mindspore.dataset.RandomSampler
- class mindspore.dataset.RandomSampler(replacement=False, num_samples=None)[source]
Samples the elements randomly.
- Parameters
- Raises
TypeError – If replacement is not of type bool.
TypeError – If num_samples is not of type int.
ValueError – If num_samples is a negative value.
Examples
>>> # creates a RandomSampler >>> sampler = ds.RandomSampler() >>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir, ... num_parallel_workers=8, ... sampler=sampler)
- add_child(sampler)
Add a sub-sampler for given sampler. The parent will receive all data from the output of sub-sampler 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=4)) >>> dataset = ds.Cifar10Dataset(cifar10_dataset_dir, sampler=sampler)
- get_child()
Get the child sampler of given sampler.
- Returns
Sampler, The child sampler of given sampler.
Examples
>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3) >>> sampler.add_child(ds.RandomSampler(num_samples=2)) >>> child_sampler = sampler.get_child()
- get_num_samples()
Get num_samples value of the current sampler instance. This parameter can be optionally passed in when defining the Sampler (default is None). This method will return the num_samples value. If the current sampler has child samplers, it will continue to access the child samplers and process the obtained value according to certain rules.
The following table shows the various possible combinations, and the final results returned.
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.
Examples
>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3) >>> num_samplers = sampler.get_num_samples()