Is there a Pytorch-internal procedure to detect NaNs in Tensors? Tensorflow has the tf.is_nan and the tf.check_numerics operations ... Does Pytorch have something similar,... moreIs there a Pytorch-internal procedure to detect NaNs in Tensors? Tensorflow has the tf.is_nan and the tf.check_numerics operations ... Does Pytorch have something similar, somewhere? I could not find something like this in the docs...I am looking specifically for a Pytorch internal routine, since I would like this to happen on the GPU as well as on the CPU. This excludes numpy - based solutions (like np.isnan(sometensor.numpy()).any()) .
I have a neural network written in PyTorch, that outputs some Tensor a on GPU. I would like to continue processing a with a highly efficient TensorFlow layer.
As far as I... moreI have a neural network written in PyTorch, that outputs some Tensor a on GPU. I would like to continue processing a with a highly efficient TensorFlow layer.
As far as I know, the only way to do this is to move a from GPU memory to CPU memory, convert to numpy, and then feed that into TensorFlow. A simplified example:
import torch import tensorflow as tf # output of some neural network written in PyTorch a = torch.ones((10, 10), dtype=torch.float32).cuda() # move to CPU / pinned memory c = a.to('cpu', non_blocking=True) # setup TensorFlow stuff (only needs to happen once) sess = tf.Session() c_ph = tf.placeholder(tf.float32, shape=c.shape) c_mean = tf.reduce_mean(c_ph) # run TensorFlow print(sess.run(c_mean, feed_dict={c_ph: c.numpy()}))
This is a bit far fetched maybe but is there a way to make it so that either
a never leaves GPU memory, or
a goes from GPU memory to Pinned Memory to GPU memory.
I attempted 2. in the code snipped above using non_blocking=True but I am not sure if it... less
I want to set some of my model frozen. Following the official docs:
with torch.no_grad():
linear = nn.Linear(1, 1)
... moreI want to set some of my model frozen. Following the official docs:
with torch.no_grad():
linear = nn.Linear(1, 1)
linear.eval()
print(linear.weight.requires_grad)
But it prints True instead of False. If I want to set the model in eval mode, what should I do?
I am reading through the documentation of PyTorch and found an example where they write
gradients = torch.FloatTensor()
y.backward(gradients)
print(x.grad)
where x was an initial... moreI am reading through the documentation of PyTorch and found an example where they write
gradients = torch.FloatTensor()
y.backward(gradients)
print(x.grad)
where x was an initial variable, from which y was constructed (a 3-vector). The question is, what are the 0.1, 1.0 and 0.0001 arguments of the gradients tensor ? The documentation is not very clear on that.
I have PyTorch installed in my machine but whenever I try to do the following-
from torchtext import data
from torchtext import datasets
I get the following... moreI have PyTorch installed in my machine but whenever I try to do the following-
from torchtext import data
from torchtext import datasets
I get the following error.
ImportError: No module named 'torchtext'
How can I install torchtext?
Is there any way, I can add simple L1/L2 regularization in PyTorch? We can probably compute the regularized loss by simply adding the data_loss with the reg_loss but is there any... moreIs there any way, I can add simple L1/L2 regularization in PyTorch? We can probably compute the regularized loss by simply adding the data_loss with the reg_loss but is there any explicit way, any support from PyTorch library to do it more easily without doing it manually?
I have two models that are in ONNX format. Both models are similar (both are pre-trained deep learning models, ex. ResNet50 models). The only difference between them is that the... moreI have two models that are in ONNX format. Both models are similar (both are pre-trained deep learning models, ex. ResNet50 models). The only difference between them is that the last layers are optimized/retrained for different data sets.
I want to merge the first k layers of these two models, as shown below. This should enhance the performance of inference.
To make my case clearer these are examples from other machine learning tools to implement this feature Ex. Pytorch, Keras.
I was looking for alternative ways to save a trained model in PyTorch. So far, I have found two alternatives.
torch.save() to save a model and torch.load() to load a... moreI was looking for alternative ways to save a trained model in PyTorch. So far, I have found two alternatives.
torch.save() to save a model and torch.load() to load a model.
model.state_dict() to save a trained model and model.load_state_dict() to load the saved model.
I have come across to this discussion where approach 2 is recommended over approach 1.
My question is, why the second approach is preferred? Is it only because torch.nn modules have those two function and we are encouraged to use them?
I am a little bit confused about the data augmentation performed in PyTorch. Now, as far as I know, when we are performing data augmentation, we are KEEPING our original dataset,... moreI am a little bit confused about the data augmentation performed in PyTorch. Now, as far as I know, when we are performing data augmentation, we are KEEPING our original dataset, and then adding other versions of it (Flipping, Cropping...etc). But that doesn't seem like happening in PyTorch. As far as I understood from the references, when we use data.transforms in PyTorch, then it applies them one by one. So for example:
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(, ),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(, ),
}
Here , for the training, we are first randomly cropping the image and resizing it to shape (224,224). Then we are taking these (224,224) images and horizontally flipping them. Therefore, our dataset is now... less
I have posted this question on Data Science StackExchange site since StackOverflow does not support LaTeX. Linking it here because this site is probably more appropriate.The... moreI have posted this question on Data Science StackExchange site since StackOverflow does not support LaTeX. Linking it here because this site is probably more appropriate.The question with correctly rendered LaTeX is here: https://datascience.stackexchange.com/questions/48062/pytorch-does-not-seem-to-be-optimizing-correctlyThe idea is that I am considering sums of sine waves with different phases. The waves are sampled with some sample rate s in the interval . I need to select phases in such a way, that the sum of the waves at any sample point is minimized.Below is the Python code. Optimization does not seem to be computed correctly.
import numpy as np
import torch
I want to return variable from function using return and after that call same function, but resume from return. It this possible? Example:
def abc(): return 5 return 6 var = abc()... moreI want to return variable from function using return and after that call same function, but resume from return. It this possible? Example:
def abc(): return 5 return 6 var = abc() # var = 5 ### var = abc() # var = 6
I need to run my python script under Azure Machine Learning, using python=3.6.8 (not the default 3.6.2). I am using the AML "PyTorch()" Estimator, setting the "conda_packages"... moreI need to run my python script under Azure Machine Learning, using python=3.6.8 (not the default 3.6.2). I am using the AML "PyTorch()" Estimator, setting the "conda_packages" arg to .
I am relying on this doc page for the PyTorch Estimator:
I expected to see python 3.6.8, since I specified that in the PyTorch Estimator's conda_packages arg.
I also tried moving the "python==3.6.8" from conda_packages to pip_packages, but received an error saying pip could not locate that package.
FYI, I have another package specified in pip_packages, and that does get installed correctly during this process. It seems like the value of the "conda_packages" arg is not being used (I can find no mention of a conda or python install error in... less
