PyTorch搭建卷积神经网络（CNN）进行视频行为识别（附源码和数据集）

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一、行为识别简介

行为识别是视频理解中的一项基础任务，它可以从视频中提取语义信息，进而可以为其他任务如行为检测，行为定位等提供通用的视频表征

现有的视频行为数据集大致可以划分为两种类型

1：场景相关数据集 这一类的数据集场景提供了较多的语义信息 仅仅通过单帧图像便能很好的判断对应的行为

2：时序相关数据集 这一类数据集对时间关系要求很高，需要足够多帧图像才能准确的识别视频中的行为。

例如骑马的例子就与场景高度相关，马和草地给出了足够多的语义信息

但是打开柜子就与时间高度相关，如果反转时序甚至容易认为在关闭柜子

如下图

二、数据准备

数据的准备包括对视频的抽帧处理，具体原理此处不再赘述

大家可自行前往官网下载数据集

视频行为识别数据集

三、模型搭建与训练

在介绍模型的搭建与训练之外，需要先了解的命令行参数，还有无名的必填参数dataset以及modality。前者用于选择数据集，后者用于确定数据集类型 是RGB图像还是Flow光流图像

过程比较繁琐 此处不再赘述

效果如下图

最终会得到如下的热力图，从红色到黄色到绿色到蓝色，网络的关注度从大到小，可以看到模块可以很好地定位到运动发生的时空区域

四、代码

项目结构如下

main函数代码

import os
import time
import shutil
import torch.nn.parallel
imd_norm_

from ops.dataset import TSNDataSet
from ops.models import TSN
from ops.transforms import *
from opts import parser
from ops import dataset_config
from ops.utils import AverageMeter, accuracy
from ops.temporal_shift import make_temporal_pool

from tensorboardX import SummaryWriter

best_prec1 = 0

def main():
    global args, best_prec1
    args = parser.parse_args()

    num_class, args.train_list, args.val_list, args.root_path, prefix = dataset_config.return_dataset(args.dataset,
                                                                                                      args.modality)
    full_arch_name = args.arch
    if args.shift:
        full_arch_name += '_shift{}_{}'.format(args.shift_div, args.shift_place)
    if args.temporal_pool:
        full_arch_name += '_tpool'
    args.store_name = '_'.join(
        ['TSM', args.dataset, args.modality, full_arch_name, args.consensus_type, 'segment%d' % args.num_segments,
         'e{}'.format(args.epochs)])
 
        args.store_name += '_nl'
    if args.suffix is not None:
        args.store_name += '_{}'.format(args.suffix)
    print('storing name: ' + args.store_name)

    check_rootfolders()

    model = TSN(num_class, args.num_segments, args.modality,
                base_model=args.arch,
                consensus_type=args.consensus_type,
                dropout=args.dropout,
                img_feature_dim=args.img_feature_dim,
                partial_bn=not args.no_partialbn,
                pretrain=args.pretrain,
                is_shift=args.shift, shift_div=args.shift_div, shift_place=args.shift_place,
                fc_lr5=not (args.tune_from and args.dataset in args.tune_from),
                temporal_pool=args.temporal_pool,
                non_local=args.non_local)

    crop_size = model.crop_size
    scale_size = model.scale_size
    input_mean = model.input_mean
    in else True)

    model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()

    optimizer = torch.optim.SGD(policies,
                                args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    if args.resume:
        if args.temporal_pool:  # early temporal pool so that we can load the state_dict
            make_temporal_pool(model.module.base_model, args.num_segments)
        if os.path.isfile(args.resume):
            print(("=> loading checkpoint '{}'".format(args.resume)))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print(("=> loaded checkpoint '{}' (epoch {})"
                   .format(args.evaluate, checkpoint['epoch'])))
        else:
            print(("=> no checkpoint found at '{}'".format(args.resume)))

  ate_dict']
        model_dict = model.state_dict()
        replace_dict = []
        for k, v in sd.items():
            if k not in model_dict and k.replace('.net', '') in model_dict:
                print('=> Load after remove .net: ', k)
                replace_dict.append((k, k.replace('.net', '')))
        for k, v in model_dict.items():
            if k not in sd and k.replace('.net', '') in sd:
                print('=> Load after adding .net: ', k)
                replace_dict.append((k.replace('.net', ''), k))

