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