def init():
global prednet_model, logistic_regression_model
nt = 10
# logistic regression model for detecing anomalies based on model output
model_path = Model.get_model_path("logistic_regression")
logistic_regression_model = joblib.load(model_path)
model_root = Model.get_model_path('prednet_UCSDped1') #, _workspace=ws)
# model_root = model_root.strip('model.json')
print(model_root)
# load json and create model
json_file = open(os.path.join(model_root, 'model.json'),
'r') # todo, this is going to the real one
# json_file = open(os.path.join(model_root, 'model', 'model.json'), 'r')
model_json = json_file.read()
json_file.close()
trained_model = model_from_json(model_json,
custom_objects={"PredNet": PredNet})
# load weights into new model
trained_model.load_weights(os.path.join(model_root, "weights.hdf5"))
# Create testing model (to output predictions)
layer_config = trained_model.layers[1].get_config()
layer_config['output_mode'] = 'prediction'
# data_format = layer_config['data_format'] if 'data_format' in layer_config else layer_config['dim_ordering']
test_prednet = PredNet(weights=trained_model.layers[1].get_weights(),
**layer_config)
input_shape = list(trained_model.layers[0].batch_input_shape[1:])
input_shape[0] = nt
inputs = Input(shape=tuple(input_shape))
predictions = test_prednet(inputs)
prednet_model = Model_keras(inputs=inputs, outputs=predictions)
def get_unet():
inputs = Input((None, None, 3))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4], axis=3)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3], axis=3)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2], axis=3)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1], axis=3)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
model = Model_keras(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=Adam(lr=1e-5),
loss=dice_coef_loss,
metrics=[dice_coef])
return model
def init():
global model
model_root = Model.get_model_path(
'kd_teach_the_student') #, _workspace=ws)
# model_root = model_root.strip('model.json')
print(model_root)
# load json and create model
weight_file = os.path.join(model_root, "squeezenet_weights.hdf5")
model = SqueezeNet(weight_decay=0.0,
image_size=299,
trainable=False,
weight_file=weight_file)
# model.load_weights(os.path.join(model_root, "squeezenet_weights.hdf5"))
model = Model_keras(model.input, model.outputs)
def init():
global prednet_model, logistic_regression_model
nt = 10
# logistic regression model for detecing anomalies based on model output
try:
model_path = Model.get_model_path("logistic_regression")
logistic_regression_model = joblib.load(model_path)
except Exception as e:
print(e)
logistic_regression_model = None
prednet_path = Model.get_model_path('prednet_UCSDped1')
# model_root = model_root.strip('model.json')
print(prednet_path)
# load json and create model
with open(os.path.join(prednet_path, 'model.json'), 'r') as json_file:
model_json = json_file.read()
trained_model = model_from_json(model_json,
custom_objects={"PredNet": PredNet})
# load weights into new model
trained_model.load_weights(os.path.join(prednet_path, "weights.hdf5"))
# Create testing model (to output predictions)
layer_config = trained_model.layers[1].get_config()
layer_config['output_mode'] = 'prediction'
test_prednet = PredNet(weights=trained_model.layers[1].get_weights(),
**layer_config)
input_shape = list(trained_model.layers[0].batch_input_shape[1:])
input_shape[0] = nt
inputs = Input(shape=tuple(input_shape))
predictions = test_prednet(inputs)
prednet_model = Model_keras(inputs=inputs, outputs=predictions)
def get_unet(imgs_width, imgs_height):
inputs = Input((imgs_width, imgs_height, n_channels))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
pool5 = MaxPooling2D(pool_size=(2, 2))(conv5)
conv6 = Conv2D(1024, (3, 3), activation='relu', padding='same')(pool5)
conv6 = Conv2D(1024, (3, 3), activation='relu', padding='same')(conv6)
up7 = concatenate([
Conv2DTranspose(512,
(2, 2), strides=(2, 2), padding='same')(conv6), conv5
],
axis=3)
conv7 = Conv2D(512, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv7)
up8 = concatenate([
Conv2DTranspose(256,
(2, 2), strides=(2, 2), padding='same')(conv7), conv4
],
axis=3)
conv8 = Conv2D(256, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv8)
up9 = concatenate([
Conv2DTranspose(128,
(2, 2), strides=(2, 2), padding='same')(conv8), conv3
],
axis=3)
conv9 = Conv2D(128, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv9)
up10 = concatenate([
Conv2DTranspose(64,
(2, 2), strides=(2, 2), padding='same')(conv9), conv2
],
axis=3)
conv10 = Conv2D(64, (3, 3), activation='relu', padding='same')(up10)
conv10 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv10)
up11 = concatenate([
Conv2DTranspose(32,
(2, 2), strides=(2, 2), padding='same')(conv10), conv1
],
axis=3)
conv11 = Conv2D(32, (3, 3), activation='relu', padding='same')(up11)
conv11 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv11)
conv12 = Conv2D(1, (1, 1), activation='sigmoid')(conv11)
model = Model_keras(inputs=[inputs], outputs=[conv12])
model.compile(optimizer=Adam(lr=1e-5),
loss=dice_coef_loss,
metrics=['accuracy', dice_coef])
return model
# Create testing model (to output predictions)
layer_config = trained_model.layers[1].get_config()
layer_config["output_mode"] = "prediction"
data_format = (
layer_config["data_format"]
if "data_format" in layer_config
else layer_config["dim_ordering"]
)
prednet = PredNet(
weights=trained_model.layers[1].get_weights(), **layer_config
)
input_shape = list(trained_model.layers[0].batch_input_shape[1:])
input_shape[0] = args.nt
inputs = Input(shape=tuple(input_shape))
predictions = prednet(inputs)
test_model = Model_keras(inputs=inputs, outputs=predictions)
# Define Generator for test sequences
test_generator = TestsetGenerator(
test_file,
test_sources,
args.nt,
data_format=data_format,
N_seq=args.N_seq
)
X_test = test_generator.create_all()
# Apply model to the test sequences
X_hat = test_model.predict(X_test, args.batch_size)
if data_format == "channels_first":
X_test = np.transpose(X_test, (0, 1, 3, 4, 2))