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train.py
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train.py
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import sys
import argparse
import time
import cv2
import wandb
from PIL import Image
import metric
import os
#For Native Torch multi GPUs
import datetime
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel
from torch.distributed import init_process_group, destroy_process_group
from torch.utils.data import DataLoader, random_split
import torch.optim as optim
import torch.nn.functional as F
import torch
import torchvision.transforms as transforms
import torch.optim.lr_scheduler as scheduler
## custom imports
# sys.path.append('./apex/')
# from apex import amp
from network.CrossU import CrossUnetAttentionGenerator, UNet
from extractor.SA_idextractor import ShapeAwareIdentityExtractor
# from network.AEI_Net import *
from network.MultiscaleDiscriminator import *
from utils.training.Dataset import FaceEmbedCombined, FaceEmbed, FaceEmbedSubdir, FaceEmbedFFHQ, FaceEmbedCelebA, FaceEmbedCustom#FaceEmbedAllFlat
from utils.training.image_processing import make_image_list, get_faceswap
from utils.training.losses import hinge_loss, compute_discriminator_loss, compute_generator_losses
from utils.training.detector import detect_landmarks, paint_eyes
from utils.training.landmark_detector import detect_all_landmarks
from AdaptiveWingLoss.core import models
from arcface_model.iresnet import iresnet100
from models.model import FlowFaceCrossAttentionModel, FlowFaceCrossAttentionLayer
import torch
def train_one_epoch(G: 'generator model',
D: 'discriminator model',
id_extractor: 'id_extractor model',
opt_G: "generator opt",
opt_D: "discriminator opt",
scheduler_G: "scheduler G opt",
scheduler_D: "scheduler D opt",
netArc: 'ArcFace model',
model_ft: 'Landmark Detector',
args: 'Args Namespace',
train_dataloader: torch.utils.data.DataLoader,
device: 'torch device',
epoch:int,
starting_iteration: 'iteration currently at',
loss_adv_accumulated:int,
config:dict
):
# ##loading pretrained models for extracting IDs
# f_3d_path = "/datasets/pretrained/pretrained_model.pth"
# f_id_path = "/datasets/pretrained/backbone.pth"
# id_extractor = ShapeAwareIdentityExtractor(f_3d_path, f_id_path, args.id_mode).to(args.device)
# id_extractor = DistributedDataParallel(id_extractor, device_ids=[config['local_rank']])
# #print(id_extractor)
if args.mixed_precision:
scaler = torch.cuda.amp.GradScaler(enabled=False)
# Xs.shape
for iteration, data in enumerate(train_dataloader):
start_time = time.time()
iteration = starting_iteration + iteration
if args.mixed_precision:
with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=True): ##input data는 일단 그대로 flaot32이지만 input이 계산될때 + output은 float16이된다
id_ext_src_input, id_ext_tgt_input, Xt_f, Xt_b, Xs_f, Xs_b, same_person = data
id_ext_src_input = id_ext_src_input.to(args.device)
id_ext_tgt_input = id_ext_tgt_input.to(args.device)
Xs_f = Xs_f.to(args.device)
# Xs.shape
Xt_f = Xt_f.to(args.device)
# Xt.shape
same_person = same_person.to(args.device)
realtime_batch_size = Xt_f.shape[0]
with torch.autocast(device_type="cuda", enabled=False):
# with torch.autocast(device_type="cuda", dtype=torch.float16): ## 이거 때문에 개망하기때문에 나중에 없애고 인덴트 들여써라
mixed_id_embedding, src_id_emb, tgt_id_emb = id_extractor.module.forward(id_ext_src_input.float(), id_ext_tgt_input.float()) ## id_embedding = [B, 769]
# print('mixed_id_embedding', mixed_id_embedding.dtype) ##mixed_id_embedding is float32 bcuz of enabled=False and id_ext_src_input.float()..
diff_person = torch.ones_like(same_person)
if args.diff_eq_same:
same_person = diff_person
# generator training
opt_G.zero_grad() ##축적된 gradients를 비워준다
swapped_face, recon_f_src, recon_f_tgt = G.module.forward(Xt_f, Xs_f, mixed_id_embedding) ##제너레이터에 target face와 source face identity를 넣어서 결과물을 만든다.
Xt_f_attrs = G.module.CUMAE_tgt(Xt_f) # UNet으로 Xt의 bottleneck 이후 feature maps -> 238번 line을 통해 forward가 돌아갈 때 한 번에 계산해놓을 수 있을듯?
# Xs_f_attrs = G.module.CUMAE_src(Xs_f) # UNet으로 Xs의 bottleneck 이후 feature maps -> 238번 line을 통해 forward가 돌아갈 때 한 번에 계산해놓을 수 있을듯?
# print('recon_f_src', recon_f_src.dtype) ## recon_f_src is still float16 because we are using autocast
with torch.autocast(device_type="cuda", enabled=False):
##swapped_emb = ArcFace value. this is for infoNCE loss mostly
swapped_id_emb = id_extractor.module.id_forward(swapped_face.float())
if args.shape_loss:
with torch.autocast(device_type="cuda", enabled=False):
q_fuse, q_r = id_extractor.module.shapeloss_forward(id_ext_src_input.float(), id_ext_tgt_input.float(), swapped_face.float()) # Y가 network의 output tensor에 denorm까지 되었다고 가정 & q_r은 지금 당장 잡아낼 수가 없으므로(swap 결과가 초반엔 별로여서) 당장은 q_fuse를 똑같이 씀
else:
pass
q_fuse, q_r = 0, 0
# Y, recon_f_src, recon_f_tgt = G(Xt, Xs, id_embedding) ##제너레이터에 target face와 source face identity를 넣어서 결과물을 만든다. MAE의 경우 Xt_embed, Xs_embed를 넣으면 될 것 같다 (same latent space)
# Xt_attrs = G.CUMAE_tgt(Xt) # UNet으로 Xt의 bottleneck 이후 feature maps -> 238번 line을 통해 forward가 돌아갈 때 한 번에 계산해놓을 수 있을듯?
# Xs_attrs = G.CUMAE_src(Xs) # UNet으로 Xs의 bottleneck 이후 feature maps -> 238번 line을 통해 forward가 돌아갈 때 한 번에 계산해놓을 수 있을듯?
Di = D.module(swapped_face) ##이렇게 나온 swapped face 결과물을 Discriminator에 넣어서 가짜로 구별을 해내는지 확인해 보는 것이다. 0과 가까우면 가짜라고하는것이다.
