def extract_features(directory, is_attention=False):
# load the model
if is_attention:
model = ResNet152()
model.layers.pop()
# extract final 49x512 conv layer for context vectors
final_conv = Reshape([49, 512])(model.layers[-4].output)
model = Model(inputs=model.inputs, outputs=final_conv)
print(model.summary())
features = dict()
else:
model = ResNet152()
# re-structure the model
model.layers.pop()
model = Model(inputs=model.inputs, outputs=model.layers[-1].output)
print(model.summary())
# extract features from each photo
features = dict()
for name in progressbar(listdir(directory)):
# ignore README
if name == 'README.md':
continue
filename = directory + '/' + name
image = load_image(filename)
# extract features
feature = model.predict(image, verbose=0)
# get image id
image_id = name.split('.')[0]
# store feature
features[image_id] = feature
print('>%s' % name)
return features
def main():
opt = parse_args()
print(json.dumps(vars(opt), indent = 2))
input_path = opt.data_path
print("Input path:"+str(input_path))
output_path = opt.feature_path
print("Output path:"+(output_path))
#loading pretrain resnet 152
resnet152_model = ResNet152(ResNet152(include_top=False, weights='imagenet', input_tensor=None, input_shape=None, pooling=None,))
count = 0
features_filename = open(output_path,"w")
for filename in os.listdir(input_path):
if filename.endswith(".jpg") or filename.endswith(".png"):
image_path = os.path.join(input_path,filename)
print("Image path:",image_path)
img = image.load_img(image_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
features = resnet152_model.predict(x)[0]
output = filename+" "+" ".join(np.asarray(features, dtype=str))+"\n"
# filename_features_format = np.insert(features.astype(str), 0, str(filename), axis=0)
features_filename.write(output)
# np.savetxt(features_filename,filename_features_format,fmt='%s')
count += 1
print("Processing "+str(count)+". Filename: "+filename)
def GenerateVisualFeatures(data_path, offset=0, limit=None, model=None):
article_paths = [
join(data_path, f) for f in listdir(data_path)
if isdir(join(data_path, f))
]
limit = limit if limit else len(article_paths) - offset
model = model if model else ResNet152(weights='imagenet',
include_top=False)
for i in range(offset, offset + limit):
path = article_paths[i]
print(i, path)
meta_path = join(path, 'img/', 'meta.json')
meta_arr = _getImagesMeta(meta_path)
for meta in meta_arr:
if 'features' in meta: continue
if meta['filename'][-4:].lower() != ".jpg": continue
img_path = join(path, 'img/', meta['filename'])
try:
features = _getImageFeatures(model, img_path)['features']
meta['features'] = [str(f) for f in features]
except Exception as e:
print("exception", str(e))
print(img_path)
continue
_dump(meta_path, json.dumps({"img_meta": meta_arr}))
def evaluation(args):
path_img_val = '../datasets/ilsvrc2012/images/val/'
path_val_info = '../datasets/ilsvrc2012/images/val.txt'
if args.model == 'vgg16':
model = VGG16(weights='imagenet')
model.summary()
elif args.model == 'resnet152':
model = ResNet152(weights='imagenet')
model.summary()
elif args.model == 'resnet152v2':
model = ResNet152V2(weights='imagenet')
model.summary()
elif args.model == 'inceptionresnetv2':
model = InceptionResNetV2(weights='imagenet')
model.summary()
elif args.model == 'densenet201':
model = DenseNet201(weights='imagenet')
model.summary()
elif args.model == 'nasnetlarge':
model = NASNetLarge(weights='imagenet')
model.summary()
name, label = load_header_imagenet(load_file(path_val_info))
pred = list()
for i, n in enumerate(name):
x = preprocessing_imagenet(path_img_val + n, args)
pred.append(np.argmax(model.predict(x), axis=1)[0])
if i % 1000 == 0:
print(n)
correct = len([p for p, l in zip(pred, label) if p == l])
print('Accuracy of the IMAGENET dataset using model %s: %.4f' %
(args.model, correct / len(label)))
def set_base_model(self):
stringWeights = None
if (self.imageNetWeights):
print("Using imagenet weights...")
