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Functions199 in github.com/Alioth2000/Hoss-ReID

⨍ Functions 199◇ Types & classes 46

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

model/backbones/vit_pytorch.py:141

(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., dr

model/backbones/vit_pytorch.py:170

(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768)

model/backbones/vit_pytorch.py:191

(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768)

model/backbones/vit_pytorch.py:215

(self, img_size=224, patch_size=16, stride_size=20, in_chans=3, embed_dim=768)

model/backbones/vit_pytorch.py:255

(self, img_size=224, patch_size=16, stride_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,

model/backbones/vit_pytorch.py:295

(self, inplanes, planes, stride=1, downsample=None)

model/backbones/resnet.py:16

(self, inplanes, planes, stride=1, downsample=None)

model/backbones/resnet.py:48

(self, optimizer: torch.optim.Optimizer, param_group_field: str,

solver/scheduler.py:25

(self, optimizer: torch.optim.Optimizer, t_initial: int, t_

solver/cosine_lr.py:26

( self, optimizer, milestones, # steps gamma=0.1,

solver/lr_scheduler.py:15

(self, dataset, transform=None, pair=False)

datasets/bases.py:83

(self, probability=0.5, sl=0.02, sh=0.4, r1=0.3, mean=(0.4914, 0.4822, 0.4465))

datasets/preprocessing.py:17

(self, root="", verbose=True, pid_begin=0, **kwargs)

datasets/pretrain.py:14

(self, root='', verbose=True, pid_begin = 0, **kwargs)

datasets/hoss.py:13

(self, data_source, batch_size, num_instances)

datasets/sampler.py:18

(self, data_source, batch_size, num_instances)

datasets/sampler_ddp.py:122

(self, num_classes=751, feat_dim=2048, use_gpu=True)

loss/center_loss.py:18

(self, in_features, num_classes, s=256, m=0.25)

loss/arcface.py:53

Constructor for the LabelSmoothing module. :param smoothing: label smoothing factor

loss/softmax_loss.py:40

(self, margin=0.3, **kwargs)

loss/metric_learning.py:10

(self, in_features, num_classes, s=256, m=0.25)

loss/metric_learning.py:46

(self, in_features, out_features, s=30.0, m=0.30)

loss/metric_learning.py:134

(self, in_features, out_features, s=30.0, m=0.30)

loss/metric_learning.py:167

(self, margin=None, hard_factor=0.0)

loss/triplet_loss.py:113

datasets/sampler.py:37

datasets/sampler_ddp.py:148

datasets/bases.py:89

datasets/sampler.py:65

datasets/sampler_ddp.py:198

loss/metric_learning.py:158

solver/lr_scheduler.py:43

Method_init_weights

model/backbones/vit_pytorch.py:356

Functioncheck_isfile

utils/iotools.py:23

Functioncosine_dist

Args: x: pytorch Variable, with shape [m, d] y: pytorch Variable, with shape [n, d] Returns: dist: pytorch Variable, with s

loss/triplet_loss.py:34

Functioncosine_similarity

utils/metrics.py:15

Functiondeit_small_patch16_224_TransReID

(img_size=(256, 128), stride_size=16, drop_path_rate=0.1, drop_rate=0.0, attn_drop_rate=0.0, camera=0, view=0,

model/backbones/vit_pytorch.py:474

(self, x, label=None)

model/make_model.py:86

(self, x, label=None, cam_label= None, img_wh=None)

model/make_model.py:199

model/backbones/vit_transoss.py:52

model/backbones/vit_transoss.py:66

model/backbones/vit_transoss.py:88

model/backbones/vit_transoss.py:117

model/backbones/vit_transoss.py:137

model/backbones/vit_transoss.py:179

model/backbones/vit_transoss.py:215

model/backbones/vit_transoss.py:232

(self, x, cam_label=None, img_wh=None)

model/backbones/vit_transoss.py:360

model/backbones/vit_pytorch.py:72

model/backbones/vit_pytorch.py:131

model/backbones/vit_pytorch.py:153

model/backbones/vit_pytorch.py:182

model/backbones/vit_pytorch.py:202

model/backbones/vit_pytorch.py:244

model/backbones/vit_pytorch.py:280

(self, x, cam_label=None, view_label=None)

model/backbones/vit_pytorch.py:407

model/backbones/resnet.py:26

model/backbones/resnet.py:61

(self, x, cam_label=None)

model/backbones/resnet.py:115

Args: x: feature matrix with shape (batch_size, feat_dim). labels: ground truth labels with shape (num_classes).

