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Functions396 in github.com/apple/ml-simplefold

↓ 1 callersFunctionrandom_rotations
Generate random rotations as 3x3 rotation matrices. Args: n: Number of rotation matrices in a batch to return. dtype: Type t
src/simplefold/utils/boltz_utils.py:190
↓ 1 callersFunctionrandomly_rotate
(coords, return_second_coords=False, second_coords=None)
src/simplefold/utils/boltz_utils.py:44
↓ 1 callersMethodreset_parameters
(self)
src/simplefold/model/torch/layers.py:130
↓ 1 callersFunctionrotate_half
(x)
src/simplefold/model/mlx/esm_rotary_embedding.py:13
↓ 1 callersFunctionroutine
(args=None)
src/simplefold/evaluation/analyze_two_state.py:362
↓ 1 callersFunctionrun_eval
(args)
src/simplefold/evaluation/analyze_folding.py:66
↓ 1 callersMethodsmooth_lddt_loss
Compute weighted alignment. Parameters ---------- pred_coords: torch.Tensor The predicted atom coordinates
src/simplefold/model/simplefold.py:152
↓ 1 callersFunctionsubmit_run
(cfg)
src/simplefold/train.py:90
↓ 1 callersFunctionsubmit_run
(cfg)
src/simplefold/test.py:86
↓ 1 callersFunctionsubmit_run
(cfg)
src/simplefold/train_fsdp.py:113
↓ 1 callersFunctionsymmetrize
Make layer symmetric in final two dimensions, used for contact prediction.
src/simplefold/model/mlx/esm_modules.py:19
↓ 1 callersFunctiontest
(cfg)
src/simplefold/test.py:25
↓ 1 callersMethodtimestep_embedding
Create sinusoidal timestep embeddings. :param t: a 1-D Tensor of N indices, one per batch element. These ma
src/simplefold/model/torch/layers.py:170
↓ 1 callersMethodtimestep_embedding
Create sinusoidal timestep embeddings. :param t: a 1-D Tensor of N indices, one per batch element. These ma
src/simplefold/model/mlx/layers.py:182
↓ 1 callersMethodtoken_drop
cond: (B, N, D) Drops conditions to enable classifier-free guidance.
src/simplefold/model/torch/layers.py:214
↓ 1 callersMethodtoken_drop
cond: (B, N, D) Drops conditions to enable classifier-free guidance.
src/simplefold/model/mlx/layers.py:226
↓ 1 callersFunctiontokenize_structure
( record, tokenizer, target_dir: Path, save_token_dir: str, save_token_record_dir: Path, )
src/simplefold/process_structure.py:45
↓ 1 callersFunctiontrain
(cfg)
src/simplefold/train.py:33
↓ 1 callersFunctiontrain
(cfg)
src/simplefold/train_fsdp.py:35
↓ 1 callersMethodtrain_dataloader
Get the training dataloader. Returns ------- DataLoader The training dataloader.
src/simplefold/datasets/train_datamodule.py:373
↓ 1 callersFunctionutils_softmax
(x, dim: int, onnx_trace: bool = False)
src/simplefold/model/mlx/esm_multihead_attention.py:14
Method__call__
Input shape: Time x Batch x Channel Args: key_padding_mask (ByteTensor, optional): mask to exclude keys that are
src/simplefold/model/mlx/esm_multihead_attention.py:76
Method__call__
(self, x, **kwargs)
src/simplefold/model/mlx/layers.py:53
Method__call__
(self, x, **kwargs)
src/simplefold/model/mlx/layers.py:90
Method__call__
(self, x)
src/simplefold/model/mlx/layers.py:153
Method__call__
(self, t)
src/simplefold/model/mlx/layers.py:206
Method__call__
(self, cond, train, force_drop_ids=None)
src/simplefold/model/mlx/layers.py:238
Method__call__
(self, x, c)
src/simplefold/model/mlx/layers.py:259
Method__call__
( self, latents, c, **kwargs, )
src/simplefold/model/mlx/blocks.py:37
Method__call__
( self, latents, **kwargs, )
src/simplefold/model/mlx/blocks.py:79
Method__call__
(self, latents, c, **kwargs)
src/simplefold/model/mlx/blocks.py:96
Method__call__
(self, x)
src/simplefold/model/mlx/esm_modules.py:51
Method__call__
( self, x, self_attn_mask=None, self_attn_padding_mask=None, need_head
src/simplefold/model/mlx/esm_modules.py:97
Method__call__
(self, features)
src/simplefold/model/mlx/esm_modules.py:136
Method__call__
(self, tokens, attentions)
src/simplefold/model/mlx/esm_modules.py:168
Method__call__
(self, q, k)
src/simplefold/model/mlx/esm_rotary_embedding.py:66
Method__call__
(self, noised_pos, t, feats, self_cond=None)
src/simplefold/model/mlx/architecture.py:195
Method__call__
(self, pos)
src/simplefold/model/mlx/pos_embed.py:23
Method__call__
Get the positional encoding for each coordinate. Args: pos: (*, in_dim) Returns: out:
src/simplefold/model/mlx/pos_embed.py:93
Method__call__
xq: [B, H, N, D] xk: [B, H, N, D] pos: [B, N, in_dim]
src/simplefold/model/mlx/pos_embed.py:193
Method__call__
( self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False )
src/simplefold/model/mlx/esm_network.py:83
Method__call__
( self, latent, feats, )
src/simplefold/model/mlx/confidence_module.py:53
Method__getitem__
Get an item from the resource.
