From dd5e2ca1663ed355953da9ea906f5131387d286a Mon Sep 17 00:00:00 2001 From: KaiyangZhou Date: Fri, 24 May 2019 16:30:24 +0100 Subject: [PATCH] 0.7.7 --- AWESOME_REID.html | 4 +- MODEL_ZOO.html | 4 +- _modules/index.html | 6 +- _modules/torchreid/data/datamanager.html | 4 +- .../torchreid/data/datasets/__init__.html | 4 +- _modules/torchreid/data/datasets/dataset.html | 4 +- .../torchreid/data/datasets/image/cuhk01.html | 4 +- .../torchreid/data/datasets/image/cuhk03.html | 4 +- .../data/datasets/image/dukemtmcreid.html | 4 +- .../torchreid/data/datasets/image/grid.html | 4 +- .../torchreid/data/datasets/image/ilids.html | 4 +- .../data/datasets/image/market1501.html | 4 +- .../torchreid/data/datasets/image/msmt17.html | 4 +- .../torchreid/data/datasets/image/prid.html | 4 +- .../data/datasets/image/sensereid.html | 4 +- .../torchreid/data/datasets/image/viper.html | 4 +- .../data/datasets/video/dukemtmcvidreid.html | 4 +- .../data/datasets/video/ilidsvid.html | 4 +- .../torchreid/data/datasets/video/mars.html | 4 +- .../data/datasets/video/prid2011.html | 4 +- _modules/torchreid/data/sampler.html | 4 +- _modules/torchreid/data/transforms.html | 4 +- _modules/torchreid/engine/engine.html | 17 +- _modules/torchreid/engine/image/softmax.html | 41 +- _modules/torchreid/engine/image/triplet.html | 45 +- _modules/torchreid/engine/video/softmax.html | 4 +- _modules/torchreid/engine/video/triplet.html | 4 +- .../torchreid/losses/cross_entropy_loss.html | 4 +- .../losses/hard_mine_triplet_loss.html | 4 +- _modules/torchreid/metrics/accuracy.html | 4 +- _modules/torchreid/metrics/distance.html | 4 +- _modules/torchreid/metrics/rank.html | 4 +- _modules/torchreid/models/__init__.html | 14 +- _modules/torchreid/models/densenet.html | 9 +- _modules/torchreid/models/hacnn.html | 4 +- .../torchreid/models/inceptionresnetv2.html | 4 +- _modules/torchreid/models/inceptionv4.html | 4 +- _modules/torchreid/models/mlfn.html | 4 +- _modules/torchreid/models/mobilenetv2.html | 4 +- _modules/torchreid/models/mudeep.html | 4 +- _modules/torchreid/models/nasnet.html | 4 +- _modules/torchreid/models/pcb.html | 4 +- _modules/torchreid/models/resnet.html | 200 +++++--- _modules/torchreid/models/resnetmid.html | 4 +- _modules/torchreid/models/resnext.html | 447 ------------------ _modules/torchreid/models/senet.html | 4 +- _modules/torchreid/models/shufflenet.html | 4 +- _modules/torchreid/models/shufflenetv2.html | 419 ++++++++++++++++ _modules/torchreid/models/squeezenet.html | 9 +- _modules/torchreid/models/xception.html | 4 +- _modules/torchreid/optim/lr_scheduler.html | 4 +- _modules/torchreid/optim/optimizer.html | 4 +- _modules/torchreid/utils/avgmeter.html | 4 +- _modules/torchreid/utils/loggers.html | 4 +- .../torchreid/utils/model_complexity.html | 4 +- _modules/torchreid/utils/reidtools.html | 4 +- _modules/torchreid/utils/tools.html | 18 +- _modules/torchreid/utils/torchtools.html | 4 +- _sources/pkg/models.rst.txt | 2 +- _static/documentation_options.js | 2 +- datasets.html | 4 +- evaluation.html | 4 +- genindex.html | 14 +- index.html | 4 +- objects.inv | Bin 1847 -> 1864 bytes pkg/data.html | 4 +- pkg/engine.html | 62 +-- pkg/losses.html | 4 +- pkg/metrics.html | 4 +- pkg/models.html | 49 +- pkg/optim.html | 4 +- pkg/utils.html | 11 +- py-modindex.html | 4 +- search.html | 4 +- searchindex.js | 2 +- user_guide.html | 4 +- 76 files changed, 835 insertions(+), 760 deletions(-) delete mode 100644 _modules/torchreid/models/resnext.html create mode 100644 _modules/torchreid/models/shufflenetv2.html diff --git a/AWESOME_REID.html b/AWESOME_REID.html index ae0cd3e..201ccc2 100644 --- a/AWESOME_REID.html +++ b/AWESOME_REID.html @@ -8,7 +8,7 @@ - Awesome-ReID — torchreid 0.7.6 documentation + Awesome-ReID — torchreid 0.7.7 documentation @@ -61,7 +61,7 @@
- 0.7.6 + 0.7.7
diff --git a/MODEL_ZOO.html b/MODEL_ZOO.html index 7a30cd0..c133068 100644 --- a/MODEL_ZOO.html +++ b/MODEL_ZOO.html @@ -8,7 +8,7 @@ - Model Zoo — torchreid 0.7.6 documentation + Model Zoo — torchreid 0.7.7 documentation @@ -60,7 +60,7 @@
- 0.7.6 + 0.7.7
diff --git a/_modules/index.html b/_modules/index.html index d1f74a9..82c2e62 100644 --- a/_modules/index.html +++ b/_modules/index.html @@ -8,7 +8,7 @@ - Overview: module code — torchreid 0.7.6 documentation + Overview: module code — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
- 0.7.6 + 0.7.7
@@ -204,9 +204,9 @@
  • torchreid.models.pcb
  • torchreid.models.resnet
  • torchreid.models.resnetmid
    • -
  • torchreid.models.resnext
  • torchreid.models.senet
  • torchreid.models.shufflenet
    • +
  • torchreid.models.shufflenetv2
  • torchreid.models.squeezenet
  • torchreid.models.xception
  • torchreid.optim.lr_scheduler
    • diff --git a/_modules/torchreid/data/datamanager.html b/_modules/torchreid/data/datamanager.html index 79f2a49..d5471ce 100644 --- a/_modules/torchreid/data/datamanager.html +++ b/_modules/torchreid/data/datamanager.html @@ -8,7 +8,7 @@ - torchreid.data.datamanager — torchreid 0.7.6 documentation + torchreid.data.datamanager — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/__init__.html b/_modules/torchreid/data/datasets/__init__.html index f96e6fb..4d74e66 100644 --- a/_modules/torchreid/data/datasets/__init__.html +++ b/_modules/torchreid/data/datasets/__init__.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.__init__ — torchreid 0.7.6 documentation + torchreid.data.datasets.__init__ — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/dataset.html b/_modules/torchreid/data/datasets/dataset.html index 02ce70e..7938700 100644 --- a/_modules/torchreid/data/datasets/dataset.html +++ b/_modules/torchreid/data/datasets/dataset.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.dataset — torchreid 0.7.6 documentation + torchreid.data.datasets.dataset — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/cuhk01.html b/_modules/torchreid/data/datasets/image/cuhk01.html index 35847c2..059d20d 100644 --- a/_modules/torchreid/data/datasets/image/cuhk01.html +++ b/_modules/torchreid/data/datasets/image/cuhk01.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.cuhk01 — torchreid 0.7.6 documentation + torchreid.data.datasets.image.cuhk01 — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/cuhk03.html b/_modules/torchreid/data/datasets/image/cuhk03.html index f8ec8fa..938832f 100644 --- a/_modules/torchreid/data/datasets/image/cuhk03.html +++ b/_modules/torchreid/data/datasets/image/cuhk03.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.cuhk03 — torchreid 0.7.6 documentation + torchreid.data.datasets.image.cuhk03 — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/dukemtmcreid.html b/_modules/torchreid/data/datasets/image/dukemtmcreid.html index 51219cb..3896bb6 100644 --- a/_modules/torchreid/data/datasets/image/dukemtmcreid.html +++ b/_modules/torchreid/data/datasets/image/dukemtmcreid.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.dukemtmcreid — torchreid 0.7.6 documentation + torchreid.data.datasets.image.dukemtmcreid — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/grid.html b/_modules/torchreid/data/datasets/image/grid.html index a894e43..e50c271 100644 --- a/_modules/torchreid/data/datasets/image/grid.html +++ b/_modules/torchreid/data/datasets/image/grid.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.grid — torchreid 0.7.6 documentation + torchreid.data.datasets.image.grid — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/ilids.html b/_modules/torchreid/data/datasets/image/ilids.html index c6a7365..7ecb0b9 100644 --- a/_modules/torchreid/data/datasets/image/ilids.html +++ b/_modules/torchreid/data/datasets/image/ilids.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.ilids — torchreid 0.7.6 documentation + torchreid.data.datasets.image.ilids — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/market1501.html b/_modules/torchreid/data/datasets/image/market1501.html index 03a18f3..1edbe45 100644 --- a/_modules/torchreid/data/datasets/image/market1501.html +++ b/_modules/torchreid/data/datasets/image/market1501.