def Train():
print('********************load data********************')
dataloader_train = get_train_dataloader(batch_size=config['BATCH_SIZE'],
shuffle=True,
num_workers=8)
dataloader_val = get_validation_dataloader(batch_size=config['BATCH_SIZE'],
shuffle=True,
num_workers=8)
#dataloader_train, dataloader_val = get_train_val_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8, split_ratio=0.1)
print('********************load data succeed!********************')
print('********************load model********************')
# initialize and load the model
if args.model == 'CXRNet':
model = CXRClassifier(num_classes=N_CLASSES,
is_pre_trained=True).cuda() #initialize model
optimizer_model = optim.Adam(model.parameters(),
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=1e-5)
lr_scheduler_model = lr_scheduler.StepLR(optimizer_model,
step_size=10,
gamma=1)
#for left_lung
model_unet_left = UNet(n_channels=3,
n_classes=1).cuda() #initialize model
CKPT_PATH = config['CKPT_PATH'] + 'best_unet_left.pkl'
if os.path.exists(CKPT_PATH):
checkpoint = torch.load(CKPT_PATH)
model_unet_left.load_state_dict(checkpoint) #strict=False
print("=> loaded well-trained unet model checkpoint: " + CKPT_PATH)
model_unet_left.eval()
#for right lung
model_unet_right = UNet(n_channels=3,
n_classes=1).cuda() #initialize model
CKPT_PATH = config['CKPT_PATH'] + 'best_unet_right.pkl'
if os.path.exists(CKPT_PATH):
checkpoint = torch.load(CKPT_PATH)
model_unet_right.load_state_dict(checkpoint) #strict=False
print("=> loaded well-trained unet model checkpoint: " + CKPT_PATH)
model_unet_right.eval()
#for heart
model_unet_heart = UNet(n_channels=3,
n_classes=1).cuda() #initialize model
CKPT_PATH = config['CKPT_PATH'] + 'best_unet_heart.pkl'
if os.path.exists(CKPT_PATH):
checkpoint = torch.load(CKPT_PATH)
model_unet_heart.load_state_dict(checkpoint) #strict=False
print("=> loaded well-trained unet model checkpoint: " + CKPT_PATH)
model_unet_heart.eval()
else:
print('No required model')
return #over
torch.backends.cudnn.benchmark = True # improve train speed slightly
bce_criterion = nn.BCELoss() #define binary cross-entropy loss
print('********************load model succeed!********************')
print('********************begin training!********************')
AUROC_best = 0.50
for epoch in range(config['MAX_EPOCHS']):
since = time.time()
print('Epoch {}/{}'.format(epoch + 1, config['MAX_EPOCHS']))
print('-' * 10)
model.train() #set model to training mode
train_loss = []
with torch.autograd.enable_grad():
for batch_idx, (image, label) in enumerate(dataloader_train):
optimizer_model.zero_grad()
#pathological regions generation
var_image = torch.autograd.Variable(image).cuda()
mask_left = model_unet_left(var_image) #for left lung
mask_right = model_unet_right(var_image) #for right lung
mask_heart = model_unet_heart(var_image) #for heart
patchs, patch_labels, globals, global_labels = ROIGeneration(
image, [mask_left, mask_right, mask_heart], label)
#training
loss_patch, loss_global = torch.FloatTensor(
[0.0]).cuda(), torch.FloatTensor([0.0]).cuda()
if len(patchs) > 0:
var_patchs = torch.autograd.Variable(patchs).cuda()
var_patch_labels = torch.autograd.Variable(
patch_labels).cuda()
out_patch = model(var_patchs, is_patch=True) #forward
loss_patch = bce_criterion(out_patch, var_patch_labels)
"""
if len(globals)>0:
var_globals = torch.autograd.Variable(globals).cuda()
var_global_labels = torch.autograd.Variable(global_labels).cuda()
out_global = model(var_globals, is_patch = False)#forward
loss_global = bce_criterion(out_global, var_global_labels)
"""
loss_tensor = loss_patch + loss_global
loss_tensor.backward()
optimizer_model.step()
train_loss.append(loss_tensor.item())
sys.stdout.write(
'\r Epoch: {} / Step: {} : train loss = {}'.format(
epoch + 1, batch_idx + 1,
float('%0.6f' % loss_tensor.item())))
sys.stdout.flush()
lr_scheduler_model.step() #about lr and gamma
print("\r Eopch: %5d train loss = %.6f" %
(epoch + 1, np.mean(train_loss)))
model.eval() #turn to test mode
val_loss = []
gt = torch.FloatTensor().cuda()
pred = torch.FloatTensor().cuda()
with torch.autograd.no_grad():
for batch_idx, (image, label) in enumerate(dataloader_val):
#pathological regions generation
var_image = torch.autograd.Variable(image).cuda()