if args.eye_detector_loss:
Xt_f_eyes, Xt_f_heatmap_left, Xt_f_heatmap_right = detect_landmarks(Xt_f, model_ft) ##detect_landmarks 부문에 다른 eye loss 뿐만이 아니라 다른 part도 계산하고 싶으면 여기다 코드를 추가해서 넣으면 될거같다
swapped_face_eyes, swapped_face_heatmap_left, swapped_face_heatmap_right = detect_landmarks(swapped_face, model_ft)
eye_heatmaps = [Xt_f_heatmap_left, Xt_f_heatmap_right, swapped_face_heatmap_left, swapped_face_heatmap_right]
else:
eye_heatmaps = None
# landmark extractor
if args.landmark_detector_loss:
Xt_f_pred_heatmap, Xt_f_landmarks = detect_all_landmarks(Xt_f, model_ft)
swapped_face_pred_heatmap, swapped_face_landmarks = detect_all_landmarks(swapped_face, model_ft)
all_landmark_heatmaps = [Xt_f_pred_heatmap, swapped_face_pred_heatmap]
all_landmarks = [Xt_f_landmarks, swapped_face_landmarks]
else:
all_landmark_heatmaps = None
all_landmarks = None
# lossG, loss_adv_accumulated, L_adv, L_id, L_attr, L_rec, L_l2_eyes, L_cycle, L_cycle_identity, L_contrastive, L_source_unet, L_target_unet, L_landmarks, L_shape = compute_generator_losses(G, swapped_face, Xt_f, Xs_f, Xt_f_attrs, Di,
# eye_heatmaps, loss_adv_accumulated,
# diff_person, same_person, src_id_emb, tgt_id_emb, swapped_id_emb, mixed_id_embedding, recon_f_src, recon_f_tgt, q_fuse, q_r, all_landmark_heatmaps, args)
lossG, loss_adv_accumulated, L_adv, L_id, L_attr, L_rec, L_l2_eyes, L_cycle, L_cycle_identity, L_contrastive, L_source_unet, L_target_unet, L_landmarks, L_shape = compute_generator_losses(G, swapped_face, Xt_f, Xs_f, Xt_f_attrs, Di,
eye_heatmaps, loss_adv_accumulated,
diff_person, same_person, mixed_id_embedding, src_id_emb, tgt_id_emb, swapped_id_emb, recon_f_src, recon_f_tgt, q_fuse, q_r, all_landmark_heatmaps, args)
lossD = compute_discriminator_loss(D, swapped_face, Xs_f, Xt_f, recon_f_src, recon_f_tgt, diff_person, args.device)
# discriminator training
opt_D.zero_grad()
else: ##mixed_precision False인 경우에는 이라는 뜻
id_ext_src_input, id_ext_tgt_input, Xt_f, Xt_b, Xs_f, Xs_b, same_person = data
id_ext_src_input = id_ext_src_input.to(args.device)
id_ext_tgt_input = id_ext_tgt_input.to(args.device)
Xs_f = Xs_f.to(args.device)
# Xs.shape
Xt_f = Xt_f.to(args.device)
# Xt.shape
same_person = same_person.to(args.device)
realtime_batch_size = Xt_f.shape[0]
# with torch.autocast(device_type="cuda", dtype=torch.float16): ## 이거 때문에 개망하기때문에 나중에 없애고 인덴트 들여써라
mixed_id_embedding, src_id_emb, tgt_id_emb = id_extractor.module.forward(id_ext_src_input, id_ext_tgt_input) ## id_embedding = [B, 769]
diff_person = torch.ones_like(same_person)
if args.diff_eq_same:
same_person = diff_person
# generator training
opt_G.zero_grad() ##축적된 gradients를 비워준다
swapped_face, recon_f_src, recon_f_tgt = G.module.forward(Xt_f, Xs_f, mixed_id_embedding) ##제너레이터에 target face와 source face identity를 넣어서 결과물을 만든다.
Xt_f_attrs = G.module.CUMAE_tgt(Xt_f) # UNet으로 Xt의 bottleneck 이후 feature maps -> 238번 line을 통해 forward가 돌아갈 때 한 번에 계산해놓을 수 있을듯?
# Xs_f_attrs = G.module.CUMAE_src(Xs_f) # UNet으로 Xs의 bottleneck 이후 feature maps -> 238번 line을 통해 forward가 돌아갈 때 한 번에 계산해놓을 수 있을듯?
##swapped_emb = ArcFace value. this is for infoNCE loss mostly
swapped_id_emb = id_extractor.module.id_forward(swapped_face)
# swapped_id_emb = swapped_id_emb.to(args.device)
if args.shape_loss:
q_fuse, q_r = id_extractor.module.shapeloss_forward(id_ext_src_input, id_ext_tgt_input, swapped_face) # Y가 network의 output tensor에 denorm까지 되었다고 가정 & q_r은 지금 당장 잡아낼 수가 없으므로(swap 결과가 초반엔 별로여서) 당장은 q_fuse를 똑같이 씀
else:
q_fuse, q_r = 0, 0
# Y, recon_f_src, recon_f_tgt = G(Xt, Xs, id_embedding) ##제너레이터에 target face와 source face identity를 넣어서 결과물을 만든다. MAE의 경우 Xt_embed, Xs_embed를 넣으면 될 것 같다 (same latent space)
# Xt_attrs = G.CUMAE_tgt(Xt) # UNet으로 Xt의 bottleneck 이후 feature maps -> 238번 line을 통해 forward가 돌아갈 때 한 번에 계산해놓을 수 있을듯?
# Xs_attrs = G.CUMAE_src(Xs) # UNet으로 Xs의 bottleneck 이후 feature maps -> 238번 line을 통해 forward가 돌아갈 때 한 번에 계산해놓을 수 있을듯?
Di = D.module(swapped_face) ##이렇게 나온 swapped face 결과물을 Discriminator에 넣어서 가짜로 구별을 해내는지 확인해 보는 것이다. 0과 가까우면 가짜라고하는것이다.
