def define_classifier_architecture(architecture_name,
image_size,
weights,
classifier_kwargs=None):
if architecture_name == 'MobileNet':
model = MobileNetV2(input_shape=image_size,
include_top=False,
weights=weights,
**classifier_kwargs)
elif architecture_name == 'VGG16':
model = VGG16(input_shape=image_size,
include_top=False,
weights=weights,
**classifier_kwargs)
elif architecture_name == 'VGG19':
model = VGG19(input_shape=image_size,
include_top=False,
weights=weights,
**classifier_kwargs)
elif architecture_name == 'NASNetMobile':
model = NASNetMobile(input_shape=image_size,
include_top=False,
weights=weights,
**classifier_kwargs)
elif architecture_name == 'NASNetLarge':
model = NASNetLarge(input_shape=image_size,
include_top=False,
weights=weights,
**classifier_kwargs)
elif architecture_name == 'InceptionV3':
model = InceptionV3(input_shape=image_size,
include_top=False,
weights=weights,
**classifier_kwargs)
elif architecture_name == 'InceptionResNetV2':
model = InceptionResNetV2(input_shape=image_size,
include_top=False,
weights=weights,
**classifier_kwargs)
elif architecture_name == 'Resnet50':
model = ResNet50(input_shape=image_size,
include_top=False,
weights=weights,
**classifier_kwargs)
elif architecture_name == 'EfficientNetB0':
model = EfficientNetB0(input_shape=image_size,
include_top=False,
weights=weights,
**classifier_kwargs)
else:
raise ValueError(
f"Classifier '{architecture_name}' is wrong or not implemented.")
return model
def get_model(architecture, iteracion, models_info, pipeline):
print("="*len(architecture))
print(architecture)
print("="*len(architecture))
if iteracion > 0:
base_model = models_info[architecture]['model_memory']
if architecture == 'InceptionV3':
from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
if iteracion == 0:
base_model = InceptionV3(weights=pipeline['weights'],include_top=False,input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
if architecture == 'InceptionV4':
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
if iteracion == 0:
base_model = InceptionResNetV2(weights=pipeline['weights'],include_top=False,input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
if architecture == 'ResNet50':
from tensorflow.keras.applications.resnet import ResNet50, preprocess_input
if iteracion == 0:
base_model = ResNet50(weights=pipeline['weights'],include_top=False,input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
if architecture == 'ResNet101':
from tensorflow.keras.applications.resnet import ResNet101, preprocess_input
if iteracion == 0:
base_model = ResNet101(weights=pipeline['weights'],include_top=False,input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
if architecture == 'ResNet152':
from tensorflow.keras.applications.resnet import ResNet152, preprocess_input
if iteracion == 0:
base_model = ResNet152(weights=pipeline['weights'],include_top=False,input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
if architecture == 'DenseNet121':
from tensorflow.keras.applications.densenet import DenseNet121, preprocess_input
if iteracion == 0:
base_model = DenseNet121(weights=pipeline['weights'],include_top=False,input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
if architecture == 'DenseNet169':
from tensorflow.keras.applications.densenet import DenseNet169, preprocess_input
if iteracion == 0:
base_model = DenseNet169(weights=pipeline['weights'],include_top=False,input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
if architecture == 'DenseNet201':
from tensorflow.keras.applications.densenet import DenseNet201, preprocess_input
if iteracion == 0:
base_model = DenseNet201(weights=pipeline['weights'],include_top=False,input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
if architecture == 'NASNetLarge':
from tensorflow.keras.applications.nasnet import NASNetLarge, preprocess_input
if iteracion == 0:
base_model = NASNetLarge(weights=pipeline['weights'],include_top=False,input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
if architecture == 'Xception':
from tensorflow.keras.applications.xception import Xception, preprocess_input
if iteracion == 0:
base_model = Xception(weights=pipeline['weights'], include_top=False, input_shape=(pipeline['img_height'], pipeline['img_width'], 3))
return base_model, preprocess_input
def main():
# setup session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
# get and shuffle batch paths
# datasetPaths= exBC.preparePaths(rootFolderLocal+'Prepared-Data')
datasetPaths = exBC.preparePaths(rootFolderGCP + 'Prepared-Data')
random.shuffle(datasetPaths)
batchAmt = len(datasetPaths)
testAmt = math.ceil(batchAmt * .1)
# 10% set aside for testing
testingPaths = datasetPaths[:testAmt]
trainingPaths = datasetPaths[testAmt:]
writeVideo(testingPaths)
print("Test Video Written")
