# Documentation¶

class kymatio.Scattering1D(J, shape, Q=1, max_order=2, average=True, oversampling=0, vectorize=True)[source]

Bases: object

The 1D scattering transform

The scattering transform computes a cascade of wavelet transforms alternated with a complex modulus non-linearity. The scattering transform of a 1D signal may be written as where , , and .

In the above formulas, denotes convolution in time. The filters and are analytic wavelets with center frequencies and , while is a real lowpass filter centered at the zero frequency.

The Scattering1D class implements the 1D scattering transform for a given set of filters whose parameters are specified at initialization. While the wavelets are fixed, other parameters may be changed after the object is created, such as whether to compute all of , , and or just and .

The scattering transform may be computed on the CPU (the default) or a GPU, if available. A Scattering1D object may be transferred from one to the other using the cuda() and cpu() methods.

Given an input Tensor x of size (B, T), where B is the number of signals to transform (the batch size) and T is the length of the signal, we compute its scattering transform by passing it to the forward() method.

Example

# Set the parameters of the scattering transform.
J = 6
T = 2**13
Q = 8

# Generate a sample signal.
x = torch.randn(1, 1, T)

# Define a Scattering1D object.
S = Scattering1D(J, T, Q)

# Calculate the scattering transform.
Sx = S.forward(x)


Above, the length of the signal is T = 2**13 = 8192, while the maximum scale of the scattering transform is set to 2**J = 2**6 = 64. The time-frequency resolution of the first-order wavelets is set to Q = 8 wavelets per octave. The second-order wavelets always have one wavelet per octave.

Parameters: J (int) – The maximum log-scale of the scattering transform. In other words, the maximum scale is given by 2**J. T (int) – The length of the input signals. Q (int >= 1) – The number of first-order wavelets per octave (second-order wavelets are fixed to one wavelet per octave). Defaults to 1. max_order (int, optional) – The maximum order of scattering coefficients to compute. Must be either 1 or 2. Defaults to 2. average (boolean, optional) – Determines whether the output is averaged in time or not. The averaged output corresponds to the standard scattering transform, while the un-averaged output skips the last convolution by . This parameter may be modified after object creation. Defaults to True. oversampling (integer >= 0, optional) – Controls the oversampling factor relative to the default as a power of two. Since the convolving by wavelets (or lowpass filters) and taking the modulus reduces the high-frequency content of the signal, we can subsample to save space and improve performance. However, this may reduce precision in the calculation. If this is not desirable, oversampling can be set to a large value to prevent too much subsampling. This parameter may be modified after object creation. Defaults to 0. vectorize (boolean, optional) – Determines wheter to return a vectorized scattering transform (that is, a large array containing the output) or a dictionary (where each entry corresponds to a separate scattering coefficient). This parameter may be modified after object creation. Defaults to True.
J

int – The maximum log-scale of the scattering transform. In other words, the maximum scale is given by 2**J.

shape

int – The length of the input signals.

Q

int – The number of first-order wavelets per octave (second-order wavelets are fixed to one wavelet per octave).

J_pad

int – The logarithm of the padded length of the signals.

pad_left

int – The amount of padding to the left of the signal.

pad_right

int – The amount of padding to the right of the signal.

phi_f

dictionary – A dictionary containing the lowpass filter at all resolutions. See filter_bank.scattering_filter_factory for an exact description.

psi1_f

dictionary – A dictionary containing all the first-order wavelet filters, each represented as a dictionary containing that filter at all resolutions. See filter_bank.scattering_filter_factory for an exact description.

psi2_f

dictionary – A dictionary containing all the second-order wavelet filters, each represented as a dictionary containing that filter at all resolutions. See filter_bank.scattering_filter_factory for an exact description. description

max_order

int – The maximum scattering order of the transform.

average

boolean – Controls whether the output should be averaged (the standard scattering transform) or not (resulting in wavelet modulus coefficients). Note that to obtain unaveraged output, the vectorize flag must be set to False.

oversampling

int – The number of powers of two to oversample the output compared to the default subsampling rate determined from the filters.

vectorize

boolean – Controls whether the output should be vectorized into a single Tensor or collected into a dictionary. For more details, see the documentation for forward().

build()[source]

Set up padding and filters

Certain internal data, such as the amount of padding and the wavelet filters to be used in the scattering transform, need to be computed from the parameters given during construction. This function is called automatically during object creation and no subsequent calls are therefore needed.

static compute_meta_scattering(J, Q, max_order=2)[source]

Get metadata on the transform.

