将C盘不规则图片整形为224×224的深度学习模型输入，并引入动态卷积到densenet 结合inception代码

首先，将不规则图片整形为224×224大小的图片可以使用OpenCV库进行操作，具体代码如下：

import cv2

img = cv2.imread("C:/image.jpg")
resized_img = cv2.resize(img, (224, 224))
cv2.imwrite("C:/resized_image.jpg", resized_img)

接下来，我们需要引入动态卷积到DenseNet结合Inception代码中。动态卷积是一种可以根据输入数据自适应地调整卷积核大小的卷积方法，可以提高模型的泛化能力。我们可以使用PyTorch框架实现动态卷积，具体代码如下：

import torch
import torch.nn as nn

class DynamicConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, padding=0, stride=1, dilation=1, groups=1,
                 bias=True, eps=1e-5, momentum=0.1, weight_decay=0.01):
        super(DynamicConv2d, self).__init__()

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.padding = padding
        self.stride = stride
        self.dilation = dilation
        self.groups = groups
        self.bias = bias
        self.eps = eps
        self.momentum = momentum
        self.weight_decay = weight_decay

        self.bn = nn.BatchNorm2d(in_channels, eps=eps, momentum=momentum)
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
                              padding=padding, stride=stride, dilation=dilation, groups=groups, bias=bias)
        self.relu = nn.ReLU(inplace=True)

        # weight
        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size))
        nn.init.kaiming_uniform_(self.weight, a=1)

        # bias
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
            nn.init.constant_(self.bias, 0)

    def forward(self, x):
        x = self.bn(x)
        weight = self.weight.abs() / (self.bn.weight.abs().unsqueeze(2).unsqueeze(3) + self.eps)
        weight = weight / (torch.sum(weight, dim=[2, 3], keepdim=True) + self.eps)
        x = nn.functional.conv2d(x, weight, bias=None, stride=self.stride,
                                 padding=self.padding, dilation=self.dilation, groups=self.groups)
        if self.bias is not None:
            x = x + self.bias.view(1, -1, 1, 1).expand_as(x)
        x = self.relu(x)
        return x

这里我们使用了BatchNorm2d来对输入进行归一化，然后使用动态卷积进行卷积操作，最后通过ReLU函数进行非线性变换。同时，在初始化时需要对权重进行Xavier初始化，对偏置进行常数初始化。

最后，我们需要将动态卷积结合DenseNet和Inception代码进行实现。具体代码如下：

import torch
import torch.nn as nn

class DenseNet_Inception(nn.Module):
    def __init__(self, num_classes=1000, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64,
                 bn_size=4, drop_rate=0, eps=1e-5, momentum=0.1, weight_decay=0.01):
        super(DenseNet_Inception, self).__init__()

        self.features = nn.Sequential()

        # First convolution
        self.features.add_module('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3,
                                                     bias=False))
        self.features.add_module('norm0', nn.BatchNorm2d(num_init_features, eps=eps, momentum=momentum))
        self.features.add_module('relu0', nn.ReLU(inplace=True))
        self.features.add_module('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = DenseBlock(num_layers=num_layers, num_input_features=num_features,
                               bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate,
                               eps=eps, momentum=momentum, weight_decay=weight_decay)
            self.features.add_module('denseblock%d' % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = InceptionTransition(num_input_features=num_features, eps=eps, momentum=momentum,
                                             weight_decay=weight_decay)
                self.features.add_module('trans%d' % (i + 1), trans)
                num_features = num_features // 2

        # Final batch norm
        self.features.add_module('norm5', nn.BatchNorm2d(num_features, eps=eps, momentum=momentum))

        # Linear layer
        self.classifier = nn.Linear(num_features, num_classes)

        # Initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        features = self.features(x)
        out = nn.functional.avg_pool2d(features, kernel_size=7, stride=1).view(features.size(0), -1)
        out = self.classifier(out)
        return out


class InceptionTransition(nn.Module):
    def __init__(self, num_input_features, eps=1e-5, momentum=0.1, weight_decay=0.01):
        super(InceptionTransition, self).__init__()

        self.num_input_features = num_input_features

        self.bn = nn.BatchNorm2d(num_input_features, eps=eps, momentum=momentum)
        self.conv = nn.Conv2d(num_input_features, num_input_features // 2, kernel_size=1, bias=False)
        self.dynamic_conv = DynamicConv2d(num_input_features // 2, num_input_features // 2, kernel_size=3, padding=1,
                                          bias=False, eps=eps, momentum=momentum, weight_decay=weight_decay)
        self.pool = nn.AvgPool2d(kernel_size=2, stride=2)

    def forward(self, x):
        x = self.bn(x)
        x = nn.functional.relu(x, inplace=True)
        x = self.conv(x)
        x = self.dynamic_conv(x)
        x = self.pool(x)
        return x


class Bottleneck(nn.Module):
    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, eps=1e-5, momentum=0.1,
                 weight_decay=0.01):
        super(Bottleneck, self).__init__()

        self.bn1 = nn.BatchNorm2d(num_input_features, eps=eps, momentum=momentum)
        self.conv1 = nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(bn_size * growth_rate, eps=eps, momentum=momentum)
        self.conv2 = DynamicConv2d(bn_size * growth_rate, growth_rate, kernel_size=3, padding=1, bias=False,
                                   eps=eps, momentum=momentum, weight_decay=weight_decay)
        self.drop_rate = drop_rate

    def forward(self, x):
        x = self.bn1(x)
        x = nn.functional.relu(x, inplace=True)
        x = self.conv1(x)
        x = self.bn2(x)
        x = nn.functional.relu(x, inplace=True)
        x = self.conv2(x)
        if self.drop_rate > 0:
            x = nn.functional.dropout(x, p=self.drop_rate, training=self.training)
        return x


class DenseBlock(nn.Module):
    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, eps=1e-5, momentum=0.1,
                 weight_decay=0.01):
        super(DenseBlock, self).__init__()

        self.num_layers = num_layers
        self.num_input_features = num_input_features
        self.bn_size = bn_size
        self.growth_rate = growth_rate
        self.drop_rate = drop_rate

        for i in range(num_layers):
            layer = Bottleneck(num_input_features=num_input_features + i * growth_rate,
                               growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate,
                               eps=eps, momentum=momentum, weight_decay=weight_decay)
            self.add_module('denselayer%d' % (i + 1), layer)

    def forward(self, x):
        features = [x]
        for i in range(self.num_layers):
            layer = getattr(self, 'denselayer%d' % (i + 1))
            new_features = layer(features[-1])
            features.append(new_features)
        return torch.cat(features, 1)

这里我们使用了DenseNet和Inception的模型结构，并将动态卷积引入到InceptionTransition中。同时，我们需要对所有的卷积层进行Xavier初始化，对所有的BatchNorm层进行$\gamma$参数初始化为1，$\beta$参数初始化为0，对所有的全连接层进行正态分布初始化。在Bottleneck中，我们使用了动态卷积代替了传统卷积。

以上就是将C盘不规则图片整形为224×224的深度学习模型输入，并引入动态卷积到DenseNet结合Inception的代码实现。