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A10G
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import functools
import torch
import torch.nn as nn
from .base_function import LayerNorm2d, ADAINHourglass, FineEncoder, FineDecoder
def convert_flow_to_deformation(flow):
r"""convert flow fields to deformations.
Args:
flow (tensor): Flow field obtained by the model
Returns:
deformation (tensor): The deformation used for warpping
"""
b,c,h,w = flow.shape
flow_norm = 2 * torch.cat([flow[:,:1,...]/(w-1),flow[:,1:,...]/(h-1)], 1)
grid = make_coordinate_grid(flow)
deformation = grid + flow_norm.permute(0,2,3,1)
return deformation
def make_coordinate_grid(flow):
r"""obtain coordinate grid with the same size as the flow filed.
Args:
flow (tensor): Flow field obtained by the model
Returns:
grid (tensor): The grid with the same size as the input flow
"""
b,c,h,w = flow.shape
x = torch.arange(w).to(flow)
y = torch.arange(h).to(flow)
x = (2 * (x / (w - 1)) - 1)
y = (2 * (y / (h - 1)) - 1)
yy = y.view(-1, 1).repeat(1, w)
xx = x.view(1, -1).repeat(h, 1)
meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2)
meshed = meshed.expand(b, -1, -1, -1)
return meshed
def warp_image(source_image, deformation):
r"""warp the input image according to the deformation
Args:
source_image (tensor): source images to be warpped
deformation (tensor): deformations used to warp the images; value in range (-1, 1)
Returns:
output (tensor): the warpped images
"""
_, h_old, w_old, _ = deformation.shape
_, _, h, w = source_image.shape
if h_old != h or w_old != w:
deformation = deformation.permute(0, 3, 1, 2)
deformation = torch.nn.functional.interpolate(deformation, size=(h, w), mode='bilinear')
deformation = deformation.permute(0, 2, 3, 1)
return torch.nn.functional.grid_sample(source_image, deformation)
class FaceGenerator(nn.Module):
def __init__(
self,
mapping_net,
warpping_net,
editing_net,
common
):
super(FaceGenerator, self).__init__()
self.mapping_net = MappingNet(**mapping_net)
self.warpping_net = WarpingNet(**warpping_net, **common)
self.editing_net = EditingNet(**editing_net, **common)
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, descriptor_nc, layer):
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,
descriptor_nc,
base_nc,
max_nc,
encoder_layer,
decoder_layer,
use_spect
):
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 = convert_flow_to_deformation(final_output['flow_field'])
final_output['warp_image'] = warp_image(input_image, deformation)
return final_output
class EditingNet(nn.Module):
def __init__(
self,
image_nc,
descriptor_nc,
layer,
base_nc,
max_nc,
num_res_blocks,
use_spect):
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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