HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Add ResNet50 and ResNet101 backbone RTDETR models (#6661)

Browse source 
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Awsome 2023-12-03 23:57:11 +08:00 • committed by GitHub
parent d12411ec0d
commit 1e1247ddee
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
7 changed files with 144 additions and 21 deletions
Download patch file Download diff file Expand all files Collapse all files

4
ultralytics/nn/modules/__init__.py
View file

@ -18,7 +18,7 @@ Example:
"""
from .block import (C1, C2, C3, C3TR, DFL, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, GhostBottleneck,
HGBlock, HGStem, Proto, RepC3)
HGBlock, HGStem, Proto, RepC3, ResNetLayer)
from .conv import (CBAM, ChannelAttention, Concat, Conv, Conv2, ConvTranspose, DWConv, DWConvTranspose2d, Focus,
GhostConv, LightConv, RepConv, SpatialAttention)
from .head import Classify, Detect, Pose, RTDETRDecoder, Segment
@ -30,4 +30,4 @@ __all__ = ('Conv', 'Conv2', 'LightConv', 'RepConv', 'DWConv', 'DWConvTranspose2d
'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3',
'C2f', 'C3x', 'C3TR', 'C3Ghost', 'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'Detect',
'Segment', 'Pose', 'Classify', 'TransformerEncoderLayer', 'RepC3', 'RTDETRDecoder', 'AIFI',
'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP')
'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP', 'ResNetLayer')

40
ultralytics/nn/modules/block.py
View file

@ -9,7 +9,7 @@ from .conv import Conv, DWConv, GhostConv, LightConv, RepConv
from .transformer import TransformerBlock
__all__ = ('DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3', 'C2f', 'C3x', 'C3TR', 'C3Ghost',
'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'RepC3')
'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'RepC3', 'ResNetLayer')
class DFL(nn.Module):
@ -331,3 +331,41 @@ class BottleneckCSP(nn.Module):
y1 = self.cv3(self.m(self.cv1(x)))
y2 = self.cv2(x)
return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
class ResNetBlock(nn.Module):
"""ResNet block with standard convolution layers."""
def __init__(self, c1, c2, s=1, e=4):
"""Initialize convolution with given parameters."""
super().__init__()
c3 = e * c2
self.cv1 = Conv(c1, c2, k=1, s=1, act=True)
self.cv2 = Conv(c2, c2, k=3, s=s, p=1, act=True)
self.cv3 = Conv(c2, c3, k=1, act=False)
self.shortcut = nn.Sequential(Conv(c1, c3, k=1, s=s, act=False)) if s != 1 or c1 != c3 else nn.Identity()
def forward(self, x):
"""Forward pass through the ResNet block."""
return F.relu(self.cv3(self.cv2(self.cv1(x))) + self.shortcut(x))
class ResNetLayer(nn.Module):
"""ResNet layer with multiple ResNet blocks."""
def __init__(self, c1, c2, s=1, is_first=False, n=1, e=4):
"""Initializes the ResNetLayer given arguments."""
super().__init__()
self.is_first = is_first
if self.is_first:
self.layer = nn.Sequential(Conv(c1, c2, k=7, s=2, p=3, act=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
else:
blocks = [ResNetBlock(c1, c2, s, e=e)]
blocks.extend([ResNetBlock(e * c2, c2, 1, e=e) for _ in range(n - 1)])
self.layer = nn.Sequential(*blocks)
def forward(self, x):
"""Forward pass through the ResNet layer."""
return self.layer(x)