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.market1501 — torchreid 0.7.6 documentation + torchreid.data.datasets.image.market1501 — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/msmt17.html b/_modules/torchreid/data/datasets/image/msmt17.html index b89d5a8..46b3e51 100644 --- a/_modules/torchreid/data/datasets/image/msmt17.html +++ b/_modules/torchreid/data/datasets/image/msmt17.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.msmt17 — torchreid 0.7.6 documentation + torchreid.data.datasets.image.msmt17 — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/prid.html b/_modules/torchreid/data/datasets/image/prid.html index 09118d1..708e81e 100644 --- a/_modules/torchreid/data/datasets/image/prid.html +++ b/_modules/torchreid/data/datasets/image/prid.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.prid — torchreid 0.7.6 documentation + torchreid.data.datasets.image.prid — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/sensereid.html b/_modules/torchreid/data/datasets/image/sensereid.html index f31e899..ac48598 100644 --- a/_modules/torchreid/data/datasets/image/sensereid.html +++ b/_modules/torchreid/data/datasets/image/sensereid.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.sensereid — torchreid 0.7.6 documentation + torchreid.data.datasets.image.sensereid — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/image/viper.html b/_modules/torchreid/data/datasets/image/viper.html index 7016d17..bbc3bc7 100644 --- a/_modules/torchreid/data/datasets/image/viper.html +++ b/_modules/torchreid/data/datasets/image/viper.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.image.viper — torchreid 0.7.6 documentation + torchreid.data.datasets.image.viper — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/video/dukemtmcvidreid.html b/_modules/torchreid/data/datasets/video/dukemtmcvidreid.html index 4055502..5ae9022 100644 --- a/_modules/torchreid/data/datasets/video/dukemtmcvidreid.html +++ b/_modules/torchreid/data/datasets/video/dukemtmcvidreid.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.video.dukemtmcvidreid — torchreid 0.7.6 documentation + torchreid.data.datasets.video.dukemtmcvidreid — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/video/ilidsvid.html b/_modules/torchreid/data/datasets/video/ilidsvid.html index 94f3b3b..9bd1cd0 100644 --- a/_modules/torchreid/data/datasets/video/ilidsvid.html +++ b/_modules/torchreid/data/datasets/video/ilidsvid.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.video.ilidsvid — torchreid 0.7.6 documentation + torchreid.data.datasets.video.ilidsvid — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/video/mars.html b/_modules/torchreid/data/datasets/video/mars.html index 40bd0ac..414377f 100644 --- a/_modules/torchreid/data/datasets/video/mars.html +++ b/_modules/torchreid/data/datasets/video/mars.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.video.mars — torchreid 0.7.6 documentation + torchreid.data.datasets.video.mars — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/datasets/video/prid2011.html b/_modules/torchreid/data/datasets/video/prid2011.html index dc5349c..5180670 100644 --- a/_modules/torchreid/data/datasets/video/prid2011.html +++ b/_modules/torchreid/data/datasets/video/prid2011.html @@ -8,7 +8,7 @@ - torchreid.data.datasets.video.prid2011 — torchreid 0.7.6 documentation + torchreid.data.datasets.video.prid2011 — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/sampler.html b/_modules/torchreid/data/sampler.html index f84473b..a7569fb 100644 --- a/_modules/torchreid/data/sampler.html +++ b/_modules/torchreid/data/sampler.html @@ -8,7 +8,7 @@ - torchreid.data.sampler — torchreid 0.7.6 documentation + torchreid.data.sampler — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/data/transforms.html b/_modules/torchreid/data/transforms.html index 80090c5..75c36b7 100644 --- a/_modules/torchreid/data/transforms.html +++ b/_modules/torchreid/data/transforms.html @@ -8,7 +8,7 @@ - torchreid.data.transforms — torchreid 0.7.6 documentation + torchreid.data.transforms — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/engine/engine.html b/_modules/torchreid/engine/engine.html index d3c540c..e621ebb 100644 --- a/_modules/torchreid/engine/engine.html +++ b/_modules/torchreid/engine/engine.html @@ -8,7 +8,7 @@ - torchreid.engine.engine — torchreid 0.7.6 documentation + torchreid.engine.engine — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    @@ -219,7 +219,7 @@ max_epoch (int): maximum epoch. start_epoch (int, optional): starting epoch. Default is 0. fixbase_epoch (int, optional): number of epochs to train ``open_layers`` (new layers) - while keeping base layers frozen. Default is 0. ``fixbase_epoch`` is not counted + while keeping base layers frozen. Default is 0. ``fixbase_epoch`` is counted in ``max_epoch``. open_layers (str or list, optional): layers (attribute names) open for training. start_eval (int, optional): from which epoch to start evaluation. Default is 0. @@ -263,15 +263,8 @@ time_start = time.time() print('=> Start training') - if fixbase_epoch>0 and (open_layers is not None): - print('Pretrain open layers ({}) for {} epochs'.format(open_layers, fixbase_epoch)) - for epoch in range(fixbase_epoch): - self.train(epoch, trainloader, fixbase=True, open_layers=open_layers, - print_freq=print_freq) - print('Done. From now on all layers are open to train for {} epochs'.format(max_epoch)) - for epoch in range(start_epoch, max_epoch): - self.train(epoch, trainloader, print_freq=print_freq) + self.train(epoch, max_epoch, trainloader, fixbase_epoch, open_layers, print_freq) if (epoch+1)>start_eval and eval_freq>0 and (epoch+1)%eval_freq==0 and (epoch+1)!=max_epoch: rank1 = self.test( @@ -362,7 +355,7 @@ for name in targets: domain = 'source' if name in self.datamanager.sources else 'target' - print('\n##### Evaluating {} ({}) #####'.format(name, domain)) + print('##### Evaluating {} ({}) #####'.format(name, domain)) queryloader = testloader[name]['query'] galleryloader = testloader[name]['gallery'] rank1 = self._evaluate( diff --git a/_modules/torchreid/engine/image/softmax.html b/_modules/torchreid/engine/image/softmax.html index 2649af2..38fe2d3 100644 --- a/_modules/torchreid/engine/image/softmax.html +++ b/_modules/torchreid/engine/image/softmax.html @@ -8,7 +8,7 @@ - torchreid.engine.image.softmax — torchreid 0.7.6 documentation + torchreid.engine.image.softmax — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    @@ -182,7 +182,7 @@
    [docs]class ImageSoftmaxEngine(engine.Engine): - """Softmax-loss engine for image-reid. + r"""Softmax-loss engine for image-reid. Args: datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager`` @@ -239,24 +239,15 @@ label_smooth=label_smooth ) -
    [docs] def train(self, epoch, trainloader, fixbase=False, open_layers=None, print_freq=10): - """Trains the model for one epoch on source datasets using softmax loss. - - Args: - epoch (int): current epoch. - trainloader (Dataloader): training dataloader. - fixbase (bool, optional): whether to fix base layers. Default is False. - open_layers (str or list, optional): layers open for training. - print_freq (int, optional): print frequency. Default is 10. - """ +
    [docs] def train(self, epoch, max_epoch, trainloader, fixbase_epoch=0, open_layers=None, print_freq=10): losses = AverageMeter() accs = AverageMeter() batch_time = AverageMeter() data_time = AverageMeter() self.model.train() - - if fixbase and (open_layers is not None): + if (epoch+1)<=fixbase_epoch and open_layers is not None: + print('* Only train {} (epoch: {}/{})'.format(open_layers, epoch+1, fixbase_epoch)) open_specified_layers(self.model, open_layers) else: open_all_layers(self.model) @@ -282,20 +273,30 @@ accs.update(metrics.accuracy(outputs, pids)[0].item()) if (batch_idx+1) % print_freq == 0: - print('Epoch: [{0}][{1}/{2}]\t' + # estimate remaining time + num_batches = len(trainloader) + eta_seconds = batch_time.avg * (num_batches-(batch_idx+1) + (max_epoch-(epoch+1))*num_batches) + eta_str = str(datetime.timedelta(seconds=int(eta_seconds))) + print('Epoch: [{0}/{1}][{2}/{3}]\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' - 'Acc {acc.val:.2f} ({acc.avg:.2f})\t'.format( - epoch + 1, batch_idx + 1, len(trainloader), + 'Acc {acc.val:.2f} ({acc.avg:.2f})\t' + 'Lr {lr:.6f}\t' + 'Eta {eta}'.format( + epoch+1, max_epoch, batch_idx+1, len(trainloader), batch_time=batch_time, data_time=data_time, loss=losses, - acc=accs)) + acc=accs, + lr=self.optimizer.param_groups[0]['lr'], + eta=eta_str + ) + ) end = time.time() - if (self.scheduler is not None) and (not fixbase): + if self.scheduler is not None: self.scheduler.step()
