打印conv22_cv2_0_2的形状
时间: 2023-08-04 15:05:13 浏览: 52
要打印 conv22_cv2_0_2 层的形状,您可以使用以下代码:
```cpp
Dims conv22_cv2_0_2_outputDims = conv22_cv2_0_2->getOutput(0)->getDimensions();
std::cout << "conv22_cv2_0_2 output shape: " << conv22_cv2_0_2_outputDims.d[0] << " x " << conv22_cv2_0_2_outputDims.d[1] << " x " << conv22_cv2_0_2_outputDims.d[2] << " x " << conv22_cv2_0_2_outputDims.d[3] << std::endl;
```
这将打印出 conv22_cv2_0_2 层输出的形状,其中 conv22_cv2_0_2_outputDims 是一个 Dims 类型的对象,包含了输出的高度、宽度和通道数。请确保在使用之前已经创建了网络并添加了相应的层。
相关问题
conv22_cv2_0_2的信息
要打印 conv22_cv2_0_2 层的权重和偏置信息,您可以使用以下代码:
```cpp
const float* conv22_cv2_0_2_weights = reinterpret_cast<const float*>(weightMap["model.22.cv2.0.2.weight"].values);
const float* conv22_cv2_0_2_bias = reinterpret_cast<const float*>(weightMap["model.22.cv2.0.2.bias"].values);
int conv22_cv2_0_2_weightSize = weightMap["model.22.cv2.0.2.weight"].count;
int conv22_cv2_0_2_biasSize = weightMap["model.22.cv2.0.2.bias"].count;
std::cout << "conv22_cv2_0_2 weights:" << std::endl;
for (int i = 0; i < conv22_cv2_0_2_weightSize; i++) {
std::cout << conv22_cv2_0_2_weights[i] << " ";
}
std::cout << std::endl;
std::cout << "conv22_cv2_0_2 bias:" << std::endl;
for (int i = 0; i < conv22_cv2_0_2_biasSize; i++) {
std::cout << conv22_cv2_0_2_bias[i] << " ";
}
std::cout << std::endl;
```
这将打印出 conv22_cv2_0_2 层的权重和偏置值。请确保在使用之前已经正确加载了权重和偏置。
ITensor* inputTensor22_0[] = { conv22_cv2_0_2->getOutput(0), conv22_cv3_0_2->getOutput(0) }; 打印一下conv22_cv3_0_2的形状
要打印 `conv22_cv3_0_2` 的形状,您可以使用 `getDimensions()` 方法获取张量的维度信息,并使用 `nbDims()` 方法获取维度的数量。以下是一种示例代码:
```cpp
ITensor* conv22_cv3_0_2_tensor = conv22_cv3_0_2->getOutput(0);
nvinfer1::Dims dims = conv22_cv3_0_2_tensor->getDimensions();
std::cout << "conv22_cv3_0_2 shape: ";
for (int i = 0; i < dims.nbDims; ++i) {
std::cout << dims.d[i] << " ";
}
std::cout << std::endl;
```
上述代码会打印出 `conv22_cv3_0_2` 张量的形状信息,例如 `[batch_size, channels, height, width]`。
请注意,具体的维度顺序和数量可能因您的模型和框架而有所不同,您需要根据实际情况进行适当的调整。