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bubbliiiing / deeplabv3-plus-pytorch

这是一个deeplabv3-plus-pytorch的源码，可以用于训练自己的模型。
MIT License
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MobileNetV3网络在DeepLabv3+中的使用 #81

Open Voyagerlemon opened 1 year ago

Voyagerlemon commented 1 year ago

MobileNetV3网络

import torch.nn as nn import math import torch

all = ['build_mobilenetv3_small', 'build_mobilenetv3_large']

def _make_divisible(v, divisor, min_value=None): if min_value is None: min_value = divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)

--------------------------------------

# 确保调整后的值不会比原始值下降超过10%
#--------------------------------------#
if new_v < 0.9 * v:
    new_v += divisor
return new_v

class Sigmoid(nn.Module): def init(self, inplace=True): super(Sigmoid, self).init() self.relu6 = nn.ReLU6(inplace=inplace)

def forward(self, x):
    return self.relu6(x + 3) / 6

class HardSwish(nn.Module): def init(self, inplace=True): super(HardSwish, self).init() self.sigmoid = Sigmoid(inplace=inplace)

def forward(self, x):
    return x * self.sigmoid(x)

class SEInvertedBottleneck(nn.Module): def init(self, channel, reduction=4): super(SEInvertedBottleneck, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.SEblock = nn.Sequential( nn.Linear(channel, _make_divisible(channel // reduction, 8)),

nn.ReLU(inplace=True),

        nn.ReLU6(inplace=True),
        nn.Linear(_make_divisible(channel // reduction, 8), channel),
        # Sigmoid()
        HardSwish(inplace=True)
    )

def forward(self, x):
    b, c, _, _ = x.size()
    y = self.avg_pool(x).view(b, c)
    y = self.SEblock(y).view(b, c, 1, 1)
    return x * y

--------------------------------

带有批归一化和激活函数的卷积层

--------------------------------

def conv_3x3_bn(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), HardSwish() )

----------------------------------

带有批归一化和激活函数的1×1卷积层

----------------------------------

def conv_1x1_bn(inp, oup): return nn.Sequential( nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), HardSwish() )

-------------------------

MobileNetV3的倒残差网络

-------------------------

class InvertedResidual(nn.Module): def init(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs): super(InvertedResidual, self).init() assert stride in [1, 2]

    self.identity = stride == 1 and inp == oup

    if inp == hidden_dim:
        self.conv = nn.Sequential(
            #----------------------------------------------#
            # 进行3x3的逐层卷积, 进行跨特征点的特征提取(dw)
            #----------------------------------------------#
            nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                      bias=False),
            nn.BatchNorm2d(hidden_dim),
            #  HardSwish() if use_hs else nn.ReLU(inplace=True)
            HardSwish() if use_hs else nn.ReLU6(inplace=True),

            #---------------#
            # 使用SE模块
            #---------------# 
            SEInvertedBottleneck(hidden_dim) if use_se else nn.Identity(),

            #---------------------------------------#
            # 利用1x1卷积进行通道数的调整(pw-linear)
            #---------------------------------------#
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        )
    else:
        self.conv = nn.Sequential(
            #---------------------------------#
            # 利用1x1卷积进行通道数的调整(pw)
            #---------------------------------#
            nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
            nn.BatchNorm2d(hidden_dim),
            HardSwish() if use_hs else nn.ReLU(inplace=True),

            #----------------------------------------------#
            # 进行3x3的逐层卷积, 进行跨特征点的特征提取(dw)
            #----------------------------------------------#
            nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                      bias=False),
            nn.BatchNorm2d(hidden_dim),

            #---------------#
            # 使用SE模块
            #---------------# 
            SEInvertedBottleneck(hidden_dim) if use_se else nn.Identity(),
            HardSwish() if use_hs else nn.ReLU(inplace=True),

            #----------------------------------------#
            # 利用1x1卷积进行通道数的调整(pw-linear)
            #----------------------------------------#
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        )

def forward(self, x):
    if self.identity:
        return x + self.conv(x)
    else:
        return self.conv(x)

class MobileNetV3(nn.Module): def init(self, cfgs, mode, num_classes=1000, width_mult=1.): super(MobileNetV3, self).init()

