name stringlengths 15 255 | question stringlengths 20 1.77k | questionUpvotes int64 0 23 | timeCreated stringlengths 24 24 | answer stringlengths 9 1.09k | answerUpvotes int64 0 75 | timeAnswered stringlengths 24 24 | answerURL stringlengths 50 285 | context stringlengths 244 1.73k | answer_start int64 0 3.45k | answers stringlengths 46 1.14k |
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Constant Prediction in CNN | Ok, so I have a model to predict the class of image, cat or dog. I receive %95 accuracy in training. But for some reason, I stuck with constant output when I try to predict single image.
I read similar topics from forum but that hasn’t contributed much in my case.
Below, you can find all info. Pls… | 0 | 2020-07-19T02:51:06.473Z | Hey, problem was, when I predicting on single image, I did not scale the image, which is nothing but a single line code: x= x/255.0
Now, this works fantastic! Thanks a lot for your instructions too ^^. | 1 | 2020-07-20T18:20:38.221Z | https://discuss.pytorch.org/t/constant-prediction-in-cnn/89745/8 | Yes, resizing in general changes the pixel values. For resizing targets (e.g. segmentation masks) we usually apply “nearest” interpolation mode. In this mode, resizing is approx equivalent to removing columns and lines => that new pixels are exactly the same to the source. For other modes, new pixel… It seems... | 613 | {'text': ['Hey, problem was, when I predicting on single image, I did not scale the image, which is nothing but a single line code: x= x/255.0\n\nNow, this works fantastic! Thanks a lot for your instructions too ^^.'], 'answer_start': [613]} |
Get encoder from trained UNet | Hi,
I have a trained a UNet model on some images but now, I want to extract the encoder part of the model. My UNet has the following architecture:
UNet(
(conv_final): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))
(down_convs): ModuleList(
(0): DownConv(
(conv1): Conv2d(1, 8, kernel… | 0 | 2020-09-03T11:31:59.573Z | Thanks for the notebook.
The DownConv layer returns a tuple in <a href="https://github.com/Flock1/solar/blob/6545a8165e622f5dc27ce34f8b3cece9b7eb832f/model.py#L70">this line of code</a>, which doesn’t work in an nn.Sequential container and standard layers.
If you want to accept the tuple in the next layer, you could ... | 1 | 2020-09-21T05:57:09.251Z | https://discuss.pytorch.org/t/get-encoder-from-trained-unet/95053/21 | Thanks for the notebook.
The DownConv layer returns a tuple in <a href="https://github.com/Flock1/solar/blob/6545a8165e622f5dc27ce34f8b3cece9b7eb832f/model.py#L70">this line of code</a>, which doesn’t work in an nn.Sequential container and standard layers.
If you want to accept the tuple in the next layer, you could ... | 1,630 | {'text': ['Thanks for the notebook.\n\nThe DownConv layer returns a tuple in <a href="https://github.com/Flock1/solar/blob/6545a8165e622f5dc27ce34f8b3cece9b7eb832f/model.py#L70">this line of code</a>, which doesn’t work in an nn.Sequential container and standard layers.\n\nIf you want to accept the tuple in the next la... |
Can someone provide the steps to upload my pytorch project on Google Colab? | Hi all,
I want to run my project on google colab, as dnt have GPU facility. Uptill now I have uploaded the complete project file in my drive. What should be next, Plz give me a step wise process . My project has several parts (.py files) and dataset too.
Regards | 0 | 2020-11-02T08:25:56.158Z | Sorry for the late reply :pray:
I don’t know about you but when I program, I like to run my code on the terminal of the operating system I’m using windows or Linux and not on the IDE (this is just my preference)
So it’s kinda similar to colab.
Colab uses Linux as it’s operating system and the a p… | 1 | 2020-11-04T13:45:21.505Z | https://discuss.pytorch.org/t/can-someone-provide-the-steps-to-upload-my-pytorch-project-on-google-colab/101324/9 | Thanks for the notebook.
The DownConv layer returns a tuple in <a href="https://github.com/Flock1/solar/blob/6545a8165e622f5dc27ce34f8b3cece9b7eb832f/model.py#L70">this line of code</a>, which doesn’t work in an nn.Sequential container and standard layers.
If you want to accept the tuple in the next layer, you could ... | 1,198 | {'text': ['Sorry for the late reply :pray:\n\nI don’t know about you but when I program, I like to run my code on the terminal of the operating system I’m using windows or Linux and not on the IDE (this is just my preference)\n\nSo it’s kinda similar to colab.\n\nColab uses Linux as it’s operating system and the a p&he... |
Self-defined function for data augmentation | I define a image flipping class. I am wondering whether this class will realize image flipping during training
#flip image
class HorizontallyFlipline(object):
def __call__(self, img, xx3,xx4):
if abs(xx3[-1]-0.5)>0.1 or abs(xx4[-1]-0.5)>0.1:
return img.transpose(Image.FLIP_LEFT_RIGH… | 0 | 2018-10-11T14:07:22.560Z | [image] woaichipinngguo:
if self.mirror:
Assuming that you set self.mirror only during training, the code looks alright to me. | 0 | 2018-10-12T05:53:33.929Z | https://discuss.pytorch.org/t/self-defined-function-for-data-augmentation/27055/4 | Thanks for the notebook.
The DownConv layer returns a tuple in <a href="https://github.com/Flock1/solar/blob/6545a8165e622f5dc27ce34f8b3cece9b7eb832f/model.py#L70">this line of code</a>, which doesn’t work in an nn.Sequential container and standard layers.
If you want to accept the tuple in the next layer, you could ... | 692 | {'text': ['[image] woaichipinngguo:\n\nif self.mirror:\n\nAssuming that you set self.mirror only during training, the code looks alright to me.'], 'answer_start': [692]} |
How partially load big tensor from file? | I can load a tensor from file like this:
X = torch.load(filename)
This tensor has a shape torch.Size([30000000]).
But I can’t load dataset fully due to memory limits.
How can I load only first 3000 numbers from file?
And then second portion of 3000 numbers without full load? | 0 | 2020-06-19T16:06:12.828Z | the tricky part about reading your data as a byte stream is that there is no data structure.
let say x = (BxCxWxH), then your bytes stream has a size of x = (B * C *W * H). Then you must know the type of your data, i.e int, float, double …etc, and figure out of many bytes each type are to read cor… | 2 | 2020-06-23T17:27:48.425Z | https://discuss.pytorch.org/t/how-partially-load-big-tensor-from-file/86088/4 | the tricky part about reading your data as a byte stream is that there is no data structure.
let say x = (BxCxWxH), then your bytes stream has a size of x = (B * C *W * H). Then you must know the type of your data, i.e int, float, double …etc, and figure out of many bytes each type are to read cor… Didn’t you ... | 1,640 | {'text': ['the tricky part about reading your data as a byte stream is that there is no data structure.\n\nlet say x = (BxCxWxH), then your bytes stream has a size of x = (B * C *W * H). Then you must know the type of your data, i.e int, float, double …etc, and figure out of many bytes each type are to read cor&hellip... |
I'm not getting the correct output from model | Hello. I trained an autoencoder/decoder and saved the model. I loaded the model and took the decoder portion off, in order to extract the features from the middle of the encoder. But when i run the encoder, it states the following error;
AttributeError: ‘tuple’ object has no attribute ‘dim’
I … | 0 | 2019-11-08T19:00:23.786Z | Didn’t you just said that you removed the encoder? So you removed the unpooling layer no?
You can see examples of this reshaping in any cnn example, <a href="https://github.com/pytorch/examples/blob/60108edfa3838a823220e16428cb5f98e8e88d53/mnist/main.py#L23" rel="nofollow noopener">this one</a> for example. | 0 | 2019-11-08T21:31:03.511Z | https://discuss.pytorch.org/t/im-not-getting-the-correct-output-from-model/60409/6 | the tricky part about reading your data as a byte stream is that there is no data structure.
let say x = (BxCxWxH), then your bytes stream has a size of x = (B * C *W * H). Then you must know the type of your data, i.e int, float, double …etc, and figure out of many bytes each type are to read cor… Didn’t you ... | 1,129 | {'text': ['Didn’t you just said that you removed the encoder? So you removed the unpooling layer no?\n\nYou can see examples of this reshaping in any cnn example, <a href="https://github.com/pytorch/examples/blob/60108edfa3838a823220e16428cb5f98e8e88d53/mnist/main.py#L23" rel="nofollow noopener">this one</a> for exampl... |
Model's parameters update during DDP training | I’m using DDP to train Neural Architecture Search networks which contained a controller and a model network. During training, my controller predictss a model’s architecture that maximize reward. the call looks like this.
# both model and controller are torch.nn.DistributedDataParallel
arch = contro… | 0 | 2020-06-23T19:13:02.493Z | IIUC, that will still remove DDP autograd hooks on self._arch.
Question, do you need the backward pass to compute the gradients for self._arch? If not, you can explicitly setting self._arch.requires_grad = False before passing the model to DDP ctor to tell DDP to ignore self._arch. Then, the above … | 0 | 2020-06-24T14:19:13.241Z | https://discuss.pytorch.org/t/models-parameters-update-during-ddp-training/86601/9 | the tricky part about reading your data as a byte stream is that there is no data structure.
let say x = (BxCxWxH), then your bytes stream has a size of x = (B * C *W * H). Then you must know the type of your data, i.e int, float, double …etc, and figure out of many bytes each type are to read cor… Didn’t you ... | 619 | {'text': ['IIUC, that will still remove DDP autograd hooks on self._arch.\n\nQuestion, do you need the backward pass to compute the gradients for self._arch? If not, you can explicitly setting self._arch.requires_grad = False before passing the model to DDP ctor to tell DDP to ignore self._arch. Then, the above &hellip... |
Cross Entropy Loss for imbalanced set (binary classification) | Dear community,
I am trying to use the weights for the binary classification problem for CrossEntropyLoss and by now I am so lost in it….
In my network I set the output size as 1 and have sigmoid activation function at the end to ensure I get values between 0 and 1. I assume it is probability in m… | 0 | 2020-12-18T19:43:39.718Z | Hi Alice!
Let me answer your question(s) two different ways.
[image] Alice_NL:
Can I then transform logits to probabilities by a new network model
You could, but doing so would be overkill. You can just call the
function (or class) version of sigmoid() directly:
my_logits = my_model (my_… | 1 | 2020-12-20T16:36:58.658Z | https://discuss.pytorch.org/t/cross-entropy-loss-for-imbalanced-set-binary-classification/106554/9 | Hi Alice!
Let me answer your question(s) two different ways.
[image] Alice_NL:
Can I then transform logits to probabilities by a new network model
You could, but doing so would be overkill. You can just call the
function (or class) version of sigmoid() directly:
my_logits = my_model (my_… What about havin... | 1,854 | {'text': ['Hi Alice!\n\nLet me answer your question(s) two different ways.\n\n[image] Alice_NL:\n\nCan I then transform logits to probabilities by a new network model\n\nYou could, but doing so would be overkill. You can just call the\n\nfunction (or class) version of sigmoid() directly:\n\nmy_logits = my_model (my_&h... |
Semantic Segmenataion Model Problem | I am working on semantic Segmentation on Pascal VOC 2012 dataset and my model is not working.
Please help.
My model is like.
<a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/2X/a/a8e8ffa09e32b959f567d9234fe6d055f59ef44e.jpeg" data-download-href="https://discuss.pytorch.org/uploads/defau... | 0 | 2019-03-23T05:22:09.769Z | What about having some modification on lr_scheduler? | 1 | 2019-03-31T01:24:15.380Z | https://discuss.pytorch.org/t/semantic-segmenataion-model-problem/40678/20 | Hi Alice!
Let me answer your question(s) two different ways.
[image] Alice_NL:
Can I then transform logits to probabilities by a new network model
You could, but doing so would be overkill. You can just call the
function (or class) version of sigmoid() directly:
my_logits = my_model (my_… What about havin... | 1,231 | {'text': ['What about having some modification on lr_scheduler?'], 'answer_start': [1231]} |
Cpu inference - ram increases every iteration | The model I’m running causes memory to increase with every iteration.
to load it I do the following:
def _load_model(model_path):
model = ModelDef(num_classes=35)
model.load_state_dict(torch.load(model_path, map_location="cpu"), strict=False)
model.eval()
return model
to run it I … | 0 | 2019-11-04T17:46:50.853Z | So after some more debugging I found that if I switch to pytorch cpu the ram stays stable.
So it looks like it was a pytorch related bug after all. None the less I loved your keep it simple stupid debugging methods. I think i’ll hang them up on the wall.
Thanks,
Dan | 2 | 2019-11-05T11:13:39.263Z | https://discuss.pytorch.org/t/cpu-inference-ram-increases-every-iteration/59973/10 | Hi Alice!
Let me answer your question(s) two different ways.
[image] Alice_NL:
Can I then transform logits to probabilities by a new network model
You could, but doing so would be overkill. You can just call the
function (or class) version of sigmoid() directly:
my_logits = my_model (my_… What about havin... | 357 | {'text': ['So after some more debugging I found that if I switch to pytorch cpu the ram stays stable.\n\nSo it looks like it was a pytorch related bug after all. None the less I loved your keep it simple stupid debugging methods. I think i’ll hang them up on the wall.\n\nThanks,\n\nDan'], 'answer_start': [357]} |
Properly implementing DDP in training loop with cleanup, barrier, and its expected output | Hi,
I’m currently trying to figure out how to properly implement DDP with cleanup, barrier, and its expected output. While I think gives the dpp tutorial <a href="https://pytorch.org/tutorials/intermediate/ddp_tutorial.html" class="inline-onebox" rel="noopener nofollow ugc">Getting Started with Distributed Data Parall... | 0 | 2022-03-15T08:44:38.155Z | Thanks <a class="mention" href="/u/dmack">@dmack</a> for trying out DDP! Here is my understanding:
One way to think about data parallel training is that it increases the effective batch size. If each worker in a world of size W operates on a batch size B, then the effective batch size is W * B.
DDP computes the loss ... | 1 | 2022-03-16T16:24:12.926Z | https://discuss.pytorch.org/t/properly-implementing-ddp-in-training-loop-with-cleanup-barrier-and-its-expected-output/146465/4 | Thanks <a class="mention" href="/u/dmack">@dmack</a> for trying out DDP! Here is my understanding:
One way to think about data parallel training is that it increases the effective batch size. If each worker in a world of size W operates on a batch size B, then the effective batch size is W * B.
DDP computes the loss ... | 1,252 | {'text': ['Thanks <a class="mention" href="/u/dmack">@dmack</a> for trying out DDP! Here is my understanding:\n\nOne way to think about data parallel training is that it increases the effective batch size. If each worker in a world of size W operates on a batch size B, then the effective batch size is W * B.\n\nDDP com... |
IndexError: pop from empty list in grad_sample_module.py for opacus version > 0.9 | Hi,
Im using Opacus to make CT-GAN (<a href="https://github.com/sdv-dev/CTGAN" class="inline-onebox" rel="noopener nofollow ugc">GitHub - sdv-dev/CTGAN: Conditional GAN for generating synthetic tabular data.</a>) differntial private.
There is already an implementation who does this: (<a href="https://github.com/open... | 0 | 2021-08-07T18:38:25.754Z | <a class="mention" href="/u/shaanchandra">@shaanchandra</a> From the discussion above, it seems that there are two potential solutions:
As <a class="mention" href="/u/knilox">@knilox</a> mentioned above, remove the gradient regularization loss entirely - this may seem that we are not faithfully reproducing the origina... | 2 | 2021-08-26T16:48:29.167Z | https://discuss.pytorch.org/t/indexerror-pop-from-empty-list-in-grad-sample-module-py-for-opacus-version-0-9/128843/12 | Thanks <a class="mention" href="/u/dmack">@dmack</a> for trying out DDP! Here is my understanding:
One way to think about data parallel training is that it increases the effective batch size. If each worker in a world of size W operates on a batch size B, then the effective batch size is W * B.
DDP computes the loss ... | 973 | {'text': ['<a class="mention" href="/u/shaanchandra">@shaanchandra</a> From the discussion above, it seems that there are two potential solutions:\n\nAs <a class="mention" href="/u/knilox">@knilox</a> mentioned above, remove the gradient regularization loss entirely - this may seem that we are not faithfully reproducin... |
Getting Runtime error: element 0 of tensors does not require grad and does not have a grad_fn | Hi there!
I am trying to run a simple CNN2LSTM model and facing this error:
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn.
The strange part is that the current model is a simpler version of my previous model which worked absolutely fine.
