def main(hp, model_id, save_base_dir, data_dir, train_epochs, total_epochs, epoch_index): # Xinyi modified tf.logging.set_verbosity(tf.logging.ERROR) model_dir = save_base_dir + str(model_id) for name in list(flags.FLAGS): delattr(flags.FLAGS, name) define_cifar_flags(hp, model_id, model_dir, data_dir, train_epochs, total_epochs, epoch_index) absl_app.parse_flags_with_usage(sys.argv) return start(0)
def parse_flags(argv=None): """Reset flags and reparse. Currently only used in testing.""" flags.FLAGS.unparse_flags() absl_app.parse_flags_with_usage(argv or sys.argv)
"Uses --dataset_path and --feature_spec" "Overrides synthetic dataset dimension flags, other than the number of batches") flags.DEFINE_integer('synthetic_dataset_train_batches', default=64008, help='Number of training batches in the synthetic dataset') flags.DEFINE_integer('synthetic_dataset_valid_batches', default=1350, help='Number of validation batches in the synthetic dataset') flags.DEFINE_list('synthetic_dataset_cardinalities', default=26*[1000], help='Number of categories for each embedding table of the synthetic dataset') flags.DEFINE_integer('synthetic_dataset_num_numerical_features', default=13, help='Number of numerical features of the synthetic dataset') define_command_line_flags() FLAGS = flags.FLAGS app.define_help_flags() app.parse_flags_with_usage(sys.argv) if FLAGS.xla: if FLAGS.cpu: os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible --tf_xla_cpu_global_jit' else: os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible' import time from lr_scheduler import LearningRateScheduler import tensorflow as tf import tensorflow_addons as tfa import numpy as np from utils import IterTimer, init_logging, dist_print from dataloader import create_input_pipelines
def _flags_parser(argv): argv = _benchmark.Initialize(argv) return app.parse_flags_with_usage(argv)
def flags_parser(args): # Plumbs the flags defined in this file to the main module, mostly for the # console script wrapper tb-gcp-uploader. for flag in set(flags.FLAGS.get_key_flags_for_module(__name__)): flags.FLAGS.register_flag_by_module(args[0], flag) return app.parse_flags_with_usage(args)
import tensorflow as tf import numpy as np from PIL import Image from absl import flags, app from yolov3_tf2.models import (YoloV3, YoloV3Tiny) from yolov3_tf2.utils import draw_outputs, load_darknet_weights from yolov3_tf2.dataset import transform_images app.parse_flags_with_usage(['yolo_iou_threshold']) def get_model(path): yolo = YoloV3(classes=80) yolo.load_weights(path) return yolo def find_cars(image, yolo): #img_arr = np.array(image_orig) if type(image) == list: images = np.array([np.array(i) for i in image]) image = transform_images(images, 416) else: image = tf.expand_dims(np.array(image), axis=0) image = transform_images(image, 416) boxes, scores, classes, nums = yolo.predict(image, steps=1) filtered = []