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import torch.nn.functional as F
import pdb
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch import nn
from Models.Swapper_Units import Encoder, Decoder, Discriminator, Decoder_Enlarge, Encoder_noBNIN, VGGPerceptualLoss, Decoder_Enlarge_for_onnx
from Models.arcface_resnet import resnet50, resnet_face18
# from Model.MultiScaleDiscriminator import MultiscaleDiscriminator
from Models.iresnet import iresnet100
from backbones import get_model
import numpy as np
# from Model.loss import GANLoss, AEI_Loss
batch_size = 1
class Normalize(nn.Module):
def __init__(self, mean, std):
super(Normalize, self).__init__()
self.mean = mean
self.std = std
def forward(self, x):
x = x - self.mean
x = x / self.std
return x
class ResNet_ID_encoder(pl.LightningModule):
def __init__(self, ema_path):
super(ResNet_ID_encoder, self).__init__()
self.resnet = resnet50()
#pdb.set_trace()
self.ema = nn.Linear(512, 512)
self.ema.weight.data = torch.nn.Parameter(torch.from_numpy(np.load(ema_path)).permute(1,0)).cuda()
self.ema.bias.data.fill_(0.0)
#self.ema = torch.from_numpy(np.load(ema_path)).cuda()
def processing_ema_only(self,latent):
x = self.ema(latent)
# x = torch.matmul(x,self.ema)
out = torch.div(x, torch.linalg.norm(x, dim=1, keepdim=True))
return out
def forward(self, inputs):
x = self.resnet(inputs)
x = self.ema(x)
#x = torch.matmul(x,self.ema)
out = torch.div(x, torch.linalg.norm(x, dim=1, keepdim=True))
return out
class Swapper_Enlarge(nn.Module): #class Swapper_Enlarge(pl.LightningModule):
def __init__(self, source_dim,exist_BN=True):
super(Swapper_Enlarge, self).__init__()
self.source_dim = source_dim
if exist_BN:
self.E = Encoder(self.source_dim)
else:
self.E = Encoder_noBNIN(self.source_dim)
self.G = Decoder_Enlarge(1024, 3)
self.dis = None
self.train_adv = None
def forward(self, target, source,get_latent=False):
# source = np.dot(source, self.ema)
# source /= np.linalg.norm(source)
output = self.E(target, source)
if get_latent==True:
output,latent = self.G(output,get_latent=get_latent)
return output,latent
else:
output = self.G(output)
return output
def forward(self, target, source,get_latent=False):
# source = np.dot(source, self.ema)
# source /= np.linalg.norm(source)
output = self.E(target, source)
if get_latent==True:
output,latent = self.G(output,get_latent=get_latent)
return output,latent
else:
output = self.G(output)
return output
class Swapper_Enlarge_for_onnx(nn.Module): #class Swapper_Enlarge(pl.LightningModule):
def __init__(self, source_dim,exist_BN=True):
super(Swapper_Enlarge_for_onnx, self).__init__()
self.source_dim = source_dim
if exist_BN:
self.E = Encoder(self.source_dim)
else:
self.E = Encoder_noBNIN(self.source_dim)
self.G = Decoder_Enlarge_for_onnx(1024, 3)
self.dis = None
self.train_adv = None
def forward(self, target, source):
# source = np.dot(source, self.ema)
# source /= np.linalg.norm(source)
output = self.E(target, source)
output = self.G(output)
return output
def forward(self, target, source):
# source = np.dot(source, self.ema)
# source /= np.linalg.norm(source)
output = self.E(target, source)
output = self.G(output)
return output
class Swapper(nn.Module): #class Swapper(pl.LightningModule):
