PytorchBackend

class PytorchBackend(device='cuda', float_dtype=None, complex_dtype=None, int_dtype=None, overflow_safe_dtype=None, **kwargs)

Bases: NumpyFFTWBackend

A pytorch-based matching backend.

Methods

PytorchBackend.abs(*args, **kwargs)	Compute the absolute of array elements.
PytorchBackend.add(*args, **kwargs)	Element-wise addition of arrays.
PytorchBackend.arange(*args, **kwargs)	Arange values in evenly spaced interval.
PytorchBackend.argsort(*args, **kwargs)	Compute the indices to sort a given input array.
PytorchBackend.astype(arr, dtype)	Change the datatype of arr.
PytorchBackend.build_fft(fast_shape, ...[, ...])	Build forward and inverse real fourier transform functions.
PytorchBackend.center_of_mass(arr[, cutoff])	Computes the center of mass of a numpy ndarray instance using all available elements.
PytorchBackend.clip(*args, **kwargs)	Clip elements of arr.
PytorchBackend.compute_convolution_shapes(...)	Computes regular, optimized and fourier convolution shape.
PytorchBackend.concatenate(*args, **kwargs)	Join a sequence of objects along an existing axis.
PytorchBackend.datatype_bytes(dtype)	Return the number of bytes occupied by a given datatype.
PytorchBackend.device_count()	Returns the number of available GPU devices.
PytorchBackend.divide(*args, **kwargs)	Element-wise division of arrays.
PytorchBackend.dot(*args, **kwargs)
PytorchBackend.einsum(*args, **kwargs)	Compute the einstein notation based summation.
PytorchBackend.eps(dtype)	Returns the minimal difference representable by dtype.
PytorchBackend.extract_center(arr, newshape)	Extract the centered portion of an array based on a new shape.
PytorchBackend.fill(arr, value)	Fills arr in-place with a given value.
PytorchBackend.flip(a, axis, **kwargs)
PytorchBackend.free_cache()	Free cached objects allocated by backend.
PytorchBackend.from_sharedarr(args)	Returns an array of given shape and dtype from shared memory location.
PytorchBackend.full(shape, fill_value[, dtype])	Returns an array filled with fill_value of specified shape and dtype.
PytorchBackend.get_available_memory()	Returns the available memory available for computations in bytes.
PytorchBackend.get_fundamental_dtype(arr)	Given an array instance, returns the corresponding fundamental python type, i.e., int, float or complex.
PytorchBackend.identity(*args, **kwargs)
PytorchBackend.indices(shape)	Creates an array representing the index grid of an input.
PytorchBackend.max(*args, **kwargs)	Compute the maximum of array elements.
PytorchBackend.max_filter_coordinates(...)	Identifies local maxima in score_space separated by min_distance.
PytorchBackend.max_score_over_rotations(...)	Update elements in max_scores and rotations where scores is larger than max_scores with score and rotation_index, respectivelty.
PytorchBackend.maximum(x1, x2, *args, **kwargs)	Compute the element wise maximum of arr1 and arr2.
PytorchBackend.mean(*args, **kwargs)	Compute the mean of array elements.
PytorchBackend.min(*args, **kwargs)	Compute the minimum of array elements.
PytorchBackend.minimum(x1, x2, *args, **kwargs)	Compute the element wise minimum of arr1 and arr2.
PytorchBackend.mod(x1, x2, *args, **kwargs)	Element-wise modulus of arrays.
PytorchBackend.multiply(*args, **kwargs)	Element-wise multiplication of arrays.
PytorchBackend.norm_scores(arr, exp_sq, ...)	Normalizes arr by the standard deviation ensuring numerical stability.
PytorchBackend.power(*args, **kwargs)	Compute the n-th power of an array.
PytorchBackend.repeat(*args, **kwargs)	Repeat each array element a specified number of times.
PytorchBackend.reshape(*args, **kwargs)
PytorchBackend.reverse(arr)	Reverse the order of elements in an array along all its axes.
PytorchBackend.rigid_transform(arr, ...[, ...])	Rotates the given tensor arr based on the provided rotation_matrix.
PytorchBackend.roll(a, shift, axis, **kwargs)	Roll array elements along a specified axis.
PytorchBackend.set_device(device_index)	Context manager that sets active compute device device for operations.
PytorchBackend.size(arr)	Compute the number of elements of arr.
PytorchBackend.sqrt(*args, **kwargs)	Compute the square root of array elements.
PytorchBackend.square(*args, **kwargs)	Compute the square of array elements.
PytorchBackend.stack(*args, **kwargs)	Join a sequence of objects along a new axis.
PytorchBackend.std(*args, **kwargs)	Compute the standad deviation of array elements.
PytorchBackend.subtract(*args, **kwargs)	Element-wise subtraction of arrays.
PytorchBackend.sum(*args, **kwargs)	Compute the sum of array elements.
PytorchBackend.to_backend_array(arr)	Convert a numpy array instance to backend array type.
PytorchBackend.to_cpu_array(arr)	Convert an array of a given backend to a CPU array of that backend.
PytorchBackend.to_numpy_array(arr)	Convert an array of given backend to a numpy array.
PytorchBackend.to_sharedarr(arr[, ...])	Converts an array to an object shared in memory.
PytorchBackend.tobytes(arr)	Compute the bytestring representation of arr.
PytorchBackend.topk_indices(arr, k)	Determinces the indices of largest elements.
PytorchBackend.topleft_pad(arr, shape[, padval])	Returns an array that has been padded to a specified shape with a padding value at the top-left corner.
PytorchBackend.transpose(arr)	Compute the transpose of arr.
PytorchBackend.tril_indices(*args, **kwargs)	Compute indices of upper triangular matrix
PytorchBackend.unique(ar[, return_index, ...])	Find the unique elements of an array.
PytorchBackend.unravel_index(indices, shape)	Convert flat index to array indices.
PytorchBackend.where(*args, **kwargs)	Return elements from input depending on condition.
PytorchBackend.zeros(shape[, dtype])	Returns an aligned array of zeros with specified shape and dtype.