def create_model(self):
super().create_model()
encoder_model = densenet.DenseNet169(include_top=False,
weights='imagenet',
pooling=None)
input_image = encoder_model.input
# input_image = layers.Input(batch_shape=(self.BATCH_SIZE, *self.MODEL_INPUT_IMAGE_SHAPE), name='input_1')
# encoder_model = densenet.DenseNet169(include_top=False, weights='imagenet',
# input_tensor=input_image,
# # input_shape=self.MODEL_INPUT_IMAGE_SHAPE,
# pooling=None)
input_male = layers.Input(shape=(1, ), name='input_male')
# input_male = layers.Input(batch_shape=(self.BATCH_SIZE, 1), name='input_male')
x_image = encoder_model.output
x_image = layers.GlobalAveragePooling2D(
name='encoder_pooling')(x_image)
# x_image = layers.Reshape(target_shape=(-1, 1,))(x_image)
# x_image = layers.Flatten()(x_image)
x_image = layers.Reshape(target_shape=(-1, ))(x_image)
x_male = layers.Dense(32, activation='relu')(input_male)
x = layers.concatenate([x_image, x_male], axis=-1)
x = layers.Dense(1000, activation='relu')(x)
x = layers.Dense(1000, activation='relu')(x)
output_age = layers.Dense(1, activation='linear', name='output_age')(x)
self.model = keras.models.Model(inputs=[input_image, input_male],
outputs=output_age)
self.model.summary(line_length=150)
def build_base_model(self, inputs, blocks=None):
inputs = keras.layers.Lambda(
lambda x: keras_densenet.preprocess_input(x))(inputs)
if self.backbone_name == 'densenet121':
densenet = keras_densenet.DenseNet121(include_top=False,
weights='imagenet',
input_tensor=inputs)
elif self.backbone_name == 'densenet169':
densenet = keras_densenet.DenseNet169(include_top=False,
input_tensor=inputs,
weights='imagenet')
elif self.backbone_name == 'densenet201':
densenet = keras_densenet.DenseNet201(include_top=False,
input_tensor=inputs,
weights='imagenet')
elif self.backbone_name == 'densenet':
if blocks is None:
raise ValueError(
'blocks must be specified to use custom densenet backbone')
densenet = keras_densenet.DenseNet(blocks=blocks,
include_top=False,
input_tensor=inputs,
weights='imagenet')
else:
raise ValueError("Backbone '{}' not recognized.".format(
self.backbone_name))
return densenet
def dense_net169(self, percent2retrain): 'Returns a Densenet169 architecture NN' dense_model = densenet.DenseNet169(input_shape=self.input_dim, weights='imagenet', include_top=False) # freeze base layers if percent2retrain < 1: for layer in dense_model.layers[:-int(len(dense_model.layers)*percent2retrain)]: layer.trainable = False # add classification top layer model = Sequential() model.add(dense_model) model.add(Flatten()) model.add(Dense(512, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(self.n_classes, activation='sigmoid')) return model
def DenseNet3D(input_shape, growth_rate=32, block_config=(6, 12, 24, 16),
num_init_features=64, bn_size=4, drop_rate=0, num_classes=5):
r"""Densenet-BC model class, based on
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
Args:
growth_rate (int) - how many filters to add each layer (`k` in paper)
block_config (list of 4 ints) - how many layers in each pooling block
num_init_features (int) - the number of filters to learn in the first convolution layer
bn_size (int) - multiplicative factor for number of bottle neck layers
(i.e. bn_size * k features in the bottleneck layer)
drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes
"""
#-----------------------------------------------------------------
inp_2d = (Input(shape=(224,224,3), name='2d_input'))
batch_densenet = densenet.DenseNet169(include_top=False, input_shape=(224,224,3), input_tensor=inp_2d, weights='imagenet')
for layer in batch_densenet.layers:
layer.trainable = False
# Configure the 2D CNN to take batches of pictures
inp_2d_batch = (Input(shape=input_shape, name='2d_input_batch'))
batch_densenet = TimeDistributed(batch_densenet)(inp_2d_batch)
batch_densenet = Model(inputs=inp_2d_batch, outputs=batch_densenet)
#-----------------------------------------------------------------
# inp_3d = (Input(shape=input_shape, name='3d_input'))
t3d = T3D169(include_top=False, input_shape=input_shape)
#--------------from 2d densenet model-----------------
x = GlobalAveragePooling3D(name='avg_pool_t3d')(t3d.output)
y = GlobalAveragePooling3D(name='avg_pool_densnet3d')(batch_densenet.output)
#-----------------------------------------------------
x = keras.layers.concatenate([x,y])
