def getCNN_Model(use_vgg16=use_vgg16):
if use_vgg16:
modelID = 'VGG16'
inp = (224, 224, 3)
modelPackage = vgg16
margins = (8, 8, 48, 48)
Target_Frame_Shape = (240, 320, 3)
cnn_model = vgg16.VGG16(weights='imagenet',
input_shape=inp,
include_top=include_vgg_top)
elif True:
inp = (299, 299, 3)
modelPackage = inception_v3
modelID = 'InceptionV3'
margins = (0, 1, 51, 50)
Target_Frame_Shape = (300, 400, 3)
cnn_model = inception_v3.InceptionV3(weights='imagenet',
input_shape=inp,
include_top=include_vgg_top)
else:
inp = (331, 331, 3)
modelPackage = nasnet
modelID = 'NASNetLarge'
margins = (14, 15, 74, 75)
Target_Frame_Shape = (360, 480, 3)
cnn_model = nasnet.NASNetLarge(weights='imagenet',
input_shape=inp,
include_top=include_vgg_top)
def preprocess_input(imagePath):
return preprocess_input_for_model(imagePath, Target_Frame_Shape,
margins, modelPackage)
if include_vgg_top:
modelID = modelID + '_inc_top'
#cnn_model = addDropout(cnn_model)
cnn_model.layers.pop()
cnn_model.outputs = [cnn_model.layers[-1].output]
cnn_model.output_layers = [cnn_model.layers[-1]]
cnn_model.layers[-1].outbound_nodes = []
for layer in cnn_model.layers:
layer.trainable = False
x = cnn_model.layers[-1].output #
"""
x = Dropout(0.25, name = 'dropout3_025')(x) #
x = Dense(1024, activation='relu', name='fc1024')(x) #
x = Dropout(0.25, name = 'dropout_025')(x) #
x = Dense(num_outputs, name = 'fc3')(x) #
"""
#a = Input(shape=(num_outputs, ), name='aux_input0')
#x = (concatenate([x, a], axis = 1))#
cnn_model = Model(inputs=cnn_model.input, outputs=x)
return inp, cnn_model, modelID, preprocess_input
def __init__(self, model, input_size):
input_shape = (input_size, input_size, 3)
if model == 'xception':
base_model = xception.Xception(weights='imagenet',
include_top=False,
pooling='max',
input_shape=input_shape)
elif model == 'vgg16':
base_model = vgg16.VGG16(weights='imagenet',
include_top=False,
pooling='max',
input_shape=input_shape)
elif model == 'vgg19':
base_model = vgg19.VGG19(weights='imagenet',
include_top=False,
pooling='max',
input_shape=input_shape)
elif model == 'inception_v3':
base_model = inception_v3.InceptionV3(weights='imagenet',
include_top=False,
pooling='max',
input_shape=input_shape)
elif model == 'mobilenet':
base_model = mobilenet.MobileNet(weights='imagenet',
include_top=False,
pooling='max',
input_shape=input_shape)
elif model == 'inception_resnet_v2':
base_model = inception_resnet_v2.InceptionResNetV2(
weights='imagenet',
include_top=False,
pooling='max',
input_shape=input_shape)
elif model == 'resnet50':
base_model = resnet50.ResNet50(weights='imagenet',
include_top=False,
pooling='max',
input_shape=input_shape)
elif model == 'nasnetlarge':
base_model = nasnet.NASNetLarge(weights='imagenet',
include_top=False,
pooling='max',
input_shape=input_shape)
else:
base_model = nasnet.NASNetMobile(weights='imagenet',
include_top=False,
pooling='max',
input_shape=input_shape)
self.input_size = input_size
self.model = base_model
self.graph = tf.get_default_graph()
base_model.summary()
def load_imagenet_model(self):
"""
Initialize the pre-trained model architecture and load the model weights.
The downloaded weights contains only the convolution base. It does not
contain the top two dense layers. We will have to manually define the top
two dense layers. The size_dict dictionary object will hold the input sizes
for various models, which will be further used to train the respective models
with the given input image dimensions.
Arguments:
-model_name : Name of the model, for example - vgg16, inception_v3, resnet50 etc
"""
if (self.input_params['model_name'] == "vgg16"):
base_model = vgg16.VGG16(weights=None, include_top=False)
base_model.load_weights(
self.path_dict["weights_path"] +
"vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5")
elif (self.input_params['model_name'] == "inceptionv3"):
base_model = inception_v3.InceptionV3(weights=None,
include_top=False)
base_model.load_weights(
self.path_dict["weights_path"] +
"inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5")
elif (self.input_params['model_name'] == "resnet50"):
base_model = resnet.ResNet50(weights=None, include_top=False)
base_model.load_weights(
self.path_dict["weights_path"] +
"resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5")
elif (self.input_params['model_name'] == "inception_resnet"):
base_model = inception_resnet_v2.InceptionResNetV2(
weights=None, include_top=False)
base_model.load_weights(
self.path_dict["weights_path"] +
"inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5"
)
elif (self.input_params['model_name'] == "nasnet"):
base_model = nasnet.NASNetLarge(weights=None, include_top=False)
base_model.load_weights(self.path_dict["weights_path"] +
"NASNet-large-no-top.h5")
elif (self.input_params['model_name'] == "xception"):
base_model = xception.Xception(weights=None, include_top=False)
base_model.load_weights(
self.path_dict["weights_path"] +
"xception_weights_tf_dim_ordering_tf_kernels.h5")
return base_model
def create_model(feature_extraction_method, num_classes, path_cnn_pre_trained,
input_size):
if (feature_extraction_method == 'fine_tuning_lenet'):
model = load_model(path_cnn_pre_trained)
input_image = input_size
if (feature_extraction_method == 'fine_tuning_vgg16'):
model = vgg16.VGG16(weights='imagenet', include_top=True)
#layer_name = 'fc2'
input_image = 224
elif (feature_extraction_method == 'fine_tuning_vgg19'):
model = vgg19.VGG19(weights='imagenet', include_top=True)
#layer_name = 'fc2'
input_image = 224
elif (feature_extraction_method == 'fine_tuning_xception'):
model = xception.Xception(weights='imagenet', include_top=True)
#layer_name = 'avg_pool'
input_image = 299
elif (feature_extraction_method == 'fine_tuning_resnet'):
model = resnet.ResNet50(weights='imagenet', include_top=True)
#layer_name = 'avg_pool'
input_image = 224
elif (feature_extraction_method == 'fine_tuning_inception_resnet'):
model = inception_resnet.InceptionResNetV2(weights='imagenet',
include_top=True)
#layer_name = 'avg_pool'
input_image = 299
elif (feature_extraction_method == 'fine_tuning_nasnet'):
model = nasnet.NASNetLarge(weights='imagenet', include_top=True)
#layer_name = 'global_average_pooling2d_1'
input_image = 331
#Removing the last layer
model.layers.pop()
new_layer = Dense(num_classes, activation='softmax', name='predictions')
model = Model(model.input, new_layer(model.layers[-1].output))
model.summary()
return model, input_image
def __init__(self, weights=None, cnn_model_type='nasnet', n_gpu=1):
"""Either load pretrained from imagenet, or load our saved
weights from our own training."""
