use relative imports everywhere (ML4GW#190)

ml4gw/dataloading/chunked_dataset.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from ml4gw.types import WaveformTensor
+from ..types import WaveformTensor
 
 
 class ChunkedTimeSeriesDataset(torch.utils.data.IterableDataset):

ml4gw/dataloading/hdf5_dataset.py

@@ -5,7 +5,7 @@
 import numpy as np
 import torch
 
-from ml4gw.types import WaveformTensor
+from ..types import WaveformTensor
 
 
 class ContiguousHdf5Warning(Warning):

ml4gw/dataloading/in_memory_dataset.py

@@ -5,7 +5,7 @@
 from jaxtyping import Float
 from torch import Tensor
 
-from ml4gw.utils.slicing import slice_kernels
+from ..utils.slicing import slice_kernels
 
 
 class InMemoryDataset(torch.utils.data.IterableDataset):

ml4gw/gw.py

@@ -16,8 +16,10 @@
 from jaxtyping import Float
 from torch import Tensor
 
-from ml4gw.constants import C
-from ml4gw.types import (
+from ml4gw.utils.interferometer import InterferometerGeometry
+
+from .constants import C
+from .types import (
     BatchTensor,
     NetworkDetectorTensors,
     NetworkVertices,
@@ -26,7 +28,6 @@
     VectorGeometry,
     WaveformTensor,
 )
-from ml4gw.utils.interferometer import InterferometerGeometry
 
 
 def outer(x: VectorGeometry, y: VectorGeometry) -> TensorGeometry:

ml4gw/nn/autoencoder/base.py

@@ -4,7 +4,7 @@
 import torch
 from torch import Tensor
 
-from ml4gw.nn.autoencoder.skip_connection import SkipConnection
+from .skip_connection import SkipConnection
 
 
 class Autoencoder(torch.nn.Module):

ml4gw/nn/autoencoder/convolutional.py

@@ -4,9 +4,9 @@
 import torch
 from torch import Tensor
 
-from ml4gw.nn.autoencoder.base import Autoencoder
-from ml4gw.nn.autoencoder.skip_connection import SkipConnection
-from ml4gw.nn.autoencoder.utils import match_size
+from .base import Autoencoder
+from .skip_connection import SkipConnection
+from .utils import match_size
 
 Module = Callable[[...], torch.nn.Module]
 

ml4gw/nn/autoencoder/skip_connection.py

@@ -1,7 +1,7 @@
 import torch
 from torch import Tensor
 
-from ml4gw.nn.autoencoder.utils import match_size
+from .utils import match_size
 
 
 class SkipConnection(torch.nn.Module):

ml4gw/nn/resnet/resnet_1d.py

@@ -13,7 +13,7 @@
 import torch.nn as nn
 from torch import Tensor
 
-from ml4gw.nn.norm import GroupNorm1DGetter, NormLayer
+from ..norm import GroupNorm1DGetter, NormLayer
 
 
 def convN(

ml4gw/nn/resnet/resnet_2d.py

@@ -10,7 +10,7 @@
 import torch.nn as nn
 from torch import Tensor
 
-from ml4gw.nn.norm import GroupNorm2DGetter, NormLayer
+from ..norm import GroupNorm2DGetter, NormLayer
 
 
 def convN(

ml4gw/nn/streaming/online_average.py

@@ -4,7 +4,7 @@
 from jaxtyping import Float
 from torch import Tensor
 
-from ml4gw.utils.slicing import unfold_windows
+from ...utils.slicing import unfold_windows
 
 
 class OnlineAverager(torch.nn.Module):

ml4gw/nn/streaming/snapshotter.py

@@ -4,7 +4,7 @@
 from jaxtyping import Float
 from torch import Tensor
 
-from ml4gw.utils.slicing import unfold_windows
+from ...utils.slicing import unfold_windows
 
 
 class Snapshotter(torch.nn.Module):

ml4gw/spectral.py

@@ -15,7 +15,7 @@
 from jaxtyping import Float
 from torch import Tensor
 
-from ml4gw.types import (
+from .types import (
     FrequencySeries1to3d,
     PSDTensor,
     TimeSeries1to3d,

ml4gw/transforms/pearson.py

@@ -2,8 +2,8 @@
 from jaxtyping import Float
 from torch import Tensor
 
-from ml4gw.types import TimeSeries1to3d
-from ml4gw.utils.slicing import unfold_windows
+from ..types import TimeSeries1to3d
+from ..utils.slicing import unfold_windows
 
 
 class ShiftedPearsonCorrelation(torch.nn.Module):

ml4gw/transforms/qtransform.py

@@ -7,8 +7,8 @@
 from jaxtyping import Float, Int
 from torch import Tensor
 
-from ml4gw.transforms.spline_interpolation import SplineInterpolate
-from ml4gw.types import FrequencySeries1to3d, TimeSeries1to3d, TimeSeries3d
+from ..types import FrequencySeries1to3d, TimeSeries1to3d, TimeSeries3d
+from .spline_interpolation import SplineInterpolate
 