    #------------------#
    # 设置倒残差网络块
    #------------------#
    self.cfgs = cfgs
    assert mode in ['large', 'small']

    #--------------#
    # 构建第一层
    #--------------#
    input_channel = _make_divisible(16 * width_mult, 8)

    #------------------------------#
    # 对特征层进行高和宽的压缩
    # 1024×2048×3-->512×1024×3
    #------------------------------#
    self.features = [conv_3x3_bn(3, input_channel, 2)]

    #------------------#
    # 构建倒残差网络块
    #------------------#
    block = InvertedResidual

    exp_size = None
    output_channel = None

    for i in range(4):
        for k, t, c, use_se, use_hs, s in self.cfgs[i]:
            output_channel = _make_divisible(c * width_mult, 8)
            exp_size = _make_divisible(input_channel * t, 8)
            self.features.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
            input_channel = output_channel

    #------------------#
    # 构建最后几层网络
    #------------------#
    self.features = nn.Sequential(*self.features)
    self.conv = conv_1x1_bn(input_channel, exp_size)
    self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

    output_channel = {'large': 1280, 'small': 1024}
    output_channel = _make_divisible(output_channel[mode] * width_mult, 8) if width_mult > 1.0 else output_channel[
        mode]
    self.classifier = nn.Sequential(
        nn.Linear(exp_size, output_channel),
        HardSwish(),
        nn.Dropout(0.2),
        nn.Linear(output_channel, num_classes),
    )

    self._initialize_weights()

def forward(self, x):
    #--------------------------------------#
    # downsample: [2  4  8  16  32]
    #--------------------------------------# 
    x = self.features(x) 
    x = self.conv(x)
    x = self.avgpool(x)
    x = x.view(x.size(0), -1)
    x = self.classifier(x)
    return x

def _initialize_weights(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, math.sqrt(2. / n))
            if m.bias is not None:
                m.bias.data.zero_()
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, nn.Linear):
            m.weight.data.normal_(0, 0.01)
            m.bias.data.zero_()

def mobilenetv3_large(**kwargs): """ Constructs a MobileNetV3-Large model

8x download factor
"""
cfgs = [
    # k, t, c, SE, HS, s
    [[3, 1, 16, 0, 0, 1],
     [3, 4, 24, 0, 0, 2],
     [3, 3, 24, 0, 0, 1]],
    [[5, 3, 40, 1, 0, 2],
     [5, 3, 40, 1, 0, 1],
     [5, 3, 40, 1, 0, 1]],
    [[3, 6, 80, 0, 1, 1],
     [3, 2.5, 80, 0, 1, 1],
     [3, 2.3, 80, 0, 1, 1],
     [3, 2.3, 80, 0, 1, 1],
     [3, 6, 112, 1, 1, 1],
     [3, 6, 112, 1, 1, 1]],
    [[5, 6, 160, 1, 1, 1],
     [5, 6, 160, 1, 1, 1],
     [5, 6, 160, 1, 1, 1]]
]
return MobileNetV3(cfgs, mode='large', **kwargs)

def mobilenetv3_small(**kwargs): """ Constructs a MobileNetV3-Small model 8x download factor """ cfgs = [

k, t, c, SE, HS, s

    [[3, 1, 16, 1, 0, 2]],
    [[3, 4.5, 24, 0, 0, 2],
     [3, 3.67, 24, 0, 0, 1]],
    [[5, 4, 40, 1, 1, 1],
     [5, 6, 40, 1, 1, 1],
     [5, 6, 40, 1, 1, 1],
     [5, 3, 48, 1, 1, 1],
     [5, 3, 48, 1, 1, 1]],
    [[5, 6, 96, 1, 1, 1],
     [5, 6, 96, 1, 1, 1],
     [5, 6, 96, 1, 1, 1]],
]

return MobileNetV3(cfgs, mode='small', **kwargs)

def load_and_convert(net, state_dict): net_dict = net.state_dict().copy() net_list = list(net_dict.keys()) trained_list = list(state_dict.keys()) assert len(net_list) == len(trained_list), 'Learning parameters do not match, check net and trained state_dict' for i in range(len(net_list)): net_dict[net_list[i]] = state_dict[trained_list[i]] net.load_state_dict(net_dict)

