def compile(self,
loss_function=weighted_loss(cce_iou_dice, weights_list),
metrics=None):
if metrics is None:
metrics = [dice, iou]
self.model.compile(optimizer=self.optimizer,
loss=loss_function,
metrics=metrics)
def fit(self, x_train: np.ndarray, y_train: np.ndarray, x_val: np.ndarray, y_val: np.ndarray, class_weights=None):
if class_weights is None:
class_weights = weights_list
self.compile(loss_function=weighted_loss(cce_iou_dice, class_weights))
return self.model.fit(x_train, y_train,
steps_per_epoch=x_train.shape[0] // batch_size,
validation_data=[x_val, y_val],
validation_steps=x_val.shape[0] // batch_size,
batch_size=batch_size,
epochs=epochs,
shuffle=True)
def __init__(self, weight_url=None):
if weight_url:
self.model = load_model(filepath=weight_url,
custom_objects={
'dice':
dice,
'iou':
iou,
'loss_function':
weighted_loss(cce_iou_dice,
weights_list)
})
else:
model_input = Input((512, 512, 512))
x00 = conv2d(filters=int(16 * number_of_filters))(model_input)
x00 = BatchNormalization()(x00)
x00 = LeakyReLU(0.01)(x00)
x00 = Dropout(0.2)(x00)
x00 = conv2d(filters=int(16 * number_of_filters))(x00)
x00 = BatchNormalization()(x00)
x00 = LeakyReLU(0.01)(x00)
x00 = Dropout(0.2)(x00)
p0 = MaxPooling2D(pool_size=(2, 2))(x00)
x10 = conv2d(filters=int(32 * number_of_filters))(p0)
x10 = BatchNormalization()(x10)
x10 = LeakyReLU(0.01)(x10)
x10 = Dropout(0.2)(x10)
x10 = conv2d(filters=int(32 * number_of_filters))(x10)
x10 = BatchNormalization()(x10)
x10 = LeakyReLU(0.01)(x10)
x10 = Dropout(0.2)(x10)
p1 = MaxPooling2D(pool_size=(2, 2))(x10)
x01 = conv2dtranspose(int(16 * number_of_filters))(x10)
x01 = concatenate([x00, x01])
x01 = conv2d(filters=int(16 * number_of_filters))(x01)
x01 = BatchNormalization()(x01)
x01 = LeakyReLU(0.01)(x01)
x01 = conv2d(filters=int(16 * number_of_filters))(x01)
x01 = BatchNormalization()(x01)
x01 = LeakyReLU(0.01)(x01)
x01 = Dropout(0.2)(x01)
x20 = conv2d(filters=int(64 * number_of_filters))(p1)
x20 = BatchNormalization()(x20)
x20 = LeakyReLU(0.01)(x20)
x20 = Dropout(0.2)(x20)
x20 = conv2d(filters=int(64 * number_of_filters))(x20)
x20 = BatchNormalization()(x20)
x20 = LeakyReLU(0.01)(x20)
x20 = Dropout(0.2)(x20)
p2 = MaxPooling2D(pool_size=(2, 2))(x20)
x11 = conv2dtranspose(int(16 * number_of_filters))(x20)
x11 = concatenate([x10, x11])
x11 = conv2d(filters=int(16 * number_of_filters))(x11)
x11 = BatchNormalization()(x11)
x11 = LeakyReLU(0.01)(x11)
x11 = conv2d(filters=int(16 * number_of_filters))(x11)
x11 = BatchNormalization()(x11)
x11 = LeakyReLU(0.01)(x11)
x11 = Dropout(0.2)(x11)
x02 = conv2dtranspose(int(16 * number_of_filters))(x11)
x02 = concatenate([x00, x01, x02])
x02 = conv2d(filters=int(16 * number_of_filters))(x02)
x02 = BatchNormalization()(x02)
x02 = LeakyReLU(0.01)(x02)
x02 = conv2d(filters=int(16 * number_of_filters))(x02)
x02 = BatchNormalization()(x02)
x02 = LeakyReLU(0.01)(x02)
x02 = Dropout(0.2)(x02)
x30 = conv2d(filters=int(128 * number_of_filters))(p2)
x30 = BatchNormalization()(x30)
x30 = LeakyReLU(0.01)(x30)
