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| import torch.nn as nn | |
| from torch.nn import TransformerDecoder | |
| from .model import Model | |
| from .transformer import PositionalEncoding, TransformerDecoderLayer | |
| class BCNLanguage(Model): | |
| def __init__(self, dataset_max_length, null_label, num_classes, d_model=512, nhead=8, d_inner=2048, dropout=0.1, | |
| activation='relu', num_layers=4, detach=True, use_self_attn=False, loss_weight=1.0, | |
| global_debug=False): | |
| super().__init__(dataset_max_length, null_label) | |
| self.detach = detach | |
| self.loss_weight = loss_weight | |
| self.proj = nn.Linear(num_classes, d_model, False) | |
| self.token_encoder = PositionalEncoding(d_model, max_len=self.max_length) | |
| self.pos_encoder = PositionalEncoding(d_model, dropout=0, max_len=self.max_length) | |
| decoder_layer = TransformerDecoderLayer(d_model, nhead, d_inner, dropout, | |
| activation, self_attn=use_self_attn, debug=global_debug) | |
| self.model = TransformerDecoder(decoder_layer, num_layers) | |
| self.cls = nn.Linear(d_model, num_classes) | |
| def forward(self, tokens, lengths): | |
| """ | |
| Args: | |
| tokens: (N, T, C) where T is length, N is batch size and C is classes number | |
| lengths: (N,) | |
| """ | |
| if self.detach: | |
| tokens = tokens.detach() | |
| embed = self.proj(tokens) # (N, T, E) | |
| embed = embed.permute(1, 0, 2) # (T, N, E) | |
| embed = self.token_encoder(embed) # (T, N, E) | |
| padding_mask = self._get_padding_mask(lengths, self.max_length) | |
| zeros = embed.new_zeros(*embed.shape) | |
| qeury = self.pos_encoder(zeros) | |
| location_mask = self._get_location_mask(self.max_length, tokens.device) | |
| output = self.model(qeury, embed, | |
| tgt_key_padding_mask=padding_mask, | |
| memory_mask=location_mask, | |
| memory_key_padding_mask=padding_mask) # (T, N, E) | |
| output = output.permute(1, 0, 2) # (N, T, E) | |
| logits = self.cls(output) # (N, T, C) | |
| pt_lengths = self._get_length(logits) | |
| res = {'feature': output, 'logits': logits, 'pt_lengths': pt_lengths, | |
| 'loss_weight': self.loss_weight, 'name': 'language'} | |
| return res | |