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Functions2,893 in github.com/QWTforGithub/T2LDM

↓ 1 callersMethod_forward_jit
A cut & paste of the contents of the PyTorch BatchNorm2d forward function
timm/models/layers/norm_act.py:29
↓ 1 callersMethod_forward_python
(self, x)
timm/models/layers/norm_act.py:56
↓ 1 callersFunction_gen_fbnetc
FBNet-C Paper: https://arxiv.org/abs/1812.03443 Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcn
timm/models/efficientnet.py:705
↓ 1 callersFunction_gen_mnasnet_small
Creates a mnasnet-b1 model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet Paper: https://arxiv.org/pdf/1807
timm/models/efficientnet.py:646
↓ 1 callersFunction_gen_mobilenet_v3_rw
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:239
↓ 1 callersFunction_gen_spnasnet
Creates the Single-Path NAS model from search targeted for Pixel1 phone. Paper: https://arxiv.org/abs/1904.02877 Args: channel_multipl
timm/models/efficientnet.py:735
↓ 1 callersMethod_get_lr
(param_group, param_state)
timm/optim/adafactor.py:54
↓ 1 callersMethod_get_options
(param_group, param_shape)
timm/optim/adafactor.py:65
↓ 1 callersFunction_get_pixels
(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda')
timm/data/random_erasing.py:13
↓ 1 callersFunction_get_return_layers
(feature_info, out_map)
timm/models/features.py:145
↓ 1 callersMethod_get_stage_params
(self, cfg, default_stride=2, output_stride=32, drop_path_rate=0.)
timm/models/regnet.py:284
↓ 1 callersMethod_initialize_weights
(self)
timm/models/vgg.py:148
↓ 1 callersFunction_interpolation
(kwargs)
timm/data/auto_augment.py:42
↓ 1 callersFunction_kernel_valid
(k)
timm/models/layers/selective_kernel.py:14
↓ 1 callersFunction_label_from_paths
(*path, leaf_only=True)
timm/data/parsers/parser_image_in_tar.py:118
↓ 1 callersMethod_load_checkpoint
(self, checkpoint_path)
timm/utils/model_ema.py:51
↓ 1 callersFunction_load_weights
(model: nn.Module, checkpoint_path: str, prefix: str = 'resnet/')
timm/models/resnetv2.py:432
↓ 1 callersFunction_load_weights
Load weights from .npz checkpoints for official Google Brain Flax implementation
timm/models/vision_transformer.py:408
↓ 1 callersFunction_lovasz_hinge
Binary Lovasz hinge loss logits: [B, H, W] Logits at each pixel (between -infinity and +infinity) labels: [B, H, W] Tensor, bi
utils/common.py:521
↓ 1 callersFunction_lovasz_softmax
Multi-class Lovasz-Softmax loss Args: @param probas: [B, C, H, W] Class probabilities at each prediction (between 0 and 1). Int
utils/common.py:574
↓ 1 callersMethod_make_block
(self, ba, block_idx, block_count)
timm/models/efficientnet_builder.py:298
↓ 1 callersMethod_make_branches
(self, num_branches, block, num_blocks, num_channels)
timm/models/hrnet.py:435
↓ 1 callersMethod_make_fuse_layers
(self)
timm/models/hrnet.py:442
↓ 1 callersMethod_make_one_branch
(self, branch_index, block, num_blocks, num_channels, stride=1)
timm/models/hrnet.py:418
↓ 1 callersMethod_mix_batch
(self, x)
timm/data/mixup.py:196
↓ 1 callersMethod_mix_batch_collate
(self, output, batch)
timm/data/mixup.py:280
↓ 1 callersMethod_mix_elem
(self, x)
timm/data/mixup.py:159
↓ 1 callersMethod_mix_elem_collate
(self, output, batch, half=False)
timm/data/mixup.py:227
↓ 1 callersMethod_mix_pair
(self, x)
timm/data/mixup.py:176
↓ 1 callersMethod_mix_pair_collate
(self, output, batch)
timm/data/mixup.py:252
↓ 1 callersFunction_no_grad_trunc_normal_
(tensor, mean, std, a, b)
timm/models/layers/weight_init.py:8
↓ 1 callersMethod_padding
(self, x)
timm/models/layers/median_pool.py:25
↓ 1 callersMethod_phi_center
(self, H, device, dtype)
models/T2LDM.py:168
↓ 1 callersFunction_posterize_level_to_arg
(level, _hparams)
timm/data/auto_augment.py:220
↓ 1 callersFunction_select_rand_weights
(weight_idx=0, transforms=None)
timm/data/auto_augment.py:625
↓ 1 callersFunction_solarize_level_to_arg
(level, _hparams)
timm/data/auto_augment.py:241
↓ 1 callersMethod_theta_center
(self, W, device, dtype)
models/T2LDM.py:162
↓ 1 callersFunction_transform
(n_px)
models/CLIP/clip/clip.py:79
↓ 1 callersFunction_weight_init
(m)
timm/models/senet.py:62
↓ 1 callersFunction_xception
(variant, pretrained=False, **kwargs)
timm/models/xception.py:222
↓ 1 callersFunctionact_with_gamma
(act_type, gamma: float = 1.)
