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| import torch | |
| import torch.nn as nn | |
| from utils import CharsetMapper | |
| _default_tfmer_cfg = dict(d_model=512, nhead=8, d_inner=2048, # 1024 | |
| dropout=0.1, activation='relu') | |
| class Model(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.max_length = config.dataset_max_length + 1 | |
| self.charset = CharsetMapper(config.dataset_charset_path, max_length=self.max_length) | |
| def load(self, source, device=None, strict=True): | |
| state = torch.load(source, map_location=device) | |
| self.load_state_dict(state['model'], strict=strict) | |
| def _get_length(self, logit, dim=-1): | |
| """ Greed decoder to obtain length from logit""" | |
| out = (logit.argmax(dim=-1) == self.charset.null_label) | |
| abn = out.any(dim) | |
| out = ((out.cumsum(dim) == 1) & out).max(dim)[1] | |
| out = out + 1 # additional end token | |
| out = torch.where(abn, out, out.new_tensor(logit.shape[1])) | |
| return out | |
| def _get_padding_mask(length, max_length): | |
| length = length.unsqueeze(-1) | |
| grid = torch.arange(0, max_length, device=length.device).unsqueeze(0) | |
| return grid >= length | |
| def _get_square_subsequent_mask(sz, device, diagonal=0, fw=True): | |
| r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf'). | |
| Unmasked positions are filled with float(0.0). | |
| """ | |
| mask = (torch.triu(torch.ones(sz, sz, device=device), diagonal=diagonal) == 1) | |
| if fw: mask = mask.transpose(0, 1) | |
| mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) | |
| return mask | |
| def _get_location_mask(sz, device=None): | |
| mask = torch.eye(sz, device=device) | |
| mask = mask.float().masked_fill(mask == 1, float('-inf')) | |
| return mask | |