mindspore.dataset.SequentialSampler
- class mindspore.dataset.SequentialSampler(start_index=None, num_samples=None)[source]
Samples the dataset elements sequentially, same as not having a sampler.
- Parameters
Examples
>>> # creates a SequentialSampler >>> sampler = ds.SequentialSampler() >>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir, ... num_parallel_workers=8, ... sampler=sampler)
- Raises
TypeError – If start_index is not an integer value.
TypeError – If num_samples is not an integer value.
RuntimeError – If start_index is a negative value.
RuntimeError – If num_samples is a negative value.
- add_child(sampler)
Add a sub-sampler for given sampler. The sub-sampler will receive all data from the output of parent sampler and apply its sample logic to return new samples.
- Parameters
sampler (Sampler) – Object used to choose samples from the dataset. Only builtin samplers(DistributedSampler, PKSampler, RandomSampler, SequentialSampler, SubsetRandomSampler, WeightedRandomSampler) are supported.
Examples
>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3) >>> sampler.add_child(ds.RandomSampler(num_samples=2)) >>> dataset = ds.Cifar10Dataset(cifar10_dataset_dir, sampler=sampler)
- get_child()
add a child sampler
- get_num_samples()
All samplers can contain a numeric num_samples value (or it can be set to None). A child sampler can exist or be None. If a child sampler exists, then the child sampler count can be a numeric value or None. These conditions impact the resultant sampler count that is used. The following table shows the possible results from calling this function.
child sampler
num_samples
child_samples
result
T
x
y
min(x, y)
T
x
None
x
T
None
y
y
T
None
None
None
None
x
n/a
x
None
None
n/a
None
- Returns
int, the number of samples, or None
- parse_child()
Parse the child sampler.
- parse_child_for_minddataset()
Parse the child sampler for MindRecord.