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Functions1,823 in github.com/amazon-science/mm-cot

⨍ Functions 1,823◇ Types & classes 268
↓ 108 callersFunction_cfg
(url='', **kwargs)
timm/models/efficientnet.py:54
↓ 79 callersFunction_cfg
(url='', **kwargs)
timm/models/resnet.py:24
↓ 79 callersFunction_create_resnet
(variant, pretrained=False, **kwargs)
timm/models/resnet.py:683
↓ 52 callersMethodget
Get value by key at specified index (indices) if idx == None, returns value for key at each output index if idx is an integer, return
timm/models/features.py:36
↓ 47 callersMethodformat
(self, record)
timm/utils/log.py:13
↓ 44 callersFunctionbuild_model_with_cfg
Build model with specified default_cfg and optional model_cfg This helper fn aids in the construction of a model including: * handling def
timm/models/helpers.py:397
↓ 43 callersFunction_create_normfreenet
(variant, pretrained=False, **kwargs)
timm/models/nfnet.py:588
↓ 43 callersFunction_dcfg
(url='', **kwargs)
timm/models/nfnet.py:35
↓ 43 callersFunction_gen_efficientnet
Creates an EfficientNet model. Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py Pap
timm/models/efficientnet.py:758
↓ 33 callersFunction_create_vision_transformer
(variant, pretrained=False, default_cfg=None, **kwargs)
timm/models/vision_transformer.py:519
↓ 32 callersFunction_cfg
(url='', **kwargs)
timm/models/vision_transformer.py:43
↓ 30 callersFunction_n2p
(w, t=True)
timm/models/vision_transformer.py:404
↓ 24 callersFunction_cfg
(url='', **kwargs)
timm/models/mlp_mixer.py:54
↓ 24 callersFunction_cfg
(url='', **kwargs)
timm/models/regnet.py:60
↓ 24 callersFunction_create_mixer
(variant, pretrained=False, **kwargs)
timm/models/mlp_mixer.py:350
↓ 24 callersFunction_create_regnet
(variant, pretrained, **kwargs)
timm/models/regnet.py:344
↓ 24 callersFunction_mcfg
(**kwargs)
timm/models/regnet.py:25
↓ 22 callersFunction_cfg
(url='', **kwargs)
timm/models/resnetv2.py:45
↓ 21 callersFunction_cfg
(url='', **kwargs)
timm/models/gluon_resnet.py:14
↓ 21 callersFunction_create_resnet
(variant, pretrained=False, **kwargs)
timm/models/gluon_resnet.py:60
↓ 21 callersFunction_nfnet_cfg
( depths, channels=(256, 512, 1536, 1536), group_size=128, bottle_ratio=0.5, feat_mult=2., act
timm/models/nfnet.py:193
↓ 20 callersFunctiondecode_arch_def
(arch_def, depth_multiplier=1.0, depth_trunc='ceil', experts_multiplier=1, fix_first_last=False)
timm/models/efficientnet_builder.py:238
↓ 20 callersFunctionresolve_bn_args
(kwargs)
timm/models/efficientnet_builder.py:42
↓ 19 callersMethoddownsample
Feature map down-sampling.
timm/models/coat.py:270
↓ 17 callersFunction_cfg
(url='', **kwargs)
timm/models/vision_transformer_hybrid.py:30
↓ 17 callersFunction_cfg
(url='', **kwargs)
timm/models/byobnet.py:44
↓ 17 callersFunction_create_byobnet
(variant, pretrained=False, **kwargs)
timm/models/byobnet.py:1150
↓ 17 callersFunction_create_vision_transformer_hybrid
(variant, backbone, pretrained=False, **kwargs)
timm/models/vision_transformer_hybrid.py:143
↓ 16 callersFunction_cfg
(url='', **kwargs)
timm/models/mobilenetv3.py:29
↓ 16 callersFunction_cfg
(url='', **kwargs)
timm/models/byoanet.py:23
↓ 16 callersFunction_create_byoanet
(variant, cfg_variant=None, pretrained=False, **kwargs)
timm/models/byoanet.py:311
↓ 16 callersFunction_create_effnet
(variant, pretrained=False, **kwargs)
timm/models/efficientnet.py:543
↓ 16 callersFunctionresolve_act_layer
(kwargs, default='relu')
timm/models/efficientnet_builder.py:53
↓ 15 callersFunctioninterleave_blocks
interleave 2 block types in stack
timm/models/byobnet.py:159
↓ 15 callersFunctiont2p
Possibly convert HWIO to OIHW.
