def main():
#create model
best_prec1 = 0
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.set_device(0)
model = 0
if args.basenet == 'MultiModal':
model = MultiModalNet('se_resnet50', 'DPN26', 0.5)
#model = MultiModalNet('se_resnet50', 'DPN26', 0.5)
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
elif args.basenet == 'oct_resnet101':
model = oct_resnet101()
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
assert not model==0
model = model.cuda()
cudnn.benchmark = True
# Dataset
Aug = Augmentation()
Dataset_train = MM_BDXJTU2019(root = args.dataset_root, mode = 'MM_cleaned_train', transform = Aug)
#weights = [class_ration[label] for data,label in Dataset_train]
Dataloader_train = data.DataLoader(Dataset_train, args.batch_size,
num_workers = args.num_workers,
shuffle = True, pin_memory = True)
Dataset_val = MM_BDXJTU2019(root = args.dataset_root, mode = 'val')
Dataloader_val = data.DataLoader(Dataset_val, batch_size = 8,
num_workers = args.num_workers,
shuffle = True, pin_memory = True)
criterion = nn.CrossEntropyLoss(weight = weights).cuda()
print("info:",args.batch_size)
print("load pretrained model from model3 _____21_6000___adam")
state_dict1 = torch.load('model3/BDXJTU2019_SGD_21_6000.pth')
model.load_state_dict(state_dict1, strict=False)
# Optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr = args.lr, momentum = args.momentum,
# weight_decay = args.weight_decay)
Optimizer=optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),lr=0.001)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(Optimizer, epoch)
# train for one epoch
train(Dataloader_train, model, criterion, Optimizer, epoch) #train(Dataloader_train, Network, criterion, Optimizer, epoch)
# evaluate on validation set
#_,_ = validate(Dataloader_val, model, criterion) #prec1 = validate(Dataloader_val, Network, criterion)
# remember best prec@1 and save checkpoint
#is_best = prec1 > best_prec1
#best_prec1 = max(prec1, best_prec1)
#if is_best:
if epoch%1 == 0:
1#torch.save(model.state_dict(), 'model3/BDXJTU2019_SGD_' + repr(epoch) + '.pth')
def GetNetTensor():
# Priors
torch.set_default_tensor_type('torch.cuda.FloatTensor')
Dataset_val = MM_BDXJTU2019(root = 'data', mode = 'val', TRAIN_IMAGE_DIR = 'train_image_raw')
Dataloader_val = data.DataLoader(Dataset_val, 1, num_workers = 0, shuffle = False, pin_memory = False)
# Network
cudnn.benchmark = True
MODEL_NAME = [i for i in os.listdir('./weights') if i!='best_models'][0]
BEST_DIR = op.join('weights','best_models')
# Find & Load Best_Model
if op.isdir(BEST_DIR) and len(os.listdir(BEST_DIR))>0:
best_model = MultiModalNet(MODEL_NAME, 'DPN26', 0.5).cuda()
pthlist = [i for i in os.listdir(BEST_DIR) if i[-4:]=='.pth']
pthlist.sort(key=lambda x:eval(re.findall(r'\d+',x)[-1]))
best_model.load_state_dict(torch.load(op.join(BEST_DIR,pthlist[-1])))
best_model.eval()
TensorGenerator( Dataloader_val, NET_VAL_DATA_PATH, best_model)
else:
print('No best_model pth found in ./weights/best_models.')
