import time
from datetime import datetime
from pathlib import Path
import xlwt

from src.dataset.data_loader import GMDataset, get_dataloader
from src.evaluation_metric import *
from src.parallel import DataParallel
from src.utils.model_sl import load_model
from src.utils.data_to_cuda import data_to_cuda
from src.utils.timer import Timer

from src.utils.config import cfg
from pygmtools.benchmark import Benchmark


def eval_model(model, classes, bm, last_epoch=True, verbose=False, xls_sheet=None):
    print('Start evaluation...')
    since = time.time()

    device = next(model.parameters()).device

    was_training = model.training
    model.eval()

    dataloaders = []

    for cls in classes:
        image_dataset = GMDataset(name=cfg.DATASET_FULL_NAME,
                                  bm=bm,
                                  problem=cfg.PROBLEM.TYPE,
                                  length=cfg.EVAL.SAMPLES,
                                  cls=cls,
                                  using_all_graphs=cfg.PROBLEM.TEST_ALL_GRAPHS)
        torch.manual_seed(cfg.RANDOM_SEED)
        dataloader = get_dataloader(image_dataset, shuffle=True)
        dataloaders.append(dataloader)

    recalls = []
    precisions = []
    f1s = []
    coverages = []
    pred_time = []
    objs = torch.zeros(len(classes), device=device)
    cluster_acc = []
    cluster_purity = []
    cluster_ri = []

    timer = Timer()

    prediction = []

    for i, cls in enumerate(classes):
        if verbose:
            print('Evaluating class {}: {}/{}'.format(cls, i, len(classes)))

        running_since = time.time()
        iter_num = 0

        pred_time_list = []
        obj_total_num = torch.zeros(1, device=device)
        cluster_acc_list = []
        cluster_purity_list = []
        cluster_ri_list = []
        prediction_cls = []

        for inputs in dataloaders[i]:
            if iter_num >= cfg.EVAL.SAMPLES / inputs['batch_size']:
                break
            if model.module.device != torch.device('cpu'):
                inputs = data_to_cuda(inputs)

            batch_num = inputs['batch_size']

            iter_num = iter_num + 1

            with torch.set_grad_enabled(False):
                timer.tick()
                outputs = model(inputs)
                pred_time_list.append(torch.full((batch_num,), timer.toc() / batch_num))

            # Evaluate matching accuracy
            if cfg.PROBLEM.TYPE == '2GM':
                assert 'perm_mat' in outputs

                for b in range(outputs['perm_mat'].shape[0]):
                    perm_mat = outputs['perm_mat'][b, :outputs['ns'][0][b], :outputs['ns'][1][b]].cpu()
                    perm_mat = perm_mat.numpy()
                    eval_dict = dict()
                    id_pair = inputs['id_list'][0][b], inputs['id_list'][1][b]
                    eval_dict['ids'] = id_pair
                    eval_dict['cls'] = cls
                    eval_dict['perm_mat'] = perm_mat
                    prediction.append(eval_dict)
                    prediction_cls.append(eval_dict)

                if 'aff_mat' in outputs:
                    pred_obj_score = objective_score(outputs['perm_mat'], outputs['aff_mat'])
                    gt_obj_score = objective_score(outputs['gt_perm_mat'], outputs['aff_mat'])
                    objs[i] += torch.sum(pred_obj_score / gt_obj_score)
                    obj_total_num += batch_num
            elif cfg.PROBLEM.TYPE in ['MGM', 'MGM3']:
                assert 'graph_indices' in outputs
                assert 'perm_mat_list' in outputs

                ns = outputs['ns']
                idx = -1
                for x_pred, (idx_src, idx_tgt) in \
                        zip(outputs['perm_mat_list'], outputs['graph_indices']):
                    idx += 1
                    for b in range(x_pred.shape[0]):
                        perm_mat = x_pred[b, :ns[idx_src][b], :ns[idx_tgt][b]].cpu()
                        perm_mat = perm_mat.numpy()
                        eval_dict = dict()
                        id_pair = inputs['id_list'][idx_src][b], inputs['id_list'][idx_tgt][b]
                        eval_dict['ids'] = id_pair
                        if cfg.PROBLEM.TYPE == 'MGM3':
                            eval_dict['cls'] = bm.data_dict[id_pair[0]]['cls']
                        else:
                            eval_dict['cls'] = cls
                        eval_dict['perm_mat'] = perm_mat
                        prediction.append(eval_dict)
                        prediction_cls.append(eval_dict)

            else:
                raise ValueError('Unknown problem type {}'.format(cfg.PROBLEM.TYPE))

