利用pytorch框架设计基于DPN92模型的图像检测与分类输出坐标、大小和种类

首先，我们需要安装PyTorch和其他必要的库。可以使用以下命令安装PyTorch：

pip install torch torchvision

其他库可以使用以下命令安装：

pip install numpy pandas matplotlib opencv-python

接下来，我们需要下载DPN92预训练模型的权重。可以使用以下命令下载：

wget https://github.com/c0nn3r/DPN/releases/download/v2.0/DPN92_extra_5k.pth.tar

现在开始设计模型。我们将使用PyTorch中的预训练模型和自定义头来实现图像检测和分类。以下是完整的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import cv2
import numpy as np

# Define the custom head for object detection
class DetectionHead(nn.Module):
    def __init__(self, in_channels, num_classes):
        super(DetectionHead, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(in_channels)
        self.conv3 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(in_channels)
        self.conv4 = nn.Conv2d(in_channels, num_classes * 5, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.relu(self.bn3(self.conv3(x)))
        x = self.conv4(x)
        return x

# Define the model
class DPN92Detection(nn.Module):
    def __init__(self, num_classes):
        super(DPN92Detection, self).__init__()
        self.dpn92 = torch.hub.load('rwightman/pytorch-dpn-pretrained', 'dpn92', pretrained=True)
        self.head = DetectionHead(2688, num_classes)

    def forward(self, x):
        x = self.dpn92.features(x)
        x = F.avg_pool2d(x, kernel_size=7, stride=1)
        x = x.view(x.size(0), -1)
        x = self.head(x)
        return x

# Define the class names
class_names = ['class0', 'class1', 'class2', 'class3', 'class4']

# Load the model and the weights
model = DPN92Detection(num_classes=len(class_names))
model.load_state_dict(torch.load('DPN92_extra_5k.pth.tar', map_location='cpu')['state_dict'])

# Set the model to evaluation mode
model.eval()

# Define the image transformer
image_transforms = transforms.Compose([
    transforms.ToPILImage(),
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

# Load the image
image = cv2.imread('test.jpg')

# Transform the image
input_image = image_transforms(image)
input_image = input_image.unsqueeze(0)

# Make a prediction
with torch.no_grad():
    output = model(input_image)

# Get the class probabilities
class_probs = F.softmax(output[:, :len(class_names)], dim=1)

# Get the bounding box coordinates, sizes and class indices
coords_sizes_classes = output[:, len(class_names):].view(-1, 5)
coords_sizes_classes[:, :2] = torch.sigmoid(coords_sizes_classes[:, :2])
coords_sizes_classes[:, 2] = torch.exp(coords_sizes_classes[:, 2])
coords_sizes_classes[:, 3:5] = torch.argmax(coords_sizes_classes[:, 3:], dim=1).unsqueeze(1)
coords_sizes_classes = coords_sizes_classes.cpu().numpy()

# Filter out the boxes with low confidence
conf_threshold = 0.5
filtered_boxes = coords_sizes_classes[class_probs[0] > conf_threshold]

# Draw the boxes on the image
for box in filtered_boxes:
    x, y, w, h, c = box
    x *= image.shape[1]
    y *= image.shape[0]
    w *= image.shape[1]
    h *= image.shape[0]
    x1 = int(x - w / 2)
    y1 = int(y - h / 2)
    x2 = int(x + w / 2)
    y2 = int(y + h / 2)
    cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
    cv2.putText(image, class_names[int(c)], (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

# Show the image
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

在上面的代码中，我们定义了一个名为`DetectionHead`的自定义头，用于检测图像中的对象，并输出它们的坐标、大小和类别。然后，我们定义了一个名为`DPN92Detection`的模型，该模型使用DPN92预训练模型和自定义头进行图像检测和分类。我们还定义了一些变量，如类名、图像变换器、置信度阈值等。最后，我们将模型和权重加载到内存中，并使用`cv2`库加载图像。我们将图像传递给模型，然后使用`softmax`函数获取类别概率，使用`sigmoid`和`exp`函数获取边界框的坐标和大小，并使用`argmax`函数获取类别索引。最后，我们过滤掉低置信度的边界框，并将它们绘制在原始图像上。