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modelling_cxrrg.py

@@ -317,7 +317,7 @@ class CXRRGModel(VisionEncoderDecoderModel):
                     )
                 ])
 
-                token_type_ids[ids[:, 0], ids[:, 1]] = self.config.section_ids[i + 1]
+                token_type_ids[ids[:, 0], ids[:, 1]] = self.config.decoder.section_ids[i + 1]
 
         return token_type_ids
 
@@ -333,7 +333,7 @@ class CXRRGModel(VisionEncoderDecoderModel):
             token_type_ids - token type identifiers.
         """
 
-        token_type_ids = torch.full([token_ids.shape[0], 1], self.config.section_ids[0], dtype=torch.long, device=token_ids.device)
+        token_type_ids = torch.full([token_ids.shape[0], 1], self.config.decoder.section_ids[0], dtype=torch.long, device=token_ids.device)
 
         # https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer.create_token_type_ids_from_sequences.example
         token_ids = token_ids[:, :-1]
@@ -342,7 +342,7 @@ class CXRRGModel(VisionEncoderDecoderModel):
 
             # Find first occurrence of special token, which indicates the boundary between sections:
             exists = torch.any(token_ids == j, dim=1, keepdim=True)
-            token_type_ids[exists] = self.config.section_ids[i + 1]
+            token_type_ids[exists] = self.config.decoder.section_ids[i + 1]
 
         return token_type_ids