Repository: chufengt/iccv19_attribute Branch: master Commit: 800b969b1349 Files: 12 Total size: 15.2 MB Directory structure: gitextract_37ig3rjb/ ├── LICENSE ├── README.md ├── data_list/ │ ├── pa-100k/ │ │ ├── test.txt │ │ └── train_val.txt │ ├── peta/ │ │ ├── PETA_test_list.txt │ │ └── PETA_train_list.txt │ └── rap/ │ ├── test.txt │ └── train.txt ├── main.py ├── model/ │ ├── __init__.py │ └── inception_iccv.py └── utils/ └── datasets.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ # Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization Code for the paper "Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization", ICCV 2019, Seoul. [[Paper]](https://arxiv.org/abs/1910.04562) [[Poster]](https://chufengt.github.io/publication/pedestrian-attribute/iccv_poster_id2029.pdf) Contact: chufeng.t@foxmail.com or tcf18@mails.tsinghua.edu.cn ## Environment - Python 3.6+ - PyTorch 0.4+ ## Datasets - RAP: http://rap.idealtest.org/ - PETA: http://mmlab.ie.cuhk.edu.hk/projects/PETA.html - PA-100K: https://github.com/xh-liu/HydraPlus-Net The original datasets should be processed to match the DataLoader. We provide the label lists for training and testing. ## Training and Testing ``` python main.py --approach=inception_iccv --experiment=rap ``` ``` python main.py --approach=inception_iccv --experiment=rap -e --resume='model_path' ``` ## Pretrained Models We provide the pretrained models for reference, the results may slightly different with the values reported in our paper. | Dataset | mA | Link | | ------- | ----- | ------------------------------------------------------------ | | PETA | 86.34 | [Model](https://drive.google.com/file/d/1cvX43Qn_vydzT_jnmgwYUUe9hIA161PH/view?usp=sharing) | | RAP | 81.86 | [Model](https://drive.google.com/file/d/15paMK0-rKDsuzptDPK5kH2JuL8QO0HyS/view?usp=sharing) | | PA-100K | 80.45 | [Model](https://drive.google.com/file/d/1xIw3jpvE1pDC3U464kcFJ58iSKCRNQ63/view?usp=sharing) | ## Reference If this work is useful to your research, please cite: ``` @inproceedings{tang2019improving, title={Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization}, author={Tang, Chufeng and Sheng, Lu and Zhang, Zhaoxiang and Hu, Xiaolin}, booktitle={Proceedings of the IEEE International Conference on Computer Vision}, pages={4997--5006}, year={2019} } ``` ================================================ FILE: data_list/pa-100k/test.txt ================================================ 090001.jpg 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 090002.jpg 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 090003.jpg 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 090004.jpg 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 090005.jpg 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 090006.jpg 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 090007.jpg 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 090008.jpg 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 090009.jpg 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 090010.jpg 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 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0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 CAM16_2014-03-20_20140320160121-20140320160709_tarid13_frame473_line1.png 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM21_2014-03-02_20140302143752-20140302144704_tarid479_frame6018_line1.png 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM27_2014-02-26_20140226112532-20140226113304_tarid199_frame4880_line1.png 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 CAM18_2014-03-20_20140320163556-20140320164144_tarid365_frame3626_line1.png 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM20_2014-03-10_20140310134306-20140310134858_tarid11_frame1116_line1.png 1 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 CAM12_2014-02-26_20140226111833-20140226112421_tarid901_frame3881_line1.png 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 CAM16_2014-02-26_20140226114745-20140226115333_tarid133_frame1779_line1.png 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 CAM22_2014-03-01_20140301104500-20140301105016_tarid256_frame2008_line2.png 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 CAM06_2014-03-03_20140303134555-20140303135143_tarid40_frame3961_line1.png 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 CAM12_2014-02-25_20140225152721-20140225154153_tarid1078_frame7518_line1.png 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM25_2014-03-05_20140305114538-20140305115126_tarid110_frame684_line3.png 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM28_2014-03-03_20140303134843-20140303135759_tarid133_frame3504_line1.png 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM14_2014-02-22_20140222103216-20140222103804_tarid218_frame2340_line1.png 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 CAM18_2014-03-01_20140301103714-20140301104414_tarid10_frame402_line1.png 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM09_2014-02-17_20140217103851-20140217104439_tarid315_frame3297_line1.png 1 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 CAM12_2014-03-03_20140303131556-20140303132147_tarid585_frame2742_line1.png 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM10_2014-02-15_20140215162933-20140215163605_tarid303_frame2046_line1.png 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 CAM29_2014-03-12_20140312174231-20140312175659_tarid385_frame4987_line2.png 1 0 0 1 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM17_2014-02-26_20140226115321-20140226115909_tarid422_frame3534_line2.png 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CAM28_2014-02-22_20140222102700-20140222104112_tarid57_frame9906_line1.png 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 ================================================ FILE: main.py ================================================ import argparse import os import shutil import time import sys import numpy as np import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim import model as models from utils.datasets import Get_Dataset parser = argparse.ArgumentParser(description='Pedestrian Attribute Framework') parser.add_argument('--experiment', default='rap', type=str, required=True, help='(default=%(default)s)') parser.add_argument('--approach', default='inception_iccv', type=str, required=True, help='(default=%(default)s)') parser.add_argument('--epochs', default=60, type=int, required=False, help='(default=%(default)d)') parser.add_argument('--batch_size', default=32, type=int, required=False, help='(default=%(default)d)') parser.add_argument('--lr', '--learning-rate', default=0.0001, type=float, required=False, help='(default=%(default)f)') parser.add_argument('--optimizer', default='adam', type=str, required=False, help='(default=%(default)s)') parser.add_argument('--momentum', default=0.9, type=float, required=False, help='(default=%(default)f)') parser.add_argument('--weight_decay', default=0.0005, type=float, required=False, help='(default=%(default)f)') parser.add_argument('--start-epoch', default=0, type=int, required=False, help='(default=%(default)d)') parser.add_argument('--print_freq', default=100, type=int, required=False, help='(default=%(default)d)') parser.add_argument('--save_freq', default=10, type=int, required=False, help='(default=%(default)d)') parser.add_argument('--resume', default='', type=str, required=False, help='(default=%(default)s)') parser.add_argument('--decay_epoch', default=(20,40), type=eval, required=False, help='(default=%(default)d)') parser.add_argument('--prefix', default='', type=str, required=False, help='(default=%(default)s)') parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', required=False, help='evaluate model on validation set') # Seed np.random.seed(1) torch.manual_seed(1) if torch.cuda.is_available(): torch.cuda.manual_seed(1) else: print('[CUDA unavailable]'); sys.exit() best_accu = 0 EPS = 1e-12 ##################################################################################################### def main(): global args, best_accu args = parser.parse_args() print('=' * 100) print('Arguments = ') for arg in vars(args): print('\t' + arg + ':', getattr(args, arg)) print('=' * 100) # Data loading code train_dataset, val_dataset, attr_num, description = Get_Dataset(args.experiment, args.approach) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4, pin_memory=True) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=32, shuffle=False, num_workers=4, pin_memory=True) # create model model = models.