def NASNetLarge(*args, **kwargs):
return nasnet.NASNetLarge(*args, **kwargs)
def get_models(model_name, NUM_CLASSES):
#region Normal Inceptionv3,Xception,InceptionResnetV2
if model_name == 'InceptionV3':
model = InceptionV3(include_top=True,
input_shape=(299, 299, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32
# BATCH_SIZE_TRAIN = 64 # 增加GPU之后
elif model_name == 'InceptionV4':
from LIBS.Neural_Networks.classification import inception_v4
model = inception_v4.create_inception_v4(nb_classes=NUM_CLASSES,
load_weights=False)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 16
elif model_name == 'Xception':
model = Xception(include_top=True,
input_shape=(299, 299, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 28 # 增加GPU之后
elif model_name == 'InceptionResNetV2':
# following keras implemantation don't use dropout
# 标准的不用dropout，收敛快了许多, train准确率提高
model = InceptionResNetV2(include_top=True,
input_shape=(299, 299, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32 # 增加GPU之后
elif model_name == 'MobileNetV2': #Total params: 2,261,827
# model = MobileNetV2(include_top=True, input_shape=(256, 256, 3), weights=None, classes=NUM_CLASSES)
model = MobileNetV2(include_top=True,
input_shape=(224, 224, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 64
elif model_name == 'My_Xception':
model = my_xception.Xception(classes=NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32 # 增加GPU之后
elif model_name == 'DRN_A18': #params:20,631,682
from LIBS.Neural_Networks.classification import my_DRN_A
model = my_DRN_A.DRN_A_Builder.build_DRN_A_18(input_shape=(256, 256,
3),
num_classes=NUM_CLASSES)
IMAGE_SIZE = 256
BATCH_SIZE_TRAIN = 32
elif model_name == 'DRN_A34':
from LIBS.Neural_Networks.classification import my_DRN_A_1
# model = my_DRN_A.DRN_A_Builder.build_DRN_A_34(input_shape=(256, 256, 3), num_classes=NUM_CLASSES)
model = my_DRN_A_1.DRN_A_Builder.build_DRN_A_34(
input_shape=(256, 256, 3), num_classes=NUM_CLASSES)
IMAGE_SIZE = 256
BATCH_SIZE_TRAIN = 32
elif model_name == 'DRN_A50':
from LIBS.Neural_Networks.classification import my_DRN_A
model = my_DRN_A.DRN_A_Builder.build_DRN_A_50(input_shape=(256, 256,
3),
num_classes=NUM_CLASSES)
IMAGE_SIZE = 256
BATCH_SIZE_TRAIN = 32
elif model_name == 'DRN_A101':
from LIBS.Neural_Networks.classification import my_DRN_A
model = my_DRN_A.DRN_A_Builder.build_DRN_A_101(input_shape=(256, 256,
3),
num_classes=NUM_CLASSES)
IMAGE_SIZE = 256
BATCH_SIZE_TRAIN = 32
elif model_name == 'DRN_C26': #params:20,631,682
from LIBS.Neural_Networks.classification import my_DRN_C
model = my_DRN_C.DRN_C_Builder.build_DRN_C_26(input_shape=(224, 224,
3),
num_classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 32
elif model_name == 'DRN_C42': # params:30,750,978
from LIBS.Neural_Networks.classification import my_DRN_C
model = my_DRN_C.DRN_C_Builder.build_DRN_C_42(input_shape=(224, 224,
3),
num_classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 32
elif model_name == 'DPN92': #DPN92,DPN98,DPN107,DPN137
from LIBS.Neural_Networks.classification import my_dpn
model = my_dpn.DPN92(input_shape=(224, 224, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 16 # GTX1080 ti 32 exhausted
elif model_name == 'DPN98':
from LIBS.Neural_Networks.classification import my_dpn
model = my_dpn.DPN92(input_shape=(224, 224, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 16 # GTX1080 ti 32 exhausted
elif model_name == 'DPN107':
from LIBS.Neural_Networks.classification import my_dpn
model = my_dpn.DPN107(input_shape=(224, 224, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 16 # GTX1080 ti 32 exhausted
elif model_name == 'DPN137':
from LIBS.Neural_Networks.classification import my_dpn
model = my_dpn.DPN137(input_shape=(224, 224, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 16 # GTX1080 ti 32 exhausted
elif model_name == 'DenseNet121': #121,169,201
model = densenet.DenseNet121(input_shape=(224, 224, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 32
elif model_name == 'DenseNet169':
