update readme
KaiyangZhou
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@ -33,6 +33,7 @@ You can find some research projects that are built on top of Torchreid `here <ht
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What's new
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------------
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- [May 2020] Added the person attribute recognition code used in [Omni-Scale Feature Learning for Person Re-Identification (ICCV'19)](https://arxiv.org/abs/1905.00953).
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- [May 2020] ``1.2.1``: Added a simple API for feature extraction (``torchreid/utils/feature_extractor.py``). See the `documentation <https://kaiyangzhou.github.io/deep-person-reid/user_guide.html>`_ for the instruction.
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- [Apr 2020] Code for reproducing the experiments of `deep mutual learning <https://zpascal.net/cvpr2018/Zhang_Deep_Mutual_Learning_CVPR_2018_paper.pdf>`_ in the `OSNet paper <https://arxiv.org/pdf/1905.00953v6.pdf>`__ (Supp. B) has been released at ``projects/DML``.
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- [Apr 2020] Upgraded to ``1.2.0``. The engine class has been made more model-agnostic to improve extensibility. See `Engine <torchreid/engine/engine.py>`_ and `ImageSoftmaxEngine <torchreid/engine/image/softmax.py>`_ for more details. Credit to `Dassl.pytorch <https://github.com/KaiyangZhou/Dassl.pytorch>`_.
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@ -1,4 +1,5 @@
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Here are some research projects built on [Torchreid](https://arxiv.org/abs/1910.10093).
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+ [Learning Generalisable Omni-Scale Representations for Person Re-Identification](OSNet_AIN)
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+ [Deep Mutual Learning (CVPR'18)](DML)
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+ `OSNet_AIN`: [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://arxiv.org/abs/1910.06827)
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+ `DML`: [Deep Mutual Learning (CVPR'18)](https://arxiv.org/abs/1706.00384)
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+ `attribute_recognition`: [Omni-Scale Feature Learning for Person Re-Identification (ICCV'19)](https://arxiv.org/abs/1905.00953)
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@ -0,0 +1,18 @@
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# Person Attribute Recognition
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This code was developed for the experiment of person attribute recognition in [Omni-Scale Feature Learning for Person Re-Identification (ICCV'19)](https://arxiv.org/abs/1905.00953).
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## Download data
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Download the PA-100K dataset from [https://github.com/xh-liu/HydraPlus-Net](https://github.com/xh-liu/HydraPlus-Net), and extract the file under the folder where you store your data (say $DATASET). The folder structure should look like
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```bash
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$DATASET/
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pa100k/
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data/ # images
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annotation/
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annotation.mat
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```
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## Train
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The training command is provided in `train.sh`. Run `bash train.sh $DATASET` to start training.
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## Test
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To test a pretrained model, add the following two arguments to `train.sh`: `--load-weights $PATH_TO_WEIGHTS --evaluate`.
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@ -53,6 +53,7 @@ def init_dataset(use_gpu):
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mode='train',
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verbose=True
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)
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valset = datasets.init_dataset(
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args.dataset,
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root=args.root,
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@ -60,6 +61,7 @@ def init_dataset(use_gpu):
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mode='val',
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verbose=False
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)
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testset = datasets.init_dataset(
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args.dataset,
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root=args.root,
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@ -108,6 +110,7 @@ def main():
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use_gpu = torch.cuda.is_available() and not args.use_cpu
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log_name = 'test.log' if args.evaluate else 'train.log'
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sys.stdout = Logger(osp.join(args.save_dir, log_name))
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print('** Arguments **')
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arg_keys = list(args.__dict__.keys())
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arg_keys.sort()
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@ -116,6 +119,7 @@ def main():
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print('\n')
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print('Collecting env info ...')
