def log_epoch_stats(self, cur_epoch):
"""
Log the stats of the current epoch.
Args:
cur_epoch (int): the number of current epoch.
"""
eta_sec = self.iter_timer.seconds() * (
self.MAX_EPOCH - (cur_epoch + 1) * self.epoch_iters)
eta = str(datetime.timedelta(seconds=int(eta_sec)))
stats = {
"_type": "train_epoch",
"epoch": "{}/{}".format(cur_epoch + 1, self._cfg.SOLVER.MAX_EPOCH),
"dt": self.iter_timer.seconds(),
"dt_data": self.data_timer.seconds(),
"dt_net": self.net_timer.seconds(),
"eta": eta,
"lr": self.lr,
"gpu_mem": "{:.2f}G".format(misc.gpu_mem_usage()),
"RAM": "{:.2f}/{:.2f}G".format(*misc.cpu_mem_usage()),
}
if not self._cfg.DATA.MULTI_LABEL:
top1_err = self.num_top1_mis / self.num_samples
top5_err = self.num_top5_mis / self.num_samples
avg_loss = self.loss_total / self.num_samples
stats["top1_err"] = top1_err
stats["top5_err"] = top5_err
stats["loss"] = avg_loss
logging.log_json_stats(stats)
def log_epoch_stats(self, cur_epoch):
"""
Log the stats of the current epoch.
Args:
cur_epoch (int): the number of current epoch.
"""
stats = {
"_type": "val_epoch",
"epoch": "{}/{}".format(cur_epoch + 1, self._cfg.SOLVER.MAX_EPOCH),
"time_diff": self.iter_timer.seconds(),
"gpu_mem": "{:.2f}G".format(misc.gpu_mem_usage()),
"RAM": "{:.2f}/{:.2f}G".format(*misc.cpu_mem_usage()),
}
if self._cfg.DATA.MULTI_LABEL:
stats["map"] = get_map(
torch.cat(self.all_preds).cpu().numpy(),
torch.cat(self.all_labels).cpu().numpy(),
)
else:
top1_err = self.num_top1_mis / self.num_samples
top5_err = self.num_top5_mis / self.num_samples
self.min_top1_err = min(self.min_top1_err, top1_err)
self.min_top5_err = min(self.min_top5_err, top5_err)
stats["top1_err"] = top1_err
stats["top5_err"] = top5_err
stats["min_top1_err"] = self.min_top1_err
stats["min_top5_err"] = self.min_top5_err
logging.log_json_stats(stats)
def log_iter_stats(self, cur_epoch, cur_iter):
"""
log the stats of the current iteration.
Args:
cur_epoch (int): the number of current epoch.
cur_iter (int): the number of current iteration.
"""
if (cur_iter + 1) % self._cfg.LOG_PERIOD != 0:
return
eta_sec = self.iter_timer.seconds() * (
self.MAX_EPOCH - (cur_epoch * self.epoch_iters + cur_iter + 1))
eta = str(datetime.timedelta(seconds=int(eta_sec)))
stats = {
"_type": "train_iter",
"epoch": "{}/{}".format(cur_epoch + 1, self._cfg.SOLVER.MAX_EPOCH),
"iter": "{}/{}".format(cur_iter + 1, self.epoch_iters),
"dt": self.iter_timer.seconds(),
"dt_data": self.data_timer.seconds(),
"dt_net": self.net_timer.seconds(),
"eta": eta,
"loss": self.loss.get_win_median(),
"lr": self.lr,
"gpu_mem": "{:.2f}G".format(misc.gpu_mem_usage()),
}
if not self._cfg.DATA.MULTI_LABEL:
stats["top1_err"] = self.mb_top1_err.get_win_median()
stats["top5_err"] = self.mb_top5_err.get_win_median()
logging.log_json_stats(stats)
def finalize_metrics(self, ks=(1, 5)):
"""
Calculate and log the final ensembled metrics.
ks (tuple): list of top-k values for topk_accuracies. For example,
ks = (1, 5) correspods to top-1 and top-5 accuracy.
