def get_model():
if model_index == 0:
return mobilenet_v1.MobileNetV1()
elif model_index == 1:
return mobilenet_v2.MobileNetV2()
elif model_index == 2:
return mobilenet_v3_large.MobileNetV3Large()
elif model_index == 3:
return mobilenet_v3_small.MobileNetV3Small()
elif model_index == 4:
return efficientnet.efficient_net_b0()
elif model_index == 5:
return efficientnet.efficient_net_b1()
elif model_index == 6:
return efficientnet.efficient_net_b2()
elif model_index == 7:
return efficientnet.efficient_net_b3()
elif model_index == 8:
return efficientnet.efficient_net_b4()
elif model_index == 9:
return efficientnet.efficient_net_b5()
elif model_index == 10:
return efficientnet.efficient_net_b6()
elif model_index == 11:
return efficientnet.efficient_net_b7()
elif model_index == 12:
return resnext.ResNeXt50()
elif model_index == 13:
return resnext.ResNeXt101()
elif model_index == 14:
return inception_v4.InceptionV4()
elif model_index == 15:
return inception_resnet_v1.InceptionResNetV1()
elif model_index == 16:
return inception_resnet_v2.InceptionResNetV2()
elif model_index == 17:
return se_resnet.se_resnet_50()
elif model_index == 18:
return se_resnet.se_resnet_101()
elif model_index == 19:
return se_resnet.se_resnet_152()
elif model_index == 20:
return squeezenet.SqueezeNet()
elif model_index == 21:
return densenet.densenet_121()
elif model_index == 22:
return densenet.densenet_169()
elif model_index == 23:
return densenet.densenet_201()
elif model_index == 24:
return densenet.densenet_264()
elif model_index == 25:
return shufflenet_v2.shufflenet_0_5x()
elif model_index == 26:
return shufflenet_v2.shufflenet_1_0x()
elif model_index == 27:
return shufflenet_v2.shufflenet_1_5x()
elif model_index == 28:
return shufflenet_v2.shufflenet_2_0x()
Y = tf.placeholder(tf.float32, [None, 1000])
dg = DataGenerator('./data/val224_compressed.pkl', model=args.model, dtype='float32')
with tf.device('/cpu:0'):
if args.model == 'resnet':
logits = resnet50.ResNet50(X, weights)
elif args.model == 'inception':
logits = inception_v3.InceptionV3(X, weights)
elif args.model == 'vgg':
logits = vgg16.VGG16(X, weights)
elif args.model == 'mobilenet':
logits = mobilenet.MobileNet(X, weights, args.alpha)
elif args.model == 'xception':
logits = xception.Xception(X, weights)
else:
logits = squeezenet.SqueezeNet(X, weights)
prediction = tf.nn.softmax(logits)
pred = tf.argmax(prediction, 1)
acc = 0.
acc_top5 = 0.
print('Start evaluating {}'.format(args.model))
with tf.Session() as sess:
for im, label in dg.generator():
t1, t5 = sess.run([pred, prediction], feed_dict={X: im})
if t1[0] == label:
acc += 1
if label in top5_acc(t5[0].tolist()):
acc_top5 += 1
print('Top1 accuracy: {}'.format(acc / 50000))
print('Top5 accuracy: {}'.format(acc_top5 / 50000))
def get_model(name="squeezenet"):
if name == "squeezenet":
from models import squeezenet
return squeezenet.SqueezeNet()
def main(args):
BATCH_SIZE = args.batch_size
LR = args.learning_rate
EPOCH = args.epoch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
use_gpu = torch.cuda.is_available()
data_transforms = {
transforms.Compose([
transforms.Resize(320),
transforms.CenterCrop(299),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
}
transform = transforms.Compose([
transforms.Resize(size=(227, 227)),
transforms.RandomRotation(20),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), # 将图片转换为Tensor,归一化至[0,1]
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = torchvision.datasets.ImageFolder(root=args.train_images,
transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
# 从文件夹中读取validation数据
validation_dataset = torchvision.datasets.ImageFolder(
root=args.test_images, transform=transform)
print(validation_dataset.class_to_idx)
