batch data con only contains: tensor, numpy.ndarray, dict, list, number, but got <class 'PIL.Image.Image'>

这个错误通常出现在使用深度学习框架训练模型时，输入数据的格式不正确。在深度学习中，常用的输入数据类型包括张量（Tensor）、Numpy 数组、字典、列表、数值等，但是 PIL.Image.Image 类型的数据不在支持的数据类型范围内，所以会报错。

如果要使用 PIL.Image.Image 类型的数据作为输入，需要将其转换为支持的数据类型，例如将其转换为 Numpy 数组或张量。可以使用 PIL 库提供的方法将图片转换为 Numpy 数组，然后再将其转换为张量。示例代码如下：

from PIL import Image
import numpy as np
import torch

# 读取图片并转换为 Numpy 数组
img = Image.open('example.jpg')
img_array = np.array(img)

# 将 Numpy 数组转换为张量
img_tensor = torch.from_numpy(img_array)

这样就可以将 PIL.Image.Image 类型的数据转换为支持的数据类型，然后再进行训练或预测。

相关问题

#encoding=utf8 import torch import torch.nn as nn from torch.autograd import Variable import torch.utils.data as Data import torchvision import os if os.path.exists('/data/workspace/course/step3/cnn.pkl'): os.remove('/data/workspace/course/step3/cnn.pkl') #加载数据 train_data = torchvision.datasets.MNIST( root='/data/workspace/course/step3/mnist', train=True, # this is training data transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to download=False, ) #取6000个样本为训练集 train_data_tiny = [] for i in range(6000): train_data_tiny.append(train_data[i]) train_data = train_data_tiny #********* Begin *********# #********* End *********# #保存模型 torch.save(cnn.state_dict(), '/data/workspace/course/step3/cnn.pkl') 帮我补全begin-end区域代码

# 定义卷积神经网络模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential( # input shape (1, 28, 28)
nn.Conv2d(
in_channels=1, # input height
out_channels=16, # n_filters
kernel_size=5, # filter size
stride=1, # filter movement/step
padding=2,
), # output shape (16, 28, 28)
nn.ReLU(), # activation
nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)
)
self.conv2 = nn.Sequential( # input shape (16, 14, 14)
nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
nn.ReLU(), # activation
nn.MaxPool2d(2), # output shape (32, 7, 7)
)
self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes

def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
output = self.out(x)
return output

# 实例化卷积神经网络模型
cnn = CNN()

# 定义损失函数和优化器
loss_func = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=0.001)

# 定义数据集和数据加载器
train_loader = Data.DataLoader(dataset=train_data, batch_size=64, shuffle=True)

# 训练模型
for epoch in range(1):
for step, (x, y) in enumerate(train_loader):
b_x = Variable(x) # batch x
b_y = Variable(y) # batch y
output = cnn(b_x)
loss = loss_func(output, b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 100 == 0:
test_output = cnn(torch.unsqueeze(train_data_tiny[0][0], dim=0))
pred_y = torch.max(test_output, 1)[1].data.squeeze()
accuracy = sum(pred_y == train_data_tiny[0][1]) / float(train_data_tiny[0][1].size)
print('Epoch:', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)

# 保存模型
torch.save(cnn.state_dict(), '/data/workspace/course/step3/cnn.pkl')

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。