torch.argmax dim=1 和axis

torch.argmax(dim=1)和axis的作用是一样的，都是在指定维度上取最大值的索引。在PyTorch中，dim是指定维度的参数，而在NumPy和Pandas中，axis是指定维度的参数。两者的用法类似，只是名称不同。例如，对于一个2维的张量，dim=0表示在行上进行操作，dim=1表示在列上进行操作。

相关问题

torch.argmax(label_count, dim=1)

This code will return the index of the maximum value in each row of the tensor "label_count".

For example, if "label_count" is a tensor of shape (3, 5), where each row represents the count of a label in a dataset of 5 classes, the code will return a tensor of shape (3,) containing the index of the most frequent label in each row.

Note that "dim=1" specifies that the operation is performed along the second axis (columns) of the tensor.

这是对单个文件进行预测“import os import json import torch from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt from model import convnext_tiny as create_model def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(f"using {device} device.") num_classes = 5 img_size = 224 data_transform = transforms.Compose( [transforms.Resize(int(img_size * 1.14)), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # load image img_path = "../tulip.jpg" assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path) img = Image.open(img_path) plt.imshow(img) # [N, C, H, W] img = data_transform(img) # expand batch dimension img = torch.unsqueeze(img, dim=0) # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path) with open(json_path, "r") as f: class_indict = json.load(f) # create model model = create_model(num_classes=num_classes).to(device) # load model weights model_weight_path = "./weights/best_model.pth" model.load_state_dict(torch.load(model_weight_path, map_location=device)) model.eval() with torch.no_grad(): # predict class output = torch.squeeze(model(img.to(device))).cpu() predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)], predict[predict_cla].numpy()) plt.title(print_res) for i in range(len(predict)): print("class: {:10} prob: {:.3}".format(class_indict[str(i)], predict[i].numpy())) plt.show() if name == 'main': main()”，改为对指定文件夹下的左右文件进行预测，并绘制混淆矩阵，

好的，为了实现您的需求，您需要进行以下几个步骤：

1. 加载模型和类别标签：

您需要在代码中加载您的模型和类别标签。您可以使用之前的代码中的方式来加载它们，只需要做一些小的改动即可。例如，您需要将模型和类别标签的路径作为参数传递给您的函数。

2. 加载测试集：

您需要加载您的测试集。您可以使用 `torchvision.datasets.ImageFolder` 来加载测试集。这个函数会将每个文件夹中的所有图像文件都加载到一个 tensor 中，并自动为每个文件夹分配一个标签。

3. 进行预测：

您需要对测试集中的每个图像进行预测，并将预测结果与真实标签进行比较。您可以使用之前的代码中的方式来预测每个图像，只需要做一些小的改动即可。例如，您需要将预测结果保存到一个列表中，并将真实标签保存到另一个列表中。

4. 绘制混淆矩阵：

最后，您需要使用预测结果和真实标签来绘制混淆矩阵。您可以使用 `sklearn.metrics.confusion_matrix` 来计算混淆矩阵，并使用 `matplotlib` 来绘制它。

下面是修改后的代码示例：

import os
import json
import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import numpy as np
from model import convnext_tiny as create_model

def predict_folder(model_path, json_path, folder_path):
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print(f"using {device} device.")
    num_classes = 5
    img_size = 224

    data_transform = transforms.Compose([
        transforms.Resize(int(img_size * 1.14)),
        transforms.CenterCrop(img_size),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

    # load class_indict json
    with open(json_path, "r") as f:
        class_indict = json.load(f)

    # create model
    model = create_model(num_classes=num_classes).to(device)

    # load model weights
    model.load_state_dict(torch.load(model_path, map_location=device))
    model.eval()

    y_true = []
    y_pred = []

    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if file.endswith(".jpg") or file.endswith(".jpeg"):
                img_path = os.path.join(root, file)
                assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)

                img = Image.open(img_path)

                # [N, C, H, W]
                img = data_transform(img)

                # expand batch dimension
                img = torch.unsqueeze(img, dim=0)

                # predict class
                with torch.no_grad():
                    output = torch.squeeze(model(img.to(device))).cpu()
                    predict = torch.softmax(output, dim=0)
                    predict_cla = torch.argmax(predict).numpy()

                y_true.append(class_indict[os.path.basename(root)])
                y_pred.append(predict_cla)

    # plot confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    fig, ax = plt.subplots(figsize=(5, 5))
    ax.imshow(cm, cmap=plt.cm.Blues, aspect='equal')
    ax.set_xlabel('Predicted label')
    ax.set_ylabel('True label')
    ax.set_xticks(np.arange(len(class_indict)))
    ax.set_yticks(np.arange(len(class_indict)))
    ax.set_xticklabels(class_indict.values(), rotation=90)
    ax.set_yticklabels(class_indict.values())
    ax.tick_params(axis=u'both', which=u'both',length=0)
    for i in range(len(class_indict)):
        for j in range(len(class_indict)):
            text = ax.text(j, i, cm[i, j], ha="center", va="center", color="white" if cm[i, j] > cm.max() / 2. else "black")
    fig.tight_layout()
    plt.show()

if __name__ == '__main__':
    # set the paths for the model, class_indict json, and test data folder
    model_path = './weights/best_model.pth'
    json_path = './class_indices.json'
    folder_path = './test_data'

    predict_folder(model_path, json_path, folder_path)

请注意，这个函数的参数需要您自己根据您的实际情况进行设置，以匹配模型、类别标签和测试集的路径。