timm/models/nfnet.py:298
↓ 1 callersFunctionadapt_model_from_file
(parent_module, model_variant)
timm/models/helpers.py:329
↓ 1 callersFunctionadapt_model_from_string
(parent_module, model_string)
timm/models/helpers.py:279
↓ 1 callersFunctionadaptive_clip_grad
(parameters, clip_factor=0.01, eps=1e-3, norm_type=2.0)
timm/utils/agc.py:30
↓ 1 callersFunctionadaptive_pool_feat_mult
(pool_type='avg')
timm/models/layers/adaptive_avgmax_pool.py:17
↓ 1 callersFunctionadd_weight_decay
(model, weight_decay=1e-5, skip_list=())
timm/optim/optim_factory.py:31
↓ 1 callersFunctionadjust_widths_groups_comp
Adjusts the compatibility of widths and groups.
timm/models/regnet.py:107
↓ 1 callersMethodany_requires_grad
(self, x)
timm/models/densenet.py:67
↓ 1 callersMethodattention
(self, x: torch.Tensor)
models/CLIP/clip/model.py:189
↓ 1 callersFunctionaugment_and_mix_transform
Create AugMix PyTorch transform :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated
timm/data/auto_augment.py:818
↓ 1 callersFunctionaugmix_ops
(magnitude=10, hparams=None, transforms=None)
timm/data/auto_augment.py:738
↓ 1 callersFunctionauto_augment_policy
(name='v0', hparams=None)
timm/data/auto_augment.py:497
↓ 1 callersFunctionauto_augment_policy_original
(hparams)
timm/data/auto_augment.py:431
↓ 1 callersFunctionauto_augment_policy_originalr
(hparams)
timm/data/auto_augment.py:464
↓ 1 callersFunctionauto_augment_policy_v0
(hparams)
timm/data/auto_augment.py:364
↓ 1 callersFunctionauto_augment_policy_v0r
(hparams)
timm/data/auto_augment.py:397
↓ 1 callersFunctionauto_augment_transform
Create a AutoAugment transform :param config_str: String defining configuration of auto augmentation. Consists of multiple sections separate
timm/data/auto_augment.py:532
↓ 1 callersFunctionavailable_models
Returns the names of available CLIP models
models/CLIP/clip/clip.py:89
↓ 1 callersFunctionbasic_clean
(text)
models/CLIP/clip/simple_tokenizer.py:50
↓ 1 callersMethodbpe
(self, token)
models/CLIP/clip/simple_tokenizer.py:80
↓ 1 callersMethodbuild_attention_mask
(self)
models/CLIP/clip/model.py:332
↓ 1 callersFunctionbuild_model
(dataset_name, model_name, device='cpu')
eval/__init__.py:41
↓ 1 callersFunctionbuild_model
(state_dict: dict)
models/CLIP/clip/model.py:455
↓ 1 callersFunctioncalculate_frechet_distance
Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2
eval/fid_score.py:116
↓ 1 callersMethodcall_checkpoint_bottleneck
(self, x)
timm/models/densenet.py:75
↓ 1 callersMethodchannels
feature channels accessor
timm/models/features.py:62
↓ 1 callersFunctioncheck_semantic_object
(semantic, scene=True)
data/nuScenes/descriptor_plus.py:85
↓ 1 callersFunctioncheck_text
(infos=None)
data/nuScenes/descriptor_plus.py:1877
↓ 1 callersFunctionclass_descripter
(items)
data/nuScenes/descriptor_plus.py:638
↓ 1 callersFunctionclass_descripter
(items)
data/nuScenes/descriptor.py:584
↓ 1 callersFunctionclass_name
(index)
data/nuScenes/descriptor.py:97
↓ 1 callersFunctioncompute_cd
Calculate score of Chamfer Distance (CD)
eval/eval_utils.py:28
↓ 1 callersFunctioncompute_emd
Calculate score of Earth Mover's Distance (EMD)
eval/eval_utils.py:44
