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  },
  {
    "path": "README.md",
    "content": "# Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization\n\nCode for the paper \"Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization\", ICCV 2019, Seoul.\n\n[[Paper]](https://arxiv.org/abs/1910.04562) [[Poster]](https://chufengt.github.io/publication/pedestrian-attribute/iccv_poster_id2029.pdf)\n\nContact: chufeng.t@foxmail.com or tcf18@mails.tsinghua.edu.cn\n\n## Environment\n\n- Python 3.6+\n- PyTorch 0.4+\n\n## Datasets\n\n- RAP: http://rap.idealtest.org/\n- PETA: http://mmlab.ie.cuhk.edu.hk/projects/PETA.html\n- PA-100K: https://github.com/xh-liu/HydraPlus-Net\n\nThe original datasets should be processed to match the DataLoader.\n\nWe provide the label lists for training and testing.\n\n## Training and Testing\n\n```\npython main.py --approach=inception_iccv --experiment=rap\n```\n\n```\npython main.py --approach=inception_iccv --experiment=rap -e --resume='model_path'\n```\n\n## Pretrained Models\n\nWe provide the pretrained models for reference, the results may slightly different with the values reported in our paper.\n\n| Dataset | mA    | Link                                                         |\n| ------- | ----- | ------------------------------------------------------------ |\n| PETA    | 86.34 | [Model](https://drive.google.com/file/d/1cvX43Qn_vydzT_jnmgwYUUe9hIA161PH/view?usp=sharing) |\n| RAP     | 81.86 | [Model](https://drive.google.com/file/d/15paMK0-rKDsuzptDPK5kH2JuL8QO0HyS/view?usp=sharing) |\n| PA-100K | 80.45 | [Model](https://drive.google.com/file/d/1xIw3jpvE1pDC3U464kcFJ58iSKCRNQ63/view?usp=sharing) |\n\n## Reference\n\nIf this work is useful to your research, please cite:\n\n```\n@inproceedings{tang2019improving,\n  title={Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization},\n  author={Tang, Chufeng and Sheng, Lu and Zhang, Zhaoxiang and Hu, Xiaolin},\n  booktitle={Proceedings of the IEEE International Conference on Computer Vision},\n  pages={4997--5006},\n  year={2019}\n}\n```\n"
  },
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  },
  {
    "path": "main.py",
    "content": "import argparse\r\nimport os\r\nimport shutil\r\nimport time\r\nimport sys\r\nimport numpy as np\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.parallel\r\nimport torch.backends.cudnn as cudnn\r\nimport torch.optim\r\nimport model as models\r\n\r\nfrom utils.datasets import Get_Dataset\r\n\r\nparser = argparse.ArgumentParser(description='Pedestrian Attribute Framework')\r\nparser.add_argument('--experiment', default='rap', type=str, required=True, help='(default=%(default)s)')\r\nparser.add_argument('--approach', default='inception_iccv', type=str, required=True, help='(default=%(default)s)')\r\nparser.add_argument('--epochs', default=60, type=int, required=False, help='(default=%(default)d)')\r\nparser.add_argument('--batch_size', default=32, type=int, required=False, help='(default=%(default)d)')\r\nparser.add_argument('--lr', '--learning-rate', default=0.0001, type=float, required=False, help='(default=%(default)f)')\r\nparser.add_argument('--optimizer', default='adam', type=str, required=False, help='(default=%(default)s)')\r\nparser.add_argument('--momentum', default=0.9, type=float, required=False, help='(default=%(default)f)')\r\nparser.add_argument('--weight_decay', default=0.0005, type=float, required=False, help='(default=%(default)f)')\r\nparser.add_argument('--start-epoch', default=0, type=int, required=False, help='(default=%(default)d)')\r\nparser.add_argument('--print_freq', default=100, type=int, required=False, help='(default=%(default)d)')\r\nparser.add_argument('--save_freq', default=10, type=int, required=False, help='(default=%(default)d)')\r\nparser.add_argument('--resume', default='', type=str, required=False, help='(default=%(default)s)')\r\nparser.add_argument('--decay_epoch', default=(20,40), type=eval, required=False, help='(default=%(default)d)')\r\nparser.add_argument('--prefix', default='', type=str, required=False, help='(default=%(default)s)')\r\nparser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', required=False, help='evaluate model on validation set')\r\n\r\n# Seed\r\nnp.random.seed(1)\r\ntorch.manual_seed(1)\r\nif torch.cuda.is_available(): torch.cuda.manual_seed(1)\r\nelse: print('[CUDA unavailable]'); sys.exit()\r\nbest_accu = 0\r\nEPS = 1e-12\r\n\r\n#####################################################################################################\r\n\r\n\r\ndef main():\r\n    global args, best_accu\r\n    args = parser.parse_args()\r\n\r\n    print('=' * 100)\r\n    print('Arguments = ')\r\n    for arg in vars(args):\r\n        print('\\t' + arg + ':', getattr(args, arg))\r\n    print('=' * 100)\r\n\r\n    # Data loading code\r\n    train_dataset, val_dataset, attr_num, description = Get_Dataset(args.experiment, args.approach)\r\n\r\n    train_loader = torch.utils.data.DataLoader(\r\n        train_dataset,\r\n        batch_size=args.batch_size, shuffle=True, num_workers=4, pin_memory=True)\r\n\r\n    val_loader = torch.utils.data.DataLoader(\r\n        val_dataset,\r\n        batch_size=32, shuffle=False, num_workers=4, pin_memory=True)\r\n\r\n    # create model\r\n    model = models.__dict__[args.approach](pretrained=True, num_classes=attr_num)\r\n\r\n    # get the number of model parameters\r\n    print('Number of model parameters: {}'.format(\r\n        sum([p.data.nelement() for p in model.parameters()])))\r\n    print('')\r\n\r\n    # for training on multiple GPUs.\r\n    # Use CUDA_VISIBLE_DEVICES=0,1 to specify which GPUs to use\r\n    model = torch.nn.DataParallel(model).cuda()\r\n\r\n    # optionally resume from a checkpoint\r\n    if args.resume:\r\n        if os.path.isfile(args.resume):\r\n            print(\"=> loading checkpoint '{}'\".format(args.resume))\r\n            checkpoint = torch.load(args.resume)\r\n            args.start_epoch = checkpoint['epoch']\r\n            best_accu = checkpoint['best_accu']\r\n            model.load_state_dict(checkpoint['state_dict'])\r\n            print(\"=> loaded checkpoint '{}' (epoch {})\"\r\n                  .format(args.resume, checkpoint['epoch']))\r\n        else:\r\n            print(\"=> no checkpoint found at '{}'\".format(args.resume))\r\n\r\n    cudnn.benchmark = False\r\n    cudnn.deterministic = True\r\n\r\n    # define loss function\r\n    criterion = Weighted_BCELoss(args.experiment)\r\n\r\n    if args.optimizer == 'adam':\r\n        optimizer = torch.optim.Adam(model.parameters(), lr=args.lr,\r\n                                    betas=(0.9, 0.999),\r\n                                    weight_decay=args.weight_decay)\r\n    else:\r\n        optimizer = torch.optim.SGD(model.parameters(), args.lr,\r\n                                    momentum=args.momentum,\r\n                                    weight_decay=args.weight_decay)\r\n\r\n\r\n    if args.evaluate:\r\n        test(val_loader, model, attr_num, description)\r\n        return\r\n\r\n    for epoch in range(args.start_epoch, args.epochs):\r\n        adjust_learning_rate(optimizer, epoch, args.decay_epoch)\r\n\r\n        # train for one epoch\r\n        train(train_loader, model, criterion, optimizer, epoch)\r\n\r\n        # evaluate on validation set\r\n        accu = validate(val_loader, model, criterion, epoch)\r\n\r\n        test(val_loader, model, attr_num, description)\r\n\r\n        # remember best Accu and save checkpoint\r\n        is_best = accu > best_accu\r\n        best_accu = max(accu, best_accu)\r\n\r\n        if epoch in args.decay_epoch:\r\n            save_checkpoint({\r\n                'epoch': epoch + 1,\r\n                'state_dict': model.state_dict(),\r\n                'best_accu': best_accu,\r\n            }, epoch+1, args.prefix)\r\n\r\ndef train(train_loader, model, criterion, optimizer, epoch):\r\n    \"\"\"Train for one epoch on the training set\"\"\"\r\n    batch_time = AverageMeter()\r\n    losses = AverageMeter()\r\n    top1 = AverageMeter()\r\n    model.train()\r\n\r\n    end = time.time()\r\n    for i, _ in enumerate(train_loader):\r\n        input, target = _\r\n        target = target.cuda(non_blocking=True)\r\n        input = input.cuda(non_blocking=True)\r\n        output = model(input)\r\n\r\n        bs = target.size(0)\r\n\r\n        if type(output) == type(()) or type(output) == type([]):\r\n            loss_list = []\r\n            # deep supervision\r\n            for k in range(len(output)):\r\n                out = output[k]\r\n                loss_list.append(criterion.forward(torch.sigmoid(out), target, epoch))\r\n            loss = sum(loss_list)\r\n            # maximum voting\r\n            output = torch.max(torch.max(torch.max(output[0],output[1]),output[2]),output[3])\r\n        else:\r\n            loss = criterion.forward(torch.sigmoid(output), target, epoch)\r\n\r\n        # measure accuracy and record loss\r\n        accu = accuracy(output.data, target)\r\n        losses.update(loss.data, bs)\r\n        top1.update(accu, bs)\r\n\r\n        # compute gradient and do SGD step\r\n        optimizer.zero_grad()\r\n        loss.backward()\r\n        optimizer.step()\r\n\r\n        # measure elapsed time\r\n        batch_time.update(time.time() - end)\r\n        end = time.time()\r\n\r\n        if i % args.print_freq == 0:\r\n            print('Epoch: [{0}][{1}/{2}]\\t'\r\n                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\\t'\r\n                  