crowsonkb

"""Computes the covariance matrix in PyTorch."""


def cov_mean(input, unbiased=True, keepdims=False):
    n = input.shape[-1] - unbiased
    mean = input.mean(dim=-1, keepdims=True)
    dev = input - mean
    mean = mean if keepdims else mean[..., 0]
    return dev @ dev.transpose(-1, -2) / n, mean

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from torch.nn import functional as F
from torchvision import transforms


def random_shift(input, max_shift, mode='replicate', value=0):
    padded = F.pad(input, max_shift, mode=mode, value=value)
    return transforms.RandomCrop(input.shape[2:])(padded)

crowsonkb

from torch import nn


class Lambda(nn.Module):
    """Wraps a callable in an :class:`nn.Module` without registering it."""

    def __init__(self, func):
        super().__init__()
        object.__setattr__(self, 'forward', func)

crowsonkb

from prettyprinter import register_pretty, pretty_call
import pyparsing as pp

@register_pretty(pp.ParseResults)
def pretty_parse_results(value, ctx):
    return pretty_call(ctx, pp.ParseResults, value.asList(), value.asDict())

crowsonkb

# The name of the Pygments (syntax highlighting) style to use.
sys.path.append('.')
import pygments.styles
import base16_spacemacs_kat
# pygments.styles.base16_spacemacs_kat = base16_spacemacs_kat
sys.modules['pygments.styles.' + 'base16_spacemacs_kat'] = base16_spacemacs_kat
pygments.styles.STYLE_MAP['base16-spacemacs-kat'] = 'base16_spacemacs_kat::Base16SpacemacsStyle'
pygments_style = 'base16-spacemacs-kat'  # 'friendly'

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# This file provides configuration information about non-Python dependencies for
# numpy.distutils-using packages. Create a file like this called "site.cfg" next
# to your package's setup.py file and fill in the appropriate sections. Not all
# packages will use all sections so you should leave out sections that your
# package does not use.

# To assist automatic installation like easy_install, the user's home directory
# will also be checked for the file ~/.numpy-site.cfg .

# The format of the file is that of the standard library's ConfigParser module.

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name: "thing"
layer {
    name: "data"
    type: "Data"
    top: "big_data"
    top: "label"
    include { phase: TRAIN }
    transform_param {
       mean_value: 103.939
       mean_value: 116.779

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"""A with-statement context manager to benchmark code."""

import time

__all__ = ['Benchmark']


class Benchmark:
    """A with-statement context manager to benchmark code."""
    def __init__(self, timer=time.perf_counter):

crowsonkb

"""The prompt-toolkit utility function :func:`format_fragments`."""

from prompt_toolkit.formatted_text import FormattedText


def format_fragments(fragments, *args, **kwargs):
    """Applies :meth:`str.format` to each text fragment in `fragments`.

    Args:
        fragments (FormattedText): The input list of text fragments.

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import inspect
import typing
from typing import get_type_hints, TypeVar, Any, AnyStr, Generic, Union

from sphinx.util import logging
from sphinx.util.inspect import Signature

try:
    from typing_extensions import Protocol
except ImportError: