/
solver.py
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/
solver.py
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import os
import numpy as np
import time
import datetime
import matplotlib.pyplot as plt
import torch
import torchvision
from torch import optim
from torch.autograd import Variable
import torch.nn.functional as F
from evaluation import *
from network import U_Net,R2U_Net,AttU_Net,R2AttU_Net
import csv
class Solver(object):
def __init__(self, config, train_loader, valid_loader, test_loader):
# Data loader
self.train_loader = train_loader
self.valid_loader = valid_loader
self.test_loader = test_loader
# Models
self.unet = None
self.optimizer = None
self.img_ch = config.img_ch
self.output_ch = config.output_ch
self.criterion = torch.nn.BCELoss()
self.augmentation_prob = config.augmentation_prob
# Hyper-parameters
self.lr = config.lr
self.beta1 = config.beta1
self.beta2 = config.beta2
# Training settings
self.num_epochs = config.num_epochs
self.num_epochs_decay = config.num_epochs_decay
self.batch_size = config.batch_size
# Step size
self.log_step = config.log_step
self.val_step = config.val_step
# Path
self.model_path = config.model_path
self.result_path = config.result_path
self.mode = config.mode
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model_type = config.model_type
self.t = config.t
self.build_model()
def build_model(self):
"""Build generator and discriminator."""
if self.model_type =='U_Net':
self.unet = U_Net(img_ch=3,output_ch=1)
elif self.model_type =='R2U_Net':
self.unet = R2U_Net(img_ch=3,output_ch=1,t=self.t)
elif self.model_type =='AttU_Net':
self.unet = AttU_Net(img_ch=3,output_ch=1)
elif self.model_type == 'R2AttU_Net':
self.unet = R2AttU_Net(img_ch=3,output_ch=1,t=self.t)
self.optimizer = optim.Adam(list(self.unet.parameters()),
self.lr, [self.beta1, self.beta2])
self.unet.to(self.device)
# self.print_network(self.unet, self.model_type)
def print_network(self, model, name):
"""Print out the network information."""
num_params = 0
for p in model.parameters():
num_params += p.numel()
print(model)
print(name)
print("The number of parameters: {}".format(num_params))
def to_data(self, x):
"""Convert variable to tensor."""
if torch.cuda.is_available():
x = x.cpu()
return x.data
def update_lr(self, g_lr, d_lr):
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr
def reset_grad(self):
"""Zero the gradient buffers."""
self.unet.zero_grad()
def compute_accuracy(self,SR,GT):
SR_flat = SR.view(-1)
GT_flat = GT.view(-1)
acc = GT_flat.data.cpu()==(SR_flat.data.cpu()>0.5)
def tensor2img(self,x):
img = (x[:,0,:,:]>x[:,1,:,:]).float()
img = img*255
return img
def plot(self,SR, GT, img):
SR =SR>0.5
SR = torch.squeeze(SR).cpu().numpy()*255
GT = torch.squeeze(GT == torch.max(GT)).cpu().numpy()*255
fig= plt.figure()
fig.add_subplot(1,3,3)
plt.imshow(SR)
fig.add_subplot(1, 3, 2)
plt.imshow(GT)
fig.add_subplot(1, 3, 1)
plt.imshow(img.transpose_(1,3).transpose_(1,2)[0].cpu().numpy())
plt.show()
def train(self):
"""Train encoder, generator and discriminator."""
#====================================== Training ===========================================#
#===========================================================================================#
unet_path = os.path.join(self.model_path, '%s-%d-%.4f-%d-%.4f.pkl' %(self.model_type,self.num_epochs,self.lr,self.num_epochs_decay,self.augmentation_prob))
# U-Net Train
if os.path.isfile(unet_path):
# Load the pretrained Encoder
self.unet.load_state_dict(torch.load(unet_path))
print('%s is Successfully Loaded from %s'%(self.model_type,unet_path))
else:
# Train for Encoder
lr = self.lr
best_unet_score = 0.
for epoch in range(self.num_epochs):
self.unet.train(True)
epoch_loss = 0
acc = 0. # Accuracy
SE = 0. # Sensitivity (Recall)
SP = 0. # Specificity
PC = 0. # Precision
F1 = 0. # F1 Score
JS = 0. # Jaccard Similarity
DC = 0. # Dice Coefficient
length = 0
for i, (images, GT) in enumerate(self.train_loader):
# GT : Ground Truth
images = images.to(self.device)
GT = GT.to(self.device)
# SR : Segmentation Result
SR = self.unet(images)
SR_probs = F.sigmoid(SR)
SR_flat = SR_probs.view(SR_probs.size(0),-1)
GT_flat = GT.view(GT.size(0),-1)
loss = self.criterion(SR_flat,GT_flat)
epoch_loss += loss.item()
# Backprop + optimize
self.reset_grad()
loss.backward()
self.optimizer.step()
acc += get_accuracy(SR,GT)
SE += get_sensitivity(SR,GT)
SP += get_specificity(SR,GT)
PC += get_precision(SR,GT)
F1 += get_F1(SR,GT)
JS += get_JS(SR,GT)
DC += get_DC(SR,GT)
length += images.size(0)
acc = acc/length
SE = SE/length
SP = SP/length
PC = PC/length
F1 = F1/length
JS = JS/length
DC = DC/length
# Print the log info
print('Epoch [%d/%d], Loss: %.4f, \n[Training] Acc: %.4f, SE: %.4f, SP: %.4f, PC: %.4f, F1: %.4f, JS: %.4f, DC: %.4f' % (
epoch+1, self.num_epochs, \
epoch_loss,\
acc,SE,SP,PC,F1,JS,DC))
# Decay learning rate
if (epoch+1) > (self.num_epochs - self.num_epochs_decay):
lr -= (self.lr / float(self.num_epochs_decay))
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr
print ('Decay learning rate to lr: {}.'.format(lr))
#===================================== Validation ====================================#
self.unet.train(False)
self.unet.eval()
acc = 0. # Accuracy
SE = 0. # Sensitivity (Recall)
SP = 0. # Specificity
PC = 0. # Precision
F1 = 0. # F1 Score
JS = 0. # Jaccard Similarity
DC = 0. # Dice Coefficient
length=0
for i, (images, GT) in enumerate(self.valid_loader):
images = images.to(self.device)
GT = GT.to(self.device)
SR = F.sigmoid(self.unet(images))
acc += get_accuracy(SR,GT)
SE += get_sensitivity(SR,GT)
SP += get_specificity(SR,GT)
PC += get_precision(SR,GT)
F1 += get_F1(SR,GT)
JS += get_JS(SR,GT)
DC += get_DC(SR,GT)
length += images.size(0)
acc = acc/length
SE = SE/length
SP = SP/length
PC = PC/length
F1 = F1/length
JS = JS/length
DC = DC/length
unet_score = JS + DC
print('[Validation] Acc: %.4f, SE: %.4f, SP: %.4f, PC: %.4f, F1: %.4f, JS: %.4f, DC: %.4f'%(acc,SE,SP,PC,F1,JS,DC))
'''
torchvision.utils.save_image(images.data.cpu(),
os.path.join(self.result_path,
'%s_valid_%d_image.png'%(self.model_type,epoch+1)))
torchvision.utils.save_image(SR.data.cpu(),
os.path.join(self.result_path,
'%s_valid_%d_SR.png'%(self.model_type,epoch+1)))
torchvision.utils.save_image(GT.data.cpu(),
os.path.join(self.result_path,
'%s_valid_%d_GT.png'%(self.model_type,epoch+1)))
'''
# Save Best U-Net model
if unet_score > best_unet_score:
best_unet_score = unet_score
best_epoch = epoch
best_unet = self.unet.state_dict()
print('Best %s model score : %.4f'%(self.model_type,best_unet_score))
torch.save(best_unet,unet_path)
#===================================== Test ====================================#
del self.unet
del best_unet
self.build_model()
self.unet.load_state_dict(torch.load(unet_path))
self.unet.train(False)
self.unet.eval()
acc = 0. # Accuracy
SE = 0. # Sensitivity (Recall)
SP = 0. # Specificity
PC = 0. # Precision
F1 = 0. # F1 Score
JS = 0. # Jaccard Similarity
DC = 0. # Dice Coefficient
length=0
for i, (images, GT) in enumerate(self.test_loader):
images = images.to(self.device)
GT = GT.to(self.device)
SR = F.sigmoid(self.unet(images))
acc += get_accuracy(SR,GT)
SE += get_sensitivity(SR,GT)
SP += get_specificity(SR,GT)
PC += get_precision(SR,GT)
F1 += get_F1(SR,GT)
JS += get_JS(SR,GT)
DC += get_DC(SR,GT)
length += images.size(0)
acc = acc/length
SE = SE/length
SP = SP/length
PC = PC/length
F1 = F1/length
JS = JS/length
DC = DC/length
unet_score = JS + DC
f = open(os.path.join(self.result_path,'result.csv'), 'a', encoding='utf-8', newline='')
wr = csv.writer(f)
wr.writerow([self.model_type,acc,SE,SP,PC,F1,JS,DC,self.lr,best_epoch,self.num_epochs,self.num_epochs_decay,self.augmentation_prob])
f.close()
def test(self):
# ===================================== Test ====================================#
unet_path = os.path.join(self.model_path, '%s-%d-%.4f-%d-%.4f.pkl' % (
self.model_type, self.num_epochs, self.lr, self.num_epochs_decay, self.augmentation_prob))
unet_path = os.path.join(self.model_path, 'U_Net-150-0.0002-57-0.0808.pkl')
# U-Net Train
if os.path.isfile(unet_path):
# Load the pretrained Encoder
self.unet.load_state_dict(torch.load(unet_path))
print('%s is Successfully Loaded from %s' % (self.model_type, unet_path))
self.build_model()
self.unet.load_state_dict(torch.load(unet_path))
self.unet.train(False)
self.unet.eval()
acc = 0. # Accuracy
SE = 0. # Sensitivity (Recall)
SP = 0. # Specificity
PC = 0. # Precision
F1 = 0. # F1 Score
JS = 0. # Jaccard Similarity
DC = 0. # Dice Coefficient
length = 0
for i, (images, GT) in enumerate(self.valid_loader):
images = images.to(self.device)
GT = GT.to(self.device)
SR = F.sigmoid(self.unet(images))
self.plot(SR, GT, images)
acc += get_accuracy(SR, GT)
SE += get_sensitivity(SR, GT)
SP += get_specificity(SR, GT)
PC += get_precision(SR, GT)
F1 += get_F1(SR, GT)
JS += get_JS(SR, GT)
DC += get_DC(SR, GT)
length += images.size(0)
acc = acc / length
SE = SE / length
SP = SP / length
PC = PC / length
F1 = F1 / length
JS = JS / length
DC = DC / length
unet_score = JS + DC
f = open(os.path.join(self.result_path, 'result.csv'), 'a', encoding='utf-8', newline='')
wr = csv.writer(f)
wr.writerow(
[self.model_type, acc, SE, SP, PC, F1, JS, DC, self.lr, 2, self.num_epochs, self.num_epochs_decay,
self.augmentation_prob])
f.close()