from __future__ import print_function
from __future__ import division

import os
import sys
import time
import datetime
import argparse
import os.path as osp
import numpy as np

import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.optim import lr_scheduler

import data_manager
from dataset_loader import ImageDataset, VideoDataset
import transforms as T
import models
from losses import CrossEntropyLabelSmooth, TripletLoss
from utils.iotools import save_checkpoint, check_isfile
from utils.avgmeter import AverageMeter
from utils.logger import Logger
from utils.torchtools import count_num_param
from utils.reidtools import visualize_ranked_results
from eval_metrics import evaluate
from samplers import RandomIdentitySampler
from optimizers import init_optim


parser = argparse.ArgumentParser(description='Train video model with cross entropy loss')
# Datasets
parser.add_argument('--root', type=str, default='data',
                    help="root path to data directory")
parser.add_argument('-d', '--dataset', type=str, default='mars',
                    choices=data_manager.get_names())
parser.add_argument('-j', '--workers', default=4, type=int,
                    help="number of data loading workers (default: 4)")
parser.add_argument('--height', type=int, default=256,
                    help="height of an image (default: 256)")
parser.add_argument('--width', type=int, default=128,
                    help="width of an image (default: 128)")
parser.add_argument('--seq-len', type=int, default=15,
                    help="number of images to sample in a tracklet")
# Optimization options
parser.add_argument('--optim', type=str, default='adam',
                    help="optimization algorithm (see optimizers.py)")
parser.add_argument('--max-epoch', default=500, type=int,
                    help="maximum epochs to run")
parser.add_argument('--start-epoch', default=0, type=int,
                    help="manual epoch number (useful on restarts)")
parser.add_argument('--train-batch', default=32, type=int,
                    help="train batch size")
parser.add_argument('--test-batch', default=5, type=int,
                    help="test batch size (number of tracklets)")
parser.add_argument('--lr', '--learning-rate', default=0.0003, type=float,
                    help="initial learning rate")
parser.add_argument('--stepsize', default=[300, 400], nargs='+', type=int,
                    help="stepsize to decay learning rate")
parser.add_argument('--gamma', default=0.1, type=float,
                    help="learning rate decay")
parser.add_argument('--weight-decay', default=5e-04, type=float,
                    help="weight decay (default: 5e-04)")
parser.add_argument('--margin', type=float, default=0.3,
                    help="margin for triplet loss")
parser.add_argument('--num-instances', type=int, default=4,
                    help="number of instances per identity")
parser.add_argument('--htri-only', action='store_true', default=False,
                    help="if this is True, only htri loss is used in training")
# Architecture
parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names())
parser.add_argument('--pool', type=str, default='avg', choices=['avg', 'max'])
# Miscs
parser.add_argument('--print-freq', type=int, default=10,
                    help="print frequency")
parser.add_argument('--seed', type=int, default=1,
                    help="manual seed")
parser.add_argument('--resume', type=str, default='', metavar='PATH')
parser.add_argument('--load-weights', type=str, default='',
                    help="load pretrained weights but ignores layers that don't match in size")
parser.add_argument('--evaluate', action='store_true',
                    help="evaluation only")
parser.add_argument('--eval-step', type=int, default=-1,
                    help="run evaluation for every N epochs (set to -1 to test after training)")
parser.add_argument('--start-eval', type=int, default=0,
                    help="start to evaluate after specific epoch")
parser.add_argument('--save-dir', type=str, default='log')
parser.add_argument('--use-cpu', action='store_true',
                    help="use cpu")
parser.add_argument('--gpu-devices', default='0', type=str,
                    help='gpu device ids for CUDA_VISIBLE_DEVICES')
parser.add_argument('--vis-ranked-res', action='store_true',
                    help="visualize ranked results, only available in evaluation mode (default: False)")

args = parser.parse_args()


def main():
    torch.manual_seed(args.seed)
    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
    use_gpu = torch.cuda.is_available()
    if args.use_cpu: use_gpu = False

