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Functions34 in github.com/ajtejankar/byol-convert

⨍ Functions 34 ◇ Types & classes 5

↓ 10 callersFunction_resnet

(arch, block, layers, pretrained, progress, **kwargs)

↓ 6 callersFunctionpad_same

↓ 4 callersMethod_make_layer

(self, block, planes, blocks, stride=1, dilate=False)

↓ 4 callersMethodupdate

(self, val, n=1)

validate.py:114

↓ 3 callersFunctionconv1x1

1x1 convolution

↓ 3 callersFunctionconv3x3

3x3 convolution with padding

↓ 2 callersMethod__init__

(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=6

↓ 2 callersFunctionresnet200

r"""ResNet-200 2x model from BYOL Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If T

↓ 2 callersFunctionresnet50

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

↓ 1 callersMethod_forward_impl

↓ 1 callersMethod_get_batch_fmtstr

(self, num_batches)

validate.py:136

↓ 1 callersFunctionaccuracy

Computes the accuracy over the k top predictions for the specified values of k

validate.py:142

↓ 1 callersFunctioncalc_padding_same

(in_height, in_width, strides, filter_height, filter_width)

↓ 1 callersMethoddisplay

validate.py:131

↓ 1 callersFunctionload_checkpoint

(checkpoint_path)

↓ 1 callersFunctionmain

↓ 1 callersMethodreset

validate.py:108

↓ 1 callersFunctionvalidate

(val_loader, model)

(self, name, fmt=':f')

validate.py:103

(self, num_batches, meters, prefix="")

validate.py:126

(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm

(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm

validate.py:120

Functionresnet101

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

Functionresnet152

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

Functionresnet18

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

Functionresnet34

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

Functionresnext101_32x8d

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

Functionresnext50_32x4d

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

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 fo

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 for