To solve this err… | 0 | 2022-03-25T23:39:06.812Z | The point of initialization wouldn’t matter since you are currently not using outputs at all.
I assume you would like to assign some computed values to it at one point, but this code seems to be missing. | 1 | 2022-03-29T17:24:42.588Z | https://discuss.pytorch.org/t/getting-runtime-error-element-0-of-tensors-does-not-require-grad-and-does-not-have-a-grad-fn/147459/10 | Thanks <a class="mention" href="/u/dmack">@dmack</a> for trying out DDP! Here is my understanding:
One way to think about data parallel training is that it increases the effective batch size. If each worker in a world of size W operates on a batch size B, then the effective batch size is W * B.
DDP computes the loss ... | 741 | {'text': ['The point of initialization wouldn’t matter since you are currently not using outputs at all.\n\nI assume you would like to assign some computed values to it at one point, but this code seems to be missing.'], 'answer_start': [741]} |
Error in loss function | Hello, i’m making some changes on a normal CNN, to be compatible with other model, that i create.
I get the following error:
File “C:/Users/user/.spyder-py3/cnn.py”, line 105, in
loss = criterion(output,img)
RuntimeError: The size of tensor a (10) must match the size of tensor b (28) at non-sin… | 0 | 2020-05-14T15:52:14.948Z | I modified your code to this in the for loop:
label=torch.tensor([[0, 0, 0], [0, 1, 0], [0, 0, 2]])
output=torch.randn(3, 3, requires_grad=True)
loss=criterion(output, label.float())
And it works.
If you want to do classification you need to use cross entropy loss. Remember to check s… | 0 | 2020-05-18T04:53:24.771Z | https://discuss.pytorch.org/t/error-in-loss-function/81228/20 | I modified your code to this in the for loop:
label=torch.tensor([[0, 0, 0], [0, 1, 0], [0, 0, 2]])
output=torch.randn(3, 3, requires_grad=True)
loss=criterion(output, label.float())
And it works.
If you want to do classification you need to use cross entropy loss. Remember to check s… So: tested h5 dataset... | 1,890 | {'text': ['I modified your code to this in the for loop:\n\nlabel=torch.tensor([[0, 0, 0], [0, 1, 0], [0, 0, 2]])\n\noutput=torch.randn(3, 3, requires_grad=True)\n\nloss=criterion(output, label.float())\n\nAnd it works.\n\nIf you want to do classification you need to use cross entropy loss. Remember to check s…'... |
Dataloader eating ram | I have a dataset of 9 gigs of wav files for music synthesis, and to manage batches across different files i load each file into custom WavFileDataset which i then combine in ConcatDataset to use as a dataset for dataloader. Problems begin when i try to sample from dataloader, even with batch_size = … | 0 | 2020-03-22T22:22:00.031Z | So: tested h5 dataset, dataloader still crashed my session
Turns out, dataset shuffling needs ram, and it needs a lot of it in my case
By turning it off it successfully next(iter(dataloader))'s new batches of data
Gotta figure out how to do shuffling
But at least it is working now | 0 | 2020-03-28T20:07:20.710Z | https://discuss.pytorch.org/t/dataloader-eating-ram/74064/6 | I modified your code to this in the for loop:
label=torch.tensor([[0, 0, 0], [0, 1, 0], [0, 0, 2]])
output=torch.randn(3, 3, requires_grad=True)
loss=criterion(output, label.float())
And it works.
If you want to do classification you need to use cross entropy loss. Remember to check s… So: tested h5 dataset... | 1,244 | {'text': ['So: tested h5 dataset, dataloader still crashed my session\n\nTurns out, dataset shuffling needs ram, and it needs a lot of it in my case\n\nBy turning it off it successfully next(iter(dataloader))'s new batches of data\n\nGotta figure out how to do shuffling\n\nBut at least it is working now'], 'answer_... |
MobileNetV2 + SSDLite quantization results in different model definition | I’m trying to quantize a mobilenetv2 + SSDLite model from https://github.com/qfgaohao/pytorch-ssd
I followed the tutorial here https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html doing Post-training static quantization
Before quantizing the model definition looks like this
S… | 0 | 2020-03-22T18:16:27.184Z | yeah, you’ll need to quantize lq_model after lq_model = create_mobilenetv2_ssd_lite(len(class_names), is_test=True) before you load from the quantized model | 0 | 2020-03-27T23:26:37.720Z | https://discuss.pytorch.org/t/mobilenetv2-ssdlite-quantization-results-in-different-model-definition/74056/3 | I modified your code to this in the for loop:
label=torch.tensor([[0, 0, 0], [0, 1, 0], [0, 0, 2]])
output=torch.randn(3, 3, requires_grad=True)
loss=criterion(output, label.float())
And it works.
If you want to do classification you need to use cross entropy loss. Remember to check s… So: tested h5 dataset... | 589 | {'text': ['yeah, you’ll need to quantize lq_model after lq_model = create_mobilenetv2_ssd_lite(len(class_names), is_test=True) before you load from the quantized model'], 'answer_start': [589]} |
Laptop shuts down while trainining | My laptop automatically shuts without warning (sometimes) during training.
Machine config - Lenovo y540, RTX 2060, Ubuntu 18.04, PyTorch 1.4. I tried training a simple binary image classification model (4 conv layers with batchnorm and Dropout). The model trained for 20 epochs (batch size = 8) and … | 0 | 2020-03-10T12:25:14.301Z | Hello Mr. Air!
[image] theairbend3r:
Mar 10 17:10:11 maverick kernel: [ 319.690728] mce: CPU11: Core temperature above threshold, cpu clock throttled (total events = 75)
...
Mar 10 17:10:11 maverick kernel: [ 319.690730] mce: CPU11: Package temperature above threshold, cpu clock throttled (to… | 2 | 2020-03-10T13:46:17.462Z | https://discuss.pytorch.org/t/laptop-shuts-down-while-trainining/72711/2 | Hello Mr. Air!
[image] theairbend3r:
Mar 10 17:10:11 maverick kernel: [ 319.690728] mce: CPU11: Core temperature above threshold, cpu clock throttled (total events = 75)
...
Mar 10 17:10:11 maverick kernel: [ 319.690730] mce: CPU11: Package temperature above threshold, cpu clock throttled (to… Yo, so I’ve ... | 1,492 | {'text': ['Hello Mr. Air!\n\n[image] theairbend3r:\n\nMar 10 17:10:11 maverick kernel: [ 319.690728] mce: CPU11: Core temperature above threshold, cpu clock throttled (total events = 75)\n\n...\n\nMar 10 17:10:11 maverick kernel: [ 319.690730] mce: CPU11: Package temperature above threshold, cpu clock throttled (to&h... |
Accuracy of signal prediction model stuck at 51% after few epochs | So, I’m training a neural network architecture on a particular wave signal detection.
the raw data are in .npy files and contains the time domain signal.
I did some feature extraction on the time domain signal like:
Discrete Fourier Transform to convert the time domain signal to frequency domain … | 0 | 2021-08-23T19:53:24.809Z | Yo, so I’ve fixed the problem finally
The issue was that the NNModel.train() was outside the epoch loop, and in the epoch loop, the testing function is called at the 0th epoch and it toggles the NNModel.train() to NNModel.eval().
So since the NNModel.train() was outside the loop, after the 0th epo… | 1 | 2021-08-24T16:18:53.437Z | https://discuss.pytorch.org/t/accuracy-of-signal-prediction-model-stuck-at-51-after-few-epochs/130082/27 | Hello Mr. Air!
[image] theairbend3r:
Mar 10 17:10:11 maverick kernel: [ 319.690728] mce: CPU11: Core temperature above threshold, cpu clock throttled (total events = 75)
...
Mar 10 17:10:11 maverick kernel: [ 319.690730] mce: CPU11: Package temperature above threshold, cpu clock throttled (to… Yo, so I’ve ... | 1,054 | {'text': ['Yo, so I’ve fixed the problem finally\n\nThe issue was that the NNModel.train() was outside the epoch loop, and in the epoch loop, the testing function is called at the 0th epoch and it toggles the NNModel.train() to NNModel.eval().\n\nSo since the NNModel.train() was outside the loop, after the 0th epo&hell... |
Issue using ._parameters internal method | I’m trying to access model parameters using the internal ._parameters method. When I define the model as below, I get model parameters without any issue
model = nn.Linear(10, 10)
print(model._parameters)
However, when I use this method to get parameters of a model defined as a class, I get an empt… | 0 | 2021-10-18T15:55:41.362Z | This is indeed unrelated.
If you enable anomaly mode, you will see that the problem is that some of the params are saved for backward but modified inplace. The fix is to make sure they are not:
for i in range(epochs):
model.train()
train_loss = 0
params = dict(model.named_parameters())… | 1 | 2021-10-21T16:03:38.506Z | https://discuss.pytorch.org/t/issue-using-parameters-internal-method/134549/12 | Hello Mr. Air!
[image] theairbend3r:
Mar 10 17:10:11 maverick kernel: [ 319.690728] mce: CPU11: Core temperature above threshold, cpu clock throttled (total events = 75)
...
Mar 10 17:10:11 maverick kernel: [ 319.690730] mce: CPU11: Package temperature above threshold, cpu clock throttled (to… Yo, so I’ve ... | 617 | {'text': ['This is indeed unrelated.\n\nIf you enable anomaly mode, you will see that the problem is that some of the params are saved for backward but modified inplace. The fix is to make sure they are not:\n\nfor i in range(epochs):\n\nmodel.train()\n\ntrain_loss = 0\n\nparams = dict(model.named_parameters())…... |
nn.BatchNorm vs MyBatchNorm | I have reimplemented BatchNorm1D based on the implementation provided by <a class="mention" href="/u/ptrblck">@ptrblck</a> (greatly appreciated!), here: <a href="https://github.com/ptrblck/pytorch_misc/blob/master/batch_norm_manual.py" rel="nofollow noopener">https://github.com/ptrblck/pytorch_misc/blob/master/batch_no... | 0 | 2020-05-24T12:07:16.735Z | Ah OK.
The backward pass of repeat_interleave is not deterministic as explained in the linked docs:
Additionally, the backward path for repeat_interleave() operates nondeterministically on the CUDA backend because repeat_interleave() is implemented using index_select() , the backward path fo… | 1 | 2020-05-26T21:26:01.317Z | https://discuss.pytorch.org/t/nn-batchnorm-vs-mybatchnorm/82682/9 | Ah OK.
The backward pass of repeat_interleave is not deterministic as explained in the linked docs:
Additionally, the backward path for repeat_interleave() operates nondeterministically on the CUDA backend because repeat_interleave() is implemented using index_select() , the backward path fo… you could sp... | 1,832 | {'text': ['Ah OK.\n\nThe backward pass of repeat_interleave is not deterministic as explained in the linked docs:\n\nAdditionally, the backward path for repeat_interleave() operates nondeterministically on the CUDA backend because repeat_interleave() is implemented using index_select() , the backward path fo&helli... |
Changing DataLoader to include a FITS class | I’m wondering if there’s a way to alter the torch.utils.data.DataLoader so that one could import FITS files into Pytorch as tensors with labels?
I’m intending to load in Astronomical images from FITS format (<a href="http://docs.astropy.org/en/stable/index.html" rel="nofollow noopener">http://docs.astropy.org/en/stabl... | 0 | 2018-05-04T08:46:58.698Z | you could specify your own folder as shown in <a href="https://discuss.pytorch.org/t/imagefolder-data-shuffle/17731/5?u=justusschock">this post</a> | 0 | 2018-05-08T12:39:12.314Z | https://discuss.pytorch.org/t/changing-dataloader-to-include-a-fits-class/17495/3 | Ah OK.
The backward pass of repeat_interleave is not deterministic as explained in the linked docs:
Additionally, the backward path for repeat_interleave() operates nondeterministically on the CUDA backend because repeat_interleave() is implemented using index_select() , the backward path fo… you could sp... | 1,224 | {'text': ['you could specify your own folder as shown in <a href="https://discuss.pytorch.org/t/imagefolder-data-shuffle/17731/5?u=justusschock">this post</a>'], 'answer_start': [1224]} |
Reliably measure module latency + Repeatability | For NAS (Network Architecture Search) I need to measure the latency of operation that are present in my search space.
Therefore, I tried different approaches to measure the latency of a nn.Module:
pytorch autograd profiler
“normal” time.time() measurements
cuda events
Of course I used torch.cuda… | 0 | 2020-03-05T10:11:24.702Z | Hi themozel!
Unfortunately I didn’t find a solution for measuring cells with small tensors. A “hacky solution” was to just multiply H and W with a constant factor of e.g. 8. Although this leads to “correct” (i.e. expected) latency differences between operations of different complexity, it can also … | 0 | 2020-06-25T09:37:37.502Z | https://discuss.pytorch.org/t/reliably-measure-module-latency-repeatability/72126/9 | Ah OK.
The backward pass of repeat_interleave is not deterministic as explained in the linked docs:
Additionally, the backward path for repeat_interleave() operates nondeterministically on the CUDA backend because repeat_interleave() is implemented using index_select() , the backward path fo… you could sp... | 456 | {'text': ['Hi themozel!\n\nUnfortunately I didn’t find a solution for measuring cells with small tensors. A “hacky solution” was to just multiply H and W with a constant factor of e.g. 8. Although this leads to “correct” (i.e. expected) latency differences between operations of different complexity, it can also &hellip... |
Weights disconnection implementation | I try to implement the disconnection of weights, i.e., the specific connection is always 0. It sounds like masked_scatter_, but I found it could not be autograded.
Here is my code:
import torch
import numpy as np
x = torch.rand((3, 1))
# tensor([[ 0.8525],
# [ 0.1509],
# [ 0.9724]… | 0 | 2018-06-07T04:11:31.167Z | While I am not clear about the picture you have posted - (why is the shape of x (3,2) if there are just three input nodes). One clear issue with your code though is that none of the variables have a requires_grad=True. For autograd to track stuff at least one of the inputs should have requires_grad=… | 0 | 2018-06-07T07:48:04.980Z | https://discuss.pytorch.org/t/weights-disconnection-implementation/19314/5 | While I am not clear about the picture you have posted - (why is the shape of x (3,2) if there are just three input nodes). One clear issue with your code though is that none of the variables have a requires_grad=True. For autograd to track stuff at least one of the inputs should have requires_grad=… You need to... | 1,528 | {'text': ['While I am not clear about the picture you have posted - (why is the shape of x (3,2) if there are just three input nodes). One clear issue with your code though is that none of the variables have a requires_grad=True. For autograd to track stuff at least one of the inputs should have requires_grad=…'... |
Move tensor failed? | my code looks like this, i write a forward hook,and try to move ‘QPs’ to the ‘output’ tensor device then do some operations, but my ‘assert’ code failed , because two tensor are not on the same device, i am confused…BTY,i use dataparallel but i think it doesn’t matter… so what happens?
def layer1_h… | 0 | 2019-11-07T08:06:04.174Z | You need to do QPs = QPs.to(output.device) for it to work as to does not change the Tensor inplace.
Also you can try QPs = output.new(QPs, device=output.device). | 1 | 2019-11-07T15:07:26.248Z | https://discuss.pytorch.org/t/move-tensor-failed/60248/2 | While I am not clear about the picture you have posted - (why is the shape of x (3,2) if there are just three input nodes). One clear issue with your code though is that none of the variables have a requires_grad=True. For autograd to track stuff at least one of the inputs should have requires_grad=… You need to... | 1,073 | {'text': ['You need to do QPs = QPs.to(output.device) for it to work as to does not change the Tensor inplace.\n\nAlso you can try QPs = output.new(QPs, device=output.device).'], 'answer_start': [1073]} |
Using feature extraction layers from pre-trained FRCNN | Hi all,
I have trained FRCNN using torchvision.models.detection.fasterrcnn_resnet50_fpn and now I want to use it’s feature extraction layers for something else.
To do so I first printed frcnn.modules() and see that the model has 4 major components:
0) GeneralizedRCNNTransform
BackboneWithFPN
RP… | 0 | 2020-01-25T11:29:43.906Z | You don’t necessarily need to wrap it in an nn.Sequential module, as it is already a working module.
