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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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139 changed files with 6870 additions and 5125 deletions
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64
ultralytics/nn/modules/conv.py
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@ -7,8 +7,21 @@ import numpy as np
import torch
import torch.nn as nn
__all__ = ('Conv', 'Conv2', 'LightConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 'GhostConv',
'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'RepConv')
__all__ = (
"Conv",
"Conv2",
"LightConv",
"DWConv",
"DWConvTranspose2d",
"ConvTranspose",
"Focus",
"GhostConv",
"ChannelAttention",
"SpatialAttention",
"CBAM",
"Concat",
"RepConv",
)
def autopad(k, p=None, d=1): # kernel, padding, dilation
@ -22,6 +35,7 @@ def autopad(k, p=None, d=1): # kernel, padding, dilation
class Conv(nn.Module):
"""Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""
default_act = nn.SiLU() # default activation
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
@ -60,9 +74,9 @@ class Conv2(Conv):
"""Fuse parallel convolutions."""
w = torch.zeros_like(self.conv.weight.data)
i = [x // 2 for x in w.shape[2:]]
w[:, :, i[0]:i[0] + 1, i[1]:i[1] + 1] = self.cv2.weight.data.clone()
w[:, :, i[0] : i[0] + 1, i[1] : i[1] + 1] = self.cv2.weight.data.clone()
self.conv.weight.data += w
self.__delattr__('cv2')
self.__delattr__("cv2")
self.forward = self.forward_fuse
@ -102,6 +116,7 @@ class DWConvTranspose2d(nn.ConvTranspose2d):
class ConvTranspose(nn.Module):
"""Convolution transpose 2d layer."""
default_act = nn.SiLU() # default activation
def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True):
@ -164,6 +179,7 @@ class RepConv(nn.Module):
This module is used in RT-DETR.
Based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
"""
default_act = nn.SiLU() # default activation
def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False):
@ -214,7 +230,7 @@ class RepConv(nn.Module):
beta = branch.bn.bias
eps = branch.bn.eps
elif isinstance(branch, nn.BatchNorm2d):
if not hasattr(self, 'id_tensor'):
if not hasattr(self, "id_tensor"):
input_dim = self.c1 // self.g
kernel_value = np.zeros((self.c1, input_dim, 3, 3), dtype=np.float32)
for i in range(self.c1):
@ -232,29 +248,31 @@ class RepConv(nn.Module):
def fuse_convs(self):
"""Combines two convolution layers into a single layer and removes unused attributes from the class."""
if hasattr(self, 'conv'):
if hasattr(self, "conv"):
return
kernel, bias = self.get_equivalent_kernel_bias()
self.conv = nn.Conv2d(in_channels=self.conv1.conv.in_channels,
out_channels=self.conv1.conv.out_channels,
kernel_size=self.conv1.conv.kernel_size,
stride=self.conv1.conv.stride,
padding=self.conv1.conv.padding,
dilation=self.conv1.conv.dilation,
groups=self.conv1.conv.groups,
bias=True).requires_grad_(False)
self.conv = nn.Conv2d(
in_channels=self.conv1.conv.in_channels,
out_channels=self.conv1.conv.out_channels,
kernel_size=self.conv1.conv.kernel_size,
stride=self.conv1.conv.stride,
padding=self.conv1.conv.padding,
dilation=self.conv1.conv.dilation,
groups=self.conv1.conv.groups,
bias=True,
).requires_grad_(False)
self.conv.weight.data = kernel
self.conv.bias.data = bias
for para in self.parameters():
para.detach_()
self.__delattr__('conv1')
self.__delattr__('conv2')
if hasattr(self, 'nm'):
self.__delattr__('nm')
if hasattr(self, 'bn'):
self.__delattr__('bn')
if hasattr(self, 'id_tensor'):
self.__delattr__('id_tensor')
self.__delattr__("conv1")
self.__delattr__("conv2")
if hasattr(self, "nm"):
self.__delattr__("nm")
if hasattr(self, "bn"):
self.__delattr__("bn")
if hasattr(self, "id_tensor"):
self.__delattr__("id_tensor")
class ChannelAttention(nn.Module):
@ -278,7 +296,7 @@ class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
"""Initialize Spatial-attention module with kernel size argument."""
super().__init__()
assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
assert kernel_size in (3, 7), "kernel size must be 3 or 7"
padding = 3 if kernel_size == 7 else 1
self.cv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
self.act = nn.Sigmoid()