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 <BaseModel.get_current_losses>
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 <BaseModel.get_current_visuals>
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 <BaseModel.save_networks> and <BaseModel.load_networks>
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 <BaseModel.setup>.
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 <optimize_parameters> and <test>."""
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