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# TODO
import functools
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import flow_util
from models.base_blocks import LayerNorm2d, ADAINHourglass, FineEncoder, FineDecoder
# DNet
class DNet(nn.Module):
def __init__(self):
super(DNet, self).__init__()
self.mapping_net = MappingNet()
self.warpping_net = WarpingNet()
self.editing_net = EditingNet()
def forward(self, input_image, driving_source, stage=None):
if stage == 'warp':
descriptor = self.mapping_net(driving_source)
output = self.warpping_net(input_image, descriptor)
else:
descriptor = self.mapping_net(driving_source)
output = self.warpping_net(input_image, descriptor)
output['fake_image'] = self.editing_net(input_image, output['warp_image'], descriptor)
return output
class MappingNet(nn.Module):
def __init__(self, coeff_nc=73, descriptor_nc=256, layer=3):
super( MappingNet, self).__init__()
self.layer = layer
nonlinearity = nn.LeakyReLU(0.1)
self.first = nn.Sequential(
torch.nn.Conv1d(coeff_nc, descriptor_nc, kernel_size=7, padding=0, bias=True))
for i in range(layer):
net = nn.Sequential(nonlinearity,
torch.nn.Conv1d(descriptor_nc, descriptor_nc, kernel_size=3, padding=0, dilation=3))
setattr(self, 'encoder' + str(i), net)
self.pooling = nn.AdaptiveAvgPool1d(1)
self.output_nc = descriptor_nc
def forward(self, input_3dmm):
out = self.first(input_3dmm)
for i in range(self.layer):
model = getattr(self, 'encoder' + str(i))
out = model(out) + out[:,:,3:-3]
out = self.pooling(out)
return out
class WarpingNet(nn.Module):
def __init__(
self,
image_nc=3,
descriptor_nc=256,
base_nc=32,
max_nc=256,
encoder_layer=5,
decoder_layer=3,
use_spect=False
):
super( WarpingNet, self).__init__()
nonlinearity = nn.LeakyReLU(0.1)
norm_layer = functools.partial(LayerNorm2d, affine=True)
kwargs = {'nonlinearity':nonlinearity, 'use_spect':use_spect}
self.descriptor_nc = descriptor_nc
self.hourglass = ADAINHourglass(image_nc, self.descriptor_nc, base_nc,
max_nc, encoder_layer, decoder_layer, **kwargs)
self.flow_out = nn.Sequential(norm_layer(self.hourglass.output_nc),
nonlinearity,
nn.Conv2d(self.hourglass.output_nc, 2, kernel_size=7, stride=1, padding=3))
self.pool = nn.AdaptiveAvgPool2d(1)
def forward(self, input_image, descriptor):
final_output={}
output = self.hourglass(input_image, descriptor)
final_output['flow_field'] = self.flow_out(output)
deformation = flow_util.convert_flow_to_deformation(final_output['flow_field'])
final_output['warp_image'] = flow_util.warp_image(input_image, deformation)
return final_output
class EditingNet(nn.Module):
def __init__(
self,
image_nc=3,
descriptor_nc=256,
layer=3,
base_nc=64,
max_nc=256,
num_res_blocks=2,
use_spect=False):
super(EditingNet, self).__init__()
nonlinearity = nn.LeakyReLU(0.1)
norm_layer = functools.partial(LayerNorm2d, affine=True)
kwargs = {'norm_layer':norm_layer, 'nonlinearity':nonlinearity, 'use_spect':use_spect}
self.descriptor_nc = descriptor_nc
# encoder part
self.encoder = FineEncoder(image_nc*2, base_nc, max_nc, layer, **kwargs)
self.decoder = FineDecoder(image_nc, self.descriptor_nc, base_nc, max_nc, layer, num_res_blocks, **kwargs)
def forward(self, input_image, warp_image, descriptor):
x = torch.cat([input_image, warp_image], 1)
x = self.encoder(x)
gen_image = self.decoder(x, descriptor)
return gen_image
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