def test_xception_variable_input_channels():
input_shape = (1, None, None) if K.image_data_format() == 'channels_first' else (None, None, 1)
model = applications.Xception(weights=None, include_top=False, input_shape=input_shape)
assert model.output_shape == (None, None, None, 2048)
input_shape = (4, None, None) if K.image_data_format() == 'channels_first' else (None, None, 4)
model = applications.Xception(weights=None, include_top=False, input_shape=input_shape)
assert model.output_shape == (None, None, None, 2048)
def existing_model(base_model, base_model_layer, l):
if base_model == 'VGG16':
base_model = applications.VGG16(
weights="imagenet",
include_top=False,
input_shape=(constants.IMAGE_HEIGHT, constants.IMAGE_WIDTH, 3))
elif base_model == 'ResNet50':
base_model = applications.ResNet50(
weights="imagenet",
include_top=False,
input_shape=(constants.IMAGE_HEIGHT, constants.IMAGE_WIDTH, 3))
elif base_model == 'Xception':
base_model = applications.Xception(
weights="imagenet",
include_top=False,
input_shape=(constants.IMAGE_HEIGHT, constants.IMAGE_WIDTH, 3))
else:
raise "Unrecognized existing model {}".format(base_model)
if base_model_layer == -1:
x = base_model.layers[-1].output
else:
for layer in base_model.layers:
layer.trainable = False
x = base_model.layers[base_model_layer].output
x = Flatten()(x)
x = Dense(512,
activation="relu",
kernel_regularizer=regularizers.l2(l))(x)
x = Dense(128,
activation="relu",
kernel_regularizer=regularizers.l2(l))(x)
x = Dense(2, activation='sigmoid')(x)
return Model(input=base_model.input, output=x)
def callModel(model_picked = 'vgg16'):
'''function returns the model picked based on input
Input choices:
'vgg16' - VGG16
'vgg19' - VGG19
'res50' - ResNet50
'xception' - Xception
'inception' - InceptionV3
'monet' - MobileNetV2
'''
#The models have a series of convolutional layers and then they have dense(deeply connected layers)
#include_top = False only gets the convo layers and ignores the dense layer
#imagenet is a huge image dataset on which the models are trained. if weights ='imagenet' means the weights are acquired from that.
if model_picked == 'vgg16':
model = applications.VGG16(include_top=False, weights='imagenet')
elif model_picked =='vgg19':
model = applications.VGG19(include_top=False, weights='imagenet')
elif model_picked == 'res50':
model = applications.ResNet50(include_top=False, weights='imagenet')
elif model_picked == 'xception':
model = applications.Xception(include_top=False, weights='imagenet')
elif model_picked == 'inception':
model = applications.InceptionV3(include_top=False, weights='imagenet')
elif model_picked == 'monet':
model = applications.MobileNetV2(include_top=False, weights='imagenet',
input_shape=(224,224,3))
return model
def build_xception_classifier(input_shape, num_classes, l2_coeff=0.01):
xception = applications.Xception(include_top=False,
weights=None,
input_shape=(input_shape[0],
input_shape[1], 3))
xception.trainable = True
inputs = Input(shape=(input_shape[0], input_shape[1], 3), name='in1')
x = xception(inputs)
x = GlobalAveragePooling2D()(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dense(1024, kernel_regularizer=regularizers.l2(l2_coeff))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dropout(0.5)(x)
x = Dense(512, kernel_regularizer=regularizers.l2(l2_coeff))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dropout(0.5)(x)
x = Dense(num_classes)(x)
x = Activation('sigmoid')(x)
model = Model(inputs, x, name='xception_based_classifier')
