mindspore.dataset.PKSampler
- class mindspore.dataset.PKSampler(num_val, num_class=None, shuffle=False, class_column='label', num_samples=None)[source]
Samples K elements for each P class in the dataset.
- Parameters
num_val (int) – Number of elements to sample for each class.
num_class (int, optional) – Number of classes to sample (default=None, sample all classes). The parameter does not support to specify currently.
shuffle (bool, optional) – If True, the class IDs are shuffled, otherwise it will not be shuffled (default=False).
class_column (str, optional) – Name of column with class labels for MindDataset (default=’label’).
num_samples (int, optional) – The number of samples to draw (default=None, which means sample all elements).
- Raises
TypeError – If shuffle is not of type bool.
TypeError – If class_column is not of type str.
TypeError – If num_samples is not of type int.
NotImplementedError – If num_class is not None.
RuntimeError – If num_val is not a positive value.
ValueError – If num_samples is a negative value.
Examples
>>> # creates a PKSampler that will get 3 samples from every class. >>> sampler = ds.PKSampler(3) >>> 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()