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train_personalized_batch_norm.py
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train_personalized_batch_norm.py
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import time
from options.train_options import TrainOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import Visualizer
import numpy as np, h5py
from skimage.metrics import peak_signal_noise_ratio as psnr
from skimage.metrics import structural_similarity as ssim
import random
import torch
print(torch.__version__)
import sys
import os
os.environ['QT_QPA_PLATFORM']='offscreen'
import matplotlib.pyplot as plt
def print_log(logger,message):
print(message, flush=True)
if logger:
logger.write(str(message) + '\n')
def plot(L1_avg, psnr_avg, ssim_avg, epoch, save_dir, name):
plt.plot(np.mean(L1_avg, axis=1)[:epoch])
plt.savefig(os.path.join(save_dir, name + '_l1_avg_loss.png'))
plt.close()
plt.plot(np.mean(psnr_avg, axis=1)[:epoch])
plt.savefig(os.path.join(save_dir, name + '_mean_psnr.png'))
plt.close()
plt.plot(np.std(psnr_avg, axis=1)[:epoch])
plt.savefig(os.path.join(save_dir, name + '_std_psnr.png'))
plt.close()
plt.plot(np.mean(ssim_avg, axis=1)[:epoch])
plt.savefig(os.path.join(save_dir, name + '_mean_ssim.png'))
plt.close()
def one_epoch(opt, dataset, model,one_hot, visualizer, total_steps, dataset_val, L1_avg, psnr_avg, ssim_avg, dset):
opt.phase='train'
epoch_iter = 0
iter_data_time = time.time()
epoch_start_time = time.time()
for i, data in enumerate(dataset):
iter_start_time = time.time()
if total_steps % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
visualizer.reset()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data, one_hot,dset)
model.optimize_parameters()
if total_steps % opt.display_freq == 0:
save_result = total_steps % opt.update_html_freq == 0
temp_visuals=model.get_current_visuals()
if temp_visuals['real_A'].shape[2]==1:
temp_visuals['real_A']=np.concatenate((temp_visuals['real_A'],np.zeros((temp_visuals['real_A'].shape[0],temp_visuals['real_A'].shape[1],2),dtype=np.uint8)),axis=2)
elif temp_visuals['real_A'].shape[2]==2:
temp_visuals['real_A']=np.concatenate((temp_visuals['real_A'],np.zeros((temp_visuals['real_A'].shape[0],temp_visuals['real_A'].shape[1],1),dtype=np.uint8)),axis=2)
else:
temp_visuals['real_A']=temp_visuals['real_A'][:,:,0:3]
if temp_visuals['fake_B'].shape[2]==2:
temp_visuals['fake_B']=np.concatenate((temp_visuals['fake_B'],np.zeros((temp_visuals['fake_B'].shape[0],temp_visuals['fake_B'].shape[1],1),dtype=np.uint8)),axis=2)
temp_visuals['real_B']=np.concatenate((temp_visuals['real_B'],np.zeros((temp_visuals['real_B'].shape[0],temp_visuals['real_B'].shape[1],1),dtype=np.uint8)),axis=2)
temp_visuals['real_B']=temp_visuals['real_B'][:,:,0:3]
temp_visuals['fake_B']=temp_visuals['fake_B'][:,:,0:3]
visualizer.display_current_results(temp_visuals, epoch, save_result)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t, t_data)
# if opt.display_id > 0:
# visualizer.plot_current_errors(epoch, float(epoch_iter) / dataset_size, opt, errors)
if total_steps % opt.save_latest_freq == 0:
print(dset + ': saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save(dset + '_latest')
iter_data_time = time.time()
#Validation step
logger = open(os.path.join(save_dir, 'log.txt'), 'a')
print(opt.dataset_mode)
opt.phase='val'
for i, data_val in enumerate(dataset_val):
model.set_input(data_val, one_hot,dset)
model.test()
fake_im=model.fake_B.cpu().data.numpy()
real_im=model.real_B.cpu().data.numpy()
real_im=real_im*0.5+0.5
fake_im=fake_im*0.5+0.5
fake_im[fake_im<0]=0
L1_avg[epoch-1,i]=abs(fake_im-real_im).mean()
psnr_avg[epoch-1,i]=psnr(real_im/real_im.max(), fake_im/fake_im.max(),data_range =1)
ssim_avg[epoch-1,i]=ssim(real_im[0, 0]/real_im.max(), fake_im[0, 0]/fake_im.max(),data_range =1)
if epoch % opt.save_epoch_freq == 0:
print(dset + ': saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save(dset + '_latest')
