Python layers.MaxPool2D方法代码示例

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def gettest_model():
    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    A = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    B = Activation("relu", name='relu1')(A)
    C = MaxPool2D(pool_size=2)(B)
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(C)
    x = Activation("relu", name='relu2')(x)
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    K = Activation("relu", name='relu3')(x)


    x = Flatten()(K)
    dense = Dense(2,name = "dense")(x)
    output = Activation("relu", name='relu4')(dense)
    x = Model([input], [output])
    x.load_weights("./model/model12.h5")
    ok = Model([input], [dense])

    for layer in ok.layers:
        print(layer)

    return ok 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def convolutional_autoencoder():

    input_shape=(28,28,1)
    n_channels = input_shape[-1]
    model = Sequential()
    model.add(Conv2D(32, (3,3), activation='relu', padding='same', input_shape=input_shape))
    model.add(MaxPool2D(padding='same'))
    model.add(Conv2D(16, (3,3), activation='relu', padding='same'))
    model.add(MaxPool2D(padding='same'))
    model.add(Conv2D(8, (3,3), activation='relu', padding='same'))
    model.add(UpSampling2D())
    model.add(Conv2D(16, (3,3), activation='relu', padding='same'))
    model.add(UpSampling2D())
    model.add(Conv2D(32, (3,3), activation='relu', padding='same'))
    model.add(Conv2D(n_channels, (3,3), activation='sigmoid', padding='same'))
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def model_definition():
        """ Keras RNetwork for MTCNN """
        input_ = Input(shape=(24, 24, 3))
        var_x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input_)
        var_x = PReLU(shared_axes=[1, 2], name='prelu1')(var_x)
        var_x = MaxPool2D(pool_size=3, strides=2, padding='same')(var_x)

        var_x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(var_x)
        var_x = PReLU(shared_axes=[1, 2], name='prelu2')(var_x)
        var_x = MaxPool2D(pool_size=3, strides=2)(var_x)

        var_x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(var_x)
        var_x = PReLU(shared_axes=[1, 2], name='prelu3')(var_x)
        var_x = Permute((3, 2, 1))(var_x)
        var_x = Flatten()(var_x)
        var_x = Dense(128, name='conv4')(var_x)
        var_x = PReLU(name='prelu4')(var_x)
        classifier = Dense(2, activation='softmax', name='conv5-1')(var_x)
        bbox_regress = Dense(4, name='conv5-2')(var_x)
        return [input_], [classifier, bbox_regress] 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def create_Kao_Onet( weight_path = 'model48.h5'):
    input = Input(shape = [48,48,3])
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1,2],name='prelu1')(x)
    x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1,2],name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1,2],name='prelu3')(x)
    x = MaxPool2D(pool_size=2)(x)
    x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x)
    x = PReLU(shared_axes=[1,2],name='prelu4')(x)
    x = Permute((3,2,1))(x)
    x = Flatten()(x)
    x = Dense(256, name='conv5') (x)
    x = PReLU(name='prelu5')(x)

    classifier = Dense(2, activation='softmax',name='conv6-1')(x)
    bbox_regress = Dense(4,name='conv6-2')(x)
    landmark_regress = Dense(10,name='conv6-3')(x)
    model = Model([input], [classifier, bbox_regress, landmark_regress])
    model.load_weights(weight_path, by_name=True)

    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def create_Kao_Rnet (weight_path = 'model24.h5'):
    input = Input(shape=[24, 24, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
    x = MaxPool2D(pool_size=3,strides=2, padding='same')(x)

    x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)