'Loss {loss.val:.4f} ({loss.avg:.4f})\\t'\r\n                  'Accu {top1.val:.3f} ({top1.avg:.3f})'.format(\r\n                      epoch, i, len(train_loader), batch_time=batch_time,\r\n                      loss=losses, top1=top1))\r\n\r\n\r\ndef validate(val_loader, model, criterion, epoch):\r\n    \"\"\"Perform validation on the validation set\"\"\"\r\n    batch_time = AverageMeter()\r\n    losses = AverageMeter()\r\n    top1 = AverageMeter()\r\n    model.eval()\r\n\r\n    end = time.time()\r\n    for i, _ in enumerate(val_loader):\r\n        input, target = _\r\n        target = target.cuda(non_blocking=True)\r\n        input = input.cuda(non_blocking=True)\r\n        output = model(input)\r\n\r\n        bs = target.size(0)\r\n\r\n        if type(output) == type(()) or type(output) == type([]):\r\n            loss_list = []\r\n            # deep supervision\r\n            for k in range(len(output)):\r\n                out = output[k]\r\n                loss_list.append(criterion.forward(torch.sigmoid(out), target, epoch))\r\n            loss = sum(loss_list)\r\n            # maximum voting\r\n            output = torch.max(torch.max(torch.max(output[0],output[1]),output[2]),output[3])\r\n        else:\r\n            loss = criterion.forward(torch.sigmoid(output), target, epoch)\r\n\r\n        # measure accuracy and record loss\r\n        accu = accuracy(output.data, target)\r\n        losses.update(loss.data, bs)\r\n        top1.update(accu, bs)\r\n\r\n        # measure elapsed time\r\n        batch_time.update(time.time() - end)\r\n        end = time.time()\r\n\r\n        if i % args.print_freq == 0:\r\n            print('Test: [{0}/{1}]\\t'\r\n                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\\t'\r\n                  'Loss {loss.val:.4f} ({loss.avg:.4f})\\t'\r\n                  'Accu {top1.val:.3f} ({top1.avg:.3f})'.format(\r\n                      i, len(val_loader), batch_time=batch_time, loss=losses,\r\n                      top1=top1))\r\n\r\n    print(' * Accu {top1.avg:.3f}'.format(top1=top1))\r\n    return top1.avg\r\n\r\n\r\ndef test(val_loader, model, attr_num, description):\r\n    model.eval()\r\n\r\n    pos_cnt = []\r\n    pos_tol = []\r\n    neg_cnt = []\r\n    neg_tol = []\r\n\r\n    accu = 0.0\r\n    prec = 0.0\r\n    recall = 0.0\r\n    tol = 0\r\n\r\n    for it in range(attr_num):\r\n        pos_cnt.append(0)\r\n        pos_tol.append(0)\r\n        neg_cnt.append(0)\r\n        neg_tol.append(0)\r\n\r\n    for i, _ in enumerate(val_loader):\r\n        input, target = _\r\n        target = target.cuda(non_blocking=True)\r\n        input = input.cuda(non_blocking=True)\r\n        output = model(input)\r\n        bs = target.size(0)\r\n\r\n        # maximum voting\r\n        if type(output) == type(()) or type(output) == type([]):\r\n            output = torch.max(torch.max(torch.max(output[0],output[1]),output[2]),output[3])\r\n\r\n\r\n        batch_size = target.size(0)\r\n        tol = tol + batch_size\r\n        output = torch.sigmoid(output.data).cpu().numpy()\r\n        output = np.where(output > 0.5, 1, 0)\r\n        target = target.cpu().numpy()\r\n\r\n        for it in range(attr_num):\r\n            for jt in range(batch_size):\r\n                if target[jt][it] == 1:\r\n                    pos_tol[it] = pos_tol[it] + 1\r\n                    if output[jt][it] == 1:\r\n                        pos_cnt[it] = pos_cnt[it] + 1\r\n                if target[jt][it] == 0:\r\n                    neg_tol[it] = neg_tol[it] + 1\r\n                    if output[jt][it] == 0:\r\n                        neg_cnt[it] = neg_cnt[it] + 1\r\n\r\n        if attr_num == 1:\r\n            continue\r\n        for jt in range(batch_size):\r\n            tp = 0\r\n            fn = 0\r\n            fp = 0\r\n            for it in range(attr_num):\r\n                if output[jt][it] == 1 and target[jt][it] == 1:\r\n                    tp = tp + 1\r\n                elif output[jt][it] == 0 and target[jt][it] == 1:\r\n                    fn = fn + 1\r\n                elif output[jt][it] == 1 and target[jt][it] == 0:\r\n                    fp = fp + 1\r\n            if tp + fn + fp != 0:\r\n                accu = accu +  1.0 * tp / (tp + fn + fp)\r\n            if tp + fp != 0:\r\n                prec = prec + 1.0 * tp / (tp + fp)\r\n            if tp + fn != 0:\r\n                recall = recall + 1.0 * tp / (tp + fn)\r\n\r\n    print('=' * 100)\r\n    print('\\t     Attr              \\tp_true/n_true\\tp_tol/n_tol\\tp_pred/n_pred\\tcur_mA')\r\n    mA = 0.0\r\n    for it in range(attr_num):\r\n        cur_mA = ((1.0*pos_cnt[it]/pos_tol[it]) + (1.0*neg_cnt[it]/neg_tol[it])) / 2.0\r\n        mA = mA + cur_mA\r\n        print('\\t#{:2}: {:18}\\t{:4}\\{:4}\\t{:4}\\{:4}\\t{:4}\\{:4}\\t{:.5f}'.format(it,description[it],pos_cnt[it],neg_cnt[it],pos_tol[it],neg_tol[it],(pos_cnt[it]+neg_tol[it]-neg_cnt[it]),(neg_cnt[it]+pos_tol[it]-pos_cnt[it]),cur_mA))\r\n    mA = mA / attr_num\r\n    print('\\t' + 'mA:        '+str(mA))\r\n\r\n    if attr_num != 1:\r\n        accu = accu / tol\r\n        prec = prec / tol\r\n        recall = recall / tol\r\n        f1 = 2.0 * prec * recall / (prec + recall)\r\n        print('\\t' + 'Accuracy:  '+str(accu))\r\n        print('\\t' + 'Precision: '+str(prec))\r\n        print('\\t' + 'Recall:    '+str(recall))\r\n        print('\\t' + 'F1_Score:  '+str(f1))\r\n    print('=' * 100)\r\n\r\n\r\ndef save_checkpoint(state, epoch, prefix, filename='.pth.tar'):\r\n    \"\"\"Saves checkpoint to disk\"\"\"\r\n    directory = \"your_path\" + args.experiment + '/' + args.approach + '/'\r\n    if not os.path.exists(directory):\r\n        os.makedirs(directory)\r\n    if prefix == '':\r\n        filename = directory + str(epoch) + filename\r\n    else:\r\n        filename = directory + prefix + '_' + str(epoch) + filename\r\n    torch.save(state, filename)\r\n\r\nclass AverageMeter(object):\r\n    \"\"\"Computes and stores the average and current value\"\"\"\r\n    def __init__(self):\r\n        self.reset()\r\n\r\n    def reset(self):\r\n        self.val = 0\r\n        self.avg = 0\r\n        self.sum = 0\r\n        self.count = 0\r\n\r\n    def update(self, val, n=1):\r\n        self.val = val\r\n        self.sum += val * n\r\n        self.count += n\r\n        self.avg = self.sum / self.count\r\n\r\ndef adjust_learning_rate(optimizer, epoch, decay_epoch):\r\n    lr = args.lr\r\n    for epc in decay_epoch:\r\n        if epoch >= epc:\r\n            lr = lr * 0.1\r\n        else:\r\n            break\r\n    print()\r\n    print('Learning Rate:', lr)\r\n    print()\r\n    for param_group in optimizer.param_groups:\r\n        param_group['lr'] = lr\r\n\r\n\r\ndef accuracy(output, target):\r\n    batch_size = target.size(0)\r\n    attr_num = target.size(1)\r\n\r\n    output = torch.sigmoid(output).cpu().numpy()\r\n    output = np.where(output > 0.5, 1, 0)\r\n    pred = torch.from_numpy(output).long()\r\n    target = target.cpu().long()\r\n    correct = pred.eq(target)\r\n    correct = correct.numpy()\r\n\r\n    res = []\r\n    for k in range(attr_num):\r\n        res.append(1.0*sum(correct[:,k]) / batch_size)\r\n    return sum(res) / attr_num\r\n\r\n\r\nclass Weighted_BCELoss(object):\r\n    \"\"\"\r\n        Weighted_BCELoss was proposed in \"Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios\"[13].\r\n    \"\"\"\r\n    def __init__(self, experiment):\r\n        super(Weighted_BCELoss, self).__init__()\r\n        self.weights = None\r\n        if experiment == 'pa100k':\r\n            self.weights = torch.Tensor([0.460444444444,\r\n                                        0.0134555555556,\r\n                                        0.924377777778,\r\n                                        0.0621666666667,\r\n                                        0.352666666667,\r\n                                        0.294622222222,\r\n                                        0.352711111111,\r\n                                        0.0435444444444,\r\n                                        0.179977777778,\r\n                                        0.185,\r\n                                        0.192733333333,\r\n                                        0.1601,\r\n                                        0.00952222222222,\r\n                                        0.5834,\r\n                                        0.4166,\r\n                                        0.0494777777778,\r\n                                        0.151044444444,\r\n                                        0.107755555556,\r\n                                        0.0419111111111,\r\n                                        0.00472222222222,\r\n                                        0.0168888888889,\r\n                                        0.0324111111111,\r\n                                        0.711711111111,\r\n                                        0.173444444444,\r\n                                        0.114844444444,\r\n                                        0.006]).cuda()\r\n        elif experiment == 'rap':\r\n            self.weights = torch.Tensor([0.311434,\r\n                                        0.009980,\r\n                                        0.430011,\r\n                                        0.560010,\r\n                                        0.144932,\r\n                                        0.742479,\r\n                                        