            # Evaluate clustering accuracy
            if cfg.PROBLEM.TYPE == 'MGM3':
                assert 'pred_cluster' in outputs
                assert 'cls' in outputs

                pred_cluster = outputs['pred_cluster']
                cls_gt_transpose = [[] for _ in range(batch_num)]
                for batched_cls in outputs['cls']:
                    for b, _cls in enumerate(batched_cls):
                        cls_gt_transpose[b].append(_cls)
                cluster_acc_list.append(clustering_accuracy(pred_cluster, cls_gt_transpose))
                cluster_purity_list.append(clustering_purity(pred_cluster, cls_gt_transpose))
                cluster_ri_list.append(rand_index(pred_cluster, cls_gt_transpose))

            if iter_num % cfg.STATISTIC_STEP == 0 and verbose:
                running_speed = cfg.STATISTIC_STEP * batch_num / (time.time() - running_since)
                print('Class {:<8} Iteration {:<4} {:>4.2f}sample/s'.format(cls, iter_num, running_speed))
                running_since = time.time()

        objs[i] = objs[i] / obj_total_num
        pred_time.append(torch.cat(pred_time_list))
        if cfg.PROBLEM.TYPE == 'MGM3':
            cluster_acc.append(torch.cat(cluster_acc_list))
            cluster_purity.append(torch.cat(cluster_purity_list))
            cluster_ri.append(torch.cat(cluster_ri_list))

        if verbose:
            if cfg.PROBLEM.TYPE != 'MGM3':
                bm.eval_cls(prediction_cls, cls, verbose=verbose)
            print('Class {} norm obj score = {:.4f}'.format(cls, objs[i]))
            print('Class {} pred time = {}s'.format(cls, format_metric(pred_time[i])))
            if cfg.PROBLEM.TYPE == 'MGM3':
                print('Class {} cluster acc={}'.format(cls, format_metric(cluster_acc[i])))
                print('Class {} cluster purity={}'.format(cls, format_metric(cluster_purity[i])))
                print('Class {} cluster rand index={}'.format(cls, format_metric(cluster_ri[i])))

    if cfg.PROBLEM.TYPE == 'MGM3':
        result = bm.eval(prediction, classes[0], verbose=True)
        for cls in classes[0]:
            precision = result[cls]['precision']
            recall = result[cls]['recall']
            f1 = result[cls]['f1']
            coverage = result[cls]['coverage']

            recalls.append(recall)
            precisions.append(precision)
            f1s.append(f1)
            coverages.append(coverage)
    else:
        result = bm.eval(prediction, classes, verbose=True)
        for cls in classes:
            precision = result[cls]['precision']
            recall = result[cls]['recall']
            f1 = result[cls]['f1']
            coverage = result[cls]['coverage']

            recalls.append(recall)
            precisions.append(precision)
            f1s.append(f1)
            coverages.append(coverage)

    time_elapsed = time.time() - since
    print('Evaluation complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))

    model.train(mode=was_training)

    if xls_sheet:
        for idx, cls in enumerate(classes):
            xls_sheet.write(0, idx+1, cls)
        xls_sheet.write(0, idx+2, 'mean')

