Functions — github.com/ZhangYuanhan-AI/OmniBenchmark

hub / github.com/ZhangYuanhan-AI/OmniBenchmark / functions

Functions269 in github.com/ZhangYuanhan-AI/OmniBenchmark

⨍ Functions 269◇ Types & classes 49

Methodforward

(self, image, text)

linear_probe/models/vit.py:414

Methodforward

(self, x)

linear_probe/models/timm_rn101.py:38

Methodforward

(self, x)

linear_probe/models/timm_mlp_mixer.py:32

Methodforward

(self, x: torch.Tensor)

linear_probe/models/modified_resnet.py:104

Methodforward

(self, x)

linear_probe/models/modified_resnet.py:130

Methodforward

(self, x)

linear_probe/models/modified_resnet.py:229

Methodforward

(self, x)

linear_probe/models/timm_beit.py:46

Methodforward

(self, x: torch.Tensor)

linear_probe/models/text_transformer.py:18

Methodforward

(self, x: torch.Tensor)

linear_probe/models/text_transformer.py:27

Methodforward

(self, x: torch.Tensor)

linear_probe/models/text_transformer.py:49

Methodforward

(self, x: torch.Tensor)

linear_probe/models/text_transformer.py:72

Methodforward

(self, texts)

linear_probe/models/text_transformer.py:185

Methodforward

(self, x)

linear_probe/models/timm_vit_modified.py:79

Methodforward

(self, x)

linear_probe/models/timm_effnet.py:35

Methodforward

(self, x)

linear_probe/models/timm_vit.py:42

Methodforward

(self, x)

linear_probe/models/timm_swinTransformer.py:27

Methodforward

(self, x)

linear_probe/models/timm_inceptionv4.py:28

Methodforward

(self, x)

linear_probe/models/resnet.py:38

Methodforward

(self, x)

linear_probe/models/resnet.py:73

Methodforward

(self, x)

linear_probe/models/resnet.py:161

Methodforward

(self, features, labels=None, sup_logits=None)

ReCo/losses.py:21

Methodforward

(self, features, labels=None,sup_logits=None)

ReCo/losses.py:135

Methodforward

(self, x, *args)

ReCo/moco/builder.py:14

Methodforward

(self, im_q, im_k=None, labels=None)

ReCo/moco/builder.py:228

Methodforward

(self, x: Tensor)

ReCo/models/resnet.py:66

Methodforward

(self, x: Tensor)

ReCo/models/resnet.py:120

Methodforward

(self, x: Tensor)

ReCo/models/resnet.py:247

Functionget_lr

(optimizer)

linear_probe/utils.py:106

Functionidentity

(img, **__)

ReCo/randaugment.py:120

Functioninvert

(img, **__)

ReCo/randaugment.py:116

Functionis_image_file

Checks if a file is an allowed image extension. Args: filename (string): path to a file Returns: bool: True if the filename

linear_probe/dataset.py:72

Functionmodified_res101

(**kwargs)

linear_probe/models/modified_resnet.py:264

Functionmodified_res50

(**kwargs)

linear_probe/models/modified_resnet.py:253

Functionmodified_res50x16

(**kwargs)

linear_probe/models/modified_resnet.py:275

Functionposterize

(img, bits_to_keep, **__)

ReCo/randaugment.py:147

Functionprint_pass

(*args)

ReCo/reco_imagenet.py:174

Functionresnet101

r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained

linear_probe/models/resnet.py:247

Functionresnet101

r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained

ReCo/models/resnet.py:310

Functionresnet152

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

ReCo/models/resnet.py:324

Functionresnet18

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

ReCo/models/resnet.py:268

Functionresnet200

r"""ResNet-200 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained

ReCo/models/resnet.py:336

Functionresnet34

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

ReCo/models/resnet.py:282

Functionresnet50

Constructs a ResNet-50 model.

linear_probe/models/resnet.py:240

Functionresnet50

r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (

ReCo/models/resnet.py:296

Functionresnetal50

Constructs a ResNet-50 model.

linear_probe/models/resnet.py:259

Functionresnext101_32x8d

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

ReCo/models/resnet.py:364

Functionresnext50_32x4d

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

ReCo/models/resnet.py:348

Functionrotate

(img, degrees, **kwargs)

ReCo/randaugment.py:80

Functionsharpness

(img, factor, **__)

ReCo/randaugment.py:165

Functionshear_x

(img, factor, **kwargs)

ReCo/randaugment.py:48

Functionshear_y

(img, factor, **kwargs)

ReCo/randaugment.py:53

Functionsolarize

(img, thresh, **__)

ReCo/randaugment.py:128

Functionsolarize_add

(img, add, thresh=128, **__)

ReCo/randaugment.py:132

Methodstem

(x)

linear_probe/models/modified_resnet.py:230

Functiontimm_beit

(**kwargs)

linear_probe/models/timm_beit.py:52

Functiontimm_effnet

(**kwargs)

linear_probe/models/timm_effnet.py:43

Functiontimm_inceptionv4

(**kwargs)

linear_probe/models/timm_inceptionv4.py:35

Functiontimm_mlp_mixer

(**kwargs)

linear_probe/models/timm_mlp_mixer.py:39

Functiontimm_rn101

(**kwargs)

linear_probe/models/timm_rn101.py:45

Functiontimm_swinTransformer

(**kwargs)

linear_probe/models/timm_swinTransformer.py:33

Functiontimm_vit

(**kwargs)

linear_probe/models/timm_vit.py:47

Functiontimm_vit_modified

(**kwargs)

linear_probe/models/timm_vit_modified.py:84

Functiontranslate_x_abs

(img, pixels, **kwargs)

ReCo/randaugment.py:70

Functiontranslate_x_rel

(img, pct, **kwargs)

ReCo/randaugment.py:58

Functiontranslate_y_abs

(img, pixels, **kwargs)

ReCo/randaugment.py:75

Functiontranslate_y_rel

(img, pct, **kwargs)

ReCo/randaugment.py:64

Functiontrim_key

(key)

linear_probe/dataset.py:20

Functionwide_resnet101_2

r"""Wide ResNet-101-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_. The model is the same as ResNet except f

ReCo/models/resnet.py:400

Functionwide_resnet50_2

r"""Wide ResNet-50-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_. The model is the same as ResNet except fo

ReCo/models/resnet.py:380

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