return model
def get_pretrained_model(model, img_size):
possible_names = ["vgg16", "vgg19", "mobilenet", "xception"]
if model not in possible_names:
raise ValueError(f"model needs to be either {possible_names}")
logger.debug(f"backend for model is {model}")
width, height = img_size
if K.image_data_format() == 'channels_first':
input_shape = (3, width, height)
else:
input_shape = (width, height, 3)
logger.debug(
f"backend suggests that we have the following input shape: {input_shape}"
)
models = {
"vgg16":
applications.VGG16(input_shape=input_shape,
include_top=False,
weights='imagenet'),
"vgg19":
applications.VGG19(input_shape=input_shape,
include_top=False,
weights='imagenet'),
"mobilenet":
applications.MobileNet(input_shape=input_shape,
include_top=False,
weights='imagenet'),
"xception":
applications.Xception(input_shape=input_shape,
include_top=False,
weights='imagenet')
}
return models[model]
def save_bottlebeck_features():
datagen = ImageDataGenerator(rescale=1. / 255)
# build the VGG16 network
model = applications.Xception(include_top=False, weights='imagenet')
generator = datagen.flow_from_directory(train_data_dir,
target_size=(img_width,
img_height),
batch_size=batch_size,
class_mode='categorical',
shuffle=False)
bottleneck_features_train = model.predict_generator(
generator, nb_train_samples // batch_size)
np.save(open('xception_bottleneck_features_train.npy', 'wb'),
bottleneck_features_train)
generator = datagen.flow_from_directory(validation_data_dir,
target_size=(img_width,
img_height),
batch_size=batch_size,
class_mode='categorical',
shuffle=False)
bottleneck_features_validation = model.predict_generator(
generator, nb_validation_samples // batch_size)
np.save(open('xception_bottleneck_features_validation.npy', 'wb'),
bottleneck_features_validation)
class_dictionary = generator.class_indices
print(class_dictionary)
def callModel(model_picked='vgg16'):
'''function returns the model picked based on input
Input choices:
'vgg16' - VGG16
'vgg19' - VGG19
'res50' - ResNet50
'xception' - Xception
'inception' - InceptionV3
'monet' - MobileNetV2
'''
if model_picked == 'vgg16':
model = applications.VGG16(include_top=False, weights='imagenet')
elif model_picked == 'vgg19':
model = applications.VGG19(include_top=False, weights='imagenet')
elif model_picked == 'res50':
model = applications.ResNet50(include_top=False, weights='imagenet')
elif model_picked == 'xception':
model = applications.Xception(include_top=False, weights='imagenet')
elif model_picked == 'inception':
model = applications.InceptionV3(include_top=False, weights='imagenet')
elif model_picked == 'monet':
model = applications.MobileNetV2(include_top=False,
weights='imagenet',
input_shape=(224, 224, 3))
return model
def model_define(modeltype, inputshape):
if modeltype == 'define':
model = customize_mode()
print('Model: define !')
elif modeltype == 'EfficientNetB3':
model = efn.EfficientNetB3(include_top=False,
weights='imagenet',
input_tensor=None,
input_shape=inputshape,
pooling=None)
freeze_layers(model)
print('Model: EfficientNetB3, weights loaded!')
elif modeltype == 'ResNet50':
model = applications.ResNet50(include_top=False,
weights='imagenet',
input_shape=inputshape,
pooling='avg')
freeze_layers(model)
print('Model: ResNet50, weights loaded!')
elif modeltype == 'Xception':
model = applications.Xception(include_top=False,
weights='imagenet',
input_shape=inputshape)
freeze_layers(model)
print('Model: Xception, weights loaded!')