def GeResult():
# Dataset
Dataset_val = MM_BDXJTU2019(root='/home/dell/Desktop/2019BaiduXJTU/data',
mode='1_val')
Dataloader_val = data.DataLoader(Dataset_val,
batch_size=1,
num_workers=4,
shuffle=True,
pin_memory=True)
class_names = [
'001', '002', '003', '004', '005', '006', '007', '008', '009'
]
# construct network
epoch = 12
net = MultiModalNet('se_resnet152', 'DPN26', 0.5)
# if torch.cuda.device_count() > 1:
# print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
# net = nn.DataParallel(net)
net.to(device)
# net.load_state_dict(torch.load('/home/dell/Desktop/2019BaiduXJTU/weights/se_resnet152_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_' + str(epoch) + '.pth'))
net.load_state_dict(
torch.load(
'/home/dell/Desktop/2019BaiduXJTU/weights/se_resnet152_se_resnext50_32x4d_resample_pretrained_80w_1/inception_005.pth'
))
print('load ' + str(epoch) + ' epoch model')
net.eval()
results = []
results_anno = []
for i, (Input_img, Input_vis, Anno) in enumerate(Dataloader_val):
Input_img = Input_img.to(device)
Input_vis = Input_vis.to(device)
ConfTensor = net.forward(Input_img, Input_vis)
_, pred = ConfTensor.data.topk(1, 1, True, False)
results.append(pred.item())
results_anno.append(Anno) #append annotation results
if ((i + 1) % 1000 == 0):
print(i + 1)
print('Accuracy of Orignal Input: %0.6f' %
(accuracy_score(results, results_anno, normalize=True)))
# print accuracy of different input
print('Accuracy of Orignal Input: %0.6f' %
(accuracy_score(results, results_anno, normalize=True)))
cnf_matrix = confusion_matrix(results_anno, results)
cnf_tr = np.trace(cnf_matrix)
cnf_tr = cnf_tr.astype('float')
print(cnf_tr / len(Dataset_val))
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=class_names,
title='Confusion matrix, without normalization')
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=class_names,
normalize=True,
title='Normalized confusion matrix')
plt.show()
def main():
#create model
best_prec1 = 0
if args.basenet == 'se_resnet152':
model = MultiModalNet('se_resnet152', 'DPN26', 0.5)
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
elif args.basenet == 'se_resnext50_32x4d':
model = MultiModalNet1('se_resnext50_32x4d', 'DPN26', 0.5)
elif args.basenet == 'se_resnet50':
model = MultiModalNet1('se_resnet50', 'DPN26', 0.5)
elif args.basenet == 'densenet201':
model = MultiModalNet2('densenet201', 'DPN26', 0.5)
elif args.basenet == 'oct_resnet101':
model = oct_resnet101()
# print("load pretrained model from /home/dell/Desktop/2019BaiduXJTU/weights/densenet201_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_4.pth")
# pre='/home/dell/Desktop/2019BaiduXJTU/weights/densenet201_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_1.pth'
# model.load_state_dict(torch.load(pre))
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
model = nn.DataParallel(model)
model.to(device)
# Dataset
Aug = Augmentation()
Dataset_train = MM_BDXJTU2019(root='/home/dell/Desktop/2019BaiduXJTU/data',
mode='MM_1_train',
transform=Aug)
#weights = [class_ration[label] for data,label in Dataset_train]
Dataloader_train = data.DataLoader(Dataset_train,
128,
num_workers=4,
shuffle=True,
pin_memory=True)
Dataset_val = MM_BDXJTU2019(root='/home/dell/Desktop/2019BaiduXJTU/data',
mode='val')
Dataloader_val = data.DataLoader(Dataset_val,
batch_size=32,
num_workers=4,
shuffle=True,
pin_memory=True)
# criterion = nn.CrossEntropyLoss(weight = weights).cuda()
criterion = nn.CrossEntropyLoss().to(device)
# Optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr = args.lr, momentum = args.momentum,
# weight_decay = args.weight_decay)
Optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(Optimizer, epoch)
# train for one epoch
train(Dataloader_train, model, criterion, Optimizer, epoch
) #train(Dataloader_train, Network, criterion, Optimizer, epoch)
# evaluate on validation set
#_,_ = validate(Dataloader_val, model, criterion) #prec1 = validate(Dataloader_val, Network, criterion)
