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Functions1,241 in github.com/ALRhub/X_IL

⨍ Functions 1,241◇ Types & classes 225↳ Endpoints 7
Method__init__
( self, embed_dim: int, n_heads: int, attn_pdrop: float,
agents/models/beso/models/networks/transformers/transformer_blocks.py:345
Method__init__
( self, embed_dim: int, n_heads: int, attn_pdrop: float,
agents/models/beso/models/networks/transformers/transformer_blocks.py:384
Method__init__
( self, embed_dim: int, n_heads: int, attn_pdrop: float,
agents/models/beso/models/networks/transformers/transformer_blocks.py:427
Method__init__
( self, embed_dim: int, n_heads: int, attn_pdrop: float,
agents/models/beso/models/networks/transformers/transformer_blocks.py:468
Method__init__
( self, embed_dim: int, n_heads: int, attn_pdrop: float,
agents/models/beso/models/networks/transformers/transformer_blocks.py:510
Method__init__
( self, embed_dim: int, n_heads: int, attn_pdrop: float,
agents/models/beso/models/networks/transformers/transformer_blocks.py:573
Method__init__
( self, embed_dim: int, n_heads: int, attn_pdrop: float,
agents/models/beso/models/networks/transformers/transformer_blocks.py:637
Method__init__
( self, embed_dim: int, n_heads: int, attn_pdrop: float,
agents/models/beso/models/networks/transformers/transformer_blocks.py:678
Method__init__
Multi-Input Multi-Headed Attention Operation
agents/models/beso/models/networks/transformers/transformer_blocks.py:719
Method__init__
Multiheaded Attention Pooling Block -- note that for MAP, we adopt earlier post-norm conventions.
agents/models/beso/models/networks/transformers/transformer_blocks.py:748
Method__init__
( self, embed_dim: int, n_heads: int, attn_pdrop: float,
agents/models/beso/models/networks/transformers/transformer_blocks.py:795
Method__init__
(self, clip_style, token_dim=384, clip_token_index=0, num_token=4)
agents/models/beso/models/networks/transformers/transformer_blocks.py:837
Method__init__
(self, token_dim)
agents/models/beso/models/networks/transformers/transformer_blocks.py:874
Method__init__
(self, embed_dim, scale=30.)
agents/models/beso/models/edm_diffusion/utils.py:24
Method__init__
(self, time_embed_dim, device, in_features=1, std=1.)
agents/models/beso/models/edm_diffusion/utils.py:36
Method__init__
(self, time_embed_dim, device)
agents/models/beso/models/edm_diffusion/utils.py:52
Method__init__
(self, time_embed_dim, device)
agents/models/beso/models/edm_diffusion/utils.py:68
Method__init__
(self, d_model, dropout=0.1, max_len=5000)
agents/models/beso/models/edm_diffusion/utils.py:84
Method__init__
(self, dim)
agents/models/beso/models/edm_diffusion/utils.py:104
Method__init__
(self, input_dim, latent_dim)
agents/models/beso/models/edm_diffusion/utils.py:120
Method__init__
(self, x, t0, t1, seed=None, **kwargs)
agents/models/beso/models/edm_diffusion/gc_sampling.py:115
Method__init__
(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x)
agents/models/beso/models/edm_diffusion/gc_sampling.py:153
Method__init__
(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8)
agents/models/beso/models/edm_diffusion/gc_sampling.py:497
Method__init__
(self, model, extra_args=None, eps_callback=None, info_callback=None)
agents/models/beso/models/edm_diffusion/gc_sampling.py:527
Method__init__
(self, inner_model, sigma_data=1.)