self.weights = weights # so we can check elsewhere which model
if weights is None:
# Get model with pretrained weights.
if cnn_model_type == 'InceptionV3':
self.model = inception_v3.InceptionV3(
weights='imagenet',pooling='avg',
include_top=False
)
elif cnn_model_type == 'nasnet':
base_model = nasnet.NASNetLarge(
weights='imagenet',
include_top=True
)
# issue https://github.com/keras-team/keras/issues/10109
self.model = Model(
inputs=base_model.input,
outputs=base_model.get_layer('global_average_pooling2d_1').output
)
else:
# Load the model first.
self.model = load_model(weights)
# Then remove the top so we get features not predictions.
# From: https://github.com/fchollet/keras/issues/2371
self.model.layers.pop()
self.model.layers.pop() # two pops to get to pool layer
self.model.outputs = [self.model.layers[-1].output]
self.model.output_layers = [self.model.layers[-1]]
self.model.layers[-1].outbound_nodes = []
if n_gpu>1:
self.model = multi_gpu_model(self.model,n_gpu)
def create_model(feature_extraction_method, path_cnn_pre_trained, input_size):
if (feature_extraction_method == 'pretrained_lenet'):
model = load_model(path_cnn_pre_trained)
input_image = input_size
elif (feature_extraction_method == 'pretrained_vgg16'):
model = vgg16.VGG16(weights='imagenet', include_top=True)
#layer_name = 'fc2'
input_image = 224
elif (feature_extraction_method == 'pretrained_vgg19'):
model = vgg19.VGG19(weights='imagenet', include_top=True)
#layer_name = 'fc2'
input_image = 224
elif (feature_extraction_method == 'pretrained_xception'):
model = xception.Xception(weights='imagenet', include_top=True)
#layer_name = 'avg_pool'
input_image = 299
elif (feature_extraction_method == 'pretrained_resnet'):
model = resnet.ResNet50(weights='imagenet', include_top=True)
#layer_name = 'avg_pool'
input_image = 224
elif (feature_extraction_method == 'pretrained_inception_resnet'):
model = inception_resnet.InceptionResNetV2(weights='imagenet',
include_top=True)
#layer_name = 'avg_pool'
input_image = 299
elif (feature_extraction_method == 'pretrained_nasnet'):
model = nasnet.NASNetLarge(weights='imagenet', include_top=True)
#layer_name = 'global_average_pooling2d_1'
input_image = 331
intermediate_layer_model = Model(inputs=model.input,
outputs=model.layers[-2].output)
model.summary()
return intermediate_layer_model, input_image
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("train_class", type=str, choices=classes.keys())
parser.add_argument("data_dir", type=str)
parser.add_argument('-e', "--epoch",
required=False,
type=int,
default=64,
dest="epoch")
parser.add_argument('-ef', "--epoch-fine-tune",
required=False,
type=int,
default=200,
dest="epoch_fine_tune")
parser.add_argument('-b', '--batch-size',
required=False,
default=1024,
type=int,
dest="batch")
parser.add_argument('-lr', '--learning-rate',
required=False,
default=1e-4,
type=float,
dest="lr")
parser.add_argument('-decay', '--learning-rate-decay',
required=False,
default=1e-6,
type=float,
dest="decay")
parser.add_argument('-ignore-npz', '--ignore-precomputed-learning-file',
required=False,
default=False,
type=bool,
dest="ignore_npz")
parser.add_argument('-ri', '--use-random-weight-initialisation',
required=False,
default=False,
type=bool,
dest="random_init")
parser.add_argument('-ua', '--unfroze-all-convolution-layer-directly',
required=False,
default=False,
type=bool,
dest="unfroze_all")
parser.add_argument('-m', '--model-name',
required=False,
default="MobileNetV2",
type=str,
dest="model_name")
parser.add_argument('-d', '--dense-layer-size',
required=False,
nargs="*",
default=[],
type=int,
dest="dense_size")
parser.add_argument('-is', '--input-size',
required=False,
default=96,
type=int,
dest="input_size")
parser.add_argument('-viz', '--data-visualisation',
required=False,
default=False,
type=bool,
dest="data_visualisation")
args = parser.parse_args()
batch_size = args.batch
class_name = args.train_class
out_classes = classes[class_name]["signs_classes"]
rotation_and_flips = classes[class_name]["rotation_and_flips"]
h_symmetry_classes = classes[class_name]["h_symmetry"]
try:
merge_sign_classes = classes[class_name]["merge_sign_classes"]
except KeyError:
merge_sign_classes = None
mapping = {c: i for i, c in enumerate(out_classes)}
mapping_id_to_name = {i: c for c, i in mapping.items()}
os.makedirs(class_name, exist_ok=True)
x_train, y_train, x_test, y_test = get_data_for_master_class(class_name=class_name,
mapping=mapping,
mapping_id_to_name=mapping_id_to_name,
rotation_and_flips=rotation_and_flips,
data_dir=args.data_dir,
merge_sign_classes=merge_sign_classes,
h_symmetry_classes=h_symmetry_classes,
image_size=(args.input_size, args.input_size),
ignore_npz=args.ignore_npz,
out_classes=out_classes)
if args.data_visualisation:
preprocess_input = lambda x: x
model = None
else:
if args.random_init:
weights = None
else:
weights = 'imagenet'
if args.model_name == "MobileNetV2":
preprocess_input = mobilenetv2.preprocess_input
base_model = mobilenetv2.MobileNetV2(weights=weights,
include_top=False,
input_shape=(args.input_size, args.input_size, 3),
pooling='avg')
elif args.model_name == "InceptionResNetV2":
preprocess_input = inception_resnet_v2.preprocess_input
base_model = inception_resnet_v2.InceptionResNetV2(weights=weights,
include_top=False,
input_shape=(args.input_size, args.input_size, 3),
pooling='avg')
elif args.model_name == "NASNetLarge":
preprocess_input = nasnet.preprocess_input
base_model = nasnet.NASNetLarge(weights=weights,
include_top=False,
input_shape=(args.input_size, args.input_size, 3),
pooling='avg')
else:
raise ValueError("unknown model name {}, should be one of {}".format(args.model_name,
["MobileNetV2", "InceptionResNetV2",
"NASNetLarge"]))
predictions = base_model.outputs[0]
for s in args.dense_size:
predictions = Dense(s, activation='relu')(predictions)
predictions = Dense(len(out_classes), activation='softmax')(predictions)
model = Model(inputs=base_model.input, outputs=predictions)
# model.summary()
# blocks = {}
# for i, layer in enumerate(base_model.layers):
# s = layer.name.split('_')
# if s[0] == "block":
# b = int(s[1])
# if b not in blocks:
# blocks[b] = [i]
# else:
# blocks[b].append(i)
# exit(0)
callbacks = [ModelCheckpoint(filepath="{}/checkpoint.h5".format(class_name),
monitor="val_loss",
mode='min',
verbose=0,
save_best_only="True",
save_weights_only=False,
period=1),
EarlyStopping(monitor='val_acc',
mode='max',
min_delta=0.001,
patience=40,
verbose=1,
restore_best_weights=True)
]
x_test = np.stack([preprocess_input(i) for i in x_test])
datagen = ImageDataGenerator(featurewise_center=False,
featurewise_std_normalization=False,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
brightness_range=(0.5, 1.4),
shear_range=3.0,
zoom_range=(0.7, 1.1),
fill_mode='nearest',
horizontal_flip=False,
