numpytorch 0.1.2

Monkey-patched numpy with pytorch syntax

numpytorch

Monkey-patched numpy with pytorch syntax.

If you are also tired of dim, axis, keepdim, keepdims, cat, concatenate , or wasted enough time debugging repeat(), meshgrid(), this package provides a dirty solution:

# Just replace the import

#import numpy as np
import numpytorch as np

# use the torch syntax:
x = np.randn(2, 3)
x = x.permute(1, 0).unsqueeze(-1)
x = x.add(1).abs().sin()

# while it won't break the original numpy syntax:
x = np.random.rand(2, 3)
x = np.expand_dims(x.transpose(1, 0), -1)
x = np.sin(np.abs(x + 1))

Features

• fully compatible with pure numpy code.

• patched most pytorch functions and Tensor methods into numpy and ndarray.

Install

Only Cpython is supported since we use forbiddenfruit to extend the built-in np.ndarray.

pip install numpytorch

Documentations

Since there are conflicted names in numpy and pytorch, such as np.stack() & torch.stack(), ndarray.view() & Tensor.view(), two modes are provided to handle these conflicts: compatible or override.

In the default compatible mode, all of the names in numpy are kept unchanged:

• If the name is conflicted, we add torch_{name} to distinguish from the original numpy method.

  np.torch_stack()
  arr.torch_view()
  np.stack() # original numpy stack()
  arr.view() # original numpy.ndarray view()
  

• If the name is not conflicted, we add both torch_{name} and {name}.

  np.torch_randn()
  arr.torch_permute()
  np.randn() # alias of torch_randn()
  arr.permute() # alias of torch_permute()
  

In the override mode, we instead keep the torch functions unchanged and rename numpy functions. {name} & torch_{name} are always added (except some special functions like view(), size), and the conflicted numpy versions are renamed to numpy_{name}. However, this is only experimental and may lead to unexpected bugs since it may break some numpy functions. Use at your own risk!

# 'compatible' mode is invoked by default at import
import numpytorch as np

# invoke override mode 
np.set_patch_mode('override')

# invoke compatible mode
np.set_patch_mode('compatible')

# remove all patches
np.set_patch_mode('none')

# list current patches
np.list_patches()

All of the patched functions and methods are listed below. Unless specifically mentioned, they should behave similarly as the torch counterparts.

============== np.* ==============

torch_cat (tensors: Sequence[numpy.ndarray], dim: int = 0, out: Union[numpy.ndarray, NoneType] = None)

cat (tensors: Sequence[numpy.ndarray], dim: int = 0, out: Union[numpy.ndarray, NoneType] = None)

torch_chunk (input: numpy.ndarray, chunks: int, dim: int = 0)

chunk (input: numpy.ndarray, chunks: int, dim: int = 0)

torch_gather (input: numpy.ndarray, dim: int, index: numpy.ndarray, out: Union[numpy.ndarray, NoneType] = None)

gather (input: numpy.ndarray, dim: int, index: numpy.ndarray, out: Union[numpy.ndarray, NoneType] = None)

torch_index_select (input: numpy.ndarray, dim: int, index: numpy.ndarray, out: Union[numpy.ndarray, NoneType] = None)

index_select (input: numpy.ndarray, dim: int, index: numpy.ndarray, out: Union[numpy.ndarray, NoneType] = None)

torch_masked_select (input: numpy.ndarray, mask: numpy.ndarray, out: Union[numpy.ndarray, NoneType] = None)

masked_select (input: numpy.ndarray, mask: numpy.ndarray, out: Union[numpy.ndarray, NoneType] = None)

torch_movedim (input: numpy.ndarray, source: Union[int, Tuple[int]], destination: Union[int, Tuple[int]])

movedim (input: numpy.ndarray, source: Union[int, Tuple[int]], destination: Union[int, Tuple[int]])

torch_swapdims (input: numpy.ndarray, dim0: int, dim1: int)