model = densenet.DenseNet169(input_shape=(224, 224, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 32
elif model_name == 'DenseNet201':
model = densenet.DenseNet201(input_shape=(224, 224, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 16
elif model_name == 'NasnetMobile':
model = nasnet.NASNetMobile(input_shape=(224, 224, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 32
elif model_name == 'my_Mnasnet':
from LIBS.Neural_Networks.classification import my_Mnasnet
model = my_Mnasnet.MnasNet(input_shape=(224, 224, 3),
classes=NUM_CLASSES)
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 64
elif model_name == "Mnasnet":
from LIBS.Neural_Networks.classification import my_Mnasnet
model = my_Mnasnet.MnasNet(classes=NUM_CLASSES,
input_shape=(224, 224, 3))
IMAGE_SIZE = 224
BATCH_SIZE_TRAIN = 64
elif model_name == 'NasnetMedium': #Trainable params: 22,695,624
model = nasnet.NASNet(
input_shape=(299, 299, 3),
penultimate_filters=1920,
num_blocks=7,
stem_block_filters=80, # Large:96, Mobile:32
classes=NUM_CLASSES,
default_size=299)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 16
elif model_name == 'NasnetLarge':
model = nasnet.NASNetLarge(input_shape=(331, 331, 3),
weights=None,
classes=NUM_CLASSES)
IMAGE_SIZE = 331
BATCH_SIZE_TRAIN = 8
#endregion
#region All kinds of ResNet, ResNeXt
# 7*32=224, 256, 288, 320, 352, 384
elif model_name == 'ResNet50':
from LIBS.Neural_Networks.classification import my_resnet
model = my_resnet.ResnetBuilder.build_resnet_50((256, 256, 3),
NUM_CLASSES)
IMAGE_SIZE = 256
BATCH_SIZE_TRAIN = 32
elif model_name == 'ResNet50_288':
from LIBS.Neural_Networks.classification import my_resnet
model = my_resnet.ResnetBuilder.build_resnet_50((288, 288, 3),
NUM_CLASSES)
IMAGE_SIZE = 288
BATCH_SIZE_TRAIN = 32
elif model_name == 'ResNet101':
from LIBS.Neural_Networks.classification import my_resnet
model = my_resnet.ResnetBuilder.build_resnet_101((256, 256, 3),
NUM_CLASSES)
IMAGE_SIZE = 256
BATCH_SIZE_TRAIN = 32
elif model_name == 'ResNet448':
from LIBS.Neural_Networks.classification import my_resnet
# model = my_resnet.ResnetBuilder.build_resnet_mymodel_34_64_5((448, 448, 3), NUM_CLASSES)
model = my_resnet.ResnetBuilder.build_resnet_448_1((448, 448, 3),
NUM_CLASSES)
IMAGE_SIZE = 448
BATCH_SIZE_TRAIN = 32 # 增加GPU之后
elif model_name == 'ResNext448':
from LIBS.Neural_Networks.classification import resNeXt
model = resNeXt.my_ResNext(input_shape=(448, 448, 3),
classes=NUM_CLASSES)
IMAGE_SIZE = 448
BATCH_SIZE_TRAIN = 16 # 增加GPU之后
#endregion
#region multi label (Inceptionv3, Xception, InceptionResnetV2)
elif model_name == 'Multi_label_InceptionV3':
base_model = InceptionV3(include_top=False, weights=None)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32
elif model_name == 'Multi_label_SE_InceptionV3':
from LIBS.Neural_Networks.classification import my_se_inception_v3
base_model = my_se_inception_v3.SE_InceptionV3((299, 299, 3),
include_top=False)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32
elif model_name == 'Multi_label_Xception':
base_model = Xception(include_top=False, weights=None)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32 # 增加GPU之后
elif model_name == 'Multi_label_my_Xception':
from LIBS.Neural_Networks.classification import my_xception
model = my_xception.Xception(classes=NUM_CLASSES, multi_labels=True)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32 # 增加GPU之后
elif model_name == 'Multi_label_InceptionResNetV2':
base_model = InceptionResNetV2(include_top=False, weights=None)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32
elif model_name == 'Multi_label_DRN_A_Xception':
from LIBS.Neural_Networks import DRN_A_Xception_Builder
base_model = DRN_A_Xception_Builder.build_DRN_A_xception(
input_shape=(288, 288, 3), num_classes=29, include_top=False)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 288
BATCH_SIZE_TRAIN = 32
elif model_name == 'Multi_NasnetMedium':
base_model = nasnet.NASNet(
input_shape=(299, 299, 3),
penultimate_filters=1920,
num_blocks=7,
stem_block_filters=80, # Large:96, Mobile:32
