guided-pix2pix 代码略解

本文主要是介绍guided-pix2pix 代码略解，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

《Guided Image-to-Image Translation with Bi-Directional Feature Transformation》

train.py

            model.set_input(data)model.optimize_parameters()

开始训练

models/guided_pix2pix_model.py

    def set_input(self, input):self.real_A = input['A'].to(self.device)self.real_B = input['B'].to(self.device)self.guide = input['guide'].to(self.device)

这里的guide是指的是引导的image/pose，real_A指的是作为输入的image，real_B指的是GT

    def forward(self):self.fake_B = self.netG(self.real_A, self.guide)

# load/define networks
self.netG = networks.define_G(input_nc=opt.input_nc, guide_nc=opt.guide_nc, output_nc=opt.output_nc, ngf=opt.ngf, netG=opt.netG, n_layers=opt.n_layers, norm=opt.norm, init_type=opt.init_type, init_gain=opt.init_gain, gpu_ids=self.gpu_ids)

models/networks.py

def define_G(input_nc, guide_nc, output_nc, ngf, netG, n_layers=8, n_downsampling=3, n_blocks=9, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]):net = Nonenorm_layer = get_norm_layer(norm_type=norm)if netG == 'bFT_resnet':net = bFT_Resnet(input_nc, guide_nc, output_nc, ngf, norm_layer=norm_layer, n_blocks=n_blocks)elif netG == 'bFT_unet':net = bFT_Unet(input_nc, guide_nc, output_nc, n_layers, ngf, norm_layer=norm_layer)else:raise NotImplementedError('Generator model name [%s] is not recognized' % netG)net = init_net(net, init_type, init_gain, gpu_ids)return net

