seg_model.eval()
时间: 2024-05-20 17:14:46 浏览: 10
这段代码是将一个名为 `seg_model` 的模型设置为评估模式,即不进行梯度计算和参数更新。在 PyTorch 中,有两种模型模式:训练模式和评估模式。在训练模式下,模型会计算梯度并更新参数,而在评估模式下,模型只进行前向传递计算,不进行梯度计算和参数更新,这样可以提高模型的计算效率。一般在使用模型进行推理时需要将模型设置为评估模式。
相关问题
PermissionError Traceback (most recent call last) Cell In[2], line 6 3 model.load_weights('./checkpoints/seg_model'); 5 # the SAM model checkpoints can be downloaded from: https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth ----> 6 sam = sam_model_registry["default"](checkpoint="/Users/hp/segmenteverygrain-main/segmenteverygrain/Segmentation") File D:\Anaconda\lib\site-packages\segment_anything\build_sam.py:15, in build_sam_vit_h(checkpoint) 14 def build_sam_vit_h(checkpoint=None): ---> 15 return _build_sam( 16 encoder_embed_dim=1280, 17 encoder_depth=32, 18 encoder_num_heads=16, 19 encoder_global_attn_indexes=[7, 15, 23, 31], 20 checkpoint=checkpoint, 21 ) File D:\Anaconda\lib\site-packages\segment_anything\build_sam.py:104, in _build_sam(encoder_embed_dim, encoder_depth, encoder_num_heads, encoder_global_attn_indexes, checkpoint) 102 sam.eval() 103 if checkpoint is not None: --> 104 with open(checkpoint, "rb") as f: 105 state_dict = torch.load(f) 106 sam.load_state_dict(state_dict) PermissionError: [Errno 13] Permission denied: '/Users/hp/segmenteverygrain-main/segmenteverygrain/Segmentation'
这个错误是由于权限问题导致的,系统拒绝访问 '/Users/hp/segmenteverygrain-main/segmenteverygrain/Segmentation' 文件。
请确保您具有足够的权限来访问该文件。您可以尝试以下解决方法:
1. 检查文件路径是否正确。确保路径中没有拼写错误或其他问题。
2. 确保您有读取该文件的权限。您可以尝试在命令行或文件资源管理器中手动打开该文件,以验证是否有读取权限。
3. 如果文件位于受限制的目录下,尝试将文件移动到其他位置,例如您的用户目录或公共目录,并更新代码中的文件路径。
4. 如果您正在使用 Jupyter Notebook 或类似的环境,请确保您有足够的权限来读取该文件。有时,Jupyter Notebook 在某些目录下可能会受到限制。
如果上述方法仍然无法解决问题,请考虑联系系统管理员或具有适当权限的用户以获取进一步的帮助。
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。
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