Using a personnal dataset¶
In addition to the datasets embedded in HyperNOMAD, the user can choose to use a personnal dataset by specifying the following informations in the parameter file:
DATASET CUSTOM NUMBER_OF_CLASSES 20
When using a CUSTOM dataset, it is mandatory to provide HyperNOMAD with the number of classes. The user is also responsible of plugging the 3 datasets (training, validation and testing) into the blackbox. In the file blackbox.py, the lines 80 to 84 must be completed with the adequate information.
# Load the data print('> Preparing the data..') if dataset is not 'CUSTOM': dataloader = DataHandler(dataset, batch_size) image_size, number_classes = dataloader.get_info_data trainloader, validloader, testloader = dataloader.get_loaders() else: # Add here the adequate information image_size = None number_classes = None trainloader = None validloader = None testloader = None
The image size is a tuple of the form : (number_input_channels, length_image, width_image). In the case of MNIST, the image size is (1, 28, 28).
The trainload, validloader and testloader must be instances of ‘torch.utils.data.dataloader.DataLoader’.