https://discuss.pytorch.org/t/data-must-be-a-sequence-got-numpy-int64/20697 https://discuss.pytorch.org/t/solved-convert-tensors-into-sequences-vs-tuples-issue/4116
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or batch_size <= 0: raise ValueError("batch_size should be a positive integeral value, " "but got batch_size={}".format(batch_size)) if not isinstance(drop_last, bool): raise ValueError("drop_last should be...
(target, tgt_vocab, num_steps) # 生成迭代器 data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len) data_iter = load_array(data_arrays, batch_size) return data_iter, src_vocab, tgt_vocab def load_array(data_arrays, batch_size, is_train=True): """Construct a PyTorch...
this option to True is not needed and often can be worked aroundin a much more efficient way. Defaults to the value of``create_graph``.create_graph (bool, optional): If ``True``, graph of the derivative willbe constructed, allowing to compute higher order derivativeproducts. Defaults to ...
pad_sequences_3d用于将一批序列填充到统一的长度,确保批中的每个序列具有相同数量的元素(或时间步长)。这在许多机器学习任务中尤其重要,因为输入数据必须具有一致的形状。 # Define a function for paddingdef pad_sequences_3d(sequences, max_len=None, pad...
值得一提的是,PyTorch 源码中并没有提供默认的__len__()方法实现,原因是 return NotImplemented 或者 raise NotImplementedError() 之类的默认实现都会存在各自的问题,这点我们在源码 pytorch/torch/utils/data/sampler.py 中的注释也可以得到解释。 1.2 Iterable-style dataset ...
🐛 Describe the bug Hi. I got a C++ compile error when trying to compile a sequence of PyTorch instructions shown below for CPU: import torch a = torch.rand([2]) b = torch.rand([2]) def forward(a, b): a = torch.nn.functional.pad(a, (0, -1...
As you can see, our predicted sentence is not matched very well, so in order to get higher accuracy, you need to train with a lot more data and try to add more iterations and number of layers using Sequence to sequence learning.
parameters())), ( "DistributedDataParallel is not needed when a module " "doesn't have any parameter that requires a gradient." ) self.is_multi_device_module = len({p.device for p in module.parameters()}) > 1 distinct_device_types = {p.device.type for p in module.parameters()} ...
classCausalSelfAttention(nn.Module):def__init__(self, num_heads:int, embed_dimension:int, bias:bool=False, is_causal:bool=False, dropout:float=0.0):super().__init__()assertembed_dimension % num_heads ==0# key, query, value projections for all heads, but in a batchself.c_attn = ...