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utility.py
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utility.py
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import os
import math
import time
import datetime
from functools import reduce
import imageio
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import scipy.misc as misc
import pickle
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lrs
from skimage.measure import compare_psnr
from scipy.ndimage import zoom
class timer():
def __init__(self):
self.acc = 0
self.tic()
def tic(self):
self.t0 = time.time()
def toc(self):
return time.time() - self.t0
def hold(self):
self.acc += self.toc()
def release(self):
ret = self.acc
self.acc = 0
return ret
def reset(self):
self.acc = 0
class checkpoint():
def __init__(self, args):
self.args = args
self.ok = True
self.log = torch.Tensor()
now = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
if args.load == '.':
if args.save == '.': args.save = now
self.dir = 'experiment/' + args.save
# self.dir = args.save
else:
self.dir = 'experiment/' + args.load
if not os.path.exists(self.dir):
args.load = '.'
else:
self.log = torch.load(self.dir + '/psnr_log.pt')
print('Continue from epoch {}...'.format(len(self.log)))
if args.reset:
os.system('rm -rf ' + self.dir)
args.load = '.'
def _make_dir(path):
if not os.path.exists(path): os.makedirs(path)
_make_dir(self.dir)
_make_dir(self.dir + '/model')
_make_dir(self.dir + '/results')
open_type = 'a' if os.path.exists(self.dir + '/log.txt') else 'w'
self.log_file = open(self.dir + '/log.txt', open_type)
with open(self.dir + '/config.txt', open_type) as f:
f.write(now + '\n\n')
for arg in vars(args):
f.write('{}: {}\n'.format(arg, getattr(args, arg)))
f.write('\n')
def save(self, trainer, epoch, is_best=False):
trainer.model.save(self.dir, epoch, is_best=is_best)
trainer.loss.save(self.dir)
trainer.loss.plot_loss(self.dir, epoch)
self.plot_psnr(epoch)
torch.save(self.log, os.path.join(self.dir, 'psnr_log.pt'))
torch.save(
trainer.optimizer.state_dict(),
os.path.join(self.dir, 'optimizer.pt')
)
def save_train(self, trainer, epoch):
trainer.model.save(self.dir, epoch)
torch.save(self.log, os.path.join(self.dir, 'psnr_log.pt'))
torch.save(
trainer.optimizer.state_dict(),
os.path.join(self.dir, 'optimizer.pt')
)
def add_log(self, log):
self.log = torch.cat([self.log, log])
def write_log(self, log, refresh=False):
print(log)
self.log_file.write(log + '\n')
if refresh:
self.log_file.close()
self.log_file = open(self.dir + '/log.txt', 'a')
def done(self):
self.log_file.close()
def plot_psnr(self, epoch):
axis = np.linspace(1, epoch, epoch)
label = 'SR on {}'.format(self.args.data_test)
fig = plt.figure()
plt.title(label)
for idx_scale, scale in enumerate(self.args.scale):
plt.plot(
axis,
self.log[:, idx_scale].numpy(),
label='Scale {}'.format(scale)
)
plt.legend()
plt.xlabel('Epochs')
plt.ylabel('PSNR')
plt.grid(True)
plt.savefig('{}/test_{}.pdf'.format(self.dir, self.args.data_test))
plt.close(fig)
def norm(self, inp):
inp = inp.astype(float)
inp = inp - inp.min()
return inp/inp.max()
def save_results(self, filename, save_list, scale):
postfix = ('SAG','COR','REF')
if not os.path.isdir('{}/results/raw/'.format(self.dir)):
os.mkdir('{}/results/raw/'.format(self.dir))
for v, p in zip(save_list, postfix):
v = np.clip(v[0].cpu().data.numpy().round(),0,4000).astype('uint16')
filename1 = '{}/results/raw/{}_x{}_'.format(self.dir, filename, scale)
pickle.dump(v, open('{}{}.pt'.format(filename1, p), 'wb'))
def calc_psnr(sr, hr, scale):
# clipping values in range, normalize to 0-1
# print(sr_ref.shape,sr_sag.shape,sr_cor.shape, hr.shape,scale)
for i in range(len(sr)):
sr[i] = np.clip(sr[i][0].cpu().data.numpy(),0,4000)/4000
# remove the observed slides
sr[i] = np.delete(np.clip(sr[i], 0, 1), np.s_[::scale], axis=2)
# calculate the central region to avoid empty pixels
sr[i] = sr[i][128:384,128:384]
hr = hr[0].cpu().data.numpy()/4000
hr = np.delete(np.clip(hr, 0, 1), np.s_[::scale], axis=2)
hr = hr[128:384,128:384]
output = []
for i in range(len(sr)):
output.append(np.around(compare_psnr(sr[i], hr),2))
return output
def make_optimizer(args, my_model):
trainable = filter(lambda x: x.requires_grad, my_model.parameters())
if args.optimizer == 'SGD':
optimizer_function = optim.SGD
kwargs = {'momentum': args.momentum}
elif args.optimizer == 'ADAM':
optimizer_function = optim.Adam
kwargs = {
'betas': (args.beta1, args.beta2),
'eps': args.epsilon
}
elif args.optimizer == 'RMSprop':
optimizer_function = optim.RMSprop
kwargs = {'eps': args.epsilon}
kwargs['lr'] = args.lr
kwargs['weight_decay'] = args.weight_decay
return optimizer_function(trainable, **kwargs)
def make_scheduler(args, my_optimizer):
if args.decay_type == 'step':
scheduler = lrs.StepLR(
my_optimizer,
step_size=args.lr_decay,
gamma=args.gamma
)
elif args.decay_type.find('step') >= 0:
milestones = args.decay_type.split('_')
milestones.pop(0)
milestones = list(map(lambda x: int(x), milestones))
scheduler = lrs.MultiStepLR(
my_optimizer,
milestones=milestones,
gamma=args.gamma
)
return scheduler