# remember best prec@1 and save checkpoint
#is_best = prec1 > best_prec1
#best_prec1 = max(prec1, best_prec1)
#if is_best:
if epoch % 1 == 0:
torch.save(
model.module.state_dict(), 'weights/' + args.basenet +
'_se_resnext50_32x4d_resample_pretrained_80w_1/' +
'BDXJTU2019_SGD_' + repr(epoch) + '.pth')
def main():
###Enter Main Func:
mp.set_start_method('spawn')
#create model
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.set_device(0)
MODEL_NAME = 'multiscale_se_resnext_HR'
MODEL_DIR = op.join('weights', MODEL_NAME)
BEST_DIR = op.join('weights', 'best_models')
if not op.isdir(MODEL_DIR):
os.mkdir(MODEL_DIR)
if not op.isdir(BEST_DIR):
os.mkdir(BEST_DIR)
# if args.basenet == 'MultiModal':
model = MultiModalNet(MODEL_NAME, 'DPN26', 0.5)
# #net = Networktorch.nn.DataParallel(Network, device_ids=[0])
# elif args.basenet == 'oct_resnet101':
# model = oct_resnet101()
# #net = Networktorch.nn.DataParallel(Network, device_ids=[0])
model = model.cuda()
cudnn.benchmark = True
RESUME = False
# MODEL_PATH = './weights/best_models/se_resnext50_32x4d_SGD_w_46.pth'
pthlist = [i for i in os.listdir(MODEL_DIR) if i[-4:] == '.pth']
if len(pthlist) > 0:
pthlist.sort(key=lambda x: eval(re.findall(r'\d+', x)[-1]))
MODEL_PATH = op.join(MODEL_DIR, pthlist[-1])
model.load_state_dict(torch.load(MODEL_PATH))
RESUME = True
# Dataset
Aug = Augmentation()
Dataset_train = MM_BDXJTU2019(root=args.dataset_root,
mode='train',
transform=Aug,
TRAIN_IMAGE_DIR='train_image_raw')
#weights = [class_ration[label] for data,label in Dataset_train]
Dataloader_train = data.DataLoader(Dataset_train,
args.batch_size,
num_workers=args.num_workers,
shuffle=True,
pin_memory=True)
Dataset_val = MM_BDXJTU2019(root=args.dataset_root,
mode='val',
TRAIN_IMAGE_DIR='train_image_raw')
Dataloader_val = data.DataLoader(Dataset_val,
batch_size=64,
num_workers=args.num_workers,
shuffle=True,
pin_memory=True)
criterion = nn.CrossEntropyLoss(weight=weights).cuda()
Optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
args.start_epoch = eval(re.findall(r'\d+',
MODEL_PATH)[-1]) if RESUME else 0
best_pred1, best_preds = 0, {}
if RESUME and op.isfile(best_log):
best_preds = json.load(open(best_log))
best_pred1 = best_preds['best_pred1']
elif len(os.listdir(BEST_DIR)) > 0:
best_model = MultiModalNet(MODEL_NAME, 'DPN26', 0.5).cuda()
pthlist = [i for i in os.listdir(BEST_DIR) if i[-4:] == '.pth']
pthlist.sort(key=lambda x: eval(re.findall(r'\d+', x)[-1]))
best_model.load_state_dict(torch.load(op.join(BEST_DIR, pthlist[-1])))
best_pred1 = validate(Dataloader_val,
best_model,
criterion,
printable=False)[0]
log_dict(eval(re.findall(r'\d+', pthlist[-1])[-1]), best_pred1)
log('#Resume: Another Start from Epoch {}'.format(args.start_epoch + 1))
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(Optimizer, epoch)
# train for one epoch
train(Dataloader_train, model, criterion, Optimizer, epoch
) #train(Dataloader_train, Network, criterion, Optimizer, epoch)
# evaluate on validation set
pred1, pred5 = validate(
Dataloader_val, model,
criterion) #pred1 = validate(Dataloader_val, Network, criterion)
# remember best pred@1 and save checkpoint
COMMON_MODEL_PATH = op.join(MODEL_DIR,
'SGD_fold1_{}.pth'.format(epoch + 1))
BEST_MODEL_PATH = op.join(
BEST_DIR, '{}_SGD_fold1_{}.pth'.format(MODEL_NAME, epoch + 1))
torch.save(model.state_dict(), COMMON_MODEL_PATH)
if pred1 > best_pred1:
best_pred1 = max(pred1, best_pred1)
torch.save(model.state_dict(), BEST_MODEL_PATH)
log_dict(epoch + 1, best_pred1)
log('Epoch:{}\tpred1:{}\tBest_pred1:{}\n'.format(
epoch + 1, pred1, best_pred1))
def GeResult():
# Dataset
Dataset_val = MM_BDXJTU2019(root='/home/dell/Desktop/2019BaiduXJTU/data',
mode='val')
Dataloader_val = data.DataLoader(Dataset_val,
batch_size=1,
num_workers=2,
shuffle=True,
pin_memory=True)
class_names = [
'001', '002', '003', '004', '005', '006', '007', '008', '009'
]
net1 = MultiModalNet1('se_resnet50', 'DPN26', 0.5)
net1.load_state_dict(
