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import torch
import torch.nn as nn
# import torch.nn.functional as F
from .gaussian_projection import GaussianProjection
[docs]class DistCost(nn.Module):
def __init__(self, weight=None, vec_weight=None, gaussian_params={}, device=torch.device('cpu'), float_dtype=torch.float32, **kwargs):
super(DistCost, self).__init__()
self.device = device
self.float_dtype = float_dtype
self.weight = torch.as_tensor(weight, device=device, dtype=float_dtype)
if(vec_weight is not None):
self.vec_weight = torch.as_tensor(vec_weight, device=device, dtype=float_dtype)
else:
self.vec_weight = 1.0
self.proj_gaussian = GaussianProjection(gaussian_params=gaussian_params)
[docs] def forward(self, disp_vec, dist_type="l2", beta=1.0, RETURN_GOAL_DIST=False):
inp_device = disp_vec.device
disp_vec = self.vec_weight * disp_vec.to(self.device)
if dist_type == 'l2':
dist = torch.norm(disp_vec, p=2, dim=-1,keepdim=False)
elif dist_type == 'squared_l2':
dist = (torch.sum(torch.square(disp_vec), dim=-1,keepdim=False))
elif dist_type == 'l1':
dist = torch.norm(disp_vec, p=1, dim=-1,keepdim=False)
elif dist_type == 'smooth_l1':
l1_dist = torch.norm(disp_vec, p=1, dim=-1)
dist = None
raise NotImplementedError
cost = self.weight * self.proj_gaussian(dist)
if(RETURN_GOAL_DIST):
return cost.to(inp_device), dist.to(inp_device)
return cost.to(inp_device)