ecg_encoder.py

import torch
import torch.nn as nn
from transformers import AutoModel


class ECGEncoder(nn.Module):
    def __init__(self, **kwargs):
        super(ECGEncoder, self).__init__()
        # Load the pre-trained model from Hugging Face
        # Note: Ensure that the model is available and accessible
        # Use bfloat16 by default.
        self.encoder = AutoModel.from_pretrained("fuyingw/MELP_Encoder", 
            torch_dtype=torch.bfloat16,
            trust_remote_code=True, 
            revision="main")
    
    def forward(self, input_embeds, audio_attention_mask):
        x = input_embeds.permute(0, 2, 1)  # Change shape from (batch_size, seq_len, features) to (batch_size, features, seq_len)
        output = self.encoder(x)
        # bz, 128, 768
        mask = None
        return output["ecg_token_emb"], mask

    def gradient_checkpointing_enable(self):
        pass