def test_create_subset_random_sampler_range_str(self):
""" Tests whther SubsetRandomSampler accepts 'indices' with the option 2: range as str. """
range_str = '0, 20'
config = ConfigInterface()
config.add_default_params({'name': 'SubsetRandomSampler',
'indices': range_str})
# Create the sampler.
sampler = SamplerFactory.build(TestProblemMockup(), config)
# Check number of samples.
self.assertEqual(len(sampler), 20)
def test_create_subset_random_sampler_list_of_indices(self):
""" Tests whther SubsetRandomSampler accepts 'indices' with the option 3: list of indices. """
yaml_list = yaml.load('[0, 2, 5, 10]')
config = ConfigInterface()
config.add_default_params({'name': 'SubsetRandomSampler',
'indices': yaml_list})
# Create the sampler.
sampler = SamplerFactory.build(TestProblemMockup(), config)
# Check number of samples.
self.assertEqual(len(sampler), 4)
def test_create_subset_random_sampler_range(self):
""" Tests whther SubsetRandomSampler accepts 'indices' with the option 1: range. """
indices = range(20)
config = ConfigInterface()
config.add_default_params({
'type': 'SubsetRandomSampler',
'indices': indices
})
# Create the sampler.
sampler = SamplerFactory.build(TestTaskMockup(), config, "training")
# Check number of samples.
self.assertEqual(len(sampler), 20)
def test_create_subset_random_sampler_file(self):
""" Tests whther SubsetRandomSampler accepts 'indices' with the option 4: name of the file containing indices. """
filename = "/tmp/tmp_indices.txt"
# Store indices to file.
indices = np.asarray([1,2,3,4,5],dtype=int)
# Write array to file, separate elements with commas.
indices.tofile(filename, sep=',', format="%s")
config = ConfigInterface()
config.add_default_params({'name': 'SubsetRandomSampler',
'indices': filename})
# Create the sampler.
sampler = SamplerFactory.build(TestProblemMockup(), config)
# Check number of samples.
self.assertEqual(len(sampler), 5)
#if __name__ == "__main__":
# unittest.main()
def build(self, log=True):
"""
Method creates a problem on the basis of configuration section.
:param log: Logs information and the detected errors (DEFAULT: TRUE)
:return: number of detected errors
"""
try:
# Create component.
component, class_obj = ComponentFactory.build(
"problem", self.config["problem"])
# Check if class is derived (even indirectly) from Problem.
if not ComponentFactory.check_inheritance(class_obj,
ptp.Problem.__name__):
raise ConfigurationError(
"Class '{}' is not derived from the Problem class!".format(
class_obj.__name__))
# Set problem.
self.problem = component
# Try to build the sampler.
self.sampler = SamplerFactory.build(self.problem,
self.config["sampler"])
if self.sampler is not None:
# Set shuffle to False - REQUIRED as those two are exclusive.
self.config['dataloader'].add_config_params({'shuffle': False})
# build the DataLoader on top of the validation problem
self.dataloader = DataLoader(
dataset=self.problem,
batch_size=self.config['problem']['batch_size'],
shuffle=self.config['dataloader']['shuffle'],
sampler=self.sampler,
batch_sampler=self.config['dataloader']['batch_sampler'],
num_workers=self.config['dataloader']['num_workers'],
collate_fn=self.problem.collate_fn,
pin_memory=self.config['dataloader']['pin_memory'],
drop_last=self.config['dataloader']['drop_last'],
timeout=self.config['dataloader']['timeout'],
worker_init_fn=self.worker_init_fn)
# Display sizes.
if log:
self.logger.info("Problem for '{}' loaded (size: {})".format(
self.name, len(self.problem)))
if (self.sampler is not None):
self.logger.info(
"Sampler for '{}' created (size: {})".format(
self.name, len(self.sampler)))
# Ok, success.
return 0
except ConfigurationError as e:
if log:
self.logger.error(
"Detected configuration error while creating the problem instance:\n {}"
.format(e))
# Return error.
return 1
except KeyError as e:
if log:
self.logger.error(
"Detected key error while creating the problem instance: required key {} is missing"
.format(e))
# Return error.
return 1