Class PositionalEncoding

model.py:232–249

Implement the PE function.

class PositionalEncoding(nn.Module):
    "Implement the PE function."

    def __init__(self, d_model, max_len=10000):
        super(PositionalEncoding, self).__init__()
        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) *
                             -(math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).permute(0,2,1) # of shape (1, d_model, l)
        self.pe = nn.Parameter(pe, requires_grad=True)
#         self.register_buffer('pe', pe)

    def forward(self, x):
        return x + self.pe[:, :, 0:x.shape[2]]

class Encoder(nn.Module):
    def __init__(self, num_layers, r1, r2, num_f_maps, input_dim, num_classes, channel_masking_rate, att_type, alpha):