test_loader = torch.utils.data.DataLoader(validation_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
if args.model_name == "densenet":
Net = densenet.DenseNet().to(device)
if args.model_name == "alexnet":
Net = alexnet.AlexNet().to(device)
if args.model_name == "googlenet":
Net = googlenet.GoogLeNet().to(device)
if args.model_name == "mobilenet":
Net = mobilenet.MobileNetV2().to(device)
if args.model_name == "mnasnet":
Net = mnasnet.mnasnet1_0().to(device)
if args.model_name == "squeezenet":
Net = squeezenet.SqueezeNet().to(device)
if args.model_name == "resnet":
Net = resnet.resnet50().to(device)
if args.model_name == "vgg":
Net = vgg.vgg19().to(device)
if args.model_name == "shufflenetv2":
Net = shufflenetv2.shufflenet_v2_x1_0().to(device)
criterion = nn.CrossEntropyLoss()
opti = torch.optim.Adam(Net.parameters(), lr=LR)
if __name__ == '__main__':
Accuracy_list = []
Loss_list = []
for epoch in range(EPOCH):
sum_loss = 0.0
correct1 = 0
total1 = 0
for i, (images, labels) in enumerate(train_loader):
num_images = images.size(0)
images = Variable(images.to(device))
labels = Variable(labels.to(device))
if args.model_name == 'googlenet':
out = Net(images)
out = out[0]
else:
out = Net(images)
_, predicted = torch.max(out.data, 1)
total1 += labels.size(0)
correct1 += (predicted == labels).sum().item()
loss = criterion(out, labels)
print(loss)
opti.zero_grad()
loss.backward()
opti.step()
# 每训练100个batch打印一次平均loss
sum_loss += loss.item()
if i % 10 == 9:
print('train loss [%d, %d] loss: %.03f' %
(epoch + 1, i + 1, sum_loss / 2000))
print("train acc %.03f" % (100.0 * correct1 / total1))
sum_loss = 0.0
Accuracy_list.append(100.0 * correct1 / total1)
print('accurary={}'.format(100.0 * correct1 / total1))
Loss_list.append(loss.item())
x1 = range(0, EPOCH)
x2 = range(0, EPOCH)
y1 = Accuracy_list
y2 = Loss_list
total_test = 0
correct_test = 0
for i, (images, labels) in enumerate(test_loader):
start_time = time.time()
print('time_start', start_time)
num_images = images.size(0)
print('num_images', num_images)
images = Variable(images.to(device))
labels = Variable(labels.to(device))
print("GroundTruth", labels)
if args.model_name == 'googlenet':
out = Net(images)[0]
out = out[0]
else:
out = Net(images)
_, predicted = torch.max(out.data, 1)
print("predicted", predicted)
correct_test += (predicted == labels).sum().item()
total_test += labels.size(0)
print('time_usage', (time.time() - start_time) / args.batch_size)
print('total_test', total_test)
print('correct_test', correct_test)
print('accurary={}'.format(100.0 * correct_test / total_test))
plt.subplot(2, 1, 1)
plt.plot(x1, y1, 'o-')
plt.title('Train accuracy vs. epoches')
plt.ylabel('Train accuracy')
plt.subplot(2, 1, 2)
plt.plot(x2, y2, '.-')
plt.xlabel('Train loss vs. epoches')
plt.ylabel('Train loss')
# plt.savefig("accuracy_epoch" + str(EPOCH) + ".png")
plt.savefig(args.output_dir + '/' + 'accuracy_epoch' + str(EPOCH) +
'.png')
plt.show()
torch.save(args.output_dir, args.model_name + '.pth')
# create model
if args.model == 'vgg':
model = vgg16.VGG16(input_shape=(224, 224, 3))
elif args.model == 'inception':
model = inception_v3.InceptionV3(input_shape=(299, 299, 3))
elif args.model == 'inception_resnet':
model = inception_resnet_v2.InceptionResNetV2(input_shape=(299, 299, 3))
elif args.model == 'xception':
model = xception.Xception(input_shape=(299, 299, 3))
elif args.model == 'resnet':
model = resnet_50.ResNet50(input_shape=(224, 224, 3))
elif args.model == 'densenet':
model = densenet_121.DenseNet(reduction=0.5, classes=1000)
elif args.model == 'squeezenet':
model = squeezenet.SqueezeNet(input_shape=(227, 227, 3), classes=1000)
elif args.model == 'mobilenet':
model = mobilenet.MobileNet(input_shape=(224, 224, 3), alpha=args.alpha)
else:
raise ValueError("Do not support {}".format(args.model))
model.summary()
model_name = args.model if args.model != 'mobilenet' else args.model + '_' + str(args.alpha)
model.load_weights('./weights/{}'.format(weights[model_name]), by_name=True)
print('Evaluating {0} with {1} data type'.format(args.model, args.dtype))
dg = DataGenerator('./data/val224_compressed.pkl', model=args.model, dtype=args.dtype)
acc = 0
acc_top5 = 0
for im, label in dg.generator():
out = model.predict(im)
pred = np.argmax(out[0])
from keras.utils import get_file
from keras.utils import layer_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD, Adam
from models import resnet, squeezenet, inceptionv3, alexnet
output_directory = '/home/student/Desktop/NurtientDeficiencyDetection/'
h5f = h5py.File("{}data_training.h5".format(output_directory), 'r')
train_X = h5f['X_train']
train_Y = h5f['Y_train']
h5f1 = h5py.File("{}data_testing.h5".format(output_directory), 'r')
test_X = h5f1['X_test']
print(test_X.shape)
test_Y = h5f1['Y_test']
input_shape = [224, 224, 3]
model = squeezenet.SqueezeNet(2)
optimizer1 = keras.optimizers.Adam(learning_rate=0.003,
beta_1=0.9,
beta_2=0.999,
amsgrad=False)
model.compile(optimizer=optimizer1,
loss='categorical_crossentropy',
metrics=['accuracy'])
# add this line before model.fit
checkpointer = ModelCheckpoint(
filepath='/home/student/Desktop/ganesh/weights_squeezenet_3.h5',
verbose=1,
save_best_only=True)
#change model.fit in this way
def get_model():
if model_index == 0:
return mobilenet_v1.MobileNetV1()
elif model_index == 1:
return mobilenet_v2.MobileNetV2()
elif model_index == 2:
return mobilenet_v3_large.MobileNetV3Large()
elif model_index == 3:
return mobilenet_v3_small.MobileNetV3Small()
elif model_index == 4:
return efficientnet.efficient_net_b0()
elif model_index == 5:
return efficientnet.efficient_net_b1()
elif model_index == 6:
return efficientnet.efficient_net_b2()
elif model_index == 7:
return efficientnet.efficient_net_b3()
elif model_index == 8:
return efficientnet.efficient_net_b4()
elif model_index == 9:
return efficientnet.efficient_net_b5()
elif model_index == 10:
return efficientnet.efficient_net_b6()
elif model_index == 11:
return efficientnet.efficient_net_b7()
elif model_index == 12:
return resnext.ResNeXt50()
elif model_index == 13:
return resnext.ResNeXt101()
elif model_index == 14:
return inception_v4.InceptionV4()
elif model_index == 15:
return inception_resnet_v1.InceptionResNetV1()
elif model_index == 16:
return inception_resnet_v2.InceptionResNetV2()
elif model_index == 17:
return se_resnet.se_resnet_50()
elif model_index == 18:
return se_resnet.se_resnet_101()
elif model_index == 19:
return se_resnet.se_resnet_152()
elif model_index == 20:
return squeezenet.SqueezeNet()
elif model_index == 21:
return densenet.densenet_121()
elif model_index == 22:
return densenet.densenet_169()
elif model_index == 23:
return densenet.densenet_201()
elif model_index == 24:
return densenet.densenet_264()
elif model_index == 25:
return shufflenet_v2.shufflenet_0_5x()
elif model_index == 26:
return shufflenet_v2.shufflenet_1_0x()
elif model_index == 27:
return shufflenet_v2.shufflenet_1_5x()
elif model_index == 28:
return shufflenet_v2.shufflenet_2_0x()
elif model_index == 29:
return resnet.resnet_18()
elif model_index == 30:
return resnet.resnet_34()
elif model_index == 31:
return resnet.resnet_50()
elif model_index == 32:
return resnet.resnet_101()
elif model_index == 33:
return resnet.resnet_152()
elif model_index == 34:
return vgg16.VGG16()
elif model_index == 35:
return vgg16_mini.VGG16()
elif model_index == 36:
return VGG16_self.VGG16()
elif model_index == 10086:
return diy_resnet.resnet_50()
else:
raise ValueError("The model_index does not exist.")