mask_left = model_unet_left(var_image) #for left lung
mask_right = model_unet_right(var_image) #for right lung
mask_heart = model_unet_heart(var_image) #for heart
patchs, patch_labels, globals, global_labels = ROIGeneration(
image, [mask_left, mask_right, mask_heart], label)
#training
loss_patch, loss_global = torch.FloatTensor(
[0.0]).cuda(), torch.FloatTensor([0.0]).cuda()
if len(patchs) > 0:
var_patchs = torch.autograd.Variable(patchs).cuda()
var_patch_labels = torch.autograd.Variable(
patch_labels).cuda()
out_patch = model(var_patchs, is_patch=True) #forward
loss_patch = bce_criterion(out_patch, var_patch_labels)
gt = torch.cat((gt, patch_labels.cuda()), 0)
pred = torch.cat((pred, out_patch.data), 0)
"""
if len(globals)>0:
var_globals = torch.autograd.Variable(globals).cuda()
var_global_labels = torch.autograd.Variable(global_labels).cuda()
out_global = model(var_globals, is_patch = False)#forward
loss_global = bce_criterion(out_global, var_global_labels)
gt = torch.cat((gt, global_labels.cuda()), 0)
pred = torch.cat((pred, out_global.data), 0)
"""
loss_tensor = loss_patch + loss_global
val_loss.append(loss_tensor.item())
sys.stdout.write(
'\r Epoch: {} / Step: {} : validation loss = {}'.format(
epoch + 1, batch_idx + 1,
float('%0.6f' % loss_tensor.item())))
sys.stdout.flush()
#evaluation
AUROCs_avg = np.array(compute_AUCs(gt, pred)).mean()
print("\r Eopch: %5d validation loss = %.6f, average AUROC=%.4f" %
(epoch + 1, np.mean(val_loss), AUROCs_avg))
#save checkpoint
if AUROC_best < AUROCs_avg:
AUROC_best = AUROCs_avg
torch.save(model.state_dict(),
config['CKPT_PATH'] + 'best_model_CXRNet.pkl')
print(' Epoch: {} model has been already save!'.format(epoch + 1))
time_elapsed = time.time() - since
print('Training epoch: {} completed in {:.0f}m {:.0f}s'.format(
epoch + 1, time_elapsed // 60, time_elapsed % 60))
def Train():
print('********************load data********************')
dataloader_train = get_train_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8)
dataloader_val = get_validation_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8)
#dataloader_train, dataloader_val = get_train_val_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8, split_ratio=0.1)
print('********************load data succeed!********************')
print('********************load model********************')
# initialize and load the model
if args.model == 'CXRNet':
model_img = CXRClassifier(num_classes=N_CLASSES, is_pre_trained=True, is_roi=False).cuda()#initialize model
#model_img = nn.DataParallel(model_img).cuda() # make model available multi GPU cores training
optimizer_img = optim.Adam(model_img.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
lr_scheduler_img = lr_scheduler.StepLR(optimizer_img, step_size = 10, gamma = 1)
roigen = ROIGenerator()
model_roi = CXRClassifier(num_classes=N_CLASSES, is_pre_trained=True, is_roi=True).cuda()
#model_roi = nn.DataParallel(model_roi).cuda()
optimizer_roi = optim.Adam(model_roi.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
lr_scheduler_roi = lr_scheduler.StepLR(optimizer_roi, step_size = 10, gamma = 1)
model_fusion = FusionClassifier(input_size=2048, output_size=N_CLASSES).cuda()
#model_fusion = nn.DataParallel(model_fusion).cuda()
optimizer_fusion = optim.Adam(model_fusion.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
lr_scheduler_fusion = lr_scheduler.StepLR(optimizer_fusion, step_size = 10, gamma = 1)
else:
print('No required model')
return #over
torch.backends.cudnn.benchmark = True # improve train speed slightly
bce_criterion = nn.BCELoss() #define binary cross-entropy loss
print('********************load model succeed!********************')
print('********************begin training!********************')
AUROC_best = 0.50
for epoch in range(config['MAX_EPOCHS']):
since = time.time()
print('Epoch {}/{}'.format(epoch+1 , config['MAX_EPOCHS']))
print('-' * 10)
model_img.train() #set model to training mode
model_roi.train()
model_fusion.train()
train_loss = []
with torch.autograd.enable_grad():
for batch_idx, (image, label) in enumerate(dataloader_train):
optimizer_img.zero_grad()
optimizer_roi.zero_grad()
optimizer_fusion.zero_grad()
#image-level
var_image = torch.autograd.Variable(image).cuda()
var_label = torch.autograd.Variable(label).cuda()
