def check_model_params()

in XLM/src/model/__init__.py [0:0]

• 53 lines of code
• 20 McCabe index (conditional complexity)

def check_model_params(params):
    """
    Check models parameters.
    """
    # masked language modeling task parameters
    assert params.bptt >= 1
    assert 0 <= params.word_pred < 1
    assert 0 <= params.sample_alpha < 1
    s = params.word_mask_keep_rand.split(',')
    assert len(s) == 3
    s = [float(x) for x in s]
    assert all([0 <= x <= 1 for x in s]) and sum(s) == 1
    params.word_mask = s[0]
    params.word_keep = s[1]
    params.word_rand = s[2]

    # input sentence noise for DAE
    if len(params.ae_steps) == 0:
        assert params.word_shuffle == 0
        assert params.word_dropout == 0
        assert params.word_blank == 0
    else:
        assert params.word_shuffle == 0 or params.word_shuffle > 1
        assert 0 <= params.word_dropout < 1
        assert 0 <= params.word_blank < 1

    # model dimensions
    if params.emb_dim_encoder == 0 and params.emb_dim_decoder == 0:
        assert params.emb_dim > 0
        params.emb_dim_encoder = params.emb_dim
        params.emb_dim_decoder = params.emb_dim
    else:
        assert params.emb_dim == 0
        assert params.emb_dim_encoder > 0 and params.emb_dim_decoder > 0
        if params.emb_dim_encoder == params.emb_dim_decoder:
            params.emb_dim = params.emb_dim_decoder
        else:
            assert params.reload_emb == "", 'Pre-trained embeddings are not supported when the embedding size of the ' \
                                            'encoder and the decoder do not match '
    assert params.emb_dim_encoder % params.n_heads == 0
    assert params.emb_dim_decoder % params.n_heads == 0

    if params.n_layers_encoder == 0 and params.n_layers_decoder == 0:
        assert params.n_layers > 0
        params.n_layers_encoder = params.n_layers
        params.n_layers_decoder = params.n_layers
    else:
        assert params.n_layers == 0

    assert params.n_layers_encoder > 0 and params.n_layers_decoder > 0

    # reload pretrained word embeddings
    if params.reload_emb != '':
        assert os.path.isfile(params.reload_emb)

    # reload a pretrained model
    if params.reload_model != '':
        if params.encoder_only:
            assert os.path.isfile(params.reload_model)
        else:
            s = params.reload_model.split(',')
            assert len(s) == 2
            assert all([x == '' or os.path.isfile(x) for x in s])

    assert not (params.beam_size > 1 and params.number_samples >
                1), 'Cannot sample when already doing beam search'
    assert (params.eval_temperature is None) == (params.number_samples <=
                                                 1), 'Eval temperature should be set if and only if taking several samples at eval time'