| 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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| import numpy as np
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| import functools
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| from .blocks import CondGatedConv2d, CondTransposeGatedConv2d, Conv2dBlock
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|
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| class G3(nn.Module):
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| def __init__(self, cfg):
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| super(G3, self).__init__()
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|
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| input_nc = cfg['input_nc']
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| ngf = cfg['ngf']
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| output_nc = cfg['output_nc']
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| lab_nc = cfg['lab_dim'] + 1
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| g_norm = cfg['G_norm_type']
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| self.enc1 = CondGatedConv2d(input_nc, ngf, lab_nc, kernel_size=3, stride=1, padding=1, norm=g_norm, activation='lrelu')
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| self.enc2 = CondGatedConv2d(ngf, ngf * 2, lab_nc, kernel_size=3, stride=2, padding=1, norm=g_norm, activation='lrelu')
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| self.enc3 = CondGatedConv2d(ngf * 2, ngf * 4, lab_nc, kernel_size=3, stride=2, padding=1, norm=g_norm, activation='lrelu')
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| self.enc4 = CondGatedConv2d(ngf * 4, ngf * 4, lab_nc, kernel_size=3, stride=2, padding=1, norm=g_norm, activation='lrelu')
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| self.enc5 = CondGatedConv2d(ngf * 4, ngf * 8, lab_nc, kernel_size=3, stride=2, padding=1, norm=g_norm, activation='lrelu')
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| self.enc6 = CondGatedConv2d(ngf * 8, ngf * 8, lab_nc, kernel_size=3, stride=2, padding=1, norm=g_norm, activation='lrelu')
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| self.enc7 = CondGatedConv2d(ngf * 8, ngf * 16, lab_nc, kernel_size=3, stride=2, padding=1, norm=g_norm, activation='lrelu')
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| self.enc8 = CondGatedConv2d(ngf * 16, ngf * 16, lab_nc, kernel_size=3, stride=2, padding=1, norm=g_norm, activation='lrelu')
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| self.enc9 = CondGatedConv2d(ngf * 16, ngf * 32, lab_nc, kernel_size=3, stride=1, padding=1, dilation=1, norm=g_norm,
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| activation='lrelu')
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| self.dec8 = CondTransposeGatedConv2d(ngf * 32 + ngf * 16, ngf * 16, lab_nc, kernel_size=3, stride=1, padding=1, norm=g_norm,
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| activation='lrelu', spade_norm=True, cfg=cfg)
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| self.dec7 = CondTransposeGatedConv2d(ngf * 16 + ngf * 8, ngf * 8, lab_nc, kernel_size=3, stride=1, padding=1, norm=g_norm,
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| activation='lrelu', spade_norm=True, cfg=cfg)
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| self.dec6 = CondTransposeGatedConv2d(ngf * 8 + ngf * 8, ngf * 8, lab_nc, kernel_size=3, stride=1, padding=1, norm=g_norm,
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| activation='lrelu', spade_norm=True, cfg=cfg)
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| self.dec5 = CondTransposeGatedConv2d(ngf * 8 + ngf * 4, ngf * 4, lab_nc, kernel_size=3, stride=1, padding=1, norm=g_norm,
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| activation='lrelu', spade_norm=True, cfg=cfg)
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| self.dec4 = CondTransposeGatedConv2d(ngf * 4 + ngf * 4, ngf * 2, lab_nc, kernel_size=3, stride=1, padding=1, norm=g_norm,
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| activation='lrelu', spade_norm=True, cfg=cfg)
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| self.dec3 = CondTransposeGatedConv2d(ngf * 2 + ngf * 2, ngf, lab_nc, kernel_size=3, stride=1, padding=1, norm=g_norm,
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| activation='lrelu', spade_norm=True, cfg=cfg)
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| self.dec2 = CondTransposeGatedConv2d(ngf, ngf, lab_nc, kernel_size=3, stride=1, padding=1, norm=g_norm,
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| activation='lrelu', spade_norm=True, cfg=cfg)
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| self.dec1 = Conv2dBlock(ngf, output_nc, kernel_size=3, stride=1, padding=1, norm='none', activation='tanh')
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| def forward(self, input, segmap, mask, style_codes):
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|
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| e1 = self.enc1(input, segmap, mask)
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| e2 = self.enc2(e1, segmap, mask)
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| e3 = self.enc3(e2, segmap, mask)
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| e4 = self.enc4(e3, segmap, mask)
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| e5 = self.enc5(e4, segmap, mask)
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| e6 = self.enc6(e5, segmap, mask)
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| e7 = self.enc7(e6, segmap, mask)
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| e8 = self.enc8(e7, segmap, mask)
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| e9 = self.enc9(e8, segmap, mask)
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|
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| d8 = self.dec8(e9, segmap, mask, skip=e7, style_codes=style_codes)
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| d7 = self.dec7(d8, segmap, mask, skip=e6, style_codes=style_codes)
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| d6 = self.dec6(d7, segmap, mask, skip=e5, style_codes=style_codes)
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| d5 = self.dec5(d6, segmap, mask, skip=e4, style_codes=style_codes)
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| d4 = self.dec4(d5, segmap, mask, skip=e3, style_codes=style_codes)
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| d3 = self.dec3(d4, segmap, mask, skip=e2, style_codes=style_codes)
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| d2 = self.dec2(d3, segmap, mask, style_codes=style_codes)
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| d1 = self.dec1(d2)
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|
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| return d1 |
