alper111

import torch
import torchvision

class VGGPerceptualLoss(torch.nn.Module):
    def __init__(self, resize=True):
        super(VGGPerceptualLoss, self).__init__()
        blocks = []
        blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval())

ericclemmons

Using <details> in GitHub

Suppose you're opening an issue and there's a lot noisey logs that may be useful.

Rather than wrecking readability, wrap it in a <details> tag!

<details>
 Summary Goes Here

cgddrd

\lstdefinestyle{customc++}{
  belowcaptionskip=1\baselineskip,
  breaklines=true,
  frame=L,
  language=C++,
  showstringspaces=false,
  basicstyle=\footnotesize\ttfamily,
  keywordstyle=\bfseries\color{OliveGreen},
  commentstyle=\itshape\color{black},
  identifierstyle=\color{blue},

vitormil

the problem:

subl somefile
zsh: correct 'subl' to 'ul' [nyae]? n

node -v
zsh: correct 'node' to 'od' [nyae]? n
v0.8.16

adobkin

# Find the CUnit headers and libraries
#
#  CUNIT_INCLUDE_DIRS - The CUnit include directory (directory where CUnit/CUnit.h was found)
#  CUNIT_LIBRARIES    - The libraries needed to use CUnit
#  CUNIT_FOUND        - True if CUnit found in system


FIND_PATH(CUNIT_INCLUDE_DIR NAMES CUnit/CUnit.h)
MARK_AS_ADVANCED(CUNIT_INCLUDE_DIR)

endolith

function [maxtab, mintab]=peakdet(v, delta, x)
%PEAKDET Detect peaks in a vector
%        [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
%        maxima and minima ("peaks") in the vector V.
%        MAXTAB and MINTAB consists of two columns. Column 1
%        contains indices in V, and column 2 the found values.
%      
%        With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices
%        in MAXTAB and MINTAB are replaced with the corresponding
%        X-values.