1z\"], params[\"s2z\"] = params[\"chi1\"], params[\"chi2\"]\n", "\n", "hc, hp = waveform_generator(**params)\n", "print(hc.shape, hp.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have 500 BBH waveforms, 8 seconds long and sampled at 2048 Hz. We can plot one of these as well:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "times = torch.arange(0, waveform_duration, 1 / sample_rate)\n", "plt.plot(times, hp[0].cpu())\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Strain\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Waveform projection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now project these waveforms and get the observed strain. At present, the projection is the most basic, assuming a fixed orientation between the detector and the source over the duration of the signal. The source code for these functions can be found [here](https://github.com/ML4GW/ml4gw/blob/main/ml4gw/gw.py).\n", "\n", "Future feature:\n", "- Account for the Earth's rotation and orbit" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([500, 2, 16384])\n" ] } ], "source": [ "from ml4gw.gw import get_ifo_geometry, compute_observed_strain\n", "\n", "# Define probability distributions for sky location and polarization angle\n", "dec = Cosine()\n", "psi = Uniform(0, torch.pi)\n", "phi = Uniform(-torch.pi, torch.pi)\n", "\n", "# The interferometer geometry for V1 and K1 are also in ml4gw\n", "ifos = [\"H1\", \"L1\"]\n", "tensors, vertices = get_ifo_geometry(*ifos)\n", "\n", "# Pass the detector geometry, along with the polarizations and sky parameters,\n", "# to get the observed strain\n", "waveforms = compute_observed_strain(\n", " dec=dec.sample((num_waveforms,)).to(device),\n", " psi=psi.sample((num_waveforms,)).to(device),\n", " phi=phi.sample((num_waveforms,)).to(device),\n", " detector_tensors=tensors.to(device),\n", " detector_vertices=vertices.to(device),\n", " sample_rate=sample_rate,\n", " cross=hc,\n", " plus=hp,\n", ")\n", "print(waveforms.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have a batch of multi-channel time-series data. The first dimension is the batch dimension, and corresponds to the number of waveforms that were generated. The second dimension is the channel dimension, and corresponds to the interferometers that we chose to use in the order they were specified. The third dimension is the time dimension. \n", "\n", "We can plot this as well, though there won't be much difference from before." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(times, waveforms[0, 0].cpu(), label=\"H1\", alpha=0.5)\n", "plt.plot(times, waveforms[0, 1].cpu(), label=\"L1\", alpha=0.5)\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Strain\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PSD Estimation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our waveforms generated, one thing we might want to do is calculate their SNRs with respect to some background data. To do that, we'll need the power spectral density of the background. The [`SpectralDensity`](https://github.com/ML4GW/ml4gw/blob/main/ml4gw/transforms/spectral.py) module can take a batch of multi-channel timeseries data and compute the PSD along the time dimension. We'll begin by downloading some background data from the Gravitational Wave Open Science Center (GWOSC). This data comes from the Hanford and Livingston and was taken during O3.\n", "\n", "One important piece to note is that, due to the scale of the strain, the background data is cast to `double` precision before being given to the module to avoid certain values being zeroed out.\n", "\n", "Future feature:\n", "- Automatically cast input data to `double` unless the user specifies otherwise" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "from gwpy.timeseries import TimeSeries, TimeSeriesDict\n", "from pathlib import Path\n", "\n", "# Point this to whatever directory you want to house\n", "# all of the data products this notebook creates\n", "data_dir = Path(\"./data\")\n", "\n", "# And this to the directory where you want to download the data\n", "background_dir = data_dir / \"background_data\"\n", "background_dir.mkdir(parents=True, exist_ok=True)\n", "\n", "# These are the GPS time of the start and end of the segments.\n", "# There's no particular reason for these times, other than that they\n", "# contain analysis-ready data\n", "segments = [\n", " (1240579783, 1240587612), \n", " (1240594562, 1240606748), \n", " (1240624412, 1240644412),\n", " (1240644412, 1240654372),\n", " (1240658942, 1240668052),\n", "]\n", "\n", "for (start, end) in segments:\n", " # Download the data from GWOSC. This will take a few minutes.\n", " duration = end - start\n", " fname = background_dir / f\"background-{start}-{duration}.hdf5\"\n", " if fname.exists():\n", " continue\n", "\n", " ts_dict = TimeSeriesDict()\n", " for ifo in ifos:\n", " ts_dict[ifo] = TimeSeries.fetch_open_data(ifo, start, end, cache=True)\n", " ts_dict = ts_dict.resample(sample_rate)\n", " ts_dict.write(fname, format=\"hdf5\")" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 2049])\n" ] } ], "source": [ "from ml4gw.transforms import SpectralDensity\n", "import h5py\n", "\n", "fftlength = 2\n", "spectral_density = SpectralDensity(\n", " sample_rate=sample_rate,\n", " fftlength=fftlength,\n", " overlap=None,\n", " average=\"median\",\n", ").to(device)\n", "\n", "# This is H1 and L1 data from O3 that I downloaded earlier\n", "# We have tools for dataloading that I'll get to later\n", "background_file = background_dir / \"background-1240579783-7829.hdf5\"\n", "with h5py.File(background_file, \"r\") as f:\n", " background = [torch.Tensor(f[ifo][:]) for ifo in ifos]\n", " background = torch.stack(background).to(device)\n", "\n", "# Note cast to double\n", "psd = spectral_density(background.double())\n", "print(psd.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the PSDs and see that they look as expected:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "freqs = torch.linspace(0, nyquist, psd.shape[-1])\n", "plt.plot(freqs, psd.cpu()[0], label=\"H1\")\n", "plt.plot(freqs, psd.cpu()[1], label=\"L1\")\n", "plt.xscale(\"log\")\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"Frequency (Hz)\")\n", "plt.ylabel(\"PSD (1/Hz)\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SNR Calculation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the PSD from H1 and L1, we can calculate the SNRs of the waveforms we generated.\n", "\n", "Note: the shape of the PSDs is determined by the sampling rate and the FFT length used in the calculation, and so may not match up with the shape of the FFT'ed waveforms. We can interpolate the PSDs so that the dimensions match.\n", "\n", "Future feature:\n", "- Do this interpolation automatically somewhere unless the user specifies otherwise" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "from ml4gw.gw import compute_ifo_snr, compute_network_snr\n", "\n", "# Note need to interpolate\n", "if psd.shape[-1] != num_freqs:\n", " # Adding dummy dimensions for consistency\n", " while psd.ndim < 3:\n", " psd = psd[None]\n", " psd = torch.nn.functional.interpolate(\n", " psd, size=(num_freqs,), mode=\"linear\"\n", " )\n", "\n", "# We can compute both the individual and network SNRs\n", "# The SNR calculation starts at the minimum frequency we\n", "# specified earlier and goes to the maximum\n", "# TODO: There's probably no reason to have multiple functions\n", "h1_snr = compute_ifo_snr(\n", " responses=waveforms[:, 0],\n", " psd=psd[:, 0],\n", " sample_rate=sample_rate,\n", " highpass=f_min,\n", ")\n", "l1_snr = compute_ifo_snr(\n", " responses=waveforms[:, 1],\n", " psd=psd[:, 1],\n", " sample_rate=sample_rate,\n", " highpass=f_min,\n", ")\n", "network_snr = compute_network_snr(\n", " responses=waveforms, psd=psd, sample_rate=sample_rate, highpass=f_min\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot histograms of each of these SNR arrays:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(h1_snr.cpu(), bins=25, alpha=0.5, label=\"H1\")\n", "plt.hist(l1_snr.cpu(), bins=25, alpha=0.5, label=\"L1\")\n", "plt.hist(network_snr.cpu(), bins=25, alpha=0.5, label=\"Network\")\n", "plt.xlabel(\"SNR\")\n", "plt.ylabel(\"Count\")\n", "plt.xlim(0, 100)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks more or less like expected. But we've got a lot of low SNR signals, which a search will have trouble detecting. We can rescale our waveforms so that the SNR distribution is more favorable. This could be useful for something like curriculum learning: your network could begin with higher SNR events, and slowly be shown lower SNR events as training continues.\n", "\n", "We'll create an array of target SNRs based on a power law distribution that very roughly matches the above, but with a minimum SNR of 12 and maximum of 100." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ml4gw.gw import reweight_snrs\n", "\n", "target_snrs = PowerLaw(12, 100, -3).sample((num_waveforms,)).to(device)\n", "# Each waveform will be scaled by the ratio of its target SNR to its current SNR\n", "waveforms = reweight_snrs(\n", " responses=waveforms,\n", " target_snrs=target_snrs,\n", " psd=psd,\n", " sample_rate=sample_rate,\n", " highpass=f_min,\n", ")\n", "\n", "network_snr = compute_network_snr(\n", " responses=waveforms, psd=psd, sample_rate=sample_rate, highpass=f_min\n", ")\n", "\n", "plt.hist(network_snr.cpu(), bins=25, alpha=0.5, label=\"Network\")\n", "plt.xlabel(\"SNR\")\n", "plt.ylabel(\"Count\")\n", "plt.xlim(0, 100)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have a set of waveforms that are much easier to detect." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataloading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When training a model, it's beneficial to provide as much variety in data as you can. We benefit from having multiple independent detectors, which allows for combinatorially more background samples than a single detector would, as the samples don't have to be coincident.\n", "\n", "The [`Hdf5TimeSeriesDataset`](https://github.com/ML4GW/ml4gw/blob/6c68f4bade88362d5d6d27e912410a84765d09a0/ml4gw/dataloading/hdf5_dataset.py#L15) allows for randomly sampling windows of data from hdf5 files, provided they all have the same structure. Here, we have a set of background files, each containing H1 and L1 strain data. We could chop this data up into samples prior to training, but we'd be limited by the number of samples we could create and fit on disk. Instead, during training, we grab the data we need for each batch by randomly sampling from all of these files, giving us effectively unlimited background data to train on.\n", "\n", "Future feature:\n", "- Allow sampling from multiple datasets within the same file" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "background-1240579783-7829.hdf5 background-1240644412-9960.hdf5\n", "background-1240594562-12186.hdf5 background-1240658942-9110.hdf5\n", "background-1240624412-20000.hdf5\n" ] } ], "source": [ "!ls data/background_data/" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([1000, 2, 39936])\n" ] } ], "source": [ "from ml4gw.dataloading import Hdf5TimeSeriesDataset\n", "\n", "# Defining some parameters for future use, and to\n", "# determine the size of the windows to sample.\n", "# We're going to be whitening the last part of each\n", "# window with a PSD calculated from the first part,\n", "# so we need to grab enough data to do that\n", "\n", "# Length of data used to estimate PSD\n", "psd_length = 16\n", "psd_size = int(psd_length * sample_rate)\n", "\n", "# Length of filter. A segment of length fduration / 2\n", "# will be cropped from either side after whitening\n", "fduration = 2\n", "\n", "# Length of window of data we'll feed to our network\n", "kernel_length = 1.5\n", "kernel_size = int(1.5 * sample_rate)\n", "\n", "# Total length of data to sample\n", "window_length = psd_length + fduration + kernel_length\n", "\n", "fnames = list(background_dir.iterdir())\n", "dataloader = Hdf5TimeSeriesDataset(\n", " fnames=fnames,\n", " channels=ifos,\n", " kernel_size=int(window_length * sample_rate),\n", " batch_size=2\n", " * num_waveforms, # Grab twice as many background samples as we have waveforms\n", " batches_per_epoch=1, # Just doing 1 here for demonstration purposes\n", " coincident=False,\n", ")\n", "\n", "background_samples = [x for x in dataloader][0].to(device)\n", "print(background_samples.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Whitening" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's crucial to normalize your data before putting it through a neural network, and whitening provides a physically meaningful way of doing that. The [`Whiten`](https://github.com/ML4GW/ml4gw/blob/6c68f4bade88362d5d6d27e912410a84765d09a0/ml4gw/transforms/whitening.py#L15) module will whiten and optionally highpass batches of multi-channel data with a provided set of PSDs. If the tensor of PSDs is 2D, each batch element of data will be whitened using the same PSDs. If 3D, each batch element will be whitened by the PSDs contained along the 0th dimension. In other words, you can provide a single PSD for each channel to whiten all elements in that channel, or you can provide a PSD for each element. We're doing the latter here.\n", "\n", "Note that the whitening process automatically removes `fduration / 2` seconds from either side of the input data.\n", "\n", "Future feature:\n", "- Add an option to not crop" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PSD shape: torch.Size([1000, 2, 2049])\n", "Kernel shape: torch.Size([1000, 2, 7168])\n", "Whitened kernel shape: torch.Size([1000, 2, 3072])\n" ] } ], "source": [ "from ml4gw.transforms import Whiten\n", "\n", "whiten = Whiten(\n", " fduration=fduration, sample_rate=sample_rate, highpass=f_min\n", ").to(device)\n", "\n", "# Create PSDs using the first psd_length seconds of each sample\n", "# with the SpectralDensity module we defined earlier\n", "psd = spectral_density(background_samples[..., :psd_size].double())\n", "print(f\"PSD shape: {psd.shape}\")\n", "\n", "# Take everything after the first psd_length as our input kernel\n", "kernel = background_samples[..., psd_size:]\n", "# And whiten using our PSDs\n", "whitened_kernel = whiten(kernel, psd)\n", "print(f\"Kernel shape: {kernel.shape}\")\n", "print(f\"Whitened kernel shape: {whitened_kernel.shape}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the first segment of strain, we can see that the data looks as expected. For visual clarity, the plots show only H1 data. Again, note the difference in length before and after whitening." ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "times = torch.arange(0, kernel_length + fduration, 1 / sample_rate)\n", "plt.plot(times, kernel[0, 0].cpu())\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Strain\")\n", "plt.show()\n", "\n", "times = torch.arange(0, kernel_length, 1 / sample_rate)\n", "plt.plot(times, whitened_kernel[0, 0].cpu())\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Whitened strain\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we can add our waveforms into half of these background samples, taking care not to add them into data that will be removed during whitening. We'll use only the final `kernel_length` seconds of each waveform." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "pad = int(fduration / 2 * sample_rate)\n", "injected = kernel.detach().clone()\n", "# Inject waveforms into every other background sample\n", "injected[::2, :, pad:-pad] += waveforms[..., -kernel_size:]\n", "# And whiten with the same PSDs as before\n", "whitened_injected = whiten(injected, psd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot one of these as well, using the loudest signal for visibility." ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Factor of 2 because we injected every other sample\n", "idx = 2 * torch.argmax(network_snr)\n", "\n", "times = torch.arange(0, kernel_length + fduration, 1 / sample_rate)\n", "plt.plot(times, injected[idx, 0].cpu())\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Strain\")\n", "plt.show()\n", "\n", "times = torch.arange(0, kernel_length, 1 / sample_rate)\n", "plt.plot(times, whitened_injected[idx, 0].cpu())\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Whitened strain\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use this dataset we just created as a validation dataset during the training example, so I'll create a tensor of labels and save everything to file." ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "y = torch.zeros(len(injected))\n", "y[::2] = 1\n", "with h5py.File(data_dir / \"validation_dataset.hdf5\", \"w\") as f:\n", " f.create_dataset(\"X\", data=whitened_injected.cpu())\n", " f.create_dataset(\"y\", data=y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transforms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wanted to, we could stop with our data as-is and train a time-domain model (which is what we'll do in the example). But we could also transform our data into the time-frequency domain to create spectrograms, and treat this as an image classification problem. `ml4gw` has two methods for doing that: the [`MultiResolutionSpectrogram`](https://github.com/ML4GW/ml4gw/blob/6c68f4bade88362d5d6d27e912410a84765d09a0/ml4gw/transforms/spectrogram.py#L13) and the [`Q-transform`](https://github.com/ML4GW/ml4gw/blob/6c68f4bade88362d5d6d27e912410a84765d09a0/ml4gw/transforms/qtransform.py#L154).\n", "\n", "The multi-resolution spectrogram module creates a single spectrogram by combining multiple spectrograms that were calculated using different numbers of FFTs. This is similar to what PySTAMPAS does as part of their pipeline. The Q-transform is a translation of GWpy's implementation, with some torch-specific speed-ups.\n", "\n", "The spectrogram module uses torchaudio's [`Spectrogram`](https://pytorch.org/audio/main/generated/torchaudio.transforms.Spectrogram.html) object, and can accept any of the same keyword arguments, given in a list. " ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "from ml4gw.transforms import (\n", " MultiResolutionSpectrogram,\n", " QScan,\n", " SingleQTransform,\n", ")\n", "\n", "mrs = MultiResolutionSpectrogram(\n", " kernel_length=kernel_length,\n", " sample_rate=sample_rate,\n", " n_fft=[\n", " 64,\n", " 128,\n", " 256,\n", " ], # Specififying just one value will create a single-resolution spectrogram\n", ").to(device)\n", "\n", "# The Q-transform can be accessed either through the QScan,\n", "# which will look over a range of q values, or through the\n", "# SingleQTransform, which uses a given q value\n", "qscan = QScan(\n", " duration=kernel_length,\n", " sample_rate=sample_rate,\n", " spectrogram_shape=[512, 512],\n", " qrange=[4, 128],\n", ").to(device)\n", "sqt = SingleQTransform(\n", " duration=kernel_length,\n", " sample_rate=sample_rate,\n", " spectrogram_shape=[512, 512],\n", " q=12,\n", ").to(device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll pass our whitened and injected data to each of these transforms, which will process the entire batch at once, and plot the first sample.