stringWeights = "imagenet"
else:
print("Using random initialization of the weights...")
stringWeights = None
if (self.base_model_type == "vgg16"):
self.base_model = keras.applications.vgg16.VGG16(
include_top=False,
weights=stringWeights,
input_shape=self.input_shape,
pooling='max')
elif (self.base_model_type == "resnet50"):
self.base_model = ResNet50(include_top=False,
weights=stringWeights,
input_shape=self.input_shape,
pooling='max')
elif (self.base_model_type == "resnet101"):
self.base_model = ResNet101(include_top=False,
weights=stringWeights,
input_shape=self.input_shape,
pooling='max')
elif (self.base_model_type == "resnet152"):
self.base_model = ResNet152(include_top=False,
weights=stringWeights,
input_shape=self.input_shape,
pooling='max')
elif (self.base_model_type == "inceptionv3"):
self.base_model = keras.applications.inception_v3.InceptionV3(
include_top=False, weights=stringWeights, pooling='max')
def build_cnn_model():
base_model = ResNet152((224,224,3), weights="imagenet")
cnn_model = Model(input=base_model.input, output=base_model.get_layer('avg_pool').output)
opt = optimizers.SGD(lr=0.01, decay=1e-4, momentum=0.9)
cnn_model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
return cnn_model
def make_encoder(input, name='resnet50', pretrained=True):
if name == 'resnet18':
from classification_models.keras import Classifiers
ResNet18, _ = Classifiers.get('resnet18')
model = ResNet18(
weights='imagenet' if pretrained else None,
input_tensor=input,
include_top=False
)
elif name == 'resnet50':
from keras.applications.resnet import ResNet50
model = ResNet50(
weights='imagenet' if pretrained else None,
input_tensor=input,
include_top=False
)
elif name == 'resnet101':
from keras.applications.resnet import ResNet101
model = ResNet101(
weights='imagenet' if pretrained else None,
input_tensor=input,
include_top=False
)
elif name == 'resnet152':
from keras.applications.resnet import ResNet152
model = ResNet152(
weights='imagenet' if pretrained else None,
input_tensor=input,
include_top=False
)
elif name == 'vgg16':
from keras.applications.vgg16 import VGG16
model = VGG16(
weights='imagenet' if pretrained else None,
input_tensor=input,
include_top=False
)
elif name == 'vgg19':
from keras.applications.vgg19 import VGG19
model = VGG19(
weights='imagenet' if pretrained else None,
input_tensor=input,
include_top=False
)
else:
raise Exception(f'unknown encoder {name}')
return model
def train(self):
# re-size all the images to this
IMAGE_SIZE = [224, 224]
# add preprocessing layer to the front of VGG
resnet = ResNet152(input_shape=IMAGE_SIZE + [3],
weights='imagenet',
include_top=False)
# don't train existing weights
for layer in resnet.layers:
layer.trainable = False
# useful for getting number of classes
folders = glob(self.train_path + '*')
# our layers - you can add more if you want
x = Flatten()(resnet.output)
prediction = Dense(len(folders), activation='sigmoid')(x)
# create a model object
model = Model(inputs=resnet.input, outputs=prediction)
# view the structure of the model
model.summary()
# tell the model what cost and optimization method to use
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Use the Image Data Generator to import the images from the dataset
train_datagen = ImageDataGenerator(rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
training_set = train_datagen.flow_from_directory(
self.train_path,
target_size=(224, 224),
batch_size=32,
class_mode='categorical')
test_set = test_datagen.flow_from_directory(self.train_path,
target_size=(224, 224),
