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Fix offline training for model comparison ignoring shared context #139

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merged 3 commits into from
Mar 1, 2024
Merged

Fix offline training for model comparison ignoring shared context #139

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eb35a14
Include shared context in MultiSimulationDataset for offline training
elseml Feb 27, 2024
9a5b125
Merge remote-tracking branch 'upstream/Development' into Development
elseml Feb 29, 2024
56a8dd5
Fix last epoch validation loss not saving
elseml Mar 1, 2024
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8 changes: 8 additions & 0 deletions bayesflow/helper_classes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,10 +141,18 @@ def __init__(self, forward_dict, batch_size, buffer_size=1024):
self.iters = [iter(d) for d in self.datasets]
self.batch_size = batch_size

# Include further keys (= shared context) from forward_dict
self.further_keys = {}
for key, value in forward_dict.items():
if key not in [DEFAULT_KEYS["model_outputs"], DEFAULT_KEYS["model_indices"]]:
self.further_keys[key] = value

def __next__(self):
if self.current_it < self.num_batches:
outputs = [next(d) for d in self.iters]
output_dict = {DEFAULT_KEYS["model_outputs"]: outputs, DEFAULT_KEYS["model_indices"]: self.model_indices}
if self.further_keys:
output_dict.update(self.further_keys)
self.current_it += 1
return output_dict
self.current_it = 0
Expand Down
13 changes: 6 additions & 7 deletions bayesflow/trainers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -454,8 +454,8 @@ def train_online(
p_bar.update(1)

# Store and compute validation loss, if specified
self._save_trainer(save_checkpoint)
self._validation(ep, validation_sims, **kwargs)
self._save_trainer(save_checkpoint)

# Check early stopping, if specified
if self._check_early_stopping(early_stopper):
Expand Down Expand Up @@ -579,13 +579,13 @@ def train_offline(
# Format for display on progress bar
disp_str = format_loss_string(ep, bi, loss, avg_dict, lr=lr, it_str="Batch")

# Update progress
# Update progress bar
p_bar.set_postfix_str(disp_str, refresh=False)
p_bar.update(1)

# Store and compute validation loss, if specified
self._save_trainer(save_checkpoint)
self._validation(ep, validation_sims, **kwargs)
self._save_trainer(save_checkpoint)

# Check early stopping, if specified
if self._check_early_stopping(early_stopper):
Expand Down Expand Up @@ -762,15 +762,14 @@ def train_from_presimulation(
p_bar.update(1)

# Store after each epoch, if specified
self._save_trainer(save_checkpoint)

self._validation(ep, validation_sims, **kwargs)
self._save_trainer(save_checkpoint)

# Check early stopping, if specified
if self._check_early_stopping(early_stopper):
break

# Remove reference to optimizer, if not set to persistent
# Remove optimizer reference, if not set as persistent
if not reuse_optimizer:
self.optimizer = None
return self.loss_history.get_plottable()
Expand Down Expand Up @@ -906,8 +905,8 @@ def train_experience_replay(
p_bar.update(1)

# Store and compute validation loss, if specified
self._save_trainer(save_checkpoint)
self._validation(ep, validation_sims, **kwargs)
self._save_trainer(save_checkpoint)

# Check early stopping, if specified
if self._check_early_stopping(early_stopper):
Expand Down