conv_fea_img, fc_fea_img, out_img = model_img(var_image)#forward
loss_img = bce_criterion(out_img, var_label)
#ROI-level
cls_weights = list(model_img.parameters())
weight_softmax = np.squeeze(cls_weights[-5].data.cpu().numpy())
roi = roigen.ROIGeneration(image, conv_fea_img, weight_softmax, label.numpy())
var_roi = torch.autograd.Variable(roi).cuda()
_, fc_fea_roi, out_roi = model_roi(var_roi)
loss_roi = bce_criterion(out_roi, var_label)
#Fusion
fc_fea_fusion = torch.cat((fc_fea_img,fc_fea_roi), 1)
var_fusion = torch.autograd.Variable(fc_fea_fusion).cuda()
out_fusion = model_fusion(var_fusion)
loss_fusion = bce_criterion(out_fusion, var_label)
#backward and update parameters
loss_tensor = 0.7*loss_img + 0.2*loss_roi + 0.1*loss_fusion
loss_tensor.backward()
optimizer_img.step()
optimizer_roi.step()
optimizer_fusion.step()
train_loss.append(loss_tensor.item())
#print([x.grad for x in optimizer.param_groups[0]['params']])
sys.stdout.write('\r Epoch: {} / Step: {} : image loss ={}, roi loss ={}, fusion loss = {}, train loss = {}'
.format(epoch+1, batch_idx+1, float('%0.6f'%loss_img.item()), float('%0.6f'%loss_roi.item()),
float('%0.6f'%loss_fusion.item()), float('%0.6f'%loss_tensor.item()) ))
sys.stdout.flush()
lr_scheduler_img.step() #about lr and gamma
lr_scheduler_roi.step()
lr_scheduler_fusion.step()
print("\r Eopch: %5d train loss = %.6f" % (epoch + 1, np.mean(train_loss)))
model_img.eval() #turn to test mode
model_roi.eval()
model_fusion.eval()
loss_img_all, loss_roi_all, loss_fusion_all = [], [], []
val_loss = []
gt = torch.FloatTensor().cuda()
pred_img = torch.FloatTensor().cuda()
pred_roi = torch.FloatTensor().cuda()
pred_fusion = torch.FloatTensor().cuda()
with torch.autograd.no_grad():
for batch_idx, (image, label) in enumerate(dataloader_val):
gt = torch.cat((gt, label.cuda()), 0)
#image-level
var_image = torch.autograd.Variable(image).cuda()
var_label = torch.autograd.Variable(label).cuda()
conv_fea_img, fc_fea_img, out_img = model_img(var_image)#forward
loss_img = bce_criterion(out_img, var_label)
pred_img = torch.cat((pred_img, out_img.data), 0)
#ROI-level
cls_weights = list(model_img.parameters())
weight_softmax = np.squeeze(cls_weights[-5].data.cpu().numpy())
roi = roigen.ROIGeneration(image, conv_fea_img, weight_softmax, label.numpy())
var_roi = torch.autograd.Variable(roi).cuda()
_, fc_fea_roi, out_roi = model_roi(var_roi)
loss_roi = bce_criterion(out_roi, var_label)
pred_roi = torch.cat((pred_roi, out_roi.data), 0)
#Fusion
fc_fea_fusion = torch.cat((fc_fea_img,fc_fea_roi), 1)
var_fusion = torch.autograd.Variable(fc_fea_fusion).cuda()
out_fusion = model_fusion(var_fusion)
loss_fusion = bce_criterion(out_fusion, var_label)
pred_fusion = torch.cat((pred_fusion, out_fusion.data), 0)
#loss
loss_tensor = 0.7*loss_img + 0.2*loss_roi + 0.1*loss_fusion
val_loss.append(loss_tensor.item())
sys.stdout.write('\r Epoch: {} / Step: {} : image loss ={}, roi loss ={}, fusion loss = {}, train loss = {}'
.format(epoch+1, batch_idx+1, float('%0.6f'%loss_img.item()), float('%0.6f'%loss_roi.item()),
float('%0.6f'%loss_fusion.item()), float('%0.6f'%loss_tensor.item()) ))
sys.stdout.flush()
loss_img_all.append(loss_img.item())
loss_roi_all.append(loss_roi.item())
loss_fusion_all.append(loss_fusion.item())
#evaluation
AUROCs_img = np.array(compute_AUCs(gt, pred_img)).mean()
AUROCs_roi = np.array(compute_AUCs(gt, pred_roi)).mean()
AUROCs_fusion = np.array(compute_AUCs(gt, pred_fusion)).mean()
print("\r Eopch: %5d validation loss = %.6f, Validataion AUROC image=%.4f roi=%.4f fusion=%.4f"
% (epoch + 1, np.mean(val_loss), AUROCs_img, AUROCs_roi, AUROCs_fusion))
logger.info("\r Eopch: %5d validation loss = %.4f, image loss = %.4f, roi loss =%.4f fusion loss =%.4f"
% (epoch + 1, np.mean(val_loss), np.mean(loss_img_all), np.mean(loss_roi_all), np.mean(loss_fusion_all)))
#save checkpoint
if AUROC_best < AUROCs_fusion:
AUROC_best = AUROCs_fusion
#torch.save(model.module.state_dict(), CKPT_PATH)
torch.save(model_img.state_dict(), config['CKPT_PATH'] + 'img_model.pkl') #Saving torch.nn.DataParallel Models
torch.save(model_roi.state_dict(), config['CKPT_PATH'] + 'roi_model.pkl')
torch.save(model_fusion.state_dict(), config['CKPT_PATH'] + 'fusion_model.pkl')
print(' Epoch: {} model has been already save!'.format(epoch+1))