0.097728,\r\n                                        0.946303,\r\n                                        0.048287,\r\n                                        0.004328,\r\n                                        0.189323,\r\n                                        0.944764,\r\n                                        0.016713,\r\n                                        0.072959,\r\n                                        0.010461,\r\n                                        0.221186,\r\n                                        0.123434,\r\n                                        0.057785,\r\n                                        0.228857,\r\n                                        0.172779,\r\n                                        0.315186,\r\n                                        0.022147,\r\n                                        0.030299,\r\n                                        0.017843,\r\n                                        0.560346,\r\n                                        0.000553,\r\n                                        0.027991,\r\n                                        0.036624,\r\n                                        0.268342,\r\n                                        0.133317,\r\n                                        0.302465,\r\n                                        0.270891,\r\n                                        0.124059,\r\n                                        0.012432,\r\n                                        0.157340,\r\n                                        0.018132,\r\n                                        0.064182,\r\n                                        0.028111,\r\n                                        0.042155,\r\n                                        0.027558,\r\n                                        0.012649,\r\n                                        0.024504,\r\n                                        0.294601,\r\n                                        0.034099,\r\n                                        0.032800,\r\n                                        0.091812,\r\n                                        0.024552,\r\n                                        0.010388,\r\n                                        0.017603,\r\n                                        0.023446,\r\n                                        0.128917]).cuda()\r\n        elif experiment == 'peta':\r\n            self.weights = torch.Tensor([0.5016,\r\n                                        0.3275,\r\n                                        0.1023,\r\n                                        0.0597,\r\n                                        0.1986,\r\n                                        0.2011,\r\n                                        0.8643,\r\n                                        0.8559,\r\n                                        0.1342,\r\n                                        0.1297,\r\n                                        0.1014,\r\n                                        0.0685,\r\n                                        0.314,\r\n                                        0.2932,\r\n                                        0.04,\r\n                                        0.2346,\r\n                                        0.5473,\r\n                                        0.2974,\r\n                                        0.0849,\r\n                                        0.7523,\r\n                                        0.2717,\r\n                                        0.0282,\r\n                                        0.0749,\r\n                                        0.0191,\r\n                                        0.3633,\r\n                                        0.0359,\r\n                                        0.1425,\r\n                                        0.0454,\r\n                                        0.2201,\r\n                                        0.0178,\r\n                                        0.0285,\r\n                                        0.5125,\r\n                                        0.0838,\r\n                                        0.4605,\r\n                                        0.0124]).cuda()\r\n        #self.weights = None\r\n\r\n    def forward(self, output, target, epoch):\r\n        if self.weights is not None:\r\n            cur_weights = torch.exp(target + (1 - target * 2) * self.weights)\r\n            loss = cur_weights *  (target * torch.log(output + EPS)) + ((1 - target) * torch.log(1 - output + EPS))\r\n        else:\r\n            loss = target * torch.log(output + EPS) + (1 - target) * torch.log(1 - output + EPS)\r\n        return torch.neg(torch.mean(loss))\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n"
  },
  {
    "path": "model/__init__.py",
    "content": "from .inception_iccv import *"
  },
  {
    "path": "model/inception_iccv.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.tensor as tensor\nfrom torch.nn import functional as F\n\n__all__ = ['inception_iccv']\n\ndef inception_iccv(pretrained=True, debug=False, **kwargs):\n    model = InceptionNet(**kwargs)\n    \"\"\"\n        Pretrained model: 'https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/bninception.py'\n        Initializing with basedline models (trained BN-Inception) can obtain better results.\n    \"\"\"\n    if pretrained:\n        pretrained_dict = torch.load('model/bn_inception-52deb4733.pth')\n        model_dict = model.state_dict()\n        new_dict = {}\n        for k,_ in model_dict.items():\n            raw_name = k.replace('main_branch.', '')\n            if raw_name in pretrained_dict:\n                new_dict[k] = pretrained_dict[raw_name]\n        model_dict.update(new_dict)\n        model.load_state_dict(model_dict)\n    return model\n\n\nclass ChannelAttn(nn.Module):\n    def __init__(self, in_channels, reduction_rate=16):\n        super(ChannelAttn, self).__init__()\n        assert in_channels%reduction_rate == 0\n        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction_rate, kernel_size=1, stride=1, padding=0)\n        self.conv2 = nn.Conv2d(in_channels // reduction_rate, in_channels, kernel_size=1, stride=1, padding=0)\n\n    def forward(self, x):\n        # squeeze operation (global average pooling)\n        x = F.avg_pool2d(x, x.size()[2:])\n        # excitation operation (2 conv layers)\n        x = F.relu(self.conv1(x))\n        x = self.conv2(x)\n        return torch.sigmoid(x)\n\n\nclass SpatialTransformBlock(nn.Module):\n    def __init__(self, num_classes, pooling_size, channels):\n        super(SpatialTransformBlock, self).__init__()\n        self.num_classes = num_classes\n        self.spatial = pooling_size\n\n        self.global_pool = nn.AvgPool2d((pooling_size, pooling_size//2), stride=1, padding=0, ceil_mode=True, count_include_pad=True)\n\n        self.gap_list = nn.ModuleList()\n        self.fc_list = nn.ModuleList()\n        self.att_list = nn.ModuleList()\n        self.stn_list = nn.ModuleList()\n        for i in range(self.num_classes):\n            self.gap_list.append(nn.AvgPool2d((pooling_size, pooling_size//2), stride=1, padding=0, ceil_mode=True, count_include_pad=True))\n            self.fc_list.append(nn.Linear(channels, 1))\n            self.att_list.append(ChannelAttn(channels))\n            self.stn_list.append(nn.Linear(channels, 4))\n\n    def stn(self, x, theta):\n        grid = F.affine_grid(theta, x.size())\n        x = F.grid_sample(x, grid, padding_mode='border')\n        return x.cuda()\n\n    def transform_theta(self, theta_i, region_idx):\n        theta = torch.zeros(theta_i.size(0), 2, 3)\n        theta[:,0,0] = torch.sigmoid(theta_i[:,0])\n        theta[:,1,1] = torch.sigmoid(theta_i[:,1])\n        theta[:,0,2] = torch.tanh(theta_i[:,2])\n        theta[:,1,2] = torch.tanh(theta_i[:,3])\n        theta = theta.cuda()\n        return theta\n\n    def forward(self, features):\n        pred_list = []\n        bs = features.size(0)\n        for i in range(self.num_classes):\n            stn_feature = features * self.att_list[i](features) + features\n\n            theta_i = self.stn_list[i](F.avg_pool2d(stn_feature, stn_feature.size()[2:]).view(bs,-1)).view(-1,4)\n            theta_i = self.transform_theta(theta_i, i)\n\n            sub_feature = self.stn(stn_feature, theta_i)\n            pred = self.gap_list[i](sub_feature).view(bs,-1)\n            pred = self.fc_list[i](pred)\n            pred_list.append(pred)\n        pred = torch.cat(pred_list, 1)\n        return pred\n\n\nclass InceptionNet(nn.Module):\n    def __init__(self, num_classes=51):\n        super(InceptionNet, self).__init__()\n        self.num_classes = num_classes\n        self.main_branch = BNInception()\n        self.global_pool = nn.AvgPool2d((8,4), stride=1, padding=0, ceil_mode=True, count_include_pad=True)\n        self.finalfc = nn.Linear(1024, num_classes)\n\n        self.st_3b = SpatialTransformBlock(num_classes, 32, 256*3)\n        self.st_4d = SpatialTransformBlock(num_classes, 16, 256*2)\n        self.st_5b = SpatialTransformBlock(num_classes, 8, 256)\n\n        # Lateral layers\n        self.latlayer_3b = nn.Conv2d(320, 256, kernel_size=1, stride=1, padding=0)\n        self.latlayer_4d = nn.Conv2d(608, 256, kernel_size=1, stride=1, padding=0)\n        self.latlayer_5b = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)\n\n    def _upsample_add(self, x, y):\n        _,_,H,W = y.size()\n        up_feat = F.interpolate(x, (H, W), mode='bilinear', align_corners=False)\n        return torch.cat([up_feat,y], 1)\n\n    def forward(self, input):\n        bs = input.size(0)\n        feat_3b, feat_4d, feat_5b = self.main_branch(input)\n        main_feat = self.global_pool(feat_5b).view(bs,-1)\n        main_pred = self.finalfc(main_feat)\n\n        fusion_5b = self.latlayer_5b(feat_5b)\n        fusion_4d = self._upsample_add(fusion_5b, self.latlayer_4d(feat_4d))\n        fusion_3b = self._upsample_add(fusion_4d, self.latlayer_3b(feat_3b))\n\n        pred_3b = self.st_3b(fusion_3b)\n        pred_4d = self.st_4d(fusion_4d)\n        pred_5b = self.st_5b(fusion_5b)\n\n        return pred_3b, pred_4d, pred_5b, main_pred\n\nclass BNInception(nn.Module):\n    \"\"\"\n        Copy from 'https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/bninception.py'\n    \"\"\"\n    def __init__(self):\n        super(BNInception, self).