↓ 1 callersFunctioncompute_fpvd
Calculate the score of Fréchet Point-based Volume Distance (FPVD)
eval/eval_utils.py:127
↓ 1 callersFunctioncompute_fsvd
Calculate the score of Fréchet Sparse Volume Distance (FSVD)
eval/eval_utils.py:114
↓ 1 callersFunctioncompute_jsd
Calculate the score of Jensen-Shannon Divergence (JSD)
eval/eval_utils.py:79
↓ 1 callersFunctioncompute_mmd
Calculate the score of Minimum Matching Distance (MMD)
eval/eval_utils.py:60
↓ 1 callersFunctioncompute_pairwise_cd
(x, y, module=None)
eval/metric_utils.py:484
↓ 1 callersFunctioncompute_pairwise_emd
(x, y, module=None)
eval/metric_utils.py:516
↓ 1 callersFunctionconvert_weights
Convert applicable model parameters to fp16
models/CLIP/clip/model.py:431
↓ 1 callersFunctioncosine_beta_schedule
cosine schedule as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
models/diffusion/discrete_time.py:23
↓ 1 callersFunctioncount_everyclass_text
(infos)
data/kitti_semantic/descriptor.py:830
↓ 1 callersFunctioncount_text
()
data/kitti_semantic/descriptor.py:882
↓ 1 callersFunctioncreate_block
(block: Union[str, nn.Module], **kwargs)
timm/models/byobnet.py:1178
↓ 1 callersFunctioncreate_byob_stages
( cfg: ByoModelCfg, drop_path_rate: float, output_stride: int, stem_feat: Dict[str, Any], feat
timm/models/byobnet.py:1319
↓ 1 callersFunctioncreate_byob_stem
(in_chs, out_chs, stem_type='', pool_type='', feat_prefix='stem', layers: LayerFn = None)
timm/models/byobnet.py:1232
↓ 1 callersFunctioncreate_optimizer_v2
Create an optimizer. TODO currently the model is passed in and all parameters are selected for optimization. For more general use an interfa
timm/optim/optim_factory.py:75
↓ 1 callersFunctioncreate_stem
( in_chans=3, out_chs=32, kernel_size=3, stride=2, pool='', act_layer=None, norm_layer=None, a
timm/models/cspnet.py:131
↓ 1 callersFunctioncreate_stem
(in_chs, out_chs, stem_type='', conv_layer=None, act_layer=None, preact_feature=True)
timm/models/nfnet.py:396
↓ 1 callersFunctioncreate_transform
( input_size, is_training=False, use_prefetcher=False, no_aug=False, s
timm/data/transforms_factory.py:167
↓ 1 callersMethoddecode
(self, tokens)
models/CLIP/clip/simple_tokenizer.py:129
↓ 1 callersFunctiondefault_bpe
()
models/CLIP/clip/simple_tokenizer.py:11
↓ 1 callersFunctiondel_key
(info, key)
data/nuScenes/descriptor_plus.py:187
↓ 1 callersFunctiondel_key
(info, key)
data/nuScenes/descriptor.py:163
↓ 1 callersFunctiondescripter
(items)
data/nuScenes/descriptor_plus.py:679
↓ 1 callersFunctiondescripter
(items)
data/nuScenes/descriptor.py:625
↓ 1 callersFunctiondistorted_bounding_box_crop
Generates cropped_image using one of the bboxes randomly distorted. See `tf.image.sample_distorted_bounding_box` for more documentation. Arg
timm/data/tf_preprocessing.py:32
↓ 1 callersFunctiondownload_cached_file
(url, check_hash=True, progress=False)
timm/models/hub.py:41
↓ 1 callersFunctiondownsample_avg
( in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None)
timm/models/resnet.py:444
↓ 1 callersFunctiondownsample_conv
( in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None)
timm/models/resnet.py:430
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