Functions — github.com/TencentYoutuResearch/PersonReID-YouReID

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Functions293 in github.com/TencentYoutuResearch/PersonReID-YouReID

⨍ Functions 293◇ Types & classes 61

Method__init__

(self)

train/Nafs_Trainer.py:25

Method__init__

(self)

train/BaseTrainer.py:30

dataset/testdata.py:63

Method__len__

(self)

dataset/formatdata.py:263

(self, output, target)

models/baseline.py:123

Methodcompute_loss

(self, output, target)

models/pcb.py:87

Methodcompute_loss

(self, global_img_feat, global_text_feat, local_img_query, local_img_value, local_text_key, local_text_value,

models/nafs.py:756

Methodcompute_loss

(self, output, target )

models/pyramid.py:65

Methodcompute_loss

(self, output, target)

models/cacenet.py:122

Functionconvert_dsbn

(model)

core/layers.py:269

Functionconvert_dsbnShare

(model, constant_batch=32)

Functioneval_part_result

(info, use_rerank=False, use_pcb_format=True, logger=None)

utils/evaluate.py:71

Functioneval_result

(data, root, use_rerank=False, use_pcb_format=True, logger=None)

utils/evaluate.py:13

Functionevaluate_rank

Evaluates CMC rank. Args: distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery). q_pids (numpy.ndarray): 1-D

dataset/formatdata.py:15

Methodforward

Args: inputs (torch.Tensor): feature matrix with shape (batch_size, feat_dim). targets (torch.LongTensor): ground tru

core/loss.py:66

Methodforward

Args: inputs (torch.Tensor): feature matrix with shape (batch_size, feat_dim). targets (torch.LongTensor): ground tru

core/loss.py:143

Methodforward

(self, input_0, input_1, targets)

core/loss.py:160

Methodforward

Args: inputs (torch.Tensor): feature matrix with shape (batch_size, feat_dim). targets (torch.LongTensor): ground tru

core/loss.py:184

Methodforward

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

(self, features, targets)

core/loss.py:365

Methodforward

(self, feats, labels)

core/loss.py:392

Methodforward

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

core/loss.py:439

Methodforward

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

:param x: (b, c, t, h, w) :return:

core/layers.py:121

Methodforward

:param x: (b, c, t, h, w) :return:

('input.shape:', (64, 3, 384, 128))

models/mgn.py:90

Methodforward

(self, x, label=None)

models/baseline.py:93

Methodforward

(self, x, label=None)

(self, tokens, segments, input_masks)

models/nafs.py:519

Methodforward

(self, images, tokens, segments, input_masks, sep_tokens, sep_segments, sep_input_masks, n_sep, p2=None, p3=No

models/nafs.py:587

Methodforward

Returns: feat_list: each member with shape [N, C] logits_list: each member with shape [N, num_classes]

models/pyramid.py:99

Methodforward

(self, x, label=None)

models/cacenet.py:103

Methodforward

(self, x)

models/backbones/resnet_ibn_a.py:41

Methodforward

(self, x)

models/backbones/resnet_ibn_a.py:117

Methodforward

(self, x)

models/backbones/senet.py:97

Methodforward

(self, x)

models/backbones/senet.py:111

Methodforward

(self, x)

models/backbones/senet.py:372

Methodforward

(self, x)

models/backbones/resnet.py:56

Methodforward

(self, x)

models/backbones/resnet.py:133

Methodget_imgs

(self)

dataset/OccludedData.py:202

Methodget_imgs

(self)

dataset/OccludedData.py:270

models/backbones/senet.py:349

Functionload_pickle

Check and load pickle object. According to this post: https://stackoverflow.com/a/41733927, cPickle and disabling garbage collector helps with loa

tools/transform_format.py:41

Functionmain

tools/inference_onnx.cpp:187

Functionmake_dataset

(root, config, class_to_idx, classes)

dataset/formatdata.py:30

Functionmake_query

(root, config)

dataset/formatdata.py:62

Functionmask_distance

(query, gallery)

utils/metric.py:25

Methodparse_im_name

Get the person id or cam from an image name.

dataset/formatdata.py:344

Functionpart_distance

models/backbones/resnet.py:173

Functionre_ranking

(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3)

utils/re_ranking.py:14

Functionread_image

Reads image from path using ``PIL.Image``. Args: path (str): path to an image. Returns: PIL image

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True,

models/backbones/resnet.py:205

Functionresnet101_ibn_a

Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

models/backbones/resnet_ibn_a.py:151

Functionresnet152

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True,

models/backbones/resnet.py:217

Functionresnet152_ibn_a

Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

models/backbones/resnet_ibn_a.py:172

Functionresnet50

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True,

models/backbones/resnet.py:193

Functionresnet50_ibn_a

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

models/backbones/resnet_ibn_a.py:135

Functionresnext101_32x8d

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True,

models/backbones/resnet.py:229

Functionse_resnet101

(num_classes=1000, pretrained='imagenet')

models/backbones/senet.py:414

Functionse_resnet152

(num_classes=1000, pretrained='imagenet')

models/backbones/senet.py:425

Functionse_resnet50

(num_classes=1000, pretrained='imagenet')

models/backbones/senet.py:403

Functionse_resnext101_32x4d

(last_stride=2, pretrained=True, **kwargs)

models/backbones/senet.py:447

Functionse_resnext50_32x4d

(num_classes=1000, pretrained='imagenet')

models/backbones/senet.py:436

Functionsenet154

(num_classes=1000, pretrained='imagenet')

models/backbones/senet.py:394

Functionsetup_logger

(name, save_dir, distributed_rank)

utils/logger.py:12

Methodstep

Step could be called after every batch update Example: >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)

utils/epoch_lr.py:121

Methodstep

Step could be called after every batch update Example: >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)

utils/epoch_lr.py:218

Functionweights_init_kaiming

(path, signature, data)

utils/evaluate.py:129

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