__init__()\n        inplace = True\n        self.conv1_7x7_s2 = nn.Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))\n        self.conv1_7x7_s2_bn = nn.BatchNorm2d(64, affine=True)\n        self.conv1_relu_7x7 = nn.ReLU(inplace)\n        self.pool1_3x3_s2 = nn.MaxPool2d((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True)\n        self.conv2_3x3_reduce = nn.Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.conv2_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)\n        self.conv2_relu_3x3_reduce = nn.ReLU(inplace)\n        self.conv2_3x3 = nn.Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.conv2_3x3_bn = nn.BatchNorm2d(192, affine=True)\n        self.conv2_relu_3x3 = nn.ReLU(inplace)\n        self.pool2_3x3_s2 = nn.MaxPool2d((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True)\n        self.inception_3a_1x1 = nn.Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3a_1x1_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_3a_relu_1x1 = nn.ReLU(inplace)\n        self.inception_3a_3x3_reduce = nn.Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3a_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_3a_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_3a_3x3 = nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_3a_3x3_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_3a_relu_3x3 = nn.ReLU(inplace)\n        self.inception_3a_double_3x3_reduce = nn.Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3a_double_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_3a_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_3a_double_3x3_1 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_3a_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_3a_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_3a_double_3x3_2 = nn.Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_3a_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_3a_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_3a_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)\n        self.inception_3a_pool_proj = nn.Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3a_pool_proj_bn = nn.BatchNorm2d(32, affine=True)\n        self.inception_3a_relu_pool_proj = nn.ReLU(inplace)\n        self.inception_3b_1x1 = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3b_1x1_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_3b_relu_1x1 = nn.ReLU(inplace)\n        self.inception_3b_3x3_reduce = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3b_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_3b_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_3b_3x3 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_3b_3x3_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_3b_relu_3x3 = nn.ReLU(inplace)\n        self.inception_3b_double_3x3_reduce = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3b_double_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_3b_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_3b_double_3x3_1 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_3b_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_3b_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_3b_double_3x3_2 = nn.Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_3b_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_3b_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_3b_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)\n        self.inception_3b_pool_proj = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3b_pool_proj_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_3b_relu_pool_proj = nn.ReLU(inplace)\n        self.inception_3c_3x3_reduce = nn.Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3c_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_3c_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_3c_3x3 = nn.Conv2d(128, 160, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n        self.inception_3c_3x3_bn = nn.BatchNorm2d(160, affine=True)\n        self.inception_3c_relu_3x3 = nn.ReLU(inplace)\n        self.inception_3c_double_3x3_reduce = nn.Conv2d(320, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_3c_double_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_3c_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_3c_double_3x3_1 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_3c_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_3c_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_3c_double_3x3_2 = nn.Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n        self.inception_3c_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_3c_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_3c_pool = nn.MaxPool2d((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True)\n        self.inception_4a_1x1 = nn.Conv2d(576, 224, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4a_1x1_bn = nn.BatchNorm2d(224, affine=True)\n        self.inception_4a_relu_1x1 = nn.ReLU(inplace)\n        self.inception_4a_3x3_reduce = nn.Conv2d(576, 64, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4a_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)\n        self.inception_4a_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4a_3x3 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4a_3x3_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_4a_relu_3x3 = nn.ReLU(inplace)\n        self.inception_4a_double_3x3_reduce = nn.Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4a_double_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_4a_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4a_double_3x3_1 = nn.Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4a_double_3x3_1_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4a_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_4a_double_3x3_2 = nn.Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4a_double_3x3_2_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4a_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_4a_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)\n        self.inception_4a_pool_proj = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4a_pool_proj_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4a_relu_pool_proj = nn.ReLU(inplace)\n        self.inception_4b_1x1 = nn.Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4b_1x1_bn = nn.BatchNorm2d(192, affine=True)\n        self.inception_4b_relu_1x1 = nn.ReLU(inplace)\n        self.inception_4b_3x3_reduce = nn.Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4b_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_4b_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4b_3x3 = nn.Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4b_3x3_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4b_relu_3x3 = nn.ReLU(inplace)\n        self.inception_4b_double_3x3_reduce = nn.Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4b_double_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_4b_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4b_double_3x3_1 = nn.Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4b_double_3x3_1_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4b_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_4b_double_3x3_2 = nn.Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4b_double_3x3_2_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4b_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_4b_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)\n        self.inception_4b_pool_proj = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4b_pool_proj_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4b_relu_pool_proj = nn.ReLU(inplace)\n        self.inception_4c_1x1 = nn.Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4c_1x1_bn = nn.BatchNorm2d(160, affine=True)\n        self.inception_4c_relu_1x1 = nn.ReLU(inplace)\n        self.inception_4c_3x3_reduce = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4c_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4c_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4c_3x3 = nn.Conv2d(128, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4c_3x3_bn = nn.BatchNorm2d(160, affine=True)\n        self.inception_4c_relu_3x3 = nn.ReLU(inplace)\n        self.inception_4c_double_3x3_reduce = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4c_double_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4c_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4c_double_3x3_1 = nn.Conv2d(128, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4c_double_3x3_1_bn = nn.BatchNorm2d(160, affine=True)\n        self.inception_4c_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_4c_double_3x3_2 = nn.Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4c_double_3x3_2_bn = nn.BatchNorm2d(160, affine=True)\n        self.inception_4c_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_4c_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)\n        self.inception_4c_pool_proj = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4c_pool_proj_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4c_relu_pool_proj = nn.ReLU(inplace)\n        self.inception_4d_1x1 = nn.Conv2d(608, 96, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4d_1x1_bn = nn.BatchNorm2d(96, affine=True)\n        self.inception_4d_relu_1x1 = nn.ReLU(inplace)\n        self.inception_4d_3x3_reduce = nn.Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4d_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4d_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4d_3x3 = nn.Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4d_3x3_bn = nn.BatchNorm2d(192, affine=True)\n        self.inception_4d_relu_3x3 = nn.ReLU(inplace)\n        self.inception_4d_double_3x3_reduce = nn.Conv2d(608, 160, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4d_double_3x3_reduce_bn = nn.BatchNorm2d(160, affine=True)\n        self.inception_4d_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4d_double_3x3_1 = nn.Conv2d(160, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4d_double_3x3_1_bn = nn.BatchNorm2d(192, affine=True)\n        self.inception_4d_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_4d_double_3x3_2 = nn.Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4d_double_3x3_2_bn = nn.BatchNorm2d(192, affine=True)\n        self.inception_4d_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_4d_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)\n        self.inception_4d_pool_proj = nn.Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4d_pool_proj_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4d_relu_pool_proj = nn.ReLU(inplace)\n        self.inception_4e_3x3_reduce = nn.Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4e_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_4e_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4e_3x3 = nn.Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n        self.inception_4e_3x3_bn = nn.BatchNorm2d(192, affine=True)\n        self.inception_4e_relu_3x3 = nn.ReLU(inplace)\n        self.inception_4e_double_3x3_reduce = nn.Conv2d(608, 192, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_4e_double_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True)\n        self.inception_4e_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_4e_double_3x3_1 = nn.Conv2d(192, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_4e_double_3x3_1_bn = nn.BatchNorm2d(256, affine=True)\n        self.inception_4e_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_4e_double_3x3_2 = nn.Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n        self.inception_4e_double_3x3_2_bn = nn.BatchNorm2d(256, affine=True)\n        self.inception_4e_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_4e_pool = nn.MaxPool2d((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True)\n        self.inception_5a_1x1 = nn.Conv2d(1056, 352, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_5a_1x1_bn = nn.BatchNorm2d(352, affine=True)\n        self.inception_5a_relu_1x1 = nn.ReLU(inplace)\n        self.inception_5a_3x3_reduce = nn.Conv2d(1056, 192, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_5a_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True)\n        self.inception_5a_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_5a_3x3 = nn.Conv2d(192, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_5a_3x3_bn = nn.BatchNorm2d(320, affine=True)\n        self.inception_5a_relu_3x3 = nn.ReLU(inplace)\n        self.inception_5a_double_3x3_reduce = nn.Conv2d(1056, 160, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_5a_double_3x3_reduce_bn = nn.BatchNorm2d(160, affine=True)\n        self.inception_5a_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_5a_double_3x3_1 = nn.Conv2d(160, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_5a_double_3x3_1_bn = nn.BatchNorm2d(224, affine=True)\n        self.inception_5a_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_5a_double_3x3_2 = nn.Conv2d(224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_5a_double_3x3_2_bn = nn.BatchNorm2d(224, affine=True)\n        self.inception_5a_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_5a_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)\n        self.inception_5a_pool_proj = nn.Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_5a_pool_proj_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_5a_relu_pool_proj = nn.ReLU(inplace)\n        self.inception_5b_1x1 = nn.Conv2d(1024, 352, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_5b_1x1_bn = nn.BatchNorm2d(352, affine=True)\n        self.inception_5b_relu_1x1 = nn.ReLU(inplace)\n        self.inception_5b_3x3_reduce = nn.Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_5b_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True)\n        self.inception_5b_relu_3x3_reduce = nn.ReLU(inplace)\n        self.inception_5b_3x3 = nn.Conv2d(192, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_5b_3x3_bn = nn.BatchNorm2d(320, affine=True)\n        self.inception_5b_relu_3x3 = nn.ReLU(inplace)\n        self.inception_5b_double_3x3_reduce = nn.Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_5b_double_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True)\n        self.inception_5b_relu_double_3x3_reduce = nn.ReLU(inplace)\n        self.inception_5b_double_3x3_1 = nn.Conv2d(192, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_5b_double_3x3_1_bn = nn.BatchNorm2d(224, affine=True)\n        self.inception_5b_relu_double_3x3_1 = nn.ReLU(inplace)\n        self.inception_5b_double_3x3_2 = nn.Conv2d(224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        self.inception_5b_double_3x3_2_bn = nn.BatchNorm2d(224, affine=True)\n        self.inception_5b_relu_double_3x3_2 = nn.ReLU(inplace)\n        self.inception_5b_pool = nn.MaxPool2d((3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), ceil_mode=True)\n        self.inception_5b_pool_proj = nn.Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1))\n        self.inception_5b_pool_proj_bn = nn.BatchNorm2d(128, affine=True)\n        self.inception_5b_relu_pool_proj = nn.ReLU(inplace)\n\n    def features(self, input):\n        conv1_7x7_s2_out = self.conv1_7x7_s2(input)\n        conv1_7x7_s2_bn_out = self.conv1_7x7_s2_bn(conv1_7x7_s2_out)\n        conv1_relu_7x7_out = self.conv1_relu_7x7(conv1_7x7_s2_bn_out)\n        pool1_3x3_s2_out = self.pool1_3x3_s2(conv1_relu_7x7_out)\n        conv2_3x3_reduce_out = self.conv2_3x3_reduce(pool1_3x3_s2_out)\n        conv2_3x3_reduce_bn_out = self.conv2_3x3_reduce_bn(conv2_3x3_reduce_out)\n        conv2_relu_3x3_reduce_out = self.conv2_relu_3x3_reduce(conv2_3x3_reduce_bn_out)\n        conv2_3x3_out = self.conv2_3x3(conv2_relu_3x3_reduce_out)\n        conv2_3x3_bn_out = self.conv2_3x3_bn(conv2_3x3_out)\n        conv2_relu_3x3_out = self.conv2_relu_3x3(conv2_3x3_bn_out)\n        pool2_3x3_s2_out = self.pool2_3x3_s2(conv2_relu_3x3_out)\n        inception_3a_1x1_out = self.inception_3a_1x1(pool2_3x3_s2_out)\n        inception_3a_1x1_bn_out = self.inception_3a_1x1_bn(inception_3a_1x1_out)\n        inception_3a_relu_1x1_out = self.inception_3a_relu_1x1(inception_3a_1x1_bn_out)\n        inception_3a_3x3_reduce_out = self.inception_3a_3x3_reduce(pool2_3x3_s2_out)\n        inception_3a_3x3_reduce_bn_out = self.inception_3a_3x3_reduce_bn(inception_3a_3x3_reduce_out)\n        inception_3a_relu_3x3_reduce_out = self.inception_3a_relu_3x3_reduce(inception_3a_3x3_reduce_bn_out)\n        inception_3a_3x3_out = self.inception_3a_3x3(inception_3a_relu_3x3_reduce_out)\n        inception_3a_3x3_bn_out = self.inception_3a_3x3_bn(inception_3a_3x3_out)\n        inception_3a_relu_3x3_out = self.inception_3a_relu_3x3(inception_3a_3x3_bn_out)\n        inception_3a_double_3x3_reduce_out = self.inception_3a_double_3x3_reduce(pool2_3x3_s2_out)\n        inception_3a_double_3x3_reduce_bn_out = self.inception_3a_double_3x3_reduce_bn(inception_3a_double_3x3_reduce_out)\n        inception_3a_relu_double_3x3_reduce_out = self.inception_3a_relu_double_3x3_reduce(inception_3a_double_3x3_reduce_bn_out)\n        inception_3a_double_3x3_1_out = self.inception_3a_double_3x3_1(inception_3a_relu_double_3x3_reduce_out)\n        inception_3a_double_3x3_1_bn_out = self.inception_3a_double_3x3_1_bn(inception_3a_double_3x3_1_out)\n        inception_3a_relu_double_3x3_1_out = self.inception_3a_relu_double_3x3_1(inception_3a_double_3x3_1_bn_out)\n        inception_3a_double_3x3_2_out = self.inception_3a_double_3x3_2(inception_3a_relu_double_3x3_1_out)\n        inception_3a_double_3x3_2_bn_out = self.inception_3a_double_3x3_2_bn(inception_3a_double_3x3_2_out)\n        inception_3a_relu_double_3x3_2_out = self.inception_3a_relu_double_3x3_2(inception_3a_double_3x3_2_bn_out)\n        inception_3a_pool_out = self.inception_3a_pool(pool2_3x3_s2_out)\n        inception_3a_pool_proj_out = self.inception_3a_pool_proj(inception_3a_pool_out)\n        inception_3a_pool_proj_bn_out = self.inception_3a_pool_proj_bn(inception_3a_pool_proj_out)\n        inception_3a_relu_pool_proj_out = self.inception_3a_relu_pool_proj(inception_3a_pool_proj_bn_out)\n        