Umit Kacar, PhD umitkacar

## Pytorch transform.txt
from torchvision import transforms

transform = transforms.Compose([
    transforms.ToTensor(),  # Bu metot PIL.Image veya numpy.ndarray (H x W x C) [0, 255] verilerini
                             # torch.FloatTensor (C x H x W) [0.0, 1.0] formatına dönüştürür.
])

class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, image_paths, transform=None):
        self.image_paths = image_paths

## tmux
tmux
tmux ls
tmux attach -d -t 0

## RedLines
from redlines import Redlines

diff = Redlines(text,response)
display(Markdown(diff.output_markdown))

## cuda_11.3_installation_on_Ubuntu_20.04
#!/bin/bash

## https://deeplearningcrashcourse.org/setup_ubuntu/

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

## Bash script in Python
#!/bin/bash

import subprocess
subprocess.run(["./zip.sh"])
subprocess.run(["zip", "-r", "test.zip", "test/"], capture_output=True)

# https://docs.python.org/3/library/subprocess.html

## Install_OpenCV4_CUDA11_CUDNN8.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                umitkacar
                / Install_OpenCV4_CUDA11_CUDNN8.md
            
            
              Created
              November 14, 2022 09:25
                — forked from raulqf/Install_OpenCV4_CUDA11_CUDNN8.md
            
              
                How to install OpenCV 4.5 with CUDA 11.2 in Ubuntu 20.04
              
          
    How to install OpenCV 4.5.2 with CUDA 11.2 and CUDNN 8.2 in Ubuntu 20.04

First of all install update and upgrade your system:
    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

  
## pytorch_cute.py
dataset, val_set = torch.utils.data.random_split(dataset,[int(len(dataset) * 0.85),
                                                 len(dataset) - int(len(dataset) * 0.85)])

config.manual_seed = 33
print("Random Seed: ", config.manual_seed)
random.seed(config.manual_seed)
torch.manual_seed(config.manual_seed)

if torch.cuda.is_available():
    torch.cuda.manual_seed_all(config.manual_seed)

## static_class_methods.py
# use of class method and static method.
from datetime import date

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    # a class method to create a Person object by birth year.
    @classmethod

## __str__.py
class Address:
    def __init__(self, street, city, state, zipcode, street2=''):
        self.street = street
        self.street2 = street2
        self.city = city
        self.state = state
        self.zipcode = zipcode

    def __str__(self):
        lines = [self.street]

## make folder.py
if not os.path.exists("output"):
  os.makedirs("output")
	from torchvision import transforms

	transform = transforms.Compose([
	transforms.ToTensor(), # Bu metot PIL.Image veya numpy.ndarray (H x W x C) [0, 255] verilerini
	# torch.FloatTensor (C x H x W) [0.0, 1.0] formatına dönüştürür.
	])

	class CustomDataset(torch.utils.data.Dataset):
	def __init__(self, image_paths, transform=None):
	self.image_paths = image_paths
	from redlines import Redlines

	diff = Redlines(text,response)
	display(Markdown(diff.output_markdown))
	#!/bin/bash

	## https://deeplearningcrashcourse.org/setup_ubuntu/

	### steps ####
	# verify the system has a cuda-capable gpu
	# download and install the nvidia cuda toolkit and cudnn
	# setup environmental variables
	# verify the installation
	###
	#!/bin/bash

	import subprocess
	subprocess.run(["./zip.sh"])
	subprocess.run(["zip", "-r", "test.zip", "test/"], capture_output=True)

	# https://docs.python.org/3/library/subprocess.html
	dataset, val_set = torch.utils.data.random_split(dataset,[int(len(dataset) * 0.85),
	len(dataset) - int(len(dataset) * 0.85)])

	config.manual_seed = 33
	print("Random Seed: ", config.manual_seed)
	random.seed(config.manual_seed)
	torch.manual_seed(config.manual_seed)

	if torch.cuda.is_available():
	torch.cuda.manual_seed_all(config.manual_seed)
	# use of class method and static method.
	from datetime import date

	class Person:
	def __init__(self, name, age):
	self.name = name
	self.age = age

	# a class method to create a Person object by birth year.
	@classmethod
	class Address:
	def __init__(self, street, city, state, zipcode, street2=''):
	self.street = street
	self.street2 = street2
	self.city = city
	self.state = state
	self.zipcode = zipcode

	def __str__(self):
	lines = [self.street]