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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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