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remove extra rfd3na configs

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Raktim Mitra
2026-03-20 16:07:07 -07:00
parent 001709433c
commit e94ea147a6
4 changed files with 0 additions and 386 deletions
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100
models/rfd3/configs/experiment/rfd3na.yaml
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@@ -1,100 +0,0 @@
# @package _global_
# Training configuration for RFD3
defaults:
#- /debug/default
- override /model: rfd3_base
- override /logger: wandb
- override /datasets: design_base_rfd3na
- _self_
name: rfd3na_scratch_clean_test
tags: [print-model]
#ckpt: null
#ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na-fine-tune/2026-02-17_15-21_JOB_3608285/ckpt/epoch-0590.ckpt
ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_scratch_clean_test/2026-02-19_01-58_JOB_3986244/ckpt/epoch-0180.ckpt
model:
net:
token_initializer:
token_1d_features:
ref_motif_token_type: 3
restype: 32
is_dna_token: 1
is_rna_token: 1
is_protein_token: 1
token_2d_features:
bp_partners: 3 # Unspecified, pair, loop
atom_1d_features:
ref_atom_name_chars: 256
ref_element: 128
ref_charge: 1
ref_mask: 1
ref_is_motif_atom_with_fixed_coord: 1
ref_is_motif_atom_unindexed: 1
has_zero_occupancy: 1
ref_pos: 3
# Guided features
ref_atomwise_rasa: 3
active_donor: 1
active_acceptor: 1
is_atom_level_hotspot: 1
diffusion_module:
n_recycle: 2
use_local_token_attention: True
diffusion_transformer:
n_local_tokens: 32
n_keys: 128
inference_sampler:
num_timesteps: 100
datasets:
diffusion_batch_size_train: 16
crop_size: 256
max_atoms_in_crop: 2560 # ~10x crop size.
global_transform_args:
meta_conditioning_probabilities:
p_is_nucleic_ss_example: 0.25
p_nucleic_ss_show_partial_feats: 0.7
p_canonical_bp_filter: 0.2
#calculate_NA_SS: 0.3
association_scheme: atom23
#add_na_pair_features: true
train_conditions:
unconditional:
frequency: 2.0
island:
frequency: 2.0
sequence_design:
frequency: 0.5
tipatom:
frequency: 5.0
ppi:
frequency: 0.0
train:
# These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data.
pdb:
probability: 0.6
rna_monomer_distillation:
probability: 0.3
monomer_distillation:
probability: 0.1
val:
pseudoknot:
dataset:
# eval_every_n: 10
eval_every_n: 5
trainer:
#devices_per_node: 1
#limit_train_batches: 10
#limit_val_batches: 1
validate_every_n_epochs: 5
prevalidate: true

99
models/rfd3/configs/experiment/rfd3na_af.yaml
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@@ -1,99 +0,0 @@
# @package _global_
# Training configuration for RFD3
defaults:
- /debug/default
- override /model: rfd3_base
# - override /logger: wandb
- override /datasets: design_base_rfd3na
- _self_
name: rfd3na-fine-tune
tags: [print-model]
ckpt_path: /projects/ml/aa_design/models/rfd3_latest_foundry.ckpt
model:
net:
token_initializer:
token_1d_features:
ref_motif_token_type: 3
restype: 32
is_dna_token: 1
is_rna_token: 1
is_protein_token: 1
token_2d_features:
bp_partners: 3 # Unspecified, pair, loop
atom_1d_features:
ref_atom_name_chars: 256
ref_element: 128
ref_charge: 1
ref_mask: 1
ref_is_motif_atom_with_fixed_coord: 1
ref_is_motif_atom_unindexed: 1
has_zero_occupancy: 1
ref_pos: 3
# Guided features
ref_atomwise_rasa: 3
active_donor: 1
active_acceptor: 1
is_atom_level_hotspot: 1
diffusion_module:
n_recycle: 2
use_local_token_attention: True
diffusion_transformer:
n_local_tokens: 32
n_keys: 128
inference_sampler:
num_timesteps: 100
datasets:
diffusion_batch_size_train: 16
crop_size: 256
max_atoms_in_crop: 2560 # ~10x crop size.
global_transform_args:
meta_conditioning_probabilities:
# p_is_nucleic_ss_example: 0.25
# p_nucleic_ss_show_partial_feats: 0.7
# p_canonical_bp_filter: 0.2
p_is_nucleic_ss_example: 1.0
p_nucleic_ss_show_partial_feats: 0.0
p_canonical_bp_filter: 0.0
#calculate_NA_SS: 0.3
association_scheme: atom23
#add_na_pair_features: true
train_conditions:
unconditional:
frequency: 2.0
island:
frequency: 2.0
sequence_design:
frequency: 0.5
tipatom:
frequency: 5.0
ppi:
frequency: 0.0
train:
# These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data.
pdb:
probability: 0.0
rna_monomer_distillation:
probability: 1.0
monomer_distillation:
probability: 0.0
val:
pseudoknot:
dataset:
# eval_every_n: 10
eval_every_n: 5
trainer:
#devices_per_node: 1
#limit_train_batches: 10
#limit_val_batches: 1
validate_every_n_epochs: 5
prevalidate: true

