The TorchVision datasets subpackage is a convenient utility for accessing well-known public image and video datasets. You can use these tools to start training new computer vision models very quickly. TorchVision Datasets Example To get started, all you have to do is import one of the Dataset classes. Then, instantiate it and access one of the samples with indexing: from torchvision import datasets dataset = datasets.MNIST(root="./", download=True) img, label = dataset[10] img.size # Expected result # (28, 28) You’ll get a tuple with a Pillow image and an integer label back: The TorchVision datasets implement __len__() and __getitem__() methods, which means that in addition to getting specific elements by index, you can also get the number of samples with the len() function: len(dataset) # Expected result # 60000 Additionally, DataLoader classes can use TorchVision Dataset objects to create automatic batches for training. Since they mostly return Pillow images, you do need to pass in a transform to convert the image to a tensor: import torch from torchvision import transforms dataset = datasets.MNIST( root="./", transform=transforms.ToTensor() ) data_loader = torch.utils.data.DataLoader(dataset, batch_size=4) x, y = next(iter(data_loader)) x.shape # Expected result # torch.Size([4, 1, 28, 28]) API The interface for the TorchVision Dataset classes is somewhat inconsistent because every dataset has a slightly different set of constraints. For example, many of the datasets return (PIL.Image, int) tuples, but this obviously wouldn’t work for videos (TorchVision packs them into tensors). But generally speaking, the constructors take the following arguments: root: where to download the raw dataset or where the Dataset class should expect to find a raw dataset that has already been downloaded.split: which holdout to use. This can be train, test, val, extra… best to look at the docs for the dataset you want to use.download: a boolean indicating whether TorchVision should download the raw data for you. Although setting this argument to true will raise an error for datasets like ImageNet. More on this below.transform: a TorchVision transform to apply to the input image or video. A word about ImageNet ImageNet is no longer available for small companies or independent researchers. This is a real shame because pre-trained classifiers in model zoos are almost always trained on ImageNet. However, it is possible to download most of the ImageNet dataset from Academic Torrents. I cannot endorse this strategy because I don’t know if it’s allowed. If you did want to download the train and validation sets from ImageNet 2012, here are some steps you could follow: Launch an Amazon Linux EC2 instance with at least 200GB of storage. The whole process takes about 2 hours on a c5.xlarge instance. 2. Install the aria2c command-line tool (instructions here). 3. Download the tar files: # Download the validation set aria2c https://academictorrents.com/download/dfa9ab2528ce76b907047aa8cf8fc792852facb9.torrent # Download the train set aria2c https://academictorrents.com/download/a306397ccf9c2ead27155983c254227c0fd938e2.torrent 4. Make sure the files match the MD5 hashes (helpfully provided by the TorchVision team): # Check the validation file md5sum ILSVRC2012_img_val.tar # Expected result # 29b22e2961454d5413ddabcf34fc5622 # Check the train file md5sum ILSVRC2012_img_train.tar # Expected result # 1d675b47d978889d74fa0da5fadfb00e 5. Upload the files to S3 — hosting the files costs a little over $3 per month. 6. Terminate the instance. 7. Send the Academic Torrents team some Bitcoin to say thank you. Summary And that’s all you need to know to get started with TorchVision Datasets. For production machine learning pipelines, you probably want to implement your own Dataset class, but the datasets that come out of the box with TorchVision are a great way to experiment quickly! The post TorchVision Datasets: Getting Started appeared first on Sparrow Computing.

