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"""
continuous_munit_cyclepoint_residual.py
This is CoMo-MUNIT *logic*, so how the network is trained.
"""
import math
import torch
import itertools
from .base_model import BaseModel
from .backbones import comomunit as networks
import random
import munch
def ModelOptions():
mo = munch.Munch()
# Generator
mo.gen_dim = 64
mo.style_dim = 8
mo.gen_activ = 'relu'
mo.n_downsample = 2
mo.n_res = 4
mo.gen_pad_type = 'reflect'
mo.mlp_dim = 256
# Discriminiator
mo.disc_dim = 64
mo.disc_norm = 'none'
mo.disc_activ = 'lrelu'
mo.disc_n_layer = 4
mo.num_scales = 3 # TODO change for other experiments!
mo.disc_pad_type = 'reflect'
# Initialization
mo.init_type_gen = 'kaiming'
mo.init_type_disc = 'normal'
mo.init_gain = 0.02
# Weights
mo.lambda_gan = 1
mo.lambda_rec_image = 10
mo.lambda_rec_style = 1
mo.lambda_rec_content = 1
mo.lambda_rec_cycle = 10
mo.lambda_vgg = 0.1
mo.lambda_idt = 1
mo.lambda_Phinet_A = 1
# Continuous settings
mo.resblocks_cont = 1
mo.lambda_physics = 10
mo.lambda_compare = 10
mo.lambda_physics_compare = 1
return mo
class CoMoMUNITModel(BaseModel):
def __init__(self, opt):
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 = ['D_A', 'G_A', 'cycle_A', 'rec_A', 'rec_style_B', 'rec_content_A', 'vgg_A', 'phi_net_A',
'D_B', 'G_B', 'cycle_B', 'rec_B', 'rec_style_A', 'rec_content_B', 'vgg_B', 'idt_B',
'recon_physics', 'phi_net']
# specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
visual_names_A = ['x', 'y', 'rec_A_img', 'rec_A_cycle', 'y_M_tilde', 'y_M']
visual_names_B = ['y_tilde', 'fake_A', 'rec_B_img', 'rec_B_cycle', 'idt_B_img']
self.visual_names = visual_names_A + visual_names_B # combine visualizations for A and B
# specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>.
self.model_names = ['G_A', 'D_A', 'G_B', 'D_B', 'DRB', 'Phi_net', 'Phi_net_A']
self.netG_A = networks.define_G_munit(opt.input_nc, opt.output_nc, opt.gen_dim, opt.style_dim, opt.n_downsample,
opt.n_res, opt.gen_pad_type, opt.mlp_dim, opt.gen_activ, opt.init_type_gen,
opt.init_gain, self.gpu_ids)
self.netG_B = networks.define_G_munit(opt.output_nc, opt.input_nc, opt.gen_dim, opt.style_dim, opt.n_downsample,
opt.n_res, opt.gen_pad_type, opt.mlp_dim, opt.gen_activ, opt.init_type_gen,
opt.init_gain, self.gpu_ids)
self.netDRB = networks.define_DRB_munit(opt.resblocks_cont, opt.gen_dim * (2 ** opt.n_downsample), 'instance', opt.gen_activ,
opt.gen_pad_type, opt.init_type_gen, opt.init_gain, self.gpu_ids)
# define discriminators
self.netD_A = networks.define_D_munit(opt.output_nc, opt.disc_dim, opt.disc_norm, opt.disc_activ, opt.disc_n_layer,
opt.gan_mode, opt.num_scales, opt.disc_pad_type, opt.init_type_disc,
opt.init_gain, self.gpu_ids)
self.netD_B = networks.define_D_munit(opt.input_nc, opt.disc_dim, opt.disc_norm, opt.disc_activ, opt.disc_n_layer,
opt.gan_mode, opt.num_scales, opt.disc_pad_type, opt.init_type_disc,
opt.init_gain, self.gpu_ids)
# We use munit style encoder as phinet/phinet_A
self.netPhi_net = networks.init_net(networks.StyleEncoder(4, opt.input_nc * 2, opt.gen_dim, 2, norm='instance',
