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16 changes: 11 additions & 5 deletions ppsci/solver/eval.py
Original file line number Diff line number Diff line change
Expand Up @@ -167,6 +167,10 @@ def _eval_by_dataset(
for metric_name, metric_func in _validator.metric.items():
# NOTE: compute metric with entire output and label
metric_dict = metric_func(all_output, all_label)
assert metric_name not in metric_dict_group, (
f"Metric name({metric_name}) already exists, please ensure all metric "
"names are unique over all validators."
)
metric_dict_group[metric_name] = {
k: float(v) for k, v in metric_dict.items()
}
Expand Down Expand Up @@ -215,7 +219,6 @@ def _eval_by_batch(
num_samples = _get_dataset_length(_validator.data_loader)

loss_dict = misc.Prettydefaultdict(float)
metric_dict_group: Dict[str, Dict[str, float]] = misc.PrettyOrderedDict()
reader_tic = time.perf_counter()
batch_tic = time.perf_counter()
for iter_id, batch in enumerate(_validator.data_loader, start=1):
Expand Down Expand Up @@ -251,9 +254,12 @@ def _eval_by_batch(

# collect batch metric
for metric_name, metric_func in _validator.metric.items():
assert metric_name not in metric_dict_group, (
f"Metric name({metric_name}) already exists, please ensure all metric "
"names are unique over all validators."
)
metric_dict_group[metric_name] = misc.Prettydefaultdict(list)
metric_dict = metric_func(output_dict, label_dict)
if metric_name not in metric_dict_group:
metric_dict_group[metric_name] = misc.Prettydefaultdict(list)
for var_name, metric_value in metric_dict.items():
metric_dict_group[metric_name][var_name].append(
metric_value
Expand Down Expand Up @@ -284,9 +290,9 @@ def _eval_by_batch(
# concatenate all metric and discard metric of padded sample(s)
for metric_name, metric_dict in metric_dict_group.items():
for var_name, metric_value in metric_dict.items():
# NOTE: concat all metric(scalars) into metric vector
# NOTE: concat single metric(scalar) list into metric vector
metric_value = paddle.concat(metric_value)[:num_samples]
# NOTE: compute metric via averaging metric vector,
# NOTE: compute metric via averaging metric over all samples,
# this might be not general for certain evaluation case
metric_value = float(metric_value.mean())
metric_dict_group[metric_name][var_name] = metric_value
Expand Down