(self, attention, d_model, d_ff=None, moving_avg=25, dropout=0.1, activation="relu")
| 82 | """ |
| 83 | |
| 84 | def __init__(self, attention, d_model, d_ff=None, moving_avg=25, dropout=0.1, activation="relu"): |
| 85 | super(EncoderLayer, self).__init__() |
| 86 | d_ff = d_ff or 4 * d_model |
| 87 | self.attention = attention |
| 88 | self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1, bias=False) |
| 89 | self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1, bias=False) |
| 90 | self.decomp1 = series_decomp(moving_avg) |
| 91 | self.decomp2 = series_decomp(moving_avg) |
| 92 | self.dropout = nn.Dropout(dropout) |
| 93 | self.activation = F.relu if activation == "relu" else F.gelu |
| 94 | |
| 95 | def forward(self, x, attn_mask=None): |
| 96 | new_x, attn = self.attention( |
nothing calls this directly
no test coverage detected