Python densenet.DenseNet121方法代碼示例

# 需要導入模塊: from keras.applications import densenet [as 別名]
# 或者: from keras.applications.densenet import DenseNet121 [as 別名]
def get_model_pretrain(arch):
    modlrate = 1
    if   "VGG16" in arch:       base_model = vgg16.VGG16
    elif "VGG19" in arch:       base_model = vgg19.VGG19
    elif "RESNET50" in arch:    base_model = resnet50.ResNet50
    elif "DENSENET121" in arch: base_model = densenet.DenseNet121
    elif "MOBILENET" in arch:
        base_model = mobilenet.MobileNet
        modlrate = 10
    else: print("model not avaiable"); exit()
    base_model = base_model(weights='imagenet', include_top=False)
    return base_model, modlrate 

# 需要導入模塊: from keras.applications import densenet [as 別名]
# 或者: from keras.applications.densenet import DenseNet121 [as 別名]
def test_DenseNet121(self):
        from keras.applications.densenet import DenseNet121
        model = DenseNet121(include_top=True, weights='imagenet')
        res = run_image(model, self.model_files, img_path)
        self.assertTrue(*res) 

# 需要導入模塊: from keras.applications import densenet [as 別名]
# 或者: from keras.applications.densenet import DenseNet121 [as 別名]
def get_densenet121_unet_softmax(input_shape, weights='imagenet'):
    blocks = [6, 12, 24, 16]
    img_input = Input(input_shape + (4,))
    
    x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
    x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                           name='conv1/bn')(x)
    x = Activation('relu', name='conv1/relu')(x)
    conv1 = x
    x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
    x = MaxPooling2D(3, strides=2, name='pool1')(x)
    x = dense_block(x, blocks[0], name='conv2')
    conv2 = x
    x = transition_block(x, 0.5, name='pool2')
    x = dense_block(x, blocks[1], name='conv3')
    conv3 = x
    x = transition_block(x, 0.5, name='pool3')
    x = dense_block(x, blocks[2], name='conv4')
    conv4 = x
    x = transition_block(x, 0.5, name='pool4')
    x = dense_block(x, blocks[3], name='conv5')
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                           name='bn')(x)
    conv5 = x 
    
    conv6 = conv_block(UpSampling2D()(conv5), 320)
    conv6 = concatenate([conv6, conv4], axis=-1)
    conv6 = conv_block(conv6, 320)

    conv7 = conv_block(UpSampling2D()(conv6), 256)
    conv7 = concatenate([conv7, conv3], axis=-1)
    conv7 = conv_block(conv7, 256)

    conv8 = conv_block(UpSampling2D()(conv7), 128)
    conv8 = concatenate([conv8, conv2], axis=-1)
    conv8 = conv_block(conv8, 128)

    conv9 = conv_block(UpSampling2D()(conv8), 96)
    conv9 = concatenate([conv9, conv1], axis=-1)
    conv9 = conv_block(conv9, 96)

    conv10 = conv_block(UpSampling2D()(conv9), 64)
    conv10 = conv_block(conv10, 64)
    res = Conv2D(3, (1, 1), activation='softmax')(conv10)
    model = Model(img_input, res)
    
    if weights == 'imagenet':
        densenet = DenseNet121(input_shape=input_shape + (3,), weights=weights, include_top=False)
        w0 = densenet.layers[2].get_weights()
        w = model.layers[2].get_weights()
        w[0][:, :, [0, 1, 2], :] = 0.9 * w0[0][:, :, :3, :]
        w[0][:, :, 3, :] = 0.1 * w0[0][:, :, 1, :]
        model.layers[2].set_weights(w)
        for i in range(3, len(densenet.layers)):
            model.layers[i].set_weights(densenet.layers[i].get_weights())
            model.layers[i].trainable = False
    
    return model 

# 需要導入模塊: from keras.applications import densenet [as 別名]
# 或者: from keras.applications.densenet import DenseNet121 [as 別名]
def get_tst_neural_net(type):
    model = None
    custom_objects = dict()
    if type == 'mobilenet_small':
        from keras.applications.mobilenet import MobileNet
        model = MobileNet((128, 128, 3), depth_multiplier=1, alpha=0.25, include_top=True, weights='imagenet')
    elif type == 'mobilenet':
        from keras.applications.mobilenet import MobileNet
        model = MobileNet((224, 224, 3), depth_multiplier=1, alpha=1.0, include_top=True, weights='imagenet')
    elif type == 'mobilenet_v2':
        from keras.applications.mobilenetv2 import MobileNetV2
        model = MobileNetV2((224, 224, 3), depth_multiplier=1, alpha=1.4, include_top=True, weights='imagenet')
    elif type == 'resnet50':
        from keras.applications.resnet50 import ResNet50
        model = ResNet50(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'inception_v3':
        from keras.applications.inception_v3 import InceptionV3
        model = InceptionV3(input_shape=(299, 299, 3), include_top=True, weights='imagenet')
    elif type == 'inception_resnet_v2':
        from keras.applications.inception_resnet_v2 import InceptionResNetV2
        model = InceptionResNetV2(input_shape=(299, 299, 3), include_top=True, weights='imagenet')
    elif type == 'xception':
        from keras.applications.xception import Xception
        model = Xception(input_shape=(299, 299, 3), include_top=True, weights='imagenet')
    elif type == 'densenet121':
        from keras.applications.densenet import DenseNet121
        model = DenseNet121(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'densenet169':
        from keras.applications.densenet import DenseNet169
        model = DenseNet169(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'densenet201':
        from keras.applications.densenet import DenseNet201
        model = DenseNet201(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'nasnetmobile':
        from keras.applications.nasnet import NASNetMobile
        model = NASNetMobile(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'nasnetlarge':
        from keras.applications.nasnet import NASNetLarge
        model = NASNetLarge(input_shape=(331, 331, 3), include_top=True, weights='imagenet')
    elif type == 'vgg16':
        from keras.applications.vgg16 import VGG16
        model = VGG16(input_shape=(224, 224, 3), include_top=False, pooling='avg', weights='imagenet')
    elif type == 'vgg19':
        from keras.applications.vgg19 import VGG19
        model = VGG19(input_shape=(224, 224, 3), include_top=False, pooling='avg', weights='imagenet')
    elif type == 'multi_io':
        model = get_custom_multi_io_model()
    elif type == 'multi_model_layer_1':
        model = get_custom_model_with_other_model_as_layer()
    elif type == 'multi_model_layer_2':
        model = get_small_model_with_other_model_as_layer()
    elif type == 'Conv2DTranspose':
        model = get_Conv2DTranspose_model()
    elif type == 'RetinaNet':
        model, custom_objects = get_RetinaNet_model()
    elif type == 'conv3d_model':
        model = get_simple_3d_model()
    return model, custom_objects