Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,496 Bytes
9b33fca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 |
"""Various positional encodings for the transformer."""
import math
import torch
from torch import Tensor, nn
class PositionEmbeddingSineHW(nn.Module):
"""A more standard version of the position embedding.
It is very similar to the one used by the Attention is all you need paper,
generalized to work on images.
"""
def __init__(
self,
num_pos_feats: int = 64,
temperatureH: int = 10000,
temperatureW: int = 10000,
normalize: bool = False,
scale: float | None = None,
) -> None:
"""Constructor method for PositionEmbeddingSineHW class."""
super().__init__()
self.num_pos_feats = num_pos_feats
self.temperatureH = temperatureH
self.temperatureW = temperatureW
self.normalize = normalize
if scale is not None and normalize is False:
raise ValueError("normalize should be True if scale is passed")
if scale is None:
scale = 2 * math.pi
self.scale = scale
def forward(self, x: Tensor, mask: Tensor | None = None):
assert mask is not None
not_mask = ~mask
y_embed = not_mask.cumsum(1, dtype=torch.float32)
x_embed = not_mask.cumsum(2, dtype=torch.float32)
if self.normalize:
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_tx = torch.arange(
self.num_pos_feats, dtype=torch.float32, device=x.device
)
dim_tx = self.temperatureW ** (
2
* (torch.div(dim_tx, 2, rounding_mode="floor"))
/ self.num_pos_feats
)
pos_x = x_embed[:, :, :, None] / dim_tx
dim_ty = torch.arange(
self.num_pos_feats, dtype=torch.float32, device=x.device
)
dim_ty = self.temperatureH ** (
2
* (torch.div(dim_ty, 2, rounding_mode="floor"))
/ self.num_pos_feats
)
pos_y = y_embed[:, :, :, None] / dim_ty
pos_x = torch.stack(
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
).flatten(3)
pos_y = torch.stack(
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
return pos
def get_sine_pos_embed(
pos_tensor: torch.Tensor,
num_pos_feats: int = 128,
temperature: int = 10000,
exchange_xy: bool = True,
):
"""Generate sine position embedding from a position tensor.
Args:
pos_tensor (torch.Tensor): shape: [..., n].
num_pos_feats (int): projected shape for each float in the tensor.
temperature (int): temperature in the sine/cosine function.
exchange_xy (bool, optional): exchange pos x and pos y. \
For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
Returns:
pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
"""
scale = 2 * math.pi
dim_t = torch.arange(
num_pos_feats, dtype=torch.float32, device=pos_tensor.device
)
dim_t = temperature ** (
2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats
)
def sine_func(x: torch.Tensor):
sin_x = x * scale / dim_t
sin_x = torch.stack(
(sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3
).flatten(2)
return sin_x
pos_res = [
sine_func(x)
for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)
]
if exchange_xy:
pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
pos_res = torch.cat(pos_res, dim=-1)
return pos_res
def gen_sineembed_for_position(pos_tensor):
# n_query, bs, _ = pos_tensor.size()
# sineembed_tensor = torch.zeros(n_query, bs, 256)
scale = 2 * math.pi
dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode="floor")) / 128)
x_embed = pos_tensor[:, :, 0] * scale
y_embed = pos_tensor[:, :, 1] * scale
pos_x = x_embed[:, :, None] / dim_t
pos_y = y_embed[:, :, None] / dim_t
pos_x = torch.stack(
(pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3
).flatten(2)
pos_y = torch.stack(
(pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3
).flatten(2)
if pos_tensor.size(-1) == 2:
pos = torch.cat((pos_y, pos_x), dim=2)
elif pos_tensor.size(-1) == 4:
w_embed = pos_tensor[:, :, 2] * scale
pos_w = w_embed[:, :, None] / dim_t
pos_w = torch.stack(
(pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3
).flatten(2)
h_embed = pos_tensor[:, :, 3] * scale
pos_h = h_embed[:, :, None] / dim_t
pos_h = torch.stack(
(pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3
).flatten(2)
pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
else:
raise ValueError(
"Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1))
)
pos = pos.to(pos_tensor.dtype)
return pos
def coordinate_to_encoding(
coord_tensor: Tensor,
num_feats: int = 128,
temperature: int = 10000,
scale: float = 2 * math.pi,
) -> Tensor:
"""Convert coordinate tensor to positional encoding.
Args:
coord_tensor (Tensor): Coordinate tensor to be converted to
positional encoding. With the last dimension as 2 or 4.
num_feats (int, optional): The feature dimension for each position
along x-axis or y-axis. Note the final returned dimension
for each position is 2 times of this value. Defaults to 128.
temperature (int, optional): The temperature used for scaling
the position embedding. Defaults to 10000.
scale (float, optional): A scale factor that scales the position
embedding. The scale will be used only when `normalize` is True.
Defaults to 2*pi.
Returns:
Tensor: Returned encoded positional tensor.
"""
dim_t = torch.arange(
num_feats, dtype=torch.float32, device=coord_tensor.device
)
dim_t = temperature ** (2 * (dim_t // 2) / num_feats)
x_embed = coord_tensor[..., 0] * scale
y_embed = coord_tensor[..., 1] * scale
pos_x = x_embed[..., None] / dim_t
pos_y = y_embed[..., None] / dim_t
pos_x = torch.stack(
(pos_x[..., 0::2].sin(), pos_x[..., 1::2].cos()), dim=-1
).flatten(2)
pos_y = torch.stack(
(pos_y[..., 0::2].sin(), pos_y[..., 1::2].cos()), dim=-1
).flatten(2)
if coord_tensor.size(-1) == 2:
pos = torch.cat((pos_y, pos_x), dim=-1)
elif coord_tensor.size(-1) == 4:
w_embed = coord_tensor[..., 2] * scale
pos_w = w_embed[..., None] / dim_t
pos_w = torch.stack(
(pos_w[..., 0::2].sin(), pos_w[..., 1::2].cos()), dim=-1
).flatten(2)
h_embed = coord_tensor[..., 3] * scale
pos_h = h_embed[..., None] / dim_t
pos_h = torch.stack(
(pos_h[..., 0::2].sin(), pos_h[..., 1::2].cos()), dim=-1
).flatten(2)
pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=-1)
else:
raise ValueError(
"Unknown pos_tensor shape(-1):{}".format(coord_tensor.size(-1))
)
return pos
|