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