def build_mobilenetv3_large(pretrained=True, width_mult=1.): net = mobilenetv3_large(width_mult=width_mult) if pretrained: eps = 1e-5 if abs(1.0 - width_mult) < eps: weights = './initmodel/mobilenetv3-large-1cd25616.pth' state_dict = torch.load(weights) elif abs(0.75 - width_mult) < eps: weights = './initmodel/mobilenetv3-large-0.75-9632d2a8.pth' state_dict = torch.load(weights) else: raise RuntimeError("Not support width_mult: {}".format(width_mult)) load_and_convert(net, state_dict) return net

def build_mobilenetv3_small(pretrained=True, width_mult=1.): net = mobilenetv3_small(width_mult=width_mult) if pretrained: eps = 1e-5 if abs(1.0 - width_mult) < eps: weights = './initmodel/mobilenetv3-small-55df8e1f.pth' state_dict = torch.load(weights) elif abs(0.75 - width_mult) < eps: weights = './initmodel/mobilenetv3-small-0.75-86c972c3.pth' state_dict = torch.load(weights) else: raise RuntimeError("Not support width_mult: {}".format(width_mult)) load_and_convert(net, state_dict) return net

if name == 'main': import torch

def params(net):
    return sum(param.numel() for param in net.parameters())

net = build_mobilenetv3_large(pretrained=False, width_mult=1.)
input = torch.randn((1, 3, 224, 224))
out = net(input)
print('Out shape ', out.size())

####################################################################### 请问各位大佬这样构建有问题吗，在deeplabv3_plus.py中，这样的写法如何更改呢？？请up住及各位大佬解答一下！！！！！ class MobileNetV3(nn.Module): def init(self, pretrained=True): super(MobileNetV3, self).init() from functools import partial

    model           = build_mobilenetv3_large(pretrained)
    self.features   = model.features[:4]

def forward(self, x):
    #-----------------------------------#
    # 获得DeepLabv3+的深层特征和浅层特征
    #-----------------------------------#
    low_level_features = self.features[:4](x)
    x = self.features[4:](low_level_features)
    return low_level_features, x

在class DeepLab(nn.Module)中： elif backbone == "mobilenetv3": self.backbone = MobileNetV3(pretrained=pretrained) in_channels = 320 low_level_channels = 24

bubbliiiing commented 1 year ago

你这个代码……有点乱，看不太懂啊，为什么有些事代码格式有些不是

Leochen9 commented 1 year ago

MobileNetV3网络

导入火炬.nn 作为 NN 导入数学导入火炬

all = ['build_mobilenetv3_small'， 'build_mobilenetv3_large']

def _make_divisible（v， divisor， min_value=None）： 如果min_value为 None： min_value = 除数 new_v = max（min_value， int（v + 除数 / 2） // 除数 除数） #--------------------------------------# # 确保调整后的值不会比原始值下降超过10% #--------------------------------------# 如果new_v < 0.9 v： new_v += 除数返回new_v

类 Sigmoid（nn.模块）： def init（self， inplace=True）： super（Sigmoid， self）.init（） self.relu6 = nn.ReLU6（就地=就地）

def forward(self, x):
    return self.relu6(x + 3) / 6

类硬斯威什（nn.模块）：def init（self， inplace=True）： super（HardSwish， self）。init（） self.sigmoid = Sigmoid（inplace=inplace）

def forward(self, x):
    return x * self.sigmoid(x)

类 SEInvertedBottleneck（nn.模块）： def init（self， channel， reduction=4）： super（SEInvertedBottleneck， self）.init（） self.avg_pool = nn。AdaptiveAvgPool2d（1） self.SEblock = nn。顺序（ nn.线性（通道， _make_divisible（通道 // 缩减， 8））， # nn.ReLU（inplace=True）， nn.ReLU6（inplace=True）， nn.线性（_make_divisible（channel // reduction， 8）， channel）， # Sigmoid（） HardSwish（inplace=True） )

def forward(self, x):
    b, c, _, _ = x.size()
    y = self.avg_pool(x).view(b, c)
    y = self.SEblock(y).view(b, c, 1, 1)
    return x * y

--------------------------------

带有批归一化和激活函数的卷积层

--------------------------------#def conv_3x3_bn（inp， oup， stride）： return nn.顺序（ nn.Conv2d（inp， oup， 3， stride， 1， bias=False）， nn.BatchNorm2d（oup）， HardSwish（） )