However, since the FeaturePyramidNetwork is used internally, you will get the OrderedDict as the output as seen in <a href="https://github.com/pytorch/vision/blob/bb5af1d77658133af8be8c9b1a13139722315c3a/torchvision/op... | 0 | 2020-01-26T08:35:07.531Z | https://discuss.pytorch.org/t/using-feature-extraction-layers-from-pre-trained-frcnn/67621/8 | While I am not clear about the picture you have posted - (why is the shape of x (3,2) if there are just three input nodes). One clear issue with your code though is that none of the variables have a requires_grad=True. For autograd to track stuff at least one of the inputs should have requires_grad=… You need to... | 472 | {'text': ['You don’t necessarily need to wrap it in an nn.Sequential module, as it is already a working module.\n\nHowever, since the FeaturePyramidNetwork is used internally, you will get the OrderedDict as the output as seen in <a href="https://github.com/pytorch/vision/blob/bb5af1d77658133af8be8c9b1a13139722315c3a/t... |
AttributeError: 'GradSampleModule' object has no attribute for method | Hello,
I am using the flower as an FL framework and I am trying to put DP support by using Opacus.
Here is the problem I meet:
I am using a very common MNIST model and inherited to get a new class:
# https://github.com/pytorch/examples/blob/main/mnist/main.py
class Net(nn.Module):
def __ini… | 1 | 2022-07-21T04:30:17.619Z | <a class="mention" href="/u/leonmac">@Leonmac</a> The problem here is that privacy_engine.make_private wraps your model object with GradSampleModule(model). The latter is an instance of nn.Module which can do forward/backward passes. The difference from the original model is that 1) it computes per-sample gradients (th... | 0 | 2022-07-27T15:47:24.898Z | https://discuss.pytorch.org/t/attributeerror-gradsamplemodule-object-has-no-attribute-for-method/157135/8 | <a class="mention" href="/u/leonmac">@Leonmac</a> The problem here is that privacy_engine.make_private wraps your model object with GradSampleModule(model). The latter is an instance of nn.Module which can do forward/backward passes. The difference from the original model is that 1) it computes per-sample gradients (th... | 1,826 | {'text': ['<a class="mention" href="/u/leonmac">@Leonmac</a> The problem here is that privacy_engine.make_private wraps your model object with GradSampleModule(model). The latter is an instance of nn.Module which can do forward/backward passes. The difference from the original model is that 1) it computes per-sample gr... |
Pytorch uses wrong cuda version | Hello everybody,
PyTorch seems to use the wrong cuda version.
I create a fresh conda environment with
conda create -n myenv
Then in this environment I install torch via
conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
Afterwards if I start python in this … | 0 | 2022-07-19T08:52:40.440Z | Thanks for the update.
So the install command seems to work as conda list shows the right binary:
pytorch 1.12.0 py3.9_cuda11.6_cudnn8.3.2_0
but you have multiple PyTorch binaries installed where the one installed via pip seems to use the CUDA 10.2 runtime and is an old… | 1 | 2022-07-19T09:06:35.610Z | https://discuss.pytorch.org/t/pytorch-uses-wrong-cuda-version/156954/4 | <a class="mention" href="/u/leonmac">@Leonmac</a> The problem here is that privacy_engine.make_private wraps your model object with GradSampleModule(model). The latter is an instance of nn.Module which can do forward/backward passes. The difference from the original model is that 1) it computes per-sample gradients (th... | 1,263 | {'text': ['Thanks for the update.\n\nSo the install command seems to work as conda list shows the right binary:\n\npytorch 1.12.0 py3.9_cuda11.6_cudnn8.3.2_0\n\nbut you have multiple PyTorch binaries installed where the one installed via pip seems to use the CUDA 10.2 runtime and is an old&he... |
Learning Translation with Kornia | Hi,
I have been trying to learn translation (x, y) parameters with Kornia in the following manner:
class DTranslation(nn.Module):
def __init__(self, x_translation, y_translation):
super(DTranslation, self).__init__()
self.translations = torch.stack([x_translation, y_translation… | 0 | 2020-09-15T06:52:09.733Z | The issue is solved, with both the kind help from <a class="mention" href="/u/juanfmontesinos">@JuanFMontesinos</a> and at <a href="https://github.com/kornia/kornia/issues/682" rel="nofollow noopener">https://github.com/kornia/kornia/issues/682</a>
<a class="lightbox" href="https://discuss.pytorch.org/uploads/default/... | 0 | 2020-09-21T15:00:36.336Z | https://discuss.pytorch.org/t/learning-translation-with-kornia/96314/12 | <a class="mention" href="/u/leonmac">@Leonmac</a> The problem here is that privacy_engine.make_private wraps your model object with GradSampleModule(model). The latter is an instance of nn.Module which can do forward/backward passes. The difference from the original model is that 1) it computes per-sample gradients (th... | 659 | {'text': ['The issue is solved, with both the kind help from <a class="mention" href="/u/juanfmontesinos">@JuanFMontesinos</a> and at <a href="https://github.com/kornia/kornia/issues/682" rel="nofollow noopener">https://github.com/kornia/kornia/issues/682</a>\n\n<a class="lightbox" href="https://discuss.pytorch.org/upl... |
Gradient Doesn't Compute Backward | I want to make a custom loss function of MSE Loss by doing GMM computation for the result.
The MSE Loss will be computed using the GMM result and target value.
Here is my code
class StyleLoss(nn.Module):
def __init__(self, target_feature, ncomp, initial_mus, initial_covs, initial_priors):
… | 0 | 2020-03-11T07:32:14.056Z | self.input_gmm was also detached in the line before.
The general rule is, as long as you use PyTorch functions, don’t detach the tensors (via recreating new tensors, calling .detach() or item()), Autograd will be able to track the computation graph and calculate the gradients. | 0 | 2020-03-11T08:42:30.916Z | https://discuss.pytorch.org/t/gradient-doesnt-compute-backward/72831/4 | self.input_gmm was also detached in the line before.
The general rule is, as long as you use PyTorch functions, don’t detach the tensors (via recreating new tensors, calling .detach() or item()), Autograd will be able to track the computation graph and calculate the gradients. I’m not sure I understand what you mean b... | 2,882 | {'text': ['self.input_gmm was also detached in the line before.\n\nThe general rule is, as long as you use PyTorch functions, don’t detach the tensors (via recreating new tensors, calling .detach() or item()), Autograd will be able to track the computation graph and calculate the gradients.'], 'answer_start': [2882]} |
How dataloader shuffled with enumerate()? | Hello.
I am coding for training loop with dataloader which is flagged shuffle=True.
to my knowledge, people usually coding epoch and iteration for dataloader as follows:
for epoch in epochs:
for iter, (input, target) in enumerate(dataloader):
"""Do Training"""
With this commonsense, ... | 0 | 2020-07-23T10:47:37.632Z | I’m not sure I understand what you mean by
[image] GB_K:
and the loader will shuffle if it reaches to the end by enumerate.
but the data is already shuffled at the time you call enumerate() on the dataloader.
The dataloader first shuffles the data and puts it into batches. When you call enu… | 1 | 2020-07-23T11:31:28.483Z | https://discuss.pytorch.org/t/how-dataloader-shuffled-with-enumerate/90264/5 | self.input_gmm was also detached in the line before.
The general rule is, as long as you use PyTorch functions, don’t detach the tensors (via recreating new tensors, calling .detach() or item()), Autograd will be able to track the computation graph and calculate the gradients. I’m not sure I understand what you mean b... | 1,720 | {'text': ['I’m not sure I understand what you mean by\n\n[image] GB_K:\n\nand the loader will shuffle if it reaches to the end by enumerate.\n\nbut the data is already shuffled at the time you call enumerate() on the dataloader.\n\nThe dataloader first shuffles the data and puts it into batches. When you call enu&helli... |
Single Machine DDP Issue on A6000 GPU | Hi,
I’ve recently gotten access to some A6000 GPUs. The machine has CUDA11.3 installed, and my environment has the latest PyTorch release (1.10.0) with the CUDA11.3 build: torch==1.10.0+cu113.
It seems like single GPU training works well, but as soon as I switch to DDP (initiated when torch.cuda.d… | 0 | 2021-10-22T14:52:37.188Z | Upon cancelling the hang, I get a timeout error:
fd_event_list = self._selector.poll(timeout)
It seems like the processes can’t communicate with each other. So I’ve tried wrapping the training loop with model.no_sync(), and the code progresses well.
As you mentioned, this definitely does look lik… | 0 | 2021-10-23T12:12:49.967Z | https://discuss.pytorch.org/t/single-machine-ddp-issue-on-a6000-gpu/134869/7 | self.input_gmm was also detached in the line before.
The general rule is, as long as you use PyTorch functions, don’t detach the tensors (via recreating new tensors, calling .detach() or item()), Autograd will be able to track the computation graph and calculate the gradients. I’m not sure I understand what you mean b... | 583 | {'text': ['Upon cancelling the hang, I get a timeout error:\n\nfd_event_list = self._selector.poll(timeout)\n\nIt seems like the processes can’t communicate with each other. So I’ve tried wrapping the training loop with model.no_sync(), and the code progresses well.\n\nAs you mentioned, this definitely does look lik&he... |
ResNet reproducibility | Hi everyone :slight_smile:
I have two models that are essentially the same (same architecture, same number of parameters) but they yield different results. The first model is one from the PyTorch model selection (a ResNet18 without pretrained weights) and the other one is essentially copy pasted co… | 0 | 2020-11-17T12:44:37.349Z | I get the same results, if I try to make sure to use the same calls into the PRNG:
torch.manual_seed(2809)
modelA = ResNet(BasicBlock, [2, 2, 2, 2], 1000)
in_ = modelA.fc.in_features
classes = 10
modelA.fc = nn.Linear(in_features=in_, out_features=classes)
torch.manual_seed(2809)
modelB = ResNet(Ba… | 3 | 2020-11-19T10:57:05.255Z | https://discuss.pytorch.org/t/resnet-reproducibility/103113/13 | I get the same results, if I try to make sure to use the same calls into the PRNG:
torch.manual_seed(2809)
modelA = ResNet(BasicBlock, [2, 2, 2, 2], 1000)
in_ = modelA.fc.in_features
classes = 10
modelA.fc = nn.Linear(in_features=in_, out_features=classes)
torch.manual_seed(2809)
modelB = ResNet(Ba… Hi,
... | 1,782 | {'text': ['I get the same results, if I try to make sure to use the same calls into the PRNG:\n\ntorch.manual_seed(2809)\n\nmodelA = ResNet(BasicBlock, [2, 2, 2, 2], 1000)\n\nin_ = modelA.fc.in_features\n\nclasses = 10\n\nmodelA.fc = nn.Linear(in_features=in_, out_features=classes)\n\ntorch.manual_seed(2809)\n\nmodelB ... |
requires_grad=True for two variables | Hi all,
I have a model that has multiple inputs, and I was wondering if it is possible to find the gradients of the output with respect to the inputs.
The layout is as follows:
output = f(inp1, inp2, …)
To do so, we minimize the loss: Solve MSE optimization problem loss ||output-target||
Then a… | 0 | 2020-04-23T19:39:16.609Z | Hi,
First, make sure your inputs require gradients:
If they don’t, just call requires_grad_() on them before giving them to your net
If they do and are leaf (inp1.is_leaf()) then you’re good to go
If they do but are not leafs, you can do inp1.retain_grad() to make sure the .grad field will be pop… | 3 | 2020-04-23T19:47:12.077Z | https://discuss.pytorch.org/t/requires-grad-true-for-two-variables/78119/2 | I get the same results, if I try to make sure to use the same calls into the PRNG:
torch.manual_seed(2809)
modelA = ResNet(BasicBlock, [2, 2, 2, 2], 1000)
in_ = modelA.fc.in_features
classes = 10
modelA.fc = nn.Linear(in_features=in_, out_features=classes)
torch.manual_seed(2809)
modelB = ResNet(Ba… Hi,
... | 1,206 | {'text': ['Hi,\n\nFirst, make sure your inputs require gradients:\n\nIf they don’t, just call requires_grad_() on them before giving them to your net\n\nIf they do and are leaf (inp1.is_leaf()) then you’re good to go\n\nIf they do but are not leafs, you can do inp1.retain_grad() to make sure the .grad field will be pop... |
Can pytorch provide some sample code on training ResNet? | I recently need to train ResNet50 and do some experiments, I know there are bunch of pretrained models on github, but I feel more interested on the training process(like how to preprocess, set the LR and so on)…
The dataset I am using is standard CIFAR100. On github, few repo claim that they can ac… | 0 | 2019-12-14T14:02:19.671Z | Review the <a href="https://arxiv.org/abs/1812.01187" rel="nofollow noopener">Bag of Tricks for Image Classification with Convolutional Neural Networks</a> for some pointers.
Preprocessing: Zeros padding with value=4 and then randomly crop a 32x32 image. For normalization use mean=[0.491, 0.482, 0.447] and std=[0.247,... | 2 | 2019-12-14T14:15:24.562Z | https://discuss.pytorch.org/t/can-pytorch-provide-some-sample-code-on-training-resnet/64051/5 | I get the same results, if I try to make sure to use the same calls into the PRNG:
torch.manual_seed(2809)
modelA = ResNet(BasicBlock, [2, 2, 2, 2], 1000)
in_ = modelA.fc.in_features
classes = 10
modelA.fc = nn.Linear(in_features=in_, out_features=classes)
torch.manual_seed(2809)
modelB = ResNet(Ba… Hi,
... | 625 | {'text': ['Review the <a href="https://arxiv.org/abs/1812.01187" rel="nofollow noopener">Bag of Tricks for Image Classification with Convolutional Neural Networks</a> for some pointers.\n\nPreprocessing: Zeros padding with value=4 and then randomly crop a 32x32 image. For normalization use mean=[0.491, 0.482, 0.447] an... |
Torch_dir not found | I use the cmaker and the cmakerlists.txt and I do the configuration but an error which appears to me is torch_dir not found. what am i doing? | 0 | 2020-06-20T17:35:55.511Z | Is this the output of the command? If yes, then it is an empty folder, please download the LibTorch binary at <a href="https://pytorch.org/" rel="nofollow noopener">https://pytorch.org/</a> and extract them into this folder. | 0 | 2020-06-22T17:36:21.191Z | https://discuss.pytorch.org/t/torch-dir-not-found/86208/15 | Is this the output of the command? If yes, then it is an empty folder, please download the LibTorch binary at <a href="https://pytorch.org/" rel="nofollow noopener">https://pytorch.org/</a> and extract them into this folder. checkpoint is for cases where at least one argument has requires_grad=True, using it like that ... | 2,008 | {'text': ['Is this the output of the command? If yes, then it is an empty folder, please download the LibTorch binary at <a href="https://pytorch.org/" rel="nofollow noopener">https://pytorch.org/</a> and extract them into this folder.'], 'answer_start': [2008]} |
Trying to understand torch.utils.checkpoint | I am trying to understand how to use checkpoints to optimize my training. My basic understanding was that it trades increased compute for a lower memory footprint (by re-computing instead of storing data for the backward pass). Naively then I would assume that any time I use it I should decrease mem… | 0 | 2020-09-04T19:29:36.506Z | checkpoint is for cases where at least one argument has requires_grad=True, using it like that skips self.model(i.e. resnet) training. | 2 | 2020-09-05T03:00:38.974Z | https://discuss.pytorch.org/t/trying-to-understand-torch-utils-checkpoint/95224/6 | Is this the output of the command? If yes, then it is an empty folder, please download the LibTorch binary at <a href="https://pytorch.org/" rel="nofollow noopener">https://pytorch.org/</a> and extract them into this folder. checkpoint is for cases where at least one argument has requires_grad=True, using it like that ... | 1,229 | {'text': ['checkpoint is for cases where at least one argument has requires_grad=True, using it like that skips self.model(i.e. resnet) training.'], 'answer_start': [1229]} |
Segmentation Fault bias initialisation Conv2d | Hi!,
I face a problem using pytorch 1.3.0 on Cuda V100. Here the code originating from
<a href="https://github.com/cszn/DnCNN/blob/master/TrainingCodes/dncnn_pytorch/main_train.py" target="_blank" rel="nofollow noopener">cszn/DnCNN/blob/master/TrainingCodes/dncnn_pytorch/main_train.py</a>
# -*- coding: utf-8 -*-
# ... | 0 | 2019-12-16T15:05:01.493Z | Interesting. So I guess the pip-version linked to a wrong mkl version. Hence causing the issue !
In general, I would advise to use the conda install of pytorch if you’re in a conda environment. That will make sure you don’t have such issues !
Happy this is fixed. | 0 | 2019-12-17T16:42:42.866Z | https://discuss.pytorch.org/t/segmentation-fault-bias-initialisation-conv2d/64227/17 | Is this the output of the command? If yes, then it is an empty folder, please download the LibTorch binary at <a href="https://pytorch.org/" rel="nofollow noopener">https://pytorch.org/</a> and extract them into this folder. checkpoint is for cases where at least one argument has requires_grad=True, using it like that ... | 360 | {'text': ['Interesting. So I guess the pip-version linked to a wrong mkl version. Hence causing the issue !\n\nIn general, I would advise to use the conda install of pytorch if you’re in a conda environment. That will make sure you don’t have such issues !\n\nHappy this is fixed.'], 'answer_start': [360]} |
Why doesnt these variables get updated? | Hello all, I created a simple network where a convolutional layers weight matrix is altered by a custom function.
