EigenGS/utils.py at main · vllab/EigenGS · GitHub

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import os
import torch.nn.functional as F
from pytorch_msssim import ms_ssim, ssim
import torch
import numpy as np

# class LogWriter:
#     def __init__(self, file_path, train=True):
#         os.makedirs(file_path, exist_ok=True)
#         self.file_path = os.path.join(file_path, "train.txt" if train else "test.txt")

#     def write(self, text, show=True):
#         if show:
#             print(text)
#         with open(self.file_path, 'a') as file:
#             file.write(text + '\n')


def loss_fn(pred, target, loss_type='L2', lambda_value=0.7):
    target = target.detach()
    pred = pred.float()
    target  = target.float()
    if loss_type == 'L2':
        loss = F.mse_loss(pred, target)
    elif loss_type == 'L1':
        loss = F.l1_loss(pred, target)
    elif loss_type == 'SSIM':
        loss = 1 - ssim(pred, target, data_range=1, size_average=True)
    elif loss_type == 'Fusion1':
        loss = lambda_value * F.mse_loss(pred, target) + (1-lambda_value) * (1 - ssim(pred, target, data_range=1, size_average=True))
    elif loss_type == 'Fusion2':
        loss = lambda_value * F.l1_loss(pred, target) + (1-lambda_value) * (1 - ssim(pred, target, data_range=1, size_average=True))
    elif loss_type == 'Fusion3':
        loss = lambda_value * F.mse_loss(pred, target) + (1-lambda_value) * F.l1_loss(pred, target)
    elif loss_type == 'Fusion4':
        loss = lambda_value * F.l1_loss(pred, target) + (1-lambda_value) * (1 - ms_ssim(pred, target, data_range=1, size_average=True))
    elif loss_type == 'Fusion_hinerv':
        loss = lambda_value * F.l1_loss(pred, target) + (1-lambda_value)  * (1 - ms_ssim(pred, target, data_range=1, size_average=True, win_size=5))
    return loss

def strip_lowerdiag(L):
    if L.shape[1] == 3:
        uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda")
        uncertainty[:, 0] = L[:, 0, 0]
        uncertainty[:, 1] = L[:, 0, 1]
        uncertainty[:, 2] = L[:, 0, 2]
        uncertainty[:, 3] = L[:, 1, 1]
        uncertainty[:, 4] = L[:, 1, 2]
        uncertainty[:, 5] = L[:, 2, 2]

    elif L.shape[1] == 2:
        uncertainty = torch.zeros((L.shape[0], 3), dtype=torch.float, device="cuda")
        uncertainty[:, 0] = L[:, 0, 0]
        uncertainty[:, 1] = L[:, 0, 1]
        uncertainty[:, 2] = L[:, 1, 1]
    return uncertainty

def strip_symmetric(sym):
    return strip_lowerdiag(sym)

def build_rotation(r):
    norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3])

    q = r / norm[:, None]

    R = torch.zeros((q.size(0), 3, 3), device='cuda')

    r = q[:, 0]
    x = q[:, 1]
    y = q[:, 2]
    z = q[:, 3]

    R[:, 0, 0] = 1 - 2 * (y*y + z*z)
    R[:, 0, 1] = 2 * (x*y - r*z)
    R[:, 0, 2] = 2 * (x*z + r*y)
    R[:, 1, 0] = 2 * (x*y + r*z)
    R[:, 1, 1] = 1 - 2 * (x*x + z*z)
    R[:, 1, 2] = 2 * (y*z - r*x)
    R[:, 2, 0] = 2 * (x*z - r*y)
    R[:, 2, 1] = 2 * (y*z + r*x)
    R[:, 2, 2] = 1 - 2 * (x*x + y*y)
    return R

def build_scaling_rotation(s, r):
    L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda")
    R = build_rotation(r)

    L[:,0,0] = s[:,0]
    L[:,1,1] = s[:,1]
    L[:,2,2] = s[:,2]

    L = R @ L
    return L

def build_rotation_2d(r):
    '''
    Build rotation matrix in 2D.
    '''
    R = torch.zeros((r.size(0), 2, 2), device='cuda')
    R[:, 0, 0] = torch.cos(r)[:, 0]
    R[:, 0, 1] = -torch.sin(r)[:, 0]
    R[:, 1, 0] = torch.sin(r)[:, 0]
    R[:, 1, 1] = torch.cos(r)[:, 0]
    return R

def build_scaling_rotation_2d(s, r, device):
    L = torch.zeros((s.shape[0], 2, 2), dtype=torch.float, device='cuda')
    R = build_rotation_2d(r, device)
    L[:,0,0] = s[:,0]
    L[:,1,1] = s[:,1]
    L = R @ L
    return L

def build_covariance_from_scaling_rotation_2d(scaling, scaling_modifier, rotation, device):
    '''
    Build covariance metrix from rotation and scale matricies.
    '''
    L = build_scaling_rotation_2d(scaling_modifier * scaling, rotation, device)
    actual_covariance = L @ L.transpose(1, 2)
    return actual_covariance

def build_triangular(r):
    R = torch.zeros((r.size(0), 2, 2), device=r.device)
    R[:, 0, 0] = r[:, 0]
    R[:, 1, 0] = r[:, 1]
    R[:, 1, 1] = r[:, 2]
    return R