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）(pytorch卷积操作)

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）

推荐整理分享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）(pytorch卷积操作)，希望有所帮助，仅作参考，欢迎阅读内容。

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需要数据集和源码请点赞关注收藏后评论区留下QQ邮箱~~~

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 osimport timeimport shutilimport torch.nn.parallelimd_norm_from ops.dataset import TSNDataSetfrom ops.models import TSNfrom ops.transforms import *from opts import parserfrom ops import dataset_configfrom ops.utils import AverageMeter, accuracyfrom ops.temporal_shift import make_temporal_poolfrom tensorboardX import SummaryWriterbest_prec1 = 0def 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.avgdef 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 argparseparser = 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 testingfrom ops.transforms import *from ops import dataset_configfrom torch.nn import functional as F# optionsparser = argparse.ArgumentParser(description="TSM testing on the full validation set")parser.add_argument('dataset', type=str)# may contain splitsparsparser.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 testparser.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.countdef 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 resdef 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, Noneweights_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 = Nonefor 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, labelproc_start_time = time.time()max_num = args.max_num if args.max_num > 0 else total_numtop1 = 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_cntprint(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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