dataiter | Python classes for data manipulation
kandi X-RAY | dataiter Summary
kandi X-RAY | dataiter Summary
[Downloads] Dataiter currently includes the following classes. DataFrame is a class for tabular data similar to R’s data.frame or pandas.DataFrame. It is under the hood a dictionary of NumPy arrays and thus capable of fast vectorized operations. You can consider this to be a light-weight alternative to Pandas with a simple and consistent API. Performance-wise Dataiter relies on NumPy and Numba and is likely to be at best comparable to Pandas. ListOfDicts is a class useful for manipulating data from JSON APIs. It provides functionality similar to libraries such as Underscore.js, with manipulation functions that iterate over the data and return a shallow modified copy of the original. attd.AttributeDict is used to provide convenient access to dictionary keys. GeoJSON is a simple wrapper class that allows reading a GeoJSON file into a DataFrame and writing a data frame to a GeoJSON file. Any operations on the data are thus done with methods provided by the data frame class. Geometry is read as-is into the "geometry" column, but no special geometric operations are currently supported.
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Top functions reviewed by kandi - BETA
- Aggregate columns
- Determines if a NumPy array is used
- Convert to boolean
- Calculate the sum of the values
- Compute True if x is True
- Join two columns
- Split the DataFrame according to the given criteria
- Iterate the columns of the DataFrame
- Return NaT value
- True if this is an integer
- Calculate the sum
- Filter rows in the table
- Calculate the minimum value
- Perform inner join
- Compute mode of group
- Return the dtype of the data type
- Filter out rows from rows
- Boolean aggregation function
- Read features from a JSON file
- Compute the quantile of x
- Calculate the mean of the data
- Calculate the nth element of x
- Calculate the median function
- Join two collections
- Write the geometry to a file
- Aggregate the values for each group
- Join two DataFrames
dataiter Key Features
dataiter Examples and Code Snippets
Community Discussions
Trending Discussions on dataiter
QUESTION
I was looking at the activation maps of vgg19 in pytorch. I found that all the values of the maps are positive even before I applied the ReLU.
This seems very strange to me... If this would be correct (could be that I not used the register_forward_hook method correctly?) why would one then apply ReLu at all?
This is my code to produce this:
...ANSWER
Answered 2022-Feb-22 at 04:04You should clone the output in
QUESTION
I'm trying to convert my tensorflow code to pytorch.
Simply speaking, it estimates 7 values (number) from images using CNN.(regressor)
The backbone network is vgg16 with pretrained weights, I'd like to convert last fcl (actually due to ImageNet dataset, the last fcl output is 1000 classes), to (4096 x 4096), and add more fcls.
before :
vgg last fcl (4096 x 1000)
after:
vgg last fcl (change to 4096 x 4096)
----add fcl1 (4096 x 4096)
----add fcl2 (4096 x 2048)
└ add fclx (2048 x 3)
└ add fclq (2048 x 4)
: fcl2 is connected to two different tensors, with size of 3 and 4
Here, I tried to do it with only one image (for just debugging) and GT values (7 values) with L2 Loss. If I do that using Tensorflow, the loss decreases drastically, and When I Infer an image, it gives almost similar values to GT.
However, If I try to do it using Pytorch, It looks like training doesn't work well.
I guess the loss should sharply decrease while training (almost for every iteration)
What's the problem?
- The loss is actually |x-x'|^2 + b|q-q'|^2, well-known as L2-norm used in PoseNet(Kendall, 2015). x has three values of position and q has four values of quaternion(rotation). b is the hyperparameter determined by user.
ANSWER
Answered 2021-Sep-10 at 12:03Under my test .cpu() does not affects BP
I noticed that you added a .cpu() to the final loss, which PyTorch just can't pass the gradient from CPU to GPU (I guess a new comutational graph is created). Just remove the .cpu() in the PoseLoss and remain all tensors on GPU. Also the Variable API has been needless since PyTorch supported automatic marking of leaf node of computation graph.
