id stringlengths 3 8 | text stringlengths 1 115k |
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st104300 | Hi,
pytorch has done a great job for normal dense tensor, and I have used it for not only deep learning applications, but also some basic gpu boosted inference task, such as kmeans and tsne.
However, currently the sparse tensor support is not so good. According to my observation, only matrix multiplication and element-... |
st104301 | Yes, in the long term we’d like to support more operations on sparse. There is an issue open for slice but I’m sure about reduce sum and elementwise comparision: https://github.com/pytorch/pytorch/issues 44. |
st104302 | Hi everybody,
I am currently trying to train a regressor that accepts 5 dimensional features an outputs a single value. The neural network architecture that I used accepts batch of input data and I use batchNorm in the first layer.
But beacuse of the nature of batchNorm network generates normalized predictions. For eva... |
st104303 | BatchNorm doesn’t necessarily generate normalized features, if affine=True.
The additional weights and bias can scale and shift the input again, but that’s only a side note.
During evaluation you should use the running statistics and not calculate the current batch statistics anymore.
You can do this by setting your mo... |
st104304 | Thanks for your reply, I have already used model.train() and model.eval() features of pyTorch. But still I get normalized outputs. But I think in order to evaluate the model in a valid manner I need same normalization scheme for the ground truth data for the regression problem. |
st104305 | Hello,
I’m trying to work my way through Mixtures Density Network, and while I managed to figure it out for a single feature for the output, I’m stuck when it comes to several features. Among my problems, here’s the one concerning the Normal distribution class.
Here’s an example:
import torch
from torch.distributions i... |
st104306 | Never mind, I figured it out !
I think that Normal generates one normal distribution per feature. Hence, you have as many log_probs as you have distributions.
However, in the case of multi-features, here’s what I propose:
Either keep Normal, but sum log probs (or multiply probs) to get the probability you seek given m... |
st104307 | I have converted a model from crnn in torch7 (https://github.com/bgshih/crnn 1), but the predictions are completely wrong. How can I debug it?
I want to print the tensor value in each layer in torch7 and compare it with pytorch, but it seems that there is no print layer in torch7. Now I am trying to load the model in t... |
st104308 | Did you make sure to set your model to evaluation with model.eval() before comparing its output to the torch7 model?
I think in torch7 you can just print the output of each layer.
Could you try to call this, if you used a Sequential model:
net.modules[n].output |
st104309 | 4D1E6621519DC40F0FD85FE766B719C6.jpg1308×504 93.2 KB
I download pytorch from this link and get it installed in my server. It works fine in a test file. However,
LD_LIBRARY_PATH contains no cudnn. I wondering pytorch installed in this way get no utilization
of cudnn lib, leading to the slow training in DNNs.
If I add... |
st104310 | Solved by ptrblck in post #2
You can print the version with:
print(torch.backends.cudnn.version())
The wheels come with some pre-built libraties (CUDA, cuDNN, etc.). |
st104311 | You can print the version with:
print(torch.backends.cudnn.version())
The wheels come with some pre-built libraties (CUDA, cuDNN, etc.). |
st104312 | Hello ,
I can’t figure out this error , it seems to be linked to input dimension to my RNNclassifier.
Thanks.
class RNNClassifier(nn.Module):
def __init__(self, input_size, hidden_size, output_size, n_layers=1):
super(RNNClassifier, self).__init__()
self.hidden_size = hidden_size
self.n_lay... |
st104313 | Is there any experience for predict online with one image input and predict multiprocessing ? |
st104314 | In the Pytorch transfer learning tutorial 6, the following line of code appears:
scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
and during the model training:
scheduler.step()
I can’t find the documentation of lr_scheduler (only the method itself). Will someone please help me understand what exa... |
st104315 | Solved by ptrblck in post #2
The learning rate scheduler adjusts the learning rates stored in the optimizer.
In your example a step function is used to lower the learning rate after 7 steps with a multiplicative factor of gamma=0.1.
Assuming that your initial learning ra... |
st104316 | The learning rate scheduler adjusts the learning rates stored in the optimizer.
In your example a step function is used to lower the learning rate after 7 steps with a multiplicative factor of gamma=0.1.
