import tensorflow as tf


import time
import numpy as np
import tensorflow as tf
import random
from tensorflow.contrib import slim

from npu_bridge.estimator import npu_ops
from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig

tf.app.flags.DEFINE_integer('input_size', 512, '')
tf.app.flags.DEFINE_integer('batch_size_per_gpu', 14, '')
tf.app.flags.DEFINE_integer('num_readers', 16, '')
tf.app.flags.DEFINE_float('learning_rate', 0.0001, '')
tf.app.flags.DEFINE_integer('max_steps', 100000, '')
tf.app.flags.DEFINE_integer('loss_scale', 1024, '')
tf.app.flags.DEFINE_float('moving_average_decay', 0.997, '')
tf.app.flags.DEFINE_string('gpu_list', '1', '')
tf.app.flags.DEFINE_string('checkpoint_path', '/tmp/east_resnet_v1_50_rbox/', '')
tf.app.flags.DEFINE_boolean('restore', False, 'whether to resotre from checkpoint')
tf.app.flags.DEFINE_integer('save_checkpoint_steps', 1000, '')
tf.app.flags.DEFINE_integer('save_summary_steps', 100, '')
tf.app.flags.DEFINE_string('pretrained_model_path', None, '')
tf.app.flags.DEFINE_boolean('allow_mix_precision', False, 'whether to allow mix precision')
tf.app.flags.DEFINE_boolean('auto_tune', False, 'whether to autotune')
tf.app.flags.DEFINE_boolean('use_processed_data', False, 'whether to use processed data')
tf.app.flags.DEFINE_string('processed_data', './processed_dataset/', 'where to save preprocessed datasets')

import model
import icdar
