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Functions18,851 in github.com/bilibili/Index-anisora

⨍ Functions 18,851◇ Types & classes 4,392↳ Endpoints 34
↓ 10 callersMethodfrom_pretrained_base
Load a pretrained checkpoint of the current model. Args: name: The identifier of the pretrained model. arg
anisoraV1_train_npu/swissarmytransformer-npu_t_sp/sat/model/base_model.py:186
↓ 10 callersMethodfrom_pretrained_base
Load a pretrained checkpoint of the current model. Args: name: The identifier of the pretrained model. arg
anisoraV1_infer/sat/model/base_model.py:186
↓ 10 callersMethodfrom_pretrained_base
Load a pretrained checkpoint of the current model. Args: name: The identifier of the pretrained model. arg
anisoraV1_infer/sat/sat/model/base_model.py:186
↓ 10 callersMethodfrom_pretrained_base
Load a pretrained checkpoint of the current model. Args: name: The identifier of the pretrained model. arg
anisoraV1_train_gpu/swissarmytransformer-npu_t_sp/sat/model/base_model.py:186
↓ 10 callersMethodget_args
Get the parsed args of the current model. Args: **kwargs: will override the default args. Returns:
anisoraV1_train_npu/swissarmytransformer-npu_t_sp/sat/model/base_model.py:285
↓ 10 callersMethodget_args
Get the parsed args of the current model. Args: **kwargs: will override the default args. Returns:
anisoraV1_infer/sat/model/base_model.py:285
↓ 10 callersMethodget_args
Get the parsed args of the current model. Args: **kwargs: will override the default args. Returns:
anisoraV1_train_gpu/swissarmytransformer-npu_t_sp/sat/model/base_model.py:285
↓ 10 callersMethodget_sigma_gen
(self, num_sigmas)
anisora_rl/sat/sgm/modules/diffusionmodules/sampling.py:62
↓ 10 callersMethodget_sigma_gen
(self, num_sigmas)
anisoraV1_train_npu/sgm/modules/diffusionmodules/sampling.py:65
↓ 10 callersMethodget_sigma_gen
(self, num_sigmas)
anisoraV1_train_npu/sat/sgm/modules/diffusionmodules/sampling.py:62
↓ 10 callersMethodget_sigma_gen
(self, num_sigmas)
anisoraV1_infer/fastercache/models/cogvideox/sgm/modules/diffusionmodules/sampling.py:62
↓ 10 callersMethodget_sigma_gen
(self, num_sigmas)
anisoraV1_train_gpu/sgm/modules/diffusionmodules/sampling.py:65
↓ 10 callersMethodget_sigma_gen
(self, num_sigmas)
anisoraV1_train_gpu/sat/sgm/modules/diffusionmodules/sampling.py:62
↓ 10 callersFunctioninitialize_context_parallel
(context_parallel_size)
anisora_rl/sat/vae_modules/utils.py:25
↓ 10 callersFunctionload_checkpoint
Load a model checkpoint.
anisoraV1_infer/sat/training/model_io.py:266
↓ 10 callersFunctionload_image
(image_file)
reward/mantis/mllm_tools/mllm_utils.py:6
↓ 10 callersMethodload_state_dict
(self, state_dict: Dict[str, Any])
anisoraV1_infer/sat/examples/yolos/util/scheduler.py:63
↓ 10 callersMethodlog
(self, name: str, data: Scalar, step: int)
reward/character/samurai/sam2/training/utils/logger.py:167
↓ 10 callersFunctionmerge_images
Merge multiple images into one image Args: image_links (List, optional): List of image links. Defaults to []. Returns:
reward/mantis/mllm_tools/mllm_utils.py:26
↓ 10 callersFunctionnormalization
Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization.
anisoraV1_train_npu/sgm/modules/diffusionmodules/util.py:259
↓ 10 callersFunctionnormalization
Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization.
