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train_centralized.py
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train_centralized.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 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()
opt.display_freq = 1
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)#,i)
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 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')
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()
#Training data
opt.phase='train'
opt.dataset_name = "combined"
data_loader_MRNet = CreateDataLoader(opt)
dataset_MRNet = data_loader_MRNet.load_data()
dataset_size = len(data_loader_MRNet)
print('#'+opt.dataset_name+' training images = %d' % dataset_size)
##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()
#validation data
opt.phase='val'
data_loader_val_MRNet = CreateDataLoader(opt)
dataset_val_MRNet = data_loader_val_MRNet.load_data()
dataset_size_val = len(data_loader_val_MRNet)
print('#'+opt.dataset_name+' validation images = %d' % dataset_size_val)
L1_avg_MRNet = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_MRNet)])
psnr_avg_MRNet = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_MRNet)])
ssim_avg_MRNet = np.zeros([opt.niter + opt.niter_decay,len(dataset_val_MRNet)])
model_MRNet = create_model(opt)
visualizer_MRNet = Visualizer(opt)
MRNet_netG_SD = model_MRNet.netG.state_dict()
total_steps = 0
one_hot_MRNet = torch.tensor([[1.0, 0.0,0.0,0.0]], 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
_, model_MRNet, _, L1_avg_MRNet, psnr_avg_MRNet, ssim_avg_MRNet = one_epoch(opt, dataset_MRNet, model_MRNet,one_hot_MRNet, visualizer_MRNet, total_steps,
dataset_val_MRNet, L1_avg_MRNet, psnr_avg_MRNet, ssim_avg_MRNet, opt.dataset_name)