miniworld.utils.DataClass.MiniWorldDataClass

class miniworld.utils.DataClass.MiniWorldDataClass(data_dict)

__init__(data_dict)

For test memory leak. data_dict :

{ # L_crop can be less than params[‘CROP’] ‘msa’ : {

‘sample_seq’: torch.tensor, (MAXCYCLE, L_crop) ‘sample_msa_clust’: torch.tensor, (MAXCYCLE, Nclust, L_crop) ‘sample_msa_seed’: torch.tensor, (MAXCYCLE, Nclust, L_crop, 23 + 23 + 2 + 2) ‘sample_msa_extra’: torch.tensor, (MAXCYCLE, N_extra, L_crop, 23 + 1 + 2) ‘sample_mask_pos’: torch.tensor (MAXCYCLE, Nclust, L_crop)

}, ‘template’ : {

‘xyz’ : torch.tensor (params[‘N_PICK_GLOBAL’], L_crop, 27, 3) ‘template_1D’ : torch.tensor (params[‘N_PICK_GLOBAL’], L_crop, NUM_CLASSES + 1) ‘template_atom_mask’: torch.tensor (params[‘N_PICK_GLOBAL’], L_crop, 27)

}, ‘label’ : {

‘sequence’{

‘sequence’ : torch.tensor # (L_chain, NUM_CLASSES)

}, ‘structure’ : {

‘xyz’ : torch.tensor, # (L_chain, 27, 3) ‘atom_mask’ : torch.tensor, # (L_chain, 27) ‘position_mask’ : torch.tensor, # (L_chain, 27) ‘has_multiple_chains’ : bool, ‘has_multiple_models’ : bool

}, ‘occupancy’ : occupancy,

}, ‘prev’ : {

‘xyz’ : torch.tensor, # (L_crop, 27, 3) ‘atom_mask’ : torch.tensor, # (L_crop, 27)

} ‘symmetry_related_info’ : symmetry_related_info, ‘crop_idx’ : torch.tensor, # (L_crop) ‘chain_break’ : dictionary, # (N_chain, 2) ‘ID’ : ID, ‘has_label_structure’ : has_label_structure (bool), ‘source’ : source (str),

}

Methods

__init__(data_dict)

For test memory leak. data_dict : { # L_crop can be less than params['CROP'] 'msa' : { 'sample_seq': torch.tensor, (MAXCYCLE, L_crop) 'sample_msa_clust': torch.tensor, (MAXCYCLE, Nclust, L_crop) 'sample_msa_seed': torch.tensor, (MAXCYCLE, Nclust, L_crop, 23 + 23 + 2 + 2) 'sample_msa_extra': torch.tensor, (MAXCYCLE, N_extra, L_crop, 23 + 1 + 2) 'sample_mask_pos': torch.tensor (MAXCYCLE, Nclust, L_crop) }, 'template' : { 'xyz' : torch.tensor (params['N_PICK_GLOBAL'], L_crop, 27, 3) 'template_1D' : torch.tensor (params['N_PICK_GLOBAL'], L_crop, NUM_CLASSES + 1) 'template_atom_mask': torch.tensor (params['N_PICK_GLOBAL'], L_crop, 27) }, 'label' : { 'sequence' : { 'sequence' : torch.tensor # (L_chain, NUM_CLASSES) }, 'structure' : { 'xyz' : torch.tensor, # (L_chain, 27, 3) 'atom_mask' : torch.tensor, # (L_chain, 27) 'position_mask' : torch.tensor, # (L_chain, 27) 'has_multiple_chains' : bool, 'has_multiple_models' : bool }, 'occupancy' : occupancy, }, 'prev' : { 'xyz' : torch.tensor, # (L_crop, 27, 3) 'atom_mask' : torch.tensor, # (L_crop, 27) } 'symmetry_related_info' : symmetry_related_info, 'crop_idx' : torch.tensor, # (L_crop) 'chain_break' : dictionary, # (N_chain, 2) 'ID' : ID, 'has_label_structure' : has_label_structure (bool), 'source' : source (str), }.