    xls_row = 1

    # show result
    if xls_sheet:
        xls_sheet.write(xls_row, 0, 'precision')
        xls_sheet.write(xls_row+1, 0, 'recall')
        xls_sheet.write(xls_row+2, 0, 'f1')
        xls_sheet.write(xls_row+3, 0, 'coverage')
    for idx, (cls, cls_p, cls_r, cls_f1, cls_cvg) in enumerate(zip(classes, precisions, recalls, f1s, coverages)):
        if xls_sheet:
            xls_sheet.write(xls_row, idx+1, '{:.4f}'.format(cls_p)) #'{:.4f}'.format(torch.mean(cls_p)))
            xls_sheet.write(xls_row+1, idx+1, '{:.4f}'.format(cls_r)) #'{:.4f}'.format(torch.mean(cls_r)))
            xls_sheet.write(xls_row+2, idx+1, '{:.4f}'.format(cls_f1)) #'{:.4f}'.format(torch.mean(cls_f1)))
            xls_sheet.write(xls_row+3, idx+1, '{:.4f}'.format(cls_cvg))
    if xls_sheet:
        xls_sheet.write(xls_row, idx+2, '{:.4f}'.format(result['mean']['precision'])) #'{:.4f}'.format(torch.mean(torch.cat(precisions))))
        xls_sheet.write(xls_row+1, idx+2, '{:.4f}'.format(result['mean']['recall'])) #'{:.4f}'.format(torch.mean(torch.cat(recalls))))
        xls_sheet.write(xls_row+2, idx+2, '{:.4f}'.format(result['mean']['f1'])) #'{:.4f}'.format(torch.mean(torch.cat(f1s))))
        xls_row += 4

    if not torch.any(torch.isnan(objs)):
        print('Normalized objective score')
        if xls_sheet: xls_sheet.write(xls_row, 0, 'norm objscore')
        for idx, (cls, cls_obj) in enumerate(zip(classes, objs)):
            print('{} = {:.4f}'.format(cls, cls_obj))
            if xls_sheet: xls_sheet.write(xls_row, idx+1, cls_obj.item()) #'{:.4f}'.format(cls_obj))
        print('average objscore = {:.4f}'.format(torch.mean(objs)))
        if xls_sheet:
            xls_sheet.write(xls_row, idx+2, torch.mean(objs).item()) #'{:.4f}'.format(torch.mean(objs)))
            xls_row += 1

    if cfg.PROBLEM.TYPE == 'MGM3':
        print('Clustering accuracy')
        if xls_sheet: xls_sheet.write(xls_row, 0, 'cluster acc')
        for idx, (cls, cls_acc) in enumerate(zip(classes, cluster_acc)):
            print('{} = {}'.format(cls, format_metric(cls_acc)))
            if xls_sheet: xls_sheet.write(xls_row, idx+1, torch.mean(cls_acc).item()) #'{:.4f}'.format(torch.mean(cls_acc)))
        print('average clustering accuracy = {}'.format(format_metric(torch.cat(cluster_acc))))
        if xls_sheet:
            xls_sheet.write(xls_row, idx+2, torch.mean(torch.cat(cluster_acc)).item()) #'{:.4f}'.format(torch.mean(torch.cat(cluster_acc))))
            xls_row += 1

        print('Clustering purity')
        if xls_sheet: xls_sheet.write(xls_row, 0, 'cluster purity')
        for idx, (cls, cls_acc) in enumerate(zip(classes, cluster_purity)):
            print('{} = {}'.format(cls, format_metric(cls_acc)))
            if xls_sheet: xls_sheet.write(xls_row, idx+1, torch.mean(cls_acc).item()) #'{:.4f}'.format(torch.mean(cls_acc)))
        print('average clustering purity = {}'.format(format_metric(torch.cat(cluster_purity))))
        if xls_sheet:
            xls_sheet.write(xls_row, idx+2, torch.mean(torch.cat(cluster_purity)).item()) #'{:.4f}'.format(torch.mean(torch.cat(cluster_purity))))
            xls_row += 1

        print('Clustering rand index')
        if xls_sheet: xls_sheet.write(xls_row, 0, 'rand index')
        for idx, (cls, cls_acc) in enumerate(zip(classes, cluster_ri)):
            print('{} = {}'.format(cls, format_metric(cls_acc)))
            if xls_sheet: xls_sheet.write(xls_row, idx+1, torch.mean(cls_acc).item()) #'{:.4f}'.format(torch.mean(cls_acc)))
        print('average rand index = {}'.format(format_metric(torch.cat(cluster_ri))))
        if xls_sheet:
            xls_sheet.write(xls_row, idx+2, torch.mean(torch.cat(cluster_ri)).item()) #'{:.4f}'.format(torch.mean(torch.cat(cluster_ri))))
            xls_row += 1

