(
self,
dim,
kernel_size=7,
padding=3,
drop_path=0.0,
layer_scale_init_value=1e-6,
use_dwconv=True,
)
| 72 | """ |
| 73 | |
| 74 | def __init__( |
| 75 | self, |
| 76 | dim, |
| 77 | kernel_size=7, |
| 78 | padding=3, |
| 79 | drop_path=0.0, |
| 80 | layer_scale_init_value=1e-6, |
| 81 | use_dwconv=True, |
| 82 | ): |
| 83 | super().__init__() |
| 84 | self.dwconv = nn.Conv2d( |
| 85 | dim, |
| 86 | dim, |
| 87 | kernel_size=kernel_size, |
| 88 | padding=padding, |
| 89 | groups=dim if use_dwconv else 1, |
| 90 | ) # depthwise conv |
| 91 | self.norm = LayerNorm2d(dim, eps=1e-6) |
| 92 | self.pwconv1 = nn.Linear( |
| 93 | dim, 4 * dim |
| 94 | ) # pointwise/1x1 convs, implemented with linear layers |
| 95 | self.act = nn.GELU() |
| 96 | self.pwconv2 = nn.Linear(4 * dim, dim) |
| 97 | self.gamma = ( |
| 98 | nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) |
| 99 | if layer_scale_init_value > 0 |
| 100 | else None |
| 101 | ) |
| 102 | self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() |
| 103 | |
| 104 | def forward(self, x): |
| 105 | input = x |
nothing calls this directly
no test coverage detected