"""
Author: Benny
Date: Nov 2019
"""
import datetime
import importlib
import logging
import os
import shutil
from pathlib import Path

import hydra
import numpy as np
import torch
from omegaconf import OmegaConf
from torch.optim.lr_scheduler import MultiStepLR
from tqdm import tqdm

import provider
from dataset import PartNormalDataset

seg_classes = {'Earphone': [16, 17, 18], 'Motorbike': [30, 31, 32, 33, 34, 35], 'Rocket': [41, 42, 43],
               'Car': [8, 9, 10, 11], 'Laptop': [28, 29], 'Cap': [6, 7], 'Skateboard': [44, 45, 46], 'Mug': [36, 37],
               'Guitar': [19, 20, 21], 'Bag': [4, 5], 'Lamp': [24, 25, 26, 27], 'Table': [47, 48, 49],
               'Airplane': [0, 1, 2, 3], 'Pistol': [38, 39, 40], 'Chair': [12, 13, 14, 15], 'Knife': [22, 23]}
seg_label_to_cat = {}  # {0:Airplane, 1:Airplane, ...49:Table}
for cat in seg_classes.keys():
    for label in seg_classes[cat]:
        seg_label_to_cat[label] = cat


def inplace_relu(m):
    classname = m.__class__.__name__
    if classname.find('ReLU') != -1:
        m.inplace=True

def to_categorical(y, num_classes):
    """ 1-hot encodes a tensor """
    new_y = torch.eye(num_classes)[y.cpu().data.numpy(),]
    if (y.is_cuda):
        return new_y.cuda()
    return new_y

@hydra.main(config_path='config', config_name='partseg')
def main(cfg):
    OmegaConf.set_struct(cfg, False)

    '''HYPER PARAMETER'''
    os.environ["CUDA_VISIBLE_DEVICES"] = str(cfg.gpu)
    logger = logging.getLogger(__name__)

    print(OmegaConf.to_yaml(cfg))
    print('\n')

    root = hydra.utils.to_absolute_path('data/shapenetcore_partanno_segmentation_benchmark_v0_normal')

    TRAIN_DATASET = PartNormalDataset(root=root, npoints=cfg.num_point, split='trainval', normal_channel=cfg.normal)
    trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=cfg.batch_size, shuffle=True, num_workers=cfg.num_workers, drop_last=True)
    TEST_DATASET = PartNormalDataset(root=root, npoints=cfg.num_point, split='test', normal_channel=cfg.normal)
    testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=cfg.batch_size, shuffle=False, num_workers=cfg.num_workers)

    '''MODEL LOADING'''
    cfg.input_dim = (6 if cfg.normal else 3) + 16
    cfg.num_class = 50
    num_category = 16
    num_part = cfg.num_class
    shutil.copy(hydra.utils.to_absolute_path('models/{}/model.py'.format(cfg.model.name)), '.')

    model = getattr(importlib.import_module('models.{}.model'.format(cfg.model.name)), 'PointTransformerSeg')(cfg).cuda()
    criterion = torch.nn.CrossEntropyLoss()

    try:
        checkpoint = torch.load('best_model.pth')
        start_epoch = checkpoint['epoch']
        model.load_state_dict(checkpoint['model_state_dict'])
        logger.info('Use pretrain model')
    except:
        logger.info('No existing model, starting training from scratch...')
        start_epoch = 0