torch.load(
'/home/dell/Desktop/2019BaiduXJTU/weights/se_resnet50_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_9.pth'
))
net1.to(device)
net1.eval()
net2 = MultiModalNet('se_resnet152', 'DPN26', 0.5)
net2.load_state_dict(
torch.load(
'/home/dell/Desktop/2019BaiduXJTU/weights/se_resnet152_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_4.pth'
))
net2.to(device)
net2.eval()
net3 = MultiModalNet2('densenet201', 'DPN26', 0.5)
net3.load_state_dict(
torch.load(
'/home/dell/Desktop/2019BaiduXJTU/weights/densenet201_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_3.pth'
))
net3.to(device)
net3.eval()
# construct network
# net1 =MultiModalNet('se_resnext50_32x4d', 'DPN26', 0.5)
# net1.load_state_dict(torch.load('/home/dell/Desktop/2019BaiduXJTU/models/BDXJTU2019_SGD_16.pth'))
# net1.eval()
# net2 = MultiModalNet('se_resnext50_32x4d', 'DPN26', 0.5)
# net2.load_state_dict(torch.load('/home/dell/Desktop/2019BaiduXJTU/models/BDXJTU2019_SGD_26.pth'))
# net2.eval()
# net3 =MultiModalNet('se_resnext50_32x4d', 'DPN26', 0.5)
# net3.load_state_dict(torch.load('/home/dell/Desktop/2019BaiduXJTU/models/BDXJTU2019_SGD_50.pth'))
# net3.eval()
results = []
results_anno = []
for i, (Input_img, Input_vis, Anno) in enumerate(Dataloader_val):
Input_img = Input_img.to(device)
Input_vis = Input_vis.to(device)
ConfTensor1 = net1.forward(Input_img, Input_vis)
ConfTensor2 = net2.forward(Input_img, Input_vis)
ConfTensor3 = net3.forward(Input_img, Input_vis)
ConfTensor = (torch.nn.functional.normalize(ConfTensor1) +
torch.nn.functional.normalize(ConfTensor2) +
torch.nn.functional.normalize(ConfTensor3)) / 3
score, pred = ConfTensor.data.topk(1, 1, True, False)
#print(score.item())
if (score.item() > 0.85):
results.append(pred.item())
results_anno.append(Anno) #append annotation results
if ((i + 1) % 2000 == 0):
print(i + 1)
print(len(results))
print('Accuracy of Orignal Input: %0.6f' %
(accuracy_score(results, results_anno, normalize=True)))
# print accuracy of different input
print('Accuracy of Orignal Input: %0.6f' %
(accuracy_score(results, results_anno, normalize=True)))
cnf_matrix = confusion_matrix(results_anno, results)
cnf_tr = np.trace(cnf_matrix)
cnf_tr = cnf_tr.astype('float')
print(cnf_tr / len(Dataset_val))
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=class_names,
title='Confusion matrix, without normalization')
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=class_names,
normalize=True,
title='Normalized confusion matrix')
plt.show()
def GeResult():
# Dataset
Dataset_val = MM_BDXJTU2019(root='data', mode='1_val')
Dataloader_val = data.DataLoader(Dataset_val,
batch_size=1,
num_workers=2,
shuffle=True,
pin_memory=True)
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.set_device(0)
class_names = [
'001', '002', '003', '004', '005', '006', '007', '008', '009'
]
# construct network
epoch = 80
net = MultiModalNet('se_resnext50_32x4d', 'DPN26', 0.5)
net.load_state_dict(
torch.load(
'/home/zxw/2019BaiduXJTU/weights/MultiModal_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_'
+ str(epoch) + '.pth'))
print('load ' + str(epoch) + ' epoch model')
net.eval()
results = []
results_anno = []
for i, (Input_img, Input_vis, Anno) in enumerate(Dataloader_val):
Input_img = Input_img.cuda()
Input_vis = Input_vis.cuda()
ConfTensor = net.forward(Input_img, Input_vis)
_, pred = ConfTensor.data.topk(1, 1, True, False)
results.append(pred.item())
results_anno.append(Anno) #append annotation results
if ((i + 1) % 1000 == 0):
print(i + 1)
print('Accuracy of Orignal Input: %0.6f' %
(accuracy_score(results, results_anno, normalize=True)))
# print accuracy of different input
print('Accuracy of Orignal Input: %0.6f' %
(accuracy_score(results, results_anno, normalize=True)))
cnf_matrix = confusion_matrix(results_anno, results)
cnf_tr = np.trace(cnf_matrix)
cnf_tr = cnf_tr.astype('float')
print(cnf_tr / len(Dataset_val))
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=class_names,
