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-rw-r--r--models/model.py13
1 files changed, 7 insertions, 6 deletions
diff --git a/models/model.py b/models/model.py
index e8d79f9..3cae788 100644
--- a/models/model.py
+++ b/models/model.py
@@ -115,11 +115,11 @@ class Model:
optim_hp = self.hp.get('optimizer', {})
sched_hp = self.hp.get('scheduler', {})
self.rgb_pn = RGBPartNet(self.train_size, self.in_channels, **model_hp)
+ # Try to accelerate computation using CUDA or others
+ self.rgb_pn = self._accelerate(self.rgb_pn)
self.optimizer = optim.Adam(self.rgb_pn.parameters(), **optim_hp)
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, **sched_hp)
self.writer = SummaryWriter(self._log_name)
- # Try to accelerate computation using CUDA or others
- self._accelerate()
self.rgb_pn.train()
# Init weights at first iter
@@ -176,11 +176,12 @@ class Model:
self.writer.close()
break
- def _accelerate(self):
+ def _accelerate(self, model: nn.Module) -> nn.Module:
if not self.disable_acc:
if torch.cuda.device_count() > 1:
- self.rgb_pn = nn.DataParallel(self.rgb_pn)
- self.rgb_pn = self.rgb_pn.to(self.device)
+ model = nn.DataParallel(model)
+ model = model.to(self.device)
+ return model
def predict_all(
self,
@@ -204,7 +205,7 @@ class Model:
model_hp = self.hp.get('model', {})
self.rgb_pn = RGBPartNet(ae_in_channels=self.in_channels, **model_hp)
# Try to accelerate computation using CUDA or others
- self._accelerate()
+ self.rgb_pn = self._accelerate(self.rgb_pn)
self.rgb_pn.eval()
gallery_samples, probe_samples = [], {}