While programming in python, comparison has to be done very often for checking different conditions. We may need to compare two variables or two groups of variables for a single condition checking. In this article we will try different ways to compare two lists in python. While comparing, we will have to check if both the lists contain the same elements or not irrespective of the order in which the elements are present in the lists. Accordingly, we will have to print the result. Compare two lists using sort() method To check whether two lists contain the same elements or not, we can use  the sort() method to sort the elements of the lists first. Then, we can compare the two lists. For comparison,first we will check if the length of the lists are equal or not. If the lengths are not equal, the lists will be automatically considered as different.  If the length of the lists are the same, we will sort the two lists. Then we will compare the lists using the == operator to check whether the lists are equal or not. If the sorted lists are equal, it will establish that both the original lists contain the same elements. This can be implemented as follows. # function to compare lists def compare(l1, l2): # here l1 and l2 must be lists if len(l1) != len(l2): return False l1.sort() l2.sort() if l1 == l2: return True else: return False list1 = [1, 2, 3, 4] list2 = [1, 4, 3, 2] list3 = [2, 3, 4, 5] print("list1 is:",list1) print("list2 is:",list2) print("list3 is:",list3) # comparing list1 and list 2 print("list1 and list2 contain same elements:",compare(list1, list2)) #comparing list2 and list3 print("list1 and list3 contain same elements:",compare(list1, list3)) Output: list1 is: [1, 2, 3, 4] list2 is: [1, 4, 3, 2] list3 is: [2, 3, 4, 5] list1 and list2 contain same elements: True list1 and list3 contain same elements: False Compare using sets in Python To compare two lists in python, we can use sets. A set in python only allows unique values in it. We can use this property of sets to find if two lists have the same elements or not. For comparison,first we will check if the length of the lists are equal or not. If the lengths are not equal, the lists will be automatically flagged as different. After that, we will convert the lists into sets using set() constructor. We can compare the two sets using the == operator to check if both the sets are equal or not. If both sets are equal, it will be established that both the lists contain equal values. Following example illustrates this concept. # function to compare lists def compare(l1, l2): # here l1 and l2 must be lists if len(l1) != len(l2): return False set1 = set(l1) set2 = set(l2) if set1 == set2: return True else: return False list1 = [1, 2, 3, 4] list2 = [1, 4, 3, 2] list3 = [2, 3, 4, 5] print("list1 is:", list1) print("list2 is:", list2) print("list3 is:", list3) # comparing list1 and list 2 print("list1 and list2 contain same elements:", compare(list1, list2)) # comparing list2 and list3 print("list1 and list3 contain same elements:", compare(list1, list3)) Output: list1 is: [1, 2, 3, 4] list2 is: [1, 4, 3, 2] list3 is: [2, 3, 4, 5] list1 and list2 contain same elements: True list1 and list3 contain same elements: False Compare two lists using frequency counter We can also compare two lists without comparing their lengths. For this, first we will have to create a python dictionary for each list which will keep track of the frequency of the elements in the lists. After creating the dictionaries where elements of the lists are stored as keys and their frequency are stored as values, we can compare the frequency of each element in the two dictionaries. If the frequency of each elements becomes equal, it will be confirmed that both the lists contained equal elements. For this task, we can use the counter() method. The counter() method, when invoked on a list, creates a python dictionary and stores the elements as keys and their frequency as values in it. After invoking the counter() method, we can compare the created dictionary using == operator to check whether the frequency of every element is equal or not. If the result is True, the lists contain equal elements. Otherwise not. This can be seen from the following example. import collections # function to compare lists def compare(l1, l2): # here l1 and l2 must be lists if len(l1) != len(l2): return False counter1 = collections.Counter(l1) counter2=collections.Counter(l2) if counter1 == counter2: return True else: return False list1 = [1, 2, 3, 4] list2 = [1, 4, 3, 2] list3 = [2, 3, 4, 5] print("list1 is:", list1) print("list2 is:", list2) print("list3 is:", list3) # comparing list1 and list 2 print("list1 and list2 contain same elements:", compare(list1, list2)) # comparing list2 and list3 print("list1 and list3 contain same elements:", compare(list1, list3)) Output: list1 is: [1, 2, 3, 4] list2 is: [1, 4, 3, 2] list3 is: [2, 3, 4, 5] list1 and list2 contain same elements: True list1 and list3 contain same elements: False Conclusion In this article, we have seen three different ways to compare two lists in python and have checked if they contain the same elements without considering the position of the elements. To read more about lists, read this article on list comprehension. In the compare() function used in the examples, it may be possible that user passes two other objects instead of lists. In such cases, the program may run into error. To avoid this, we can use exception handling using python try except to avoid runtime errors by applying type checking using type() method in try-except block to check if the objects passed as arguments are lists. The post Compare two lists in Python appeared first on PythonForBeginners.com.

