Spaces:
Sleeping
Sleeping
File size: 9,143 Bytes
81ecb2b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
import torch as th
import numpy as np
import logging
from .vgg import VGGLossMasked
logger = logging.getLogger("dva.{__name__}")
class DCTLoss(nn.Module):
def __init__(self, weights):
super().__init__()
self.weights = weights
def forward(self, inputs, preds, iteration=None):
loss_dict = {"loss_total": 0.0}
target = inputs['gt']
recon = preds['recon']
posterior = preds['posterior']
fft_gt = th.view_as_real(th.fft.fft(target.reshape(target.shape[0], -1)))
fft_recon = th.view_as_real(th.fft.fft(recon.reshape(recon.shape[0], -1)))
loss_recon_dct_l1 = th.mean(th.abs(fft_gt - fft_recon))
loss_recon_l1 = th.mean(th.abs(target - recon))
loss_kl = posterior.kl().mean()
loss_dict.update(loss_recon_l1=loss_recon_l1, loss_recon_dct_l1=loss_recon_dct_l1, loss_kl=loss_kl)
loss_total = self.weights.recon * loss_recon_dct_l1 + self.weights.kl * loss_kl
loss_dict["loss_total"] = loss_total
return loss_total, loss_dict
class VAESepL2Loss(nn.Module):
def __init__(self, weights):
super().__init__()
self.weights = weights
def forward(self, inputs, preds, iteration=None):
loss_dict = {"loss_total": 0.0}
target = inputs['gt']
recon = preds['recon']
posterior = preds['posterior']
recon_diff = (target - recon) ** 2
loss_recon_sdf_l1 = th.mean(recon_diff[:, 0:1, ...])
loss_recon_rgb_l1 = th.mean(recon_diff[:, 1:4, ...])
loss_recon_mat_l1 = th.mean(recon_diff[:, 4:6, ...])
loss_kl = posterior.kl().mean()
loss_dict.update(loss_sdf_l1=loss_recon_sdf_l1, loss_rgb_l1=loss_recon_rgb_l1, loss_mat_l1=loss_recon_mat_l1, loss_kl=loss_kl)
loss_total = self.weights.sdf * loss_recon_sdf_l1 + self.weights.rgb * loss_recon_rgb_l1 + self.weights.mat * loss_recon_mat_l1
if "kl" in self.weights:
loss_total += self.weights.kl * loss_kl
loss_dict["loss_total"] = loss_total
return loss_total, loss_dict
class VAESepLoss(nn.Module):
def __init__(self, weights):
super().__init__()
self.weights = weights
def forward(self, inputs, preds, iteration=None):
loss_dict = {"loss_total": 0.0}
target = inputs['gt']
recon = preds['recon']
posterior = preds['posterior']
recon_diff = th.abs(target - recon)
loss_recon_sdf_l1 = th.mean(recon_diff[:, 0:1, ...])
loss_recon_rgb_l1 = th.mean(recon_diff[:, 1:4, ...])
loss_recon_mat_l1 = th.mean(recon_diff[:, 4:6, ...])
loss_kl = posterior.kl().mean()
loss_dict.update(loss_sdf_l1=loss_recon_sdf_l1, loss_rgb_l1=loss_recon_rgb_l1, loss_mat_l1=loss_recon_mat_l1, loss_kl=loss_kl)
loss_total = self.weights.sdf * loss_recon_sdf_l1 + self.weights.rgb * loss_recon_rgb_l1 + self.weights.mat * loss_recon_mat_l1
if "kl" in self.weights:
loss_total += self.weights.kl * loss_kl
loss_dict["loss_total"] = loss_total
return loss_total, loss_dict
class VAELoss(nn.Module):
def __init__(self, weights):
super().__init__()
self.weights = weights
def forward(self, inputs, preds, iteration=None):
loss_dict = {"loss_total": 0.0}
target = inputs['gt']
recon = preds['recon']
posterior = preds['posterior']
loss_recon_l1 = th.mean(th.abs(target - recon))
loss_kl = posterior.kl().mean()
loss_dict.update(loss_recon_l1=loss_recon_l1, loss_kl=loss_kl)
loss_total = self.weights.recon * loss_recon_l1 + self.weights.kl * loss_kl
loss_dict["loss_total"] = loss_total
return loss_total, loss_dict
class PrimSDFLoss(nn.Module):
def __init__(self, weights, shape_opt_steps=2000, tex_opt_steps=6000):
super().__init__()
self.weights = weights
self.shape_opt_steps = shape_opt_steps
self.tex_opt_steps = tex_opt_steps
def forward(self, inputs, preds, iteration=None):
loss_dict = {"loss_total": 0.0}
if iteration < self.shape_opt_steps:
target_sdf = inputs['sdf']
sdf = preds['sdf']
