def Run(self) -> None:
"""Run the epoch worker thread."""
rolling_results = batches.RollingResults()
for i, batch in enumerate(self.batch_iterator.batches):
self.batch_count += 1
self.ctx.i += batch.graph_count
# Record the unique graph IDs.
for graph_id in batch.graph_ids:
self.graph_ids.add(graph_id)
# We have run out of batches.
if batch.end_of_batches:
break
# Skip an empty batch.
if not batch.graph_count:
continue
# Run the batch through the model.
with self.ctx.Profile(
3,
lambda t: (f"Batch {i+1} with "
f"{batch.graph_count} graphs: "
f"{batch_results}"),
) as batch_timer:
batch_results = self.model.RunBatch(self.epoch_type, batch)
# Record the batch results.
self.logger.OnBatchEnd(
run_id=self.model.run_id,
epoch_type=self.epoch_type,
epoch_num=self.model.epoch_num,
batch_num=i + 1,
timer=batch_timer,
data=batch,
results=batch_results,
)
rolling_results.Update(batch,
batch_results,
weight=batch_results.target_count)
self.ctx.bar.set_postfix(
loss=rolling_results.loss,
acc=rolling_results.accuracy,
prec=rolling_results.precision,
rec=rolling_results.recall,
)
self.results = epoch.Results.FromRollingResults(rolling_results)
self.logger.OnEpochEnd(self.model.run_id, self.epoch_type,
self.model.epoch_num, self.results)
def test_RollingResults_iteration_count(weight: float):
"""Test aggreation of model iteration count and convergence."""
rolling_results = batches.RollingResults()
data = batches.Data(graph_ids=[1], data=None)
results = batches.Results.Create(
np.random.rand(1, 10),
np.random.rand(1, 10),
iteration_count=1,
model_converged=True,
)
for _ in range(10):
rolling_results.Update(data, results, weight=weight)
assert rolling_results.iteration_count == 1
assert rolling_results.model_converged == 1