Python Qt5 : application with QML file.

The PyQt5 includes QML as a means of declaratively describing a user interface and is possible to write complete standalone QML applications.
Using QML file is different from the versions PyQt5 and old PyQt4.
Using this type of application can let you solve custom and style application.
I create a simple example but can create your python module with a class with new type of style.
This can be used with qmlRegisterType for your new python class type.
Let's see the example:
The main python file:

from PyQt5.QtNetwork import *
from PyQt5.QtQml import *
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *

class MainWin(object):
    def __init__(self):
        self.eng = QQmlApplicationEngine()
        self.eng.load('win.qml')
        win = self.eng.rootObjects()[0]   
        win.show()

if __name__ == '__main__':
    import sys
    App = QApplication(sys.argv)
    Win = MainWin()
    sys.exit(App.exec_())

The QML file:

import QtQuick 2.2
import QtQuick.Controls 1.0
ApplicationWindow {
    id: main
    width: 640
    height: 480
    color: 'blue'
 }

The result is a blue window.

Using pytorch - another way.

Yes. I used pytorch and is working well. Is not perfect the GitHub come every day with a full stack of issues.
Let's continue this series with another step: torchvision.
If you take a closer look at that gift, you will see that it comes with a special label that can really help us.
This label is a named torchvision.
The torchvision python module is a package consists of popular datasets, model architectures, and common image transformations for computer vision.
Most operations pass through filters and date already recognized.

• torchvision.datasets: (MNIST,Fashion-MNIST,EMNIST,COCO,LSUN,ImageFolder,DatasetFolder,Imagenet-12,CIFAR,STL10,SVHN,PhotoTour,SBU,Flickr,VOC)
• torchvision.models: (Alexnet,VGG,ResNet,SqueezeNet,DenseNet,Inception v3)
• torchvision.transforms: (Transforms on PIL Image,Transforms on torch.*Tensor,Conversion Transforms,Generic Transforms,Functional Transforms)
• torchvision.utils

This part of the gift help you to load and prepare dataset but into certain order.
Using this special label, we will be able to use the gift-breaking information.
Let's see the example:

C:\Python364>python.exe
Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:54:40) [MSC v.1900 64 bit (AMD64)]
 on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torchvision
>>> import torchvision.transforms as transforms
>>> transform = transforms.Compose(
...     [transforms.ToTensor(),
...      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
>>> trainset = torchvision.datasets.CIFAR10(root='./data', train=True,download=T
rue, transform=transform)
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data\ci
far-10-python.tar.gz

You will ask me: How is this special gift label linked?
In this way:

>>> import torch
>>> trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,shuffle=Tru
e, num_workers=2)

Let's take a closer look at the information in the special label.

>>> print(trainset)
Dataset CIFAR10
    Number of datapoints: 50000
    Split: train
    Root Location: ./data
    Transforms (if any): Compose(
                             ToTensor()
                             Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)
)
                         )
    Target Transforms (if any): None
>>> print(dir(trainset))
['__add__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq_
_', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash
__', '__init__', '__init_subclass__', '__le__', '__len__', '__lt__', '__module__
', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__'
, '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_check_integrity'
, 'base_folder', 'download', 'filename', 'root', 'target_transform', 'test_list'
, 'tgz_md5', 'train', 'train_data', 'train_labels', 'train_list', 'transform', '
url']

Let's look more closely at the information that can be used by the gift with the special label.

>>> print(trainloader)

>>> print(dir(trainloader))
['_DataLoader__initialized', '__class__', '__delattr__', '__dict__', '__dir__',
'__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__ha
sh__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__
', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__',
 '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'bat
ch_sampler', 'batch_size', 'collate_fn', 'dataset', 'drop_last', 'num_workers',
'pin_memory', 'sampler', 'timeout', 'worker_init_fn']

Beware, CIFAR10 is just one of the training databases.
About the CIFAR-10 dataset, that consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class.
There are 50,000 training images (5,000 per class) and 10,000 test images.

Fix errors when write files.