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print('** System info **\n{}\n'.format(collect_env_info()))
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if use_gpu:
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torch.backends.cudnn.benchmark = True
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else:
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@ -123,9 +127,8 @@ def main():
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'Currently using CPU, however, GPU is highly recommended'
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)
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trainloader, valloader, testloader, num_attrs, attr_dict = init_dataset(
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use_gpu
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)
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dataset_vars = init_dataset(use_gpu)
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trainloader, valloader, testloader, num_attrs, attr_dict = dataset_vars
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if args.weighted_bce:
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print('Use weighted binary cross entropy')
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@ -139,6 +142,7 @@ def main():
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print('BCE weights: {}'.format(bce_weights))
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bce_weights = bce_weights.expand(args.batch_size, num_attrs)
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criterion = nn.BCEWithLogitsLoss(weight=bce_weights)
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else:
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print('Use plain binary cross entropy')
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criterion = nn.BCEWithLogitsLoss()
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@ -147,7 +151,7 @@ def main():
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model = models.build_model(
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args.arch,
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num_attrs,
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pretrained=(not args.no_pretrained),
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pretrained=not args.no_pretrained,
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use_gpu=use_gpu
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)
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num_params, flops = compute_model_complexity(
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@ -181,12 +185,9 @@ def main():
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optimizer.load_state_dict(checkpoint['optimizer'])
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start_epoch = checkpoint['epoch']
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best_result = checkpoint['label_mA']
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print("Loaded checkpoint from '{}'".format(args.resume))
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print(
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"- start_epoch: {}\n- label_mA: {}".format(
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start_epoch, best_result
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)
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)
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print('Loaded checkpoint from "{}"'.format(args.resume))
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print('- start epoch: {}'.format(start_epoch))
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print('- label_mA: {}'.format(best_result))
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time_start = time.time()
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@ -199,6 +200,7 @@ def main():
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is_best = label_mA > best_result
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if is_best:
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best_result = label_mA
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save_checkpoint(
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{
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'state_dict': model.state_dict(),
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@ -340,7 +342,7 @@ def test(model, testloader, attr_dict, use_gpu):
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if (batch_idx+1) % args.print_freq == 0:
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print(
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"Processed batch {}/{}".format(batch_idx + 1, len(testloader))
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'Processed batch {}/{}'.format(batch_idx + 1, len(testloader))
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)
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if args.save_prediction:
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@ -349,24 +351,24 @@ def test(model, testloader, attr_dict, use_gpu):
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img_path = img_paths[idx]
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probs = orig_outputs[idx, :]
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labels = attrs[idx, :]
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txtfile.write("{}\n".format(img_path))
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txtfile.write("*** Correct prediction ***\n")
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txtfile.write('{}\n'.format(img_path))
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txtfile.write('*** Correct prediction ***\n')
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for attr_idx, (label, prob) in enumerate(zip(labels, probs)):
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if label:
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attr_name = attr_dict[attr_idx]
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info = "{}: {:.1%} ".format(attr_name, prob)
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info = '{}: {:.1%} '.format(attr_name, prob)
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txtfile.write(info)
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txtfile.write("\n*** Incorrect prediction ***\n")
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txtfile.write('\n*** Incorrect prediction ***\n')
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for attr_idx, (label, prob) in enumerate(zip(labels, probs)):
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if not label and prob > 0.5:
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attr_name = attr_dict[attr_idx]
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info = "{}: {:.1%} ".format(attr_name, prob)
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info = '{}: {:.1%} '.format(attr_name, prob)
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txtfile.write(info)
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txtfile.write("\n\n")
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txtfile.write('\n\n')
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txtfile.close()
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print(
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"=> BatchTime(s)/BatchSize(img): {:.4f}/{}".format(
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'=> BatchTime(s)/BatchSize(img): {:.4f}/{}'.format(
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batch_time.avg, args.batch_size
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)
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)
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@ -381,14 +383,14 @@ def test(model, testloader, attr_dict, use_gpu):
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label_mA_verbose = (term1+term2) * 0.5
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label_mA = label_mA_verbose.mean()
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print("* Results *")
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print(" # test persons: {}".format(num_persons))
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print(" (instance-based) accuracy: {:.1%}".format(ins_acc))
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print(" (instance-based) precition: {:.1%}".format(ins_prec))
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print(" (instance-based) recall: {:.1%}".format(ins_rec))
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print(" (instance-based) f1-score: {:.1%}".format(ins_f1))
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print(" (label-based) mean accuracy: {:.1%}".format(label_mA))
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print(" mA for each attribute: {}".format(label_mA_verbose))
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print('* Results *')
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print(' # test persons: {}'.format(num_persons))
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print(' (instance-based) accuracy: {:.1%}'.format(ins_acc))
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print(' (instance-based) precition: {:.1%}'.format(ins_prec))
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print(' (instance-based) recall: {:.1%}'.format(ins_rec))
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print(' (instance-based) f1-score: {:.1%}'.format(ins_f1))
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print(' (label-based) mean accuracy: {:.1%}'.format(label_mA))
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print(' mA for each attribute: {}'.format(label_mA_verbose))
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return label_mA, ins_acc, ins_prec, ins_rec, ins_f1
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