Switching backends

The default backend for this package is Tensorflow/Keras, but a full PyTorch backend is also included, with a dedicated TFRecord reader/writer that ensures saved image tiles can be served to both Tensorflow and PyTorch models in cross-compatible fashion.

If using the Tensorflow backend, PyTorch does not need to be installed; the reverse is true as well.

To switch backends, simply set the environmental variable SF_BACKEND equal to either torch or tensorflow:

export SF_BACKEND=torch

TFRecord DataLoader

In addition to using the built-in training tools, you can use tiles that have been extracted with Slideflow with completely external projects. The slideflow.Dataset class includes both torch() and tensorflow() functions to prepare a DataLoader or Tensorflow tf.data.Dataset instance that interleaves and processs images from stored TFRecords.

from slideflow import Project

P = Project('/project/path', ...)
dts = P.dataset(tile_px=299, tile_um=302, filters=None)

If you want to perform any balancing, use the slideflow.Datset.balance() method:

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

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
)

The returned dataloader can then be used directly with your external PyTorch applications.