vertical_flip=False,
preprocessing_function=preprocess_input)
datagen.fit(x_train)
if args.data_visualisation:
for b in datagen.flow(x_train, y_train, batch_size=1):
im, im_class = b[0][0], b[1][0]
im_class = int(np.argmax(im_class))
plt.imshow(im.astype(np.int))
plt.title(out_classes[im_class])
plt.show()
return
if not args.random_init:
# if the network is not randomly initialized, we first fine tune the last layers
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer=rmsprop(lr=args.lr, decay=args.decay),
loss='categorical_crossentropy', metrics=["accuracy"])
history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
steps_per_epoch=ceil(len(x_train) / batch_size),
epochs=args.epoch,
verbose=1,
validation_data=(x_test, y_test),
use_multiprocessing=True,
callbacks=callbacks)
plot_history(history, "{0}/{1}_{0}_dense_".format(class_name, args.model_name))
model.save("{0}/{1}_{0}_dense.h5".format(class_name, args.model_name), overwrite=True)
if not args.unfroze_all:
# unfroze the 3 last blocks of mobile net
for layer in model.layers[:113]:
layer.trainable = False
for layer in model.layers[113:]:
layer.trainable = True
model.compile(optimizer=SGD(lr=args.lr, momentum=0.9, decay=args.decay),
loss='categorical_crossentropy', metrics=["accuracy"])
history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
steps_per_epoch=ceil(len(x_train) / batch_size),
epochs=args.epoch_fine_tune,
verbose=1,
validation_data=(x_test, y_test),
use_multiprocessing=True,
callbacks=callbacks)
plot_history(history, "{0}/{1}_{0}_fine_tuning_1_".format(class_name, args.model_name))
model.save("{0}/{1}_{0}_1.h5".format(class_name, args.model_name), overwrite=True)
# unfroze the 6 last blocks of mobile net
for layer in model.layers[:87]:
layer.trainable = False
for layer in model.layers[87:]:
layer.trainable = True
model.compile(optimizer=SGD(lr=args.lr, momentum=0.9, decay=args.decay),
loss='categorical_crossentropy', metrics=["accuracy"])
history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
steps_per_epoch=ceil(len(x_train) / batch_size),
epochs=args.epoch_fine_tune,
verbose=1,
validation_data=(x_test, y_test),
use_multiprocessing=True,
callbacks=callbacks)
plot_history(history, "{0}/{1}_{0}_fine_tuning_2_".format(class_name, args.model_name))
model.save("{0}/{1}_{0}_2.h5".format(class_name, args.model_name), overwrite=True)
# unfroze all model
for layer in model.layers:
layer.trainable = True
model.compile(optimizer=SGD(lr=args.lr, momentum=0.9, decay=args.decay),
loss='categorical_crossentropy', metrics=["accuracy"])
history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
steps_per_epoch=ceil(len(x_train) / batch_size),
epochs=args.epoch_fine_tune,
verbose=1,
validation_data=(x_test, y_test),
use_multiprocessing=True,
callbacks=callbacks)
plot_history(history, "{0}/{1}_{0}_fine_tuning_f_".format(class_name, args.model_name))
model.save("{0}/{1}_{0}_final.h5".format(class_name, args.model_name), overwrite=True)
if K.image_data_format() == 'channels_first':
combination_image = K.placeholder((1, 3, img_nrows, img_ncols))
else:
combination_image = K.placeholder((1, img_nrows, img_ncols, 3))
# combine the 3 images into a single Keras tensor
input_tensor = K.concatenate(
[base_image, style_reference_image, combination_image], axis=0)
print(input_tensor)
# build the VGG16 network with our 3 images as input
# the model will be loaded with pre-trained ImageNet weights
# model = vgg19.VGG19(input_tensor=input_tensor,
# weights='imagenet', include_top=False)
model = nasnet.NASNetLarge(input_tensor=input_tensor,
input_shape=(400, 300, 3),
weights='imagenet',
include_top=False)
# model = vgg19.VGG19(input_tensor=input_tensor, input_shape=(400, 300, 3),
# weights='imagenet', include_top=False)
print('Model loaded.')
# get the symbolic outputs of each "key" layer (we gave them unique names).
outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])
for i in outputs_dict:
print(i)
# compute the neural style loss
# first we need to define 4 util functions
# the gram matrix of an image tensor (feature-wise outer product)
def nasnet_download():
print("开始下载NASNetLarge")
nasnet.NASNetLarge()
print("完成下载NASNetLarge")
def naslarge(input_image, **kwargs):
from keras.applications import nasnet
model = nasnet.NASNetLarge(input_tensor=input_image,
include_top=False,
**kwargs)
return model
#print('Total params: {:,}'.format(trainable_count + non_trainable_count))
#print('Trainable params: {:,}'.format(trainable_count))
#print('Non-trainable params: {:,}'.format(non_trainable_count))
paramcount_list= []
for model_name in ["vgg16","nasnet","inception_resnet","inceptionv3","xception"]:
if(model_name=="vgg16"):
base_model = vgg16.VGG16(weights=None, include_top=False)
elif(model_name=="inceptionv3"):
base_model = inception_v3.InceptionV3(weights=None, include_top=False)
elif(model_name=="resnet50"):
base_model = resnet.ResNet50(weights=None, include_top=False)
elif(model_name=="inception_resnet"):
base_model = inception_resnet_v2.InceptionResNetV2(weights=None, include_top=False)
elif(model_name=="nasnet"):
base_model = nasnet.NASNetLarge(weights=None, include_top=False)
elif(model_name=="xception"):
base_model = xception.Xception(weights=None, include_top=False)
print("model name is:",model_name)
paramcount_list.append((model_name,"base",count_params(base_model)))
#Adding a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
customlayers= customlayer()
output_layer = customlayers(x)
model_stg1 = Model(inputs=base_model.input, outputs=output_layer)
model_json = model_stg1.to_json()
modelpath= liby+model_name+".json"
with open(modelpath, "w") as json_file:
json_file.write(model_json)
modelpath= liby+model_name+".pdf"
def save_bottlebeck_features(model_name):
if model_name=='resnet50':
model = resnet50.ResNet50(weights='imagenet', include_top=False, pooling='avg')
# 2048 dimensional features
# pooling: 1) None: output is 16x16x2048, 2) avg: 1x1x2048, 3) max: 1x1x2048
#base_model=resnet50.ResNet50(input_shape=(img_height,img_width,3),weights='imagenet', include_top=False)
#model = Model(inputs=base_model.input, outputs=base_model.get_layer('activation_25').output)
elif model_name=='nasnet_large':
model=nasnet.NASNetLarge(input_shape=(img_height,img_width,3),weights='imagenet', include_top=False, pooling='avg')
#4032 dimensional features
elif model_name=='xception':
model=xception.Xception(input_shape=(img_height,img_width,3),weights='imagenet', include_top=False, pooling='avg')
#2048 dimensional features
elif model_name=='inceptionv3':
model=inception_v3.InceptionV3(input_shape=(img_height,img_width,3),weights='imagenet', include_top=False, pooling='avg')
#2048 dimensional features
elif model_name=='inceptionresnetv2':
model=inception_resnet_v2.InceptionResNetV2(input_shape=(img_height,img_width,3),weights='imagenet', include_top=False, pooling='avg')
