def _predict_func(self,
func,
input,
batch_size=8224,
numpy=None,
eval_=True,
grads=False,
to_cpu=False,
num_workers=0,
is_dataloader=None,
**kwargs):
"""Get predictions from `input` which can be data or a DataLoader.
`func` can be anything and is not concatenated to `self.net` or `self.net.predict`.
This is different from `predict` and `predict_net` which both use call `self.net`.
"""
if is_data(input) or (is_dataloader is False):
dl = self.make_dataloader_predict(input,
batch_size,
shuffle=False,
num_workers=num_workers,
**kwargs)
elif is_dl(input) or (is_dataloader is True):
dl = input
else:
raise ValueError(
"Did not recognize data type. You can set `is_dataloader to `Ture`"
+ + " or `False` to force usage.")
to_cpu = numpy or to_cpu
preds = self._predict_func_dl(func, dl, numpy, eval_, grads, to_cpu)
return array_or_tensor(preds, numpy, input)
def fit(
self,
input,
target=None,
batch_size=256,
epochs=1,
callbacks=None,
verbose=True,
num_workers=0,
shuffle=True,
metrics=None,
val_data=None,
val_batch_size=8224,
**kwargs,
):
"""Fit model with inputs and targets.
Arguments:
input {np.array, tensor or tuple} -- Input (x) passed to net.
target {np.array, tensor or tuple} -- Target (y) passed to loss function.
Keyword Arguments:
batch_size {int} -- Elemets in each batch (default: {256})
epochs {int} -- Number of epochs (default: {1})
callbacks {list} -- list of callbacks (default: {None})
verbose {bool} -- Print progress (default: {True})
num_workers {int} -- Number of workers used in the dataloader (default: {0})
shuffle {bool} -- If we should shuffle the order of the dataset (default: {True})
**kwargs -- Passed to the 'make_dataloader' method. Set e.g. `torch_ds_dl to use
the TensorDataset and DataLoader provided by torch instead of the torchtuples
implementations.
Returns:
TrainingLogger -- Training log
"""
if target is not None:
input = (input, target)
dataloader = self.make_dataloader(input, batch_size, shuffle,
num_workers, **kwargs)
val_dataloader = val_data
if (is_dl(val_data) is False) and (val_data is not None):
val_dataloader = self.make_dataloader(val_data,
val_batch_size,
shuffle=False,
num_workers=num_workers,
**kwargs)
log = self.fit_dataloader(dataloader, epochs, callbacks, verbose,
metrics, val_dataloader)
return log