[aisingapore#61] Replaced deprecated functional.tanh with torch.tanh

ktyap · Nov 4, 2022 · 5da1a48 · 5da1a48
1 parent 97bc12f
commit 5da1a48
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Showing 3 changed files with 4 additions and 4 deletions.
diff --git a/sgnlp/models/drnn_roberta/model.py b/sgnlp/models/drnn_roberta/model.py
@@ -104,7 +104,7 @@ def forward(self, M, x, mask=None):
             M_ = M.transpose(0,1) # batch, seqlen, mem_dim
             x_ = x.unsqueeze(1).expand(-1,M.size()[0],-1) # batch, seqlen, cand_dim
             M_x_ = torch.cat([M_,x_],2) # batch, seqlen, mem_dim+cand_dim
-            mx_a = F.tanh(self.transform(M_x_)) # batch, seqlen, alpha_dim
+            mx_a = torch.tanh(self.transform(M_x_)) # batch, seqlen, alpha_dim
             alpha = F.softmax(self.vector_prod(mx_a),1).transpose(1,2) # batch, 1, seqlen
 
         attn_pool = torch.bmm(alpha, M.transpose(0,1))[:,0,:] # batch, mem_dim
@@ -426,7 +426,7 @@ def forward(
                 att_features = torch.cat(att_features, dim=0)
                 hidden = F.relu(self.linear(att_features))
             else:
-                hidden = F.tanh(self.linear(features))
+                hidden = torch.tanh(self.linear(features))
 
 
             log_prob = F.log_softmax(self.smax_fc(hidden), 2)

diff --git a/sgnlp/models/drnn_roberta/modeling.py b/sgnlp/models/drnn_roberta/modeling.py
@@ -260,7 +260,7 @@ def forward(
                 att_features = torch.cat(att_features, dim=0)
                 hidden = F.relu(self.linear(att_features))
             else:
-                hidden = F.tanh(self.linear(features))
+                hidden = torch.tanh(self.linear(features))
 
 
             log_prob = F.log_softmax(self.smax_fc(hidden), 2)

diff --git a/sgnlp/models/drnn_roberta/modules.py b/sgnlp/models/drnn_roberta/modules.py
@@ -91,7 +91,7 @@ def forward(self, M, x, mask=None):
             M_ = M.transpose(0,1) # batch, seqlen, mem_dim
             x_ = x.unsqueeze(1).expand(-1,M.size()[0],-1) # batch, seqlen, cand_dim
             M_x_ = torch.cat([M_,x_],2) # batch, seqlen, mem_dim+cand_dim
-            mx_a = F.tanh(self.transform(M_x_)) # batch, seqlen, alpha_dim
+            mx_a = torch.tanh(self.transform(M_x_)) # batch, seqlen, alpha_dim
             alpha = F.softmax(self.vector_prod(mx_a),1).transpose(1,2) # batch, 1, seqlen
 
         attn_pool = torch.bmm(alpha, M.transpose(0,1))[:,0,:] # batch, mem_dim