batch_size=32,
class_mode='categorical')
# fit the model
r = model.fit_generator(training_set,
validation_data=test_set,
epochs=10,
steps_per_epoch=2,
validation_steps=len(test_set))
model.save(self.model_save_path)
def load_model(model):
K.clear_session()
if model == "ResNet152":
base_model = ResNet152(include_top=False,
weights='imagenet',
input_shape=(299, 299, 3),
pooling="max")
if model == "InceptionV3":
base_model = InceptionV3(include_top=False,
weights='imagenet',
input_shape=(299, 299, 3),
pooling="max")
if model == "InceptionResNetV2":
base_model = InceptionResNetV2(include_top=False,
weights='imagenet',
input_shape=(299, 299, 3),
pooling="max")
if model == "Xception":
base_model = Xception(include_top=False,
weights='imagenet',
input_shape=(299, 299, 3),
pooling="max")
if model == "VGG":
base_model = VGG19(include_top=False,
weights='imagenet',
input_shape=(299, 299, 3),
pooling="max")
base_model.summary()
x = base_model.output
dense1_ = Dense(512, activation='relu')
dense1 = dense1_(x)
x = Dropout(0.2)(dense1)
dense2 = Dense(256, activation='relu')(x)
x = Dropout(0.2)(dense2)
dense3 = Dense(128, activation='relu')(x)
pred_output = Dense(1, activation='sigmoid')(dense3)
model = Model(inputs=base_model.input, outputs=[pred_output])
model.summary()
return model
def Image_model():
'''
Description: build Image model
Arguments:
Returns:
Pretrained image model
'''
print("Create Image Model ...")
model = ResNet152()
model.input
model.layers.pop()
new_layer = Dense(1024, name='FC-1024')
inp = model.input
out = new_layer(model.layers[-1].output)
model = Model(inp, out)
return model
class VGG16Model:
# Process Model
# model = VGG19(
model = ResNet152(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000)
# model = MobileNetV2(weights='imagenet', include_top=False)
def infern(self, img: np.ndarray):
x = np.expand_dims(img, axis=0)
x = preprocess_input(x)
predictions = self.model.predict(x)
return decode_predictions(predictions, top=3)
def test(self):
# image = load_img('../4051378654_238ca94313.jpg', target_size=(224, 224))
image = load_img('../1480654305.jpg', target_size=(224, 224))
image = img_to_array(image)
print(self.infern(image))
def ResNet(input_shape = (const.X_Height, const.X_Width, const.X_Channels), classes = const.Y_Classes, Layers = 50, source = 'keras', weights = 'imagenet'):
"""
create RestNet model
Arguments:
input_shape = Height, Width and channels for each input image
classes = how many classes model will be trainned on
Layers: how many layers; should be one of [18, 34, 50, 101, 152]
source: 'keras' (use built-in model) or 'manual' (use my custom model above)
weights: 'imagenet' (load weights from keras lib) or None (no weights loading)
'imagenet' only available if layers in [50,101,152]
"""
# validate parameters
if (Layers not in [18, 34, 50, 101, 152]):
raise ValueError('Invalid layer number: ' + str(Layers) + ' (must be one of [18, 34, 50, 101, 152]).')
if (source not in ['keras', 'manual']):
raise ValueError('Invalid model source: ' + str(source) + " (must be 'keras' or 'manual'.")
if (weights not in [None, 'imagenet']):
raise ValueError('Invalid weights definition: ' + str(weights) + " (must be None or 'imagenet'.")
if (Layers in [18, 34]):
if (source == 'keras'):
raise ValueError("No keras model available for small ResNets. 'source' parameter must be 'manual' when layers are 18 or 34.")
if (weights != None):
raise ValueError("No weights available for small ResNets. 'weights' Parameter must be None when layers are 18 or 34.")
