2019-03-25 01:22:43 +08:00
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2019-03-25 01:22:43 +08:00
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2019-04-28 06:32:21 +08:00
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< h1 > Source code for torchreid.engine.video.triplet< / h1 > < div class = "highlight" > < pre >
< span > < / span > < span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > absolute_import< / span >
< span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > print_function< / span >
< span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > division< / span >
< span class = "kn" > import< / span > < span class = "nn" > time< / span >
< span class = "kn" > import< / span > < span class = "nn" > datetime< / span >
< span class = "kn" > import< / span > < span class = "nn" > torch< / span >
< span class = "kn" > import< / span > < span class = "nn" > torchreid< / span >
< span class = "kn" > from< / span > < span class = "nn" > torchreid.engine.image< / span > < span class = "k" > import< / span > < span class = "n" > ImageTripletEngine< / span >
< span class = "kn" > from< / span > < span class = "nn" > torchreid.engine.video< / span > < span class = "k" > import< / span > < span class = "n" > VideoSoftmaxEngine< / span >
< div class = "viewcode-block" id = "VideoTripletEngine" > < a class = "viewcode-back" href = "../../../../pkg/engine.html#torchreid.engine.video.triplet.VideoTripletEngine" > [docs]< / a > < span class = "k" > class< / span > < span class = "nc" > VideoTripletEngine< / span > < span class = "p" > (< / span > < span class = "n" > ImageTripletEngine< / span > < span class = "p" > ,< / span > < span class = "n" > VideoSoftmaxEngine< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " Triplet-loss engine for video-reid.< / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``< / span >
< span class = "sd" > or ``torchreid.data.VideoDataManager``.< / span >
< span class = "sd" > model (nn.Module): model instance.< / span >
< span class = "sd" > optimizer (Optimizer): an Optimizer.< / span >
< span class = "sd" > margin (float, optional): margin for triplet loss. Default is 0.3.< / span >
< span class = "sd" > weight_t (float, optional): weight for triplet loss. Default is 1.< / span >
< span class = "sd" > weight_x (float, optional): weight for softmax loss. Default is 1.< / span >
< span class = "sd" > scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.< / span >
< span class = "sd" > use_cpu (bool, optional): use cpu. Default is False.< / span >
< span class = "sd" > label_smooth (bool, optional): use label smoothing regularizer. Default is True.< / span >
< span class = "sd" > pooling_method (str, optional): how to pool features for a tracklet.< / span >
< span class = "sd" > Default is " avg" (average). Choices are [" avg" , " max" ].< / span >
< span class = "sd" > Examples::< / span >
< span class = "sd" > < / span >
< span class = "sd" > import torch< / span >
< span class = "sd" > import torchreid< / span >
< span class = "sd" > # Each batch contains batch_size*seq_len images< / span >
< span class = "sd" > # Each identity is sampled with num_instances tracklets < / span >
< span class = "sd" > datamanager = torchreid.data.VideoDataManager(< / span >
< span class = "sd" > root=' path/to/reid-data' ,< / span >
< span class = "sd" > sources=' mars' ,< / span >
< span class = "sd" > height=256,< / span >
< span class = "sd" > width=128,< / span >
< span class = "sd" > combineall=False,< / span >
< span class = "sd" > num_instances=4,< / span >
< span class = "sd" > train_sampler=' RandomIdentitySampler' < / span >
< span class = "sd" > batch_size=8, # number of tracklets< / span >
< span class = "sd" > seq_len=15 # number of images in each tracklet< / span >
< span class = "sd" > )< / span >
< span class = "sd" > model = torchreid.models.build_model(< / span >
< span class = "sd" > name=' resnet50' ,< / span >
< span class = "sd" > num_classes=datamanager.num_train_pids,< / span >
< span class = "sd" > loss=' triplet' < / span >
< span class = "sd" > )< / span >
< span class = "sd" > model = model.cuda()< / span >
< span class = "sd" > optimizer = torchreid.optim.build_optimizer(< / span >
< span class = "sd" > model, optim=' adam' , lr=0.0003< / span >
< span class = "sd" > )< / span >
< span class = "sd" > scheduler = torchreid.optim.build_lr_scheduler(< / span >
< span class = "sd" > optimizer,< / span >
< span class = "sd" > lr_scheduler=' single_step' ,< / span >
< span class = "sd" > stepsize=20< / span >
< span class = "sd" > )< / span >
< span class = "sd" > engine = torchreid.engine.VideoTripletEngine(< / span >
< span class = "sd" > datamanager, model, optimizer, margin=0.3,< / span >
< span class = "sd" > weight_t=0.7, weight_x=1, scheduler=scheduler,< / span >
< span class = "sd" > pooling_method=' avg' < / span >
< span class = "sd" > )< / span >
< span class = "sd" > engine.run(< / span >
< span class = "sd" > max_epoch=60,< / span >
< span class = "sd" > save_dir=' log/resnet50-triplet-mars' ,< / span >
< span class = "sd" > print_freq=10< / span >
< span class = "sd" > )< / span >
2019-05-06 17:48:46 +08:00
< span class = "sd" > " " " < / span >
2019-03-25 01:22:43 +08:00
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > datamanager< / span > < span class = "p" > ,< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > optimizer< / span > < span class = "p" > ,< / span > < span class = "n" > margin< / span > < span class = "o" > =< / span > < span class = "mf" > 0.3< / span > < span class = "p" > ,< / span >
< span class = "n" > weight_t< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > weight_x< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > scheduler< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span > < span class = "n" > use_cpu< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > label_smooth< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "n" > pooling_method< / span > < span class = "o" > =< / span > < span class = "s1" > ' avg' < / span > < span class = "p" > ):< / span >
< span class = "nb" > super< / span > < span class = "p" > (< / span > < span class = "n" > VideoTripletEngine< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "fm" > __init__< / span > < span class = "p" > (< / span > < span class = "n" > datamanager< / span > < span class = "p" > ,< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > optimizer< / span > < span class = "p" > ,< / span > < span class = "n" > margin< / span > < span class = "o" > =< / span > < span class = "n" > margin< / span > < span class = "p" > ,< / span >
< span class = "n" > weight_t< / span > < span class = "o" > =< / span > < span class = "n" > weight_t< / span > < span class = "p" > ,< / span > < span class = "n" > weight_x< / span > < span class = "o" > =< / span > < span class = "n" > weight_x< / span > < span class = "p" > ,< / span >
< span class = "n" > scheduler< / span > < span class = "o" > =< / span > < span class = "n" > scheduler< / span > < span class = "p" > ,< / span > < span class = "n" > use_cpu< / span > < span class = "o" > =< / span > < span class = "n" > use_cpu< / span > < span class = "p" > ,< / span >
< span class = "n" > label_smooth< / span > < span class = "o" > =< / span > < span class = "n" > label_smooth< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > pooling_method< / span > < span class = "o" > =< / span > < span class = "n" > pooling_method< / span > < / div >
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