def __init__(self, source_dim,exist_BN=True):
super(Swapper, self).__init__()
self.source_dim = source_dim
if exist_BN:
self.E = Encoder(self.source_dim)
else:
self.E = Encoder_noBNIN(self.source_dim)
self.G = Decoder(1024, 3)
self.dis = None
self.train_adv = None
def forward(self, target, source,get_latent=False):
# source = np.dot(source, self.ema)
# source /= np.linalg.norm(source)
output = self.E(target, source)
if get_latent==True:
output,latent = self.G(output,get_latent=get_latent)
return output,latent
else:
output = self.G(output)
return output
class Doppelganger(pl.LightningModule):
def __init__(self, Swapper, id_encoder,fine_tune=False):
super(Doppelganger, self).__init__()
self.Swapper = Swapper.cuda()
#self.Id_encoder = None
self.fine_tune = fine_tune
self.Id_encoder = id_encoder.cuda()
self.dis = None
self.train_adv = False
self.feats_extractor = None
def Prepareing_adversrial_learning(self,img_size,max_conv_size):
self.dis = Discriminator(img_size=img_size, max_conv_dim=max_conv_size).cuda()
self.train_adv = True
def Preparing_VGG_percet_loss(self):
self.feats_extractor = VGGPerceptualLoss(layer_ids=[3, 8, 15, 22]).cuda()
def set_grads(self):
swapper_param_list = list(self.Swapper.parameters())
for param in swapper_param_list:
param.requires_grad = True
idc_param_list = list(self.Id_encoder.parameters())
for param in idc_param_list:
param.requires_grad = False
# if self.fine_tune==False:
# idc_param_list = list(self.Id_encoder.parameters())
# for param in idc_param_list:
# param.requires_grad = False
if self.train_adv==True:
adv_param_list = list(self.dis.parameters())
for param in adv_param_list:
param.requires_grad = True
def save(self, fname, step):
print('Saving checkpoint into %s...' % fname)
PATH_Swapper = fname + '_swapper_%d' % (step) + '.pt'
PATH_IDC = fname + '_idc_%d' % (step) + '.pt'
PATH_DIS = fname + '_dis_%d' % (step) + '.pt'
PATH_Swapper_last = fname + '_swapper_last' + '.pt'
PATH_IDC_last = fname + '_idc_last' + '.pt'
PATH_DIS_last = fname + '_dis_last' + '.pt'
# Save the mask module
torch.save(self.Swapper.state_dict(), PATH_Swapper)
torch.save(self.Swapper.state_dict(), PATH_Swapper_last)
torch.save(self.ID_encoder.state_dict(), PATH_IDC)
torch.save(self.ID_encoder.state_dict(), PATH_IDC_last)
# Save discriminator
if self.train_adv==True:
torch.save(self.discriminator.state_dict(), PATH_DIS)
torch.save(self.discriminator.state_dict(), PATH_DIS_last)
# Save the mask module
def load(self, fname, step=None):
print('Loading checkpoint from %s...' % fname)
if step == None:
PATH_Swapper = fname + '_swapper_last' + '.pt'
PATH_IDC = fname + '_idc_last' + '.pt'
PATH_DIS = fname + '_dis_last' + '.pt'
else:
PATH_Swapper = fname + '_swapper_%d' % (step) + '.pt'
PATH_IDC = fname + '_idc_%d' % (step) + '.pt'
PATH_DIS = fname + '_dis_%d' % (step) + '.pt'
# Save the mask module
self.Swapper.load_state_dict(torch.load(PATH_Swapper))
# Save discriminator
self.dis.load_state_dict(torch.load(PATH_DIS))
if self.fine_tune==False:
self.Id_encoder.load_state_dict(torch.load(PATH_IDC))
def get_id_code(self,source):
return self.Id_encoder(source).detach()
def forward(self, target, source,get_latent=False):