# prepare the model
# untrainedModel = prepModel((64, 64, 3))
# untrainedModel.summary()
# train the model
# trainedModel = trainModelHDD(trainingPaths, testingPaths, untrainedModel)
base = NASNetLarge(weights='imagenet', include_top=False)
new = base.output
new = GlobalAveragePooling2D()(new)
new = Dense(1024, activation='relu', kernel_regularizer=regularizers.l2(0.01))(new)
new = Dense(512, activation='relu')(new)
final = Dense(12, activation='sigmoid')(new)
untrainedModel = Model(inputs=base.input, outputs=final)
for layer in base.layers:
layer.trainable = False
untrainedModel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])
untrainedModel.summary()
trainedModel = trainModelHDD(trainingPaths, testingPaths, untrainedModel)
print("model training finished")
# save the model for the server
trainedModel.save("model.h5")
print("Model Saved")
testModel(testingPaths, trainedModel)
print("Complete")
def __init__(self, name):
if name == 'nas':
from tensorflow.keras.applications.nasnet import NASNetLarge, preprocess_input
self.model = NASNetLarge(include_top=False,
weights='imagenet',
pooling='avg')
self.size = 331
if name == 'res':
from tensorflow.keras.applications.resnet import ResNet50, preprocess_input
self.model = ResNet50(include_top=False,
weights='imagenet',
pooling='avg')
self.size = 224
self.proc = preprocess_input
self.model.trainable = False
def get_model_nasnet_large(num_class):
base_model = NASNetLarge(weights='imagenet', include_top=False)
x = base_model.output
# custom top
predictions = get_custom_top(x, num_class)
model = Model(inputs=base_model.input, outputs=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def get_basemodel(model_name):
if model_name == "MobileNetV2":
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
return MobileNetV2(include_top=True,weights='imagenet',classes=1000)
elif model_name == "ResNet50":
from tensorflow.keras.applications.resnet50 import ResNet50
return ResNet50(include_top=True,weights='imagenet',classes=1000)
elif model_name=="InceptionV2":
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2
return InceptionResNetV2(include_top=True,weights='imagenet',classes=1000)
elif model_name=="DenseNet201":
from tensorflow.keras.applications.densenet import DenseNet201
return DenseNet201(include_top=True,weights='imagenet',classes=1000)
elif model_name=="NASNetLarge":
from tensorflow.keras.applications.nasnet import NASNetLarge
return NASNetLarge(include_top=True,weights='imagenet',classes=1000)
def get_nasnetlarge(classes=9,
input_shape=(331, 331, 3),
base_layer_trainable=False):
from tensorflow.keras.applications.nasnet import NASNetLarge
base_model = NASNetLarge(include_top=False, input_shape=input_shape)
for layer in base_model.layers:
layer.trainable = base_layer_trainable
head_model = KL.GlobalMaxPool2D()(base_model.output)
head_model = KL.Dense(1024,
activation='relu',
name='0000',
kernel_initializer='he_uniform')(head_model)
head_model = KL.Dropout(0.5)(head_model)
head_model = KL.Dense(1024,
activation='relu',
name='1111',
kernel_initializer='he_uniform')(head_model)
head_model = KL.Dropout(0.5)(head_model)
head_model = KL.Dense(classes, activation='softmax',
name='3333')(head_model)
model = KM.Model(inputs=base_model.input, outputs=head_model)
return model
zoom_range=0.1,
channel_shift_range=0.0,
fill_mode="nearest",
cval=0.0,
horizontal_flip=True,
vertical_flip=False,
rescale=1. / 255,
)
datagen = data.flow_from_directory(train_data_dir,
target_size=(331, 331),
batch_size=101,
shuffle=True,
class_mode='categorical')
base_model = NASNetLarge(weights='imagenet',
input_shape=(331, 331, 3),
include_top=False)
#base_model = tf.keras.applications.EfficientNetB7(weights='imagenet',input_shape=(360,360,3), include_top=False)
for layer in base_model.layers:
layer.trainable = False
x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dropout(0.4)(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Dense(512, activation='relu')(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Dense(256, activation='relu')(x)
def get_model(architecture, iteracion, models_info, pipeline):
print("=" * len(architecture))
print(architecture)
print("=" * len(architecture))
if iteracion > 0 and not pipeline["restart_weights"]:
print("USING MODELS FROM MEMORY")
base_model = models_info[architecture]['model_memory']
print("OK - USING MODELS FROM MEMORY")
if architecture == 'InceptionV3':
from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = InceptionV3(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'], 3))
print(f"OK - RESTARTING WEIGHTS FROM IMAGENET FOR {architecture}")
if architecture == 'InceptionV4':
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = InceptionResNetV2(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'],
3))
print(f"OK - RESTARTING WEIGHTS FROM IMAGENET FOR {architecture}")
if architecture == 'ResNet50':
