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train.py
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train.py
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import torch
import torch.nn as nn
import numpy as np
import os
from models import *
from utils import *
from data_loading import *
import cv2
from metrics import compute_psnr,compute_ssim
import pytorch_msssim
## TODOS:
## 1. Dump SH in file
##
##
## Notes:
## 1. SH is not normalized
## 2. Face is normalized and denormalized - shall we not normalize in the first place?
# Enable WANDB Logging
WANDB_ENABLE = False
def predict_celeba(sfs_net_model, dl, train_epoch_num = 0,
use_cuda = False, out_folder = None, wandb = None, suffix = 'CelebA_Val', dump_all_images = False):
# debugging flag to dump image
fix_bix_dump = 0
recon_loss = nn.L1Loss()
if use_cuda:
recon_loss = recon_loss.cuda()
tloss = 0 # Total loss
rloss = 0 # Reconstruction loss
for bix, data in enumerate(dl):
face = data
if use_cuda:
face = face.cuda()
# predicted_face == reconstruction
predicted_normal, predicted_albedo, predicted_sh, predicted_shading, predicted_face = sfs_net_model(face)
# coba
# file_name = out_folder
# save_image(predicted_normal, path = file_name+'_normal.png')
# save_image(predicted_albedo, path = file_name+'_albedo.png')
# save_image(predicted_shading, path = file_name+'_shading.png')
# save_image(predicted_face, path = file_name+'_recon.png')
if bix == fix_bix_dump or dump_all_images:
# save predictions in log folder
file_name = out_folder + suffix + '_' + str(train_epoch_num) + '_' + str(bix)
# log images
predicted_normal = get_normal_in_range(predicted_normal)
save_image(predicted_normal, path = file_name+'_normal.png')
save_image(predicted_albedo, path = file_name+'_albedo.png')
save_image(predicted_shading, path = file_name+'_shading.png')
save_image(predicted_face, path = file_name+'_recon.png')
save_image(face, path = file_name+'_face.png')
# TODO:
# Dump SH as CSV or TXT file
# Loss computation
# Reconstruction loss
total_loss = recon_loss(predicted_face, face)
# Logging for display and debugging purposes
tloss += total_loss.item()
len_dl = len(dl)
if(wandb is not None):
wandb.log({suffix+' Total loss': tloss/len_dl}, step=train_epoch_num)
# return average loss over dataset
return tloss / len_dl
def predict_synthetic(sfs_net_model, dl, train_epoch_num = 0,
use_cuda = False, out_folder = None, wandb = None, suffix = 'Val'):
# debugging flag to dump image
#sfs_net_model.eval()
fix_bix_dump = 0
normal_loss = nn.L1Loss()
albedo_loss = nn.L1Loss()
sh_loss = nn.MSELoss()
recon_loss = nn.L1Loss()
corrector_loss = nn.L1Loss()
lamda_recon = 0.5
lamda_albedo = 0.5
lamda_normal = 0.5
lamda_sh = 0.1
lamda_cor = 0.5
if use_cuda:
normal_loss = normal_loss.cuda()
albedo_loss = albedo_loss.cuda()
sh_loss = sh_loss.cuda()
recon_loss = recon_loss.cuda()
corrector_loss = corrector_loss.cuda()
tloss = 0 # Total loss
nloss = 0 # Normal loss
aloss = 0 # Albedo loss
shloss = 0 # SH loss
rloss = 0 # Reconstruction loss
closs = 0
for bix, data in enumerate(dl):
albedo, normal, sh, face = data
if use_cuda:
albedo = albedo.cuda()
normal = normal.cuda()
sh = sh.cuda()
face = face.cuda()
# Apply Mask on input image
# face = applyMask(face, mask)
# predicted_face == reconstruction
predicted_normal, predicted_albedo, predicted_sh, predicted_shading, predicted_face = sfs_net_model(face)
if fix_bix_dump == bix :
# save predictions in log folder
save_p_normal = get_normal_in_range(predicted_normal)
save_gt_normal = get_normal_in_range(normal)
# log images
for i in range(face.size()[0]):
file_name = out_folder + suffix + '_' + str(train_epoch_num) + '_' + str(i) + '_' + str(bix)+'.png'
tensor = torch.cat([predicted_albedo.unsqueeze(0)[:,i,:,:], save_p_normal.unsqueeze(0)[:,i,:,:],predicted_shading.unsqueeze(0)[:,i,:,:], \
predicted_face.unsqueeze(0)[:,i,:,:],face.unsqueeze(0)[:,i,:,:]], dim=0)
utils.save_image(tensor.cpu(), file_name , nrow=5)
# Loss computation
# Normal loss
current_normal_loss = normal_loss(predicted_normal, normal)
# Albedo loss
current_albedo_loss = albedo_loss(predicted_albedo, albedo)
# SH loss
current_sh_loss = sh_loss(predicted_sh, sh)
# Reconstruction loss
current_recon_loss = recon_loss(predicted_face, face)
total_loss = lamda_recon * current_recon_loss + lamda_normal * current_normal_loss \