I came up with this :
class snet(nn.Module):
def __init__(self, num_classes=3):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 2, 1, 0)
shape = self.conv… | 0 | 2019-07-21T07:54:12.892Z | Had a missing line of code above. This
def forward(self, x):
self.some_function()
...
The values of kernel won’t be optimised directly, instead the optimizer will optimise values of var1 and var2 only as they are the parameters.
You can check that out by tracing the grad_fn backwards (tedious… | 1 | 2019-07-22T16:32:13.716Z | https://discuss.pytorch.org/t/why-doesnt-these-variables-get-updated/51187/8 | Had a missing line of code above. This
def forward(self, x):
self.some_function()
...
The values of kernel won’t be optimised directly, instead the optimizer will optimise values of var1 and var2 only as they are the parameters.
You can check that out by tracing the grad_fn backwards (tedious… You can retur... | 1,250 | {'text': ['Had a missing line of code above. This\n\ndef forward(self, x):\n\nself.some_function()\n\n...\n\nThe values of kernel won’t be optimised directly, instead the optimizer will optimise values of var1 and var2 only as they are the parameters.\n\nYou can check that out by tracing the grad_fn backwards (tedious&... |
Is it professional when dealing with the softmax layer in mobile | Is it possible to do the task of softmax layer in pytorch, I know Tensorflow can do it | 0 | 2019-12-31T02:15:00.295Z | You can return dicts in your forward method:
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.fc1 = nn.Linear(1, 1)
self.fc2 = nn.Linear(1, 1)
def forward(self, x):
x1 = self.fc1(x)
x2 = self.fc2(x)
r… | 0 | 2020-01-07T04:01:41.709Z | https://discuss.pytorch.org/t/is-it-professional-when-dealing-with-the-softmax-layer-in-mobile/65424/19 | Had a missing line of code above. This
def forward(self, x):
self.some_function()
...
The values of kernel won’t be optimised directly, instead the optimizer will optimise values of var1 and var2 only as they are the parameters.
You can check that out by tracing the grad_fn backwards (tedious… You can retur... | 932 | {'text': ['You can return dicts in your forward method:\n\nclass MyModel(nn.Module):\n\ndef __init__(self):\n\nsuper(MyModel, self).__init__()\n\nself.fc1 = nn.Linear(1, 1)\n\nself.fc2 = nn.Linear(1, 1)\n\ndef forward(self, x):\n\nx1 = self.fc1(x)\n\nx2 = self.fc2(x)\n\nr…'], 'answer_start': [932]} |
Training process is terminated when node fails for torch elastic | Hi!
I am recently using torch elastic with c10d and min_nodes=1. I have succeeded in joining the existing training from other nodes dynamically. The training process blocks for rendezvous and restarts from the latest checkpoint with a new remaining iteration number (because of the updated world siz… | 0 | 2021-11-01T05:53:57.974Z | I can confirm this is indeed a bug. Please track the progress of the fix: <a href="https://github.com/pytorch/pytorch/issues/67742" class="inline-onebox" rel="noopener nofollow ugc">[torch/elastic] Scale down does not work correctly when agent is killed with SIGINT, SIGTERM · Issue #67742 · pytorch/pytorch · GitHub</a>... | 0 | 2021-11-03T03:41:21.593Z | https://discuss.pytorch.org/t/training-process-is-terminated-when-node-fails-for-torch-elastic/135580/8 | Had a missing line of code above. This
def forward(self, x):
self.some_function()
...
The values of kernel won’t be optimised directly, instead the optimizer will optimise values of var1 and var2 only as they are the parameters.
You can check that out by tracing the grad_fn backwards (tedious… You can retur... | 559 | {'text': ['I can confirm this is indeed a bug. Please track the progress of the fix: <a href="https://github.com/pytorch/pytorch/issues/67742" class="inline-onebox" rel="noopener nofollow ugc">[torch/elastic] Scale down does not work correctly when agent is killed with SIGINT, SIGTERM · Issue #67742 · pytorch/pytorch ·... |
Problems iterating through loader when using sampler | **Hi there! **
I have a custom dataset in which (even though I have more or less the same number of samples for each class) it missclassified some of the data for some specific classes. So what I’m trying to do is oversample by using “sampler” in the dataloader and WeightedRandomSampler as follows: … | 0 | 2021-02-17T12:21:54.132Z | Which sklearn version are you using, as I’m getting an error running your code with random target data:
targets = np.random.randint(0, 100, (100,))
class_weights = compute_class_weight('balanced', np.unique(targets), targets,classes=np.arange(NUM_POSITIONS))
> TypeError: compute_class_weight() got &hellip... | 0 | 2021-02-19T08:41:44.255Z | https://discuss.pytorch.org/t/problems-iterating-through-loader-when-using-sampler/112157/9 | Which sklearn version are you using, as I’m getting an error running your code with random target data:
targets = np.random.randint(0, 100, (100,))
class_weights = compute_class_weight('balanced', np.unique(targets), targets,classes=np.arange(NUM_POSITIONS))
> TypeError: compute_class_weight() got &hellip... | 1,824 | {'text': ['Which sklearn version are you using, as I’m getting an error running your code with random target data:\n\ntargets = np.random.randint(0, 100, (100,))\n\nclass_weights = compute_class_weight('balanced', np.unique(targets), targets,classes=np.arange(NUM_POSITIONS))\n\n> TypeError: compute_class_wei... |
How to dropout with non zero value? | I want to use feature dropout like dropout2d and fill it with mean value (or Gaussian noise for example) instead of zeros.
How to do so?
Easiest thing to do is runing dropout2d and fill zeros, but i have zeros in data. | 0 | 2020-05-03T18:18:28.109Z | It depends, what you want to achieve.
The original mask before the unsqueeze and expand operations can be used to index x directly, which would yield:
x = torch.ones([1, 167, 128])
batch_size, length, features = x.size()
p = 0.5
mask = torch.distributions.Bernoulli(
probs=(1 - p)).sample((batc… | 0 | 2020-10-20T19:26:36.995Z | https://discuss.pytorch.org/t/how-to-dropout-with-non-zero-value/79561/10 | Which sklearn version are you using, as I’m getting an error running your code with random target data:
targets = np.random.randint(0, 100, (100,))
class_weights = compute_class_weight('balanced', np.unique(targets), targets,classes=np.arange(NUM_POSITIONS))
> TypeError: compute_class_weight() got &hellip... | 1,234 | {'text': ['It depends, what you want to achieve.\n\nThe original mask before the unsqueeze and expand operations can be used to index x directly, which would yield:\n\nx = torch.ones([1, 167, 128])\n\nbatch_size, length, features = x.size()\n\np = 0.5\n\nmask = torch.distributions.Bernoulli(\n\nprobs=(1 - p)).sample((b... |
Saving and Loading Optimizer Params | Hi,
I’m trying to save and load optimizer params as we do for a model, but although i tried in many different ways, still i couldn’t work it. Here is the code:
best_model_wts = copy.deepcopy(model.state_dict())
best_optim_pars = copy.deepcopy(optimizer.state_dict())
for epoch in range(num_epochs)… | 0 | 2020-11-30T07:44:59.432Z | Did you try not to use deepcopy at all in your code?
Also, did you check if your code updating or reassigning a model parameters somewhere (as in linked thread I posted before)?
Can you update to a current pytorch version? | 1 | 2020-11-30T12:07:46.670Z | https://discuss.pytorch.org/t/saving-and-loading-optimizer-params/104594/12 | Which sklearn version are you using, as I’m getting an error running your code with random target data:
targets = np.random.randint(0, 100, (100,))
class_weights = compute_class_weight('balanced', np.unique(targets), targets,classes=np.arange(NUM_POSITIONS))
> TypeError: compute_class_weight() got &hellip... | 631 | {'text': ['Did you try not to use deepcopy at all in your code?\n\nAlso, did you check if your code updating or reassigning a model parameters somewhere (as in linked thread I posted before)?\n\nCan you update to a current pytorch version?'], 'answer_start': [631]} |
Pytorch - lstm yields retain_graph error - how do I get around this? | I am training a simple LSTM model however pytorch gives me an error saying that I need to set retain_graph=True. However this takes the model longer to train and I do not think I need to do this.
class SequenceModel(nn.Module):
def __init__(self):
super().__init__()
self.lstm =… | 0 | 2020-03-03T12:19:46.566Z | If you want to learn the hidden layer initial state, starting from 0:
class SequenceModel(nn.Module):
def __init__(self):
super().__init__()
self.lstm = nn.LSTM(input_size = 3, hidden_size = 3, bidirectional=False)
self.hidden = nn.ParameterList((nn.Parameter(torch.zero… | 3 | 2020-03-03T15:22:30.270Z | https://discuss.pytorch.org/t/pytorch-lstm-yields-retain-graph-error-how-do-i-get-around-this/71837/8 | If you want to learn the hidden layer initial state, starting from 0:
class SequenceModel(nn.Module):
def __init__(self):
super().__init__()
self.lstm = nn.LSTM(input_size = 3, hidden_size = 3, bidirectional=False)
self.hidden = nn.ParameterList((nn.Parameter(torch.zero… Yes you are right, but if that happe... | 1,710 | {'text': ['If you want to learn the hidden layer initial state, starting from 0:\n\nclass SequenceModel(nn.Module):\n\ndef __init__(self):\n\nsuper().__init__()\n\nself.lstm = nn.LSTM(input_size = 3, hidden_size = 3, bidirectional=False)\n\nself.hidden = nn.ParameterList((nn.Parameter(torch.zero…'], 'answer_star... |
Loss is 1 but gradients are zero | I am facing this issue of gradient being 0 even though the loss is not zero. loss stays at 1 while gradients are 0. I’m using the MSE loss function. Can anyone please help me here in debugging this?
Training code snippet:
# Train network
max_epochs = max_epochs+1
epoch = 1
last_acc = 0… | 0 | 2022-04-16T06:03:04.191Z | Yes you are right, but if that happens, then the loss itself will become zero which is not the case here.
Using regularization helped here to make sure layer_and_weights doesn’t become zero by default. And gradient started flowing again. So I guess this solves the problem for now.
Thanks | 0 | 2022-04-21T15:05:23.530Z | https://discuss.pytorch.org/t/loss-is-1-but-gradients-are-zero/149296/22 | If you want to learn the hidden layer initial state, starting from 0:
class SequenceModel(nn.Module):
def __init__(self):
super().__init__()
self.lstm = nn.LSTM(input_size = 3, hidden_size = 3, bidirectional=False)
self.hidden = nn.ParameterList((nn.Parameter(torch.zero… Yes you are right, but if that happe... | 1,139 | {'text': ['Yes you are right, but if that happens, then the loss itself will become zero which is not the case here.\n\nUsing regularization helped here to make sure layer_and_weights doesn’t become zero by default. And gradient started flowing again. So I guess this solves the problem for now.\n\nThanks'], 'answer_sta... |
RuntimeError: Calculated padded input size per channel: (1 x 1). Kernel size: (4 x 4). Kernel size can't be greater than actual input size | I am trying to train GAN to transfer style. I am getting error when passing images through discriminator
for epoch in range(epochs):
#code for stats
for real_images in tqdm(t_dl):
optimizer["discriminator"].zero_grad()
real_preds = model["discriminator"](re… | 0 | 2022-08-27T17:31:16.040Z | Thanks for help <a class="mention" href="/u/ptrblck">@ptrblck</a>!
[image] ptrblck:
Based on the updated code I would start with checking fake_images.
I checked it and it was the problem.Generator generated images of wrong shape,so discriminator was getting 112*112. | 0 | 2022-08-28T11:51:21.132Z | https://discuss.pytorch.org/t/runtimeerror-calculated-padded-input-size-per-channel-1-x-1-kernel-size-4-x-4-kernel-size-cant-be-greater-than-actual-input-size/160184/11 | If you want to learn the hidden layer initial state, starting from 0:
class SequenceModel(nn.Module):
def __init__(self):
super().__init__()
self.lstm = nn.LSTM(input_size = 3, hidden_size = 3, bidirectional=False)
self.hidden = nn.ParameterList((nn.Parameter(torch.zero… Yes you are right, but if that happe... | 575 | {'text': ['Thanks for help <a class="mention" href="/u/ptrblck">@ptrblck</a>!\n\n[image] ptrblck:\n\nBased on the updated code I would start with checking fake_images.\n\nI checked it and it was the problem.Generator generated images of wrong shape,so discriminator was getting 112*112.'], 'answer_start': [575]} |
Image Sizing Is too large when trying to display image from deep learning pipeline | thanks so much for this forum, its helping me to learn so much! I will try to answer a few questions as well. Still being a newbie my question is below.
I am trying to display an image from a deep learning pipeline with the associated label. The error I am getting is that the image is too large … | 1 | 2021-06-29T20:31:05.790Z | I see, right before show(org_image, title=sentence) inside load_image_and_predict:
Do you printing out the org_image size by adding org_image.size before the show(org_image, title=sentence) line? As well as print the sentence if possible!
print(org_image.size, sentence)
(What if the caption is so… | 0 | 2021-06-29T21:00:36.201Z | https://discuss.pytorch.org/t/image-sizing-is-too-large-when-trying-to-display-image-from-deep-learning-pipeline/125399/10 | I see, right before show(org_image, title=sentence) inside load_image_and_predict:
Do you printing out the org_image size by adding org_image.size before the show(org_image, title=sentence) line? As well as print the sentence if possible!
print(org_image.size, sentence)
(What if the caption is so… If you are ... | 1,688 | {'text': ['I see, right before show(org_image, title=sentence) inside load_image_and_predict:\n\nDo you printing out the org_image size by adding org_image.size before the show(org_image, title=sentence) line? As well as print the sentence if possible!\n\nprint(org_image.size, sentence)\n\n(What if the caption is so&he... |
Understanding tensor.backwards() | Hello, so I don’t really get why it is that we need to give a grad tensor to tensor.backwards(). They say the grad should be the gradient of the tensor w.r.t itself but wouldn’t that just be a tensor of all ones?
If not could you please give an example where the gradient wouldn’t be all ones?
I fe… | 0 | 2020-06-12T20:56:38.683Z | If you are trying to calculate the dLoss/dLoss, then it would be torch.ones, that’s correct.
For other use cases it might be different. Recently such a use case was described <a href="https://discuss.pytorch.org/t/propagate-manually-computed-gradients/85884">here</a>. | 1 | 2020-06-20T07:35:40.853Z | https://discuss.pytorch.org/t/understanding-tensor-backwards/85269/6 | I see, right before show(org_image, title=sentence) inside load_image_and_predict:
Do you printing out the org_image size by adding org_image.size before the show(org_image, title=sentence) line? As well as print the sentence if possible!
print(org_image.size, sentence)
(What if the caption is so… If you are ... | 1,153 | {'text': ['If you are trying to calculate the dLoss/dLoss, then it would be torch.ones, that’s correct.\n\nFor other use cases it might be different. Recently such a use case was described <a href="https://discuss.pytorch.org/t/propagate-manually-computed-gradients/85884">here</a>.'], 'answer_start': [1153]} |
Why this convnet with SGD (batch size 1) fail? | I’d like to understand why this script:
<a href="https://github.com/pytorch/examples/blob/master/mnist/main.py" target="_blank" rel="nofollow noopener">pytorch/examples/blob/master/mnist/main.py</a>
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional... | 0 | 2018-01-04T22:03:26.864Z | Well, as you can see the network architecture in Keras is quite different from the example in Pytorch. Anyway, lower the learning rate to 0.001 and you should be fine with batch_size=1. | 0 | 2018-01-04T23:26:43.873Z | https://discuss.pytorch.org/t/why-this-convnet-with-sgd-batch-size-1-fail/11887/7 | I see, right before show(org_image, title=sentence) inside load_image_and_predict:
Do you printing out the org_image size by adding org_image.size before the show(org_image, title=sentence) line? As well as print the sentence if possible!
print(org_image.size, sentence)
(What if the caption is so… If you are ... | 579 | {'text': ['Well, as you can see the network architecture in Keras is quite different from the example in Pytorch. Anyway, lower the learning rate to 0.001 and you should be fine with batch_size=1.'], 'answer_start': [579]} |
Problems with LAPACK on iOS | Hello!
