Parameters of Model Loading before Inference

[rose4labour]

Below is the model loading code,
for the code “class_imbalance_ratio = train_ratios,“
My test indicates that loading the model requires specifying the train_ratios.
I only need inference without a test csv file, so I specify 138 values fo train_ratios, like [0.1,0.2,...]
I've tested it several times, and different 138 values of train_ratios seem to be independent of the inference results. Is this correct?
if I only need to do inference, which part of parameters can I modify for a specific model weight file (such as https://github.com/ARY2260/openpom/blob/main/examples/example_model.pt)?
THANK U!

model = MPNNPOMModel(n_tasks=n_tasks,
batch_size=128,
learning_rate=learning_rate,
class_imbalance_ratio = train_ratios,
loss_aggr_type = 'sum',
node_out_feats = 100,
edge_hidden_feats = 75,
edge_out_feats = 100,
num_step_message_passing = 5,
mpnn_residual = True,
message_aggregator_type = 'sum',
mode = 'classification',
number_atom_features = GraphConvConstants.ATOM_FDIM,
number_bond_features = GraphConvConstants.BOND_FDIM,
n_classes = 1,
readout_type = 'set2set',
num_step_set2set = 3,
num_layer_set2set = 2,
ffn_hidden_list= [392, 392],
ffn_embeddings = 256,
ffn_activation = 'relu',
ffn_dropout_p = 0.12,
ffn_dropout_at_input_no_act = False,
weight_decay = 1e-5,
self_loop = False,
optimizer_name = 'adam',
log_frequency = 32,
#model_dir = f'./example_model.pt',
device_name='cuda')

[ARY2260]

Yes, you are correct. Imbalance ratios are used only by the loss function during training of the model. It doesn't have any impact on inferences since its only a forward pass.

Thank you for reporting this bug!

For the work around during inference, you can use an arbitrary n_tasks size array. The value n_tasks should be the same as the value used during training of the model.

[rose4labour]

Yes, you are correct. Imbalance ratios are used only by the loss function during training of the model. It doesn't have any impact on inferences since its only a forward pass.

Thank you for reporting this bug!

For the work around during inference, you can use an arbitrary n_tasks size array. The value n_tasks should be the same as the value used during training of the model.

Thanks for your answer.
What about the best training parameters?
For example,
num_step_message_passing = 5
what about num_step_message_passing =4 or 6?