Transformer解码器架构实现 基于前面的可配置Transformer模块,我们构建了一个标准的Transformer解码器架构。这一实现遵循了典型的Transformer架构设计范式,同时保持了足够的灵活性以适应不同的实验需求。 classMyDecoder(nn.Module):def__init__(self,block_fn,num_tokens,dim,num_heads,num_layers,max_seq_len,pad_idx...
src_pad_mask = x == self.pad_idx dst_pad_mask = y == self.pad_idx src_mask = self.generate_mask(src_pad_mask, src_pad_mask, False) dst_mask = self.generate_mask(dst_pad_mask, dst_pad_mask, True) src_dst_mask = self.generate_mask(dst_pad_mask, src_pad_mask, False) enco...
# Initialize padded_sequences with the pad_value padded_sequences = torch.full((batch_size, max_len, feature_size), fill_value=pad_value, dtype=sequences.dtype, device=sequences.device) # Pad each sequence to the max_len padded_sequences[:, :seq_len, :] = sequences return padded_seq...
d_model = 8state_size = 128 # Example state sizeseq_len = 100 # Example sequence lengthbatch_size = 256 # Example batch sizelast_batch_size = 81 # only for the very last batch of the datasetcurrent_batch_size = batch_sizedifferent_...
waveforms = pad_sequence(waveforms, batch_first=True) # 13283为对齐后的音频长度 for i, waveform in enumerate(waveforms): torchaudio.save(f'./dataset/waves_yesno_pad/{i}_{labels[i]}.wav', waveform.view(1, 13283), 8000) 1. 2. ...
pytorch搭建transformer分类 一、 裁剪——Crop 1.随机裁剪:transforms.RandomCrop** class torchvision.transforms.RandomCrop(size, padding=None, pad_if_needed=False, fill=0, padding_mode=‘constant’) 1. 功能:依据给定的size随机裁剪 参数: size- (sequence or int),若为sequence,则为(h,w),若为int,...
# 将一个序列中所有的词记录在all_tokens中以便之后构造词典,然后在该序列后面添加PAD直到序列# 长度变为max_seq_len,然后将序列保存在all_seqs中defprocess_one_seq(seq_tokens,all_tokens,all_seqs,max_seq_len):all_tokens.extend(seq_tokens)seq_tokens+=[EOS]+[PAD]*(max_seq_len-len(seq_tokens)-...
seq_len = 100 # Example sequence length batch_size = 256 # Example batch size last_batch_size = 81 # only for the very last batch of the dataset current_batch_size = batch_size different_batch_size = False h_new = None temp_buffer = None ...
seq_len = 100 # Example sequence length batch_size = 256 # Example batch size last_batch_size = 81 # only for the very last batch of the dataset current_batch_size = batch_size different_batch_size = False h_new = None temp_buffer = None ...
defpad_sequences_3d(sequences,max_len=None,pad_value=0): # Assuming sequences is a tensor of shape (batch_size, seq_len, feature_size) batch_size, seq_len,feature_size=sequences.shape ifmax_lenisNone:max_len=seq_len+1 # Initialize padded_sequences with the pad_valuepadded_sequences...