def load_models():
"""Load models """
model_list = []
model_list.append(VGGNet("model/vggnet.h5"))
model_list.append(ResNet("model/resnet.h5"))
model_list.append(VGGNet5("model/vggnet5.h5"))
return model_list
if args.model_type == 'ex_atten':
model = VGG_interpretable_atten(num_classes=num_classe)
elif args.model_type == 'ex':
model = VGG_interpretable(num_classes=num_classe)
elif args.model_type == 'atten':
model = VGG_atten(num_classes=num_classe)
elif args.model_type == 'gradcam':
model = VGG_gradcam(num_classes=num_classe)
elif args.model_type == 'ex_gradcam':
model = VGG_interpretable_gradcam(num_classes=num_classe)
elif args.model_type == 'ex_gradcam5':
model = VGG_interpretable_gradcam(num_classes=num_classe)
elif args.model_type == 'ex_gradcam2':
model = VGG_interpretable_gradcam2(num_classes=num_classe)
else:
model = VGGNet(num_classes=num_classe)
#model = VGG_gradcam(num_classes=num_classe)
elif args.model == 'resnet':
if args.model_type == 'ex_atten':
model = VGG_interpretable_atten(num_classes=num_classe)
elif args.model_type == 'ex':
model = Resnet_interpretable(num_classes=num_classe)
elif args.model_type == 'ex_gradcam':
model = Resnet_interpretable_gradcam(num_classes=num_classe)
elif args.model_type == 'ex_gradcam2':
model = VGG_interpretable_gradcam2(num_classes=num_classe)
else:
model = Resnet(num_classes=num_classe)
elif args.model == 'mobilenet':
if args.model_type == 'ex_atten':
model = VGG_interpretable_atten(num_classes=num_classe)
def train_factory(MODEL_NAME):
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
# model = CCR(input_shape=(img_width,img_height,1),classes=charset_size)
# model = LeNet.build(width=img_width, height=img_height, depth=1, classes=charset_size)
# model = ResNet.build_model(SHAPE=(img_width,img_height,1), classes=charset_size)
# vgg net 5
# MODEL_PATH='trained_model/vggnet5.hdf5'
# model=VGGNet5.vgg(input_shape=(img_width,img_height,1),classes=charset_size)
model=None
if(MODEL_NAME=='inception_resnet_v2'):
model=InceptionResNetV2.inception_resnet_v2(input_shape=(img_width,img_height,3),classes=charset_size,weights='./trained_model/inception_resnet_v2/inception_resnet_v2.12-0.8244.hdf5')
elif(MODEL_NAME=='xception'):
# xeception
model=Xception.Xception((img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='mobilenet_v2'):
#mobilenet v2
model=MobileNetv2.MobileNet_v2((img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='inception_v3'):
#mobilenet v2
model=Inception_v3.inception((img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='vgg16'):
model=VGGNet.vgg(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='vgg19'):
model=VGG19.VGG19(input_shape=(img_width,img_height,3),classes=charset_size,weights='weights/vgg19_weights_tf_dim_ordering_tf_kernels.h5')
elif(MODEL_NAME=='resnet50'):
model=ResNet50.resnet(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='inception_v4'):
model=inception_v4.inception_v4(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='resnet34'):
model=ResNet34.ResNet34(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='densenet121'):
model=DenseNet.DenseNet(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='densenet161'):
model=DenseNet.DenseNet(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='shufflenet_v2'):
model=ShuffleNetV2.ShuffleNetV2(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='resnet_attention_56'):
model=Resnet_Attention_56.Resnet_Attention_56(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='squeezenet'):
model=SqueezeNet.SqueezeNet(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='seresnet50'):
model=SEResNet50.SEResNet50(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='se_resnext'):
model=SEResNext.SEResNext(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='nasnet'):
model=NASNet.NASNetLarge(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='custom'):
model=Custom_Network.Custom_Network(input_shape=(img_width,img_height,3),classes=charset_size)
elif(MODEL_NAME=='resnet18'):
model=ResnetBuilder.build_resnet_18(input_shape=(img_width,img_height,3),num_outputs=charset_size)
print(model.summary())
train(model,MODEL_NAME)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
valloader = torch.utils.data.DataLoader(valset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
# Build model
vgg16 = VGGNet(10).to(device)
# Loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(vgg16.parameters(), lr=lr, momentum=momentum)
# Training
print("Start training")
best_val_acc = 0.0
for epoch in range(epochs):
# train part
for i, data in enumerate(trainloader, 0):
inputs, labels = data[0].to(device), data[1].to(device)
optimizer.zero_grad()
import numpy as np
import argparse
import glob
from vgg16 import VGGNet
ap = argparse.ArgumentParser()
ap.add_argument("--index", required=True, help="Name of index file")
args = vars(ap.parse_args())
if __name__ == "__main__":
feats = []
names = []
model = VGGNet()
# i=0
for imgPath in glob.glob("../../" + "database" + "/*.jpg"):
imageId = imgPath[imgPath.rfind("/") + 1:]
norm_feat = model.extract_feat(imgPath)
feats.append(norm_feat)
names.append(imageId)
# print("extracting feature from image No. %d " %((i+1)))
# i+=1
feats = np.array(feats)
output = args["index"]
h5f = h5py.File(output, 'w')
h5f.create_dataset('dataset_1', data=feats)
testset = torchvision.datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transform)
testloader = torch.utils.data.DataLoader(testset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
# 저장한 모델 불러오기
vgg16 = VGGNet(10).to(device)
optimizer = optim.SGD(vgg16.parameters(), lr=lr, momentum=momentum)
checkpoint = torch.load('vgg16_ckpt.pt')
vgg16.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
loss = checkpoint['loss']
# PATH = 'vgg16.pth'
# vgg16.load_state_dict(torch.load(PATH))
# 전체 test 데이터셋에 대한 정확도
correct = 0
total = 0