activ='lrelu', pad_type=opt.gen_pad_type), init_type=opt.init_type_gen,
init_gain = opt.init_gain, gpu_ids = opt.gpu_ids)
self.netPhi_net_A = networks.init_net(networks.StyleEncoder(4, opt.input_nc, opt.gen_dim, 1, norm='instance',
activ='lrelu', pad_type=opt.gen_pad_type), init_type=opt.init_type_gen,
init_gain = opt.init_gain, gpu_ids = opt.gpu_ids)
# define loss functions
self.reconCriterion = torch.nn.L1Loss()
self.criterionPhysics = torch.nn.L1Loss()
self.criterionIdt = torch.nn.L1Loss()
# initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
if opt.lambda_vgg > 0:
self.instance_norm = torch.nn.InstanceNorm2d(512)
self.vgg = networks.Vgg16()
self.vgg.load_state_dict(torch.load('res/vgg_imagenet.pth'))
self.vgg.eval()
for param in self.vgg.parameters():
param.requires_grad = False
def configure_optimizers(self):
opt_G = torch.optim.Adam(itertools.chain(self.netG_A.parameters(), self.netG_B.parameters(),
self.netDRB.parameters(), self.netPhi_net.parameters(),
self.netPhi_net_A.parameters()),
weight_decay=0.0001, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
opt_D = torch.optim.Adam(itertools.chain(self.netD_A.parameters(), self.netD_B.parameters()),
weight_decay=0.0001, lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
scheduler_G = self.get_scheduler(self.opt, opt_G)
scheduler_D = self.get_scheduler(self.opt, opt_D)
return [opt_D, opt_G], [scheduler_D, scheduler_G]
def set_input(self, input):
# Input image. everything is mixed so we only have one style
self.x = input['A']
# Paths just because maybe they are needed
self.image_paths = input['A_paths']
# Desired continuity value which is used to render self.y_M_tilde
# Desired continuity value which is used to render self.y_M_tilde
self.phi = input['phi'].float()
self.cos_phi = input['cos_phi'].float()
self.sin_phi = input['sin_phi'].float()
# Term used to train SSN
self.phi_prime = input['phi_prime'].float()
self.cos_phi_prime = input['cos_phi_prime'].float()
self.sin_phi_prime = input['sin_phi_prime'].float()
# physical model applied to self.x with continuity self.continuity
self.y_M_tilde = input['A_cont']
# physical model applied to self.x with continuity self.continuity_compare
self.y_M_tilde_prime = input['A_cont_compare']
# Other image, in reality the two will belong to the same domain
self.y_tilde = input['B']
def __vgg_preprocess(self, batch):
tensortype = type(batch)
(r, g, b) = torch.chunk(batch, 3, dim=1)
batch = torch.cat((b, g, r), dim=1) # convert RGB to BGR
batch = (batch + 1) * 255 * 0.5 # [-1, 1] -> [0, 255]
mean = tensortype(batch.data.size()).to(self.device)
mean[:, 0, :, :] = 103.939
mean[:, 1, :, :] = 116.779
mean[:, 2, :, :] = 123.680
batch = batch.sub(mean) # subtract mean
return batch
def __compute_vgg_loss(self, img, target):
img_vgg = self.__vgg_preprocess(img)
target_vgg = self.__vgg_preprocess(target)
img_fea = self.vgg(img_vgg)
target_fea = self.vgg(target_vgg)
return torch.mean((self.instance_norm(img_fea) - self.instance_norm(target_fea)) ** 2)
def forward(self, img, phi = None, style_B_fake = None):
"""Run forward pass; called by both functions <optimize_parameters> and <test>."""