\n", "\n", "Note: the axes on each plot correspond to a bin index, not the actual time and frequency\n", "\n", "Note: the multi-resolution spectrogram has linearly-spaced frequency bins, while the Q-transform has log-spaced bins\n", "\n", "Neither of these aspects are really a problem for training a model (all that really matters is that you treat all your data the same way), but it's annoying when it comes to making plots.\n", "\n", "Future features:\n", "- Store the time and frequency values corresponding to each bin as attributes of the object\n", "- Allow either frequency bin spacing" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "specgram = mrs(whitened_injected)\n", "plt.imshow(specgram[idx, 0].cpu(), aspect=\"auto\", origin=\"lower\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "specgram = qscan(whitened_injected)\n", "plt.imshow(specgram[idx, 0].cpu(), aspect=\"auto\", origin=\"lower\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "specgram = sqt(whitened_injected)\n", "plt.imshow(specgram[idx, 0].cpu(), aspect=\"auto\", origin=\"lower\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The big benefit here is _speed_. We computed 2,000 Q-transforms in a fraction of a second. The downside is that we needed to use the same q value for each of them, but there are applications where that may not be a problem. For instance, BBH signals are typically better represented with lower q values. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are other [transforms](https://github.com/ML4GW/ml4gw/tree/main/ml4gw/transforms) that I don't have time to go into:\n", "- Channel-wise scaler\n", "- Spline interpolation (though note the caveats)\n", "- Fixed whitener, which is fit to a set PSD\n", "- Pearson correlation calculator\n", "\n", "Future features:\n", "- Fix edge effects of spline interpolation\n", "- Multi-rate resampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Architectures\n", "\n", "We've implemented a couple basic [neural network architectures](https://github.com/ML4GW/ml4gw/tree/main/ml4gw/nn) for out-of-the-box convenience. Today, we'll be using a 1D ResNet, which is mostly copied from PyTorch's implementation, but with a few key differences:\n", "- Arbitrary kernel sizes\n", "- `GroupNorm` as the default normalization layer, as training statistics in general won't match testing statistics\n", "- Custom `GroupNorm` implementation which is faster than the standard PyTorch version at inference time" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[-0.3213]], device='cuda:0')\n" ] } ], "source": [ "from ml4gw.nn.resnet import ResNet1D\n", "\n", "architecture = ResNet1D(\n", " in_channels=2, # H1 and L1 as input channels\n", " layers=[2, 2], # Keep things small and do a ResNet10\n", " classes=1, # Single scalar-valued output\n", " kernel_size=3, # Size of convolutional kernels, not to be confused with data size\n", ").to(device)\n", "\n", "# And we can, e.g., pass the first element of our validation set\n", "with torch.no_grad():\n", " print(architecture(whitened_injected[0][None]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example training setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll now go through an example of putting many of these pieces together into a single model, implemented using th [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/) framework.\n", "\n", "Begin by clearing the GPU:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "import gc\n", "\n", "gc.collect()\n", "torch.cuda.empty_cache()\n", "torch.cuda.reset_peak_memory_stats()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "from ml4gw import augmentations, distributions, gw, transforms, waveforms\n", "from ml4gw.dataloading import ChunkedTimeSeriesDataset, Hdf5TimeSeriesDataset\n", "from ml4gw.utils.slicing import sample_kernels\n", "import torch\n", "from lightning import pytorch as pl\n", "import torchmetrics\n", "from torchmetrics.classification import BinaryAUROC\n", "\n", "from typing import Callable, Dict, List\n", "\n", "\n", "class Ml4gwDetectionModel(pl.LightningModule):\n", " \"\"\"\n", " Model with methods for generating waveforms and\n", " performing our preprocessing augmentations in\n", " real-time on the GPU. Also loads training background\n", " in chunks from disk, then samples batches from chunks.\n", " \"\"\"\n", "\n", " def __init__(\n", " self,\n", " architecture: torch.nn.Module,\n", " metric: torchmetrics.Metric,\n", " ifos: List[str] = [\"H1\", \"L1\"],\n", " kernel_length: float = 1.5,\n", " # PSD/whitening args\n", " fduration: float = 2,\n", " psd_length: float = 16,\n", " sample_rate: float = 2048,\n", " fftlength: float = 2,\n", " highpass: float = 32,\n", " # Dataloading args\n", " chunk_length: float = 128, # we'll talk about chunks in a second\n", " reads_per_chunk: int = 40,\n", " learning_rate: float = 0.005,\n", " batch_size: int = 256,\n", " # Waveform generation args\n", " waveform_prob: float = 0.5,\n", " approximant: Callable = waveforms.cbc.IMRPhenomD,\n", " param_dict: Dict[str, torch.distributions.Distribution] = param_dict,\n", " waveform_duration: float = 8,\n", " f_min: float = 20,\n", " f_max: float = None,\n", " f_ref: float = 20,\n", " # Augmentation args\n", " inversion_prob: float = 0.5,\n", " reversal_prob: float = 0.5,\n", " min_snr: float = 12,\n", " max_snr: float = 100,\n", " ) -> None:\n", " super().