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import math |
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import torch |
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from torch import nn |
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from uniperceiver.utils.registry import Registry |
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POSITION_ENC_REGISTRY = Registry("POSITION_ENC") |
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POSITION_ENC_REGISTRY.__doc__ = """ |
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Registry for positional encoding |
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""" |
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__all__ = ["SinusoidEncoding", "NNEmbeddingEncoding"] |
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def build_position_encoding(cfg, dim, max_len): |
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name = cfg.MODEL.TOKEN_EMBED.POSITION |
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return POSITION_ENC_REGISTRY.get(name)(dim, max_len) |
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@POSITION_ENC_REGISTRY.register() |
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class SinusoidEncoding(nn.Module): |
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def __init__(self, dim, max_len): |
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super(SinusoidEncoding, self).__init__() |
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pe = torch.zeros(max_len, dim) |
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position = torch.arange(0, max_len).unsqueeze(1).float() |
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div_term = torch.exp(torch.arange(0, dim, 2).float() * |
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-(math.log(max_len * 2.0) / dim)) |
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pe[:, 0::2] = torch.sin(position * div_term) |
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pe[:, 1::2] = torch.cos(position * div_term) |
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pe = pe.unsqueeze(0) |
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self.register_buffer('pe', pe) |
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def forward(self, x): |
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if isinstance(x, int): |
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return self.pe[:, x] |
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else: |
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x_size = x.size(1) |
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return self.pe[:, :x_size] |
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@POSITION_ENC_REGISTRY.register() |
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class NNEmbeddingEncoding(nn.Module): |
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def __init__(self, dim, max_len): |
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super(NNEmbeddingEncoding, self).__init__() |
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self.position_embeddings = nn.Embedding(max_len, dim) |
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def forward(self, x, start_time=0): |
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if isinstance(x, int): |
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position_embeddings = self.position_embeddings(torch.tensor([x], dtype=torch.long).cuda()) |
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elif isinstance(x, torch.Tensor) and x.dim()==1: |
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position_embeddings = self.position_embeddings(x) |
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else: |
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x_size = x.size(1) |
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position_ids = torch.arange(x_size, dtype=torch.long, device=x.device) + start_time |
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position_embeddings = self.position_embeddings(position_ids) |
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return position_embeddings |