    diff --git a/_modules/torchreid/engine/image/triplet.html b/_modules/torchreid/engine/image/triplet.html index f61c764..f2f5885 100644 --- a/_modules/torchreid/engine/image/triplet.html +++ b/_modules/torchreid/engine/image/triplet.html @@ -8,7 +8,7 @@ - torchreid.engine.image.triplet — torchreid 0.7.6 documentation + torchreid.engine.image.triplet — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    @@ -182,7 +182,7 @@
    [docs]class ImageTripletEngine(engine.Engine): - """Triplet-loss engine for image-reid. + r"""Triplet-loss engine for image-reid. Args: datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager`` @@ -250,16 +250,7 @@ label_smooth=label_smooth ) -
    [docs] def train(self, epoch, trainloader, fixbase=False, open_layers=None, print_freq=10): - """Trains the model for one epoch on source datasets using hard mining triplet loss. - - Args: - epoch (int): current epoch. - trainloader (Dataloader): training dataloader. - fixbase (bool, optional): whether to fix base layers. Default is False. - open_layers (str or list, optional): layers open for training. - print_freq (int, optional): print frequency. Default is 10. - """ +
    [docs] def train(self, epoch, max_epoch, trainloader, fixbase_epoch=0, open_layers=None, print_freq=10): losses_t = AverageMeter() losses_x = AverageMeter() accs = AverageMeter() @@ -267,8 +258,8 @@ data_time = AverageMeter() self.model.train() - - if fixbase and (open_layers is not None): + if (epoch+1)<=fixbase_epoch and open_layers is not None: + print('* Only train {} (epoch: {}/{})'.format(open_layers, epoch+1, fixbase_epoch)) open_specified_layers(self.model, open_layers) else: open_all_layers(self.model) @@ -297,22 +288,32 @@ accs.update(metrics.accuracy(outputs, pids)[0].item()) if (batch_idx+1) % print_freq == 0: - print('Epoch: [{0}][{1}/{2}]\t' + # estimate remaining time + num_batches = len(trainloader) + eta_seconds = batch_time.avg * (num_batches-(batch_idx+1) + (max_epoch-(epoch+1))*num_batches) + eta_str = str(datetime.timedelta(seconds=int(eta_seconds))) + print('Epoch: [{0}/{1}][{2}/{3}]\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' - 'Triplet {loss_t.val:.4f} ({loss_t.avg:.4f})\t' - 'Softmax {loss_x.val:.4f} ({loss_x.avg:.4f})\t' - 'Acc {acc.val:.2f} ({acc.avg:.2f})\t'.format( - epoch + 1, batch_idx + 1, len(trainloader), + 'Loss_t {loss_t.val:.4f} ({loss_t.avg:.4f})\t' + 'Loss_x {loss_x.val:.4f} ({loss_x.avg:.4f})\t' + 'Acc {acc.val:.2f} ({acc.avg:.2f})\t' + 'Lr {lr:.6f}\t' + 'Eta {eta}'.format( + epoch+1, max_epoch, batch_idx+1, len(trainloader), batch_time=batch_time, data_time=data_time, loss_t=losses_t, loss_x=losses_x, - acc=accs)) + acc=accs, + lr=self.optimizer.param_groups[0]['lr'], + eta=eta_str + ) + ) end = time.time() - if (self.scheduler is not None) and (not fixbase): + if self.scheduler is not None: self.scheduler.step()
    diff --git a/_modules/torchreid/engine/video/softmax.html b/_modules/torchreid/engine/video/softmax.html index e6e532d..d057512 100644 --- a/_modules/torchreid/engine/video/softmax.html +++ b/_modules/torchreid/engine/video/softmax.html @@ -8,7 +8,7 @@ - torchreid.engine.video.softmax — torchreid 0.7.6 documentation + torchreid.engine.video.softmax — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/engine/video/triplet.html b/_modules/torchreid/engine/video/triplet.html index 4c8587e..0308b39 100644 --- a/_modules/torchreid/engine/video/triplet.html +++ b/_modules/torchreid/engine/video/triplet.html @@ -8,7 +8,7 @@ - torchreid.engine.video.triplet — torchreid 0.7.6 documentation + torchreid.engine.video.triplet — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/losses/cross_entropy_loss.html b/_modules/torchreid/losses/cross_entropy_loss.html index f46bc80..9bd4d61 100644 --- a/_modules/torchreid/losses/cross_entropy_loss.html +++ b/_modules/torchreid/losses/cross_entropy_loss.html @@ -8,7 +8,7 @@ - torchreid.losses.cross_entropy_loss — torchreid 0.7.6 documentation + torchreid.losses.cross_entropy_loss — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/losses/hard_mine_triplet_loss.html b/_modules/torchreid/losses/hard_mine_triplet_loss.html index 860ea27..6c54935 100644 --- a/_modules/torchreid/losses/hard_mine_triplet_loss.html +++ b/_modules/torchreid/losses/hard_mine_triplet_loss.html @@ -8,7 +8,7 @@ - torchreid.losses.hard_mine_triplet_loss — torchreid 0.7.6 documentation + torchreid.losses.hard_mine_triplet_loss — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/metrics/accuracy.html b/_modules/torchreid/metrics/accuracy.html index 9c84bde..dec1c3f 100644 --- a/_modules/torchreid/metrics/accuracy.html +++ b/_modules/torchreid/metrics/accuracy.html @@ -8,7 +8,7 @@ - torchreid.metrics.accuracy — torchreid 0.7.6 documentation + torchreid.metrics.accuracy — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/metrics/distance.html b/_modules/torchreid/metrics/distance.html index e920466..32882f2 100644 --- a/_modules/torchreid/metrics/distance.html +++ b/_modules/torchreid/metrics/distance.html @@ -8,7 +8,7 @@ - torchreid.metrics.distance — torchreid 0.7.6 documentation + torchreid.metrics.distance — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/metrics/rank.html b/_modules/torchreid/metrics/rank.html index 4eaf73f..1e9c193 100644 --- a/_modules/torchreid/metrics/rank.html +++ b/_modules/torchreid/metrics/rank.html @@ -8,7 +8,7 @@ - torchreid.metrics.rank — torchreid 0.7.6 documentation + torchreid.metrics.rank — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/_modules/torchreid/models/__init__.html b/_modules/torchreid/models/__init__.html index d0a4338..ff6c8ac 100644 --- a/_modules/torchreid/models/__init__.html +++ b/_modules/torchreid/models/__init__.html @@ -8,7 +8,7 @@ - torchreid.models.__init__ — torchreid 0.7.6 documentation + torchreid.models.__init__ — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    @@ -171,7 +171,6 @@ from .resnet import * from .resnetmid import * -from .resnext import * from .senet import * from .densenet import * from .inceptionresnetv2 import * @@ -182,6 +181,7 @@ from .mobilenetv2 import * from .shufflenet import * from .squeezenet import * +from .shufflenetv2 import * from .mudeep import * from .hacnn import * @@ -196,9 +196,9 @@ 'resnet50': resnet50, 'resnet101': resnet101, 'resnet152': resnet152, - 'resnet50_fc512': resnet50_fc512, 'resnext50_32x4d': resnext50_32x4d, - 'resnext50_32x4d_fc512': resnext50_32x4d_fc512, + 'resnext101_32x8d': resnext101_32x8d, + 'resnet50_fc512': resnet50_fc512, 'se_resnet50': se_resnet50, 'se_resnet50_fc512': se_resnet50_fc512, 'se_resnet101': se_resnet101, @@ -220,6 +220,10 @@ 'squeezenet1_0': squeezenet1_0, 'squeezenet1_0_fc512': squeezenet1_0_fc512, 'squeezenet1_1': squeezenet1_1, + 'shufflenet_v2_x0_5': shufflenet_v2_x0_5, + 'shufflenet_v2_x1_0': shufflenet_v2_x1_0, + 'shufflenet_v2_x1_5': shufflenet_v2_x1_5, + 'shufflenet_v2_x2_0': shufflenet_v2_x2_0, # reid-specific models 'mudeep': MuDeep, 'resnet50mid': resnet50mid, diff --git a/_modules/torchreid/models/densenet.html b/_modules/torchreid/models/densenet.html index 875e344..df7f3fb 100644 --- a/_modules/torchreid/models/densenet.html +++ b/_modules/torchreid/models/densenet.html @@ -8,7 +8,7 @@ - torchreid.models.densenet — torchreid 0.7.6 documentation + torchreid.models.densenet — torchreid 0.7.7 documentation @@ -59,7 +59,7 @@
    - 0.7.6 + 0.7.7
    @@ -165,7 +165,10 @@