+ lamda_albedo * current_albedo_loss + lamda_sh * current_sh_loss
# Logging for display and debugging purposes
tloss += total_loss.item()
nloss += current_normal_loss.item()
aloss += current_albedo_loss.item()
shloss += current_sh_loss.item()
rloss += current_recon_loss.item()
len_dl = len(dl)
# return average loss over dataset
return tloss / len_dl, nloss / len_dl, aloss / len_dl, shloss / len_dl, rloss / len_dl
def predict_sfsnet(sfs_net_model, dl, train_epoch_num = 0,
use_cuda = False, out_folder = None, wandb = None, suffix = 'Val'):
# debugging flag to dump image
#sfs_net_model.eval()
fix_bix_dump = 0
normal_loss = nn.L1Loss()
albedo_loss = nn.L1Loss()
sh_loss = nn.MSELoss()
recon_loss = nn.L1Loss()
lamda_recon = 0.5
lamda_albedo = 0.5
lamda_normal = 0.5
lamda_sh = 0.1
if use_cuda:
normal_loss = normal_loss.cuda()
albedo_loss = albedo_loss.cuda()
sh_loss = sh_loss.cuda()
recon_loss = recon_loss.cuda()
tloss = 0 # Total loss
nloss = 0 # Normal loss
aloss = 0 # Albedo loss
shloss = 0 # SH loss
rloss = 0 # Reconstruction loss
for bix, data in enumerate(dl):
albedo, normal, sh, face ,faceHR= data
if use_cuda:
albedo = albedo.cuda()
normal = normal.cuda()
sh = sh.cuda()
face = face.cuda()
faceHR = faceHR.cuda()
# Apply Mask on input image
# face = applyMask(face, mask)
# predicted_face == reconstruction
predicted_normal, predicted_albedo, predicted_sh, predicted_shading, predicted_face = sfs_net_model(faceHR)
#if fix_bix_dump == bix :
# save predictions in log folder
save_p_normal = get_normal_in_range(predicted_normal)
save_gt_normal = get_normal_in_range(normal)
# log images
for i in range(face.size()[0]):
file_name = out_folder + suffix + '_' + str(train_epoch_num) + '_' + str(i) + '_' + str(bix)+'.png'
tensor = torch.cat([predicted_albedo.unsqueeze(0)[:,i,:,:], save_p_normal.unsqueeze(0)[:,i,:,:],predicted_shading.unsqueeze(0)[:,i,:,:], \
predicted_face.unsqueeze(0)[:,i,:,:],face.unsqueeze(0)[:,i,:,:]], dim=0)
utils.save_image(tensor.cpu(), file_name , nrow=5)
# Loss computation
# Normal loss
current_normal_loss = normal_loss(predicted_normal, normal)
# Albedo loss
current_albedo_loss = albedo_loss(predicted_albedo, albedo)
# SH loss
current_sh_loss = sh_loss(predicted_sh, sh)
# Reconstruction loss
current_recon_loss = recon_loss(predicted_face, face)
total_loss = lamda_recon * current_recon_loss + lamda_normal * current_normal_loss \
+ lamda_albedo * current_albedo_loss + lamda_sh * current_sh_loss
# Logging for display and debugging purposes
tloss += total_loss.item()
nloss += current_normal_loss.item()
aloss += current_albedo_loss.item()
shloss += current_sh_loss.item()
rloss += current_recon_loss.item()
len_dl = len(dl)
# return average loss over dataset
return tloss / len_dl, nloss / len_dl, aloss / len_dl, shloss / len_dl, rloss / len_dl
def predict_corrector(model, dl, perceptual_loss=None, train_epoch_num = 0,
use_cuda = False, out_folder = None, wandb = None, suffix = 'Val'):
# debugging flag to dump image
#sfs_net_model.eval()
fix_bix_dump = 0
albedo_corrector_loss = nn.L1Loss()
shading_corrector_loss = nn.L1Loss()
recon_corrector_loss = nn.L1Loss()
alcorloss = 0
shcorloss = 0
recorloss = 0
for bix, data in enumerate(dl):
attr, face,target = data
if use_cuda:
target = target.cuda()
face = face.cuda()
attr = attr.cuda()
_, target_albedo, _, target_shading, target_recon, _, _,_= model(target)
_, _, _, _, _, albedo_correct, shading_correct,recon_correct= model(face)
if fix_bix_dump == bix :
# save predictions in log folder
# save_p_normal = get_normal_in_range(predicted_normal)
# save_gt_normal = get_normal_in_range(normal)
# log images
for i in range(face.size()[0]):
file_name = out_folder + suffix + '_' + str(train_epoch_num) + '_' + str(i) + '_' + str(bix)
tensor = torch.cat([face.unsqueeze(0)[:,i,:,:],albedo_correct.unsqueeze(0)[:,i,:,:],target_albedo.unsqueeze(0)[:,i,:,:],\
shading_correct.unsqueeze(0)[:,i,:,:],target_shading.unsqueeze(0)[:,i,:,:],recon_correct.unsqueeze(0)[:,i,:,:],target.unsqueeze(0)[:,i,:,:]], dim=0)
utils.save_image(tensor.cpu(), file_name+'.png' , nrow=7)
# save_image(recon_correct.unsqueeze(0)[:,i,:,:], path = file_name+'_recon.png')
# save_image(target.unsqueeze(0)[:,i,:,:], path = file_name+'_target.png')
#np.savetxt(file_name+'_cls.txt', label[i].cpu().detach().numpy(), delimiter='\t')