swapdims (input: numpy.ndarray, dim0: int, dim1: int)

torch_narrow (input: numpy.ndarray, dim: int, start: int, length: int)

narrow (input: numpy.ndarray, dim: int, start: int, length: int)

torch_nonzero (input: numpy.ndarray, as_tuple: bool = False)

torch_scatter (input: numpy.ndarray, dim: int, index: numpy.ndarray, src: numpy.ndarray, reduce: Union[str, NoneType] = None)

scatter (input: numpy.ndarray, dim: int, index: numpy.ndarray, src: numpy.ndarray, reduce: Union[str, NoneType] = None)

torch_scatter_add (input: numpy.ndarray, dim: int, index: numpy.ndarray, src: numpy.ndarray)

scatter_add (input: numpy.ndarray, dim: int, index: numpy.ndarray, src: numpy.ndarray)

torch_split (input: numpy.ndarray, split_size_or_sections: Union[int, Sequence[int]], dim: int = 0)

torch_squeeze (input: numpy.ndarray, dim: Union[int, NoneType] = None)

torch_stack (input: Sequence[numpy.ndarray], dim: int = 0, out: Union[numpy.ndarray, NoneType] = None)

torch_unsqueeze (input: numpy.ndarray, dim: int)

unsqueeze (input: numpy.ndarray, dim: int)

torch_unbind (input: numpy.ndarray, dim: int = 0)

unbind (input: numpy.ndarray, dim: int = 0)

torch_meshgrid (*xi: numpy.ndarray)

torch_clone (input: numpy.ndarray)

clone (input: numpy.ndarray)

torch_is_contiguous (input: numpy.ndarray)

is_contiguous (input: numpy.ndarray)

torch_contiguous (input: numpy.ndarray)

contiguous (input: numpy.ndarray)

torch_repeat (input: numpy.ndarray, *sizes: int)

torch_repeat_interleave (input: numpy.ndarray, repeats: Union[int, numpy.ndarray], dim: Union[int, NoneType] = None)

repeat_interleave (input: numpy.ndarray, repeats: Union[int, numpy.ndarray], dim: Union[int, NoneType] = None)

torch_permute (input: numpy.ndarray, *axes: int)

permute (input: numpy.ndarray, *axes: int)

torch_view (input: numpy.ndarray, *sizes: int)

view (input: numpy.ndarray, *sizes: int)

torch_view_as (input: numpy.ndarray, other: numpy.ndarray)

view_as (input: numpy.ndarray, other: numpy.ndarray)

torch_expand (input: numpy.ndarray, *sizes: int)

Note: the output is read-only since we use np.broadcast_to to return a view of the input, which different from the original behavior of torch.expand().

expand (input: numpy.ndarray, *sizes: int)

torch_expand_as (input: numpy.ndarray, other: numpy.ndarray)

Note: the output is read-only.

expand_as (input: numpy.ndarray, other: numpy.ndarray)

torch_t (input: numpy.ndarray)

t (input: numpy.ndarray)

torch_seed ()

seed ()

torch_manual_seed (seed: int)

manual_seed (seed: int)

torch_rand (*size: int, out: Union[numpy.ndarray, NoneType] = None)

rand (*size: int, out: Union[numpy.ndarray, NoneType] = None)

torch_rand_like (input: numpy.ndarray)

rand_like (input: numpy.ndarray)

torch_randint (low: int, high: Union[int, NoneType] = None, size: Union[Sequence[int], NoneType] = None, out: Union[numpy.ndarray, NoneType] = None)

randint (low: int, high: Union[int, NoneType] = None, size: Union[Sequence[int], NoneType] = None, out: Union[numpy.ndarray, NoneType] = None)

torch_randint_like (input: numpy.ndarray, low: int, high: Union[int, NoneType] = None)

randint_like (input: numpy.ndarray, low: int, high: Union[int, NoneType] = None)

torch_randn (*size: int, out: Union[numpy.ndarray, NoneType] = None)