__dict__[args.approach](pretrained=True, num_classes=attr_num) # get the number of model parameters print('Number of model parameters: {}'.format( sum([p.data.nelement() for p in model.parameters()]))) print('') # for training on multiple GPUs. # Use CUDA_VISIBLE_DEVICES=0,1 to specify which GPUs to use model = torch.nn.DataParallel(model).cuda() # optionally resume from a checkpoint if args.resume: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) args.start_epoch = checkpoint['epoch'] best_accu = checkpoint['best_accu'] model.load_state_dict(checkpoint['state_dict']) print("=> loaded checkpoint '{}' (epoch {})" .format(args.resume, checkpoint['epoch'])) else: print("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = False cudnn.deterministic = True # define loss function criterion = Weighted_BCELoss(args.experiment) if args.optimizer == 'adam': optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999), weight_decay=args.weight_decay) else: optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) if args.evaluate: test(val_loader, model, attr_num, description) return for epoch in range(args.start_epoch, args.epochs): adjust_learning_rate(optimizer, epoch, args.decay_epoch) # train for one epoch train(train_loader, model, criterion, optimizer, epoch) # evaluate on validation set accu = validate(val_loader, model, criterion, epoch) test(val_loader, model, attr_num, description) # remember best Accu and save checkpoint is_best = accu > best_accu best_accu = max(accu, best_accu) if epoch in args.decay_epoch: save_checkpoint({ 'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_accu': best_accu, }, epoch+1, args.prefix) def train(train_loader, model, criterion, optimizer, epoch): """Train for one epoch on the training set""" batch_time = AverageMeter() losses = AverageMeter() top1 = AverageMeter() model.train() end = time.time() for i, _ in enumerate(train_loader): input, target = _ target = target.cuda(non_blocking=True) input = input.cuda(non_blocking=True) output = model(input) bs = target.size(0) if type(output) == type(()) or type(output) == type([]): loss_list = [] # deep supervision for k in range(len(output)): out = output[k] loss_list.append(criterion.forward(torch.sigmoid(out), target, epoch)) loss = sum(loss_list) # maximum voting output = torch.max(torch.max(torch.max(output[0],output[1]),output[2]),output[3]) else: loss = criterion.forward(torch.sigmoid(output), target, epoch) # measure accuracy and record loss accu = accuracy(output.data, target) losses.update(loss.data, bs) top1.update(accu, bs) # compute gradient and do SGD step optimizer.zero_grad() loss.backward() optimizer.step() # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: print('Epoch: [{0}][{1}/{2}]\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' 'Accu {top1.val:.3f} ({top1.avg:.3f})'.format( epoch, i, len(train_loader), batch_time=batch_time, loss=losses, top1=top1)) def validate(val_loader, model, criterion, epoch): """Perform validation on the validation set""" batch_time = AverageMeter() losses = AverageMeter() top1 = AverageMeter() model.eval() end = time.time() for i, _ in enumerate(val_loader): input, target = _ target = target.cuda(non_blocking=True) input = input.cuda(non_blocking=True) output = model(input) bs = target.size(0) if type(output) == type(()) or type(output) == type([]): loss_list = [] # deep supervision for k in range(len(output)): out = output[k] loss_list.append(criterion.forward(torch.sigmoid(out), target, epoch)) loss = sum(loss_list) # maximum voting output = torch.max(torch.max(torch.max(output[0],output[1]),output[2]),output[3]) else: loss = criterion.forward(torch.sigmoid(output), target, epoch) # measure accuracy and record loss accu = accuracy(output.data, target) losses.update(loss.data, bs) top1.update(accu, bs) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: print('Test: [{0}/{1}]\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' 'Accu {top1.val:.3f} ({top1.avg:.3f})'.format( i, len(val_loader), batch_time=batch_time, loss=losses, top1=top1)) print(' * Accu {top1.avg:.3f}'.format(top1=top1)) return top1.avg def test(val_loader, model, attr_num, description): model.eval() pos_cnt = [] pos_tol = [] neg_cnt = [] neg_tol = [] accu = 0.0 prec = 0.0 recall = 0.0 tol = 0 for it in range(attr_num): pos_cnt.append(0) pos_tol.append(0) neg_cnt.append(0) neg_tol.append(0) for i, _ in enumerate(val_loader): input, target = _ target = target.cuda(non_blocking=True) input = input.cuda(non_blocking=True) output = model(input) bs = target.size(0) # maximum voting if type(output) == type(()) or type(output) == type([]): output = torch.max(torch.max(torch.max(output[0],output[1]),output[2]),output[3]) batch_size = target.size(0) tol = tol + batch_size output = torch.sigmoid(output.data).cpu().numpy() output = np.where(output > 0.5, 1, 0) target = target.cpu().numpy() for it in range(attr_num): for jt in range(batch_size): if target[jt][it] == 1: pos_tol[it] = pos_tol[it] + 1 if output[jt][it] == 1: pos_cnt[it] = pos_cnt[it] + 1 if target[jt][it] == 0: neg_tol[it] = neg_tol[it] + 1 if output[jt][it] == 0: neg_cnt[it] = neg_cnt[it] + 1 if attr_num == 1: continue for jt in range(batch_size): tp = 0 fn = 0 fp = 0 for it in range(attr_num): if output[jt][it] == 1 and target[jt][it] == 1: tp = tp + 1 elif output[jt][it] == 0 and target[jt][it] == 1: fn = fn + 1 elif output[jt][it] == 1 and target[jt][it] == 0: fp = fp + 1 if tp + fn + fp != 0: accu = accu + 1.0 * tp / (tp + fn + fp) if tp + fp != 0: prec = prec + 1.0 * tp / (tp + fp) if tp + fn != 0: recall = recall + 1.0 * tp / (tp + fn) print('=' * 100) print('\t Attr \tp_true/n_true\tp_tol/n_tol\tp_pred/n_pred\tcur_mA') mA = 0.0 for it in range(attr_num): cur_mA = ((1.0*pos_cnt[it]/pos_tol[it]) + (1.0*neg_cnt[it]/neg_tol[it])) / 2.0 mA = mA + cur_mA 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)) mA = mA / attr_num print('\t' + 'mA: '+str(mA)) if attr_num != 1: accu = accu / tol prec = prec / tol recall = recall / tol f1 = 2.0 * prec * recall / (prec + recall) print('\t' + 'Accuracy: '+str(accu)) print('\t' + 'Precision: '+str(prec)) print('\t' + 'Recall: '+str(recall)) print('\t' + 'F1_Score: '+str(f1)) print('=' * 100) def save_checkpoint(state, epoch, prefix, filename='.pth.tar'): """Saves checkpoint to disk""" directory = "your_path" + args.experiment + '/' + args.approach + '/' if not os.path.exists(directory): os.makedirs(directory) if prefix == '': filename = directory + str(epoch) + filename else: filename = directory + prefix + '_' + str(epoch) + filename torch.save(state, filename) class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def adjust_learning_rate(optimizer, epoch, decay_epoch): lr = args.lr for epc in decay_epoch: if epoch >= epc: lr = lr * 0.1 else: break print() print('Learning Rate:', lr) print() for param_group in optimizer.param_groups: param_group['lr'] = lr def accuracy(output, target): batch_size = target.size(0) attr_num = target.size(1) output = torch.sigmoid(output).cpu().numpy() output = np.where(output > 0.5, 1, 0) pred = torch.from_numpy(output).long() target = target.cpu().long() correct = pred.eq(target) correct = correct.numpy() res = [] for k in range(attr_num): res.append(1.0*sum(correct[:,k]) / batch_size) return sum(res) / attr_num class Weighted_BCELoss(object): """ Weighted_BCELoss was proposed in "Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios"[13]. """ def __init__(self, experiment): super(Weighted_BCELoss, self).