pytorch | Dynamic neural networks in Python with strong GPU | Machine Learning library

 by   pytorch Python Version: v2.0.1 License: Non-SPDX

kandi X-RAY | pytorch Summary

kandi X-RAY | pytorch Summary

pytorch is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Numpy applications. pytorch has no bugs, it has build file available and it has medium support. However pytorch has 1 vulnerabilities and it has a Non-SPDX License. You can download it from GitHub, Maven.

At a granular level, PyTorch is a library that consists of the following components:.

            kandi-support Support

              pytorch has a medium active ecosystem.
              It has 67874 star(s) with 18602 fork(s). There are 1649 watchers for this library.
              It had no major release in the last 12 months.
              There are 11237 open issues and 23283 have been closed. On average issues are closed in 60 days. There are 922 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pytorch is v2.0.1

            kandi-Quality Quality

              pytorch has 0 bugs and 0 code smells.

            kandi-Security Security

              pytorch has 1 vulnerability issues reported (1 critical, 0 high, 0 medium, 0 low).
              pytorch code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              pytorch has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              pytorch releases are available to install and integrate.
              Deployable package is available in Maven.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 497661 lines of code, 39721 functions and 2295 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

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            pytorch Key Features

            No Key Features are available at this moment for pytorch.

            pytorch Examples and Code Snippets

            Pytorch RuntimeError: expected scalar type Double but found Float
            Pythondot img1Lines of Code : 2dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
             return torch.tensor(batch_x).float(), torch.tensor(batch_t)
            How to use the DeBERTa model by He et al. (2022) on Spyder?
            Pythondot img2Lines of Code : 12dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from transformers import DebertaTokenizer, DebertaModel
            import torch
            # downloading the models
            tokenizer = DebertaTokenizer.from_pretrained("microsoft/deberta-base")
            model = DebertaModel.from_pretrained("microsoft/deberta-base")
            # tokenizin
            I am running into a gradient computation inplace error
            Pythondot img3Lines of Code : 14dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def backward(self, unet_loss, dis_loss):
                    dis_loss.backward(retain_graph = True)
            def backward(self, unet_los
            How can I find multiple maximum indices of a torch tensor?
            Pythondot img4Lines of Code : 2dot img4License : Strong Copyleft (CC BY-SA 4.0)
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            (x == torch.max(x)).nonzero()
            speeding up 1d convolution in PyTorch
            Pythondot img5Lines of Code : 3dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            out = torch.conv1d(x_batch.unsqueeze(0), y_batch.unsqueeze(1).flip(2), padding=y_batch.size(1)-1, groups=x_batch.size(0))
            print(torch.allclose(out, res1))  # True
            speeding up 1d convolution in PyTorch
            Pythondot img6Lines of Code : 12dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import torch
            from torch.nn import functional as F
            num_vectors = 100
            len_vectors = 9
            v1 = torch.rand((num_vectors, 1, len_vectors))
            v2 = torch.rand(1, 1, 6)
            padding = torch.min(torch.tensor([v1.shape[-1], v2.shape[
            Combine Camembert & CRF for token classification
            Pythondot img7Lines of Code : 70dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import torch
            import torch.nn as nn
            from torchcrf import CRF
            from transformers import CamembertModel, CamembertTokenizerFast
            class CamemBERTCRF(nn.Module):
              def __init__(self, num_labels):
                super(CamemBERTCRF, self).__init__()
            How to use pt file
            Pythondot img8Lines of Code : 6dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            file = ""
            model = your_model()
            # this will automatically load the file and load the parameters into the model.
            Simple Neural Network in Pytorch with 3 inputs (Numerical Values)
            Pythondot img9Lines of Code : 18dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from torch import nn
            import torch.nn.functional as F
            def network(nn.Module):
                def __init__(self, M):
                    # M is the dimension of input feature
                    super(network, self).__init__()
                    self.layer1 = nn.Linear(M, 100)
            How can I determine validation loss for faster RCNN (PyTorch)?
            Pythondot img10Lines of Code : 127dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from typing import Tuple, List, Dict, Optional
            import torch
            from torch import Tensor
            from collections import OrderedDict
            from torchvision.models.detection.roi_heads import fastrcnn_loss
            from torchvision.models.detection.rpn import concat_b

            Community Discussions


            Syntax for making objects callable in python
            Asked 2022-Mar-26 at 18:08

            I understand that in python user-defined objects can be made callable by defining a __call__() method in the class definition. For example,



            Answered 2022-Mar-26 at 18:08

            Functions are normal first-class objects in python. The name to with which you define a function object, e.g. with a def statement, is not set in stone, any more than it would be for an int or list. Just as you can do



            What is the proper way to make an object with unpickable fields pickable?
            Asked 2022-Jan-26 at 00:11

            For me what I do is detect what is unpickable and make it into a string (I guess I could have deleted it too but then it will falsely tell me that field didn't exist but I'd rather have it exist but be a string). But I wanted to know if there was a less hacky more official way to do this.

