x, segment = torch.arange(-2, 2, 0.01), torch.tensor([-1.5, 1])
时间: 2024-04-29 07:20:14 浏览: 8
这是一个使用PyTorch创建的张量x和分段张量segment的示例。具体来说,x是一个从-2到2,以0.01为步长的等差数列张量,包含了401个元素。而segment是一个长度为2的一维张量,它的第一个元素是-1.5,第二个元素是1,可以将其视为将x分成了两个部分,第一个部分是从-2到-1.5,第二个部分是从-1.5到1。这种分段的方式可以在一些计算中非常有用,例如在实现分段函数时,可以将输入张量x与分段张量segment进行比较,然后根据比较结果选择不同的计算方式。
相关问题
s = 256 # 2x min stride m.inplace = self.inplace forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward check_anchor_order(m) m.anchors /= m.stride.view(-1, 1, 1) self.stride = m.stride self._initialize_biases() # only run once
这段代码看起来像是在进行目标检测模型的初始化过程,其中包括计算输入图像的 stride,调整锚框的大小以适应不同尺度的特征图,以及初始化偏置参数等。具体来说:
- 代码开头的 s = 256 表示输入图像的大小为 256x256。
- 接下来的 forward 函数是为了通过模型前向传播得到特征图的大小,以便计算 stride。其中 isinstance(m, Segment) 是为了检查模型是否为分割模型,因为分割模型需要将输出的大小与输入大小一致。
- 然后通过计算 s / x.shape[-2] 得到 stride,其中 x.shape[-2] 是特征图的高度或宽度,取其中较小值是为了保证特征图不会超出输入图像的大小。
- check_anchor_order 函数是为了检查锚框的大小是否符合规范。
- 最后通过将锚框大小除以 stride,得到调整后的锚框大小,以适应不同尺度的特征图。
值得注意的是,这段代码中还有一些其他函数和参数,可能需要结合具体模型来理解其含义。
import jieba import torch from sklearn.metrics.pairwise import cosine_similarity from transformers import BertTokenizer, BertModel seed_words = ['姓名'] # 加载微博文本数据 text_data = [] with open("output/weibo1.txt", "r", encoding="utf-8") as f: for line in f: text_data.append(line.strip()) # 加载BERT模型和分词器 tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') model = BertModel.from_pretrained('bert-base-chinese') seed_tokens = ["[CLS]"] + seed_words + ["[SEP]"] seed_token_ids = tokenizer.convert_tokens_to_ids(seed_tokens) seed_segment_ids = [0] * len(seed_token_ids) # 转换为张量,调用BERT模型进行编码 seed_token_tensor = torch.tensor([seed_token_ids]) seed_segment_tensor = torch.tensor([seed_segment_ids]) with torch.no_grad(): seed_outputs = model(seed_token_tensor, seed_segment_tensor) seed_encoded_layers = seed_outputs[0] jieba.load_userdict('data/userdict.txt') # 构建隐私词库 privacy_words = set() for text in text_data: words = jieba.lcut(text.strip()) tokens = ["[CLS]"] + words + ["[SEP]"] token_ids = tokenizer.convert_tokens_to_ids(tokens) segment_ids = [0] * len(token_ids) # 转换为张量,调用BERT模型进行编码 token_tensor = torch.tensor([token_ids]) segment_tensor = torch.tensor([segment_ids]) with torch.no_grad(): outputs = model(token_tensor, segment_tensor) encoded_layers = outputs[0] # 对于每个词,计算它与种子词的相似度 for i in range(1, len(tokens)-1): word = tokens[i] if word in seed_words: continue word_tensor = encoded_layers[0][i].reshape(1, -1) seed_tensors =seed_encoded_layers[0][i].reshape(1, -1) # 计算当前微博词汇与种子词的相似度 sim = cosine_similarity(word_tensor, seed_tensors, dense_output=False)[0].max() print(sim, word) if sim > 0.5 and len(word) > 1: privacy_words.add(word) print(privacy_words) 上述代码运行之后有错误,报错信息为:Traceback (most recent call last): File "E:/PyCharm Community Edition 2020.2.2/Project/WordDict/newsim.py", line 397, in <module> seed_tensors =seed_encoded_layers[0][i].reshape(1, -1) IndexError: index 3 is out of bounds for dimension 0 with size 3. 请帮我修改
根据报错信息,问题出在获取seed_encoded_layers的第三个元素时,超过了张量的维度范围。可以通过打印输出seed_encoded_layers的形状,来确定张量的维度情况,看看是否与预期一致。
同时,可以检查一下seed_tokens和seed_token_ids的值,确保它们正确。
下面是修改后的代码:
```
import jieba
import torch
from sklearn.metrics.pairwise import cosine_similarity
from transformers import BertTokenizer, BertModel
seed_words = ['姓名']
# 加载微博文本数据
text_data = []
with open("output/weibo1.txt", "r", encoding="utf-8") as f:
for line in f:
text_data.append(line.strip())
# 加载BERT模型和分词器
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
model = BertModel.from_pretrained('bert-base-chinese')
seed_tokens = ["[CLS]"] + seed_words + ["[SEP]"]
seed_token_ids = tokenizer.convert_tokens_to_ids(seed_tokens)
seed_segment_ids = [0] * len(seed_token_ids)
# 转换为张量,调用BERT模型进行编码
seed_token_tensor = torch.tensor([seed_token_ids])
seed_segment_tensor = torch.tensor([seed_segment_ids])
with torch.no_grad():
seed_outputs = model(seed_token_tensor, seed_segment_tensor)
seed_encoded_layers = seed_outputs[0]
jieba.load_userdict('data/userdict.txt')
# 构建隐私词库
privacy_words = set()
for text in text_data:
words = jieba.lcut(text.strip())
tokens = ["[CLS]"] + words + ["[SEP]"]
token_ids = tokenizer.convert_tokens_to_ids(tokens)
segment_ids = [0] * len(token_ids)
# 转换为张量,调用BERT模型进行编码
token_tensor = torch.tensor([token_ids])
segment_tensor = torch.tensor([segment_ids])
with torch.no_grad():
outputs = model(token_tensor, segment_tensor)
encoded_layers = outputs[0]
# 对于每个词,计算它与种子词的相似度
for i in range(1, len(tokens)-1):
word = tokens[i]
if word in seed_words:
continue
word_tensor = encoded_layers[0][i].reshape(1, -1)
seed_tensors = seed_encoded_layers[0][0].reshape(1, -1) # 修改此处,取第一个元素
# 计算当前微博词汇与种子词的相似度
sim = cosine_similarity(word_tensor, seed_tensors, dense_output=False)[0].max()
print(sim, word)
if sim > 0.5 and len(word) > 1:
privacy_words.add(word)
print(privacy_words)
```