# Loss computation
current_albedo_cor_loss = shading_corrector_loss(albedo_correct,target_albedo) #+ perceptual_loss(albedo_correct,target_albedo)
current_shading_cor_loss = albedo_corrector_loss(shading_correct,target_shading) + perceptual_loss(shading_correct,target_shading)
current_recon_cor_loss = recon_corrector_loss(recon_correct,target_recon) + perceptual_loss(recon_correct,target_recon)
alcorloss += current_albedo_cor_loss.item()
shcorloss += current_shading_cor_loss.item()
recorloss += current_recon_cor_loss.item()
len_dl = len(dl)
# return average loss over dataset
return alcorloss/len_dl, shcorloss/len_dl , recorloss/len_dl
def predict_light(model, dl, perceptual_loss=None, train_epoch_num = 0,
use_cuda = False, out_folder = None, iteration = None, wandb = None, suffix = 'Val'):
# debugging flag to dump image
fix_bix_dump = 0
recon_corrector_loss = nn.L1Loss()
perceptual_loss = perceptual_loss
alcorloss = 0
shcorloss = 0
recorloss = 0
for bix, data in enumerate(dl):
face,target = data
if use_cuda:
target = target.cuda()
face = face.cuda()
fake_image = model(face)
if fix_bix_dump == bix :
# save predictions in log folder
# save_p_normal = get_normal_in_range(predicted_normal)
# save_gt_normal = get_normal_in_range(normal)
# log images
for i in range(face.size()[0]-5,face.size()[0]):
file_name = out_folder + suffix + '_' + str(train_epoch_num) + '_' + str(iteration) + '_' + str(i) + '_' + str(bix)
tensor = torch.cat([face.unsqueeze(0)[:,i,:,:],fake_image.unsqueeze(0)[:,i,:,:],target.unsqueeze(0)[:,i,:,:]], dim=0)
utils.save_image(tensor.cpu(), file_name+'.png' , nrow=3)
# save_image(fake_image.unsqueeze(0)[:,i,:,:], path = file_name+'_recon.png')
# save_image(target.unsqueeze(0)[:,i,:,:], path = file_name+'_target.png')
#np.savetxt(file_name+'_cls.txt', label[i].cpu().detach().numpy(), delimiter='\t')
# Loss computation
current_recon_cor_loss = 10*recon_corrector_loss(fake_image,target) + perceptual_loss(fake_image,target)
recorloss += current_recon_cor_loss.item()
len_dl = len(dl)
# return average loss over dataset
return recorloss/5
def predict_fixer(model,fixer, dl, perceptual_loss=None, train_epoch_num = 0,
use_cuda = False, out_folder = None, iteration = None, wandb = None, suffix = 'Val'):
# debugging flag to dump image
fix_bix_dump = 0
recon_corrector_loss = nn.L1Loss()
alcorloss = 0
shcorloss = 0
recorloss = 0
for bix, data in enumerate(dl):
face,target = data
if use_cuda:
target = target.cuda()
face = face.cuda()
fake = model(face)
fake_image = fixer(fake)
if fix_bix_dump == bix :
# save predictions in log folder
# save_p_normal = get_normal_in_range(predicted_normal)
# save_gt_normal = get_normal_in_range(normal)
# log images
for i in range(face.size()[0]-5,face.size()[0]):
file_name = out_folder + suffix + '_' + str(train_epoch_num) + '_' + str(iteration) + '_' + str(i) + '_' + str(bix)
tensor = torch.cat([face.unsqueeze(0)[:,i,:,:],fake.unsqueeze(0)[:,i,:,:],fake_image.unsqueeze(0)[:,i,:,:],target.unsqueeze(0)[:,i,:,:]], dim=0)
utils.save_image(tensor.cpu(), file_name+'.png' , nrow=4)
# save_image(fake_image.unsqueeze(0)[:,i,:,:], path = file_name+'_recon.png')
# save_image(target.unsqueeze(0)[:,i,:,:], path = file_name+'_target.png')
#np.savetxt(file_name+'_cls.txt', label[i].cpu().detach().numpy(), delimiter='\t')
# Loss computation
current_recon_cor_loss = recon_corrector_loss(fake_image,target) #+ perceptual_loss(fake_image,target)
recorloss += current_recon_cor_loss.item()
len_dl = len(dl)
# return average loss over dataset
return recorloss/5
def Calculate_PSNR(model, dl, use_cuda = False, out_folder = None):
# debugging flag to dump image
#sfs_net_model.eval()
file = open(out_folder+'psnr_results.txt','w')
fix_bix_dump = 0
for bix, data in enumerate(dl):
attr, face,target = data
if use_cuda:
target = target.cuda()
face = face.cuda()
attr = attr.cuda()
fake_image = model(face)
for i in range(face.size()[0]):
file_name = out_folder + str(bix) + '_' + str(i)
tensor = torch.cat([face.unsqueeze(0)[:,i,:,:],fake_image.unsqueeze(0)[:,i,:,:],target.unsqueeze(0)[:,i,:,:]], dim=0)
utils.save_image(tensor.cpu(), file_name+'.png' , nrow=3)
psnr = compute_psnr(fake_image.unsqueeze(0)[:,i,:,:],target.unsqueeze(0)[:,i,:,:])
file.write(str(bix) + " PSNR results : "+str(psnr.item())+'\n')
#torch.save(psnr, file_name)