if args.eye_detector_loss:
Xt_f_eyes, Xt_f_heatmap_left, Xt_f_heatmap_right = detect_landmarks(Xt_f, model_ft) ##detect_landmarks 부문에 다른 eye loss 뿐만이 아니라 다른 part도 계산하고 싶으면 여기다 코드를 추가해서 넣으면 될거같다
swapped_face_eyes, swapped_face_heatmap_left, swapped_face_heatmap_right = detect_landmarks(swapped_face, model_ft)
eye_heatmaps = [Xt_f_heatmap_left, Xt_f_heatmap_right, swapped_face_heatmap_left, swapped_face_heatmap_right]
else:
eye_heatmaps = None
# landmark extractor
if args.landmark_detector_loss:
Xt_f_pred_heatmap, Xt_f_landmarks = detect_all_landmarks(Xt_f, model_ft)
swapped_face_pred_heatmap, swapped_face_landmarks = detect_all_landmarks(swapped_face, model_ft)
all_landmark_heatmaps = [Xt_f_pred_heatmap, swapped_face_pred_heatmap]
all_landmarks = [Xt_f_landmarks, swapped_face_landmarks]
else:
all_landmark_heatmaps = None
all_landmarks = None
lossG, loss_adv_accumulated, L_adv, L_id, L_attr, L_rec, L_l2_eyes, L_cycle, L_cycle_identity, L_contrastive, L_source_unet, L_target_unet, L_landmarks, L_shape = compute_generator_losses(G, swapped_face, Xt_f, Xs_f, Xt_f_attrs, Di,
eye_heatmaps, loss_adv_accumulated,
diff_person, same_person, mixed_id_embedding, src_id_emb, tgt_id_emb, swapped_id_emb, recon_f_src, recon_f_tgt, q_fuse, q_r, all_landmark_heatmaps, args)
# discriminator training
opt_D.zero_grad()
lossD = compute_discriminator_loss(D, swapped_face, Xs_f, Xt_f, recon_f_src, recon_f_tgt, diff_person, args.device)
# if (iteration + 1) % 100 != 0 and not last_step: # Accumulate gradients for 100 steps
# with G.no_sync() and D.no_sync(): # Disable gradient synchronization
# loss = loss_fn(model(data), labels) # Forward step
# loss.backward() # Backward step + gradient ACCUMULATION
if args.mixed_precision:
##for amp implementation (@hojun Seo)
scaler.scale(lossG).backward()
scaler.step(opt_G)
if args.scheduler:
scheduler_G.step()
else:
lossG.backward()
opt_G.step()
if args.scheduler:
scheduler_G.step()
if args.mixed_precision:
##for amp implementation (@hojun Seo)
scaler.scale(lossD).backward()
if (not args.discr_force) or (loss_adv_accumulated < 4.):
scaler.step(opt_D)
if args.scheduler:
##https://aimaster.tistory.com/83
scheduler_D.step()
else:
lossD.backward()
if (not args.discr_force) or (loss_adv_accumulated < 4.):
opt_D.step()
if args.scheduler:
##https://aimaster.tistory.com/83
scheduler_D.step()
if args.mixed_precision:
scaler.update() ##even tho we have 2 loss backwards, update should only be done once
else:
pass
'''
Here onwards, we must (maybe) convert amp mixed precision tensors in autocast region manually if we want to use them in float32 format
'''
# print('Xt_f data type: ', Xt_f.dtype)
# print('swapped_face data type: ', Xt_f.dtype)
# print(f'lossD: {lossD.item()}')
batch_time = time.time() - start_time
if iteration % args.show_step == 0:
images = [Xs_f, Xt_f, swapped_face]
if args.eye_detector_loss:
Xt_f_eyes_img = paint_eyes(Xt_f, Xt_f_eyes)
# print(f'eyes: ', {Xt_f_eyes.shape})
# break
Yt_f_eyes_img = paint_eyes(swapped_face, swapped_face_eyes)
images.extend([Xt_f_eyes_img, Yt_f_eyes_img])
image = make_image_list(images)
if (args.use_wandb) and config['global_rank'] == 0:
wandb.log({"gen_images":wandb.Image(image, caption=f"{epoch:03}" + '_' + f"{iteration:06}")})
else:
cv2.imwrite('./images/generated_image.jpg', image[:,:,::-1])
if (args.use_wandb) and (args.use_reconsimg) and config['global_rank'] == 0:
images = [Xs_f, recon_f_src, Xt_f, recon_f_tgt]
image = make_image_list(images)
wandb.log({"OrgSrc_ReconSrc_OrgTgt_ReconTgt":wandb.Image(image, caption=f"{epoch:03}" + '_' + f"{iteration:06}")})
if iteration % 10 == 0:
print(f'GPU {config["local_rank"]} epoch: {epoch} current iteration: {iteration} / max iteration size: {len(train_dataloader)}')
print(f'GPU {config["local_rank"]} lossD: {lossD.item()} lossG: {lossG.item()} batch_time: {batch_time}s')
print(f'GPU {config["local_rank"]} L_adv: {L_adv.item()} L_id: {L_id.item()} L_attr: {L_attr.item()} L_rec: {L_rec.item()} \n')
if args.eye_detector_loss:
print(f'GPU {config["local_rank"]} L_l2_eyes: {L_l2_eyes.item()} \n')
if args.landmark_detector_loss:
print(f'GPU {config["local_rank"]} L_landmarks: {L_landmarks.item()} \n')
if args.cycle_loss:
print(f'GPU {config["local_rank"]} L_cycle: {L_cycle.item()} \n')
# if args.cycle_identity_loss:
print(f'GPU {config["local_rank"]} L_cycle_identity: {L_cycle_identity.item()} \n')
if args.contrastive_loss:
print(f'GPU {config["local_rank"]} L_contrastive: {L_contrastive.item()} \n')
if args.unet_loss:
print(f'GPU {config["local_rank"]} L_source_unet: {L_source_unet.item()} \n')
print(f'GPU {config["local_rank"]} L_target_unet: {L_target_unet.item()} \n')
if args.shape_loss:
print(f'GPU {config["local_rank"]} L_shape: {L_shape.item()} \n')
print(f'GPU {config["local_rank"]} loss_adv_accumulated: {loss_adv_accumulated} \n')
if args.scheduler:
print(f'GPU {config["local_rank"]} scheduler_G lr: {scheduler_G.get_last_lr()} scheduler_D lr: {scheduler_D.get_last_lr()} \n')
if args.use_wandb and config['global_rank'] == 0:
if args.eye_detector_loss:
wandb.log({"loss_eyes": L_l2_eyes.item()}, commit=False)
if args.landmark_detector_loss:
wandb.log({"loss_landmarks": L_landmarks.item()}, commit=False)
if args.cycle_loss:
wandb.log({"loss_cycle": L_cycle.item()}, commit=False)
# if args.cycle_identity_loss:
wandb.log({"loss_cycle_identity": L_cycle_identity.item()}, commit=False)
if args.contrastive_loss:
wandb.log({"loss_contrastive": L_contrastive.item()}, commit=False)
if args.unet_loss:
wandb.log({"loss_source_unet": L_source_unet.item()}, commit=False)
wandb.log({"loss_target_unet": L_target_unet.item()}, commit=False)
if args.shape_loss:
wandb.log({"loss_shape": L_shape.item()}, commit=False)
# 설정 필요하면 args에 true false 추가
# wandb.log({"loss_source_unet": L_source_unet.item()}, commit=False)
# wandb.log({"loss_target_unet": L_target_unet.item()}, commit=False)
# wandb.log({"loss_shape": L_shape.item()}, commit=False)
wandb.log({
"loss_id": L_id.item(),
"lossD": lossD.item(),
"lossG": lossG.item(),
"loss_adv": L_adv.item(),
"loss_attr": L_attr.item(),
"loss_rec": L_rec.item(),
# "loss_cycle": L_cycle.item(),
# "loss_cycle_identity": L_cycle_identity.item(),
# "loss_contrastive": L_contrastive.item(),
# "loss_source_unet": L_source_unet.item(),
# "loss_target_unet": L_target_unet.item(),
# "loss_landmarks": L_landmarks.item()
})
#if iteration % 10000 == 0:
#if epoch % args.max_epoch == 0: # From this
if iteration == 0 & epoch % args.save_epoch == 0: # To this
if config['global_rank'] == 0:
# torch.save(G.module.state_dict(), f'./saved_models_{args.run_name}/G_latest.pth')
# torch.save(D.module.state_dict(), f'./saved_models_{args.run_name}/D_latest.pth')
# torch.save(G.module.state_dict(), f'./current_models_{args.run_name}/G_' + str(epoch)+ '_' + f"{iteration:06}" + '.pth')
# torch.save(D.module.state_dict(), f'./current_models_{args.run_name}/D_' + str(epoch)+ '_' + f"{iteration:06}" + '.pth')
torch.save({
'epoch': epoch,
'iteration': iteration,
'batch_size': args.batch_size,
'model_state_dict': G.module.state_dict(),
'optimizer_state_dict': opt_G.state_dict(),
'wandb_project': args.wandb_project,
'wandb_entity': args.wandb_entity
}, f'./saved_models_{args.run_name}/G_latest.pth')
print('Generator model checkpoint saved')
torch.save({
'epoch': epoch,
'iteration': iteration,
'batch_size': args.batch_size,
'model_state_dict': D.module.state_dict(),
'optimizer_state_dict': opt_D.state_dict(),
'wandb_project': args.wandb_project,
'wandb_entity': args.wandb_entity
}, f'./saved_models_{args.run_name}/D_latest.pth')
print('Discriminator model checkpoint saved')
torch.save({
'epoch': epoch,
'iteration': iteration,
'batch_size': args.batch_size,
'model_state_dict': G.module.state_dict(),
'optimizer_state_dict': opt_G.state_dict(),
'wandb_project': args.wandb_project,
'wandb_entity': args.wandb_entity
}, f'./current_models_{args.run_name}/G_' + str(epoch)+ '_' + f"{iteration:06}" + '.pth')
torch.save({
'epoch': epoch,
'iteration': iteration,
'batch_size': args.batch_size,
'model_state_dict': D.module.state_dict(),
'optimizer_state_dict': opt_D.state_dict(),
'wandb_project': args.wandb_project,
'wandb_entity': args.wandb_entity
}, f'./current_models_{args.run_name}/D_' + str(epoch)+ '_' + f"{iteration:06}" + '.pth')
#if (iteration % 100 == 0) and (args.use_wandb) and config['global_rank'] == 0:
# if (iteration % 1000 == 0) and (args.use_wandb) and config['global_rank'] == 0:
# G.eval()
# res1 = get_faceswap('examples/images/training/source1.png', 'examples/images/training/target1.png', G, id_extractor, device)
# res2 = get_faceswap('examples/images/training/source2.png', 'examples/images/training/target2.png', G, id_extractor, device)
# res3 = get_faceswap('examples/images/training/source3.png', 'examples/images/training/target3.png', G, id_extractor, device)
# res4 = get_faceswap('examples/images/training/source4.png', 'examples/images/training/target4.png', G, id_extractor, device)
# res5 = get_faceswap('examples/images/training/source5.png', 'examples/images/training/target5.png', G, id_extractor, device)
# res6 = get_faceswap('examples/images/training/source6.png', 'examples/images/training/target6.png', G, id_extractor, device)
# output1 = np.concatenate((res1, res2, res3), axis=0)
# output2 = np.concatenate((res4, res5, res6), axis=0)
# output = np.concatenate((output1, output2), axis=1)
# wandb.log({"our_images":wandb.Image(output, caption=f"{epoch:03}" + '_' + f"{iteration:06}")})
# G.train()
# def train(args, config):
def train(args, config):