else:
pass
return model
def Xception(shape, num_classes, last_activation):
base_model = applications.Xception(weights='imagenet', include_top=False, input_shape=shape)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(num_classes, activation=last_activation)(x)
model = Model(base_model.input, x)
return model
def train_pretrained_model(model_name='xception',
hidden_layers=2,
hidden_neurons=512,
loss_function='binary_crossentropy',
top_epochs=100,
batch_size=16,
optimizer=Adagrad(lr=.0001),
patience=3,
validation_split=.1,
dropout=.2):
config = tf.ConfigProto()
set_session(tf.Session(config=config))
# load training data
train_X = np.load(os.path.join('training_data', 'train_X.npy'))
train_Y = np.load(os.path.join('training_data', 'train_Y.npy'))
classes = len(train_Y[0])
checkpoint_path = os.path.join(
'checkpoints', model_name + '{epoch:02d}-{val_loss:.3f}.mod')
# xception
base_model = applications.Xception(include_top=False,
weights='imagenet',
input_shape=(299, 299, 3))
# don't train base layers
for layer in base_model.layers:
layer.trainable = False
# add new top model
x = base_model.output
x = Flatten()(x)
for i in range(0, hidden_layers):
x = Dense(hidden_neurons,
activation='relu',
kernel_initializer='glorot_uniform')(x)
x = Dropout(dropout)(x)
predictions = Dense(classes, activation='sigmoid')(x)
model = Model(inputs=base_model.inputs, outputs=predictions)
model.summary()
# train new top layers
model.compile(optimizer=optimizer, metrics=[], loss=loss_function)
callbacks = [
EarlyStopping(monitor='val_loss', min_delta=0.001, patience=patience),
ModelCheckpoint(checkpoint_path, period=5)
]
model.fit(train_X,
train_Y,
epochs=top_epochs,
batch_size=batch_size,
validation_split=validation_split,
callbacks=callbacks)
save_path = os.path.join('checkpoints', model_name + '_trained')
model.save(save_path)
def models_factory(model_type, image_size):
if model_type == "vgg16":
base_model = applications.VGG16(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "vgg19":
base_model = applications.VGG19(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "resnet50":
base_model = applications.ResNet50(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "inceptionv3":
base_model = applications.InceptionV3(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "xception":
base_model = applications.Xception(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "mobilenet":
base_model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "inceptionresnetv2":
base_model = applications.InceptionResNetV2(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1],
3))
elif model_type == "nasnet":
base_model = applications.nasnet.NASNetLarge(
weights='imagenet',
include_top=False,
input_shape=(image_size[0], image_size[1], 3))
for layer in base_model.layers:
layer.trainable = False
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(
Dense(1024, kernel_initializer='glorot_uniform', activation='relu'))
top_model.add(
Dense(1024, kernel_initializer='glorot_uniform', activation='relu'))
top_model.add(Dense(1, activation='sigmoid'))
model = Model(input=base_model.input, output=top_model(base_model.output))
return model, base_model
def Shared_Xception():
xception_base = applications.Xception(weights= None,include_top = False)
left_input = Input(shape=(250,250,3))
right_input = Input(shape=(250,250,3))
left_features = xception_base(left_input)
right_input = xception_base(right_input)
merged_features = layers.concatenate([left_features,right_input],axis=-1)
def Xception(input_shape = (256, 256, 3), weights = 'imagenet', num_classes = 5): model_origin = applications.Xception(include_top = False, weights = weights, input_shape = input_shape) for layer in model_origin.layers: layer.trainable = True #layer_name = 'block13_sepconv2_bn' p = GlobalAveragePooling2D()(model_origin.output) o = Dense(num_classes, activation = 'softmax')(p) model = Model(inputs = model_origin.input, output = [o]) return model
def get_imagenet_architecture(architecture, variant, size, alpha, output_layer, include_top=False, weights='imagenet'):
from keras import applications, Model
if include_top:
assert output_layer == 'last'
if size == 'auto':
size = get_image_size(architecture, variant, size)
shape = (size, size, 3)
if architecture == 'densenet':
if variant == 'auto':
variant = 'densenet-121'
if variant == 'densenet-121':
model = applications.DenseNet121(weights=weights, include_top=include_top, input_shape=shape)
elif variant == 'densenet-169':
model = applications.DenseNet169(weights=weights, include_top=include_top, input_shape=shape)
elif variant == 'densenet-201':
model = applications.DenseNet201(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'inception-resnet-v2':
model = applications.InceptionResNetV2(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'mobilenet':
model = applications.MobileNet(weights=weights, include_top=include_top, input_shape=shape, alpha=alpha)
elif architecture == 'mobilenet-v2':
model = applications.MobileNetV2(weights=weights, include_top=include_top, input_shape=shape, alpha=alpha)
elif architecture == 'nasnet':
if variant == 'auto':
variant = 'large'
if variant == 'large':
model = applications.NASNetLarge(weights=weights, include_top=include_top, input_shape=shape)
else:
model = applications.NASNetMobile(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'resnet-50':
model = applications.ResNet50(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'vgg-16':
model = applications.VGG16(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'vgg-19':
model = applications.VGG19(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'xception':
model = applications.Xception(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'inception-v3':
model = applications.InceptionV3(weights=weights, include_top=include_top, input_shape=shape)
if output_layer != 'last':
try:
if isinstance(output_layer, int):
layer = model.layers[output_layer]
else:
layer = model.get_layer(output_layer)
except Exception:
raise VergeMLError('layer not found: {}'.format(output_layer))
model = Model(inputs=model.input, outputs=layer.output)
return model
def create_Xception(image_size, num_class):
resnet_conv = applications.Xception(weights='imagenet',
include_top=False,
input_shape=(image_size, image_size,
3))
model = models.Sequential()
model.add(resnet_conv)
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dense(num_class, activation='softmax'))
return model
pass
def test_big_models(self):
"""
A test for bigger commonly used pretrained models (for this we skip the weights)
:return:
"""
kapp_kwargs = dict(
input_shape=(99, 99, 3),
weights=None,
include_top=False # so we can use different sizes
)
test_models = []
test_models += [('Resnet50', kapps.ResNet50(**kapp_kwargs))]
test_models += [('InceptionV3', kapps.InceptionV3(**kapp_kwargs))]
test_models += [('VGG19', kapps.VGG19(**kapp_kwargs))]
test_models += [('Xception', kapps.Xception(**kapp_kwargs))]
for i, (model_name, cur_keras_model) in enumerate(test_models):
model_layers = ','.join(
map(lambda x: x.name, cur_keras_model.layers))
out_path = "%04d.pkl" % i
try:
c_model_pkl = KerasTestCase.export_keras_model(
cur_keras_model, out_path, model_name=model_layers)
except td.UnknownOperationException as uoe:
print('Model {}: {}'.format(i, model_layers),
'could not be serialized', uoe)
bad_layer_count = sum([
us_layer in model_layers for us_layer in UNSUPPORTED_LAYERS
])
self.assertGreater(
bad_layer_count, 0,
"Model contains no unsupported layers {}, "
"Unsupported Layers:{}".format(model_layers,
UNSUPPORTED_LAYERS))
continue
except tf.errors.RESOURCE_EXHAUSTED:
# many of the bigger models take up quite a bit of GPU memory
print('Model {} with #{} layers is too big for memory'.format(
model_name, len(cur_keras_model.layers)))
result = KerasTestCase.deploy_model(
c_model_pkl, np.random.uniform(0, 1, size=(299, 299, 3)))
self.assertIsNotNone(result, "Result should not be empty")
self.assertEqual(
len(result.shape), 4,
"Output should be 4D Tensor: {}".format(result.shape))
os.remove(c_model_pkl['path'])
def getModel(self):
# Gets Xception model. The classification layer of the model is sliced off by setting include_top=False. New classification layer is added as the model has typically 1000 classes
base_model = applications.Xception(weights="imagenet",
include_top=False,
input_shape=(img_width, img_height,
3))
x = base_model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation="softmax")(x)
for layer in base_model.layers:
layer.trainable = False
model = Model(input=base_model.input, output=predictions)
model.summary()
return model
def get_model(name='VGG19'):
if name=='VGG16':
return applications.VGG16(),(224, 224)
elif name=='VGG19':
return applications.VGG19(),(224, 224)
elif name=='ResNet50':
return applications.ResNet50(),(224, 224)
elif name=='InceptionV3':
return applications.InceptionV3(),(299,299)
elif name=='MobileNet':
return applications.MobileNet(),(224, 224) # any>32*32
elif name=='Xception':
return applications.Xception(),(299,299)
else:
return None, None
def getModel():
xception_base = applications.Xception(
weights=None, include_top=False) # 使用图像处理基础模型Xception，不包括顶部
left_input = Input(shape=(250, 250, 3))
right_input = Input(shape=(250, 250, 3))
left_features = xception_base(left_input) # 将模型xception_base当作层来使用
right_features = xception_base(right_input)
merged_features = layers.concatenate([left_features, right_features],
axis=-1)
model = models.Model([left_input, right_input], merged_features)
return model
def bottom_layers_builder(originalSize,resizeFactor): """ [email protected] """ img_size = originalSize*resizeFactor if k.image_data_format() == 'channels_first': input_shape = (3, img_size, img_size) else: input_shape = (img_size, img_size, 3) #model = applications.InceptionV3(weights = "imagenet", include_top=False, input_shape = (img_size, img_size, 3)) model = applications.Xception(weights = "imagenet", include_top=False, input_shape = input_shape) for layer in model.layers : layer.trainable = False return model
def model_app(arch, input_tensor):
"""Loads the appropriate convolutional neural network (CNN) model
Args:
arch: String key for model to be loaded.
