(
self,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten_embedding: bool = True,
)
| 35 | """ |
| 36 | |
| 37 | def __init__( |
| 38 | self, |
| 39 | img_size: Union[int, Tuple[int, int]] = 224, |
| 40 | patch_size: Union[int, Tuple[int, int]] = 16, |
| 41 | in_chans: int = 3, |
| 42 | embed_dim: int = 768, |
| 43 | norm_layer: Optional[Callable] = None, |
| 44 | flatten_embedding: bool = True, |
| 45 | ) -> None: |
| 46 | super().__init__() |
| 47 | |
| 48 | image_HW = make_2tuple(img_size) |
| 49 | patch_HW = make_2tuple(patch_size) |
| 50 | patch_grid_size = ( |
| 51 | image_HW[0] // patch_HW[0], |
| 52 | image_HW[1] // patch_HW[1], |
| 53 | ) |
| 54 | |
| 55 | self.img_size = image_HW |
| 56 | self.patch_size = patch_HW |
| 57 | self.patches_resolution = patch_grid_size |
| 58 | self.num_patches = patch_grid_size[0] * patch_grid_size[1] |
| 59 | |
| 60 | self.in_chans = in_chans |
| 61 | self.embed_dim = embed_dim |
| 62 | |
| 63 | self.flatten_embedding = flatten_embedding |
| 64 | |
| 65 | self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) |
| 66 | self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() |
| 67 | |
| 68 | def forward(self, x: Tensor) -> Tensor: |
| 69 | _, _, H, W = x.shape |
nothing calls this directly
no test coverage detected