file.close()
def Calculate_metrics(dl, use_cuda = False, out_folder = None):
# debugging flag to dump image
#sfs_net_model.eval()
total_ssim = 0
total_psnr = 0
total_ms = 0
fix_bix_dump = 0
for bix, data in enumerate(dl):
face,target = data
for i in range(face.size()[0]):
file_name = out_folder + str(bix) + '_' + str(i)
tensor = torch.cat([face.unsqueeze(0)[:,i,:,:],target.unsqueeze(0)[:,i,:,:]], dim=0)
utils.save_image(tensor.cpu(), file_name+'.png' , nrow=2)
ssim = compute_ssim(face.unsqueeze(0)[:,i,:,:],target.unsqueeze(0)[:,i,:,:])
psnr = compute_psnr(face.unsqueeze(0)[:,i,:,:],target.unsqueeze(0)[:,i,:,:])
mssim= pytorch_msssim.msssim(face.unsqueeze(0)[:,i,:,:],target.unsqueeze(0)[:,i,:,:])
# with open(out_folder+'ssim_results.txt', 'a') as f:
# f.write('\n'+str(bix)+". ssim score : "+str(ssim.item()))
total_ssim += ssim.item()
total_psnr += psnr.item()
total_ms += mssim.item()
print(file_name +": " + str(ssim.item()) +"+"+ str(psnr.item()) +"+"+ str(mssim.item()))
print("Average SSIM : " +str(total_ssim/len(dl)))
print("Average PSNR : " +str(total_psnr/len(dl)))
#print("Average MS-SSIM : " +str(total_ms/len(dl)))
# with open(out_folder+'ssim_results.txt', 'a') as f:
# f.write('\n'+"average score : "+str(total/len(dl)))
def train_synthetic(sfs_net_model, syn_data, celeba_data=None,rgb_data=None, read_first=None,
batch_size = 10, num_epochs = 10, log_path = './results/metadata/', use_cuda=False, wandb=None,
lr = 0.01, wt_decay=0.005, training_syn=False):
# data processing
syn_train_csv = syn_data + '/train.csv'
syn_test_csv = syn_data + '/test.csv'
celeba_train_csv = None
celeba_test_csv = None
val_celeba_dl = None
#tambahanku
if celeba_data is not None:
# celeba_train_csv = celeba_data + '/train.csv'
# celeba_test_csv = celeba_data + '/test.csv'
if training_syn:
celeba_dt, _ = get_celeba_dataset(dir=celeba_data ,read_from_csv=None, read_first=batch_size, validation_split=99.9)
val_celeba_dl = DataLoader(celeba_dt, batch_size=batch_size, shuffle=True)
# Load Synthetic dataset
train_dataset, val_dataset = get_sfsnet_dataset(read_from_csv=syn_train_csv, read_celeba_csv=celeba_train_csv ,read_first=read_first, validation_split=2, training_syn = training_syn)
test_dataset, _ = get_sfsnet_dataset(read_from_csv=syn_test_csv, read_celeba_csv=celeba_test_csv,read_first=100, validation_split=0, training_syn = training_syn)
syn_train_dl = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
syn_val_dl = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
syn_test_dl = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
print('Synthetic dataset: Train data: ', len(syn_train_dl), ' Val data: ', len(syn_val_dl), ' Test data: ', len(syn_test_dl))
model_checkpoint_dir = log_path + 'checkpoints/'
out_images_dir = log_path + 'out_images/'
out_syn_images_dir = out_images_dir
os.system('mkdir -p {}'.format(model_checkpoint_dir))
os.system('mkdir -p {}'.format(out_syn_images_dir + 'train/'))
os.system('mkdir -p {}'.format(out_syn_images_dir + 'val/'))
os.system('mkdir -p {}'.format(out_syn_images_dir + 'test/'))
if val_celeba_dl is not None:
os.system('mkdir -p {}'.format(out_syn_images_dir + 'celeba_val/'))
# Collect model parameters
model_parameters = sfs_net_model.parameters()
optimizer = torch.optim.Adam(model_parameters, lr=lr, weight_decay=wt_decay)
#scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=6, gamma=0.1)
normal_loss = nn.MSELoss()
albedo_loss = nn.MSELoss()
sh_loss = nn.MSELoss()
recon_loss = nn.MSELoss()
if use_cuda:
normal_loss = normal_loss.cuda()
albedo_loss = albedo_loss.cuda()
sh_loss = sh_loss.cuda()
recon_loss = recon_loss.cuda()
lamda_recon = 1
lamda_albedo = 1
lamda_normal = 1
lamda_sh = 1
if use_cuda:
normal_loss = normal_loss.cuda()
albedo_loss = albedo_loss.cuda()
sh_loss = sh_loss.cuda()
recon_loss = recon_loss.cuda()
syn_train_len = len(syn_train_dl)
for epoch in range(1, num_epochs+1):
tloss = 0 # Total loss
nloss = 0 # Normal loss
aloss = 0 # Albedo loss
shloss = 0 # SH loss
rloss = 0 # Reconstruction loss
for bix, data in enumerate(syn_train_dl):
albedo, normal, sh, face = data
if use_cuda:
albedo = albedo.cuda()
normal = normal.cuda()
sh = sh.cuda()
face = face.cuda()
# Apply Mask on input image
# face = applyMask(face, mask)
predicted_normal, predicted_albedo, predicted_sh, out_shading, out_recon = sfs_net_model(face)
