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VESBD-ODE/checkpoint.ckpt

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+oid sha256:f16700a860c1e88d117a64c1a41396027927a7f45185fbb5eaf2d4ce43317b8e
+size 148095124

VESBD-ODE/train.yaml

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+model:
+  si:
+    class_path: omg.si.stochastic_interpolants.StochasticInterpolants
+    init_args:
+      stochastic_interpolants:
+        # chemical species
+        - class_path: omg.si.single_stochastic_interpolant_identity.SingleStochasticInterpolantIdentity
+        # fractional coordinates
+        - class_path: omg.si.single_stochastic_interpolant_os.SingleStochasticInterpolantOS
+          init_args:
+            interpolant: 
+              class_path: omg.si.interpolants.PeriodicScoreBasedDiffusionModelInterpolantVE
+              init_args:
+                sigma:
+                  class_path: omg.si.sigma.GeometricSigma
+                  init_args:
+                    sigma_min: 0.007753186833706728
+                    sigma_max: 0.5165059747015202
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 0.0030999124784898413
+            correct_center_of_mass_motion: true
+            predict_velocity: true
+        # lattice vectors
+        - class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.interpolants.TrigonometricInterpolant
+            gamma: 
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 0.024482789522429726
+            epsilon: 
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 9.940425570212101
+                mu: 0.24041599621265147
+                sigma: 0.021132860336543085
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.0026332451961934566
+            velocity_annealing_factor: 14.933642154361792
+            correct_center_of_mass_motion: false
+      data_fields:
+        # if the order of the data_fields changes,
+        # the order of the above StochasticInterpolant inputs must also change
+        - "species"
+        - "pos"
+        - "cell"
+      integration_time_steps: 380
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.979954187812053
+    cell_loss_b: 0.01866918394074503
+    cell_loss_z: 0.0013766282472020075
+  sampler:
+    class_path: omg.sampler.sample_from_rng.SampleFromRNG
+    init_args:
+      pos_distribution: 
+        class_path: omg.sampler.distributions.NormalDistribution
+        init_args:
+          scale: 8.955438982782663
+      cell_distribution: 
+        class_path: omg.sampler.distributions.InformedLatticeDistribution
+        init_args:
+          dataset_name: perov_5
+      species_distribution:
+        class_path: omg.sampler.distributions.MirrorData
+  model:
+    class_path: omg.model.model.Model
+    init_args:
+      encoder:
+        class_path: omg.model.encoders.cspnet_full.CSPNetFull
+      head:
+        class_path: omg.model.heads.pass_through.PassThrough
+      time_embedder:
+        class_path: omg.model.model_utils.SinusoidalTimeEmbeddings
+        init_args:
+          dim: 256
+  use_min_perm_dist: False
+  float_32_matmul_precision: "high"
+  validation_mode: "match_rate"
+  dataset_name: "perov_5"
+data:
+  train_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/perov_5/train.lmdb"
+      niggli: False
+  val_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/perov_5/val.lmdb"
+      niggli: False
+  predict_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/perov_5/test.lmdb"
+      niggli: False
+  batch_size: 256
+  num_workers: 4
+  pin_memory: True
+  persistent_workers: True
+trainer:
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        filename: "best_val_loss_total"
+        save_top_k: 1
+        monitor: "val_loss_total"
+        save_weights_only: true
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        filename: "best_val_match_rate"
+        save_top_k: 1
+        monitor: "match_rate"
+        save_weights_only: true
+        mode: 'max'
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        filename: "best_val_rmsd"
+        save_top_k: 1
+        monitor: "mean_rmsd"
+        save_weights_only: true
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        save_top_k: -1  # Store every checkpoint after 100 epochs.
+        monitor: "val_loss_total"
+        every_n_epochs: 100
+        save_weights_only: false
+  gradient_clip_val: 0.5
+  num_sanity_val_steps: 0
+  precision: "32-true"
+  max_epochs: 6000
+  enable_progress_bar: false
+  check_val_every_n_epoch: 100
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 0.0077762908469486665