numpytorch 0.1.0

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.

</code></pre>
<h3>Documentations</h3>
<p>Since there are conflicted names in <code>numpy</code> and <code>pytorch</code>, such as <code>np.stack() & torch.stack()</code>, <code>ndarray.view() & Tensor.view()</code>, two modes are provided to handle these conflicts: <code>compatible</code> or <code>override</code>.</p>
<p>In the default <code>compatible</code> mode, all of the names in <code>numpy</code> are kept unchanged:</p>
<ul>
<li>
<p>If the name is conflicted, we add <code>torch_{name}</code> to distinguish from the original <code>numpy</code> method.</p>
<pre lang="python"><code>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) expand (input: numpy.ndarray, *sizes: int) torch_expand_as (input: numpy.ndarray, other: numpy.ndarray) 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)