I wrote a simple code that maybe someone here can re-use. I wanted to make something that pads a generic dim, and I don’t use an RNN of any type so PackedSequence was a bit of overkill for me. It’s simple, but it works for me. 1 def pad_tensor(vec, pad, dim): 2 """ ...
在使用深度学习特别是LSTM进行文本分析时,经常会遇到文本长度不一样的情况,此时就需要对同一个batch中的不同文本使用padding的方式进行文本长度对齐,方便将训练数据输入到LSTM模型进行训练,同时为了保证模型训练的精度,应该同时告诉LSTM相关padding的情况,此时,pytorch中的pack_padded_sequence就有了用武之地。 直接从文本...
在这里,我们定义了一个名为pad_sequence的辅助函数,其作用便是对传入的一个batch的序列按指定条件进行padding处理。具体实现代码如下: 1defpad_sequence(sequences,batch_first=False,max_len=None,padding_value=0):2ifmax_lenisNone:3max_len=max([s.size(0)forsinsequences])4out_tensors=[]5fortensorins...
v: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch. cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths of the sequences in the batch, used to index into q. cu_seqlens_k: (batch_size + 1,), dtype ...
🐛 Describe the bug An issue was identified with the reflection_pad3d function in PyTorch regarding the handling of its padding argument. The function expects padding to be an array of length 6, but currently, there is no validation check...
sequences=["I've been waiting for a HuggingFace course my whole life.","This course is amazing!",]batch=tokenizer(sequences,padding=True,truncation=True,return_tensors="pt")batch['labels']=torch.tensor([1,1])# tokenizer出来的结果是一个dictionary,所以可以直接加入新的 key-value ...
Sorry, something went wrong. NicolasHugmentioned this issueOct 11, 2024 RuntimeError: Argument #4: Padding size should be less than the corresponding input dimension for v2 transformspytorch/vision#8622 Open
问AssertionError: Padding_idx必须在num_embeddings内EN我的本科与硕士都是非科班，几乎没有相关的基础。
示例代码(假设使用Python和PyTorch): python import torch # 假设你有一个包含多个不同长度序列的batch sequences = [ torch.tensor([1, 2, 3]), torch.tensor([1, 2]), torch.tensor([1, 2, 3, 4, 5]) ] # 定义padding token的ID(假设为0) pad_token_id = 0 # 计算最长序列的长度 max_len...
q=torch.randn(3,1,10)# source sequence length 3, batch size 1, embedding size 10attn=nn.MultiheadAttention(10,1)# embedding size 10, one headattn(q,q,q)# self attention forattn_mask, we need matrix of shape (S, S), defsrc_mask(sz):mask=(torch.triu(torch.ones(sz,sz))==1)...