# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

import argparse
from collections import OrderedDict
import importlib
import os
import shutil
import time

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models

from apex import amp

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--data_dir', type=str,
                    help='path to dataset')
parser.add_argument('--module', '-m', required=True,
                    help='name of module that contains full model definition')
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('--eval-batch-size', default=100, type=int,
                    help='eval mini-batch size (default: 100)')
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('--fp16', action='store_true',
                    help='train model in fp16 precision')
parser.add_argument('--forward_only', action='store_true',
                    help="Run forward pass only")
parser.add_argument('--num_minibatches', default=None, type=int,
                    help="Number of minibatches to run")
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-s', '--synthetic_data', action='store_true',
                    help="Use synthetic data")
parser.add_argument('--world-size', default=1, type=int,
                    help='number of distributed processes')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='gloo', type=str,
                    help='distributed backend')

best_prec1 = 0
args = parser.parse_args()

# initialize Amp
amp_handle = amp.init(enabled=args.fp16)


class SyntheticDataset(torch.utils.data.dataset.Dataset):
    def __init__(self, input_size, length, num_classes=1000):
        self.tensor = Variable(torch.rand(*input_size)).type(torch.FloatTensor)
        self.target = torch.Tensor(1).random_(0, num_classes)[0].type(torch.LongTensor)
        self.length = length

    def __getitem__(self, index):
        return self.tensor, self.target

    def __len__(self):
        return self.length


def main():
    global args, best_prec1

    args.distributed = args.world_size > 1

    if args.distributed:
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size)

    # create model
    module = importlib.import_module(args.module)
    args.arch = module.arch()
    model = module.full_model()

    model = model.cuda()

    if not args.distributed:
        model = torch.nn.DataParallel(model).cuda()
    else:
        model.cuda()
        model = torch.nn.parallel.DistributedDataParallel(model)

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

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

    # 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_dir, 'train')
    valdir = os.path.join(args.data_dir, 'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    if args.arch == 'inception_v3':
        train_dataset = datasets.ImageFolder(
            traindir,
            transforms.Compose([
                transforms.RandomResizedCrop(299),
                transforms.ToTensor(),
                normalize,
            ])
        )
        if args.synthetic_data:
            train_dataset = SyntheticDataset((3, 299, 299), len(train_dataset))
    else:
        train_dataset = datasets.ImageFolder(
            traindir,
            transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ]))
        if args.synthetic_data:
            train_dataset = SyntheticDataset((3, 224, 224), len(train_dataset))

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    else:
        train_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

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

    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        adjust_learning_rate(optimizer, epoch)

        # train or run forward pass only for one epoch
        if args.forward_only:
            validate(train_loader, model, criterion, epoch)
        else:
            train(train_loader, model, criterion, optimizer, epoch)

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

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


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

    # switch to train mode
    model.train()

    end = time.time()
    epoch_start_time = time.time()
    for i, (input, target) in enumerate(train_loader):
        if args.num_minibatches is not None and i >= args.num_minibatches:
            break

        # measure data loading time
        data_time.update(time.time() - end)

        target = target.cuda(non_blocking=True)

        # compute output
        output = model(input)
        if isinstance(output, tuple):
            loss = sum((criterion(output_elem, target) for output_elem in output))
        else:
            loss = criterion(output, target)

        # measure accuracy and record loss
        if isinstance(output, tuple):
            prec1, prec5 = accuracy(output[0], target, topk=(1, 5))
        else:
            prec1, prec5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), input.size(0))
        top1.update(prec1[0], input.size(0))
        top5.update(prec5[0], input.size(0))

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

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

        if i % args.print_freq == 0:
            n = len(train_loader)
            if args.num_minibatches is not None:
                n = args.num_minibatches
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data: {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Memory: {memory:.3f} ({cached_memory:.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, n, batch_time=batch_time,
                   data_time=data_time, loss=losses, top1=top1, top5=top5,
                   memory=(float(torch.cuda.memory_allocated()) / 10**9),
                   cached_memory=(float(torch.cuda.memory_cached()) / 10**9)))
            import sys; sys.stdout.flush()

    print("Epoch %d: %.3f seconds" % (epoch, time.time() - epoch_start_time))
    print("Epoch start time: %.3f, epoch end time: %.3f" % (epoch_start_time, time.time()))


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

    # switch to evaluate mode
    model.eval()

    epoch_start_time = time.time()
    with torch.no_grad():
        end = time.time()
        for i, (input, target) in enumerate(val_loader):
            if args.num_minibatches is not None and i >= args.num_minibatches:
                break
            target = target.cuda(non_blocking=True)

            # compute output
            output = model(input)
            loss = criterion(output, target)

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

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

            if i % args.print_freq == 0:
                n = len(val_loader)
                if args.num_minibatches is not None:
                    n = args.num_minibatches
                print('Test: [{0}][{1}/{2}]\t'
                      'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Memory: {memory:.3f} ({cached_memory:.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, n, batch_time=batch_time, loss=losses,
                       top1=top1, top5=top5,
                       memory=(float(torch.cuda.memory_allocated()) / 10**9),
                       cached_memory=(float(torch.cuda.memory_cached()) / 10**9)))
                import sys; sys.stdout.flush()

        print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))
        print('Epoch %d: %.3f seconds' % (epoch, time.time() - epoch_start_time))
        print("Epoch start time: %.3f, epoch end time: %.3f" % (epoch_start_time, time.time()))

    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"""
    with torch.no_grad():
        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].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


if __name__ == '__main__':
    main()