x = Dropout(0.65)(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.35)(x)
out = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=[inp_2d_batch, t3d.input], outputs=[out])
# model.summary()
return model
def get_model():
num_classes = 10
input_shape = (MODELS[MODEL]['size'], MODELS[MODEL]['size'], 3)
#preprocess = imagenet_utils.preprocess_input
input_image = Input(shape=input_shape)
if MODEL == "densenet121":
base_model = densenet.DenseNet121(include_top=False,
pooling=None,
weights='imagenet',
input_shape=input_shape)
elif MODEL == "densenet169":
base_model = densenet.DenseNet169(include_top=False,
pooling=None,
weights='imagenet',
input_shape=input_shape)
elif MODEL == "densenet201":
base_model = densenet.DenseNet201(include_top=False,
pooling=None,
weights='imagenet',
input_shape=input_shape)
elif MODEL == "inceptionresnet":
base_model = inception_resnet_v2.InceptionResNetV2(
include_top=False,
pooling=None,
weights='imagenet',
input_shape=input_shape)
elif MODEL == "inception":
base_model = inception_v3.InceptionV3(include_top=False,
pooling=None,
weights='imagenet',
input_shape=input_shape)
elif MODEL == "mobilenet":
base_model = mobilenet.MobileNet(include_top=False,
pooling=None,
weights='imagenet',
input_shape=input_shape)
elif MODEL == "resnet":
base_model = resnet50.ResNet50(include_top=False,
pooling=None,
weights='imagenet',
input_shape=input_shape)
elif MODEL == "vgg16":
base_model = vgg16.VGG16(include_top=False,
pooling=None,
weights='imagenet',
input_shape=input_shape)
elif MODEL == "vgg19":
base_model = vgg19.VGG19(include_top=False,
pooling=None,
weights='imagenet',
input_shape=input_shape)
else:
print("Bad model type:", MODEL)
sys.exit(-1)
x = input_image
x = base_model(x)
x = Reshape((-1, ))(x)
#x = Dropout(rate=?)(x)
x = Dense(512, activation='relu', name='fc1')(x)
x = Dropout(0.3, name='dropout_fc1')(x)
x = Dense(128, activation='relu', name='fc2')(x)
x = Dropout(0.3, name='dropout_fc2')(x)
prediction = Dense(nclass, activation="softmax", name="predictions")(x)
# this is the model we will train
my_model = Model(inputs=(input_image), outputs=prediction)
# compile the model (should be done *after* setting layers to non-trainable)
opt = optimizers.Adam(lr=1e-4)
my_model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['acc'])
my_model.summary()
return my_model
def DenseNet3D(input_shape, growth_rate=32, block_config=(6, 12, 24, 16),
num_init_features=64, bn_size=4, drop_rate=0, num_classes=5):
r"""Densenet-BC model class, based on
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
Args:
growth_rate (int) - how many filters to add each layer (`k` in paper)
block_config (list of 4 ints) - how many layers in each pooling block
num_init_features (int) - the number of filters to learn in the first convolution layer
bn_size (int) - multiplicative factor for number of bottle neck layers
(i.e. bn_size * k features in the bottleneck layer)
drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes
"""
#-----------------------------------------------------------------
inp_2d = (Input(shape=(224,224,3), name='2d_input'))
pretrained_densenet = densenet.DenseNet169(include_top=False, input_shape=(224,224,3), input_tensor=inp_2d, weights='imagenet')
for layer in pretrained_densenet.layers:
layer.trainable = False
#-----------------------------------------------------------------
# First convolution-----------------------
inp_3d = (Input(shape=input_shape, name='3d_input'))
# need to check padding
x = (Conv3D(num_init_features, kernel_size=(3, 7, 7),
strides=2, padding='same', use_bias=False))(inp_3d)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# need to check padding
x = MaxPooling3D(pool_size=(3, 3, 3), strides=(
2, 2, 2), padding='valid')(x)
# Each denseblock
num_features = num_init_features
for i, num_layers in enumerate(block_config):
# print('Pass', i)
x = _DenseBlock(x, num_layers=num_layers,
bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
# print('Not Last layer, so adding Temporal Transition Layer')
x = _TTL(x)
# num_features = 128*3
x = _Transition(x, num_output_features=num_features)
num_features = num_features
# Final batch norm
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = AveragePooling3D(pool_size=(1, 7, 7))(x)
x = Flatten(name='flatten_3d')(x)
x = Dense(1024, activation='relu')(x)
#--------------fron 2d densenet model-----------------