    Source code for torchreid.models.densenet

    -from __future__ import absolute_import
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import absolute_import
 from __future__ import division
 
 __all__ = ['densenet121', 'densenet169', 'densenet201', 'densenet161', 'densenet121_fc512']
diff --git a/_modules/torchreid/models/hacnn.html b/_modules/torchreid/models/hacnn.html
index fd21a28..511a4b1 100644
--- a/_modules/torchreid/models/hacnn.html
+++ b/_modules/torchreid/models/hacnn.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.hacnn — torchreid 0.7.6 documentation
+  torchreid.models.hacnn — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/inceptionresnetv2.html b/_modules/torchreid/models/inceptionresnetv2.html
index d0a4fe1..a19f22f 100644
--- a/_modules/torchreid/models/inceptionresnetv2.html
+++ b/_modules/torchreid/models/inceptionresnetv2.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.inceptionresnetv2 — torchreid 0.7.6 documentation
+  torchreid.models.inceptionresnetv2 — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/inceptionv4.html b/_modules/torchreid/models/inceptionv4.html
index 3b185f4..5612e02 100644
--- a/_modules/torchreid/models/inceptionv4.html
+++ b/_modules/torchreid/models/inceptionv4.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.inceptionv4 — torchreid 0.7.6 documentation
+  torchreid.models.inceptionv4 — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/mlfn.html b/_modules/torchreid/models/mlfn.html
index 345b606..edad60e 100644
--- a/_modules/torchreid/models/mlfn.html
+++ b/_modules/torchreid/models/mlfn.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.mlfn — torchreid 0.7.6 documentation
+  torchreid.models.mlfn — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/mobilenetv2.html b/_modules/torchreid/models/mobilenetv2.html
index 4283987..67ea4a1 100644
--- a/_modules/torchreid/models/mobilenetv2.html
+++ b/_modules/torchreid/models/mobilenetv2.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.mobilenetv2 — torchreid 0.7.6 documentation
+  torchreid.models.mobilenetv2 — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/mudeep.html b/_modules/torchreid/models/mudeep.html
index 64555b8..78ebb2e 100644
--- a/_modules/torchreid/models/mudeep.html
+++ b/_modules/torchreid/models/mudeep.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.mudeep — torchreid 0.7.6 documentation
+  torchreid.models.mudeep — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/nasnet.html b/_modules/torchreid/models/nasnet.html
index c6e3af3..44edac2 100644
--- a/_modules/torchreid/models/nasnet.html
+++ b/_modules/torchreid/models/nasnet.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.nasnet — torchreid 0.7.6 documentation
+  torchreid.models.nasnet — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/pcb.html b/_modules/torchreid/models/pcb.html
index 3ef2582..3073829 100644
--- a/_modules/torchreid/models/pcb.html
+++ b/_modules/torchreid/models/pcb.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.pcb — torchreid 0.7.6 documentation
+  torchreid.models.pcb — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/resnet.html b/_modules/torchreid/models/resnet.html
index 99e3706..45418de 100644
--- a/_modules/torchreid/models/resnet.html
+++ b/_modules/torchreid/models/resnet.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.resnet — torchreid 0.7.6 documentation
+  torchreid.models.resnet — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
@@ -165,10 +165,14 @@
            
    
             
   
    Source code for torchreid.models.resnet
    -from __future__ import absolute_import
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import absolute_import
 from __future__ import division
 
-__all__ = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnet50_fc512']
+__all__ = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d',
+           'resnext101_32x8d', 'resnet50_fc512']
 
 import torch
 from torch import nn
@@ -183,30 +187,45 @@
     'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
     'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
     'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
+    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
 }
 
 
-def conv3x3(in_planes, out_planes, stride=1):
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
     """3x3 convolution with padding"""
     return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
+                     padding=dilation, groups=groups, bias=False, dilation=dilation)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
 
 
 class BasicBlock(nn.Module):
     expansion = 1
 
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
+    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None):
         super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
         self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = nn.BatchNorm2d(planes)
+        self.bn1 = norm_layer(planes)
         self.relu = nn.ReLU(inplace=True)
         self.conv2 = conv3x3(planes, planes)
-        self.bn2 = nn.BatchNorm2d(planes)
+        self.bn2 = norm_layer(planes)
         self.downsample = downsample
         self.stride = stride
 
     def forward(self, x):
-        residual = x
+        identity = x
 
         out = self.conv1(x)
         out = self.bn1(out)
@@ -216,9 +235,9 @@
         out = self.bn2(out)
 
         if self.downsample is not None:
-            residual = self.downsample(x)
+            identity = self.downsample(x)
 
-        out += residual
+        out += identity
         out = self.relu(out)
 
         return out
@@ -227,21 +246,25 @@
 class Bottleneck(nn.Module):
     expansion = 4
 
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
+    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None):
         super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        width = int(planes * (base_width / 64.)) * groups
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(inplanes, width)
+        self.bn1 = norm_layer(width)
+        self.conv2 = conv3x3(width, width, stride, groups, dilation)
+        self.bn2 = norm_layer(width)
+        self.conv3 = conv1x1(width, planes * self.expansion)
+        self.bn3 = norm_layer(planes * self.expansion)
         self.relu = nn.ReLU(inplace=True)
         self.downsample = downsample
         self.stride = stride
 
     def forward(self, x):
-        residual = x
+        identity = x
 
         out = self.conv1(x)
         out = self.bn1(out)
@@ -255,9 +278,9 @@
         out = self.bn3(out)
 
         if self.downsample is not None:
-            residual = self.downsample(x)
+            identity = self.downsample(x)
 
-        out += residual
+        out += identity
         out = self.relu(out)
 
         return out
@@ -267,7 +290,8 @@
     """Residual network.
     
     Reference:
-        He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
 
     Public keys:
         - ``resnet18``: ResNet18.
@@ -275,49 +299,80 @@
         - ``resnet50``: ResNet50.
         - ``resnet101``: ResNet101.
         - ``resnet152``: ResNet152.
+        - ``resnext50_32x4d``: ResNeXt50.
+        - ``resnext101_32x8d``: ResNeXt101.
         - ``resnet50_fc512``: ResNet50 + FC.
     """
-    
-    def __init__(self, num_classes, loss, block, layers,
-                 last_stride=2,
-                 fc_dims=None,
-                 dropout_p=None,
-                 **kwargs):
-        self.inplanes = 64
+
+    def __init__(self, num_classes, loss, block, layers, zero_init_residual=False,
+                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
+                 norm_layer=None, last_stride=2, fc_dims=None, dropout_p=None, **kwargs):
         super(ResNet, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer
         self.loss = loss
         self.feature_dim = 512 * block.expansion
-        
-        # backbone network
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            # each element in the tuple indicates if we should replace
+            # the 2x2 stride with a dilated convolution instead
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError("replace_stride_with_dilation should be None "
+                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+        self.groups = groups
+        self.base_width = width_per_group
+        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = norm_layer(self.inplanes)
         self.relu = nn.ReLU(inplace=True)
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
         self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=last_stride)
-        
-        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
+                                       dilate=replace_stride_with_dilation[0])
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
+                                       dilate=replace_stride_with_dilation[1])
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=last_stride,
+                                       dilate=replace_stride_with_dilation[2])
+        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
         self.fc = self._construct_fc_layer(fc_dims, 512 * block.expansion, dropout_p)
         self.classifier = nn.Linear(self.feature_dim, num_classes)
 
         self._init_params()
 
-    def _make_layer(self, block, planes, blocks, stride=1):
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
         downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
         if stride != 1 or self.inplanes != planes * block.expansion:
             downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
             )
 
         layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
+        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
+                            self.base_width, previous_dilation, norm_layer))
         self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups=self.groups,
+                                base_width=self.base_width, dilation=self.dilation,
+                                norm_layer=norm_layer))
 
         return nn.Sequential(*layers)
 
@@ -409,17 +464,7 @@
     model.load_state_dict(model_dict)
 
 
-"""
-Residual network configurations:
---
-resnet18: block=BasicBlock, layers=[2, 2, 2, 2]
-resnet34: block=BasicBlock, layers=[3, 4, 6, 3]
-resnet50: block=Bottleneck, layers=[3, 4, 6, 3]
-resnet101: block=Bottleneck, layers=[3, 4, 23, 3]
-resnet152: block=Bottleneck, layers=[3, 8, 36, 3]
-"""
-
-
+"""ResNet"""
 def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
     model = ResNet(
         num_classes=num_classes,
@@ -500,8 +545,45 @@
     return model
 