x30 = Dropout(0.2)(x30)
x30 = conv2d(filters=int(128 * number_of_filters))(x30)
x30 = BatchNormalization()(x30)
x30 = LeakyReLU(0.01)(x30)
x30 = Dropout(0.2)(x30)
p3 = MaxPooling2D(pool_size=(2, 2))(x30)
x21 = conv2dtranspose(int(16 * number_of_filters))(x30)
x21 = concatenate([x20, x21])
x21 = conv2d(filters=int(16 * number_of_filters))(x21)
x21 = BatchNormalization()(x21)
x21 = LeakyReLU(0.01)(x21)
x21 = conv2d(filters=int(16 * number_of_filters))(x21)
x21 = BatchNormalization()(x21)
x21 = LeakyReLU(0.01)(x21)
x21 = Dropout(0.2)(x21)
x12 = conv2dtranspose(int(16 * number_of_filters))(x21)
x12 = concatenate([x10, x11, x12])
x12 = conv2d(filters=int(16 * number_of_filters))(x12)
x12 = BatchNormalization()(x12)
x12 = LeakyReLU(0.01)(x12)
x12 = conv2d(filters=int(16 * number_of_filters))(x12)
x12 = BatchNormalization()(x12)
x12 = LeakyReLU(0.01)(x12)
x12 = Dropout(0.2)(x12)
x03 = conv2dtranspose(int(16 * number_of_filters))(x12)
x03 = concatenate([x00, x01, x02, x03])
x03 = conv2d(filters=int(16 * number_of_filters))(x03)
x03 = BatchNormalization()(x03)
x03 = LeakyReLU(0.01)(x03)
x03 = conv2d(filters=int(16 * number_of_filters))(x03)
x03 = BatchNormalization()(x03)
x03 = LeakyReLU(0.01)(x03)
x03 = Dropout(0.2)(x03)
m = conv2d(filters=int(256 * number_of_filters))(p3)
m = BatchNormalization()(m)
m = LeakyReLU(0.01)(m)
m = conv2d(filters=int(256 * number_of_filters))(m)
m = BatchNormalization()(m)
m = LeakyReLU(0.01)(m)
m = Dropout(0.2)(m)
x31 = conv2dtranspose(int(128 * number_of_filters))(m)
x31 = concatenate([x31, x30])
x31 = conv2d(filters=int(128 * number_of_filters))(x31)
x31 = BatchNormalization()(x31)
x31 = LeakyReLU(0.01)(x31)
x31 = conv2d(filters=int(128 * number_of_filters))(x31)
x31 = BatchNormalization()(x31)
x31 = LeakyReLU(0.01)(x31)
x31 = Dropout(0.2)(x31)
x22 = conv2dtranspose(int(64 * number_of_filters))(x31)
x22 = concatenate([x22, x20, x21])
x22 = conv2d(filters=int(64 * number_of_filters))(x22)
x22 = BatchNormalization()(x22)
x22 = LeakyReLU(0.01)(x22)
x22 = conv2d(filters=int(64 * number_of_filters))(x22)
x22 = BatchNormalization()(x22)
x22 = LeakyReLU(0.01)(x22)
x22 = Dropout(0.2)(x22)
x13 = conv2dtranspose(int(32 * number_of_filters))(x22)
x13 = concatenate([x13, x10, x11, x12])
x13 = conv2d(filters=int(32 * number_of_filters))(x13)
x13 = BatchNormalization()(x13)
x13 = LeakyReLU(0.01)(x13)
x13 = conv2d(filters=int(32 * number_of_filters))(x13)
x13 = BatchNormalization()(x13)
x13 = LeakyReLU(0.01)(x13)
x13 = Dropout(0.2)(x13)
x04 = conv2dtranspose(int(16 * number_of_filters))(x13)
x04 = concatenate([x04, x00, x01, x02, x03], axis=3)
x04 = conv2d(filters=int(16 * number_of_filters))(x04)
x04 = BatchNormalization()(x04)
x04 = LeakyReLU(0.01)(x04)
x04 = conv2d(filters=int(16 * number_of_filters))(x04)
x04 = BatchNormalization()(x04)
x04 = LeakyReLU(0.01)(x04)
x04 = Dropout(0.2)(x04)
output = Conv2D(num_classes,
kernel_size=(1, 1),
activation='softmax')(x04)
self.model = tf.keras.Model(inputs=[model_input], outputs=[output])
self.optimizer = Adam(lr=0.0005)