def __getitem__(self, index): _targets = Variable(torch.from_numpy(self._targets[index]).float(), requires_grad=False) _inputs = self._inputs_transform(_targets) if self._use_cuda: _targets = _targets.cuda() _inputs = _inputs.cuda() return _targets, _inputs
时间: 2023-03-21 19:01:50 浏览: 61
这是一个 Python 类的方法,该方法名为 "__getitem__",它接收一个索引 "index" 作为输入,并返回 "_targets" 和 "_inputs" 两个变量。
在方法中,首先将输入的索引用于 "_targets" 数组中,将其转换为 PyTorch 中的张量(Tensor),并将其设置为不需要梯度计算。然后,将 "_targets" 作为输入,调用了另一个函数 "_inputs_transform",将其转换为另一个张量 "_inputs"。
如果使用了 CUDA 加速,那么代码会将 "_targets" 和 "_inputs" 转移到 GPU 上。最后,方法返回 "_targets" 和 "_inputs" 两个变量。
相关问题
""" 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")
```
def train(device, model, opt, loss_fn, train_loader): model.train() epoch_loss = 0 f1 = [] for g in train_loader: g = g.to(device) feat = g.ndata['feat'] label = g.ndata['label'] logits = model(g, feat) loss = loss_fn(logits, label) f1.append(get_f1(logits.detach().cpu().numpy(), label.detach().cpu().numpy())) epoch_loss += loss.data.item() opt.zero_grad() loss.backward() opt.step() return epoch_loss / len(train_loader), np.mean(f1),写一个train_loader以调用该函数
这里提供一个简单的train_loader示例:
```
from torch.utils.data import Dataset, DataLoader
class GraphDataset(Dataset):
def __init__(self, graphs, targets):
self.graphs = graphs
self.targets = targets
def __len__(self):
return len(self.graphs)
def __getitem__(self, idx):
return self.graphs[idx], self.targets[idx]
train_dataset = GraphDataset(train_graphs, train_targets)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
```
其中,`train_graphs` 是一个包含多个图的列表,而 `train_targets` 是相应的目标标签。`GraphDataset` 类将每个图和其对应的标签打包成一个数据样本,并且通过 `DataLoader` 对象以批量的方式进行训练。在训练时,可以使用 `train()` 函数进行模型训练和评估。