QUESTION
from torchvision import datasets, transforms
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,)),])
trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True) # A
trainloader = torch.utils.data.DataLoader(trainset.train_data, batch_size=64, shuffle=True) # B
dataiter = iter(trainloader)
images, labels = dataiter.next() # A
images = dataiter.next() # B
images.shape
ANSWER
Answered 2021-Sep-09 at 07:40The second dimension describes the color channels which for grayscale is 1. RGB images would have 3 channels (red, green and blue) and would look something like 64, 3, W, H.
So when working with CNNs your data normally has to be in shape batchsize, channels, width, height therefore 64, 1, 28, 28 is correct.
QUESTION
I want to get through Fashion_Mnist data, I would like to see the output gradient which might be mean squared sum between first and second layer
My code first below
...ANSWER
Answered 2021-May-30 at 12:28The error is caused by the number of samples in the dataset and the batch size.
In more detail, the training MNIST dataset includes 60,000 samples, your current batch_size is 128 and you will need 60000/128=468.75 loops to finish training on one epoch. So the problem comes from here, for 468 loops, your data will have 128 samples but the last loop just contains 60000 - 468*128 = 96 samples.
To solve this problem, I think you need to find the suitable batch_size and the number of neural in your model as well.
I think it should work for computing loss
QUESTION
I am working on the pytorch to learn.
And There is a question how to check the output gradient by each layer in my code.
My code is below
...ANSWER
Answered 2021-May-29 at 11:31Well, this is a good question if you need to know the inner computation within your model. Let me explain to you!
So firstly when you print the model variable you'll get this output:
QUESTION
I am new to pytorch and I am following a tutorial but when i try to modify the code to use 64x64x3 images instead of 32x32x3 images, i get a buch of errors. Here is the code from the tutorial:
...ANSWER
Answered 2021-May-02 at 11:41I think this should work because after performing 2nd Pooling operation the output feature map is coming N x C x 13 x 13
self.fc1 = nn.Linear(16 * 13 * 13, 120)
x = x.view(-1, 16 * 13 * 13)
QUESTION
I have the following error in my training loop and I don't really understand what the issue is. I am currently in the process of writing this code so stuff isn't final but I cannot figure out what this problem is.
I have tried googling the error and read some of the answers but still couldn't seem to understand the crux of the issue.
Dataset and Dataloader (X and Y are already given to me, they are both [2000, 40, 1] tensors)
...ANSWER
Answered 2021-Apr-10 at 22:56 def forward(self, x_c, y_c):
return self.layers(x_c, y_c)
QUESTION
I've 2 folders.One image in 1 folder and another in another folder. I have to compare two images and find the dissimilarity but the code is written random folder.
...ANSWER
Answered 2020-Dec-03 at 12:18Just by assigning should_get_same_class=0 in __getitem__ function of your custom dataset class, InferenceSiameseNetworkDataset you can ensure that two images belong to different class/folder.
Secondly, You should not concatinate samples from two batches that may not satisfy your condition. You should use x0,x1,label2 = next(dataiter) under the scope of loop followed by concatination.
QUESTION
I follow the tutorials in pytorch.org
It occurs error：TensorBoard logging requires TensorBoard version 1.15 or above,but I have install TensorBoard already.
Here is the code:
ANSWER
Answered 2020-Aug-11 at 14:29Uninstall tensorflow, tensorboard, tensorboardx and tensorboard-plugin-wit.
Install only tensorboard with conda after that.
If this doesn't work, recreate your conda environment only with tensorboard. If you need tensorflow as well install it beforehand.
QUESTION
DISCLAIMER I know, this question has already asked multiple times, but i tried their solutions, none of them worked for me, so after all those effort, i can't find anything else and eventually i have to ask again.
I'm doing image classification with cnns (PYTORCH), i wan't to train it on GPU (nvidia gpu, compatible with cuda/cuda installed), i successfully managed to put net on it, but the problem is with data.
...ANSWER
Answered 2020-Jul-21 at 01:39Your images tensor is located on the CPU while your net is located on the GPU. Even when evaluating you want to make sure that your input tensors and model are located on the same device otherwise you will get tensor data type errors.
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