    print('Predict time')
    if xls_sheet: xls_sheet.write(xls_row, 0, 'time')
    for idx, (cls, cls_time) in enumerate(zip(classes, pred_time)):
        print('{} = {}'.format(cls, format_metric(cls_time)))
        if xls_sheet: xls_sheet.write(xls_row, idx + 1, torch.mean(cls_time).item()) #'{:.4f}'.format(torch.mean(cls_time)))
    print('average time = {}'.format(format_metric(torch.cat(pred_time))))
    if xls_sheet:
        xls_sheet.write(xls_row, idx+2, torch.mean(torch.cat(pred_time)).item()) #'{:.4f}'.format(torch.mean(torch.cat(pred_time))))
        xls_row += 1

    bm.rm_gt_cache(last_epoch=last_epoch)

    return torch.Tensor(recalls)


if __name__ == '__main__':
    from src.utils.dup_stdout_manager import DupStdoutFileManager
    from src.utils.parse_args import parse_args
    from src.utils.print_easydict import print_easydict

    args = parse_args('Deep learning of graph matching evaluation code.')

    import importlib
    mod = importlib.import_module(cfg.MODULE)
    Net = mod.Net

    torch.manual_seed(cfg.RANDOM_SEED)

    ds_dict = cfg[cfg.DATASET_FULL_NAME] if ('DATASET_FULL_NAME' in cfg) and (cfg.DATASET_FULL_NAME in cfg) else {}
    benchmark = Benchmark(name=cfg.DATASET_FULL_NAME,
                          sets='test',
                          problem=cfg.PROBLEM.TYPE,
                          obj_resize=cfg.PROBLEM.RESCALE,
                          filter=cfg.PROBLEM.FILTER,
                          **ds_dict)

    cls = None if cfg.EVAL.CLASS in ['none', 'all'] else cfg.EVAL.CLASS
    if cls is None:
        clss = benchmark.classes
    else:
        clss = [cls]

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    model = Net()
    model = model.to(device)
    model = DataParallel(model, device_ids=cfg.GPUS)

    if not Path(cfg.OUTPUT_PATH).exists():
        Path(cfg.OUTPUT_PATH).mkdir(parents=True)
    now_time = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
    wb = xlwt.Workbook()
    ws = wb.add_sheet('epoch{}'.format(cfg.EVAL.EPOCH))
    with DupStdoutFileManager(str(Path(cfg.OUTPUT_PATH) / ('eval_log_' + now_time + '.log'))) as _:
        print_easydict(cfg)

        model_path = ''
        if cfg.EVAL.EPOCH is not None and cfg.EVAL.EPOCH > 0:
            model_path = str(Path(cfg.OUTPUT_PATH) / 'params' / 'params_{:04}.pt'.format(cfg.EVAL.EPOCH))
        if len(cfg.PRETRAINED_PATH) > 0:
            model_path = cfg.PRETRAINED_PATH
        if len(model_path) > 0:
            print('Loading model parameters from {}'.format(model_path))
            load_model(model, model_path)

        pcks = eval_model(
            model, clss,
            benchmark,
            verbose=True,
            xls_sheet=ws
        )
    wb.save(str(Path(cfg.OUTPUT_PATH) / ('eval_result_' + now_time + '.xls')))

import time
from datetime import datetime
from pathlib import Path
import xlwt

from src.dataset.data_loader import GMDataset, get_dataloader
from src.evaluation_metric import *
from src.parallel import DataParallel
from src.utils.model_sl import load_model
from src.utils.data_to_cuda import data_to_cuda
from src.utils.timer import Timer

from src.utils.config import cfg
from pygmtools.benchmark import Benchmark


def eval_model(model, classes, bm, last_epoch=True, verbose=False, xls_sheet=None):
    print('Start evaluation...')
    since = time.time()

