torch_utils/models.py

# Copyright (c) SenseTime Research. All rights reserved.

# https://github.com/rosinality/stylegan2-pytorch/blob/master/model.py

import math
import random
import functools
import operator

import torch
from torch import nn
from torch.nn import functional as F
import torch.nn.init as init
from torch.autograd import Function

from .op_edit import FusedLeakyReLU, fused_leaky_relu, upfirdn2d


class PixelNorm(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, input):
        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)


def make_kernel(k):
    k = torch.tensor(k, dtype=torch.float32)
    if k.ndim == 1:
        k = k[None, :] * k[:, None]
    k /= k.sum()
    return k


class Upsample(nn.Module):
    def __init__(self, kernel, factor=2):
        super().__init__()

        self.factor = factor
        kernel = make_kernel(kernel) * (factor ** 2)
        self.register_buffer("kernel", kernel)

        p = kernel.shape[0] - factor

        pad0 = (p + 1) // 2 + factor - 1
        pad1 = p // 2

        self.pad = (pad0, pad1)

    def forward(self, input):
        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
        return out


class Downsample(nn.Module):
    def __init__(self, kernel, factor=2):
        super().__init__()

        self.factor = factor
        kernel = make_kernel(kernel)
        self.register_buffer("kernel", kernel)

        p = kernel.shape[0] - factor

        pad0 = (p + 1) // 2
        pad1 = p // 2

        self.pad = (pad0, pad1)

    def forward(self, input):
        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
        return out


class Blur(nn.Module):
    def __init__(self, kernel, pad, upsample_factor=1):
        super().__init__()

        kernel = make_kernel(kernel)

        if upsample_factor > 1:
            kernel = kernel * (upsample_factor ** 2)

        self.register_buffer("kernel", kernel)

        self.pad = pad

    def forward(self, input):
        out = upfirdn2d(input, self.kernel, pad=self.pad)
        return out


class EqualConv2d(nn.Module):
    def __init__(
        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
    ):
        super().__init__()

        self.weight = nn.Parameter(
            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
        )
        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)

        self.stride = stride
        self.padding = padding

        if bias:
            self.bias = nn.Parameter(torch.zeros(out_channel))

        else:
            self.bias = None

    def forward(self, input):
        out = F.conv2d(
            input,
            self.weight * self.scale,
            bias=self.bias,
            stride=self.stride,
            padding=self.padding,
        )
        return out

    def __repr__(self):
        return (
            f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},"
            f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})"
        )


class EqualLinear(nn.Module):
    def __init__(
        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
    ):
        super().__init__()

        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))

        if bias:
            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
        else:
            self.bias = None

        self.activation = activation

        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
        self.lr_mul = lr_mul

    def forward(self, input):
        if self.activation:
            out = F.linear(input, self.weight * self.scale)
            out = fused_leaky_relu(out, self.bias * self.lr_mul)
        else:
            out = F.linear(
                input, self.weight * self.scale, bias=self.bias * self.lr_mul
            )
        return out

    def __repr__(self):
        return (
            f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})"
        )


class ScaledLeakyReLU(nn.Module):
    def __init__(self, negative_slope=0.2):
        super().__init__()
        self.negative_slope = negative_slope

    def forward(self, input):
        out = F.leaky_relu(input, negative_slope=self.negative_slope)
        return out * math.sqrt(2)


class ModulatedConv2d(nn.Module):
    def __init__(
        self,
        in_channel,
        out_channel,
        kernel_size,
        style_dim,
        demodulate=True,
        upsample=False,
        downsample=False,
        blur_kernel=[1, 3, 3, 1],
    ):
        super().__init__()

        self.eps = 1e-8
        self.kernel_size = kernel_size
        self.in_channel = in_channel
        self.out_channel = out_channel
        self.upsample = upsample
        self.downsample = downsample

        if upsample:
            factor = 2
            p = (len(blur_kernel) - factor) - (kernel_size - 1)
            pad0 = (p + 1) // 2 + factor - 1
            pad1 = p // 2 + 1
            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)

        if downsample:
            factor = 2
            p = (len(blur_kernel) - factor) + (kernel_size - 1)
            pad0 = (p + 1) // 2
            pad1 = p // 2
            self.blur = Blur(blur_kernel, pad=(pad0, pad1))

        fan_in = in_channel * kernel_size ** 2
        self.scale = 1 / math.sqrt(fan_in)
        self.padding = kernel_size // 2
        self.weight = nn.Parameter(
            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
        )
        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
        self.demodulate = demodulate

    def __repr__(self):
        return (
            f"{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, "
            f"upsample={self.upsample}, downsample={self.downsample})"
        )

    def forward(self, input, style):
        batch, in_channel, height, width = input.shape

