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| author | Jordan Gong <jordan.gong@protonmail.com> | 2021-04-04 17:44:23 +0800 |
|---|---|---|
| committer | Jordan Gong <jordan.gong@protonmail.com> | 2021-04-04 17:44:23 +0800 |
| commit | 6f3dd9109b8ae7b37e3373d844a6c406d83c2b35 (patch) | |
| tree | a530221dfef3100a236c4091c3d0c15ea636d9e5 /models/model.py | |
| parent | 6a8824e4fb8bdd1f3e763b78b765830788415cfb (diff) | |
| parent | 85627d4cfb495453a7c28b3f131b84b1038af674 (diff) | |
Merge branch 'disentangling_only' into disentangling_only_py3.8disentangling_only_py3.8
Diffstat (limited to 'models/model.py')
| -rw-r--r-- | models/model.py | 24 |
1 files changed, 19 insertions, 5 deletions
diff --git a/models/model.py b/models/model.py index 46987ca..ebaaaf1 100644 --- a/models/model.py +++ b/models/model.py @@ -54,6 +54,7 @@ class Model: self.total_iters = self.meta.get('total_iters', (self.total_iter,)) self.is_train: bool = True + self.num_class: Optional[int] = None self.in_channels: int = 3 self.in_size: Tuple[int, int] = (64, 48) self.batch_size: Optional[int] = None @@ -160,8 +161,13 @@ class Model: optim_hp: Dict = self.hp.get('optimizer', {}).copy() sched_hp = self.hp.get('scheduler', {}) - self.rgb_pn = RGBPartNet(self.in_channels, self.in_size, **model_hp, - image_log_on=self.image_log_on) + self.rgb_pn = RGBPartNet( + self.num_class, + self.in_channels, + self.in_size, + **model_hp, + image_log_on=self.image_log_on + ) # Try to accelerate computation using CUDA or others self.rgb_pn = self.rgb_pn.to(self.device) @@ -202,7 +208,8 @@ class Model: # forward + backward + optimize x_c1 = batch_c1['clip'].to(self.device) x_c2 = batch_c2['clip'].to(self.device) - losses, features, images = self.rgb_pn(x_c1, x_c2) + y = batch_c1['label'].to(self.device) + losses, features, images = self.rgb_pn(x_c1, x_c2, y) loss = losses.sum() loss.backward() self.optimizer.step() @@ -254,8 +261,9 @@ class Model: batch_c1, batch_c2 = next(val_dataloader) x_c1 = batch_c1['clip'].to(self.device) x_c2 = batch_c2['clip'].to(self.device) + y = batch_c1['label'].to(self.device) with torch.no_grad(): - losses, _, _ = self.rgb_pn(x_c1, x_c2) + losses, _, _ = self.rgb_pn(x_c1, x_c2, y) loss = losses.sum() self._write_stat('Val', loss, losses) @@ -302,7 +310,12 @@ class Model: # Init models model_hp: dict = self.hp.get('model', {}).copy() - self.rgb_pn = RGBPartNet(self.in_channels, self.in_size, **model_hp) + self.rgb_pn = RGBPartNet( + self.num_class, + 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) self.rgb_pn.eval() @@ -419,6 +432,7 @@ class Model: self, dataset_config: DatasetConfiguration ) -> Union[CASIAB]: + self.num_class = dataset_config.get('train_size', 74) self.in_channels = dataset_config.get('num_input_channels', 3) self.in_size = dataset_config.get('frame_size', (64, 48)) self._dataset_sig = self._make_signature( |