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import torch
from torch import nn
import math
"""
Wrap encoding, as used by MacAodha et al
"""
class Wrap(nn.Module):
def __init__(self):
super(Wrap, self).__init__()
# adding this class variable is important to determine
# the dimension of the follow-up neural network
self.embedding_dim = 4
def forward(self, coords):
# place lon lat coordinates in a -pi, pi range
coords = torch.deg2rad(coords)
cos_lon = torch.cos(coords[:, 0]).unsqueeze(-1)
sin_lon = torch.sin(coords[:, 0]).unsqueeze(-1)
cos_lat = torch.cos(coords[:, 1]).unsqueeze(-1)
sin_lat = torch.sin(coords[:, 1]).unsqueeze(-1)
return torch.cat((cos_lon, sin_lon, cos_lat, sin_lat), 1) |