64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
import torch
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from torch import nn
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from torch.nn.utils.rnn import pad_sequence
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import PytorchBoot.stereotype as stereotype
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@stereotype.module("transformer_seq_encoder")
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class TransformerSequenceEncoder(nn.Module):
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def __init__(self, config):
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super(TransformerSequenceEncoder, self).__init__()
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self.config = config
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embed_dim = config["embed_dim"]
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encoder_layer = nn.TransformerEncoderLayer(
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d_model=embed_dim,
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nhead=config["num_heads"],
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dim_feedforward=config["ffn_dim"],
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batch_first=True,
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)
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self.transformer_encoder = nn.TransformerEncoder(
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encoder_layer, num_layers=config["num_layers"]
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)
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self.fc = nn.Linear(embed_dim, config["output_dim"])
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def encode_sequence(self, embedding_list_batch):
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lengths = []
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for embedding_list in embedding_list_batch:
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lengths.append(len(embedding_list))
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embedding_tensor = pad_sequence(embedding_list_batch, batch_first=True) # Shape: [batch_size, max_seq_len, embed_dim]
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max_len = max(lengths)
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padding_mask = torch.tensor([([0] * length + [1] * (max_len - length)) for length in lengths], dtype=torch.bool).to(embedding_tensor.device)
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transformer_output = self.transformer_encoder(embedding_tensor, src_key_padding_mask=padding_mask)
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final_feature = transformer_output.mean(dim=1)
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final_output = self.fc(final_feature)
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return final_output
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if __name__ == "__main__":
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config = {
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"embed_dim": 256,
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"num_heads": 4,
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"ffn_dim": 256,
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"num_layers": 3,
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"output_dim": 1024,
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}
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encoder = TransformerSequenceEncoder(config)
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seq_len = [5, 8, 9, 4]
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batch_size = 4
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embedding_list_batch = [
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torch.randn(seq_len[idx], config["embed_dim"]) for idx in range(batch_size)
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]
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output_feature = encoder.encode_sequence(
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embedding_list_batch
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)
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print("Encoded Feature:", output_feature)
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print("Feature Shape:", output_feature.shape)
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