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from models.networks.utils import UnormGPS
import torch.nn as nn
from torch.nn.functional import tanh
import torch
class RegressionHead(nn.Module):
def __init__(self, use_tanh=False):
super().__init__()
self.unorm = UnormGPS()
self.use_tanh = use_tanh
def forward(self, x):
"""Forward pass of the network.
x : Union[torch.Tensor, dict] with the output of the backbone.
"""
if self.use_tanh:
x = tanh(x)
gps = self.unorm(x)
return {"gps": gps}
class RegressionHeadAngle(nn.Module):
def __init__(self):
super().__init__()
self.unorm = UnormGPS()
def forward(self, x):
"""Forward pass of the network.
x : Union[torch.Tensor, dict] with the output of the backbone.
"""
x1 = x[:, 0].pow(2)
x2 = x[:, 1].pow(2)
x3 = x[:, 2].pow(2)
x4 = x[:, 3].pow(2)
cos_lambda = x1 / (x1 + x2)
sin_lambda = x2 / (x1 + x2)
cos_phi = x3 / (x3 + x4)
sin_phi = x4 / (x3 + x4)
lbd = torch.atan2(sin_lambda, cos_lambda)
phi = torch.atan2(sin_phi, cos_phi)
gps = torch.cat((lbd.unsqueeze(1), phi.unsqueeze(1)), dim=1)
# gps = self.unorm(x)
return {"gps": gps}
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