Assuming that your initial learning rate is 1e-1:
optimizer = optim.SGD(model.parameters(), lr=1e-1)
Now after sche... |
st104317 | Hi, I am reading this 1 paper about pytorch . In section 3.1, they gave an example code:
y = x.tanh()
y.add_(3)
y.backward()
I could not understand the idea behind this code snippet. Does this snippet cause an error ? Or eventhough it does not cause an error, is it illegal to use such thing ?
Thanks |
st104318 | The idea is that since add_ is an inplace operation, in this case, y will be modified inplace and so the gradient computation for the tanh operation won’t be able to be performed.
This code snipped should cause an error during the backward pass. All these potential problems are tracked properly, so if no error is raise... |
st104319 | I am experimenting with the code that accompanied the paper “Regularizing and Optimizing LSTM Language Models” (Merity et al. 2018): https://github.com/salesforce/awd-lstm-lm/ 56. In it, they apply dropout on the embedding. I was wondering if it was possible to make the implementation a bit more conservative on memory ... |
st104320 | That is a neat approach!
A small difference (I seem to see ~1e-7 often when using float32) is expected when you compute the same thing in a different way (that is mathematically equivalent).
Are you sure you have the exact same random for the dataloader and dropout? My guess would be that you have different embeddings ... |
st104321 | Yes, I wouldn’t have wondered if the two functions returned slightly different tensors. But they don’t – only their gradients differ. And you are right, the initial divergence is well in the ballpark of regular float errors. What bugs me is that behind the scenes, the two functions should be doing the same thing; all I... |
st104322 | There isn’t any symbolic code, the functions call various C++ functions for the embeddings, there is the CPU backward for dense matrices 4, next to it is a version for sparse matrices on CPU. The CUDA code is a bit more elaborate 4.
I’m not sure the chances are good to find an obvious reason for the discrepancy - essen... |
st104323 | I see. I ran the code until convergence, and the final perplexity is almost the same as with the original function (as expected), so I don’t mind this all that much. It would have been nice to have the same numbers, though. |
st104324 | I had this error running my code
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
import numpy as np
from Data import Data
... |
st104325 | You have probably cut the important part of your error message in your screenshot… |
st104326 | Screenshot from 2018-06-21 11-48-54.png733×626 86.6 KB
the bottom of the screen-shot . |
st104327 | I want to stack list of something and convert it to gpu:
torch.stack(fatoms, 0).to(device=device)
As far as I know, tensor was created on cpu firstly and then would be transferred to specified device. How to put it on gpu straight? |
st104328 | Solved by ptrblck in post #4
If your fatoms list contains CPU tensors, then your example is the way to go.
You could push the content onto the GPU before calling torch.stack, which is what my example shows. |
st104329 | If both tensors are already on the GPU, the result will also have the save device:
a = torch.randn(10, device='cuda:0')
b = torch.randn(10, device='cuda:0')
c = torch.stack((a, b))
print(c.device)
> device(type='cuda', index=0) |
st104330 | If your fatoms list contains CPU tensors, then your example is the way to go.
You could push the content onto the GPU before calling torch.stack, which is what my example shows. |
st104331 | I just started learning PyTorch after using Keras exclusively for a couple of years. I really like how models in Keras are trained, and having nice qol features like model checking and early stopping. I was wondering if there’s a way to implement each of the following in PyTorch
Reserving a portion of the training da... |
st104332 | Have a look at Ignite 21. They have some utilities and abstractions which you may find useful.
You don’t need an activation function for certain loss functions.
nn.CrossEntropyLoss for example expects logits, so that you shouldn’t have any activation at the end of your model.