    # if cfg.optimizer == 'Adam':
    #     optimizer = torch.optim.Adam(
    #         model.parameters(),
    #         lr=cfg.learning_rate,
    #         betas=(0.9, 0.999),
    #         eps=1e-08,
    #         weight_decay=cfg.weight_decay
    #     )
    # else:
    #     optimizer = torch.optim.SGD(model.parameters(), lr=cfg.learning_rate, momentum=0.9)
    
    if cfg.optimizer == 'Adam':
        optimizer = torch.optim.Adam(
            model.parameters(),
            lr=cfg.learning_rate,
            betas=(0.9, 0.999),
            eps=1e-08,
            weight_decay=cfg.weight_decay
        )
    else:
        optimizer = torch.optim.SGD(
            model.parameters(), 
            lr=cfg.learning_rate, 
            momentum=0.9,
            weight_decay=0.0001
            )
    
    scheduler = MultiStepLR(
        optimizer, 
        milestones = [120,180], 
        gamma=cfg.scheduler_gamma
        )

    def bn_momentum_adjust(m, momentum):
        if isinstance(m, torch.nn.BatchNorm2d) or isinstance(m, torch.nn.BatchNorm1d):
            m.momentum = momentum

    LEARNING_RATE_CLIP = 1e-5
    MOMENTUM_ORIGINAL = 0.1
    MOMENTUM_DECCAY = 0.5
    MOMENTUM_DECCAY_STEP = cfg.step_size

    best_acc = 0
    global_epoch = 0
    best_class_avg_iou = 0
    best_inctance_avg_iou = 0

    for epoch in range(start_epoch, cfg.epoch):
        mean_correct = []

        logger.info('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, cfg.epoch))
        '''Adjust learning rate and BN momentum'''
        lr = max(cfg.learning_rate * (cfg.lr_decay ** (epoch // cfg.step_size)), LEARNING_RATE_CLIP)
        logger.info('Learning rate:%f' % lr)
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr
        momentum = MOMENTUM_ORIGINAL * (MOMENTUM_DECCAY ** (epoch // MOMENTUM_DECCAY_STEP))
        if momentum < 0.01:
            momentum = 0.01
        print('BN momentum updated to: %f' % momentum)
        model = model.apply(lambda x: bn_momentum_adjust(x, momentum))
        model = model.train()

        '''learning one epoch'''
        for i, (points, label, target) in tqdm(enumerate(trainDataLoader), total=len(trainDataLoader), smoothing=0.9):
            points = points.data.numpy()
            points[:, :, 0:3] = provider.random_scale_point_cloud(points[:, :, 0:3])
            points[:, :, 0:3] = provider.shift_point_cloud(points[:, :, 0:3])
            points = torch.Tensor(points)

            points, label, target = points.float().cuda(), label.long().cuda(), target.long().cuda()
            optimizer.zero_grad()

            seg_pred = model(torch.cat([points, to_categorical(label, num_category).repeat(1, points.shape[1], 1)], -1))
            seg_pred = seg_pred.contiguous().view(-1, num_part)
            target = target.view(-1, 1)[:, 0]
            pred_choice = seg_pred.data.max(1)[1]

            correct = pred_choice.eq(target.data).cpu().sum()
            mean_correct.append(correct.item() / (cfg.batch_size * cfg.num_point))
            loss = criterion(seg_pred, target)
            loss.backward()
            optimizer.step()

        train_instance_acc = np.mean(mean_correct)
        logger.info('Train accuracy is: %.5f' % train_instance_acc)

        with torch.no_grad():
            test_metrics = {}
            total_correct = 0
            total_seen = 0
            total_seen_class = [0 for _ in range(num_part)]
            total_correct_class = [0 for _ in range(num_part)]
            shape_ious = {cat: [] for cat in seg_classes.keys()}
            seg_label_to_cat = {}  # {0:Airplane, 1:Airplane, ...49:Table}

            for cat in seg_classes.keys():
                for label in seg_classes[cat]:
                    seg_label_to_cat[label] = cat

            model = model.eval()

            for batch_id, (points, label, target) in tqdm(enumerate(testDataLoader), total=len(testDataLoader), smoothing=0.9):
                cur_batch_size, NUM_POINT, _ = points.size()
                points, label, target = points.float().cuda(), label.long().cuda(), target.long().cuda()
                seg_pred = model(torch.cat([points, to_categorical(label, num_category).repeat(1, points.shape[1], 1)], -1))
                cur_pred_val = seg_pred.cpu().data.numpy()
                cur_pred_val_logits = cur_pred_val
                cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32)
                target = target.cpu().data.numpy()

