wassname/Pointnet2_PyTorch
1
0
Fork 0
mirror of https://github.com/wassname/Pointnet2_PyTorch.git synced 2026-07-22 12:30:51 +08:00
Code Issues Packages Projects Releases Wiki Activity

Some faster/better kernels. Tensors with points are now kept in (b, c, ...) format as this is easier for pytorch

Browse Source
This commit is contained in:
erikwijmans
2018-02-10 20:32:52 -05:00
parent 8bce353da4
commit 65a127f3d2
19 changed files with 372 additions and 296 deletions
Download Patch File Download Diff File Expand all files Collapse all files
utils/pytorch_utils.py
+46 -1
View File
@@ -561,6 +561,8 @@ class Trainer(object):
self.checkpoint_name, self.best_name = checkpoint_name, best_name
self.eval_frequency = eval_frequency
self.training_best = {}
self.eval_best = {}
if log_name is not None:
tb_log.configure(log_name)
@@ -617,6 +619,21 @@ class Trainer(object):
self._print("Train", epoch, total_loss, eval_dict, count)
if 'loss' in self.training_best:
self.training_best['loss'] = np.min(
self.training_best['loss'], total_loss / count
)
else:
self.training_best['loss'] = total_loss / count
for k, v in eval_dict.items():
if k in self.training_best:
self.training_best[k] = np.max(
self.training_best[k], stats.means(v)
)
else:
self.training_best[k] = stats.mean(v)
def eval_epoch(self, epoch, d_loader):
if d_loader is None:
return
@@ -650,6 +667,19 @@ class Trainer(object):
self._print("Eval", epoch, total_loss, eval_dict, count)
if 'loss' in self.eval_best:
self.eval_best['loss'] = np.min(
self.eval_best['loss'], total_loss / count
)
else:
self.eval_best['loss'] = total_loss / count
for k, v in eval_dict.items():
if k in self.eval_best:
self.eval_best[k] = np.max(self.eval_best[k], stats.means(v))
else:
self.eval_best[k] = stats.mean(v)
return total_loss / count, eval_dict
def train(
@@ -689,11 +719,26 @@ class Trainer(object):
best_loss = min(best_loss, val_loss)
save_checkpoint(
checkpoint_state(
self.model, self.optimizer, val_loss, epoch
self.model, self.optimizer, val_loss, epoch + 1
),
is_best,
filename=self.checkpoint_name,
bestname=self.best_name
)
print("{0} Summary {0}".format("-" * 5))
print("** Training Stats **")
for k, v in natsorted(self.training_best.items(), key=itemgetter(0)):
if k == 'loss':
print("Best loss: {:.4e}".format(v))
else:
print("Best {}: {:2.3f}%".format(k, v * 1e2))
print("\n** Eval Stats **")
for k, v in natsorted(self.eval_best.items(), key=itemgetter(0)):
if k == 'loss':
print("Best loss: {:.4e}".format(v))
else:
print("Best {}: {:2.3f}%".format(k, v * 1e2))
return best_loss