nn.NLLLoss on the other have expects log pr... |
st104333 | [root@localhost pytorch]# source activate py35
(py35) [root@localhost pytorch]# export CMAKE_PREFIX_PATH="/root/miniconda3/bin:/usr/lib64"
(py35) [root@localhost pytorch]# python setup.py install
…
[100%] Generating lib/libnccl.so
Compiling src/libwrap.cu > /opt/pytorch/third_party/build/nccl/obj/l... |
st104334 | When I transfer my model to cuda:0:
model = model.to(device=torch.device("cuda:0"))
Everything is fine, but when I try to do the same for torch.device("cuda:1"), I got:
RuntimeError: CUDA error (10): invalid device ordinal
What I tried:
os.environ[“CUDA_VISIBLE_DEVICES”] = “1”
export CUDA_VISIBLE_DEVICES=1 and then py... |
st104335 | Solved by justusschock in post #2
If you set CUDA_VISIBLE_DEVICES = 1 your python script is only able to “see” this GPU and thus refers to it as “cuda:0” . If you start it on cuda:0 with CUDA_VISIBLE_DEVICES being set to 1 and afterwards use nvidia-smi you should see your... |
st104336 | If you set CUDA_VISIBLE_DEVICES = 1 your python script is only able to “see” this GPU and thus refers to it as “cuda:0” . If you start it on cuda:0 with CUDA_VISIBLE_DEVICES being set to 1 and afterwards use nvidia-smi you should see your model running on GPU1 |
st104337 | I have trained a pytorch model and write one test py script to predict(input one image and load model and predict the result).
I have 10 images, there are two ways:
one thread
just loop the 10 images order by order, the time of every test result is about 40ms
multithread
use python multithread, but the time of every t... |
st104338 | Can you try setting env var OMP_NUM_THREADS=1 before running your python script? |
st104339 | Hi guys, does PyTorch has the function that would return me the vectorized upper triangular matrix?
For example, I have Tensors as [ [1, 2, 3], [4, 5, 6], [7, 8, 9]], and I want [1, 2, 3, 5, 6, 9]. I know numpy has triu_indices, which will return me the indices of upper triangular matrix, and use the index I can easily... |
st104340 | I don’t think that there’s a api for directly returning the vectorized upper triangular matrix, but you can still achieve that:
>>> a = torch.arange(1, 10).view(3, 3)
>>> a
1 2 3
4 5 6
7 8 9
[torch.FloatTensor of size 3x3]
>>> triu_indices = a.triu().nonzero().transpose()
>>> triu_indices
0 0 0 1 1 2
0 1 2 1 2 2
... |
st104341 | Note that this only works when there are no zeros in the upper triangular part.
It would be cool if we could get more support for this in core pytorch. Also important is the opposite – going from a vectorization to an upper/lower triangular matrix. I haven’t been able to find a clean way to do this yet. |
st104342 | Here’s another way that should work more generally:
In [180]: x = torch.randn(3, 3)
In [181]: x
Out[181]:
1.6177 0.5082 1.3817
-0.5801 -0.4657 0.8086
-0.4783 -0.3208 1.4459
[torch.FloatTensor of size 3x3]
In [182]: x[torch.triu(torch.ones(3, 3)) == 1]
Out[182]:
1.6177
0.5082
1.3817
-0.4657
0.8086
1.4459... |
st104343 | Although this doesn’t seem to work on Variables:
RuntimeError: can't assign Variable to a torch.FloatTensor using a mask (only torch.FloatTensor or float are supported) |
st104344 | You could do this with a mask
def tril_mask(value):
n = value.size(-1)
coords = value.new(n)
torch.arange(n, out=coords)
return coords <= coords.view(n, 1)
which is used as
>>> value = torch.arange(9).view(3,3)
>>> value[tril_mask(value)]
0
3
4
6
7
8
[torch.FloatTensor of size 6] |
st104345 | Trick is to use numpy itself in torch without hurting the backpropgration.
For x as a 2D tensor this works for me:
import numpy as np
row_idx, col_idx = np.triu_indices(x.shape[1])
row_idx = torch.LongTensor(row_idx).cuda()
col_idx = torch.LongTensor(col_idx).cuda()
x = x[row_idx, col_idx]
For 3D tensor (assuming fi... |
st104346 | Hello , I have the exact opposite issue !
I have a vector with n*(n-1)/2 elements . (the elements of an upper triangular matrix matrix without the main diagonal)
I want to assign the vector into an upper triangular matrix (n by n) and still keep the whole process differentiable in pytorch.
I have tried : mat[np.triu... |
st104347 | No, but I have a suggestion without requiring grad on a leaf variable :
n = 5
mat = torch.zeros(5, 5)
vector = torch.randn(10, requires_grad=True)
mat[numpy.triu_indices(n, 1)] = vector
mat.sum().backward()
vector.grad
gives the expected vector of 10 ones. So mat doesn’t require_grad when it is instantiated, but only ... |
st104348 | @tom
I understood and reproduced your code snippet.