agents/models/beso/models/edm_diffusion/score_wrappers.py:26
Method__init__
( self, device: torch.device, freeze_backbone: bool = True, model_name: str = "RN50" )
agents/models/beso/models/perceptual_encoders/vision_clip.py:34
Method__init__
( self, device: torch.device, freeze_backbone: bool = True, model_name: str = "RN50" )
agents/models/beso/models/perceptual_encoders/vision_clip.py:51
Method__init__
Assumes rgb input: B,C,H,W Assumes low_dim input: B,D
agents/models/beso/models/perceptual_encoders/multi_image_obs_encoder.py:27
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
agents/models/beso/models/perceptual_encoders/film_resnets.py:50
Method__init__
(self, cond_dim, latent_dim=128, pretrained=False, hidden_size=512)
agents/models/beso/models/perceptual_encoders/film_resnets.py:92
Method__init__
(self, cond_dim, latent_dim=128, pretrained=False)
agents/models/beso/models/perceptual_encoders/film_resnets.py:204
Method__init__
(self)
agents/models/beso/models/perceptual_encoders/no_encoder.py:7
Method__init__
( self, latent_dim: int = 128, pretrained: bool = False, freeze_backbone: bool
agents/models/beso/models/perceptual_encoders/resnets.py:102
Method__init__
( self, latent_dim: int = 512, model_type: str = 'v-cond', device: str = 'cuda
agents/models/beso/models/perceptual_encoders/voltron_encoder.py:74
Method__init__
( self, seed: int, device: str, render: bool = True,
simulation/base_sim.py:12
Method__init__
( self, env_name: str, camera_names: ListConfig[str], img_height: int,
simulation/robocasa_pc_img_sim.py:38
Method__init__
( self, env_name: str, camera_names: ListConfig[str], img_height: int,
simulation/robocasa_sim.py:21
Method__init__
(self, num_episode, max_step_per_episode, task_suite: str,
simulation/libero_sim.py:29
Method__init__
( self, env_name: str, camera_names: ListConfig[str], img_height: int,
simulation/robocasa_pc_sim.py:38
Method__len__
(self)
environments/dataset/libero_dataset.py:197
Method__len__
(self)
environments/dataset/robocasa_pc_img_dataset_memory.py:164
Method__len__
(self)
environments/dataset/robocasa_dataset_memory.py:119
Method__len__
(self)
environments/dataset/robocasa_dataset.py:121
Method__len__
(self)
environments/dataset/robocasa_pc_dataset.py:136
Method__len__
(self)
environments/dataset/robocasa_pc_dataset_memory.py:145
Method__post_init__
(self)
agents/backbones/xlstm/mlstm_kernels/torch/backend_module.py:77
Method__repr__
Pretty print network.
agents/encoders/crop_randomizer.py:126
Method__repr__
(self)
agents/models/beso/utils/transforms.py:33
Method__repr__
(self)
agents/models/beso/utils/transforms.py:46
Method__repr__
(self)
agents/models/beso/utils/transforms.py:65
Method__repr__
(self)
agents/models/beso/utils/transforms.py:123
Function_attn_bwd
( Q, K, V, sm_scale, # DO, # DQ, DK, DV, # M, D, # shared by Q/
agents/backbones/xlstm/mlstm_kernels/baselines/flash_attention/triton_tutorial.py:402
Function_attn_bwd_preprocess
( O, DO, Delta, Z, H, N_CTX, BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, #
agents/backbones/xlstm/mlstm_kernels/baselines/flash_attention/triton_tutorial.py:256
Function_attn_fwd
( Q, K, V, sm_scale, M, Out, # stride_qz, stride_qh, stride_qm, strid
agents/backbones/xlstm/mlstm_kernels/baselines/flash_attention/triton_tutorial.py:119
Function_bwd_kernel
( Q, K, V, sm_scale, Out, DO, DQ, DK, DV, L, M, D, stride_
agents/backbones/xlstm/mlstm_kernels/baselines/flash_attention/flash_attention_triton.py:151
Function_bwd_preprocess
( Out, DO, L, NewDO, Delta, BLOCK_M: tl.constexpr, D_HEAD: tl.constexpr, )
agents/backbones/xlstm/mlstm_kernels/baselines/flash_attention/flash_attention_triton.py:127
Method_check_success
(self)
environments/wrappers/point_cloud_sampling_wrapper.py:32
Method_check_success
(self)
environments/wrappers/point_cloud_wrapper.py:36
Function_convert_image_to_rgb
(image)
agents/models/beso/models/networks/clip.py:557
Function_convert_weights_to_fp16
(l)
agents/models/beso/models/networks/clip.py:446
Function_count_ln_flops
(d)
agents/backbones/xlstm/mlstm_kernels/utils/flops/mlstm_block_flop_counts.py:47