# Random style sampling
if style_B_fake is None:
style_B_fake = torch.randn(img.size(0), self.opt.style_dim, 1, 1).to(self.device)
if phi is None:
phi = torch.zeros(1).fill_(random.random()).to(self.device) * math.pi * 2
self.cos_phi = torch.cos(phi)
self.sin_phi = torch.sin(phi)
# Encoding
self.content_A, self.style_A_real = self.netG_A.encode(img)
features_A = self.netG_B.assign_adain(self.content_A, style_B_fake)
features_A_real, features_A_physics = self.netDRB(features_A, self.cos_phi, self.sin_phi)
fake_B = self.netG_B.decode(features_A_real)
return fake_B
def training_step_D(self):
with torch.no_grad():
# Random style sampling
self.style_A_fake = torch.randn(self.x.size(0), self.opt.style_dim, 1, 1).to(self.device)
self.style_B_fake = torch.randn(self.y_tilde.size(0), self.opt.style_dim, 1, 1).to(self.device)
self.content_A, self.style_A_real = self.netG_A.encode(self.x)
features_A = self.netG_B.assign_adain(self.content_A, self.style_B_fake)
features_A_real, features_A_physics = self.netDRB(features_A, self.cos_phi, self.sin_phi)
self.y = self.netG_B.decode(features_A_real)
# Encoding
self.content_B, self.style_B_real = self.netG_B.encode(self.y_tilde)
features_B = self.netG_A.assign_adain(self.content_B, self.style_A_fake)
features_B_real, _ = self.netDRB(features_B,
torch.ones(self.cos_phi.size()).to(self.device),
torch.zeros(self.sin_phi.size()).to(self.device)
)
self.fake_A = self.netG_A.decode(features_B_real)
self.loss_D_A = self.netD_A.calc_dis_loss(self.y, self.y_tilde) * self.opt.lambda_gan
self.loss_D_B = self.netD_B.calc_dis_loss(self.fake_A, self.x) * self.opt.lambda_gan
loss_D = self.loss_D_A + self.loss_D_B
return loss_D
def phi_loss_fn(self):
# the distance between the generated image and the image at the output of the
# physical model should be zero
input_zerodistance = torch.cat((self.y, self.y_M_tilde), dim = 1)
# Distance between generated image and other image of the physical model should be
# taken from the ground truth value
input_normaldistance = torch.cat((self.y, self.y_M_tilde_prime), dim = 1)
# same for this, but this does not depend on a GAN generation so it's used as a regularization term
input_regolarize = torch.cat((self.y_M_tilde, self.y_M_tilde_prime), dim = 1)
# essentailly, ground truth distance given by the physical model renderings
# Cosine distance, we are trying to encode cyclic stuff
distance_cos = (torch.cos(self.phi) - torch.cos(self.phi_prime)) / 2
distance_sin = (torch.sin(self.phi) - torch.sin(self.phi_prime)) / 2
# We evaluate the angle distance and we normalize it in -1/1
output_zerodistance = torch.tanh(self.netPhi_net(input_zerodistance))#[0])
output_normaldistance = torch.tanh(self.netPhi_net(input_normaldistance))#[0])
output_regolarize = torch.tanh(self.netPhi_net(input_regolarize))#[0])
loss_cos = torch.pow(output_zerodistance[:, 0] - 0, 2).mean()
loss_cos += torch.pow(output_normaldistance[:, 0] - distance_cos, 2).mean()
loss_cos += torch.pow(output_regolarize[:, 0] - distance_cos, 2).mean()
loss_sin = torch.pow(output_zerodistance[:, 1] - 0, 2).mean()
loss_sin += torch.pow(output_normaldistance[:, 1] - distance_sin, 2).mean()
loss_sin += torch.pow(output_regolarize[:, 1] - distance_sin, 2).mean()
# additional terms on the other image generated by the GAN, i.e. something that should resemble exactly
# the image generated by the physical model
# This terms follow the same reasoning as before and weighted differently
input_physics_zerodistance = torch.cat((self.y_M, self.y_M_tilde), dim = 1)
input_physics_regolarize = torch.cat((self.y_M, self.y_M_tilde_prime), dim = 1)
