def finalize_metrics(self, log=True):
"""
Calculate and log the final AVA metrics.
"""
all_preds = torch.cat(self.all_preds, dim=0)
all_ori_boxes = torch.cat(self.all_ori_boxes, dim=0)
all_metadata = torch.cat(self.all_metadata, dim=0)
if self.mode == "test" or (self.full_ava_test and self.mode == "val"):
groundtruth = self.full_groundtruth
else:
groundtruth = self.mini_groundtruth
self.full_map = evaluate_ava(
all_preds,
all_ori_boxes,
all_metadata.tolist(),
self.class_whitelist,
self.categories,
groundtruth=groundtruth,
video_idx_to_name=self.video_idx_to_name,
)
if log:
stats = {"mode": self.mode, "map": self.full_map}
logging.log_json_stats(stats)
def finalize_metrics(self, log=True):
"""
Calculate and log the final AVA metrics.
"""
all_preds = torch.cat(self.all_preds, dim=0)
all_ori_boxes = torch.cat(self.all_ori_boxes, dim=0)
all_metadata = torch.cat(self.all_metadata, dim=0)
if self.mode == "test" or (self.full_ava_test and self.mode == "val"):
groundtruth = self.full_groundtruth
else:
groundtruth = self.mini_groundtruth
self.full_map = evaluate_ava(
all_preds,
all_ori_boxes,
all_metadata.tolist(),
self.excluded_keys,
self.class_whitelist,
self.categories,
groundtruth=groundtruth,
video_idx_to_name=self.video_idx_to_name,
)
if log:
stats = {"mode": self.mode, "map": self.full_map}
logging.log_json_stats(stats, self.output_dir)
map_str = "{:.{prec}f}".format(self.full_map * 100.0, prec=2)
self.min_top1_err = self.full_map
self.stats["top1_acc"] = map_str
self.stats["top5_acc"] = map_str