I have a folder of images and a csv file with a label corresponding to each image. I am trying to train a pytorch model, the problem is data loading seems to be a bottleneck,so i want to store the images to a HDf5 file and read them.
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Currently I am working on U-Net model. I have images and corresponding mask files in different folders and now I want to apply segmentation using U net but as my image data size is huge it doesn't fit into memory and my RAM crashes. I want to use ImageDataGenerator to load my dataset into batches. The problem is for using ImageDataGenerator, our data should have class wise images in separate folder but as we know for segmentation task our classes are each distinct pixels from our images. So, I am stuck at how to do it without having class-wise images directory structure?
I've been following the fastai course on machine learning. Got up to lesson four and thought I'd use what I've learned to create a model that predicts hand-written letters. The code they used to load their training dataset is as follows:
pets1 = DataBlock(blocks = (ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(seed=42),
get_y=using_attr(RegexLabeller(r'(.+)_\d+.jpg$'), 'name'))
pets1.summary(path/"images")
This works when you have image files but the dataset files I have are
emnist-letters-train-images-idx3-ubyte
emnist-letters-train-labels-idx1-ubyte
emnist-letters-test-images-idx3-ubyte
emnist-letters-test-labels-idx1-ubyte
I could extract all the images from those files but is there a way I can load the ubyte files into my program? The files have the same format as the MNIST digits dataset.
I want to train a LayoutLM through huggingface transformer, however I need help in creating the training data for LayoutLM from my pdf documents.
Multi page Document Classification can be effectively done by SequenceClassifiers. So here, is a strategy:
Convert Your PDF pages into images and make directory for each different category.
Iterate through all images and create a csv with image Path and label.
Then define your important features and encode the dataset.
Save it in your disk.
Load it back when you need it using Load_from_disk and dataloader.
I'm new to PyTorch; trying to implement a model I developed in TF and compare the results. The model is an Autoencoder model. The input data is a csv file including n samples each with m features (a n*m numerical matrix in a csv file). The targets (the labels) are in another csv file with the same format as the input file. I've been looking online but couldn't find a good documentation for reading non-image data from csv file with multiple labels. Any idea how can I read my data and iterate over it during training?
Thank you
Might you be looking for something like TabularDataset?
class
torchtext.data.TabularDataset(path, format, fields, skip_header=False, csv_reader_params={}, **kwargs)
Defines a Dataset of columns stored in CSV, TSV, or JSON format.
It will take a path to a CSV file and build a dataset from it. You also need to specify the names of the columns which will then become the data fields.
In general, all of implementations of torch.Dataset for specific types of data are located outside of pytorch in the torchvision, torchtext, and torchaudio libraries.
I am using Keras ImageDataGenerator with flow_from_directory.
For training data, each class folder has 10,000-20,000 jpg files each, with 13 classes. While training, every hour or so, before full data has been trained once, I get error from Keras indicating that a particular jpg file could not be read. Sure enough, I explore into the file location, and can see file is corrupt.
problem is this wastes lot of time, and on each error, I have to restart the training.
is there a way to modify Keras flow_from_directory or to ensure that corrupt files are skipped and training goes on without error
if 1 is not possible, is there a way to quickly(I have 1.1 TB of data) verify corrput files in all class folders and remove them /delete them from those folders, before starting the training.