def test_init(self):
unet_config = UnetConfig(input_size=(16, 16, 3),
filters=10,
dropout=0.6,
batchnorm=False)
unet = Unet(config=unet_config)
unet.compile(loss="binary_crossentropy", metrics=["accuracy"])
unet.summary()
import cv2
from models.unet import Unet
from data_augmentation.data_augmentation import DataAugmentation
import numpy as np
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
# Initialize
IMAGE_PATH = "dataset/Original/Testing/"
MASK_PATH = "dataset/MASKS/Testing/"
IMAGE_FILE = "Frame00314-org"
model = Unet(input_shape=(224, 224, 1)).build()
model.load_weights("models/model_weight.h5")
model.summary()
print("yeah")
def convert_to_tensor(numpy_image):
numpy_image = np.expand_dims(numpy_image, axis=2)
numpy_image = np.expand_dims(numpy_image, axis=0)
tensor_image = tf.convert_to_tensor(numpy_image)
return tensor_image
def predict(image):
process_obj = DataAugmentation(input_size=224, output_size=224)
image_processed = process_obj.data_process_test(image)
tensor_image = convert_to_tensor(image_processed)
predicted_mask = model.predict(tensor_image)
def test_default_init(self):
unet = Unet()
unet.compile()
unet.summary()
def lr_scheduler(epoch):
lr = learning_rate
new_lr = lr * 0.1**(epoch // 10)
return max(new_lr, 1e-10)
with tf.device('/device:GPU:0'):
unet = Unet().build(IMAGE_SHAPE)
model_json = unet.to_json()
with open(os.path.join(SEGMENT_RESULT_PATH, 'model.json'), 'w') as f:
f.write(json.dumps(model_json))
unet.compile(loss=loss_func, optimizer=optim, metrics=[monitors])
unet.summary()
# with open(os.path.join(SEGMENT_RESULT_PATH,'model.json'), 'r') as f:
# model_json = json.loads(f.read())
# unet = keras.models.model_from_json(model_json)
augm = {
"gamma": True,
"rotate": True,
"flip": True,
"hiseq": False,
"normal": False,
"invert": False,
"crop": True
}