Are you interested in sharing your Python project with the broader world? Would you like to make it easily installable using pip? How do you create Python packages that share your code in a scalable and maintainable way? This week on the show, Real Python author and former guest Dane Hillard returns to talk about his new book, "Publishing Python Packages." [ Improve Your Python With ? Python Tricks ? – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Introduction The Portable Document Format (PDF) is not a WYSIWYG (What You See is What You Get) format. It was developed to be platform-agnostic, independent of the underlying operating system and rendering engines. To achieve this, PDF was constructed to be interacted with via something more like a programming language, and relies on a series of instructions and operations to achieve a result. In fact, PDF is based on a scripting language - PostScript, which was the first device-independent Page Description Language. In this guide, we'll be using borb - a Python library dedicated to reading, manipulating and generating PDF documents. It offers both a low-level model (allowing you access to the exact coordinates and layout if you choose to use those) and a high-level model (where you can delegate the precise calculations of margins, positions, etc to a layout manager). Matplotlib is a data visualization library that fueled an entire generation of engineers to start visualizing data, and the engine behind many other popular libraries such as Seaborn. Given how common PDF documents are for creating reports (which oftentimes include graphs), we'll take a look at how to integrate Matplotlib charts in a PDF document using borb. Installing borb (and Matplotlib) borb can be downloaded from source on GitHub, or installed via pip: $ pip install borb Matplotlib can be installed via pip: $ pip install matplotlib Integrating Matplotlib Charts in PDF Documents with borb Before we can create a chart, such as a Pie Chart, we're going to write a small utility function that generates N colors, evenly distributed among the color-spectrum. This will help us whenever we need to create a plot and color each section: from borb.pdf.canvas.color.color import HSVColor, HexColor from decimal import Decimal import typing def create_n_colors(n: int) -> typing.List[str]: # The base color is borb-blue base_hsv_color: HSVColor = HSVColor.from_rgb(HexColor("56cbf9")) # This array comprehension creates n HSVColor objects, transforms then to RGB, and then returns their hex string return [HSVColor(base_hsv_color.hue + Decimal(x / 360), Decimal(1), Decimal(1)).to_rgb().to_hex_string() for x in range(0, 360, int(360/n))] Note: HSL (hue, saturation, lightness) and HSV/HSB (hue, saturation, value/hue, saturation, brightness) are alternative representations of the RGB color model. HSL and HSV/HSB were designed in the 1970s by computer graphics researchers to more closely align with the way human vision perceives color-making attributes. In these models, colors of each hue are arranged in a radial slice, around a central axis of neutral colors which ranges from black at the bottom to white at the top: Credits: Wikimedia (CC BY-SA 3.0) license The advantage of using this representation for Color is that we can easily divide the color-spectrum in equal parts. Now we can define a create_pie_chart() function (or a function for other types of plots): # New import(s) import matplotlib.pyplot as plt from borb.pdf.canvas.layout.image.chart import Chart from borb.pdf.canvas.layout.layout_element import Alignment def create_piechart(labels: typing.List[str], data: typing.List[float]): # Symetric figure to ensure equal aspect ratio fig1, ax1 = plt.subplots(figsize=(4, 4)) ax1.pie( data, explode=[0 for _ in range(0, len(labels))], labels=labels, autopct="%1.1f%%", shadow=True, startangle=90, colors=create_n_colors(len(labels)), ) ax1.axis("equal") # Equal aspect ratio ensures that pie is drawn as a circle. return Chart( plt.gcf(), width=Decimal(200), height=Decimal(200), horizontal_alignment=Alignment.CENTERED, ) Here, we've used Matplotlib to create a pie chart, via the pie() function. If you'd like to learn more about creating Pie Charts, read our Guide to Matplotlib Pie Charts! The gcf() function of the PyPlot instance returns the current figure (get current figure). This figure can be embedded into a PDF document, by injecting it in a Chart constructor, alongside your customization arguments such as the width, height and horizontal_alignment. That's it! You just supply a Matplotlib figure to the Chart contructor. Adding a Matplotlib Chart to a PDF Document Now it's time to create our basic PDF Document and add content to it. # New import(s) from borb.pdf.document import Document from borb.pdf.page.page import Page from borb.pdf.pdf import PDF from borb.pdf.canvas.layout.page_layout.multi_column_layout import MultiColumnLayout from borb.pdf.canvas.layout.page_layout.page_layout import PageLayout from borb.pdf.canvas.layout.text.paragraph import Paragraph # Create empty Document pdf = Document() # Create empty Page page = Page() # Add Page to Document pdf.append_page(page) # Create PageLayout layout: PageLayout = MultiColumnLayout(page) # Write title layout.add(Paragraph("About Lorem Ipsum", font_size=Decimal(20), font="Helvetica-Bold")) We'll be using hyphenation in this PDF to ensure the text can be laid out even more smoothly. Hyphenation in borb is pretty straightforward: # New import(s) from borb.pdf.canvas.layout.hyphenation.hyphenation import Hyphenation # Create hyphenation algorithm hyphenation_algorithm: Hyphenation = Hyphenation("en-gb") # Write paragraph layout.add(Paragraph( """ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. """, text_alignment=Alignment.JUSTIFIED, hyphenation=hyphenation_algorithm)) Now we can add a piechart using the function we declared earlier; # Write graph layout.add(create_piechart(["Loren", "Ipsum", "Dolor"], [0.6, 0.3, 0.1])) Next we're going to write three more Paragraph objects. One of them is going to be more of a quote (border on the side, different font, etc). # Write paragraph layout.add(Paragraph( """ Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. """, text_alignment=Alignment.JUSTIFIED, hyphenation=hyphenation_algorithm)) # Write paragraph layout.add(Paragraph( """ Lorem Ipsum is simply dummy text of the printing and typesetting industry. """, font="Courier-Bold", text_alignment=Alignment.JUSTIFIED, hyphenation=hyphenation_algorithm, border_color=HexColor("56cbf9"), border_width=Decimal(3), border_left=True, padding_left=Decimal(5), padding_bottom=Decimal(5), )) # Write paragraph layout.add(Paragraph( """ Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. """, text_alignment=Alignment.JUSTIFIED, hyphenation=hyphenation_algorithm)) Let's add another plot # Write graph layout.add(create_piechart(["Loren", "Ipsum", "Dolor", "Sit", "Amet"], [600, 30, 89, 100, 203])) And one more Paragraph of content # Write paragraph layout.add(Paragraph( """ It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). """, text_alignment=Alignment.JUSTIFIED, hyphenation=hyphenation_algorithm)) Finally, we can store the Document: # Write to disk with open("output.pdf", "wb") as pdf_file_handle: PDF.dumps(pdf_file_handle, pdf) Running this code results in a PDF document that looks like this: Conclusion In this guide you've learned how to integrate Matplotlib charts in a PDF using borb. From here, the sky is the limit! The more creative you get with data visualization, the nicer your PDFs will be.