看一下bFT_resent

class bFT_Resnet(nn.Module):def __init__(self, input_nc, guide_nc, output_nc, ngf=64, n_blocks=9, norm_layer=nn.BatchNorm2d,padding_type='reflect', bottleneck_depth=100):super(bFT_Resnet, self).__init__()self.activation = nn.ReLU(True)n_downsampling=3## inputpadding_in = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0)]self.padding_in = nn.Sequential(*padding_in)self.conv1 = nn.Conv2d(ngf, ngf * 2, kernel_size=3, stride=2, padding=1)self.conv2 = nn.Conv2d(ngf * 2, ngf * 4, kernel_size=3, stride=2, padding=1)self.conv3 = nn.Conv2d(ngf * 4, ngf * 8, kernel_size=3, stride=2, padding=1)## guidepadding_g = [nn.ReflectionPad2d(3), nn.Conv2d(guide_nc, ngf, kernel_size=7, padding=0)]self.padding_g = nn.Sequential(*padding_g)self.conv1_g = nn.Conv2d(ngf, ngf * 2, kernel_size=3, stride=2, padding=1)self.conv2_g = nn.Conv2d(ngf * 2, ngf * 4, kernel_size=3, stride=2, padding=1)self.conv3_g = nn.Conv2d(ngf * 4, ngf * 8, kernel_size=3, stride=2, padding=1)# bottleneck1self.bottleneck_alpha_1 = self.bottleneck_layer(ngf, bottleneck_depth)self.G_bottleneck_alpha_1 = self.bottleneck_layer(ngf, bottleneck_depth)self.bottleneck_beta_1 = self.bottleneck_layer(ngf, bottleneck_depth)self.G_bottleneck_beta_1 = self.bottleneck_layer(ngf, bottleneck_depth)# bottleneck2self.bottleneck_alpha_2 = self.bottleneck_layer(ngf*2, bottleneck_depth)self.G_bottleneck_alpha_2 = self.bottleneck_layer(ngf*2, bottleneck_depth)self.bottleneck_beta_2 = self.bottleneck_layer(ngf*2, bottleneck_depth)self.G_bottleneck_beta_2 = self.bottleneck_layer(ngf*2, bottleneck_depth)# bottleneck3self.bottleneck_alpha_3 = self.bottleneck_layer(ngf*4, bottleneck_depth)self.G_bottleneck_alpha_3 = self.bottleneck_layer(ngf*4, bottleneck_depth)self.bottleneck_beta_3 = self.bottleneck_layer(ngf*4, bottleneck_depth)self.G_bottleneck_beta_3 = self.bottleneck_layer(ngf*4, bottleneck_depth)# bottleneck4self.bottleneck_alpha_4 = self.bottleneck_layer(ngf*8, bottleneck_depth)self.G_bottleneck_alpha_4 = self.bottleneck_layer(ngf*8, bottleneck_depth)self.bottleneck_beta_4 = self.bottleneck_layer(ngf*8, bottleneck_depth)self.G_bottleneck_beta_4 = self.bottleneck_layer(ngf*8, bottleneck_depth)### 这些bottlenect_layer都是由1x1的卷积，激活层，1x1的卷积组成的，做从nc->nc的映射resnet = []mult = 2**n_downsamplingfor i in range(n_blocks):resnet += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=self.activation, norm_layer=norm_layer)]self.resnet = nn.Sequential(*resnet)decoder = []for i in range(n_downsampling):mult = 2**(n_downsampling - i)decoder += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1),norm_layer(int(ngf * mult / 2)), self.activation]self.pre_decoder = nn.Sequential(*decoder)self.decoder = nn.Sequential(*[nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()])def bottleneck_layer(self, nc, bottleneck_depth):return nn.Sequential(*[nn.Conv2d(nc, bottleneck_depth, kernel_size=1), self.activation, nn.Conv2d(bottleneck_depth, nc, kernel_size=1)])def get_FiLM_param_(self, X, i, guide=False):x = X.clone()# bottleneckif guide:if (i==1):alpha_layer = self.G_bottleneck_alpha_1beta_layer = self.G_bottleneck_beta_1elif (i==2):alpha_layer = self.G_bottleneck_alpha_2beta_layer = self.G_bottleneck_beta_2elif (i==3):alpha_layer = self.G_bottleneck_alpha_3beta_layer = self.G_bottleneck_beta_3elif (i==4):alpha_layer = self.G_bottleneck_alpha_4beta_layer = self.G_bottleneck_beta_4else:if (i==1):alpha_layer = self.bottleneck_alpha_1beta_layer = self.bottleneck_beta_1elif (i==2):alpha_layer = self.bottleneck_alpha_2beta_layer = self.bottleneck_beta_2elif (i==3):alpha_layer = self.bottleneck_alpha_3beta_layer = self.bottleneck_beta_3elif (i==4):alpha_layer = self.bottleneck_alpha_4beta_layer = self.bottleneck_beta_4alpha = alpha_layer(x)beta = beta_layer(x)return alpha, betadef forward(self, input, guidance):input = self.padding_in(input)  guidance = self.padding_g(guidance)g_alpha1, g_beta1 = self.get_FiLM_param_(guidance, 1, guide=True)i_alpha1, i_beta1 = self.get_FiLM_param_(input, 1)guidance = affine_transformation(guidance, i_alpha1, i_beta1)input = affine_transformation(input, g_alpha1, g_beta1)input = self.activation(input)guidance = self.activation(guidance)input = self.conv1(input)guidance = self.conv1_g(guidance)g_alpha2, g_beta2 = self.get_FiLM_param_(guidance, 2, guide=True)i_alpha2, i_beta2 = self.get_FiLM_param_(input, 2)input = affine_transformation(input, g_alpha2, g_beta2)guidance = affine_transformation(guidance, i_alpha2, i_beta2)input = self.activation(input)guidance = self.activation(guidance)input = self.conv2(input)guidance = self.conv2_g(guidance)g_alpha3, g_beta3 = self.get_FiLM_param_(guidance, 3, guide=True)i_alpha3, i_beta3 = self.get_FiLM_param_(input, 3)input = affine_transformation(input, g_alpha3, g_beta3)guidance = affine_transformation(guidance, i_alpha3, i_beta3)input = self.activation(input)guidance = self.activation(guidance)input = self.conv3(input)guidance = self.conv3_g(guidance)g_alpha4, g_beta4 = self.get_FiLM_param_(guidance, 4, guide=True)# guidance在这一步之后就舍弃了input = affine_transformation(input, g_alpha4, g_beta4)input = self.activation(input)input = self.resnet(input)input = self.pre_decoder(input)output = self.decoder(input)return output

 

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