47 lines
2.4 KiB
Python
47 lines
2.4 KiB
Python
import torch
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from torch import nn
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import PytorchBoot.stereotype as stereotype
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import sys; sys.path.append(r"C:\Document\Local Project\nbv_rec\nbv_reconstruction")
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from modules.seq_encoder.abstract_seq_encoder import SequenceEncoder
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@stereotype.module("transformer_seq_encoder")
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class TransformerSequenceEncoder(SequenceEncoder):
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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['pts_embed_dim'] + config['pose_embed_dim']
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self.positional_encoding = nn.Parameter(torch.zeros(1, config['max_seq_len'], embed_dim))
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encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=config['num_heads'], dim_feedforward=config['ffn_dim'])
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self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=config['num_layers'])
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self.fc = nn.Linear(embed_dim, config['output_dim'])
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def encode_sequence(self, pts_embedding_list, pose_embedding_list):
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combined_features = [torch.cat((pts_embed, pose_embed), dim=-1) for pts_embed, pose_embed in zip(pts_embedding_list[:-1], pose_embedding_list[:-1])]
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combined_tensor = torch.stack(combined_features)
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pos_encoding = self.positional_encoding[:, :combined_tensor.size(0), :]
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combined_tensor = combined_tensor.unsqueeze(0) + pos_encoding
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transformer_output = self.transformer_encoder(combined_tensor).squeeze(0)
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final_feature = transformer_output.mean(dim=0)
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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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'pts_embed_dim': 1024, # 每个点云embedding的维度
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'pose_embed_dim': 256, # 每个姿态embedding的维度
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'num_heads': 4, # 多头注意力机制的头数
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'ffn_dim': 256, # 前馈神经网络的维度
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'num_layers': 3, # Transformer 编码层数
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'max_seq_len': 10, # 最大序列长度
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'output_dim': 2048, # 输出特征维度
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}
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encoder = TransformerSequenceEncoder(config)
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seq_len = 5
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pts_embedding_list = [torch.randn(config['pts_embed_dim']) for _ in range(seq_len)]
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pose_embedding_list = [torch.randn(config['pose_embed_dim']) for _ in range(seq_len)]
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output_feature = encoder.encode_sequence(pts_embedding_list, pose_embedding_list)
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print("Encoded Feature:", output_feature)
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print("Feature Shape:", output_feature.shape) |