----------------------------------

带有批归一化和激活函数的1×1卷积层

----------------------------------#def conv_1x1_bn（inp， oup）： return nn.顺序（ nn.Conv2d（inp， oup， 1， 1， 0， bias=False）， nn.BatchNorm2d（oup）， HardSwish（） )

-------------------------

MobileNetV3的倒残差网络

-------------------------#类 倒置残差（nn.模块）：def init（self， inp， hidden_dim， oup， kernel_size， stride， use_se， use_hs）： super（InvertedResidual， self）.init（） 断言在 [1， 2] 中的步幅

    self.identity = stride == 1 and inp == oup

    if inp == hidden_dim:
        self.conv = nn.Sequential(
            #----------------------------------------------#
            # 进行3x3的逐层卷积, 进行跨特征点的特征提取(dw)
            #----------------------------------------------#
            nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                      bias=False),
            nn.BatchNorm2d(hidden_dim),
            #  HardSwish() if use_hs else nn.ReLU(inplace=True)
            HardSwish() if use_hs else nn.ReLU6(inplace=True),

            #---------------#
            # 使用SE模块
            #---------------# 
            SEInvertedBottleneck(hidden_dim) if use_se else nn.Identity(),

            #---------------------------------------#
            # 利用1x1卷积进行通道数的调整(pw-linear)
            #---------------------------------------#
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        )
    else:
        self.conv = nn.Sequential(
            #---------------------------------#
            # 利用1x1卷积进行通道数的调整(pw)
            #---------------------------------#
            nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
            nn.BatchNorm2d(hidden_dim),
            HardSwish() if use_hs else nn.ReLU(inplace=True),

            #----------------------------------------------#
            # 进行3x3的逐层卷积, 进行跨特征点的特征提取(dw)
            #----------------------------------------------#
            nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                      bias=False),
            nn.BatchNorm2d(hidden_dim),

            #---------------#
            # 使用SE模块
            #---------------# 
            SEInvertedBottleneck(hidden_dim) if use_se else nn.Identity(),
            HardSwish() if use_hs else nn.ReLU(inplace=True),

            #----------------------------------------#
            # 利用1x1卷积进行通道数的调整(pw-linear)
            #----------------------------------------#
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        )

def forward(self, x):
    if self.identity:
        return x + self.conv(x)
    else:
        return self.conv(x)

类 MobileNetV3（nn.模块）： def init（self， cfgs， mode， num_classes=1000， width_mult=1.）： super（MobileNetV3， self）.初始化()

    #------------------#
    # 设置倒残差网络块
    #------------------#
    self.cfgs = cfgs
    assert mode in ['large', 'small']

    #--------------#
    # 构建第一层
    #--------------#
    input_channel = _make_divisible(16 * width_mult, 8)

    #------------------------------#
    # 对特征层进行高和宽的压缩
    # 1024×2048×3-->512×1024×3
    #------------------------------#
    self.features = [conv_3x3_bn(3, input_channel, 2)]

    #------------------#
    # 构建倒残差网络块
    #------------------#
    block = InvertedResidual

    exp_size = None
    output_channel = None

    for i in range(4):
        for k, t, c, use_se, use_hs, s in self.cfgs[i]:
            output_channel = _make_divisible(c * width_mult, 8)
            exp_size = _make_divisible(input_channel * t, 8)
            self.features.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
            input_channel = output_channel

    #------------------#
    # 构建最后几层网络
    #------------------#
    self.features = nn.Sequential(*self.features)
    self.conv = conv_1x1_bn(input_channel, exp_size)
    self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

    output_channel = {'large': 1280, 'small': 1024}
    output_channel = _make_divisible(output_channel[mode] * width_mult, 8) if width_mult > 1.0 else output_channel[
        mode]
    self.classifier = nn.Sequential(
        nn.Linear(exp_size, output_channel),
        HardSwish(),
        nn.Dropout(0.2),
        nn.Linear(output_channel, num_classes),
    )

    self._initialize_weights()

def forward(self, x):
    #--------------------------------------#
    # downsample: [2  4  8  16  32]
    #--------------------------------------# 
    x = self.features(x) 
    x = self.conv(x)
    x = self.avgpool(x)
    x = x.view(x.size(0), -1)
    x = self.classifier(x)
    return x

