t-8ch

CFLAGS=$(shell pkg-config --cflags libmutter-1) -Wl,-rpath,/usr/lib/mutter
LDLIBS=$(shell pkg-config --libs libmutter-1)

all: main

alsrgv

# Copyright 2018 Uber Technologies, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

samoshkin

bind -T root F12  \
  set prefix None \;\
  set key-table off \;\
  set status-style "fg=$color_status_text,bg=$color_window_off_status_bg" \;\
  set window-status-current-format "#[fg=$color_window_off_status_bg,bg=$color_window_off_status_current_bg]$separator_powerline_right#[default] #I:#W# #[fg=$color_window_off_status_current_bg,bg=$color_window_off_status_bg]$separator_powerline_right#[default]" \;\
  set window-status-current-style "fg=$color_dark,bold,bg=$color_window_off_status_current_bg" \;\
  if -F '#{pane_in_mode}' 'send-keys -X cancel' \;\
  refresh-client -S \;\

bind -T off F12 \

ajbrock

## Wide ResNet with Shift and incorrect hyperparams.
# Based on code by xternalz: https://github.com/xternalz/WideResNet-pytorch
# WRN by Sergey Zagoruyko and Nikos Komodakis
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable as V
import torch.optim as optim

wikrie

#!/bin/bash
# Steps to build and install tmux from source on Ubuntu.
# Takes < 25 seconds on EC2 env [even on a low-end config instance].
VERSION=2.5
sudo apt-get -y remove tmux
sudo apt-get -y install wget tar libevent-dev libncurses-dev
wget https://github.com/tmux/tmux/releases/download/${VERSION}/tmux-${VERSION}.tar.gz
tar xf tmux-${VERSION}.tar.gz
rm -f tmux-${VERSION}.tar.gz
cd tmux-${VERSION}

yongjun823

import tensorflow as tf
import numpy as np
import base64
import csv
import os
from PIL import Image
from utils import dataset_util

"""
csv format

mkolod

.global main
.func main

main:
  LDR R0, =random_seed
  LDR R0, [R0]
  MOV R1, #num_elements
  BL _rng_loop
  LDR R0, =unordered_msg
  BL printf

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

kylehounslow

from __future__ import print_function
import requests
import json
import cv2

addr = 'http://localhost:5000'
test_url = addr + '/api/test'

# prepare headers for http request
content_type = 'image/jpeg'

felixgwu

import torch
from torch import nn

__all__ = ['FCDenseNet', 'fcdensenet_tiny', 'fcdensenet56_nodrop',
           'fcdensenet56', 'fcdensenet67', 'fcdensenet103',
           'fcdensenet103_nodrop']


class DenseBlock(nn.Module):