    device = next(model.parameters()).device

    was_training = model.training
    model.eval()

    dataloaders = []

    for cls in classes:
        image_dataset = GMDataset(name=cfg.DATASET_FULL_NAME,
                                  bm=bm,
                                  problem=cfg.PROBLEM.TYPE,
                                  length=cfg.EVAL.SAMPLES,
                                  cls=cls,
                                  using_all_graphs=cfg.PROBLEM.TEST_ALL_GRAPHS)
        torch.manual_seed(cfg.RANDOM_SEED)
        dataloader = get_dataloader(image_dataset, shuffle=True)
        dataloaders.append(dataloader)

    recalls = []
    precisions = []
    f1s = []
    coverages = []
    pred_time = []
    objs = torch.zeros(len(classes), device=device)
    cluster_acc = []
    cluster_purity = []
    cluster_ri = []

    timer = Timer()

    prediction = []

    for i, cls in enumerate(classes):
        if verbose:
            print('Evaluating class {}: {}/{}'.format(cls, i, len(classes)))

        running_since = time.time()
        iter_num = 0

        pred_time_list = []
        obj_total_num = torch.zeros(1, device=device)
        cluster_acc_list = []
        cluster_purity_list = []
        cluster_ri_list = []
        prediction_cls = []

        for inputs in dataloaders[i]:
            if iter_num >= cfg.EVAL.SAMPLES / inputs['batch_size']:
                break
            if model.module.device != torch.device('cpu'):
                inputs = data_to_cuda(inputs)

            batch_num = inputs['batch_size']

            iter_num = iter_num + 1

            with torch.set_grad_enabled(False):
                timer.tick()
                outputs = model(inputs)
                pred_time_list.append(torch.full((batch_num,), timer.toc() / batch_num))

            # Evaluate matching accuracy
            if cfg.PROBLEM.TYPE == '2GM':
                assert 'perm_mat' in outputs

                for b in range(outputs['perm_mat'].shape[0]):
                    perm_mat = outputs['perm_mat'][b, :outputs['ns'][0][b], :outputs['ns'][1][b]].cpu()
                    perm_mat = perm_mat.numpy()
                    eval_dict = dict()
                    id_pair = inputs['id_list'][0][b], inputs['id_list'][1][b]
                    eval_dict['ids'] = id_pair
                    eval_dict['cls'] = cls
                    eval_dict['perm_mat'] = perm_mat
                    prediction.append(eval_dict)
                    prediction_cls.append(eval_dict)

                if 'aff_mat' in outputs:
                    pred_obj_score = objective_score(outputs['perm_mat'], outputs['aff_mat'])
                    gt_obj_score = objective_score(outputs['gt_perm_mat'], outputs['aff_mat'])
                    objs[i] += torch.sum(pred_obj_score / gt_obj_score)
                    obj_total_num += batch_num
            elif cfg.PROBLEM.TYPE in ['MGM', 'MGM3']:
                assert 'graph_indices' in outputs
                assert 'perm_mat_list' in outputs

                ns = outputs['ns']
                idx = -1
                for x_pred, (idx_src, idx_tgt) in \
                        zip(outputs['perm_mat_list'], outputs['graph_indices']):
                    idx += 1
                    for b in range(x_pred.shape[0]):
                        perm_mat = x_pred[b, :ns[idx_src][b], :ns[idx_tgt][b]].cpu()
                        perm_mat = perm_mat.numpy()
                        eval_dict = dict()
                        id_pair = inputs['id_list'][idx_src][b], inputs['id_list'][idx_tgt][b]
                        eval_dict['ids'] = id_pair
                        if cfg.PROBLEM.TYPE == 'MGM3':
                            eval_dict['cls'] = bm.data_dict[id_pair[0]]['cls']
                        else:
                            eval_dict['cls'] = cls
                        eval_dict['perm_mat'] = perm_mat
                        prediction.append(eval_dict)
                        prediction_cls.append(eval_dict)

            else:
                raise ValueError('Unknown problem type {}'.format(cfg.PROBLEM.TYPE))

