diff options
| -rw-r--r-- | models/model.py | 3 | ||||
| -rw-r--r-- | models/rgb_part_net.py | 5 |
2 files changed, 5 insertions, 3 deletions
diff --git a/models/model.py b/models/model.py index 5899fc0..90d48e0 100644 --- a/models/model.py +++ b/models/model.py @@ -314,7 +314,8 @@ class Model: ) # Init models - model_hp = self.hp.get('model', {}) + model_hp: dict = self.hp.get('model', {}).copy() + 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 = self.rgb_pn.to(self.device) diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py index 936ec46..4367c62 100644 --- a/models/rgb_part_net.py +++ b/models/rgb_part_net.py @@ -74,8 +74,8 @@ class RGBPartNet(nn.Module): def _disentangle(self, x_c1_t2, x_c2_t2=None): n, t, c, h, w = x_c1_t2.size() device = x_c1_t2.device - x_c1_t1 = x_c1_t2[:, torch.randperm(t), :, :, :] if self.training: + x_c1_t1 = x_c1_t2[:, torch.randperm(t), :, :, :] ((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2) # Decode features x_c = self._decode_cano_feature(f_c_, n, t, device) @@ -98,7 +98,8 @@ class RGBPartNet(nn.Module): else: # evaluating f_c_, f_p_ = self.ae(x_c1_t2) x_c = self._decode_cano_feature(f_c_, n, t, device) - x_p = self._decode_pose_feature(f_p_, n, t, c, h, w, device) + x_p_ = self._decode_pose_feature(f_p_, n, t, c, h, w, device) + x_p = x_p_.view(n, t, self.pn_in_channels, self.h // 4, self.w // 4) return (x_c, x_p), None, None def _decode_appr_feature(self, f_a_, n, t, device): |