input : visual feature [batch_size x T x input_size] output : contextual feature [batch_size x T x output_size]
(self, input)
| 86 | self.linear = nn.Linear(hidden_size * 2, output_size) |
| 87 | |
| 88 | def forward(self, input): |
| 89 | """ |
| 90 | input : visual feature [batch_size x T x input_size] |
| 91 | output : contextual feature [batch_size x T x output_size] |
| 92 | """ |
| 93 | self.rnn.flatten_parameters() |
| 94 | recurrent, _ = self.rnn(input) # batch_size x T x input_size -> batch_size x T x (2*hidden_size) |
| 95 | output = self.linear(recurrent) # batch_size x T x output_size |
| 96 | return output |
| 97 | |
| 98 | # class BidirectionalGRU(nn.Module): |
| 99 | # |
nothing calls this directly
no outgoing calls
no test coverage detected