with open('your_file_path.txt', 'r') as file: data = file.read()将数据读为tensor类型

您可以使用以下代码将读入的数据转换为PyTorch张量：

import torch

with open('your_file_path.txt', 'r') as file:
    data = file.read()

data = data.strip().split()  # split the data into a list of strings
tensor_data = torch.tensor([int(d) for d in data])  # convert the list to a PyTorch tensor

请注意，此代码假设文件中的数据已经是数字，每个数字之间使用空格分隔。如果数据以其他格式存在，请根据需要修改代码。

相关问题

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，你的问题是如何将上述代码修改为对指定文件夹下的所有文件进行预测，并绘制混淆矩阵。

要实现这个功能，你需要进行以下几个步骤：

1. 导入必要的包：

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

2. 定义函数 `predict_folder`，用于对指定文件夹下的所有文件进行预测：

   def predict_folder(folder_path, model_weight_path, json_path, img_size, num_classes, device):
       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])
       ])

       # read class_indict
       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_weight_path, map_location=device))
       model.eval()

       y_true = []
       y_pred = []

       for root, dirs, files in os.walk(folder_path):
           for file in files:
               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)

               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()

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

       return y_true, y_pred

这个函数接受五个参数：

- `folder_path`：要预测的文件夹路径。
- `model_weight_path`：模型权重文件路径。
- `json_path`：类别标签文件路径。
- `img_size`：输入图片的大小。
- `num_classes`：分类器的类别数。
- `device`：设备类型。

函数会返回两个列表 `y_true` 和 `y_pred`，分别代表真实标签和预测标签。

3. 加载类别标签：

   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)

4. 调用 `predict_folder` 函数进行预测：

   folder_path = './test'
   assert os.path.exists(folder_path), "folder: '{}' dose not exist.".format(folder_path)

   y_true, y_pred = predict_folder(folder_path, "./weights/best_model.pth", json_path, 224, 5, device)

这里假设要预测的文件夹路径为 `./test`，模型权重文件路径为 `./weights/best_model.pth`，输入图片大小为 224，分类器的类别数为 5。

5. 绘制混淆矩阵：

   cm = confusion_matrix(y_true, y_pred)

   fig, ax = plt.subplots()

   im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
   ax.figure.colorbar(im, ax=ax)
   ax.set(xticks=np.arange(cm.shape[1]),
          yticks=np.arange(cm.shape[0]),
          xticklabels=list(class_indict.values()), yticklabels=list(class_indict.values()),
          title='Confusion matrix',
          ylabel='True label',
          xlabel='Predicted label')

   plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
            rotation_mode="anchor")

   fmt = 'd'
   thresh = cm.max() / 2.
   for i in range(cm.shape[0]):
       for j in range(cm.shape[1]):
           ax.text(j, i, format(cm[i, j], fmt),
                   ha="center", va="center",
                   color="white" if cm[i, j] > thresh else "black")
   fig.tight_layout()
   plt.show()

这里使用了 `sklearn.metrics` 中的 `confusion_matrix` 函数进行混淆矩阵的计算。然后使用 `matplotlib` 绘制混淆矩阵图像。

yolov1的软件代码解读 class VOCDataset(torch.utils.data.Dataset): def __init__(self, csv_file, img_dir, label_dir, S=7, B=2, C=20, transform=None): self.annotations = pd.read_csv(csv_file) self.img_dir = img_dir self.label_dir = label_

dir self.S = S # S: 每个cell预测的边界框数量 self.B = B # B: 每个边界框预测的坐标数 self.C = C # C: 物体类别数 self.transform = transform def __len__(self): return len(self.annotations) def __getitem__(self, index): label_path = os.path.join(self.label_dir, self.annotations.iloc[index, 1]) boxes = [] with open(label_path) as f: for label in f.readlines(): class_label, x, y, width, height = [float(x) if float(x) != int(float(x)) else int(x) for x in label.replace("\n", "").split()] boxes.append([class_label, x, y, width, height]) img_path = os.path.join(self.img_dir, self.annotations.iloc[index, 0]) image = Image.open(img_path) boxes = torch.tensor(boxes) if self.transform: image, boxes = self.transform(image, boxes) label_matrix = torch.zeros((self.S, self.S, self.C + 5 * self.B)) for box in boxes: class_label, x, y, width, height = box class_label = int(class_label) # i,j represents the cell row and cell column i, j = int(self.S * y), int(self.S * x) x_cell, y_cell = self.S * x - j, self.S * y - i # width and height of cell width_cell, height_cell = (width * self.S, height * self.S) if label_matrix[i, j, 20] == 0: # Set that there exists an object # Coordinates of bounding box box_coordinates = torch.tensor([x_cell, y_cell, width_cell, height_cell]) label_matrix[i, j, 20:25] = box_coordinates # Set the class label_matrix[i, j, class_label] = 1 # Set no-object confidence to 0 label_matrix[i, j, 25:] = torch.tensor([0] * 5) return image, label_matrix

这是一个 PyTorch 中的数据集类，用于加载 Pascal VOC 数据集的图片和标签。在 `__init__` 方法中，我们读取了 CSV 文件，设置了图片和标签的路径，以及每个 cell 预测的边界框数、每个边界框预测的坐标数和物体类别数。在 `__getitem__` 方法中，我们首先读取了标签文件，解析出每个边界框的类别、坐标和大小，然后加载对应的图片，对图片和标签进行了预处理，并最终返回图片和标签矩阵。其中，标签矩阵的大小为 `(S, S, C + 5B)`，其中 `S` 表示每个 cell 的大小，`C` 表示物体类别数，`B` 表示每个边界框预测的坐标数，这里为 4 个坐标值和 1 个置信度值。在标签矩阵中，每个 cell 中包含了表示是否存在物体的置信度值和对应边界框的坐标和类别信息。