            # Evaluate clustering accuracy
            if cfg.PROBLEM.TYPE == 'MGM3':
                assert 'pred_cluster' in outputs
                assert 'cls' in outputs

                pred_cluster = outputs['pred_cluster']
                cls_gt_transpose = [[] for _ in range(batch_num)]
                for batched_cls in outputs['cls']:
                    for b, _cls in enumerate(batched_cls):
                        cls_gt_transpose[b].append(_cls)
                cluster_acc_list.append(clustering_accuracy(pred_cluster, cls_gt_transpose))
                cluster_purity_list.append(clustering_purity(pred_cluster, cls_gt_transpose))
                cluster_ri_list.append(rand_index(pred_cluster, cls_gt_transpose))

            if iter_num % cfg.STATISTIC_STEP == 0 and verbose:
                running_speed = cfg.STATISTIC_STEP * batch_num / (time.time() - running_since)
                print('Class {:<8} Iteration {:<4} {:>4.2f}sample/s'.format(cls, iter_num, running_speed))
                running_since = time.time()

        objs[i] = objs[i] / obj_total_num
        pred_time.append(torch.cat(pred_time_list))
        if cfg.PROBLEM.TYPE == 'MGM3':
            cluster_acc.append(torch.cat(cluster_acc_list))
            cluster_purity.append(torch.cat(cluster_purity_list))
            cluster_ri.append(torch.cat(cluster_ri_list))

        if verbose:
            if cfg.PROBLEM.TYPE != 'MGM3':
                bm.eval_cls(prediction_cls, cls, verbose=verbose)
            print('Class {} norm obj score = {:.4f}'.format(cls, objs[i]))
            print('Class {} pred time = {}s'.format(cls, format_metric(pred_time[i])))
            if cfg.PROBLEM.TYPE == 'MGM3':
                print('Class {} cluster acc={}'.format(cls, format_metric(cluster_acc[i])))
                print('Class {} cluster purity={}'.format(cls, format_metric(cluster_purity[i])))
                print('Class {} cluster rand index={}'.format(cls, format_metric(cluster_ri[i])))

    if cfg.PROBLEM.TYPE == 'MGM3':
        result = bm.eval(prediction, classes[0], verbose=True)
        for cls in classes[0]:
            precision = result[cls]['precision']
            recall = result[cls]['recall']
            f1 = result[cls]['f1']
            coverage = result[cls]['coverage']

            recalls.append(recall)
            precisions.append(precision)
            f1s.append(f1)
            coverages.append(coverage)
    else:
        result = bm.eval(prediction, classes, verbose=True)
        for cls in classes:
            precision = result[cls]['precision']
            recall = result[cls]['recall']
            f1 = result[cls]['f1']
            coverage = result[cls]['coverage']

            recalls.append(recall)
            precisions.append(precision)
            f1s.append(f1)
            coverages.append(coverage)

    time_elapsed = time.time() - since
    print('Evaluation complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))

    model.train(mode=was_training)

    if xls_sheet:
        for idx, cls in enumerate(classes):
            xls_sheet.write(0, idx+1, cls)
        xls_sheet.write(0, idx+2, 'mean')

    xls_row = 1

    # show result
    if xls_sheet:
        xls_sheet.write(xls_row, 0, 'precision')
        xls_sheet.write(xls_row+1, 0, 'recall')
        xls_sheet.write(xls_row+2, 0, 'f1')
        xls_sheet.write(xls_row+3, 0, 'coverage')
    for idx, (cls, cls_p, cls_r, cls_f1, cls_cvg) in enumerate(zip(classes, precisions, recalls, f1s, coverages)):
        if xls_sheet:
            xls_sheet.write(xls_row, idx+1, '{:.4f}'.format(cls_p)) #'{:.4f}'.format(torch.mean(cls_p)))
            xls_sheet.write(xls_row+1, idx+1, '{:.4f}'.format(cls_r)) #'{:.4f}'.format(torch.mean(cls_r)))
            xls_sheet.write(xls_row+2, idx+1, '{:.4f}'.format(cls_f1)) #'{:.4f}'.format(torch.mean(cls_f1)))
            xls_sheet.write(xls_row+3, idx+1, '{:.4f}'.format(cls_cvg))
    if xls_sheet:
        xls_sheet.write(xls_row, idx+2, '{:.4f}'.format(result['mean']['precision'])) #'{:.4f}'.format(torch.mean(torch.cat(precisions))))
        xls_sheet.write(xls_row+1, idx+2, '{:.4f}'.format(result['mean']['recall'])) #'{:.4f}'.format(torch.mean(torch.cat(recalls))))
        xls_sheet.write(xls_row+2, idx+2, '{:.4f}'.format(result['mean']['f1'])) #'{:.4f}'.format(torch.mean(torch.cat(f1s))))
        xls_row += 4

