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import torch
import torch.nn.functional as F
from .base_model import BaseModel
from . import networks, losses
class TC(BaseModel):
"""This class implements the transformer for image completion"""
def name(self):
return "Transformer Image Completion"
@staticmethod
def modify_options(parser, is_train=True):
"""Add new options and rewrite default values for existing options"""
parser.add_argument('--coarse_or_refine', type=str, default='refine', help='train the transform or refined network')
parser.add_argument('--down_layers', type=int, default=4, help='# times down sampling for refine generator')
parser.add_argument('--mid_layers', type=int, default=6, help='# times middle layers for refine generator')
if is_train:
parser.add_argument('--lambda_rec', type=float, default=10.0, help='weight for image reconstruction loss')
parser.add_argument('--lambda_g', type=float, default=1.0, help='weight for discriminator loss')
parser.add_argument('--lambda_lp', type=float, default=10.0, help='weight for the perceptual loss')
parser.add_argument('--lambda_gradient', type=float, default=0.0, help='weight for the gradient penalty')
return parser
def __init__(self, opt):
"""inital the Transformer model"""
BaseModel.__init__(self, opt)
self.visual_names = ['img_org', 'img_m', 'img_g', 'img_out']
self.model_names = ['E', 'G', 'D', 'T']
self.loss_names = ['G_rec', 'G_lp', 'G_GAN', 'D_real', 'D_fake']
self.netE = networks.define_E(opt)
self.netT = networks.define_T(opt)
self.netG = networks.define_G(opt)
self.netD = networks.define_D(opt, opt.fixed_size)
if 'refine' in self.opt.coarse_or_refine:
opt = self._refine_opt(opt)
self.netG_Ref = networks.define_G(opt)
self.netD_Ref = networks.define_D(opt, opt.fine_size)
self.visual_names += ['img_ref', 'img_ref_out']
self.model_names += ['G_Ref', 'D_Ref']
if self.isTrain:
# define the loss function
self.L1loss = torch.nn.L1Loss()
self.GANloss = losses.GANLoss(opt.gan_mode).to(self.device)
self.NormalVGG = losses.Normalization(self.device)
self.LPIPSloss = losses.LPIPSLoss(ckpt_path=opt.lipip_path).to(self.device)
if len(self.opt.gpu_ids) > 0:
self.LPIPSloss = torch.nn.parallel.DataParallel(self.LPIPSloss, self.opt.gpu_ids)
# define the optimizer
if 'coarse' in self.opt.coarse_or_refine:
self.optimizerG = torch.optim.Adam(list(self.netE.parameters()) + list(self.netG.parameters())
+ list(self.netT.parameters()), lr=opt.lr, betas=(opt.beta1, opt.beta2))
self.optimizerD = torch.optim.Adam(self.netD.parameters(), lr=opt.lr * 4, betas=(opt.beta1, opt.beta2))
self.optimizers.append(self.optimizerG)
self.optimizers.append(self.optimizerD)
if 'refine' in self.opt.coarse_or_refine:
self.optimizerGRef = torch.optim.Adam(self.netG_Ref.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
self.optimizerDRef = torch.optim.Adam(self.netD_Ref.parameters(), lr=opt.lr * 4, betas=(opt.beta1, opt.beta2))
self.optimizers.append(self.optimizerGRef)
self.optimizers.append(self.optimizerDRef)
else:
self.visual_names = ['img_org', 'img_m', 'img_out']
if 'refine' in self.opt.coarse_or_refine:
self.visual_names += ['img_ref_out']
def set_input(self, input):
"""Unpack input data from the data loader and perform necessary pre-process steps"""
self.input = input
self.image_paths = self.input['img_path']
self.img_org = input['img_org'].to(self.device) * 2 - 1
self.img = input['img'].to(self.device) * 2 - 1
self.mask = input['mask'].to(self.device)
# get I_m and I_c for image with mask and complement regions for training
self.img_m = self.mask * self.img_org
@torch.no_grad()
def test(self):
"""Run forward processing for testing"""
