ortegatron

from detectron2.engine.hooks import HookBase
from detectron2.evaluation import inference_context
from detectron2.utils.logger import log_every_n_seconds
from detectron2.data import DatasetMapper, build_detection_test_loader
import detectron2.utils.comm as comm
import torch
import time
import datetime

class LossEvalHook(HookBase):

rudolfratusinski

Preparation

• In open Ubuntu 18.04 machine click Parallels Actions -> "Install Parallels Tools"

• A "Parallels Tools" CD will popup on your Ubuntu desktop.

• Open it by double mouse click, copy all the content to a new, empty directory on a desktop, name it for e.g. "parallels_fixed"

• Open terminal, change directory to parallels_fixed (cd ~/Desktop/parallels_fixed)

• Make command line installer executable (chmod +x install)

• Change directory to "installer" (cd installer)

• Make few other scripts executable: chmod +x installer.* *.sh prl_*

gyglim

"""Simple example on how to log scalars and images to tensorboard without tensor ops.

License: BSD License 2.0
"""
__author__ = "Michael Gygli"

import tensorflow as tf
from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

tiann

#! /usr/bin/env python
# coding: utf-8

'''
auto switch keyboard between different applications

if you want to change the app list, modify the var 'ignore_list'
'''

from AppKit import NSWorkspace, NSWorkspaceDidActivateApplicationNotification, NSWorkspaceApplicationKey

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)