from __future__ import print_function, absolute_import
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
import transforms as T
import models
from losses import CrossEntropyLabelSmooth, CenterLoss
from utils import AverageMeter, Logger, save_checkpoint
from eval_metrics import evaluate

parser = argparse.ArgumentParser(description='Train image model with center loss')
# Datasets
parser.add_argument('-d', '--dataset', type=str, default='market1501',
                    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('--split-id', type=int, default=0, help="split index")
# CUHK03-specific setting
parser.add_argument('--cuhk03-labeled', action='store_true',
                    help="whether to use labeled images, if false, detected images are used (default: False)")
parser.add_argument('--cuhk03-classic-split', action='store_true',
                    help="whether to use classic split by Li et al. CVPR'14 (default: False)")
parser.add_argument('--use-metric-cuhk03', action='store_true',
                    help="whether to use cuhk03-metric (default: False)")
# Optimization options
parser.add_argument('--max-epoch', default=60, 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=32, type=int, help="test batch size")
parser.add_argument('--lr', '--learning-rate', default=0.0003, type=float,
                    help="initial learning rate")
parser.add_argument('--lr-cent', default=0.5, type=float,
                    help="learning rate for center loss")
parser.add_argument('--weight-cent', type=float, default=0.0005, help="weight for center loss")
parser.add_argument('--stepsize', default=20, type=int,
                    help="stepsize to decay learning rate (>0 means this is enabled)")
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)")
# Architecture
parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names())
# 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('--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('--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')

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_dataset(
        name=args.dataset, split_id=args.split_id,
        cuhk03_labeled=args.cuhk03_labeled, cuhk03_classic_split=args.cuhk03_classic_split,
    )

    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

    trainloader = DataLoader(
        ImageDataset(dataset.train, transform=transform_train),
        batch_size=args.train_batch, shuffle=True, num_workers=args.workers,
        pin_memory=pin_memory, drop_last=True,
    )

    queryloader = DataLoader(
        ImageDataset(dataset.query, transform=transform_test),
        batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
        pin_memory=pin_memory, drop_last=False,
    )

    galleryloader = DataLoader(
        ImageDataset(dataset.gallery, 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={'cent'})
    print("Model size: {:.5f}M".format(sum(p.numel() for p in model.parameters())/1000000.0))

    criterion_xent = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
    criterion_cent = CenterLoss(num_classes=dataset.num_train_pids, feat_dim=model.feat_dim, use_gpu=use_gpu)

    optimizer_model = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
    optimizer_cent = torch.optim.SGD(criterion_cent.parameters(), lr=args.lr_cent)
    
    if args.stepsize > 0:
        scheduler = lr_scheduler.StepLR(optimizer_model, step_size=args.stepsize, gamma=args.gamma)
    start_epoch = args.start_epoch

    if args.resume:
        print("Loading checkpoint from '{}'".format(args.resume))
        checkpoint = torch.load(args.resume)
        model.load_state_dict(checkpoint['state_dict'])
        start_epoch = checkpoint['epoch']

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

    if args.evaluate:
        print("Evaluate only")
        test(model, queryloader, galleryloader, use_gpu)
        return

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

    for epoch in range(start_epoch, args.max_epoch):
        train(epoch, model, criterion_xent, criterion_cent, optimizer_model, optimizer_cent, trainloader, use_gpu)
        
        if args.stepsize > 0: scheduler.step()
        
        if args.eval_step > 0 and (epoch+1) % args.eval_step == 0 or (epoch+1) == args.max_epoch:
            print("==> Test")
            rank1 = test(model, queryloader, galleryloader, 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))
    print("Finished. Total elapsed time (h:m:s): {}".format(elapsed))

def train(epoch, model, criterion_xent, criterion_cent, optimizer_model, optimizer_cent, trainloader, use_gpu):
    model.train()
    losses = AverageMeter()

    for batch_idx, (imgs, pids, _) in enumerate(trainloader):
        if use_gpu:
            imgs, pids = imgs.cuda(), pids.cuda()
        outputs, features = model(imgs)
        xentloss = criterion_xent(outputs, pids)
        centloss = criterion_cent(features, pids) * args.weight_cent
        loss = xentloss + centloss
        optimizer_model.zero_grad()
        optimizer_cent.zero_grad()
        loss.backward()
        optimizer_model.step()
        # remove the impact of weight_cent in learning centers
        for param in criterion_cent.parameters():
            param.grad.data *= (1. / args.weight_cent)
        optimizer_cent.step()
        losses.update(loss.item(), pids.size(0))

        if (batch_idx+1) % args.print_freq == 0:
            print("Epoch {}/{}\t Batch {}/{}\t Loss {:.6f} ({:.6f})".format(
                epoch+1, args.max_epoch, batch_idx+1, len(trainloader), losses.val, losses.avg
            ))

def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
    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()
            features = model(imgs)
            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()
            features = model(imgs)
            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("Computing distance matrix")

    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, use_metric_cuhk03=args.use_metric_cuhk03)

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

    return cmc[0]

if __name__ == '__main__':
    main()