anisoraV1_train_gpu/sgm/modules/diffusionmodules/util.py:259
↓ 10 callersFunctionpack_one
(t, pattern)
anisora_rl/sat/sgm/modules/autoencoding/magvit2_pytorch.py:68
↓ 10 callersFunctionpack_one
(t, pattern)
anisoraV1_train_npu/sgm/modules/autoencoding/magvit2_pytorch.py:61
↓ 10 callersFunctionpack_one
(t, pattern)
anisoraV1_train_npu/sat/sgm/modules/autoencoding/magvit2_pytorch.py:68
↓ 10 callersFunctionpack_one
(t, pattern)
anisoraV1_infer/fastercache/models/cogvideox/sgm/modules/autoencoding/magvit2_pytorch.py:68
↓ 10 callersFunctionpack_one
(t, pattern)
anisoraV1_train_gpu/sgm/modules/autoencoding/magvit2_pytorch.py:61
↓ 10 callersFunctionpack_one
(t, pattern)
anisoraV1_train_gpu/sat/sgm/modules/autoencoding/magvit2_pytorch.py:68
↓ 10 callersMethodpredict
Run Kalman filter prediction step. Parameters ---------- mean : ndarray The 8 dimensional mean vector of the obje
reward/character/samurai/sam2/sam2/utils/kalman_filter.py:87
↓ 10 callersMethodprepare_sampling_loop
(self, x, cond, uc=None, num_steps=None)
anisora_rl/sat/sgm/modules/diffusionmodules/sampling.py:485
↓ 10 callersMethodprepare_sampling_loop
(self, x, cond, uc=None, num_steps=None)
anisoraV1_train_npu/sgm/modules/diffusionmodules/sampling.py:536
↓ 10 callersMethodprepare_sampling_loop
(self, x, cond, uc=None, num_steps=None)
anisoraV1_train_npu/sat/sgm/modules/diffusionmodules/sampling.py:485
↓ 10 callersMethodprepare_sampling_loop
(self, x, cond, uc=None, num_steps=None)
anisoraV1_infer/fastercache/models/cogvideox/sgm/modules/diffusionmodules/sampling.py:485
↓ 10 callersMethodprepare_sampling_loop
(self, x, cond, uc=None, num_steps=None)
anisoraV1_train_gpu/sgm/modules/diffusionmodules/sampling.py:536
↓ 10 callersMethodprepare_sampling_loop
(self, x, cond, uc=None, num_steps=None)
anisoraV1_train_gpu/sat/sgm/modules/diffusionmodules/sampling.py:485
↓ 10 callersFunctionset_pad
(name: str, dim_size: int, parallel_group: dist.ProcessGroup)
anisoraV1_infer/videosys/core/comm.py:373
↓ 10 callersFunctionsplit_sequence
(input_, process_group, dim, grad_scale=1.0, pad=0)
anisoraV1_infer/videosys/core/comm.py:358
↓ 10 callersFunctiontimestep_embedding
Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be
anisoraV1_infer/fastercache/models/cogvideox/sgm/modules/diffusionmodules/util.py:180
↓ 10 callersFunctionunpack_one
(t, ps, pattern)
anisora_rl/sat/sgm/modules/autoencoding/magvit2_pytorch.py:72
↓ 10 callersFunctionunpack_one
(t, ps, pattern)
anisoraV1_train_npu/sgm/modules/autoencoding/magvit2_pytorch.py:64
↓ 10 callersFunctionunpack_one
(t, ps, pattern)
anisoraV1_train_npu/sat/sgm/modules/autoencoding/magvit2_pytorch.py:72
↓ 10 callersFunctionunpack_one
(t, ps, pattern)
anisoraV1_infer/fastercache/models/cogvideox/sgm/modules/autoencoding/magvit2_pytorch.py:72
↓ 10 callersFunctionunpack_one
(t, ps, pattern)
anisoraV1_train_gpu/sgm/modules/autoencoding/magvit2_pytorch.py:64
↓ 10 callersFunctionunpack_one
(t, ps, pattern)
anisoraV1_train_gpu/sat/sgm/modules/autoencoding/magvit2_pytorch.py:72
↓ 10 callersFunctionuseMessagesSnackbar
()
reward/character/samurai/sam2/demo/frontend/src/common/components/snackbar/useMessagesSnackbar.ts:36
↓ 9 callersFunctionNormalize
(in_channels)
anisora_rl/SwissArmyTransformer-main/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:34
↓ 9 callersFunctionNormalize
(in_channels)
anisoraV1_train_npu/swissarmytransformer-npu_t_sp/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:34
↓ 9 callersFunctionNormalize
(in_channels, num_groups=32)