# Loss computation
# Normal loss
current_normal_loss = normal_loss(predicted_normal, normal)
# Albedo loss
current_albedo_loss = albedo_loss(predicted_albedo, albedo)
# SH loss
current_sh_loss = sh_loss(predicted_sh, sh)
current_recon_loss = recon_loss(out_recon, face)
total_loss = lamda_normal * current_normal_loss \
+ lamda_albedo * current_albedo_loss + lamda_sh * current_sh_loss # + lamda_recon * current_recon_loss
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
# Logging for display and debugging purposes
tloss += total_loss.item()
nloss += current_normal_loss.item()
aloss += current_albedo_loss.item()
shloss += current_sh_loss.item()
rloss += current_recon_loss.item()
#scheduler.step(epoch)
epoch_alert='Epoch: {} Learning rate: {} - Total Loss: {}, Normal Loss: {}, Albedo Loss: {}, SH Loss: {}, Recon Loss: {}'.format(epoch , optimizer.param_groups[0]['lr'],\
tloss / syn_train_len, nloss / syn_train_len, aloss / syn_train_len, shloss / syn_train_len, rloss / syn_train_len)
print(epoch_alert)
with open(log_path+'details.txt', 'a') as f:
f.write('\n'+epoch_alert)
with torch.no_grad():
log_prefix = 'Syn Data'
if celeba_data is not None:
log_prefix = 'Mix Data '
# Model saving
# torch.save({'epoch': epoch,
# 'model_state_dict': sfs_net_model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict()
# }, model_checkpoint_dir + 'skipnet_model_'+str(epoch) +'.pkl')
if epoch % 5 == 0:
v_total, v_normal, v_albedo, v_sh, v_recon = predict_synthetic(sfs_net_model, syn_val_dl, train_epoch_num=epoch, use_cuda=use_cuda,
out_folder=out_syn_images_dir+'/val/')
val_alert='Val Synthetic set results: Total Loss: {}, Normal Loss: {}, Albedo Loss: {}, SH Loss: {}, Recon Loss: {}'.format(v_total,
v_normal, v_albedo, v_sh, v_recon)
print(val_alert)
#write to log
with open(log_path+'details.txt', 'a') as f:
f.write('\n'+val_alert)
if epoch % 10 == 0:
t_total, t_normal, t_albedo, t_sh, t_recon = predict_synthetic(sfs_net_model, syn_test_dl, train_epoch_num=epoch, use_cuda=use_cuda,
out_folder=out_syn_images_dir + '/test/', suffix='Test')
test_alert= 'Test-set results: Total Loss: {}, Normal Loss: {}, Albedo Loss: {}, SH Loss: {}, Recon Loss: {}'.format(t_total,
t_normal, t_albedo, t_sh, t_recon)
print(test_alert)
#write to log
with open(log_path+'details.txt', 'a') as f:
f.write('\n'+test_alert)
if val_celeba_dl is not None:
lossku=predict_celeba(sfs_net_model, val_celeba_dl, train_epoch_num = epoch,
use_cuda = use_cuda, out_folder = out_syn_images_dir + 'celeba_val/', dump_all_images = True)
real_test='Test-celeba set results: Total Loss:{}\n'.format(lossku)
print(real_test)
#write to log
with open(log_path+'details.txt', 'a') as f:
f.write('\n'+real_test)
def train(sfs_net_model, syn_data, celeba_data=None,read_first=None,
batch_size = 10, num_epochs = 10, log_path = './results/metadata/', use_cuda=False, wandb=None,
lr = 0.01, wt_decay=0.005, training_syn=False, optim_state=None, last_epoch = None):
# data processing
syn_train_csv = syn_data + '/train.csv'
syn_test_csv = syn_data + '/test.csv'
celeba_train_csv = celeba_data + '/train.csv'
celeba_test_csv = celeba_data + '/test.csv'
# Load Synthetic dataset
train_dataset, _ = get_sfsnet_dataset(syn_dir=syn_data+'train/', read_from_csv=syn_train_csv, read_celeba_csv=celeba_train_csv, read_first=read_first, validation_split=0, training_syn = training_syn)
test_dataset, _ = get_sfsnet_dataset(syn_dir=syn_data+'test/', read_from_csv=syn_test_csv, read_celeba_csv=celeba_test_csv,read_first=100, validation_split=0, training_syn = training_syn)
syn_train_dl = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
syn_test_dl = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
print('Synthetic dataset: Train data: ', len(syn_test_dl),' Test data: ', len(syn_test_dl))
model_checkpoint_dir = log_path + 'checkpoints/'
out_images_dir = log_path + 'out_images/'
out_syn_images_dir = out_images_dir
os.system('mkdir -p {}'.format(model_checkpoint_dir))
os.system('mkdir -p {}'.format(out_syn_images_dir + 'train/'))
os.system('mkdir -p {}'.format(out_syn_images_dir + 'test/'))
# Collect model parameters
model_parameters = sfs_net_model.parameters()
optimizer = torch.optim.Adam(model_parameters, lr=lr, weight_decay=wt_decay)
if (optim_state is not None ):
optimizer.load_state_dict(optim_state)
#optimizer.param_groups[0]['lr']=0.1
#scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=4, gamma=0.1)
#scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=2, verbose=True, threshold=0.1,threshold_mode='abs', min_lr= 0.0001)
normal_loss = nn.L1Loss()
albedo_loss = nn.L1Loss()
sh_loss = nn.MSELoss()
recon_loss = nn.L1Loss()
shading_loss = nn.L1Loss()
if use_cuda:
normal_loss = normal_loss.cuda()
albedo_loss = albedo_loss.cuda()
sh_loss = sh_loss.cuda()
recon_loss = recon_loss.cuda()
shading_loss = shading_loss.cuda()
lamda_recon = 0.5
lamda_albedo = 0.5
lamda_normal = 0.5
lamda_sh = 0.1
lamda_shading = 0.5
syn_train_len = len(syn_test_dl)
start_epoch = 1
if last_epoch is not None :
start_epoch = last_epoch+1
#with mlflow.start_run(experiment_id=0):
for epoch in range(start_epoch, num_epochs+1):
#sfs_net_model.train()
tloss = 0 # Total loss
nloss = 0 # Normal loss
aloss = 0 # Albedo loss
shloss = 0 # SH loss
rloss = 0 # Reconstruction loss
for bix, data in enumerate(syn_test_dl):
albedo, normal, sh, face, faceHR = data
if use_cuda:
albedo = albedo.cuda()
normal = normal.cuda()
sh = sh.cuda()
face = face.cuda()
faceHR = faceHR.cuda()
shading = get_shading(normal, sh).cuda()
else :
shading = get_shading(normal, sh)
# Apply Mask on input image
# a=face[0].view(3,128,128).permute(1,2,0)
# a=a.detach().cpu().numpy()
# #b=nn.functional.interpolate(faceHR, size=128)
# b=normal[0].view(3,128,128).permute(1,2,0)
# b=b.detach().cpu().numpy()
# fig=plt.figure(figsize=(3, 3))
# fig.add_subplot(131)
# plt.imshow(a)
# fig.add_subplot(132)
# plt.imshow(b)
# plt.show()
#Train Discriminator
predicted_normal, predicted_albedo, predicted_sh, out_shading, out_recon = sfs_net_model(faceHR)
# Normal loss
current_normal_loss = normal_loss(predicted_normal, normal)
# Albedo loss
current_albedo_loss = albedo_loss(predicted_albedo, albedo)
# SH loss
current_sh_loss = sh_loss(predicted_sh, sh)
# Reconstruction loss
# Edge case: Shading generation requires denormalized normal and sh
# Hence, denormalizing face here
# Base
current_recon_loss = recon_loss(out_recon, face)
total_loss = lamda_normal * current_normal_loss \
+ lamda_albedo * current_albedo_loss + lamda_sh * current_sh_loss + lamda_recon * current_recon_loss
#base
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
# Logging for display and debugging purposes
tloss += total_loss.item()
nloss += current_normal_loss.item()
aloss += current_albedo_loss.item()
shloss += current_sh_loss.item()
rloss += current_recon_loss.item()
train_alert ='Epoch: {} - Learning Rate: {}. Total Loss: {}, Normal Loss: {}, Albedo Loss: {}, SH Loss: {}, Recon Loss: {}'.format(\
epoch, optimizer.param_groups[0]['lr'] , tloss / syn_train_len, nloss / syn_train_len, aloss / syn_train_len, shloss / syn_train_len , rloss / syn_train_len)
print(train_alert)
with open(log_path+'train_details.txt', 'a') as f:
f.write('\n'+str(epoch)+','+str(optimizer.param_groups[0]['lr'])+','+str(round(aloss / syn_train_len,4))+','+str(round(nloss / syn_train_len,4))+\
','+str(round(shloss / syn_train_len,4))+','+str(round(rloss / syn_train_len,4))+','+str(round(tloss / syn_train_len,4)))
print("sampai test")
with torch.no_grad():
log_prefix = 'Syn Data'
if celeba_data is not None:
log_prefix = 'Mix Data '
#Model saving
# torch.save({'epoch': epoch,
# 'model_state_dict': sfs_net_model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict()
# }, model_checkpoint_dir + 'sfs_net_model_'+str(epoch)+'.pkl')
if epoch % 5 == 0:
t_total, t_normal, t_albedo, t_sh, t_recon = predict_sfsnet(sfs_net_model, syn_test_dl, train_epoch_num=epoch, use_cuda=use_cuda,
out_folder=out_syn_images_dir + '/test/', wandb=wandb, suffix='Test')
test_alert = 'Test-set results: Total Loss: {}, Normal Loss: {}, Albedo Loss: {}, SH Loss: {}, Recon Loss: {}\n'.format(t_total,
t_normal, t_albedo, t_sh, t_recon)
print(test_alert)
with open(log_path+'test_details.txt', 'a') as f:
f.write('\n'+str(epoch)+','+str(optimizer.param_groups[0]['lr'])+','+str(round(t_albedo,4))+','+str(round(t_normal,4))+','+str(round(t_sh,4))+','+str(round(t_recon,4))+','+str(round(t_total,4)))
print("selesai test")
def train_withPretrain(sfs_net_model, syn_data, celeba_data=None, rgb_syn_data=None, rgb_real_data=None, read_first=None,
batch_size = 10, num_epochs = 10, log_path = './results/metadata/', use_cuda=False, wandb=None,
