可以发现该类比较简单,主要进行的是对堆叠的操作。首先将拿到的encoder_layer通过_get_clones()函数克隆若干份,然后在forward中进行循环的调用。我们可以看下)_get_clones方法的实现: def _get_clones(module, N): return ModuleList([copy.deepcopy(module) for i
"" super().__init__() self.layers = _get_clones(decoder_layer, num_layers) self.num_layers = num_layers self.hidden_dim = hidden_dim self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx def forward( self, embed, # decoder embeddings refer_bbox, # anchor fea...
class Encoder(nn.Module): "Encoder是N个EncoderLayer的堆积而成" def __init__(self, layer, N): super(Encoder, self).__init__() #layer是一个SubLayer,我们clone N个 self.layers = clones(layer, N) #再加一个LayerNorm层 self.norm = LayerNorm(layer.size) def forward(self, x, mask):...
def _get_clones(module, N): return ModuleList([copy.deepcopy(module) for i in range(N)]) class LocalformerEncoder(nn.Module): __constants__ = ["norm"] def __init__(self, encoder_layer, num_layers, d_model): super(LocalformerEncoder, self).__init__() self.layers = _...
# Track clones to best test accuracy a1_reals.add(a1.real) a1_imags.add(a1.imag) a1_grad_reals.add(a1.grad.real) a1_grad_imags.add(a1.grad.imag) losses.add(loss) return loss def closure2(): optim2.zero_grad() a1_reals.pop_check_set(a1_real, self) a1_imags.pop_...
self.sublayer = clones(SublayerConnection(size, dropout), 3) # 解码器有三个子层 def forward(self, x, memory, src_mask, tgt_mask): # memory为编码器输出隐藏表示 m = memory # 自注意力机制,q、k、v均来自解码器隐表示 (子层一) x = self.sublayer[0](x, lambda x: self.self_attn(x...
(5)最后,向大家推荐的是国内领先的人工智能教育平台——七月在线的PyTorch入门与实战系列课:julyedu.com/course/getD。课程虽然是收费课程,但课程包含PyTorch语法、深度学习基础、词向量基础、NLP和CV的项目应用、实战等,理论和实战相结合,确实比其它课程讲的更详细,推荐给大家。 三、NLP&PyTorch实战 (1)Pytorch te...
spm_id_from=333.999.0.0importcopyimportmathfromcollectionsimportnamedtupleimportnumpyasnpimporttorchimporttorch.nnasnnimporttorch.nn.functionalasFfromtorch.autogradimportVariableHypothesis = namedtuple('Hypothesis', ['value','score'])defclones(module, n):returnnn.ModuleList([copy.deepcopy(module)for_in...
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The Dockerfile begins with the`ubuntu:22.04`base image. It sets up the ROCm and AMDGPU repositories, installs the necessary dependencies, and also installs Python packages essential for our work. The second stage clones the ONNX Runtime repository, checks out the specific commit we want, and...