inception_3a_output_out = torch.cat([inception_3a_relu_1x1_out,inception_3a_relu_3x3_out,inception_3a_relu_double_3x3_2_out ,inception_3a_relu_pool_proj_out], 1)\n        inception_3b_1x1_out = self.inception_3b_1x1(inception_3a_output_out)\n        inception_3b_1x1_bn_out = self.inception_3b_1x1_bn(inception_3b_1x1_out)\n        inception_3b_relu_1x1_out = self.inception_3b_relu_1x1(inception_3b_1x1_bn_out)\n        inception_3b_3x3_reduce_out = self.inception_3b_3x3_reduce(inception_3a_output_out)\n        inception_3b_3x3_reduce_bn_out = self.inception_3b_3x3_reduce_bn(inception_3b_3x3_reduce_out)\n        inception_3b_relu_3x3_reduce_out = self.inception_3b_relu_3x3_reduce(inception_3b_3x3_reduce_bn_out)\n        inception_3b_3x3_out = self.inception_3b_3x3(inception_3b_relu_3x3_reduce_out)\n        inception_3b_3x3_bn_out = self.inception_3b_3x3_bn(inception_3b_3x3_out)\n        inception_3b_relu_3x3_out = self.inception_3b_relu_3x3(inception_3b_3x3_bn_out)\n        inception_3b_double_3x3_reduce_out = self.inception_3b_double_3x3_reduce(inception_3a_output_out)\n        inception_3b_double_3x3_reduce_bn_out = self.inception_3b_double_3x3_reduce_bn(inception_3b_double_3x3_reduce_out)\n        inception_3b_relu_double_3x3_reduce_out = self.inception_3b_relu_double_3x3_reduce(inception_3b_double_3x3_reduce_bn_out)\n        inception_3b_double_3x3_1_out = self.inception_3b_double_3x3_1(inception_3b_relu_double_3x3_reduce_out)\n        inception_3b_double_3x3_1_bn_out = self.inception_3b_double_3x3_1_bn(inception_3b_double_3x3_1_out)\n        inception_3b_relu_double_3x3_1_out = self.inception_3b_relu_double_3x3_1(inception_3b_double_3x3_1_bn_out)\n        inception_3b_double_3x3_2_out = self.inception_3b_double_3x3_2(inception_3b_relu_double_3x3_1_out)\n        inception_3b_double_3x3_2_bn_out = self.inception_3b_double_3x3_2_bn(inception_3b_double_3x3_2_out)\n        inception_3b_relu_double_3x3_2_out = self.inception_3b_relu_double_3x3_2(inception_3b_double_3x3_2_bn_out)\n        inception_3b_pool_out = self.inception_3b_pool(inception_3a_output_out)\n        inception_3b_pool_proj_out = self.inception_3b_pool_proj(inception_3b_pool_out)\n        inception_3b_pool_proj_bn_out = self.inception_3b_pool_proj_bn(inception_3b_pool_proj_out)\n        inception_3b_relu_pool_proj_out = self.inception_3b_relu_pool_proj(inception_3b_pool_proj_bn_out)\n        inception_3b_output_out = torch.cat([inception_3b_relu_1x1_out,inception_3b_relu_3x3_out,inception_3b_relu_double_3x3_2_out,inception_3b_relu_pool_proj_out], 1)\n        inception_3c_3x3_reduce_out = self.inception_3c_3x3_reduce(inception_3b_output_out)\n        inception_3c_3x3_reduce_bn_out = self.inception_3c_3x3_reduce_bn(inception_3c_3x3_reduce_out)\n        inception_3c_relu_3x3_reduce_out = self.inception_3c_relu_3x3_reduce(inception_3c_3x3_reduce_bn_out)\n        inception_3c_3x3_out = self.inception_3c_3x3(inception_3c_relu_3x3_reduce_out)\n        inception_3c_3x3_bn_out = self.inception_3c_3x3_bn(inception_3c_3x3_out)\n        inception_3c_relu_3x3_out = self.inception_3c_relu_3x3(inception_3c_3x3_bn_out)\n        inception_3c_double_3x3_reduce_out = self.inception_3c_double_3x3_reduce(inception_3b_output_out)\n        inception_3c_double_3x3_reduce_bn_out = self.inception_3c_double_3x3_reduce_bn(inception_3c_double_3x3_reduce_out)\n        inception_3c_relu_double_3x3_reduce_out = self.inception_3c_relu_double_3x3_reduce(inception_3c_double_3x3_reduce_bn_out)\n        inception_3c_double_3x3_1_out = self.inception_3c_double_3x3_1(inception_3c_relu_double_3x3_reduce_out)\n        inception_3c_double_3x3_1_bn_out = self.inception_3c_double_3x3_1_bn(inception_3c_double_3x3_1_out)\n        inception_3c_relu_double_3x3_1_out = self.inception_3c_relu_double_3x3_1(inception_3c_double_3x3_1_bn_out)\n        inception_3c_double_3x3_2_out = self.inception_3c_double_3x3_2(inception_3c_relu_double_3x3_1_out)\n        inception_3c_double_3x3_2_bn_out = self.inception_3c_double_3x3_2_bn(inception_3c_double_3x3_2_out)\n        inception_3c_relu_double_3x3_2_out = self.inception_3c_relu_double_3x3_2(inception_3c_double_3x3_2_bn_out)\n        inception_3c_pool_out = self.inception_3c_pool(inception_3b_output_out)\n        inception_3c_output_out = torch.cat([inception_3c_relu_3x3_out,inception_3c_relu_double_3x3_2_out,inception_3c_pool_out], 1)\n        inception_4a_1x1_out = self.inception_4a_1x1(inception_3c_output_out)\n        inception_4a_1x1_bn_out = self.inception_4a_1x1_bn(inception_4a_1x1_out)\n        inception_4a_relu_1x1_out = self.inception_4a_relu_1x1(inception_4a_1x1_bn_out)\n        inception_4a_3x3_reduce_out = self.inception_4a_3x3_reduce(inception_3c_output_out)\n        inception_4a_3x3_reduce_bn_out = self.inception_4a_3x3_reduce_bn(inception_4a_3x3_reduce_out)\n        inception_4a_relu_3x3_reduce_out = self.inception_4a_relu_3x3_reduce(inception_4a_3x3_reduce_bn_out)\n        inception_4a_3x3_out = self.inception_4a_3x3(inception_4a_relu_3x3_reduce_out)\n        inception_4a_3x3_bn_out = self.inception_4a_3x3_bn(inception_4a_3x3_out)\n        inception_4a_relu_3x3_out = self.inception_4a_relu_3x3(inception_4a_3x3_bn_out)\n        inception_4a_double_3x3_reduce_out = self.inception_4a_double_3x3_reduce(inception_3c_output_out)\n        inception_4a_double_3x3_reduce_bn_out = self.inception_4a_double_3x3_reduce_bn(inception_4a_double_3x3_reduce_out)\n        inception_4a_relu_double_3x3_reduce_out = self.inception_4a_relu_double_3x3_reduce(inception_4a_double_3x3_reduce_bn_out)\n        inception_4a_double_3x3_1_out = self.inception_4a_double_3x3_1(inception_4a_relu_double_3x3_reduce_out)\n        inception_4a_double_3x3_1_bn_out = self.inception_4a_double_3x3_1_bn(inception_4a_double_3x3_1_out)\n        inception_4a_relu_double_3x3_1_out = self.inception_4a_relu_double_3x3_1(inception_4a_double_3x3_1_bn_out)\n        inception_4a_double_3x3_2_out = self.inception_4a_double_3x3_2(inception_4a_relu_double_3x3_1_out)\n        inception_4a_double_3x3_2_bn_out = self.inception_4a_double_3x3_2_bn(inception_4a_double_3x3_2_out)\n        inception_4a_relu_double_3x3_2_out = self.inception_4a_relu_double_3x3_2(inception_4a_double_3x3_2_bn_out)\n        inception_4a_pool_out = self.inception_4a_pool(inception_3c_output_out)\n        inception_4a_pool_proj_out = self.inception_4a_pool_proj(inception_4a_pool_out)\n        inception_4a_pool_proj_bn_out = self.inception_4a_pool_proj_bn(inception_4a_pool_proj_out)\n        inception_4a_relu_pool_proj_out = self.inception_4a_relu_pool_proj(inception_4a_pool_proj_bn_out)\n        inception_4a_output_out = torch.cat([inception_4a_relu_1x1_out,inception_4a_relu_3x3_out,inception_4a_relu_double_3x3_2_out,inception_4a_relu_pool_proj_out], 1)\n        inception_4b_1x1_out = self.inception_4b_1x1(inception_4a_output_out)\n        inception_4b_1x1_bn_out = self.inception_4b_1x1_bn(inception_4b_1x1_out)\n        inception_4b_relu_1x1_out = self.inception_4b_relu_1x1(inception_4b_1x1_bn_out)\n        inception_4b_3x3_reduce_out = self.inception_4b_3x3_reduce(inception_4a_output_out)\n        inception_4b_3x3_reduce_bn_out = self.inception_4b_3x3_reduce_bn(inception_4b_3x3_reduce_out)\n        inception_4b_relu_3x3_reduce_out = self.inception_4b_relu_3x3_reduce(inception_4b_3x3_reduce_bn_out)\n        inception_4b_3x3_out = self.inception_4b_3x3(inception_4b_relu_3x3_reduce_out)\n        inception_4b_3x3_bn_out = self.inception_4b_3x3_bn(inception_4b_3x3_out)\n        inception_4b_relu_3x3_out = self.inception_4b_relu_3x3(inception_4b_3x3_bn_out)\n        inception_4b_double_3x3_reduce_out = self.inception_4b_double_3x3_reduce(inception_4a_output_out)\n        inception_4b_double_3x3_reduce_bn_out = self.inception_4b_double_3x3_reduce_bn(inception_4b_double_3x3_reduce_out)\n        inception_4b_relu_double_3x3_reduce_out = self.inception_4b_relu_double_3x3_reduce(inception_4b_double_3x3_reduce_bn_out)\n        inception_4b_double_3x3_1_out = self.inception_4b_double_3x3_1(inception_4b_relu_double_3x3_reduce_out)\n        inception_4b_double_3x3_1_bn_out = self.inception_4b_double_3x3_1_bn(inception_4b_double_3x3_1_out)\n        inception_4b_relu_double_3x3_1_out = self.inception_4b_relu_double_3x3_1(inception_4b_double_3x3_1_bn_out)\n        inception_4b_double_3x3_2_out = self.inception_4b_double_3x3_2(inception_4b_relu_double_3x3_1_out)\n        inception_4b_double_3x3_2_bn_out = self.inception_4b_double_3x3_2_bn(inception_4b_double_3x3_2_out)\n        inception_4b_relu_double_3x3_2_out = self.inception_4b_relu_double_3x3_2(inception_4b_double_3x3_2_bn_out)\n        inception_4b_pool_out = self.inception_4b_pool(inception_4a_output_out)\n        inception_4b_pool_proj_out = self.inception_4b_pool_proj(inception_4b_pool_out)\n        inception_4b_pool_proj_bn_out = self.inception_4b_pool_proj_bn(inception_4b_pool_proj_out)\n        inception_4b_relu_pool_proj_out = self.inception_4b_relu_pool_proj(inception_4b_pool_proj_bn_out)\n        inception_4b_output_out = torch.cat([inception_4b_relu_1x1_out,inception_4b_relu_3x3_out,inception_4b_relu_double_3x3_2_out,inception_4b_relu_pool_proj_out], 1)\n        inception_4c_1x1_out = self.inception_4c_1x1(inception_4b_output_out)\n        inception_4c_1x1_bn_out = self.inception_4c_1x1_bn(inception_4c_1x1_out)\n        inception_4c_relu_1x1_out = self.inception_4c_relu_1x1(inception_4c_1x1_bn_out)\n        inception_4c_3x3_reduce_out = self.inception_4c_3x3_reduce(inception_4b_output_out)\n        inception_4c_3x3_reduce_bn_out = self.inception_4c_3x3_reduce_bn(inception_4c_3x3_reduce_out)\n        inception_4c_relu_3x3_reduce_out = self.inception_4c_relu_3x3_reduce(inception_4c_3x3_reduce_bn_out)\n        inception_4c_3x3_out = self.inception_4c_3x3(inception_4c_relu_3x3_reduce_out)\n        inception_4c_3x3_bn_out = self.inception_4c_3x3_bn(inception_4c_3x3_out)\n        inception_4c_relu_3x3_out = self.inception_4c_relu_3x3(inception_4c_3x3_bn_out)\n        inception_4c_double_3x3_reduce_out = self.inception_4c_double_3x3_reduce(inception_4b_output_out)\n        inception_4c_double_3x3_reduce_bn_out = self.inception_4c_double_3x3_reduce_bn(inception_4c_double_3x3_reduce_out)\n        inception_4c_relu_double_3x3_reduce_out = self.inception_4c_relu_double_3x3_reduce(inception_4c_double_3x3_reduce_bn_out)\n        inception_4c_double_3x3_1_out = self.inception_4c_double_3x3_1(inception_4c_relu_double_3x3_reduce_out)\n        inception_4c_double_3x3_1_bn_out = self.inception_4c_double_3x3_1_bn(inception_4c_double_3x3_1_out)\n        inception_4c_relu_double_3x3_1_out = self.inception_4c_relu_double_3x3_1(inception_4c_double_3x3_1_bn_out)\n        inception_4c_double_3x3_2_out = self.inception_4c_double_3x3_2(inception_4c_relu_double_3x3_1_out)\n        inception_4c_double_3x3_2_bn_out = self.inception_4c_double_3x3_2_bn(inception_4c_double_3x3_2_out)\n        inception_4c_relu_double_3x3_2_out = self.inception_4c_relu_double_3x3_2(inception_4c_double_3x3_2_bn_out)\n        inception_4c_pool_out = self.inception_4c_pool(inception_4b_output_out)\n        inception_4c_pool_proj_out = self.inception_4c_pool_proj(inception_4c_pool_out)\n        inception_4c_pool_proj_bn_out = self.inception_4c_pool_proj_bn(inception_4c_pool_proj_out)\n        inception_4c_relu_pool_proj_out = self.inception_4c_relu_pool_proj(inception_4c_pool_proj_bn_out)\n        inception_4c_output_out = torch.cat([inception_4c_relu_1x1_out,inception_4c_relu_3x3_out,inception_4c_relu_double_3x3_2_out,inception_4c_relu_pool_proj_out], 1)\n        inception_4d_1x1_out = self.inception_4d_1x1(inception_4c_output_out)\n        inception_4d_1x1_bn_out = self.inception_4d_1x1_bn(inception_4d_1x1_out)\n        inception_4d_relu_1x1_out = self.inception_4d_relu_1x1(inception_4d_1x1_bn_out)\n        inception_4d_3x3_reduce_out = self.inception_4d_3x3_reduce(inception_4c_output_out)\n        inception_4d_3x3_reduce_bn_out = self.inception_4d_3x3_reduce_bn(inception_4d_3x3_reduce_out)\n        inception_4d_relu_3x3_reduce_out = self.inception_4d_relu_3x3_reduce(inception_4d_3x3_reduce_bn_out)\n        inception_4d_3x3_out = self.inception_4d_3x3(inception_4d_relu_3x3_reduce_out)\n        inception_4d_3x3_bn_out = self.inception_4d_3x3_bn(inception_4d_3x3_out)\n        inception_4d_relu_3x3_out = self.inception_4d_relu_3x3(inception_4d_3x3_bn_out)\n        inception_4d_double_3x3_reduce_out = self.inception_4d_double_3x3_reduce(inception_4c_output_out)\n        inception_4d_double_3x3_reduce_bn_out = self.inception_4d_double_3x3_reduce_bn(inception_4d_double_3x3_reduce_out)\n        inception_4d_relu_double_3x3_reduce_out = self.inception_4d_relu_double_3x3_reduce(inception_4d_double_3x3_reduce_bn_out)\n        inception_4d_double_3x3_1_out = self.inception_4d_double_3x3_1(inception_4d_relu_double_3x3_reduce_out)\n        inception_4d_double_3x3_1_bn_out = self.inception_4d_double_3x3_1_bn(inception_4d_double_3x3_1_out)\n        inception_4d_relu_double_3x3_1_out = self.inception_4d_relu_double_3x3_1(inception_4d_double_3x3_1_bn_out)\n        inception_4d_double_3x3_2_out = self.inception_4d_double_3x3_2(inception_4d_relu_double_3x3_1_out)\n        inception_4d_double_3x3_2_bn_out = self.inception_4d_double_3x3_2_bn(inception_4d_double_3x3_2_out)\n        inception_4d_relu_double_3x3_2_out = self.inception_4d_relu_double_3x3_2(inception_4d_double_3x3_2_bn_out)\n        inception_4d_pool_out = self.inception_4d_pool(inception_4c_output_out)\n        inception_4d_pool_proj_out = self.inception_4d_pool_proj(inception_4d_pool_out)\n        inception_4d_pool_proj_bn_out = self.inception_4d_pool_proj_bn(inception_4d_pool_proj_out)\n        inception_4d_relu_pool_proj_out = self.inception_4d_relu_pool_proj(inception_4d_pool_proj_bn_out)\n        inception_4d_output_out = torch.cat([inception_4d_relu_1x1_out,inception_4d_relu_3x3_out,inception_4d_relu_double_3x3_2_out,inception_4d_relu_pool_proj_out], 1)\n        inception_4e_3x3_reduce_out = self.inception_4e_3x3_reduce(inception_4d_output_out)\n        inception_4e_3x3_reduce_bn_out = self.inception_4e_3x3_reduce_bn(inception_4e_3x3_reduce_out)\n        inception_4e_relu_3x3_reduce_out = self.inception_4e_relu_3x3_reduce(inception_4e_3x3_reduce_bn_out)\n        inception_4e_3x3_out = self.inception_4e_3x3(inception_4e_relu_3x3_reduce_out)\n        inception_4e_3x3_bn_out = self.inception_4e_3x3_bn(inception_4e_3x3_out)\n        inception_4e_relu_3x3_out = self.inception_4e_relu_3x3(inception_4e_3x3_bn_out)\n        inception_4e_double_3x3_reduce_out = self.inception_4e_double_3x3_reduce(inception_4d_output_out)\n        inception_4e_double_3x3_reduce_bn_out = self.inception_4e_double_3x3_reduce_bn(inception_4e_double_3x3_reduce_out)\n        inception_4e_relu_double_3x3_reduce_out = self.inception_4e_relu_double_3x3_reduce(inception_4e_double_3x3_reduce_bn_out)\n        inception_4e_double_3x3_1_out = self.inception_4e_double_3x3_1(inception_4e_relu_double_3x3_reduce_out)\n        inception_4e_double_3x3_1_bn_out = self.inception_4e_double_3x3_1_bn(inception_4e_double_3x3_1_out)\n        inception_4e_relu_double_3x3_1_out = self.inception_4e_relu_double_3x3_1(inception_4e_double_3x3_1_bn_out)\n        inception_4e_double_3x3_2_out = self.inception_4e_double_3x3_2(inception_4e_relu_double_3x3_1_out)\n        inception_4e_double_3x3_2_bn_out = self.inception_4e_double_3x3_2_bn(inception_4e_double_3x3_2_out)\n        inception_4e_relu_double_3x3_2_out = self.inception_4e_relu_double_3x3_2(inception_4e_double_3x3_2_bn_out)\n        inception_4e_pool_out = self.inception_4e_pool(inception_4d_output_out)\n        inception_4e_output_out = torch.cat([inception_4e_relu_3x3_out,inception_4e_relu_double_3x3_2_out,inception_4e_pool_out], 1)\n        inception_5a_1x1_out = self.inception_5a_1x1(inception_4e_output_out)\n        inception_5a_1x1_bn_out = self.inception_5a_1x1_bn(inception_5a_1x1_out)\n        inception_5a_relu_1x1_out = self.inception_5a_relu_1x1(inception_5a_1x1_bn_out)\n        inception_5a_3x3_reduce_out = self.inception_5a_3x3_reduce(inception_4e_output_out)\n        inception_5a_3x3_reduce_bn_out = self.inception_5a_3x3_reduce_bn(inception_5a_3x3_reduce_out)\n        inception_5a_relu_3x3_reduce_out = self.inception_5a_relu_3x3_reduce(inception_5a_3x3_reduce_bn_out)\n        inception_5a_3x3_out = self.inception_5a_3x3(inception_5a_relu_3x3_reduce_out)\n        inception_5a_3x3_bn_out = self.inception_5a_3x3_bn(inception_5a_3x3_out)\n        inception_5a_relu_3x3_out = self.inception_5a_relu_3x3(inception_5a_3x3_bn_out)\n        inception_5a_double_3x3_reduce_out = self.inception_5a_double_3x3_reduce(inception_4e_output_out)\n        inception_5a_double_3x3_reduce_bn_out = self.inception_5a_double_3x3_reduce_bn(inception_5a_double_3x3_reduce_out)\n        inception_5a_relu_double_3x3_reduce_out = self.inception_5a_relu_double_3x3_reduce(inception_5a_double_3x3_reduce_bn_out)\n        inception_5a_double_3x3_1_out = self.inception_5a_double_3x3_1(inception_5a_relu_double_3x3_reduce_out)\n        inception_5a_double_3x3_1_bn_out = self.inception_5a_double_3x3_1_bn(inception_5a_double_3x3_1_out)\n        inception_5a_relu_double_3x3_1_out = self.inception_5a_relu_double_3x3_1(inception_5a_double_3x3_1_bn_out)\n        inception_5a_double_3x3_2_out = self.inception_5a_double_3x3_2(inception_5a_relu_double_3x3_1_out)\n        inception_5a_double_3x3_2_bn_out = self.inception_5a_double_3x3_2_bn(inception_5a_double_3x3_2_out)\n        inception_5a_relu_double_3x3_2_out = self.inception_5a_relu_double_3x3_2(inception_5a_double_3x3_2_bn_out)\n        inception_5a_pool_out = self.inception_5a_pool(inception_4e_output_out)\n        inception_5a_pool_proj_out = self.inception_5a_pool_proj(inception_5a_pool_out)\n        inception_5a_pool_proj_bn_out = self.inception_5a_pool_proj_bn(inception_5a_pool_proj_out)\n        inception_5a_relu_pool_proj_out = self.inception_5a_relu_pool_proj(inception_5a_pool_proj_bn_out)\n        inception_5a_output_out = torch.cat([inception_5a_relu_1x1_out,inception_5a_relu_3x3_out,inception_5a_relu_double_3x3_2_out,inception_5a_relu_pool_proj_out], 1)\n        inception_5b_1x1_out = self.inception_5b_1x1(inception_5a_output_out)\n        inception_5b_1x1_bn_out = self.inception_5b_1x1_bn(inception_5b_1x1_out)\n        inception_5b_relu_1x1_out = self.inception_5b_relu_1x1(inception_5b_1x1_bn_out)\n        inception_5b_3x3_reduce_out = self.inception_5b_3x3_reduce(inception_5a_output_out)\n        inception_5b_3x3_reduce_bn_out = self.inception_5b_3x3_reduce_bn(inception_5b_3x3_reduce_out)\n        inception_5b_relu_3x3_reduce_out = self.inception_5b_relu_3x3_reduce(inception_5b_3x3_reduce_bn_out)\n        inception_5b_3x3_out = self.inception_5b_3x3(inception_5b_relu_3x3_reduce_out)\n        inception_5b_3x3_bn_out = self.inception_5b_3x3_bn(inception_5b_3x3_out)\n        inception_5b_relu_3x3_out = self.inception_5b_relu_3x3(inception_5b_3x3_bn_out)\n        