I’m trying to run my model on iOS and encounter a problem with LibTorch. The problem is in the call of SVD function.
The following code:
#import <torch/script.h>
#import <ATen/Functions.h>
at::Tensor tensor = torch::ones({3,3});
auto result = at::svd(tensor);
crushes with error as executi&hell... | 0 | 2020-02-10T16:01:53.440Z | Hi <a class="mention" href="/u/kulikovv">@kulikovv</a>,
We didn’t compile LAPACK into our binary. However, since LAPCK is supported by the Accelerate.framework on iOS. You can manually enable it by following the steps below
Add a compiler flag - CMAKE_ARGS+=("-DUSE_LAPACK=ON") in build_ios.sh to tell cmake ... | 5 | 2020-02-10T18:34:46.810Z | https://discuss.pytorch.org/t/problems-with-lapack-on-ios/69240/5 | Hi <a class="mention" href="/u/kulikovv">@kulikovv</a>,
We didn’t compile LAPACK into our binary. However, since LAPCK is supported by the Accelerate.framework on iOS. You can manually enable it by following the steps below
Add a compiler flag - CMAKE_ARGS+=("-DUSE_LAPACK=ON") in build_ios.sh to tell cmake ... | 1,528 | {'text': ['Hi <a class="mention" href="/u/kulikovv">@kulikovv</a>,\n\nWe didn’t compile LAPACK into our binary. However, since LAPCK is supported by the Accelerate.framework on iOS. You can manually enable it by following the steps below\n\nAdd a compiler flag - CMAKE_ARGS+=("-DUSE_LAPACK=ON") in build_ios.sh... |
How to fix size mismatch pretrained model for large input image sizes? | Hi,
So I understand that pretrained models WITH dense layers require the exact image size the network was originally trained on for input. I know you can feed in different image sizes provided you add additional layers but I was wondering what is the best/optimal way.
Currently, I have input sizes… | 0 | 2022-02-13T02:09:07.702Z | So the main difference here is that your feature extraction is using average pooling, while the torchvision implementations of models e.g., DenseNet use adaptive average pooling (in this case also referred to as "global average pooling) to guarantee the output spatial dimensions are reduced to [1,1]… | 1 | 2022-02-14T00:59:16.240Z | https://discuss.pytorch.org/t/how-to-fix-size-mismatch-pretrained-model-for-large-input-image-sizes/144025/4 | Hi <a class="mention" href="/u/kulikovv">@kulikovv</a>,
We didn’t compile LAPACK into our binary. However, since LAPCK is supported by the Accelerate.framework on iOS. You can manually enable it by following the steps below
Add a compiler flag - CMAKE_ARGS+=("-DUSE_LAPACK=ON") in build_ios.sh to tell cmake ... | 1,125 | {'text': ['So the main difference here is that your feature extraction is using average pooling, while the torchvision implementations of models e.g., DenseNet use adaptive average pooling (in this case also referred to as "global average pooling) to guarantee the output spatial dimensions are reduced to [1,1]&hel... |
Swap axes in pytorch? | Hi, in tensorflow, we have data_format option in tf.nn.conv2d which could specify the data format as NHWC or NCHW.
Is there equivalent operation in pytorch?
If not, should we convert Variable to numpy.array, use np.swapaxes and convert it back into Variable?
And under such circumstances, will the… | 6 | 2017-03-09T13:08:30.505Z | <a class="mention" href="/u/veril">@Veril</a> transpose only applies to 2 axis, while permute can be applied to all the axes at the same time.
For example
a = torch.rand(1,2,3,4)
print(a.transpose(0,3).transpose(1,2).size())
print(a.permute(3,2,1,0).size())
BTW, <a href="https://github.com/pytorch/pytorch/blob/mas... | 24 | 2017-03-09T14:37:12.290Z | https://discuss.pytorch.org/t/swap-axes-in-pytorch/970/5 | Hi <a class="mention" href="/u/kulikovv">@kulikovv</a>,
We didn’t compile LAPACK into our binary. However, since LAPCK is supported by the Accelerate.framework on iOS. You can manually enable it by following the steps below
Add a compiler flag - CMAKE_ARGS+=("-DUSE_LAPACK=ON") in build_ios.sh to tell cmake ... | 675 | {'text': ['<a class="mention" href="/u/veril">@Veril</a> transpose only applies to 2 axis, while permute can be applied to all the axes at the same time.\n\nFor example\n\na = torch.rand(1,2,3,4)\n\nprint(a.transpose(0,3).transpose(1,2).size())\n\nprint(a.permute(3,2,1,0).size())\n\nBTW, <a href="https://github.com/pyt... |
Resnet is not giving me any accuracy | Well, while using Desnsenet169, I am getting proper accuracies, but in the case of ResNet - 101/50, I am not getting any accuracies. It is printing like this -
[image] Deb_Prakash_Chatterj:
Well, while using Desnsenet169, I am getting proper accuracies, but in the case of ResNet - 101/50, I am … | 0 | 2019-02-25T09:08:58.224Z | Sure, maybe this can be best explained by an example:
In [1]: import torch
In [2]: a = torch.tensor([1, 2, 3])
In [3]: b = a.clone().view(-1, 1)
… | 1 | 2019-02-25T16:13:14.555Z | https://discuss.pytorch.org/t/resnet-is-not-giving-me-any-accuracy/38166/7 | Sure, maybe this can be best explained by an example:
In [1]: import torch
In [2]: a = torch.tensor([1, 2, 3])
In [3]: b = a.clone().view(-1, 1)
… Thanks for the code.
Since you are rewrapping some modules and remove the last linear layer, the output shape will be the last activation after the average pooli... | 2,154 | {'text': ['Sure, maybe this can be best explained by an example:\n\nIn [1]: import torch\n\nIn [2]: a = torch.tensor([1, 2, 3])\n\nIn [3]: b = a.clone().view(-1, 1)\n\n…'], 'answer_start': [2154]} |
Size of extracted feature | Hello, I am new in pytorch and currently I trained mnist data in resnet 34 model. After I extracting my feature the size is [1, 512, 10, 10] which I was expecting [1, 512, 1, 1]. Can someone explain to me what does 10 10 stands for? | 0 | 2020-02-09T12:26:35.458Z | Thanks for the code.
Since you are rewrapping some modules and remove the last linear layer, the output shape will be the last activation after the average pooling layer.
For your input shape, it’ll be [batch_size, 512, 10, 10].
If you want to get a single pixel in the spatial size, you would hav… | 1 | 2020-02-13T19:47:58.134Z | https://discuss.pytorch.org/t/size-of-extracted-feature/69126/9 | Sure, maybe this can be best explained by an example:
In [1]: import torch
In [2]: a = torch.tensor([1, 2, 3])
In [3]: b = a.clone().view(-1, 1)
… Thanks for the code.
Since you are rewrapping some modules and remove the last linear layer, the output shape will be the last activation after the average pooli... | 1,235 | {'text': ['Thanks for the code.\n\nSince you are rewrapping some modules and remove the last linear layer, the output shape will be the last activation after the average pooling layer.\n\nFor your input shape, it’ll be [batch_size, 512, 10, 10].\n\nIf you want to get a single pixel in the spatial size, you would hav&he... |
4D tensor equivalent neural network layer in PyTorch | Hello,
How can I define a layer like below code in pytorch?
InputLayer(
shape=(None, 1, input_height, input_width),
)
(The input is a 4 Dimensional tensor.) | 0 | 2020-06-13T19:10:54.280Z | I do not remember the details of Theano’s memory layout, but I am assuming it uses the NCHW format, which means your input dimensions (10, 1, 20, 224) corresponds to
batch size of 10,
channel depth of 1,
image height of 20 pixels,
image width of 224 pixels.
(The image height of 20 pixels does see… | 0 | 2020-06-14T10:36:33.939Z | https://discuss.pytorch.org/t/4d-tensor-equivalent-neural-network-layer-in-pytorch/85360/9 | Sure, maybe this can be best explained by an example:
In [1]: import torch
In [2]: a = torch.tensor([1, 2, 3])
In [3]: b = a.clone().view(-1, 1)
… Thanks for the code.
Since you are rewrapping some modules and remove the last linear layer, the output shape will be the last activation after the average pooli... | 467 | {'text': ['I do not remember the details of Theano’s memory layout, but I am assuming it uses the NCHW format, which means your input dimensions (10, 1, 20, 224) corresponds to\n\nbatch size of 10,\n\nchannel depth of 1,\n\nimage height of 20 pixels,\n\nimage width of 224 pixels.\n\n(The image height of 20 pixels does ... |
Cannot calculate second order gradients even though `create_graph=True` | I have a training loop iteration that looks like this…
mu, logvar = m(x)
alpha = torch.zeros(size, requires_grad=True)
loss = alpha * criterion(mu, logvar)
loss.backward(retain_graph=True, create_graph=True)
for p in m.parameters():
p = p - LR * p.grad
x_next, y_next = data[j + 1]
mu_next, log… | 0 | 2020-04-27T19:29:01.287Z | I think the problem in your code is:
for p in m.parameters():
p = p - LR * p.grad
This does not modify p inplace ! It just assigns the result to a variable name p that you override just after. So you could remove these lines and your code would run the same.
This is why you don’t see the link.
… | 0 | 2020-04-30T15:24:09.642Z | https://discuss.pytorch.org/t/cannot-calculate-second-order-gradients-even-though-create-graph-true/78711/12 | I think the problem in your code is:
for p in m.parameters():
p = p - LR * p.grad
This does not modify p inplace ! It just assigns the result to a variable name p that you override just after. So you could remove these lines and your code would run the same.
This is why you don’t see the link.
… This seems ... | 1,554 | {'text': ['I think the problem in your code is:\n\nfor p in m.parameters():\n\np = p - LR * p.grad\n\nThis does not modify p inplace ! It just assigns the result to a variable name p that you override just after. So you could remove these lines and your code would run the same.\n\nThis is why you don’t see the link.\n\... |
How to get multi-target NLL in C++? (multi-target not supported at ClassNLLCriterion.c) | In the <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss" rel="nofollow noopener">python API</a>, the NLLLoss is allowed to take a target shape (N, d1, …, dk). However, in the c++ api, the torch::nll_loss will crash with an exception multi-target not supported at C:\w\1\s\windows\pytorch\aten\src\THNN/g... | 0 | 2019-10-22T16:15:10.302Z | This seems to be indeed the right shape and I remembered I’ve seen this issue before!
Could you check, if nll_loss2d is defined and if so use it instead of nll_loss (I’m currently not on my machine to check it)? | 1 | 2019-10-22T16:42:46.543Z | https://discuss.pytorch.org/t/how-to-get-multi-target-nll-in-c-multi-target-not-supported-at-classnllcriterion-c/58929/6 | I think the problem in your code is:
for p in m.parameters():
p = p - LR * p.grad
This does not modify p inplace ! It just assigns the result to a variable name p that you override just after. So you could remove these lines and your code would run the same.
This is why you don’t see the link.
… This seems ... | 1,086 | {'text': ['This seems to be indeed the right shape and I remembered I’ve seen this issue before!\n\nCould you check, if nll_loss2d is defined and if so use it instead of nll_loss (I’m currently not on my machine to check it)?'], 'answer_start': [1086]} |
Image Classification completely wrong with PyTorch Mobile iOS example | Dear all,
My trained/traced model got a good performance on PC. However, when i ship the model.pt to PyTorch Mobile and tested on iOS. the classification of same Image is completely wrong.
i have no idea where is the problem and how to solve it.
my model.pt is generated using Transfer learning wi… | 0 | 2020-05-17T01:35:38.133Z | <a class="mention" href="/u/ptrblck">@ptrblck</a> <a class="mention" href="/u/xta0">@xta0</a>
I have figured out where is the problem.
i double checked the predict output of Desktop and iOS, the torch.max gives the same tensor value(more or less same)
But in iOS, the label.txt should be generated along with PyTorch ... | 2 | 2020-05-19T18:58:26.121Z | https://discuss.pytorch.org/t/image-classification-completely-wrong-with-pytorch-mobile-ios-example/81585/9 | I think the problem in your code is:
for p in m.parameters():
p = p - LR * p.grad
This does not modify p inplace ! It just assigns the result to a variable name p that you override just after. So you could remove these lines and your code would run the same.
This is why you don’t see the link.
… This seems ... | 522 | {'text': ['<a class="mention" href="/u/ptrblck">@ptrblck</a> <a class="mention" href="/u/xta0">@xta0</a>\n\nI have figured out where is the problem.\n\ni double checked the predict output of Desktop and iOS, the torch.max gives the same tensor value(more or less same)\n\nBut in iOS, the label.txt should be generated al... |
Error in QAT evaluate | When i run QAT, training is normal, but when i want to evaluate the qat model, an error length of scales must equal to channel confuse me.
I use pytorch 1.4.0, and my code is
# Traing is Normal
net = MyQATNet()
net.fuse_model()
net.qconfig = torch.quantization.get_default_qat_config("fbgemm")
net_&helli... | 0 | 2020-06-10T12:21:35.850Z | I think somebody have the same error: <a href="https://discuss.pytorch.org/t/issue-with-quantization/67457" class="inline-onebox">Issue with Quantization</a>
I think i know the answew:
# pytorch 1.4
#save
torch.save(net.state_dict(),'xx') # fp32 model
#load
model.qconfig = torch.quantization.get_default_q... | 1 | 2020-06-11T08:14:20.200Z | https://discuss.pytorch.org/t/error-in-qat-evaluate/84901/10 | I think somebody have the same error: <a href="https://discuss.pytorch.org/t/issue-with-quantization/67457" class="inline-onebox">Issue with Quantization</a>
I think i know the answew:
# pytorch 1.4
#save
torch.save(net.state_dict(),'xx') # fp32 model
#load
model.qconfig = torch.quantization.get_default_q... | 1,818 | {'text': ['I think somebody have the same error: <a href="https://discuss.pytorch.org/t/issue-with-quantization/67457" class="inline-onebox">Issue with Quantization</a>\n\nI think i know the answew:\n\n# pytorch 1.4\n\n#save\n\ntorch.save(net.state_dict(),'xx') # fp32 model\n\n#load\n\nmodel.qconfig = torch.qua... |
Outputs = func(*inputs) TypeError: ‘Tensor’ object is not callable | Hello
I aimed to calculate the jacobian of a tensor (n by m) with respect a tensor (m by d) so i tried this code:
torch.autograd.functional.jacobian(output,W)
where output is the output of my network and gives me the following error
outputs = func(*inputs)
TypeError: ‘Tensor’ object is not call… | 0 | 2020-05-17T09:22:27.141Z | You can do that.
But if you already have a module, you can do:
mod = nn.Linear(10, 10)
jacobian(func, x) # To get the jacobian of the output wrt x
You should set create_graph=True if you want to backprop through that operation. | 1 | 2020-05-19T19:23:57.675Z | https://discuss.pytorch.org/t/outputs-func-inputs-typeerror-tensor-object-is-not-callable/81620/10 | I think somebody have the same error: <a href="https://discuss.pytorch.org/t/issue-with-quantization/67457" class="inline-onebox">Issue with Quantization</a>
I think i know the answew:
# pytorch 1.4
#save
torch.save(net.state_dict(),'xx') # fp32 model
#load
model.qconfig = torch.quantization.get_default_q... | 1,338 | {'text': ['You can do that.\n\nBut if you already have a module, you can do:\n\nmod = nn.Linear(10, 10)\n\njacobian(func, x) # To get the jacobian of the output wrt x\n\nYou should set create_graph=True if you want to backprop through that operation.'], 'answer_start': [1338]} |
Shape '[32, 150528]' is invalid for input of size 1492992 | Using GPU: True
Epoch 1/10
----------
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-13-cd4c03781ec7> in <module>()
8 exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_… | 0 | 2020-08-21T14:42:50.803Z | I expect, your problem is when using view x = x.view(x.size(0), 3 * 224 * 224) which is giving error,
without providing explicit shape it might be better to use
x.view(x.shape[0],-1)
Another problem might be in self.fc1 and self.fc2
As we know, torch.nn.Linear(in_features, out_features… | 0 | 2020-08-21T15:16:05.373Z | https://discuss.pytorch.org/t/shape-32-150528-is-invalid-for-input-of-size-1492992/93641/2 | I think somebody have the same error: <a href="https://discuss.pytorch.org/t/issue-with-quantization/67457" class="inline-onebox">Issue with Quantization</a>
I think i know the answew:
# pytorch 1.4
#save
torch.save(net.state_dict(),'xx') # fp32 model
#load
model.qconfig = torch.quantization.get_default_q... | 661 | {'text': ['I expect, your problem is when using view x = x.view(x.size(0), 3 * 224 * 224) which is giving error,\n\nwithout providing explicit shape it might be better to use\n\nx.view(x.shape[0],-1)\n\nAnother problem might be in self.fc1 and self.fc2\n\nAs we know, torch.nn.Linear(in_features, out_features&... |
Loss.backward throwing CUDA Errors | Sorry that I am asking this again but I would need a confirmation before I try to switch to the nightly builds for the issue since they seem to be a bit unstable.
