128 lines
6.4 KiB
Python
128 lines
6.4 KiB
Python
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import torch
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from .base_model import BaseModel
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from . import networks
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class Pix2PixModel(BaseModel):
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""" This class implements the pix2pix model, for learning a mapping from input images to output images given paired data.
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The model training requires '--dataset_mode aligned' dataset.
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By default, it uses a '--netG unet256' U-Net generator,
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a '--netD basic' discriminator (PatchGAN),
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and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper).
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pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf
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"""
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@staticmethod
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def modify_commandline_options(parser, is_train=True):
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"""Add new dataset-specific options, and rewrite default values for existing options.
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Parameters:
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parser -- original option parser
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is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
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Returns:
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the modified parser.
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For pix2pix, we do not use image buffer
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The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1
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By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets.
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"""
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# changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/)
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parser.set_defaults(norm='batch', netG='unet_256', dataset_mode='aligned')
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if is_train:
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parser.set_defaults(pool_size=0, gan_mode='vanilla')
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parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss')
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return parser
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def __init__(self, opt):
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"""Initialize the pix2pix class.
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Parameters:
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opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
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"""
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BaseModel.__init__(self, opt)
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# specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
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self.loss_names = ['G_GAN', 'G_L1', 'D_real', 'D_fake']
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# specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
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self.visual_names = ['real_A', 'fake_B', 'real_B']
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# specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
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if self.isTrain:
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self.model_names = ['G', 'D']
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else: # during test time, only load G
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self.model_names = ['G']
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# define networks (both generator and discriminator)
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self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
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not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
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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
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self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
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opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
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if self.isTrain:
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# define loss functions
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self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
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self.criterionL1 = torch.nn.L1Loss()
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# initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
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self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
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self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
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self.optimizers.append(self.optimizer_G)
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self.optimizers.append(self.optimizer_D)
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def set_input(self, input):
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"""Unpack input data from the dataloader and perform necessary pre-processing steps.
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Parameters:
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input (dict): include the data itself and its metadata information.
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The option 'direction' can be used to swap images in domain A and domain B.
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"""
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AtoB = self.opt.direction == 'AtoB'
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self.real_A = input['A' if AtoB else 'B'].to(self.device)
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self.real_B = input['B' if AtoB else 'A'].to(self.device)
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self.image_paths = input['A_paths' if AtoB else 'B_paths']
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def forward(self):
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"""Run forward pass; called by both functions <optimize_parameters> and <test>."""
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self.fake_B = self.netG(self.real_A) # G(A)
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def backward_D(self):
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"""Calculate GAN loss for the discriminator"""
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# Fake; stop backprop to the generator by detaching fake_B
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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
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pred_fake = self.netD(fake_AB.detach())
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self.loss_D_fake = self.criterionGAN(pred_fake, False)
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# Real
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real_AB = torch.cat((self.real_A, self.real_B), 1)
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pred_real = self.netD(real_AB)
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self.loss_D_real = self.criterionGAN(pred_real, True)
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# combine loss and calculate gradients
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self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
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self.loss_D.backward()
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def backward_G(self):
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"""Calculate GAN and L1 loss for the generator"""
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# First, G(A) should fake the discriminator
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fake_AB = torch.cat((self.real_A, self.fake_B), 1)
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pred_fake = self.netD(fake_AB)
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self.loss_G_GAN = self.criterionGAN(pred_fake, True)
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# Second, G(A) = B
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self.loss_G_L1 = self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1
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# combine loss and calculate gradients
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self.loss_G = self.loss_G_GAN + self.loss_G_L1
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self.loss_G.backward()
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def optimize_parameters(self):
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self.forward() # compute fake images: G(A)
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# update D
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self.set_requires_grad(self.netD, True) # enable backprop for D
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self.optimizer_D.zero_grad() # set D's gradients to zero
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self.backward_D() # calculate gradients for D
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self.optimizer_D.step() # update D's weights
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# update G
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self.set_requires_grad(self.netD, False) # D requires no gradients when optimizing G
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self.optimizer_G.zero_grad() # set G's gradients to zero
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self.backward_G() # calculate graidents for G
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self.optimizer_G.step() # update G's weights
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