Upload HuBERTECG

config.json

@@ -5,6 +5,10 @@
     "HuBERTECG"
   ],
   "attention_dropout": 0.1,
+  "auto_map": {
+    "AutoConfig": "hubert_ecg.HuBERTECGConfig",
+    "AutoModel": "hubert_ecg.HuBERTECG"
+  },
   "bos_token_id": 1,
   "classifier_proj_size": 256,
   "conv_bias": false,

hubert_ecg.py

@@ -0,0 +1,47 @@
+import torch
+import torch.nn as nn
+from transformers import HubertConfig, HubertModel
+from typing import List
+
+class HuBERTECGConfig(HubertConfig):
+    
+    model_type = "hubert_ecg"
+    
+    def __init__(self, ensemble_length: int = 1, vocab_sizes: List[int] = [100], **kwargs):
+        super().__init__(**kwargs)
+        self.ensemble_length = ensemble_length
+        self.vocab_sizes = vocab_sizes if isinstance(vocab_sizes, list) else [vocab_sizes]
+
+class HuBERTECG(HubertModel):
+    
+    config_class = HuBERTECGConfig
+    
+    def __init__(self, config: HuBERTECGConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.pretraining_vocab_sizes = config.vocab_sizes
+            
+        assert config.ensemble_length > 0 and config.ensemble_length == len(config.vocab_sizes), f"ensemble_length {config.ensemble_length} must be equal to len(vocab_sizes) {len(config.vocab_sizes)}"
+
+        # final projection layer to map encodings into the space of the codebook
+        self.final_proj = nn.ModuleList([nn.Linear(config.hidden_size, config.classifier_proj_size) for _ in range(config.ensemble_length)])
+
+        # embedding for codebooks
+        self.label_embedding = nn.ModuleList([nn.Embedding(vocab_size, config.classifier_proj_size) for vocab_size in config.vocab_sizes])
+        
+        assert len(self.final_proj) == len(self.label_embedding), f"final_proj and label_embedding must have the same length"
+        
+    def logits(self, transformer_output: torch.Tensor) -> torch.Tensor:
+        # takes (B, T, D)
+        
+        # compute a projected output for each ensemble
+        projected_outputs = [final_projection(transformer_output) for final_projection in self.final_proj]
+        
+        ensemble_logits = [torch.cosine_similarity(
+            projected_output.unsqueeze(2),
+            label_emb.weight.unsqueeze(0).unsqueeze(0),
+            dim=-1,
+        ) / 0.1 for projected_output, label_emb in zip(projected_outputs, self.label_embedding)]
+        
+        return ensemble_logits # returns [(BS, T, V)] * ensemble_length