time_elapsed = time.time() - since
print('Training epoch: {} completed in {:.0f}m {:.0f}s'.format(epoch+1, time_elapsed // 60 , time_elapsed % 60))
def Train():
print('********************load data********************')
dataloader_train = get_train_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8)
dataloader_val = get_validation_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8)
#dataloader_train, dataloader_val = get_train_val_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8, split_ratio=0.1)
print('********************load data succeed!********************')
print('********************load model********************')
# initialize and load the model
if args.model == 'CXRNet':
#for left_lung
model_unet_left = UNet(n_channels=3, n_classes=1).cuda()#initialize model
CKPT_PATH = config['CKPT_PATH'] + 'best_unet_left.pkl'
if os.path.exists(CKPT_PATH):
checkpoint = torch.load(CKPT_PATH)
model_unet_left.load_state_dict(checkpoint) #strict=False
print("=> loaded well-trained unet model checkpoint: "+CKPT_PATH)
model_unet_left.eval()
model_left = CXRClassifier(num_classes=N_CLASSES, is_pre_trained=True).cuda()#initialize model
optimizer_left = optim.Adam(model_left.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
lr_scheduler_left = lr_scheduler.StepLR(optimizer_left, step_size = 10, gamma = 1)
#for right lung
model_unet_right = UNet(n_channels=3, n_classes=1).cuda()#initialize model
CKPT_PATH = config['CKPT_PATH'] + 'best_unet_right.pkl'
if os.path.exists(CKPT_PATH):
checkpoint = torch.load(CKPT_PATH)
model_unet_right.load_state_dict(checkpoint) #strict=False
print("=> loaded well-trained unet model checkpoint: "+CKPT_PATH)
model_unet_right.eval()
model_right = CXRClassifier(num_classes=N_CLASSES, is_pre_trained=True).cuda()#initialize model
optimizer_right = optim.Adam(model_right.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
lr_scheduler_right = lr_scheduler.StepLR(optimizer_right, step_size = 10, gamma = 1)
#for heart
model_unet_heart = UNet(n_channels=3, n_classes=1).cuda()#initialize model
CKPT_PATH = config['CKPT_PATH'] + 'best_unet_heart.pkl'
if os.path.exists(CKPT_PATH):
checkpoint = torch.load(CKPT_PATH)
model_unet_heart.load_state_dict(checkpoint) #strict=False
print("=> loaded well-trained unet model checkpoint: "+CKPT_PATH)
model_unet_heart.eval()
model_heart = CXRClassifier(num_classes=N_CLASSES, is_pre_trained=True).cuda()#initialize model
optimizer_heart = optim.Adam(model_heart.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
lr_scheduler_heart = lr_scheduler.StepLR(optimizer_heart, step_size = 10, gamma = 1)
else:
print('No required model')
return #over
torch.backends.cudnn.benchmark = True # improve train speed slightly
bce_criterion = nn.BCELoss() #define binary cross-entropy loss
print('********************load model succeed!********************')
print('********************begin training!********************')
AUROC_best = 0.50
for epoch in range(config['MAX_EPOCHS']):
since = time.time()
print('Epoch {}/{}'.format(epoch+1 , config['MAX_EPOCHS']))
print('-' * 10)
model_left.train() #set model to training mode
model_right.train()
model_heart.train()
train_loss = []
with torch.autograd.enable_grad():
for batch_idx, (image, label) in enumerate(dataloader_train):
optimizer_left.zero_grad()
optimizer_right.zero_grad()
optimizer_heart.zero_grad()
var_image = torch.autograd.Variable(image).cuda()
var_label = torch.autograd.Variable(label).cuda()
#for left lung
mask = model_unet_left(var_image)
roi = ROIGeneration(image, mask)
var_roi = torch.autograd.Variable(roi).cuda()
out_left = model_left(var_roi)#forward
loss_left = bce_criterion(out_left, var_label)
loss_left.backward()
optimizer_left.step()
#for right lung
mask = model_unet_right(var_image)
roi = ROIGeneration(image, mask)
var_roi = torch.autograd.Variable(roi).cuda()
out_right = model_right(var_roi)#forward
loss_right = bce_criterion(out_right, var_label)
loss_right.backward()
optimizer_right.step()
#for heart
mask = model_unet_heart(var_image)
roi = ROIGeneration(image, mask)
var_roi = torch.autograd.Variable(roi).cuda()
out_heart = model_heart(var_roi)#forward
loss_heart = bce_criterion(out_heart, var_label)
loss_heart.backward()
optimizer_heart.step()
#loss sum
