这是什么意思:data_folder = args.data data = Data(data_folder)
时间: 2024-06-04 12:11:24 浏览: 15
这段代码的意思是创建一个名为"data_folder"的变量,并将其值设置为"args.data",然后将"data_folder"变量的值作为参数传递给名为"Data"的函数,从而创建一个名为"data"的数据对象。具体来说,该数据对象可能包含用于训练或测试机器学习模型的数据集、标签、特征等信息。
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下面这段代码的作用是什么class CasSeqGCNTrainer(object): def __init__(self, args): self.args = args self.setup_model() def enumerate_unique_labels_and_targets(self): """ Enumerating the features and targets. """ print("\nEnumerating feature and target values.\n") #枚举数据集 ending = "*.json" self.graph_paths = sorted(glob.glob(self.args.graph_folder + ending), key = os.path.getmtime)#获取self.args.graph_folder目录下所有的json文件 features = set() data_dict = dict() for path in tqdm(self.graph_paths):#加载所有的json文件,将数据存储在上面的features和data_dict中 data = json.load(open(path)) data_dict = data for i in range(0, len(data) - self.args.sub_size): graph_num = 'graph_' + str(i) features = features.union(set(data[graph_num]['labels'].values())) self.number_of_nodes = self.args.number_of_nodes self.feature_map = utils.create_numeric_mapping(features) #依赖的其他文件提供的能力,看上去是将数据集根据特性进行整理 self.number_of_features = len(self.feature_map)#将特性的map的长度赋值给特性数量
这段代码定义了一个名为 CasSeqGCNTrainer 的类,它包含了初始化函数 __init__(self, args) 和一个枚举数据集的函数 enumerate_unique_labels_and_targets(self)。其中,初始化函数接收一个参数 args,表示训练器的一些配置参数;setup_model() 方法用于构建模型。而枚举数据集的函数则用于加载数据集,将数据存储在 data_dict 中,并枚举所有数据中出现的特征(features)和目标(targets)。最终,该类还定义了两个实例变量:number_of_nodes 表示节点数量,feature_map 是一个字典,用于将特征映射到数字编号。
""" 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")
```
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