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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.
# ------------------------------------------------------------------------
from typing import Dict, List
import torch
from torch import nn
from rfdetr.util.misc import NestedTensor
from rfdetr.models.position_encoding import build_position_encoding
from rfdetr.models.backbone.backbone import *
from typing import Callable
class Joiner(nn.Sequential):
def __init__(self, backbone, position_embedding):
super().__init__(backbone, position_embedding)
self._export = False
def forward(self, tensor_list: NestedTensor):
""" """
x = self[0](tensor_list)
pos = []
for x_ in x:
pos.append(self[1](x_, align_dim_orders=False).to(x_.tensors.dtype))
return x, pos
def export(self):
self._export = True
self._forward_origin = self.forward
self.forward = self.forward_export
for name, m in self.named_modules():
if (
hasattr(m, "export")
and isinstance(m.export, Callable)
and hasattr(m, "_export")
and not m._export
):
m.export()
def forward_export(self, inputs: torch.Tensor):
feats, masks = self[0](inputs)
poss = []
for feat, mask in zip(feats, masks):
poss.append(self[1](mask, align_dim_orders=False).to(feat.dtype))
return feats, None, poss
def build_backbone(
encoder,
vit_encoder_num_layers,
pretrained_encoder,
window_block_indexes,
drop_path,
out_channels,
out_feature_indexes,
projector_scale,
use_cls_token,
hidden_dim,
position_embedding,
freeze_encoder,
layer_norm,
target_shape,
rms_norm,
backbone_lora,
force_no_pretrain,
gradient_checkpointing,
load_dinov2_weights,
patch_size,
num_windows,
positional_encoding_size,
):
"""
Useful args:
- encoder: encoder name
- lr_encoder:
- dilation
- use_checkpoint: for swin only for now
"""
position_embedding = build_position_encoding(hidden_dim, position_embedding)
backbone = Backbone(
encoder,
pretrained_encoder,
window_block_indexes=window_block_indexes,
drop_path=drop_path,
out_channels=out_channels,
out_feature_indexes=out_feature_indexes,
projector_scale=projector_scale,
use_cls_token=use_cls_token,
layer_norm=layer_norm,
freeze_encoder=freeze_encoder,
target_shape=target_shape,
rms_norm=rms_norm,
backbone_lora=backbone_lora,
gradient_checkpointing=gradient_checkpointing,
load_dinov2_weights=load_dinov2_weights,
patch_size=patch_size,
num_windows=num_windows,
positional_encoding_size=positional_encoding_size,
)
model = Joiner(backbone, position_embedding)
return model
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