class TensorDataset(Dataset[Tuple[Tensor, ...]]):这段代码是什么意思
时间: 2023-05-26 17:07:48 浏览: 53
这段代码定义了一个新的类TensorDataset,这个类继承自PyTorch中的Dataset类,表示一个基于Tensor的数据集,每个数据样本可以包含多个Tensor。它的类型参数Tuple[Tensor, ...]表示每个数据样本是一个元组,元组中的元素都是Tensor类型。
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class MyDataSet(Dataset): """自定义数据集""" def __init__(self, images_path: list, images_class: list, transform=None): self.images_path = images_path self.images_class = images_class self.transform = transform def __len__(self): return len(self.images_path) def __getitem__(self, item): img = Image.open(self.images_path[item]) # RGB为彩色图片,L为灰度图片 if img.mode != 'RGB': raise ValueError("image: {} isn't RGB mode.".format(self.images_path[item])) label = self.images_class[item] if self.transform is not None: img = self.transform(img) print(label) return img, label @staticmethod def collate_fn(batch): # 官方实现的default_collate可以参考 # https://github.com/pytorch/pytorch/blob/67b7e751e6b5931a9f45274653f4f653a4e6cdf6/torch/utils/data/_utils/collate.py images, labels = tuple(zip(*batch)) images = torch.stack(images, dim=0) labels = torch.as_tensor(np.array(labels[:, 0])) return images, labels
这段代码是一个自定义的 PyTorch 数据集类 `MyDataSet`,它继承自 PyTorch 内置的 `Dataset` 类。在 `__init__` 方法中,该类接收两个列表 `images_path` 和 `images_class`,分别代表图片的路径和对应的类别。还可以传入一个可选的 `transform` 参数,用于对图片进行预处理。在 `__getitem__` 方法中,该类会根据索引 `item` 加载对应路径的图片,并返回预处理后的图片张量和对应的类别标签。
这个类还定义了一个静态方法 `collate_fn`,它接收一个批次的数据,对其进行处理并返回。在这个方法中,首先使用 Python 的 `zip` 函数和 `*` 运算符将批次中的图片张量和对应的标签分别打包成两个元组。然后使用 `torch.stack` 函数将图片张量拼接成一个大的张量,`dim=0` 表示在第 0 维进行拼接。最后,代码使用 NumPy 的 `array` 函数将标签列表转换为 NumPy 数组,然后使用 `[:, 0]` 语法取出了每个元组中的第一个标签,最后使用 `torch.as_tensor` 函数将其转换为 PyTorch 张量。
这个自定义数据集类的实现比较简单,但需要注意一些细节,比如 `zip` 函数的使用,`labels[:, 0]` 的含义等等。
可以将seg.train转化成未封装好的代码吗
当然可以,以下是将 `seg.train` 转化为未封装的代码的步骤:
1. 导入相关库
```python
import json
import random
from collections import Counter
from functools import partial
from pathlib import Path
from typing import List, Tuple
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
```
2. 准备数据
```python
def load_data(file_path: str) -> List[Tuple[List[str], List[str]]]:
"""加载数据"""
with open(file_path, "r", encoding="utf-8") as f:
data = f.read().splitlines()
data = [line.split("\t") for line in data]
return [(text.split(), label.split()) for text, label in data]
train_data = load_data("train.txt")
valid_data = load_data("dev.txt")
test_data = load_data("test.txt")
```
3. 构建词表
```python
def build_vocab(data: List[Tuple[List[str], List[str]]], min_freq: int = 2) -> Tuple[Dict[str, int], Dict[int, str]]:
"""构建词表"""
counter = Counter([word for text, _ in data for word in text])
counter = {word: freq for word, freq in counter.items() if freq >= min_freq}
word_list = sorted(list(counter.keys()))
word2id = {"[PAD]": 0, "[UNK]": 1, "[CLS]": 2, "[SEP]": 3}
for word in word_list:
word2id[word] = len(word2id)
id2word = {id: word for word, id in word2id.items()}
return word2id, id2word
word2id, id2word = build_vocab(train_data)
```
4. 定义数据处理函数
```python
def process_data(data: List[Tuple[List[str], List[str]]], word2id: Dict[str, int]) -> List[Tuple[List[int], List[int]]]:
"""数据处理函数"""
return [(torch.tensor([word2id.get(word, word2id["[UNK]"]) for word in text]), torch.tensor([int(label) for label in labels])) for text, labels in data]
train_data = process_data(train_data, word2id)
valid_data = process_data(valid_data, word2id)
test_data = process_data(test_data, word2id)
```
5. 定义数据集和数据加载器
```python
class SegDataset(Dataset):
"""分词数据集"""
def __init__(self, data: List[Tuple[List[int], List[int]]]):
self.data = data
def __len__(self):
return len(self.data)
def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor]:
return self.data[index]
def collate_fn(batch: List[Tuple[torch.Tensor, torch.Tensor]]) -> Tuple[torch.Tensor, torch.Tensor]:
"""数据处理函数"""
texts = [item[0] for item in batch]
labels = [item[1] for item in batch]
max_len = max([len(text) for text in texts])
texts = [torch.cat([text, torch.tensor([0] * (max_len - len(text)))]) for text in texts]
labels = [torch.cat([label, torch.tensor([-1] * (max_len - len(label)))]) for label in labels]
mask = torch.tensor([[1] * len(text) + [0] * (max_len - len(text)) for text in texts])
return torch.stack(texts), torch.stack(labels), mask
train_dataset = SegDataset(train_data)
valid_dataset = SegDataset(valid_data)
test_dataset = SegDataset(test_data)
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True, collate_fn=collate_fn)
valid_loader = DataLoader(valid_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)
test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)
```
6. 定义模型
```python
class SegModel(torch.nn.Module):
"""分词模型"""
def __init__(self, vocab_size: int, embedding_size: int, hidden_size: int):
super().__init__()
self.embedding = torch.nn.Embedding(vocab_size, embedding_size, padding_idx=0)
self.lstm = torch.nn.LSTM(embedding_size, hidden_size, batch_first=True, bidirectional=True)
self.linear = torch.nn.Linear(2 * hidden_size, 1)
self.sigmoid = torch.nn.Sigmoid()
def forward(self, inputs: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
embeddings = self.embedding(inputs)
outputs, _ = self.lstm(embeddings)
logits = self.linear(outputs)
logits = logits.squeeze(-1)
logits = self.sigmoid(logits)
logits = logits * mask
return logits
model = SegModel(len(word2id), 128, 256)
```
7. 定义训练函数
```python
def train(model: torch.nn.Module, optimizer: torch.optim.Optimizer, data_loader: DataLoader, device: str):
"""训练函数"""
model.train()
loss_func = torch.nn.BCELoss(reduction="none")
total_loss = 0
total_num = 0
for texts, labels, mask in tqdm(data_loader, desc="Train"):
texts = texts.to(device)
labels = labels.float().to(device)
mask = mask.float().to(device)
logits = model(texts, mask)
loss = loss_func(logits, labels)
loss = (loss * mask).sum() / mask.sum()
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item() * mask.sum().item()
total_num += mask.sum().item()
return total_loss / total_num
def evaluate(model: torch.nn.Module, data_loader: DataLoader, device: str):
"""评估函数"""
model.eval()
loss_func = torch.nn.BCELoss(reduction="none")
total_loss = 0
total_num = 0
with torch.no_grad():
for texts, labels, mask in tqdm(data_loader, desc="Evaluate"):
texts = texts.to(device)
labels = labels.float().to(device)
mask = mask.float().to(device)
logits = model(texts, mask)
loss = loss_func(logits, labels)
loss = (loss * mask).sum() / mask.sum()
total_loss += loss.item() * mask.sum().item()
total_num += mask.sum().item()
return total_loss / total_num
def train_and_evaluate(model: torch.nn.Module, optimizer: torch.optim.Optimizer, train_loader: DataLoader,
valid_loader: DataLoader, num_epochs: int, device: str):
"""训练和评估函数"""
best_valid_loss = float("inf")
for epoch in range(num_epochs):
train_loss = train(model, optimizer, train_loader, device)
valid_loss = evaluate(model, valid_loader, device)
print(f"Epoch [{epoch + 1}/{num_epochs}], Train Loss: {train_loss:.4f}, Valid Loss: {valid_loss:.4f}")
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), "best_model.pt")
model.load_state_dict(torch.load("best_model.pt"))
test_loss = evaluate(model, test_loader, device)
print(f"Test Loss: {test_loss:.4f}")
```
8. 训练模型
```python
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters())
train_and_evaluate(model, optimizer, train_loader, valid_loader, 10, device)
```
以上就是将 `seg.train` 转化为未封装的代码的步骤,你可以根据需要进行修改和调整。