input_tensor: Keras tensor to use as image input for the model.
Returns:
model: The specified Keras Model instance with ImageNet weights loaded and without the top classification layer.
"""
# function that loads the appropriate model
if arch == 'Xception':
model = applications.Xception(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('Xception loaded')
elif arch == 'VGG16':
model = applications.VGG16(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('VGG16 loaded')
elif arch == 'VGG19':
model = applications.VGG19(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('VGG19 loaded')
elif arch == 'ResNet50':
model = applications.ResNet50(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('ResNet50 loaded')
elif arch == 'InceptionV3':
model = applications.InceptionV3(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('InceptionV3 loaded')
elif arch == 'InceptionResNetV2':
model = applications.InceptionResNetV2(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('InceptionResNetV2 loaded')
elif arch == 'MobileNet':
model = applications.MobileNet(input_shape=(224, 224, 3), weights='imagenet', include_top=False,
input_tensor=input_tensor)
print('MobileNet loaded')
elif arch == 'DenseNet121':
model = applications.DenseNet121(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('DenseNet121 loaded')
elif arch == 'NASNetLarge':
model = applications.NASNetLarge(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('NASNetLarge loaded')
elif arch == 'MobileNetV2':
model = applications.MobileNetV2(input_shape=(224, 224, 3), weights='imagenet', include_top=False,
input_tensor=input_tensor)
print('MobileNetV2 loaded')
else:
print('Invalid model selected')
model = False
return model
def get_base_model(model_type):
input_tensor = Input(shape=(img_width,img_height,3))
input_shape = shape=(img_width,img_height,3)
if(model_type == 'vgg16'):
# build the VGG16 network
#NOTE: VGG16 code needs the base model downloaded or else it will try to download it
preferred_size = (224,224)
return applications.VGG16(include_top=False, weights='imagenet',input_tensor=input_tensor)
elif(model_type == 'inception'):
preferred_size = (299,299)
if (preferred_size_matches_scale()):
return applications.InceptionV3(include_top=False, weights='imagenet')
else:
return applications.InceptionV3(include_top=False, weights='imagenet', input_shape=input_shape)
elif(model_type == 'xception'):
print('Using Xception')
return applications.Xception(include_top=False, weights='imagenet', input_shape=input_shape)
elif(model_type == 'resnet50'):
preferred_size = (224,224)
return applications.ResNet50(include_top=False, weights='imagenet')
def get_xception_model(num_classes, input_shape):
model = applications.Xception(weights="imagenet",
include_top=False,
input_shape=input_shape)
