# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the BSD 3-Clause License  (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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 argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import torch.multiprocessing as mp
import torch.distributed as dist
import torch.npu

from apex import amp

model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__")
                     and callable(models.__dict__[name]))

model_names.append('mobilenet')

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--data', metavar='DIR', default="/data/imagenet",
                    help='path to dataset')
parser.add_argument('--arch', '-a', metavar='ARCH', default='mobilenet',
                    choices=model_names,
                    help='model architecture: ' +
                         ' | '.join(model_names) +
                         ' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch_size', default=256, type=int,
                    metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default=None, type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    default=0,
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--device-list', type=str, default='',
                    help='device id list')
parser.add_argument('--run-prof', default=False, action='store_true',
                    help='run prof')
parser.add_argument('--rank', default=0, type=int, metavar='N',
                    help='local rank')
parser.add_argument('--ngpu', default=1, type=int, metavar='N',
                    help='number of gpu')
parser.add_argument('--device_id', default=0, type=int, metavar='N',
                    help='id of gpu')
best_prec1 = 0


class Net(nn.Module):
    def __init__(self, num_classes=1000):
        super(Net, self).__init__()

        def conv_bn(inp, oup, stride):
            return nn.Sequential(
                nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
                nn.BatchNorm2d(oup),
                nn.ReLU(inplace=True)
            )

        def conv_dw(inp, oup, stride):
            return nn.Sequential(
                nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
                nn.BatchNorm2d(inp),
                nn.ReLU(inplace=True),

                nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
                nn.ReLU(inplace=True),
            )

        self.model = nn.Sequential(
            conv_bn(3, 32, 2),
            conv_dw(32, 64, 1),
            conv_dw(64, 128, 2),
            conv_dw(128, 128, 1),
            conv_dw(128, 256, 2),
            conv_dw(256, 256, 1),
            conv_dw(256, 512, 2),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 1024, 2),
            conv_dw(1024, 1024, 1),
            nn.AvgPool2d(7),
        )
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.model(x)
        x = x.view(-1, 1024)
        x = self.fc(x)
        return x


def mobilenet(path="./checkpoint.pth.tar", num_classes=1000):
    net = Net()
    pretrained_dict = torch.load(path, map_location="cpu")["state_dict"]
    model.load_state_dict({k.replace('module.', '', 1): v for k, v in pretrained_dict.items()})

    if 'fc.weight' in pretrained_dict:
        pretrained_dict.pop('fc.weight')
        pretrained_dict.pop('fc.bias')
    if 'module.fc.weight' in pretrained_dict:
        pretrained_dict.pop('module.fc.weight')
        pretrained_dict.pop('module.fc.bias')

    for param in net.parameters():
        param.requires_gard = False

    net.fc = nn.Linear(1024, num_classes)
    net.load_state_dict(pretrained_dict, strict=False)
    return net


def do_main(arg):
    global args, best_prec1
    args = arg
    rank = args.rank
    ngpu = args.ngpu
    gpuid = args.device_id if ngpu == 1 else args.rank
    os.environ['MASTER_ADDR'] = '127.0.0.1'
    os.environ['MASTER_PORT'] = '50000'
    dist.init_process_group(backend='hccl', world_size=ngpu, rank=rank)
    print('Initializing process rank', rank, 'with gpuid', gpuid)
    device = torch.device("npu:{}".format(gpuid))
    torch.npu.set_device(device)
    args.device = device

    # create model
    if args.pretrained:
        print("=> using pre-trained model '{}'".format(args.arch))
        model = mobilenet(path='model_best.pth.tar', num_classes=1000)
    else:
        print("=> creating model '{}'".format(args.arch))
        if args.arch.startswith('mobilenet'):
            model = Net(num_classes=1000)
            print(model)
        else:
            model = models.__dict__[args.arch]()

    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    model = model.to(device)
    model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale=64.0)
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpuid], broadcast_buffers=False)

    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().to(device)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    val_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    if args.evaluate:
        validate(val_loader, model, criterion)
        return
    else:
        train_dataset = datasets.ImageFolder(traindir, transforms.Compose([
            transforms.RandomSizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ])
                                             )
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        train_loader = torch.utils.data.DataLoader(
            train_dataset,
            batch_size=args.batch_size,
            sampler=train_sampler,
            num_workers=args.workers, pin_memory=True)

    for epoch in range(args.start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch)

        # train for one epoch
        train_sampler.set_epoch(epoch)

        train(train_loader, model, criterion, optimizer, epoch)

        # evaluate on validation set
        prec1 = validate(val_loader, model, criterion)

        # remember best prec@1 and save checkpoint
        is_best = prec1 > best_prec1
        best_prec1 = max(prec1, best_prec1)
        if args.rank == 0:
            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'best_prec1': best_prec1,
                'optimizer': optimizer.state_dict(),
            }, is_best)


def train(train_loader, model, criterion, optimizer, epoch):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()
    tot_time = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()

    for i, (input, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        target = target.to(torch.int32)
        target = target.to(args.device, non_blocking=True)
        input = input.to(args.device, non_blocking=True)
        input_var = torch.autograd.Variable(input)
        target_var = torch.autograd.Variable(target)

        if args.run_prof and i == 5:
            with torch.autograd.profiler.profile(use_cuda=True) as prof:
                out = model(input_var)
                loss = criterion(out, target_var)
                optimizer.zero_grad()
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
                optimizer.step()
            prof.export_chrome_trace("output.prof")  # "output.prof"为输出文件地址
            exit(0)

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.data.item(), input.size(0))
        top1.update(prec1.item(), input.size(0))
        top5.update(prec5.item(), input.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        with amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_loss.backward()
        optimizer.step()

        # measure elapsed time
        current_batch_time = time.time() - end
        batch_time.update(current_batch_time)
        end = time.time()

        fps = args.batch_size * args.ngpu / current_batch_time
        if i >= 2:
            tot_time.update(current_batch_time)

        if i % args.print_freq == 0 and args.rank == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'FPS = {fps:.2f}\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                epoch, i, len(train_loader), fps=fps, batch_time=batch_time,
                data_time=data_time, loss=losses, top1=top1, top5=top5))
    if args.rank == 0:
        print('FPS = {:.2f}\t'.format(args.batch_size * args.ngpu / tot_time.avg))


def validate(val_loader, model, criterion):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    for i, (input, target) in enumerate(val_loader):
        target = target.to(torch.int32)
        target = target.npu()
        input_var = input.npu()
        target_var = target

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.item(), input.size(0))
        top1.update(prec1.item(), input.size(0))
        top5.update(prec5.item(), input.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0 and args.rank == 0:
            print('Test: [{0}/{1}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                i, len(val_loader), batch_time=batch_time, loss=losses,
                top1=top1, top5=top5))

    if args.rank == 0:
        print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))

    return top1.avg


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].reshape(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


def main():
    args = parser.parse_args()
    # assert args.device_list != ''
    # ngpu = len(args.device_list.split(','))
    # mp.spawn(do_main, nprocs=ngpu, args=(args,))
    do_main(arg=args)


if __name__ == '__main__':
    main()