Update video_regression.py

AMAAI-Lab · Nov 3, 2023 · 9352f8a · 9352f8a
1 parent 9bb1e7d
commit 9352f8a
Showing 1 changed file with 0 additions and 22 deletions.
diff --git a/model/video_regression.py b/model/video_regression.py
@@ -19,7 +19,6 @@ def __init__(self, n_layers=2, d_model=64, dropout=0.1, max_sequence_video=300,
         self.total_vf_dim = total_vf_dim
         self.regModel = regModel
 
-        #self.lstm = nn.LSTM(self.total_vf_dim, self.d_model, self.nlayers, bidirectional=True)
         self.bilstm = nn.LSTM(self.total_vf_dim, self.d_model, self.nlayers, bidirectional=True)
         self.bigru = nn.GRU(self.total_vf_dim, self.d_model, self.nlayers, bidirectional=True)
         self.bifc = nn.Linear(self.d_model * 2, 2)
@@ -28,10 +27,6 @@ def __init__(self, n_layers=2, d_model=64, dropout=0.1, max_sequence_video=300,
         self.gru = nn.GRU(self.total_vf_dim, self.d_model, self.nlayers)
         self.fc = nn.Linear(self.d_model, 2)
 
-        # self.attention = nn.Sequential(
-        #     nn.Linear(self.total_vf_dim, 1),
-        #     nn.Tanh(),
-        # )
 
     def forward(self, feature_semantic_list, feature_scene_offset, feature_motion, feature_emotion):
         ### Video (SemanticList + SceneOffset + Motion + Emotion) (ENCODER) ###
@@ -43,9 +38,6 @@ def forward(self, feature_semantic_list, feature_scene_offset, feature_motion, f
         vf_concat = torch.cat([vf_concat, feature_motion.unsqueeze(-1).float()], dim=-1) 
         vf_concat = torch.cat([vf_concat, feature_emotion.float()], dim=-1) 
 
-        # hidden_state = torch.zeros(2, vf_concat.shape[0] , self.d_model).to(get_device())
-        # cell_state = torch.zeros(2, vf_concat.shape[0] , self.d_model).to(get_device())
-
         vf_concat = vf_concat.permute(1,0,2)
         vf_concat = F.dropout(vf_concat, p=self.dropout, training=self.training)
 
@@ -66,18 +58,4 @@ def forward(self, feature_semantic_list, feature_scene_offset, feature_motion, f
             out = out.permute(1,0,2)
             out = self.fc(out)
         return out
-
-        # # Add attention mechanism
-        # attn_weights = F.softmax(self.attention(vf_concat), dim=1)
-        # out = torch.bmm(attn_weights.transpose(1, 2), vf_concat)
-
-        # # Add attention mechanism
-        # attn_weights = F.softmax(self.attention(out), dim=1)
-        # out = torch.bmm(attn_weights.transpose(1, 2), out)
-
-        # # Add regularization with dropout
-        # out = F.dropout(out, p=self.dropout, training=self.training)
 
-        # out, _ = self.lstm(out)
-        # out = self.fc(out)
-        # return out