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from typing import Dict, List, Optional, Tuple, Union
import math
import torch
import torch.nn as nn
from ...modules.autoencoding.lpips.loss.lpips import LPIPS
from ...modules.encoders.modules import GeneralConditioner
from ...util import append_dims, instantiate_from_config
from .denoiser import Denoiser
class StandardDiffusionLoss(nn.Module):
def __init__(
self,
sigma_sampler_config: dict,
loss_weighting_config: dict,
loss_type: str = "l2",
offset_noise_level: float = 0.0,
batch2model_keys: Optional[Union[str, List[str]]] = None,
):
super().__init__()
assert loss_type in ["l2", "l1", "lpips"]
self.sigma_sampler = instantiate_from_config(sigma_sampler_config)
self.loss_weighting = instantiate_from_config(loss_weighting_config)
self.loss_type = loss_type
self.offset_noise_level = offset_noise_level
if loss_type == "lpips":
self.lpips = LPIPS().eval()
if not batch2model_keys:
batch2model_keys = []
if isinstance(batch2model_keys, str):
batch2model_keys = [batch2model_keys]
self.batch2model_keys = set(batch2model_keys)
def get_noised_input(
self, sigmas_bc: torch.Tensor, noise: torch.Tensor, input: torch.Tensor
) -> torch.Tensor:
noised_input = input + noise * sigmas_bc
return noised_input
def forward(
self,
network: nn.Module,
denoiser: Denoiser,
conditioner: GeneralConditioner,
input: torch.Tensor,
batch: Dict,
) -> torch.Tensor:
cond = conditioner(batch)
return self._forward(network, denoiser, cond, input, batch)
def _forward(
self,
network: nn.Module,
denoiser: Denoiser,
cond: Dict,
input: torch.Tensor,
batch: Dict,
) -> Tuple[torch.Tensor, Dict]:
additional_model_inputs = {
key: batch[key] for key in self.batch2model_keys.intersection(batch)
}
sigmas = self.sigma_sampler(input.shape[0]).to(input)
noise = torch.randn_like(input)
if self.offset_noise_level > 0.0:
offset_shape = (
(input.shape[0], 1, input.shape[2])
if self.n_frames is not None
else (input.shape[0], input.shape[1])
)
noise = noise + self.offset_noise_level * append_dims(
torch.randn(offset_shape, device=input.device),
input.ndim,
)
sigmas_bc = append_dims(sigmas, input.ndim)
noised_input = self.get_noised_input(sigmas_bc, noise, input)
model_output = denoiser(
network, noised_input, sigmas, cond, **additional_model_inputs
)
w = append_dims(self.loss_weighting(sigmas), input.ndim)
return self.get_loss(model_output, input, w)
def get_loss(self, model_output, target, w):
if self.loss_type == "l2":
return torch.mean(
(w * (model_output - target) ** 2).reshape(target.shape[0], -1), 1
)
elif self.loss_type == "l1":
return torch.mean(
(w * (model_output - target).abs()).reshape(target.shape[0], -1), 1
)
elif self.loss_type == "lpips":
loss = self.lpips(model_output, target).reshape(-1)
return loss
else:
raise NotImplementedError(f"Unknown loss type {self.loss_type}")
class StandardDiffusionLossImgRef(nn.Module):
def __init__(
self,
sigma_sampler_config: dict,
sigma_sampler_config_ref: dict,
type: str = "l2",
offset_noise_level: float = 0.0,
batch2model_keys: Optional[Union[str, List[str]]] = None,
):
super().__init__()
assert type in ["l2", "l1", "lpips"]
self.sigma_sampler = instantiate_from_config(sigma_sampler_config)
self.sigma_sampler_ref = None
if sigma_sampler_config_ref is not None:
self.sigma_sampler_ref = instantiate_from_config(sigma_sampler_config_ref)
self.type = type
self.offset_noise_level = offset_noise_level
if type == "lpips":
self.lpips = LPIPS().eval()
if not batch2model_keys:
batch2model_keys = []
if isinstance(batch2model_keys, str):
batch2model_keys = [batch2model_keys]
self.batch2model_keys = set(batch2model_keys)