 
+"""ResNeXt"""
+def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 6, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        groups=32,
+        width_per_group=4,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnext50_32x4d'])
+    return model
+
+
+def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ResNet(
+        num_classes=num_classes,
+        loss=loss,
+        block=Bottleneck,
+        layers=[3, 4, 23, 3],
+        last_stride=2,
+        fc_dims=None,
+        dropout_p=None,
+        groups=32,
+        width_per_group=8,
+        **kwargs
+    )
+    if pretrained:
+        init_pretrained_weights(model, model_urls['resnext101_32x8d'])
+    return model
+
+
 """
-resnet + fc
+ResNet + FC
 """
 def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
     model = ResNet(
diff --git a/_modules/torchreid/models/resnetmid.html b/_modules/torchreid/models/resnetmid.html
index 0e60e6f..6412fb4 100644
--- a/_modules/torchreid/models/resnetmid.html
+++ b/_modules/torchreid/models/resnetmid.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.resnetmid — torchreid 0.7.6 documentation
+  torchreid.models.resnetmid — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/resnext.html b/_modules/torchreid/models/resnext.html
deleted file mode 100644
index 336955c..0000000
--- a/_modules/torchreid/models/resnext.html
+++ /dev/null
@@ -1,447 +0,0 @@
-
-
-
-
-  
-
-  
-  
-  
-  
-  torchreid.models.resnext — torchreid 0.7.6 documentation
-  
-
-  
-  
-  
-  
-
-  
-  
-  
-    
-      
-        
-        
-        
-        
-        
-    
-    
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-    
-
-  
-  
-  
-    
-     
-
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-
-
-   
-  
    
-    
-    
-
-    
    
-
-      
-      
-
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-      
    
-        
-        
    
-        
-          
-
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-
-
-
-
-
-
-
-
-
-
-
-
-
-
    
-
-  
      
-    
-      
    • Docs »
      • 
-        
-          
    • Module code »
      • 
-        
-      
    • torchreid.models.resnext
      • 
-    
-    
-      
    • 
-        
-      
      • 
-    
-  
    
-
-  
-  
    
-
    
-          
    
-           
    
-            
-  
    Source code for torchreid.models.resnext
    -from __future__ import absolute_import
-from __future__ import division
-
-__all__ = ['resnext50_32x4d', 'resnext50_32x4d_fc512']
-
-import math
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-import torchvision
-import torch.utils.model_zoo as model_zoo
-
-
-model_urls = {
-    # top1 = 76.3
-    'resnext50_32x4d': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/resnext50_32x4d-453b60f8.pth',
-}
-
-
-class ResNeXtBottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, groups=32, base_width=4, stride=1, downsample=None):
-        super(ResNeXtBottleneck, self).__init__()
-        width = int(math.floor(planes * (base_width / 64.)) * groups)
-        self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
-        self.bn1 = nn.BatchNorm2d(width)
-        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
-        self.bn2 = nn.BatchNorm2d(width)
-        self.conv3 = nn.Conv2d(width, planes * self.expansion, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-
    [docs]class ResNeXt(nn.Module):
-    """ResNeXt.
-    
-    Reference:
-        Xie et al. Aggregated Residual Transformations for Deep
-        Neural Networks. CVPR 2017.
-
-    Public keys:
-        - ``resnext50_32x4d``: ResNeXt50 (groups=32, width=4).
-        - ``resnext50_32x4d_fc512`` ResNeXt50 (groups=32, width=4) + FC.
-    """
-    
-    def __init__(self, num_classes, loss, block, layers,
-                 groups=32,
-                 base_width=4,
-                 last_stride=2,
-                 fc_dims=None,
-                 dropout_p=None,
-                 **kwargs):
-        self.inplanes = 64
-        super(ResNeXt, self).__init__()
-        self.loss = loss
-        self.feature_dim = 512 * block.expansion
-        
-        # backbone network
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0], groups, base_width)
-        self.layer2 = self._make_layer(block, 128, layers[1], groups, base_width, stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], groups, base_width, stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], groups, base_width, stride=last_stride)
-        
-        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
-        self.fc = self._construct_fc_layer(fc_dims, 512 * block.expansion, dropout_p)
-        self.classifier = nn.Linear(self.feature_dim, num_classes)
-
-        self._init_params()
-
-    def _make_layer(self, block, planes, blocks, groups, base_width, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, groups, base_width, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups, base_width))
-
-        return nn.Sequential(*layers)
-
-    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
-        """Constructs fully connected layer.
-
-        Args:
-            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
-            input_dim (int): input dimension
-            dropout_p (float): dropout probability, if None, dropout is unused
-        """
-        if fc_dims is None:
-            self.feature_dim = input_dim
-            return None
-        
-        assert isinstance(fc_dims, (list, tuple)), 'fc_dims must be either list or tuple, but got {}'.format(type(fc_dims))
-        
-        layers = []
-        for dim in fc_dims:
-            layers.append(nn.Linear(input_dim, dim))
-            layers.append(nn.BatchNorm1d(dim))
-            layers.append(nn.ReLU(inplace=True))
-            if dropout_p is not None:
-                layers.append(nn.Dropout(p=dropout_p))
-            input_dim = dim
-        
-        self.feature_dim = fc_dims[-1]
-        
-        return nn.Sequential(*layers)
-
-    def _init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm1d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def featuremaps(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        return x
-
-    def forward(self, x):
-        f = self.featuremaps(x)
-        v = self.global_avgpool(f)
-        v = v.view(v.size(0), -1)
-        
-        if self.fc is not None:
-            v = self.fc(v)
-        
-        if not self.training:
-            return v
-        
-        y = self.classifier(v)
-        
-        if self.loss == 'softmax':
-            return y
-        elif self.loss == 'triplet':
-            return y, v
-        else:
-            raise KeyError("Unsupported loss: {}".format(self.loss))
    
-
-
-def init_pretrained_weights(model, model_url):
-    """Initializes model with pretrained weights.
-    
-    Layers that don't match with pretrained layers in name or size are kept unchanged.
-    """
-    pretrain_dict = model_zoo.load_url(model_url)
-    model_dict = model.state_dict()
-    pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
-    model_dict.update(pretrain_dict)
-    model.load_state_dict(model_dict)
-
-
-def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
-    model = ResNeXt(
-        num_classes=num_classes,
-        loss=loss,
-        block=ResNeXtBottleneck,
-        layers=[3, 4, 6, 3],
-        groups=32,
-        base_width=4,
-        last_stride=2,
-        fc_dims=None,
-        dropout_p=None,
-        **kwargs
-    )
-    if pretrained:
-        init_pretrained_weights(model, model_urls['resnext50_32x4d'])
-    return model
-
-
-def resnext50_32x4d_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
-    model = ResNeXt(
-        num_classes=num_classes,
-        loss=loss,
-        block=ResNeXtBottleneck,
-        layers=[3, 4, 6, 3],
-        groups=32,
-        base_width=4,
-        last_stride=1,
-        fc_dims=[512],
-        dropout_p=None,
-        **kwargs
-    )
-    if pretrained:
-        init_pretrained_weights(model, model_urls['resnext50_32x4d'])
-    return model
-
    
-
-           
    
-           
-          
    
-          
-
-        
    
-      
    
-
-    
    
-
-  
    
-  
-
-
-  
-
-  
-  
-    
-   
-
-
-
\ No newline at end of file
diff --git a/_modules/torchreid/models/senet.html b/_modules/torchreid/models/senet.html
index 03a414e..65de3e9 100644
--- a/_modules/torchreid/models/senet.html
+++ b/_modules/torchreid/models/senet.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.senet — torchreid 0.7.6 documentation
+  torchreid.models.senet — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/shufflenet.html b/_modules/torchreid/models/shufflenet.html
index b644bca..bf63502 100644
--- a/_modules/torchreid/models/shufflenet.html
+++ b/_modules/torchreid/models/shufflenet.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.shufflenet — torchreid 0.7.6 documentation
+  torchreid.models.shufflenet — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/models/shufflenetv2.html b/_modules/torchreid/models/shufflenetv2.html
new file mode 100644
index 0000000..3ad9e5b
--- /dev/null
+++ b/_modules/torchreid/models/shufflenetv2.html
@@ -0,0 +1,419 @@
+
+
+
+
+  
+
+  
+  
+  
+  
+  torchreid.models.shufflenetv2 — torchreid 0.7.7 documentation
+  
+
+  
+  
+  
+  
+
+  
+  
+  
+    
+      
+        
+        
+        
+        
+        
+    
+    
+
+    
+
+  
+  
+  
+    
+     
+
+
+
+
+   
+  
    