I have an issue though in integrating it in a custom layer.
My goal is to compute gradients for Q (3x3 matrix) !
My problem is that I cannot compute gradients (None is shown)
I suspect there is something wrong with the computational graph or an inplace operation that ... |
st104349 | One couldn’t reproduce this, as it won’t run (triple backticks “```” around the code comment would help the formatting, too).
nn.Parameter breaks the computational graph and should only be used for model state, not for computed variables, so does Variable(...). It is almost certainly an error to instantiate new nn.Para... |
st104350 | I am looking into the adam optimizer code and I ran into the following code.
if group['weight_decay'] != 0:
grad = grad.add(group['weight_decay'], p.data)
It is clear that weight_decay and p.data are muiltiplied and then added to the grad. However, checking the documentation, I cannot see the add method which does... |
st104351 | It’s the second entry:
https://pytorch.org/docs/stable/torch.html?highlight=add#torch.add 13
torch.add(input, value=1, other, out=None)
But we should really make a note of it in the tensor.add docs. |
st104352 | Hello!
I have just started to pytorch-ing. I saw that recently a new pytorch version (0.4) has been released. I wonder do the examples 1 or tutorials 1 in the official pytorch webpage is modified accordingly? For example, I heard that there is no more Variable type in pytorch but I still saw them in the examples I shar... |
st104353 | All of the examples and tutorials should be modified accordingly. Variable is deprecated, but you can still use it for backwards compatibility reasons. However, as you noted, it is better to not use Variables in your code: could you point me to where those are still being used in the examples/tutorials? |
st104354 | coyote:
examples
Sure, today I saw usage of variable only 1 on this example. I guess others have already been updated accordingly. |
st104355 | My bad. As you said, the variable I was talking about is part of TF code not pytorch. Then, I guess I should close this thread since I do not have any other example for now. |
st104356 | Hello,
I have a fresh installation of cuda 8.0 and pytorch. I ran the following simple code:
import torch
a = torch.rand(4)
a.cuda()
When I try to use the GPU from pytorch I get the error:
THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1524577177097/work/aten/src/THC/THCTensorRandom.cu line=25 error=30 : unknown er... |
st104357 | Hi! Have you solved the problem? I met the same mistake and failed to solve it after various attempts. Could you help? Thanks very much. |
st104358 | Hi,
I am losing my mind a bit, I guess I missed something in the documentation somewhere but I cannot figure it out. I am taking the derivative of the sum of distances from one point (0,0) to 9 other points ( [-1,-1],[-1,0],…,[1,1] - AKA 3x3 grid positions).
When I reshape one of the variables from (9x2) to (9x2) the d... |
st104359 | Ah! I’m a fool!
github.com/torch/cutorch
Issue: reshape, view, resize differences 40
opened by szagoruyko
on 2015-01-10
closed by szagoruyko
on 2015-01-12
Could someone please clear the differences between THCudaTensor_resize(n)d and :view, :reshape? In which cases resize doesn't... |
st104360 | Hi! This is actually a bug. I’m tracking it at https://github.com/pytorch/pytorch/issues/8626 44 and working on it currently. Sorry about it! |
st104361 | Interesting I assumed that since a reshape copied over data it somehow lost the history in the process. With view it seems to be much more reasonable… at least in my case. Would this bug also affect contiguous and view calls after expand? or just reshape? |
st104362 | In this case view and reshape does the same thing. You can either replace reshape with contiguous() or clone() here as a workaround. |
st104363 | Awesome! Thanks for the quick reply Maybe this fix will make some of the existing code I have run a bit better , in the mean time will use workarounds! Thanks again! |
st104364 | What’s the main difference between legacy.nn and the new nn?
Is legacy.nn slower or what problem does it have? Is there any documentation about why it became legacy?
I like the legacy code because it’s easy to read while I can’t even find the implementation of updateGradInput and accGradParameters functions in the new... |
st104365 | legacy.nn is there for backwards compatibility with lua torch.
If you can find implementations for operations in python, then that means those ops are not being implemented in c++. In general our c++ implementations are faster than their python counterparts.