 """
 All based on https://github.com/gwpy/gwpy/blob/v3.0.8/gwpy/signal/qtransform.py

ml4gw/transforms/scaler.py

@@ -4,7 +4,7 @@
 from jaxtyping import Float
 from torch import Tensor
 
-from ml4gw.transforms.transform import FittableTransform
+from .transform import FittableTransform
 
 
 class ChannelWiseScaler(FittableTransform):

ml4gw/transforms/snr_rescaler.py

@@ -2,9 +2,9 @@
 
 import torch
 
-from ml4gw.gw import compute_network_snr
-from ml4gw.transforms.transform import FittableSpectralTransform
-from ml4gw.types import BatchTensor, TimeSeries2d, WaveformTensor
+from ..gw import compute_network_snr
+from ..types import BatchTensor, TimeSeries2d, WaveformTensor
+from .transform import FittableSpectralTransform
 
 
 class SnrRescaler(FittableSpectralTransform):

ml4gw/transforms/spectral.py

@@ -4,8 +4,8 @@
 from jaxtyping import Float
 from torch import Tensor
 
-from ml4gw.spectral import fast_spectral_density, spectral_density
-from ml4gw.types import FrequencySeries1to3d, TimeSeries1to3d
+from ..spectral import fast_spectral_density, spectral_density
+from ..types import FrequencySeries1to3d, TimeSeries1to3d
 
 
 class SpectralDensity(torch.nn.Module):

ml4gw/transforms/spectrogram.py

@@ -7,7 +7,7 @@
 from torch import Tensor
 from torchaudio.transforms import Spectrogram
 
-from ml4gw.types import TimeSeries3d
+from ..types import TimeSeries3d
 
 
 class MultiResolutionSpectrogram(torch.nn.Module):

ml4gw/transforms/transform.py

@@ -2,8 +2,8 @@
 
 import torch
 
-from ml4gw.spectral import spectral_density
-from ml4gw.types import FrequencySeries1to3d, TimeSeries1to3d
+from ..spectral import spectral_density
+from ..types import FrequencySeries1to3d, TimeSeries1to3d
 
 
 class FittableTransform(torch.nn.Module):

ml4gw/transforms/waveforms.py

@@ -4,8 +4,8 @@
 from jaxtyping import Float
 from torch import Tensor
 
-from ml4gw import gw
-from ml4gw.types import BatchTensor
+from .. import gw
+from ..types import BatchTensor
 
 
 # TODO: should these live in ml4gw.waveforms submodule?

ml4gw/transforms/whitening.py

@@ -2,14 +2,14 @@
 
 import torch
 
-from ml4gw import spectral
-from ml4gw.transforms.transform import FittableSpectralTransform
-from ml4gw.types import (
+from .. import spectral
+from ..types import (
     FrequencySeries1d,
     FrequencySeries1to3d,
     TimeSeries1d,
     TimeSeries3d,
 )
+from .transform import FittableSpectralTransform
 
 
 class Whiten(torch.nn.Module):

ml4gw/utils/slicing.py

@@ -5,12 +5,7 @@
 from torch import Tensor
 from torch.nn.functional import unfold
 
-from ml4gw.types import (
-    TimeSeries1d,
-    TimeSeries1to3d,
-    TimeSeries2d,
-    TimeSeries3d,
-)
+from ..types import TimeSeries1d, TimeSeries1to3d, TimeSeries2d, TimeSeries3d
 
 BatchTimeSeriesTensor = Union[Float[Tensor, "batch time"], TimeSeries3d]
 

ml4gw/waveforms/cbc/phenom_d.py

@@ -1,9 +1,8 @@
 import torch
 from jaxtyping import Float
 
-from ml4gw.constants import MTSUN_SI, PI
-from ml4gw.types import BatchTensor, FrequencySeries1d
-
+from ...constants import MTSUN_SI, PI
+from ...types import BatchTensor, FrequencySeries1d
 from .phenom_d_data import QNMData_a, QNMData_fdamp, QNMData_fring
 from .taylorf2 import TaylorF2
 

ml4gw/waveforms/cbc/taylorf2.py

@@ -1,10 +1,10 @@
 import torch
 from jaxtyping import Float
 
-from ml4gw.constants import MPC_SEC, MTSUN_SI, PI
-from ml4gw.constants import EulerGamma as GAMMA
-from ml4gw.types import BatchTensor, FrequencySeries1d
-from ml4gw.waveforms.conversion import chirp_mass_and_mass_ratio_to_components
+from ...constants import MPC_SEC, MTSUN_SI, PI
+from ...constants import EulerGamma as GAMMA
+from ...types import BatchTensor, FrequencySeries1d
+from ..conversion import chirp_mass_and_mass_ratio_to_components
 
 
 class TaylorF2(torch.nn.Module):

ml4gw/waveforms/conversion.py

@@ -1,7 +1,7 @@
 import torch
 
-from ml4gw.constants import MTSUN_SI, PI
-from ml4gw.types import BatchTensor
+from ..constants import MTSUN_SI, PI
+from ..types import BatchTensor
 
 
 def rotate_z(angle: BatchTensor, x, y, z):