pytorch | Dynamic neural networks in Python with strong GPU | Machine Learning library
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kandi X-RAY | pytorch Summary
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Lines of Code : 2License : Strong Copyleft (CC BY-SA 4.0)
Copy return torch.tensor(batch_x).float(), torch.tensor(batch_t)
from transformers import DebertaTokenizer, DebertaModel
import torch
# downloading the models
tokenizer = DebertaTokenizer.from_pretrained("microsoft/deberta-base")
model = DebertaModel.from_pretrained("microsoft/deberta-base")
# tokenizindef backward(self, unet_loss, dis_loss):
dis_loss.backward(retain_graph = True)
self.dis_optimizer.step()
unet_loss.backward()
self.unet_optimizer.step()
def backward(self, unet_los(x == torch.max(x)).nonzero()
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
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[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__()
file = "model.pt"
model = your_model()
model.load_state_dict(torch.load(file))
# this will automatically load the file and load the parameters into the model.
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)
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_bCommunity Discussions
Trending Discussions on pytorch
QUESTION
I understand that in python user-defined objects can be made callable by defining a __call__() method in the class definition. For example,
ANSWER
Answered 2022-Mar-26 at 18:08Functions 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
QUESTION
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:
...ANSWER
Answered 2022-Jan-19 at 22:30Yes, 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:
QUESTION
Goal: I am trying to import a graph FROM networkx into PyTorch geometric and set labels and node features.
(This is in Python)
Question(s):
- How do I do this [the conversion from networkx to PyTorch geometric]? (presumably by using the
from_networkxfunction) - 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):
...ANSWER
Answered 2021-Dec-22 at 18:32The 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.
- Instead of text as labels, you probably want to have a categorial representation, e.g. 1 stands for Ford.
- If you want to match the "usual convention". Then you name your input features
xand your labels/ground truthy. - 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 introductionfor an example, which uses the Cora dataset.
QUESTION
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:
...ANSWER
Answered 2021-Dec-01 at 15:00While 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.
QUESTION
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.
...ANSWER
Answered 2021-Nov-18 at 03:08It 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: https://github.com/pytorch/pytorch/issues/47702#issuecomment-965625139
TL;DR: a public beta is at least 4 months out.
QUESTION
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)
ANSWER
Answered 2021-Oct-31 at 10:39The output spatial dimensions of nn.ConvTranspose2d are given by:
QUESTION
I have been reading the official guide here (https://www.tensorflow.org/text/tutorials/transformer) 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: https://www.tensorflow.org/datasets/catalog/wmt14_translate#wmt14_translatede-en .
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.
...ANSWER
Answered 2021-Oct-11 at 23:00You can build your own tokenizer following this tutorial https://www.tensorflow.org/text/guide/subwords_tokenizer
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:
QUESTION
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!
...ANSWER
Answered 2021-Sep-27 at 18:31The 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/symbolic_opset12.py:
QUESTION
ANSWER
Answered 2021-Sep-22 at 15:21I 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:
QUESTION
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.
Example:
...ANSWER
Answered 2021-Sep-08 at 12:16What you are looking for is torch.scatter_ with the value option.
Tensor.scatter_(dim, index, src, reduce=None) → Tensor
Writes all values from the tensorsrcintoselfat the indices specified in theindextensor. For each value insrc, its outputindexis specified by its index in src fordimension != dimand by the corresponding value in index fordimension = 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:
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