Paul has a tutorial on testing and TDD with React and TypeScript. We discuss workflow and the differences, similarities between testing with React/TypeScript and Python. We also discuss what lessons that we can bring from front end testing to Python testing.Special Guest: Paul Everitt.Sponsored By:Patreon Supporters: Help support the show with as little as $1 per month and be the first to know when new episodes come out.Support Test & Code in PythonLinks:React, TypeScript, and TDD — Paul Everitt's tutorialReact Testing Library<p>Paul has a tutorial on testing and TDD with React and TypeScript. <br> We discuss workflow and the differences, similarities between testing with React/TypeScript and Python.<br> We also discuss what lessons that we can bring from front end testing to Python testing.</p><p>Special Guest: Paul Everitt.</p><p>Sponsored By:</p><ul><li><a href="https://www.patreon.com/testpodcast" rel="nofollow">Patreon Supporters</a>: <a href="https://www.patreon.com/testpodcast" rel="nofollow">Help support the show with as little as $1 per month and be the first to know when new episodes come out.</a></li></ul><p><a href="https://www.patreon.com/testpodcast" rel="payment">Support Test & Code in Python</a></p><p>Links:</p><ul><li><a href="https://www.jetbrains.com/webstorm/guide/tutorials/react_typescript_tdd/" title="React, TypeScript, and TDD" rel="nofollow">React, TypeScript, and TDD</a> &mdash; Paul Everitt's tutorial</li><li><a href="https://testing-library.com/docs/react-testing-library/intro/" title="React Testing Library" rel="nofollow">React Testing Library</a></li></ul>

