def test_agent_with_generator(self):
self.api_workflow_client.embedding_id = "embedding_id_xyz"
width = 32
height = 32
no_classes = 13
agent = ActiveLearningAgent(
self.api_workflow_client,
preselected_tag_name="preselected_tag_name_xyz")
method = SamplingMethod.CORAL
n_samples = len(agent.labeled_set) + 2
n_predictions = len(agent.query_set)
predictions = np.random.rand(n_predictions, no_classes, width,
height).astype(np.float32)
predictions_normalized = predictions / np.sum(predictions,
axis=1)[:, np.newaxis]
predictions_generator = (predictions_normalized[i]
for i in range(n_predictions))
al_scorer = ScorerSemanticSegmentation(predictions_generator)
sampler_config = SamplerConfig(n_samples=n_samples, method=method)
agent.query(sampler_config=sampler_config, al_scorer=al_scorer)
# make sure we throw an error if generator is already consumed
with self.assertRaises(ValueError):
agent.upload_scores(al_scorer)
def test_agent_only_upload_scores(self):
self.api_workflow_client.embedding_id = "embedding_id_xyz"
agent = ActiveLearningAgent(
self.api_workflow_client,
preselected_tag_name="preselected_tag_name_xyz",
)
n_predictions = len(agent.query_set)
predictions = np.random.rand(n_predictions, 10).astype(np.float32)
predictions_normalized = predictions / np.sum(predictions,
axis=1)[:, np.newaxis]
al_scorer = ScorerClassification(predictions_normalized)
agent.upload_scores(al_scorer)