anisoraV1_infer/videosys/models/autoencoders/autoencoder_kl_open_sora_plan_v120.py:150
↓ 9 callersFunctionNormalize
(in_channels, num_groups=32)
anisoraV1_infer/fastercache/models/opensora_plan/modules/normalize.py:25
↓ 9 callersFunctionNormalize
(in_channels)
anisoraV1_infer/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:34
↓ 9 callersFunctionNormalize
(in_channels)
anisoraV1_infer/sat/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:34
↓ 9 callersFunctionNormalize
(in_channels)
anisoraV1_train_gpu/swissarmytransformer-npu_t_sp/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:34
↓ 9 callersMethod__init__
(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam
anisora_rl/SwissArmyTransformer-main/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:196
↓ 9 callersMethod__init__
(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam
anisoraV1_train_npu/swissarmytransformer-npu_t_sp/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:196
↓ 9 callersMethod__init__
(self, config: RWConfig)
reward/mantis/mllm_tools/model_utils/otter/models/falcon/modelling_RW.py:348
↓ 9 callersMethod__init__
(self, config)
reward/mantis/models/mfuyu/modeling_persimmon.py:203
↓ 9 callersMethod__init__
(self, in_channels, out_channels)
anisoraV1_infer/fastercache/models/opensora_plan/modules/updownsample.py:26
↓ 9 callersMethod__init__
(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam
anisoraV1_infer/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:196
↓ 9 callersMethod__init__
(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam
anisoraV1_infer/sat/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:196
↓ 9 callersMethod__init__
(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam
anisoraV1_train_gpu/swissarmytransformer-npu_t_sp/sat/tokenization/cogview/vqvae/vqvae_diffusion.py:196
↓ 9 callersMethod_updateStreamingState
( state: StreamingState, forceUpdate: boolean = false, )
reward/character/samurai/sam2/demo/frontend/src/common/tracker/SAM2Model.ts:568
↓ 9 callersMethodabortStreamMasks
()
reward/character/samurai/sam2/demo/frontend/src/common/tracker/Tracker.ts:73
↓ 9 callersMethodbackward
(ctx, grad_output: torch.Tensor)
anisoraV1_infer/sat/quantization/kernels.py:61
↓ 9 callersMethodcleanup
()
reward/character/samurai/sam2/demo/frontend/src/common/components/video/effects/Effect.ts:66
↓ 9 callersFunctioncopy_layer_param
in-place copy from src to dst src and dst should be the same layer type, e.g., both are LayerNorm or both are Linear. Or at least, bo
anisoraV1_infer/sat/examples/llama/transform_param.py:54
↓ 9 callersFunctioncopy_layer_param
in-place copy from src to dst src and dst should be the same layer type, e.g., both are LayerNorm or both are Linear. Or at least, bo
anisoraV1_infer/sat/examples/clip/transform_param.py:25
↓ 9 callersFunctioncopy_layer_param
in-place copy from src to dst src and dst should be the same layer type, e.g., both are LayerNorm or both are Linear. Or at least, bo
anisoraV1_infer/sat/examples/clip/transform_param_new.py:22
↓ 9 callersFunctioncopy_layer_param
in-place copy from src to dst src and dst should be the same layer type, e.g., both are LayerNorm or both are Linear. Or at least, bo
anisoraV1_infer/sat/examples/bert/transform_param.py:68
↓ 9 callersFunctioncreate_vit
(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0)
reward/models/blip.py:196
↓ 9 callersMethodend
()
reward/character/samurai/sam2/demo/frontend/src/debug/stats/Stats.ts:138
↓ 9 callersMethodexists
(model_path)
anisoraV1_infer/sat/sat/tokenization/glm/sp_tokenizer.py:69