        for k, k_new in replace_dict:
            sd[k_new] = sd.pop(k)
        keys1 = set(list(sd.keys()))
        keys2 = set(list(model_dict.keys()))
        set_diff = (keys1 - keys2) | (keys2 - keys1)
        print('#### Notice: keys that failed to load: {}'.format(set_diff))
        if args.dataset not in args.tune_from:  # new dataset
            print('=> New dataset, do not load fc weights')
            sd = {k: v for k, v in sd.items() if 'fc' not in k}
        if te_dict(model_dict)

    if args.temporal_pool and not args.resume:
        make_temporal_pool(model.module.base_model, args.num_segments)

    cudnn.benchmark = True

    # Data loading code
    if args.modality != 'RGBDiff':
        normalize = GroupNormalize(input_mean, input_std)
    else:
        normalize = IdentityTransform()

    if args.modality == 'RGB':
        data_length = 1
    elif args.modality in ['Flow', 'RGBDiff']:
        data_length = 5

    train_loader = torch.utils.data.DataLoader(
        TSNDataSet(args.root_path, args.train_list, num_segments=args.num_segments,
                   new_length=data_length,
                   modality=args.modality,
                   image_tmpl=prefix,
                   transform=torchvision.transforms.Compose([
                       train_augmentation,
                       Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
                       ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
                       normalize,
                   ]), dense_sample=args.dense_sample),
        batch_size=args.batch_size, shuffle=True,
        num_workers=args.workers, pin_memory=True,
        drop_last=True)  # prevent something not % n_GPU

    val_loader = torch.utils.data.DataLoader(
        TSNDataSet(args.root_path, args.val_list, num_segments=args.num_segments,
                   new_length=data_length,
                   modality=args.modality,
                   image_tmpl=prefix,
                   random_shift=False,
                   transform=torchvision.transforms.Compose([
                       GroupScale(int(scale_size)),
                       GroupCenterCrop(crop_size),
                       Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])),
                       ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])),
                       normalize,
                   ]), dense_sample=args.dense_sample),
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    # define loss function (criterion) and optimizer
    if args.loss_type == 'nll':
        criterion = torch.nn.CrossEntropyLoss().cuda()
    else:
        raise ValueError("Unknown loss type")

    for group in policies:
        print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
            group['name'], len(group['params']), group['lr_mult'], group['decay_mult'])))

    if args.evaluate:
        validate(val_loader, model, criterion, 0)
        return

    log_training = open(os.path.join(args.root_log, args.store_name, 'log.csv'), 'w')
    with open(os.path.join(args.root_log, args.store_name, 'args.txt'), 'w') as f:
        f.write(str(args))
    tf_writer = SummaryWriter(log_dir=os.path.join(args.root_log, args.store_name))
    for epoch in range(args.start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch, args.lr_type, args.lr_steps)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, log_training, tf_writer)

        # evaluate on validation set
        if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
            prec1 = validate(val_loader, model, criterion, epoch, log_training, tf_writer)

            # remember best prec@1 and save checkpoint
            is_best = prec1 > best_prec1
            best_prec1 = max(prec1, best_prec1)
            tf_writer.add_scalar('acc/test_top1_best', best_prec1, epoch)

            output_best = 'Best Prec@1: %.3f\n' % (best_prec1)
            print(output_best)
            log_training.write(output_best + '\n')
            log_training.flush()

            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'optimizer': optimizer.state_dict(),
                'best_prec1': best_prec1,
            }, is_best)

def train(train_loader, model, criterion, optimizer, epoch, log, tf_writer):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    if args.no_partialbn:
        model.module.partialBN(False)
    else:
        model.module.partialBN(True)

    # switch to train mode
    model.train()

    end = time.time()
    for i, (input, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        target = target.cuda()
        input_var = torch.autograd.Variable(input)
        target_var = torch.autograd.Variable(target)

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.item(), input.size(0))
        top1.update(prec1.item(), input.size(0))
        top5.update(prec5.item(), input.size(0))

        # compute gradient and do SGD step
        loss.backward()

        if args.clip_gradient is not None:
            total_norm = clip_grad_norm_(model.parameters(), args.clip_gradient)

        optimizer.step()
        optimizer.zero_grad()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            output = ('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                      'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                epoch, i, len(train_loader), batch_time=batch_time,
                data_time=data_time, loss=losses, top1=top1, top5=top5, lr=optimizer.param_groups[-1]['lr'] * 0.1))  # TODO
            print(output)
            log.write(output + '\n')
            log.flush()

    tf_writer.add_scalar('loss/train', losses.avg, epoch)
    tf_writer.add_scalar('acc/train_top1', top1.avg, epoch)
    tf_writer.add_scalar('acc/train_top5', top5.avg, epoch)
    tf_writer.add_scalar('lr', optimizer.param_groups[-1]['lr'], epoch)

def validate(val_loader, model, criterion, epoch, log=None, tf_writer=None):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    with torch.no_grad():
        for i, (input, target) in enumerate(val_loader):
            target = target.cuda()

            # compute output
            output = model(input)
            loss = criterion(output, target)

            # measure accuracy and record loss
            prec1, prec5 = accuracy(output.data, target, topk=(1, 5))

            losses.update(loss.item(), input.size(0))
            top1.update(prec1.item(), input.size(0))
            top5.update(prec5.item(), input.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                output = ('Test: [{0}/{1}]\t'
                          'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                          'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                          'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                          'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                    i, len(val_loader), batch_time=batch_time, loss=losses,
                    top1=top1, top5=top5))
                print(output)
                if log is not None:
                    log.write(output + '\n')
                    log.flush()

    output = ('Testing Results: Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f} Loss {loss.avg:.5f}'
              .format(top1=top1, top5=top5, loss=losses))
    print(output)
    if log is not None:
        log.write(output + '\n')
        log.flush()

    if tf_writer is not None:
        tf_writer.add_scalar('loss/test', losses.avg, epoch)
        tf_writer.add_scalar('acc/test_top1', top1.avg, epoch)
        tf_writer.add_scalar('acc/test_top5', top5.avg, epoch)

    return top1.avg

def save_checkpoint(state, is_best):
    filename = '%s/%s/ckpt.pth.tar' % (args.root_model, args.store_name)
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, filename.replace('pth.tar', 'best.pth.tar'))

def adjust_learning_rate(optimizer, epoch, lr_type, lr_steps):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    if lr_type == 'step':
        decay = 0.1 ** (sum(epoch >= np.array(lr_steps)))
        lr = args.lr * decay
        decay = args.weight_decay
    elif lr_type == 'cos':
        import math
        lr = 0.5 * args.lr * (1 + math.cos(math.pi * epoch / args.epochs))
        decay = args.weight_decay
    else:
        raise NotImplementedError
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr * param_group['lr_mult']
        param_group['weight_decay'] = decay * param_group['decay_mult']

def check_rootfolders():
    """Create log and model folder"""
    folders_util = [args.root_log, args.root_model,
                    os.path.join(args.root_log, args.store_name),
                    os.path.join(args.root_model, args.store_name)]
    for folder in folders_util:
        if not os.path.exists(folder):
            print('creating folder ' + folder)
            os.mkdir(folder)

if __name__ == '__main__':
    main()

opts类代码如下

#这里下面的参数应该要自行输入

import argparse
parser = argparse.ArgumentParser(description="PyTorch implementation of Temporal Segment Networks")
parser.add_argument('dataset', default="")
parser.add_argument('modality', default="RGB", choices=['RGB', 'Flow'])
parser.add_argument('--train_list', type=str, default="")
parser.add_argument('--val_list', type=str, default="")
parser.add_argument('--root_path', type=str, default="")
parser.add_argument('--store_name', type=str, default="")
# ========================= Model Configs ==========================
parser.add_argument('--arch', type=str, default="BNInception")
parser.add_argument('--num_segments', type=int, default=3)
parser.add_argument('--consensus_type', type=str, default='avg')
parser.add_argument('--k', type=int, default=3)

parser.add_argument('--dropout', '--do', default=0.5, type=float,
                    metavar='DO', help='dropout ratio (default: 0.5)')
parser.add_argument('--loss_type', type=str, default="nll",
                    choices=['nll'])
parser.add_argument('--img_feature_dim', default=256, type=int, help="the feature dimension for each frame")
parser.add_argument('--suffix', type=str, default=None)
parser.add_argument('--pretrain', type=str, default='imagenet')
parser.add_argument('--tune_from', type=str, default=None, help='fine-tune from checkpoint')

