def main(_argv):
if FLAGS.tiny:
yolo = YoloV3Tiny(classes=FLAGS.num_classes)
else:
yolo = YoloV3(classes=FLAGS.num_classes)
yolo.summary()
logging.info('model created')
load_darknet_weights(yolo, FLAGS.weights, FLAGS.tiny)
logging.info('weights loaded')
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo(img)
logging.info('sanity check passed')
yolo.save_weights(FLAGS.output)
logging.info('weights saved')
def main(_argv):
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
yolo = yolo_v3(classes=FLAGS.num_classes)
yolo.summary()
logging.info('model created')
load_darknet_weights(yolo, FLAGS.weights, FLAGS.tiny)
logging.info('weights loaded')
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo(img)
logging.info('sanity check passed')
yolo.save_weights(FLAGS.output)
logging.info('weights saved')
def main(_argv):
if tf.test.is_gpu_available():
tf.config.gpu.set_per_process_memory_fraction(FLAGS.gpu_fraction)
if FLAGS.tiny:
yolo = YoloV3Tiny()
else:
yolo = YoloV3()
yolo.summary()
logging.info('model created')
load_darknet_weights(yolo, FLAGS.weights, FLAGS.tiny)
logging.info('weights loaded')
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo(img)
logging.info('sanity check passed')
yolo.save_weights(FLAGS.output)
logging.info('weights saved')
def main(_argv):
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
tf.compat.v1.InteractiveSession(config=config)
if FLAGS.tiny:
yolo = YoloV3Tiny(classes=FLAGS.num_classes)
else:
yolo = YoloV3(classes=FLAGS.num_classes)
yolo.summary()
logging.info('model created')
load_darknet_weights(yolo, FLAGS.weights, FLAGS.tiny)
logging.info('weights loaded')
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo(img)
logging.info('sanity check passed')
yolo.save_weights(FLAGS.output)
logging.info('weights saved')
def main(_argv):
if tf_version == '2':
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
size = None if FLAGS.size == 0 else FLAGS.size
if FLAGS.tiny:
yolo = YoloV3Tiny(size=size, classes=FLAGS.num_classes, training=FLAGS.training)
elif FLAGS.lite:
yolo = YoloV2Lite(size=size, classes=FLAGS.num_classes, training=FLAGS.training)
else:
yolo = YoloV3(size=size, classes=FLAGS.num_classes, training=FLAGS.training)
yolo.summary()
logging.info('model created')
if FLAGS.from_h5:
yolo.load_weights(FLAGS.weights)
else:
load_darknet_weights(yolo, FLAGS.weights, FLAGS.tiny, FLAGS.lite)
logging.info('weights loaded')
imsize = 320 if size is None else size
img = np.random.random((1, imsize, imsize, 3)).astype(np.float32)
output = yolo(img)
logging.info('sanity check passed')
predict(yolo)
if FLAGS.output.endswith(('.h5', '.hdf5')):
yolo.save(FLAGS.output)
logging.info('keras model saved')
else:
yolo.save_weights(FLAGS.output)
logging.info('weights saved')
if tf_version == '1':
convert_to_pb(yolo)
logging.info('pb saved')
def convert_weights_to_tf():
if FLAGS.tiny:
yolo = YoloV3Tiny(classes=80)
load_darknet_weights(model=yolo, weights_file=FLAGS.tiny_weights, tiny=FLAGS.tiny)
logging.info('weights loaded')
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo(img)
logging.info('sanity check passed')
yolo.save_weights('./checkpoints/yolov3-tiny.tf')
logging.info('weights saved')
else:
yolo = YoloV3(classes=80)
load_darknet_weights(model=yolo, weights_file=FLAGS.weights, tiny=FLAGS.tiny)
logging.info('weights loaded')
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo(img)
logging.info('sanity check passed')
yolo.save_weights(FLAGS.output)
logging.info('weights saved')
import numpy as np
from yolov3_tf2.models_simple import YoloV3, YoloV3Tiny
from yolov3_tf2.utils import load_darknet_weights
import tensorflow as tf
tiny = False
num_classes = 80
path_weights = './data/yolov3.weights'
path_output = './checkpoints/yolov3.tf'
if tiny:
path_weights = path_weights[:-8] + '-tiny.weights'
path_output = path_output[:-3] + '-tiny.tf'
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
if tiny:
yolo = YoloV3Tiny(classes=num_classes)
else:
yolo = YoloV3(classes=num_classes)
yolo.summary()
load_darknet_weights(yolo, path_weights, tiny)
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo(img)
yolo.save_weights(path_output)