#model.save(epoch)
print_log(logger,'Epoch %3d l1_avg_loss: %.5f mean_psnr: %.3f std_psnr:%.3f mean_ssim: %.3f' % \
(epoch, np.mean(L1_avg[epoch-1]), np.mean(psnr_avg[epoch-1]), np.std(psnr_avg[epoch-1]), 100 * np.mean(ssim_avg[epoch-1])))
print_log(logger,'')
logger.close()
f = h5py.File(opt.checkpoints_dir+opt.name+'.mat', "w")
f.create_dataset(dset + '_L1_avg', data=L1_avg)
f.create_dataset(dset + '_psnr_avg', data=psnr_avg)
f.close()
print(dset + ': End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
model.update_learning_rate()
return opt, model, total_steps, L1_avg, psnr_avg, ssim_avg
if __name__ == '__main__':
opt = TrainOptions().parse()
##logger ##
save_dir = os.path.join(opt.checkpoints_dir, opt.name)
logger = open(os.path.join(save_dir, 'log.txt'), 'w+')
print_log(logger,opt.name)
logger.close()
#IXI
#Training data
opt.dataset_name = "IXI"
opt.phase='train'
data_loader_IXI = CreateDataLoader(opt)
dataset_IXI = data_loader_IXI.load_data()
dataset_IXI_size = len(data_loader_IXI)
print('#'+opt.dataset_name+' training images = %d' % dataset_IXI_size)
#validation data
opt.phase='val'
data_loader_val_IXI = CreateDataLoader(opt)
dataset_val_IXI = data_loader_val_IXI.load_data()
dataset_size_val = len(data_loader_val_IXI)
print('#'+opt.dataset_name+' validation images = %d' % dataset_size_val)
#BRATS
#Training data
opt.dataroot = opt.dataroot2
opt.dataset_name = "BRATS"
opt.phase='train'
data_loader_BRATS = CreateDataLoader(opt)
dataset_BRATS = data_loader_BRATS.load_data()
dataset_BRATS_size = len(data_loader_BRATS)
print('#'+opt.dataset_name+' training images = %d' % dataset_BRATS_size)
#validation data
opt.phase='val'
data_loader_val_BRATS = CreateDataLoader(opt)
dataset_val_BRATS = data_loader_val_BRATS.load_data()
dataset_size_val = len(data_loader_val_BRATS)
print('#'+opt.dataset_name+' validation images = %d' % dataset_size_val)
#MIDAS
#Training data
opt.dataroot = opt.dataroot3
opt.dataset_name = "MIDAS"
opt.phase='train'
data_loader_MIDAS = CreateDataLoader(opt)
dataset_MIDAS = data_loader_MIDAS.load_data()
dataset_MIDAS_size = len(data_loader_MIDAS)
print('#'+opt.dataset_name+' training images = %d' % dataset_MIDAS_size)
#validation data
opt.phase='val'
data_loader_val_MIDAS = CreateDataLoader(opt)
dataset_val_MIDAS = data_loader_val_MIDAS.load_data()
dataset_size_val = len(data_loader_val_MIDAS)
print('#'+opt.dataset_name+' validation images = %d' % dataset_size_val)
#fastMRI
#Training data
opt.dataroot = opt.dataroot4
opt.dataset_name = "fastMRI"
opt.phase='train'
data_loader_fastMRI = CreateDataLoader(opt)
dataset_fastMRI = data_loader_fastMRI.load_data()
dataset_fastMRI_size = len(data_loader_fastMRI)
print('#'+opt.dataset_name+' training images = %d' % dataset_fastMRI_size)
#validation data
opt.phase='val'
data_loader_val_fastMRI = CreateDataLoader(opt)
dataset_val_fastMRI = data_loader_val_fastMRI.load_data()
dataset_size_val = len(data_loader_val_fastMRI)
print('#'+opt.dataset_name+' validation images = %d' % dataset_size_val)
total_size = dataset_IXI_size+dataset_BRATS_size+dataset_MIDAS_size+dataset_fastMRI_size
ixi_coef = dataset_IXI_size/total_size
brats_coef = dataset_BRATS_size/total_size
midas_coef = dataset_MIDAS_size/total_size
fastmri_coef = dataset_fastMRI_size/total_size
print(ixi_coef)
print(brats_coef)
print(midas_coef)
print(fastmri_coef)
L1_avg_IXI = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_IXI)])
psnr_avg_IXI = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_IXI)])
ssim_avg_IXI = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_IXI)])
L1_avg_BRATS = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_BRATS)])
psnr_avg_BRATS = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_BRATS)])
ssim_avg_BRATS = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_BRATS)])
L1_avg_MIDAS = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_MIDAS)])