__init__()\n", " self.save_hyperparameters(\n", " ignore=[\"architecture\", \"metric\", \"approximant\"]\n", " )\n", " self.nn = architecture\n", " self.metric = metric\n", "\n", " self.inverter = augmentations.SignalInverter(prob=inversion_prob)\n", " self.reverser = augmentations.SignalReverser(prob=reversal_prob)\n", "\n", " # real-time transformations defined with torch Modules\n", " self.spectral_density = transforms.SpectralDensity(\n", " sample_rate, fftlength, average=\"median\", fast=False\n", " )\n", " self.whitener = transforms.Whiten(\n", " fduration, sample_rate, highpass=highpass\n", " )\n", "\n", " # get some geometry information about\n", " # the interferometers we're going to project to\n", " detector_tensors, vertices = gw.get_ifo_geometry(*ifos)\n", " self.register_buffer(\"detector_tensors\", detector_tensors)\n", " self.register_buffer(\"detector_vertices\", vertices)\n", "\n", " # define some sky parameter distributions\n", " self.param_dict = param_dict\n", " self.dec = distributions.Cosine()\n", " self.psi = torch.distributions.Uniform(0, torch.pi)\n", " self.phi = torch.distributions.Uniform(\n", " -torch.pi, torch.pi\n", " ) # relative RAs of detector and source\n", " self.waveform_generator = TimeDomainCBCWaveformGenerator(\n", " approximant=approximant(),\n", " sample_rate=sample_rate,\n", " duration=waveform_duration,\n", " f_min=f_min,\n", " f_ref=f_ref,\n", " right_pad=0.5,\n", " ).to(self.device)\n", "\n", " # rather than sample distances, we'll sample target SNRs.\n", " # This way we can ensure we train our network on\n", " # signals that are more detectable. We'll use a distribution\n", " # that looks roughly like the natural sampled SNR distribution\n", " self.snr = distributions.PowerLaw(min_snr, max_snr, -3)\n", "\n", " # up front let's define some properties in units of samples\n", " # Note the different usage of window_size from earlier\n", " self.kernel_size = int(kernel_length * sample_rate)\n", " self.window_size = self.kernel_size + int(fduration * sample_rate)\n", " self.psd_size = int(psd_length * sample_rate)\n", "\n", " def forward(self, X):\n", " return self.nn(X)\n", "\n", " def training_step(self, batch):\n", " X, y = batch\n", " y_hat = self(X)\n", " loss = torch.nn.functional.binary_cross_entropy_with_logits(y_hat, y)\n", " self.log(\"train_loss\", loss, on_step=True, prog_bar=True)\n", " return loss\n", "\n", " def validation_step(self, batch):\n", " X, y = batch\n", " y_hat = self(X)\n", " self.metric.update(y_hat, y)\n", " self.log(\"valid_auroc\", self.metric, on_epoch=True, prog_bar=True)\n", "\n", " def configure_optimizers(self):\n", " parameters = self.nn.parameters()\n", " optimizer = torch.optim.AdamW(parameters, self.hparams.learning_rate)\n", " scheduler = torch.optim.lr_scheduler.OneCycleLR(\n", " optimizer,\n", " self.hparams.learning_rate,\n", " pct_start=0.1,\n", " total_steps=self.trainer.estimated_stepping_batches,\n", " )\n", " scheduler_config = dict(scheduler=scheduler, interval=\"step\")\n", " return dict(optimizer=optimizer, lr_scheduler=scheduler_config)\n", "\n", " def configure_callbacks(self):\n", " chkpt = pl.callbacks.ModelCheckpoint(monitor=\"valid_auroc\", mode=\"max\")\n", " return [chkpt]\n", "\n", " def generate_waveforms(self, batch_size: int) -> tuple[torch.Tensor, ...]:\n", " rvs = torch.rand(size=(batch_size,))\n", " mask = rvs < self.hparams.waveform_prob\n", " num_injections = mask.sum().item()\n", "\n", " params = {\n", " k: v.sample((num_injections,)).to(device)\n", " for k, v in self.param_dict.items()\n", " }\n", "\n", " params[\"s1z\"], params[\"s2z\"] = (\n", " params[\"chi1\"], params[\"chi2\"]\n", " )\n", " params[\"mass_1\"], params[\"mass_2\"] = waveforms.conversion.chirp_mass_and_mass_ratio_to_components(\n", " params[\"chirp_mass\"], params[\"mass_ratio\"]\n", " )\n", "\n", " hc, hp = self.waveform_generator(**params)\n", " return hc, hp, mask\n", "\n", " def project_waveforms(\n", " self, hc: torch.Tensor, hp: torch.Tensor\n", " ) -> torch.Tensor:\n", " # sample sky parameters\n", " N = len(hc)\n", " dec = self.dec.sample((N,)).to(hc)\n", " psi = self.psi.sample((N,)).to(hc)\n", " phi = self.phi.sample((N,)).to(hc)\n", "\n", " # project to interferometer response\n", " return