#1536 dimensional features
elif model_name=='densenet':
model=densenet.DenseNet201(input_shape=(img_height,img_width,3),weights='imagenet', include_top=False, pooling='avg')
# 1920 dimensional features
else:
model=vgg19.VGG19(weights='imagenet', include_top=False, pooling='avg')
# 512 dimensional features
#base_model=vgg19.VGG19(input_shape=(img_height,img_width,3),weights='imagenet', include_top=False)
#model=Model(inputs=base_model.input,outputs=base_model.get_layer('block4_pool').output)
images = os.listdir(wsi_dir)
for image_name in images:
if '.svs' in image_name:
patient_id=image_name[0:23]
image_features = []
image_names = []
#patient_id='TCGA-2F-A9KT'
#patches=os.listdir(train_data_dir[ind]+patient_id+'*.png')
patches=glob.glob(data_dir+patient_id+'*.png')
for patch_name in patches:
patch_split=patch_name.split("\\")
img = image.load_img(patch_name, target_size=(img_height,img_width))
# convert image to numpy array
x = image.img_to_array(img)
# the image is now in an array of shape (224, 224, 3)
# need to expand it to (1, 224, 224, 3) as it's expecting a list
x = np.expand_dims(x, axis=0)
#imshow(np.uint8(x[0,:,:,:]))
if model_name=='resnet50':
x = resnet50.preprocess_input(x)
elif model_name=='nasnet_large':
x = nasnet.preprocess_input(x)
elif model_name == 'xception':
x = xception.preprocess_input(x)
elif model_name=='inceptionv3':
x=inception_v3.preprocess_input(x)
elif model_name == 'inceptionresnetv2':
x=inception_resnet_v2.preprocess_input(x)
elif model_name=='densenet':
x=densenet.preprocess_input(x)
else:
x=vgg19.preprocess_input(x)
# extract the features
features = model.predict(x)[0]
#features=np.mean(features,axis=(0,1))
image_features.append(features)
image_names.append(patch_split[1])
if save_features==True:
scipy.io.savemat('./step2_output/'+patient_id+'_feat.mat', mdict={'image_features': image_features, 'image_names':image_names})
def build_cnn():
nasnet_inst = nasnet.NASNetLarge(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=(HEIGHT, WIDTH, 3),
pooling=None,
classes=1000)
x = nasnet_inst.output
x = Dense(64, activation="relu")(x)
x = Dense(2, activation="softmax")(x)
model = Model(inputs=nasnet_inst.inputs, outputs=x)
for i in model.layers:
i.trainable = True
trainable_layers = [
"input_1", "stem_conv1", "stem_bn1", "activation_1",
"reduction_conv_1_stem_1", "reduction_bn_1_stem_1", "activation_2",
"activation_4", "separable_conv_1_reduction_left",
"separable_conv_1_reduction_1_st", "separable_conv_1_bn_reduction_l",
"separable_conv_1_bn_reduction_1", "activation_3", "activation_5",
"separable_conv_2_reduction_left", "separable_conv_2_reduction_1_st",
"separable_conv_2_bn_reduction_l", "separable_conv_2_bn_reduction_1",
"activation_6", "reduction_add_1_stem_1",
"separable_conv_1_reduction_righ", "activation_8", "activation_10",
"separable_conv_1_bn_reduction_r", "separable_conv_1_reduction_righ",
"separable_conv_1_reduction_left", "activation_7",
"separable_conv_1_bn_reduction_r", "separable_conv_1_bn_reduction_l",
"separable_conv_2_reduction_righ", "activation_9", "activation_11",
"reduction_left2_stem_1", "separable_conv_2_bn_reduction_r",
"separable_conv_2_reduction_righ", "separable_conv_2_reduction_left",
"adjust_relu_1_stem_2", "reduction_add_2_stem_1",
"reduction_left3_stem_1", "separable_conv_2_bn_reduction_r",
"reduction_left4_stem_1", "separable_conv_2_bn_reduction_l",
"reduction_right5_stem_1", "zero_padding2d_1", "reduction_add3_stem_1",
"add_1", "reduction_add4_stem_1", "cropping2d_1",
"reduction_concat_stem_1", "adjust_avg_pool_1_stem_2",
"adjust_avg_pool_2_stem_2", "activation_12", "adjust_conv_1_stem_2",
"adjust_conv_2_stem_2", "reduction_conv_1_stem_2", "concatenate_1",
"reduction_bn_1_stem_2", "adjust_bn_stem_2", "activation_13",
"activation_15", "separable_conv_1_reduction_left",
"separable_conv_1_reduction_1_st", "separable_conv_1_bn_reduction_l",
"separable_conv_1_bn_reduction_1", "activation_14", "activation_16",
"separable_conv_2_reduction_left", "separable_conv_2_reduction_1_st",
"separable_conv_2_bn_reduction_l", "separable_conv_2_bn_reduction_1",
"activation_17", "reduction_add_1_stem_2",
"separable_conv_1_reduction_righ", "activation_19", "activation_21",
"separable_conv_1_bn_reduction_r", "separable_conv_1_reduction_righ",
"separable_conv_1_reduction_left", "activation_18",
"separable_conv_1_bn_reduction_r", "separable_conv_1_bn_reduction_l",
"separable_conv_2_reduction_righ", "activation_20", "activation_22",
"reduction_left2_stem_2", "separable_conv_2_bn_reduction_r",
"separable_conv_2_reduction_righ", "separable_conv_2_reduction_left",
"adjust_relu_1_0", "reduction_add_2_stem_2", "reduction_left3_stem_2",
"separable_conv_2_bn_reduction_r", "reduction_left4_stem_2",
"separable_conv_2_bn_reduction_l", "reduction_right5_stem_2",
"zero_padding2d_2", "reduction_add3_stem_2", "add_2",
"reduction_add4_stem_2", "cropping2d_2", "reduction_concat_stem_2",
"adjust_avg_pool_1_0", "adjust_avg_pool_2_0", "adjust_conv_1_0",
"adjust_conv_2_0", "activation_23", "concatenate_2", "normal_conv_1_0",
"adjust_bn_0", "normal_bn_1_0", "activation_24", "activation_26",
"activation_28", "activation_30", "activation_32",
"separable_conv_1_normal_left1_0", "separable_conv_1_normal_right1_",
"separable_conv_1_normal_left2_0", "separable_conv_1_normal_right2_",
"separable_conv_1_normal_left5_0", "separable_conv_1_bn_normal_left",
"separable_conv_1_bn_normal_righ", "separable_conv_1_bn_normal_left",
"separable_conv_1_bn_normal_righ", "separable_conv_1_bn_normal_left",
"activation_25", "activation_27", "activation_29", "activation_31",
"activation_33", "separable_conv_2_normal_left1_0",
"separable_conv_2_normal_right1_", "separable_conv_2_normal_left2_0",
"separable_conv_2_normal_right2_", "separable_conv_2_normal_left5_0",
"separable_conv_2_bn_normal_left", "separable_conv_2_bn_normal_righ",
"separable_conv_2_bn_normal_left", "separable_conv_2_bn_normal_righ",
"normal_left3_0", "normal_left4_0", "normal_right4_0",
"separable_conv_2_bn_normal_left", "normal_add_1_0", "normal_add_2_0",
"normal_add_3_0", "normal_add_4_0", "normal_add_5_0",
"normal_concat_0", "activation_34", "activation_35",
"adjust_conv_projection_1", "normal_conv_1_1", "adjust_bn_1",
"normal_bn_1_1", "activation_36", "activation_38", "activation_40",
"activation_42", "activation_44", "separable_conv_1_normal_left1_1",
"separable_conv_1_normal_right1_", "separable_conv_1_normal_left2_1",
"separable_conv_1_normal_right2_", "separable_conv_1_normal_left5_1",
"separable_conv_1_bn_normal_left", "separable_conv_1_bn_normal_righ",
"separable_conv_1_bn_normal_left", "separable_conv_1_bn_normal_righ",
"separable_conv_1_bn_normal_left", "activation_37", "activation_39",
"activation_41", "activation_43", "activation_45",