fixed_img = F.interpolate(self.img_m, size=[self.opt.fixed_size, self.opt.fixed_size], mode='bicubic', align_corners=True).clamp(-1, 1)
fixed_mask = (F.interpolate(self.mask, size=[self.opt.fixed_size, self.opt.fixed_size], mode='bicubic', align_corners=True) > 0.9).type_as(fixed_img)
out, mask = self.netE(fixed_img, mask=fixed_mask, return_mask=True)
out = self.netT(out, mask, bool_mask=False)
# sample result
for i in range(self.opt.nsampling):
img_g = self.netG(out, mask=self.mask)
img_g_org = F.interpolate(img_g, size=self.img_org.size()[2:], mode='bicubic', align_corners=True).clamp(-1, 1)
self.img_out = self.mask * self.img_org + (1 - self.mask) * img_g_org
# save for multiple results
self.save_results(self.img_out, path=self.opt.save_dir + '/img_out', data_name=i)
if 'refine' in self.opt.coarse_or_refine:
img_ref = self.netG_Ref(self.img_out, mask=self.mask)
self.img_ref_out = self.mask * self.img_org + (1 - self.mask) * img_ref
# save for multiple results
self.save_results(self.img_ref_out, path=self.opt.save_dir + '/img_ref_out', data_name=i)
def forward(self):
"""Run forward processing to get the outputs"""
fixed_img = F.interpolate(self.img_m, size=[self.opt.fixed_size, self.opt.fixed_size], mode='bicubic', align_corners=True).clamp(-1, 1)
self.fixed_mask = (F.interpolate(self.mask, size=[self.opt.fixed_size, self.opt.fixed_size], mode='bicubic', align_corners=True) > 0.9).type_as(fixed_img)
out, mask = self.netE(fixed_img, mask=self.fixed_mask, return_mask=True)
out = self.netT(out, mask, bool_mask=False)
self.img_g = self.netG(out, mask=self.mask)
img_g_org = F.interpolate(self.img_g, size=self.img_org.size()[2:], mode='bicubic', align_corners=True).clamp(-1, 1)
self.img_out = self.mask * self.img_org + (1 - self.mask) * img_g_org
if 'refine' in self.opt.coarse_or_refine:
self.img_ref = self.netG_Ref(self.img_out, self.mask)
self.img_ref_out = self.mask * self.img_org + (1 - self.mask) * self.img_ref
def backward_D_basic(self, netD, real, fake):
"""
Calculate GAN loss for the discriminator
:param netD: the discriminator D
:param real: real examples
:param fake: examples generated by a generator
:return: discriminator loss
"""
self.loss_D_real = self.GANloss(netD(real), True, is_dis=True)
self.loss_D_fake = self.GANloss(netD(fake), False, is_dis=True)
loss_D = self.loss_D_real + self.loss_D_fake
if self.opt.lambda_gradient > 0:
self.loss_D_Gradient, _ = losses.cal_gradient_penalty(netD, real, fake, real.device, lambda_gp=self.opt.lambda_gradient)
loss_D += self.loss_D_Gradient
loss_D.backward()
return loss_D
def backward_D(self):
"""Calculate the GAN loss for discriminator"""
self.loss_D = 0
if 'coarse' in self.opt.coarse_or_refine:
self.set_requires_grad([self.netD], True)
self.optimizerD.zero_grad()
real = self.img.detach()
fake = self.img_g.detach()
self.loss_D += self.backward_D_basic(self.netD, real, fake) if self.opt.lambda_g > 0 else 0
if 'refine' in self.opt.coarse_or_refine:
self.set_requires_grad([self.netD_Ref], True)
self.optimizerDRef.zero_grad()
real = self.img_org.detach()
fake = self.img_ref.detach()
self.loss_D += self.backward_D_basic(self.netD_Ref, real, fake) if self.opt.lambda_g > 0 else 0
def backward_G(self):
"""Calculate the loss for generator"""
self.loss_G_GAN = 0
self.loss_G_rec = 0
self.loss_G_lp =0
if 'coarse' in self.opt.coarse_or_refine:
self.set_requires_grad([self.netD], False)
self.optimizerG.zero_grad()
self.loss_G_GAN += self.GANloss(self.netD(self.img_g), True) * self.opt.lambda_g if self.opt.lambda_g > 0 else 0
self.loss_G_rec += (self.L1loss(self.img_g * (1 - self.fixed_mask), self.img * (1 - self.fixed_mask)) * 3 +