It depends on what operation you’re looking for, but a number... |
st104366 | >>> import torch
>>> import numpy as np
>>> torch.cat((np.ones((5,5)), np.zeros((5,5))),1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: expected Variable as element 0 in argument 0, but got tuple
Well this error message didnt make sense to me, especially when Variables are depre... |
st104367 | Thank you for the report, I’ve filed an issue here: https://github.com/pytorch/pytorch/issues/8694 118 |
st104368 | When I call:
torch.nn.functional.grid_sample(array_5d, indices, mode='anything')
mode can be anything. Why is that? Is there no mode to choose?
I would like to choose mode=‘trilinear’, is that available? In the docs I can see there is mode=‘bilinear’ as default. |
st104369 | Only bilinear is supported: https://pytorch.org/docs/master/nn.html?highlight=grid_sample#torch.nn.functional.grid_sample 18
But the code should really throw an error if you try to pass anything other than bilinear. I’ve opened an issue here: https://github.com/pytorch/pytorch/issues/8693 22 for this. |
st104370 | Hi, I used to create leaf variable like:
y = torch.autograd.Variable(torch.zeros([batch_size, c, h, w]), requires_grad=True)
Then I want to assign value to indexed parts of y like below,(y_local is a Variable computed based on other variables and I want to assign the value of y_local to part of the y and ensure that th... |
st104371 | Gradients won’t flow if you call .data and then index into it. They will flow, however, if you don’t use .data:
y[:,:,local_x[i]:local_x[i+1],local_y[i]:local_y[i+1]] = y_local |
st104372 | Hi there friends!
I’m building an RNN that uses nn.embedding() for its input and target. Unfortunately, I’m having trouble with the MSELoss, NLLLoss and other loss functions, which give me this error message when I try to run loss.backward:
AssertionError: nn criterions don't compute the gradient w.r.t. targets - pleas... |
st104373 | I don’t understand. Are you trying to learn the targets? Why do the targets come from an embedding?
If you really want to back propagate to the targets you can just compute the MSE loss by:
loss = ((input - target)**2).mean() |
st104374 | Hey! Thanks so much for your response. I super appreciate it. I’m not sure I explained my model well, so I apologize for any confusion.
I’m building a word-level LSTM RNN for text generation (I’ve already built the char-level and am hoping to compare the convergence rate of the two).
The corpus is so large, though, it’... |
st104375 | I think one normally passes the predicted embeddings through an nn.Linear(n_embed, n_vocab) (and then Softmax or LogSoftmax). That should give you relative “probabilities” of the next word. |
st104376 | Thanks for the response!
So I already have the predicted embeddings, I need to back-propagate the results. But when I pass in the two embeddings (one being the predicted embeddings, the other being the target embedding) to the loss function, I get this:
AssertionError: nn criterions don't compute the gradient w.r.t. ta... |
st104377 | (Let me know if seeing any of my code would be helpful and what in particular I should share.) |
st104378 | If your targets are fixed (i.e. you are not optimizing the targets), do loss_fn(input, target.detach()).
If you are also optimizing the targets, use the expression above. |
st104379 | Hi cooganb, when your model was done training, and you had it generate some text for you, what did you do when the predicted embedding wasn’t an actual word? It seems unreasonable to expect all 100 dimensions of the prediction to perfectly match a real word, so did you find the euclidean-closest embedding to the predic... |
st104380 | As far as I understand, the usual thing to do is take a softmax with the inner products of embeddings with the output. This then puts you in the same place as you would be with a classification problem at the softmax layer.
Best regards
Thomas |
st104381 | I am curious about what loss function did you use in the end? Which gave the best performance? Also what optimizer worked the best? |
st104382 | Hello every body,
Can you take 2min and run this program on your system and tell me the execution time please ?
It’s just for me to be sure that my config system run correctly because on my system the program takes 6min against 1min30 on the web site normaly.
You can download the script at the end of the page!
code li... |
st104383 | I have a 8-GPU server, however the performance of training using torchvision has no advantage over 4-GPU server.
