def classification(
shape: Tuple[int, int, int] = (224, 224, 3),
n_class: int = 3,
model_net: str = "Resnet50V2",
resnet_train: bool = True,
) -> Model:
"""
Modelo de classificação entre covid, normal e pneumonia
Args:
-----
input_size (tuple, optional): Tamanho da imagem de entrada.
Defaults to (224, 224, 3).
n_class (int, optional): Número de classes de saída.
Defaults to 3.
Returns:
--------
(keras.Model) : Modelo do keras
"""
inputs = Input(shape, name="entrada_modelo")
model = Conv2D(
filters=3,
kernel_size=(3, 3),
padding="same",
activation="relu",
name="conv_gray_rgb",
)(inputs)
params = {
"include_top": False,
"weights": "imagenet",
"input_shape": (shape[0], shape[1], 3),
"pooling": "avg",
}
if model_net == "VGG19":
base_model = VGG19(**params)
elif model_net == "InceptionResNetV2":
base_model = InceptionV3(**params)
elif model_net == "MobileNetV2":
base_model = MobileNetV3Small(**params)
elif model_net == "DenseNet201":
base_model = DenseNet201(**params)
else:
base_model = ResNet50V2(**params)
base_model.trainable = resnet_train
model = base_model(model)
model = Dropout(0.5, name="drop_0")(model)
model = Dense(units=256, name="dense_0")(model)
model = Dropout(0.5, name="drop_1")(model)
model = Dense(units=n_class, name="classifier")(model)
predictions = Activation(activation="softmax", name="output")(model)
return Model(inputs=inputs, outputs=predictions)
def init_model(model_name):
if (model_name == "VGG19"): # Initialisierung des VGG19
return VGG19(include_top=True, weights='imagenet')
if (model_name == "VGG16"):
return tf.keras.applications.VGG16(include_top=True,
weights='imagenet')
if (model_name == "ResNet50"):
return ResNet50(include_top=True, weights="imagenet")
if (model_name == "DenseNet201"):
return DenseNet201(include_top=True, weights="imagenet")
if (model_name == "DenseNet121"):
return DenseNet121(include_top=True, weights="imagenet")
if (model_name == "InceptionResNetV2"):
return InceptionResNetV2(include_top=True, weights="imagenet")
def __init__(self, top_model, dog_breed_names, image_shape=(224, 224, 3)):
'''
Initializes the model.
:param top_model: trained dog breed detector
:param image_shape: image shape
'''
self.cnn_model = DenseNet201(weights='imagenet', include_top=False, input_shape=image_shape)
self.top_model = top_model
self.model = Model(inputs=self.cnn_model.input,
outputs=self.top_model(self.cnn_model.output))
self.model.compile(loss='categorical_crossentropy', optimizer='adam',
metrics=['accuracy'])
self.dog_breed_names = dog_breed_names
def __init__(self,
train_data_dir,
valid_data_dir,
test_data_dir,
image_shape=(224, 224, 3)):
'''
Initializes the instance
:param train_data_dir: directory containing train dog images. Expected structure:
one subdirectory for one breed that contains corresponding
dog breed images as jpegs
:param valid_data_dir: same as train_data_dir for validation images
:param test_data_dir: same as train_data_dir for test images
:param images_shape: target image shape for the image resize
'''
# data directories
self.train_data_dir = train_data_dir
self.valid_data_dir = valid_data_dir
self.test_data_dir = test_data_dir
# image generator
self.batch_size = 16
self.image_shape = image_shape
self.train_image_gen = ImageDataGenerator(
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
preprocessing_function=preprocess_input)
self.test_image_gen = ImageDataGenerator(
preprocessing_function=preprocess_input)
# DenseNet
self.model = DenseNet201(weights='imagenet',
include_top=False,
input_shape=self.image_shape)
# file names
bottleneck_feature_dir = "bottleneck_features"
if not os.path.exists(bottleneck_feature_dir):