self.L1loss(self.img_g * self.fixed_mask, self.img_g * self.fixed_mask)) * self.opt.lambda_rec
norm_real = self.NormalVGG((self.img + 1) * 0.5)
norm_fake = self.NormalVGG((self.img_g + 1) * 0.5)
self.loss_G_lp += (self.LPIPSloss(norm_real, norm_fake).mean()) * self.opt.lambda_lp if self.opt.lambda_lp > 0 else 0
if 'refine' in self.opt.coarse_or_refine:
self.set_requires_grad([self.netD_Ref], False)
self.optimizerGRef.zero_grad()
self.loss_G_GAN += self.GANloss(self.netD_Ref(self.img_ref), True) * self.opt.lambda_g if self.opt.lambda_g > 0 else 0
self.loss_G_rec += (self.L1loss(self.img_ref * (1 - self.mask), self.img_org * (1 - self.mask)) * 3 +
self.L1loss(self.img_ref * self.mask, self.img_org * self.mask)) * self.opt.lambda_rec
norm_real = self.NormalVGG((self.img_org + 1) * 0.5)
norm_fake = self.NormalVGG((self.img_ref + 1) * 0.5)
self.loss_G_lp += (self.LPIPSloss(norm_real, norm_fake).mean()) * self.opt.lambda_lp if self.opt.lambda_lp > 0 else 0
self.loss_G = self.loss_G_GAN + self.loss_G_rec + self.loss_G_lp
self.loss_G.backward()
def optimize_parameters(self):
"""update network weights"""
# forward
self.set_requires_grad([self.netE, self.netT, self.netG], 'coarse' in self.opt.coarse_or_refine)
self.forward()
# update D
self.backward_D()
if 'coarse' in self.opt.coarse_or_refine:
self.optimizerD.step()
if 'refine' in self.opt.coarse_or_refine:
self.optimizerDRef.step()
# update G
self.backward_G()
if 'coarse' in self.opt.coarse_or_refine:
self.optimizerG.step()
if 'refine' in self.opt.coarse_or_refine:
self.optimizerGRef.step()
def configure_optimizers(self):
"""
Following minGPT:
This long function is unfortunately doing something very simple and is being very defensive:
We are separating out all parameters of the model into two buckets: those that will experience
weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
We are then returning the PyTorch optimizer object.
"""
# separate out all parameters to those that will and won't experience regularizing weight decay
decay = set()
no_decay = set()
whitelist_weight_modules = (torch.nn.Linear, torch.nn.Conv2d)
blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
for mn, m in self.netT.named_modules():
for pn, p in m.named_parameters():
fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
if pn.endswith('bias') or pn.endswith('alpha'):
# all biases will not be decayed
no_decay.add(fpn)
elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
# weights of whitelist modules will be weight decayed
decay.add(fpn)
elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
# weights of blacklist modules will NOT be weight decayed
no_decay.add(fpn)
# validate that we considered every parameter
param_dict = {pn: p for pn, p in self.netT.named_parameters()}
inter_params = decay & no_decay
union_params = decay | no_decay
assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params),)
assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
% (str(param_dict.keys() - union_params),)
# create the pytorch optimizer object
optim_groups = [
{"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": 0.01, "betas":(0.9, 0.95)},
{"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0, "betas":(0.9, 0.95)},
{"params": list(filter(lambda p: p.requires_grad, self.netE.parameters()))},
{"params": list(filter(lambda p: p.requires_grad, self.netG.parameters()))}
]
optimizer = torch.optim.Adam(optim_groups, lr=self.opt.lr, betas=(self.opt.beta1, self.opt.beta2))
return optimizer
def _refine_opt(self, opt):
"""modify the opt for refine generator and discriminator"""
opt.netG = 'refine'
opt.netD = 'style'
return opt |