def GeResult():
# Dataset
Dataset = BDXJTU2019_test(root='/home/dell/Desktop/2019BaiduXJTU/data')
Dataloader = data.DataLoader(Dataset,
1,
num_workers=1,
shuffle=False,
pin_memory=True)
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_6.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()
net4 = MultiModalNet2('densenet201', 'DPN26', 0.5)
net4.load_state_dict(
torch.load(
'/home/dell/Desktop/2019BaiduXJTU/weights/densenet201_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_10.pth'
))
net4.to(device)
net4.eval()
net5 = MultiModalNet1('multiscale_se_resnext', 'DPN26', 0.5)
net5.load_state_dict(
torch.load(
'/home/dell/Desktop/2019BaiduXJTU/weights/multiscale_se_resnext_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_11.pth'
))
net5.to(device)
net5.eval()
net6 = MultiModalNet1('multiscale_resnet', 'DPN26', 0.5)
net6.load_state_dict(
torch.load(
'/home/dell/Desktop/2019BaiduXJTU/weights/multiscale_resnet_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_10.pth'
))
net6.to(device)
net6.eval()
net7 = MultiModalNet2('densenet201', 'DPN26', 0.5)
net7.load_state_dict(
torch.load(
'/home/dell/Desktop/2019BaiduXJTU/weights/densenet201_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_4.pth'
))
net7.to(device)
net7.eval()
#Network = pnasnet5large(6, None)
#Network = ResNeXt101_64x4d(6)
# net1 =MultiModalNet('se_resnext50_32x4d', 'DPN26', 0.5)
# net1.load_state_dict(torch.load('/home/zxw/2019BaiduXJTU/weights/MultiModal_se_resnext50_32x4d_resample_pretrained/BDXJTU2019_SGD_16.pth'))
# net1.eval()
# net2 = MultiModalNet('multiscale_se_resnext_HR', 'DPN26', 0.5)
# net2.load_state_dict(torch.load('/home/zxw/2019BaiduXJTU/weights/MultiModal_50_MS_resample_pretrained_HR/BDXJTU2019_SGD_26.pth'))
# net2.eval()
# net3 = MultiModalNet('se_resnext50_32x4d', 'DPN26', 0.5)
# net3.load_state_dict(torch.load('/home/zxw/2019BaiduXJTU/weights/MultiModal_se_resnext50_32x4d_resample_pretrained_w/BDXJTU2019_SGD_50.pth'))
# net3.eval()
# net4 = MultiModalNet('se_resnext50_32x4d', 'DPN26', 0.5)
# net4.load_state_dict(torch.load('/home/zxw/2019BaiduXJTU/weights/MultiModal_se_resnext50_32x4d_resample_pretrained_1/BDXJTU2019_SGD_80.pth'))
# net4.eval()
filename = 'MM_ensemble4_TTA.txt'
f = open(filename, 'w')
for (Input_O, Input_H, visit_tensor, anos) in Dataloader:
ConfTensor_O = net1.forward(Input_O.to(device),
visit_tensor.to(device))
ConfTensor_H = net2.forward(Input_O.to(device),
visit_tensor.to(device))
ConfTensor_V = net3.forward(Input_O.to(device),
visit_tensor.to(device))
ConfTensor_V0 = net3.forward(Input_H.to(device),
visit_tensor.to(device))
ConfTensor_1 = net4.forward(Input_O.to(device),
visit_tensor.to(device))
ConfTensor_10 = net4.forward(Input_H.to(device),
visit_tensor.to(device))
ConfTensor_2 = net5.forward(Input_O.to(device),
visit_tensor.to(device))
ConfTensor_20 = net5.forward(Input_H.to(device),
visit_tensor.to(device))
ConfTensor_3 = net6.forward(Input_O.to(device),
visit_tensor.to(device))
ConfTensor_4 = net7.forward(Input_O.to(device),
visit_tensor.to(device))
preds = torch.nn.functional.normalize(
ConfTensor_O) + torch.nn.functional.normalize(
ConfTensor_H) + 2 * torch.nn.functional.normalize(
ConfTensor_V) + torch.nn.functional.normalize(
ConfTensor_V0) + torch.nn.functional.normalize(
ConfTensor_1
) + torch.nn.functional.normalize(
ConfTensor_10
) + 2 * torch.nn.functional.normalize(
ConfTensor_2) + torch.nn.functional.normalize(
ConfTensor_20
) + torch.nn.functional.normalize(
ConfTensor_3
) + 2 * torch.nn.functional.normalize(ConfTensor_4)
_, pred = preds.data.topk(1, 1, True, True)
#f.write(anos[0] + ',' + CLASSES[4] + '\r\n')
print(anos[0][:-4] + '\t' + CLASSES[pred[0][0]] + '\n')
f.writelines(anos[0][:-4] + '\t' + CLASSES[pred[0][0]] + '\n')
f.close()
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 = 13
net = MultiModalNet1('multiscale_resnet', '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/multiscale_resnet_se_resnext50_32x4d_resample_pretrained_80w_1/BDXJTU2019_SGD_'
+ str(epoch) + '.pth'))
# net.load_state_dict(torch.load('/home/dell/Desktop/2019BaiduXJTU/weights/densenet201_se_resnext50_32x4d_resample_pretrained_80w_1/inception_008.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 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()