loss_sdf_l1 = th.mean(th.abs(sdf - target_sdf))
loss_dict.update(loss_sdf_l1=loss_sdf_l1)
loss_total = self.weights.sdf_l1 * loss_sdf_l1
prim_scale = preds["prim_scale"]
# we use 1/scale instead of the original 100/scale as our scale is normalized to [-1, 1] cube
if "vol_sum" in self.weights:
loss_prim_vol_sum = th.mean(th.sum(th.prod(1 / prim_scale, dim=-1), dim=-1))
loss_dict.update(loss_prim_vol_sum=loss_prim_vol_sum)
loss_total += self.weights.vol_sum * loss_prim_vol_sum
if iteration >= self.shape_opt_steps and iteration < self.tex_opt_steps:
target_tex = inputs['tex']
tex = preds['tex']
loss_tex_l1 = th.mean(th.abs(tex - target_tex))
loss_dict.update(loss_tex_l1=loss_tex_l1)
loss_total = (
self.weights.rgb_l1 * loss_tex_l1
)
if "mat_l1" in self.weights:
target_mat = inputs['mat']
mat = preds['mat']
loss_mat_l1 = th.mean(th.abs(mat - target_mat))
loss_dict.update(loss_mat_l1=loss_mat_l1)
loss_total += self.weights.mat_l1 * loss_mat_l1
if "grad_l2" in self.weights:
loss_grad_l2 = th.mean((preds["grad"] - inputs["grad"]) ** 2)
loss_total += self.weights.grad_l2 * loss_grad_l2
loss_dict.update(loss_grad_l2=loss_grad_l2)
loss_dict["loss_total"] = loss_total
return loss_total, loss_dict
class TotalMVPLoss(nn.Module):
def __init__(self, weights, assets=None):
super().__init__()
self.weights = weights
if "vgg" in self.weights:
self.vgg_loss = VGGLossMasked()
def forward(self, inputs, preds, iteration=None):
loss_dict = {"loss_total": 0.0}
B = inputs["image"].shape
# rgb
target_rgb = inputs["image"].permute(0, 2, 3, 1)
# removing the mask
target_rgb = target_rgb * inputs["image_mask"][:, 0, :, :, np.newaxis]
rgb = preds["rgb"]
loss_rgb_mse = th.mean(((rgb - target_rgb) / 16.0) ** 2.0)
loss_dict.update(loss_rgb_mse=loss_rgb_mse)
alpha = preds["alpha"]
# mask loss
target_mask = inputs["image_mask"][:, 0].to(th.float32)
loss_mask_mae = th.mean((target_mask - alpha).abs())
loss_dict.update(loss_mask_mae=loss_mask_mae)
B = alpha.shape[0]
# beta prior on opacity
loss_alpha_prior = th.mean(
th.log(0.1 + alpha.reshape(B, -1))
+ th.log(0.1 + 1.0 - alpha.reshape(B, -1))
- -2.20727
)
loss_dict.update(loss_alpha_prior=loss_alpha_prior)
prim_scale = preds["prim_scale"]
loss_prim_vol_sum = th.mean(th.sum(th.prod(100.0 / prim_scale, dim=-1), dim=-1))
loss_dict.update(loss_prim_vol_sum=loss_prim_vol_sum)
loss_total = (
self.weights.rgb_mse * loss_rgb_mse
+ self.weights.mask_mae * loss_mask_mae
+ self.weights.alpha_prior * loss_alpha_prior
+ self.weights.prim_vol_sum * loss_prim_vol_sum
)
if "embs_l2" in self.weights:
loss_embs_l2 = th.sum(th.norm(preds["embs"], dim=1))
loss_total += self.weights.embs_l2 * loss_embs_l2
loss_dict.update(loss_embs_l2=loss_embs_l2)
if "vgg" in self.weights:
loss_vgg = self.vgg_loss(
rgb.permute(0, 3, 1, 2),
target_rgb.permute(0, 3, 1, 2),
inputs["image_mask"],
)
loss_total += self.weights.vgg * loss_vgg
loss_dict.update(loss_vgg=loss_vgg)
if "prim_scale_var" in self.weights:
log_prim_scale = th.log(prim_scale)
# NOTE: should we detach this?
log_prim_scale_mean = th.mean(log_prim_scale, dim=1, keepdim=True)
loss_prim_scale_var = th.mean((log_prim_scale - log_prim_scale_mean) ** 2.0)
loss_total += self.weights.prim_scale_var * loss_prim_scale_var
loss_dict.update(loss_prim_scale_var=loss_prim_scale_var)
loss_dict["loss_total"] = loss_total
return loss_total, loss_dict
def process_losses(loss_dict, reduce=True, detach=True):
"""Preprocess the dict of losses outputs."""
result = {
k.replace("loss_", ""): v for k, v in loss_dict.items() if k.startswith("loss_")
}
if detach:
result = {k: v.detach() for k, v in result.items()}
if reduce:
result = {k: float(v.mean().item()) for k, v in result.items()}
return result
|