src/simplefold/process_mmcif.py:67
Method__getitem__
Get an item from the dataset. Returns ------- Dict[str, Tensor] The sampled data features.
src/simplefold/datasets/test_datamodule.py:55
Method__getitem__
Get an item from the dataset. Parameters ---------- idx : int The data index. Returns -------
src/simplefold/datasets/train_datamodule.py:104
Method__init__
Initialize the redis database.
src/simplefold/process_mmcif.py:56
Method__init__
( self, simplefold_model, plddt=False, ckpt_dir="./artifacts", backend
src/simplefold/wrapper.py:51
Method__init__
( self, output_dir, prediction_dir, num_steps, nsample_per_protein,
src/simplefold/wrapper.py:200
Method__init__
Initializes a multi-GPU-friendly python command line logger that logs on all processes with their rank prefixed in the log message. :
src/simplefold/utils/pylogger.py:15
Method__init__
( self, optimizer: torch.optim.Optimizer, max_lr: float = 0.1, min_lr: float =
src/simplefold/utils/lr_scheduler.py:12
Method__init__
Initialize the writer. Parameters ---------- output_dir : str The directory to save the predictions.
src/simplefold/boltz_data_pipeline/write/writer.py:32
Method__init__
Initialize the cropper. Modulates the type of cropping to be performed. Smaller neighborhoods result in more spatial cropping
src/simplefold/boltz_data_pipeline/crop/slice.py:43
Method__init__
Initialize the cropper. Modulates the type of cropping to be performed. Smaller neighborhoods result in more spatial cropping
src/simplefold/boltz_data_pipeline/crop/boltz.py:138
Method__init__
Initialize the filter. Parameters ---------- min_len : float, optional The minimum allowed length. max_le
src/simplefold/boltz_data_pipeline/filter/static/polymer.py:27
Method__init__
Initialize the filter. Parameters ---------- max_dist : float, optional The maximum allowed distance.
src/simplefold/boltz_data_pipeline/filter/static/polymer.py:114
Method__init__
Initialize the filter. Parameters ---------- dist : float, optional The maximum distance for a clash. fre
src/simplefold/boltz_data_pipeline/filter/static/polymer.py:195
Method__init__
Initialize the filter. Parameters ---------- min_chains : int The minimum number of chains allowed. max_c
src/simplefold/boltz_data_pipeline/filter/dynamic/size.py:16
Method__init__
Initialize the filter. Parameters ---------- subset : str The subset of data to consider, one per line.
src/simplefold/boltz_data_pipeline/filter/dynamic/subset.py:18
Method__init__
Initialize the filter. Parameters ---------- resolution : float, optional The maximum allowed resolution.
