def eval(self, model):
"""
Performs evaluation on a given model.
:param model: The model on which to perform evaluation
:type model: AdaptiveModel
:return all_results: A list of dictionaries, one for each prediction head. Each dictionary contains the metrics
and reports generated during evaluation.
:rtype all_results: list of dicts
"""
model.eval()
# init empty lists per prediction head
loss_all = [0 for _ in model.prediction_heads]
preds_all = [[] for _ in model.prediction_heads]
label_all = [[] for _ in model.prediction_heads]
for step, batch in enumerate(
tqdm(self.data_loader, desc="Evaluating", mininterval=10)
):
batch = {key: batch[key].to(self.device) for key in batch}
with torch.no_grad():
logits = model.forward(**batch)
# TODO logits_to_loss should be a single, overloaded function
losses_per_head = model.logits_to_loss_per_head(logits=logits, **batch)
preds = model.logits_to_preds(
logits=logits, **batch
)
labels = model.prepare_labels(**batch)
# stack results of all batches per prediction head
for head_num, head in enumerate(model.prediction_heads):
loss_all[head_num] += np.sum(to_numpy(losses_per_head[head_num]))
if head.model_type == "span_classification":
# TODO check why adaptive model doesnt pack preds into list of list of tuples
preds_all[head_num] += preds
label_all[head_num] += labels
else:
preds_all[head_num] += list(to_numpy(preds[head_num]))
label_all[head_num] += list(to_numpy(labels[head_num]))
# Evaluate per prediction head
all_results = []
for head_num, head in enumerate(model.prediction_heads):
if head.model_type == "multilabel_text_classification":
# converting from string preds back to multi-hot encoding
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer(classes=head.label_list)
# TODO check why .fit() should be called on predictions, rather than on labels
preds_all[head_num] = mlb.fit_transform(preds_all[head_num])
label_all[head_num] = mlb.transform(label_all[head_num])
elif head.model_type == "span_classification":
temp = head._aggregate_preds(preds_all[head_num])
preds_all[head_num] = temp
result = {"loss": loss_all[head_num] / len(self.data_loader.dataset),
"task_name": head.task_name}
result.update(
compute_metrics(metric=head.metric, preds=preds_all[head_num], labels=label_all[head_num]
)
)
# Select type of report depending on prediction head output type
if self.classification_report:
if head.ph_output_type == "per_token":
report_fn = token_classification_report
elif head.ph_output_type == "per_sequence":
report_fn = classification_report
elif head.ph_output_type == "per_token_squad":
report_fn = lambda *args, **kwargs: "not Implemented"
elif head.ph_output_type == "per_sequence_continuous":
report_fn = r2_score
else:
raise NotImplementedError
# CHANGE PARAMETERS, not all report_fn accept digits
if head.ph_output_type in ["per_sequence_continuous","per_token"]:
result["report"] = report_fn(
label_all[head_num], preds_all[head_num]
)
else:
# supply labels as all possible combination because if ground truth labels do not cover
# all values in label_list (maybe dev set is small), the report will break
result["report"] = report_fn(
label_all[head_num],
preds_all[head_num],
digits=4,
labels=head.label_list,
target_names=head.label_list)
all_results.append(result)
return all_results
def eval(self, model):
"""
Performs evaluation on a given model.
:param model: The model on which to perform evaluation
:type model: AdaptiveModel
:return all_results: A list of dictionaries, one for each prediction head. Each dictionary contains the metrics
and reports generated during evaluation.
:rtype all_results: list of dicts
"""
model.eval()
# init empty lists per prediction head
loss_all = [0 for _ in model.prediction_heads]
preds_all = [[] for _ in model.prediction_heads]
label_all = [[] for _ in model.prediction_heads]
for step, batch in enumerate(
tqdm(self.data_loader, desc="Evaluating", mininterval=10)):
batch = {key: batch[key].to(self.device) for key in batch}
with torch.no_grad():
logits = model.forward(**batch)
# TODO logits_to_loss should be a single, overloaded function
losses_per_head = model.logits_to_loss_per_head(logits=logits,
**batch)
preds = model.logits_to_preds(logits=logits, **batch)
labels = model.prepare_labels(**batch)
# stack results of all batches per prediction head
for head_num, head in enumerate(model.prediction_heads):
loss_all[head_num] += np.sum(
to_numpy(losses_per_head[head_num]))
preds_all[head_num] += list(to_numpy(preds[head_num]))
label_all[head_num] += list(to_numpy(labels[head_num]))
# Evaluate per prediction head
all_results = []
for head_num, head in enumerate(model.prediction_heads):
result = {
"loss": loss_all[head_num] / len(self.data_loader.dataset),
"task_name": head.task_name
}
result.update(
compute_metrics(metric=head.metric,
preds=preds_all[head_num],
labels=label_all[head_num]))
# Select type of report depending on prediction head output type
if self.classification_report:
if head.ph_output_type == "per_token":
report_fn = token_classification_report
elif head.ph_output_type == "per_sequence":
report_fn = classification_report
elif head.ph_output_type == "per_token_squad":
report_fn = lambda *args, **kwargs: "not Implemented"
elif head.ph_output_type == "per_sequence_continuous":
report_fn = r2_score
else:
raise NotImplementedError
# CHANGE PARAMETERS, not all report_fn accept digits
if head.ph_output_type == "per_sequence_continuous":
result["report"] = report_fn(label_all[head_num],
preds_all[head_num])
else:
result["report"] = report_fn(label_all[head_num],
preds_all[head_num],
digits=4)
all_results.append(result)
return all_results