def _initialize_weights(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, math.sqrt(2. / n))
            if m.bias is not None:
                m.bias.data.zero_()
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, nn.Linear):
            m.weight.data.normal_(0, 0.01)
            m.bias.data.zero_()

def mobilenetv3_large（**kwargs）： “”“ 构造一个 MobileNetV3-Large 模型

8x download factor
"""
cfgs = [
    # k, t, c, SE, HS, s
    [[3, 1, 16, 0, 0, 1],
     [3, 4, 24, 0, 0, 2],
     [3, 3, 24, 0, 0, 1]],
    [[5, 3, 40, 1, 0, 2],
     [5, 3, 40, 1, 0, 1],
     [5, 3, 40, 1, 0, 1]],
    [[3, 6, 80, 0, 1, 1],
     [3, 2.5, 80, 0, 1, 1],
     [3, 2.3, 80, 0, 1, 1],
     [3, 2.3, 80, 0, 1, 1],
     [3, 6, 112, 1, 1, 1],
     [3, 6, 112, 1, 1, 1]],
    [[5, 6, 160, 1, 1, 1],
     [5, 6, 160, 1, 1, 1],
     [5, 6, 160, 1, 1, 1]]
]
return MobileNetV3(cfgs, mode='large', **kwargs)

def mobilenetv3_small（**kwargs）： “”“ 构造一个 MobileNetV3-Small 模型 8x 下载因子 ”“” cfgs = [ # k， t， c， SE， HS， s [[3， 1， 16， 1， 0， 2]]， [[3， 4.5， 24， 0， 0， 2]， [3， 3.67， 24， 0， 0， 1]]， [[5， 4， 40， 1， 1， 1]， [5， 6， 40， 1， 1， 1]， [5， 6， 40， 1， 1， 1， 5， 3， 48， 1， 1， 1， 5， 3]， [48， 1， 1， 1， 5， 6]]， [[96， 1， 1， 1， 5， 6]， [96， 1， 1， 1， 5， 6]， [96， 1， 1， 1， <>， <>]]， ]

return MobileNetV3(cfgs, mode='small', **kwargs)

def load_and_convert（net， state_dict）： net_dict = net.state_dict（）.copy（） net_list = list（net_dict.keys（）） trained_list = list（state_dict.keys（）） assert len（net_list） == len（trained_list）， 'Learning parameters not match， check net and train state_dict' for i in range（len（net_list））： net_dict[net_list[i]] = state_dict[trained_list[i]] net.load_state_dict（net_dict）

def build_mobilenetv3_large（pretrained=True， width_mult=1.）： net = mobilenetv3_large（width_mult=width_mult） 如果预训练： eps = 1e-5 如果 abs（1.0 - width_mult） < EPS： weights = './initmodel/mobilenetv3-large-1cd25616.pth' state_dict = Torch.load（weights） elif abs（0.75 - width_mult） < EPS： weights = './initmodel/mobilenetv3-large-0.75-9632d2a8.pth' state_dict = torch.load（weights） else： raise RuntimeError（“Not support width_mult： {}”.format（width_mult）） load_and_convert（net， state_dict） return net

def build_mobilenetv3_small（pretrained=True， width_mult=1.）： net = mobilenetv3_small（width_mult=width_mult） 如果预训练： eps = 1e-5 如果 abs（1.0 - width_mult） < EPS： weights = './initmodel/mobilenetv3-small-55df8e1f.pth' state_dict = Torch.load（weights） elif abs（0.75 - width_mult） < EPS： weights = './initmodel/mobilenetv3-small-0.75-86c972c3.pth' state_dict = torch.load（weights） else： raise RuntimeError（“Not support width_mult： {}”.format（width_mult）） load_and_convert（net， state_dict） return net

如果名称 == “主”：导入割炬

def params(net):
    return sum(param.numel() for param in net.parameters())

net = build_mobilenetv3_large(pretrained=False, width_mult=1.)
input = torch.randn((1, 3, 224, 224))
out = net(input)
print('Out shape ', out.size())

