two_branches(x) print(f"conv1(x) + conv2(x) tooks {perf_counter() - start:.6f}s") start = perf_counter() conv_fused(x) print(f"conv_fused(x) tooks {perf_counter() - start:.6f}s") 速度快了一倍 conv1(x) + conv2(x) tooks 0.000421sconv_fused(x) tooks 0.000215s 3、Fu...
为了进一步减少参数和计算量,采用了interleave groupwise的3×3卷积层以换取效率。其中,RepVGG-A的第3、5、7、…、21层以及RepVGG-B额外的第23、25、27层设置组数g。为了简单起见,对于这些层,g被全局地设置为1、2或4,而没有进行分层调整。 3、实验结果 REPVGG-A0在准确性和速度方面比RESNET-18好1.25%和33%...
repeat_interleave( valid_lens, repeats=self.num_heads, dim=0) # output的形状:(batch_size*num_heads,查询的个数, # num_hiddens/num_heads) output = self.attention(queries, keys, values, valid_lens) # output_concat的形状:(batch_size,查询的个数,num_hiddens) output_concat = transpose_output...
为了进一步减少参数和计算量,采用了interleave groupwise的3×3卷积层以换取效率。其中,RepVGG-A的第3、5、7、…、21层以及RepVGG-B额外的第23、25、27层设置组数g。为了简单起见,对于这些层,g被全局地设置为1、2或4,而没有进行分层调整。 3、实验结果REPVGG-A0在准确性和速度方面比RESNET-18好1.25%和33%,...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/torch/tensor.py at master · bwasti/pytorch
This changes the default behavior of zero_grad() to zero out the grads by setting them to None instead of zero tensors. In other words, the set_to_none kwarg is now True by default instead of False. Setting grads to None reduces peak memory usage and increases performance. This will br...
torch.repeat_interleave pyRepeatInterleave Not applicable torch.reshape pyView Not applicable torch.rsqrt pyRsqrt Not applicable torch.size pySize Not applicable torch.sin pySin Not applicable torch.split pySplitWithSizes Not applicable torch.sqrt pyElementwiseBinary Not applicable torch.square pySquare...
目标检测与R-CNN——pytorch与paddle实现目标检测与R-CNN 本文将深入探讨目标检测与R-CNN的理论基础,并通过PyTorch和PaddlePaddle两个深度学习框架来展示如何实现R-CNN模型。我们将首先介绍目标检测与R-CNN的基本概念,这些理论基础是理解和实现目标检测与R-...
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)...
1、pytorch中 Variable、Parameter、Tensor之间的使用差异 参考博客:[1]Pytorch 中的 Tensor , Variable和Parameter区别与联系 (1)Tensor pytorch中的Tensor类似于numpy中的array,而不直接用array的原因,是因为Tensor能够更方便地在GPU上进行运算。pytorch为Tensor设计了许多方便的操作,同时Tensor也可以轻松地和numpy数组进...