        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
        weight = self.scale * self.weight * style

        if self.demodulate:
            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)

        weight = weight.view(
            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
        )

        if self.upsample:
            input = input.view(1, batch * in_channel, height, width)
            weight = weight.view(
                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
            )
            weight = weight.transpose(1, 2).reshape(
                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
            )
            out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)
            out = self.blur(out)

        elif self.downsample:
            input = self.blur(input)
            _, _, height, width = input.shape
            input = input.view(1, batch * in_channel, height, width)
            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)

        else:
            input = input.view(1, batch * in_channel, height, width)
            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)

        return out


class NoiseInjection(nn.Module):
    def __init__(self):
        super().__init__()
        self.weight = nn.Parameter(torch.zeros(1))

    def forward(self, image, noise=None):
        if noise is None:
            batch, _, height, width = image.shape
            noise = image.new_empty(batch, 1, height, width).normal_()
        return image + self.weight * noise


class ConstantInput(nn.Module):
    def __init__(self, channel, size=4):
        super().__init__()
        self.input = nn.Parameter(torch.randn(1, channel, size, size // 2))

    def forward(self, input):
        batch = input.shape[0]
        out = self.input.repeat(batch, 1, 1, 1)
        return out


class StyledConv(nn.Module):
    def __init__(
        self,
        in_channel,
        out_channel,
        kernel_size,
        style_dim,
        upsample=False,
        blur_kernel=[1, 3, 3, 1],
        demodulate=True,
    ):
        super().__init__()
        self.conv = ModulatedConv2d(
            in_channel,
            out_channel,
            kernel_size,
            style_dim,
            upsample=upsample,
            blur_kernel=blur_kernel,
            demodulate=demodulate,
        )
        self.noise = NoiseInjection()
        self.activate = FusedLeakyReLU(out_channel)

    def forward(self, input, style, noise=None):
        out = self.conv(input, style)
        out = self.noise(out, noise=noise)
        out = self.activate(out)
        return out


class ToRGB(nn.Module):
    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
        super().__init__()
        if upsample:
            self.upsample = Upsample(blur_kernel)

        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))

    def forward(self, input, style, skip=None):
        out = self.conv(input, style)
        out = out + self.bias

        if skip is not None:
            skip = self.upsample(skip)
            out = out + skip

        return out


class Generator(nn.Module):
    def __init__(
        self,
        size,
        style_dim,
        n_mlp,
        channel_multiplier=1,
        blur_kernel=[1, 3, 3, 1],
        lr_mlp=0.01,
        small=False,
        small_isaac=False,
    ):
        super().__init__()

        self.size = size

        if small and size > 64:
            raise ValueError("small only works for sizes <= 64")

        self.style_dim = style_dim
        layers = [PixelNorm()]

        for i in range(n_mlp):
            layers.append(
                EqualLinear(
                    style_dim, style_dim, lr_mul=lr_mlp, activation="fused_lrelu"
                )
            )

        self.style = nn.Sequential(*layers)

        if small:
            self.channels = {
                4: 64 * channel_multiplier,
                8: 64 * channel_multiplier,
                16: 64 * channel_multiplier,
                32: 64 * channel_multiplier,
                64: 64 * channel_multiplier,
            }
        elif small_isaac:
            self.channels = {4: 256, 8: 256, 16: 256, 32: 256, 64: 128, 128: 128}
        else:
            self.channels = {
                4: 512,
                8: 512,
                16: 512,
                32: 512,
                64: 256 * channel_multiplier,
                128: 128 * channel_multiplier,
                256: 64 * channel_multiplier,
                512: 32 * channel_multiplier,
                1024: 16 * channel_multiplier,
            }

        self.input = ConstantInput(self.channels[4])
        self.conv1 = StyledConv(
            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
        )
        self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)

        self.log_size = int(math.log(size, 2))
        self.num_layers = (self.log_size - 2) * 2 + 1

        self.convs = nn.ModuleList()
        self.upsamples = nn.ModuleList()
        self.to_rgbs = nn.ModuleList()
        self.noises = nn.Module()