##Multi GPU setting
assert torch.cuda.is_available(), "Training on CPU is not supported as Multi-GPU strategy is set"
device = args.device
print(f"[GPU {config['local_rank']}] is using device: {args.device}")
print(f"[GPU {config['local_rank']}] is loading dataset")
# training params
batch_size = args.batch_size
max_epoch = args.max_epoch
# # training params
# batch_size = config['batch_size
# max_epoch = config['max_epoch
## initializing id extractor model
f_3d_path = "/datasets/pretrained/pretrained_model.pth"
f_id_path = "/datasets/pretrained/backbone.pth"
id_extractor = ShapeAwareIdentityExtractor(f_3d_path, f_id_path, args.mixed_precision, args.id_mode).to(args.device)
id_extractor = DistributedDataParallel(id_extractor, device_ids=[config['local_rank']])
id_extractor.eval()
# initializing main models
# G = AEI_Net(config['backbone, num_blocks=config['num_blocks, c_id=512).to(device)
G = CrossUnetAttentionGenerator(backbone='unet', num_adain = args.num_adain).to(args.device)
opt_G = optim.Adam(G.parameters(), lr=args.lr_G, betas=(0, 0.999), weight_decay=1e-4)
# G, opt_G = amp.initialize(G, opt_G, opt_level=args.optim_level)
G = DistributedDataParallel(G, device_ids=[config['local_rank']])
D = MultiscaleDiscriminator(input_nc=3, n_layers=5, norm_layer=torch.nn.InstanceNorm2d).to(args.device)
opt_D = optim.Adam(D.parameters(), lr=args.lr_D, betas=(0, 0.999), weight_decay=1e-4)
# D, opt_D = amp.initialize(D, opt_D, opt_level=args.optim_level)
D = DistributedDataParallel(D, device_ids=[config['local_rank']])
# initializing model for identity extraction
if args.mixed_precision == True:
netArc = iresnet100(fp16=True)
else:
netArc = iresnet100(fp16=False)
netArc.load_state_dict(torch.load('/datasets/pretrained/backbone.pth'))
netArc = netArc.to(args.device)
# netArc=netArc.cuda()
netArc = DistributedDataParallel(netArc, device_ids=[config['local_rank']])
netArc.eval()
if args.eye_detector_loss:
model_ft = models.FAN(4, "False", "False", 98)
# checkpoint = torch.load('./AdaptiveWingLoss/AWL_detector/WFLW_4HG.pth')
checkpoint = torch.load('/datasets/pretrained/WFLW_4HG.pth')
if 'state_dict' not in checkpoint:
model_ft.load_state_dict(checkpoint)
else:
pretrained_weights = checkpoint['state_dict']
model_weights = model_ft.state_dict()
pretrained_weights = {k: v for k, v in pretrained_weights.items() \
if k in model_weights}
model_weights.update(pretrained_weights)
model_ft.load_state_dict(model_weights)
model_ft = model_ft.to(args.device)
model_ft = DistributedDataParallel(model_ft, device_ids=[config['local_rank']])
model_ft.eval()
else:
model_ft=None
# opt_G = optim.Adam(G.parameters(), lr=args.lr_G, betas=(0, 0.999), weight_decay=1e-4)
# opt_D = optim.Adam(D.parameters(), lr=args.lr_D, betas=(0, 0.999), weight_decay=1e-4)
# G, opt_G = amp.initialize(G, opt_G, opt_level=args.optim_level)
# D, opt_D = amp.initialize(D, opt_D, opt_level=args.optim_level)
if args.scheduler:
scheduler_G = scheduler.StepLR(opt_G, step_size=args.scheduler_step, gamma=args.scheduler_gamma)
scheduler_D = scheduler.StepLR(opt_D, step_size=args.scheduler_step, gamma=args.scheduler_gamma)
else:
scheduler_G = None
scheduler_D = None
starting_epoch = 0
if args.pretrained:
try:
# G.module.load_state_dict(torch.load(args.G_path, map_location=torch.device(config['local_rank'])), strict=False)
# D.module.load_state_dict(torch.load(args.D_path, map_location=torch.device(config['local_rank'])), strict=False)
# G.module.load_state_dict(torch.load(args.G_path, map_location=torch.device('cpu')), strict=False)
# D.module.load_state_dict(torch.load(args.D_path, map_location=torch.device('cpu')), strict=False)
G_state = torch.load(f'./saved_models_{args.run_name}/G_latest.pth')
D_state = torch.load(f'./saved_models_{args.run_name}/D_latest.pth')
G.load_state_dict(G_state['model_state_dict'])
starting_epoch = G_state['epoch'] + 1
starting_iteration = G_state['iteration'] + 1
opt_G.load_state_dict(G_state['optimizer_state_dict'])
print(f'GPU {config["local_rank"]} - Preloading model ./saved_models_{args.run_name}/G_latest.pt')
D.load_state_dict(D_state['model_state_dict'])
starting_epoch = D_state['epoch'] + 1
starting_iteration = D_state['iteration'] + 1
opt_D.load_state_dict(D_state['optimizer_state_dict'])
print(f'GPU {config["local_rank"]} - Preloading model ./saved_models_{args.run_name}/D_latest.pt')
print(f'[GPU {config["local_rank"]}]: Loaded pretrained weights for G and D')
except FileNotFoundError as e:
print(f'[GPU {config["local_rank"]}]: Not found pretrained weights. Continue without any pretrained weights.')