The python is a very versatile programming language.
The tutorial for today is about:

• check the type of variables;
• see the list error of writelines with the list output;
• fix errors for writelines;

One good example for some errors can be this:

>>> file.writelines(paragraphs)
Traceback (most recent call last):
  File "", line 1, in 
TypeError: a bytes-like object is required, not 'str'
>>> file.writelines(paragraphs.decode('utf-8'))
Traceback (most recent call last):
  File "", line 1, in 
AttributeError: 'list' object has no attribute 'decode'

Is a common issue for list and writing file.
The result of paragraphs is a list, see:

>>> type(paragraphs)

The list can be write with te writelines into the file like this:

>>> file = open("out.txt","wb")
>>> file.writelines([word.encode('utf-8') for word in paragraphs])

The file is a open file variable and the paragraphs is a list.

Using pytorch - a simpler perspective.

Suppose this module PyTorch is a data extravagance circuit that allows us to filter information several times, and we can decide each time we decide the final result.
A simpler perspective of how to work with PyTorch can be explained by a simple example.
It's like a Christmas baby (PyTorch) that opens a multi-packed gift until it gets the final product - the desired gift.
The opening operations of the package involve smart moves called: forward and backward passes.
The child's feedback can be called: loss and backpropagate.
In this case, the child will try to remove from his package until he is satisfied and will not be lost (loss and backpropagate functions).
To compute the backward pass for a gradient and every time we backpropagate the gradient from a variable, the gradient is accumulative instead of being reset and replaced (most of networks designs call backward multiple times).
PyTorch comes with many loss functions.
Most of examples code create a mean square error loss function and later backpropagate the gradients based on the loss.
Will you ask me if the gift is shaped? I can tell you that the gift can contain from Verge à Saint-Nicolas (unidimensional) to complex (multidimensional) structures - the most simplistic and worn out is the square one (two-dimensional matrix).
This gift is packed with magic in mathematical functions which allows the child to understand what is in the gift.
But the child is more special. He recognizes forms (matrices, shapes, simple formulas) and this allows him to open parts of the gift.
El poate roti acesta parti din cadou (mm).
The mm is a matrix multiplication.
He can see the corners he can get from the gift.
ReLU stands for "rectified linear unit" and is a type of activation function.
Mathematically, it is defined as y = max(0, x).
He can see which parts of the gift are bigger or smaller so he can understand the gift.
This clamp function clamps all elements in input into the returns[ min, max ] and returns a resulting tensor:
The clamp should only affect gradients for values outside the min and max range.
The pow function power with the exponent.
The clone returns a copy of the self tensor. The copy has the same size and data type as self.
A common example is: clamp(min=0) is exactly ReLU().
PyTorch provides ReLU and its variants through the torch.nn module.
If you run the program to look at the output, you will understand that the child has only five operations left and is already pleased with the way the gift result.
The source code is based on one example from here:

import torch 
dtype = torch.float
device = torch.device("cpu")
batch,input,hidden,output = 2,10,2,5
x = torch.randn(batch,input,device=device,dtype=dtype)
y = torch.randn(hidden,output,device=device,dtype=dtype)
w1 = torch.randn(input,hidden,device=device,dtype=dtype)
w2 = torch.randn(hidden,output,device=device,dtype=dtype)

l_r = 1e-6
for t in range(5):
 h = x.mm(w1)
 h_r = h.clamp(min=0)
 y_p = h_r.mm(w2)
 loss = (y_p - y).pow(2).sum().item()
 print("t=",t,"loss=",loss,"\n")
 g_y_p = 2.0 * (y_p -y)
 g_w2 = h_r.t().mm(g_y_p)
 g_h_r = g_y_p.mm(w2.t())
 g_h = g_h_r.clone()
 g_h[h<0 -="l_r" 0="" g_w1="" g_w2="" n="" print="" w1=",w1," w2=",w2,">

The child's result after five operations.

...
t= 4 loss= 25.40263557434082

w1= tensor([[ 1.5933,  0.3818],
        [-1.0043, -1.3362],
        [ 0.5841, -1.9811],
        [ 2.3483,  0.5748],
        [ 0.5904, -0.2521],
        [-0.6612,  2.7945],
        [ 0.4841, -0.5894],
        [-1.4434, -0.1421],
        [-1.2712, -1.4269],
        [ 0.7929,  0.2040]]) w2= tensor([[ 1.7389,  0.4337,  0.4557,  1.3704,  0
.3819],
        [ 0.2937,  0.0212, -0.4604, -1.0564, -1.5403]])

Using pytorch - install it on Windows OS.