"separable_conv_2_normal_left1_1", "separable_conv_2_normal_right1_",
"separable_conv_2_normal_left2_1", "separable_conv_2_normal_right2_",
"separable_conv_2_normal_left5_1", "separable_conv_2_bn_normal_left",
"separable_conv_2_bn_normal_righ", "separable_conv_2_bn_normal_left",
"separable_conv_2_bn_normal_righ", "normal_left3_1", "normal_left4_1",
"normal_right4_1", "separable_conv_2_bn_normal_left"
]
# "normal_add_1_1","normal_add_2_1","normal_add_3_1","normal_add_4_1","normal_add_5_1",
# "normal_concat_1","activation_46","activation_47","adjust_conv_projection_2","normal_conv_1_2","adjust_bn_2","normal_bn_1_2","activation_48","activation_50","activation_52",
# "activation_54","activation_56","separable_conv_1_normal_left1_2","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_2","separable_conv_1_normal_right2_",
# "separable_conv_1_normal_left5_2","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ",
# "separable_conv_1_bn_normal_left","activation_49","activation_51","activation_53","activation_55","activation_57","separable_conv_2_normal_left1_2","separable_conv_2_normal_right1_",
# "separable_conv_2_normal_left2_2","separable_conv_2_normal_right2_","separable_conv_2_normal_left5_2","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ",
# "separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_2","normal_left4_2","normal_right4_2","separable_conv_2_bn_normal_left","normal_add_1_2",
# "normal_add_2_2","normal_add_3_2","normal_add_4_2","normal_add_5_2","normal_concat_2","activation_58","activation_59","adjust_conv_projection_3","normal_conv_1_3","adjust_bn_3",
# "normal_bn_1_3","activation_60","activation_62","activation_64","activation_66","activation_68","separable_conv_1_normal_left1_3","separable_conv_1_normal_right1_",
# "separable_conv_1_normal_left2_3","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_3","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ",
# "separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_61","activation_63","activation_65","activation_67","activation_69",
# "separable_conv_2_normal_left1_3","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_3","separable_conv_2_normal_right2_","separable_conv_2_normal_left5_3",
# "separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_3","normal_left4_3",
# "normal_right4_3","separable_conv_2_bn_normal_left","normal_add_1_3","normal_add_2_3","normal_add_3_3","normal_add_4_3","normal_add_5_3","normal_concat_3","activation_70",
# "activation_71","adjust_conv_projection_4","normal_conv_1_4","adjust_bn_4","normal_bn_1_4","activation_72","activation_74","activation_76","activation_78","activation_80",
# "separable_conv_1_normal_left1_4","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_4","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_4",
# "separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left",
# "activation_73","activation_75","activation_77","activation_79","activation_81","separable_conv_2_normal_left1_4","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_4",
# "separable_conv_2_normal_right2_","separable_conv_2_normal_left5_4","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left",
# "separable_conv_2_bn_normal_righ","normal_left3_4","normal_left4_4","normal_right4_4","separable_conv_2_bn_normal_left","normal_add_1_4","normal_add_2_4","normal_add_3_4",
# "normal_add_4_4","normal_add_5_4","normal_concat_4","activation_82","activation_83","adjust_conv_projection_5","normal_conv_1_5","adjust_bn_5","normal_bn_1_5","activation_84",
# "activation_86","activation_88","activation_90","activation_92","separable_conv_1_normal_left1_5","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_5",
# "separable_conv_1_normal_right2_","separable_conv_1_normal_left5_5","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left",
# "separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_85","activation_87","activation_89","activation_91","activation_93","separable_conv_2_normal_left1_5",
# "separable_conv_2_normal_right1_","separable_conv_2_normal_left2_5","separable_conv_2_normal_right2_","separable_conv_2_normal_left5_5","separable_conv_2_bn_normal_left",
# "separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_5","normal_left4_5","normal_right4_5",
# "separable_conv_2_bn_normal_left","normal_add_1_5","normal_add_2_5","normal_add_3_5","normal_add_4_5","normal_add_5_5","normal_concat_5","activation_95","activation_94",
# "reduction_conv_1_reduce_6","adjust_conv_projection_reduce_6","reduction_bn_1_reduce_6","adjust_bn_reduce_6","activation_96","activation_98","separable_conv_1_reduction_left",
# "separable_conv_1_reduction_1_re","separable_conv_1_bn_reduction_l","separable_conv_1_bn_reduction_1","activation_97","activation_99","separable_conv_2_reduction_left",
# "separable_conv_2_reduction_1_re","separable_conv_2_bn_reduction_l","separable_conv_2_bn_reduction_1","activation_100","reduction_add_1_reduce_6","separable_conv_1_reduction_righ",
# "activation_102","activation_104","separable_conv_1_bn_reduction_r","separable_conv_1_reduction_righ","separable_conv_1_reduction_left","activation_101","separable_conv_1_bn_reduction_r",
# "separable_conv_1_bn_reduction_l","separable_conv_2_reduction_righ","activation_103","activation_105","reduction_left2_reduce_6","separable_conv_2_bn_reduction_r",
# "separable_conv_2_reduction_righ","separable_conv_2_reduction_left","adjust_relu_1_7","reduction_add_2_reduce_6","reduction_left3_reduce_6","separable_conv_2_bn_reduction_r",
# "reduction_left4_reduce_6","separable_conv_2_bn_reduction_l","reduction_right5_reduce_6","zero_padding2d_3","reduction_add3_reduce_6","add_3","reduction_add4_reduce_6",
# "cropping2d_3","reduction_concat_reduce_6","adjust_avg_pool_1_7","adjust_avg_pool_2_7","adjust_conv_1_7","adjust_conv_2_7","activation_106","concatenate_3","normal_conv_1_7",
# "adjust_bn_7","normal_bn_1_7","activation_107","activation_109","activation_111","activation_113","activation_115","separable_conv_1_normal_left1_7","separable_conv_1_normal_right1_",
# "separable_conv_1_normal_left2_7","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_7","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ",
# "separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_108","activation_110","activation_112","activation_114",
# "activation_116","separable_conv_2_normal_left1_7","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_7","separable_conv_2_normal_right2_","separable_conv_2_normal_left5_7",
# "separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_7","normal_left4_7","normal_right4_7",
# "separable_conv_2_bn_normal_left","normal_add_1_7","normal_add_2_7","normal_add_3_7","normal_add_4_7","normal_add_5_7","normal_concat_7","activation_117","activation_118","adjust_conv_projection_8",