loss_tensor = loss_left + loss_right + loss_heart
train_loss.append(loss_tensor.item())
#print([x.grad for x in optimizer.param_groups[0]['params']])
sys.stdout.write('\r Epoch: {} / Step: {} : train loss = {}'.format(epoch+1, batch_idx+1, float('%0.6f'%loss_tensor.item()) ))
sys.stdout.flush()
lr_scheduler_left.step() #about lr and gamma
lr_scheduler_right.step()
lr_scheduler_heart.step()
print("\r Eopch: %5d train loss = %.6f" % (epoch + 1, np.mean(train_loss)))
model_left.eval() #turn to test mode
model_right.eval()
model_heart.eval()
val_loss = []
gt = torch.FloatTensor().cuda()
preds = torch.FloatTensor().cuda()
with torch.autograd.no_grad():
for batch_idx, (image, label) in enumerate(dataloader_val):
pred = torch.FloatTensor().cuda()
gt = torch.cat((gt, label.cuda()), 0)
var_image = torch.autograd.Variable(image).cuda()
var_label = torch.autograd.Variable(label).cuda()
#for left lung
mask = model_unet_left(var_image)
roi = ROIGeneration(image, mask)
var_roi = torch.autograd.Variable(roi).cuda()
out_left = model_left(var_roi)#forward
pred = torch.cat((pred, out_left.data.unsqueeze(0)), 0)
#for right lung
mask = model_unet_right(var_image)
roi = ROIGeneration(image, mask)
var_roi = torch.autograd.Variable(roi).cuda()
out_right = model_right(var_roi)#forward
pred = torch.cat((pred, out_right.data.unsqueeze(0)), 0)
#for heart
mask = model_unet_heart(var_image)
roi = ROIGeneration(image, mask)
var_roi = torch.autograd.Variable(roi).cuda()
out_heart = model_heart(var_roi)#forward
pred = torch.cat((pred, out_heart.data.unsqueeze(0)), 0)
#prediction
pred = torch.max(pred, 0)[0] #torch.mean
preds = torch.cat((preds, pred.data), 0)
loss_tensor = bce_criterion(pred, var_label)
val_loss.append(loss_tensor.item())
sys.stdout.write('\r Epoch: {} / Step: {} : validation loss = {}'.format(epoch+1, batch_idx+1, float('%0.6f'%loss_tensor.item()) ))
sys.stdout.flush()
#evaluation
AUROCs_avg = np.array(compute_AUCs(gt, preds)).mean()
print("\r Eopch: %5d validation loss = %.6f, average AUROC=%.4f"% (epoch + 1, np.mean(val_loss), AUROCs_avg))
#save checkpoint
if AUROC_best < AUROCs_avg:
AUROC_best = AUROCs_avg
torch.save(model_img.state_dict(), config['CKPT_PATH'] + 'left_model.pkl') #Saving torch.nn.DataParallel Models
torch.save(model_roi.state_dict(), config['CKPT_PATH'] + 'right_model.pkl')
torch.save(model_fusion.state_dict(), config['CKPT_PATH'] + 'heart_model.pkl')
print(' Epoch: {} model has been already save!'.format(epoch+1))
time_elapsed = time.time() - since
print('Training epoch: {} completed in {:.0f}m {:.0f}s'.format(epoch+1, time_elapsed // 60 , time_elapsed % 60))
def Train():
print('********************load data********************')
dataloader_train = get_train_dataloader(batch_size=config['BATCH_SIZE'],
shuffle=True,
num_workers=8)
dataloader_val = get_validation_dataloader(batch_size=config['BATCH_SIZE'],
shuffle=False,
num_workers=8)
#dataloader_train, dataloader_val = get_train_val_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8)
print('********************load data succeed!********************')
print('********************load model********************')
# initialize and load the model
if args.model == 'CXRNet':
model = CXRNet(num_classes=N_CLASSES,
is_pre_trained=True).cuda() #initialize model
optimizer_model = optim.Adam(model.parameters(),
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=1e-5)
lr_scheduler_model = lr_scheduler.StepLR(optimizer_model,
step_size=10,
gamma=1)
torch.backends.cudnn.benchmark = True # improve train speed slightly
bce_criterion = nn.BCELoss() #define binary cross-entropy loss
#mse_criterion = nn.MSELoss() #define regression loss
model_unet = UNet(n_channels=3, n_classes=1).cuda() #initialize model
CKPT_PATH = config['CKPT_PATH'] + 'best_unet.pkl'
if os.path.exists(CKPT_PATH):
checkpoint = torch.load(CKPT_PATH)
model_unet.load_state_dict(checkpoint) #strict=False
print("=> loaded well-trained unet model checkpoint: " + CKPT_PATH)
model_unet.eval()
else:
print('No required model')
return #over
print('********************load model succeed!********************')
print('********************begin training!********************')
AUROC_best = 0.50
for epoch in range(config['MAX_EPOCHS']):
since = time.time()
print('Epoch {}/{}'.format(epoch + 1, config['MAX_EPOCHS']))