# ========================= Learning Configs ==========================
parser.add_argument('--epochs', default=120, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('-b', '--batch-size', default=128, type=int,
                    metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--lr_type', default='step', type=str,
                    metavar='LRtype', help='learning rate type')
parser.add_argument('--lr_steps', default=[50, 100], type=float, nargs="+",
                    metavar='LRSteps', help='epochs to decay learning rate by 10')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
                    metavar='W', help='weight decay (default: 5e-4)')
parser.add_argument('--clip-gradient', '--gd', default=None, type=float,
                    metavar='W', help='gradient norm clipping (default: disabled)')
parser.add_argument('--no_partialbn', '--npb', default=False, action="store_true")

# ========================= Monitor Configs ==========================
parser.add_argument('--print-freq', '-p', default=20, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--eval-freq', '-ef', default=5, type=int,
                    metavar='N', help='evaluation frequency (default: 5)')

# ========================= Runtime Configs ==========================
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
                    help='number of data loading workers (default: 8)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--snapshot_pref', type=str, default="")
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('--gpus', nargs='+', type=int, default=None)
parser.add_argument('--flow_prefix', default="", type=str)
parser.add_argument('--root_log',type=str, default='log')
parser.add_argument('--root_model', type=str, default='checkpoint')

parser.add_argument('--shift', default=False, action="store_true", help='use shift for models')
parser.add_argument('--shift_div', default=8, type=int, help='number of div for shift (default: 8)')
parser.add_argument('--shift_place', default='blockres', type=str, help='place for shift (default: stageres)')

parser.add_argument('--temporal_pool', default=False, action="store_true", help='add temporal pooling')
parser.add_argument('--non_local', default=False, action="store_true", help='add non local block')

parser.add_argument('--dense_sample', default=False, action="store_true", help='use dense sample for video dataset')

test_models类代码如下

# Notice that this file has been modified to support ensemble testing

from ops.transforms import *
from ops import dataset_config
from torch.nn import functional as F

# options
parser = argparse.ArgumentParser(description="TSM testing on the full validation set")
parser.add_argument('dataset', type=str)

# may contain splits
pars
parser.add_argument('--test_crops', type=int, default=1)
parser.add_argument('--coeff', type=str, default=None)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
                    help='number of data loading workers (default: 8)')

# for true test
parser.add_argument('--test_list', type=str, default=None)
parser.add_argument('--csv_file', type=str, default=None)

parser.add_argument('--softmax', default=False, action="store_true", help='use softmax')

parser.add_argument('--max_num', type=int, default=-1)
parser.add_argument('--input_size', type=int, default=224)
parser.add_argument('--crop_fusion_type', type=str, default='avg')
parser.add_argument('--gpus', nargs='+', type=int, default=None)
parser.add_argument('--img_feature_dim',type=int, default=256)
parser.add_argument('--num_set_segments',type=int, default=1,help='TODO: select multiply set of n-frames from a video')
parser.add_argument('--pretrain', type=str, default='imagenet')

args = parser.parse_args()

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)
    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))
    res = []
    for k in topk:
         correct_k = correct[:k].view(-1).float().sum(0)
         res.append(correct_k.mul_(100.0 / batch_size))
    return res

def parse_shift_option_from_log_name(log_name):
    if 'shift' in log_name:
        strings = log_name.split('_')
        for i, s in enumerate(strings):
            if 'shift' in s:
                break
        return True, int(strings[i].replace('shift', '')), strings[i + 1]
    else:
        return False, None, None

weights_list = args.weights.split(',')
test_segments_list = [int(s) for s in args.test_segments.split(',')]
assert len(weights_list) == len(test_segments_list)
if args.coeff is None:
    coeff_list = [1] * len(weights_list)
else:
    coeff_list = [float(c) for c in args.coeff.split(',')]

if args.test_list is not None:
    test_file_list = args.test_list.split(',')
else:
    test_file_list = [None] * len(weights_list)

data_iter_list = []
net_list = []
modality_list = []