randn (*size: int, out: Union[numpy.ndarray, NoneType] = None)

torch_randn_like (input: numpy.ndarray)

randn_like (input: numpy.ndarray)

torch_randperm (n: int, out: Union[numpy.ndarray, NoneType] = None)

randperm (n: int, out: Union[numpy.ndarray, NoneType] = None)

torch_clamp (input: numpy.ndarray, min: Union[numbers.Number, numpy.ndarray, NoneType] = None, max: Union[numbers.Number, numpy.ndarray, NoneType] = None)

clamp (input: numpy.ndarray, min: Union[numbers.Number, numpy.ndarray, NoneType] = None, max: Union[numbers.Number, numpy.ndarray, NoneType] = None)

torch_max (input: numpy.ndarray, dim: Union[int, numpy.ndarray, NoneType] = None, keepdim: bool = False, out: Union[numpy.ndarray, NoneType] = None)

max (input: numpy.ndarray, dim: Union[int, numpy.ndarray, NoneType] = None, keepdim: bool = False, out: Union[numpy.ndarray, NoneType] = None)

torch_min (input: numpy.ndarray, dim: Union[int, numpy.ndarray, NoneType] = None, keepdim: bool = False, out: Union[numpy.ndarray, NoneType] = None)

min (input: numpy.ndarray, dim: Union[int, numpy.ndarray, NoneType] = None, keepdim: bool = False, out: Union[numpy.ndarray, NoneType] = None)

torch_flatten (input: numpy.ndarray, start_dim: int = 0, end_dim: int = -1)

flatten (input: numpy.ndarray, start_dim: int = 0, end_dim: int = -1)

========== np.ndarray.* ==========

torch_dim attribute dim attribute torch_numel (self)

numel (self)

torch_index_select (input: numpy.ndarray, dim: int, index: numpy.ndarray, out: Union[numpy.ndarray, NoneType] = None)

index_select (input: numpy.ndarray, dim: int, index: numpy.ndarray, out: Union[numpy.ndarray, NoneType] = None)

torch_squeeze_ (input: numpy.ndarray, dim: Union[int, NoneType] = None)

squeeze_ (input: numpy.ndarray, dim: Union[int, NoneType] = None)

torch_unsqueeze (input: numpy.ndarray, dim: int)

unsqueeze (input: numpy.ndarray, dim: int)

torch_unsqueeze_ (input: numpy.ndarray, dim: int)

unsqueeze_ (input: numpy.ndarray, dim: int)

torch_is_contiguous (input: numpy.ndarray)

is_contiguous (input: numpy.ndarray)

torch_contiguous (input: numpy.ndarray)

contiguous (input: numpy.ndarray)

torch_clone (input: numpy.ndarray)

clone (input: numpy.ndarray)

torch_repeat (input: numpy.ndarray, *sizes: int)

torch_repeat_interleave (input: numpy.ndarray, repeats: Union[int, numpy.ndarray], dim: Union[int, NoneType] = None)

repeat_interleave (input: numpy.ndarray, repeats: Union[int, numpy.ndarray], dim: Union[int, NoneType] = None)

torch_view (input: numpy.ndarray, *sizes: int)

torch_permute (input: numpy.ndarray, *axes: int)

permute (input: numpy.ndarray, *axes: int)

torch_expand (input: numpy.ndarray, *sizes: int)

expand (input: numpy.ndarray, *sizes: int)

torch_expand_as (input: numpy.ndarray, other: numpy.ndarray)

expand_as (input: numpy.ndarray, other: numpy.ndarray)

torch_uniform_ (input: numpy.ndarray, fro: int = 0, to: int = 1)

uniform_ (input: numpy.ndarray, fro: int = 0, to: int = 1)

torch_normal_ (input: numpy.ndarray, mean: int = 0, std: int = 1)

normal_ (input: numpy.ndarray, mean: int = 0, std: int = 1)

torch_zero_ (input: numpy.ndarray)