inception_5b_double_3x3_reduce_out = self.inception_5b_double_3x3_reduce(inception_5a_output_out)\n        inception_5b_double_3x3_reduce_bn_out = self.inception_5b_double_3x3_reduce_bn(inception_5b_double_3x3_reduce_out)\n        inception_5b_relu_double_3x3_reduce_out = self.inception_5b_relu_double_3x3_reduce(inception_5b_double_3x3_reduce_bn_out)\n        inception_5b_double_3x3_1_out = self.inception_5b_double_3x3_1(inception_5b_relu_double_3x3_reduce_out)\n        inception_5b_double_3x3_1_bn_out = self.inception_5b_double_3x3_1_bn(inception_5b_double_3x3_1_out)\n        inception_5b_relu_double_3x3_1_out = self.inception_5b_relu_double_3x3_1(inception_5b_double_3x3_1_bn_out)\n        inception_5b_double_3x3_2_out = self.inception_5b_double_3x3_2(inception_5b_relu_double_3x3_1_out)\n        inception_5b_double_3x3_2_bn_out = self.inception_5b_double_3x3_2_bn(inception_5b_double_3x3_2_out)\n        inception_5b_relu_double_3x3_2_out = self.inception_5b_relu_double_3x3_2(inception_5b_double_3x3_2_bn_out)\n        inception_5b_pool_out = self.inception_5b_pool(inception_5a_output_out)\n        inception_5b_pool_proj_out = self.inception_5b_pool_proj(inception_5b_pool_out)\n        inception_5b_pool_proj_bn_out = self.inception_5b_pool_proj_bn(inception_5b_pool_proj_out)\n        inception_5b_relu_pool_proj_out = self.inception_5b_relu_pool_proj(inception_5b_pool_proj_bn_out)\n        inception_5b_output_out = torch.cat([inception_5b_relu_1x1_out,inception_5b_relu_3x3_out,inception_5b_relu_double_3x3_2_out,inception_5b_relu_pool_proj_out], 1)\n        return inception_3b_output_out,inception_4d_output_out,inception_5b_output_out\n\n    def forward(self, input):\n        return self.features(input)\n"
  },
  {
    "path": "utils/datasets.py",
    "content": "import os\r\nimport sys\r\nfrom PIL import Image\r\nimport torch\r\nimport numpy as np\r\nimport torch.utils.data as data\r\nimport torchvision.transforms as transforms\r\nimport torchvision.datasets as datasets\r\n\r\ndef default_loader(path):\r\n    return Image.open(path).convert('RGB')\r\nclass MultiLabelDataset(data.Dataset):\r\n    def __init__(self, root, label, transform = None, loader = default_loader):\r\n        images = []\r\n        labels = open(label).readlines()\r\n        for line in labels:\r\n            items = line.split()\r\n            img_name = items.pop(0)\r\n            if os.path.isfile(os.path.join(root, img_name)):\r\n                cur_label = tuple([int(v) for v in items])\r\n                images.append((img_name, cur_label))\r\n            else:\r\n                print(os.path.join(root, img_name) + 'Not Found.')\r\n        self.root = root\r\n        self.images = images\r\n        self.transform = transform\r\n        self.loader = loader\r\n\r\n    def __getitem__(self, index):\r\n        img_name, label = self.images[index]\r\n        img = self.loader(os.path.join(self.root, img_name))\r\n        raw_img = img.copy()\r\n        if self.transform is not None:\r\n            img = self.transform(img)\r\n        return img, torch.Tensor(label)\r\n\r\n    def __len__(self):\r\n        return len(self.images)\r\n\r\n\r\nattr_nums = {}\r\nattr_nums['pa100k'] = 26\r\nattr_nums['rap'] = 51\r\nattr_nums['peta'] = 35\r\n\r\ndescription = {}\r\ndescription['pa100k'] = ['Female',\r\n                        'AgeOver60',\r\n                        'Age18-60',\r\n                        'AgeLess18',\r\n                        'Front',\r\n                        'Side',\r\n                        'Back',\r\n                        'Hat',\r\n                        'Glasses',\r\n                        'HandBag',\r\n                        'ShoulderBag',\r\n                        'Backpack',\r\n                        'HoldObjectsInFront',\r\n                        'ShortSleeve',\r\n                        'LongSleeve',\r\n                        'UpperStride',\r\n                        'UpperLogo',\r\n                        'UpperPlaid',\r\n                        'UpperSplice',\r\n                        'LowerStripe',\r\n                        'LowerPattern',\r\n                        'LongCoat',\r\n                        'Trousers',\r\n                        'Shorts',\r\n                        'Skirt&Dress',\r\n                        'boots']\r\n\r\ndescription['peta'] = ['Age16-30',\r\n                        'Age31-45',\r\n                        'Age46-60',\r\n                        'AgeAbove61',\r\n                        'Backpack',\r\n                        'CarryingOther',\r\n                        'Casual lower',\r\n                        'Casual upper',\r\n                        'Formal lower',\r\n                        'Formal upper',\r\n                        'Hat',\r\n                        'Jacket',\r\n                        'Jeans',\r\n                        'Leather Shoes',\r\n                        'Logo',\r\n                        'Long hair',\r\n                        'Male',\r\n                        'Messenger Bag',\r\n                        'Muffler',\r\n                        'No accessory',\r\n                        'No carrying',\r\n                        'Plaid',\r\n                        'PlasticBags',\r\n                        'Sandals',\r\n                        'Shoes',\r\n                        'Shorts',\r\n                        'Short Sleeve',\r\n                        'Skirt',\r\n                        'Sneaker',\r\n                        'Stripes',\r\n                        'Sunglasses',\r\n                        'Trousers',\r\n                        'Tshirt',\r\n                        'UpperOther',\r\n                        'V-Neck']\r\n\r\ndescription['rap'] = ['Female',\r\n                        'AgeLess16',\r\n                        'Age17-30',\r\n                        'Age31-45',\r\n                        'BodyFat',\r\n                        'BodyNormal',\r\n                        'BodyThin',\r\n                        'Customer',\r\n                        'Clerk',\r\n                        'BaldHead',\r\n                        'LongHair',\r\n                        'BlackHair',\r\n                        'Hat',\r\n                        'Glasses',\r\n                        'Muffler',\r\n                        'Shirt',\r\n                        'Sweater',\r\n                        'Vest',\r\n                        'TShirt',\r\n                        'Cotton',\r\n                        'Jacket',\r\n                        'Suit-Up',\r\n                        'Tight',\r\n                        'ShortSleeve',\r\n                        'LongTrousers',\r\n                        'Skirt',\r\n                        'ShortSkirt',\r\n                        'Dress',\r\n                        'Jeans',\r\n                        'TightTrousers',\r\n                        'LeatherShoes',\r\n                        'SportShoes',\r\n                        'Boots',\r\n                        'ClothShoes',\r\n                        'CasualShoes',\r\n                        'Backpack',\r\n                        'SSBag',\r\n                        'HandBag',\r\n                        'Box',\r\n                        'PlasticBag',\r\n                        'PaperBag',\r\n                        'HandTrunk',\r\n                        'OtherAttchment',\r\n                        'Calling',\r\n                        'Talking',\r\n                        'Gathering',\r\n                        'Holding',\r\n                        'Pusing',\r\n                        'Pulling',\r\n                        'CarryingbyArm',\r\n                        'CarryingbyHand']\r\n\r\n\r\n\r\n\r\ndef Get_Dataset(experiment, approach):\r\n\r\n    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\r\n    transform_train = transforms.Compose([\r\n        transforms.Resize(size=(256, 128)),\r\n        transforms.RandomHorizontalFlip(),\r\n        # transforms.ColorJitter(hue=.05, saturation=.05),\r\n        # transforms.RandomRotation(20, resample=Image.BILINEAR),\r\n        transforms.ToTensor(),\r\n        normalize\r\n        ])\r\n    transform_test = transforms.Compose([\r\n        transforms.Resize(size=(256, 128)),\r\n        transforms.ToTensor(),\r\n        normalize\r\n        ])\r\n\r\n    if experiment == 'pa100k':\r\n        train_dataset = MultiLabelDataset(root='data_path',\r\n                    label='train_list_path', transform=transform_train)\r\n        val_dataset = MultiLabelDataset(root='data_path',\r\n                    label='val_list_path', transform=transform_test)\r\n        return train_dataset, val_dataset, attr_nums['pa100k'], description['pa100k']\r\n    elif experiment == 'rap':\r\n        train_dataset = MultiLabelDataset(root='data_path',\r\n                    label='train_list_path', transform=transform_train)\r\n        val_dataset = MultiLabelDataset(root='data_path',\r\n                    label='val_list_path', transform=transform_test)\r\n        return train_dataset, val_dataset, attr_nums['rap'], description['rap']\r\n    elif experiment == 'peta':\r\n        train_dataset = MultiLabelDataset(root='data_path',\r\n                    label='train_list_path', transform=transform_train)\r\n        val_dataset = MultiLabelDataset(root='data_path',\r\n                    label='val_list_path', transform=transform_test)\r\n        return train_dataset, val_dataset, attr_nums['peta'], description['peta']\r\n"
  }
]