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- import torch
- import torch.nn as nn
- from .rtdetr_basic import TRDecoderLayer
- # Transformer Decoder Module
- class MemoryCompressor(nn.Module):
- def __init__(self, cfg, in_dim):
- super().__init__()
- # -------------------- Basic Parameters ---------------------
- self.d_model = in_dim
- self.ffn_dim = round(cfg['com_dim_feedforward']*cfg['width'])
- self.compressed_vector = nn.Embedding(cfg['dim_compressed'], in_dim)
- # -------------------- Network Parameters ---------------------
- self.compress_layer = TRDecoderLayer(
- d_model=in_dim,
- dim_feedforward=self.ffn_dim,
- num_heads=cfg['com_num_heads'],
- dropout=cfg['com_dropout'],
- act_type=cfg['com_act']
- )
- def forward(self, memory, memory_pos):
- bs = memory.size(0)
- output = self.compressed_vector.weight[None].repeat(bs, 1, 1)
- output = self.compress_layer(output, None, memory, memory_pos)
- return output
- def build_compressor(cfg, in_dim):
- return MemoryCompressor(cfg, in_dim)
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