Custom Training Loops

To use *.tfrecords from extracted tiles in a custom training loop or entirely separate architecture (such as StyleGAN2 or YoloV5), Tensorflow tf.data.Dataset or PyTorch torch.utils.data.DataLoader objects can be created for easily serving processed images to your custom trainer.

TFRecord DataLoader

The slideflow.Dataset class includes functions to prepare a Tensorflow tf.data.Dataset or PyTorch torch.utils.data.DataLoader object to interleave and process images from stored TFRecords. First, create a Dataset object at a given tile size:

from slideflow import Project

P = Project('/project/path', ...)
dts = P.dataset(tile_px=299, tile_um=302)

If you want to perform any mini-batch balancing, use the .balance() method:

dts = dts.balance('HPV_status', strategy='category')

Other dataset options can also be applied at this step. For example, to clip the maximum number of tiles to take from a slide, use the .clip() method:

dts = dts.clip(500)

Finally, use the slideflow.Dataset.torch() method to create a DataLoader object:

dataloader = dts.torch(
    labels       = ...       # Your outcome label
    batch_size   = 64,       # Batch size
    num_workers  = 6,        # Number of workers reading tfrecords
    infinite     = True,     # True for training, False for validation
    augment      = True,     # Flip/rotate/compression augmentation
    standardize  = True,     # Standardize images: mean 0, variance of 1
    pin_memory   = False,    # Pin memory to GPUs
)

or the slideflow.Dataset.tensorflow() method to create a tf.data.Dataset:

dataloader = dts.tensorflow(
    labels       = ...       # Your outcome label
    batch_size   = 64,       # Batch size
    infinite     = True,     # True for training, False for validation
    augment      = True,     # Flip/rotate/compression augmentation
    standardize  = True,     # Standardize images
)

The returned dataloaders can then be used directly with your external applications. Read more about creating and using dataloaders.