total_num = None
for this_weights, this_test_segments, test_file in zip(weights_list, test_segments_list, test_file_list):
    is_shift, shift_div, shift_place = parse_shift_option_from_log_name(this_weights)
    if 'RGB' in this_weights:
        modality = 'RGB'
    else:
        modality = 'Flow'
    this_arch = this_weights.split('TSM_')[1].split('_')[2]
    modality_list.append(modality)
    num_class, args.train_list, val_list, root_path, prefix = dataset_config.return_dataset(args.dataset,
                                                                                            modality)
    print('=> shift: {}, shift_div: {}, shift_place: {}'.format(is_shift, shift_div, shift_place))
    net = TSN(num_class, this_test_segments if is_shift else 1, modality,
              base_model=this_arch,
              consensus_type=args.crop_fusion_type,
              img_feature_dim=args.img_feature_dim,
              pretrain=args.pretrain,
              is_shift=is_shift, shift_div=shift_div, shift_place=shift_place,
              non_local='_nl' in this_weights,
              )

    if 'tpool' in this_weights:
        from ops.temporal_shift import make_temporal_pool
        make_temporal_pool(net.base_model, this_test_segments)  # since DataParallel

    checkpoint = torch.load(this_weights)
    checkpoint = checkpoint['state_dict']

    # base_dict = {('base_model.' + k).replace('base_model.fc', 'new_fc'): v for k, v in list(checkpoint.items())}
    base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint.items())}
    replace_dict = {'base_model.classifier.weight': 'new_fc.weight',
                    'base_model.classifier.bias': 'new_fc.bias',
                    }
    for k, v in replace_dict.items():
        if k in base_dict:
            base_dict[v] = base_dict.pop(k)

    net.load_state_dict(base_dict)

    input_size = net.scale_size if args.full_res else net.input_size
    if args.test_crops == 1:
        cropping = torchvision.transforms.Compose([
            GroupScale(net.scale_size),
            GroupCenterCrop(input_size),
        ])
    elif args.test_crops == 3:  # do not flip, so only 5 crops
        cropping = torchvision.transforms.Compose([
            GroupFullResSample(input_size, net.scale_size, flip=False)
        ])
    elif args.test_crops == 5:  # do not flip, so only 5 crops
        cropping = torchvision.transforms.Compose([
            GroupOverSample(input_size, net.scale_size, flip=False)
        ])
    elif args.test_crops == 10:
        cropping = torchvision.transforms.Compose([
            GroupOverSample(input_size, net.scale_size)
        ])
    else:
        raise ValueError("Only 1, 5, 10 crops are supported while we got {}".format(args.test_crops))

    data_loader = torch.utils.data.DataLoader(
            TSNDataSet(root_path, test_file if test_file is not None else val_list, num_segments=this_test_segments,
                       new_length=1 if modality == "RGB" else 5,
                       modality=modality,
                       image_tmpl=prefix,
                       test_mode=True,
                       remove_missing=len(weights_list) == 1,
                       transform=torchvision.transforms.Compose([
                           cropping,
                           Stack(roll=(this_arch in ['BNInception', 'InceptionV3'])),
                           ToTorchFormatTensor(div=(this_arch not in ['BNInception', 'InceptionV3'])),
                           GroupNormalize(net.input_mean, net.input_std),
                       ]), dense_sample=args.dense_sample, twice_sample=args.twice_sample),
            batch_size=args.batch_size, shuffle=False,
            num_workers=args.workers, pin_memory=True,
    )

    if args.gpus is not None:
        devices = [args.gpus[i] for i in range(args.workers)]
    else:
        devices = list(range(args.workers))

    net = torch.nn.DataParallel(net.cuda())
    net.eval()

    data_gen = enumerate(data_loader)

    if total_num is None:
        total_num = len(data_loader.dataset)
    else:
        assert total_num == len(data_loader.dataset)

    data_iter_list.append(data_gen)
    net_list.append(net)

output = []

def eval_video(video_data, net, this_test_segments, modality):
    net.eval()
    with torch.no_grad():
        i, data, label = video_data
        batch_size = label.numel()
        num_crop = args.test_crops
        if args.dense_sample:
            num_crop *= 10  # 10 clips for testing when using dense sample