This information specifies the content of each scattering coefficient, which order, which frequencies, which filters were used, and so on.

Parameters: J (int) – The maximum log-scale of the scattering transform. In other words, the maximum scale is given by 2**J. Q (int >= 1) – The number of first-order wavelets per octave. Second-order wavelets are fixed to one wavelet per octave. max_order (int, optional) – The maximum order of scattering coefficients to compute. Must be either equal to 1 or 2. Defaults to 2. meta – A dictionary with the following keys: ’order’ : tensor A Tensor of length C, the total number of scattering coefficients, specifying the scattering order. ’xi’ : tensor A Tensor of size (C, max_order), specifying the center frequency of the filter used at each order (padded with NaNs). ’sigma’ : tensor A Tensor of size (C, max_order), specifying the frequency bandwidth of the filter used at each order (padded with NaNs). ’j’ : tensor A Tensor of size (C, max_order), specifying the dyadic scale of the filter used at each order (padded with NaNs). ’n’ : tensor A Tensor of size (C, max_order), specifying the indices of the filters used at each order (padded with NaNs). ’key’ : list The tuples indexing the corresponding scattering coefficient in the non-vectorized output. dictionary
forward(x)[source]

Apply the scattering transform

Given an input Tensor of size (B, T0), where B is the batch size and T0 is the length of the individual signals, this function computes its scattering transform. If the vectorize flag is set to True, the output is in the form of a Tensor or size (B, C, T1), where T1 is the signal length after subsampling to the scale 2**J (with the appropriate oversampling factor to reduce aliasing), and C is the number of scattering coefficients. If vectorize is set False, however, the output is a dictionary containing C keys, each a tuple whose length corresponds to the scattering order and whose elements are the sequence of filter indices used.

Furthermore, if the average flag is set to False, these outputs are not averaged, but are simply the wavelet modulus coefficients of the filters.

Parameters: x (tensor) – An input Tensor of size (B, T0). S – If the vectorize flag is True, the output is a Tensor containing the scattering coefficients, while if vectorize is False, it is a dictionary indexed by tuples of filter indices. tensor or dictionary
meta()[source]

Get meta information on the transform

Calls the static method compute_meta_scattering() with the parameters of the transform object.

Returns: meta – See the documentation for compute_meta_scattering(). dictionary
output_size(detail=False)[source]

Get size of the scattering transform

Calls the static method precompute_size_scattering() with the parameters of the transform object.

Parameters: detail (boolean, optional) – Specifies whether to provide a detailed size (number of coefficient per order) or an aggregate size (total number of coefficients). size – See the documentation for precompute_size_scattering(). int or tuple
static precompute_size_scattering(J, Q, max_order=2, detail=False)[source]

Get size of the scattering transform

The number of scattering coefficients depends on the filter configuration and so can be calculated using a few of the scattering transform parameters.

Parameters: J (int) – The maximum log-scale of the scattering transform. In other words, the maximum scale is given by 2**J. Q (int >= 1) – The number of first-order wavelets per octave. Second-order wavelets are fixed to one wavelet per octave. max_order (int, optional) – The maximum order of scattering coefficients to compute. Must be either equal to 1 or 2. Defaults to 2. detail (boolean, optional) – Specifies whether to provide a detailed size (number of coefficient per order) or an aggregate size (total number of coefficients). size – If detail is False, returns the number of coefficients as an integer. If True, returns a tuple of size max_order containing the number of coefficients in each order. int or tuple
class kymatio.Scattering2D(J, shape, L=8, max_order=2, pre_pad=False)[source]