from tensorflow.keras.applications.resnet import ResNet50, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = ResNet50(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'], 3))
if architecture == 'ResNet101':
from tensorflow.keras.applications.resnet import ResNet101, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = ResNet101(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'], 3))
if architecture == 'ResNet152':
from tensorflow.keras.applications.resnet import ResNet152, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = ResNet152(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'], 3))
print(f"OK - RESTARTING WEIGHTS FROM IMAGENET FOR {architecture}")
if architecture == 'DenseNet121':
from tensorflow.keras.applications.densenet import DenseNet121, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = DenseNet121(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'], 3))
if architecture == 'DenseNet169':
from tensorflow.keras.applications.densenet import DenseNet169, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = DenseNet169(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'], 3))
if architecture == 'DenseNet201':
from tensorflow.keras.applications.densenet import DenseNet201, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = DenseNet201(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'], 3))
if architecture == 'NASNetLarge':
from tensorflow.keras.applications.nasnet import NASNetLarge, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = NASNetLarge(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'], 3))
if architecture == 'Xception':
from tensorflow.keras.applications.xception import Xception, preprocess_input
if iteracion == 0 or pipeline["restart_weights"]:
base_model = Xception(weights=pipeline['weights'],
include_top=False,
input_shape=(pipeline['img_height'],
pipeline['img_width'], 3))
return base_model, preprocess_input
elif args.net == 'inception_v3':
base_model = InceptionV3(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
elif args.net == 'xception':
base_model = Xception(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
elif args.net == 'resnet_50':
base_model = ResNet50(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
elif args.net == 'densenet_121':
base_model = DenseNet121(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
elif args.net == 'densenet_169':
base_model = DenseNet169(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
elif args.net == 'densenet_201':
base_model = DenseNet201(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
elif args.net == 'mobilenet_v2':
base_model = MobileNetV2(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
elif args.net == 'nasnetlarge':
base_model = NASNetLarge(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
elif args.net == 'nasnetmobile':
base_model = NASNetMobile(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
elif args.net == 'inceptionresnet_v2':
base_model = InceptionResNetV2(input_shape=(dim, dim, 3), weights='imagenet', include_top=False)
else:
print('Not supported network type')
sys.exit()
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(rate=args.dropout0)(x)
x = Dense(args.dense1, use_bias=False, kernel_regularizer=l2(args.l21))(x)
if args.bn1:
x = BatchNormalization()(x)
x = Activation('relu')(x)
def get_base_model(self, name='vgg16'):
print('using model : ', name)
if name == 'mobilenet_v2':
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
base_model = MobileNetV2(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'mobilenet':
from tensorflow.keras.applications.mobilenet import MobileNet
base_model = MobileNet(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'densenet121':
from tensorflow.keras.applications.densenet import DenseNet121
base_model = DenseNet121(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'densenet169':
from tensorflow.keras.applications.densenet import DenseNet169
base_model = DenseNet169(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'densenet201':
from tensorflow.keras.applications.densenet import DenseNet201
base_model = DenseNet201(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'inception_resnet_v2':
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2
base_model = InceptionResNetV2(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'inception_v3':
from tensorflow.keras.applications.inception_v3 import InceptionV3
base_model = InceptionV3(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'nasnet_large':
from tensorflow.keras.applications.nasnet import NASNetLarge
base_model = NASNetLarge(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'nasnet_mobile':
from tensorflow.keras.applications.nasnet import NASNetMobile
base_model = NASNetMobile(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'resnet50':
from tensorflow.keras.applications.resnet50 import ResNet50
base_model = ResNet50(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'xception':
