networks/encoder.py

import math
import torch
from torch import nn
from torch.nn import functional as F

def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
    return F.leaky_relu(input + bias, negative_slope) * scale

class FusedLeakyReLU(nn.Module):
    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
        super().__init__()
        self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1))
        self.negative_slope = negative_slope
        self.scale = scale

    def forward(self, input):
        out = fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
        return out


def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
    _, minor, in_h, in_w = input.shape
    kernel_h, kernel_w = kernel.shape

    out = input.view(-1, minor, in_h, 1, in_w, 1)
    out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
    out = out.view(-1, minor, in_h * up_y, in_w * up_x)

    out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
    out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0),
          max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0), ]

    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
                      in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, )

    return out[:, :, ::down_y, ::down_x]


def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])


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


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

        self.negative_slope = negative_slope

    def forward(self, input):
        return F.leaky_relu(input, negative_slope=self.negative_slope)


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):

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

    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 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 WeightedSumLayer(nn.Module):
    def __init__(self, num_tensors=8):
        super(WeightedSumLayer, self).__init__()

        self.weights = nn.Parameter(torch.randn(num_tensors))
    
    def forward(self, tensor_list):

        weights = torch.softmax(self.weights, dim=0)
        weighted_sum = torch.zeros_like(tensor_list[0])
        for tensor, weight in zip(tensor_list, weights):
            weighted_sum += tensor * weight
        
        return weighted_sum

class EncoderApp(nn.Module):
    def __init__(self, size, w_dim=512, fusion_type=''):
        super(EncoderApp, self).__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))

        self.convs = nn.ModuleList()
        self.convs.append(ConvLayer(3, channels[size], 1))

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

        self.convs.append(EqualConv2d(in_channel, self.w_dim, 4, padding=0, bias=False))
        
        self.fusion_type = fusion_type
        assert self.fusion_type == 'weighted_sum'
        if self.fusion_type == 'weighted_sum':
            print(f'HAL layer is enabled!')
            self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1))
            self.fc1 = EqualLinear(64, 512)
            self.fc2 = EqualLinear(128, 512)
            self.fc3 = EqualLinear(256, 512)
            self.ws = WeightedSumLayer()

    def forward(self, x):

        res = []
        h = x
        pooled_h_lists = []
        for i, conv in enumerate(self.convs):
            h = conv(h)
            if self.fusion_type == 'weighted_sum':
                pooled_h = self.adaptive_pool(h).view(x.size(0), -1) 
                if i == 0:
                    pooled_h_lists.append(self.fc1(pooled_h))
                elif i == 1:
                    pooled_h_lists.append(self.fc2(pooled_h))
                elif i == 2:
                    pooled_h_lists.append(self.fc3(pooled_h))
                else:
                    pooled_h_lists.append(pooled_h)
            res.append(h)
        
        if self.fusion_type == 'weighted_sum':
            last_layer = self.ws(pooled_h_lists)
        else:  
            last_layer = res[-1].squeeze(-1).squeeze(-1)
        layer_features = res[::-1][2:]
        
        return last_layer, layer_features


class DecouplingModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(DecouplingModel, self).__init__()
        
        # identity_excluded_net is called identity encoder in the paper
        self.identity_net = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim)
        )
        
        self.identity_net_density = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim)
        )
        
        # identity_excluded_net is called motion encoder in the paper
        self.identity_excluded_net = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim)
        )

    def forward(self, x):

        id_, id_rm =  self.identity_net(x), self.identity_excluded_net(x)
        id_density = self.identity_net_density(id_)
        return id_, id_rm, id_density

class Encoder(nn.Module):
    def __init__(self, size, dim=512, dim_motion=20, weighted_sum=False):
        super(Encoder, self).__init__()

        # image encoder
        self.net_app = EncoderApp(size, dim, weighted_sum)
        
        # decouping network
        self.net_decouping = DecouplingModel(dim, dim, dim)

        # part of the motion encoder
        fc = [EqualLinear(dim, dim)]
        for i in range(3):
            fc.append(EqualLinear(dim, dim))

        fc.append(EqualLinear(dim, dim_motion))
        self.fc = nn.Sequential(*fc)

    def enc_app(self, x):

        h_source = self.net_app(x)

        return h_source

    def enc_motion(self, x):

        h, _ = self.net_app(x)
        h_motion = self.fc(h)

        return h_motion
    
    def encode_image_obj(self, image_obj):
        feat, _ = self.net_app(image_obj)
        id_emb, idrm_emb, id_density_emb = self.net_decouping(feat)
        return id_emb, idrm_emb, id_density_emb

    def forward(self, input_source, input_target, input_face, input_aug):


        if input_target is not None:

            h_source, feats = self.net_app(input_source)
            h_target, _ = self.net_app(input_target)
            h_face, _ = self.net_app(input_face)
            h_aug, _ = self.net_app(input_aug)
            
            h_source_id_emb, h_source_idrm_emb, h_source_id_density_emb = self.net_decouping(h_source)
            h_target_id_emb, h_target_idrm_emb, h_target_id_density_emb = self.net_decouping(h_target)
            h_face_id_emb, h_face_idrm_emb, h_face_id_density_emb = self.net_decouping(h_face)
            h_aug_id_emb, h_aug_idrm_emb, h_aug_id_density_emb = self.net_decouping(h_aug)

            h_target_motion_target = self.fc(h_target_idrm_emb)
            h_another_face_target =  self.fc(h_face_idrm_emb)
            
        else:
            h_source, feats = self.net_app(input_source)


        return {'h_source':h_source, 'h_motion':h_target_motion_target, 'feats':feats, 'h_another_face_target':h_another_face_target, 'h_face':h_face, \
                'h_source_id_emb':h_source_id_emb, 'h_source_idrm_emb':h_source_idrm_emb,  'h_source_id_density_emb':h_source_id_density_emb, \
                'h_target_id_emb':h_target_id_emb, 'h_target_idrm_emb':h_target_idrm_emb,  'h_target_id_density_emb':h_target_id_density_emb, \
                'h_face_id_emb':h_face_id_emb, 'h_face_idrm_emb':h_face_idrm_emb, 'h_face_id_density_emb':h_face_id_density_emb, \
                'h_aug_id_emb':h_aug_id_emb, 'h_aug_idrm_emb':h_aug_idrm_emb ,'h_aug_id_density_emb':h_aug_id_density_emb, \
                }