#######################################################################请问各位大佬这样构建有问题吗，在deeplabv3_plus.py中，这样的写法如何更改呢？？请up住及各位大佬解答一下!!!! class MobileNetV3（nn.模块）： def init（self， pretrained=True）： super（MobileNetV3， self）.init（） 从 functools 导入部分

    model           = build_mobilenetv3_large(pretrained)
    self.features   = model.features[:4]

def forward(self, x):
    #-----------------------------------#
    # 获得DeepLabv3+的深层特征和浅层特征
    #-----------------------------------#
    low_level_features = self.features[:4](x)
    x = self.features[4:](low_level_features)
    return low_level_features, x

在类 DeepLab（nn.模块）中： elif 骨干 == “mobilenetv3”： self.backbone = MobileNetV3（pretrained=pretrained） in_channels = 320 low_level_channels = 24

你好，请问你解决了吗？我也想更换一下主干网络，但是一直会出错，想请教你一下，谢谢

Voyagerlemon commented 1 year ago

你这个代码……有点乱，看不太懂啊，为什么有些事代码格式有些不是 重新粘贴一下代码，确实太乱了：


import torch.nn as nn
import math
import torch

all = ['build_mobilenetv3_small', 'build_mobilenetv3_large']

def _make_divisible(v, divisor, min_value=None): if min_value is None: min_value = divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)

--------------------------------------

# 确保调整后的值不会比原始值下降超过10%
#--------------------------------------#
if new_v < 0.9 * v:
    new_v += divisor
return new_v

class Sigmoid(nn.Module): def init(self, inplace=True): super(Sigmoid, self).init() self.relu6 = nn.ReLU6(inplace=inplace)

def forward(self, x):
    return self.relu6(x + 3) / 6

class HardSwish(nn.Module): def init(self, inplace=True): super(HardSwish, self).init() self.sigmoid = Sigmoid(inplace=inplace)

def forward(self, x):
    return x * self.sigmoid(x)

class SEInvertedBottleneck(nn.Module): def init(self, channel, reduction=4): super(SEInvertedBottleneck, self).init() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.SEblock = nn.Sequential( nn.Linear(channel, _make_divisible(channel // reduction, 8)),

nn.ReLU(inplace=True),

        nn.ReLU6(inplace=True),
        nn.Linear(_make_divisible(channel // reduction, 8), channel),
        # Sigmoid()
        HardSwish(inplace=True)
    )

def forward(self, x):
    b, c, _, _ = x.size()
    y = self.avg_pool(x).view(b, c)
    y = self.SEblock(y).view(b, c, 1, 1)
    return x * y

--------------------------------

带有批归一化和激活函数的卷积层

--------------------------------

def conv_3x3_bn(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), HardSwish() )

----------------------------------

带有批归一化和激活函数的1×1卷积层

----------------------------------

def conv_1x1_bn(inp, oup): return nn.Sequential( nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), HardSwish() )

-------------------------

MobileNetV3的倒残差网络

-------------------------

class InvertedResidual(nn.Module): def init(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs): super(InvertedResidual, self).init() assert stride in [1, 2]

    self.identity = stride == 1 and inp == oup

    if inp == hidden_dim:
        self.conv = nn.Sequential(
            #----------------------------------------------#
            # 进行3x3的逐层卷积, 进行跨特征点的特征提取(dw)
            #----------------------------------------------#
            nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                      bias=False),
            nn.BatchNorm2d(hidden_dim),
            #  HardSwish() if use_hs else nn.ReLU(inplace=True)
            HardSwish() if use_hs else nn.ReLU6(inplace=True),

            #---------------#
            # 使用SE模块
            #---------------# 
            SEInvertedBottleneck(hidden_dim) if use_se else nn.Identity(),

            #---------------------------------------#
            # 利用1x1卷积进行通道数的调整(pw-linear)
            #---------------------------------------#
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        )
    else:
        self.conv = nn.Sequential(
            #---------------------------------#
            # 利用1x1卷积进行通道数的调整(pw)
            #---------------------------------#
            nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
            nn.BatchNorm2d(hidden_dim),
            HardSwish() if use_hs else nn.ReLU(inplace=True),

            #----------------------------------------------#
            # 进行3x3的逐层卷积, 进行跨特征点的特征提取(dw)
            #----------------------------------------------#
            nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                      bias=False),
            nn.BatchNorm2d(hidden_dim),

