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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['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
'''
model
'''
params['model'] = 'ResidualFCNet' # ResidualFCNet, LinNet
params['num_filts'] = 256 # embedding dimension
params['input_enc'] = 'sin_cos' # sin_cos, env, sin_cos_env
params['depth'] = 4
'''
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'
'''
geo prior
'''
params['batch_size'] = 2048
'''
geo feature
'''
params['cell_size'] = 25
params = apply_overrides(params, overrides)
return params
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