def __init__(
self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [
img_size[0] // patch_size[0],
img_size[1] // patch_size[1],
]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
self.proj = nn.Conv2D(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
)
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def __init__(self,
backbone,
img_size=224,
feature_size=None,
in_chans=3,
embed_dim=768):
super().__init__()
assert isinstance(backbone, nn.Layer)
img_size = to_2tuple(img_size)
self.img_size = img_size
self.backbone = backbone
if feature_size is None:
with paddle.no_grad():
training = backbone.training
if training:
backbone.eval()
o = self.backbone(
paddle.zeros((1, in_chans, img_size[0], img_size[1])))[-1]
feature_size = o.shape[-2:]
feature_dim = o.shape[1]
backbone.train(training)
else:
feature_size = to_2tuple(feature_size)
feature_dim = self.backbone.feature_info.channels()[-1]
self.num_patches = feature_size[0] * feature_size[1]
self.proj = nn.Conv2D(feature_dim, embed_dim, kernel_size=1)
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2D(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
)
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
new_patch_size = to_2tuple(patch_size // 2)
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] //
patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.embed_dim = embed_dim
self.conv1 = nn.Conv2D(in_chans,
128,
kernel_size=7,
stride=2,
padding=3,
bias_attr=False) # 112x112
self.bn1 = nn.BatchNorm2D(128)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2D(128,
128,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False) # 112x112
self.bn2 = nn.BatchNorm2D(128)
self.conv3 = nn.Conv2D(128,
128,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn3 = nn.BatchNorm2D(128)
self.proj = nn.Conv2D(128,
embed_dim,
kernel_size=new_patch_size,
stride=new_patch_size)
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
assert (
img_size[0] % patch_size[0] == 0
and img_size[1] % patch_size[1] == 0
), f"img_size {img_size} should be divided by patch_size {patch_size}."
# Note: self.H, self.W and self.num_patches are not used
self.H, self.W = img_size[0] // patch_size[0], img_size[
1] // patch_size[1]
# since the image size may change on the fly.
self.num_patches = self.H * self.W
self.proj = nn.Conv2D(in_chans,
embed_dim,
kernel_size=patch_size,
stride=patch_size)
self.norm = nn.LayerNorm(embed_dim)
def __init__(
self,
dim,
input_resolution,
num_heads,
window_size=7,
shift_size=0,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert (
0 <= self.shift_size < self.window_size
), "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop,
)
if self.shift_size > 0:
# calculate attention mask for SW-MSA
H, W = self.input_resolution
img_mask = paddle.zeros((1, H, W, 1)) # 1 H W 1
h_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
w_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
# nW, window_size, window_size, 1
mask_windows = window_partition(img_mask, self.window_size)
mask_windows = mask_windows.reshape(
(-1, self.window_size * self.window_size)
)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
_h = paddle.full_like(attn_mask, -100.0, dtype="float32")
_z = paddle.full_like(attn_mask, 0.0, dtype="float32")
attn_mask = paddle.where(attn_mask != 0, _h, _z)
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
