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Functions503 in github.com/Hawkeye-FineGrained/Hawkeye

⨍ Functions 503◇ Types & classes 105

Functionget_mask

(conv5, layer_weights, mask=None, rate=0.8, img_size=448)

model/methods/MGE_CNN/MGE.py:30

Methodget_optimizer

(self, config)

Examples/PeerLearning.py:41

Methodget_optimizer

(self, config)

Examples/Baseline.py:32

Examples/APINet.py:34

Methodget_optimizer

(self, config)

Examples/ProtoTreeNet.py:21

Examples/MGE_CNN.py:18

Examples/OSMENet.py:35

Methodget_optimizer

(self, config)

Examples/CrossX.py:35

Methodget_optimizer

(self, config)

Examples/PairConfusion.py:17

Examples/NTSNet.py:15

Methodget_optimizer

(self, config)

Examples/InterpPartsNet.py:39

Methodget_optimizer

(self, config)

Examples/DCL.py:74

Methodget_performance_meters

(self)

Examples/PeerLearning.py:25

Methodget_scheduler

(self, config)

Examples/PeerLearning.py:56

Methodget_scheduler

(self, config)

Examples/Baseline.py:35

Examples/APINet.py:45

Methodget_scheduler

(self, config)

Examples/ProtoTreeNet.py:70

Examples/MGE_CNN.py:25

Examples/OSMENet.py:45

Methodget_scheduler

(self, config)

Examples/CrossX.py:39

Methodget_scheduler

(self, config)

Examples/PairConfusion.py:25

Examples/NTSNet.py:21

Methodget_scheduler

(self, config)

Examples/InterpPartsNet.py:64

Methodget_scheduler

(self, config)

Examples/DCL.py:90

Methodget_transformers

(self, config)

Examples/Baseline.py:12

Methodget_transformers

(self, config)

Examples/APCNN.py:18

Methodget_transformers

(self, config)

Examples/S3N.py:17

Methodget_transformers

(self, config)

Examples/CrossX.py:16

Methodget_transformers

(self, config)

Examples/InterpPartsNet.py:17

Methodget_transformers

(self, config)

Examples/DCL.py:19

Methodget_weighted_sampler

(self)

dataset/dataset_DCL.py:94

Functioninitialize_weights

(m)

model/utils.py:5

Functioninitialize_weights_xavier

model/methods/ProtoTree/leaf.py:90

Methodleaves

(self)

model/methods/ProtoTree/node.py:26

Methodleaves

(self)

model/methods/ProtoTree/branch.py:92

Methodleaves

(self)

model/methods/ProtoTree/prototree.py:237

Methodleaves_require_grad

(self)

model/methods/ProtoTree/prototree.py:81

Functionlist_loss

(logits, targets)

model/loss/NTS_loss.py:32

Functionnms_pytorch

Apply non-maximum suppression to avoid detecting too many overlapping bounding boxes for a given object. Args: boxes: (tensor) The loc

model/methods/nms.py:4

Methodnode_depths

(self)

model/methods/ProtoTree/prototree.py:222

Methodnodes

(self)

model/methods/ProtoTree/node.py:22

Methodnodes

(self)

model/methods/ProtoTree/prototree.py:214

Methodnodes_by_index

(self)

model/methods/ProtoTree/leaf.py:98

Methodnodes_by_index

(self)

model/methods/ProtoTree/node.py:34

Methodnodes_by_index

(self)

model/methods/ProtoTree/branch.py:102

Methodnodes_by_index

(self)

model/methods/ProtoTree/prototree.py:218

Methodnum_branches

(self)

model/methods/ProtoTree/leaf.py:102

Methodnum_branches

(self)

model/methods/ProtoTree/node.py:38

Methodnum_branches

(self)

model/methods/ProtoTree/branch.py:108

Methodnum_branches

(self)

model/methods/ProtoTree/prototree.py:241

Methodnum_leaves

(self)

model/methods/ProtoTree/leaf.py:106

Methodnum_leaves

(self)

model/methods/ProtoTree/node.py:42

Methodnum_leaves

(self)

model/methods/ProtoTree/branch.py:112

Methodnum_leaves

(self)

model/methods/ProtoTree/prototree.py:245

Examples/APINet.py:86

Methodon_start_epoch

(self, config)

Examples/ProtoTreeNet.py:83

Methodpath_to

(self, node: Node)

model/methods/ProtoTree/prototree.py:309

Methodprototypes_require_grad

(self)

model/methods/ProtoTree/prototree.py:90

Functionpth_nms_merge

(dets, thresh, topk)

model/methods/APCNN.py:36

Functionranking_loss

(score, targets, proposal_num=6)

model/loss/NTS_loss.py:38

Methodregister

(self, module)

utils/repository.py:10

Methodrequires_grad

(self)

model/methods/ProtoTree/leaf.py:78

Functionresnet101

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

model/methods/APCNN.py:611

Functionresnet101

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

model/methods/NTS_Net/resnet.py:191

Functionresnet152

r"""ResNet-152 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained

model/backbone/resnet.py:323

Functionresnet152

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

model/methods/NTS_Net/resnet.py:203

Functionresnet18

r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (

model/backbone/resnet.py:271

Functionresnet18

Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

model/methods/NTS_Net/resnet.py:155

Functionresnet34

r"""ResNet-34 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (

model/backbone/resnet.py:284

Functionresnet34

Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

model/methods/NTS_Net/resnet.py:167

Functionresnext101_32x8d

r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_. Arg

model/backbone/resnet.py:351

Functionresnext50_32x4d

r"""ResNeXt-50 32x4d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_. Args

model/backbone/resnet.py:336

Methodroot

(self)

model/methods/ProtoTree/prototree.py:77

Methodsave_gradient

(self, grad)

model/methods/MGE_CNN/grad_cam.py:15

Methodsave_state

(self, directory_path: str)

model/methods/ProtoTree/prototree.py:256

Methodsize

(self)

model/methods/ProtoTree/node.py:18

Methodupdate

(self, new_value)

utils/utils.py:21

Methodupdate_performance_meter

(self, split)

Examples/PeerLearning.py:104

r"""VGG 11-layer model (configuration "A") from `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.

model/backbone/vgg.py:90

Functionvgg11_bn

r"""VGG 11-layer model (configuration "A") with batch normalization `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https:/

model/backbone/vgg.py:103

Functionvgg13

r"""VGG 13-layer model (configuration "B") `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.

model/backbone/vgg.py:116

Functionvgg13_bn

r"""VGG 13-layer model (configuration "B") with batch normalization `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https:/

model/backbone/vgg.py:129

Functionvgg16_bn

r"""VGG 16-layer model (configuration "D") with batch normalization `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https:/

model/backbone/vgg.py:155

Functionvgg19

r"""VGG 19-layer model (configuration "E") `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.

model/backbone/vgg.py:168

Functionvgg19_bn

r"""VGG 19-layer model (configuration 'E') with batch normalization `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https:/

model/backbone/vgg.py:181

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