+    
+    
+
+    
    
+
+      
+      
+
+
+      
    
+        
+        
    
+        
+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    
+
+  
      
+    
+      
    • Docs »
      • 
+        
+          
    • Module code »
      • 
+        
+      
    • torchreid.models.shufflenetv2
      • 
+    
+    
+      
    • 
+        
+      
      • 
+    
+  
    
+
+  
+  
    
+
    
+          
    
+           
    
+            
+  
    Source code for torchreid.models.shufflenetv2
    +"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import absolute_import
+from __future__ import division
+
+__all__ = ['shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0']
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+import torchvision
+import torch.utils.model_zoo as model_zoo
+
+
+model_urls = {
+    'shufflenetv2_x0.5': 'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
+    'shufflenetv2_x1.0': 'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
+    'shufflenetv2_x1.5': None,
+    'shufflenetv2_x2.0': None,
+}
+
+
+def channel_shuffle(x, groups):
+    batchsize, num_channels, height, width = x.data.size()
+    channels_per_group = num_channels // groups
+
+    # reshape
+    x = x.view(batchsize, groups,
+               channels_per_group, height, width)
+
+    x = torch.transpose(x, 1, 2).contiguous()
+
+    # flatten
+    x = x.view(batchsize, -1, height, width)
+
+    return x
+
+
+class InvertedResidual(nn.Module):
+    
+    def __init__(self, inp, oup, stride):
+        super(InvertedResidual, self).__init__()
+
+        if not (1 <= stride <= 3):
+            raise ValueError('illegal stride value')
+        self.stride = stride
+
+        branch_features = oup // 2
+        assert (self.stride != 1) or (inp == branch_features << 1)
+
+        if self.stride > 1:
+            self.branch1 = nn.Sequential(
+                self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
+                nn.BatchNorm2d(inp),
+                nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+                nn.BatchNorm2d(branch_features),
+                nn.ReLU(inplace=True),
+            )
+
+        self.branch2 = nn.Sequential(
+            nn.Conv2d(inp if (self.stride > 1) else branch_features,
+                      branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+            nn.BatchNorm2d(branch_features),
+            nn.ReLU(inplace=True),
+            self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
+            nn.BatchNorm2d(branch_features),
+            nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+            nn.BatchNorm2d(branch_features),
+            nn.ReLU(inplace=True),
+        )
+
+    @staticmethod
+    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
+        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
+
+    def forward(self, x):
+        if self.stride == 1:
+            x1, x2 = x.chunk(2, dim=1)
+            out = torch.cat((x1, self.branch2(x2)), dim=1)
+        else:
+            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
+
+        out = channel_shuffle(out, 2)
+
+        return out
+
+
+
    [docs]class ShuffleNetV2(nn.Module):
+    """ShuffleNetV2.
+    
+    Reference:
+        Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.
+
+    Public keys:
+        - ``shufflenet_v2_x0_5``: ShuffleNetV2 x0.5.
+        - ``shufflenet_v2_x1_0``: ShuffleNetV2 x1.0.
+        - ``shufflenet_v2_x1_5``: ShuffleNetV2 x1.5.
+        - ``shufflenet_v2_x2_0``: ShuffleNetV2 x2.0.
+    """
+
+    def __init__(self, num_classes, loss, stages_repeats, stages_out_channels, **kwargs):
+        super(ShuffleNetV2, self).__init__()
+        self.loss = loss
+
+        if len(stages_repeats) != 3:
+            raise ValueError('expected stages_repeats as list of 3 positive ints')
+        if len(stages_out_channels) != 5:
+            raise ValueError('expected stages_out_channels as list of 5 positive ints')
+        self._stage_out_channels = stages_out_channels
+
+        input_channels = 3
+        output_channels = self._stage_out_channels[0]
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
+            nn.BatchNorm2d(output_channels),
+            nn.ReLU(inplace=True),
+        )
+        input_channels = output_channels
+
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
+        for name, repeats, output_channels in zip(
+                stage_names, stages_repeats, self._stage_out_channels[1:]):
+            seq = [InvertedResidual(input_channels, output_channels, 2)]
+            for i in range(repeats - 1):
+                seq.append(InvertedResidual(output_channels, output_channels, 1))
+            setattr(self, name, nn.Sequential(*seq))
+            input_channels = output_channels
+
+        output_channels = self._stage_out_channels[-1]
+        self.conv5 = nn.Sequential(
+            nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(output_channels),
+            nn.ReLU(inplace=True),
+        )
+        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+        self.classifier = nn.Linear(output_channels, num_classes)
+
+    def featuremaps(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = self.conv5(x)
+        return x
+
+    def forward(self, x):
+        f = self.featuremaps(x)
+        v = self.global_avgpool(f)
+        v = v.view(v.size(0), -1)
+        
+        if not self.training:
+            return v
+        
+        y = self.classifier(v)
+        
+        if self.loss == 'softmax':
+            return y
+        elif self.loss == 'triplet':
+            return y, v
+        else:
+            raise KeyError("Unsupported loss: {}".format(self.loss))
    
+
+
+def init_pretrained_weights(model, model_url):
+    """Initializes model with pretrained weights.
+    
+    Layers that don't match with pretrained layers in name or size are kept unchanged.
+    """
+    if model_url is None:
+        import warnings
+        warnings.warn('ImageNet pretrained weights are unavailable for this model')
+        return
+    pretrain_dict = model_zoo.load_url(model_url)
+    model_dict = model.state_dict()
+    pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
+    model_dict.update(pretrain_dict)
+    model.load_state_dict(model_dict)
+
+
+def shufflenet_v2_x0_5(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(num_classes, loss, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x0.5'])
+    return model
+
+
+def shufflenet_v2_x1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(num_classes, loss, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x1.0'])
+    return model
+
+
+def shufflenet_v2_x1_5(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(num_classes, loss, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x1.5'])
+    return model
+
+
+def shufflenet_v2_x2_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+    model = ShuffleNetV2(num_classes, loss, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)
+    if pretrained:
+        init_pretrained_weights(model, model_urls['shufflenetv2_x2.0'])
+    return model
+
    
+
+           
    
+           
+          
    
+          
+
+        
    
+      
    
+
+    
    
+
+  
    
+  
+
+
+  
+
+  
+  
+    
+   
+
+
+
\ No newline at end of file
diff --git a/_modules/torchreid/models/squeezenet.html b/_modules/torchreid/models/squeezenet.html
index a7a5a1e..5b93d03 100644
--- a/_modules/torchreid/models/squeezenet.html
+++ b/_modules/torchreid/models/squeezenet.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.squeezenet — torchreid 0.7.6 documentation
+  torchreid.models.squeezenet — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
@@ -165,7 +165,10 @@
            
    
             
   
    Source code for torchreid.models.squeezenet
    -from __future__ import absolute_import
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import absolute_import
 from __future__ import division
 
 __all__ = [
diff --git a/_modules/torchreid/models/xception.html b/_modules/torchreid/models/xception.html
index 5d08f36..7f85a86 100644
--- a/_modules/torchreid/models/xception.html
+++ b/_modules/torchreid/models/xception.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.models.xception — torchreid 0.7.6 documentation
+  torchreid.models.xception — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/optim/lr_scheduler.html b/_modules/torchreid/optim/lr_scheduler.html
index 9bdf9f0..a1d8705 100644
--- a/_modules/torchreid/optim/lr_scheduler.html
+++ b/_modules/torchreid/optim/lr_scheduler.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.optim.lr_scheduler — torchreid 0.7.6 documentation
+  torchreid.optim.lr_scheduler — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/optim/optimizer.html b/_modules/torchreid/optim/optimizer.html
index d9173b2..8dcb348 100644
--- a/_modules/torchreid/optim/optimizer.html
+++ b/_modules/torchreid/optim/optimizer.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.optim.optimizer — torchreid 0.7.6 documentation
+  torchreid.optim.optimizer — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/utils/avgmeter.html b/_modules/torchreid/utils/avgmeter.html
index 774eade..6829733 100644
--- a/_modules/torchreid/utils/avgmeter.html
+++ b/_modules/torchreid/utils/avgmeter.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.utils.avgmeter — torchreid 0.7.6 documentation
+  torchreid.utils.avgmeter — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/utils/loggers.html b/_modules/torchreid/utils/loggers.html
index 39ef183..3c14be2 100644
--- a/_modules/torchreid/utils/loggers.html
+++ b/_modules/torchreid/utils/loggers.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.utils.loggers — torchreid 0.7.6 documentation
+  torchreid.utils.loggers — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/utils/model_complexity.html b/_modules/torchreid/utils/model_complexity.html
index ee93c6f..6042abb 100644
--- a/_modules/torchreid/utils/model_complexity.html
+++ b/_modules/torchreid/utils/model_complexity.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.utils.model_complexity — torchreid 0.7.6 documentation
+  torchreid.utils.model_complexity — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/utils/reidtools.html b/_modules/torchreid/utils/reidtools.html
index 23b9b8d..bd6c701 100644
--- a/_modules/torchreid/utils/reidtools.html
+++ b/_modules/torchreid/utils/reidtools.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.utils.reidtools — torchreid 0.7.6 documentation
+  torchreid.utils.reidtools — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_modules/torchreid/utils/tools.html b/_modules/torchreid/utils/tools.html
index c44220b..ea4211c 100644
--- a/_modules/torchreid/utils/tools.html
+++ b/_modules/torchreid/utils/tools.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.utils.tools — torchreid 0.7.6 documentation
+  torchreid.utils.tools — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
@@ -170,7 +170,7 @@
 from __future__ import division
 
