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Functions2,488 in github.com/TPCD/LifelongReID

⨍ Functions 2,488◇ Types & classes 338↳ Endpoints 1

(self, config, loader)

lreid/core/base_metagraph_p_s.py:37

(self, optimizer, last_epoch=-1)

lreid/core/torch16_lr_scheduler.py:26

lreid/core/torch16_lr_scheduler.py:128

(self, optimizer, lr_lambda, last_epoch=-1)

lreid/core/torch16_lr_scheduler.py:178

(self, optimizer, lr_lambda, last_epoch=-1)

lreid/core/torch16_lr_scheduler.py:258

(self, optimizer, milestones, gamma=0.1, last_epoch=-1)

lreid/core/torch16_lr_scheduler.py:388

(self, optimizer, gamma, last_epoch=-1)

lreid/core/torch16_lr_scheduler.py:419

(self, optimizer, T_max, eta_min=0, last_epoch=-1)

lreid/core/torch16_lr_scheduler.py:476

(self, optimizer, mode='min', factor=0.1, patience=10, verbose=False, threshold=1e-4, thresho

lreid/core/torch16_lr_scheduler.py:553

(self, optimizer, base_lr, max_lr, step_si

lreid/core/torch16_lr_scheduler.py:767

(self, optimizer, T_0, T_mult=1, eta_min=0, last_epoch=-1)

lreid/core/torch16_lr_scheduler.py:925

lreid/core/torch16_lr_scheduler.py:999

(self, optimizer, max_lr, total_steps=None,

lreid/core/torch16_lr_scheduler.py:1107

(self, optimizer, milestones, gamma=0.1, last_epoch=-1)

lreid/core/lr_schedulers.py:32

(self, height, width, p=0.5, interpolation=Image.BILINEAR)

lreid/data/transforms.py:28

( self, probability=0.5, sl=0.02, sh=0.4, r1=0.3, mean=[0.4914

lreid/data/transforms.py:68

lreid/data/transforms.py:121

( self, prob_happen=0.5, pool_capacity=50000, min_sample_size=100, pat

lreid/data/transforms.py:160

( self, root='', sources=None, targets=None, height=256, width

lreid/data/datamanager.py:153

( self, root='', sources=None, targets=None, height=256, width

lreid/data/datamanager.py:411

(self, data_source, batch_size, num_instances)

lreid/data/sampler.py:23

(self, data_source, batch_size, n_domain)

lreid/data/sampler.py:99

(self, data_source, batch_size, n_dataset)

lreid/data/sampler.py:158

(self, train, query, gallery, **kwargs)

lreid/data/datasets/dataset.py:314

( self, train, query, gallery, seq_len=15, sample_method='even

lreid/data/datasets/dataset.py:374

(self, root, transforms, mode='train')

lreid/data/datasets/ps_dataset.py:52

(self, root='', **kwargs)

lreid/data/datasets/image/dukemtmcreid.py:26

(self, root='', split_id=0, **kwargs)

lreid/data/datasets/image/cuhk01.py:30

( self, root='', split_id=0, cuhk03_labeled=False, cuhk03_classic_spli

lreid/data/datasets/image/cuhk03.py:26

(self, root='', split_id=0, **kwargs)

lreid/data/datasets/image/grid.py:28

(self, root='', market1501_500k=False, **kwargs)

lreid/data/datasets/image/market1501.py:26

(self, root='', split_id=0, **kwargs)

lreid/data/datasets/image/prid.py:29

(self, root='', split_id=0, **kwargs)

lreid/data/datasets/image/ilids.py:27

(self, root='', **kwargs)

lreid/data/datasets/image/msmt17.py:41

(self, root='', **kwargs)

lreid/data/datasets/image/cuhk02.py:30

(self, root='', **kwargs)

lreid/data/datasets/image/sensereid.py:27

(self, root='', split_id=0, **kwargs)

lreid/data/datasets/image/viper.py:27

(self, root='', split_id=0, **kwargs)

lreid/data/datasets/video/prid2011.py:27

(self, root='', **kwargs)

lreid/data/datasets/video/mars.py:25

(self, root='', split_id=0, **kwargs)

lreid/data/datasets/video/ilidsvid.py:27

(self, root='', min_seq_len=0, **kwargs)

lreid/data/datasets/video/dukemtmcvidreid.py:29

(self, model_func, n_way, n_support, tf_path=None, approx=False, dropout_method='none', dropout_rate=0., drop

lreid/methods/maml.py:11

(self, method='gaussian', rate=0.1, schedule='constant')

lreid/methods/drop_grad.py:5

(self, indim, outdim)

lreid/methods/backbone.py:21

lreid/methods/backbone.py:37

(self, in_features, out_features)

lreid/methods/backbone.py:45

(self, in_channels, out_channels, kernel_size, stride=1,padding=0, bias = True)

lreid/methods/backbone.py:58

(self, num_features)