    x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
    x = Permute((3, 2, 1))(x)
    x = Flatten()(x)
    x = Dense(128, name='conv4')(x)
    x = PReLU( name='prelu4')(x)
    classifier = Dense(2, activation='softmax', name='conv5-1')(x)
    bbox_regress = Dense(4, name='conv5-2')(x)
    model = Model([input], [classifier, bbox_regress])
    model.load_weights(weight_path, by_name=True)
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def get_convnet_landslide_all(args) -> Model:
    input_shape = (args.area_size, args.area_size, 14)
    model = Sequential()
    model.add(Conv2D(8, 3, 3, input_shape=input_shape, init='normal'))
    model.add(Activation('relu'))
    model.add(Conv2D(8, 3, 3, init='normal'))
    model.add(Activation('relu'))
    model.add(MaxPool2D((1, 1), strides=(1, 1)))
    model.add(Dropout(0.25))
    model.add(Flatten(name="flatten"))
    #
    model.add(Dense(512, activation='relu', name='dense', init='normal'))
    model.add(Dropout(0.25))
    model.add(Dense(1, name='last_layer'))
    model.add(Activation('sigmoid'))
    
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def get_model_1(args):
    model = Sequential()
    model.add(Conv2D(32, (5, 5), input_shape=(args.area_size, args.area_size, 14)))
    model.add(Activation('relu'))
    model.add(Conv2D(16, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPool2D((1, 1), strides=(1, 1)))
    model.add(Dropout(0.25))
    #
    model.add(AvgPool2D((3, 3), strides=(1, 1)))
    model.add(Flatten(name="flatten"))
    #
    model.add(Dense(1, name='last_layer'))
    model.add(Activation('sigmoid'))

    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def get_model_cifar(args):
    model = Sequential()

    model.add(Conv2D(32, (3, 3), padding='same', input_shape=(args.area_size, args.area_size, 14)))
    model.add(Activation('relu'))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(64, (3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def create_Pnet(weight_path):
    input = Input(shape=[None, None, 3])

    x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1,2],name='PReLU1')(x)
    x = MaxPool2D(pool_size=2)(x)

    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1,2],name='PReLU2')(x)

    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1,2],name='PReLU3')(x)

    classifier = Conv2D(2, (1, 1), activation='softmax', name='conv4-1')(x)
    # 无激活函数，线性。
    bbox_regress = Conv2D(4, (1, 1), name='conv4-2')(x)

    model = Model([input], [classifier, bbox_regress])
    model.load_weights(weight_path, by_name=True)
    return model

#-----------------------------#
#   mtcnn的第二段
#   精修框
#-----------------------------# 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def down_sample(self, x, filters):
        x_filters = int(x.shape[-1])
        x_conv = layers.Conv2D(filters - x_filters, kernel_size=3, strides=(2, 2), padding='same')(x)
        x_pool = layers.MaxPool2D()(x)
        x = layers.concatenate([x_conv, x_pool], axis=-1)
        x = layers.BatchNormalization()(x)
        x = layers.Activation('relu')(x)
        return x 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def _SPP_block(inp, kernels, strides):
    pools = [MaxPool2D(pool_size = pool_size, strides = stride, padding = 'same')(inp) \
             for pool_size, stride in zip(kernels, strides)]
    pools = [inp] + pools
    return concatenate(pools)


#Downsampling block is common to all YOLO-v3 models and are unaffected by the SPP or fully connected blocks or the number of labes 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def create_Pnet(weight_path):
    # h,w
    input = Input(shape=[None, None, 3])

    # h,w,3 -> h/2,w/2,10
    x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1,2],name='PReLU1')(x)
    x = MaxPool2D(pool_size=2)(x)

    # h/2,w/2,10 -> h/2,w/2,16
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1,2],name='PReLU2')(x)
    # h/2,w/2,32
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1,2],name='PReLU3')(x)

    # h/2, w/2, 2
    classifier = Conv2D(2, (1, 1), activation='softmax', name='conv4-1')(x)
    # 无激活函数，线性。
    # h/2, w/2, 4
    bbox_regress = Conv2D(4, (1, 1), name='conv4-2')(x)

    model = Model([input], [classifier, bbox_regress])
    model.load_weights(weight_path, by_name=True)
    return model

#-----------------------------#
#   mtcnn的第二段
#   精修框
#-----------------------------# 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def create_Rnet(weight_path):
    input = Input(shape=[24, 24, 3])
    # 24,24,3 -> 11,11,28
    x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
    x = MaxPool2D(pool_size=3,strides=2, padding='same')(x)

