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import torch |
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import torch.nn as nn |
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from utils import CharsetMapper |
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_default_tfmer_cfg = dict(d_model=512, nhead=8, d_inner=2048, |
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dropout=0.1, activation='relu') |
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class Model(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.max_length = config.dataset_max_length + 1 |
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self.charset = CharsetMapper(config.dataset_charset_path, max_length=self.max_length) |
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def load(self, source, device=None, strict=True): |
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state = torch.load(source, map_location=device) |
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self.load_state_dict(state['model'], strict=strict) |
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def _get_length(self, logit, dim=-1): |
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""" Greed decoder to obtain length from logit""" |
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out = (logit.argmax(dim=-1) == self.charset.null_label) |
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abn = out.any(dim) |
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out = ((out.cumsum(dim) == 1) & out).max(dim)[1] |
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out = out + 1 |
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out = torch.where(abn, out, out.new_tensor(logit.shape[1])) |
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return out |
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@staticmethod |
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def _get_padding_mask(length, max_length): |
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length = length.unsqueeze(-1) |
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grid = torch.arange(0, max_length, device=length.device).unsqueeze(0) |
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return grid >= length |
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@staticmethod |
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def _get_square_subsequent_mask(sz, device, diagonal=0, fw=True): |
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r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). |
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Unmasked positions are filled with float(0.0). |
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""" |
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mask = (torch.triu(torch.ones(sz, sz, device=device), diagonal=diagonal) == 1) |
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if fw: mask = mask.transpose(0, 1) |
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mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) |
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return mask |
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@staticmethod |
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def _get_location_mask(sz, device=None): |
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mask = torch.eye(sz, device=device) |
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mask = mask.float().masked_fill(mask == 1, float('-inf')) |
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return mask |
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