Versatile-Diffusion/versatile_diffusion/lib/model_zoo/autokl_utils.py

import torch
import torch.nn as nn
import functools

class ActNorm(nn.Module):
    def __init__(self, num_features, logdet=False, affine=True,
                 allow_reverse_init=False):
        assert affine
        super().__init__()
        self.logdet = logdet
        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
        self.allow_reverse_init = allow_reverse_init

        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))

    def initialize(self, input):
        with torch.no_grad():
            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
            mean = (
                flatten.mean(1)
                .unsqueeze(1)
                .unsqueeze(2)
                .unsqueeze(3)
                .permute(1, 0, 2, 3)
            )
            std = (
                flatten.std(1)
                .unsqueeze(1)
                .unsqueeze(2)
                .unsqueeze(3)
                .permute(1, 0, 2, 3)
            )

            self.loc.data.copy_(-mean)
            self.scale.data.copy_(1 / (std + 1e-6))

    def forward(self, input, reverse=False):
        if reverse:
            return self.reverse(input)
        if len(input.shape) == 2:
            input = input[:,:,None,None]
            squeeze = True
        else:
            squeeze = False

        _, _, height, width = input.shape

        if self.training and self.initialized.item() == 0:
            self.initialize(input)
            self.initialized.fill_(1)

        h = self.scale * (input + self.loc)

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)

        if self.logdet:
            log_abs = torch.log(torch.abs(self.scale))
            logdet = height*width*torch.sum(log_abs)
            logdet = logdet * torch.ones(input.shape[0]).to(input)
            return h, logdet

        return h

    def reverse(self, output):
        if self.training and self.initialized.item() == 0:
            if not self.allow_reverse_init:
                raise RuntimeError(
                    "Initializing ActNorm in reverse direction is "
                    "disabled by default. Use allow_reverse_init=True to enable."
                )
            else:
                self.initialize(output)
                self.initialized.fill_(1)

        if len(output.shape) == 2:
            output = output[:,:,None,None]
            squeeze = True
        else:
            squeeze = False

        h = output / self.scale - self.loc

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)
        return h

#################
# Discriminator #
#################

def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)

class NLayerDiscriminator(nn.Module):
    """Defines a PatchGAN discriminator as in Pix2Pix
        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
    """
    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
        """Construct a PatchGAN discriminator
        Parameters:
            input_nc (int)  -- the number of channels in input images
            ndf (int)       -- the number of filters in the last conv layer
            n_layers (int)  -- the number of conv layers in the discriminator
            norm_layer      -- normalization layer
        """
        super(NLayerDiscriminator, self).__init__()
        if not use_actnorm:
            norm_layer = nn.BatchNorm2d
        else:
            norm_layer = ActNorm
        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
            use_bias = norm_layer.func != nn.BatchNorm2d
        else:
            use_bias = norm_layer != nn.BatchNorm2d

        kw = 4
        padw = 1
        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
        nf_mult = 1
        nf_mult_prev = 1
        for n in range(1, n_layers):  # gradually increase the number of filters
            nf_mult_prev = nf_mult
            nf_mult = min(2 ** n, 8)
            sequence += [
                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
                norm_layer(ndf * nf_mult),
                nn.LeakyReLU(0.2, True)
            ]

        nf_mult_prev = nf_mult
        nf_mult = min(2 ** n_layers, 8)
        sequence += [
            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
            norm_layer(ndf * nf_mult),
            nn.LeakyReLU(0.2, True)
        ]

        sequence += [
            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]  # output 1 channel prediction map
        self.main = nn.Sequential(*sequence)

    def forward(self, input):
        """Standard forward."""
        return self.main(input)

#########
# LPIPS #
#########

class ScalingLayer(nn.Module):
    def __init__(self):
        super(ScalingLayer, self).__init__()
        self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
        self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])

    def forward(self, inp):
        return (inp - self.shift) / self.scale

class NetLinLayer(nn.Module):
    """ A single linear layer which does a 1x1 conv """
    def __init__(self, chn_in, chn_out=1, use_dropout=False):
        super(NetLinLayer, self).__init__()
        layers = [nn.Dropout(), ] if (use_dropout) else []
        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
        self.model = nn.Sequential(*layers)

from collections import namedtuple
from torchvision import models
from torchvision.models import VGG16_Weights

class vgg16(torch.nn.Module):
    def __init__(self, requires_grad=False, pretrained=True):
        super(vgg16, self).__init__()
        if pretrained:
            vgg_pretrained_features = models.vgg16(weights=VGG16_Weights.IMAGENET1K_V1).features
        self.slice1 = torch.nn.Sequential()
        self.slice2 = torch.nn.Sequential()
        self.slice3 = torch.nn.Sequential()
        self.slice4 = torch.nn.Sequential()
        self.slice5 = torch.nn.Sequential()
        self.N_slices = 5
        for x in range(4):
            self.slice1.add_module(str(x), vgg_pretrained_features[x])
        for x in range(4, 9):
            self.slice2.add_module(str(x), vgg_pretrained_features[x])
        for x in range(9, 16):
            self.slice3.add_module(str(x), vgg_pretrained_features[x])
        for x in range(16, 23):
            self.slice4.add_module(str(x), vgg_pretrained_features[x])
        for x in range(23, 30):
            self.slice5.add_module(str(x), vgg_pretrained_features[x])
        if not requires_grad:
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, X):
        h = self.slice1(X)
        h_relu1_2 = h
        h = self.slice2(h)
        h_relu2_2 = h
        h = self.slice3(h)
        h_relu3_3 = h
        h = self.slice4(h)
        h_relu4_3 = h
        h = self.slice5(h)
        h_relu5_3 = h
        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
        return out