loss/center_loss.py:29

(self, input, label)

loss/arcface.py:35

Args: inputs: prediction matrix (before softmax) with shape (batch_size, num_classes) targets: ground truth labels wi

loss/softmax_loss.py:23

(self, x, target)

loss/softmax_loss.py:50

(self, inputs, targets)

loss/metric_learning.py:14

(self, input, label)

loss/metric_learning.py:101

(self, input, label)

loss/metric_learning.py:143

loss/metric_learning.py:176

Methodget_classifier

model/backbones/vit_transoss.py:313

Methodget_classifier

model/backbones/vit_pytorch.py:369

Methodget_cycle_length

(self, cycles=0)

solver/cosine_lr.py:108

Methodget_epoch_values

(self, epoch: int)

solver/cosine_lr.py:96

Methodget_update_values

(self, num_updates: int)

solver/cosine_lr.py:102

Methodload_param

(self, model_path)

model/backbones/vit_transoss.py:364

Methodload_param

(self, model_path)

model/backbones/vit_pytorch.py:411

Methodload_param

(self, model_path)

model/backbones/resnet.py:127

Methodload_param_finetune

(self, model_path)

model/make_model.py:118

Methodload_param_finetune

(self, model_path)

model/make_model.py:244

Methodload_state_dict

(self, state_dict: Dict[str, Any])

solver/scheduler.py:58

Functionloss_func

(score, feat, target)

loss/make_loss.py:33

Methodno_weight_decay

model/backbones/vit_transoss.py:310

Methodno_weight_decay

model/backbones/vit_pytorch.py:366

model/backbones/vit_transoss.py:14

model/backbones/vit_pytorch.py:36

Methodprint_dataset_statistics

(self, train, query, gallery)

datasets/bases.py:65

Methodrandom_init

model/backbones/resnet.py:134

Functionread_json

utils/iotools.py:30

Methodreset_classifier

(self, num_classes, global_pool='')

model/backbones/vit_transoss.py:316

Methodreset_classifier

(self, num_classes, global_pool='')

model/backbones/vit_pytorch.py:372

Functionshuffle_unit

(features, shift, group, begin=1)

model/make_model.py:8

Methodstep_update

(self, num_updates: int, metric: float = None)

solver/scheduler.py:74

Functiontrain_collate_fn

# collate_fn这个函数的输入就是一个list，list的长度是一个batch size，list中的每个元素都是__getitem__得到的结果

datasets/make_dataloader.py:21

Functiontrain_pair_collate_fn

# collate_fn这个函数的输入就是一个list，list的长度是一个batch size，list中的每个元素都是__getitem__得到的结果

datasets/make_dataloader.py:33

Functionval_collate_fn

datasets/make_dataloader.py:46

Functionvit_base_patch16_224_TransOSS

(img_size=(256, 128), stride_size=16, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, camera=0, local_f

model/backbones/vit_transoss.py:414

Functionvit_base_patch16_224_TransReID

(img_size=(256, 128), stride_size=16, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, camera=0, view=0,

model/backbones/vit_pytorch.py:457

Functionvit_small_patch16_224_TransReID

(img_size=(256, 128), stride_size=16, drop_rate=0., attn_drop_rate=0.,drop_path_rate=0.1, camera=0, view=0, lo

model/backbones/vit_pytorch.py:465

Functionweights_init_classifier

model/make_model.py:44

Functionweights_init_kaiming

model/make_model.py:28

Functionwrite_json

utils/iotools.py:36

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