If you’re building production ML systems, dev containers are the killer feature of VS Code. Dev containers give you full VS Code functionality inside a Docker container. This lets you unify your dev and production environments if production is a Docker container. But even if you’re not targeting a Docker deployment, running your code in a container prevents you from fighting with local Python environments across development machines: if you configure your dev container correctly, it will work exactly the same for every member of your team. In this post, I’ll give you a quick look at how development containers work in VS Code so you can try them for yourself. There are other ways to go about this, but for a basic workflow, I recommend a few things: A Dockerfile in the root folder to define the development containerA configuration file at .devcontainer/devcontainer.jsonWhatever project files you need This means, for a new project, the folder structure will look something like the following: . ├── .devcontainer │   └── devcontainer.json ├── Dockerfile ├── LICENSE ├── README.md ├── main.py ├── requirements-dev.txt └── requirements.txt The Dockerfile can be very flexible. Typically you’ll want to install dependencies unless your Docker image already has them installed. Here’s a very basic one you can use to start: FROM python:3.9 COPY requirements.txt . RUN pip install requirements.txt The devcontainer.json file is also highly customizable, but a few things are particularly important: The build definitionWorkspace settings for VS Code (things like the path to the Python interpreter, what linter to use, etc)Extensions to useAdditional commands to run inside the container Here’s what a basic devcontainer.json looks like: { "name": "Python 3", "build": { "dockerfile": "../Dockerfile", }, // Set *default* container specific settings.json values on container create. "settings": { "python.pythonPath": "/usr/local/bin/python", "python.languageServer": "Pylance", "python.linting.enabled": true, "python.formatting.blackPath": "/usr/local/bin/black", "python.formatting.provider": "black", "editor.formatOnSave": true, }, // Add the IDs of extensions you want installed when the container is created. "extensions": [ "ms-python.python", "ms-python.vscode-pylance" ], "postCreateCommand": "pip install -r requirements-dev.txt" } Once you have these files setup in your VS Code session, you can open the project in a container. Open the Command Palette with F1 and run Remote-Containers: Open Folder in Container... From there, you can open a terminal and run your code. Notice if you modify print("hello world") to print('hello world') (with a single quotation) Black will automatically change it for you on save. Nifty! One other note: the community seems to be switching to Poetry for dependency management in Python and I think this is a good trend. Version conflicts are a huge pain in the regular pip toolchain. I will save that for another post, but just know that you can swap out the pip requirements files for Poetry. The post Development containers in VS Code: a quick start guide appeared first on Sparrow Computing.

As the author of PEP 563, I can summarize my position as follows: had PEP 563 never been implemented, it would be easy to accept PEP 649. However, in the current situation it’s not that clear because I find it very important to allow writing a single codebase that works on Python 3.7 - 3.11. If we can secure this behavior, I’m +1 to accepting PEP 649. If not, I have an alternative idea.

Furniture Hardware Market Drivers And Restraints For Each Segment – Puck77  Puck77

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Enhanced Profiles Drive BlackRock, Invesco and Boutique  GlobeNewswire

The 2021 Apple Hardware Lineup Will Make Holiday Shopping A Blast  Men's Gear

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