97
models/rfd3/configs/experiment/rfd3na_no_distill.yaml
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@@ -1,97 +0,0 @@
# @package _global_
# Training configuration for RFD3
defaults:
#- /debug/default
- override /model: rfd3_base
- override /logger: wandb
- override /datasets: design_base_rfd3na
- _self_
name: rfd3na_no_distill
tags: [print-model]
#ckpt_path: null
ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_no_distill/2026-02-19_02-16_JOB_3988845/ckpt/epoch-0170.ckpt
model:
net:
token_initializer:
token_1d_features:
ref_motif_token_type: 3
restype: 32
is_dna_token: 1
is_rna_token: 1
is_protein_token: 1
token_2d_features:
bp_partners: 3 # Unspecified, pair, loop
atom_1d_features:
ref_atom_name_chars: 256
ref_element: 128
ref_charge: 1
ref_mask: 1
ref_is_motif_atom_with_fixed_coord: 1
ref_is_motif_atom_unindexed: 1
has_zero_occupancy: 1
ref_pos: 3
# Guided features
ref_atomwise_rasa: 3
active_donor: 1
active_acceptor: 1
is_atom_level_hotspot: 1
diffusion_module:
n_recycle: 2
use_local_token_attention: True
diffusion_transformer:
n_local_tokens: 32
n_keys: 128
inference_sampler:
num_timesteps: 100
datasets:
diffusion_batch_size_train: 16
crop_size: 256
max_atoms_in_crop: 2560 # ~10x crop size.
global_transform_args:
meta_conditioning_probabilities:
p_is_nucleic_ss_example: 0.25
p_nucleic_ss_show_partial_feats: 0.7
p_canonical_bp_filter: 0.2
#calculate_NA_SS: 0.3
association_scheme: atom23
#add_na_pair_features: true
train_conditions:
unconditional:
frequency: 2.0
island:
frequency: 2.0
sequence_design:
frequency: 0.5
tipatom:
frequency: 5.0
ppi:
frequency: 0.0
train:
# These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data.
pdb:
probability: 0.7
rna_monomer_distillation:
probability: 0.3
monomer_distillation:
probability: 0.0
val:
pseudoknot:
dataset:
# eval_every_n: 10
eval_every_n: 5
trainer:
#devices_per_node: 1
#limit_train_batches: 10
#limit_val_batches: 1
validate_every_n_epochs: 5
prevalidate: true

90
models/rfd3/configs/experiment/rfd3na_rm.yaml
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@@ -1,90 +0,0 @@
# @package _global_
# Training configuration for RFD3
defaults:
#- /debug/default
- override /model: rfd3_base
- override /logger: wandb
- override /datasets: design_base_rfd3na
- _self_
name: rfd3na
tags: [print-model]
ckpt_path: null
model:
net:
token_initializer:
token_1d_features:
ref_motif_token_type: 3
restype: 32
is_dna_token: 1
is_rna_token: 1
is_protein_token: 1
#token_2d_features:
#bp_partners: 3 # Unspecified, pair, loop
atom_1d_features:
ref_atom_name_chars: 256
ref_element: 128
ref_charge: 1
ref_mask: 1
ref_is_motif_atom_with_fixed_coord: 1
ref_is_motif_atom_unindexed: 1
has_zero_occupancy: 1
ref_pos: 3
# Guided features
ref_atomwise_rasa: 3
active_donor: 1
active_acceptor: 1
is_atom_level_hotspot: 1
diffusion_module:
n_recycle: 2
use_local_token_attention: True
diffusion_transformer:
n_local_tokens: 32
n_keys: 128
inference_sampler:
num_timesteps: 100
datasets:
diffusion_batch_size_train: 16
crop_size: 256
max_atoms_in_crop: 2560 # ~10x crop size.
global_transform_args:
meta_conditioning_probabilities:
calculate_NA_SS: 0.0
association_scheme: atom23
#add_na_pair_features: true
train_conditions:
unconditional:
frequency: 2.0
island:
frequency: 2.0
sequence_design:
frequency: 0.5
tipatom:
frequency: 5.0
ppi:
frequency: 0.0
train:
# These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data.
pdb:
probability: 0.5
rna_monomer_distillation:
probability: 0.5
val:
pseudoknot:
dataset:
# eval_every_n: 10
eval_every_n: 5
trainer:
#devices_per_node: 1
#limit_train_batches: 10
limit_val_batches: 1
validate_every_n_epochs: 5
prevalidate: false