Bases: object

Main module implementing the scattering transform in 2D. The scattering transform computes two wavelet transform followed by modulus non-linearity. It can be summarized as:

S_J x = [S_J^0 x, S_J^1 x, S_J^2 x]


where:

S_J^0 x = x * phi_J
S_J^1 x = [|x * psi^1_lambda| * phi_J]_lambda
S_J^2 x = [||x * psi^1_lambda| * psi^2_mu| * phi_J]_{lambda, mu}


where * denotes the convolution (in space), phi_J is a low pass filter, psi^1_lambda is a family of band pass filters and psi^2_mu is another family of band pass filters. Only Morlet filters are used in this implementation. Convolutions are efficiently performed in the Fourier domain with this implementation.

Example

# 1) Define a Scattering object as: s = Scattering2D(J, shape=(M, N)) # where (M, N) are the image sizes and 2**J the scale of the scattering # 2) Forward on an input Tensor x of shape B x M x N, # where B is the batch size. result_s = s(x)

Parameters: J (int) – logscale of the scattering shape (tuple of int) – spatial support (M, N) of the input L (int, optional) – number of angles used for the wavelet transform max_order (int, optional) – The maximum order of scattering coefficients to compute. Must be either 1 or 2. Defaults to 2. pre_pad (boolean, optional) – controls the padding: if set to False, a symmetric padding is applied on the signal. If set to true, the software will assume the signal was padded externally.
J

int – logscale of the scattering

shape

tuple of int – spatial support (M, N) of the input

L

int, optional – number of angles used for the wavelet transform

max_order

int, optional – The maximum order of scattering coefficients to compute. Must be either equal to 1 or 2. Defaults to 2.

pre_pad

boolean – controls the padding

Psi

dictionary – containing the wavelets filters at all resolutions. See filter_bank.filter_bank for an exact description.

Phi

dictionary – containing the low-pass filters at all resolutions. See filter_bank.filter_bank for an exact description.

M_padded, N_padded

int – spatial support of the padded input

Notes

The design of the filters is optimized for the value L = 8

pre_pad is particularly useful when doing crops of a bigger
image because the padding is then extremely accurate. Defaults to False.
forward(input)[source]

Forward pass of the scattering.

Parameters: input (tensor) – tensor with 3 dimensions where are arbitrary. typically is the batch size, whereas is the number of input channels. S – scattering of the input, a 4D tensor where corresponds to a new channel dimension and are downsampled sizes by a factor . tensor
class kymatio.HarmonicScattering3D(J, shape, L=3, sigma_0=1, max_order=2, rotation_covariant=True, method='standard', points=None, integral_powers=(0.5, 1.0, 2.0))[source]

Bases: object

3D Solid Harmonic scattering .

This class implements solid harmonic scattering on an input 3D image. For details see https://arxiv.org/abs/1805.00571.

Instantiates and initializes a 3d solid harmonic scattering object.

Parameters: J (int) – number of scales shape (tuple of int) – shape (M, N, O) of the input signal L (int, optional) – Number of l values. Defaults to 3. sigma_0 (float, optional) – Bandwidth of mother wavelet. Defaults to 1. max_order (int, optional) – The maximum order of scattering coefficients to compute. Must be either 1 or 2. Defaults to 2. rotation_covariant (bool, optional) – if set to True the first order moduli take the form: if set to False the first order moduli take the form: The second order moduli change analogously Defaut: True method (string, optional) – specifies the method for obtaining scattering coefficients (“standard”,”local”,”integral”). Default: “standard” points (array-like, optional) – List of locations in which to sample wavelet moduli. Used when method == ‘local’ integral_powers (array-like) – List of exponents to the power of which moduli are raised before integration. Used with method == ‘standard’, method == ‘integral’
forward(input_array)[source]

The forward pass of 3D solid harmonic scattering

Parameters: input_array (torch tensor) – input of size (batchsize, M, N, O) output – if max_order is 1 it returns a torch tensor with the first order scattering coefficients if max_order is 2 it returns a torch tensor with the first and second order scattering coefficients, concatenated along the feature axis tuple | torch tensor