Delete EncDec-SDE-Gamma

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