A minimal example with toy data set

I was trying to use the toy data-sets but when I got errors like train doesn’t exist when trying to loop through the batches. Can we have a tiny minimal example to loop through the data for toy data sets?

My attempt

from torchmeta.toy import Sinusoid
#from torchmeta.datasets.helpers import omniglot
from torchmeta.utils.data import BatchMetaDataLoader

from tqdm import tqdm

num_samples_per_task = 10
dataset = Sinusoid(num_samples_per_task, num_tasks=10, noise_std=None,
    transform=None, target_transform=None, dataset_transform=None)
#dataset = omniglot("data", ways=5, shots=5, test_shots=15, meta_train=True, download=True)
dataloader = BatchMetaDataLoader(dataset, batch_size=5, num_workers=4)
print(f'len(dataset) = {len(dataset)}')
print(f'len(dataloader) = {len(dataloader)}')
for batch in dataloader:
    train_inputs, train_targets = batch["train"]

other weird things was like the tensors being of size 16 but my meta-batch size being of size 5…

[tristandeleu]

MiniImagenet does not have a num_samples_per_task argument (this is specific to toy regression datasets). But you can indeed see this as being similar to the 600 images per class: it corresponds to the number of possible examples to sample from for this task. In the case of toy regression tasks, this is simply the number of support + number of query examples (5 + 10 here).

[tristandeleu]

Data transforms (like ClassSplitter) can either be used as a data_transform, or as a wrapper (the wrapper is here just as syntactic sugar). The following two are equivalent

• ClassSplitter as a dataset_transform argument

from torchmeta.toy import Sinusoid
from torchmeta.transforms import ClassSplitter

dataset = Sinusoid(num_samples_per_task=15,
    dataset_transform=ClassSplitter(num_train_per_class=5, num_test_per_class=10))

task = dataset.sample_task()
print(task)  # OrderedDict([('train', <torchmeta.utils.data.task.SubsetTask object at 0x11ba07dd8>), ('test', <torchmeta.utils.data.task.SubsetTask object at 0x11ba10240>)])

• ClassSplitter as a wrapper

from torchmeta.toy import Sinusoid
from torchmeta.transforms import ClassSplitter

dataset = Sinusoid(num_samples_per_task=15)
dataset = ClassSplitter(dataset, num_train_per_class=5, num_test_per_class=10)

task = dataset.sample_task()
print(task)  # OrderedDict([('train', <torchmeta.utils.data.task.SubsetTask object at 0x12078eda0>), ('test', <torchmeta.utils.data.task.SubsetTask object at 0x120797208>)])