    if not torch.any(torch.isnan(objs)):
        print('Normalized objective score')
        if xls_sheet: xls_sheet.write(xls_row, 0, 'norm objscore')
        for idx, (cls, cls_obj) in enumerate(zip(classes, objs)):
            print('{} = {:.4f}'.format(cls, cls_obj))
            if xls_sheet: xls_sheet.write(xls_row, idx+1, cls_obj.item()) #'{:.4f}'.format(cls_obj))
        print('average objscore = {:.4f}'.format(torch.mean(objs)))
        if xls_sheet:
            xls_sheet.write(xls_row, idx+2, torch.mean(objs).item()) #'{:.4f}'.format(torch.mean(objs)))
            xls_row += 1

    if cfg.PROBLEM.TYPE == 'MGM3':
        print('Clustering accuracy')
        if xls_sheet: xls_sheet.write(xls_row, 0, 'cluster acc')
        for idx, (cls, cls_acc) in enumerate(zip(classes, cluster_acc)):
            print('{} = {}'.format(cls, format_metric(cls_acc)))
            if xls_sheet: xls_sheet.write(xls_row, idx+1, torch.mean(cls_acc).item()) #'{:.4f}'.format(torch.mean(cls_acc)))
        print('average clustering accuracy = {}'.format(format_metric(torch.cat(cluster_acc))))
        if xls_sheet:
            xls_sheet.write(xls_row, idx+2, torch.mean(torch.cat(cluster_acc)).item()) #'{:.4f}'.format(torch.mean(torch.cat(cluster_acc))))
            xls_row += 1

        print('Clustering purity')
        if xls_sheet: xls_sheet.write(xls_row, 0, 'cluster purity')
        for idx, (cls, cls_acc) in enumerate(zip(classes, cluster_purity)):
            print('{} = {}'.format(cls, format_metric(cls_acc)))
            if xls_sheet: xls_sheet.write(xls_row, idx+1, torch.mean(cls_acc).item()) #'{:.4f}'.format(torch.mean(cls_acc)))
        print('average clustering purity = {}'.format(format_metric(torch.cat(cluster_purity))))
        if xls_sheet:
            xls_sheet.write(xls_row, idx+2, torch.mean(torch.cat(cluster_purity)).item()) #'{:.4f}'.format(torch.mean(torch.cat(cluster_purity))))
            xls_row += 1

        print('Clustering rand index')
        if xls_sheet: xls_sheet.write(xls_row, 0, 'rand index')
        for idx, (cls, cls_acc) in enumerate(zip(classes, cluster_ri)):
            print('{} = {}'.format(cls, format_metric(cls_acc)))
            if xls_sheet: xls_sheet.write(xls_row, idx+1, torch.mean(cls_acc).item()) #'{:.4f}'.format(torch.mean(cls_acc)))
        print('average rand index = {}'.format(format_metric(torch.cat(cluster_ri))))
        if xls_sheet:
            xls_sheet.write(xls_row, idx+2, torch.mean(torch.cat(cluster_ri)).item()) #'{:.4f}'.format(torch.mean(torch.cat(cluster_ri))))
            xls_row += 1