os.makedirs(bottleneck_feature_dir)
self.train_bottleneck_features_filename = bottleneck_feature_dir + '/train.npy'
self.valid_bottleneck_features_filename = bottleneck_feature_dir + '/valid.npy'
self.test_bottleneck_features_filename = bottleneck_feature_dir + '/test.npy'
def get_model(self, model_name, weights):
if model_name == 'InceptionV3':
from tensorflow.python.keras.applications.inception_v3 import InceptionV3
base_model = InceptionV3(weights=weights, include_top=False)
elif model_name == 'NASNetLarge':
from tensorflow.python.keras.applications.nasnet import NASNetLarge
base_model = NASNetLarge(weights=weights, include_top=False)
elif model_name == 'DenseNet201':
from tensorflow.python.keras.applications.densenet import DenseNet201
base_model = DenseNet201(weights=weights, include_top=False)
elif model_name == 'Xception':
from tensorflow.python.keras.applications.xception import Xception
base_model = Xception(weights=weights, include_top=False)
elif model_name == 'VGG16':
from tensorflow.python.keras.applications.vgg16 import VGG16
base_model = VGG16(weights=weights, include_top=False)
elif model_name == 'VGG19':
from tensorflow.python.keras.applications.vgg19 import VGG19
base_model = VGG19(weights=weights, include_top=False)
elif model_name == 'NASNetMobile':
from tensorflow.python.keras.applications.nasnet import NASNetMobile
base_model = NASNetMobile(weights=weights, include_top=False)
elif model_name == 'MobileNetV2':
from tensorflow.python.keras.applications.mobilenet_v2 import MobileNetV2
base_model = MobileNetV2(weights=weights, include_top=False)
elif model_name == 'ResNet50':
from tensorflow.python.keras.applications.resnet50 import ResNet50
base_model = ResNet50(weights=weights, include_top=False)
elif model_name == 'InceptionResNetV2':
from tensorflow.python.keras.applications.inception_resnet_v2 import InceptionResNetV2
base_model = InceptionResNetV2(weights=weights, include_top=False, )
else:
raise KeyError('Unknown network.')
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)
return model
def get_model(self, model_name, weights='imagenet'):
if model_name == 'InceptionV3':
from tensorflow.python.keras.applications.inception_v3 import InceptionV3
base_model = InceptionV3(weights=weights, include_top=False)
elif model_name == 'NASNetLarge':
from tensorflow.python.keras.applications.nasnet import NASNetLarge
base_model = NASNetLarge(weights=weights, include_top=False)
elif model_name == 'DenseNet201':
from tensorflow.python.keras.applications.densenet import DenseNet201
base_model = DenseNet201(weights=weights, include_top=False)
elif model_name == 'Xception':
from tensorflow.python.keras.applications.xception import Xception
base_model = Xception(weights=weights, include_top=False)
elif model_name == 'VGG19':
from tensorflow.python.keras.applications.vgg19 import VGG19
base_model = VGG19(weights=weights, include_top=False)
elif model_name == 'NASNetMobile':
from tensorflow.python.keras.applications.nasnet import NASNetMobile
base_model = NASNetMobile(weights=weights, include_top=False)
elif model_name == 'MobileNetV2':
from tensorflow.python.keras.applications.mobilenet_v2 import MobileNetV2
base_model = MobileNetV2(weights=weights, include_top=False)
elif model_name == 'ResNet50':
from tensorflow.python.keras.applications.resnet50 import ResNet50
base_model = ResNet50(weights=weights, include_top=False)
elif model_name == 'InceptionResNetV2':
from tensorflow.python.keras.applications.inception_resnet_v2 import InceptionResNetV2
base_model = InceptionResNetV2(weights=weights, include_top=False)
else:
raise KeyError('Unknown network.')
