关于python:睿智的目标检测53Pytorch搭建YoloX目标检测平台

import torch
from torch import nn
class SiLU(nn.Module):

@staticmethod
def forward(x):
    return x * torch.sigmoid(x)

def get_activation(name=”silu”, inplace=True):

if name == "silu":
    module = SiLU()
elif name == "relu":
    module = nn.ReLU(inplace=inplace)
elif name == "lrelu":
    module = nn.LeakyReLU(0.1, inplace=inplace)
else:
    raise AttributeError("Unsupported act type: {}".format(name))
return module

class Focus(nn.Module):

def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="silu"):
    super().__init__()
    self.conv = BaseConv(in_channels * 4, out_channels, ksize, stride, act=act)
def forward(self, x):
    patch_top_left  = x[...,  ::2,  ::2]
    patch_bot_left  = x[..., 1::2,  ::2]
    patch_top_right = x[...,  ::2, 1::2]
    patch_bot_right = x[..., 1::2, 1::2]
    x = torch.cat((patch_top_left, patch_bot_left, patch_top_right, patch_bot_right,), dim=1,)
    return self.conv(x)

class BaseConv(nn.Module):

def __init__(self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu"):
    super().__init__()
    pad         = (ksize - 1) // 2
    self.conv   = nn.Conv2d(in_channels, out_channels, kernel_size=ksize, stride=stride, padding=pad, groups=groups, bias=bias)
    self.bn     = nn.BatchNorm2d(out_channels)
    self.act    = get_activation(act, inplace=True)
def forward(self, x):
    return self.act(self.bn(self.conv(x)))
def fuseforward(self, x):
    return self.act(self.conv(x))

class DWConv(nn.Module):

def __init__(self, in_channels, out_channels, ksize, stride=1, act="silu"):
    super().__init__()
    self.dconv = BaseConv(in_channels, in_channels, ksize=ksize, stride=stride, groups=in_channels, act=act,)
    self.pconv = BaseConv(in_channels, out_channels, ksize=1, stride=1, groups=1, act=act)
def forward(self, x):
    x = self.dconv(x)
    return self.pconv(x)

class SPPBottleneck(nn.Module):

def __init__(self, in_channels, out_channels, kernel_sizes=(5, 9, 13), activation="silu"):
    super().__init__()
    hidden_channels = in_channels // 2
    self.conv1      = BaseConv(in_channels, hidden_channels, 1, stride=1, act=activation)
    self.m          = nn.ModuleList([nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2) for ks in kernel_sizes])
    conv2_channels  = hidden_channels * (len(kernel_sizes) + 1)
    self.conv2      = BaseConv(conv2_channels, out_channels, 1, stride=1, act=activation)
def forward(self, x):
    x = self.conv1(x)
    x = torch.cat([x] + [m(x) for m in self.m], dim=1)
    x = self.conv2(x)
    return x

class Bottleneck(nn.Module):

# Standard bottleneck
def __init__(self, in_channels, out_channels, shortcut=True, expansion=0.5, depthwise=False, act="silu",):
    super().__init__()
    hidden_channels = int(out_channels * expansion)
    Conv = DWConv if depthwise else BaseConv
    self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
    self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act)
    self.use_add = shortcut and in_channels == out_channels
def forward(self, x):
    y = self.conv2(self.conv1(x))
    if self.use_add:
        y = y + x
    return y

class CSPLayer(nn.Module):

def __init__(self, in_channels, out_channels, n=1, shortcut=True, expansion=0.5, depthwise=False, act="silu",):
    # ch_in, ch_out, number, shortcut, groups, expansion
    super().__init__()
    hidden_channels = int(out_channels * expansion)  # hidden channels
    self.conv1  = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
    self.conv2  = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
    self.conv3  = BaseConv(2 * hidden_channels, out_channels, 1, stride=1, act=act)
    module_list = [Bottleneck(hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act) for _ in range(n)]
    self.m      = nn.Sequential(*module_list)
def forward(self, x):
    x_1 = self.conv1(x)
    x_2 = self.conv2(x)
    x_1 = self.m(x_1)
    x = torch.cat((x_1, x_2), dim=1)
    return self.conv3(x)

class CSPDarknet(nn.Module):

def __init__(self, dep_mul, wid_mul, out_features=("dark3", "dark4", "dark5"), depthwise=False, act="silu",):
    super().__init__()
    assert out_features, "please provide output features of Darknet"
    self.out_features = out_features
    Conv = [PayPal 下载](https://www.gendan5.com/wallet/PayPal.html)DWConv if depthwise else BaseConv
    base_channels   = int(wid_mul * 64)  # 64
    base_depth      = max(round(dep_mul * 3), 1)  # 3
    # stem
    self.stem = Focus(3, base_channels, ksize=3, act=act)
    # dark2
    self.dark2 = nn.Sequential(Conv(base_channels, base_channels * 2, 3, 2, act=act),
        CSPLayer(base_channels * 2, base_channels * 2, n=base_depth, depthwise=depthwise, act=act),
    )
    # dark3
    self.dark3 = nn.Sequential(Conv(base_channels * 2, base_channels * 4, 3, 2, act=act),
        CSPLayer(base_channels * 4, base_channels * 4, n=base_depth * 3, depthwise=depthwise, act=act),
    )
    # dark4
    self.dark4 = nn.Sequential(Conv(base_channels * 4, base_channels * 8, 3, 2, act=act),
        CSPLayer(base_channels * 8, base_channels * 8, n=base_depth * 3, depthwise=depthwise, act=act),
    )
    # dark5
    self.dark5 = nn.Sequential(Conv(base_channels * 8, base_channels * 16, 3, 2, act=act),
        SPPBottleneck(base_channels * 16, base_channels * 16, activation=act),
        CSPLayer(base_channels * 16, base_channels * 16, n=base_depth, shortcut=False, depthwise=depthwise, act=act),
    )
def forward(self, x):
    outputs = {}
    x = self.stem(x)
    outputs["stem"] = x
    x = self.dark2(x)
    outputs["dark2"] = x
    x = self.dark3(x)
    outputs["dark3"] = x
    x = self.dark4(x)
    outputs["dark4"] = x
    x = self.dark5(x)
    outputs["dark5"] = x
    return {k: v for k, v in outputs.items() if k in self.out_features}