output_physics_zerodistance = torch.tanh(self.netPhi_net(input_physics_zerodistance))#[0])
output_physics_regolarize = torch.tanh(self.netPhi_net(input_physics_regolarize))#[0])
loss_cos += torch.pow(output_physics_zerodistance[:, 0] - 0, 2).mean() * self.opt.lambda_physics_compare
loss_cos += torch.pow(output_physics_regolarize[:, 0] - distance_cos,
2).mean() * self.opt.lambda_physics_compare
loss_sin += torch.pow(output_physics_zerodistance[:, 1] - 0, 2).mean() * self.opt.lambda_physics_compare
loss_sin += torch.pow(output_physics_regolarize[:, 1] - distance_sin,
2).mean() * self.opt.lambda_physics_compare
# Also distance between the two outputs of the gan should be 0
input_twoheads = torch.cat((self.y_M, self.y), dim = 1)
output_twoheads = torch.tanh(self.netPhi_net(input_twoheads))#[0])
loss_cos += torch.pow(output_twoheads[:, 0] - 0, 2).mean()
loss_sin += torch.pow(output_twoheads[:, 1] - 0, 2).mean()
loss = loss_cos + loss_sin * 0.5
return loss
def training_step_G(self):
self.style_B_fake = torch.randn(self.y_tilde.size(0), self.opt.style_dim, 1, 1).to(self.device)
self.style_A_fake = torch.randn(self.x.size(0), self.opt.style_dim, 1, 1).to(self.device)
self.content_A, self.style_A_real = self.netG_A.encode(self.x)
self.content_B, self.style_B_real = self.netG_B.encode(self.y_tilde)
self.phi_est = torch.sigmoid(self.netPhi_net_A.forward(self.y_tilde).view(self.y_tilde.size(0), -1)).view(self.y_tilde.size(0)) * 2 * math.pi
self.estimated_cos_B = torch.cos(self.phi_est)
self.estimated_sin_B = torch.sin(self.phi_est)
# Reconstruction
features_A_reconstruction = self.netG_A.assign_adain(self.content_A, self.style_A_real)
features_A_reconstruction, _ = self.netDRB(features_A_reconstruction,
torch.ones(self.estimated_cos_B.size()).to(self.device),
torch.zeros(self.estimated_sin_B.size()).to(self.device))
self.rec_A_img = self.netG_A.decode(features_A_reconstruction)
features_B_reconstruction = self.netG_B.assign_adain(self.content_B, self.style_B_real)
features_B_reconstruction, _ = self.netDRB(features_B_reconstruction, self.estimated_cos_B, self.estimated_sin_B)
self.rec_B_img = self.netG_B.decode(features_B_reconstruction)
# Cross domain
features_A = self.netG_B.assign_adain(self.content_A, self.style_B_fake)
features_A_real, features_A_physics = self.netDRB(features_A, self.cos_phi, self.sin_phi)
self.y_M = self.netG_B.decode(features_A_physics)
self.y = self.netG_B.decode(features_A_real)
features_B = self.netG_A.assign_adain(self.content_B, self.style_A_fake)
features_B_real, _ = self.netDRB(features_B,
torch.ones(self.cos_phi.size()).to(self.device),
torch.zeros(self.sin_phi.size()).to(self.device))
self.fake_A = self.netG_A.decode(features_B_real)
self.rec_content_B, self.rec_style_A = self.netG_A.encode(self.fake_A)
self.rec_content_A, self.rec_style_B = self.netG_B.encode(self.y)
if self.opt.lambda_rec_cycle > 0:
features_A_reconstruction_cycle = self.netG_A.assign_adain(self.rec_content_A, self.style_A_real)
features_A_reconstruction_cycle, _ = self.netDRB(features_A_reconstruction_cycle,
torch.ones(self.cos_phi.size()).to(self.device),
torch.zeros(self.sin_phi.size()).to(self.device))
self.rec_A_cycle = self.netG_A.decode(features_A_reconstruction_cycle)
features_B_reconstruction_cycle = self.netG_B.assign_adain(self.rec_content_B, self.style_B_real)
features_B_reconstruction_cycle, _ = self.netDRB(features_B_reconstruction_cycle, self.estimated_cos_B, self.estimated_sin_B)
self.rec_B_cycle = self.netG_B.decode(features_B_reconstruction_cycle)