A few days ago I install the pytorch on my Windows 8.1 OS and today I will able to install on Fedora 29 distro.
I will try to make a series of pytorch tutorials with Linux and Windows OS on my blogs.
If you want to install it on Fedora 29 you need to follow my Fedora blog post.
For installation on Windows OS, you can read the official webpage.
Because I don't have a new CUDA GPU, the only one video card is an NVIDIA video card 740M on my laptop and my Linux is an Intel onboard video card, I''m not able to solve issues with CUDA and pytorch.
Anyway, this will be a good start to see how to use pytorch.
Let's start the install into default way on Scripts folder from my python version 3.6.4 folder installation.

C:\Python364\Scripts>pip3 install https://download.pytorch.org/whl/cpu/torch-1.0
.0-cp36-cp36m-win_amd64.whl
Collecting torch==1.0.0 from https://download.pytorch.org/whl/cpu/torch-1.0.0-cp
36-cp36m-win_amd64.whl
  Downloading https://download.pytorch.org/whl/cpu/torch-1.0.0-cp36-cp36m-win_am
d64.whl (71.0MB)
    100% |████████████████████████████████| 71.0MB 100kB/s
Installing collected packages: torch
  Found existing installation: torch 0.4.1
    Uninstalling torch-0.4.1:
      Successfully uninstalled torch-0.4.1
Successfully installed torch-1.0.0

C:\Python364\Scripts>pip3 install torchvision

After I install the pytorch python module I import the pytorch and torchvision python modules.
First, as I expected the CUDA feature:

>>> import torch
>>> torch.cuda.is_available()
False

Let's make another test with pytorch:

>>> x = torch.rand(76, 79)
>>> x.size()
torch.Size([76, 79])
>>> print(x)
tensor([[0.1981, 0.3841, 0.9276,  ..., 0.3753, 0.7137, 0.7702],
        [0.8202, 0.9564, 0.5590,  ..., 0.0914, 0.4983, 0.7163],
        [0.0864, 0.4588, 0.0669,  ..., 0.3939, 0.0318, 0.8650],
        ...,
        [0.9028, 0.8431, 0.8592,  ..., 0.3825, 0.2537, 0.7901],
        [0.2055, 0.3003, 0.8085,  ..., 0.0724, 0.9226, 0.9559],
        [0.3671, 0.1178, 0.3837,  ..., 0.7181, 0.5704, 0.9268]])
>>> torch.tensor([[1., -1.], [1., -1.]])
tensor([[ 1., -1.],
        [ 1., -1.]])
>>> torch.zeros([1, 4], dtype=torch.int32)
tensor([[0, 0, 0, 0]], dtype=torch.int32)
>>> torch.zeros([2, 4], dtype=torch.int32)
tensor([[0, 0, 0, 0],
        [0, 0, 0, 0]], dtype=torch.int32)
>>> torch.zeros([3, 4], dtype=torch.int32)
tensor([[0, 0, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0]], dtype=torch.int32)

You can make many tests and check your instalation.
This is a screenshot with all features show by dir with pytorch and torchvision:

Python Qt5 : simple checkbox example.

Today we created a simple tutorial about QCheckBox and QLabel.
The purpose of this tutorial is to use QCheckBox in a GUI interface.
When we check QCheckBox, this will change the text from a QLabel.
The variables used by QCheckBox are my_checkbox and my_label for QLabel.
The result of my source code is this:

Let's see the source code:

# -*- coding: utf-8 -*-
"""
@author: catafest
"""
import sys
from PyQt5.QtCore import Qt
from PyQt5.QtWidgets import QWidget, QCheckBox, QLabel, QApplication

class MyCheckBox(QWidget):
 def __init__(self):
  super().__init__()
 
  my_checkbox = QCheckBox("Check this , see result", self)
  my_checkbox.move(50,60)
  my_checkbox.stateChanged.connect(self.change_my_option)
  

  self.my_label = QLabel("You can visit free-tutorial.org ", self)
  self.my_label.move(50,30)

  #self.my_label.setAlignment(Qt.AlignCenter)
  
  self.setGeometry(420,420,640,100)
  self.setWindowTitle("free-tutorials.org PyQt5 ChecBox ")
  

  
 def change_my_option(self, state):
  if state  == Qt.Checked:
   self.my_label.setText("Thank's by free-tutorial.org")
  else:
   self.my_label.setText("You can visit free-tutorial.org")
   
if __name__ == '__main__':
 app = QApplication(sys.argv)
 win = MyCheckBox()
 win.show()
 sys.exit(app.exec_())