"""
if not all(self.clip_count == self.num_clips):
logger.warning("clip count {} ~= num clips {}".format(
", ".join([
"{}: {}".format(i, k)
for i, k in enumerate(self.clip_count.tolist())
]),
self.num_clips,
))
self.stats = {"split": "test_final"}
if self.multi_label:
map = get_map(self.video_preds.cpu().numpy(),
self.video_labels.cpu().numpy())
self.stats["map"] = map
else:
num_topks_correct = metrics.topks_correct(self.video_preds,
self.video_labels, ks)
topks = [(x / self.video_preds.size(0)) * 100.0
for x in num_topks_correct]
assert len({len(ks), len(topks)}) == 1
for k, topk in zip(ks, topks):
self.stats["top{}_acc".format(k)] = "{:.{prec}f}".format(
topk, prec=2)
logging.log_json_stats(self.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)
def log_iter_stats(self, cur_epoch, cur_iter):
"""
Log the stats.
Args:
cur_epoch (int): the current epoch.
cur_iter (int): the current iteration.
"""
if (cur_iter + 1) % self.cfg.LOG_PERIOD != 0:
return
eta_sec = self.iter_timer.seconds() * (self.overall_iters - cur_iter)
eta = str(datetime.timedelta(seconds=int(eta_sec)))
if self.mode == "train":
stats = {
"_type": "{}_iter".format(self.mode),
"cur_epoch": "{}".format(cur_epoch + 1),
"cur_iter": "{}".format(cur_iter + 1),
"eta": eta,
"dt": self.iter_timer.seconds(),
"dt_data": self.data_timer.seconds(),
"dt_net": self.net_timer.seconds(),
"mode": self.mode,
"loss": self.loss.get_win_median(),
"lr": self.lr,
}
elif self.mode == "val":
stats = {
"_type": "{}_iter".format(self.mode),
"cur_epoch": "{}".format(cur_epoch + 1),
"cur_iter": "{}".format(cur_iter + 1),
"eta": eta,
"dt": self.iter_timer.seconds(),
"dt_data": self.data_timer.seconds(),
"dt_net": self.net_timer.seconds(),
"mode": self.mode,
}
elif self.mode == "test":
stats = {
"_type": "{}_iter".format(self.mode),
"cur_iter": "{}".format(cur_iter + 1),
"eta": eta,
"dt": self.iter_timer.seconds(),
"dt_data": self.data_timer.seconds(),
"dt_net": self.net_timer.seconds(),
"mode": self.mode,
}
else:
raise NotImplementedError("Unknown mode: {}".format(self.mode))
logging.log_json_stats(stats)
def log_iter_stats(self, cur_iter):
"""
Log the stats.
Args:
cur_iter (int): the current iteration of testing.
"""
eta_sec = self.iter_timer.seconds() * (self.overall_iters - cur_iter)
eta = str(datetime.timedelta(seconds=int(eta_sec)))
stats = {
"split": "test_iter",
"cur_iter": "{}".format(cur_iter + 1),
"eta": eta,
"time_diff": self.iter_timer.seconds(),
}
logging.log_json_stats(stats)
def log_epoch_stats(self, cur_epoch):
"""
Log the stats of the current epoch.
Args:
cur_epoch (int): the number of current epoch.
"""
if self.mode in ["val", "test"]:
self.finalize_metrics(log=False)
stats = {
"_type": "{}_epoch".format(self.mode),
"cur_epoch": "{}".format(cur_epoch + 1),
"mode": self.mode,
"map": self.full_map,
"gpu_mem": "{:.2f}G".format(misc.gpu_mem_usage()),
"RAM": "{:.2f}/{:.2f}G".format(*misc.cpu_mem_usage()),
}
logging.log_json_stats(stats)