#if it's the fine-tunning process, only identity code (512D) will be provided, so, no need to use Id_encoder
if self.fine_tune==False:
source = self.Id_encoder(source)
#Else
output = self.Swapper(target, source,get_latent=get_latent)
return output
def add_batch_instant_norm2swapper(self):
bn_layer = nn.BatchNorm2d(1024) # BatchNorm after fusion
in_layer = nn.InstanceNorm2d(1024, affine=True) # InstanceNorm after fusion
#name, module = self.Swapper.E.Encoder['layer_3'].items()
self.Swapper.E.Encoder['layer_3'] = nn.Sequential(
self.Swapper.E.Encoder['layer_3'] , # Original Feature_Fusion_Block
bn_layer, # BatchNorm
in_layer, # InstanceNorm
)
#pdb.set_trace()
for i in range(5): # Since we need norm layers between 6 fusion layers, we only need 5 sets of norms
out_channels = 1024 # Assuming fusion layer output size is 2048
# Directly add BatchNorm and InstanceNorm as attributes to the model
setattr(self.Swapper.E, f'batch_norm_{i}', nn.BatchNorm2d(out_channels))
setattr(self.Swapper.E, f'instance_norm_{i}', nn.InstanceNorm2d(out_channels, affine=True))
# def enlarge_384(self):
# #pdb.set_trace()
# name_list = []
# for name, module in self.Swapper.G.named_children():
# print(name, module)
# name_list.append(name)
#
# #print(self.Swapper.G)
# with torch.no_grad():
#
# # For Conv1
# print(self.Swapper.G)
# gen_conv1_old_weight = self.Swapper.G.Conv1.weight.data
# _shape = np.shape(gen_conv1_old_weight)[0:2]
# #pdb.set_trace()
# new_conv = nn.Conv2d(in_channels=_shape[1], out_channels=_shape[0]*2, kernel_size=self.Swapper.G.Conv1.kernel_size[0], padding=self.Swapper.G.Conv1.padding[0])
# new_weight = new_conv.weight.data
# new_weight[:_shape[0], :, :, :] = gen_conv1_old_weight
# new_weight[_shape[0]:, :, :, :] = 0.0
# if self.Swapper.G.Conv1.bias is not None and new_conv.bias is not None:
# new_conv.bias.data[:_shape[0]] = self.Swapper.G.Conv1.bias.data
# new_conv.bias.data[_shape[0]:] = 0.0
# self.Swapper.G.Conv1 = new_conv
#
# #Conv2
# gen_conv2_old_weight = self.Swapper.G.Conv2.weight.data
# _shape = np.shape(gen_conv2_old_weight)[0:2]
# #pdb.set_trace()
# new_conv = nn.Conv2d(in_channels=_shape[1]*2, out_channels=_shape[0] * 2,
# kernel_size=self.Swapper.G.Conv2.kernel_size[0],
# padding=self.Swapper.G.Conv2.padding[0])
# new_weight = new_conv.weight.data
# new_weight[:_shape[0], :_shape[1], :, :] = gen_conv2_old_weight
# new_weight[_shape[0]:, _shape[1]:, :, :] = 0.0
# if self.Swapper.G.Conv2.bias is not None and new_conv.bias is not None:
# new_conv.bias.data[:_shape[0]] = self.Swapper.G.Conv2.bias.data
# new_conv.bias.data[_shape[0]:] = 0.0
# self.Swapper.G.Conv2 = new_conv
#
# #Conv3
# gen_conv3_old_weight = self.Swapper.G.Conv3.weight.data
# _shape = np.shape(gen_conv3_old_weight)[0:2]
# #pdb.set_trace()
# new_conv = nn.Conv2d(in_channels=_shape[1]*2, out_channels=_shape[0] * 2,
# kernel_size=self.Swapper.G.Conv2.kernel_size[0],
# padding=self.Swapper.G.Conv2.padding[0])
# new_weight = new_conv.weight.data
# new_weight[:_shape[0], :_shape[1], :, :] = gen_conv3_old_weight
# new_weight[_shape[0]:, _shape[1]:, :, :] = 0.0
# if self.Swapper.G.Conv3.bias is not None and new_conv.bias is not None:
# new_conv.bias.data[:_shape[0]] = self.Swapper.G.Conv3.bias.data
# new_conv.bias.data[_shape[0]:] = 0.0
# self.Swapper.G.Conv3 = new_conv
#
# #pdb.set_trace()
# #print(self.Swapper.G)
# layers = list(self.Swapper.G.children())
#
# # Conv4
# gen_conv4_old_weight = self.Swapper.G.Conv4.weight.data
# _shape = np.shape(gen_conv4_old_weight)[0:2]
# #pdb.set_trace()
#
# new_conv1 = nn.Conv2d(in_channels=_shape[1]*2, out_channels=_shape[1],
# kernel_size=3,
# padding=1)
# new_conv2 = nn.Conv2d(in_channels=_shape[1], out_channels=_shape[0],
# kernel_size=self.Swapper.G.Conv4.kernel_size[0],
# padding=self.Swapper.G.Conv4.padding[0])
#
# layers.insert(4, new_conv1)
# new_weight = new_conv2.weight.data
# new_weight[:_shape[0], :_shape[1], :, :] = gen_conv4_old_weight
# new_weight[_shape[0]:, _shape[1]:, :, :] = 0.0
#
# self.Swapper.G.Conv4 = new_conv2
# #pdb.set_trace()
# self.Swapper.G = nn.Sequential(*layers)
# print(self.Swapper.G)
# self.Swapperc
# .G = self.Swapper.G.cuda()
def build_arch(fine_tune=False,exist_BN=True,enlarge=False,new_id_model=False):
#Swapper
if enlarge==True:
swapper = Swapper_Enlarge_for_onnx(512)
else:
swapper = Swapper(512,exist_BN)
swapper = swapper.cuda()
#pdb.set_trace()
# swapper_enlarged = Swapper_Enlarge(512)
# swapper_enlarged.E.load_state_dict(torch.load('./Models/MMNet.pt'), strict=False)
# swapper_enlarged= swapper_enlarged.cuda()
#ID encoder
#pdb.set_trace()
if new_id_model==False:
ID_encoder = ResNet_ID_encoder(ema_path='./Models/emp.npy')
ID_encoder.resnet.load_state_dict(torch.load('./Models/arcface_w600k_r50_pytorch.pt', map_location=torch.device('cuda')))
else:
weight = torch.load('./vit_b_fr_pgair.pt')
ID_encoder = get_model('vit_b', dropout=0, fp16=False).cuda()
ID_encoder.load_state_dict(weight)
#Framework
DPG = Doppelganger(swapper, ID_encoder, fine_tune=fine_tune)
return DPG
def build_models(config=None,fine_tune=False,adv_train=True,exist_BN=True,enlarge=False,new_id_model=False,from_scretch=False):
#Swapper
if enlarge==True:
swapper = Swapper_Enlarge(512)
if from_scretch==False:
print('Loading pre-trained model for the swapper')
swapper.load_state_dict(torch.load('./Models/MMNet.pt',weights_only=True), strict=False)
else:
swapper = Swapper(512,exist_BN)
if from_scretch==False:
print('Loading pre-trained model for the swapper')
swapper.load_state_dict(torch.load('./Models/MMNet.pt'), strict=False)
#pdb.set_trace()
swapper = swapper.cuda()
#pdb.set_trace()
# swapper_enlarged = Swapper_Enlarge(512)
# swapper_enlarged.E.load_state_dict(torch.load('./Models/MMNet.pt'), strict=False)
# swapper_enlarged= swapper_enlarged.cuda()
#ID encoder
#pdb.set_trace()
print('Loading pre-trained model for the ID encoder')
if new_id_model==False:
ID_encoder = ResNet_ID_encoder(ema_path='./Models/emp.npy')
ID_encoder.resnet.load_state_dict(torch.load('./Models/arcface_w600k_r50_pytorch.pt', map_location=torch.device('cuda')))
else:
weight = torch.load(config.id_network_path)
ID_encoder = get_model(config.id_network, dropout=0, fp16=False).cuda()
ID_encoder.load_state_dict(weight)
#Framework
DPG = Doppelganger(swapper, ID_encoder, fine_tune=fine_tune)
if adv_train==True:
DPG.Prepareing_adversrial_learning(img_size=256,max_conv_size=512)
DPG.Preparing_VGG_percet_loss()
return DPG
if __name__ == '__main__':
build_models()
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