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https://github.com/RosettaCommons/foundry.git
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5a492032d5
* Initial commit of chiral changes Initial checkin of chiral feature code Add chiral metric * Update the way chiral features are incorporated into the model Move initialization to new func use default pytorch reset parameters fix initialization for chirals config rename argument of confidence head fix initialization for chirals * refactor: src nest, rename rf2aa to modelhub * refactor: initial commit without projects * Initial commit of chiral changes * Initial checkin of chiral feature code * Add chiral metric * Remove option for double residual connection. Add kq_norm oiptions to base (20250125) config. * Restoring flag * config * rename argument of confidence head * Update the way chiral features are incorporated into the model * config * rename argument of confidence head * Update the way chiral features are incorporated into the model * Initial commit of chiral changes Initial checkin of chiral feature code Add chiral metric * Update the way chiral features are incorporated into the model Move initialization to new func use default pytorch reset parameters fix initialization for chirals config rename argument of confidence head fix initialization for chirals * refactor: new modelhub --------- Co-authored-by: fdimaio <dimaio@uw.edu> Co-authored-by: HaotianZhangAI4Science <haotianzhang@zju.edu.cn>
45 lines
1.3 KiB
YAML
45 lines
1.3 KiB
YAML
defaults:
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- af3_net
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# Model architecture
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_target_: modelhub.model.AF3.AF3WithConfidence
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# +---------- Mini rollout sampler ----------+
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# From the AF-3 main text:
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# > ...To remedy this, we developed a diffusion ‘rollout’ procedure for the full-structure prediction generation during training (using a larger step size than normal)
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# They do not further elaborate on how they adjusted the step size during diffusion rollout, but this may be a fruitful area of exploration moving forwards
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mini_rollout_sampler:
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solver: "af3"
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num_timesteps: 20 # 20 timesteps for the mini-rollout (vs. 200 for the full rollout during inference)
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min_t: 0
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max_t: 1
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sigma_data: ${model.net.diffusion_module.sigma_data}
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s_min: 4e-4
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s_max: 160
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p: 7
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gamma_0: 0.8
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gamma_min: 1.0
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noise_scale: 1.003
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step_scale: 1.5
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# +---------- Confidence head architecture ----------+
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confidence_head:
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c_s: ${model.net.c_s}
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c_z: ${model.net.c_z}
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n_pairformer_layers: 4
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pairformer:
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p_drop: 0.25
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triangle_multiplication:
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d_hidden: 128
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triangle_attention:
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n_head: 4
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d_hidden: 32
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attention_pair_bias:
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n_head: 16
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n_bins_pae: 64
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n_bins_pde: 64
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n_bins_plddt: 50
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n_bins_exp_resolved: 2
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use_Cb_distances: False
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use_af3_style_binning_and_final_layer_norms: True
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symmetrize_Cb_logits: True |