def weights_init()

in model/train.py [0:0]

• 38 lines of code
• 29 McCabe index (conditional complexity)

def weights_init(m):
    classname = m.__class__.__name__
    if classname.find("Linear") != -1:
        if hasattr(m, "weight") and m.weight is not None:
            init_weight(m.weight)
        if hasattr(m, "bias") and m.bias is not None:
            init_bias(m.bias)
    elif classname.find("AdaptiveEmbedding") != -1:
        if hasattr(m, "emb_projs"):
            for i in range(len(m.emb_projs)):
                if m.emb_projs[i] is not None:
                    init_embed(m.emb_projs[i])
    elif classname.find("Embedding") != -1:
        if hasattr(m, "weight"):
            init_weight(m.weight)
    elif classname.find("ProjectedAdaptiveLogSoftmax") != -1:
        if hasattr(m, "cluster_weight") and m.cluster_weight is not None:
            init_weight(m.cluster_weight)
        if hasattr(m, "cluster_bias") and m.cluster_bias is not None:
            init_bias(m.cluster_bias)
        if hasattr(m, "out_projs"):
            for i in range(len(m.out_projs)):
                if m.out_projs[i] is not None:
                    init_embed(m.out_projs[i])
    elif classname.find("LayerNorm") != -1:
        if hasattr(m, "weight"):
            nn.init.normal_(m.weight, 1.0, cfg.INITIALIZER.base_init[1])
        if hasattr(m, "bias") and m.bias is not None:
            init_bias(m.bias)
    elif classname.find("TransformerLM") != -1:
        if hasattr(m, "r_emb"):
            init_weight(m.r_emb)
        if hasattr(m, "r_w_bias"):
            init_weight(m.r_w_bias)
        if hasattr(m, "r_r_bias"):
            init_weight(m.r_r_bias)
        if hasattr(m, "r_bias"):
            init_bias(m.r_bias)