lreid/methods/backbone.py:79

(self, indim, outdim, pool = True, padding = 1)

lreid/methods/backbone.py:97

(self, indim, outdim, half_res)

lreid/methods/backbone.py:127

(self, indim, outdim, half_res)

lreid/methods/backbone.py:182

(self, depth, flatten = True)

lreid/methods/backbone.py:238

(self, model_func, n_way, n_support, tf_path=None, change_way=True)

lreid/methods/meta_template.py:8

(self, datasets_root, relabel=True, combineall=False)

lreid/datasets/dukemtmcreid.py:15

(self, root='', **kwargs)

lreid/datasets/dukemtmcreid.py:74

(self, datasets_root, relabel=True, combineall=False, split_id = 0)

lreid/datasets/cuhk01.py:16

(self, root='', split_id=0, **kwargs)

lreid/datasets/cuhk01.py:138

(self, datasets_root, relabel=True, combineall=False, split_id=0, cuhk03_labeled=False, cuhk03_classic

lreid/datasets/cuhk03.py:18

( self, root='', split_id=0, cuhk03_labeled=False, cuhk03_classic_spli

lreid/datasets/cuhk03.py:307

(self, datasets_root, relabel=True, combineall=False)

lreid/datasets/mix.py:26

(self, root='', **kwargs)

lreid/datasets/mix.py:133

(self, datasets_root, relabel=True, combineall=False, split_id=0)

lreid/datasets/grid.py:28

(self, root='', split_id=0, **kwargs)

lreid/datasets/grid.py:141

(self, datasets_root, relabel=True, combineall=False)

lreid/datasets/market1501.py:17

(self, root='', market1501_500k=False, **kwargs)

lreid/datasets/market1501.py:74

(self, datasets_root, relabel=True, combineall=False, split_id=0)

lreid/datasets/prid.py:29

(self, root='', split_id=0, **kwargs)

lreid/datasets/prid.py:124

(self, datasets_root, relabel=True, combineall=False, split_id=0)

lreid/datasets/ilids.py:28

(self, root='', split_id=0, **kwargs)

lreid/datasets/ilids.py:149

(self, datasets_root, relabel=True, combineall=False)

lreid/datasets/msmt17.py:29

(self, root='', **kwargs)

lreid/datasets/msmt17.py:102

(self, datasets_root, relabel=True, combineall=False, split_id = 0)

lreid/datasets/cuhk02.py:15

(self, root='', **kwargs)

lreid/datasets/cuhk02.py:104

(self, datasets_root, relabel=True, combineall=False, use_subset_train=True)

lreid/datasets/sensereid.py:14

(self, root='', **kwargs)

lreid/datasets/sensereid.py:76

(self, datasets_root, relabel=True, combineall=False, split_id=0)

lreid/datasets/viper.py:27

(self, datasets_root, relabel=True, combineall=False, split_id=0)

lreid/datasets/viper.py:146

(self, datasets_root, relabel=True, combineall=False)

lreid/datasets/cuhksysu.py:21

(self, datasets_root, relabel=True, combineall=False, use_subset_train=True)

lreid/datasets/cuhksysu.py:209

(self, root='', **kwargs)

lreid/datasets/cuhksysu.py:418

(self, in_dim, class_num)

lreid/models/bnneck.py:27

(self, in_dim, class_num)

lreid/models/bnneck.py:51

(self, hidden_dim, sparse_inputs=False, act=nn.Tanh(), bias=True, dropout=0.6)

lreid/models/metagraph_fd.py:17

(self, hidden_dim, input_dim, sigma=2.0, proto_graph_vertex_num=16, meta_graph_vertex_num=128)

lreid/models/metagraph_fd.py:135

(self, pool_mode='avg')

lreid/models/LwFnet.py:32

(self, class_num_list, pretrained=True)

lreid/models/LwFnet.py:131

(self, class_num_list, pretrained=True)

lreid/models/LwFnet.py:197

(self, num_classes, epsilon=0.1, use_gpu=True)

lreid/losses/loss.py:34

lreid/losses/loss.py:57

:param margin: float or 'soft', for MarginRankingLoss with margin and soft margin :param bh: batch hard :param metric: l2 distance or cosine di

lreid/losses/loss.py:96

:param margin: float or 'soft', for MarginRankingLoss with margin and soft margin :param bh: batch hard :param metric: l2 distance or cosine di

lreid/losses/loss.py:151

(self, margin=None, alpha=None, tval=None, normalize_feature=False)

lreid/losses/ranked_list_loss.py:72

(self, dist, mode)

lreid/evaluation/reid.py:28

(self, dist, mode)

lreid/evaluation/reid.py:136

lreid/data_loader/loader.py:27

lreid/data_loader/loader.py:96

lreid/data_loader/loader.py:173

lreid/data/sampler.py:50

lreid/data_loader/loader.py:31

lreid/data_loader/loader.py:100

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