lr = 0.01, wt_decay=0.005, training_syn=False, optim_state=None, last_epoch = None, perceptual_loss =None):
# data processing
syn_train_csv = syn_data + '/train.csv'
syn_test_csv = syn_data + '/test.csv'
celeba_train_csv = celeba_data + '/train.csv'
celeba_test_csv = celeba_data + '/test.csv'
rgb_synt_train_csv = rgb_syn_data + '/rgb_train.csv'
rgb_synt_test_csv = rgb_syn_data + '/rgb_test.csv'
rgb_real_train_csv = rgb_real_data + '/rgb_real_train.csv'
rgb_real_test_csv = rgb_real_data + '/rgb_real_test.csv'
# Load Synthetic dataset
train_dataset,_ = get_light_dataset(syn_dir=syn_data+'train/', read_from_csv=syn_train_csv, read_celeba_csv=celeba_train_csv,read_rgb_synt_csv=rgb_synt_train_csv, \
read_rgb_real_csv=rgb_real_train_csv,read_first=read_first, validation_split=0, training_syn = training_syn)
test_dataset, _ = get_light_dataset(syn_dir=syn_data+'test/', read_from_csv=syn_test_csv, read_celeba_csv=celeba_test_csv,read_rgb_synt_csv=rgb_synt_test_csv,\
read_rgb_real_csv=rgb_real_test_csv,read_first=read_first, validation_split=0, training_syn = training_syn)
syn_train_dl = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
syn_test_dl = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
print('Synthetic dataset: Train data: ', len(syn_train_dl),' Test data: ', len(syn_test_dl))
model_checkpoint_dir = log_path + 'checkpoints/'
out_images_dir = log_path + 'out_images/'
out_syn_images_dir = out_images_dir
os.system('mkdir -p {}'.format(model_checkpoint_dir))
os.system('mkdir -p {}'.format(out_syn_images_dir + 'train/'))
os.system('mkdir -p {}'.format(out_syn_images_dir + 'test/'))
# Collect model parameters
model_parameters = sfs_net_model.parameters()
optimizer = torch.optim.Adam(model_parameters, lr=lr, weight_decay=wt_decay)
if (optim_state is not None ):
optimizer.load_state_dict(optim_state)
#optimizer.param_groups[0]['lr']=0.1
#scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=4, gamma=0.1)
#scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=2, verbose=True, threshold=0.1,threshold_mode='abs', min_lr= 0.0001)
albedo_corrector_loss = nn.L1Loss()
shading_corrector_loss = nn.MSELoss()
recon_corrector_loss = nn.L1Loss()
if use_cuda:
shading_corrector_loss = shading_corrector_loss.cuda()
albedo_corrector_loss = albedo_corrector_loss.cuda()
recon_corrector_loss = recon_corrector_loss.cuda()
# lamda_recon = 0.5
# lamda_albedo = 0.5
# lamda_normal = 0.5
# lamda_sh = 0.1
# lamda_shading = 0.5
syn_train_len = len(syn_test_dl)
start_epoch = 1
if last_epoch is not None :
start_epoch = last_epoch+1
#with mlflow.start_run(experiment_id=0):
for epoch in range(start_epoch, num_epochs+1):
#sfs_net_model.train()
alcorloss = 0
shcorloss = 0
recorloss = 0
tloss = 0
for bix, data in enumerate(syn_test_dl):
_, face, target = data
if use_cuda:
face = face.cuda()
target = target.cuda()
# Apply Mask on input image
_, target_albedo, _, target_shading, target_recon, _, _,_ = sfs_net_model(target)
_, _, _, _, _, albedo_correct, shading_correct,recon_correct = sfs_net_model(face)
# a=target_shading[0].view(3,256,256).permute(1,2,0)
# a=a.detach().cpu().numpy()
# b=target[0].view(3,256,256).permute(1,2,0)
# b=b.detach().cpu().numpy()
# fig=plt.figure(figsize=(3, 3))
# fig.add_subplot(131)
# plt.imshow(a)
# fig.add_subplot(132)
# plt.imshow(b)
# plt.show()
#print(pytorch_ssim.ssim(shading_correct, target_shading))
current_shading_cor_loss = shading_corrector_loss(shading_correct,target_shading) + 0.2* perceptual_loss(shading_correct,target_shading) #pytorch_ssim.ssim(shading_correct, target_shading)
current_albedo_cor_loss = albedo_corrector_loss(albedo_correct,target_albedo) + 0.8* perceptual_loss(albedo_correct,target_albedo)
current_recon_cor_loss = recon_corrector_loss(recon_correct,target_recon) + perceptual_loss(recon_correct,target_recon)
total_loss = current_shading_cor_loss + current_albedo_cor_loss + current_recon_cor_loss
#base
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
# if bix==0:
# print(sfs_net_model.module.albedo_corrector_model.conv1[0].weight.grad[0])
# Logging for display and debugging purposes
tloss += total_loss.item()
shcorloss += current_shading_cor_loss.item()
alcorloss += current_albedo_cor_loss.item()
recorloss +=current_recon_cor_loss.item()
