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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 ViTDet (https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
"""
Projector
"""
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
"""
A LayerNorm variant, popularized by Transformers, that performs point-wise mean and
variance normalization over the channel dimension for inputs that have shape
(batch_size, channels, height, width).
https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119
"""
def __init__(self, normalized_shape, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.normalized_shape = (normalized_shape,)
def forward(self, x):
"""
LayerNorm forward
TODO: this is a hack to avoid overflow when using fp16
"""
#if x.dtype == torch.half:
# x = x / (x.max() + self.eps)
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
def get_norm(norm, out_channels):
"""
Args:
norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
or a callable that takes a channel number and returns
the normalization layer as a nn.Module.
Returns:
nn.Module or None: the normalization layer
"""
if norm is None:
return None
if isinstance(norm, str):
if len(norm) == 0:
return None
norm = {
"LN": lambda channels: LayerNorm(channels),
}[norm]
return norm(out_channels)
def get_activation(name, inplace=False):
""" get activation """
if name == "silu":
module = nn.SiLU(inplace=inplace)
elif name == "relu":
module = nn.ReLU(inplace=inplace)
elif name in ["LeakyReLU", 'leakyrelu', 'lrelu']:
module = nn.LeakyReLU(0.1, inplace=inplace)
elif name is None:
module = nn.Identity()
else:
raise AttributeError("Unsupported act type: {}".format(name))
return module
class ConvX(nn.Module):
""" Conv-bn module"""
def __init__(self, in_planes, out_planes, kernel=3, stride=1, groups=1, dilation=1, act='relu', layer_norm=False, rms_norm=False):
super(ConvX, self).__init__()
if not isinstance(kernel, tuple):
kernel = (kernel, kernel)
padding = (kernel[0] // 2, kernel[1] // 2)
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel,
stride=stride, padding=padding, groups=groups,
dilation=dilation, bias=False)
if rms_norm:
self.bn = nn.RMSNorm(out_planes)
else:
self.bn = get_norm('LN', out_planes) if layer_norm else nn.BatchNorm2d(out_planes)
self.act = get_activation(act, inplace=True)
def forward(self, x):
""" forward """
out = self.act(self.bn(self.conv(x)))
return out
class Bottleneck(nn.Module):
"""Standard bottleneck."""
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5, act='silu', layer_norm=False, rms_norm=False):
""" ch_in, ch_out, shortcut, groups, kernels, expand """
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = ConvX(c1, c_, k[0], 1, act=act, layer_norm=layer_norm, rms_norm=rms_norm)
self.cv2 = ConvX(c_, c2, k[1], 1, groups=g, act=act, layer_norm=layer_norm, rms_norm=rms_norm)
self.add = shortcut and c1 == c2
def forward(self, x):
"""'forward()' applies the YOLOv5 FPN to input data."""
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class C2f(nn.Module):
"""Faster Implementation of CSP Bottleneck with 2 convolutions."""
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, act='silu', layer_norm=False, rms_norm=False):
""" ch_in, ch_out, number, shortcut, groups, expansion """
super().__init__()
self.c = int(c2 * e) # hidden channels
self.cv1 = ConvX(c1, 2 * self.c, 1, 1, act=act, layer_norm=layer_norm, rms_norm=rms_norm)
self.cv2 = ConvX((2 + n) * self.c, c2, 1, act=act, layer_norm=layer_norm, rms_norm=rms_norm) # optional act=FReLU(c2)
self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=(3, 3), e=1.0, act=act, layer_norm=layer_norm, rms_norm=rms_norm) for _ in range(n))
def forward(self, x):
"""Forward pass using split() instead of chunk()."""
y = list(self.cv1(x).split((self.c, self.c), 1))
y.extend(m(y[-1]) for m in self.m)
return self.cv2(torch.cat(y, 1))
class MultiScaleProjector(nn.Module):
"""
This module implements MultiScaleProjector in :paper:`lwdetr`.
It creates pyramid features built on top of the input feature map.
"""
def __init__(
self,
in_channels,
out_channels,
scale_factors,
num_blocks=3,
layer_norm=False,
rms_norm=False,
survival_prob=1.0,
force_drop_last_n_features=0,
):
"""
Args:
net (Backbone): module representing the subnetwork backbone.
Must be a subclass of :class:`Backbone`.
out_channels (int): number of channels in the output feature maps.
scale_factors (list[float]): list of scaling factors to upsample or downsample
the input features for creating pyramid features.