# build model
if (source == 'keras'):
# load base model from keras
if (Layers == 50):
from keras.applications.resnet import ResNet50
baseModel = ResNet50(include_top = False, weights = weights, input_shape = input_shape)
elif (Layers == 101):
from keras.applications.resnet import ResNet101
baseModel = ResNet101(include_top = False, weights = weights, input_shape = input_shape)
elif (Layers == 152):
from keras.applications.resnet import ResNet152
baseModel = ResNet152(include_top = False, weights = weights, input_shape = input_shape)
elif (source == 'manual'):
# load model from my implementation
if (Layers in [18,34]):
baseModel = ResNetSmall(input_shape=input_shape, classes=classes, Layers=Layers)
else:
baseModel = ResNetLarge(input_shape=input_shape, classes=classes, Layers=Layers, weights=weights)
# add final layers to built-in keras model
from keras.models import Model
from keras.layers import Dense, Flatten, AveragePooling2D
X = baseModel.output
X = AveragePooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None)(X)
X = Flatten()(X)
Preds = Dense(const.Y_Classes, activation='softmax', name='fc' + str(const.Y_Classes))(X)
model = Model(inputs=baseModel.input, outputs=Preds)
# return the model
return model
def chosen_model(choice):
global base_model
if choice == 19:
model_exist()
else:
while (1):
print()
print(
'Transfer Learning? - Will use pre-trained model with imagenet weights'
)
print('y')
print('n')
weights_wanted = input()
if weights_wanted.upper() != 'Y' and weights_wanted.upper() != 'N':
print('ERROR: Please enter a valid choice')
else:
break
if choice == 1:
print('Selected Model = Xception')
if weights_wanted.upper() == 'Y':
base_model = Xception(weights='imagenet', include_top=False)
else:
base_model = Xception(weights=None, include_top=False)
if choice == 2:
print('Selected Model = VGG16')
if weights_wanted.upper() == 'Y':
base_model = VGG16(weights='imagenet', include_top=False)
else:
base_model = VGG16(weights=None, include_top=False)
if choice == 3:
print('Selected Model = VGG19')
if weights_wanted.upper() == 'Y':
base_model = VGG19(weights='imagenet', include_top=False)
else:
base_model = VGG19(weights=None, include_top=False)
if choice == 4:
print('Selected Model = ResNet50')
if weights_wanted.upper() == 'Y':
base_model = ResNet50(weights='imagenet', include_top=False)
else:
base_model = ResNet50(weights=None, include_top=False)
if choice == 5:
print('Selected Model = ResNet101')
if weights_wanted.upper() == 'Y':
base_model = ResNet101(weights='imagenet', include_top=False)
else:
base_model = ResNet101(weights=None, include_top=False)
if choice == 6:
print('Selected Model = ResNet152')
if weights_wanted.upper() == 'Y':
base_model = ResNet152(weights='imagenet', include_top=False)
else:
base_model = ResNet152(weights=None, include_top=False)
if choice == 7:
print('Selected Model = ResNet50V2')
if weights_wanted.upper() == 'Y':
base_model = ResNet50V2(weights='imagenet', include_top=False)
else:
base_model = ResNet50V2(weights=None, include_top=False)
if choice == 8:
print('Selected Model = ResNet101V2')
if weights_wanted.upper() == 'Y':
base_model = ResNet101V2(weights='imagenet', include_top=False)
else:
base_model = ResNet101V2(weights=None, include_top=False)
if choice == 9:
print('Selected Model = ResNet152V2')
if weights_wanted.upper() == 'Y':
base_model = ResNet152V2(weights='imagenet', include_top=False)
else:
base_model = ResNet152V2(weights=None, include_top=False)
if choice == 10:
print('Selected Model = InceptionV3')
if weights_wanted.upper() == 'Y':
base_model = InceptionV3(weights='imagenet', include_top=False)
else:
base_model = InceptionV3(weights=None, include_top=False)
if choice == 11:
print('Selected Model = InceptionResNetV2')
if weights_wanted.upper() == 'Y':
