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# ------------------------------------------------------------------------
# RF-DETR
# Copyright (c) 2025 Roboflow. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
# Copyright (c) 2024 Baidu. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Conditional DETR (https://github.com/Atten4Vis/ConditionalDETR)
# Copyright (c) 2021 Microsoft. All Rights Reserved.
# ------------------------------------------------------------------------
# Copied from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
"""
Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn
from rfdetr.util.misc import NestedTensor
class PositionEmbeddingSine(nn.Module):
"""
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
self._export = False
def export(self):
self._export = True
self._forward_origin = self.forward
self.forward = self.forward_export
def forward(self, tensor_list: NestedTensor, align_dim_orders = True):
x = tensor_list.tensors
mask = tensor_list.mask
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_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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 = 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)
if align_dim_orders:
pos = torch.cat((pos_y, pos_x), dim=3).permute(1, 2, 0, 3)
# return: (H, W, bs, C)
else:
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
# return: (bs, C, H, W)
return pos
def forward_export(self, mask:torch.Tensor, align_dim_orders = True):
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_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=mask.device)
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 = 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)
if align_dim_orders:
pos = torch.cat((pos_y, pos_x), dim=3).permute(1, 2, 0, 3)
# return: (H, W, bs, C)
else:
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
# return: (bs, C, H, W)
return pos
class PositionEmbeddingLearned(nn.Module):
"""
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()
self._export = False
def export(self):
raise NotImplementedError
def reset_parameters(self):
nn.init.uniform_(self.row_embed.weight)
nn.init.uniform_(self.col_embed.weight)
def forward(self, tensor_list: NestedTensor):
x = tensor_list.tensors
h, w = x.shape[:2]
i = torch.arange(w, device=x.device)
j = torch.arange(h, device=x.device)
x_emb = self.col_embed(i)
y_emb = self.row_embed(j)
pos = torch.cat([
x_emb.unsqueeze(0).repeat(h, 1, 1),
y_emb.unsqueeze(1).repeat(1, w, 1),
], dim=-1).unsqueeze(2).repeat(1, 1, x.shape[2], 1)
# return: (H, W, bs, C)
return pos
def build_position_encoding(hidden_dim, position_embedding):
N_steps = hidden_dim // 2
if position_embedding in ('v2', 'sine'):
# TODO find a better way of exposing other arguments
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
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