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EncDec-SDE-Gamma/checkpoint.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ea7b9cdd147c4f463b65fd3250146c509dfc17fd678acde7c00eabcecc9576c
+size 49642338

EncDec-SDE-Gamma/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.SingleStochasticInterpolant
+          init_args:
+            interpolant: 
+              class_path: omg.si.interpolants.PeriodicEncoderDecoderInterpolant
+              init_args:
+                switch_time: 0.42184997325946555
+                power: 0.5
+            gamma: 
+              class_path: omg.si.gamma.LatentGammaEncoderDecoder
+              init_args:
+                a: 0.03989185248799893
+                switch_time: 0.42184997325946555
+                power: 0.5
+            epsilon: 
+              class_path: omg.si.epsilon.VanishingEpsilon
+              init_args:
+                c: 2.3996529332194574
+                mu: 0.25251095399328916
+                sigma: 0.03759134500470063
+            differential_equation_type: "SDE"
+            integrator_kwargs:
+              method: "euler"
+              dt: 0.0014076164225116372
+            velocity_annealing_factor: 3.7755089557808477
+            correct_center_of_mass_motion: true
+        # lattice vectors
+        - class_path: omg.si.single_stochastic_interpolant.SingleStochasticInterpolant
+          init_args:
+            interpolant: omg.si.interpolants.LinearInterpolant
+            gamma: 
+              class_path: omg.si.gamma.LatentGammaSqrt
+              init_args:
+                a: 4.961271013084809
+            epsilon: null
+            differential_equation_type: "ODE"
+            integrator_kwargs:
+              method: "euler"
+            velocity_annealing_factor: 1.1379701544400436
+            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: 710
+  relative_si_costs:
+    species_loss: 0.0
+    pos_loss_b: 0.6143090042317803
+    pos_loss_z: 0.3794040725288834
+    cell_loss_b: 0.00628692323933625
+  sampler:
+    class_path: omg.sampler.sample_from_rng.SampleFromRNG
+    init_args:
+      pos_distribution: null
+      cell_distribution: 
+        class_path: omg.sampler.distributions.InformedLatticeDistribution
+        init_args:
+          dataset_name: mp_20
+      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: "mp_20"
+data:
+  train_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/mp_20/train.lmdb"
+      niggli: True
+  val_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/mp_20/val.lmdb"
+      niggli: True
+  predict_dataset:
+    class_path: omg.datamodule.dataloader.OMGTorchDataset
+    init_args:
+      dataset:
+        class_path: omg.datamodule.datamodule.DataModule
+        init_args:
+          lmdb_paths:
+           - "data/mp_20/test.lmdb"
+      niggli: True
+  batch_size: 32
+  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: 2000
+  enable_progress_bar: false
+  check_val_every_n_epoch: 100
+optimizer:
+  class_path: torch.optim.Adam
+  init_args:
+    lr: 0.00018567271191860665