psnr_avg_MIDAS = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_MIDAS)])
ssim_avg_MIDAS = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_MIDAS)])
L1_avg_fastMRI = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_fastMRI)])
psnr_avg_fastMRI = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_fastMRI)])
ssim_avg_fastMRI = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_fastMRI)])
model_IXI = create_model(opt)
visualizer_IXI = Visualizer(opt)
IXI_netG_SD = model_IXI.netG.state_dict()
model_BRATS = create_model(opt)
visualizer_BRATS = Visualizer(opt)
BRATS_netG_SD = model_BRATS.netG.state_dict()
model_MIDAS = create_model(opt)
visualizer_MIDAS = Visualizer(opt)
MIDAS_netG_SD = model_MIDAS.netG.state_dict()
model_fastMRI = create_model(opt)
visualizer_fastMRI = Visualizer(opt)
fastMRI_netG_SD = model_fastMRI.netG.state_dict()
total_steps = 0
# site information
one_hot_IXI = torch.tensor([[1.0, 0.0,0.0,0.0]], requires_grad=False ).cuda(opt.gpu_ids[0])
one_hot_BRATS = torch.tensor([[0.0, 1.0,0.0,0.0]], requires_grad=False).cuda(opt.gpu_ids[0])
one_hot_MIDAS = torch.tensor([[0.0, 0.0,1.0,0.0]], requires_grad=False ).cuda(opt.gpu_ids[0])
one_hot_fastMRI = torch.tensor([[0.0, 0.0,0.0,1.0]], requires_grad=False ).cuda(opt.gpu_ids[0])
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
#Epoch number
opt.epoch_number=epoch+1
#Training step
opt.dataset_name = 'ixi'
_, model_IXI, _, L1_avg_IXI, psnr_avg_IXI, ssim_avg_IXI = one_epoch(opt, dataset_IXI, model_IXI, one_hot_IXI,visualizer_IXI, total_steps,
dataset_val_IXI, L1_avg_IXI, psnr_avg_IXI, ssim_avg_IXI, opt.dataset_name)
plot(L1_avg_IXI, psnr_avg_IXI, ssim_avg_IXI, epoch, save_dir, opt.dataset_name)
opt.dataset_name = 'brats'
_, model_BRATS, _, L1_avg_BRATS, psnr_avg_BRATS, ssim_avg_BRATS = one_epoch(opt, dataset_BRATS, model_BRATS,one_hot_BRATS, visualizer_BRATS, total_steps,
dataset_val_BRATS, L1_avg_BRATS, psnr_avg_BRATS, ssim_avg_BRATS, opt.dataset_name)
plot(L1_avg_BRATS, psnr_avg_BRATS, ssim_avg_BRATS, epoch, save_dir, opt.dataset_name)
opt.dataset_name = 'midas'
_, model_MIDAS, _, L1_avg_MIDAS, psnr_avg_MIDAS, ssim_avg_MIDAS = one_epoch(opt, dataset_MIDAS, model_MIDAS,one_hot_MIDAS, visualizer_MIDAS, total_steps,
dataset_val_MIDAS, L1_avg_MIDAS, psnr_avg_MIDAS, ssim_avg_MIDAS, opt.dataset_name)
plot(L1_avg_MIDAS, psnr_avg_MIDAS, ssim_avg_MIDAS, epoch, save_dir, opt.dataset_name)
opt.dataset_name = 'fastmri'
_, model_fastMRI, _, L1_avg_fastMRI, psnr_avg_fastMRI, ssim_avg_fastMRI = one_epoch(opt, dataset_fastMRI, model_fastMRI,one_hot_fastMRI, visualizer_fastMRI, total_steps,
dataset_val_fastMRI, L1_avg_fastMRI, psnr_avg_fastMRI, ssim_avg_fastMRI, opt.dataset_name)
plot(L1_avg_fastMRI, psnr_avg_fastMRI, ssim_avg_fastMRI, epoch, save_dir, opt.dataset_name)
IXI_netG_SD = model_IXI.netG.state_dict()
BRATS_netG_SD = model_BRATS.netG.state_dict()
MIDAS_netG_SD = model_MIDAS.netG.state_dict()
fastMRI_netG_SD = model_fastMRI.netG.state_dict()
for key in IXI_netG_SD:
# print(key)
if 'norm' not in key and 'bn' not in key:# and 'running_mean' not in key and 'running_var' not in key and 'num_batches_tracked' not in key:
# print(key)
temp = ixi_coef * IXI_netG_SD[key] + brats_coef * BRATS_netG_SD[key] + midas_coef * MIDAS_netG_SD[key] + fastmri_coef * fastMRI_netG_SD[key]
IXI_netG_SD[key] = temp
BRATS_netG_SD[key] = temp
MIDAS_netG_SD[key] = temp
fastMRI_netG_SD[key] = temp
model_IXI.netG.load_state_dict(IXI_netG_SD)
model_BRATS.netG.load_state_dict(IXI_netG_SD)
model_MIDAS.netG.load_state_dict(IXI_netG_SD)
model_fastMRI.netG.load_state_dict(IXI_netG_SD)
opt.dataset_name = 'ixi'
model_IXI.save(opt.dataset_name + '_latest')
opt.dataset_name = 'brats'
model_BRATS.save(opt.dataset_name + '_latest')
opt.dataset_name = 'midas'
model_MIDAS.save(opt.dataset_name + '_latest')
opt.dataset_name = 'fastmri'
model_fastMRI.save(opt.dataset_name + '_latest')