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import torch.nn.functional as F
from models.networks.base_network import BaseNetwork
import torch
import torch.nn as nn
from .generator_module.StyTR import StyTr
from torchvision.utils import save_image
from .generator_module.transformerEncoder import TransformerEncoder
from .generator_module.HEtransformerEncoder import HeTransformerEncoder
from .generator_module.schedule import CosineAnnealingWarmUpLR
from .generator_module.DecoderCNN import Decoder_MV, vgg_structures,decoder_stem # DecoderCNN
from .generator_module.transformer_decoder import TransformerDecoder
class TSITGenerator(BaseNetwork):
@staticmethod
def modify_commandline_options(parser, is_train):
parser.set_defaults(norm_G='spectralfadesyncbatch3x3')
parser.add_argument('--num_upsampling_layers',
choices=('normal', 'more', 'most'), default='more',
help="If 'more', adds upsampling layer between the two middle resnet blocks."
"If 'most', also add one more upsampling + resnet layer at the end of the generator."
"We only use 'more' as the default setting.")
parser.add_argument('--lr_decay', type=float, default=1e-4, help='learning rate decay')
parser.add_argument('--lr_stytr2', type=float, default=5e-4, help='initial learning rate for adam')
return parser
def __init__(self, opt):
super().__init__()
self.vgg_path = r'models/networks/experiments/vgg_normalised.pth'
self.CNNdecoder = Decoder_MV(d_model=768,seq_input=True)
self.HeTransEncoder=HeTransformerEncoder(img_size=224,patch_size=2,in_chans=3,embed_dim=192,depths=[2, 2, 2],nhead=[3, 6, 12],strip_width=[2, 4, 7],drop_path_rate=0.,patch_norm=True)
self.TransEncoder= TransformerEncoder(img_size=224,patch_size=2,in_chans=3,embed_dim=192,depths=[2, 2, 2],nhead=[3, 6, 12],strip_width=[2, 4, 7],drop_path_rate=0.,patch_norm=True)
self.TransDecoder = TransformerDecoder(nlayer=3,d_model=768,nhead=8,mlp_ratio=4,qkv_bias=False,attn_drop=0.,drop=0.,drop_path=0.,act_layer=nn.GELU,norm_layer=nn.LayerNorm,norm_first=True)
self.vgg=vgg_structures
self.content_weight= opt.content_weight
self.style_weight = opt.style_weight
self.lr_decay = opt.lr_decay
self.lr_stytr2 = opt.lr_stytr2
self.device="cuda" if torch.cuda.is_available() else "cpu"
self.iters_count=0
self.save_dir= opt.save_dir
self.opt=opt
self.mode=opt.mode
def SetUp_model(self, TransEncoder,HeTransEncoder ,CNN_decoder,TransDecoder,vgg,alpha,mode):
vgg.load_state_dict(torch.load(self.vgg_path))
# vgg = nn.Sequential(*list(vgg.children())[:44])
network=StyTr(TransEncoder,HeTransEncoder,CNN_decoder,TransDecoder,vgg,alpha,mode)
network.train()
network.to(self.device)
return network
def forward(self):
network = self.SetUp_model(self.TransEncoder,self.HeTransEncoder,self.CNNdecoder, self.TransDecoder, self.vgg,self.opt.alpha,self.mode)
return network
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