base_model = InceptionResNetV2(weights='imagenet',
include_top=False)
else:
base_model = InceptionResNetV2(weights=None, include_top=False)
if choice == 12:
print('Selected Model = MobileNet')
if weights_wanted.upper() == 'Y':
base_model = MobileNet(weights='imagenet', include_top=False)
else:
base_model = MobileNet(weights=None, include_top=False)
if choice == 13:
print('Selected Model = MobileNetV2')
if weights_wanted.upper() == 'Y':
base_model = MobileNetV2(weights='imagenet', include_top=False)
else:
base_model = MobileNetV2(weights=None, include_top=False)
if choice == 14:
print('Selected Model = DenseNet121')
if weights_wanted.upper() == 'Y':
base_model = DenseNet121(weights='imagenet', include_top=False)
else:
base_model = DenseNet121(weights=None, include_top=False)
if choice == 15:
print('Selected Model = DenseNet169')
if weights_wanted.upper() == 'Y':
base_model = DenseNet169(weights='imagenet', include_top=False)
else:
base_model = DenseNet169(weights=None, include_top=False)
if choice == 16:
print('Selected Model = DenseNet201')
if weights_wanted.upper() == 'Y':
base_model = DenseNet201(weights='imagenet', include_top=False)
else:
base_model = DenseNet201(weights=None, include_top=False)
if choice == 17:
print('Selected Model = NASNetLarge')
if weights_wanted.upper() == 'Y':
base_model = NASNetLarge(weights='imagenet', include_top=False)
else:
base_model = NASNetLarge(weights=None, include_top=False)
if choice == 18:
print('Selected Model = NASNetMobile')
if weights_wanted.upper() == 'Y':
base_model = NASNetMobile(weights='imagenet',
include_top=False)
else:
base_model = NASNetMobile(weights=None, include_top=False)
CLASSES = len(os.listdir('data/train'))
print('Number of Classes = {}'.format(CLASSES))
x = base_model.output
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dropout(0.4)(x)
predictions = Dense(CLASSES, activation='softmax')(x)
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'])
training(model)
labels = [
int(str(p).split("/")[1].split("_")[0][-2:]) for p in image_paths
]
# Commented models. For testing, please check the README to adjust image dimensions and feature vector size
#model = Xception(include_top=False, weights='imagenet', pooling='avg')
#model = NASNetLarge(include_top=False, weights='imagenet', pooling='avg')
#model = InceptionResNetV2(include_top=False, weights='imagenet', pooling='avg')
#model = VGG16(include_top=False, weights='imagenet', pooling='avg')
#model = cifar100vgg(train=False)
#my_layer = model.model.layers[56]
#model = Model(model.model.input, outputs=my_layer.output)
# Define the model
model = ResNet152(include_top=False, weights='imagenet', pooling='avg')
n_clusters = 58
model.layers[0].trainable = False
dims = [224, 224]
vect_len = 2048
# Define list to store vector values
feature_vects = np.zeros((len(image_paths), vect_len), dtype=float)
# Extract vectors
for idx, img in enumerate(image_paths):
# Print
print("Extracting vector features for image: " + str(idx))
# Load and reshape the image to input to the network
img = cv2.resize(cv2.imread(img), (dims[0], dims[1]))
def extract_features(nn_model, fine_tune=False):
if fine_tune is True:
if nn_model == 'resnet50':
model = ResNet50(include_top=False, weights=None, pooling='avg')
elif nn_model == 'resnet101':
model = ResNet101(include_top=False, weights=None, pooling='avg')
elif nn_model == 'resnet152':
model = ResNet152(include_top=False, weights=None, pooling='avg')
elif nn_model == 'densenet121':
model = DenseNet121(include_top=False, weights=None, pooling='avg')
elif nn_model == 'densenet169':
model = DenseNet169(include_top=False, weights=None, pooling='avg')
elif nn_model == 'densenet201':
model = DenseNet201(include_top=False, weights=None, pooling='avg')
else:
raise NotImplementedError("The NN model is not implemented!")
