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position_encoding.py
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position_encoding.py
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import math
from typing import Optional
import paddle
import paddle.nn as nn
import paddle.nn.initializer as init
class NestedTensor(object):
def __init__(self, tensors, mask: Optional[paddle.Tensor]):
self.tensors = tensors
self.mask = mask
def decompose(self):
return self.tensors, self.mask
def __repr__(self):
return str(self.tensors)
class PositionEmbeddingSine(nn.Layer):
"""
This is a more standard version of the position embedding, 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=64, temperature=10000, normalize=False, scale=None
):
super().__init__()
self.num_pos_feats = num_pos_feats
self.temperature = temperature
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, mask):
assert mask is not None
y_embed = mask.cumsum(axis=1, dtype="float32")
x_embed = mask.cumsum(axis=2, dtype="float32")
if self.normalize:
eps = 1e-06
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_t = paddle.arange(end=self.num_pos_feats, dtype="float32")
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = paddle.stack(
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), axis=4
).flatten(3)
pos_y = paddle.stack(
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), axis=4
).flatten(3)
pos = paddle.concat((pos_y, pos_x), axis=3).transpose(perm=[0, 3, 1, 2])
return pos
class PositionEmbeddingLearned(nn.Layer):
"""
Absolute pos embedding, learned.
"""
def __init__(self, num_pos_feats=256):
super().__init__()
self.row_embed = nn.Embedding(50, num_pos_feats)
self.col_embed = nn.Embedding(50, num_pos_feats)
self.reset_parameters()
def reset_parameters(self):
init_Constant = init.Uniform()
init_Constant(self.row_embed.weight)
init_Constant(self.col_embed.weight)
def forward(self, tensor_list: NestedTensor):
x = tensor_list.tensors
h, w = x.shape[-2:]
i = paddle.arange(end=w)
j = paddle.arange(end=h)
x_emb = self.col_embed(i)
y_emb = self.row_embed(j)
pos = (
paddle.concat(
[
x_emb.unsqueeze(0).tile([h, 1, 1]),
y_emb.unsqueeze(1).tile([1, w, 1]),
],
axis=-1,
)
.transpose([2, 0, 1])
.unsqueeze(0)
.tile([x.shape[0], 1, 1, 1])
)
return pos
def build_position_encoding(hidden_dim=512, position_embedding="sine"):
N_steps = hidden_dim // 2
if position_embedding in ("v2", "sine"):
position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
elif position_embedding in ("v3", "learned"):
position_embedding = PositionEmbeddingLearned(N_steps)
else:
raise ValueError(f"not supported {position_embedding}")
return position_embedding