(self,
in_channels,
out_channels,
n_head=None,
num_encoder_blocks=3,
num_decoder_blocks=3,
beam_size=0,
max_len=25,
residual_dropout_rate=0.1,
add_conv=False,
**kwargs)
| 194 | class CDistNetDecoder(nn.Module): |
| 195 | |
| 196 | def __init__(self, |
| 197 | in_channels, |
| 198 | out_channels, |
| 199 | n_head=None, |
| 200 | num_encoder_blocks=3, |
| 201 | num_decoder_blocks=3, |
| 202 | beam_size=0, |
| 203 | max_len=25, |
| 204 | residual_dropout_rate=0.1, |
| 205 | add_conv=False, |
| 206 | **kwargs): |
| 207 | super(CDistNetDecoder, self).__init__() |
| 208 | dst_vocab_size = out_channels |
| 209 | self.ignore_index = dst_vocab_size - 1 |
| 210 | self.bos = dst_vocab_size - 2 |
| 211 | self.eos = 0 |
| 212 | self.beam_size = beam_size |
| 213 | self.max_len = max_len |
| 214 | self.add_conv = add_conv |
| 215 | d_model = in_channels |
| 216 | dim_feedforward = d_model * 4 |
| 217 | n_head = n_head if n_head is not None else d_model // 32 |
| 218 | |
| 219 | if add_conv: |
| 220 | self.convbnrelu = ConvBnRelu( |
| 221 | in_channels=in_channels, |
| 222 | out_channels=in_channels, |
| 223 | kernel_size=(1, 3), |
| 224 | stride=(1, 2), |
| 225 | ) |
| 226 | if num_encoder_blocks > 0: |
| 227 | self.positional_encoding = PositionalEncoding( |
| 228 | dropout=0.1, |
| 229 | dim=d_model, |
| 230 | ) |
| 231 | self.trans_encoder = Transformer_Encoder( |
| 232 | n_layers=num_encoder_blocks, |
| 233 | n_head=n_head, |
| 234 | d_model=d_model, |
| 235 | d_inner=dim_feedforward, |
| 236 | ) |
| 237 | else: |
| 238 | self.trans_encoder = None |
| 239 | self.semantic_branch = SEM_Pre( |
| 240 | d_model=d_model, |
| 241 | dst_vocab_size=dst_vocab_size, |
| 242 | residual_dropout_rate=residual_dropout_rate, |
| 243 | ) |
| 244 | self.positional_branch = POS_Pre(d_model=d_model) |
| 245 | |
| 246 | self.mdcdp = MDCDP(d_model, n_head, dim_feedforward // 2, |
| 247 | num_decoder_blocks) |
| 248 | self._reset_parameters() |
| 249 | |
| 250 | self.tgt_word_prj = nn.Linear( |
| 251 | d_model, dst_vocab_size - 2, |
| 252 | bias=False) # We don't predict <bos> nor <pad> |
| 253 | self.tgt_word_prj.weight.data.normal_(mean=0.0, std=d_model**-0.5) |
nothing calls this directly
no test coverage detected