Fixes for evaluation

Project_Model/Libs/Transformer/Models/NanoSocrates.py

@@ -83,7 +83,14 @@ class NanoSocratesCore(torch.nn.Module):
         x, padding = args
 
         encoder_tensor = self.__encoder_embedder(x)
-        BATCH, SEQ_LEN, _ = x.shape
+
+        BATCH: int
+
+        if len(x.shape) > 2:
+            BATCH, SEQ_LEN, _ = x.shape
+        else:
+            _, SEQ_LEN = x.shape
+            BATCH = 1
 
         encoder_output, _ = self.__encoder((encoder_tensor, padding))
 
@@ -95,25 +102,32 @@ class NanoSocratesCore(torch.nn.Module):
 
         while continue_generating:
 
-            decoder_in = self.__decoder_embedder(decoder_in)
+            decoder_in_x = self.__decoder_embedder(decoder_in)
 
             decoder_output, _, _, _, _, _ = self.__decoder(
-                (decoder_in, encoder_output, encoder_output, padding, decoder_in_pad_mask, False)
+                (decoder_in_x, encoder_output, encoder_output, padding, decoder_in_pad_mask, False)
             )
 
             logits: torch.Tensor = self.__detokener(decoder_output)
 
             logits = torch.softmax(logits, 2)
 
-            tokens = torch.argmax(logits)
+            tokens = torch.argmax(logits, 2)
+
+            if token_idx < self.__sentence_len - 1:
+                decoder_in[:,token_idx + 1] = tokens[:,token_idx]
+                decoder_in_pad_mask = decoder_in.eq(self.__pad)
+
+            if token_idx == self.__sentence_len - 1:
+                continue_generating = False
+                continue
 
             if tokens.shape[0] == 1 and tokens[0,token_idx] == self.__eos:
                 continue_generating = False
                 continue
 
-            if token_idx < self.__sentence_len - 1:
-                decoder_in[:,token_idx + 1] = tokens[:,token_idx]
-                decoder_in_pad_mask = decoder_in.eq(self.__pad)
+
+            token_idx += 1
 
         return decoder_in
 
@@ -130,7 +144,7 @@ class NanoSocratesCore(torch.nn.Module):
 
         logits = torch.softmax(logits, 2)
 
-        tokens = torch.argmax(logits)
+        tokens = torch.argmax(logits, 2)
 
         return tokens
 
@@ -146,31 +160,56 @@ class NanoSocratesCore(torch.nn.Module):
 
         while continue_generating:
 
-            decoder_in = self.__decoder_embedder(decoder_in)
+            decoder_x = self.__decoder_embedder(decoder_in)
 
             decoder_output, _, _, _, _, _ = self.__decoder(
-                (decoder_in, decoder_in, decoder_in, decoder_in_prefix_mask, decoder_in_pad_mask, False)
+                (decoder_x, decoder_in, decoder_in, decoder_in_prefix_mask, decoder_in_pad_mask, True)
             )
 
             logits: torch.Tensor = self.__detokener(decoder_output)
 
             logits = torch.softmax(logits, 2)
 
-            tokens = torch.argmax(logits)
+            tokens = torch.argmax(logits, 2)
+
+            if token_idx < self.__sentence_len - 1:
+                decoder_in[:,token_idx + 1] = tokens[:,token_idx]
+                decoder_in_pad_mask = decoder_in.eq(self.__pad)
+
+            if token_idx == self.__sentence_len - 1:
+                continue_generating = False
+                continue
 
             if tokens.shape[0] == 1 and tokens[0,token_idx] == self.__eos:
                 continue_generating = False
                 continue
 
-            if token_idx < self.__sentence_len - 1:
-                decoder_in[:,token_idx + 1] = tokens[:,token_idx]
-                decoder_in_pad_mask = decoder_in.eq(self.__pad)
+            token_idx += 1
+
+
+
 
         return decoder_in
 
     def take_pieces(self):
 
         return (
-            (self.__encoder_embedder, self.__encoder),
+            (self.__encoder_embedder, self.__encoder, self.__encoder_detokener),
             (self.__decoder_embedder, self.__decoder, self.__detokener)
-        )
+        )
+
+    def load_pieces(
+        self,
+        encoder_embedder: Embedder.NanoSocratesEmbedder,
+        decoder_embedder: Embedder.NanoSocratesEmbedder,
+        encoder: torch.nn.Sequential,
+        decoder: torch.nn.Sequential,
+        encoder_detokener: DeToken,
+        decoder_detokener: DeToken
+    ):
+        self.__encoder_embedder = encoder_embedder
+        self.__decoder_embedder = decoder_embedder
+        self.__encoder = encoder
+        self.__decoder = decoder
+        self.__encoder_detokener = encoder_detokener
+        self.__detokener = decoder_detokener

Project_Model/Libs/Transformer/Models/__init__.py

@@ -1,9 +1,11 @@
 from .TrainingModel import TrainingModel
 from .NanoSocratEncoder import NanoSocratEncoder
 from .NanoSocraDecoder import NanoSocraDecoder
+from .NanoSocrates import NanoSocratesCore
 
 __all__ = [
     "TrainingModel",
     "NanoSocratEncoder",
-    "NanoSocraDecoder"
+    "NanoSocraDecoder",
+    "NanoSocratesCore"
 ]

Project_Model/Libs/Transformer/Utils/task_type.py

@@ -1,6 +1,7 @@
 from enum import Enum, auto
 
 class TaskType(Enum):
+    TEXT2RDF = auto()
     RDF2TEXT = auto()
     MASK = auto()
     COMPLETATION = auto()