Functions — github.com/HazyResearch/butterfly

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Functions1,242 in github.com/HazyResearch/butterfly

⨍ Functions 1,242◇ Types & classes 260

Method__init__

(self, in_planes, planes, stride=1)

convolution/models/resnet.py:48

Method__init__

(self, num_classes=10)

convolution/models/resnet.py:113

Method__init__

(self, num_classes=10)

convolution/models/resnet.py:120

Method__init__

(self, num_classes=10)

convolution/models/resnet.py:127

Method__init__

(self, num_classes=10)

convolution/models/resnet.py:134

Method__init__

(self, num_classes=10)

convolution/models/resnet.py:141

Method__init__

(self, **kwargs)

convolution/datamodules/cifar.py:29

Method__init__

(self, latent_dim, output_dim, dropout_prob)

gumbel-sinkhorn/my_sorting_model.py:49

Method__init__

Fixed permutation. Used to store argmax of Permutation. Parameter: permutation: (n, ) tensor of ints

butterfly/permutation.py:61

Method__init__

(self, size)

butterfly/permutation.py:86

Method__init__

(self, in_size, out_size, matrix_batch=1, bias=True, complex=False, tied_weight=True, increas

butterfly/butterfly.py:343

Method__init__

(self, size)

learning_transforms/hstack_diag.py:49

Method__init__

(self, factors, n_terms=None, complex=False, fixed_order=False, softmax_fn='softmax')

learning_transforms/butterfly_old.py:110

Method__init__

(self, size, n_terms=None, complex=False, fixed_order=False, softmax_fn='softmax', learn_perm=False)

learning_transforms/butterfly_old.py:167

Method__init__

Parameters: size: size of butterfly matrix complex: real or complex matrix ABCD: block of [[A, B], [C, D]

learning_transforms/butterfly_old.py:209

Method__init__

(self, size, complex=False, decreasing_size=True, ortho_init=False)

learning_transforms/butterfly_old.py:269

Method__init__

(self, size, rank=1, complex=False, twiddle=None, ortho_init=False)

learning_transforms/butterfly_old.py:298

Method__init__

Parameters: size: input has shape (stack, ..., size) stack: number of stacked components, output has shape (stack, ..

learning_transforms/butterfly_old.py:350

Method__init__

(self, in_size, out_size, complex=False, decreasing_size=True, tied_weight=True, bias=True)

learning_transforms/butterfly_old.py:403

Method__init__

Parameters: size: size of butterfly matrix complex: real or complex matrix ABCD: block of [[A, B], [C, D]

learning_transforms/butterfly_old.py:466

Method__init__

(self, size, complex=False, decreasing_size=True)

learning_transforms/butterfly_old.py:506

Method__init__

Parameters: size: size of permutation matrix complex: real of complex input logit: (3, ) nn.Parameter, co

learning_transforms/butterfly_old.py:537

Method__init__

(self, size, complex=False, share_logit=False, increasing_size=True)

learning_transforms/butterfly_old.py:600

Method__init__

Fixed permutation. Used to store argmax of BlockPerm and BlockPermProduct. Parameter: permutation: (n, ) tensor of ints

learning_transforms/butterfly_old.py:643

Method__init__

(self, loader, prefetch=True)

cnn/imagenet_experiment.py:240

Method__init__

(self)

cnn/imagenet_experiment.py:400

Method__init__

(self, groups)

cnn/shufflenet_imagenet.py:75

Method__init__

(self, in_planes, out_planes, stride, groups, grouped_conv_1st_layer=True, shuffle='P', preact=False)

cnn/shufflenet_imagenet.py:87

Method__init__

(self, num_classes=1000, groups=8, width_mult=1.0, shuffle='P', preact=False)

cnn/shufflenet_imagenet.py:144

Method__init__

(self, module)

cnn/distributed.py:48

Method__init__

structure: list of string

cnn/mobilenet_imagenet.py:102

Method__init__

(self)

cnn/imagenet_amp.py:419

Method__init__

(self)

cnn/teacher_covariance.py:169

Method__init__

(self)

cnn/train_utils.py:24

Method__init__

(self, loader, prefetch=True)

cnn/train_utils.py:40

Method__init__

(self, batch_size, num_threads, device_id, data_dir, crop, dali_cpu=False)

cnn/imagenet/dataloaders.py:20

Method__init__

(self, batch_size, num_threads, device_id, data_dir, crop, size)

cnn/imagenet/dataloaders.py:70

Method__init__

(self, dataloader, num_classes, fp16, one_hot)

cnn/imagenet/dataloaders.py:227

Method__init__

(self)

cnn/imagenet/logger.py:7

Method__init__

(self)

cnn/imagenet/logger.py:24

Method__init__

(self, ret_last=True, ret_val=True)

cnn/imagenet/logger.py:41

Method__init__

(self, decay)

cnn/imagenet/logger.py:72

Method__init__

(self, print_interval, backends, verbose=False)

cnn/imagenet/logger.py:92

Method__init__

(self, filename, log_level=0)

cnn/imagenet/logger.py:173

Method__init__

(self, iters, val_iters, epochs, log_level=0)

