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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 PositionEncodingSine(nn.Module):
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"""
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This is a sinusoidal position encoding that generalized to 2-dimensional images
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"""
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def __init__(self, d_model, max_shape=(256, 256), temp_bug_fix=False):
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"""
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Args:
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max_shape (tuple): for 1/8 featmap, the max length of 256 corresponds to 2048 pixels
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temp_bug_fix (bool): As noted in this [issue](https://github.com/zju3dv/LoFTR/issues/41),
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the original implementation of LoFTR includes a bug in the pos-enc impl, which has little impact
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on the final performance. For now, we keep both impls for backward compatability.
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We will remove the buggy impl after re-training all variants of our released models.
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"""
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super().__init__()
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pe = torch.zeros((d_model, *max_shape))
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y_position = torch.ones(max_shape).cumsum(0).float().unsqueeze(0)
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x_position = torch.ones(max_shape).cumsum(1).float().unsqueeze(0)
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if temp_bug_fix:
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div_term = torch.exp(torch.arange(0, d_model//2, 2).float() * (-math.log(10000.0) / (d_model//2)))
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else: # a buggy implementation (for backward compatability only)
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div_term = torch.exp(torch.arange(0, d_model//2, 2).float() * (-math.log(10000.0) / d_model//2))
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div_term = div_term[:, None, None] # [C//4, 1, 1]
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pe[0::4, :, :] = torch.sin(x_position * div_term)
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pe[1::4, :, :] = torch.cos(x_position * div_term)
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pe[2::4, :, :] = torch.sin(y_position * div_term)
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pe[3::4, :, :] = torch.cos(y_position * div_term)
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self.register_buffer('pe', pe.unsqueeze(0), persistent=False) # [1, C, H, W]
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def forward(self, x):
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"""
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Args:
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x: [N, C, H, W]
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"""
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return x + self.pe[:, :, :x.size(2), :x.size(3)].to(x.device)
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