65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py
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"""
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Various positional encodings for the transformer.
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"""
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import math
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import torch
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from torch import nn
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class PositionEmbeddingSine(nn.Module):
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"""
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This is a more standard version of the position embedding, very similar to the one
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used by the Attention is all you need paper, generalized to work on images.
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"""
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def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
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super().__init__()
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self.num_pos_feats = num_pos_feats
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self.temperature = temperature
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self.normalize = normalize
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if scale is not None and normalize is False:
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raise ValueError("normalize should be True if scale is passed")
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if scale is None:
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scale = 2 * math.pi
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self.scale = scale
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def forward(self, x, mask=None):
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if mask is None:
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mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
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not_mask = ~mask
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y_embed = not_mask.cumsum(1, dtype=x.dtype)
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x_embed = not_mask.cumsum(2, dtype=x.dtype)
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if self.normalize:
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eps = 1e-6
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y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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dim_t = torch.arange(self.num_pos_feats, dtype=x.dtype, device=x.device)
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dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
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pos_x = x_embed[:, :, :, None] / dim_t
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pos_y = y_embed[:, :, :, None] / dim_t
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pos_x = torch.stack(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos_y = torch.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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return pos
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def __repr__(self, _repr_indent=4):
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head = "Positional encoding " + self.__class__.__name__
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body = [
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"num_pos_feats: {}".format(self.num_pos_feats),
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"temperature: {}".format(self.temperature),
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"normalize: {}".format(self.normalize),
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"scale: {}".format(self.scale),
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]
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# _repr_indent = 4
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lines = [head] + [" " * _repr_indent + line for line in body]
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return "\n".join(lines)
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