train_alert ='Epoch: {} - Learning Rate: {}. Total Loss: {}, Shading Corrector Loss: {}, Albedo Corrector Loss: {}, Recon Corrector Loss: {}'.format(\
epoch, optimizer.param_groups[0]['lr'] , tloss / syn_train_len, shcorloss / syn_train_len, alcorloss / syn_train_len, recorloss/syn_train_len)
print(train_alert)
# with open(log_path+'train_details.txt', 'a') as f:
# f.write('\n'+str(epoch)+','+str(optimizer.param_groups[0]['lr'])+','+str(round(aloss / syn_train_len,4))+','+str(round(nloss / syn_train_len,4))+\
# ','+str(round(shloss / syn_train_len,4))+','+str(round(rloss / syn_train_len,4))+','+str(round(tloss / syn_train_len,4)))
with torch.no_grad():
log_prefix = 'Syn Data'
if celeba_data is not None:
log_prefix = 'Mix Data '
#Model saving
# torch.save({'epoch': epoch,
# 'model_state_dict': sfs_net_model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict()
# }, model_checkpoint_dir + 'sfs_net_model_'+str(epoch)+'.pkl')
if epoch % 5 == 0:
t_albedo, t_shading,t_recon = predict_corrector(sfs_net_model,syn_test_dl, perceptual_loss, train_epoch_num=epoch, use_cuda=use_cuda,
out_folder=out_syn_images_dir + '/test/', wandb=wandb, suffix='Test')
test_alert = 'Test-set results: Albedo Loss: {}, shading Loss: {} , Recon Loss: {}\n'.format(t_albedo, t_shading,t_recon)
print(test_alert)
# with open(log_path+'test_details.txt', 'a') as f:
# f.write('\n'+str(epoch)+','+str(optimizer.param_groups[0]['lr'])+','+str(round(t_albedo,4))+','+str(round(t_normal,4))+','+str(round(t_sh,4))+','+str(round(t_recon,4))+','+str(round(t_total,4)))
# print("selesai test")
#Train for Original Skin Recovery Network
def train_new_style(generator,discriminator,rgb_syn_data=None, rgb_real_data=None, read_first=None,
batch_size = 10, num_epochs = 10, log_path = './results/metadata/', use_cuda=False, wandb=None,
lr = 0.01, wt_decay=0.005, training_syn=False, last_g_state=None, last_d_state=None, last_epoch = None, perceptual_loss=None):
#Prepare to read from CSV
rgb_synt_train_csv = rgb_syn_data + '/rgb_train.csv'
rgb_synt_test_csv = rgb_syn_data + '/rgb_test.csv'
rgb_real_train_csv = rgb_real_data + '/rgb_real_train.csv'
rgb_real_test_csv = rgb_real_data + '/rgb_real_test.csv'
val_celeba_dl = None
# Load dataset
train_dataset,_ = get_light_dataset(read_rgb_synt_csv=rgb_synt_train_csv,read_rgb_real_csv=rgb_real_train_csv,read_first=read_first, validation_split=0, training_syn = training_syn)
test_dataset, _ = get_light_dataset(read_rgb_synt_csv=rgb_synt_test_csv,read_rgb_real_csv=rgb_real_test_csv,read_first=10, validation_split=0, training_syn = training_syn)
# Create Dataloader
syn_train_dl = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
syn_test_dl = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
print('Synthetic dataset: Train data: ', len(syn_train_dl), ' Test data: ', len(syn_test_dl))
#Create Checkpoints & sample training result folder
model_checkpoint_dir = log_path + 'checkpoints/'
out_images_dir = log_path + 'out_images/'
out_syn_images_dir = out_images_dir
os.system('mkdir -p {}'.format(model_checkpoint_dir))
os.system('mkdir -p {}'.format(out_syn_images_dir + 'train/'))
os.system('mkdir -p {}'.format(out_syn_images_dir + 'test/'))
if val_celeba_dl is not None:
os.system('mkdir -p {}'.format(out_syn_images_dir + 'celeba_val/'))
# Collect model parameters
generator_parameters = generator.parameters()
discriminator_parameters = discriminator.parameters()
G_optimizer = torch.optim.Adam(generator_parameters, lr=lr, betas=(0.5, 0.999))
D_optimizer = torch.optim.Adam(discriminator_parameters,lr=lr, betas=(0.5, 0.999))
# Load last checkpoint if want to continue
if (last_g_state is not None ):
G_optimizer.load_state_dict(last_g_state)
D_optimizer.load_state_dict(last_d_state)
#Create Loss
perceptual_loss =perceptual_loss
image_loss = nn.L1Loss()
#assign Loss to CUDA
if use_cuda:
image_loss = image_loss.cuda()
#Hyperparameter tuning
lambda_perceptual = 1.0
lambda_gp = 10.0
lambda_recon = 10.0
lambda_adv = 0.1
syn_train_len = len(syn_train_dl)
start_epoch = 1
if last_epoch is not None :
start_epoch = last_epoch+1
#i = Iteration
i = 0
best_record = 0
for epoch in range(start_epoch, num_epochs+1):
t_d_loss_fake = 0
t_d_loss_real = 0
t_d_loss_gp = 0
t_dloss = 0
t_g_loss_fake = 0
t_g_loss_real = 0
t_g_loss_recon = 0
t_gloss = 0
t_g_loss_perceptual=0
for bix, data in enumerate(syn_train_dl):
face, target = data