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solver.py
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solver.py
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import os
import time
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.optim as optim
from prep import printProgressBar
from networks import RED_CNN
from measure import compute_measure
class Solver(object):
def __init__(self, args, data_loader):
self.mode = args.mode
self.load_mode = args.load_mode
self.data_loader = data_loader
if args.device:
self.device = torch.device(args.device)
else:
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.norm_range_min = args.norm_range_min
self.norm_range_max = args.norm_range_max
self.trunc_min = args.trunc_min
self.trunc_max = args.trunc_max
self.save_path = args.save_path
self.multi_gpu = args.multi_gpu
self.num_epochs = args.num_epochs
self.print_iters = args.print_iters
self.decay_iters = args.decay_iters
self.save_iters = args.save_iters
self.test_iters = args.test_iters
self.result_fig = args.result_fig
self.patch_size = args.patch_size
self.REDCNN = RED_CNN()
if (self.multi_gpu) and (torch.cuda.device_count() > 1):
print('Use {} GPUs'.format(torch.cuda.device_count()))
self.REDCNN = nn.DataParallel(self.REDCNN)
self.REDCNN.to(self.device)
self.lr = args.lr
self.criterion = nn.MSELoss()
self.optimizer = optim.Adam(self.REDCNN.parameters(), self.lr)
def save_model(self, iter_):
f = os.path.join(self.save_path, 'REDCNN_{}iter.ckpt'.format(iter_))
torch.save(self.REDCNN.state_dict(), f)
def load_model(self, iter_):
f = os.path.join(self.save_path, 'REDCNN_{}iter.ckpt'.format(iter_))
if self.multi_gpu:
state_d = OrderedDict()
for k, v in torch.load(f):
n = k[7:]
state_d[n] = v
self.REDCNN.load_state_dict(state_d)
else:
self.REDCNN.load_state_dict(torch.load(f))
def lr_decay(self):
lr = self.lr * 0.5
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr
def denormalize_(self, image):
image = image * (self.norm_range_max - self.norm_range_min) + self.norm_range_min
return image
def trunc(self, mat):
mat[mat <= self.trunc_min] = self.trunc_min
mat[mat >= self.trunc_max] = self.trunc_max
return mat
def save_fig(self, x, y, pred, fig_name, original_result, pred_result):
x, y, pred = x.numpy(), y.numpy(), pred.numpy()
f, ax = plt.subplots(1, 3, figsize=(30, 10))
ax[0].imshow(x, cmap=plt.cm.gray, vmin=self.trunc_min, vmax=self.trunc_max)
ax[0].set_title('Quarter-dose', fontsize=30)
ax[0].set_xlabel("PSNR: {:.4f}\nSSIM: {:.4f}\nRMSE: {:.4f}".format(original_result[0],
original_result[1],
original_result[2]), fontsize=20)
ax[1].imshow(pred, cmap=plt.cm.gray, vmin=self.trunc_min, vmax=self.trunc_max)
ax[1].set_title('Result', fontsize=30)
ax[1].set_xlabel("PSNR: {:.4f}\nSSIM: {:.4f}\nRMSE: {:.4f}".format(pred_result[0],
pred_result[1],
pred_result[2]), fontsize=20)
ax[2].imshow(y, cmap=plt.cm.gray, vmin=self.trunc_min, vmax=self.trunc_max)
ax[2].set_title('Full-dose', fontsize=30)
f.savefig(os.path.join(self.save_path, 'fig', 'result_{}.png'.format(fig_name)))
plt.close()
def train(self):
train_losses = []
total_iters = 0
start_time = time.time()
for epoch in range(1, self.num_epochs):
self.REDCNN.train(True)
for iter_, (x, y) in enumerate(self.data_loader):
total_iters += 1
# add 1 channel
x = x.unsqueeze(0).float().to(self.device)
y = y.unsqueeze(0).float().to(self.device)
if self.patch_size: # patch training
x = x.view(-1, 1, self.patch_size, self.patch_size)
y = y.view(-1, 1, self.patch_size, self.patch_size)
pred = self.REDCNN(x)
loss = self.criterion(pred, y)
self.REDCNN.zero_grad()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
train_losses.append(loss.item())
# print
if total_iters % self.print_iters == 0:
print("STEP [{}], EPOCH [{}/{}], ITER [{}/{}] \nLOSS: {:.8f}, TIME: {:.1f}s".format(total_iters, epoch,
self.num_epochs, iter_+1,
len(self.data_loader), loss.item(),
time.time() - start_time))
# learning rate decay
if total_iters % self.decay_iters == 0:
self.lr_decay()
# save model
if total_iters % self.save_iters == 0:
self.save_model(total_iters)
np.save(os.path.join(self.save_path, 'loss_{}_iter.npy'.format(total_iters)), np.array(train_losses))
def test(self):
del self.REDCNN
# load
self.REDCNN = RED_CNN().to(self.device)
self.load_model(self.test_iters)
# compute PSNR, SSIM, RMSE
ori_psnr_avg, ori_ssim_avg, ori_rmse_avg = 0, 0, 0
pred_psnr_avg, pred_ssim_avg, pred_rmse_avg = 0, 0, 0
with torch.no_grad():
for i, (x, y) in enumerate(self.data_loader):
shape_ = x.shape[-1]
x = x.unsqueeze(0).float().to(self.device)
y = y.unsqueeze(0).float().to(self.device)
pred = self.REDCNN(x)
# denormalize, truncate
x = self.trunc(self.denormalize_(x.view(shape_, shape_).cpu().detach()))
y = self.trunc(self.denormalize_(y.view(shape_, shape_).cpu().detach()))
pred = self.trunc(self.denormalize_(pred.view(shape_, shape_).cpu().detach()))
data_range = self.trunc_max - self.trunc_min
original_result, pred_result = compute_measure(x, y, pred, data_range)
ori_psnr_avg += original_result[0]
ori_ssim_avg += original_result[1]
ori_rmse_avg += original_result[2]
pred_psnr_avg += pred_result[0]
pred_ssim_avg += pred_result[1]
pred_rmse_avg += pred_result[2]
# save result figure
if self.result_fig:
self.save_fig(x, y, pred, i, original_result, pred_result)
printProgressBar(i, len(self.data_loader),
prefix="Compute measurements ..",
suffix='Complete', length=25)
print('\n')
print('Original === \nPSNR avg: {:.4f} \nSSIM avg: {:.4f} \nRMSE avg: {:.4f}'.format(ori_psnr_avg/len(self.data_loader),
ori_ssim_avg/len(self.data_loader),
ori_rmse_avg/len(self.data_loader)))
print('\n')
print('Predictions === \nPSNR avg: {:.4f} \nSSIM avg: {:.4f} \nRMSE avg: {:.4f}'.format(pred_psnr_avg/len(self.data_loader),
pred_ssim_avg/len(self.data_loader),
pred_rmse_avg/len(self.data_loader)))