            Current code I use:



            Answered 2022-Jan-19 at 22:30

            Yes, a try/except is the best way to go about this.

            Per the docs, pickle is capable of recursively pickling objects, that is to say, if you have a list of objects that are pickleable, it will pickle all objects inside of that list if you attempt to pickle that list. This means that you cannot feasibly test to see if an object is pickleable without pickling it. Because of that, your structure of:



            How to load in graph from networkx into PyTorch geometric and set node features and labels?
            Asked 2022-Jan-02 at 14:31

            Goal: I am trying to import a graph FROM networkx into PyTorch geometric and set labels and node features.

            (This is in Python)


            1. How do I do this [the conversion from networkx to PyTorch geometric]? (presumably by using the from_networkx function)
            2. How do I transfer over node features and labels? (more important question)

            I have seen some other/previous posts with this question but they weren't answered (correct me if I am wrong).

            Attempt: (I have just used an unrealistic example below, as I cannot post anything real on here)

            Let us imagine we are trying to do a graph learning task (e.g. node classification) on a group of cars (not very realistic as I said). That is, we have a group of cars, an adjacency matrix, and some features (e.g. price at the end of the year). We want to predict the node label (i.e. brand of the car).

            I will be using the following adjacency matrix: (apologies, cannot use latex to format this)

            A = [(0, 1, 0, 1, 1), (1, 0, 1, 1, 0), (0, 1, 0, 0, 1), (1, 1, 0, 0, 0), (1, 0, 1, 0, 0)]

            Here is the code (for Google Colab environment):



            Answered 2021-Dec-22 at 18:32

            The easiest way is to add all information to the networkx graph and directly create it in the way you need it. I guess you want to use some Graph Neural Networks. Then you want to have something like below.

            1. Instead of text as labels, you probably want to have a categorial representation, e.g. 1 stands for Ford.
            2. If you want to match the "usual convention". Then you name your input features x and your labels/ground truth y.
            3. The splitting of the data into train and test is done via mask. So the graph still contains all information, but only part of it is used for training. Check the PyTorch Geometric introduction for an example, which uses the Cora dataset.



            Why should I use a 2**N value and how do I choose the right one?
            Asked 2021-Dec-09 at 20:13

            I'm working through the lessons on building a neural network and I'm confused as to why 512 is used for the linear_relu_stack in the example code:



            Answered 2021-Dec-01 at 15:00

            While there are unsubstantiated claims that powers of 2 help to optimize performance for various parts of a neural network, it is a convenient method of selecting/testing/finding the right order of magnitude to use for various parameters/hyperparameters.



            How to run Pytorch on Macbook pro (M1) GPU?
            Asked 2021-Nov-18 at 03:08

            I tried to train a model using PyTorch on my Macbook pro. It uses the new generation apple M1 CPU. However, PyTorch couldn't recognize my GPUs.



            Answered 2021-Nov-18 at 03:08

            It looks like PyTorch support for the M1 GPU is in the works, but is not yet complete.

            From @soumith on GitHub:

            So, here's an update. We plan to get the M1 GPU supported. @albanD, @ezyang and a few core-devs have been looking into it. I can't confirm/deny the involvement of any other folks right now.

            So, what we have so far is that we had a prototype that was just about okay. We took the wrong approach (more graph-matching-ish), and the user-experience wasn't great -- some operations were really fast, some were really slow, there wasn't a smooth experience overall. One had to guess-work which of their workflows would be fast.

            So, we're completely re-writing it using a new approach, which I think is a lot closer to your good ole PyTorch, but it is going to take some time. I don't think we're going to hit a public alpha in the next ~4 months.

            We will open up development of this backend as soon as we can.

            That post:

            TL;DR: a public beta is at least 4 months out.