        in_channel = self.channels[4]

        for layer_idx in range(self.num_layers):
            res = (layer_idx + 5) // 2
            shape = [1, 1, 2 ** res, 2 ** res // 2]
            self.noises.register_buffer(
                "noise_{}".format(layer_idx), torch.randn(*shape)
            )

        for i in range(3, self.log_size + 1):
            out_channel = self.channels[2 ** i]

            self.convs.append(
                StyledConv(
                    in_channel,
                    out_channel,
                    3,
                    style_dim,
                    upsample=True,
                    blur_kernel=blur_kernel,
                )
            )

            self.convs.append(
                StyledConv(
                    out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
                )
            )

            self.to_rgbs.append(ToRGB(out_channel, style_dim))
            in_channel = out_channel

        self.n_latent = self.log_size * 2 - 2

    def make_noise(self):
        device = self.input.input.device

        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2 // 2, device=device)]

        for i in range(3, self.log_size + 1):
            for _ in range(2):
                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i // 2, device=device))

        return noises

    def mean_latent(self, n_latent):
        latent_in = torch.randn(
            n_latent, self.style_dim, device=self.input.input.device
        )
        latent = self.style(latent_in).mean(0, keepdim=True)

        return latent

    def get_latent(self, input):
        return self.style(input)

    def forward(
        self,
        styles,
        return_latents=False,
        return_features=False,
        inject_index=None,
        truncation=1,
        truncation_latent=None,
        input_is_latent=False,
        noise=None,
        randomize_noise=True,
        real=False,
    ):
        if not input_is_latent:
            styles = [self.style(s) for s in styles]
        if noise is None:
            if randomize_noise:
                noise = [None] * self.num_layers
            else:
                noise = [
                    getattr(self.noises, "noise_{}".format(i))
                    for i in range(self.num_layers)
                ]

        if truncation < 1:
            # print('truncation_latent: ', truncation_latent.shape)
            if not real: #if type(styles) == list:
                style_t = []
                for style in styles:
                    style_t.append(
                        truncation_latent + truncation * (style - truncation_latent) 
                    ) # (-1.1162e-03-(-1.0914e-01))*0.8+(-1.0914e-01)
                styles = style_t
            else: # styles are latent (tensor: 1,18,512), for real PTI output
                truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512)
                styles = torch.add(truncation_latent,torch.mul(torch.sub(styles,truncation_latent),truncation))
                # print('now styles after truncation : ', styles)
        #if type(styles) == list and len(styles) < 2: # this if for input as list of [(1,512)]
        if not real:
            if len(styles) < 2:
                inject_index = self.n_latent
                if styles[0].ndim < 3:
                    latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
                else:
                    latent = styles[0]
            elif type(styles) == list:
                if inject_index is None:
                    inject_index = 4
                
                latent = styles[0].unsqueeze(0)
                if latent.shape[1] == 1:
                    latent = latent.repeat(1, inject_index, 1)
                else:
                    latent = latent[:, :inject_index, :]
                latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
                latent = torch.cat([latent, latent2], 1)
        else: # input is tensor of size with torch.Size([1, 18, 512]), for real PTI output
            latent = styles

        # print(f'processed latent: {latent.shape}')

        features = {}
        out = self.input(latent)
        features["out_0"] = out
        out = self.conv1(out, latent[:, 0], noise=noise[0])
        features["conv1_0"] = out

        skip = self.to_rgb1(out, latent[:, 1])
        features["skip_0"] = skip
        i = 1
        for conv1, conv2, noise1, noise2, to_rgb in zip(
            self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
        ):
            out = conv1(out, latent[:, i], noise=noise1)
            features["conv1_{}".format(i)] = out
            out = conv2(out, latent[:, i + 1], noise=noise2)
            features["conv2_{}".format(i)] = out
            skip = to_rgb(out, latent[:, i + 2], skip)
            features["skip_{}".format(i)] = skip

            i += 2

        image = skip

        if return_latents:
            return image, latent
        elif return_features:
            return image, features
        else:
            return image, None


class ConvLayer(nn.Sequential):
    def __init__(
        self,
        in_channel,
        out_channel,
        kernel_size,
        downsample=False,
        blur_kernel=[1, 3, 3, 1],
        bias=True,
        activate=True,
    ):
        layers = []

        if downsample:
            factor = 2
            p = (len(blur_kernel) - factor) + (kernel_size - 1)
            pad0 = (p + 1) // 2
            pad1 = p // 2

