I defined a three layer convolution layer(self.convs) ,the input tensor has the shape([100,10,24]) When I excuate x_convs = self.convs(Variable(torch.from_numpy(X).type(torch.FloatTensor))), it gives me the error The ConvBlock is defined as below The “forward” function has correct indent, so I cannot figure it out what is going on. Answer You are trying to call a ModuleList, which is a list (i.e. a
Tag: pytorch
Convert a list of tensors to tensors of tensors pytorch
I have this code: I am getting the error: ValueError: only one element tensors can be converted to Python scalars How can I convert the list of tensors to a tensor of tensors in pytorch? Answer Here is a solution:
PyTorch DataLoader shuffle
I did an experiment and I did not get the result I was expecting. For the first part, I am using I save trainloader.dataset.targets to the variable a, and trainloader.dataset.data to the variable b before training my model. Then, I train the model using trainloader. After the training is finished, I save trainloader.dataset.targets to the variable c, and trainloader.dataset.data to
How to generate accurate masks for an image from Mask R-CNN prediction in PyTorch?
I have trained a Mask RCNN network for instance segmentation of apples. I am able to load the weights and generate predictions for my test images. The masks being generated seem to be in the correct location, but the mask itself has no real form.. it just looks like a bunch of pixels Training is done based on the dataset
IndexError: tensors used as indices must be long, byte or bool tensors
I am getting this error only during the testing phase, but I do not face any problem in the training and validation phase. I get this error for the last line in the given code snippet. The code snippet looks like the one below, The “lab” is a tensor value and prints out the range in such a way, (Note*:
Gaussian filter in PyTorch
I am looking for a way to apply a Gaussian filter to an image (tensor) only using PyTorch functions. Using numpy, the equivalent code is The closest suggestion I found is based on this post: But it gives me the error NameError: name ‘gaussian_weights’ is not defined. How can I make it work? Answer There is a Pytorch class to
PyTorch RuntimeError: DataLoader worker (pid(s) 15332) exited unexpectedly
I am a beginner at PyTorch and I am just trying out some examples on this webpage. But I can’t seem to get the ‘super_resolution’ program running due to this error: RuntimeError: DataLoader worker (pid(s) 15332) exited unexpectedly I searched the Internet and found that some people suggest setting num_workers to 0. But if I do that, the program tells
RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same
This: Gives the error: RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same Answer You get this error because your model is on the GPU, but your data is on the CPU. So, you need to send your input tensors to the GPU. Or like this, to stay consistent with the rest of your code: The same
PyTorch embedding layer raises “expected…cuda…but got…cpu” error
I’m working on translating a PyTorch model from CPU (where it works) to GPU (where it so far doesn’t). The error message (clipped to the important bits) is as follows: Here is the full model definition: This type of error typically occurs when there is a tensor in the model that should be on GPU but is on CPU instead.
How to change activation layer in Pytorch pretrained module?
How to change the activation layer of a Pytorch pretrained network? Here is my code : Here is my output: Answer ._modules solves the problem for me.