def __init__(self, settings: TrainSettings):
self.settings = settings
tf.keras.backend.clear_session()
if settings.gpu_allow_growth:
from ocr4all_pixel_classifier.lib.network import tf_backend_allow_growth
tf_backend_allow_growth()
from ocr4all_pixel_classifier.lib.network import Network
self.train_net = Network("train", settings.n_classes, settings.architecture,
l_rate=settings.l_rate,
foreground_masks=settings.foreground_masks, model=settings.load,
continue_training=settings.continue_training,
input_image_dimension=settings.image_dimension,
optimizer=settings.optimizer,
optimizer_norm_clipping=settings.optimizer_norm_clipping,
optimizer_norm_clip_value=settings.optimizer_norm_clip_value,
optimizer_clipping=settings.optimizer_clipping,
optimizer_clip_value=settings.optimizer_clip_value,
loss_func=settings.loss
)
if len(settings.train_data) == 0 and settings.n_epoch > 0:
raise Exception("No training files specified. Maybe set n_iter=0")
if settings.compute_baseline:
def compute_label_percentage(label):
return np.sum([np.sum(d.mask == label) for d in settings.train_data.data]) \
/ np.sum([d.mask.shape[0] * d.mask.shape[1] for d in settings.train_data.data])
logging.info("Computing label percentage for {} files.".format(len(settings.train_data.data)))
label_percentage = [compute_label_percentage(l) for l in range(settings.n_classes)]
logging.info("Label percentage: {}".format(list(zip(range(settings.n_classes), label_percentage))))
logging.info("Baseline: {}".format(max(label_percentage)))
def __init__(self, settings: PredictSettings, network: Network = None):
self.settings = settings
self.network = network
if settings.gpu_allow_growth:
tf_backend_allow_growth()
if not network:
self.network = Network("Predict",
n_classes=settings.n_classes,
model=os.path.abspath(
self.settings.network))
if settings.output:
output_dir = settings.output
os.makedirs(os.path.join(output_dir, "overlay"), exist_ok=True)
os.makedirs(os.path.join(output_dir, "color"), exist_ok=True)
os.makedirs(os.path.join(output_dir, "inverted"), exist_ok=True)