    # 11,11,28 -> 4,4,48
    x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)

    # 4,4,48 -> 3,3,64
    x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
    # 3,3,64 -> 64,3,3
    x = Permute((3, 2, 1))(x)
    x = Flatten()(x)
    # 576 -> 128
    x = Dense(128, name='conv4')(x)
    x = PReLU( name='prelu4')(x)
    # 128 -> 2 128 -> 4
    classifier = Dense(2, activation='softmax', name='conv5-1')(x)
    bbox_regress = Dense(4, name='conv5-2')(x)
    model = Model([input], [classifier, bbox_regress])
    model.load_weights(weight_path, by_name=True)
    return model

#-----------------------------#
#   mtcnn的第三段
#   精修框并获得五个点
#-----------------------------# 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def create_Onet(weight_path):
    input = Input(shape = [48,48,3])
    # 48,48,3 -> 23,23,32
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1,2],name='prelu1')(x)
    x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
    # 23,23,32 -> 10,10,64
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1,2],name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)
    # 8,8,64 -> 4,4,64
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1,2],name='prelu3')(x)
    x = MaxPool2D(pool_size=2)(x)
    # 4,4,64 -> 3,3,128
    x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x)
    x = PReLU(shared_axes=[1,2],name='prelu4')(x)
    # 3,3,128 -> 128,3,3
    x = Permute((3,2,1))(x)

    # 1152 -> 256
    x = Flatten()(x)
    x = Dense(256, name='conv5') (x)
    x = PReLU(name='prelu5')(x)

    # 鉴别
    # 256 -> 2 256 -> 4 256 -> 10 
    classifier = Dense(2, activation='softmax',name='conv6-1')(x)
    bbox_regress = Dense(4,name='conv6-2')(x)
    landmark_regress = Dense(10,name='conv6-3')(x)

    model = Model([input], [classifier, bbox_regress, landmark_regress])
    model.load_weights(weight_path, by_name=True)

    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPool2D [as 别名]
def tiny_yolo_main(input, num_anchors, num_classes):
    network_1 = NetworkConv2D_BN_Leaky(input=input, channels=16, kernel_size=(3,3) )
    network_1 = MaxPool2D(pool_size=(2,2), strides=(2,2), padding="same")(network_1)
    network_1 = NetworkConv2D_BN_Leaky(input=network_1, channels=32, kernel_size=(3, 3))
    network_1 = MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding="same")(network_1)
    network_1 = NetworkConv2D_BN_Leaky(input=network_1, channels=64, kernel_size=(3, 3))
    network_1 = MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding="same")(network_1)
    network_1 = NetworkConv2D_BN_Leaky(input=network_1, channels=128, kernel_size=(3, 3))
    network_1 = MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding="same")(network_1)
    network_1 = NetworkConv2D_BN_Leaky(input=network_1, channels=256, kernel_size=(3, 3))

    network_2 = MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding="same")(network_1)
    network_2 = NetworkConv2D_BN_Leaky(input=network_2, channels=512, kernel_size=(3, 3))
    network_2 = MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding="same")(network_2)
    network_2 = NetworkConv2D_BN_Leaky(input=network_2, channels=1024, kernel_size=(3, 3))
    network_2 = NetworkConv2D_BN_Leaky(input=network_2, channels=256, kernel_size=(1, 1))

    network_3 = NetworkConv2D_BN_Leaky(input=network_2, channels=512, kernel_size=(3, 3))
    network_3 = Conv2D(num_anchors * (num_classes + 5),  kernel_size=(1,1))(network_3)

    network_2 = NetworkConv2D_BN_Leaky(input=network_2, channels=128, kernel_size=(1, 1))
    network_2 = UpSampling2D(2)(network_2)

    network_4 = Concatenate()([network_2, network_1])
    network_4 = NetworkConv2D_BN_Leaky(input=network_4, channels=256, kernel_size=(3, 3))
    network_4 = Conv2D(num_anchors * (num_classes + 5), kernel_size=(1,1))(network_4)

    return Model(input, [network_3, network_4])