# "normal_conv_1_8","adjust_bn_8","normal_bn_1_8","activation_119","activation_121","activation_123","activation_125","activation_127","separable_conv_1_normal_left1_8",
# "separable_conv_1_normal_right1_","separable_conv_1_normal_left2_8","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_8","separable_conv_1_bn_normal_left",
# "separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_120","activation_122",
# "activation_124","activation_126","activation_128","separable_conv_2_normal_left1_8","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_8","separable_conv_2_normal_right2_",
# "separable_conv_2_normal_left5_8","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_8",
# "normal_left4_8","normal_right4_8","separable_conv_2_bn_normal_left","normal_add_1_8","normal_add_2_8","normal_add_3_8","normal_add_4_8","normal_add_5_8","normal_concat_8","activation_129",
# "activation_130","adjust_conv_projection_9","normal_conv_1_9","adjust_bn_9","normal_bn_1_9","activation_131","activation_133","activation_135","activation_137","activation_139",
# "separable_conv_1_normal_left1_9","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_9","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_9",
# "separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left",
# "activation_132","activation_134","activation_136","activation_138","activation_140","separable_conv_2_normal_left1_9","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_9",
# "separable_conv_2_normal_right2_","separable_conv_2_normal_left5_9","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left",
# "separable_conv_2_bn_normal_righ","normal_left3_9","normal_left4_9","normal_right4_9","separable_conv_2_bn_normal_left","normal_add_1_9","normal_add_2_9","normal_add_3_9",
# "normal_add_4_9","normal_add_5_9","normal_concat_9","activation_141","activation_142","adjust_conv_projection_10","normal_conv_1_10","adjust_bn_10","normal_bn_1_10",
# "activation_143","activation_145","activation_147","activation_149","activation_151","separable_conv_1_normal_left1_1","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_1",
# "separable_conv_1_normal_right2_","separable_conv_1_normal_left5_1","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left",
# "separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_144","activation_146","activation_148","activation_150","activation_152","separable_conv_2_normal_left1_1",
# "separable_conv_2_normal_right1_","separable_conv_2_normal_left2_1","separable_conv_2_normal_right2_","separable_conv_2_normal_left5_1","separable_conv_2_bn_normal_left",
# "separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_10","normal_left4_10","normal_right4_10","separable_conv_2_bn_normal_left",
# "normal_add_1_10","normal_add_2_10","normal_add_3_10","normal_add_4_10","normal_add_5_10","normal_concat_10","activation_153","activation_154","adjust_conv_projection_11",
# "normal_conv_1_11","adjust_bn_11","normal_bn_1_11","activation_155","activation_157","activation_159","activation_161","activation_163","separable_conv_1_normal_left1_1",
# "separable_conv_1_normal_right1_","separable_conv_1_normal_left2_1","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_1","separable_conv_1_bn_normal_left",
# "separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_156","activation_158",
# "activation_160","activation_162","activation_164","separable_conv_2_normal_left1_1","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_1","separable_conv_2_normal_right2_",
# "separable_conv_2_normal_left5_1","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ",
# "normal_left3_11","normal_left4_11","normal_right4_11","separable_conv_2_bn_normal_left","normal_add_1_11","normal_add_2_11","normal_add_3_11","normal_add_4_11","normal_add_5_11",
# "normal_concat_11","activation_165","activation_166","adjust_conv_projection_12","normal_conv_1_12","adjust_bn_12","normal_bn_1_12","activation_167","activation_169","activation_171",
# "activation_173","activation_175","separable_conv_1_normal_left1_1","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_1","separable_conv_1_normal_right2_",
# "separable_conv_1_normal_left5_1","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ",
# "separable_conv_1_bn_normal_left","activation_168","activation_170","activation_172","activation_174","activation_176","separable_conv_2_normal_left1_1","separable_conv_2_normal_right1_",
# "separable_conv_2_normal_left2_1","separable_conv_2_normal_right2_","separable_conv_2_normal_left5_1","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ",
# "separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_12","normal_left4_12","normal_right4_12","separable_conv_2_bn_normal_left","normal_add_1_12",
# "normal_add_2_12","normal_add_3_12","normal_add_4_12","normal_add_5_12","normal_concat_12","activation_178","activation_177","reduction_conv_1_reduce_12","adjust_conv_projection_reduce_1",
# "reduction_bn_1_reduce_12","adjust_bn_reduce_12","activation_179","activation_181","separable_conv_1_reduction_left","separable_conv_1_reduction_1_re","separable_conv_1_bn_reduction_l",
# "separable_conv_1_bn_reduction_1","activation_180","activation_182","separable_conv_2_reduction_left","separable_conv_2_reduction_1_re","separable_conv_2_bn_reduction_l",
# "separable_conv_2_bn_reduction_1","activation_183","reduction_add_1_reduce_12","separable_conv_1_reduction_righ","activation_185","activation_187","separable_conv_1_bn_reduction_r",
# "separable_conv_1_reduction_righ","separable_conv_1_reduction_left","activation_184","separable_conv_1_bn_reduction_r","separable_conv_1_bn_reduction_l","separable_conv_2_reduction_righ",
# "activation_186","activation_188","reduction_left2_reduce_12","separable_conv_2_bn_reduction_r","separable_conv_2_reduction_righ","separable_conv_2_reduction_left",
# "adjust_relu_1_13","reduction_add_2_reduce_12","reduction_left3_reduce_12","separable_conv_2_bn_reduction_r","reduction_left4_reduce_12","separable_conv_2_bn_reduction_l",
# "reduction_right5_reduce_12","zero_padding2d_4","reduction_add3_reduce_12","add_4","reduction_add4_reduce_12","cropping2d_4","reduction_concat_reduce_12","adjust_avg_pool_1_13",
# "adjust_avg_pool_2_13","adjust_conv_1_13","adjust_conv_2_13","activation_189","concatenate_4","normal_conv_1_13","adjust_bn_13","normal_bn_1_13","activation_190","activation_192",
# "activation_194","activation_196","activation_198","separable_conv_1_normal_left1_1","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_1","separable_conv_1_normal_right2_",
# "separable_conv_1_normal_left5_1","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ",
# "separable_conv_1_bn_normal_left","activation_191","activation_193","activation_195","activation_197","activation_199","separable_conv_2_normal_left1_1","separable_conv_2_normal_right1_",