if self.opt.lambda_idt > 0:
features_B_identity = self.netG_B.assign_adain(self.content_A, torch.randn(self.style_B_fake.size()).to(self.device))
features_B_identity, _ = self.netDRB(features_B_identity,
torch.ones(self.estimated_cos_B.size()).to(self.device),
torch.zeros(self.estimated_sin_B.size()).to(self.device))
self.idt_B_img = self.netG_B.decode(features_B_identity)
if self.opt.lambda_idt > 0:
self.loss_idt_A = 0
self.loss_idt_B = self.criterionIdt(self.idt_B_img, self.x) * self.opt.lambda_gan * self.opt.lambda_idt
else:
self.loss_idt_A = 0
self.loss_idt_B = 0
continuity_angle_fake = torch.sigmoid(self.netPhi_net_A.forward(self.y).view(self.y_tilde.size(0), -1)).view(self.y_tilde.size(0)) * 2 * math.pi
continuity_cos_fake = 1 - ((torch.cos(continuity_angle_fake) + 1) / 2)
continuity_cos_gt = 1 - ((torch.cos(self.phi) + 1) / 2)
continuity_sin_fake = 1 - ((torch.sin(continuity_angle_fake) + 1) / 2)
continuity_sin_gt = 1 - ((torch.sin(self.phi) + 1) / 2)
distance_cos_fake = (continuity_cos_fake - continuity_cos_gt)
distance_sin_fake = (continuity_sin_fake - continuity_sin_gt)
self.loss_phi_net_A = (distance_cos_fake ** 2) * self.opt.lambda_Phinet_A
self.loss_phi_net_A += (distance_sin_fake ** 2) * self.opt.lambda_Phinet_A
self.loss_rec_A = self.reconCriterion(self.rec_A_img, self.x) * self.opt.lambda_rec_image
self.loss_rec_B = self.reconCriterion(self.rec_B_img, self.y_tilde) * self.opt.lambda_rec_image
self.loss_rec_style_B = self.reconCriterion(self.rec_style_B, self.style_B_fake) * self.opt.lambda_rec_style
self.loss_rec_style_A = self.reconCriterion(self.rec_style_A, self.style_A_fake) * self.opt.lambda_rec_style
self.loss_rec_content_A = self.reconCriterion(self.rec_content_A, self.content_A) * self.opt.lambda_rec_content
self.loss_rec_content_B = self.reconCriterion(self.rec_content_B, self.content_B) * self.opt.lambda_rec_content
if self.opt.lambda_rec_cycle > 0:
self.loss_cycle_A = self.reconCriterion(self.rec_A_cycle, self.x) * self.opt.lambda_rec_cycle
self.loss_cycle_B = self.reconCriterion(self.rec_B_cycle, self.y_tilde) * self.opt.lambda_rec_cycle
else:
self.loss_cycle_A = 0
self.loss_G_A = self.netD_A.calc_gen_loss(self.y) * self.opt.lambda_gan
self.loss_G_B = self.netD_B.calc_gen_loss(self.fake_A) * self.opt.lambda_gan
self.loss_recon_physics = self.opt.lambda_physics * self.criterionPhysics(self.y_M, self.y_M_tilde)
self.loss_phi_net = self.phi_loss_fn() * self.opt.lambda_compare
if self.opt.lambda_vgg > 0:
self.loss_vgg_A = self.__compute_vgg_loss(self.fake_A, self.y_tilde) * self.opt.lambda_vgg
self.loss_vgg_B = self.__compute_vgg_loss(self.y, self.x) * self.opt.lambda_vgg
else:
self.loss_vgg_A = 0
self.loss_vgg_B = 0
self.loss_G = self.loss_rec_A + self.loss_rec_style_B + self.loss_rec_content_A + \
self.loss_cycle_A + self.loss_G_B + self.loss_vgg_A + \
self.loss_rec_B + self.loss_rec_style_A + self.loss_rec_content_B + \
self.loss_cycle_B + self.loss_G_A + self.loss_vgg_B + \
self.loss_recon_physics + self.loss_phi_net + self.loss_idt_B + self.loss_phi_net_A
return self.loss_G
def training_step(self, batch, batch_idx, optimizer_idx):
self.set_input(batch)
if optimizer_idx == 0:
self.set_requires_grad([self.netD_A, self.netD_B], True)
self.set_requires_grad([self.netG_A, self.netG_B], False)
return self.training_step_D()
elif optimizer_idx == 1:
self.set_requires_grad([self.netD_A, self.netD_B], False) # Ds require no gradients when optimizing Gs
self.set_requires_grad([self.netG_A, self.netG_B], True)
return self.training_step_G()
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