I checked the p2pbandwidth and topology of the gpus. I thought the bandwidth between first 4-gpus and second 4-gpus is quite low. Is it the problem?
image.png1214×310 36 KB |
st104384 | I am browsing some code in the ATen lib and came across this 3:
if (scale_grad_by_freq) {
counts.reset(new int64_t[num_weights]);
for (int i = 0; i < numel; i++) {
counts[indices_data[i]] = 0;
}
for (int i = 0; i < numel; i++) {
counts[indices_data[i]]++;
}
}
Perhaps I’m missing som... |
st104385 | The first zeros the counts, the second then counts. If you have the same index multiple times (which is the point of counting when you want to scale by frequency), you cannot combine the two.
My guess for the first loop would be that it is cheaper to only initialize those indices that are actually used rather than all ... |
st104386 | Hi, I’m trying to do an image classification task with my first neural network. I have minimal practical experience. I have about 650 8-bit gray value images of dimensions 21x21x21 that I want to put into two classes. Starting from one of the official tutorials here 2, after some fiddling I now have this code:
import o... |
st104387 | Your last linear layer (fc3) should have 2 output units, since you are using CrossEntropyLoss.
Just change it to self.fc3 = nn.Linear(84, 2) and run it again. |
st104388 | Thanks, that did it! Well, it runs, but it doesn’t work well:
...
[2, 80] loss: 0.474
[2, 81] loss: 0.951
[2, 82] loss: 0.837
[2, 83] loss: 0.476
[2, 84] loss: 0.828
[2, 85] loss: 0.725
[2, 86] loss: 0.717
[2, 87] loss: 0.830
Finished Training
My reasoning was, since I have two classes and trut... |
st104389 | Since, you have only two classes, better to use binary cross entropy as the loss function and change your last layer to self.fc3 = nn.Linear(84, 1) as it was initially.
BCE loss in pytorch -> torch.nn.BCELoss() |
st104390 | Thanks for the tip! BCELoss() awaits an input between 0 and 1, while my net outputs have entries like -5. Now my first thought was to normalize each output vector to [0,1] but on second thought, that’s a bad idea, right? Wouldn’t that change the overall loss values in a non linear way? If yes, what can I do instead? |
st104391 | Using BCELoss as @Jk749 suggested is another valid approach.
Just use one output unit, add a sigmoid at your last layer, and try BCELoss.
...
x = F.sigmoid(self.fc3(x))
return x |
st104392 | Ah, that was easy, thanks. It does little to improve the performance though:
...
Truth: tensor([ 1., 0., 0., 0.])
Output: tensor([[ 0.5666],
[ 0.5650],
[ 0.5653],
[ 0.5655]])
[2, 85] loss: 0.703
Truth: tensor([ 1., 0., 0., 1.])
Output: tensor([[ 0.5657],
[ 0.5688],
[... |
st104393 | Have you trained the SVM on the images directly?
You could play around with the learning rate, optimizers etc.
What kind of data do you have? |
st104394 | WIll do!
Each image is a 21x21x21px 3D ROI of an 8 bit grey value phase microscopy image and the classifier should decide whether there’s a (one) cell present or not. I used the SVM from scikit learn and trained on the images directly, in the unraveled shape of 1x9261. |
st104395 | I’m running some pytorch code and it will randomly stop and output the text “Killed”. I don’t see that anywhere in my code so just wondering where it is coming from and what might be causing it?
Edit: Never mind, looks like this is a python thing that the program took up too much resources somewhere. |
st104396 | I encountered the same issue. When you said it took up too much resources, are you referring to the GPU? |
st104397 | The Linux kernel has a mechanism to kill processes taking up all CPU ram. 363
“Killed” sounds like the typical output in that case, in sudo dmesg you’ll see log entries about “OOM”.
Best regards
Thomas |
st104398 | Hi,
I’ve seen it’s possible to train with a hybrid system of gpu-cpu but in the end cpu do bottleneck
I would like to know if it’s possible to use the standard RAM as additional memory which can be used by the GPU. Of course the main idea would be computing everything with the GPU. |
st104399 | If you are able to make your own autograd.Function, you can store things on CPU:
class StorethingsOnCPU(torch.autograd.Function):
@staticmethod
def forward(ctx, inp):
#...some calculation here...
res = inp
ctx.save_for_backward(inp.cpu())
ctx._device = inp.device
return r... |
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