            #---------------#
            # 使用SE模块
            #---------------# 
            SEInvertedBottleneck(hidden_dim) if use_se else nn.Identity(),
            HardSwish() if use_hs else nn.ReLU(inplace=True),

            #----------------------------------------#
            # 利用1x1卷积进行通道数的调整(pw-linear)
            #----------------------------------------#
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        )

def forward(self, x):
    if self.identity:
        return x + self.conv(x)
    else:
        return self.conv(x)

class MobileNetV3(nn.Module): def init(self, cfgs, mode, num_classes=1000, width_mult=1.): super(MobileNetV3, self).init()

    #------------------#
    # 设置倒残差网络块
    #------------------#
    self.cfgs = cfgs
    assert mode in ['large', 'small']

    #--------------#
    # 构建第一层
    #--------------#
    input_channel = _make_divisible(16 * width_mult, 8)

    #------------------------------#
    # 对特征层进行高和宽的压缩
    # 1024×2048×3-->512×1024×3
    #------------------------------#
    self.features = [conv_3x3_bn(3, input_channel, 2)]

    #------------------#
    # 构建倒残差网络块
    #------------------#
    block = InvertedResidual

    exp_size = None
    output_channel = None

    for i in range(4):
        for k, t, c, use_se, use_hs, s in self.cfgs[i]:
            output_channel = _make_divisible(c * width_mult, 8)
            exp_size = _make_divisible(input_channel * t, 8)
            self.features.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
            input_channel = output_channel

    #------------------#
    # 构建最后几层网络
    #------------------#
    self.features = nn.Sequential(*self.features)
    self.conv = conv_1x1_bn(input_channel, exp_size)
    self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

    output_channel = {'large': 1280, 'small': 1024}
    output_channel = _make_divisible(output_channel[mode] * width_mult, 8) if width_mult > 1.0 else output_channel[
        mode]
    self.classifier = nn.Sequential(
        nn.Linear(exp_size, output_channel),
        HardSwish(),
        nn.Dropout(0.2),
        nn.Linear(output_channel, num_classes),
    )

    self._initialize_weights()

def forward(self, x):
    #--------------------------------------#
    # downsample: [2  4  8  16  32]
    #--------------------------------------# 
    x = self.features(x) 
    x = self.conv(x)
    x = self.avgpool(x)
    x = x.view(x.size(0), -1)
    x = self.classifier(x)
    return x

def _initialize_weights(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, math.sqrt(2. / n))
            if m.bias is not None:
                m.bias.data.zero_()
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, nn.Linear):
            m.weight.data.normal_(0, 0.01)
            m.bias.data.zero_()

def mobilenetv3_large(**kwargs): cfgs = [

k, t, c, SE, HS, s

    [[3, 1, 16, 0, 0, 1],
     [3, 4, 24, 0, 0, 2],
     [3, 3, 24, 0, 0, 1]],
    [[5, 3, 40, 1, 0, 2],
     [5, 3, 40, 1, 0, 1],
     [5, 3, 40, 1, 0, 1]],
    [[3, 6, 80, 0, 1, 1],
     [3, 2.5, 80, 0, 1, 1],
     [3, 2.3, 80, 0, 1, 1],
     [3, 2.3, 80, 0, 1, 1],
     [3, 6, 112, 1, 1, 1],
     [3, 6, 112, 1, 1, 1]],
    [[5, 6, 160, 1, 1, 1],
     [5, 6, 160, 1, 1, 1],
     [5, 6, 160, 1, 1, 1]]
]
return MobileNetV3(cfgs, mode='large', **kwargs)

def mobilenetv3_small(**kwargs): cfgs = [

k, t, c, SE, HS, s

    [[3, 1, 16, 1, 0, 2]],
    [[3, 4.5, 24, 0, 0, 2],
     [3, 3.67, 24, 0, 0, 1]],
    [[5, 4, 40, 1, 1, 1],
     [5, 6, 40, 1, 1, 1],
     [5, 6, 40, 1, 1, 1],
     [5, 3, 48, 1, 1, 1],
     [5, 3, 48, 1, 1, 1]],
    [[5, 6, 96, 1, 1, 1],
     [5, 6, 96, 1, 1, 1],
     [5, 6, 96, 1, 1, 1]],
]

return MobileNetV3(cfgs, mode='small', **kwargs)