y = GlobalAveragePooling2D(name='avg_pool_densnet2d')(pretrained_densenet.output)
y = Dense(1024, activation='relu')(y)
#-----------------------------------------------------
x = keras.layers.concatenate([x,y])
x = Dropout(0.65)(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.35)(x)
out = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=[inp_2d, inp_3d], outputs=[out])
model.summary()
return model
) # decode the results into a list of tuples (class, description, probability)
print(decode_prediction
) # change top to x to get x highest predictions
df.at[i, prediction_likelihood[2 * x]] = decode_prediction[0][0][
1] # save description
df.at[i,
prediction_likelihood[2 * x + 1]] = decode_prediction[0][0][
2] # save probability
print(trueLabels[x])
# initialize models
resnet_model = resnet50.ResNet50(weights='imagenet')
vgg_model = vgg16.VGG16(weights='imagenet')
densenet_model = densenet.DenseNet169(weights='imagenet')
mobile_model = mobilenet.MobileNet(weights='imagenet')
modelEnsemble = [resnet_model, vgg_model, densenet_model]
# list of used models: resnet_model, vgg_model, mobile_model, densenet_model
modelEnsemleArt = [resnet50, vgg16, densenet]
# list of model nets: resnet50, vgg16, mobilenet, densenet
model_names = ['resnet50', 'vgg16', 'densenet']
# list of model Names: 'resnet50', 'vgg16', 'mobilenet','densenet'
data = [] # images to classify
trueLabels = [] # labels of images
extend = 'likelihood' # name for dataframe columns to store likelihood
prediction_likelihood = [
] # store prediction and likelihood [house, 0.10% , room 0.2% ...]
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
def __init__(self, model_name, transfer=False, input_shape=None):
'''
Load pre-trained model.
model_name: one of available models.
transfer: whether to transfer learning. if True, exclude the fully-connected layer at
the top of the network. default False
input_shape: : Transfer learning input shape. If None, default input shape. Default None. https://keras.io/applications/
'''
self.name = model_name
# vgg16
if model_name == 'vgg16':
import keras.applications.vgg16 as vgg16
self.lib = vgg16
if transfer:
if input_shape == None:
self.input_size = (224, 224)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = vgg16.VGG16(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (224, 224)
self.model = vgg16.VGG16(include_top=(not transfer))
# vgg 19
elif model_name == 'vgg19':
import keras.applications.vgg19 as vgg19
self.lib = vgg19
if transfer:
if input_shape == None:
self.input_size = (224, 224)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = vgg19.VGG19(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (224, 224)
self.model = vgg19.VGG19(include_top=(not transfer))
# resnet50
elif model_name == 'resnet50':
import keras.applications.resnet50 as resnet50
self.lib = resnet50
if transfer:
if input_shape == None:
self.input_size = (224, 224)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = resnet50.ResNet50(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (224, 224)
self.model = resnet50.ResNet50(include_top=(not transfer))
# xception
elif model_name == 'xception':
import keras.applications.xception as xception
self.lib = xception
if transfer:
if input_shape == None:
self.input_size = (299, 299)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = xception.Xception(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (299, 299)
self.model = xception.Xception(include_top=(not transfer))
# densenet121
elif model_name == 'densenet121':
import keras.applications.densenet as densenet
self.lib = densenet
if transfer:
if input_shape == None:
self.input_size = (224, 224)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = densenet.DenseNet121(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (224, 224)
self.model = densenet.DenseNet121(include_top=(not transfer))
# densenet169
elif model_name == 'densenet169':
import keras.applications.densenet as densenet
self.lib = densenet
if transfer:
if input_shape == None:
self.input_size = (224, 224)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = densenet.DenseNet169(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (224, 224)
self.model = densenet.DenseNet169(include_top=(not transfer))
# densenet201
elif model_name == 'densenet201':
import keras.applications.densenet as densenet
self.lib = densenet
if transfer:
if input_shape == None:
self.input_size = (224, 224)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = densenet.DenseNet201(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (224, 224)
self.model = densenet.DenseNet201(include_top=(not transfer))
# inceptionResnetV2
elif model_name == 'inception_resnet_v2':
import keras.applications.inception_resnet_v2 as inception_resnet_v2
self.lib = inception_resnet_v2
if transfer:
if input_shape == None:
self.input_size = (299, 299)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = self.lib.InceptionResNetV2(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (299, 299)
self.model = self.lib.InceptionResNetV2(include_top=(not transfer))
# inceptionV3
elif model_name == 'inception_v3':
import keras.applications.inception_v3 as inception_v3
self.lib = inception_v3
if transfer:
if input_shape == None:
self.input_size = (299, 299)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = self.lib.InceptionV3(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (299, 299)
self.model = self.lib.InceptionV3(include_top=(not transfer))
# nasnet mobile
elif model_name == 'nasnet_mobile':
import keras.applications.nasnet as nasnet
self.lib = nasnet
if transfer:
if input_shape == None:
self.input_size = (224, 224)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = self.lib.NASNetMobile(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (224, 224)
self.model = self.lib.NASNetMobile(include_top=(not transfer))
# nasnet large
elif model_name == 'nasnet_large':
import keras.applications.nasnet as nasnet
self.lib = nasnet
if transfer:
if input_shape == None:
self.input_size = (331, 331)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = self.lib.NASNetLarge(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (331, 331)
self.model = self.lib.NASNetLarge(include_top=(not transfer))
# mobilenet
elif model_name == 'mobilenet':
import keras.applications.mobilenet as mobilenet
self.lib = mobilenet
if transfer:
if input_shape == None:
self.input_size = (224, 224)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = self.lib.MobileNet(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (224, 224)
self.model = self.lib.MobileNet(include_top=(not transfer))
# mobilenet v2
elif model_name == 'mobilenet_v2':
import keras.applications.mobilenet_v2 as mobilenet_v2
self.lib = mobilenet_v2
if transfer:
if input_shape == None:
self.input_size = (224, 224)
input_shape = (self.input_size[0], self.input_size[1], 3)
else:
self.input_size = (input_shape[0], input_shape[1])
self.model = self.lib.MobileNetV2(include_top=(not transfer), input_shape=input_shape)
else:
self.input_size = (224, 224)
self.model = self.lib.MobileNetV2(include_top=(not transfer))
def get_model_and_data(mode, model_name):
if model_name == 'vgg16':
model = vgg16.VGG16(weights='imagenet', include_top=True)
preprocess_func = partial(vgg16.preprocess_input, mode='tf')
target_size = (224, 224)
elif model_name == 'vgg19':
model = vgg19.VGG19(weights='imagenet', include_top=True)
preprocess_func = partial(vgg19.preprocess_input, mode='tf')
target_size = (224, 224)
elif model_name == 'resnet50':
model = resnet50.ResNet50(weights='imagenet', include_top=True)
preprocess_func = partial(resnet50.preprocess_input, mode='tf')
target_size = (224, 224)
elif model_name == 'inception_v3':
model = inception_v3.InceptionV3(weights='imagenet', include_top=True)
preprocess_func = inception_v3.preprocess_input
target_size = (299, 299)
elif model_name == 'inception_resnet_v2':
model = inception_resnet_v2.InceptionResNetV2(weights='imagenet',
include_top=True)
preprocess_func = inception_resnet_v2.preprocess_input
target_size = (299, 299)
elif model_name == 'xception':
model = xception.Xception(weights='imagenet', include_top=True)
preprocess_func = xception.preprocess_input
target_size = (299, 299)
elif model_name == 'mobilenet':
model = mobilenet.MobileNet(weights='imagenet', include_top=True)
preprocess_func = mobilenet.preprocess_input
target_size = (224, 224)
elif model_name.startswith('densenet'):
model_type = int(model_name[len('densenet'):])
if model_type == 121:
model = densenet.DenseNet121(weights='imagenet', include_top=True)
elif model_type == 169:
model = densenet.DenseNet169(weights='imagenet', include_top=True)
elif model_type == 201:
model = densenet.DenseNet201(weights='imagenet', include_top=True)
else:
raise ValueError(
f'Got incorrect DenseNet model type ({model_type}).')
