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import torch |
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import math |
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def positional_encoding_2d(d_model, height, width): |
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""" |
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:param d_model: dimension of the model |
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:param height: height of the positions |
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:param width: width of the positions |
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:return: d_model*height*width position matrix |
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""" |
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if d_model % 4 != 0: |
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raise ValueError("Cannot use sin/cos positional encoding with " |
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"odd dimension (got dim={:d})".format(d_model)) |
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pe = torch.zeros(d_model, height, width) |
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d_model = int(d_model / 2) |
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div_term = torch.exp(torch.arange(0., d_model, 2) * |
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-(math.log(10000.0) / d_model)) |
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pos_w = torch.arange(0., width).unsqueeze(1) |
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pos_h = torch.arange(0., height).unsqueeze(1) |
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pe[0:d_model:2, :, :] = torch.sin(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1) |
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pe[1:d_model:2, :, :] = torch.cos(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1) |
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pe[d_model::2, :, :] = torch.sin(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width) |
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pe[d_model + 1::2, :, :] = torch.cos(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width) |
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return pe |
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def positional_encoding_1d(d_model, length): |
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""" |
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:param d_model: dimension of the model |
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:param length: length of positions |
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:return: length*d_model position matrix |
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""" |
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if d_model % 2 != 0: |
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raise ValueError("Cannot use sin/cos positional encoding with " |
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"odd dim (got dim={:d})".format(d_model)) |
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pe = torch.zeros(length, d_model) |
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position = torch.arange(0, length).unsqueeze(1) |
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div_term = torch.exp((torch.arange(0, d_model, 2, dtype=torch.float) * |
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-(math.log(10000.0) / d_model))) |
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pe[:, 0::2] = torch.sin(position.float() * div_term) |
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pe[:, 1::2] = torch.cos(position.float() * div_term) |
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return pe |
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