setup.py

import copy
import torch

def apply_overrides(params, overrides):
    params = copy.deepcopy(params)
    for param_name in overrides:
        if param_name not in params:
             print(f'override failed: no parameter named {param_name}')
        #     raise ValueError
        params[param_name] = overrides[param_name]
    return params

def get_default_params_train(overrides={}):

    params = {}

    '''
    misc
    '''
    params['device'] = 'cuda' # cuda, cpu
    params['save_base'] = './experiments/'
    params['save_frequency'] = 5
    params['experiment_name'] = 'demo'
    params['timestamp'] = False

    '''
    data
    '''
    params['species_set'] = 'all' # all, snt_birds
    params['hard_cap_seed'] = 9472
    params['hard_cap_num_per_class'] = -1 # -1 for no hard capping
    params['aux_species_seed'] = 8099
    params['num_aux_species'] = 0 # for snt_birds case, how many other species to add in
    params['input_time'] = False # whether to input time as a feature
    params['input_time_dim'] = 0
    params['dataset'] = 'inat' # inat, iucn_inat, iucn_uniform
    params['zero_shot'] = False
    params['subset_cap_name'] = None
    params['subset_cap_num_per_class'] = -1
    # MINE - I added these -check if there is any impact
    params['seed'] = 1000
    params['add_location_noise'] = False
    params['variable_context_length'] = False
    params['eval_dataset'] = 'eval_transformer'
    params['eval_num_context'] = 20
    params['use_text_inputs'] = True
    params['use_image_inputs'] = False
    params['use_env_inputs'] = False
    params['class_token_transformation'] = 'identity'
    params['loc_prob'] = 1.0
    params['text_prob'] = 0.0
    params['image_prob'] = 0.0
    params['env_prob'] = 0.0

    '''
    data files
    '''
    params['obs_file'] = 'geo_prior_train.csv'
    params['taxa_file'] = 'geo_prior_train_meta.json'

    '''
    model
    '''
    params['model'] = 'ResidualFCNet'  # ResidualFCNet, LinNet
    params['num_filts'] = 256  # embedding dimension
    params['input_enc'] = 'sin_cos' # sin_cos, env, sin_cos_env
    params['input_dim'] = 4
    params['depth'] = 4
    params['noise_time'] = False

    params['species_dim'] = 0
    params['species_enc_depth'] = 0
    params['species_filts'] = 256
    params['species_enc'] = 'embed'
    params['text_emb_path'] = ''
    params['image_emb_path'] = ''
    params['text_learn_dim'] = 0
    params['text_hidden_dim'] = 0
    params['text_num_layers'] = 1
    params['text_batchnorm'] = False
    params['species_dropout'] = 0.0
    params['geoprior_temp'] = 0.0
    # MINE - I added these
    params['num_context'] = 50
    params['transformer_input_enc'] = 'sin_cos'
    params['transformer_dropout'] = 0.1
    params['num_heads'] = 8
    params['ema_factor'] = 0.1
    params['use_register'] = True
    params['use_pretrained_sinr']=False
    params['pretrained_loc']=''
    params['freeze_sinr']=False
    params['pos_enc_class'] = 'sinr'

    '''
    loss
    '''
    params['loss'] = 'an_full' # an_full, an_ssdl, an_slds
    params['pos_weight'] = 2048

    '''
    optimization
    '''
    params['batch_size'] = 2048
    params['lr'] = 0.0005
    params['lr_decay'] = 0.98
    params['num_epochs'] = 10

    '''
    saving
    '''
    params['log_frequency'] = 512

    params = apply_overrides(params, overrides)

    return params


def get_default_params_eval(overrides={}):

    params = {}

    '''
    misc
    '''
    params['device'] = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    params['seed'] = 2022
    params['exp_base'] = './experiments'
    params['ckp_name'] = 'model.pt'
    params['eval_type'] = 'snt' # snt, iucn, geo_prior, geo_feature
    params['experiment_name'] = 'demo'
    params['input_dim'] = 4
    params['input_time'] = False
    params['input_time_dim'] = 0
    # mine
    # params['num_samples'] = -1
    # maxs
    params['num_samples'] = 0
    params['text_section'] = ''
    params['extract_pos'] = False
    # MINE - but probably not needed anymore
    # params['target_background']=True
    '''
    geo prior
    '''
    params['batch_size'] = 2048

    '''
    geo feature
    '''
    params['cell_size'] = 25

    params = apply_overrides(params, overrides)

    return params