    if not args.evaluate:
        sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
    else:
        sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'))
    print("==========\nArgs:{}\n==========".format(args))

    if use_gpu:
        print("Currently using GPU {}".format(args.gpu_devices))
        cudnn.benchmark = True
        torch.cuda.manual_seed_all(args.seed)
    else:
        print("Currently using CPU (GPU is highly recommended)")

    print("Initializing dataset {}".format(args.dataset))
    dataset = data_manager.init_vidreid_dataset(root=args.root, name=args.dataset)

    transform_train = T.Compose([
        T.Random2DTranslation(args.height, args.width),
        T.RandomHorizontalFlip(),
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    transform_test = T.Compose([
        T.Resize((args.height, args.width)),
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    pin_memory = True if use_gpu else False

    # decompose tracklets into images for image-based training
    new_train = []
    for img_paths, pid, camid in dataset.train:
        for img_path in img_paths:
            new_train.append((img_path, pid, camid))

    trainloader = DataLoader(
        ImageDataset(new_train, transform=transform_train),
        sampler=RandomIdentitySampler(new_train, num_instances=args.num_instances),
        batch_size=args.train_batch, num_workers=args.workers,
        pin_memory=pin_memory, drop_last=True,
    )

    queryloader = DataLoader(
        VideoDataset(dataset.query, seq_len=args.seq_len, sample='evenly', transform=transform_test),
        batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
        pin_memory=pin_memory, drop_last=False,
    )

    galleryloader = DataLoader(
        VideoDataset(dataset.gallery, seq_len=args.seq_len, sample='evenly', transform=transform_test),
        batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
        pin_memory=pin_memory, drop_last=False,
    )

    print("Initializing model: {}".format(args.arch))
    model = models.init_model(name=args.arch, num_classes=dataset.num_train_pids, loss={'xent', 'htri'})
    print("Model size: {:.3f} M".format(count_num_param(model)))

    criterion_xent = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
    criterion_htri = TripletLoss(margin=args.margin)

    optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay)
    scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)

    if args.load_weights:
        # load pretrained weights but ignore layers that don't match in size
        if check_isfile(args.load_weights):
            checkpoint = torch.load(args.load_weights)
            pretrain_dict = checkpoint['state_dict']
            model_dict = model.state_dict()
            pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
            model_dict.update(pretrain_dict)
            model.load_state_dict(model_dict)
            print("Loaded pretrained weights from '{}'".format(args.load_weights))

    if args.resume:
        if check_isfile(args.resume):
            checkpoint = torch.load(args.resume)
            model.load_state_dict(checkpoint['state_dict'])
            args.start_epoch = checkpoint['epoch']
            rank1 = checkpoint['rank1']
            print("Loaded checkpoint from '{}'".format(args.resume))
            print("- start_epoch: {}\n- rank1: {}".format(args.start_epoch, rank1))

    if use_gpu:
        model = nn.DataParallel(model).cuda()

    if args.evaluate:
        print("Evaluate only")
        distmat = test(model, queryloader, galleryloader, use_gpu, return_distmat=True)
        if args.vis_ranked_res:
            visualize_ranked_results(
                distmat, dataset,
                save_dir=osp.join(args.save_dir, 'ranked_results'),
                topk=20,
            )
        return

    start_time = time.time()
    train_time = 0
    best_rank1 = -np.inf
    best_epoch = 0
    print("==> Start training")

    for epoch in range(args.start_epoch, args.max_epoch):
        start_train_time = time.time()
        train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu)
        train_time += round(time.time() - start_train_time)
        
        scheduler.step()
        
        if (epoch + 1) > args.start_eval and args.eval_step > 0 and (epoch + 1) % args.eval_step == 0 or (epoch + 1) == args.max_epoch:
            print("==> Test")
            rank1 = test(model, queryloader, galleryloader, args.pool, use_gpu)
            is_best = rank1 > best_rank1
            
