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import copy
import ml_collections as mlc
from dockformerpp.utils.config_tools import set_inf, enforce_config_constraints
def model_config(
name,
train=False,
low_prec=False,
long_sequence_inference=False
):
c = copy.deepcopy(config)
# TRAINING PRESETS
if name == "initial_training":
# AF2 Suppl. Table 4, "initial training" setting
pass
elif name == "finetune_affinity":
c.loss.affinity2d.weight = 0.5
c.loss.binding_site.weight = 0.5
c.loss.positions_inter_distogram.weight = 0.5 # this is not essential given fape?
else:
raise ValueError("Invalid model name")
c.globals.use_lma = False
if long_sequence_inference:
assert(not train)
c.globals.use_lma = True
if train:
c.globals.blocks_per_ckpt = 1
c.globals.use_lma = False
if low_prec:
c.globals.eps = 1e-4
# If we want exact numerical parity with the original, inf can't be
# a global constant
set_inf(c, 1e4)
enforce_config_constraints(c)
return c
c_z = mlc.FieldReference(128, field_type=int)
c_m = mlc.FieldReference(256, field_type=int)
c_t = mlc.FieldReference(64, field_type=int)
c_e = mlc.FieldReference(64, field_type=int)
c_s = mlc.FieldReference(384, field_type=int)
blocks_per_ckpt = mlc.FieldReference(None, field_type=int)
aux_distogram_bins = mlc.FieldReference(64, field_type=int)
aux_affinity_bins = mlc.FieldReference(32, field_type=int)
eps = mlc.FieldReference(1e-8, field_type=float)
NUM_RES = "num residues placeholder"
NUM_TOKEN = "num tokens placeholder"