model.load_weights('./finetune/' + nn_model +
'/finetune_weights_50_epoch.h5',
by_name=True)
else:
if nn_model == 'resnet50':
model = ResNet50(include_top=False,
weights='imagenet',
pooling='avg')
elif nn_model == 'resnet101':
model = ResNet101(include_top=False,
weights='imagenet',
pooling='avg')
elif nn_model == 'resnet152':
model = ResNet152(include_top=False,
weights='imagenet',
pooling='avg')
elif nn_model == 'densenet121':
model = DenseNet121(include_top=False,
weights='imagenet',
pooling='avg')
elif nn_model == 'densenet169':
model = DenseNet169(include_top=False,
weights='imagenet',
pooling='avg')
elif nn_model == 'densenet201':
model = DenseNet201(include_top=False,
weights='imagenet',
pooling='avg')
else:
raise NotImplementedError("The NN model is not implemented!")
ImgPath = './dataset/img'
ProcessedPath = './dataset/feature_' + nn_model
Lastlist = os.listdir(ImgPath)
sum = 0
for lastfolder in Lastlist:
LastPath = os.path.join(ImgPath, lastfolder)
savepath = os.path.join(ProcessedPath, lastfolder)
imagelist = os.listdir(LastPath)
for image1 in imagelist:
sum += 1
print(image1)
print('sum is ', sum)
image_pre, ext = os.path.splitext(image1)
imgfile = LastPath + '/' + image1
img = image.load_img(imgfile, target_size=(64, 64))
x = image.img_to_array(img) # shape:(64,64,3)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x) # shape:(1,64,64,3)
print(x.shape)
want = model.predict(x)
print(np.shape(want)) # shape:(1,2048)
# normalize
a = np.min(want)
b = np.max(want)
want = (want - a) / (b - a)
if (not os.path.exists(savepath)):
os.makedirs(savepath)
np.save(os.path.join(savepath, image_pre + '.npz'), want)
def __init__(self):
self.model = ResNet152(weights='imagenet', include_top=False)
from keras import backend as K sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True)) K.tensorflow_backend._get_available_gpus() ## #Se establecen los paths path_csv = '/media/user_home2/vision2020_01/Data/iWildCam2019/train.csv' path_train = '/media/user_home2/vision2020_01/Data/iWildCam2019/train_images' ## #Se crea el modelo # create the base pre-trained model base_model = ResNet152(weights='imagenet', include_top=False) # add a global spatial average pooling layer x = base_model.output x = GlobalAveragePooling2D()(x) # let's add a fully-connected layer x = Dense(1024, activation='relu')(x) # and a logistic layer -- let's say we have 200 classes predictions = Dense(14, activation='softmax')(x) # this is the model we will train model = Model(inputs=base_model.input, outputs=predictions) # first: train only the top layers (which were randomly initialized) # i.e. freeze all convolutional InceptionV3 layers for layer in base_model.layers:
def Build_Model_ResNet(px_train , pSessionParameters , pTrainingParameters ):
from keras.applications.resnet import ResNet50
from keras.applications.resnet import ResNet101
from keras.applications.resnet import ResNet152
from keras.applications.resnet_v2 import ResNet50V2
from keras.applications.resnet_v2 import ResNet101V2
from keras.applications.resnet_v2 import ResNet152V2
from keras.layers import Input
# Builds a new ResNet model
'''
Input parameters:
px_train: training data to be used to set the input shape of the model
pModelBuildParameters: Dict of Parameters to define how the model is built.