This is the code:-
@staticmethod
def backward(ctx, grad_output):
grad_label = grad_output.clone()
num_ft = grad_ou… | 0 | 2020-06-12T14:32:05.210Z | Sorry for the delayed reply, but yes it should be fixed by now. Let us know, if you run into this issue again, please. | 1 | 2020-06-12T19:27:03.764Z | https://discuss.pytorch.org/t/loss-backward-throwing-cuda-errors/85221/19 | Sorry for the delayed reply, but yes it should be fixed by now. Let us know, if you run into this issue again, please. But for 2D tensors, the transforms.ToPILImage() and transforms.ToTensor() are needed.
Yes, I see you point here. Yes, images as tensors should be 3D tensors. In case of 2D tensors, I’d recommend to up... | 1,938 | {'text': ['Sorry for the delayed reply, but yes it should be fixed by now. Let us know, if you run into this issue again, please.'], 'answer_start': [1938]} |
Transformation is reducing the number of channel | from torchvision import transforms
M = torch.randint(low=0, high=2, size=(6, 64, 64), dtype = torch.float)
N = torch.randint(low=0, high=2, size=(3, 64, 64), dtype = torch.float)
gt_trans = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((64, 64)),
… | 0 | 2020-11-17T00:09:19.495Z | But for 2D tensors, the transforms.ToPILImage() and transforms.ToTensor() are needed.
Yes, I see you point here. Yes, images as tensors should be 3D tensors. In case of 2D tensors, I’d recommend to update the way to construct the input as 3D tensor, instead of 2D tensor. And in this case, I would… | 1 | 2020-11-20T09:54:56.272Z | https://discuss.pytorch.org/t/transformation-is-reducing-the-number-of-channel/103033/8 | Sorry for the delayed reply, but yes it should be fixed by now. Let us know, if you run into this issue again, please. But for 2D tensors, the transforms.ToPILImage() and transforms.ToTensor() are needed.
Yes, I see you point here. Yes, images as tensors should be 3D tensors. In case of 2D tensors, I’d recommend to up... | 1,088 | {'text': ['But for 2D tensors, the transforms.ToPILImage() and transforms.ToTensor() are needed.\n\nYes, I see you point here. Yes, images as tensors should be 3D tensors. In case of 2D tensors, I’d recommend to update the way to construct the input as 3D tensor, instead of 2D tensor. And in this case, I would…'... |
Constant Segmentation loss | Hey, I am training a simple Unet on dice and BCE loss on the Salt segmentation challenge on Kaggle. My model’s loss is not changing at all. In this example, I pick a dataset of only 5 examples and keep interacting through and get a constant loss. My gradients are not getting backdroped I think, what… | 0 | 2018-11-17T13:07:31.611Z | Just to make sure, you are running <a href="https://gist.github.com/ptrblck/27b4de4e291ffc0d85b33858d0bc8779">this</a> code and get a constant loss?
Which PyTorch version are you using? If you are using an older version (< 0.4.0), could you wrap your tensors into Variables and run it again? | 0 | 2018-11-17T14:01:21.906Z | https://discuss.pytorch.org/t/constant-segmentation-loss/29840/9 | Sorry for the delayed reply, but yes it should be fixed by now. Let us know, if you run into this issue again, please. But for 2D tensors, the transforms.ToPILImage() and transforms.ToTensor() are needed.
Yes, I see you point here. Yes, images as tensors should be 3D tensors. In case of 2D tensors, I’d recommend to up... | 426 | {'text': ['Just to make sure, you are running <a href="https://gist.github.com/ptrblck/27b4de4e291ffc0d85b33858d0bc8779">this</a> code and get a constant loss?\n\nWhich PyTorch version are you using? If you are using an older version (< 0.4.0), could you wrap your tensors into Variables and run it again?'], 'answer_... |
Pytorch reading tensors from file of tensors | I have some really big input tensors and I was running into memory issues while building them, so I read them one by one into a .pt file. As I run the script that generates and saves the file, the file gets bigger and bigger, so I am assuming that the tensors are saving correctly. Here is that code: … | 0 | 2020-09-27T04:43:27.046Z | Ho for that, the answer is definitely yes for the first and no for the second.
Our dataloaders under the hood do the exact same thing of loading things from the disk actually in some cases.
So the fact that memory is in ram or is read on the fly does not change at all how the training is going to … | 1 | 2020-10-05T22:19:11.017Z | https://discuss.pytorch.org/t/pytorch-reading-tensors-from-file-of-tensors/97579/10 | Ho for that, the answer is definitely yes for the first and no for the second.
Our dataloaders under the hood do the exact same thing of loading things from the disk actually in some cases.
So the fact that memory is in ram or is read on the fly does not change at all how the training is going to … rand_state ... | 1,442 | {'text': ['Ho for that, the answer is definitely yes for the first and no for the second.\n\nOur dataloaders under the hood do the exact same thing of loading things from the disk actually in some cases.\n\nSo the fact that memory is in ram or is read on the fly does not change at all how the training is going to &hell... |
Multinomial changing seed | I have a pretty standard model. I need it to be reproducible, so I use a random seed. But when I insert a multinomial operation anywhere in the training code, e.g.,
torch.ones(10).multinomial(num_samples=2, replacement=False)
It changes the output/performance of the model. | 0 | 2021-01-01T15:14:46.636Z | rand_state = torch.random.get_rng_state()
torch.random.manual_seed(torch.randn(1).data)
probas.multinomial(num_samples) # (temporarily) changes seed
torch.random.set_rng_state(rand_state) | 0 | 2021-01-04T19:54:58.406Z | https://discuss.pytorch.org/t/multinomial-changing-seed/107663/11 | Ho for that, the answer is definitely yes for the first and no for the second.
Our dataloaders under the hood do the exact same thing of loading things from the disk actually in some cases.
So the fact that memory is in ram or is read on the fly does not change at all how the training is going to … rand_state ... | 1,030 | {'text': ['rand_state = torch.random.get_rng_state()\n\ntorch.random.manual_seed(torch.randn(1).data)\n\nprobas.multinomial(num_samples) # (temporarily) changes seed\n\ntorch.random.set_rng_state(rand_state)'], 'answer_start': [1030]} |
How do I pass single jpg file as an argument to predicting function in pytorch? | Hello,
i have started to work with pytorch in order to use it in my AI classes project. I have been following ‘60 minutes blitz’ tutorial and everything went quite smooth. The only difference is that im using ImageFolder - not the CIFAR10 dataset. Training my model went great but i occured a proble… | 0 | 2020-05-17T12:22:25.138Z | yes. your image is gray. Maybe you can stack your image three times and feed it into your net. | 1 | 2020-05-17T15:34:46.314Z | https://discuss.pytorch.org/t/how-do-i-pass-single-jpg-file-as-an-argument-to-predicting-function-in-pytorch/81634/10 | Ho for that, the answer is definitely yes for the first and no for the second.
Our dataloaders under the hood do the exact same thing of loading things from the disk actually in some cases.
So the fact that memory is in ram or is read on the fly does not change at all how the training is going to … rand_state ... | 501 | {'text': ['yes. your image is gray. Maybe you can stack your image three times and feed it into your net.'], 'answer_start': [501]} |
JIT does not support parameter.requires_grad? | Hi,
is it a known limitation that jit.trace will ignore temporary requires_grad = False?
Here is an example:
# EXAMPLE 1
import torch
from torch import nn, jit
from torch.optim import SGD
inputs = torch.tensor([2.0], device="cuda")
model = nn.Linear(1, 1, bias=False).to("cuda")
optimizer = ... | 0 | 2021-03-08T00:45:19.344Z | For everyone wondering: requires_grad is not supposed to work. trace only tracks tensor operations, not attributes. See <a href="https://github.com/pytorch/pytorch/issues/53515#issuecomment-793188191" class="inline-onebox" rel="noopener nofollow ugc">[JIT] jit.trace does not support parameter.requires_grad? · Issue #53... | 0 | 2021-03-09T08:21:53.498Z | https://discuss.pytorch.org/t/jit-does-not-support-parameter-requires-grad/113998/3 | For everyone wondering: requires_grad is not supposed to work. trace only tracks tensor operations, not attributes. See <a href="https://github.com/pytorch/pytorch/issues/53515#issuecomment-793188191" class="inline-onebox" rel="noopener nofollow ugc">[JIT] jit.trace does not support parameter.requires_grad? · Issue #53... | 1,190 | {'text': ['For everyone wondering: requires_grad is not supposed to work. trace only tracks tensor operations, not attributes. See <a href="https://github.com/pytorch/pytorch/issues/53515#issuecomment-793188191" class="inline-onebox" rel="noopener nofollow ugc">[JIT] jit.trace does not support parameter.requires_grad? ... |
What happens when loss are negative? | Based on my understanding of back prop and gradient descent,
Loss is multiplied to gradient when taking a step with gradient descent.
So when gradient becomes negative, gradient descent takes a step in the opposite direction.
Such idea is well captured when implementing gradient ascent,
as it ca… | 8 | 2019-06-13T17:21:07.732Z | Hello Brandon!
[image] ljj7975:
Based on my understanding of back prop and gradient descent,
Loss is multiplied to gradient when taking a step with gradient descent.
So when gradient becomes negative, gradient descent takes a step in the opposite direction.
This isn’t true. All common opt… | 16 | 2019-06-13T17:57:19.787Z | https://discuss.pytorch.org/t/what-happens-when-loss-are-negative/47883/3 | For everyone wondering: requires_grad is not supposed to work. trace only tracks tensor operations, not attributes. See <a href="https://github.com/pytorch/pytorch/issues/53515#issuecomment-793188191" class="inline-onebox" rel="noopener nofollow ugc">[JIT] jit.trace does not support parameter.requires_grad? · Issue #53... | 1,039 | {'text': ['Hello Brandon!\n\n[image] ljj7975:\n\nBased on my understanding of back prop and gradient descent,\n\nLoss is multiplied to gradient when taking a step with gradient descent.\n\nSo when gradient becomes negative, gradient descent takes a step in the opposite direction.\n\nThis isn’t true. All common opt&hel... |
Model.eval() giving 'out of bounds' error | Hi everyone! Preparing to deploy a trained model to AWS (code below). Not sure how to configure Model.eval() to take in simulated user-input and provide an output.
<a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/5/1/519b102a55d65064c9d33d748f938f76cbed34f6.png" data-download-href="htt... | 0 | 2020-03-07T20:30:26.110Z | So, the solution is: came from the shape of your tensors being correct. In order for the tensor to be valid it must have a valid length, and in this case the same columns as the training data. So in our case we would want the tensor for categorical data to be (1,7) and (1,8) for numerical data
This… | 0 | 2020-03-09T04:51:29.085Z | https://discuss.pytorch.org/t/model-eval-giving-out-of-bounds-error/72422/6 | For everyone wondering: requires_grad is not supposed to work. trace only tracks tensor operations, not attributes. See <a href="https://github.com/pytorch/pytorch/issues/53515#issuecomment-793188191" class="inline-onebox" rel="noopener nofollow ugc">[JIT] jit.trace does not support parameter.requires_grad? · Issue #53... | 748 | {'text': ['So, the solution is: came from the shape of your tensors being correct. In order for the tensor to be valid it must have a valid length, and in this case the same columns as the training data. So in our case we would want the tensor for categorical data to be (1,7) and (1,8) for numerical data\n\nThis&hellip... |
A100 training slower than V100 | I have moved my model from V100 to A100 and instead of seeing an increase in speed these has been a significant slowdown from 14.2 it/sec to 10.06 it/sec.
cuda version 11.3
Pytorch version 1.9.0+cu111
I have been specifically using the code from GitHub repository (NLSPN)
[image]
<a href="https://github.com/zzangji... | 0 | 2021-12-10T11:30:08.975Z | dist.init_process_group(backend='nccl', init_method='env://',
world_size=args.num_gpus, rank=gpu)
torch.cuda.set_device(gpu)
# Prepare dataset
data = get_data(args)
data_train = data(args, 'train')
data_val = data(args, 'val')
sampler_train = Di… | 0 | 2021-12-11T18:06:45.441Z | https://discuss.pytorch.org/t/a100-training-slower-than-v100/139055/5 | dist.init_process_group(backend='nccl', init_method='env://',
world_size=args.num_gpus, rank=gpu)
torch.cuda.set_device(gpu)
# Prepare dataset
data = get_data(args)
data_train = data(args, 'train')
data_val = data(args, 'val')
sampler_train = Di… Per <a class="mention" href... | 2,112 | {'text': ['dist.init_process_group(backend='nccl', init_method='env://',\n\nworld_size=args.num_gpus, rank=gpu)\n\ntorch.cuda.set_device(gpu)\n\n# Prepare dataset\n\ndata = get_data(args)\n\ndata_train = data(args, 'train')\n\ndata_val = data(args, 'val')\n\nsampler_train = Di…'],... |
Weighted BCE loss with logits | I am dealing with imbalanced dataset. I want to use weighted BCE loss with logits. <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.BCEWithLogitsLoss" rel="noopener nofollow ugc">nn.BCEWithLogitsLoss </a> takes pos_weight argument.
From the docs:
pos_weight (<a href="https://pytorch.org/docs/stable/tensors.h... | 0 | 2022-04-26T14:58:38.285Z | Per <a class="mention" href="/u/kfrank">@KFrank</a>’s insightful observation above, it sounds like what you really have is a single binary classification, whereas an output shaped [16,2] and a pos_weight shaped [2] is meant for a two-class binary classification. In writing this I’m realizing the terminology is confusin... | 1 | 2022-04-27T12:41:52.002Z | https://discuss.pytorch.org/t/weighted-bce-loss-with-logits/150134/8 | dist.init_process_group(backend='nccl', init_method='env://',
world_size=args.num_gpus, rank=gpu)
torch.cuda.set_device(gpu)
# Prepare dataset
data = get_data(args)
data_train = data(args, 'train')
data_val = data(args, 'val')
sampler_train = Di… Per <a class="mention" href... | 1,349 | {'text': ['Per <a class="mention" href="/u/kfrank">@KFrank</a>’s insightful observation above, it sounds like what you really have is a single binary classification, whereas an output shaped [16,2] and a pos_weight shaped [2] is meant for a two-class binary classification. In writing this I’m realizing the terminology ... |
Model training with automatic mixed precision is not learning | Using mix precision, the loss flattens out after the first few iterations. The model trains fine when mix precision is not used.
Here is an example of how it’s implemented. I am using a ctc loss function
for i, _data in enumerate(train_loader):
spectrograms, labels, input_lengths, label_length… | 0 | 2020-04-07T19:10:37.027Z | Thanks, <a class="mention" href="/u/mcarilli">@mcarilli</a>. A few iterations in this context is about 1000 iterations.
And you’re right scaler.scale(loss).backward() should be outside the autocast context. The actual fix was due to how PyTorch did dynamic scaling. For my specific use-case, I had to set the growth_int... | 3 | 2020-04-08T02:50:26.408Z | https://discuss.pytorch.org/t/model-training-with-automatic-mixed-precision-is-not-learning/75756/4 | dist.init_process_group(backend='nccl', init_method='env://',
world_size=args.num_gpus, rank=gpu)
torch.cuda.set_device(gpu)
# Prepare dataset
data = get_data(args)
data_train = data(args, 'train')
data_val = data(args, 'val')
sampler_train = Di… Per <a class="mention" href... | 642 | {'text': ['Thanks, <a class="mention" href="/u/mcarilli">@mcarilli</a>. A few iterations in this context is about 1000 iterations.\n\nAnd you’re right scaler.scale(loss).backward() should be outside the autocast context. The actual fix was due to how PyTorch did dynamic scaling. For my specific use-case, I had to set t... |
Dtype error expected long but got float | Hi
I am trying to implement an Classifier but I got the following error. I have no idea why this is happening <a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/3/c/3c02b2121905195874d99bea30466a2804aad5b6.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/3c02b2121905... | 0 | 2020-03-27T11:19:36.339Z | Thank you for the reply. I found the solution on another website.