def load_and_convert(net, state_dict): net_dict = net.state_dict().copy() net_list = list(net_dict.keys()) trained_list = list(state_dict.keys()) assert len(net_list) == len(trained_list), 'Learning parameters do not match, check net and trained state_dict' for i in range(len(net_list)): net_dict[net_list[i]] = state_dict[trained_list[i]] net.load_state_dict(net_dict)

def build_mobilenetv3_large(pretrained=True, width_mult=1.): net = mobilenetv3_large(width_mult=width_mult) if pretrained: eps = 1e-5 if abs(1.0 - width_mult) < eps: weights = './model_pretrain/mobilenetv3-large-1cd25616.pth' state_dict = torch.load(weights) elif abs(0.75 - width_mult) < eps: weights = './model_pretrain/mobilenetv3-large-0.75-9632d2a8.pth' state_dict = torch.load(weights) else: raise RuntimeError("Not support width_mult: {}".format(width_mult)) load_and_convert(net, state_dict) return net

def build_mobilenetv3_small(pretrained=True, width_mult=1.): net = mobilenetv3_small(width_mult=width_mult) if pretrained: eps = 1e-5 if abs(1.0 - width_mult) < eps: weights = './model_pretrain/mobilenetv3-small-55df8e1f.pth' state_dict = torch.load(weights) elif abs(0.75 - width_mult) < eps: weights = './model_pretrain/mobilenetv3-small-0.75-86c972c3.pth' state_dict = torch.load(weights) else: raise RuntimeError("Not support width_mult: {}".format(width_mult)) load_and_convert(net, state_dict) return net

if name == 'main':

def params(net):
    return sum(param.numel() for param in net.parameters())

net = build_mobilenetv3_large(pretrained=False, width_mult=1.)
input = torch.randn((1, 3, 224, 224))
out = net(input)
print('Out shape ', out.size())
Voyagerlemon commented 1 year ago

这是构建的mobilenetv3.py的代码，请问在deeplab_plus.py中如何使用这个模块呢？

Voyagerlemon commented 1 year ago

@Leochen9 目前还没有呢

bubbliiiing commented 1 year ago

额，你这个貌似只是作为backbone的mobilenetv3 你有看mobilenetv2的调用方式吗

DeepSpace98 commented 1 year ago

额，你这个貌似只是作为backbone的mobilenetv3 你有看mobilenetv2的调用方式吗

佬，可以发一个怎么把mobilenetv3做backbone的代码吗？还有就是我要训练自己数据集的话，是不是最好先用vooc训练个模型作为预训练模型

bubbliiiing commented 1 year ago

额，你这个貌似只是作为backbone的mobilenetv3 你有看mobilenetv2的调用方式吗

佬，可以发一个怎么把mobilenetv3做backbone的代码吗？还有就是我要训练自己数据集的话，是不是最好先用vooc训练个模型作为预训练模型

imagenet就行，我周末有空可能会弄弄

bklee1213 commented 8 months ago

额，你这个貌似只是作为backbone的mobilenetv3 你有看mobilenetv2的调用方式吗

佬，可以发一个怎么把mobilenetv3做backbone的代码吗？还有就是我要训练自己数据集的话，是不是最好先用vooc训练个模型作为预训练模型

imagenet就行，我周末有空可能会弄弄

大佬，最后有弄mobilenetv3作为backbone的版本吗

Dasenyoung commented 8 months ago

额，你这个貌似只是作为backbone的mobilenetv3 你有看mobilenetv2的调用方式吗

佬，可以发一个怎么把mobilenetv3做backbone的代码吗？还有就是我要训练自己数据集的话，是不是最好先用vooc训练个模型作为预训练模型

imagenet就行，我周末有空可能会弄弄

大佬，最后有弄mobilenetv3作为backbone的版本吗

同求

ylj183 commented 7 months ago

额，你这个貌似只是作为backbone的mobilenetv3 你有看mobilenetv2的调用方式吗

佬，可以发一个怎么把mobilenetv3做backbone的代码吗？还有就是我要训练自己数据集的话，是不是最好先用vooc训练个模型作为预训练模型

imagenet就行，我周末有空可能会弄弄

大佬，最后有弄mobilenetv3作为backbone的版本吗

同求