from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR
from util.data_util import ModelNet40
from model.GDANet_cls import GDANET
import numpy as np
from torch.utils.data import DataLoader
from util.util import cal_loss, IOStream
import sklearn.metrics as metrics


# weight initialization:
def weight_init(m):
    if isinstance(m, torch.nn.Linear):
        torch.nn.init.xavier_normal_(m.weight)
        torch.nn.init.constant_(m.bias, 0)
    elif isinstance(m, torch.nn.Conv2d):
        torch.nn.init.xavier_normal_(m.weight)
        torch.nn.init.constant_(m.bias, 0)
    elif isinstance(m, torch.nn.Conv1d):
        torch.nn.init.xavier_normal_(m.weight)
        torch.nn.init.constant_(m.bias, 0)
    elif isinstance(m, torch.nn.BatchNorm2d):
        torch.nn.init.constant_(m.weight, 1)
        torch.nn.init.constant_(m.bias, 0)
    elif isinstance(m, torch.nn.BatchNorm1d):
        torch.nn.init.constant_(m.weight, 1)
        torch.nn.init.constant_(m.bias, 0)


def _init_():
    if not os.path.exists('checkpoints'):
        os.makedirs('checkpoints')
    if not os.path.exists('checkpoints/'+args.exp_name):
        os.makedirs('checkpoints/'+args.exp_name)

    # backup the running files:
    if not args.eval:
        os.system('cp main_cls.py checkpoints' + '/' + args.exp_name + '/' + 'main.py.backup')
        os.system('cp model/GDANet_cls.py checkpoints' + '/' + args.exp_name + '/' + 'GDANet_cls.py.backup')
        os.system('cp util.GDANet_util.py checkpoints' + '/' + args.exp_name + '/' + 'GDANet_util.py.backup')
        os.system('cp util.data_util.py checkpoints' + '/' + args.exp_name + '/' + 'data_util.py.backup')


def train(args, io):
    train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=8,
                              batch_size=args.batch_size, shuffle=True, drop_last=True)
    test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=8,
                             batch_size=args.test_batch_size, shuffle=True, drop_last=False)

    device = torch.device("cuda" if args.cuda else "cpu")

    model = GDANET().to(device)
    print(str(model))

    model.apply(weight_init)
    model = nn.DataParallel(model)
    print("Let's use", torch.cuda.device_count(), "GPUs!")

    if args.use_sgd:
        print("Use SGD")
        opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-4)
        scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr)
    else:
        print("Use Adam")
        opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
        scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr/100)
    
    criterion = cal_loss

    best_test_acc = 0

    for epoch in range(args.epochs):
        scheduler.step()
        ####################
        # Train
        ####################
        train_loss = 0.0
        count = 0.0
        model.train()
        train_pred = []
        train_true = []
        for data, label in train_loader:
            data, label = data.to(device), label.to(device).squeeze()
            data = data.permute(0, 2, 1)
            batch_size = data.size()[0]
            opt.zero_grad()
            logits = model(data)
            loss = criterion(logits, label)
            loss.backward()
            opt.step()
            preds = logits.max(dim=1)[1]
            count += batch_size
            train_loss += loss.item() * batch_size
            train_true.append(label.cpu().numpy())
            train_pred.append(preds.detach().cpu().numpy())
        train_true = np.concatenate(train_true)
        train_pred = np.concatenate(train_pred)
        outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f' % (epoch,
                                                                                 train_loss*1.0/count,
                                                                                 metrics.accuracy_score(
                                                                                     train_true, train_pred),
                                                                                 metrics.balanced_accuracy_score(
                                                                                     train_true, train_pred))
        io.cprint(outstr)

        ####################
        # Test
        ####################
        test_loss = 0.0
        count = 0.0
        model.eval()
        test_pred = []
        test_true = []
        for data, label in test_loader:
            data, label = data.to(device), label.to(device).squeeze()
            data = data.permute(0, 2, 1)
            batch_size = data.size()[0]
            logits = model(data)
            loss = criterion(logits, label)
            preds = logits.max(dim=1)[1]
            count += batch_size
            test_loss += loss.item() * batch_size
            test_true.append(label.cpu().numpy())
            test_pred.append(preds.detach().cpu().numpy())
        test_true = np.concatenate(test_true)
        test_pred = np.concatenate(test_pred)
        test_acc = metrics.accuracy_score(test_true, test_pred)
        avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred)
        outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f' % (epoch,
                                                                              test_loss*1.0/count,
                                                                              test_acc,
                                                                              avg_per_class_acc)
        io.cprint(outstr)
        if test_acc >= best_test_acc:
            best_test_acc = test_acc
            io.cprint('Max Acc:%.6f' % best_test_acc)
            torch.save(model.state_dict(), 'checkpoints/%s/best_model.t7' % args.exp_name)


def test(args, io):
    test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points),
                             batch_size=args.test_batch_size, shuffle=True, drop_last=False)

    device = torch.device("cuda" if args.cuda else "cpu")

    model = GDANET().to(device)
    model = nn.DataParallel(model)
    model.load_state_dict(torch.load(args.model_path))
    model = model.eval()
    test_acc = 0.0
    count = 0.0
    test_true = []
    test_pred = []
    for data, label in test_loader:

        data, label = data.to(device), label.to(device).squeeze()
        data = data.permute(0, 2, 1)
        batch_size = data.size()[0]
        logits = model(data)
        preds = logits.max(dim=1)[1]
        test_true.append(label.cpu().numpy())
        test_pred.append(preds.detach().cpu().numpy())
    test_true = np.concatenate(test_true)
    test_pred = np.concatenate(test_pred)
    test_acc = metrics.accuracy_score(test_true, test_pred)
    avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred)
    outstr = 'Test :: test acc: %.6f, test avg acc: %.6f'%(test_acc, avg_per_class_acc)
    io.cprint(outstr)


if __name__ == "__main__":
    # Training settings
    parser = argparse.ArgumentParser(description='3D Object Classification')
    parser.add_argument('--exp_name', type=str, default='GDANet', metavar='N',
                        help='Name of the experiment')
    parser.add_argument('--batch_size', type=int, default=64, metavar='batch_size',
                        help='Size of batch)')
    parser.add_argument('--test_batch_size', type=int, default=32, metavar='batch_size',
                        help='Size of batch)')
    parser.add_argument('--epochs', type=int, default=350, metavar='N',
                        help='number of episode to train')
    parser.add_argument('--use_sgd', type=bool, default=True,
                        help='Use SGD')
    parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
                        help='learning rate (default: 0.001, 0.1 if using sgd)')
    parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                        help='SGD momentum (default: 0.9)')
    parser.add_argument('--no_cuda', type=bool, default=False,
                        help='enables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--eval', type=bool,  default=False,
                        help='evaluate the model')
    parser.add_argument('--num_points', type=int, default=1024,
                        help='num of points to use')
    parser.add_argument('--model_path', type=str, default='', metavar='N',
                        help='Pretrained model path')
    args = parser.parse_args()

    _init_()

    if not args.eval:
        io = IOStream('checkpoints/' + args.exp_name + '/%s_train.log' % (args.exp_name))
    else:
        io = IOStream('checkpoints/' + args.exp_name + '/%s_test.log' % (args.exp_name))
    io.cprint(str(args))

    args.cuda = not args.no_cuda and torch.cuda.is_available()
    torch.manual_seed(args.seed)
    if args.cuda:
        io.cprint(
            'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices')
        torch.cuda.manual_seed(args.seed)
    else:
        io.cprint('Using CPU')

    if not args.eval:
        train(args, io)
    else:
        test(args, io)