__init__() self.weights = None if experiment == 'pa100k': self.weights = torch.Tensor([0.460444444444, 0.0134555555556, 0.924377777778, 0.0621666666667, 0.352666666667, 0.294622222222, 0.352711111111, 0.0435444444444, 0.179977777778, 0.185, 0.192733333333, 0.1601, 0.00952222222222, 0.5834, 0.4166, 0.0494777777778, 0.151044444444, 0.107755555556, 0.0419111111111, 0.00472222222222, 0.0168888888889, 0.0324111111111, 0.711711111111, 0.173444444444, 0.114844444444, 0.006]).cuda() elif experiment == 'rap': self.weights = torch.Tensor([0.311434, 0.009980, 0.430011, 0.560010, 0.144932, 0.742479, 0.097728, 0.946303, 0.048287, 0.004328, 0.189323, 0.944764, 0.016713, 0.072959, 0.010461, 0.221186, 0.123434, 0.057785, 0.228857, 0.172779, 0.315186, 0.022147, 0.030299, 0.017843, 0.560346, 0.000553, 0.027991, 0.036624, 0.268342, 0.133317, 0.302465, 0.270891, 0.124059, 0.012432, 0.157340, 0.018132, 0.064182, 0.028111, 0.042155, 0.027558, 0.012649, 0.024504, 0.294601, 0.034099, 0.032800, 0.091812, 0.024552, 0.010388, 0.017603, 0.023446, 0.128917]).cuda() elif experiment == 'peta': self.weights = torch.Tensor([0.5016, 0.3275, 0.1023, 0.0597, 0.1986, 0.2011, 0.8643, 0.8559, 0.1342, 0.1297, 0.1014, 0.0685, 0.314, 0.2932, 0.04, 0.2346, 0.5473, 0.2974, 0.0849, 0.7523, 0.2717, 0.0282, 0.0749, 0.0191, 0.3633, 0.0359, 0.1425, 0.0454, 0.2201, 0.0178, 0.0285, 0.5125, 0.0838, 0.4605, 0.0124]).cuda() #self.weights = None def forward(self, output, target, epoch): if self.weights is not None: cur_weights = torch.exp(target + (1 - target * 2) * self.weights) loss = cur_weights * (target * torch.log(output + EPS)) + ((1 - target) * torch.log(1 - output + EPS)) else: loss = target * torch.log(output + EPS) + (1 - target) * torch.log(1 - output + EPS) return torch.neg(torch.mean(loss)) if __name__ == '__main__': main() ================================================ FILE: model/__init__.py ================================================ from .inception_iccv import * ================================================ FILE: model/inception_iccv.py ================================================ import torch import torch.nn as nn import torch.tensor as tensor from torch.nn import functional as F __all__ = ['inception_iccv'] def inception_iccv(pretrained=True, debug=False, **kwargs): model = InceptionNet(**kwargs) """ Pretrained model: 'https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/bninception.py' Initializing with basedline models (trained BN-Inception) can obtain better results. """ if pretrained: pretrained_dict = torch.load('model/bn_inception-52deb4733.pth') model_dict = model.state_dict() new_dict = {} for k,_ in model_dict.items(): raw_name = k.replace('main_branch.', '') if raw_name in pretrained_dict: new_dict[k] = pretrained_dict[raw_name] model_dict.update(new_dict) model.load_state_dict(model_dict) return model class ChannelAttn(nn.Module): def __init__(self, in_channels, reduction_rate=16): super(ChannelAttn, self).__init__() assert in_channels%reduction_rate == 0 self.conv1 = nn.Conv2d(in_channels, in_channels // reduction_rate, kernel_size=1, stride=1, padding=0) self.conv2 = nn.Conv2d(in_channels // reduction_rate, in_channels, kernel_size=1, stride=1, padding=0) def forward(self, x): # squeeze operation (global average pooling) x = F.avg_pool2d(x, x.size()[2:]) # excitation operation (2 conv layers) x = F.relu(self.conv1(x)) x = self.conv2(x) return torch.sigmoid(x) class SpatialTransformBlock(nn.Module): def __init__(self, num_classes, pooling_size, channels): super(SpatialTransformBlock, self).__init__() self.num_classes = num_classes self.spatial = pooling_size self.global_pool = nn.AvgPool2d((pooling_size, pooling_size//2), stride=1, padding=0, ceil_mode=True, count_include_pad=True) self.gap_list = nn.ModuleList() self.fc_list = nn.ModuleList() self.att_list = nn.ModuleList() self.stn_list = nn.ModuleList() for i in range(self.num_classes): self.gap_list.append(nn.AvgPool2d((pooling_size, pooling_size//2), stride=1, padding=0, ceil_mode=True, count_include_pad=True)) self.fc_list.append(nn.Linear(channels, 1)) self.att_list.append(ChannelAttn(channels)) self.stn_list.append(nn.Linear(channels, 4)) def stn(self, x, theta): grid = F.affine_grid(theta, x.size()) x = F.grid_sample(x, grid, padding_mode='border') return x.cuda() def transform_theta(self, theta_i, region_idx): theta = torch.zeros(theta_i.size(0), 2, 3) theta[:,0,0] = torch.sigmoid(theta_i[:,0]) theta[:,1,1] = torch.sigmoid(theta_i[:,1]) theta[:,0,2] = torch.tanh(theta_i[:,2]) theta[:,1,2] = torch.tanh(theta_i[:,3]) theta = theta.cuda() return theta def forward(self, features): pred_list = [] bs = features.size(0) for i in range(self.num_classes): stn_feature = features * self.att_list[i](features) + features theta_i = self.stn_list[i](F.avg_pool2d(stn_feature, stn_feature.size()[2:]).view(bs,-1)).view(-1,4) theta_i = self.transform_theta(theta_i, i) sub_feature = self.stn(stn_feature, theta_i) pred = self.gap_list[i](sub_feature).view(bs,-1) pred = self.fc_list[i](pred) pred_list.append(pred) pred = torch.cat(pred_list, 1) return pred class InceptionNet(nn.Module): def __init__(self, num_classes=51): super(InceptionNet, self).__init__() self.num_classes = num_classes self.main_branch = BNInception() self.global_pool = nn.AvgPool2d((8,4), stride=1, padding=0, ceil_mode=True, count_include_pad=True) self.finalfc = nn.Linear(1024, num_classes) self.st_3b = SpatialTransformBlock(num_classes, 32, 256*3) self.st_4d = SpatialTransformBlock(num_classes, 16, 256*2) self.st_5b = SpatialTransformBlock(num_classes, 8, 256) # Lateral layers self.latlayer_3b = nn.Conv2d(320, 256, kernel_size=1, stride=1, padding=0) self.latlayer_4d = nn.Conv2d(608, 256, kernel_size=1, stride=1, padding=0) self.latlayer_5b = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0) def _upsample_add(self, x, y): _,_,H,W = y.size() up_feat = F.interpolate(x, (H, W), mode='bilinear', align_corners=False) return torch.cat([up_feat,y], 1) def forward(self, input): bs = input.size(0) feat_3b, feat_4d, feat_5b = self.main_branch(input) main_feat = self.global_pool(feat_5b).view(bs,-1) main_pred = self.finalfc(main_feat) fusion_5b = self.latlayer_5b(feat_5b) fusion_4d = self._upsample_add(fusion_5b, self.latlayer_4d(feat_4d)) fusion_3b = self._upsample_add(fusion_4d, self.latlayer_3b(feat_3b)) pred_3b = self.st_3b(fusion_3b) pred_4d = self.st_4d(fusion_4d) pred_5b = self.st_5b(fusion_5b) return pred_3b, pred_4d, pred_5b, main_pred class BNInception(nn.Module): """ Copy from 'https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/bninception.py' """ def __init__(self): super(BNInception, self).__init__() inplace = True self.conv1_7x7_s2 = nn.Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)) self.conv1_7x7_s2_bn = nn.BatchNorm2d(64, affine=True) self.conv1_relu_7x7 = nn.ReLU(inplace) self.pool1_3x3_s2 = nn.MaxPool2d((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True) self.conv2_3x3_reduce = nn.Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) self.conv2_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True) self.conv2_relu_3x3_reduce = nn.ReLU(inplace) self.conv2_3x3 = nn.Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.conv2_3x3_bn = nn.BatchNorm2d(192, affine=True) self.conv2_relu_3x3 = nn.ReLU(inplace) self.pool2_3x3_s2 = nn.MaxPool2d((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True) self.inception_3a_1x1 = nn.Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1)) self.inception_3a_1x1_bn = nn.BatchNorm2d(64, affine=True) self.inception_3a_relu_1x1 = nn.ReLU(inplace) self.inception_3a_3x3_reduce = nn.Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1)) self.inception_3a_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True) self.inception_3a_relu_3x3_reduce = nn.ReLU(inplace) self.inception_3a_3x3 = nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_3a_3x3_bn = nn.BatchNorm2d(64, affine=True) self.inception_3a_relu_3x3 = nn.ReLU(inplace) self.inception_3a_double_3x3_reduce = nn.Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1)) self.inception_3a_double_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True) self.inception_3a_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_3a_double_3x3_1 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_3a_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True) self.inception_3a_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_3a_double_3x3_2 = nn.Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_3a_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True) self.inception_3a_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_3a_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True) self.inception_3a_pool_proj = nn.Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1)) self.inception_3a_pool_proj_bn = nn.BatchNorm2d(32, affine=True) self.inception_3a_relu_pool_proj = nn.ReLU(inplace) self.inception_3b_1x1 = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) self.inception_3b_1x1_bn = nn.BatchNorm2d(64, affine=True) self.inception_3b_relu_1x1 = nn.ReLU(inplace) self.inception_3b_3x3_reduce = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) self.inception_3b_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True) self.inception_3b_relu_3x3_reduce = nn.ReLU(inplace) self.inception_3b_3x3 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_3b_3x3_bn = nn.BatchNorm2d(96, affine=True) self.inception_3b_relu_3x3 = nn.ReLU(inplace) self.inception_3b_double_3x3_reduce = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) self.inception_3b_double_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True) self.inception_3b_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_3b_double_3x3_1 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_3b_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True) self.inception_3b_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_3b_double_3x3_2 = nn.Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_3b_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True) self.inception_3b_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_3b_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True) self.inception_3b_pool_proj = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) self.inception_3b_pool_proj_bn = nn.BatchNorm2d(64, affine=True) self.inception_3b_relu_pool_proj = nn.ReLU(inplace) self.inception_3c_3x3_reduce = nn.Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_3c_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True) self.inception_3c_relu_3x3_reduce = nn.ReLU(inplace) self.inception_3c_3x3 = nn.Conv2d(128, 160, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) self.inception_3c_3x3_bn = nn.BatchNorm2d(160, affine=True) self.inception_3c_relu_3x3 = nn.ReLU(inplace) self.inception_3c_double_3x3_reduce = nn.Conv2d(320, 64, kernel_size=(1, 1), stride=(1, 1)) self.inception_3c_double_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True) self.inception_3c_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_3c_double_3x3_1 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_3c_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True) self.inception_3c_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_3c_double_3x3_2 = nn.Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) self.inception_3c_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True) self.inception_3c_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_3c_pool = nn.MaxPool2d((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True) self.inception_4a_1x1 = nn.Conv2d(576, 224, kernel_size=(1, 1), stride=(1, 1)) self.inception_4a_1x1_bn = nn.BatchNorm2d(224, affine=True) self.inception_4a_relu_1x1 = nn.ReLU(inplace) self.inception_4a_3x3_reduce = nn.Conv2d(576, 64, kernel_size=(1, 1), stride=(1, 1)) self.inception_4a_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True) self.inception_4a_relu_3x3_reduce = nn.ReLU(inplace) self.inception_4a_3x3 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4a_3x3_bn = nn.BatchNorm2d(96, affine=True) self.inception_4a_relu_3x3 = nn.ReLU(inplace) self.inception_4a_double_3x3_reduce = nn.Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1)) self.inception_4a_double_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True) self.inception_4a_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_4a_double_3x3_1 = nn.Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4a_double_3x3_1_bn = nn.BatchNorm2d(128, affine=True) self.inception_4a_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_4a_double_3x3_2 = nn.Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4a_double_3x3_2_bn = nn.BatchNorm2d(128, affine=True) self.inception_4a_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_4a_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True) self.inception_4a_pool_proj = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_4a_pool_proj_bn = nn.BatchNorm2d(128, affine=True) self.inception_4a_relu_pool_proj = nn.ReLU(inplace) self.inception_4b_1x1 = nn.Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1)) self.inception_4b_1x1_bn = nn.BatchNorm2d(192, affine=True) self.inception_4b_relu_1x1 = nn.ReLU(inplace) self.inception_4b_3x3_reduce = nn.Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1)) self.inception_4b_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True) self.inception_4b_relu_3x3_reduce = nn.ReLU(inplace) self.inception_4b_3x3 = nn.Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4b_3x3_bn = nn.BatchNorm2d(128, affine=True) self.inception_4b_relu_3x3 = nn.ReLU(inplace) self.inception_4b_double_3x3_reduce = nn.Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1)) self.inception_4b_double_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True) self.inception_4b_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_4b_double_3x3_1 = nn.Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4b_double_3x3_1_bn = nn.BatchNorm2d(128, affine=True) self.inception_4b_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_4b_double_3x3_2 = nn.Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4b_double_3x3_2_bn = nn.BatchNorm2d(128, affine=True) self.inception_4b_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_4b_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True) self.inception_4b_pool_proj = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_4b_pool_proj_bn = nn.BatchNorm2d(128, affine=True) self.inception_4b_relu_pool_proj = nn.ReLU(inplace) self.inception_4c_1x1 = nn.Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1)) self.inception_4c_1x1_bn = nn.BatchNorm2d(160, affine=True) self.inception_4c_relu_1x1 = nn.ReLU(inplace) self.inception_4c_3x3_reduce = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_4c_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True) self.inception_4c_relu_3x3_reduce = nn.ReLU(inplace) self.inception_4c_3x3 = nn.Conv2d(128, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4c_3x3_bn = nn.BatchNorm2d(160, affine=True) self.inception_4c_relu_3x3 = nn.ReLU(inplace) self.inception_4c_double_3x3_reduce = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_4c_double_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True) self.inception_4c_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_4c_double_3x3_1 = nn.Conv2d(128, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4c_double_3x3_1_bn = nn.BatchNorm2d(160, affine=True) self.inception_4c_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_4c_double_3x3_2 = nn.Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4c_double_3x3_2_bn = nn.BatchNorm2d(160, affine=True) self.inception_4c_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_4c_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True) self.inception_4c_pool_proj = nn.Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_4c_pool_proj_bn = nn.BatchNorm2d(128, affine=True) self.inception_4c_relu_pool_proj = nn.ReLU(inplace) self.inception_4d_1x1 = nn.Conv2d(608, 96, kernel_size=(1, 1), stride=(1, 1)) self.inception_4d_1x1_bn = nn.BatchNorm2d(96, affine=True) self.inception_4d_relu_1x1 = nn.ReLU(inplace) self.inception_4d_3x3_reduce = nn.Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_4d_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True) self.inception_4d_relu_3x3_reduce = nn.ReLU(inplace) self.inception_4d_3x3 = nn.Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4d_3x3_bn = nn.BatchNorm2d(192, affine=True) self.inception_4d_relu_3x3 = nn.ReLU(inplace) self.inception_4d_double_3x3_reduce = nn.Conv2d(608, 160, kernel_size=(1, 1), stride=(1, 1)) self.inception_4d_double_3x3_reduce_bn = nn.BatchNorm2d(160, affine=True) self.inception_4d_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_4d_double_3x3_1 = nn.Conv2d(160, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4d_double_3x3_1_bn = nn.BatchNorm2d(192, affine=True) self.inception_4d_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_4d_double_3x3_2 = nn.Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4d_double_3x3_2_bn = nn.BatchNorm2d(192, affine=True) self.inception_4d_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_4d_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True) self.inception_4d_pool_proj = nn.Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_4d_pool_proj_bn = nn.BatchNorm2d(128, affine=True) self.inception_4d_relu_pool_proj = nn.ReLU(inplace) self.inception_4e_3x3_reduce = nn.Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_4e_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True) self.inception_4e_relu_3x3_reduce = nn.ReLU(inplace) self.inception_4e_3x3 = nn.Conv2d(128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) self.inception_4e_3x3_bn = nn.BatchNorm2d(192, affine=True) self.inception_4e_relu_3x3 = nn.ReLU(inplace) self.inception_4e_double_3x3_reduce = nn.Conv2d(608, 192, kernel_size=(1, 1), stride=(1, 1)) self.inception_4e_double_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True) self.inception_4e_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_4e_double_3x3_1 = nn.Conv2d(192, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_4e_double_3x3_1_bn = nn.BatchNorm2d(256, affine=True) self.inception_4e_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_4e_double_3x3_2 = nn.Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) self.inception_4e_double_3x3_2_bn = nn.BatchNorm2d(256, affine=True) self.inception_4e_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_4e_pool = nn.MaxPool2d((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True) self.inception_5a_1x1 = nn.Conv2d(1056, 352, kernel_size=(1, 1), stride=(1, 1)) self.inception_5a_1x1_bn = nn.BatchNorm2d(352, affine=True) self.inception_5a_relu_1x1 = nn.ReLU(inplace) self.inception_5a_3x3_reduce = nn.Conv2d(1056, 192, kernel_size=(1, 1), stride=(1, 1)) self.inception_5a_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True) self.inception_5a_relu_3x3_reduce = nn.ReLU(inplace) self.inception_5a_3x3 = nn.Conv2d(192, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_5a_3x3_bn = nn.BatchNorm2d(320, affine=True) self.inception_5a_relu_3x3 = nn.ReLU(inplace) self.inception_5a_double_3x3_reduce = nn.Conv2d(1056, 160, kernel_size=(1, 1), stride=(1, 1)) self.inception_5a_double_3x3_reduce_bn = nn.BatchNorm2d(160, affine=True) self.inception_5a_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_5a_double_3x3_1 = nn.Conv2d(160, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_5a_double_3x3_1_bn = nn.BatchNorm2d(224, affine=True) self.inception_5a_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_5a_double_3x3_2 = nn.Conv2d(224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_5a_double_3x3_2_bn = nn.BatchNorm2d(224, affine=True) self.inception_5a_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_5a_pool = nn.AvgPool2d(3, stride=1, padding=1, ceil_mode=True, count_include_pad=True) self.inception_5a_pool_proj = nn.Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_5a_pool_proj_bn = nn.BatchNorm2d(128, affine=True) self.inception_5a_relu_pool_proj = nn.ReLU(inplace) self.inception_5b_1x1 = nn.Conv2d(1024, 352, kernel_size=(1, 1), stride=(1, 1)) self.inception_5b_1x1_bn = nn.BatchNorm2d(352, affine=True) self.inception_5b_relu_1x1 = nn.ReLU(inplace) self.inception_5b_3x3_reduce = nn.Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1)) self.inception_5b_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True) self.inception_5b_relu_3x3_reduce = nn.ReLU(inplace) self.inception_5b_3x3 = nn.Conv2d(192, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_5b_3x3_bn = nn.BatchNorm2d(320, affine=True) self.inception_5b_relu_3x3 = nn.ReLU(inplace) self.inception_5b_double_3x3_reduce = nn.Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1)) self.inception_5b_double_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True) self.inception_5b_relu_double_3x3_reduce = nn.ReLU(inplace) self.inception_5b_double_3x3_1 = nn.Conv2d(192, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_5b_double_3x3_1_bn = nn.BatchNorm2d(224, affine=True) self.inception_5b_relu_double_3x3_1 = nn.ReLU(inplace) self.inception_5b_double_3x3_2 = nn.Conv2d(224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.inception_5b_double_3x3_2_bn = nn.BatchNorm2d(224, affine=True) self.inception_5b_relu_double_3x3_2 = nn.ReLU(inplace) self.inception_5b_pool = nn.MaxPool2d((3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), ceil_mode=True) self.inception_5b_pool_proj = nn.Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1)) self.inception_5b_pool_proj_bn = nn.BatchNorm2d(128, affine=True) self.inception_5b_relu_pool_proj = nn.ReLU(inplace) def features(self, input): conv1_7x7_s2_out = self.conv1_7x7_s2(input) conv1_7x7_s2_bn_out = self.conv1_7x7_s2_bn(conv1_7x7_s2_out) conv1_relu_7x7_out = self.conv1_relu_7x7(conv1_7x7_s2_bn_out) pool1_3x3_s2_out = self.pool1_3x3_s2(conv1_relu_7x7_out) conv2_3x3_reduce_out = self.conv2_3x3_reduce(pool1_3x3_s2_out) conv2_3x3_reduce_bn_out = self.conv2_3x3_reduce_bn(conv2_3x3_reduce_out) conv2_relu_3x3_reduce_out = self.conv2_relu_3x3_reduce(conv2_3x3_reduce_bn_out) conv2_3x3_out = self.conv2_3x3(conv2_relu_3x3_reduce_out) conv2_3x3_bn_out = self.conv2_3x3_bn(conv2_3x3_out) conv2_relu_3x3_out = self.conv2_relu_3x3(conv2_3x3_bn_out) pool2_3x3_s2_out = self.pool2_3x3_s2(conv2_relu_3x3_out) inception_3a_1x1_out = self.inception_3a_1x1(pool2_3x3_s2_out) inception_3a_1x1_bn_out = self.inception_3a_1x1_bn(inception_3a_1x1_out) inception_3a_relu_1x1_out = self.inception_3a_relu_1x1(inception_3a_1x1_bn_out) inception_3a_3x3_reduce_out = self.inception_3a_3x3_reduce(pool2_3x3_s2_out) inception_3a_3x3_reduce_bn_out = self.inception_3a_3x3_reduce_bn(inception_3a_3x3_reduce_out) inception_3a_relu_3x3_reduce_out = self.inception_3a_relu_3x3_reduce(inception_3a_3x3_reduce_bn_out) inception_3a_3x3_out = self.inception_3a_3x3(inception_3a_relu_3x3_reduce_out) inception_3a_3x3_bn_out = self.inception_3a_3x3_bn(inception_3a_3x3_out) inception_3a_relu_3x3_out = self.inception_3a_relu_3x3(inception_3a_3x3_bn_out) inception_3a_double_3x3_reduce_out = self.inception_3a_double_3x3_reduce(pool2_3x3_s2_out) inception_3a_double_3x3_reduce_bn_out = self.inception_3a_double_3x3_reduce_bn(inception_3a_double_3x3_reduce_out) inception_3a_relu_double_3x3_reduce_out = self.inception_3a_relu_double_3x3_reduce(inception_3a_double_3x3_reduce_bn_out) inception_3a_double_3x3_1_out = self.inception_3a_double_3x3_1(inception_3a_relu_double_3x3_reduce_out) inception_3a_double_3x3_1_bn_out = self.inception_3a_double_3x3_1_bn(inception_3a_double_3x3_1_out) inception_3a_relu_double_3x3_1_out = self.inception_3a_relu_double_3x3_1(inception_3a_double_3x3_1_bn_out) inception_3a_double_3x3_2_out = self.inception_3a_double_3x3_2(inception_3a_relu_double_3x3_1_out) inception_3a_double_3x3_2_bn_out = self.inception_3a_double_3x3_2_bn(inception_3a_double_3x3_2_out) inception_3a_relu_double_3x3_2_out = self.inception_3a_relu_double_3x3_2(inception_3a_double_3x3_2_bn_out) inception_3a_pool_out = self.inception_3a_pool(pool2_3x3_s2_out) inception_3a_pool_proj_out = self.inception_3a_pool_proj(inception_3a_pool_out) inception_3a_pool_proj_bn_out = self.inception_3a_pool_proj_bn(inception_3a_pool_proj_out) inception_3a_relu_pool_proj_out = self.inception_3a_relu_pool_proj(inception_3a_pool_proj_bn_out) 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) inception_3b_1x1_out = self.inception_3b_1x1(inception_3a_output_out) inception_3b_1x1_bn_out = self.inception_3b_1x1_bn(inception_3b_1x1_out) inception_3b_relu_1x1_out = self.inception_3b_relu_1x1(inception_3b_1x1_bn_out) inception_3b_3x3_reduce_out = self.inception_3b_3x3_reduce(inception_3a_output_out) inception_3b_3x3_reduce_bn_out = self.inception_3b_3x3_reduce_bn(inception_3b_3x3_reduce_out) inception_3b_relu_3x3_reduce_out = self.inception_3b_relu_3x3_reduce(inception_3b_3x3_reduce_bn_out) inception_3b_3x3_out = self.inception_3b_3x3(inception_3b_relu_3x3_reduce_out) inception_3b_3x3_bn_out = self.inception_3b_3x3_bn(inception_3b_3x3_out) inception_3b_relu_3x3_out = self.inception_3b_relu_3x3(inception_3b_3x3_bn_out) inception_3b_double_3x3_reduce_out = self.inception_3b_double_3x3_reduce(inception_3a_output_out) inception_3b_double_3x3_reduce_bn_out = self.inception_3b_double_3x3_reduce_bn(inception_3b_double_3x3_reduce_out) inception_3b_relu_double_3x3_reduce_out = self.inception_3b_relu_double_3x3_reduce(inception_3b_double_3x3_reduce_bn_out) inception_3b_double_3x3_1_out = self.inception_3b_double_3x3_1(inception_3b_relu_double_3x3_reduce_out) inception_3b_double_3x3_1_bn_out = self.inception_3b_double_3x3_1_bn(inception_3b_double_3x3_1_out) inception_3b_relu_double_3x3_1_out = self.inception_3b_relu_double_3x3_1(inception_3b_double_3x3_1_bn_out) inception_3b_double_3x3_2_out = self.inception_3b_double_3x3_2(inception_3b_relu_double_3x3_1_out) inception_3b_double_3x3_2_bn_out = self.inception_3b_double_3x3_2_bn(inception_3b_double_3x3_2_out) inception_3b_relu_double_3x3_2_out = self.inception_3b_relu_double_3x3_2(inception_3b_double_3x3_2_bn_out) inception_3b_pool_out = self.inception_3b_pool(inception_3a_output_out) inception_3b_pool_proj_out = self.inception_3b_pool_proj(inception_3b_pool_out) inception_3b_pool_proj_bn_out = self.inception_3b_pool_proj_bn(inception_3b_pool_proj_out) inception_3b_relu_pool_proj_out = self.inception_3b_relu_pool_proj(inception_3b_pool_proj_bn_out) 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) inception_3c_3x3_reduce_out = self.inception_3c_3x3_reduce(inception_3b_output_out) inception_3c_3x3_reduce_bn_out = self.inception_3c_3x3_reduce_bn(inception_3c_3x3_reduce_out) inception_3c_relu_3x3_reduce_out = self.inception_3c_relu_3x3_reduce(inception_3c_3x3_reduce_bn_out) inception_3c_3x3_out = self.inception_3c_3x3(inception_3c_relu_3x3_reduce_out) inception_3c_3x3_bn_out = self.inception_3c_3x3_bn(inception_3c_3x3_out) inception_3c_relu_3x3_out = self.inception_3c_relu_3x3(inception_3c_3x3_bn_out) inception_3c_double_3x3_reduce_out = self.inception_3c_double_3x3_reduce(inception_3b_output_out) inception_3c_double_3x3_reduce_bn_out = self.inception_3c_double_3x3_reduce_bn(inception_3c_double_3x3_reduce_out) inception_3c_relu_double_3x3_reduce_out = self.inception_3c_relu_double_3x3_reduce(inception_3c_double_3x3_reduce_bn_out) inception_3c_double_3x3_1_out = self.inception_3c_double_3x3_1(inception_3c_relu_double_3x3_reduce_out) inception_3c_double_3x3_1_bn_out = self.inception_3c_double_3x3_1_bn(inception_3c_double_3x3_1_out) inception_3c_relu_double_3x3_1_out = self.inception_3c_relu_double_3x3_1(inception_3c_double_3x3_1_bn_out) inception_3c_double_3x3_2_out = self.inception_3c_double_3x3_2(inception_3c_relu_double_3x3_1_out) inception_3c_double_3x3_2_bn_out = self.inception_3c_double_3x3_2_bn(inception_3c_double_3x3_2_out) inception_3c_relu_double_3x3_2_out = self.inception_3c_relu_double_3x3_2(inception_3c_double_3x3_2_bn_out) inception_3c_pool_out = self.inception_3c_pool(inception_3b_output_out) inception_3c_output_out = torch.cat([inception_3c_relu_3x3_out,inception_3c_relu_double_3x3_2_out,inception_3c_pool_out], 1) inception_4a_1x1_out = self.inception_4a_1x1(inception_3c_output_out) inception_4a_1x1_bn_out = self.inception_4a_1x1_bn(inception_4a_1x1_out) inception_4a_relu_1x1_out = self.inception_4a_relu_1x1(inception_4a_1x1_bn_out) inception_4a_3x3_reduce_out = self.inception_4a_3x3_reduce(inception_3c_output_out) inception_4a_3x3_reduce_bn_out = self.inception_4a_3x3_reduce_bn(inception_4a_3x3_reduce_out) inception_4a_relu_3x3_reduce_out = self.inception_4a_relu_3x3_reduce(inception_4a_3x3_reduce_bn_out) inception_4a_3x3_out = self.inception_4a_3x3(inception_4a_relu_3x3_reduce_out) inception_4a_3x3_bn_out = self.inception_4a_3x3_bn(inception_4a_3x3_out) inception_4a_relu_3x3_out = self.inception_4a_relu_3x3(inception_4a_3x3_bn_out) inception_4a_double_3x3_reduce_out = self.inception_4a_double_3x3_reduce(inception_3c_output_out) inception_4a_double_3x3_reduce_bn_out = self.inception_4a_double_3x3_reduce_bn(inception_4a_double_3x3_reduce_out) inception_4a_relu_double_3x3_reduce_out = self.inception_4a_relu_double_3x3_reduce(inception_4a_double_3x3_reduce_bn_out) inception_4a_double_3x3_1_out = self.inception_4a_double_3x3_1(inception_4a_relu_double_3x3_reduce_out) inception_4a_double_3x3_1_bn_out = self.inception_4a_double_3x3_1_bn(inception_4a_double_3x3_1_out) inception_4a_relu_double_3x3_1_out = self.inception_4a_relu_double_3x3_1(inception_4a_double_3x3_1_bn_out) inception_4a_double_3x3_2_out = self.inception_4a_double_3x3_2(inception_4a_relu_double_3x3_1_out) inception_4a_double_3x3_2_bn_out = self.inception_4a_double_3x3_2_bn(inception_4a_double_3x3_2_out) inception_4a_relu_double_3x3_2_out = self.inception_4a_relu_double_3x3_2(inception_4a_double_3x3_2_bn_out) inception_4a_pool_out = self.inception_4a_pool(inception_3c_output_out) inception_4a_pool_proj_out = self.inception_4a_pool_proj(inception_4a_pool_out) inception_4a_pool_proj_bn_out = self.inception_4a_pool_proj_bn(inception_4a_pool_proj_out) inception_4a_relu_pool_proj_out = self.inception_4a_relu_pool_proj(inception_4a_pool_proj_bn_out) 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) inception_4b_1x1_out = self.inception_4b_1x1(inception_4a_output_out) inception_4b_1x1_bn_out = self.inception_4b_1x1_bn(inception_4b_1x1_out) inception_4b_relu_1x1_out = self.inception_4b_relu_1x1(inception_4b_1x1_bn_out) inception_4b_3x3_reduce_out = self.inception_4b_3x3_reduce(inception_4a_output_out) inception_4b_3x3_reduce_bn_out = self.inception_4b_3x3_reduce_bn(inception_4b_3x3_reduce_out) inception_4b_relu_3x3_reduce_out = self.inception_4b_relu_3x3_reduce(inception_4b_3x3_reduce_bn_out) inception_4b_3x3_out = self.inception_4b_3x3(inception_4b_relu_3x3_reduce_out) inception_4b_3x3_bn_out = self.inception_4b_3x3_bn(inception_4b_3x3_out) inception_4b_relu_3x3_out = self.inception_4b_relu_3x3(inception_4b_3x3_bn_out) inception_4b_double_3x3_reduce_out = self.inception_4b_double_3x3_reduce(inception_4a_output_out) inception_4b_double_3x3_reduce_bn_out = self.inception_4b_double_3x3_reduce_bn(inception_4b_double_3x3_reduce_out) inception_4b_relu_double_3x3_reduce_out = self.inception_4b_relu_double_3x3_reduce(inception_4b_double_3x3_reduce_bn_out) inception_4b_double_3x3_1_out = self.inception_4b_double_3x3_1(inception_4b_relu_double_3x3_reduce_out) inception_4b_double_3x3_1_bn_out = self.inception_4b_double_3x3_1_bn(inception_4b_double_3x3_1_out) inception_4b_relu_double_3x3_1_out = self.inception_4b_relu_double_3x3_1(inception_4b_double_3x3_1_bn_out) inception_4b_double_3x3_2_out = self.inception_4b_double_3x3_2(inception_4b_relu_double_3x3_1_out) inception_4b_double_3x3_2_bn_out = self.inception_4b_double_3x3_2_bn(inception_4b_double_3x3_2_out) inception_4b_relu_double_3x3_2_out = self.inception_4b_relu_double_3x3_2(inception_4b_double_3x3_2_bn_out) inception_4b_pool_out = self.inception_4b_pool(inception_4a_output_out) inception_4b_pool_proj_out = self.inception_4b_pool_proj(inception_4b_pool_out) inception_4b_pool_proj_bn_out = self.inception_4b_pool_proj_bn(inception_4b_pool_proj_out) inception_4b_relu_pool_proj_out = self.inception_4b_relu_pool_proj(inception_4b_pool_proj_bn_out) 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) inception_4c_1x1_out = self.inception_4c_1x1(inception_4b_output_out) inception_4c_1x1_bn_out = self.inception_4c_1x1_bn(inception_4c_1x1_out) inception_4c_relu_1x1_out = self.inception_4c_relu_1x1(inception_4c_1x1_bn_out) inception_4c_3x3_reduce_out = self.inception_4c_3x3_reduce(inception_4b_output_out) inception_4c_3x3_reduce_bn_out = self.inception_4c_3x3_reduce_bn(inception_4c_3x3_reduce_out) inception_4c_relu_3x3_reduce_out = self.inception_4c_relu_3x3_reduce(inception_4c_3x3_reduce_bn_out) inception_4c_3x3_out = self.inception_4c_3x3(inception_4c_relu_3x3_reduce_out) inception_4c_3x3_bn_out = self.inception_4c_3x3_bn(inception_4c_3x3_out) inception_4c_relu_3x3_out = self.inception_4c_relu_3x3(inception_4c_3x3_bn_out) inception_4c_double_3x3_reduce_out = self.inception_4c_double_3x3_reduce(inception_4b_output_out) inception_4c_double_3x3_reduce_bn_out = self.inception_4c_double_3x3_reduce_bn(inception_4c_double_3x3_reduce_out) inception_4c_relu_double_3x3_reduce_out = self.inception_4c_relu_double_3x3_reduce(inception_4c_double_3x3_reduce_bn_out) inception_4c_double_3x3_1_out = self.inception_4c_double_3x3_1(inception_4c_relu_double_3x3_reduce_out) inception_4c_double_3x3_1_bn_out = self.inception_4c_double_3x3_1_bn(inception_4c_double_3x3_1_out) inception_4c_relu_double_3x3_1_out = self.inception_4c_relu_double_3x3_1(inception_4c_double_3x3_1_bn_out) inception_4c_double_3x3_2_out = self.inception_4c_double_3x3_2(inception_4c_relu_double_3x3_1_out) inception_4c_double_3x3_2_bn_out = self.inception_4c_double_3x3_2_bn(inception_4c_double_3x3_2_out) inception_4c_relu_double_3x3_2_out = self.inception_4c_relu_double_3x3_2(inception_4c_double_3x3_2_bn_out) inception_4c_pool_out = self.inception_4c_pool(inception_4b_output_out) inception_4c_pool_proj_out = self.inception_4c_pool_proj(inception_4c_pool_out) inception_4c_pool_proj_bn_out = self.inception_4c_pool_proj_bn(inception_4c_pool_proj_out) inception_4c_relu_pool_proj_out = self.inception_4c_relu_pool_proj(inception_4c_pool_proj_bn_out) 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) inception_4d_1x1_out = self.inception_4d_1x1(inception_4c_output_out) inception_4d_1x1_bn_out = self.inception_4d_1x1_bn(inception_4d_1x1_out) inception_4d_relu_1x1_out = self.inception_4d_relu_1x1(inception_4d_1x1_bn_out) inception_4d_3x3_reduce_out = self.inception_4d_3x3_reduce(inception_4c_output_out) inception_4d_3x3_reduce_bn_out = self.inception_4d_3x3_reduce_bn(inception_4d_3x3_reduce_out) inception_4d_relu_3x3_reduce_out = self.inception_4d_relu_3x3_reduce(inception_4d_3x3_reduce_bn_out) inception_4d_3x3_out = self.inception_4d_3x3(inception_4d_relu_3x3_reduce_out) inception_4d_3x3_bn_out = self.inception_4d_3x3_bn(inception_4d_3x3_out) inception_4d_relu_3x3_out = self.inception_4d_relu_3x3(inception_4d_3x3_bn_out) inception_4d_double_3x3_reduce_out = self.inception_4d_double_3x3_reduce(inception_4c_output_out) inception_4d_double_3x3_reduce_bn_out = self.inception_4d_double_3x3_reduce_bn(inception_4d_double_3x3_reduce_out) inception_4d_relu_double_3x3_reduce_out = self.inception_4d_relu_double_3x3_reduce(inception_4d_double_3x3_reduce_bn_out) inception_4d_double_3x3_1_out = self.inception_4d_double_3x3_1(inception_4d_relu_double_3x3_reduce_out) inception_4d_double_3x3_1_bn_out = self.inception_4d_double_3x3_1_bn(inception_4d_double_3x3_1_out) inception_4d_relu_double_3x3_1_out = self.inception_4d_relu_double_3x3_1(inception_4d_double_3x3_1_bn_out) inception_4d_double_3x3_2_out = self.inception_4d_double_3x3_2(inception_4d_relu_double_3x3_1_out) inception_4d_double_3x3_2_bn_out = self.inception_4d_double_3x3_2_bn(inception_4d_double_3x3_2_out) inception_4d_relu_double_3x3_2_out = self.inception_4d_relu_double_3x3_2(inception_4d_double_3x3_2_bn_out) inception_4d_pool_out = self.inception_4d_pool(inception_4c_output_out) inception_4d_pool_proj_out = self.inception_4d_pool_proj(inception_4d_pool_out) inception_4d_pool_proj_bn_out = self.inception_4d_pool_proj_bn(inception_4d_pool_proj_out) inception_4d_relu_pool_proj_out = self.inception_4d_relu_pool_proj(inception_4d_pool_proj_bn_out) 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) inception_4e_3x3_reduce_out = self.inception_4e_3x3_reduce(inception_4d_output_out) inception_4e_3x3_reduce_bn_out = self.inception_4e_3x3_reduce_bn(inception_4e_3x3_reduce_out) inception_4e_relu_3x3_reduce_out = self.inception_4e_relu_3x3_reduce(inception_4e_3x3_reduce_bn_out) inception_4e_3x3_out = self.inception_4e_3x3(inception_4e_relu_3x3_reduce_out) inception_4e_3x3_bn_out = self.inception_4e_3x3_bn(inception_4e_3x3_out) inception_4e_relu_3x3_out = self.inception_4e_relu_3x3(inception_4e_3x3_bn_out) inception_4e_double_3x3_reduce_out = self.inception_4e_double_3x3_reduce(inception_4d_output_out) inception_4e_double_3x3_reduce_bn_out = self.inception_4e_double_3x3_reduce_bn(inception_4e_double_3x3_reduce_out) inception_4e_relu_double_3x3_reduce_out = self.inception_4e_relu_double_3x3_reduce(inception_4e_double_3x3_reduce_bn_out) inception_4e_double_3x3_1_out = self.inception_4e_double_3x3_1(inception_4e_relu_double_3x3_reduce_out) inception_4e_double_3x3_1_bn_out = self.inception_4e_double_3x3_1_bn(inception_4e_double_3x3_1_out) inception_4e_relu_double_3x3_1_out = self.inception_4e_relu_double_3x3_1(inception_4e_double_3x3_1_bn_out) inception_4e_double_3x3_2_out = self.inception_4e_double_3x3_2(inception_4e_relu_double_3x3_1_out) inception_4e_double_3x3_2_bn_out = self.inception_4e_double_3x3_2_bn(inception_4e_double_3x3_2_out) inception_4e_relu_double_3x3_2_out = self.inception_4e_relu_double_3x3_2(inception_4e_double_3x3_2_bn_out) inception_4e_pool_out = self.inception_4e_pool(inception_4d_output_out) inception_4e_output_out = torch.cat([inception_4e_relu_3x3_out,inception_4e_relu_double_3x3_2_out,inception_4e_pool_out], 1) inception_5a_1x1_out = self.inception_5a_1x1(inception_4e_output_out) inception_5a_1x1_bn_out = self.inception_5a_1x1_bn(inception_5a_1x1_out) inception_5a_relu_1x1_out = self.inception_5a_relu_1x1(inception_5a_1x1_bn_out) inception_5a_3x3_reduce_out = self.inception_5a_3x3_reduce(inception_4e_output_out) inception_5a_3x3_reduce_bn_out = self.inception_5a_3x3_reduce_bn(inception_5a_3x3_reduce_out) inception_5a_relu_3x3_reduce_out = self.inception_5a_relu_3x3_reduce(inception_5a_3x3_reduce_bn_out) inception_5a_3x3_out = self.inception_5a_3x3(inception_5a_relu_3x3_reduce_out) inception_5a_3x3_bn_out = self.inception_5a_3x3_bn(inception_5a_3x3_out) inception_5a_relu_3x3_out = self.inception_5a_relu_3x3(inception_5a_3x3_bn_out) inception_5a_double_3x3_reduce_out = self.inception_5a_double_3x3_reduce(inception_4e_output_out) inception_5a_double_3x3_reduce_bn_out = self.inception_5a_double_3x3_reduce_bn(inception_5a_double_3x3_reduce_out) inception_5a_relu_double_3x3_reduce_out = self.inception_5a_relu_double_3x3_reduce(inception_5a_double_3x3_reduce_bn_out) inception_5a_double_3x3_1_out = self.inception_5a_double_3x3_1(inception_5a_relu_double_3x3_reduce_out) inception_5a_double_3x3_1_bn_out = self.inception_5a_double_3x3_1_bn(inception_5a_double_3x3_1_out) inception_5a_relu_double_3x3_1_out = self.inception_5a_relu_double_3x3_1(inception_5a_double_3x3_1_bn_out) inception_5a_double_3x3_2_out = self.inception_5a_double_3x3_2(inception_5a_relu_double_3x3_1_out) inception_5a_double_3x3_2_bn_out = self.inception_5a_double_3x3_2_bn(inception_5a_double_3x3_2_out) inception_5a_relu_double_3x3_2_out = self.inception_5a_relu_double_3x3_2(inception_5a_double_3x3_2_bn_out) inception_5a_pool_out = self.inception_5a_pool(inception_4e_output_out) inception_5a_pool_proj_out = self.inception_5a_pool_proj(inception_5a_pool_out) inception_5a_pool_proj_bn_out = self.inception_5a_pool_proj_bn(inception_5a_pool_proj_out) inception_5a_relu_pool_proj_out = self.inception_5a_relu_pool_proj(inception_5a_pool_proj_bn_out) 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) inception_5b_1x1_out = self.inception_5b_1x1(inception_5a_output_out) inception_5b_1x1_bn_out = self.inception_5b_1x1_bn(inception_5b_1x1_out) inception_5b_relu_1x1_out = self.inception_5b_relu_1x1(inception_5b_1x1_bn_out) inception_5b_3x3_reduce_out = self.inception_5b_3x3_reduce(inception_5a_output_out) inception_5b_3x3_reduce_bn_out = self.inception_5b_3x3_reduce_bn(inception_5b_3x3_reduce_out) inception_5b_relu_3x3_reduce_out = self.inception_5b_relu_3x3_reduce(inception_5b_3x3_reduce_bn_out) inception_5b_3x3_out = self.inception_5b_3x3(inception_5b_relu_3x3_reduce_out) inception_5b_3x3_bn_out = self.inception_5b_3x3_bn(inception_5b_3x3_out) inception_5b_relu_3x3_out = self.inception_5b_relu_3x3(inception_5b_3x3_bn_out) inception_5b_double_3x3_reduce_out = self.inception_5b_double_3x3_reduce(inception_5a_output_out) inception_5b_double_3x3_reduce_bn_out = self.inception_5b_double_3x3_reduce_bn(inception_5b_double_3x3_reduce_out) inception_5b_relu_double_3x3_reduce_out = self.inception_5b_relu_double_3x3_reduce(inception_5b_double_3x3_reduce_bn_out) inception_5b_double_3x3_1_out = self.inception_5b_double_3x3_1(inception_5b_relu_double_3x3_reduce_out) inception_5b_double_3x3_1_bn_out = self.inception_5b_double_3x3_1_bn(inception_5b_double_3x3_1_out) inception_5b_relu_double_3x3_1_out = self.inception_5b_relu_double_3x3_1(inception_5b_double_3x3_1_bn_out) inception_5b_double_3x3_2_out = self.inception_5b_double_3x3_2(inception_5b_relu_double_3x3_1_out) inception_5b_double_3x3_2_bn_out = self.inception_5b_double_3x3_2_bn(inception_5b_double_3x3_2_out) inception_5b_relu_double_3x3_2_out = self.inception_5b_relu_double_3x3_2(inception_5b_double_3x3_2_bn_out) inception_5b_pool_out = self.inception_5b_pool(inception_5a_output_out) inception_5b_pool_proj_out = self.inception_5b_pool_proj(inception_5b_pool_out) inception_5b_pool_proj_bn_out = self.inception_5b_pool_proj_bn(inception_5b_pool_proj_out) inception_5b_relu_pool_proj_out = self.inception_5b_relu_pool_proj(inception_5b_pool_proj_bn_out) 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) return inception_3b_output_out,inception_4d_output_out,inception_5b_output_out def forward(self, input): return self.features(input) ================================================ FILE: utils/datasets.py ================================================ import os import sys from PIL import Image import torch import numpy as np import torch.utils.data as data import torchvision.transforms as transforms import torchvision.datasets as datasets def default_loader(path): return Image.open(path).convert('RGB') class MultiLabelDataset(data.Dataset): def __init__(self, root, label, transform = None, loader = default_loader): images = [] labels = open(label).readlines() for line in labels: items = line.split() img_name = items.pop(0) if os.path.isfile(os.path.join(root, img_name)): cur_label = tuple([int(v) for v in items]) images.append((img_name, cur_label)) else: print(os.path.join(root, img_name) + 'Not Found.') self.root = root self.images = images self.transform = transform self.loader = loader def __getitem__(self, index): img_name, label = self.images[index] img = self.loader(os.path.join(self.root, img_name)) raw_img = img.copy() if self.transform is not None: img = self.transform(img) return img, torch.Tensor(label) def __len__(self): return len(self.images) attr_nums = {} attr_nums['pa100k'] = 26 attr_nums['rap'] = 51 attr_nums['peta'] = 35 description = {} description['pa100k'] = ['Female', 'AgeOver60', 'Age18-60', 'AgeLess18', 'Front', 'Side', 'Back', 'Hat', 'Glasses', 'HandBag', 'ShoulderBag', 'Backpack', 'HoldObjectsInFront', 'ShortSleeve', 'LongSleeve', 'UpperStride', 'UpperLogo', 'UpperPlaid', 'UpperSplice', 'LowerStripe', 'LowerPattern', 'LongCoat', 'Trousers', 'Shorts', 'Skirt&Dress', 'boots'] description['peta'] = ['Age16-30', 'Age31-45', 'Age46-60', 'AgeAbove61', 'Backpack', 'CarryingOther', 'Casual lower', 'Casual upper', 'Formal lower', 'Formal upper', 'Hat', 'Jacket', 'Jeans', 'Leather Shoes', 'Logo', 'Long hair', 'Male', 'Messenger Bag', 'Muffler', 'No accessory', 'No carrying', 'Plaid', 'PlasticBags', 'Sandals', 'Shoes', 'Shorts', 'Short Sleeve', 'Skirt', 'Sneaker', 'Stripes', 'Sunglasses', 'Trousers', 'Tshirt', 'UpperOther', 'V-Neck'] description['rap'] = ['Female', 'AgeLess16', 'Age17-30', 'Age31-45', 'BodyFat', 'BodyNormal', 'BodyThin', 'Customer', 'Clerk', 'BaldHead', 'LongHair', 'BlackHair', 'Hat', 'Glasses', 'Muffler', 'Shirt', 'Sweater', 'Vest', 'TShirt', 'Cotton', 'Jacket', 'Suit-Up', 'Tight', 'ShortSleeve', 'LongTrousers', 'Skirt', 'ShortSkirt', 'Dress', 'Jeans', 'TightTrousers', 'LeatherShoes', 'SportShoes', 'Boots', 'ClothShoes', 'CasualShoes', 'Backpack', 'SSBag', 'HandBag', 'Box', 'PlasticBag', 'PaperBag', 'HandTrunk', 'OtherAttchment', 'Calling', 'Talking', 'Gathering', 'Holding', 'Pusing', 'Pulling', 'CarryingbyArm', 'CarryingbyHand'] def Get_Dataset(experiment, approach): normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) transform_train = transforms.Compose([ transforms.Resize(size=(256, 128)), transforms.RandomHorizontalFlip(), # transforms.ColorJitter(hue=.05, saturation=.05), # transforms.RandomRotation(20, resample=Image.BILINEAR), transforms.ToTensor(), normalize ]) transform_test = transforms.Compose([ transforms.Resize(size=(256, 128)), transforms.ToTensor(), normalize ]) if experiment == 'pa100k': train_dataset = MultiLabelDataset(root='data_path', label='train_list_path', transform=transform_train) val_dataset = MultiLabelDataset(root='data_path', label='val_list_path', transform=transform_test) return train_dataset, val_dataset, attr_nums['pa100k'], description['pa100k'] elif experiment == 'rap': train_dataset = MultiLabelDataset(root='data_path', label='train_list_path', transform=transform_train) val_dataset = MultiLabelDataset(root='data_path', label='val_list_path', transform=transform_test) return train_dataset, val_dataset, attr_nums['rap'], description['rap'] elif experiment == 'peta': train_dataset = MultiLabelDataset(root='data_path', label='train_list_path', transform=transform_train) val_dataset = MultiLabelDataset(root='data_path', label='val_list_path', transform=transform_test) return train_dataset, val_dataset, attr_nums['peta'], description['peta']