# "separable_conv_2_normal_left2_1","separable_conv_2_normal_right2_","separable_conv_2_normal_left5_1","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ",
# "separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_13","normal_left4_13","normal_right4_13","separable_conv_2_bn_normal_left","normal_add_1_13",
# "normal_add_2_13","normal_add_3_13","normal_add_4_13","normal_add_5_13","normal_concat_13","activation_200","activation_201","adjust_conv_projection_14","normal_conv_1_14",
# "adjust_bn_14","normal_bn_1_14","activation_202","activation_204","activation_206","activation_208","activation_210","separable_conv_1_normal_left1_1","separable_conv_1_normal_right1_",
# "separable_conv_1_normal_left2_1","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_1","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ",
# "separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_203","activation_205","activation_207","activation_209",
# "activation_211","separable_conv_2_normal_left1_1","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_1","separable_conv_2_normal_right2_","separable_conv_2_normal_left5_1",
# "separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_14","normal_left4_14",
# "normal_right4_14","separable_conv_2_bn_normal_left","normal_add_1_14","normal_add_2_14","normal_add_3_14","normal_add_4_14","normal_add_5_14","normal_concat_14","activation_212",
# "activation_213","adjust_conv_projection_15","normal_conv_1_15","adjust_bn_15","normal_bn_1_15","activation_214","activation_216","activation_218","activation_220","activation_222",
# "separable_conv_1_normal_left1_1","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_1","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_1",
# "separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left",
# "activation_215","activation_217","activation_219","activation_221","activation_223","separable_conv_2_normal_left1_1","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_1",
# "separable_conv_2_normal_right2_","separable_conv_2_normal_left5_1","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left",
# "separable_conv_2_bn_normal_righ","normal_left3_15","normal_left4_15","normal_right4_15","separable_conv_2_bn_normal_left","normal_add_1_15","normal_add_2_15","normal_add_3_15",
# "normal_add_4_15","normal_add_5_15","normal_concat_15","activation_224","activation_225","adjust_conv_projection_16","normal_conv_1_16","adjust_bn_16","normal_bn_1_16",
# "activation_226","activation_228","activation_230","activation_232","activation_234","separable_conv_1_normal_left1_1","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_1",
# "separable_conv_1_normal_right2_","separable_conv_1_normal_left5_1","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left",
# "separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_227","activation_229","activation_231","activation_233","activation_235","separable_conv_2_normal_left1_1",
# "separable_conv_2_normal_right1_","separable_conv_2_normal_left2_1","separable_conv_2_normal_right2_","separable_conv_2_normal_left5_1","separable_conv_2_bn_normal_left",
# "separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_16","normal_left4_16","normal_right4_16","separable_conv_2_bn_normal_left",
# "normal_add_1_16","normal_add_2_16","normal_add_3_16","normal_add_4_16","normal_add_5_16","normal_concat_16","activation_236","activation_237","adjust_conv_projection_17",
# "normal_conv_1_17","adjust_bn_17","normal_bn_1_17","activation_238","activation_240","activation_242","activation_244","activation_246","separable_conv_1_normal_left1_1",
# "separable_conv_1_normal_right1_","separable_conv_1_normal_left2_1","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_1","separable_conv_1_bn_normal_left",
# "separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_239","activation_241",
# "activation_243","activation_245","activation_247","separable_conv_2_normal_left1_1","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_1","separable_conv_2_normal_right2_",
# "separable_conv_2_normal_left5_1","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","normal_left3_17",
# "normal_left4_17","normal_right4_17","separable_conv_2_bn_normal_left","normal_add_1_17","normal_add_2_17","normal_add_3_17","normal_add_4_17","normal_add_5_17","normal_concat_17",
# "activation_248","activation_249","adjust_conv_projection_18","normal_conv_1_18","adjust_bn_18","normal_bn_1_18","activation_250","activation_252","activation_254","activation_256",
# "activation_258","separable_conv_1_normal_left1_1","separable_conv_1_normal_right1_","separable_conv_1_normal_left2_1","separable_conv_1_normal_right2_","separable_conv_1_normal_left5_1",
# "separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","separable_conv_1_bn_normal_righ","separable_conv_1_bn_normal_left","activation_251",
# "activation_253","activation_255","activation_257","activation_259","separable_conv_2_normal_left1_1","separable_conv_2_normal_right1_","separable_conv_2_normal_left2_1",
# "separable_conv_2_normal_right2_","separable_conv_2_normal_left5_1","separable_conv_2_bn_normal_left","separable_conv_2_bn_normal_righ","separable_conv_2_bn_normal_left",
# "separable_conv_2_bn_normal_righ","normal_left3_18","normal_left4_18","normal_right4_18","separable_conv_2_bn_normal_left","normal_add_1_18","normal_add_2_18","normal_add_3_18",
# "normal_add_4_18","normal_add_5_18","normal_concat_18","activation_260","global_average_pooling2d_1","predictions","dense_1","dense_2"]
#
#trainable_layers = ["block3_pool","block4_conv1","block4_conv2","block4_conv3","block4_conv4","block4_pool","block5_conv1","block5_conv2","block5_conv3","block5_conv4","block5_pool","flatten","fc1","fc2","predictions","dense_1","dense_2"]
for i in trainable_layers:
try:
model.get_layer(i).trainable = False
except ValueError:
print("Layer does not exist: ", i)
sgd = SGD(lr=1e-6, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
# Create models (make sure weights are already downloaded!).
# This takes a while...
print('Loading Models')
resnet = resnet50.ResNet50(
weights='weights/resnet50_weights_tf_dim_ordering_tf_kernels.h5')
xc = xception.Xception(
weights='weights/xception_weights_tf_dim_ordering_tf_kernels.h5')
v19 = vgg19.VGG19(
weights='weights/vgg19_weights_tf_dim_ordering_tf_kernels.h5')
ic3 = inception_v3.InceptionV3(
weights='weights/inception_v3_weights_tf_dim_ordering_tf_kernels.h5')
ic_resnet = inception_resnet_v2.InceptionResNetV2(
weights='weights/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5'
)
mobile = mobilenet.MobileNet(weights='weights/mobilenet_1_0_224_tf.h5')
nn_large = nasnet.NASNetLarge(weights='weights/NASNet-large.h5')
print('Models Loaded')
print('')
