我尝试计算扩散模型中生成的图像和真实图像之间的感知损失函数（我将其用于图像到图像的转换，图像为灰度）。 这是损失函数的代码：
from .custom_loss import common
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class VGGPerceptualLoss(nn.Module):
def __init__(self, conv_index, rgb_range=1):
super(VGGPerceptualLoss, self).__init__()
vgg_features = models.vgg19(pretrained=True).features
modules = [m for m in vgg_features]
if conv_index.find('22') >= 0:
self.vgg = nn.Sequential(*modules[:8])
elif conv_index.find('54') >= 0:
self.vgg = nn.Sequential(*modules[:35])
vgg_mean = (0.485, 0.456, 0.406)
vgg_std = (0.229 * rgb_range, 0.224 * rgb_range, 0.225 * rgb_range)
self.sub_mean = common.MeanShift(rgb_range, vgg_mean, vgg_std)
for p in self.parameters():
p.requires_grad = False
def forward(self, sr, hr):
def _forward(x):
x = self.sub_mean(x)
x = self.vgg(x)
return x
vgg_sr = _forward(sr[0])
with torch.no_grad():
vgg_hr = _forward(hr[0].detach())
loss = F.mse_loss(vgg_sr, vgg_hr)
return loss
这是扩散模型的一部分：
def forward_backward(self, batch, cond):
self.mp_trainer.zero_grad()
for i in range(0, batch.shape[0], self.microbatch):
micro = batch[i : i + self.microbatch].to(dist_util.dev())
micro_cond = {
k: v[i : i + self.microbatch].to(dist_util.dev())
for k, v in cond.items()
}
last_batch = (i + self.microbatch) >= batch.shape[0]
t, weights = self.schedule_sampler.sample(micro.shape[0], dist_util.dev())
compute_losses = functools.partial(
self.diffusion.training_losses_segmentation,
self.ddp_model,
self.classifier,
self.prior,
self.posterior,
micro,
t,
model_kwargs=micro_cond,
)
if last_batch or not self.use_ddp:
losses1 = compute_losses()
else:
with self.ddp_model.no_sync():
losses1 = compute_losses()
if isinstance(self.schedule_sampler, LossAwareSampler):
self.schedule_sampler.update_with_local_losses(t, losses["loss"].detach())
losses = losses1[0]
sample = losses1[1]
conv_index='22'
perceptual_loss_fn = VGGPerceptualLoss(conv_index=conv_index)
perceptual_loss = perceptual_loss_fn(sample, batch)
loss = (losses["loss"] * weights).mean()+(perceptual_loss * self.perceptual_loss_weight)
# tensor_losses = torch.cat((losses["loss"],perceptual_loss))
# loss = (tensor_losses* weights).mean()
# print(f'weights: {weights}')
# print(f'loss: {losses["loss"]}')
# loss = (losses["loss"] * weights).mean()
lossseg = (losses["mse"] * weights).mean().detach()
losscls = (losses["vb"] * weights).mean().detach()
lossrec = loss * 0
log_loss_dict(self.diffusion, t, {k: v * weights for k, v in losses.items()})
self.mp_trainer.backward(loss)
return lossseg.detach(), losscls.detach(), lossrec.detach(), sample
这是错误：请问我该如何修复它？
/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: 参数 'pretrained' 自 0.13 起已弃用，将来可能会被删除，请使用 'weights'反而。 警告.警告( /usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: 自 0.13 起，除权重枚举或
Noneweights=VGG19_Weights.IMAGENET1K_V1weights=VGG19_Weights.DEFAULT在你的
VGGPerceptualLossvgg_mean = (0.485, 0.456, 0.406)
vgg_std = (0.229 * rgb_range, 0.224 * rgb_range, 0.225 * rgb_range)
self.sub_mean = common.MeanShift(rgb_range, vgg_mean, vgg_std)
该函数只能接受RGB通道。
但是，在您的错误消息中，
RuntimeError: Given groups=1, weight of size [3, 3, 1, 1], expected input[1, 1, 128, 128] to have 3 channels, but got 1 channels instead
您输入张量大小为
(1, 1, 128, 128)因此，您可以使用以下代码来打开您的灰度图像数据，
from PIL import Image
img = Image.open("[your training image]").convert("RGB")
您的灰度图像可以转换为3通道的RGB格式。