print('-' * 10)
train_loss = []
model.train() #set model to training mode
with torch.autograd.enable_grad():
for batch_idx, (image, label) in enumerate(dataloader_train):
optimizer_model.zero_grad()
var_image = torch.autograd.Variable(image).cuda()
var_label = torch.autograd.Variable(label).cuda()
var_mask = model_unet(var_image)
var_mask = var_mask.ge(0.5).float() #0,1 binarization
mask_np = var_mask.squeeze().cpu().numpy() #bz*224*224
patchs = torch.FloatTensor()
for i in range(0, mask_np.shape[0]):
mask = mask_np[i]
ind = np.argwhere(mask != 0)
if len(ind) > 2:
minh = min(ind[:, 0])
minw = min(ind[:, 1])
maxh = max(ind[:, 0])
maxw = max(ind[:, 1])
image_crop = image[i].permute(
1, 2, 0).squeeze().numpy() #224*224*3
image_crop = image_crop[minh:maxh, minw:maxw, :]
image_crop = cv2.resize(
image_crop,
(config['TRAN_CROP'], config['TRAN_CROP']))
image_crop = torch.FloatTensor(image_crop).permute(
2, 1, 0).unsqueeze(0) #1*3*224*224
patchs = torch.cat((patchs, image_crop), 0)
else:
image_crop = image[i].unsqueeze(0)
patchs = torch.cat((patchs, image_crop), 0)
var_patchs = torch.autograd.Variable(patchs).cuda()
var_output = model(var_patchs) #forward
loss_tensor = bce_criterion(var_output, var_label)
loss_tensor.backward()
optimizer_model.step()
train_loss.append(loss_tensor.item())
sys.stdout.write(
'\r Epoch: {} / Step: {} : train BCE loss = {}'.format(
epoch + 1, batch_idx + 1,
float('%0.6f' % loss_tensor.item())))
sys.stdout.flush()
lr_scheduler_model.step() #about lr and gamma
print("\r Eopch: %5d train loss = %.6f" %
(epoch + 1, np.mean(train_loss)))
model.eval() #turn to test mode
val_loss = []
gt = torch.FloatTensor().cuda()
pred = torch.FloatTensor().cuda()
with torch.autograd.no_grad():
for batch_idx, (image, label) in enumerate(dataloader_val):
gt = torch.cat((gt, label.cuda()), 0)
var_image = torch.autograd.Variable(image).cuda()
var_label = torch.autograd.Variable(label).cuda()
var_mask = model_unet(var_image)
var_mask = var_mask.ge(0.5).float() #0,1 binarization
mask_np = var_mask.squeeze().cpu().numpy() #bz*224*224
patchs = torch.FloatTensor()
for i in range(0, mask_np.shape[0]):
mask = mask_np[i]
ind = np.argwhere(mask != 0)
if len(ind) > 0:
minh = min(ind[:, 0])
minw = min(ind[:, 1])
maxh = max(ind[:, 0])
maxw = max(ind[:, 1])
image_crop = image[i].permute(
1, 2, 0).squeeze().numpy() #224*224*3
image_crop = image_crop[minh:maxh, minw:maxw, :]
image_crop = cv2.resize(
image_crop,
(config['TRAN_CROP'], config['TRAN_CROP']))
image_crop = torch.FloatTensor(image_crop).permute(
2, 1, 0).unsqueeze(0) #1*3*224*224
patchs = torch.cat((patchs, image_crop), 0)
else:
image_crop = image[i].unsqueeze(0)
patchs = torch.cat((patchs, image_crop), 0)
var_patchs = torch.autograd.Variable(patchs).cuda()
var_output = model(var_patchs) #forward
loss_tensor = bce_criterion(var_output, var_label)
pred = torch.cat((pred, var_output.data), 0)
val_loss.append(loss_tensor.item())
sys.stdout.write(
'\r Epoch: {} / Step: {} : validation loss = {}'.format(
epoch + 1, batch_idx + 1,
float('%0.6f' % loss_tensor.item())))
sys.stdout.flush()
#evaluation
AUROCs_avg = np.array(compute_AUCs(gt, pred)).mean()
logger.info(
"\r Eopch: %5d validation loss = %.6f, Validataion AUROC image=%.4f"
% (epoch + 1, np.mean(val_loss), AUROCs_avg))
#save checkpoint
if AUROC_best < AUROCs_avg:
AUROC_best = AUROCs_avg
torch.save(
model.state_dict(), config['CKPT_PATH'] +
'best_model_CXRNet.pkl') #Saving torch.nn.DataParallel Models
print(' Epoch: {} model has been already save!'.format(epoch + 1))
time_elapsed = time.time() - since
print('Training epoch: {} completed in {:.0f}m {:.0f}s'.format(
epoch + 1, time_elapsed // 60, time_elapsed % 60))
def Test():
print('********************load data********************')
#dataloader_train = get_train_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8)
dataloader_val = get_validation_dataloader(batch_size=config['BATCH_SIZE'],
shuffle=True,
num_workers=8)
dataloader_test = get_test_dataloader(batch_size=config['BATCH_SIZE'],
shuffle=False,
num_workers=8)
print('********************load data succeed!********************')
print('********************load model********************')
# initialize and load the model
if args.model == 'CXRNet':
model_img = CXRClassifier(num_classes=N_CLASSES,