zero_ (input: numpy.ndarray)

torch_clamp (input: numpy.ndarray, min: Union[numbers.Number, numpy.ndarray, NoneType] = None, max: Union[numbers.Number, numpy.ndarray, NoneType] = None)

clamp (input: numpy.ndarray, min: Union[numbers.Number, numpy.ndarray, NoneType] = None, max: Union[numbers.Number, numpy.ndarray, NoneType] = None)

torch_clamp_ (input: numpy.ndarray, min: Union[numbers.Number, numpy.ndarray, NoneType] = None, max: Union[numbers.Number, numpy.ndarray, NoneType] = None)

clamp_ (input: numpy.ndarray, min: Union[numbers.Number, numpy.ndarray, NoneType] = None, max: Union[numbers.Number, numpy.ndarray, NoneType] = None)

torch_clip_ (input: numpy.ndarray, min: Union[numbers.Number, numpy.ndarray, NoneType] = None, max: Union[numbers.Number, numpy.ndarray, NoneType] = None)

clip_ (input: numpy.ndarray, min: Union[numbers.Number, numpy.ndarray, NoneType] = None, max: Union[numbers.Number, numpy.ndarray, NoneType] = None)

torch_flatten (input: numpy.ndarray, start_dim: int = 0, end_dim: int = -1)

torch_max (input: numpy.ndarray, dim: Union[int, numpy.ndarray, NoneType] = None, keepdim: bool = False, out: Union[numpy.ndarray, NoneType] = None)

torch_min (input: numpy.ndarray, dim: Union[int, numpy.ndarray, NoneType] = None, keepdim: bool = False, out: Union[numpy.ndarray, NoneType] = None)

torch_amax (a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)

amax (a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)

torch_amin (a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)

amin (a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)

torch_half (self)

half (self)

torch_float (self)

float (self)

torch_double (self)

double (self)

torch_short (self)

short (self)

torch_int (self)

int (self)

torch_long (self)

long (self)

torch_bool (self)

bool (self)

torch_add (self, other)

add (self, other)

torch_add_ (self, other)

add_ (self, other)

torch_subtract (self, other)

subtract (self, other)

torch_subtract_ (self, other)

subtract_ (self, other)

torch_multiply (self, other)

multiply (self, other)

torch_multiply_ (self, other)

multiply_ (self, other)

torch_matmul (self, other)

matmul (self, other)

torch_matmul_ (self, other)

matmul_ (self, other)

torch_divide (self, other)

divide (self, other)

torch_divide_ (self, other)

divide_ (self, other)

torch_true_divide (self, other)

true_divide (self, other)

torch_true_divide_ (self, other)

true_divide_ (self, other)

torch_floor_divide (self, other)

floor_divide (self, other)

torch_floor_divide_ (self, other)

floor_divide_ (self, other)

torch_negative (self)

negative (self)

torch_negative_ (self)

negative_ (self)

torch_positive (self)

positive (self)

torch_positive_ (self)

positive_ (self)

torch_power (self, other)

power (self, other)

torch_power_ (self, other)

power_ (self, other)

torch_float_power (self, other)

float_power (self, other)

torch_float_power_ (self, other)

float_power_ (self, other)

torch_remainder (self, other)

remainder (self, other)

torch_remainder_ (self, other)

remainder_ (self, other)

torch_mod (self, other)

mod (self, other)

torch_mod_ (self, other)

mod_ (self, other)

torch_fmod (self, other)

fmod (self, other)

torch_fmod_ (self, other)

fmod_ (self, other)

torch_divmod (self, other)

divmod (self, other)

torch_divmod_ (self, other)

divmod_ (self, other)

torch_absolute (self)

absolute (self)

torch_absolute_ (self)

absolute_ (self)

torch_abs (self)

abs (self)

torch_abs_ (self)

abs_ (self)

torch_fabs (self)

fabs (self)

torch_fabs_ (self)

fabs_ (self)