↓ 9 callersFunctiongather_sequence
(input_, dim, grad_scale=1.0, pad=0)
anisoraV2_gpu/fastvideo/utils/communications.py:465
↓ 9 callersMethodget_args
Get the parsed args of the current model. Args: **kwargs: will override the default args. Returns:
anisora_rl/SwissArmyTransformer-main/sat/model/base_model.py:286
↓ 9 callersFunctionget_context_parallel_group
()
anisora_rl/sat/vae_modules_infer/utils.py:43
↓ 9 callersFunctionget_context_parallel_group
()
anisoraV1_train_npu/sat/vae_modules/utils.py:43
↓ 9 callersFunctionget_context_parallel_group
()
anisoraV1_train_gpu/sat/vae_modules/utils.py:43
↓ 9 callersFunctioninstantiate_from_config
(config)
anisora_rl/sat/vae_modules/utils.py:246
↓ 9 callersMethodis_rocm_pytorch
()
anisora_rl/SwissArmyTransformer-main/sat/ops/ops_builder/builder.py:155
↓ 9 callersMethodis_rocm_pytorch
()
anisoraV1_train_npu/swissarmytransformer-npu_t_sp/sat/ops/ops_builder/builder.py:155
↓ 9 callersMethodis_rocm_pytorch
()
anisoraV1_infer/sat/ops/ops_builder/builder.py:155
↓ 9 callersMethodis_rocm_pytorch
()
anisoraV1_infer/sat/sat/ops/ops_builder/builder.py:155
↓ 9 callersMethodis_rocm_pytorch
()
anisoraV1_train_gpu/swissarmytransformer-npu_t_sp/sat/ops/ops_builder/builder.py:155
↓ 9 callersFunctionleaky_relu
(p=0.1)
anisora_rl/sat/sgm/modules/autoencoding/losses/video_loss.py:33
↓ 9 callersFunctionleaky_relu
(p=0.1)
anisoraV1_train_npu/sgm/modules/autoencoding/losses/video_loss.py:33
↓ 9 callersFunctionleaky_relu
(p=0.1)
anisoraV1_train_npu/sat/sgm/modules/autoencoding/losses/video_loss.py:33
↓ 9 callersFunctionleaky_relu
(p=0.1)
anisoraV1_infer/fastercache/models/cogvideox/sgm/modules/autoencoding/losses/video_loss.py:33
↓ 9 callersFunctionleaky_relu
(p=0.1)
anisoraV1_train_gpu/sgm/modules/autoencoding/losses/video_loss.py:33
↓ 9 callersFunctionleaky_relu
(p=0.1)
anisoraV1_train_gpu/sat/sgm/modules/autoencoding/losses/video_loss.py:33
↓ 9 callersFunctionload_images
(image_files)
reward/mantis/mllm_tools/mllm_utils.py:16
↓ 9 callersMethodload_state_dict
(self, *args, **kwargs)
anisoraV1_train_npu/sat/sgm/modules/autoencoding/magvit2_pytorch.py:1356
↓ 9 callersMethodload_state_dict
(self, *args, **kwargs)
anisoraV1_train_gpu/sat/sgm/modules/autoencoding/magvit2_pytorch.py:1356
↓ 9 callersMethodreinit
(self, parent_model=None)
anisoraV1_infer/sat/model/base_model.py:55
↓ 9 callersFunctionrotate_half
(x)
anisora_rl/SwissArmyTransformer-main/sat/model/position_embedding/rotary_embeddings.py:74
↓ 9 callersFunctionrotate_half
(x)
anisoraV1_train_npu/swissarmytransformer-npu_t_sp/sat/model/position_embedding/rotary_embeddings.py:74
↓ 9 callersFunctionrotate_half
(x)
anisoraV1_infer/sat/model/position_embedding/rotary_embeddings.py:74
↓ 9 callersFunctionrotate_half
(x)
anisoraV1_infer/sat/sat/model/position_embedding/rotary_embeddings.py:74
↓ 9 callersFunctionrotate_half
(x)
anisoraV1_train_gpu/swissarmytransformer-npu_t_sp/sat/model/position_embedding/rotary_embeddings.py:74
↓ 9 callersMethodsave_model
Will save the model, so you can reload it using `from_pretrained()`. Will only save from the main process.
reward/mantis/train/train_openflamingo.py:186
↓ 9 callersFunctionsplit_sequence
(input_, dim, grad_scale=1.0, pad=0)
anisoraV2_gpu/fastvideo/utils/communications.py:457
↓ 9 callersMethodstep
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned m
anisoraV2_npu/fastvideo/distill/solver.py:171
↓ 9 callersFunctionto_sigma
(neg_log_sigma)
anisora_rl/sat/sgm/modules/diffusionmodules/sampling_utils.py:154
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