else:
starting_iteration = 0
# if config['vgg:
dataset = FaceEmbedCombined(ffhq_data_path = args.ffhq_data_path, same_prob=0.8, same_identity=args.same_identity)
# dataset = FaceEmbedCombined(ffhq_data_path=config['ffhq_data_path, same_prob=0.8, same_identity=config['same_identity)
# dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=8, drop_last=True)
# dataset = FaceEmbedCustom('/workspace/examples/images/training')
dataset_size = len(dataset)
train_size = int(dataset_size * args.train_ratio)
validation_size = int(dataset_size - train_size)
train_dataset, validation_dataset = random_split(dataset, [train_size, validation_size])
print(f'[GPU {config["local_rank"]}]: Training Data Size : {len(train_dataset)}')
print(f'[GPU {config["local_rank"]}]: Validation Data Size : {len(validation_dataset)}')
# dataloader = DataLoader(dataset, batch_size=config['batch_size, shuffle=True, drop_last=True)
# train_dataloader = DataLoader(train_dataset, batch_size=config['batch_size, shuffle=True, drop_last=True)
train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=False, drop_last=True, sampler=DistributedSampler(train_dataset, shuffle=True))
# valid_dataloader = DataLoader(validation_dataset, batch_size=config['batch_size, shuffle=False, drop_last=True)
# valid_dataloader = DataLoader(validation_dataset, batch_size=args.batch_size, shuffle=False, drop_last=True, sampler=DistributedSampler(validation_dataset, shuffle=True))
valid_dataloader = DataLoader(validation_dataset, batch_size=args.val_batch_size, shuffle=True, drop_last=True)
# print(next(iter(dataloader)))
# print(next(iter(dataloader))[0])
##In case of multi GPU, turn off shuffle
# dataloader = DataLoader(dataset, batch_size=config['batch_size, sampler=DistributedSampler(dataset, shuffle=True))
# Будем считать аккумулированный adv loss, чтобы обучать дискриминатор только когда он ниже порога, если discr_force=True
loss_adv_accumulated = 20.
for epoch in range(starting_epoch, max_epoch):
# if epoch >= 1:
# config['id_mode = 'Hififace'
torch.cuda.empty_cache()
G.train()
D.train()
train_one_epoch(G,
D,
id_extractor,
opt_G,
opt_D,
scheduler_G,
scheduler_D,
netArc,
model_ft,
args,
train_dataloader,
device,
epoch,
starting_iteration,
loss_adv_accumulated,
config)
if config['global_rank'] == 0:
##This below is validation part
running_vloss = 0.0
running_pose_metric = 0.0
running_id_metric = 0.0
running_fid_metric = 0.0
running_expression_metric = 0.0
# Set the model to evaluation mode, disabling dropout and using population
# statistics for batch normalization.
G.eval()
D.eval()
# ##loading pretrained models for extracting IDs
# f_3d_path = "/datasets/pretrained/pretrained_model.pth"
# f_id_path = "/datasets/pretrained/backbone.pth"
# id_extractor = ShapeAwareIdentityExtractor(f_3d_path, f_id_path, args.id_mode).to(args.device)
# id_extractor = DistributedDataParallel(id_extractor, device_ids=[config['local_rank']])
# id_extractor.eval()
# Disable gradient computation and reduce memory consumption.
with torch.no_grad():
for i, valid_minibatch in enumerate(valid_dataloader):
val_id_ext_src_input, val_id_ext_tgt_input, val_Xt_f, val_Xt_b, val_Xs_f, val_Xs_b, val_same_person = valid_minibatch
val_id_ext_src_input = val_id_ext_src_input.to(args.device)
val_id_ext_tgt_input = val_id_ext_tgt_input.to(args.device)
val_Xs_f = val_Xs_f.to(args.device)
# Xs.shape
val_Xt_f = val_Xt_f.to(args.device)
# Xt.shape
val_same_person = val_same_person.to(args.device)
val_realtime_batch_size = val_Xt_f.shape[0]
# break
##id_embedding = arcface + shapeaware embedding, [src_emb, tgt_emb] = arcface embedding
val_id_embedding, val_src_id_emb, val_tgt_id_emb = id_extractor.module.forward(val_id_ext_src_input, val_id_ext_tgt_input)
val_id_embedding, val_src_id_emb, val_tgt_id_emb = val_id_embedding.to(args.device), val_src_id_emb.to(args.device), val_tgt_id_emb.to(args.device)
val_diff_person = torch.ones_like(val_same_person)
if args.diff_eq_same:
val_same_person = val_diff_person
val_swapped_face, val_recon_f_src, val_recon_f_tgt = G.module.forward(val_Xt_f, val_Xs_f, val_id_embedding)
pose_value, fid_value, id_value, expression_value = 0, 0, 0, 0
metrics_processors = []
if args.metrics_pose:
metrics_processors.append("POSE")
if args.metrics_fid:
metrics_processors.append("FID")
if args.metrics_id:
metrics_processors.append("ID")
if args.metrics_expression:
metrics_processors.append("EXPRESSION")
result = metric.run(val_Xs_f, val_Xt_f, val_swapped_face, metrics_processors)
print("metrics result : ", result)
if args.metrics_pose:
pose_value = result["metrics.POSE"]
if args.metrics_fid:
fid_value = result["metrics.FID"]
if args.metrics_id:
id_value = result["metrics.ID"]
if args.metrics_expression:
expression_value = result["metrics.EXPRESSION"]
# running_vloss += vloss
running_pose_metric += pose_value
running_id_metric += id_value
running_fid_metric += fid_value
running_expression_metric += expression_value
if args.use_wandb and config['global_rank'] == 0:
wandb.log({"running_pose_metric": running_pose_metric, "running_id_metric": running_id_metric, "running_fid_metric": running_fid_metric}, commit=False)
if args.landmark_detector_loss:
wandb.log({"running_expression_metric": running_expression_metric}, commit=False)
if config['global_rank'] == 0:
avg_pose_metric = running_pose_metric / (i + 1)
avg_id_metric = running_id_metric / (i + 1)
avg_fid_metric = running_fid_metric / (i + 1)
avg_expression_metric = running_expression_metric / (i + 1)
wandb.log({"avg_pose_metric": avg_pose_metric, "avg_id_metric": avg_id_metric, "avg_fid_metric": avg_fid_metric}, commit=False)
if args.landmark_detector_loss:
wandb.log({"avg_expression_metric": avg_expression_metric}, commit=False)
## adding functions for WandB to log the metrics
## put up the generated validation images in WandB
## saving functions for best G and D
# torch.save(model.state_dict(), model_path)
def main(args):
config = dict()
# config.update(vars(args))
config['local_rank'] = int(os.environ['LOCAL_RANK'])
config['global_rank'] = int(os.environ['RANK'])
assert config['local_rank'] != -1, "LOCAL_RANK environment variable not set"