preprocess_func = densenet.preprocess_input
target_size = (224, 224)
else:
raise ValueError(f'Got unknown NN model ({model_name}).')
if mode == 'train':
input_dir = cfg.TRAIN_IMAGES_DIR
elif mode == 'test':
input_dir = cfg.TEST_IMAGES_DIR
else:
raise ValueError(f'Got unknown proc mode ({mode}).')
output_file = cfg.NN_IMAGE_FEATURES[model_name][mode]['memmap']
return model, input_dir, output_file, preprocess_func, target_size
def get_model_and_data(mode, model_name):
if model_name == "vgg16":
model = vgg16.VGG16(weights="imagenet", include_top=True)
preprocess_func = partial(vgg16.preprocess_input, mode="tf")
target_size = (224, 224)
elif model_name == "vgg19":
model = vgg19.VGG19(weights="imagenet", include_top=True)
preprocess_func = partial(vgg19.preprocess_input, mode="tf")
target_size = (224, 224)
elif model_name == "resnet50":
model = resnet50.ResNet50(weights="imagenet", include_top=True)
preprocess_func = partial(resnet50.preprocess_input, mode="tf")
target_size = (224, 224)
elif model_name == "inception_v3":
model = inception_v3.InceptionV3(weights="imagenet", include_top=True)
preprocess_func = inception_v3.preprocess_input
target_size = (299, 299)
elif model_name == "inception_resnet_v2":
model = inception_resnet_v2.InceptionResNetV2(weights="imagenet",
include_top=True)
preprocess_func = inception_resnet_v2.preprocess_input
target_size = (299, 299)
elif model_name == "xception":
model = xception.Xception(weights="imagenet", include_top=True)
preprocess_func = xception.preprocess_input
target_size = (299, 299)
elif model_name == "mobilenet":
model = mobilenet.MobileNet(weights="imagenet", include_top=True)
preprocess_func = mobilenet.preprocess_input
target_size = (224, 224)
elif model_name.startswith("densenet"):
model_type = int(model_name[len("densenet"):])
if model_type == 121:
model = densenet.DenseNet121(weights="imagenet", include_top=True)
elif model_type == 169:
model = densenet.DenseNet169(weights="imagenet", include_top=True)
elif model_type == 201:
model = densenet.DenseNet201(weights="imagenet", include_top=True)
else:
raise ValueError(
f"Got incorrect DenseNet model type ({model_type}).")
preprocess_func = densenet.preprocess_input
target_size = (224, 224)
else:
raise ValueError(f"Got unknown NN model ({model_name}).")
if mode == "train":
input_dir = cfg.TRAIN_IMAGES_DIR
elif mode == "test":
input_dir = cfg.TEST_IMAGES_DIR
else:
raise ValueError(f"Got unknown proc mode ({mode}).")
output_file = cfg.NN_IMAGE_FEATURES[model_name][mode]["memmap"]
return model, input_dir, output_file, preprocess_func, target_size
else:
image_test = np.append(image_test, [img_norm], axis=0);
if K.image_data_format() == 'channels_first':
image_test = image_test.reshape(image_test.shape[0], 3, img_rows, img_cols)
else:
image_test = image_test.reshape(image_test.shape[0], img_rows, img_cols, 3)
#print(image_test)
image_test = image_test.astype('float32')
image_test /= 255
print('image_test shape:', image_test.shape)
print(image_test.shape[0], 'test samples')
base_model = densenet.DenseNet169(weights='imagenet', input_shape=input_shape, include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(12, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
model.load_weights("DenseNet169.hdf5")
predict_results = model.predict(image_test);
base_model3 = densenet.DenseNet201(weights='imagenet', input_shape=input_shape, include_top=False)
x3 = base_model3.output
x3 = GlobalAveragePooling2D()(x3)
x3 = Dropout(0.5)(x3)
