giannis-papaioannou

from PyQt5 import QtCore, QtGui, QtWidgets

if __name__ == "__main__":
    import sys
    app = QtWidgets.QApplication(sys.argv)
    app.setStyle('Fusion')
    palette = QtGui.QPalette()
    palette.setColor(QtGui.QPalette.Window, QtGui.QColor(53,53,53))
    palette.setColor(QtGui.QPalette.WindowText, QtCore.Qt.white)
    palette.setColor(QtGui.QPalette.Base, QtGui.QColor(15,15,15))

weiaicunzai

from __future__ import print_function, absolute_import

__all__ = ['accuracy']

def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.topk(maxk, 1, True, True)

andrewjong

import torch
from torchvision import datasets

class ImageFolderWithPaths(datasets.ImageFolder):
    """Custom dataset that includes image file paths. Extends
    torchvision.datasets.ImageFolder
    """

    # override the __getitem__ method. this is the method that dataloader calls
    def __getitem__(self, index):

acbetter

#!/usr/bin/python3
# -*- coding: utf-8 -*-

from PyQt5.QtCore import Qt
from PyQt5.QtGui import QImage, QPixmap, QPalette, QPainter
from PyQt5.QtPrintSupport import QPrintDialog, QPrinter
from PyQt5.QtWidgets import QLabel, QSizePolicy, QScrollArea, QMessageBox, QMainWindow, QMenu, QAction, \
    qApp, QFileDialog

geoffsmith

import onnx
import torch
from caffe2.python.onnx import backend
from torch import nn
from torch.autograd import Variable
from torch.nn import PixelShuffle

class MyPixelShuffle(nn.Module):
    """
    Alternative version of pixel shuffle that was copied from the C++ version

amaotone

bikcrum

# Lint as: python2, python3
# Copyright 2018 The TensorFlow Authors. 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

A03ki

ICNR

ICNR is an initialization method for sub-pixel convolution.

References

Papar

• Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize

Github or Gist

sgraaf

#!/usr/bin/env python
# -*- coding: utf-8 -*-
from argparse import ArgumentParser

import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.distributed import DistributedSampler
from transformers import BertForMaskedLM

HanClinto