import torch from .base_model import BaseModel from . import networks class Pix2Pix4DepthModel(BaseModel): """ This class implements the pix2pix model, for learning a mapping from input images to output images given paired data. The model training requires '--dataset_mode aligned' dataset. By default, it uses a '--netG unet256' U-Net generator, a '--netD basic' discriminator (PatchGAN), and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper). pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf """ @staticmethod def modify_commandline_options(parser, is_train=True): """Add new dataset-specific options, and rewrite default values for existing options. Parameters: parser -- original option parser is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. Returns: the modified parser. For pix2pix, we do not use image buffer The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1 By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets. """ # changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/) parser.set_defaults(input_nc=2,output_nc=1,norm='none', netG='unet_1024', dataset_mode='depthmerge') if is_train: parser.set_defaults(pool_size=0, gan_mode='vanilla',) parser.add_argument('--lambda_L1', type=float, default=1000, help='weight for L1 loss') return parser def __init__(self, opt): """Initialize the pix2pix class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions """ BaseModel.__init__(self, opt) # specify the training losses you want to print out. The training/test scripts will call self.loss_names = ['G_GAN', 'G_L1', 'D_real', 'D_fake'] # self.loss_names = ['G_L1'] # specify the images you want to save/display. The training/test scripts will call if self.isTrain: self.visual_names = ['outer','inner', 'fake_B', 'real_B'] else: self.visual_names = ['fake_B'] # specify the models you want to save to the disk. The training/test scripts will call and if self.isTrain: self.model_names = ['G','D'] else: # during test time, only load G self.model_names = ['G'] # define networks (both generator and discriminator) self.netG = networks.define_G(opt.input_nc, opt.output_nc, 64, 'unet_1024', 'none', False, 'normal', 0.02, self.gpu_ids) if self.isTrain: # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids) if self.isTrain: # define loss functions self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device) self.criterionL1 = torch.nn.L1Loss() # initialize optimizers; schedulers will be automatically created by function . self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=1e-4, betas=(opt.beta1, 0.999)) self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=2e-06, betas=(opt.beta1, 0.999)) self.optimizers.append(self.optimizer_G) self.optimizers.append(self.optimizer_D) def set_input_train(self, input): self.outer = input['data_outer'].to(self.device) self.outer = torch.nn.functional.interpolate(self.outer,(1024,1024),mode='bilinear',align_corners=False) self.inner = input['data_inner'].to(self.device) self.inner = torch.nn.functional.interpolate(self.inner,(1024,1024),mode='bilinear',align_corners=False) self.image_paths = input['image_path'] if self.isTrain: self.gtfake = input['data_gtfake'].to(self.device) self.gtfake = torch.nn.functional.interpolate(self.gtfake, (1024, 1024), mode='bilinear', align_corners=False) self.real_B = self.gtfake self.real_A = torch.cat((self.outer, self.inner), 1) def set_input(self, outer, inner): inner = torch.from_numpy(inner).unsqueeze(0).unsqueeze(0) outer = torch.from_numpy(outer).unsqueeze(0).unsqueeze(0) inner = (inner - torch.min(inner))/(torch.max(inner)-torch.min(inner)) outer = (outer - torch.min(outer))/(torch.max(outer)-torch.min(outer)) inner = self.normalize(inner) outer = self.normalize(outer) self.real_A = torch.cat((outer, inner), 1).to(self.device) def normalize(self, input): input = input * 2 input = input - 1 return input def forward(self): """Run forward pass; called by both functions and .""" self.fake_B = self.netG(self.real_A) # G(A) def backward_D(self): """Calculate GAN loss for the discriminator""" # Fake; stop backprop to the generator by detaching fake_B fake_AB = torch.cat((self.real_A, self.fake_B), 1) # we use conditional GANs; we need to feed both input and output to the discriminator pred_fake = self.netD(fake_AB.detach()) self.loss_D_fake = self.criterionGAN(pred_fake, False) # Real real_AB = torch.cat((self.real_A, self.real_B), 1) pred_real = self.netD(real_AB) self.loss_D_real = self.criterionGAN(pred_real, True) # combine loss and calculate gradients self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5 self.loss_D.backward() def backward_G(self): """Calculate GAN and L1 loss for the generator""" # First, G(A) should fake the discriminator fake_AB = torch.cat((self.real_A, self.fake_B), 1) pred_fake = self.netD(fake_AB) self.loss_G_GAN = self.criterionGAN(pred_fake, True) # Second, G(A) = B self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1 # combine loss and calculate gradients self.loss_G = self.loss_G_L1 + self.loss_G_GAN self.loss_G.backward() def optimize_parameters(self): self.forward() # compute fake images: G(A) # update D self.set_requires_grad(self.netD, True) # enable backprop for D self.optimizer_D.zero_grad() # set D's gradients to zero self.backward_D() # calculate gradients for D self.optimizer_D.step() # update D's weights # update G self.set_requires_grad(self.netD, False) # D requires no gradients when optimizing G self.optimizer_G.zero_grad() # set G's gradients to zero self.backward_G() # calculate graidents for G self.optimizer_G.step() # udpate G's weights