Delete configs

configs/equiformer_v2.yaml

@@ -1,57 +0,0 @@
-# @package _global_
-# from https://github.com/deepprinciple/HORM
-
-# ocp_trainer: forces_v2
-
-
-model:
-  name: EquiformerV2
-
-  use_pbc:                  False
-  regress_forces:           True
-  otf_graph:                True
-  max_neighbors:            20
-  max_radius:               12.0
-  max_num_elements:         90
-
-  num_layers:               4
-  sphere_channels:          128
-  attn_hidden_channels:     64              # [64, 96] This determines the hidden size of message passing. Do not necessarily use 96.
-  num_heads:                4
-  attn_alpha_channels:      64              # Not used when `use_s2_act_attn` is True.
-  attn_value_channels:      16
-  ffn_hidden_channels:      128
-  norm_type:                'layer_norm_sh' # ['rms_norm_sh', 'layer_norm', 'layer_norm_sh']
-
-  lmax_list:                [4]
-  mmax_list:                [2]
-  grid_resolution:          18              # [18, 16, 14, None] For `None`, simply comment this line.
-
-  num_sphere_samples:       128
-
-  edge_channels:              128
-  use_atom_edge_embedding:    True
-  share_atom_edge_embedding:  False         # If `True`, `use_atom_edge_embedding` must be `True` and the atom edge embedding will be shared across all blocks. 
-  distance_function:          'gaussian'
-  num_distance_basis:         512         # not used
-
-  attn_activation:          'silu'
-  use_s2_act_attn:          False       # [False, True] Switch between attention after S2 activation or the original EquiformerV1 attention. 
-  use_attn_renorm:          True        # Attention re-normalization. Used for ablation study.
-  ffn_activation:           'silu'      # ['silu', 'swiglu']
-  use_gate_act:             False       # [True, False] Switch between gate activation and S2 activation
-  use_grid_mlp:             True        # [False, True] If `True`, use projecting to grids and performing MLPs for FFNs.
-  use_sep_s2_act:           True        # Separable S2 activation. Used for ablation study.
-  
-  alpha_drop:               0.1         # [0.0, 0.1]
-  drop_path_rate:           0.1         # [0.0, 0.05]
-  proj_drop:                0.0
-
-  weight_init:              'uniform'    # ['uniform', 'normal']
-
-  # Added for eigenvalue/eigenvector prediction
-  do_eigvec_1: true
-  do_eigvec_2: false  
-  do_eigval_1: true
-  do_eigval_2: true
-