更改--img-size,需要更改模型的哪个地方
时间: 2024-06-04 07:07:39 浏览: 10
更改 `img-size` 通常需要更改模型的输入层。在大多数情况下,输入层是一个卷积层或全连接层,它的输入形状与 `img-size` 相关。因此,您需要更改这个输入层的形状,使其适应新的 `img-size`。
具体来说,如果您要增大 `img-size`,则需要将输入层中的卷积核或全连接层的权重矩阵重新初始化,以适应新的输入形状。如果您要减小 `img-size`,您可以通过剪裁权重矩阵或添加 padding 等方式来调整输入层,以适应新的输入形状。
相关问题
生成基于自己创建的CNN回归模型的Grad-CAM PyTorch实现代码
以下是一个基于自己创建的CNN回归模型的Grad-CAM PyTorch实现代码的示例:
```python
import torch
import torch.nn as nn
from torch.autograd import Function
import cv2
import numpy as np
class GradCAM:
def __init__(self, model, target_layer):
self.model = model
self.target_layer = target_layer
self.feature_maps = None
self.gradient = None
self.activations = []
def save_gradient(self, grad):
self.gradient = grad
def forward(self, x):
self.feature_maps = []
self.activations = [x]
for idx, layer in enumerate(self.model.features):
x = layer(x)
self.activations.append(x)
if idx == self.target_layer:
self.feature_maps.append(x)
x.register_hook(self.save_gradient)
x = self.model.avgpool(x)
x = x.view(x.size(0), -1)
x = self.model.classifier(x)
return x
def backward(self):
gradient = self.gradient[0]
weights = torch.mean(gradient, axis=(2, 3), keepdims=True)
activations = self.feature_maps[0]
grad_CAM = torch.sum(weights * activations, axis=1, keepdims=True)
grad_CAM = nn.functional.relu(grad_CAM)
return grad_CAM.squeeze().cpu().numpy()
def get_cam(img_path, model, target_layer):
model.eval()
img = cv2.imread(img_path)
img = cv2.resize(img, (224, 224))
img = np.float32(img) / 255
x = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0)
grad_cam = GradCAM(model, target_layer)
output = grad_cam.forward(x)
output.backward()
cam = grad_cam.backward()
cam = cv2.resize(cam, (224, 224))
cam = cam - np.min(cam)
cam = cam / np.max(cam)
return cam
# Example usage
model = MyCustomCNN() # Replace with your own custom CNN model
target_layer = 5 # Replace with the index of the target layer in your model
img_path = "example.jpg" # Replace with the path to your input image
cam = get_cam(img_path, model, target_layer)
```
请注意,这个示例代码仅适用于输入尺寸为 224x224 的图像,如果你的模型和输入具有不同的尺寸,需要相应地修改代码。此外,Grad-CAM的原理是基于卷积层的特征图,因此在选择目标层时应选择最后一个卷积层。
给出一个CMSIS-NN调用模型并实现预测的完整的代码
以下是一个使用CMSIS-NN调用模型并实现预测的完整代码示例。这个例子使用的是一个预训练的MNIST手写数字识别模型。你需要根据你的模型修改一些参数和调整代码。
```c
#include "arm_math.h"
#include "arm_nnfunctions.h"
#include "arm_nnexamples_cifar10_parameter.h"
#include "arm_nnexamples_cifar10_weights.h"
#include <stdio.h>
#define IMG_SIZE 28*28
#define NUM_CLASSES 10
#define NUM_OUTPUT 10
static q7_t image_data[IMG_SIZE];
static q15_t output_data[NUM_OUTPUT];
int main()
{
arm_status status;
int index;
// Load image data
FILE* fp = fopen("test_image.bin", "rb");
fread(image_data, sizeof(q7_t), IMG_SIZE, fp);
fclose(fp);
// Allocate memory for the convolutional neural network
q7_t* buffer = (q7_t*) malloc(2*CNN_BUFF_SIZE*sizeof(q7_t));
q7_t* scratch = buffer;
q7_t* conv_out = scratch;
scratch += CONV_OUT_SIZE;
q7_t* pool_out = scratch;
scratch += POOL_OUT_SIZE;
q7_t* fc_out = scratch;
scratch += FC_OUT_SIZE;
// Load convolutional neural network parameters
const uint16_t conv_dims[4] = {28, 28, 1, 16};
const uint16_t conv_strides[2] = {1, 1};
const uint16_t conv_padding[2] = {1, 1};
const uint16_t pool_dims[4] = {14, 14, 16, 16};
const uint16_t pool_strides[2] = {2, 2};
const uint16_t pool_padding[2] = {0, 0};
const uint16_t fc_dims[4] = {5, 5, 16, 10};
// Perform convolutional neural network operations
arm_convolve_HWC_q7_basic_nonsquare(image_data, conv_dims, conv_weights, CONV_BIAS, conv_strides, conv_padding, CONV_OUT_CH, CONV_OUT_DIM, conv_out, buffer);
arm_relu_q7(conv_out, CONV_OUT_SIZE);
arm_maxpool_q7_HWC(conv_out, pool_dims, POOL_OUT_CH, pool_strides, pool_padding, pool_out, buffer);
arm_fully_connected_q7_opt(pool_out, fc_weights, FC_BIAS, FC_OUT_DIM, fc_dims, 1, 0, fc_out, buffer);
arm_softmax_q7(fc_out, NUM_CLASSES, output_data);
// Print prediction
float32_t max_val = -3.4028235E+38;
for (index = 0; index < NUM_OUTPUT; index++) {
printf("%d: %d\n", index, output_data[index]);
if (max_val < output_data[index]) {
max_val = output_data[index];
}
}
// Free memory
free(buffer);
return 0;
}
```
你需要根据自己的模型修改以下参数:
- `IMG_SIZE`: 输入图像的大小
- `NUM_CLASSES`: 分类数
- `NUM_OUTPUT`: 输出的大小
- `image_data`: 存储输入图像的数组
- `conv_dims`, `conv_strides`, `conv_padding`, `pool_dims`, `pool_strides`, `pool_padding`, `fc_dims`: 卷积神经网络的参数
- `conv_weights`, `CONV_BIAS`, `fc_weights`, `FC_BIAS`: 卷积神经网络的权重和偏置
你需要将这些参数和模型的权重和偏置导入到你的代码中。最后,你需要编译并运行你的代码,它将输出模型对输入图像的预测结果。
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