# # Freeze the layers which you don't want to train. Here I am freezing the first 5 layers.
# for layer in model.layers[:5]:
# layer.trainable = False
#
# # Adding custom Layers
x = model.output
# x = Flatten()(x)
# x = Dense(512, activation="relu")(x)
# x = Dropout(0.5)(x)
# x = Dense(512, activation="relu")(x)
predictions = Dense(num_classes, activation="softmax")(x)
# creating the final model
model_final = Model(inputs=model.input, output=predictions)
model_final.summary()
return model_final
def pretrained_model(arch_name, input_shape=(224, 224, 3)):
"""
返回在 ImageNet 训练好的网络模型
args:
arch: str, 模型名字,
eg: 'xception'
input_shape, tuple, 模型输入维度,
eg: (224, 224, 3)
return:
model, keras model, 一个没有被freeze的网络模型
"""
arch_name = arch_name.lower()
arch_set = {'xception', 'vgg16', 'vgg19', 'resnet50', 'inception_v3'}
if arch_name == 'xception':
model = applications.Xception(include_top=True,
weights='imagenet',
input_shape=input_shape)
elif arch_name == 'vgg16':
model = applications.VGG16(include_top=True,
weights='imagenet',
input_shape=input_shape)
elif arch_name == 'vgg19':
model = applications.VGG19(include_top=True,
weights='imagenet',
input_shape=input_shape)
elif arch_name == 'resnet50':
model = applications.ResNet50(include_top=True,
weights='imagenet',
input_shape=input_shape)
elif arch_name == 'inception_v3':
model = applications.InceptionV3(include_top=True,
weights='imagenet',
input_shape=input_shape)
else:
raise ValueError('只能使用以下网络结构 \n{}\n'.format(arch_set))
build_model(model)
return model
def createXception(img_rows=299, img_cols=299, channel=3, num_classes=25):
base_model = applications.Xception(input_shape=(img_rows, img_cols,
channel),
weights='imagenet',
include_top=False)
out = base_model.output
out = GlobalAveragePooling2D()(out)
out = Dropout(0.5)(out)
pred_out = Dense(num_classes, activation='softmax')(out)
# add your top layer block to your base model
model = Model(base_model.input, pred_out)
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(lr=1e-3,
momentum=0.9,
decay=1e-6,
nesterov=True),
metrics=['accuracy'])
model.summary()
return model
def create_model(self):
base_model = applications.Xception(weights='imagenet',
include_top=False,
input_shape=(self.ROWS, self.COLS,
3))
for layer in base_model.layers[:5]:
layer.trainable = False
add_model = Sequential()
add_model.add(base_model)
add_model.add(Flatten())
add_model.add(Dropout(0.2))
add_model.add(Dense(1024, activation='relu'))
add_model.add(Dropout(0.3))
add_model.add(Dense(1024, activation='relu'))
add_model.add(Dense(2, activation='softmax'))
self.model = add_model
self.model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(lr=1e-3, momentum=0.9),
metrics=['accuracy'])
self.model.summary()
def test_xception_pooling():
model = applications.Xception(weights=None,
include_top=False,
pooling='avg')
assert model.output_shape == (None, 2048)
def test_xception_notop():
model = applications.Xception(weights=None, include_top=False)
assert model.output_shape == (None, None, None, 2048)
def test_xception():
model = applications.Xception(weights=None)
assert model.output_shape == (None, 1000)
""" Test case for Keras """
from __future__ import absolute_import
import numpy as np
import keras.applications as models
from perceptron.models.classification.keras import KerasModel
from perceptron.utils.image import imagenet_example
from perceptron.benchmarks.additive_noise import AdditiveGaussianNoiseMetric
from perceptron.utils.criteria.classification import TopKMisclassification
from perceptron.utils.tools import plot_image
from perceptron.utils.tools import bcolors
# instantiate the model from keras applications
xception = models.Xception(weights='imagenet')
# initialize the KerasModel
# keras xception has input bound (0, 1)
mean = np.array([0.485, 0.456, 0.406]).reshape((1, 1, 3))
std = np.array([0.229, 0.224, 0.225]).reshape((1, 1, 3))
kmodel = KerasModel(xception, bounds=(0, 1), preprocessing=(mean, std))
# get source image and label
# the model Xception expects values in [0, 1] with shape (299, 299), and channles_last
image, _ = imagenet_example(shape=(299, 299), data_format='channels_last')
image /= 255.0
label = np.argmax(kmodel.predictions(image))
metric = AdditiveGaussianNoiseMetric(kmodel,
criterion=TopKMisclassification(10))
print(bcolors.BOLD + 'Process start' + bcolors.ENDC)