# 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.

# ============================================================================

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function



import _init_paths



import os



import torch

if torch.__version__ >= "1.8":

    import torch_npu

import torch.utils.data

from opts_pose import opts

from models.model import create_model, load_model, save_model

from models.data_parallel import DataParallel

from datasets.dataset_factory import get_dataset

from trains.train_factory import train_factory

from datasets.sample.multi_pose import Multiposebatch

from apex import amp

import apex

import torch.npu

import torch.distributed as dist

from torch.nn.parallel import DistributedDataParallel as DDP





def main(opt, qtepoch=[0,]):

  os.environ['MASTER_ADDR'] = '127.0.0.1'

  os.environ['MASTER_PORT'] = opt.port

  option = {}

  if opt.bin_mode:

      torch.npu.set_compile_mode(jit_compile=False)

  if opt.use_fp32:

      option["ACL_PRECISION_MODE"] = "must_keep_origin_dtype"

      torch.npu.config.allow_internal_format=False

      if opt.hf32:

          torch.npu.conv.allow_hf32 = True

      if opt.fp32:

          torch.npu.conv.allow_hf32 = False

          torch.npu.matmul.allow_hf32 = False

  torch.npu.set_option(option)





  torch.manual_seed(opt.seed)

  torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test

  Dataset = get_dataset(opt.dataset, opt.task)

  opt = opts().update_dataset_info_and_set_heads(opt, Dataset)

  if opt.local_rank ==0:

    print(opt)

  device_id = int(opt.device_list.split(',')[int(opt.local_rank)])

  opt.device = 'npu:{}'.format(device_id)



  torch.npu.set_device(opt.device)

  if opt.distributed_launch:

    dist.init_process_group(backend='hccl', world_size=opt.world_size, rank=opt.local_rank)



  print('process{},device:{}'.format(opt.local_rank,opt.device))

  print('Creating model...')

  model = create_model(opt.arch, opt.heads, opt.head_conv)

  model = model.to(opt.device)

  if opt.pretrained:

      checkpoint = torch.load(opt.pretrained_weight_path, map_location='cpu')

      if 'module.' in list(checkpoint['state_dict'].keys())[0]:

          checkpoint['state_dict'] = {k.replace('module.', ''): v for k, v in checkpoint['state_dict'].items()}

      model.load_state_dict(checkpoint['state_dict'], strict=False)



  if not opt.use_fp32:

    optimizer = apex.optimizers.NpuFusedAdam(model.parameters(), opt.lr)

  else:

    optimizer = torch.optim.Adam(model.parameters(), opt.lr)



  if not opt.use_fp32:

    model, optimizer = amp.initialize(model, optimizer, opt_level="O1",loss_scale=19.0,combine_grad=True)

  start_epoch = 0

  if opt.load_model != '':

    model, optimizer, start_epoch = load_model(

      model, opt.load_model, optimizer, opt.resume, opt.lr, opt.lr_step)

  print('start_epoch:{}'.format(start_epoch))

  Trainer = train_factory[opt.task]

  trainer = Trainer(opt, model, optimizer)

  if opt.distributed_launch:

    trainer.set_device(opt.device_list, opt.chunk_sizes, opt.device)

  else:

    trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)

  print('Setting up data...')

  if opt.distributed_launch:

    train_sampler = torch.utils.data.distributed.DistributedSampler(Dataset(opt, 'train'))

  kwargs = {"pin_memory_device": "npu"} if torch.__version__ >= "2.0" else {}

  val_loader = torch.utils.data.DataLoader(

      Dataset(opt, 'val'), 

      batch_size=1, 

      shuffle=False,

      num_workers=1,

      pin_memory=True,

      **kwargs

  )



  if opt.test:

    _, preds = trainer.val(0, val_loader)

    val_loader.dataset.run_eval(preds, opt.save_dir)

    return



  train_loader = torch.utils.data.DataLoader(

      Dataset(opt, 'train'),

      batch_size=opt.batch_size,

      shuffle=False if opt.distributed_launch else True,

      num_workers=opt.num_workers,

      sampler=train_sampler if opt.distributed_launch else None,

      pin_memory=True,

      drop_last=True,

      collate_fn=Multiposebatch,

      **kwargs

  )



  print('Starting training...')

  best = 1e10

  for epoch in range(start_epoch + 1, opt.num_epochs + 1):

    qtepoch.append(epoch)

    if opt.distributed_launch:

        train_sampler.set_epoch(epoch)

    mark = epoch if opt.save_all else 'last'

    log_dict_train, _ = trainer.train(epoch, train_loader)

    if opt.local_rank == 0:

        str1 ='epoch:{}|'.format(epoch)

        for k, v in log_dict_train.items():

          str2 ='{} {:8f}|'.format(k,v)

          str1 = str1 +str2

        print(str1)

        if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:

          save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)),

                     epoch, model, optimizer)



        print('best:{} metric:{}  epotchs:{}'.format(best,log_dict_train[opt.metric],epoch))



        if log_dict_train[opt.metric] < best:

          best = log_dict_train[opt.metric]

          save_model(os.path.join(opt.save_dir, 'model_best.pth'),

                       epoch, model)

        else:

          save_model(os.path.join(opt.save_dir, 'model_last.pth'),

                     epoch, model, optimizer)



    if epoch in opt.lr_step:



        if opt.local_rank == 0:

            save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)), epoch, model, optimizer)

        lr = opt.lr * (0.1 ** (opt.lr_step.index(epoch) + 1))

        if opt.local_rank == 0:

            print('Drop LR to', lr)

        for param_group in optimizer.param_groups:

            param_group['lr'] = lr





if __name__ == '__main__':

  opt = opts().parse()

  main(opt)