            Understanding the PyTorch implementation of Conv2DTranspose
            Asked 2021-Oct-31 at 10:48

            I am trying to understand an example snippet that makes use of the PyTorch transposed convolution function, with documentation here, where in the docs the author writes:

            "The padding argument effectively adds dilation * (kernel_size - 1) - padding amount of zero padding to both sizes of the input."

            Consider the snippet below where a [1, 1, 4, 4] sample image of all ones is input to a ConvTranspose2D operation with arguments stride=2 and padding=1 with a weight matrix of shape (1, 1, 4, 4) that has entries from a range between 1 and 16 (in this case dilation=1 and added_padding = 1*(4-1)-1 = 2)



            Answered 2021-Oct-31 at 10:39

            The output spatial dimensions of nn.ConvTranspose2d are given by:



            Tensorflow "Transformer model for language understanding" with another Dataset?
            Asked 2021-Oct-11 at 23:08

            I have been reading the official guide here ( to try and recreate the Vanilla Transformer in Tensorflow. I notice the dataset used is quite specific, and at the end of the guide, it says to try with a different dataset.

            But that is where I have been stuck for a long time! I am trying to use the WMT14 dataset (as used in the original paper, Vaswani et. al.) here: .

            I have also tried Multi30k and IWSLT dataset from Spacy, but are there any guides on how I can fit the dataset to what the model requires? Specifically, to tokenize it. The official TF guide uses a pretrained tokenizer, which is specific to the PR-EN dataset given.



            Answered 2021-Oct-11 at 23:00

            You can build your own tokenizer following this tutorial

            It is the exact same way they build the ted_hrlr_translate_pt_en_converter tokenizer in the transformers example, you just need to adjust it to your language.

            I rewrote it for your case but didn't test it:



            pytorch: NLLLoss ignore_index default value
            Asked 2021-Oct-11 at 22:41

            in the pytorch NLLLoss doc the default of ignore_index is -100 instead of the usual None, are there any particular reasons? seems like any negative value is equivalent.

            BTW, what may be the reason that I would want to ignore an index? Thanks!



            Answered 2021-Sep-27 at 18:31

            The value for ignore_index must be an int, that's why the default value is an int and not None. The default value is arbitrary, it could have been any negative number, i.e. anything that is not a "valid" class label. The function will ignore all elements for which the target instance has that class label. In practice, this option can be used to identify unlabeled pixels for example in dense prediction tasks.

            Edit: Tracing back the implementation of nn.NLLLoss, we can find this comment in the nll_loss implementation of torch/onnx/



            how to convert a csv file to character level one-hot-encode matrices?
            Asked 2021-Sep-22 at 15:21

            I have a CSV file that looks like this

            I want to choose the last column and make character level one-hot-encode matrices of every sequence, I use this code and it doesn't work



            Answered 2021-Sep-22 at 15:21

            I think it would be best to keep pd.DataFrame as is and do the transformation "on the fly" within PyTorch Dataset.

            First, dummy data similar to yours:



            Setting results of torch.gather(...) calls
            Asked 2021-Sep-08 at 12:16

            I have a 2D pytorch tensor of shape n by m. I want to index the second dimension using a list of indices (which could be done with torch.gather) then then also set new values to the result of the indexing.




            Answered 2021-Sep-08 at 12:16

            What you are looking for is torch.scatter_ with the value option.

            Tensor.scatter_(dim, index, src, reduce=None) → Tensor
            Writes all values from the tensor src into self at the indices specified in the index tensor. For each value in src, its output index is specified by its index in src for dimension != dim and by the corresponding value in index for dimension = dim.

            With 2D tensors as input and dim=1, the operation is:
            self[i][index[i][j]] = src[i][j]

            No mention of the value parameter though...

            With value=42, and dim=1, this will have the following effect on data:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install pytorch

            Commands to install binaries via Conda or pip wheels are on our website:
            Stable binaries: Python 3.6:
            Rolling weekly binaries: Python 3.6:
            Three-pointers to get you started:.
            Tutorials: get you started with understanding and using PyTorch
            Examples: easy to understand PyTorch code across all domains
            The API Reference


            To build documentation in various formats, you will need Sphinx and the readthedocs theme. You can then build the documentation by running make <format> from the docs/ folder. Run make to get a list of all available output formats. If you get a katex error run npm install katex. If it persists, try npm install -g katex.
            Find more information at:

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