gw.compute_observed_strain(\n", " dec=dec,\n", " psi=psi,\n", " phi=phi,\n", " detector_tensors=self.detector_tensors,\n", " detector_vertices=self.detector_vertices,\n", " sample_rate=self.hparams.sample_rate,\n", " cross=hc,\n", " plus=hp,\n", " )\n", "\n", " def rescale_snrs(\n", " self, responses: torch.Tensor, psd: torch.Tensor\n", " ) -> torch.Tensor:\n", " # make sure everything has the same number of frequency bins\n", " num_freqs = int(responses.size(-1) // 2) + 1\n", " if psd.size(-1) != num_freqs:\n", " psd = torch.nn.functional.interpolate(\n", " psd, size=(num_freqs,), mode=\"linear\"\n", " )\n", " N = len(responses)\n", " target_snrs = self.snr.sample((N,)).to(responses.device)\n", " return gw.reweight_snrs(\n", " responses=responses.double(),\n", " target_snrs=target_snrs,\n", " psd=psd,\n", " sample_rate=self.hparams.sample_rate,\n", " highpass=self.hparams.highpass,\n", " )\n", "\n", " def sample_waveforms(self, responses: torch.Tensor) -> torch.Tensor:\n", " # slice off random views of each waveform to inject in arbitrary positions\n", " responses = responses[:, :, -self.window_size :]\n", "\n", " # pad so that at least half the kernel always contains signals\n", " pad = [0, int(self.window_size // 2)]\n", " responses = torch.nn.functional.pad(responses, pad)\n", " return sample_kernels(responses, self.window_size, coincident=True)\n", "\n", " @torch.no_grad()\n", " def augment(self, X: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\n", " # break off \"background\" from target kernel and compute its PSD\n", " # (in double precision since our scale is so small)\n", " background, X = torch.split(\n", " X, [self.psd_size, self.window_size], dim=-1\n", " )\n", " psd = self.spectral_density(background.double())\n", "\n", " # Generate at most batch_size signals from our parameter distributions\n", " # Keep a mask that indicates which rows to inject in\n", " batch_size = X.size(0)\n", " hc, hp, mask = self.generate_waveforms(batch_size)\n", " hc, hp, mask = hc, hp, mask\n", "\n", " # Augment with inversion and reversal\n", " X = self.inverter(X)\n", " X = self.reverser(X)\n", "\n", " # sample sky parameters and project to responses, then\n", " # rescale the response according to a randomly sampled SNR\n", " responses = self.project_waveforms(hc, hp)\n", " responses = self.rescale_snrs(responses, psd[mask])\n", "\n", " # randomly slice out a window of the waveform, add it\n", " # to our background, then whiten everything\n", " responses = self.sample_waveforms(responses)\n", " X[mask] += responses.float()\n", " X = self.whitener(X, psd)\n", "\n", " # create labels, marking 1s where we injected\n", " y = torch.zeros((batch_size, 1), device=X.device)\n", " y[mask] = 1\n", " return X, y\n", "\n", " def on_after_batch_transfer(self, batch, _):\n", " # this is a parent method that lightning calls\n", " # between when the batch gets moved to GPU and\n", " # when it gets passed to the training_step.\n", " # Apply our augmentations here\n", " if self.trainer.training:\n", " batch = self.augment(batch)\n", " return batch\n", "\n", " def train_dataloader(self):\n", " # Because our entire training dataset is generated\n", " # on the fly, the traditional idea of an \"epoch\"\n", " # meaning one pass through the training set doesn't\n", " # apply here. Instead, we have to set the number\n", " # of batches per epoch ourselves, which really\n", " # just amounts to deciding how often we want\n", " # to run over the validation dataset.\n", " samples_per_epoch = 3000\n", " batches_per_epoch = (\n", " int((samples_per_epoch - 1) // self.hparams.batch_size) + 1\n", " )\n", " batches_per_chunk = int(batches_per_epoch // 10)\n", " chunks_per_epoch = int(batches_per_epoch // batches_per_chunk) + 1\n", "\n", " # Hdf5TimeSeries dataset samples batches from disk.\n", " # In this instance, we'll make our batches really large so that\n", " # we can treat them as chunks to sample training batches from\n", " fnames = list(background_dir.iterdir())\n", " dataset = Hdf5TimeSeriesDataset(\n", " fnames=fnames,\n", " channels=self.hparams.ifos,\n", " kernel_size=int(\n", " self.hparams.chunk_length * self.hparams.sample_rate\n", " ),\n", " batch_size=self.hparams.reads_per_chunk,\n", " batches_per_epoch=chunks_per_epoch,\n", " coincident=False,\n", " )\n", "\n", " # sample batches to pass to our NN from the chunks loaded from disk\n", " return ChunkedTimeSeriesDataset(\n", " dataset,\n", " kernel_size=self.window_size + self.psd_size,\n", " batch_size=self.hparams.batch_size,\n", " batches_per_chunk=batches_per_chunk,\n", " coincident=False,\n", " )\n", "\n", " def val_dataloader(self):\n", " with h5py.File(data_dir / \"validation_dataset.hdf5\", \"r\") as f:\n", " X = torch.Tensor(f[\"X\"][:])\n", " y = torch.Tensor(f[\"y\"][:])\n", " dataset = torch.utils.data.TensorDataset(X, y)\n", " return