Return parameters: model
'''
BatchNormFlag = pSessionParameters['BatchNorm']
NoClasses = pSessionParameters['ModelBuildParameters']['NoClasses']
Activation = pSessionParameters['ModelBuildParameters']['Activation']
IncludeTopFlag = pSessionParameters['ModelBuildParameters']['IncludeTop']
Model = pSessionParameters['ModelBuildParameters']['Model']
Version = pSessionParameters['ModelBuildParameters']['Version']
if IncludeTopFlag:
if Version==1:
if 'ResNet50' in Model:
conv_base = ResNet50(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
elif 'ResNet101' in Model:
conv_base = ResNet101(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
elif 'ResNet152' in Model:
conv_base = ResNet152(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
else: # Version 2
if 'ResNet50' in Model:
conv_base = ResNet50V2(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
elif 'ResNet101' in Model:
conv_base = ResNet101V2(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
elif 'ResNet152' in Model:
conv_base = ResNet152V2(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
model = models.Sequential()
model.add(conv_base)
else:
if Version==1:
if 'ResNet50' in Model:
conv_base = ResNet50(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
elif 'ResNet101' in Model:
conv_base = ResNet101(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
elif 'ResNet152' in Model:
conv_base = ResNet152(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
else: # Version 2
if 'ResNet50' in Model:
conv_base = ResNet50V2(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
elif 'ResNet101' in Model:
conv_base = ResNet101V2(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
elif 'ResNet152' in Model:
conv_base = ResNet152V2(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
model = models.Sequential()
model.add(conv_base)
# Add Dense Layers and Dropout and BatchNorm layers based on ModelBuildParameters
model = Build_Model_AddLayers(model , pSessionParameters )
model.add(layers.Dense(NoClasses, activation=Activation, name='dense_class'))
return model
print('train from start')
model = models.Sequential()
if '50' in args_model:
base_model = ResNet50(weights=None,
include_top=False,
input_shape=(32, 32, 3),
pooling=None)
elif '101' in args_model:
base_model = ResNet101(weights=None,
include_top=False,
input_shape=(32, 32, 3),
pooling=None)
elif '152' in args_model:
base_model = ResNet152(weights=None,
include_top=False,
input_shape=(32, 32, 3),
pooling=None)
#base_model.summary()
#pdb.set_trace()
#model.add(layers.UpSampling2D((2,2)))
#model.add(layers.UpSampling2D((2,2)))
#model.add(layers.UpSampling2D((2,2)))
model.add(base_model)
model.add(layers.Flatten())
#model.add(layers.BatchNormalization())
#model.add(layers.Dense(128, activation='relu'))
#model.add(layers.Dropout(0.5))
#model.add(layers.BatchNormalization())
def ResNet101_nn(train_path, vali_path, storageFileName):
start_time = time()
monitor = EarlyStopping(monitor='val_loss',
min_delta=1e-3,
patience=5,
verbose=1,
mode='auto',
restore_best_weights=True)
train_datagen = ImageDataGenerator(rescale=1. / 255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
vali_datagen = ImageDataGenerator(rescale=1. / 255, )
train_generator = train_datagen.flow_from_directory(
train_path,
target_size=(256, 256),
batch_size=8,
)
vail_generator = vali_datagen.flow_from_directory(
vali_path,
target_size=(256, 256),
batch_size=8,
)
baseNet = ResNet152(weights='imagenet',
include_top=False,
input_shape=INPUT_SHAPE)
net = baseNet.output
net = Flatten()(net)
net = Dropout(0.2)(net)
net = Dense(NUM_CLASSES, activation='softmax')(net)
ResNet = Model(inputs=baseNet.inputs, outputs=net)
for layer in ResNet.layers[:FREEZE_LAYERS]:
layer.trainable = False
for layer in ResNet.layers[FREEZE_LAYERS:]:
layer.trainable = True
ResNet.compile(optimizer=optimizers.RMSprop(lr=1e-5),
loss='categorical_crossentropy',
metrics=['accuracy'])
history = ResNet.fit_generator(train_generator,
steps_per_epoch=50,
epochs=30,
validation_data=vail_generator,
validation_steps=50,
callbacks=[monitor])
ResNet.save(osp.join("result", "resnet152_" + storageFileName + "_3.h5"))
plt_LineChart(history=history,
netName="resnet152_" + storageFileName + ".jpg")
end_time = time()
mins = (end_time - start_time) // 60
secs = (end_time - start_time) % 60
print("ResNet Execute time: {}:{:.2f}".format(mins, secs))