# Copyright 2021 Huawei Technologies Co., Ltd



# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at



#    http://www.apache.org/licenses/LICENSE-2.0



# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import sys

#sys.path.append('/share/home/litaotao/yzc/training_results_v0.7-master/NVIDIA/benchmarks/ssd/implementations/pytorch/')#

import os

#from base_model import Loss

from opt_loss import OptLoss

from mlperf_logger import configure_logger, log_start, log_end, log_event, set_seeds, get_rank, barrier

from mlperf_logging.mllog import constants

import torch

from torch.autograd import Variable

import time

import numpy as np

import io

from bisect import bisect       # for lr_scheduler



from ssd300 import SSD300

from master_params import create_flat_master

from parse_config import parse_args, validate_arguments, validate_group_bn



from async_evaluator import AsyncEvaluator

from eval import coco_eval



#import sys

import gc

from data.native_pipeline import build_train_pipe

# necessary pytorch imports

import torch.utils.data.distributed

import torch.distributed as dist

configure_logger(constants.SSD)

log_start(key=constants.INIT_START, log_all_ranks=True)

args = parse_args()

# make sure the epoch lists are in sorted order

args.evaluation.sort()

args.lr_decay_epochs.sort()



validate_arguments(args)



torch.set_num_threads(1)

torch.backends.cudnn.benchmark = not args.profile_cudnn_get

build_train_pipe(args)