from tensorflow.keras.applications.xception import Xception
base_model = Xception(input_shape=(self.IMAGE_SIZE,
self.IMAGE_SIZE, 3),
include_top=False,
weights='imagenet')
elif name == 'vgg19':
from tensorflow.keras.applications.vgg19 import VGG19
base_model = VGG19(input_shape=(self.IMAGE_SIZE, self.IMAGE_SIZE,
3),
include_top=False,
weights='imagenet')
else:
from tensorflow.keras.applications.vgg16 import VGG16
# 采用VGG16为基本模型，include_top为False，表示FC层是可自定义的，抛弃模型中的FC层；该模型会在~/.keras/models下载基本模型
base_model = VGG16(input_shape=(self.IMAGE_SIZE, self.IMAGE_SIZE,
3),
include_top=False,
weights='imagenet')
# self.preprocess_input = preprocess_input
return base_model
def download_for_url(self, path: str, **kwargs):
"""
Download the file at the given URL
:param path: the path to download
:param kwargs: various kwargs for customizing the underlying behavior of
the model download and setup
:return: the absolute path to the model
"""
path_split = path.split('/')
type = path_split[0]
weights_file = path_split[1]
include_top = 'no_top' in weights_file
if type == 'vgg19':
ret = VGG19(include_top=include_top, **kwargs)
elif type == 'vgg16':
ret = VGG16(include_top=include_top, **kwargs)
elif type == 'resnet50':
ret = ResNet50(include_top=include_top, **kwargs)
elif type == 'resnet101':
ret = ResNet101(include_top=include_top, **kwargs)
elif type == 'resnet152':
ret = ResNet152(include_top=include_top, **kwargs)
elif type == 'resnet50v2':
ret = ResNet50V2(include_top=include_top, **kwargs)
elif type == 'resnet101v2':
ret = ResNet101V2(include_top=include_top, **kwargs)
elif type == 'resnet152v2':
ret = ResNet152V2(include_top=include_top, **kwargs)
elif type == 'densenet121':
ret = DenseNet121(include_top=include_top)
elif type == 'densenet169':
ret = DenseNet169(include_top=include_top, **kwargs)
elif type == 'densenet201':
ret = DenseNet201(include_top=include_top, **kwargs)
elif type == 'inceptionresnetv2':
ret = InceptionResNetV2(include_top=include_top, **kwargs)
elif type == 'efficientnetb0':
ret = EfficientNetB0(include_top=include_top, **kwargs)
elif type == 'efficientnetb1':
ret = EfficientNetB1(include_top=include_top, **kwargs)
elif type == 'efficientnetb2':
ret = EfficientNetB2(include_top=include_top, **kwargs)
elif type == 'efficientnetb3':
ret = EfficientNetB3(include_top=include_top, **kwargs)
elif type == 'efficientnetb4':
ret = EfficientNetB4(include_top=include_top, **kwargs)
elif type == 'efficientnetb5':
ret = EfficientNetB5(include_top=include_top, **kwargs)
elif type == 'efficientnetb6':
ret = EfficientNetB6(include_top=include_top, **kwargs)
elif type == 'efficientnetb7':
efficient_net = EfficientNetB7(include_top=include_top, **kwargs)
elif type == 'mobilenet':
ret = MobileNet(include_top=include_top, **kwargs)
elif type == 'mobilenetv2':
ret = MobileNetV2(include_top=include_top)
# MobileNetV3() missing 2 required positional arguments: 'stack_fn' and 'last_point_ch'
#elif type == 'mobilenetv3':
# mobile_net = MobileNetV3(include_top=include_top, **kwargs)
elif type == 'inceptionv3':
ret = InceptionV3(include_top=include_top, **kwargs)
elif type == 'nasnet':
ret = NASNetLarge(include_top=include_top, **kwargs)
elif type == 'nasnet_mobile':
ret = NASNetMobile(include_top=include_top, **kwargs)
elif type == 'xception':
ret = Xception(include_top=include_top, **kwargs)
model_path = os.path.join(keras_path, weights_file)
ret.save(model_path)
return model_path
from tensorflow.keras.applications.nasnet import NASNetLarge, preprocess_input
#from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
from tensorflow.keras.preprocessing import image
import numpy as np
import json
from sklearn.metrics.pairwise import cosine_similarity
import os
import time
nas_model = NASNetLarge(
input_shape=None,
include_top=False,
weights="imagenet",
input_tensor=None,
pooling='max',
)
# inc_res_model = InceptionResNetV2(
# input_shape=None, include_top=False, weights="imagenet", input_tensor=None, pooling='max')
def extract_features(img):
"""Extract features from input image and return the same"""
print(f'>>> Entered feature extractor...', end='')
start = time.time()
img = image.load_img(img, target_size=(331, 331))
# img = image.load_img(img, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
features = nas_model.predict(x)
print(f'Feature extraction took: {time.time()-start:.2}. Exiting. ')
import tensorflow as tf
# model = keras.applications.NASNetMobile(
# input_shape=img_size + (3,),
# include_top=True,
# weights=None, #randomize de weights
# input_tensor=None,
# pooling=None,
# classes=1,
# )
model = NASNetLarge(
input_shape=img_size + (3, ),
include_top=True,
weights=None, #randomize de weights
input_tensor=None,
pooling=None,
classes=1,
)
model.summary()
#%% compilando
import tensorflow as tf
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model_checkpoint_callback = [