ferrine

#!/bin/zsh
magick convert -size 3840x2160 <(scrot -o /dev/stdout)'[384x216]'  -blur 0x5 -resize 1000% RGB:- | i3lock --raw 3840x2160:rgb --image /dev/stdin

ferrine

import torch.nn
import geoopt
# package.nn.modules.py
def create_ball(ball=None, c=None):
    if ball is None:
        assert c is not None, "curvature of the ball should be explicitly specified"
        ball = geoopt.PoincareBall(c)
    elif not isinstance(ball, geoopt.PoincareBall):
        raise ValueError("ball should be an instance of PoncareMall")
    return ball

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ferrine

import sacred.observers
import tensorboardX
import os


class TensorboardObserver(sacred.observers.FileStorageObserver, tensorboardX.SummaryWriter):
    VERSION = "TensorboardObserver-0.0.1"

    def __init__(self, basedir, resource_dir=None, source_dir=None,
                 template=None, priority=sacred.observers.file_storage.DEFAULT_FILE_STORAGE_PRIORITY,

ferrine

import torch.nn
import collections

class Builder(object):
    def __init__(self, *namespaces):
        self._namespace = collections.ChainMap(*namespaces)

    def __call__(self, name, *args, **kwargs):
        try:
            return self._namespace[name](*args, **kwargs)

ferrine

import torch
def map_nested(fn, structure, cond=lambda obj: isinstance(obj, torch.Tensor)):
    r"""
    Applies fn to an object in a possibly nested data structure and returns same
    structure with every element changed if condition satisfied.
    """

    def inner_map(obj):
        if cond(obj):
            return fn(obj)

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ferrine

import itertools
import torch


def svd(x):
    # https://discuss.pytorch.org/t/multidimensional-svd/4366/2
    batches = x.shape[:-2]
    if batches:
        n, m = x.shape[-2:]
        k = min(n, m)

ferrine