title='Confusion matrix, without normalization')
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=class_names,
normalize=True,
title='Normalized confusion matrix')
plt.show()
def GeResult():
# Priors
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.set_device(0)
model_names=["","se_resnext101_32x4d","se_resnext50_32x4d","se_resnet50","densenet169","densenet121"]
weight_names=["","model1","model2","model3","model_densenet169","model_densenet121"]
model_id=5
batch_size=4
print(model_id)
model_name=model_names[model_id]
weight_name=weight_names[model_id]
weightfiles=os.listdir(weight_name)
MAX=0
for file in weightfiles:
if file.count("_")<3:
continue
s=file.split("BDXJTU2019_SGD_")[1]
s=s.split("_")
s1=int(s[0])
s2=int(s[1].split(".")[0])
if MAX<s1*1000000+s2:
MAX=s1*1000000+s2
MAXfile=file
MAXs1=s1
print(MAXfile)
# Dataset
Dataset = MM_BDXJTU2019("data_txt", mode = 'val')
if model_name.count("dense")>0:
Dataset = MM_BDXJTU2019_for_dense("data_txt", mode='val')
elif model_name.count("nasnet")>0:
Dataset = MM_BDXJTU2019_for_nasnet("data_txt", mode='val')
print(batch_size)
Dataloader = data.DataLoader(Dataset,batch_size,
num_workers=1,
shuffle=False, pin_memory=True)
# Network
cudnn.benchmark = True
# Network = pnasnet5large(6, None)
# Network = ResNeXt101_64x4d(6)
net = MultiModalNet(model_name, 'DPN26', 0.5)
print("weights model"+str(model_id))
net.load_state_dict(torch.load(weight_name+'/'+MAXfile))
net.eval()
filename = 'val_result/['+str(model_id)+'_'+str(MAXs1)+'].txt'
import numpy as np
csvO=open('val_result/csvO['+str(model_id)+'_'+str(MAXs1)+'].csv',"w")
cnt=0
name_id=np.zeros([50000])
for i,(Input_O,visit_tensor, anoss,ids) in enumerate(Dataloader):
ConfTensor_Os = net.forward(Input_O.cuda(), visit_tensor.cuda())
for id in range(ConfTensor_Os.shape[0]):
ConfTensor_O=ConfTensor_Os[id].reshape([1,9])
anos=[anoss[id]]
name_id[cnt]=int(ids[id])
preds_temp = torch.nn.functional.normalize(ConfTensor_O)
string = ""
for _ in range(9):
string = string + str(float(preds_temp[0][_])) + ","
string = string + "\n"
csvO.write(string)
####################
preds = torch.nn.functional.normalize(ConfTensor_O)
_, pred = preds.data.topk(1, 1, True, True)
if cnt%100==0:
print(cnt)
cnt+=1
print(name_id[:5])
np.save("name_id"+str(model_id)+".npy",name_id)
csvO.close()
def main():
#create model
best_prec1 = 0
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.set_device(0)
if args.basenet == 'MultiModal':
model = MultiModalNet('se_resnext50_32x4d', 'DPN26', 0.5)
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
elif args.basenet == 'oct_resnet101':
model = oct_resnet101()
#net = Networktorch.nn.DataParallel(Network, device_ids=[0])
model = model.cuda()
cudnn.benchmark = True
# Dataset
Aug = Augmentation()
Dataset_train = MM_BDXJTU2019(root=args.dataset_root,
mode='MM_1_train',
transform=Aug)
#weights = [class_ration[label] for data,label in Dataset_train]
Dataloader_train = data.DataLoader(Dataset_train,
args.batch_size,
num_workers=args.num_workers,
shuffle=True,
pin_memory=True)
Dataset_val = BDXJTU2019(root=args.dataset_root, mode='val')
Dataloader_val = data.DataLoader(Dataset_val,
batch_size=8,
num_workers=args.num_workers,
shuffle=True,
pin_memory=True)
criterion = nn.CrossEntropyLoss(weight=weights).cuda()
Optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(Optimizer, epoch)
# train for one epoch
train(Dataloader_train, model, criterion, Optimizer, epoch
) #train(Dataloader_train, Network, criterion, Optimizer, epoch)
# evaluate on validation set
#_,_ = validate(Dataloader_val, model, criterion) #prec1 = validate(Dataloader_val, Network, criterion)
# remember best prec@1 and save checkpoint
#is_best = prec1 > best_prec1
#best_prec1 = max(prec1, best_prec1)
#if is_best:
if epoch % 1 == 0:
torch.save(
model.state_dict(), 'weights/' + args.basenet +
'_se_resnext50_32x4d_resample_pretrained_80w_1/' +
'BDXJTU2019_SGD_' + repr(epoch) + '.pth')