def UpdateIterStats(self):
"""Update tracked iteration statistics."""
for k in self.losses_and_metrics.keys():
if k in self.model.losses:
self.losses_and_metrics[k] = nu.sum_multi_gpu_blob(k)
else:
self.losses_and_metrics[k] = nu.average_multi_gpu_blob(k)
for k, v in self.smoothed_losses_and_metrics.items():
v.AddValue(self.losses_and_metrics[k])
self.iter_total_loss = np.sum(
np.array([self.losses_and_metrics[k] for k in self.model.losses]))
#print(self.model.roi_data_loader._cur_img)
for i in range(len(self.model.roi_data_loader._cur_img)):
self.model.roi_data_loader._cur_loss[
self.model.roi_data_loader._cur_img[i]] = self.iter_total_loss
self.smoothed_total_loss.AddValue(self.iter_total_loss)
self.cur_epoch_total_loss.append(self.iter_total_loss)
self.smoothed_mb_qsize.AddValue(
self.model.roi_data_loader._minibatch_queue.qsize())
if self.model.roi_data_loader.next_epoch:
self.cur_epoch += 1
self.model.roi_data_loader.next_epoch = False
self.last_epoch_mean_loss = sum(self.cur_epoch_total_loss) / len(
self.cur_epoch_total_loss)
self.cur_epoch_total_loss = []
def UpdateIterStats(self):
"""Update tracked iteration statistics."""
for k in self.losses_and_metrics.keys():
if k in self.model.losses:
self.losses_and_metrics[k] = nu.sum_multi_gpu_blob(k)
else:
self.losses_and_metrics[k] = nu.average_multi_gpu_blob(k)
for k, v in self.smoothed_losses_and_metrics.items():
v.AddValue(self.losses_and_metrics[k])
self.iter_total_loss = np.sum(
np.array([self.losses_and_metrics[k] for k in self.model.losses]))
self.smoothed_total_loss.AddValue(self.iter_total_loss)
self.smoothed_mb_qsize.AddValue(
self.model.roi_data_loader._minibatch_queue.qsize())
def UpdateIterStats(self, i_iter=0):
"""Update tracked iteration statistics."""
for k in self.losses_and_metrics.keys():
if k in self.model.losses:
self.losses_and_metrics[k] = nu.sum_multi_gpu_blob(k)
else:
self.losses_and_metrics[k] = nu.average_multi_gpu_blob(k)
for k, v in self.smoothed_losses_and_metrics.items():
v.AddValue(self.losses_and_metrics[k])
self.writer.add_scalar(k, self.losses_and_metrics[k], i_iter)
self.iter_total_loss = np.sum(
np.array([self.losses_and_metrics[k] for k in self.model.losses])
)
self.smoothed_total_loss.AddValue(self.iter_total_loss)
def UpdateIterStats(self):
"""Update tracked iteration statistics."""
for k in self.losses_and_metrics.keys():
if k in self.model.losses:
self.losses_and_metrics[k] = nu.sum_multi_gpu_blob(k)
else:
self.losses_and_metrics[k] = nu.average_multi_gpu_blob(k)
for k, v in self.smoothed_losses_and_metrics.items():
v.AddValue(self.losses_and_metrics[k])
self.iter_total_loss = np.sum(
np.array([self.losses_and_metrics[k] for k in self.model.losses])
)
self.smoothed_total_loss.AddValue(self.iter_total_loss)
self.smoothed_mb_qsize.AddValue(
self.model.roi_data_loader._minibatch_queue.qsize()
)
