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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.
# ------------------------------------------------------------------------
"""
Backbone modules.
"""
from functools import partial
import torch
import torch.nn.functional as F
from torch import nn
from transformers import AutoModel, AutoProcessor, AutoModelForCausalLM, AutoConfig, AutoBackbone
from peft import LoraConfig, get_peft_model, PeftModel
from rfdetr.util.misc import NestedTensor, is_main_process
from rfdetr.models.backbone.base import BackboneBase
from rfdetr.models.backbone.projector import MultiScaleProjector
from rfdetr.models.backbone.dinov2 import DinoV2
__all__ = ["Backbone"]
class Backbone(BackboneBase):
"""backbone."""
def __init__(self,
name: str,
pretrained_encoder: str=None,
window_block_indexes: list=None,
drop_path=0.0,
out_channels=256,
out_feature_indexes: list=None,
projector_scale: list=None,
use_cls_token: bool = False,
freeze_encoder: bool = False,
layer_norm: bool = False,
target_shape: tuple[int, int] = (640, 640),
rms_norm: bool = False,
backbone_lora: bool = False,
gradient_checkpointing: bool = False,
load_dinov2_weights: bool = True,
patch_size: int = 14,
num_windows: int = 4,
positional_encoding_size: bool = False,
):
super().__init__()
# an example name here would be "dinov2_base" or "dinov2_registers_windowed_base"
# if "registers" is in the name, then use_registers is set to True, otherwise it is set to False
# similarly, if "windowed" is in the name, then use_windowed_attn is set to True, otherwise it is set to False
# the last part of the name should be the size
# and the start should be dinov2
name_parts = name.split("_")
assert name_parts[0] == "dinov2"
size = name_parts[-1]
use_registers = False
if "registers" in name_parts:
use_registers = True
name_parts.remove("registers")
use_windowed_attn = False
if "windowed" in name_parts:
use_windowed_attn = True
name_parts.remove("windowed")
assert len(name_parts) == 2, "name should be dinov2, then either registers, windowed, both, or none, then the size"
self.encoder = DinoV2(
size=name_parts[-1],
out_feature_indexes=out_feature_indexes,
shape=target_shape,
use_registers=use_registers,
use_windowed_attn=use_windowed_attn,
gradient_checkpointing=gradient_checkpointing,
load_dinov2_weights=load_dinov2_weights,
patch_size=patch_size,
num_windows=num_windows,
positional_encoding_size=positional_encoding_size,
)
# build encoder + projector as backbone module
if freeze_encoder:
for param in self.encoder.parameters():
param.requires_grad = False
self.projector_scale = projector_scale
assert len(self.projector_scale) > 0
# x[0]
assert (
sorted(self.projector_scale) == self.projector_scale
), "only support projector scale P3/P4/P5/P6 in ascending order."
level2scalefactor = dict(P3=2.0, P4=1.0, P5=0.5, P6=0.25)
scale_factors = [level2scalefactor[lvl] for lvl in self.projector_scale]
self.projector = MultiScaleProjector(
in_channels=self.encoder._out_feature_channels,
out_channels=out_channels,
scale_factors=scale_factors,
layer_norm=layer_norm,
rms_norm=rms_norm,
)
self._export = False
def export(self):
self._export = True
self._forward_origin = self.forward
self.forward = self.forward_export
if isinstance(self.encoder, PeftModel):
print("Merging and unloading LoRA weights")
self.encoder.merge_and_unload()
def forward(self, tensor_list: NestedTensor):
""" """
# (H, W, B, C)
feats = self.encoder(tensor_list.tensors)
feats = self.projector(feats)
# x: [(B, C, H, W)]
out = []
for feat in feats:
m = tensor_list.mask
assert m is not None
mask = F.interpolate(m[None].float(), size=feat.shape[-2:]).to(torch.bool)[
0
]
out.append(NestedTensor(feat, mask))
return out
def forward_export(self, tensors: torch.Tensor):
feats = self.encoder(tensors)
feats = self.projector(feats)
out_feats = []
out_masks = []
for feat in feats:
# x: [(B, C, H, W)]
b, _, h, w = feat.shape
out_masks.append(
torch.zeros((b, h, w), dtype=torch.bool, device=feat.device)
)
out_feats.append(feat)
return out_feats, out_masks
def get_named_param_lr_pairs(self, args, prefix: str = "backbone.0"):
num_layers = args.out_feature_indexes[-1] + 1
backbone_key = "backbone.0.encoder"
named_param_lr_pairs = {}
for n, p in self.named_parameters():
n = prefix + "." + n
if backbone_key in n and p.requires_grad:
lr = (
args.lr_encoder
* get_dinov2_lr_decay_rate(
n,
lr_decay_rate=args.lr_vit_layer_decay,
num_layers=num_layers,
)
* args.lr_component_decay**2
)
wd = args.weight_decay * get_dinov2_weight_decay_rate(n)
named_param_lr_pairs[n] = {
"params": p,
"lr": lr,
"weight_decay": wd,
}
return named_param_lr_pairs
def get_dinov2_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12):
"""
Calculate lr decay rate for different ViT blocks.
Args:
name (string): parameter name.
lr_decay_rate (float): base lr decay rate.
num_layers (int): number of ViT blocks.
Returns:
lr decay rate for the given parameter.
"""
layer_id = num_layers + 1
if name.startswith("backbone"):
if "embeddings" in name:
layer_id = 0
elif ".layer." in name and ".residual." not in name:
layer_id = int(name[name.find(".layer.") :].split(".")[2]) + 1
return lr_decay_rate ** (num_layers + 1 - layer_id)
def get_dinov2_weight_decay_rate(name, weight_decay_rate=1.0):
if (
("gamma" in name)
or ("pos_embed" in name)
or ("rel_pos" in name)
or ("bias" in name)
or ("norm" in name)
or ("embeddings" in name)
):
weight_decay_rate = 0.0
return weight_decay_rate
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