is_pre_trained=True,
is_roi=False).cuda()
CKPT_PATH = config['CKPT_PATH'] + 'img_model.pkl'
checkpoint = torch.load(CKPT_PATH)
model_img.load_state_dict(checkpoint) #strict=False
print("=> loaded Image model checkpoint: " + CKPT_PATH)
model_roi = CXRClassifier(num_classes=N_CLASSES,
is_pre_trained=True,
is_roi=True).cuda()
CKPT_PATH = config['CKPT_PATH'] + 'roi_model.pkl'
checkpoint = torch.load(CKPT_PATH)
model_roi.load_state_dict(checkpoint) #strict=False
print("=> loaded ROI model checkpoint: " + CKPT_PATH)
model_fusion = FusionClassifier(input_size=2048,
output_size=N_CLASSES).cuda()
CKPT_PATH = config['CKPT_PATH'] + 'fusion_model.pkl'
checkpoint = torch.load(CKPT_PATH)
model_fusion.load_state_dict(checkpoint) #strict=False
print("=> loaded Fusion model checkpoint: " + CKPT_PATH)
roigen = ROIGenerator() #region generator
else:
print('No required model')
return #over
torch.backends.cudnn.benchmark = True # improve train speed slightly
print('******************** load model succeed!********************')
"""
print('******* begin feature extraction!*********')
gt_val = torch.FloatTensor().cuda()
fc_img_val = torch.FloatTensor().cuda()
fc_roi_val = torch.FloatTensor().cuda()
with torch.autograd.no_grad():
for batch_idx, (image, label) in enumerate(dataloader_val):
gt_val = torch.cat((gt_val, label.cuda()), 0)
#image-level
var_image = torch.autograd.Variable(image).cuda()
var_label = torch.autograd.Variable(label).cuda()
conv_fea_img, fc_fea_img, _ = model_img(var_image)#forward
fc_img_val = torch.cat((fc_img_val, fc_fea_img.cuda()), 0)
#ROI-level
cls_weights = list(model_img.parameters())
weight_softmax = np.squeeze(cls_weights[-5].data.cpu().numpy())
roi = roigen.ROIGeneration(image, conv_fea_img, weight_softmax, label.numpy())
var_roi = torch.autograd.Variable(roi).cuda()
_, fc_fea_roi, _ = model_roi(var_roi)
fc_roi_val = torch.cat((fc_roi_val, fc_fea_roi.cuda()), 0)
sys.stdout.write('\r cluster process: = {}'.format(batch_idx+1))
sys.stdout.flush()
"""
print('******* begin testing!*********')
gt = torch.FloatTensor().cuda()
pred_img = torch.FloatTensor().cuda()
pred_roi = torch.FloatTensor().cuda()
pred_fusion = torch.FloatTensor().cuda()
# switch to evaluate mode
model_img.eval() #turn to test mode
model_roi.eval()
model_fusion.eval()
cudnn.benchmark = True
with torch.autograd.no_grad():
for batch_idx, (image, label) in enumerate(dataloader_test):
gt = torch.cat((gt, label.cuda()), 0)
#image-level
var_image = torch.autograd.Variable(image).cuda()
#var_label = torch.autograd.Variable(label).cuda()
conv_fea_img, fc_fea_img, out_img = model_img(var_image) #forward
pred_img = torch.cat((pred_img, out_img.data), 0)
#ROI-level
"""
#-----predicted label(softmax)---------------
shape_l, shape_c = out_img.size()[0], out_img.size()[1]
pdlabel = torch.FloatTensor(shape_l, shape_c).zero_()
out_img = F.log_softmax(out_img, dim=1)
out_img = out_img.max(1,keepdim=True)[1]
for i in range(shape_l):
pdlabel[i,out_img[i]] = 1.0
#-----predicted label---------------
"""
"""
#---------similarity label-------------------
sim_mat = cosine_similarity(fc_fea_img.cpu().numpy(), fc_img_val.cpu().numpy())
pdlabel = torch.FloatTensor().cuda()
for i in range(sim_mat.shape[0]):
idx = np.argmax(sim_mat[i,:])
pdlabel = torch.cat((pdlabel, gt_val[idx].data), 0)
#--------similarity label-------------------
"""
"""
#-----predicted label(sigmoid)---------------
shape_l, shape_c = out_img.size()[0], out_img.size()[1]
pdlabel = torch.FloatTensor(shape_l, shape_c).zero_()
for i in range(shape_l):
for j in range(shape_c):
if out_img[i, j] > classes_threshold_common[j]:
pdlabel[i,j] = 1.0
#-----predicted label---------------
"""
cls_weights = list(model_img.parameters())
weight_softmax = np.squeeze(cls_weights[-5].data.cpu().numpy())
#label = torch.zeros_like(label)
roi = roigen.ROIGeneration(image, conv_fea_img, weight_softmax,
label.numpy())
var_roi = torch.autograd.Variable(roi).cuda()
_, fc_fea_roi, out_roi = model_roi(var_roi)
pred_roi = torch.cat((pred_roi, out_roi.data), 0)
#Fusion
fc_fea_fusion = torch.cat((fc_fea_img, fc_fea_roi), 1)
var_fusion = torch.autograd.Variable(fc_fea_fusion).cuda()
out_fusion = model_fusion(var_fusion)
pred_fusion = torch.cat((pred_fusion, out_fusion.data), 0)