                for i in range(cur_batch_size):
                    cat = seg_label_to_cat[target[i, 0]]
                    logits = cur_pred_val_logits[i, :, :]
                    cur_pred_val[i, :] = np.argmax(logits[:, seg_classes[cat]], 1) + seg_classes[cat][0]

                correct = np.sum(cur_pred_val == target)
                total_correct += correct
                total_seen += (cur_batch_size * NUM_POINT)

                for l in range(num_part):
                    total_seen_class[l] += np.sum(target == l)
                    total_correct_class[l] += (np.sum((cur_pred_val == l) & (target == l)))

                for i in range(cur_batch_size):
                    segp = cur_pred_val[i, :]
                    segl = target[i, :]
                    cat = seg_label_to_cat[segl[0]]
                    part_ious = [0.0 for _ in range(len(seg_classes[cat]))]
                    for l in seg_classes[cat]:
                        if (np.sum(segl == l) == 0) and (
                                np.sum(segp == l) == 0):  # part is not present, no prediction as well
                            part_ious[l - seg_classes[cat][0]] = 1.0
                        else:
                            part_ious[l - seg_classes[cat][0]] = np.sum((segl == l) & (segp == l)) / float(
                                np.sum((segl == l) | (segp == l)))
                    shape_ious[cat].append(np.mean(part_ious))

            all_shape_ious = []
            for cat in shape_ious.keys():
                for iou in shape_ious[cat]:
                    all_shape_ious.append(iou)
                shape_ious[cat] = np.mean(shape_ious[cat])
            mean_shape_ious = np.mean(list(shape_ious.values()))
            test_metrics['accuracy'] = total_correct / float(total_seen)
            test_metrics['class_avg_accuracy'] = np.mean(
                np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float))
            for cat in sorted(shape_ious.keys()):
                logger.info('eval mIoU of %s %f' % (cat + ' ' * (14 - len(cat)), shape_ious[cat]))
            test_metrics['class_avg_iou'] = mean_shape_ious
            test_metrics['inctance_avg_iou'] = np.mean(all_shape_ious)

        logger.info('Epoch %d test Accuracy: %f  Class avg mIOU: %f   Inctance avg mIOU: %f' % (
            epoch + 1, test_metrics['accuracy'], test_metrics['class_avg_iou'], test_metrics['inctance_avg_iou']))
        if (test_metrics['inctance_avg_iou'] >= best_inctance_avg_iou):
            logger.info('Save model...')
            savepath = 'best_model.pth'
            logger.info('Saving at %s' % savepath)
            state = {
                'epoch': epoch,
                'train_acc': train_instance_acc,
                'test_acc': test_metrics['accuracy'],
                'class_avg_iou': test_metrics['class_avg_iou'],
                'inctance_avg_iou': test_metrics['inctance_avg_iou'],
                'model_state_dict': model.state_dict(),
                'optimizer_state_dict': optimizer.state_dict(),
            }
            torch.save(state, savepath)
            logger.info('Saving model....')

        if test_metrics['accuracy'] > best_acc:
            best_acc = test_metrics['accuracy']
        if test_metrics['class_avg_iou'] > best_class_avg_iou:
            best_class_avg_iou = test_metrics['class_avg_iou']
        if test_metrics['inctance_avg_iou'] > best_inctance_avg_iou:
            best_inctance_avg_iou = test_metrics['inctance_avg_iou']
        logger.info('Best accuracy is: %.5f' % best_acc)
        logger.info('Best class avg mIOU is: %.5f' % best_class_avg_iou)
        logger.info('Best inctance avg mIOU is: %.5f' % best_inctance_avg_iou)
        global_epoch += 1


if __name__ == '__main__':
    main()