classes=NUM_CLASSES,
default_size=299,
include_top=False)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 16
elif model_name == 'Multi_DRN_A18':
from LIBS.Neural_Networks.classification import my_DRN_A
#original number of parameters, resnet34 :21.8M, resnet50:25.6M
base_model = my_DRN_A.DRN_A_Builder.build_DRN_A_18(
input_shape=(288, 288, 3),
num_classes=NUM_CLASSES,
include_top=False)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 288
BATCH_SIZE_TRAIN = 32
elif model_name == 'Multi_DRN_A34':
from LIBS.Neural_Networks.classification import my_DRN_A
base_model = my_DRN_A.DRN_A_Builder.build_DRN_A_34(
input_shape=(288, 288, 3),
num_classes=NUM_CLASSES,
include_top=False)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 288
BATCH_SIZE_TRAIN = 32
elif model_name == 'Multi_DRN_A50':
from LIBS.Neural_Networks.classification import my_DRN_A
base_model = my_DRN_A.DRN_A_Builder.build_DRN_A_50(
input_shape=(288, 288, 3),
num_classes=NUM_CLASSES,
include_top=False)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 288
BATCH_SIZE_TRAIN = 32
elif model_name == 'Multi_DRN_C26': #params:20,631,682
from LIBS.Neural_Networks.classification import my_DRN_C
base_model = my_DRN_C.DRN_C_Builder.build_DRN_C_26(
input_shape=(288, 288, 3),
num_classes=NUM_CLASSES,
include_top=False)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 288
BATCH_SIZE_TRAIN = 32
elif model_name == 'Multi_DRN_C42': # params:30,750,978
from LIBS.Neural_Networks.classification import my_DRN_C
base_model = my_DRN_C.DRN_C_Builder.build_DRN_C_42(
input_shape=(288, 288, 3),
num_classes=NUM_CLASSES,
include_top=False)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 288
BATCH_SIZE_TRAIN = 32
elif model_name == 'Multi_DRN_C58':
from LIBS.Neural_Networks.classification import my_DRN_C
base_model = my_DRN_C.DRN_C_Builder.build_DRN_C_58(
input_shape=(288, 288, 3),
num_classes=NUM_CLASSES,
include_top=False)
model = add_multilabels_top(base_model, NUM_CLASSES)
IMAGE_SIZE = 288
BATCH_SIZE_TRAIN = 32
# endregion
#region SE Net(Se_InceptionV3, Se_InceptionResNetV2等)
elif model_name == 'Se_InceptionV3':
from LIBS.Neural_Networks.classification import my_se_inception_v3
model = my_se_inception_v3.SE_InceptionV3((299, 299, 3),
classes=NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 48
elif model_name == 'Se_InceptionResNetV2':
from LIBS.Neural_Networks.classification import my_se_inception_resnet_v2
model = my_se_inception_resnet_v2.SE_InceptionResNetV2(
(299, 299, 3), classes=NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32
elif model_name == 'Se_Resnext':
from LIBS.Neural_Networks import my_se_resnext
model = my_se_resnext.SEResNextImageNet((299, 299, 3),
classes=NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 24
elif model_name == 'Se_Resnet50':
from LIBS.Neural_Networks import my_se_resnet
model = my_se_resnet.SEResNet50((299, 299, 3), classes=NUM_CLASSES)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 32
#endregion
'''other models
#region multi label SE_NET
elif model_name == 'Multi_label_Se_InceptionV3':
from Neural_Networks import my_se_inception_v3
base_model = my_se_inception_v3.SE_InceptionV3((299, 299, 3), include_top=False,
classes=29, weights=None)
x = base_model.output
from keras.layers import GlobalAveragePooling2D, Dense
from keras.models import Model
x = GlobalAveragePooling2D()(x)
predictions = Dense(NUM_CLASSES, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 48
elif model_name == 'Multi_label_Se_InceptionResNetV2':
from Neural_Networks import my_se_inception_resnet_v2
base_model = my_se_inception_resnet_v2.SE_InceptionResNetV2((299, 299, 3), include_top=False,
classes=29, weights=None)
x = base_model.output
from keras.layers import GlobalAveragePooling2D, Dense
from keras.models import Model
x = GlobalAveragePooling2D()(x)
predictions = Dense(NUM_CLASSES, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)
IMAGE_SIZE = 299
BATCH_SIZE_TRAIN = 24
BATCH_SIZE_TRAIN = 32 # 增加GPU之后
#endregion
'''
return model, IMAGE_SIZE, BATCH_SIZE_TRAIN