Add prototype predict function

1 files changed, 54 insertions, 5 deletions

diff --git a/models/model.py b/models/model.py
index 54f3441..ba29ede 100644
--- a/models/model.py
+++ b/models/model.py
@@ -8,6 +8,7 @@ import torch.optim as optim
 from torch.utils.data import DataLoader
 from torch.utils.data.dataloader import default_collate
 from torch.utils.tensorboard import SummaryWriter
+from tqdm import tqdm
 
 from models.rgb_part_net import RGBPartNet
 from utils.configuration import DataloaderConfiguration, \
@@ -88,10 +89,7 @@ class Model:
         self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, 500, 0.9)
         self.writer = SummaryWriter(self.log_name)
 
-        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)
+        self._accelerate()
 
         self.rgb_pn.train()
         # Init weights at first iter
@@ -143,6 +141,57 @@ class Model:
                 self.writer.close()
                 break
 
+    def _accelerate(self):
+        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)
+
+    def predict(
+            self,
+            iter_: int,
+            dataset_config: DatasetConfiguration,
+            dataloader_config: DataloaderConfiguration,
+    ):
+        self.is_train = False
+        dataset = self._parse_dataset_config(dataset_config)
+        dataloader = self._parse_dataloader_config(dataset, dataloader_config)
+        hp = self.hp.copy()
+        _, _ = hp.pop('lr'), hp.pop('betas')
+        dataset_name = dataset_config.get('name', 'CASIA-B')
+        if dataset_name == 'CASIA-B':
+            self.rgb_pn = RGBPartNet(124 - self.train_size,
+                                     self.in_channels,
+                                     **hp)
+        elif dataset_name == 'FVG':
+            # TODO
+            pass
+        else:
+            raise ValueError('Invalid dataset: {0}'.format(dataset_name))
+
+        self._accelerate()
+
+        self.rgb_pn.eval()
+        # Load checkpoint at iter_
+        self.curr_iter = iter_
+        checkpoint = torch.load(self.checkpoint_name)
+        self.rgb_pn.load_state_dict(checkpoint['model_state_dict'])
+
+        labels, conditions, views, features = [], [], [], []
+        for sample in tqdm(dataloader, desc='Transforming', unit='clips'):
+            label, condition, view, clip = sample.values()
+            feature = self.rgb_pn(clip).detach().cpu().numpy()
+            labels.append(label)
+            conditions.append(condition)
+            views.append(view)
+            features.append(feature)
+        labels = np.asarray(labels)
+        conditions = np.asarray(conditions)
+        views = np.asarray(views)
+        features = np.asarray(features)
+
+        # TODO Implement evaluation function here
+
     @staticmethod
     def init_weights(m):
         if isinstance(m, nn.modules.conv._ConvNd):
@@ -167,7 +216,7 @@ class Model:
         )
         self.log_name = '_'.join((self.log_name, self._dataset_sig))
         config: dict = dataset_config.copy()
-        name = config.pop('name')
+        name = config.pop('name', 'CASIA-B')
         if name == 'CASIA-B':
             return CASIAB(**config, is_train=self.is_train)
         elif name == 'FVG':