timm/models/resnetv2.py:433
↓ 14 callersFunction_create_resnetv2_bit
(variant, pretrained=False, **kwargs)
timm/models/resnetv2.py:477
↓ 13 callersFunction_resnetv2
ResNet-V2 backbone helper
timm/models/vision_transformer_hybrid.py:150
↓ 12 callersFunction_cfg
(url='', **kwargs)
timm/models/dla.py:22
↓ 12 callersFunction_create_dla
(variant, pretrained=False, **kwargs)
timm/models/dla.py:341
↓ 12 callersFunction_gen_mobilenet_v3
Creates a MobileNet-V3 model. Ref impl: ? Paper: https://arxiv.org/abs/1905.02244 Args: channel_multiplier: multiplier to number o
timm/models/mobilenetv3.py:277
↓ 12 callersMethodupdate
(self, model)
timm/utils/model_ema.py:68
↓ 11 callersFunction_cfg
(url='')
timm/models/vovnet.py:139
↓ 11 callersFunction_create_vovnet
(variant, pretrained=False, **kwargs)
timm/models/vovnet.py:339
↓ 11 callersFunctionconv2d_iabn
(ni, nf, stride, kernel_size=3, groups=1, act_layer="leaky_relu", act_param=1e-2)
timm/models/tresnet.py:60
↓ 10 callersFunction_cfg
(url='', **kwargs)
timm/models/cait.py:25
↓ 10 callersFunction_cfg
(url='', **kwargs)
timm/models/swin_transformer.py:32
↓ 10 callersFunction_create_cait
(variant, pretrained=False, **kwargs)
timm/models/cait.py:315
↓ 10 callersFunction_create_swin_transformer
(variant, pretrained=False, default_cfg=None, **kwargs)
timm/models/swin_transformer.py:541
↓ 10 callersFunction_gen_efficientnet_lite
Creates an EfficientNet-Lite model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite Paper: https://
timm/models/efficientnet.py:866
↓ 10 callersMethodstate_dict
(self)
timm/utils/cuda.py:27
↓ 9 callersMethod__init__
(self)
timm/models/inception_v4.py:44
↓ 9 callersMethod__init__
(self, in_channels, out_channels, kernel_size, stride=1, padding='')
timm/models/nasnet.py:36
↓ 9 callersFunction_cfg
(url='', **kwargs)
timm/models/senet.py:28
↓ 9 callersFunction_cfg
(url='', **kwargs)
timm/models/hrnet.py:29
↓ 9 callersFunction_cfg
(url='')
timm/models/densenet.py:23
↓ 9 callersFunction_create_densenet
(variant, growth_rate, block_config, pretrained, **kwargs)
timm/models/densenet.py:286
↓ 9 callersFunction_create_hrnet
(variant, pretrained, **model_kwargs)
timm/models/hrnet.py:774
↓ 9 callersFunction_create_resnetv2
(variant, pretrained=False, **kwargs)
timm/models/resnetv2.py:467
↓ 9 callersFunction_create_senet
(variant, pretrained=False, **kwargs)
timm/models/senet.py:399
↓ 9 callersFunction_nfres_cfg
( depths, channels=(256, 512, 1024, 2048), group_size=None, act_layer='relu', attn_layer=None, attn_kw
timm/models/nfnet.py:175
↓ 9 callersMethodtransform
(self)
timm/data/dataset.py:127
↓ 8 callersMethod__init__
(self, in_channels, pool_features, conv_block=None)
timm/models/inception_v3.py:54
↓ 8 callersMethod__init__
(self, in_chs, out_chs, kernel_size=3, stride=4, pool='maxpool', num_rep=3, num_act=None, chs
timm/models/byobnet.py:869
↓ 8 callersFunction_cfg
(url='', **kwargs)
timm/models/resnest.py:19
↓ 8 callersFunction_cfg
(url='', **kwargs)
timm/models/pit.py:30
↓ 8 callersFunction_cfg
(url='')
timm/models/rexnet.py:24
↓ 8 callersFunction_cfg
(url='', **kwargs)
timm/models/cspnet.py:28
↓ 8 callersFunction_cfg
(url='', **kwargs)
timm/models/vgg.py:24
↓ 8 callersFunction_create_cspnet
(variant, pretrained=False, **kwargs)
timm/models/cspnet.py:409