"""
super(MultiScaleProjector, self).__init__()
self.scale_factors = scale_factors
self.survival_prob = survival_prob
self.force_drop_last_n_features = force_drop_last_n_features
stages_sampling = []
stages = []
# use_bias = norm == ""
use_bias = False
self.use_extra_pool = False
for scale in scale_factors:
stages_sampling.append([])
for in_dim in in_channels:
out_dim = in_dim
layers = []
# if in_dim > 512:
# layers.append(ConvX(in_dim, in_dim // 2, kernel=1))
# in_dim = in_dim // 2
if scale == 4.0:
layers.extend([
nn.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2),
get_norm('LN', in_dim // 2),
nn.GELU(),
nn.ConvTranspose2d(in_dim // 2, in_dim // 4, kernel_size=2, stride=2),
])
out_dim = in_dim // 4
elif scale == 2.0:
# a hack to reduce the FLOPs and Params when the dimention of output feature is too large
# if in_dim > 512:
# layers = [
# ConvX(in_dim, in_dim // 2, kernel=1),
# nn.ConvTranspose2d(in_dim // 2, in_dim // 4, kernel_size=2, stride=2),
# ]
# out_dim = in_dim // 4
# else:
layers.extend([
nn.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2),
])
out_dim = in_dim // 2
elif scale == 1.0:
pass
elif scale == 0.5:
layers.extend([
ConvX(in_dim, in_dim, 3, 2, layer_norm=layer_norm),
])
elif scale == 0.25:
self.use_extra_pool = True
continue
else:
raise NotImplementedError("Unsupported scale_factor:{}".format(scale))
layers = nn.Sequential(*layers)
stages_sampling[-1].append(layers)
stages_sampling[-1] = nn.ModuleList(stages_sampling[-1])
in_dim = int(sum(in_channel // max(1, scale) for in_channel in in_channels))
layers = [
C2f(in_dim, out_channels, num_blocks, layer_norm=layer_norm),
get_norm('LN', out_channels),
]
layers = nn.Sequential(*layers)
stages.append(layers)
self.stages_sampling = nn.ModuleList(stages_sampling)
self.stages = nn.ModuleList(stages)
def forward(self, x):
"""
Args:
x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
Returns:
dict[str->Tensor]:
mapping from feature map name to pyramid feature map tensor
in high to low resolution order. Returned feature names follow the FPN
convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
["p2", "p3", ..., "p6"].
"""
num_features = len(x)
if self.survival_prob < 1.0 and self.training:
final_drop_prob = 1 - self.survival_prob
drop_p = np.random.uniform()
for i in range(1, num_features):
critical_drop_prob = i * (final_drop_prob / (num_features - 1))
if drop_p < critical_drop_prob:
x[i][:] = 0
elif self.force_drop_last_n_features > 0:
for i in range(self.force_drop_last_n_features):
# don't do it inplace to ensure the compiler can optimize out the backbone layers
x[-(i+1)] = torch.zeros_like(x[-(i+1)])
results = []
# x list of len(out_features_indexes)
for i, stage in enumerate(self.stages):
feat_fuse = []
for j, stage_sampling in enumerate(self.stages_sampling[i]):
feat_fuse.append(stage_sampling(x[j]))
if len(feat_fuse) > 1:
feat_fuse = torch.cat(feat_fuse, dim=1)
else:
feat_fuse = feat_fuse[0]
results.append(stage(feat_fuse))
if self.use_extra_pool:
results.append(
F.max_pool2d(results[-1], kernel_size=1, stride=2, padding=0)
)
return results
class SimpleProjector(nn.Module):
def __init__(self, in_dim, out_dim, factor_kernel=False):
super(SimpleProjector, self).__init__()
if not factor_kernel:
self.convx1 = ConvX(in_dim, in_dim*2, layer_norm=True, act='silu')
self.convx2 = ConvX(in_dim*2, out_dim, layer_norm=True, act='silu')
else:
self.convx1 = ConvX(in_dim, out_dim, kernel=(3, 1), layer_norm=True, act='silu')
self.convx2 = ConvX(out_dim, out_dim, kernel=(1, 3), layer_norm=True, act='silu')
self.ln = get_norm('LN', out_dim)
def forward(self, x):
""" forward """
out = self.ln(self.convx2(self.convx1(x[0])))
return [out]
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