#############################################
# RUN FRAMES THROUGH IMAGE RECOGNITION MODELS
#############################################
# Create dictionary to hold results for each model.
resnet_results = dict()
xc_results = dict()
v19_results = dict()
ic3_results = dict()
ic_resnet_results = dict()
mobile_results = dict()
nn_large_results = dict()
def save_bottlebeck_features(model_name):
if model_name == 'resnet50':
model = resnet50.ResNet50(weights='imagenet',
include_top=False,
pooling='avg')
feat_output = [
'E:/Hongming/projects/tcga-bladder-mutationburden/feature_output/P_CN_20X/2)resnet50/',
'E:/Hongming/projects/tcga-bladder-mutationburden/feature_output/P_CN_20X/2)resnet50/'
]
# 2048 dimensional features
# pooling: 1) None: output is 16x16x2048, 2) avg: 1x1x2048, 3) max: 1x1x2048
#base_model=resnet50.ResNet50(input_shape=(img_height,img_width,3),weights='imagenet', include_top=False)
#model = Model(inputs=base_model.input, outputs=base_model.get_layer('activation_25').output)
elif model_name == 'nasnet_large':
model = nasnet.NASNetLarge(input_shape=(img_height, img_width, 3),
weights='imagenet',
include_top=False,
pooling='avg')
#4032 dimensional features
elif model_name == 'xception':
model = xception.Xception(input_shape=(img_height, img_width, 3),
weights='imagenet',
include_top=False,
pooling='avg')
feat_output = [
'E:/Hongming/projects/tcga-bladder-mutationburden/feature_output/P_CN_20X/4)xception/',
'E:/Hongming/projects/tcga-bladder-mutationburden/feature_output/P_CN_20X/4)xception/'
]
#2048 dimensional features
elif model_name == 'inceptionv3':
model = inception_v3.InceptionV3(input_shape=(img_height, img_width,
3),
weights='imagenet',
include_top=False,
pooling='avg')
feat_output = [
'E:/Hongming/projects/tcga-bladder-mutationburden/feature_output/P_CN_20X/5)inceptionv3/',
'E:/Hongming/projects/tcga-bladder-mutationburden/feature_output/P_CN_20X/5)inceptionv3/'
]
#2048 dimensional features
elif model_name == 'inceptionresnetv2':
model = inception_resnet_v2.InceptionResNetV2(input_shape=(img_height,
img_width,
3),
weights='imagenet',
include_top=False,
pooling='avg')
#1536 dimensional features
elif model_name == 'densenet':
model = densenet.DenseNet201(input_shape=(img_height, img_width, 3),
weights='imagenet',
include_top=False,
pooling='avg')
# 1920 dimensional features
else:
model = vgg19.VGG19(weights='imagenet',
include_top=False,
pooling='avg')
# 512 dimensional features
#base_model=vgg19.VGG19(input_shape=(img_height,img_width,3),weights='imagenet', include_top=False)
#model=Model(inputs=base_model.input,outputs=base_model.get_layer('block4_pool').output)
#for i,layer in enumerate(model.layers):
# print(i,layer.name)
#print(model.summary())
for ind in range(1, len(img_path)):
path_ind = img_path[ind]
#path_split=path_ind.split("/")
images = os.listdir(path_ind)
for image_name in images:
if '.svs' in image_name:
patient_id = image_name[0:23]
image_features = []
image_names = []
ss = time.time()
patient_id = 'TCGA-2F-A9KO'
#patches=os.listdir(train_data_dir[ind]+patient_id+'*.png')
patches = glob.glob(train_data_dir[0] + patient_id + '*.png')
for patch_name in patches:
patch_split = patch_name.split("\\")
img = image.load_img(patch_name,
target_size=(img_height, img_width))
# convert image to numpy array
x = image.img_to_array(img)
# the image is now in an array of shape (224, 224, 3)
# need to expand it to (1, 224, 224, 3) as it's expecting a list
x = np.expand_dims(x, axis=0)
#imshow(np.uint8(x[0,:,:,:]))
if model_name == 'resnet50':
x = resnet50.preprocess_input(x)
elif model_name == 'nasnet_large':
x = nasnet.preprocess_input(x)
elif model_name == 'xception':
x = xception.preprocess_input(x)
elif model_name == 'inceptionv3':
x = inception_v3.preprocess_input(x)
elif model_name == 'inceptionresnetv2':
x = inception_resnet_v2.preprocess_input(x)
elif model_name == 'densenet':
x = densenet.preprocess_input(x)
else:
x = vgg19.preprocess_input(x)
# extract the features
features = model.predict(x)[0]
#features=np.mean(features,axis=(0,1))
image_features.append(features)
image_names.append(patch_split[1])
se = time.time() - ss
print(se)
if save_features == True:
scipy.io.savemat(feat_output[ind] + patient_id +
'_feat.mat',
mdict={
'image_features': image_features,
'image_names': image_names
})
def getFinalModel(num_outputs=num_outputs,
lr=learning_rate,
include_vgg_top=include_vgg_top,
use_vgg16=use_vgg16):
if use_vgg16:
modelID = 'CNN_VGG16'
inp = (224, 224, 3)
modelPackage = vgg16
margins = (8, 8, 48, 48)
Target_Frame_Shape = (240, 320, 3)
cnn_model = vgg16.VGG16(weights='imagenet',
input_shape=inp,
include_top=include_vgg_top)
elif True:
inp = (299, 299, 3)
modelPackage = inception_v3
modelID = 'CNN_InceptionV3'
margins = (0, 1, 51, 50)
Target_Frame_Shape = (300, 400, 3)
cnn_model = inception_v3.InceptionV3(weights='imagenet',
input_shape=inp,
include_top=include_vgg_top)
else:
inp = (331, 331, 3)
modelPackage = nasnet
modelID = 'CNN_ASNetLarge'
margins = (14, 15, 74, 75)
Target_Frame_Shape = (360, 480, 3)
cnn_model = nasnet.NASNetLarge(weights='imagenet',
input_shape=inp,
include_top=include_vgg_top)
def preprocess_input(imagePath):
return preprocess_input_for_model(imagePath, Target_Frame_Shape,
margins, modelPackage)
if include_vgg_top:
modelID = modelID + '_inc_top'
#cnn_model = addDropout(cnn_model)
cnn_model.layers.pop()
cnn_model.outputs = [cnn_model.layers[-1].output]
cnn_model.output_layers = [cnn_model.layers[-1]]
cnn_model.layers[-1].outbound_nodes = []
for layer in cnn_model.layers:
layer.trainable = False
x = cnn_model.layers[-1].output
#x = Dropout(0.25, name = 'dropout3_025')(x)
x = Dense(1024, activation='tanh', name='fc1024')(x)
#x = Dropout(0.25, name = 'dropout_025')(x)
#x = Dense(num_outputs, name = 'fc3')(x)
cnn_model = Model(inputs=cnn_model.input, outputs=x)
"""
"""
#cnn_model.summary(), shape=(inp[0], inp[1], inp[2])
cnn = Sequential()
cnn.add(cnn_model)
if not include_vgg_top:
cnn.add(Flatten())
cnn.add(Dense(num_outputs))
modelID = modelID + '_output%d' % num_outputs
modelID = modelID + '_BatchSize%d' % train_batch_size
modelID = modelID + '_inEpochs%d' % in_epochs
modelID = modelID + '_outEpochs%d' % out_epochs
#for layer in rnn.layers[:1]:
# layer.trainable = False
adam = Adam(lr=lr)
modelID = modelID + '_AdamOpt_lr-%f' % lr
cnn.compile(
optimizer=adam,
loss='mean_absolute_error') #'mean_squared_error', metrics=['mae'])#
modelID = modelID + '_%s' % now()[:-7].replace(' ', '_').replace(':', '-')
return cnn_model, cnn, modelID, preprocess_input