def get_iter_stats(self, cur_epoch, cur_iter):
mem_usage = metrics.gpu_mem_usage()
iter_stats = {
'_type': 'Val_iter',
'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
'iter': '{}/{}'.format(cur_iter + 1, self.max_iter),
'top1_err': self.mb_top1_err.get_win_median(),
}
return iter_stats
def get_epoch_stats(self, cur_epoch):
top1_err = self.num_top1_mis / self.num_samples
self.min_top1_err = min(self.min_top1_err, top1_err)
mem_usage = metrics.gpu_mem_usage()
stats = {
'_type': 'Val_epoch',
'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
'top1_err': top1_err,
'min_top1_err': self.min_top1_err
}
return stats
def get_iter_stats(self, cur_epoch, cur_iter):
mem_usage = metrics.gpu_mem_usage()
iter_stats = {
'_type': 'test_iter',
'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
'iter': '{}/{}'.format(cur_iter + 1, self.max_iter),
'time_avg': self.iter_timer.average_time,
'time_diff': self.iter_timer.diff,
'top1_err': self.mb_top1_err.get_win_median(),
'top5_err': self.mb_top5_err.get_win_median(),
'mem': int(np.ceil(mem_usage))
}
return iter_stats
def get_iter_stats(self, cur_epoch, cur_iter):
mem_usage = metrics.gpu_mem_usage()
iter_stats = {
"_type": "test_iter",
"epoch": "{}/{}".format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
"iter": "{}/{}".format(cur_iter + 1, self.max_iter),
"time_avg": self.iter_timer.average_time,
"time_diff": self.iter_timer.diff,
"top1_err": self.mb_top1_err.get_win_median(),
"top5_err": self.mb_top5_err.get_win_median(),
"mem": int(np.ceil(mem_usage)),
}
return iter_stats
def get_iter_stats(self, cur_epoch, cur_iter):
eta_sec = self.iter_timer.average_time * (
self.max_iter - (cur_epoch * self.epoch_iters + cur_iter + 1))
eta_td = datetime.timedelta(seconds=int(eta_sec))
mem_usage = metrics.gpu_mem_usage()
stats = {
'_type': 'train_iter',
'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
'iter': '{}/{}'.format(cur_iter + 1, self.epoch_iters),
'top1_err': self.mb_top1_err.get_win_median(),
'loss': self.loss.get_win_median(),
'lr': self.lr,
}
return stats
def get_epoch_stats(self, cur_epoch):
eta_sec = self.iter_timer.average_time * (
self.max_iter - (cur_epoch + 1) * self.epoch_iters)
eta_td = datetime.timedelta(seconds=int(eta_sec))
mem_usage = metrics.gpu_mem_usage()
top1_err = self.num_top1_mis / self.num_samples
avg_loss = self.loss_total / self.num_samples
stats = {
'_type': 'train_epoch',
'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
'top1_err': top1_err,
'loss': avg_loss,
'lr': self.lr,
}
return stats
def get_epoch_stats(self, cur_epoch):
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)
mem_usage = metrics.gpu_mem_usage()
stats = {
'_type': 'test_epoch',
'epoch': '{}/{}'.format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
'time_avg': self.iter_timer.average_time,
'top1_err': top1_err,
'top5_err': top5_err,
'min_top1_err': self.min_top1_err,
'min_top5_err': self.min_top5_err,
'mem': int(np.ceil(mem_usage))
}
return stats
def get_epoch_stats(self, cur_epoch):
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)
mem_usage = metrics.gpu_mem_usage()
stats = {
"_type": "test_epoch",
"epoch": "{}/{}".format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
"time_avg": self.iter_timer.average_time,
"top1_err": top1_err,
"top5_err": top5_err,
"min_top1_err": self.min_top1_err,
"min_top5_err": self.min_top5_err,
"mem": int(np.ceil(mem_usage)),
}
return stats
def get_iter_stats(self, cur_epoch, cur_iter):
eta_sec = self.iter_timer.average_time * (
self.max_iter - (cur_epoch * self.epoch_iters + cur_iter + 1))
eta_td = datetime.timedelta(seconds=int(eta_sec))
mem_usage = metrics.gpu_mem_usage()
stats = {
"_type": "train_iter",
"epoch": "{}/{}".format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
"iter": "{}/{}".format(cur_iter + 1, self.epoch_iters),
"time_avg": self.iter_timer.average_time,
"time_diff": self.iter_timer.diff,
"eta": eta_str(eta_td),
"top1_err": self.mb_top1_err.get_win_median(),
"top5_err": self.mb_top5_err.get_win_median(),
"loss": self.loss.get_win_median(),
"lr": self.lr,
"mem": int(np.ceil(mem_usage)),
}
return stats
def get_epoch_stats(self, cur_epoch):
eta_sec = self.iter_timer.average_time * (
self.max_iter - (cur_epoch + 1) * self.epoch_iters)
eta_td = datetime.timedelta(seconds=int(eta_sec))
mem_usage = metrics.gpu_mem_usage()
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 = {
"_type": "train_epoch",
"epoch": "{}/{}".format(cur_epoch + 1, cfg.OPTIM.MAX_EPOCH),
"time_avg": self.iter_timer.average_time,
"eta": eta_str(eta_td),
"top1_err": top1_err,
"top5_err": top5_err,
"loss": avg_loss,
"lr": self.lr,
"mem": int(np.ceil(mem_usage)),
}
return stats