Function_count_ln_flops
(d)
agents/backbones/xlstm/mlstm_kernels/utils/flops/slstm_block_flop_counts.py:17
Function_fwd_kernel
( Q, K, V, sm_scale, TMP, L, M, # NOTE: TMP is a scratchpad buffer to workaround
agents/backbones/xlstm/mlstm_kernels/baselines/flash_attention/flash_attention_triton.py:24
Method_get_all_attributes
(self)
agents/models/beso/utils/lr_schedulers/__init__.py:67
Method_get_default
(self, attribute_name: str)
agents/models/beso/utils/lr_schedulers/__init__.py:76
Method_get_help
(self, attribute_name: str)
agents/models/beso/utils/lr_schedulers/__init__.py:93
Method_get_input_tensors
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:31
Method_get_input_tensors
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:73
Method_get_input_tensors
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:137
Method_get_input_tensors
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:205
Method_get_input_tensors
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:482
Method_get_kernel_fn
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:54
Method_get_kernel_fn
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:105
Method_get_kernel_fn
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:160
Method_get_kernel_fn
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:232
Method_get_kernel_fn
(self)
agents/backbones/xlstm/mlstm_kernels/utils/benchmark/benchmarks/training_kernel_benchmarks.py:497
Method_get_local_action
(self, global_action: np.ndarray)
simulation/robocasa_pc_img_sim.py:214
Method_get_local_action
(self, global_action: np.ndarray)
simulation/robocasa_sim.py:150
Method_get_local_action
(self, global_action: np.ndarray)
simulation/robocasa_pc_sim.py:203
Function_get_mlstm_fw_flop_dict
( mlstm_func: Callable, sequence_length: int, chunk_size: int, multiply_by_2: bool = False )
agents/backbones/xlstm/mlstm_kernels/utils/flops/mlstm_block_flop_counts.py:352
Method_get_name
(self, attribute_name: str)
agents/models/beso/utils/lr_schedulers/__init__.py:73
Method_get_type
(self, attribute_name: str)
agents/models/beso/utils/lr_schedulers/__init__.py:90
Function_init_weights
( module, n_layer, initializer_range=0.02, # Now only used for embedding layer. rescale_preno
agents/backbones/mamba/mamba.py:99
Method_init_weights
(self, module)
agents/backbones/decoder_only.py:85
Method_init_weights
(self, module)
agents/backbones/decoder_only.py:214
Method_init_weights
(self, module)
agents/backbones/encoder_decoder.py:90
Method_init_weights
(self, module)
agents/backbones/encoder_decoder.py:287
Method_init_weights
(self, module)
agents/models/beso/models/networks/mdtv_transformer.py:197
Method_init_weights
(self, module)
agents/models/beso/models/networks/mdt_transformer.py:197
Method_loss
(self, pred, targ)
agents/models/ddpm/utils.py:83
Functionadd_text
(img, lang_text)
agents/models/beso/utils/utils.py:160
Methodallocate_inference_cache
(self, batch_size, max_seqlen, dtype=None, **kwargs)
agents/backbones/mamba/blocks.py:90
Methodallocate_inference_cache
(self, batch_size, max_seqlen, dtype=None, **kwargs)
agents/backbones/mamba/mamba.py:301
Functionapply_learned_rotations
(rotations, t, start_index = 0, freq_ranges = None)
agents/models/beso/models/networks/transformers/position_embeddings.py:73
Methodapply_position_embeddings
(self, goal_embed, state_embed, action_embed, proprio_embed, t)
agents/models/beso/models/networks/mdtv_transformer.py:284
Methodapply_position_embeddings
(self, goal_embed, state_embed, action_embed, proprio_states, t)
agents/models/beso/models/networks/mdt_transformer.py:293
Functionassert_size_at_dim
Ensure that arrays and tensors in nested dictionary or list or tuple have size @size in dim @dim. Args: x (dict or list or tuple
agents/utils/tensor_util.py:837
Functionassign_process_to_cpu
(pid, cpus)
simulation/libero_sim.py:19
Functionassign_process_to_cpu
(pid, cpus)
simulation/utils.py:3
Functionattention_causal
(q, k, v, scale=None)
agents/backbones/xlstm/mlstm_kernels/baselines/flash_attention/triton_tutorial.py:718
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