x = base_model.output
x = GlobalAveragePooling2D()(x)
# x = Dense(1024, activation='relu')(x)
output = Dense(1, activation='sigmoid')(x)
# output = SiameseLossLayer(self.batch_size, self.num_distort, self.num_level)(x)
model = Model(inputs=base_model.input, outputs=output)
self.name = model_name
return model
def get_predictions(self, in_img, network_name, top=3):
# network model selection
model = None
size = (224, 224)
int_model = False
if network_name == 'VGG19':
if self.MODEL_VGG19 is None:
from tensorflow.keras.applications.vgg19 import VGG19
self.MODEL_VGG19 = VGG19(weights='imagenet')
model = self.MODEL_VGG19
int_model = True
from tensorflow.keras.applications.vgg19 import decode_predictions
elif network_name == 'DenseNet201':
if self.MODEL_DENSENET201 is None:
from tensorflow.python.keras.applications.densenet import DenseNet201
self.MODEL_DENSENET201 = DenseNet201(weights='imagenet')
model = self.MODEL_DENSENET201
from tensorflow.keras.applications.densenet import decode_predictions
elif network_name == 'MobileNetV2':
if self.MODEL_MOBILENET is None:
from tensorflow.keras.applications.mobilenet import MobileNet # (244,244)
self.MODEL_MOBILENET = MobileNet(weights='imagenet')
model = self.MODEL_MOBILENET
from tensorflow.keras.applications.mobilenet import decode_predictions
elif network_name == 'InceptionV3':
if self.MODEL_INCEPTIONV3 is None:
from tensorflow.keras.applications.inception_v3 import InceptionV3 # (299,299)
self.MODEL_INCEPTIONV3 = InceptionV3(weights='imagenet')
model = self.MODEL_INCEPTIONV3
size = (299, 299)
from tensorflow.keras.applications.inception_v3 import decode_predictions
elif network_name == 'ResNet50':
if self.MODEL_RESNET50 is None:
from tensorflow.python.keras.applications.resnet import ResNet50 # (244,244)
self.MODEL_RESNET50 = ResNet50(weights='imagenet')
model = self.MODEL_RESNET50
int_model = True
from tensorflow.keras.applications.resnet import decode_predictions
else:
print("no valid model selected")
return
# if input is a numpy image, convert it to a cv image
if (isinstance(in_img, numpy.ndarray)):
in_img = _to_cv_image(in_img)
img = cv2.resize(in_img, dsize=size, interpolation=cv2.INTER_CUBIC)
img = _cv_to_array(img)
if (int_model):
img = img * 255
img = numpy.expand_dims(img, axis=0)
# predict model and return top predictions
features = model.predict(img)
predictions = []
for p in decode_predictions(features, top=top)[0]:
predictions.append([p[0], p[1], float(p[2])])
# get the indexes of the top predictions
class_codes = numpy.flip(numpy.argsort(features[0]))[:top]
return (predictions, class_codes) # ottiene nome della classe predetta
def create_model(self):
"""
https://github.com/tensorflow/tensorflow/issues/14356
"""
input_shape = (self.config.tfr_image_height,
self.config.tfr_image_width,
self.config.tfr_image_channels)
## VGG16
if self.config.model_name == 'vgg16':
base_model = VGG16(weights='imagenet',
include_top=False,
input_tensor=self.features,
input_shape=input_shape)
## Xception
elif self.config.model_name == 'xception':
base_model = Xception(weights='imagenet',
include_top=False,
input_tensor=self.features,
input_shape=input_shape)
## Resnet50
elif self.config.model_name == 'resnet50':
base_model = ResNet50(weights='imagenet',
include_top=False,
input_tensor=self.features,
input_shape=input_shape)
logits = self.model_top_resnet50(base_model)
## InceptionResNetV2
elif self.config.model_name == 'inception_resnet_v2':
base_model = InceptionResNetV2(weights='imagenet',
include_top=False,
input_tensor=self.features,
input_shape=input_shape)
## Densenet121
elif self.config.model_name == 'densenet121':
base_model = DenseNet121(weights='imagenet',
include_top=False,
input_tensor=self.features,
input_shape=input_shape)
logits = self.model_top_densenet121(base_model)
## Densenet169
elif self.config.model_name == 'densenet169':
base_model = DenseNet169(weights='imagenet',
include_top=False,
input_tensor=self.features,
input_shape=input_shape)
logits = self.model_top_densenet121(base_model)
## Densenet201
elif self.config.model_name == 'densenet201':
base_model = DenseNet201(weights='imagenet',
include_top=False,
input_tensor=self.features,
input_shape=input_shape)
logits = self.model_top_densenet121(base_model)
else:
logging.error('Unknown model_name {}'.format(model_name))
exit(1)
return logits
import tensorflow as tf
from tensorflow.python import keras
from tensorflow.python.keras.applications.vgg16 import VGG16
from tensorflow.python.keras.applications.vgg19 import VGG19
from tensorflow.python.keras.applications.densenet import DenseNet121
from tensorflow.python.keras.applications.densenet import DenseNet201
from tensorflow.python.keras.applications.resnet50 import ResNet50
from tensorflow.python.keras.applications.inception_resnet_v2 import InceptionResNetV2
from tensorflow.python.keras.models import Model
# Für jedes Modell wird die Summary ausgegeben
print("=== VGG 16 ===")
VGG16().summary()
print("=== VGG 19 ===")
VGG19().summary()
print("=== ResNet50 ===")
ResNet50().summary()
print("=== DenseNet121 ===")
DenseNet121().summary()
print("=== DenseNet201 ===")
DenseNet201().summary()
print("=== InceptionResNetV2 ===")
InceptionResNetV2().summary()