config = mlc.ConfigDict(
{
"data": {
"common": {
"feat": {
"aatype": [NUM_TOKEN],
"all_atom_mask": [NUM_TOKEN, None],
"all_atom_positions": [NUM_TOKEN, None, None],
"atom14_alt_gt_exists": [NUM_TOKEN, None],
"atom14_alt_gt_positions": [NUM_TOKEN, None, None],
"atom14_atom_exists": [NUM_TOKEN, None],
"atom14_atom_is_ambiguous": [NUM_TOKEN, None],
"atom14_gt_exists": [NUM_TOKEN, None],
"atom14_gt_positions": [NUM_TOKEN, None, None],
"atom37_atom_exists": [NUM_TOKEN, None],
"backbone_rigid_mask": [NUM_TOKEN],
"backbone_rigid_tensor": [NUM_TOKEN, None, None],
"chi_angles_sin_cos": [NUM_TOKEN, None, None],
"chi_mask": [NUM_TOKEN, None],
"no_recycling_iters": [],
"pseudo_beta": [NUM_TOKEN, None],
"pseudo_beta_mask": [NUM_TOKEN],
"residue_index": [NUM_TOKEN],
"in_chain_residue_index": [NUM_TOKEN],
"chain_index": [NUM_TOKEN],
"residx_atom14_to_atom37": [NUM_TOKEN, None],
"residx_atom37_to_atom14": [NUM_TOKEN, None],
"resolution": [],
"rigidgroups_alt_gt_frames": [NUM_TOKEN, None, None, None],
"rigidgroups_group_exists": [NUM_TOKEN, None],
"rigidgroups_group_is_ambiguous": [NUM_TOKEN, None],
"rigidgroups_gt_exists": [NUM_TOKEN, None],
"rigidgroups_gt_frames": [NUM_TOKEN, None, None, None],
"seq_length": [],
"token_mask": [NUM_TOKEN],
"target_feat": [NUM_TOKEN, None],
"use_clamped_fape": [],
},
"max_recycling_iters": 1,
"unsupervised_features": [
"aatype",
"residue_index",
"in_chain_residue_index",
"chain_index",
"seq_length",
"no_recycling_iters",
"all_atom_mask",
"all_atom_positions",
],
},
"supervised": {
"clamp_prob": 0.9,
"supervised_features": [
"resolution",
"use_clamped_fape",
],
},
"predict": {
"fixed_size": True,
"crop": False,
"crop_size": None,
"supervised": False,
"uniform_recycling": False,
},
"eval": {
"fixed_size": True,
"crop": False,
"crop_size": None,
"supervised": True,
"uniform_recycling": False,
},
"train": {
"fixed_size": True,
"crop": True,
"crop_size": 355,
"supervised": True,
"clamp_prob": 0.9,
"uniform_recycling": True,
"distogram_mask_prob": 0.1,
},
"data_module": {
"data_loaders": {
"batch_size": 1,
# "batch_size": 2,
"num_workers": 16,
"pin_memory": True,
"should_verify": False,
},
},
},
# Recurring FieldReferences that can be changed globally here
"globals": {
"blocks_per_ckpt": blocks_per_ckpt,
# Use Staats & Rabe's low-memory attention algorithm.
"use_lma": False,
"max_lr": 1e-3,
"c_z": c_z,
"c_m": c_m,
"c_t": c_t,
"c_e": c_e,
"c_s": c_s,
"eps": eps,
},
"model": {
"_mask_trans": False,
"structure_input_embedder": {
"protein_tf_dim": 20,
"additional_tf_dim": 3, # number of classes (prot_r, prot_l, aff)
"c_z": c_z,
"c_m": c_m,
"relpos_k": 32,
"prot_min_bin": 3.25,
"prot_max_bin": 20.75,
"prot_no_bins": 15,
"inf": 1e8,
},
"recycling_embedder": {
"c_z": c_z,
"c_m": c_m,
"min_bin": 3.25,
"max_bin": 20.75,
"no_bins": 15,
"inf": 1e8,
},
"evoformer_stack": {
"c_m": c_m,
"c_z": c_z,
"c_hidden_single_att": 32,
"c_hidden_mul": 128,
"c_hidden_pair_att": 32,
"c_s": c_s,
"no_heads_single": 8,
"no_heads_pair": 4,
# "no_blocks": 48,
"no_blocks": 2,
"transition_n": 4,
"single_dropout": 0.15,
"pair_dropout": 0.25,
"blocks_per_ckpt": blocks_per_ckpt,
"clear_cache_between_blocks": False,
"inf": 1e9,
"eps": eps, # 1e-10,
},
"structure_module": {
"c_s": c_s,
"c_z": c_z,
"c_ipa": 16,
"c_resnet": 128,
"no_heads_ipa": 12,
"no_qk_points": 4,
"no_v_points": 8,
"dropout_rate": 0.1,
"no_blocks": 8,
"no_transition_layers": 1,
"no_resnet_blocks": 2,
"no_angles": 7,
"trans_scale_factor": 10,
"epsilon": eps, # 1e-12,
"inf": 1e5,
},
"heads": {
"lddt": {
"no_bins": 50,
"c_in": c_s,
"c_hidden": 128,
},
"distogram": {
"c_z": c_z,
"no_bins": aux_distogram_bins,
},
"affinity_2d": {
"c_z": c_z,
"num_bins": aux_affinity_bins,
},
"affinity_1d": {
"c_s": c_s,
"num_bins": aux_affinity_bins,
},
"affinity_cls": {
"c_s": c_s,
"num_bins": aux_affinity_bins,
},
"binding_site": {
"c_s": c_s,
"c_out": 1,
},
"inter_contact": {
"c_s": c_s,
"c_z": c_z,
"c_out": 1,
},
},
# A negative value indicates that no early stopping will occur, i.e.
# the model will always run `max_recycling_iters` number of recycling
# iterations. A positive value will enable early stopping if the
# difference in pairwise distances is less than the tolerance between
# recycling steps.
"recycle_early_stop_tolerance": -1.
},
"relax": {
"max_iterations": 0, # no max
"tolerance": 2.39,
"stiffness": 10.0,
"max_outer_iterations": 20,
"exclude_residues": [],
},
"loss": {
"distogram": {
"min_bin": 2.3125,
"max_bin": 21.6875,
"no_bins": 64,
"eps": eps, # 1e-6,
"weight": 0.3,
},
"positions_inter_distogram": {
"max_dist": 20.0,
"weight": 0.0,
},
"positions_intra_distogram": {
"max_dist": 10.0,
"weight": 0.0,
},
"binding_site": {
"weight": 0.0,
"pos_class_weight": 20.0,
},
"inter_contact": {
"weight": 0.0,
"pos_class_weight": 200.0,
},
"affinity2d": {
"min_bin": 0,
"max_bin": 15,
"no_bins": aux_affinity_bins,
"weight": 0.0,
},
"affinity_cls": {
"min_bin": 0,
"max_bin": 15,
"no_bins": aux_affinity_bins,
"weight": 0.0,
},
"fape_backbone": {
"clamp_distance": 10.0,
"loss_unit_distance": 10.0,
"weight": 0.5,
},
"fape_sidechain": {
"clamp_distance": 10.0,
"length_scale": 10.0,
"weight": 0.5,
},
"fape_interface": {
"clamp_distance": 10.0,
"length_scale": 10.0,
"weight": 0.0,
},
"plddt_loss": {
"min_resolution": 0.1,
"max_resolution": 3.0,
"cutoff": 15.0,
"no_bins": 50,
"eps": eps, # 1e-10,
"weight": 0.01,
},
"supervised_chi": {
"chi_weight": 0.5,
"angle_norm_weight": 0.01,
"eps": eps, # 1e-6,
"weight": 1.0,
},
"chain_center_of_mass": {
"clamp_distance": -4.0,
"weight": 0.,
"eps": eps,
"enabled": False,
},
"eps": eps,
},
"ema": {"decay": 0.999},
}
)
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