def normalize_tensor(x,eps=1e-10):
    norm_factor = torch.sqrt(torch.sum(x**2,dim=1,keepdim=True))
    return x/(norm_factor+eps)

def spatial_average(x, keepdim=True):
    return x.mean([2,3],keepdim=keepdim)

def get_ckpt_path(*args, **kwargs):
    return 'pretrained/lpips.pth'

class LPIPS(nn.Module):
    # Learned perceptual metric
    def __init__(self, use_dropout=True):
        super().__init__()
        self.scaling_layer = ScalingLayer()
        self.chns = [64, 128, 256, 512, 512]  # vg16 features
        self.net = vgg16(pretrained=True, requires_grad=False)
        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
        self.load_from_pretrained()
        for param in self.parameters():
            param.requires_grad = False

    def load_from_pretrained(self, name="vgg_lpips"):
        ckpt = get_ckpt_path(name, "taming/modules/autoencoder/lpips")
        self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
        print("loaded pretrained LPIPS loss from {}".format(ckpt))

    @classmethod
    def from_pretrained(cls, name="vgg_lpips"):
        if name != "vgg_lpips":
            raise NotImplementedError
        model = cls()
        ckpt = get_ckpt_path(name)
        model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
        return model

    def forward(self, input, target):
        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
        outs0, outs1 = self.net(in0_input), self.net(in1_input)
        feats0, feats1, diffs = {}, {}, {}
        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
        for kk in range(len(self.chns)):
            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2

        res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]
        val = res[0]
        for l in range(1, len(self.chns)):
            val += res[l]
        return val

############
# The loss #
############

def adopt_weight(weight, global_step, threshold=0, value=0.):
    if global_step < threshold:
        weight = value
    return weight

def hinge_d_loss(logits_real, logits_fake):
    loss_real = torch.mean(F.relu(1. - logits_real))
    loss_fake = torch.mean(F.relu(1. + logits_fake))
    d_loss = 0.5 * (loss_real + loss_fake)
    return d_loss

def vanilla_d_loss(logits_real, logits_fake):
    d_loss = 0.5 * (
        torch.mean(torch.nn.functional.softplus(-logits_real)) +
        torch.mean(torch.nn.functional.softplus(logits_fake)))
    return d_loss

class LPIPSWithDiscriminator(nn.Module):
    def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
                 disc_loss="hinge"):

        super().__init__()
        assert disc_loss in ["hinge", "vanilla"]
        self.kl_weight = kl_weight
        self.pixel_weight = pixelloss_weight
        self.perceptual_loss = LPIPS().eval()
        self.perceptual_weight = perceptual_weight
        # output log variance
        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)

        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
                                                 n_layers=disc_num_layers,
                                                 use_actnorm=use_actnorm
                                                 ).apply(weights_init)
        self.discriminator_iter_start = disc_start
        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
        self.disc_factor = disc_factor
        self.discriminator_weight = disc_weight
        self.disc_conditional = disc_conditional

    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
        if last_layer is not None:
            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
        else:
            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]

        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
        d_weight = d_weight * self.discriminator_weight
        return d_weight

    def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
                global_step, last_layer=None, cond=None, split="train",
                weights=None):
        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
        if self.perceptual_weight > 0:
            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
            rec_loss = rec_loss + self.perceptual_weight * p_loss

        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
        weighted_nll_loss = nll_loss
        if weights is not None:
            weighted_nll_loss = weights*nll_loss
        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
        kl_loss = posteriors.kl()
        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]

        # now the GAN part
        if optimizer_idx == 0:
            # generator update
            if cond is None:
                assert not self.disc_conditional
                logits_fake = self.discriminator(reconstructions.contiguous())
            else:
                assert self.disc_conditional
                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
            g_loss = -torch.mean(logits_fake)

            if self.disc_factor > 0.0:
                try:
                    d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
                except RuntimeError:
                    assert not self.training
                    d_weight = torch.tensor(0.0)
            else:
                d_weight = torch.tensor(0.0)

            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
            loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss

            log = {"Loss": loss.clone().detach().mean(), 
                   "logvar": self.logvar.detach(),
                   "loss_kl": kl_loss.detach().mean(), 
                   "loss_nll": nll_loss.detach().mean(),
                   "loss_rec": rec_loss.detach().mean(),
                   "d_weight": d_weight.detach(),
                   "disc_factor": torch.tensor(disc_factor),
                   "loss_g": g_loss.detach().mean(),
                   }
            return loss, log

        if optimizer_idx == 1:
            # second pass for discriminator update
            if cond is None:
                logits_real = self.discriminator(inputs.contiguous().detach())
                logits_fake = self.discriminator(reconstructions.contiguous().detach())
            else:
                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))

            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)

            log = {"Loss": d_loss.clone().detach().mean(),
                   "loss_disc": d_loss.clone().detach().mean(),
                   "logits_real": logits_real.detach().mean(),
                   "logits_fake": logits_fake.detach().mean()
                   }
            return d_loss, log