            if is_best:
                best_rank1 = rank1
                best_epoch = epoch + 1

            if use_gpu:
                state_dict = model.module.state_dict()
            else:
                state_dict = model.state_dict()
            
            save_checkpoint({
                'state_dict': state_dict,
                'rank1': rank1,
                'epoch': epoch,
            }, is_best, osp.join(args.save_dir, 'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))

    print("==> Best Rank-1 {:.1%}, achieved at epoch {}".format(best_rank1, best_epoch))

    elapsed = round(time.time() - start_time)
    elapsed = str(datetime.timedelta(seconds=elapsed))
    train_time = str(datetime.timedelta(seconds=train_time))
    print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))


def train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu):
    losses = AverageMeter()
    batch_time = AverageMeter()
    data_time = AverageMeter()

    model.train()

    end = time.time()
    for batch_idx, (imgs, pids, _) in enumerate(trainloader):
        data_time.update(time.time() - end)
        
        if use_gpu:
            imgs, pids = imgs.cuda(), pids.cuda()
        
        outputs, features = model(imgs)
        if args.htri_only:
            # only use hard triplet loss to train the network
            loss = criterion_htri(features, pids)
        else:
            # combine hard triplet loss with cross entropy loss
            xent_loss = criterion_xent(outputs, pids)
            htri_loss = criterion_htri(features, pids)
            loss = xent_loss + htri_loss
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        batch_time.update(time.time() - end)

        losses.update(loss.item(), pids.size(0))

        if (batch_idx + 1) % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.4f} ({data_time.avg:.4f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
                   epoch + 1, batch_idx + 1, len(trainloader), batch_time=batch_time,
                   data_time=data_time, loss=losses))
        
        end = time.time()


def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20], return_distmat=False):
    batch_time = AverageMeter()

    model.eval()

    with torch.no_grad():
        qf, q_pids, q_camids = [], [], []
        for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
            if use_gpu: imgs = imgs.cuda()
            b, s, c, h, w = imgs.size()
            imgs = imgs.view(b*s, c, h, w)
            
            end = time.time()
            features = model(imgs)
            batch_time.update(time.time() - end)

            features = features.view(b, s, -1)
            if pool == 'avg':
                features = torch.mean(features, 1)
            else:
                features, _ = torch.max(features, 1)
            features = features.data.cpu()
            qf.append(features)
            q_pids.extend(pids)
            q_camids.extend(camids)
        qf = torch.cat(qf, 0)
        q_pids = np.asarray(q_pids)
        q_camids = np.asarray(q_camids)

        print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))

        gf, g_pids, g_camids = [], [], []
        for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
            if use_gpu: imgs = imgs.cuda()
            b, s, c, h, w = imgs.size()
            imgs = imgs.view(b*s, c, h, w)
            
            end = time.time()
            features = model(imgs)
            batch_time.update(time.time() - end)

            features = features.view(b, s, -1)
            if pool == 'avg':
                features = torch.mean(features, 1)
            else:
                features, _ = torch.max(features, 1)
            features = features.data.cpu()
            gf.append(features)
            g_pids.extend(pids)
            g_camids.extend(camids)
        gf = torch.cat(gf, 0)
        g_pids = np.asarray(g_pids)
        g_camids = np.asarray(g_camids)

        print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
    
    print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch*args.seq_len))

    m, n = qf.size(0), gf.size(0)
    distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
              torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
    distmat.addmm_(1, -2, qf, gf.t())
    distmat = distmat.numpy()

    print("Computing CMC and mAP")
    cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)

    print("Results ----------")
    print("mAP: {:.1%}".format(mAP))
    print("CMC curve")
    for r in ranks:
        print("Rank-{:<3}: {:.1%}".format(r, cmc[r-1]))
    print("------------------")

    if return_distmat:
        return distmat
    return cmc[0]


if __name__ == '__main__':
    main()