cnn/imagenet/logger.py:218

Method__init__

(self, iters, epochs, log_level=0)

cnn/imagenet/logger.py:268

Method__init__

(self, smoothing = 0.0)

cnn/imagenet/mixup.py:34

Method__init__

(self, original_loss, temperature=1.0, alpha_ce=0.5)

cnn/imagenet/smoothing.py:33

Method__init__

(self, arch, loss, pretrained_weights=None, cuda=True, fp16=False, width=1.0, n_struct_layers

cnn/imagenet/training.py:31

Method__init__

(self, version, config)

cnn/imagenet/resnet.py:11

Method__init__

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

cnn/imagenet/resnet.py:79

Method__init__

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

cnn/imagenet/resnet.py:117

Method__init__

(self, size, nstack=1)

cnn/models/circulant1x1conv.py:10

Method__init__

(self, depth, widen_factor, dropout_rate, num_classes, structure_type=None, **kwargs)

cnn/models/wide_resnet.py:71

Method__init__

(self, method='linear', **kwargs)

cnn/models/lenet.py:18

Method__init__

(self, num_classes=10, dropout=False, method='linear', tied_weight=False, **kwargs)

cnn/models/lenet.py:89

Method__init__

(self, num_blocks, cardinality, bottleneck_width, num_classes=10)

cnn/models/resnext.py:41

Method__init__

(self, in_planes, out_planes, kernel_size, stride)

cnn/models/pnasnet.py:12

Method__init__

(self, in_planes, out_planes, stride=1)

cnn/models/pnasnet.py:41

Method__init__

(self, cell_type, num_cells, num_planes)

cnn/models/pnasnet.py:72

Method__init__

(self, sz=None)

cnn/models/layers.py:7

Method__init__

(self)

cnn/models/layers.py:19

Method__init__

(self, in_planes, planes, stride=1)

cnn/models/senet.py:11

Method__init__

(self, in_planes, planes, stride=1)

cnn/models/senet.py:46

Method__init__

(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer)

cnn/models/dpn.py:8

Method__init__

(self, in_planes, growth_rate)

cnn/models/densenet.py:10

Method__init__

(self, in_planes, out_planes)

cnn/models/densenet.py:25

Method__init__

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

cnn/models/presnet.py:45

Method__init__

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

cnn/models/presnet.py:77

Method__init__

(self, w, h, method='identity', rank=2, train=True, **kwargs)

cnn/models/presnet.py:208

Method__init__

(self, size)

cnn/models/presnet.py:309

Method__init__

(self, size)

cnn/models/presnet.py:323

Method__init__

(self, size, sig='BT1', param='ortho2', stochastic=False, temp=1.0, samples=1, sample_method='gumbel', hard=Fa

cnn/models/presnet.py:348

Method__init__

(self, version=1.0, num_classes=1000)

cnn/models/squeezenet.py:41

Method__init__

(self, in_planes, planes, stride=1, option='A')

cnn/models/resnet_original.py:57

Method__init__

(self, block, num_blocks, num_classes=10)

cnn/models/resnet_original.py:87

Method__init__

(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=True, ti

cnn/models/butterfly_conv.py:131

Method__init__

(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=True, ti

cnn/models/butterfly_conv.py:214

Method__init__

(self, num_classes=10, num_structured_layers=0, structure_type='B', nblocks=0, param='regular')

cnn/models/mobilenet.py:54

Method__init__

(self, groups=2)

cnn/models/shufflenetv2.py:11

Method__init__

(self, ratio)

cnn/models/shufflenetv2.py:23

Method__init__

(self, in_channels, split_ratio=0.5)

cnn/models/shufflenetv2.py:33

Method__init__

(self, net_size)

cnn/models/shufflenetv2.py:97

Method__init__

(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=True, rank=1)

cnn/models/low_rank_conv.py:10

cnn/models/shufflenet.py:11

Method__init__

(self, cfg)

cnn/models/shufflenet.py:52

Method__init__

(self, in_size, out_size, rank=4, bias=True, corner=False)

cnn/models/toeplitzlike1x1conv.py:101

Method__init__

(self, inplanes, planes, stride=1, downsample=None, is_structured=False, structure_type='B', nblocks=1,

cnn/models/resnet_imagenet.py:52

Method__init__

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

cnn/models/resnet_imagenet.py:90

Method__init__

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

cnn/models/resnet_imagenet.py:124

Method__init__

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

cnn/models/resnet_imagenet.py:160

Method__init__

(self, num_classes=10)

cnn/models/mobilenetv2.py:50

Method__init__

(self, in_planes, planes, stride=1)

cnn/models/preact_resnet.py:41

Method__init__

(self, block, num_blocks, num_classes=10)

cnn/models/preact_resnet.py:66

Method__init__

(self, in_planes, planes, stride=1, is_structured=False, structure_type='B', **kwargs)

cnn/models/resnet.py:22

Method__init__

(self, in_planes, planes, stride=1)

cnn/models/resnet.py:74

Method__init__

(self)

cnn/models/googlenet.py:57

Method__iter__

(self)

cnn/imagenet/dataloaders.py:118

Method__iter__

(self)

cnn/imagenet/dataloaders.py:234

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