↓ 8 callersFunction_create_pit
(variant, pretrained=False, **kwargs)
timm/models/pit.py:259
↓ 8 callersFunction_create_resnest
(variant, pretrained=False, **kwargs)
timm/models/resnest.py:141
↓ 8 callersFunction_create_rexnet
(variant, pretrained, **kwargs)
timm/models/rexnet.py:184
↓ 8 callersFunction_create_vgg
(variant: str, pretrained: bool, **kwargs: Any)
timm/models/vgg.py:178
↓ 8 callersFunction_gen_efficientnet_edge
Creates an EfficientNet-EdgeTPU model Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
timm/models/efficientnet.py:805
↓ 8 callersFunction_log_info_if
(msg, condition)
timm/models/efficientnet_builder.py:64
↓ 8 callersFunction_rep_vgg_bcfg
(d=(4, 6, 16, 1), wf=(1., 1., 1., 1.), groups=0)
timm/models/byobnet.py:150
↓ 8 callersFunctionconv_bn
(in_chs, out_chs, k=3, stride=1, padding=None, dilation=1)
timm/models/selecsls.py:100
↓ 8 callersFunctionget_acc_with_contion
(res_pd, key, values)
utils_evaluate.py:15
↓ 8 callersMethodload_state_dict
(self, state_dict)
timm/utils/cuda.py:31
↓ 8 callersMethodremove_cls
Remove CLS token.
timm/models/coat.py:484
↓ 8 callersMethodupdate_groups
(self, values)
timm/scheduler/scheduler.py:81
↓ 7 callersMethod__init__
( self, img_size=224, patch_size=16, in_chans=3, n
timm/models/levit.py:405
↓ 7 callersMethod__init__
(self, scale=1.0, no_relu=False)
timm/models/inception_resnet_v2.py:199
↓ 7 callersFunction_cfg
(url='', **kwargs)
timm/models/res2net.py:18
↓ 7 callersFunction_cfg
(url='', **kwargs)
timm/models/tresnet.py:20
↓ 7 callersFunction_check_args_tf
(kwargs)
timm/data/auto_augment.py:52
↓ 7 callersFunction_create_res2net
(variant, pretrained=False, **kwargs)
timm/models/res2net.py:135
↓ 7 callersFunction_create_tresnet
(variant, pretrained=False, **kwargs)
timm/models/tresnet.py:250
↓ 7 callersFunction_dm_nfnet_cfg
(depths, channels=(256, 512, 1536, 1536), act_layer='gelu', skipinit=True)
timm/models/nfnet.py:205
↓ 7 callersMethodstages
(self, x)
timm/models/hrnet.py:691
↓ 6 callersMethod__init__
(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type='', is_reduction=Fals
timm/models/pnasnet.py:188
↓ 6 callersFunction_cfg
(url='', **kwargs)
timm/models/dpn.py:25
↓ 6 callersFunction_cfg
(url='', **kwargs)
timm/models/hardcorenas.py:14
↓ 6 callersFunction_cfg
(url='', **kwargs)
timm/models/twins.py:30
↓ 6 callersFunction_create_dpn
(variant, pretrained=False, **kwargs)
timm/models/dpn.py:264
↓ 6 callersFunction_create_twins
(variant, pretrained=False, **kwargs)
timm/models/twins.py:366
↓ 6 callersFunction_gen_efficientnet_condconv
Creates an EfficientNet-CondConv model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv
timm/models/efficientnet.py:835
↓ 6 callersFunction_gen_efficientnetv2_s
Creates an EfficientNet-V2 Small model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smaller
timm/models/efficientnet.py:936
↓ 6 callersFunction_gen_hardcorenas
Creates a hardcorenas model Ref impl: https://github.com/Alibaba-MIIL/HardCoReNAS Paper: https://arxiv.org/abs/2102.11646
timm/models/hardcorenas.py:34
↓ 6 callersFunction_gen_mixnet_m
Creates a MixNet Medium-Large model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet Paper: https://ar
timm/models/efficientnet.py:1068
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