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-rw-r--r--models/model.py13
1 files changed, 8 insertions, 5 deletions
diff --git a/models/model.py b/models/model.py
index 90d48e0..a086e7b 100644
--- a/models/model.py
+++ b/models/model.py
@@ -157,13 +157,15 @@ class Model:
self.rgb_pn.hpm_num_parts, triplet_margins
)
# Try to accelerate computation using CUDA or others
+ self.rgb_pn = nn.DataParallel(self.rgb_pn)
self.rgb_pn = self.rgb_pn.to(self.device)
+ self.ba_triplet_loss = nn.DataParallel(self.ba_triplet_loss)
self.ba_triplet_loss = self.ba_triplet_loss.to(self.device)
self.optimizer = optim.Adam([
- {'params': self.rgb_pn.ae.parameters(), **ae_optim_hp},
- {'params': self.rgb_pn.pn.parameters(), **pn_optim_hp},
- {'params': self.rgb_pn.hpm.parameters(), **hpm_optim_hp},
- {'params': self.rgb_pn.fc_mat, **fc_optim_hp}
+ {'params': self.rgb_pn.module.ae.parameters(), **ae_optim_hp},
+ {'params': self.rgb_pn.module.pn.parameters(), **pn_optim_hp},
+ {'params': self.rgb_pn.module.hpm.parameters(), **hpm_optim_hp},
+ {'params': self.rgb_pn.module.fc_mat, **fc_optim_hp}
], **optim_hp)
sched_gamma = sched_hp.get('gamma', 0.9)
sched_step_size = sched_hp.get('step_size', 500)
@@ -206,7 +208,7 @@ class Model:
y = y.repeat(self.rgb_pn.num_total_parts, 1)
triplet_loss = self.ba_triplet_loss(feature, y)
losses = torch.cat((
- ae_losses,
+ ae_losses.mean(0),
torch.stack((
triplet_loss[:self.rgb_pn.hpm_num_parts].mean(),
triplet_loss[self.rgb_pn.hpm_num_parts:].mean()
@@ -318,6 +320,7 @@ class Model:
model_hp.pop('triplet_margins', None)
self.rgb_pn = RGBPartNet(self.in_channels, self.in_size, **model_hp)
# Try to accelerate computation using CUDA or others
+ self.rgb_pn = nn.DataParallel(self.rgb_pn)
self.rgb_pn = self.rgb_pn.to(self.device)
self.rgb_pn.eval()