def _write_metrics(self, metrics_dict: dict):
"""
Args:
metrics_dict (dict): dict of scalar metrics
"""
metrics_dict = {
k: v.detach().cpu().item() if isinstance(v, torch.Tensor) else float(v)
for k, v in metrics_dict.items()
}
# gather metrics among all workers for logging
# This assumes we do DDP-style training, which is currently the only
# supported method in dl_lib.
all_metrics_dict = comm.gather(metrics_dict)
if comm.is_main_process():
if "data_time" in all_metrics_dict[0]:
# data_time among workers can have high variance. The actual latency
# caused by data_time is the maximum among workers.
data_time = np.max([x.pop("data_time") for x in all_metrics_dict])
self.storage.put_scalar("data_time", data_time)
# average the rest metrics
metrics_dict = {
k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()
}
total_losses_reduced = sum(loss for loss in metrics_dict.values())
self.storage.put_scalar("total_loss", total_losses_reduced)
if len(metrics_dict) > 1:
self.storage.put_scalars(**metrics_dict)
def evaluate(self):
if self._distributed:
comm.synchronize()
self._predictions = comm.gather(self._predictions, dst=0)
self._predictions = list(itertools.chain(*self._predictions))
if not comm.is_main_process():
return {}
if len(self._predictions) == 0:
self._logger.warning(
"[COCOEvaluator] Did not receive valid predictions.")
return {}
if self._output_dir:
PathManager.mkdirs(self._output_dir)
file_path = os.path.join(self._output_dir,
"instances_predictions.pth")
with PathManager.open(file_path, "wb") as f:
torch.save(self._predictions, f)
self._results = OrderedDict()
if "proposals" in self._predictions[0]:
self._eval_box_proposals()
if "instances" in self._predictions[0]:
self._eval_predictions(set(self._tasks))
# Copy so the caller can do whatever with results
return copy.deepcopy(self._results)
def evaluate(self):
"""
Returns:
dict: has a key "segm", whose value is a dict of "AP", "AP50", and "AP75".
"""
all_predictions = comm.gather(self._predictions, dst=0)
if not comm.is_main_process():
return
predictions = defaultdict(list)
for predictions_per_rank in all_predictions:
for clsid, lines in predictions_per_rank.items():
predictions[clsid].extend(lines)
del all_predictions
self._logger.info(
"Evaluating {} using {} metric. "
"Note that results do not use the official Matlab API.".format(
self._dataset_name, 2007 if self._is_2007 else 2012))
with tempfile.TemporaryDirectory(prefix="pascal_voc_eval_") as dirname:
res_file_template = os.path.join(dirname, "{}.txt")
aps = defaultdict(list) # iou -> ap per class
for cls_id, cls_name in enumerate(self._class_names):
lines = predictions.get(cls_id, [""])
with open(res_file_template.format(cls_name), "w") as f:
f.write("\n".join(lines))
for thresh in range(50, 100, 5):
rec, prec, ap = voc_eval(
res_file_template,
self._anno_file_template,
self._image_set_path,
cls_name,
ovthresh=thresh / 100.0,
use_07_metric=self._is_2007,
)
aps[thresh].append(ap * 100)
ret = OrderedDict()
mAP = {iou: np.mean(x) for iou, x in aps.items()}
ret["bbox"] = {
"AP": np.mean(list(mAP.values())),
"AP50": mAP[50],
"AP75": mAP[75]
}
return ret