(self, embed_dim, num_heads, num_layers, mlp_ratio=4.0, dropout=0.1)
| 115 | |
| 116 | class TransformerEncoder(nn.Module): |
| 117 | def __init__(self, embed_dim, num_heads, num_layers, mlp_ratio=4.0, dropout=0.1): |
| 118 | super().__init__() |
| 119 | self.layers = nn.ModuleList([ |
| 120 | TransformerBlock(embed_dim, num_heads, mlp_ratio, dropout) |
| 121 | for _ in range(num_layers) |
| 122 | ]) |
| 123 | self.norm = nn.LayerNorm(embed_dim) |
| 124 | |
| 125 | def forward(self, x, mask=None): |
| 126 | for layer in self.layers: |
nothing calls this directly
no test coverage detected