src/simplefold/boltz_data_pipeline/filter/dynamic/resolution.py:16
Method__init__
Initialize the filter. Parameters ---------- min_chains : int The minimum number of chains allowed. max_c
src/simplefold/boltz_data_pipeline/filter/dynamic/max_residues.py:16
Method__init__
Initialize the filter. Parameters ---------- date : str, optional The maximum date of PDB entries to filter
src/simplefold/boltz_data_pipeline/filter/dynamic/date.py:26
Method__init__
(self)
src/simplefold/model/flow.py:88
Method__init__
( self, architecture, processor, loss, path, sampler,
src/simplefold/model/simplefold.py:70
Method__init__
( self, hidden_size, num_heads=8, qkv_bias=False, qk_scale=None,
src/simplefold/model/torch/layers.py:23
Method__init__
( self, *args, **kwargs, )
src/simplefold/model/torch/layers.py:81
Method__init__
(self, dim, hidden_dim, multiple_of=256)
src/simplefold/model/torch/layers.py:119
Method__init__
(self, hidden_size, frequency_embedding_size=256)
src/simplefold/model/torch/layers.py:155
Method__init__
(self, input_dim, hidden_size, dropout_prob)
src/simplefold/model/torch/layers.py:204
Method__init__
(self, hidden_size, out_channels, c_dim=None)
src/simplefold/model/torch/layers.py:239
Method__init__
( self, self_attention_layer, hidden_size, mlp_ratio=4.0, use_swiglu=F
src/simplefold/model/torch/blocks.py:82
Method__init__
(self, block, depth)
src/simplefold/model/torch/blocks.py:117
Method__init__
( self, trunk, time_embedder, aminoacid_pos_embedder, pos_embedder,
src/simplefold/model/torch/architecture.py:16
Method__init__
(self, in_dim, embed_dim, include_input=False)
src/simplefold/model/torch/pos_embed.py:12
Method__init__
( self, in_dim, embed_dim, num_heads, base=100.0, )
src/simplefold/model/torch/pos_embed.py:154
Method__init__
( self, num_timesteps=500, t_start=1e-4, tau=0.3, log_timesteps=False,
src/simplefold/model/torch/sampler.py:16
Method__init__
( self, hidden_size, transformer_blocks, num_plddt_bins=50, )
src/simplefold/model/torch/confidence_module.py:40
Method__init__
( self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0.
src/simplefold/model/mlx/esm_multihead_attention.py:28
Method__init__
( self, hidden_size, num_heads=8, qkv_bias=False, qk_scale=None,
src/simplefold/model/mlx/layers.py:23
Method__init__
( self, *args, **kwargs, )
src/simplefold/model/mlx/layers.py:83
Method__init__
(self, dim, hidden_dim, multiple_of=256)
src/simplefold/model/mlx/layers.py:144
Method__init__
(self, hidden_size, frequency_embedding_size=256)
src/simplefold/model/mlx/layers.py:167
Method__init__
(self, input_dim, hidden_size, dropout_prob)
src/simplefold/model/mlx/layers.py:216
Method__init__
( self, self_attention_layer, hidden_size, mlp_ratio=4.0, use_swiglu=F
src/simplefold/model/mlx/blocks.py:63
Method__init__
(self, block, depth)
src/simplefold/model/mlx/blocks.py:92
Method__init__
( self, embed_dim, ffn_embed_dim, attention_heads, add_bias_kv=True,
src/simplefold/model/mlx/esm_modules.py:64
Method__init__
(self, embed_dim, output_dim, weight)
src/simplefold/model/mlx/esm_modules.py:129
Method__init__
( self, in_features: int, prepend_bos: bool, append_eos: bool, bias=Tr
src/simplefold/model/mlx/esm_modules.py:148
Method__init__
(self, dim: int, *_, **__)
src/simplefold/model/mlx/esm_rotary_embedding.py:39
Method__init__
( self, trunk, time_embedder, aminoacid_pos_embedder, pos_embedder,
src/simplefold/model/mlx/architecture.py:41
Method__init__
(self, in_dim, embed_dim, include_input=False)
src/simplefold/model/mlx/pos_embed.py:15
Method__init__
( self, in_dim, embed_dim, num_heads, base=100.0, )
src/simplefold/model/mlx/pos_embed.py:180
Method__init__
( self, num_timesteps=500, t_start=1e-4, tau=0.3, log_timesteps=False,
src/simplefold/model/mlx/sampler.py:24
Method__init__
( self, num_layers: int = 33, embed_dim: int = 1280, attention_heads: int = 20
src/simplefold/model/mlx/esm_network.py:27
Method__init__
( self, hidden_size, transformer_blocks=None, num_plddt_bins=50, )
src/simplefold/model/mlx/confidence_module.py:38
Method__init__
( self, device, scale=16.0, ref_scale=5.0, multiplicity=1,
src/simplefold/processor/protein_processor.py:23
Method__init__
Initialize the DataModule. Parameters ---------- config : DataConfig The data configuration.
src/simplefold/datasets/test_datamodule.py:130
Method__init__
( self, datasets: list[DatasetConfig], tokenizer: Tokenizer, featurizer: Boltz
src/simplefold/datasets/train_datamodule.py:214
Method__init__
(self, molseq)
src/simplefold/evaluation/analyze_two_state.py:98
Method__len__
(self)
src/simplefold/datasets/test_datamodule.py:123
Method__len__
(self)
src/simplefold/datasets/train_datamodule.py:207
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