    print('Predict time')
    if xls_sheet: xls_sheet.write(xls_row, 0, 'time')
    for idx, (cls, cls_time) in enumerate(zip(classes, pred_time)):
        print('{} = {}'.format(cls, format_metric(cls_time)))
        if xls_sheet: xls_sheet.write(xls_row, idx + 1, torch.mean(cls_time).item()) #'{:.4f}'.format(torch.mean(cls_time)))
    print('average time = {}'.format(format_metric(torch.cat(pred_time))))
    if xls_sheet:
        xls_sheet.write(xls_row, idx+2, torch.mean(torch.cat(pred_time)).item()) #'{:.4f}'.format(torch.mean(torch.cat(pred_time))))
        xls_row += 1

    bm.rm_gt_cache(last_epoch=last_epoch)

    return torch.Tensor(recalls)


if __name__ == '__main__':
    from src.utils.dup_stdout_manager import DupStdoutFileManager
    from src.utils.parse_args import parse_args
    from src.utils.print_easydict import print_easydict

    args = parse_args('Deep learning of graph matching evaluation code.')

    import importlib
    mod = importlib.import_module(cfg.MODULE)
    Net = mod.Net

    torch.manual_seed(cfg.RANDOM_SEED)

    ds_dict = cfg[cfg.DATASET_FULL_NAME] if ('DATASET_FULL_NAME' in cfg) and (cfg.DATASET_FULL_NAME in cfg) else {}
    benchmark = Benchmark(name=cfg.DATASET_FULL_NAME,
                          sets='test',
                          problem=cfg.PROBLEM.TYPE,
                          obj_resize=cfg.PROBLEM.RESCALE,
                          filter=cfg.PROBLEM.FILTER,
                          **ds_dict)

    cls = None if cfg.EVAL.CLASS in ['none', 'all'] else cfg.EVAL.CLASS
    if cls is None:
        clss = benchmark.classes
    else:
        clss = [cls]

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    model = Net()
    model = model.to(device)
    model = DataParallel(model, device_ids=cfg.GPUS)

    if not Path(cfg.OUTPUT_PATH).exists():
        Path(cfg.OUTPUT_PATH).mkdir(parents=True)
    now_time = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
    wb = xlwt.Workbook()
    ws = wb.add_sheet('epoch{}'.format(cfg.EVAL.EPOCH))
    with DupStdoutFileManager(str(Path(cfg.OUTPUT_PATH) / ('eval_log_' + now_time + '.log'))) as _:
        print_easydict(cfg)

        model_path = ''
        if cfg.EVAL.EPOCH is not None and cfg.EVAL.EPOCH > 0:
            model_path = str(Path(cfg.OUTPUT_PATH) / 'params' / 'params_{:04}.pt'.format(cfg.EVAL.EPOCH))
        if len(cfg.PRETRAINED_PATH) > 0:
            model_path = cfg.PRETRAINED_PATH
        if len(model_path) > 0:
            print('Loading model parameters from {}'.format(model_path))
            load_model(model, model_path)

        pcks = eval_model(
            model, clss,
            benchmark,
            verbose=True,
            xls_sheet=ws
        )
    wb.save(str(Path(cfg.OUTPUT_PATH) / ('eval_result_' + now_time + '.xls')))

# Evaluate matching accuracy # Evaluate clustering accuracy # show result #'{:.4f}'.format(torch.mean(cls_p))) #'{:.4f}'.format(torch.mean(cls_r))) #'{:.4f}'.format(torch.mean(cls_f1))) #'{:.4f}'.format(torch.mean(torch.cat(precisions)))) #'{:.4f}'.format(torch.mean(torch.cat(recalls)))) #'{:.4f}'.format(torch.mean(torch.cat(f1s)))) #'{:.4f}'.format(cls_obj)) #'{:.4f}'.format(torch.mean(objs))) #'{:.4f}'.format(torch.mean(cls_acc))) #'{:.4f}'.format(torch.mean(torch.cat(cluster_acc)))) #'{:.4f}'.format(torch.mean(cls_acc))) #'{:.4f}'.format(torch.mean(torch.cat(cluster_purity)))) #'{:.4f}'.format(torch.mean(cls_acc))) #'{:.4f}'.format(torch.mean(torch.cat(cluster_ri)))) #'{:.4f}'.format(torch.mean(cls_time))) #'{:.4f}'.format(torch.mean(torch.cat(pred_time))))