Simply change all the parameters of model to float by using net.float() before loss and convert the input to float. | 0 | 2020-03-28T05:47:26.891Z | https://discuss.pytorch.org/t/dtype-error-expected-long-but-got-float/74521/3 | Thank you for the reply. I found the solution on another website.
Simply change all the parameters of model to float by using net.float() before loss and convert the input to float. Yeah, that’s it! Thanks a lot for checking it out.
I experimented with different settings of cudnn and found that calling
torch.backend... | 1,984 | {'text': ['Thank you for the reply. I found the solution on another website.\n\nSimply change all the parameters of model to float by using net.float() before loss and convert the input to float.'], 'answer_start': [1984]} |
Training multiple models with one dataloader | Hi,
The bottleneck of my training routine is its data augmentation, which is “sufficiently” optimized. In order to speed-up hyperparameter search, I thought it’d be a good idea to train two models, each on another GPU, simultaneously using one dataloader.
As far as I understand, this could be seen… | 0 | 2021-11-05T18:16:11.539Z | Yeah, that’s it! Thanks a lot for checking it out.
I experimented with different settings of cudnn and found that calling
torch.backends.cudnn.deterministic = True
was sufficient to solve the issue.
Some additional info with respect to runtime per batch for future readers (ii and iii solve the i… | 0 | 2021-11-12T10:50:04.504Z | https://discuss.pytorch.org/t/training-multiple-models-with-one-dataloader/136117/10 | Thank you for the reply. I found the solution on another website.
Simply change all the parameters of model to float by using net.float() before loss and convert the input to float. Yeah, that’s it! Thanks a lot for checking it out.
I experimented with different settings of cudnn and found that calling
torch.backend... | 1,175 | {'text': ['Yeah, that’s it! Thanks a lot for checking it out.\n\nI experimented with different settings of cudnn and found that calling\n\ntorch.backends.cudnn.deterministic = True\n\nwas sufficient to solve the issue.\n\nSome additional info with respect to runtime per batch for future readers (ii and iii solve the i&... |
Dataset input Nan but fine on index | <a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/c/e/ce3b73d32bebb5e0b9d6f18c08782be18ad9510d.png" data-download-href="https://discuss.pytorch.org/uploads/default/ce3b73d32bebb5e0b9d6f18c08782be18ad9510d" title="image">[image]</a>
I get the message as below when I’m training WideResNet... | 0 | 2021-05-09T14:36:11.463Z | Thanks for the update.
Could you execute these runs and see, if the behavior changes, as a sync wouldn’t be strictly necessary, but would help us to isolate is an internal method is broken:
add synchronizations in the train_data loop before and after each line of code
set non_blocking=False
reru… | 0 | 2021-05-10T23:25:37.862Z | https://discuss.pytorch.org/t/dataset-input-nan-but-fine-on-index/120733/5 | Thank you for the reply. I found the solution on another website.
Simply change all the parameters of model to float by using net.float() before loss and convert the input to float. Yeah, that’s it! Thanks a lot for checking it out.
I experimented with different settings of cudnn and found that calling
torch.backend... | 492 | {'text': ['Thanks for the update.\n\nCould you execute these runs and see, if the behavior changes, as a sync wouldn’t be strictly necessary, but would help us to isolate is an internal method is broken:\n\nadd synchronizations in the train_data loop before and after each line of code\n\nset non_blocking=False\n\nreru&... |
DDP with multiple models | Hi,
I’m trying to train two models A and B on 4 GPUs, each being trained on 2 GPUs (and thus DDP is needed). The two models are independent, but they need to exchange some information during training (not gradients), hence I would like to execute a single command torch.distributed.launch so that th… | 0 | 2021-07-06T08:21:17.630Z | You can launch 4 processes (1 per GPU) and initialize a process group of world_size 4. You can use this process group to exchange data between A and B.
Then for each model, create a new subprocess group using <a href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.new_group" class="inline-onebox" r... | 1 | 2021-07-06T23:42:36.540Z | https://discuss.pytorch.org/t/ddp-with-multiple-models/125923/2 | You can launch 4 processes (1 per GPU) and initialize a process group of world_size 4. You can use this process group to exchange data between A and B.
Then for each model, create a new subprocess group using <a href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.new_group" class="inline-onebox" r... | 1,600 | {'text': ['You can launch 4 processes (1 per GPU) and initialize a process group of world_size 4. You can use this process group to exchange data between A and B.\n\nThen for each model, create a new subprocess group using <a href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.new_group" class="inl... |
Inconsistency During Inference | I have trained several models and would like to compare their performance on a single image. The problem is that running the same model (let’s call it A) produces different results. I have set the model to evaluation mode (i.e. A = myModel.eval()) and I am using “with torch.no_grad()” yet every time… | 0 | 2021-02-23T15:36:03.948Z | [image] zhuser:
I do agree that this has to be the case and I would like to attract your attention to the point that printing out the auto_grad status of the different layers of the model using “.named_parameters()” and then applying “.requires_grad” shows “requires_grad=True”. Given that the wh… | 0 | 2021-02-25T17:58:21.964Z | https://discuss.pytorch.org/t/inconsistency-during-inference/112736/9 | You can launch 4 processes (1 per GPU) and initialize a process group of world_size 4. You can use this process group to exchange data between A and B.
Then for each model, create a new subprocess group using <a href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.new_group" class="inline-onebox" r... | 1,250 | {'text': ['[image] zhuser:\n\nI do agree that this has to be the case and I would like to attract your attention to the point that printing out the auto_grad status of the different layers of the model using “.named_parameters()” and then applying “.requires_grad” shows “requires_grad=True”. Given that the wh…']... |
Training specific examples from CIFAR 100 | I have been working on CIFAR 100 torchvision built in dataset. I wanted to train my model for images with some specific labels and want to remove other training examples. How do do that? | 0 | 2020-05-21T15:36:29.450Z | Here is an alternative. Use <a href="https://course.fast.ai/datasets" rel="nofollow noopener">fastai_datasets</a> to get your CIFAR100 dataset. The reason being it provides data in Imagenet form i.e. every class/label image are in their separate folders and you can just delete the folders/classes that you do not want.
... | 2 | 2020-05-21T22:09:32.241Z | https://discuss.pytorch.org/t/training-specific-examples-from-cifar-100/82339/12 | You can launch 4 processes (1 per GPU) and initialize a process group of world_size 4. You can use this process group to exchange data between A and B.
Then for each model, create a new subprocess group using <a href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.new_group" class="inline-onebox" r... | 756 | {'text': ['Here is an alternative. Use <a href="https://course.fast.ai/datasets" rel="nofollow noopener">fastai_datasets</a> to get your CIFAR100 dataset. The reason being it provides data in Imagenet form i.e. every class/label image are in their separate folders and you can just delete the folders/classes that you do... |
C++ extension performance issue | I’m writing a C++ extension to optimize the performance of a custom function. As a starting point I’m only focused on the CPU version.
My optimized version should in principle be faster by ~30%.
However, it runs in about the same time. Below are my code:
Unoptimized version:
torch::Tensor symsum… | 0 | 2021-12-16T20:52:14.230Z | I tried implementing AVX version. On my PC it runs 3 times faster
Duration symsum_avx: 0.127393 s
Duration symsum_forward_unoptimized: 0.370589 s
EDIT: changed loading and storing to unaligned versions as suggested
here’s the code, if you’re interested
torch::Tensor symsum_avx(torch::Tensor inp… | 1 | 2021-12-18T16:04:58.466Z | https://discuss.pytorch.org/t/c-extension-performance-issue/139572/6 | I tried implementing AVX version. On my PC it runs 3 times faster
Duration symsum_avx: 0.127393 s
Duration symsum_forward_unoptimized: 0.370589 s
EDIT: changed loading and storing to unaligned versions as suggested
here’s the code, if you’re interested
torch::Tensor symsum_avx(torch::Tensor inp… Sorry, I do... | 2,484 | {'text': ['I tried implementing AVX version. On my PC it runs 3 times faster\n\nDuration symsum_avx: 0.127393 s\n\nDuration symsum_forward_unoptimized: 0.370589 s\n\nEDIT: changed loading and storing to unaligned versions as suggested\n\nhere’s the code, if you’re interested\n\ntorch::Tensor symsum_avx(torch::Tensor in... |
Slow Forward Time | I am implementing a network for point cloud. However, the forward time for feature_extraction Module is much slower than the detection module. Below is the network implementation. Is there a way to speed up the implementation?
class ResUNet2(ME.MinkowskiNetwork):
NORM_TYPE = None
BLOCK_NORM_TYP… | 0 | 2020-04-03T08:27:44.952Z | Sorry, I don’t know if the function you need exists. But I can show you what I will do:
coords_A = coords.view(coords.shape[0], 1, 3).repeat(1, coords.shape[0], 1)
coords_B = coords.view(1, coords.shape[0], 3).repeat(coords.shape[0], 1, 1)
coords_confusion = torch.stack((coords_A, coords_B), dim=2)… | 0 | 2020-04-03T14:46:21.681Z | https://discuss.pytorch.org/t/slow-forward-time/75207/9 | I tried implementing AVX version. On my PC it runs 3 times faster
Duration symsum_avx: 0.127393 s
Duration symsum_forward_unoptimized: 0.370589 s
EDIT: changed loading and storing to unaligned versions as suggested
here’s the code, if you’re interested
torch::Tensor symsum_avx(torch::Tensor inp… Sorry, I do... | 1,551 | {'text': ['Sorry, I don’t know if the function you need exists. But I can show you what I will do:\n\ncoords_A = coords.view(coords.shape[0], 1, 3).repeat(1, coords.shape[0], 1)\n\ncoords_B = coords.view(1, coords.shape[0], 3).repeat(coords.shape[0], 1, 1)\n\ncoords_confusion = torch.stack((coords_A, coords_B), dim=2)&... |
Converting pre Pytorch1.0 code | I have a PyTorch code written in PyTorch 0.4 and I want to upgrade it. The major part where I am getting stuck is with the CUDA kernels. I know that I need to use ATen library but I do not think it is very properly documented and hence as such I am completely stuck while trying to do the upgrade. Th… | 1 | 2020-06-25T07:35:31.836Z | Yeah. If that bothers you in the overall picture, Probably go with a custom kernel.
Best regards
Thomas | 1 | 2020-06-27T22:52:47.471Z | https://discuss.pytorch.org/t/converting-pre-pytorch1-0-code/86831/10 | I tried implementing AVX version. On my PC it runs 3 times faster
Duration symsum_avx: 0.127393 s
Duration symsum_forward_unoptimized: 0.370589 s
EDIT: changed loading and storing to unaligned versions as suggested
here’s the code, if you’re interested
torch::Tensor symsum_avx(torch::Tensor inp… Sorry, I do... | 620 | {'text': ['Yeah. If that bothers you in the overall picture, Probably go with a custom kernel.\n\nBest regards\n\nThomas'], 'answer_start': [620]} |
Training with batch_size = 1, all outputs are the same and trains poorly | I am trying to train a network to output target values (between 0 and 1). I cannot batch my inputs, so I am using a batch size of 1. Since I don’t want the sum of the loss gradients of each example, but the gradient of the average loss, I am adding item_loss/num_items for each item to end up with an… | 0 | 2020-11-12T00:42:29.409Z | I cleaned up and modularized my model and solved the case with multiple samples all having the same output. It had to do with me passing the original inputs into a linear layer instead of the convolved inputs.
Since all the inputs contain similar sets of elements, except the elements are related in… | 0 | 2020-11-16T04:57:16.680Z | https://discuss.pytorch.org/t/training-with-batch-size-1-all-outputs-are-the-same-and-trains-poorly/102477/10 | I cleaned up and modularized my model and solved the case with multiple samples all having the same output. It had to do with me passing the original inputs into a linear layer instead of the convolved inputs.
Since all the inputs contain similar sets of elements, except the elements are related in… You could r... | 1,450 | {'text': ['I cleaned up and modularized my model and solved the case with multiple samples all having the same output. It had to do with me passing the original inputs into a linear layer instead of the convolved inputs.\n\nSince all the inputs contain similar sets of elements, except the elements are related in&hellip... |
Ensemble two features | I need to ensemble the features only that help in classification which I can extract it from two different models . So I need to remove the last layer first from each model then start to concatenate them … how can the ensemble method will be ? | 0 | 2021-12-06T04:50:45.671Z | You could replace the last linear layers with nn.Identity modules and create the ensemble as described in <a href="https://discuss.pytorch.org/t/custom-ensemble-approach/52024/4">this post</a>. | 1 | 2021-12-06T07:22:17.182Z | https://discuss.pytorch.org/t/ensemble-two-features/138620/2 | I cleaned up and modularized my model and solved the case with multiple samples all having the same output. It had to do with me passing the original inputs into a linear layer instead of the convolved inputs.
Since all the inputs contain similar sets of elements, except the elements are related in… You could r... | 1,034 | {'text': ['You could replace the last linear layers with nn.Identity modules and create the ensemble as described in <a href="https://discuss.pytorch.org/t/custom-ensemble-approach/52024/4">this post</a>.'], 'answer_start': [1034]} |
Custom color mapping in data loader for UNET image segmentation | Hi,
I have a 1000x1000 image and a mask of the same size with 5 color-coded classes (blue, red, dark green, light green, and pink).
<a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/c/a/ca933445e89256b8d643d14c4dbc9da9b26c34de.png" data-download-href="https://discuss.pytorch.org/upload... | 0 | 2021-05-20T18:24:40.454Z | Figured it out! The fill tool I was using on GIMP had anti-aliasing which added additional pixel values to the mask boundary in order to smooth the edges. Thank you. | 0 | 2021-06-04T19:48:39.180Z | https://discuss.pytorch.org/t/custom-color-mapping-in-data-loader-for-unet-image-segmentation/121876/12 | I cleaned up and modularized my model and solved the case with multiple samples all having the same output. It had to do with me passing the original inputs into a linear layer instead of the convolved inputs.
Since all the inputs contain similar sets of elements, except the elements are related in… You could r... | 503 | {'text': ['Figured it out! The fill tool I was using on GIMP had anti-aliasing which added additional pixel values to the mask boundary in order to smooth the edges. Thank you.'], 'answer_start': [503]} |
Indices returned by torch.topk is of wrong order | The indices return by torch.topk is strange.
In my option, if sorted=False, then the returned indices should be sorted, that is the elements in the indices are ascending.
However, in the following example, t2 has something wrong:
2144, 20, 104, 118, 123, 136, 137, 144, 171
>>> import tor… | 0 | 2020-03-25T03:34:06.063Z | If you don’t specify sorted=True, there is no guarantee of any order (sorted or original order).
[image] ShengweiAn:
Such as the non-determinism incurred by the parallelism.
But rerunning topk for servel times gave same results.
Yes, that’s most likely the reason at least for GPU runs. I’m … | 0 | 2020-03-28T04:55:01.594Z | https://discuss.pytorch.org/t/indices-returned-by-torch-topk-is-of-wrong-order/74305/9 | If you don’t specify sorted=True, there is no guarantee of any order (sorted or original order).
[image] ShengweiAn:
Such as the non-determinism incurred by the parallelism.
But rerunning topk for servel times gave same results.
Yes, that’s most likely the reason at least for GPU runs. I’m … No problem! Seem... | 1,336 | {'text': ['If you don’t specify sorted=True, there is no guarantee of any order (sorted or original order).\n\n[image] ShengweiAn:\n\nSuch as the non-determinism incurred by the parallelism.\n\nBut rerunning topk for servel times gave same results.\n\nYes, that’s most likely the reason at least for GPU runs. I’m &helli... |
Find derivative of model's paremeters wrt to a vector | Hello,
I am trying to find a double derivative using the torch.autograd.grad fucntion. It requires a step where I have to find the double derivative of the model’s parameters wrt to a vector (in this case A). Can someone please guide me how to do so?
# reproduce error
import torch
import torch.nn … | 0 | 2021-06-14T20:30:47.190Z | No problem! Seems like since you need to update A later using this computed gradient, it actually seems like the vjp IS what you want here. In that case, I wouldn’t worry about the “entire Jacobian” to much and no further action is needed apart from just using .grad(delL_delWo, A), and using a grad_… | 0 | 2021-06-15T15:01:57.028Z | https://discuss.pytorch.org/t/find-derivative-of-models-paremeters-wrt-to-a-vector/124104/4 | If you don’t specify sorted=True, there is no guarantee of any order (sorted or original order).
[image] ShengweiAn:
Such as the non-determinism incurred by the parallelism.
But rerunning topk for servel times gave same results.