 __all__ = ['mkdir_if_missing', 'check_isfile', 'read_json', 'write_json',
-           'set_random_seed', 'download_url', 'read_image']
+           'set_random_seed', 'download_url', 'read_image', 'collect_env_info']
 
 import sys
 import os
@@ -182,6 +182,7 @@
 import warnings
 import random
 import numpy as np
+import PIL
 from PIL import Image
 
 import torch
@@ -281,6 +282,17 @@
             print('IOError incurred when reading "{}". Will redo. Don\'t worry. Just chill.'.format(img_path))
             pass
     return img
    
+
+
+
    [docs]def collect_env_info():
+    """Returns env info as a string.
+
+    Code source: github.com/facebookresearch/maskrcnn-benchmark
+    """
+    from torch.utils.collect_env import get_pretty_env_info
+    env_str = get_pretty_env_info()
+    env_str += '\n        Pillow ({})'.format(PIL.__version__)
+    return env_str
    
 
    
 
            
    
diff --git a/_modules/torchreid/utils/torchtools.html b/_modules/torchreid/utils/torchtools.html
index 28dd621..0e7fad9 100644
--- a/_modules/torchreid/utils/torchtools.html
+++ b/_modules/torchreid/utils/torchtools.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.utils.torchtools — torchreid 0.7.6 documentation
+  torchreid.utils.torchtools — torchreid 0.7.7 documentation
   
 
   
@@ -59,7 +59,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/_sources/pkg/models.rst.txt b/_sources/pkg/models.rst.txt
index 81be2bb..c8110ea 100644
--- a/_sources/pkg/models.rst.txt
+++ b/_sources/pkg/models.rst.txt
@@ -14,7 +14,6 @@ ImageNet Classification Models
 -------------------------------
 
 .. autoclass:: torchreid.models.resnet.ResNet
-.. autoclass:: torchreid.models.resnext.ResNeXt
 .. autoclass:: torchreid.models.senet.SENet
 .. autoclass:: torchreid.models.densenet.DenseNet
 .. autoclass:: torchreid.models.inceptionresnetv2.InceptionResNetV2
@@ -29,6 +28,7 @@ Lightweight Models
 .. autoclass:: torchreid.models.mobilenetv2.MobileNetV2
 .. autoclass:: torchreid.models.shufflenet.ShuffleNet
 .. autoclass:: torchreid.models.squeezenet.SqueezeNet
+.. autoclass:: torchreid.models.shufflenetv2.ShuffleNetV2
 
 
 ReID-specific Models
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index 9fb6716..bb72704 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.7.6',
+    VERSION: '0.7.7',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     FILE_SUFFIX: '.html',
diff --git a/datasets.html b/datasets.html
index e81a1a7..e00191d 100644
--- a/datasets.html
+++ b/datasets.html
@@ -8,7 +8,7 @@
   
   
   
-  Datasets — torchreid 0.7.6 documentation
+  Datasets — torchreid 0.7.7 documentation
   
 
   
@@ -61,7 +61,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/evaluation.html b/evaluation.html
index ea9eed7..3a560bb 100644
--- a/evaluation.html
+++ b/evaluation.html
@@ -8,7 +8,7 @@
   
   
   
-  Evaluation — torchreid 0.7.6 documentation
+  Evaluation — torchreid 0.7.7 documentation
   
 
   
@@ -61,7 +61,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
diff --git a/genindex.html b/genindex.html
index e16da2a..b1b5a71 100644
--- a/genindex.html
+++ b/genindex.html
@@ -9,7 +9,7 @@
   
   
   
-  Index — torchreid 0.7.6 documentation
+  Index — torchreid 0.7.7 documentation
   
 
   
@@ -60,7 +60,7 @@
             
             
               
    
-                0.7.6
+                0.7.7
               
    
             
           
@@ -227,6 +227,8 @@
       
  • check_before_run() (torchreid.data.datasets.dataset.Dataset method)
 
    • 
       
  • check_isfile() (in module torchreid.utils.tools)
+
    • 
+      
  • collect_env_info() (in module torchreid.utils.tools)
 
    • 
       
  • ColorAugmentation (class in torchreid.data.transforms)
 
    • 
@@ -236,10 +238,10 @@
         
  • (torchreid.data.datasets.video.mars.Mars method)
 
    • 
       
-      
  • compute_distance_matrix() (in module torchreid.metrics.distance)
-
    • 
   
   
   
      
       
    • ShuffleNet (class in torchreid.models.shufflenet)
+
      • 
+      
    • ShuffleNetV2 (class in torchreid.models.shufflenetv2)
 
      • 
       
    • SqueezeNet (class in torchreid.models.squeezenet)
 
      • 
diff --git a/index.html b/index.html
index 4581576..8a6d9d6 100644
--- a/index.html
+++ b/index.html
@@ -8,7 +8,7 @@
   
   
   
-  Torchreid — torchreid 0.7.6 documentation
+  Torchreid — torchreid 0.7.7 documentation
   
 
   
@@ -60,7 +60,7 @@
             
             
               
      
-                0.7.6
+                0.7.7
               
      
             
           
diff --git a/objects.inv b/objects.inv
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diff --git a/pkg/data.html b/pkg/data.html
index 8942fac..bb7c628 100644
--- a/pkg/data.html
+++ b/pkg/data.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.data — torchreid 0.7.6 documentation
+  torchreid.data — torchreid 0.7.7 documentation
   
 
   
@@ -61,7 +61,7 @@
             
             
               
      
-                0.7.6
+                0.7.7
               
      
             
           
diff --git a/pkg/engine.html b/pkg/engine.html
index fad8fd3..27d9f45 100644
--- a/pkg/engine.html
+++ b/pkg/engine.html
@@ -8,7 +8,7 @@
   
   
   
-  torchreid.engine — torchreid 0.7.6 documentation
+  torchreid.engine — torchreid 0.7.7 documentation
   
 
   
@@ -61,7 +61,7 @@
             
             
               
      
-                0.7.6
+                0.7.7
               
      
             
           
@@ -210,7 +210,7 @@ or torchreid.data.V
 
    • max_epoch (int) – maximum epoch.
      • 
 
    • start_epoch (int, optional) – starting epoch. Default is 0.
      • 
 
    • fixbase_epoch (int, optional) – number of epochs to train open_layers (new layers)
-while keeping base layers frozen. Default is 0. fixbase_epoch is not counted
+while keeping base layers frozen. Default is 0. fixbase_epoch is counted
 in max_epoch.
      • 
 
    • open_layers (str or list, optional) – layers (attribute names) open for training.
      • 
 
    • start_eval (int, optional) – from which epoch to start evaluation. Default is 0.
      • 
@@ -363,23 +363,17 @@ or torchreid.data.V
    -train(epoch, trainloader, fixbase=False, open_layers=None, print_freq=10)[source]
    -

    Trains the model for one epoch on source datasets using softmax loss.

    - --- - - - -
    Parameters:
      -
    • epoch (int) – current epoch.
      • -
    • trainloader (Dataloader) – training dataloader.
      • -
    • fixbase (bool, optional) – whether to fix base layers. Default is False.
      • -
    • open_layers (str or list, optional) – layers open for training.
      • -
    • print_freq (int, optional) – print frequency. Default is 10.
      • -
    -
    +train(epoch, max_epoch, trainloader, fixbase_epoch=0, open_layers=None, print_freq=10)[source] +

    Performs training on source datasets for one epoch.

    +

    This will be called every epoch in run(), e.g.

    +
    for epoch in range(start_epoch, max_epoch):
+    self.train(some_arguments)
+
    +
    +
    +

    Note

    +

    This needs to be implemented in subclasses.

    +
    @@ -448,23 +442,17 @@ or torchreid.data.V
    -train(epoch, trainloader, fixbase=False, open_layers=None, print_freq=10)[source]
    -

    Trains the model for one epoch on source datasets using hard mining triplet loss.