torch_sign (self)

sign (self)

torch_sign_ (self)

sign_ (self)

torch_rint (self)

rint (self)

torch_rint_ (self)

rint_ (self)

torch_conj (self)

torch_conj_ (self)

conj_ (self)

torch_exp (self)

exp (self)

torch_exp_ (self)

exp_ (self)

torch_exp2 (self)

exp2 (self)

torch_exp2_ (self)

exp2_ (self)

torch_log (self)

log (self)

torch_log_ (self)

log_ (self)

torch_log2 (self)

log2 (self)

torch_log2_ (self)

log2_ (self)

torch_log10 (self)

log10 (self)

torch_log10_ (self)

log10_ (self)

torch_sqrt (self)

sqrt (self)

torch_sqrt_ (self)

sqrt_ (self)

torch_square (self)

square (self)

torch_square_ (self)

square_ (self)

torch_cbrt (self)

cbrt (self)

torch_cbrt_ (self)

cbrt_ (self)

torch_reciprocal (self)

reciprocal (self)

torch_reciprocal_ (self)

reciprocal_ (self)

torch_gcd (self, other)

gcd (self, other)

torch_gcd_ (self, other)

gcd_ (self, other)

torch_lcm (self, other)

lcm (self, other)

torch_lcm_ (self, other)

lcm_ (self, other)

torch_expm1 (self)

expm1 (self)

torch_expm1_ (self)

expm1_ (self)

torch_log1p (self)

log1p (self)

torch_log1p_ (self)

log1p_ (self)

torch_sin (self)

sin (self)

torch_sin_ (self)

sin_ (self)

torch_cos (self)

cos (self)

torch_cos_ (self)

cos_ (self)

torch_tan (self)

tan (self)

torch_tan_ (self)

tan_ (self)

torch_arcsin (self)

arcsin (self)

torch_arcsin_ (self)

arcsin_ (self)

torch_arccos (self)

arccos (self)

torch_arccos_ (self)

arccos_ (self)

torch_arctan (self)

arctan (self)

torch_arctan_ (self)

arctan_ (self)

torch_arctan2 (self, other)

arctan2 (self, other)

torch_arctan2_ (self, other)

arctan2_ (self, other)

torch_sinh (self)

sinh (self)

torch_sinh_ (self)

sinh_ (self)

torch_cosh (self)

cosh (self)

torch_cosh_ (self)

cosh_ (self)

torch_tanh (self)

tanh (self)

torch_tanh_ (self)

tanh_ (self)

torch_arcsinh (self)

arcsinh (self)

torch_arcsinh_ (self)

arcsinh_ (self)

torch_arccosh (self)

arccosh (self)

torch_arccosh_ (self)

arccosh_ (self)

torch_arctanh (self)

arctanh (self)

torch_arctanh_ (self)

arctanh_ (self)

torch_degrees (self)

degrees (self)

torch_degrees_ (self)

degrees_ (self)

torch_radians (self)

radians (self)

torch_radians_ (self)

radians_ (self)

torch_deg2rad (self)

deg2rad (self)

torch_deg2rad_ (self)

deg2rad_ (self)

torch_rad2deg (self)

rad2deg (self)

torch_rad2deg_ (self)

rad2deg_ (self)

torch_bitwise_and (self, other)

bitwise_and (self, other)

torch_bitwise_and_ (self, other)

bitwise_and_ (self, other)

torch_bitwise_or (self, other)

bitwise_or (self, other)

torch_bitwise_or_ (self, other)

bitwise_or_ (self, other)

torch_bitwise_xor (self, other)

bitwise_xor (self, other)

torch_bitwise_xor_ (self, other)

bitwise_xor_ (self, other)

torch_invert (self)

invert (self)

torch_invert_ (self)

invert_ (self)

torch_left_shift (self, other)

left_shift (self, other)

torch_left_shift_ (self, other)

left_shift_ (self, other)

torch_right_shift (self, other)

right_shift (self, other)