            layers.append(Blur(blur_kernel, pad=(pad0, pad1)))

            stride = 2
            self.padding = 0

        else:
            stride = 1
            self.padding = kernel_size // 2

        layers.append(
            EqualConv2d(
                in_channel,
                out_channel,
                kernel_size,
                padding=self.padding,
                stride=stride,
                bias=bias and not activate,
            )
        )

        if activate:
            if bias:
                layers.append(FusedLeakyReLU(out_channel))
            else:
                layers.append(ScaledLeakyReLU(0.2))

        super().__init__(*layers)


class ResBlock(nn.Module):
    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
        super().__init__()

        self.conv1 = ConvLayer(in_channel, in_channel, 3)
        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)

        self.skip = ConvLayer(
            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
        )

    def forward(self, input):
        out = self.conv1(input)
        out = self.conv2(out)

        skip = self.skip(input)
        out = (out + skip) / math.sqrt(2)

        return out


class StyleDiscriminator(nn.Module):
    def __init__(
        self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], small=False
    ):
        super().__init__()

        if small:
            channels = {4: 64, 8: 64, 16: 64, 32: 64, 64: 64}

        else:
            channels = {
                4: 512,
                8: 512,
                16: 512,
                32: 512,
                64: 256 * channel_multiplier,
                128: 128 * channel_multiplier,
                256: 64 * channel_multiplier,
                512: 32 * channel_multiplier,
                1024: 16 * channel_multiplier,
            }

        convs = [ConvLayer(3, channels[size], 1)]

        log_size = int(math.log(size, 2))
        in_channel = channels[size]

        for i in range(log_size, 2, -1):
            out_channel = channels[2 ** (i - 1)]

            convs.append(ResBlock(in_channel, out_channel, blur_kernel))

            in_channel = out_channel

        self.convs = nn.Sequential(*convs)

        self.stddev_group = 4
        self.stddev_feat = 1

        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
        self.final_linear = nn.Sequential(
            EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"),
            EqualLinear(channels[4], 1),
        )

    
    def forward(self, input):
        h = input
        h_list = []
        
        for index, blocklist in enumerate(self.convs):
            h = blocklist(h)
            h_list.append(h)
         
        out = h
        batch, channel, height, width = out.shape
        group = min(batch, self.stddev_group)
        stddev = out.view(
            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
        )
        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
        stddev = stddev.repeat(group, 1, height, width)
        out = torch.cat([out, stddev], 1)

        out = self.final_conv(out)
        h_list.append(out)
        
        out = out.view(batch, -1)
        out = self.final_linear(out)
        
        return out, h_list


class StyleEncoder(nn.Module):
    def __init__(self, size, w_dim=512):
        super().__init__()
        
        channels = {
            4: 512,
            8: 512,
            16: 512,
            32: 512,
            64: 256,
            128: 128,
            256: 64,
            512: 32,
            1024: 16
        }        
        
        self.w_dim = w_dim
        log_size = int(math.log(size, 2))
        convs = [ConvLayer(3, channels[size], 1)]

        in_channel = channels[size]
        for i in range(log_size, 2, -1):
            out_channel = channels[2 ** (i - 1)]
            convs.append(ResBlock(in_channel, out_channel))
            in_channel = out_channel

        convs.append(EqualConv2d(in_channel,2*self.w_dim, 4, padding=0, bias=False))    

        self.convs = nn.Sequential(*convs)

    def forward(self, input):
        out = self.convs(input)
        # return out.view(len(input), self.n_latents, self.w_dim)
        reshaped =  out.view(len(input), 2*self.w_dim)
        return reshaped[:,:self.w_dim], reshaped[:,self.w_dim:]

def kaiming_init(m):
    if isinstance(m, (nn.Linear, nn.Conv2d)):
        init.kaiming_normal_(m.weight)
        if m.bias is not None:
            m.bias.data.fill_(0)
    elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
        m.weight.data.fill_(1)
        if m.bias is not None:
            m.bias.data.fill_(0)


def normal_init(m):
    if isinstance(m, (nn.Linear, nn.Conv2d)):
        init.normal_(m.weight, 0, 0.02)
        if m.bias is not None:
            m.bias.data.fill_(0)
    elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
        m.weight.data.fill_(1)
        if m.bias is not None:
            m.bias.data.fill_(0)