sys.stdout.write('\r testing process: = {}'.format(batch_idx + 1))
sys.stdout.flush()
#for evaluation
AUROC_img = compute_AUCs(gt, pred_img)
AUROC_avg = np.array(AUROC_img).mean()
for i in range(N_CLASSES):
print('The AUROC of {} is {:.4f}'.format(CLASS_NAMES[i], AUROC_img[i]))
print('The average AUROC is {:.4f}'.format(AUROC_avg))
AUROC_roi = compute_AUCs(gt, pred_roi)
AUROC_avg = np.array(AUROC_roi).mean()
for i in range(N_CLASSES):
print('The AUROC of {} is {:.4f}'.format(CLASS_NAMES[i], AUROC_roi[i]))
print('The average AUROC is {:.4f}'.format(AUROC_avg))
AUROC_fusion = compute_AUCs(gt, pred_fusion)
AUROC_avg = np.array(AUROC_fusion).mean()
for i in range(N_CLASSES):
print('The AUROC of {} is {:.4f}'.format(CLASS_NAMES[i],
AUROC_fusion[i]))
print('The average AUROC is {:.4f}'.format(AUROC_avg))
def Test():
print('********************load data********************')
#dataloader_train = get_train_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8)
dataloader_val = get_validation_dataloader(batch_size=config['BATCH_SIZE'], shuffle=True, num_workers=8)
dataloader_test = get_test_dataloader(batch_size=config['BATCH_SIZE'], shuffle=False, num_workers=8)
print('********************load data succeed!********************')
print('********************load model********************')
# initialize and load the model
if args.model == 'CXRNet':
model_img = CXRClassifier(num_classes=N_CLASSES, is_pre_trained=True, is_roi=False).cuda()
CKPT_PATH = config['CKPT_PATH'] +'img_model.pkl'
checkpoint = torch.load(CKPT_PATH)
model_img.load_state_dict(checkpoint) #strict=False
print("=> loaded Image model checkpoint: "+CKPT_PATH)
model_roi = CXRClassifier(num_classes=N_CLASSES, is_pre_trained=True, is_roi=True).cuda()
CKPT_PATH = config['CKPT_PATH'] + 'roi_model.pkl'
checkpoint = torch.load(CKPT_PATH)
model_roi.load_state_dict(checkpoint) #strict=False
print("=> loaded ROI model checkpoint: "+CKPT_PATH)
model_fusion = FusionClassifier(input_size=2048, output_size=N_CLASSES).cuda()
CKPT_PATH = config['CKPT_PATH'] + 'fusion_model.pkl'
checkpoint = torch.load(CKPT_PATH)
model_fusion.load_state_dict(checkpoint) #strict=False
print("=> loaded Fusion model checkpoint: "+CKPT_PATH)
roigen = ROIGenerator() #region generator
else:
print('No required model')
return #over
torch.backends.cudnn.benchmark = True # improve train speed slightly
print('******************** load model succeed!********************')
print('******* begin testing!*********')
gt = torch.FloatTensor().cuda()
pred_img = torch.FloatTensor().cuda()
pred_roi = torch.FloatTensor().cuda()
pred_fusion = torch.FloatTensor().cuda()
# switch to evaluate mode
model_img.eval() #turn to test mode
model_roi.eval()
model_fusion.eval()
cudnn.benchmark = True
with torch.autograd.no_grad():
for batch_idx, (image, label) in enumerate(dataloader_test):
gt = torch.cat((gt, label.cuda()), 0)
#image-level
var_image = torch.autograd.Variable(image).cuda()
#var_label = torch.autograd.Variable(label).cuda()
conv_fea_img, fc_fea_img, out_img = model_img(var_image)#forward
pred_img = torch.cat((pred_img, out_img.data), 0)
#ROI-level
roi = roigen.ROIGeneration(conv_fea_img)
var_roi = torch.autograd.Variable(roi).cuda()
out_roi = model_roi(var_roi)
pred_roi = torch.cat((pred_roi, out_roi.data), 0)
#Fusion
fc_fea_fusion = torch.cat((fc_fea_img,var_roi), 1)
var_fusion = torch.autograd.Variable(fc_fea_fusion).cuda()
out_fusion = model_fusion(var_fusion)
pred_fusion = torch.cat((pred_fusion, out_fusion.data), 0)
sys.stdout.write('\r testing process: = {}'.format(batch_idx+1))
sys.stdout.flush()
#for evaluation
AUROC_img = compute_AUCs(gt, pred_img)
AUROC_avg = np.array(AUROC_img).mean()
for i in range(N_CLASSES):
print('The AUROC of {} is {:.4f}'.format(CLASS_NAMES[i], AUROC_img[i]))
print('The average AUROC is {:.4f}'.format(AUROC_avg))
AUROC_roi = compute_AUCs(gt, pred_roi)
AUROC_avg = np.array(AUROC_roi).mean()
for i in range(N_CLASSES):
print('The AUROC of {} is {:.4f}'.format(CLASS_NAMES[i], AUROC_roi[i]))
print('The average AUROC is {:.4f}'.format(AUROC_avg))
AUROC_fusion = compute_AUCs(gt, pred_fusion)
AUROC_avg = np.array(AUROC_fusion).mean()
for i in range(N_CLASSES):
print('The AUROC of {} is {:.4f}'.format(CLASS_NAMES[i], AUROC_fusion[i]))
print('The average AUROC is {:.4f}'.format(AUROC_avg))