class MyTrainData(torch.utils.data.Dataset): def __init__(self, img, transform=None): self.img = img self.transform = transform def __getitem__(self, index): img = self.img[index] img = torch.FloatTensor(img) return img def __len__(self): return len(self.img)
时间: 2024-04-25 08:27:39 浏览: 9
这是一个 PyTorch 中用于定义自定义训练数据集的类。其中,__init__ 方法用于初始化数据集,img 参数为输入的数据,transform 参数为对数据进行的变换操作;__getitem__ 方法则用于根据索引 index 获取对应的数据,并将其转换为 PyTorch 的 Tensor 对象;__len__ 方法则用于获取数据集的长度(即数据的数量)。通过这个类定义,我们可以使用 PyTorch 中的 DataLoader 对象来对数据进行批量读取和处理。
相关问题
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。
""" This code is based on the Torchvision repository, which was licensed under the BSD 3-Clause. """ import os import pickle import sys import numpy as np import torch from PIL import Image from torch.utils.data import Dataset from utils.mypath import MyPath from torchvision.datasets.utils import check_integrity, download_and_extract_archive class simclr_c10(Dataset): base_folder = 'D:/wjd/simclr_c10' filename = "simclr_c10" def __init__(self, root=MyPath.db_root_dir('wjd/simclr_c10/'), train=True, transform=None, download=False): super('wjd/simclr_c10/', self).__init__() self.data = None self.root = root self.transform = transform self.train = train # training set or test set self.classes = ['Al', 'Ag', 'Au', 'Cu', 'W', 'V', 'Ta', 'Mo'] def __getitem__(self, index): """ Args: index (int): Index Returns: dict: {'image': image, 'target': index of target class, 'meta': dict} """ img, target = self.data[index], self.targets[index] img_size = (img.shape[0], img.shape[1]) img = Image.fromarray(img) class_name = self.classes[target] if self.transform is not None: img = self.transform(img) out = {'image': img, 'target': target, 'meta': {'im_size': img_size, 'index': index, 'class_name': class_name}} return out def get_image(self, index): img = self.data[index] return img def __len__(self): return len(self.data) def extra_repr(self): return "Split: {}".format("Train" if self.train is True else "Test") return outside function,这段代码正确吗
这段代码有一些问题:
1. `super('wjd/simclr_c10/', self).__init__()` 这句话不正确,应该是 `super(simclr_c10, self).__init__()`
2. `self.targets` 在代码中没有定义,需要在 `__init__` 函数中将其定义并初始化
3. `return outside function` 这句话不应该出现在类定义中,需要将其删除
4. 另外,`check_integrity` 和 `download_and_extract_archive` 函数在代码中没有使用,可以删除
修正后的代码如下所示:
```
import os
import pickle
import sys
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
from utils.mypath import MyPath
class simclr_c10(Dataset):
base_folder = 'D:/wjd/simclr_c10'
filename = "simclr_c10"
def __init__(self, root=MyPath.db_root_dir('wjd/simclr_c10/'), train=True, transform=None, download=False):
super(simclr_c10, self).__init__()
self.data = None
self.targets = []
self.root = root
self.transform = transform
self.train = train # training set or test set
self.classes = ['Al', 'Ag', 'Au', 'Cu', 'W', 'V', 'Ta', 'Mo']
self._load_data()
def _load_data(self):
if self.train:
data_file = os.path.join(self.root, self.base_folder, 'train.pkl')
else:
data_file = os.path.join(self.root, self.base_folder, 'test.pkl')
with open(data_file, 'rb') as f:
data = pickle.load(f)
self.data = data['data']
self.targets = data['targets']
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
dict: {'image': image, 'target': index of target class, 'meta': dict}
"""
img, target = self.data[index], self.targets[index]
img_size = (img.shape[0], img.shape[1])
img = Image.fromarray(img)
class_name = self.classes[target]
if self.transform is not None:
img = self.transform(img)
out = {'image': img, 'target': target, 'meta': {'im_size': img_size, 'index': index, 'class_name': class_name}}
return out
def get_image(self, index):
img = self.data[index]
return img
def __len__(self):
return len(self.data)
def extra_repr(self):
return "Split: {}".format("Train" if self.train is True else "Test")
```