assert config['global_rank'] != -1, "RANK environment variable not set"
# Print configuration (only once per server)
if config['local_rank'] == 0:
print("Configuration:")
for key, value in config.items():
print(f"{key:>20}: {value}")
if args.use_wandb==True and config['global_rank'] == 0:
wandb.init(project=args.wandb_project,
entity=args.wandb_entity,
settings=wandb.Settings(start_method='fork'),
# id=args.wandb_id,
resume='allow')
configs = wandb.config
# config.vgg_data_path = config['vgg_data_path
configs.ffhq_data_path = args.ffhq_data_path
# config.celeba_data_path = config['celeba_data_path
# config.dob_data_path = config['dob_data_path
configs.train_ratio = args.train_ratio
configs.G_path = args.G_path
configs.D_path = args.D_path
configs.weight_adv = args.weight_adv
configs.weight_attr = args.weight_attr
configs.weight_id = args.weight_id
configs.weight_rec = args.weight_rec
configs.weight_eyes = args.weight_eyes
configs.weight_cycle = args.weight_cycle
configs.weight_cycle_identity = args.weight_cycle_identity
configs.weight_contrastive = args.weight_contrastive
configs.weight_source_unet = args.weight_source_unet
configs.weight_target_unet = args.weight_target_unet
configs.weight_landmarks = args.weight_landmarks
configs.backbone = args.backbone
configs.num_blocks = args.num_blocks
configs.num_adain = args.num_adain
configs.id_mode = args.id_mode
configs.seq_len = args.seq_len
configs.n_head = args.n_head
configs.total_embed_dim = args.total_embed_dim
configs.q_dim = args.q_dim
configs.k_dim = args.k_dim
configs.kv_dim = args.kv_dim
configs.same_person = args.same_person
configs.same_identity = args.same_identity
configs.diff_eq_same = args.diff_eq_same
configs.discr_force = args.discr_force
configs.scheduler = args.scheduler
configs.scheduler_step = args.scheduler_step
configs.scheduler_gamma = args.scheduler_gamma
configs.eye_detector_loss = args.eye_detector_loss
configs.landmark_detector_loss = args.landmark_detector_loss
configs.cycle_loss = args.cycle_loss
configs.contrastive_loss = args.contrastive_loss
configs.shape_loss = args.shape_loss
configs.wandb_id = args.wandb_id
configs.run_name = args.run_name
configs.wandb_project = args.wandb_project
configs.batch_size = args.batch_size
configs.val_batch_size = args.val_batch_size
configs.lr_G = args.lr_G
configs.lr_D = args.lr_D
configs.max_epoch = args.max_epoch
configs.show_step = args.show_step
configs.save_epoch = args.save_epoch
configs.mixed_precision = args.mixed_precision
configs.device = args.device
configs.pretrained = args.pretrained
elif not os.path.exists('./images'):
os.mkdir('./images')
# Setup distributed training
print(f'[GPU {config["local_rank"]}]: Setting up distributed training..')
init_process_group(backend='nccl', timeout=datetime.timedelta(seconds=5400))
print(f'[GPU {config["local_rank"]}]: initiating distributed process with nccl')
torch.cuda.set_device(config['local_rank'])
print(f'[GPU {config["local_rank"]}]: setting device with cuda in local rank')
print(f'[GPU {config["local_rank"]}]: Starting training')\
# train(args, device=device)
train(args, config)
# Clean up distributed training
destroy_process_group()
print(f'[GPU {config["local_rank"]}]: destroyed distributed process after training')
# config = dict()
# config['local_rank'] = int(os.environ(['LOCAL_RANK']))
# config['global_rank'] = int(os.environ(['RANK']))
# assert config['local_rank'] != -1, "LOCAL_RANK environment variable not set"
# assert config['global_rank'] != -1, "RANK environment variable not set"
# # Print configuration (only once per server)
# if config['local_rank'] == 0:
# print("Configuration:")
# for key, value in config.items():
# print(f"{key:>20}: {value}")
# # Setup distributed training
# init_process_group(backend='nccl')
# torch.cuda.set_device(config.local_rank)
# # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# # device = 'cpu'
# if not torch.cuda.is_available():
# print('cuda is not available. using cpu. check if it\'s ok')
# print("Starting training")
# train(args, gpu_config)
# # Clean up distributed training
# destroy_process_group()
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# if not torch.cuda.is_available():
# print('cuda is not available. using cpu. check if it\'s ok')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# dataset params
##the 4 arguments are newly added by Hojun
# parser.add_argument('--vgg_data_path', default='/datasets/VGG', help='Path to the dataset. If not VGG2 dataset is used, param --vgg should be set False')
# parser.add_argument('--ffhq_data_path', default='/datasets/FFHQ', type=str,help='path to ffhq dataset in string format')
parser.add_argument('--ffhq_data_path', default='/datasets/FFHQ_parsed_img', type=str,help='path to ffhq dataset in string format')
parser.add_argument('--train_ratio', default=0.9, type=float, help='how much data to be used as training set. The rest will be used as validation set. e.g.) if 0.9, validation data will be 0.1 (10%)')
# parser.add_argument('--celeba_data_path', default='/datasets/CelebHQ/CelebA-HQ-img', help='Path to the dataset. If not VGG2 dataset is used, param --vgg should be set False')
# parser.add_argument('--dob_data_path', default='/datasets/DOB', help='Path to the dataset. If not VGG2 dataset is used, param --vgg should be set False')
parser.add_argument('--G_path', default='./saved_models/G.pth', help='Path to pretrained weights for G. Only used if pretrained=True')
parser.add_argument('--D_path', default='./saved_models/D.pth', help='Path to pretrained weights for D. Only used if pretrained=True')
# weights for loss
parser.add_argument('--weight_adv', default=2, type=float, help='Adversarial Loss weight')
parser.add_argument('--weight_attr', default=15, type=float, help='Attributes weight')
parser.add_argument('--weight_id', default=10, type=float, help='Identity Loss weight')
parser.add_argument('--weight_rec', default=10, type=float, help='Reconstruction Loss weight')
parser.add_argument('--weight_eyes', default=1.5, type=float, help='Eyes Loss weight')
parser.add_argument('--weight_cycle', default=1., type=float, help='Cycle Loss weight for generator')
