--- a
+++ b/src/models/multimodals.py
@@ -0,0 +1,59 @@
+import torch
+import torch.nn as nn
+from cnn_finetune import make_model
+from timm import create_model
+
+
+def cnnfinetune_freeze(self):
+    for param in self.parameters():
+        param.requires_grad = False
+
+    for param in self._classifier.parameters():
+        param.requires_grad = True
+
+
+def cnnfinetune_unfreeze(self):
+    for param in self.parameters():
+        param.requires_grad = True
+
+
+def make_classifier(in_features, num_classes):
+    return nn.Sequential(
+        nn.Linear(in_features, 512),
+        nn.Dropout(0.3),
+        nn.Linear(512, num_classes),
+    )
+
+
+class MultiModals(nn.Module):
+    def __init__(self, model_name, pretrained=True, num_classes=6, dropout_p=None):
+        super(MultiModals, self).__init__()
+        self.model = make_model(
+            model_name=model_name,
+            num_classes=num_classes,
+            pretrained=pretrained,
+            dropout_p=dropout_p,
+            # classifier_factory=make_classifier
+        )
+
+        in_features = self.model._classifier.in_features
+
+        self._classifier = nn.Sequential(
+            nn.Linear(in_features + 8, 512),
+            nn.Dropout(0.3),
+            nn.Linear(512, num_classes),
+        )
+
+        setattr(self, 'freeze', cnnfinetune_freeze)
+        setattr(self, 'unfreeze', cnnfinetune_unfreeze)
+
+    def forward(self, images, meta):
+        x = self.model._features(images)
+        x = self.model.pool(x)
+        x = x.view(x.size(0), -1)
+        # import pdb
+        # pdb.set_trace()
+        # if isinstance(x, torch.HalfTensor):
+        #     meta = meta.half()
+        x = torch.cat([x, meta], dim=1)
+        return self._classifier(x)