torch_right_shift_ (self, other)

right_shift_ (self, other)

torch_greater (self, other)

greater (self, other)

torch_greater_ (self, other)

greater_ (self, other)

torch_greater_equal (self, other)

greater_equal (self, other)

torch_greater_equal_ (self, other)

greater_equal_ (self, other)

torch_less (self, other)

less (self, other)

torch_less_ (self, other)

less_ (self, other)

torch_less_equal (self, other)

less_equal (self, other)

torch_less_equal_ (self, other)

less_equal_ (self, other)

torch_equal (self, other)

equal (self, other)

torch_equal_ (self, other)

equal_ (self, other)

torch_not_equal (self, other)

not_equal (self, other)

torch_not_equal_ (self, other)

not_equal_ (self, other)

torch_logical_and (self, other)

logical_and (self, other)

torch_logical_and_ (self, other)

logical_and_ (self, other)

torch_logical_or (self, other)

logical_or (self, other)

torch_logical_or_ (self, other)

logical_or_ (self, other)

torch_logical_not (self)

logical_not (self)

torch_logical_not_ (self)

logical_not_ (self)

torch_logical_xor (self, other)

logical_xor (self, other)

torch_logical_xor_ (self, other)

logical_xor_ (self, other)

torch_maximum (self, other)

maximum (self, other)

torch_maximum_ (self, other)

maximum_ (self, other)

torch_minimum (self, other)

minimum (self, other)

torch_minimum_ (self, other)

minimum_ (self, other)

torch_fmax (self, other)

fmax (self, other)

torch_fmax_ (self, other)

fmax_ (self, other)

torch_fmin (self, other)

fmin (self, other)

torch_fmin_ (self, other)

fmin_ (self, other)

torch_nextafter (self, other)

nextafter (self, other)

torch_nextafter_ (self, other)

nextafter_ (self, other)

torch_spacing (self)

spacing (self)

torch_spacing_ (self)

spacing_ (self)

torch_modf (self)

modf (self)

torch_modf_ (self)

modf_ (self)

torch_ldexp (self, other)

ldexp (self, other)

torch_ldexp_ (self, other)

ldexp_ (self, other)

torch_frexp (self)

frexp (self)

torch_frexp_ (self)

frexp_ (self)

torch_isfinite (self)

isfinite (self)

torch_isfinite_ (self)

isfinite_ (self)

torch_isinf (self)

isinf (self)

torch_isinf_ (self)

isinf_ (self)

torch_isnan (self)

isnan (self)

torch_isnan_ (self)

isnan_ (self)

torch_isnat (self)

isnat (self)

torch_isnat_ (self)

isnat_ (self)

torch_signbit (self)

signbit (self)

torch_signbit_ (self)

signbit_ (self)

torch_copysign (self, other)

copysign (self, other)

torch_copysign_ (self, other)

copysign_ (self, other)

torch_floor (self)

floor (self)

torch_floor_ (self)

floor_ (self)

torch_ceil (self)

ceil (self)

torch_ceil_ (self)

ceil_ (self)

torch_trunc (self)

trunc (self)

torch_trunc_ (self)

trunc_ (self)

torch_atan (self)

atan (self)

torch_atan_ (self)

atan_ (self)

torch_asin (self)

asin (self)

torch_asin_ (self)

asin_ (self)

torch_acos (self)

acos (self)

torch_acos_ (self)

acos_ (self)

torch_atan2 (self, other)

atan2 (self, other)

torch_atan2_ (self, other)

atan2_ (self, other)

torch_ge (self, other)

ge (self, other)

torch_ge_ (self, other)

ge_ (self, other)

torch_gt (self, other)

gt (self, other)

torch_gt_ (self, other)

gt_ (self, other)

torch_le (self, other)

le (self, other)

torch_le_ (self, other)

le_ (self, other)

torch_lt (self, other)

lt (self, other)

torch_lt_ (self, other)

lt_ (self, other)