parser.add_argument('--weight_cycle_identity', default=1., type=float, help='Cycle Identity Loss weight for generator')
parser.add_argument('--weight_contrastive', default=1.5, type=float, help='Contrastive Loss weight for idendity embedding of generator')
parser.add_argument('--weight_source_unet', default=0.5, type=float, help='Source Image Unet Reconstruction Loss weight for generator')
parser.add_argument('--weight_target_unet', default=0.5, type=float, help='Target Image Unet Reconstruction Loss weight for generator')
parser.add_argument('--weight_landmarks', default=2., type=float, help='Landmark Loss weight for generator')
parser.add_argument('--weight_shape', default=3., type=float, help='Shape Loss weight for generator')
# training params you may want to change
##parameters for model configs
parser.add_argument('--backbone', default='unet', const='unet', nargs='?', choices=['unet', 'linknet', 'resnet'], help='Backbone for attribute encoder. The other modes are not applicable')
parser.add_argument('--num_blocks', default=2, type=int, help='Numbers of AddBlocks at AddResblock')
parser.add_argument('--num_adain', default=6, type=int, help='Numbers of AdaIN_ResBlocks') # 1부터 6까지. AdaIN_Resblock을 시작점으로부터 N개 사용한다는 의미
parser.add_argument('--id_mode', default='arcface', type=str, help='Mode change is possible between 1) arcface 2) hififace') # 1부터 6까지. AdaIN_Resblock을 시작점으로부터 N개 사용한다는 의미
parser.add_argument('--seq_len', default=196, type=int, help='sequence length = height*width, number of patches of ViT. It would normally be H*W = 196 or 256')
parser.add_argument('--n_head', default=2, type=int, help='number of multi attention head')
parser.add_argument('--total_embed_dim', default=512, type=int, help="Full query dim (and query's value dimension) before dividing by num head ")
parser.add_argument('--q_dim', default=1024, type=int, help="Full query dim (and query's value dimension) before dividing by num head ")
parser.add_argument('--k_dim', default=1024, type=int, help="Full key dim (and/or key's value dimension) before dividing by num head ")
parser.add_argument('--kv_dim', default=1024, type=int, help='value dim of key before dividing by num head. Key value dimension doesnt neccessarily have to be same as key dim')
parser.add_argument('--same_person', default=0.2, type=float, help='Probability of using same person identity during training')
parser.add_argument('--same_identity', default=True, type=bool, help='Using simswap approach, when source_id = target_id. Only possible with vgg=True')
parser.add_argument('--diff_eq_same', default=False, type=bool, help='Don\'t use info about where is defferent identities')
parser.add_argument('--pretrained', default=False, type=bool, help='If using the pretrained weights for training or not')
parser.add_argument('--discr_force', default=False, type=bool, help='If True Discriminator would not train when adversarial loss is high')
parser.add_argument('--scheduler', default=False, type=bool, help='If True decreasing LR is used for learning of generator and discriminator')
parser.add_argument('--scheduler_step', default=5000, type=int)
parser.add_argument('--scheduler_gamma', default=0.2, type=float, help='It is value, which shows how many times to decrease LR')
parser.add_argument('--eye_detector_loss', default=False, type=bool, help='If True eye loss with using AdaptiveWingLoss detector is applied to generator')
parser.add_argument('--landmark_detector_loss', default=False, type=bool, help='If True landmark loss is applied to generator')
parser.add_argument('--cycle_loss', default=False, type=bool, help='If True, cycle & cycle identity losses are applied to generator')
parser.add_argument('--contrastive_loss', default=True, type=bool, help='If True, contrastive loss is applied to generator')
parser.add_argument('--unet_loss', default=True, type=bool, help='If True, unet losses for source and target are applied to generator')
parser.add_argument('--shape_loss', default=False, type=bool, help='If True, contrastive loss is applied to generator')
# info about this run
parser.add_argument('--use_wandb', default=True, type=bool, help='Use wandb to track your experiments or not')
parser.add_argument('--wandb_id', default='123456', type=bool, help='unique IDs for wandb run')
parser.add_argument('--run_name', required=True, type=str, help='Name of this run. Used to create folders where to save the weights.')
parser.add_argument('--wandb_project', default='your-project-name', type=str, help='name of project. for example, faceswap_basemodel')
parser.add_argument('--wandb_entity', default='your-login', type=str, help='name of team in wandb. ours is dob_faceswapteam')
# training params you probably don't want to change
parser.add_argument('--batch_size', default=8, type=int)
parser.add_argument('--val_batch_size', default=8, type=int)
parser.add_argument('--lr_G', default=4e-4, type=float)
parser.add_argument('--lr_D', default=4e-4, type=float)
parser.add_argument('--max_epoch', default=2000, type=int)
parser.add_argument('--show_step', default=20, type=int)
parser.add_argument('--save_epoch', default=2, type=int)
parser.add_argument('--mixed_precision', default=False, type=bool)
parser.add_argument('--device', default='cuda', type=str, help='setting device between cuda and cpu')
parser.add_argument('--use_reconsimg', default=True, type=bool, help='create reconsimg for wandb')
# metrics info
parser.add_argument('--metrics_expression', default=False, type=bool)
parser.add_argument('--metrics_fid', default=True, type=bool)
parser.add_argument('--metrics_id', default=True, type=bool)
parser.add_argument('--metrics_pose', default=True, type=bool)
args = parser.parse_args()
# if bool(config['vgg_data_path)==False and config['same_identity==True:
# raise ValueError("Sorry, you can't use some other dataset than VGG2 Faces with param same_identity=True")
# Создаем папки, чтобы было куда сохранять последние веса моделей, а также веса с каждой эпохи
if not os.path.exists(f'./saved_models_{args.run_name}'):
os.mkdir(f'./saved_models_{args.run_name}')
os.mkdir(f'./current_models_{args.run_name}')
main(args)