        if args.twice_sample:
            num_crop *= 2

        if modality == 'RGB':
            length = 3
        elif modality == 'Flow':
            length = 10
        elif modality == 'RGBDiff':
            length = 18
        else:
            raise ValueError("Unknown modality "+ modality)

        data_in = data.view(-1, length, data.size(2), data.size(3))
        if is_shift:
            data_in = data_in.view(batch_size * num_crop, this_test_segments, length, data_in.size(2), data_in.size(3))
        rst = net(data_in)
        rst = rst.reshape(batch_size, num_crop, -1).mean(1)

        if args.softmax:
            # take the softmax to normalize the output to probability
            rst = F.softmax(rst, dim=1)

        rst = rst.data.cpu().numpy().copy()

        if net.module.is_shift:
            rst = rst.reshape(batch_size, num_class)
        else:
            rst = rst.reshape((batch_size, -1, num_class)).mean(axis=1).reshape((batch_size, num_class))

        return i, rst, label

proc_start_time = time.time()
max_num = args.max_num if args.max_num > 0 else total_num

top1 = AverageMeter()
top5 = AverageMeter()

for i, data_label_pairs in enumerate(zip(*data_iter_list)):
    with torch.no_grad():
        if i >= max_num:
            break
        this_rst_list = []
        this_label = None
        for n_seg, (_, (data, label)), net, modality in zip(test_segments_list, data_label_pairs, net_list, modality_list):
            rst = eval_video((i, data, label), net, n_seg, modality)
            this_rst_list.append(rst[1])
            this_label = label
        assert len(this_rst_list) == len(coeff_list)
        for i_coeff in range(len(this_rst_list)):
            this_rst_list[i_coeff] *= coeff_list[i_coeff]
        ensembled_predict = sum(this_rst_list) / len(this_rst_list)

        for p, g in zip(ensembled_predict, this_label.cpu().numpy()):
            output.append([p[None, ...], g])
        cnt_time = time.time() - proc_start_time
        prec1, prec5 = accuracy(torch.from_numpy(ensembled_predict), this_label, topk=(1, 5))
        top1.update(prec1.item(), this_label.numel())
        top5.update(prec5.item(), this_label.numel())
        if i % 20 == 0:
            print('video {} done, total {}/{}, average {:.3f} sec/video, '
                  'moving Prec@1 {:.3f} Prec@5 {:.3f}'.format(i * args.batch_size, i * args.batch_size, total_num,
                                                              float(cnt_time) / (i+1) / args.batch_size, top1.avg, top5.avg))

video_pred = [np.argmax(x[0]) for x in output]
video_pred_top5 = [np.argsort(np.mean(x[0], axis=0).reshape(-1))[::-1][:5] for x in output]

video_labels = [x[1] for x in output]

if args.csv_file is not None:
    print('=> Writing result to csv file: {}'.format(args.csv_file))
    with open(test_file_list[0].replace('test_videofolder.txt', 'category.txt')) as f:
        categories = f.readlines()
    categories = [f.strip() for f in categories]
    with open(test_file_list[0]) as f:
        vid_names = f.readlines()
    vid_names = [n.split(' ')[0] for n in vid_names]
    assert len(vid_names) == len(video_pred)
    if args.dataset != 'somethingv2':  # only output top1
        with open(args.csv_file, 'w') as f:
            for n, pred in zip(vid_names, video_pred):
                f.write('{};{}\n'.format(n, categories[pred]))
    else:
        with open(args.csv_file, 'w') as f:
            for n, pred5 in zip(vid_names, video_pred_top5):
                fill = [n]
                for p in list(pred5):
                    fill.append(p)
                f.write('{};{};{};{};{};{}\n'.format(*fill))

cf = confusion_matrix(video_labels, video_pred).astype(float)

np.save('cm.npy', cf)
cls_cnt = cf.sum(axis=1)
cls_hit = np.diag(cf)

cls_acc = cls_hit / cls_cnt
print(cls_acc)
upper = np.mean(np.max(cf, axis=1) / cls_cnt)
print('upper bound: {}'.format(upper))

print('-----Evaluation is finished------')
print('Class Accuracy {:.02f}%'.format(np.mean(cls_acc) * 100))
print('Overall Prec@1 {:.02f}% Prec@5 {:.02f}%'.format(top1.avg, top5.avg))

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