Yes, that’s most likely the reason at least for GPU runs. I’m … No problem! Seem... | 972 | {'text': ['No problem! Seems like since you need to update A later using this computed gradient, it actually seems like the vjp IS what you want here. In that case, I wouldn’t worry about the “entire Jacobian” to much and no further action is needed apart from just using .grad(delL_delWo, A), and using a grad_…'... |
Multiple nodes with Pytorch (Only CPUs) | Hi,
For single node, I set
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
and the size is as input parameter.
However, with multiple nodes, we have to set differently. But I did now know how to set it?
For example, I know the node names with 4 nodes as ... | 0 | 2020-06-10T08:31:27.830Z | Thanks for reporting, it’s an error in the doc, I think it needs to be:
rpc.ProcessGroupRpcBackendOptions(
num_send_recv_threads=16,
rpc_timeout=datetime.timedelta(seconds=1000)
)
Let me try. | 1 | 2020-06-10T16:07:13.167Z | https://discuss.pytorch.org/t/multiple-nodes-with-pytorch-only-cpus/84865/13 | If you don’t specify sorted=True, there is no guarantee of any order (sorted or original order).
[image] ShengweiAn:
Such as the non-determinism incurred by the parallelism.
But rerunning topk for servel times gave same results.
Yes, that’s most likely the reason at least for GPU runs. I’m … No problem! Seem... | 613 | {'text': ['Thanks for reporting, it’s an error in the doc, I think it needs to be:\n\nrpc.ProcessGroupRpcBackendOptions(\n\nnum_send_recv_threads=16,\n\nrpc_timeout=datetime.timedelta(seconds=1000)\n\n)\n\nLet me try.'], 'answer_start': [613]} |
My numpy and pytorch codes have totally different results | I wanted to calculate the sum of 1st to K-th power of an array and equally calculate the sum of 1st to k-th power of a tensor. I found out that the following codes and their results are totally different and I don’t know why.
I debugged the code and I know that the results are equal in the first ro… | 0 | 2019-02-16T21:26:15.228Z | The error is regarding the shallow/deep copy.
If you add a copy() after assigning the numpy arrays, your code runs fine with an error of ~2e-7:
adj_k_prob = adj_prob.copy()
adj_k_pow = adj_prob.copy() | 1 | 2019-02-18T20:32:17.679Z | https://discuss.pytorch.org/t/my-numpy-and-pytorch-codes-have-totally-different-results/37388/10 | The error is regarding the shallow/deep copy.
If you add a copy() after assigning the numpy arrays, your code runs fine with an error of ~2e-7:
adj_k_prob = adj_prob.copy()
adj_k_pow = adj_prob.copy() .eval() changes the behavior of some modules. For example, Batchnorm uses the saved statistics instead of the curre... | 1,618 | {'text': ['The error is regarding the shallow/deep copy.\n\nIf you add a copy() after assigning the numpy arrays, your code runs fine with an error of ~2e-7:\n\nadj_k_prob = adj_prob.copy()\n\nadj_k_pow = adj_prob.copy()'], 'answer_start': [1618]} |
Possible reasons for regression problem with almost same prediction result | I have a model that predict four similar category numerical data (target values).
I use a CNN to extract feature for each category target value, before FC layers, I also add two values to the flattened extracted feature maps.
This works in Keras. I rewrite my codes with Pytorch. But the result of … | 0 | 2020-01-14T06:10:17.967Z | .eval() changes the behavior of some modules. For example, Batchnorm uses the saved statistics instead of the current batch’s ones, dropout becomes an identity, etc | 1 | 2020-01-18T05:45:50.127Z | https://discuss.pytorch.org/t/possible-reasons-for-regression-problem-with-almost-same-prediction-result/66596/10 | The error is regarding the shallow/deep copy.
If you add a copy() after assigning the numpy arrays, your code runs fine with an error of ~2e-7:
adj_k_prob = adj_prob.copy()
adj_k_pow = adj_prob.copy() .eval() changes the behavior of some modules. For example, Batchnorm uses the saved statistics instead of the curre... | 1,014 | {'text': ['.eval() changes the behavior of some modules. For example, Batchnorm uses the saved statistics instead of the current batch’s ones, dropout becomes an identity, etc'], 'answer_start': [1014]} |
Combination losses: two backward or one backward? | Hello all, I have an architecture likes
[Untitled%20Diagram%20(2)]
The input is fed to Gen network to generate a fake image (fakeA). I use L1 loss to compute the different between input and fakeA. I called it is lossGen.
The fakeA then is fed to the segmentation network to create a predA. I used … | 0 | 2019-02-21T18:53:13.581Z | Both approaches should compute the same gradients.
In the second approach you would need to call lossGen.backward(retain_graph=True), otherwise the intermediate values will be cleared and you’ll get an error calling lossSeg.backward().
However, currently you are using lossSeg to calculate gradient… | 3 | 2019-02-21T20:18:02.628Z | https://discuss.pytorch.org/t/combination-losses-two-backward-or-one-backward/37887/2 | The error is regarding the shallow/deep copy.
If you add a copy() after assigning the numpy arrays, your code runs fine with an error of ~2e-7:
adj_k_prob = adj_prob.copy()
adj_k_pow = adj_prob.copy() .eval() changes the behavior of some modules. For example, Batchnorm uses the saved statistics instead of the curre... | 370 | {'text': ['Both approaches should compute the same gradients.\n\nIn the second approach you would need to call lossGen.backward(retain_graph=True), otherwise the intermediate values will be cleared and you’ll get an error calling lossSeg.backward().\n\nHowever, currently you are using lossSeg to calculate gradient&hell... |
Weights loaded incorrectly | I was f’tuning VGG network for style transfer (Gatys et al, 2015) and kept getting results that made no sense, so after some debugging it turned out I uploaded the weights incorrectly. Can someone point out my error, because I tried it on other models, and it seemed to work?
import vgg16
pretrained… | 0 | 2019-10-15T14:38:47.991Z | Hi,
This does not work because par = foo is assigning to the object foo the new name of par. What was in par before is deleted.
If you want to write in the par Tensor, you need to use an inplace operation like par.copy_(foo) to copy into the Tensor that is in par.
Also the .to() operation is alwa… | 0 | 2019-10-15T15:30:59.186Z | https://discuss.pytorch.org/t/weights-loaded-incorrectly/58298/4 | Hi,
This does not work because par = foo is assigning to the object foo the new name of par. What was in par before is deleted.
If you want to write in the par Tensor, you need to use an inplace operation like par.copy_(foo) to copy into the Tensor that is in par.
Also the .to() operation is alwa… You need to... | 1,356 | {'text': ['Hi,\n\nThis does not work because par = foo is assigning to the object foo the new name of par. What was in par before is deleted.\n\nIf you want to write in the par Tensor, you need to use an inplace operation like par.copy_(foo) to copy into the Tensor that is in par.\n\nAlso the .to() operation is alwa&he... |
Triplet loss stuck at margin alpha value | Hi everyone
I’m struggling with the triplet loss convergence. I’m trying to do a face verification (1:1 problem) with a minimum computer calculation (since I don’t have GPU).
So I’m using the facenet-pytorch model InceptionResnetV1 pretrained with vggface2 (casia-webface gives the same results).
… | 0 | 2022-02-06T22:11:43.300Z | You need to do extra inference pass before training, gather outputs, sort triplets out, then run training with these triplets. And this is rather complicated part, since every time you update weights your outputs change and all old outputs calculated before for combining triplet pairs become outdate… | 0 | 2022-02-08T06:47:18.811Z | https://discuss.pytorch.org/t/triplet-loss-stuck-at-margin-alpha-value/143425/8 | Hi,
This does not work because par = foo is assigning to the object foo the new name of par. What was in par before is deleted.
If you want to write in the par Tensor, you need to use an inplace operation like par.copy_(foo) to copy into the Tensor that is in par.
Also the .to() operation is alwa… You need to... | 987 | {'text': ['You need to do extra inference pass before training, gather outputs, sort triplets out, then run training with these triplets. And this is rather complicated part, since every time you update weights your outputs change and all old outputs calculated before for combining triplet pairs become outdate…'... |
TypeError: object() takes no parameters | Hi all,
I have two folders, the first folder contain Original Images and the second folder contain the same Images with noise and I have no label. When I added path and loading images from paths in the code I get this error:
1 train_dataset = Dataset(path_input_1 = path_input_1
----> 2 ,path_inpu… | 0 | 2020-07-07T18:39:42.803Z | Hi <a class="mention" href="/u/nikronic">@Nikronic</a>
Thank you very much, everything works very well. | 1 | 2020-07-08T15:51:55.278Z | https://discuss.pytorch.org/t/typeerror-object-takes-no-parameters/88277/10 | Hi,
This does not work because par = foo is assigning to the object foo the new name of par. What was in par before is deleted.
If you want to write in the par Tensor, you need to use an inplace operation like par.copy_(foo) to copy into the Tensor that is in par.
Also the .to() operation is alwa… You need to... | 618 | {'text': ['Hi <a class="mention" href="/u/nikronic">@Nikronic</a>\n\nThank you very much, everything works very well.'], 'answer_start': [618]} |
Quantization/QAT causing jit.script to fail | Hello
I’m trying to do QAT -> Torchscript but am getting an error.
My model is
<details><summary>Click here</summary>import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.quantization import QuantStub, DeQuantStub
def SeperableConv2d(in_channels, out_channels, kerne... | 0 | 2020-07-09T07:27:51.326Z | Updating to torch nightly from torch 1.5.1 fixed the issue! (did not try 1.6) | 1 | 2020-08-12T03:13:28.152Z | https://discuss.pytorch.org/t/quantization-qat-causing-jit-script-to-fail/88514/13 | Updating to torch nightly from torch 1.5.1 fixed the issue! (did not try 1.6) in your code , it’s for creating val and train set .
this is one way of spliting your dataset to train and val set.
if you set sampler to None, dataloader choose samples from all semple in your dataset.
[image] muhammedcanpirincci:
i want... | 1,444 | {'text': ['Updating to torch nightly from torch 1.5.1 fixed the issue! (did not try 1.6)'], 'answer_start': [1444]} |
New batch in each epoch | Hello. I am trying to use augmented and not augmented dataset in each epoch(for example: augmented in one epoch not augmented in different epoch) but i couldn’t figure out how to do it. My approach was loading DataLoader in each epoch again and again but I think it’s wrong. Because when i print inde… | 0 | 2022-01-22T13:22:28.777Z | in your code , it’s for creating val and train set .
this is one way of spliting your dataset to train and val set.
if you set sampler to None, dataloader choose samples from all semple in your dataset.
[image] muhammedcanpirincci:
i want my program to select batch that doesnt have augmented … | 1 | 2022-01-22T16:22:48.232Z | https://discuss.pytorch.org/t/new-batch-in-each-epoch/142302/6 | Updating to torch nightly from torch 1.5.1 fixed the issue! (did not try 1.6) in your code , it’s for creating val and train set .
this is one way of spliting your dataset to train and val set.
if you set sampler to None, dataloader choose samples from all semple in your dataset.
[image] muhammedcanpirincci:
i want... | 800 | {'text': ['in your code , it’s for creating val and train set .\n\nthis is one way of spliting your dataset to train and val set.\n\nif you set sampler to None, dataloader choose samples from all semple in your dataset.\n\n[image] muhammedcanpirincci:\n\ni want my program to select batch that doesnt have augmented &hel... |
How to do sort subsampling | Can any one help me with this question?
Lets say i have a 4x4 tensor and i want to do subsampling in the following way, so for each 2x2 block i put the smallest elements together,
then the second smallest elements, and so on, so the out put will be 4 tensors with size half of the input. Stride wil… | 0 | 2018-08-07T20:31:20.761Z | You are right! Thanks for pointing this out.
Here is a (hopefully) fixed version:
kh, kw = 2, 2
dh, dw = 2, 2
input = torch.randint(10, (1,2,6,6))
input_windows = input.unfold(2, kh, dh).unfold(3, kw, dw)
input_windows = input_windows.contiguous().view(*input_windows.size()[:-2], -1)
input_windows… | 0 | 2018-08-08T19:22:12.286Z | https://discuss.pytorch.org/t/how-to-do-sort-subsampling/22616/9 | Updating to torch nightly from torch 1.5.1 fixed the issue! (did not try 1.6) in your code , it’s for creating val and train set .
this is one way of spliting your dataset to train and val set.
if you set sampler to None, dataloader choose samples from all semple in your dataset.
[image] muhammedcanpirincci:
i want... | 384 | {'text': ['You are right! Thanks for pointing this out.\n\nHere is a (hopefully) fixed version:\n\nkh, kw = 2, 2\n\ndh, dw = 2, 2\n\ninput = torch.randint(10, (1,2,6,6))\n\ninput_windows = input.unfold(2, kh, dh).unfold(3, kw, dw)\n\ninput_windows = input_windows.contiguous().view(*input_windows.size()[:-2], -1)\n\ninp... |
Freeing gradients memory after optimizer step | I am training multiple models in a sequential way on the same GPU, and I need them to share the parameters after a given number of iterations. For GPU sonsumption optimization I need to free the gradients of each model at the end of each optimizer iteration. A simple solution is to set all gradients… | 0 | 2021-03-29T13:48:30.002Z | Hi,
Depending on the particular model and training loop, it may improve perf and not.
Note that a simpler way to do this is via the regular zero grad: model.zero_grad(set_to_none=True). | 2 | 2021-03-29T13:59:29.305Z | https://discuss.pytorch.org/t/freeing-gradients-memory-after-optimizer-step/116346/2 | Hi,
Depending on the particular model and training loop, it may improve perf and not.
Note that a simpler way to do this is via the regular zero grad: model.zero_grad(set_to_none=True). Hi,
You don’t have to do each parameter one by one, you can give all the params/grads as tuples.
You get all zeros because your fu... | 1,394 | {'text': ['Hi,\n\nDepending on the particular model and training loop, it may improve perf and not.\n\nNote that a simpler way to do this is via the regular zero grad: model.zero_grad(set_to_none=True).'], 'answer_start': [1394]} |
HVP w.r.t model parameters | Hi!
I have seen that within the 1.5.0 release, the possibility to compute HVP of a function has been added.
As far as I understand from the documentation, the HVP (as well as the VHP, VJP and so on) can be computed w.r.t. the input only, and not w.r.t. some other variable (such as, for instance, t… | 0 | 2020-05-31T00:15:43.077Z | Hi,
You don’t have to do each parameter one by one, you can give all the params/grads as tuples.
You get all zeros because your function f does not use the inputs x to compute the output.
You can do something like this to use the autograd API with torch.nn:
# Utilities to make nn.Module function… | 1 | 2020-05-31T22:03:38.189Z | https://discuss.pytorch.org/t/hvp-w-r-t-model-parameters/83520/4 | Hi,
Depending on the particular model and training loop, it may improve perf and not.
Note that a simpler way to do this is via the regular zero grad: model.zero_grad(set_to_none=True). Hi,
You don’t have to do each parameter one by one, you can give all the params/grads as tuples.
You get all zeros because your fu... | 885 | {'text': ['Hi,\n\nYou don’t have to do each parameter one by one, you can give all the params/grads as tuples.\n\nYou get all zeros because your function f does not use the inputs x to compute the output.\n\nYou can do something like this to use the autograd API with torch.nn:\n\n# Utilities to make nn.Module function&... |
Loss does not improve on training | Hi,
I implemented a model which fails to learn. Loss calculated for every epoch is exactly the same and so is the sequence of losses over batches for every epoch. I have been fumbling around with this for a couple of days and apparently stumbled over the reason for this behaviour now.
As it seems … | 0 | 2019-08-01T09:26:09.551Z | Thanks tom for your reply. The learning rate was indeed set to 1.0 which is a bit high I guess but maybe not yet rediculous. Switching the learning rate back to 0.01 alone is not enough as it seems because I’m still getting my nans then.
While I have still trouble getting a proper standard deviatio… | 0 | 2019-08-08T11:47:25.855Z | https://discuss.pytorch.org/t/loss-does-not-improve-on-training/52289/11 | Hi,
Depending on the particular model and training loop, it may improve perf and not.
Note that a simpler way to do this is via the regular zero grad: model.zero_grad(set_to_none=True). Hi,
You don’t have to do each parameter one by one, you can give all the params/grads as tuples.
You get all zeros because your fu... | 497 | {'text': ['Thanks tom for your reply. The learning rate was indeed set to 1.0 which is a bit high I guess but maybe not yet rediculous. Switching the learning rate back to 0.01 alone is not enough as it seems because I’m still getting my nans then.\n\nWhile I have still trouble getting a proper standard deviatio&hellip... |
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