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"""
Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR)
Copyright(c) 2023 lyuwenyu. All Rights Reserved.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import warnings
from .common import get_activation
from ..core import register
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k]
return p
def make_divisible(c, d):
return math.ceil(c / d) * d
class Conv(nn.Module):
def __init__(self, cin, cout, k=1, s=1, p=None, g=1, act='silu') -> None:
super().__init__()
self.conv = nn.Conv2d(cin, cout, k, s, autopad(k, p), groups=g, bias=False)
self.bn = nn.BatchNorm2d(cout)
self.act = get_activation(act, inplace=True)
def forward(self, x):
return self.act(self.bn(self.conv(x)))
class Bottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, act='silu'):
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1, act=act)
self.cv2 = Conv(c_, c2, 3, 1, g=g, act=act)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class C3(nn.Module):
# CSP Bottleneck with 3 convolutions
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, act='silu'): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1, act=act)
self.cv2 = Conv(c1, c_, 1, 1, act=act)
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0, act=act) for _ in range(n)))
self.cv3 = Conv(2 * c_, c2, 1, act=act)
def forward(self, x):
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
class SPPF(nn.Module):
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
def __init__(self, c1, c2, k=5, act='silu'): # equivalent to SPP(k=(5, 9, 13))
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1, act=act)
self.cv2 = Conv(c_ * 4, c2, 1, 1, act=act)
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
def forward(self, x):
x = self.cv1(x)
with warnings.catch_warnings():
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
y1 = self.m(x)
y2 = self.m(y1)
return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
@register()
class CSPDarkNet(nn.Module):
__share__ = ['depth_multi', 'width_multi']
def __init__(self, in_channels=3, width_multi=1.0, depth_multi=1.0, return_idx=[2, 3, -1], act='silu', ) -> None:
super().__init__()
channels = [64, 128, 256, 512, 1024]
channels = [make_divisible(c * width_multi, 8) for c in channels]
depths = [3, 6, 9, 3]
depths = [max(round(d * depth_multi), 1) for d in depths]
self.layers = nn.ModuleList([Conv(in_channels, channels[0], 6, 2, 2, act=act)])
for i, (c, d) in enumerate(zip(channels, depths), 1):
layer = nn.Sequential(*[Conv(c, channels[i], 3, 2, act=act), C3(channels[i], channels[i], n=d, act=act)])
self.layers.append(layer)
self.layers.append(SPPF(channels[-1], channels[-1], k=5, act=act))
self.return_idx = return_idx
self.out_channels = [channels[i] for i in self.return_idx]
self.strides = [[2, 4, 8, 16, 32][i] for i in self.return_idx]
self.depths = depths
self.act = act
def forward(self, x):
outputs = []
for _, m in enumerate(self.layers):
x = m(x)
outputs.append(x)
return [outputs[i] for i in self.return_idx]
@register()
class CSPPAN(nn.Module):
"""
P5 ---> 1x1 ---------------------------------> concat --> c3 --> det
| up | conv /2
P4 ---> concat ---> c3 ---> 1x1 --> concat ---> c3 -----------> det
| up | conv /2
P3 -----------------------> concat ---> c3 ---------------------> det
"""
__share__ = ['depth_multi', ]
def __init__(self, in_channels=[256, 512, 1024], depth_multi=1., act='silu') -> None:
super().__init__()
depth = max(round(3 * depth_multi), 1)
self.out_channels = in_channels
self.fpn_stems = nn.ModuleList([Conv(cin, cout, 1, 1, act=act) for cin, cout in zip(in_channels[::-1], in_channels[::-1][1:])])
self.fpn_csps = nn.ModuleList([C3(cin, cout, depth, False, act=act) for cin, cout in zip(in_channels[::-1], in_channels[::-1][1:])])
self.pan_stems = nn.ModuleList([Conv(c, c, 3, 2, act=act) for c in in_channels[:-1]])
self.pan_csps = nn.ModuleList([C3(c, c, depth, False, act=act) for c in in_channels[1:]])
def forward(self, feats):
fpn_feats = []
for i, feat in enumerate(feats[::-1]):
if i == 0:
feat = self.fpn_stems[i](feat)
fpn_feats.append(feat)
else:
_feat = F.interpolate(fpn_feats[-1], scale_factor=2, mode='nearest')
feat = torch.concat([_feat, feat], dim=1)
feat = self.fpn_csps[i-1](feat)
if i < len(self.fpn_stems):
feat = self.fpn_stems[i](feat)
fpn_feats.append(feat)
pan_feats = []
for i, feat in enumerate(fpn_feats[::-1]):
if i == 0:
pan_feats.append(feat)
else:
_feat = self.pan_stems[i-1](pan_feats[-1])
feat = torch.concat([_feat, feat], dim=1)
feat = self.pan_csps[i-1](feat)
pan_feats.append(feat)
return pan_feats
if __name__ == '__main__':
data = torch.rand(1, 3, 320, 640)
width_multi = 0.75
depth_multi = 0.33
m = CSPDarkNet(3, width_multi=width_multi, depth_multi=depth_multi, act='silu')
outputs = m(data)
print([o.shape for o in outputs])
m = CSPPAN(in_channels=m.out_channels, depth_multi=depth_multi, act='silu')
outputs = m(outputs)
print([o.shape for o in outputs])
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