    - --- - - - -
    Parameters:
      -
    • epoch (int) – current epoch.
      • -
    • trainloader (Dataloader) – training dataloader.
      • -
    • fixbase (bool, optional) – whether to fix base layers. Default is False.
      • -
    • open_layers (str or list, optional) – layers open for training.
      • -
    • print_freq (int, optional) – print frequency. Default is 10.
      • -
    -
    +train(epoch, max_epoch, trainloader, fixbase_epoch=0, open_layers=None, print_freq=10)[source] +

    Performs training on source datasets for one epoch.

    +

    This will be called every epoch in run(), e.g.

    +
    for epoch in range(start_epoch, max_epoch):
+    self.train(some_arguments)
+
    +
    +
    +

    Note

    +

    This needs to be implemented in subclasses.

    +
    diff --git a/pkg/losses.html b/pkg/losses.html index 5aaafee..b374f38 100644 --- a/pkg/losses.html +++ b/pkg/losses.html @@ -8,7 +8,7 @@ - torchreid.losses — torchreid 0.7.6 documentation + torchreid.losses — torchreid 0.7.7 documentation @@ -61,7 +61,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/pkg/metrics.html b/pkg/metrics.html index 4a1d3e6..eac95e9 100644 --- a/pkg/metrics.html +++ b/pkg/metrics.html @@ -8,7 +8,7 @@ - torchreid.metrics — torchreid 0.7.6 documentation + torchreid.metrics — torchreid 0.7.7 documentation @@ -61,7 +61,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/pkg/models.html b/pkg/models.html index 73c9c30..55d2542 100644 --- a/pkg/models.html +++ b/pkg/models.html @@ -8,7 +8,7 @@ - torchreid.models — torchreid 0.7.6 documentation + torchreid.models — torchreid 0.7.7 documentation @@ -61,7 +61,7 @@
    - 0.7.6 + 0.7.7
    @@ -231,11 +231,15 @@ Default is True.

    ImageNet Classification Models

    -class torchreid.models.resnet.ResNet(num_classes, loss, block, layers, last_stride=2, fc_dims=None, dropout_p=None, **kwargs)[source]
    +class torchreid.models.resnet.ResNet(num_classes, loss, block, layers, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, last_stride=2, fc_dims=None, dropout_p=None, **kwargs)[source]

    Residual network.

    Reference:
    -
    He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
    +
      +
    • He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
      • +
    • Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
      • +
    +
    Public keys:
    • resnet18: ResNet18.
      • @@ -243,29 +247,14 @@ Default is True.
    • resnet50: ResNet50.
    • resnet101: ResNet101.
    • resnet152: ResNet152.
      • +
    • resnext50_32x4d: ResNeXt50.
      • +
    • resnext101_32x8d: ResNeXt101.
    • resnet50_fc512: ResNet50 + FC.
    -
    -
    -class torchreid.models.resnext.ResNeXt(num_classes, loss, block, layers, groups=32, base_width=4, last_stride=2, fc_dims=None, dropout_p=None, **kwargs)[source]
    -

    ResNeXt.

    -
    -
    Reference:
    -
    Xie et al. Aggregated Residual Transformations for Deep -Neural Networks. CVPR 2017.
    -
    Public keys:
    -
      -
    • resnext50_32x4d: ResNeXt50 (groups=32, width=4).
      • -
    • resnext50_32x4d_fc512 ResNeXt50 (groups=32, width=4) + FC.
      • -
    -
    -
    -
    -
    class torchreid.models.senet.SENet(num_classes, loss, block, layers, groups, reduction, dropout_p=0.2, inplanes=128, input_3x3=True, downsample_kernel_size=3, downsample_padding=1, last_stride=2, fc_dims=None, **kwargs)[source]
    @@ -424,6 +413,24 @@ and< 0.5 MB model size. arXiv:1602.07360.
    +
    +
    +class torchreid.models.shufflenetv2.ShuffleNetV2(num_classes, loss, stages_repeats, stages_out_channels, **kwargs)[source]
    +

    ShuffleNetV2.

    +
    +
    Reference:
    +
    Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.
    +
    Public keys:
    +
      +
    • shufflenet_v2_x0_5: ShuffleNetV2 x0.5.
      • +
    • shufflenet_v2_x1_0: ShuffleNetV2 x1.0.
      • +
    • shufflenet_v2_x1_5: ShuffleNetV2 x1.5.
      • +
    • shufflenet_v2_x2_0: ShuffleNetV2 x2.0.
      • +
    +
    +
    +
    +

    ReID-specific Models

    diff --git a/pkg/optim.html b/pkg/optim.html index 9bbff75..f9e22ba 100644 --- a/pkg/optim.html +++ b/pkg/optim.html @@ -8,7 +8,7 @@ - torchreid.optim — torchreid 0.7.6 documentation + torchreid.optim — torchreid 0.7.7 documentation @@ -61,7 +61,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/pkg/utils.html b/pkg/utils.html index d6a6327..1a44f9a 100644 --- a/pkg/utils.html +++ b/pkg/utils.html @@ -8,7 +8,7 @@ - torchreid.utils — torchreid 0.7.6 documentation + torchreid.utils — torchreid 0.7.7 documentation @@ -61,7 +61,7 @@
    - 0.7.6 + 0.7.7
    @@ -382,6 +382,13 @@ to show the summarized results, which are convenient for analysis.

    +
    +
    +torchreid.utils.tools.collect_env_info()[source]
    +

    Returns env info as a string.

    +

    Code source: github.com/facebookresearch/maskrcnn-benchmark

    +
    +

    ReID Tools

    diff --git a/py-modindex.html b/py-modindex.html index 84f9a87..c0a7133 100644 --- a/py-modindex.html +++ b/py-modindex.html @@ -8,7 +8,7 @@ - Python Module Index — torchreid 0.7.6 documentation + Python Module Index — torchreid 0.7.7 documentation @@ -62,7 +62,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/search.html b/search.html index 1aaeefb..278e33f 100644 --- a/search.html +++ b/search.html @@ -8,7 +8,7 @@ - Search — torchreid 0.7.6 documentation + Search — torchreid 0.7.7 documentation @@ -60,7 +60,7 @@
    - 0.7.6 + 0.7.7
    diff --git a/searchindex.js b/searchindex.js index 462d226..aa303bf 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ 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Deep:[9,1,1,""]},"torchreid.models.nasnet":{NASNetAMobile:[9,1,1,""]},"torchreid.models.pcb":{PCB:[9,1,1,""]},"torchreid.models.resnet":{ResNet:[9,1,1,""]},"torchreid.models.resnetmid":{ResNetMid:[9,1,1,""]},"torchreid.models.resnext":{ResNeXt:[9,1,1,""]},"torchreid.models.senet":{SENet:[9,1,1,""]},"torchreid.models.shufflenet":{ShuffleNet:[9,1,1,""]},"torchreid.models.squeezenet":{SqueezeNet:[9,1,1,""]},"torchreid.models.xception":{Xception:[9,1,1,""]},"torchreid.optim":{lr_scheduler:[10,0,0,"-"],optimizer:[10,0,0,"-"]},"torchreid.optim.lr_scheduler":{build_lr_scheduler:[10,4,1,""]},"torchreid.optim.optimizer":{build_optimizer:[10,4,1,""]},"torchreid.utils":{avgmeter:[11,0,0,"-"],loggers:[11,0,0,"-"],model_complexity:[11,0,0,"-"],reidtools:[11,0,0,"-"],tools:[11,0,0,"-"],torchtools:[11,0,0,"-"]},"torchreid.utils.avgmeter":{AverageMeter:[11,1,1,""]},"torchreid.utils.loggers":{Logger:[11,1,1,""],RankLogger:[11,1,1,""]},"torchreid.utils.loggers.RankLogger":{show_summary:[11,3,1,""],write:[11,3,1,""]},"torchreid.utils.model_complexity":{compute_model_complexity:[11,4,1,""]},"torchreid.utils.reidtools":{visualize_ranked_results:[11,4,1,""]},"torchreid.utils.tools":{check_isfile:[11,4,1,""],download_url:[11,4,1,""],mkdir_if_missing:[11,4,1,""],read_image:[11,4,1,""],read_json:[11,4,1,""],write_json:[11,4,1,""]},"torchreid.utils.torchtools":{count_num_param:[11,4,1,""],load_checkpoint:[11,4,1,""],load_pretrained_weights:[11,4,1,""],open_all_layers:[11,4,1,""],open_specified_layers:[11,4,1,""],resume_from_checkpoint:[11,4,1,""],save_checkpoint:[11,4,1,""]}},objnames:{"0":["py","module","Python 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\ No newline at end of file diff --git a/user_guide.html b/user_guide.html index 14dd10d..8f818ea 100644 --- a/user_guide.html +++ b/user_guide.html @@ -8,7 +8,7 @@ - How-to — torchreid 0.7.6 documentation + How-to — torchreid 0.7.7 documentation @@ -61,7 +61,7 @@
    - 0.7.6 + 0.7.7