torch.utils.data.DataLoader(\n", " dataset,\n", " batch_size=self.hparams.batch_size * 4,\n", " shuffle=False,\n", " pin_memory=True,\n", " )" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "architecture = ResNet1D(\n", " in_channels=2,\n", " layers=[2, 2],\n", " classes=1,\n", " kernel_size=3,\n", ").to(device)\n", "\n", "max_fpr = 1e-3\n", "metric = BinaryAUROC(max_fpr=max_fpr)\n", "\n", "model = Ml4gwDetectionModel(\n", " architecture=architecture,\n", " metric=metric,\n", ")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Trainer will use only 1 of 8 GPUs because it is running inside an interactive / notebook environment. You may try to set `Trainer(devices=8)` but please note that multi-GPU inside interactive / notebook environments is considered experimental and unstable. Your mileage may vary.\n", "Using 16bit Automatic Mixed Precision (AMP)\n", "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", "HPU available: False, using: 0 HPUs\n", "The following callbacks returned in `LightningModule.configure_callbacks` will override existing callbacks passed to Trainer: ModelCheckpoint\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", "Loading `train_dataloader` to estimate number of stepping batches.\n" ] }, { "data": { "text/html": [ "
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       "┃    Name                Type                            Params  Mode  ┃\n",
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       "│ 0 │ nn                 │ ResNet1D                       │  232 K │ train │\n",
       "│ 1 │ metric             │ BinaryAUROC                    │      0 │ train │\n",
       "│ 2 │ inverter           │ SignalInverter                 │      0 │ train │\n",
       "│ 3 │ reverser           │ SignalReverser                 │      0 │ train │\n",
       "│ 4 │ spectral_density   │ SpectralDensity                │      0 │ train │\n",
       "│ 5 │ whitener           │ Whiten                         │      0 │ train │\n",
       "│ 6 │ waveform_generator │ TimeDomainCBCWaveformGenerator │      0 │ train │\n",
       "└───┴────────────────────┴────────────────────────────────┴────────┴───────┘\n",
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Trainable params: 232 K                                                                                            \n",
       "Non-trainable params: 0                                                                                            \n",
       "Total params: 232 K                                                                                                \n",
       "Total estimated model params size (MB): 0                                                                          \n",
       "Modules in train mode: 45                                                                                          \n",
       "Modules in eval mode: 0                                                                                            \n",
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/home/william.benoit/ML4GW/ml4gw/.venv/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_conne\n",
       "ctor.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the \n",
       "value of the `num_workers` argument` to `num_workers=79` in the `DataLoader` to improve performance.\n",
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_dir = data_dir / \"logs\"\n",
    "\n",
    "logger = pl.loggers.CSVLogger(log_dir, name=\"ml4gw-expt\")\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=30,\n",
    "    precision=\"16-mixed\",\n",
    "    log_every_n_steps=5,\n",
    "    logger=logger,\n",
    "    callbacks=[pl.callbacks.RichProgressBar()],\n",
    "    accelerator=\"gpu\",\n",
    ")\n",
    "trainer.fit(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot the metrics from our run and see the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "path = log_dir / Path(\"ml4gw-expt\")\n",
    "# Take the most recent run, if we've done multiple\n",
    "versions = [int(str(dir).split(\"_\")[-1]) for dir in path.iterdir()]\n",
    "version = sorted(versions)[-1]\n",
    "\n",
    "with open(path / f\"version_{version}/metrics.csv\", newline=\"\") as f:\n",
    "    reader = csv.reader(f, delimiter=\",\")\n",
    "    train_steps, train_loss, valid_steps, valid_loss = [], [], [], []\n",
    "    _ = next(reader)\n",
    "    for row in reader:\n",
    "        if row[2] != \"\":\n",
    "            train_steps.append(int(row[1]))\n",
    "            train_loss.append(float(row[2]))\n",
    "        else:\n",
    "            valid_steps.append(int(row[1]))\n",
    "            valid_loss.append(float(row[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
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