torch.index_select torch.index_select(input, dim, index, out=None) → Tensor 沿着指定维度对输入进行切片,取index中指定的相应项(index为一个LongTensor),然后返回到一个新的张量, 返回的张量与原始张量_Tensor_有相同的维度(在指定轴上)。 注意: 返回的张量不与原始张量共享内存
torch.index_select(input, dim, index, out=None)→ Tensor返回一个新的张量,其索引input 张量沿尺寸 dim使用的条目中index这是一个LongTensor。返回的Tensor具有与原始Tensor相同数量的尺寸。注意: 返回的张量不与原始张量共享内存空间。参数:input (Tensor) – 输入张量 dim (int) – 索引的轴 index (Long...
因此我们要在测试代码的循环前面加上一句with torch.no_grad(): 来让pytorch不要计算梯度, 这样可以少占用很多显存.autograd模块的用法 自定义一个继承于torch.autograd.Function的类,可以做一些别样的 forward操作,同时自定义backward方法,下面是一个例子:
Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given indices with the given values. A sparse tensor can be uncoalesced, in that case, there are duplicate coordinates in the indices, and the value at that index is the sum of all duplicate value entries:...
(torch.index_select/index)') # Display the actual z values for i in range(len(batch_sizes)): for j in range(len(feature_sizes)): plt.text(j, i, f'{index_select_times[i][j]/indexing_times[i][j]:.2f}', ha='center', va='center', color='white', path_effects=[pe.with...
torch.set_default_tensor_type('torch.cuda.FloatTensor') if not args.cuda: print("WARNING: It looks like you have a CUDA device, but aren't " + "using CUDA.\nRun with --cuda for optimal training speed.") torch.set_default_tensor_type('torch.FloatTensor') ...
The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices. See below for ...
import numpy as np import pandas as pd from tqdm import tqdm path_to_file = None df = pd.read_csv(path_to_file) with open('text.txt','w') as f: for setence in df.text.values: f.write(setence+'\n') # Train a tokenizer import tokenizers from transformers import BertTokenizer,...
The numpy's index output is "The indices of the first occurrences of the unique values in the original array". If one ought to match a torch routine with that of numpy. The index output should NOT be sorted, since you want unique == x[index] is True. Without sorting the indices, I...
If set to -1, the number of classes will be inferred as one greater than the largest class value in the input tensor. Returns LongTensor that has one more dimension with 1 values at the index of last dimension indicated by the input, and 0 everywhere else. Examples >>> F.one_hot(...