def __call__(self, network, denoiser, conditioner, input, input_rgb, input_ref, pose, mask, mask_ref, opacity, batch):
cond = conditioner(batch)
additional_model_inputs = {
key: batch[key] for key in self.batch2model_keys.intersection(batch)
}
sigmas = self.sigma_sampler(input.shape[0]).to(input.device)
noise = torch.randn_like(input)
if self.offset_noise_level > 0.0:
noise = noise + self.offset_noise_level * append_dims(
torch.randn(input.shape[0], device=input.device), input.ndim
)
additional_model_inputs['pose'] = pose
additional_model_inputs['mask_ref'] = mask_ref
noised_input = input + noise * append_dims(sigmas, input.ndim)
if self.sigma_sampler_ref is not None:
sigmas_ref = self.sigma_sampler_ref(input.shape[0]).to(input.device)
if input_ref is not None:
noise = torch.randn_like(input_ref)
if self.offset_noise_level > 0.0:
noise = noise + self.offset_noise_level * append_dims(
torch.randn(input_ref.shape[0], device=input_ref.device), input_ref.ndim
)
input_ref = input_ref + noise * append_dims(sigmas_ref, input_ref.ndim)
additional_model_inputs['sigmas_ref'] = sigmas_ref
additional_model_inputs['input_ref'] = input_ref
model_output, fg_mask_list, alphas, predicted_rgb_list = denoiser(
network, noised_input, sigmas, cond, **additional_model_inputs
)
w = append_dims(denoiser.w(sigmas), input.ndim)
return self.get_loss(model_output, fg_mask_list, predicted_rgb_list, input, input_rgb, w, mask, mask_ref, opacity, alphas)
def get_loss(self, model_output, fg_mask_list, predicted_rgb_list, target, target_rgb, w, mask, mask_ref, opacity, alphas_list):
loss_rgb = []
loss_fg = []
loss_bg = []
with torch.amp.autocast(device_type='cuda', dtype=torch.float32):
if self.type == "l2":
loss = (w * (model_output - target) ** 2)
if mask is not None:
loss_l2 = (loss*mask).sum([1, 2, 3])/(mask.sum([1, 2, 3]) + 1e-6)
else:
loss_l2 = torch.mean(loss.reshape(target.shape[0], -1), 1)
if len(fg_mask_list) > 0 and len(alphas_list) > 0:
for fg_mask, alphas in zip(fg_mask_list, alphas_list):
size = int(math.sqrt(fg_mask.size(1)))
opacity = torch.nn.functional.interpolate(opacity, size=size, antialias=True, mode='bilinear').detach()
fg_mask = torch.clamp(fg_mask.reshape(-1, size*size), 0., 1.)
loss_fg_ = ((fg_mask - opacity.reshape(-1, size*size))**2).mean(1) #torch.nn.functional.binary_cross_entropy(rgb, torch.clip(mask.reshape(-1, size*size), 0., 1.), reduce=False)
loss_bg_ = (alphas - opacity.reshape(-1, size*size, 1, 1)).abs()*(1-opacity.reshape(-1, size*size, 1, 1)) #alpahs : b hw d 1
loss_bg_ = (loss_bg_*((opacity.reshape(-1, size*size, 1, 1) < 0.1)*1)).mean([1, 2, 3])
loss_fg.append(loss_fg_)
loss_bg.append(loss_bg_)
loss_fg = torch.stack(loss_fg, 1)
loss_bg = torch.stack(loss_bg, 1)
if len(predicted_rgb_list) > 0:
for rgb in predicted_rgb_list:
size = int(math.sqrt(rgb.size(1)))
mask_ = torch.nn.functional.interpolate(mask, size=size, antialias=True, mode='bilinear').detach()
loss_rgb_ = ((torch.nn.functional.interpolate(target_rgb*0.5+0.5, size=size, antialias=True, mode='bilinear').detach() - rgb.reshape(-1, size, size, 3).permute(0, 3, 1, 2)) ** 2)
loss_rgb.append((loss_rgb_*mask_).sum([1, 2, 3])/(mask.sum([1, 2, 3]) + 1e-6))
loss_rgb = torch.stack(loss_rgb, 1)
# print(loss_l2, loss_fg, loss_bg, loss_rgb)
return loss_l2, loss_fg, loss_bg, loss_rgb
elif self.type == "l1":
return torch.mean(
(w * (model_output - target).abs()).reshape(target.shape[0], -1), 1
), loss_rgb
elif self.type == "lpips":
loss = self.lpips(model_output, target).reshape(-1)
return loss, loss_rgb
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