系列文章目录
提示：本文共定义3个类（class）
1.TransformerEncoderLayer 基础的多头Encoder
2.TransformerEncoder 多个Encoder，最终调用的是以上类
3.Transformer 整体结构，包含多Encode和多Decode的使用
前言
提示：这里可以添加本文要记录的大概内容：
例如：随着人工智能的不断发展，机器学习这门技术也越来越重要，很多人都开启了学习机器学习，本文就介绍了机器学习的基础内容。
提示：以下是本篇文章正文内容，下面案例可供参考
一、先定义一个多头Transformer
代码如下（示例）：
1.nn.MultiheadAttention为自带函数，只需传入输入序列和多头的数量即可，mask不是必须
2.前向过程一般采用 forward_post，里面的Norm归一化是在注意力之后
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
activation="relu", normalize_before=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(self,
src,
src_mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None):
q = k = self.with_pos_embed(src, pos) # + (768,2,256)
src2 = self.self_attn(q, k, value=src, attn_mask=src_mask,
key_padding_mask=src_key_padding_mask)[0]
src = src + self.dropout1(src2)
src = self.norm1(src)
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
def forward_pre(self, src,
src_mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None):
src2 = self.norm1(src)
q = k = self.with_pos_embed(src2, pos)
src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask,
key_padding_mask=src_key_padding_mask)[0]
src = src + self.dropout1(src2)
src2 = self.norm2(src)
src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
src = src + self.dropout2(src2)
return src
def forward(self, src,
src_mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None):
if self.normalize_before:
return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
return self.forward_post(src, src_mask, src_key_padding_mask, pos)
那么它是如何让调用的呢？
src= self.forward_post(src, src_mask, src_key_padding_mask, pos)
# pos为位置编码，等下会写。src为输入序列，
二、多个Encoder：
代码如下（示例）：
class TransformerEncoder(nn.Module):
def __init__(self, encoder_layer, num_layers, norm=None):
super().__init__()
##-----------------主要调用的层--------------------##
self.layers = _get_clones(encoder_layer, num_layers)
##-------------------------------------------------##
self.num_layers = num_layers
self.norm = norm
def forward(self, src,
mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None):
output = src
#----- 主要的前向函数。mask为None , padding_mask为(bs,h*w),-------
for layer in self.layers:
output = layer(output, src_mask=mask,
src_key_padding_mask=src_key_padding_mask, pos=pos)
#------------------------------------------------------------------
if self.norm is not None:
output = self.norm(output)
return output
其中克隆的多个encoder_layer, 由以下类Transformer来调用最上面的多头Trans
class Transformer(nn.Module):
def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,
activation="relu", normalize_before=False,
return_intermediate_dec=False):
super().__init__()
encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward,
dropout, activation, normalize_before)
encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
def forward(self, src, mask, pos_embed):
memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) # ([768, 2, 256])
hs = self.decoder(tgt, memory, memory_key_padding_mask=mask,
pos=pos_embed, query_pos=query_embed) # (6,100,2,256)
return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)
三、多个Encoder使用方法
memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) # ([768, 2, 256])
# self.encoder的定义为上方Transformer类的最后一行
那么最终这个大的Transformer结构是如何定义和使用的呢？
from .transformer import build_transformer
self.transformer = transformer
hs = self.transformer(self.input_proj(src), mask, pos[-1])[0]
四、最后看一下两个重要函数
均来自model/transformer.py
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
def build_transformer(args):
return Transformer(
d_model=args.hidden_dim,
dropout=args.dropout,
nhead=args.nheads,
dim_feedforward=args.dim_feedforward,
num_encoder_layers=args.enc_layers,
num_decoder_layers=args.dec_layers,
normalize_before=args.pre_norm,
return_intermediate_dec=True,
)
五、DETR中的mask与pos
mask = F.interpolate(tensor_list.mask[None].float(), size=x.shape[-2:]).bool()[0] # ([2, 38, 25])
## 以输入为（2，3，1194，800）为例，利用输入tensor_list的mask[None]，尺度也是1194，800。
这里猜测tensor_list.mask来源于实例分割的标签
pos=self[1](x).to(x.tensors.dtype) # x为特征图（2，2048，38，25）
self[1]= position_embedding = build_position_encoding(args)
这里的位置编码采用的是正余弦，在models/position_encode里是一个单独的类
position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
# N_steps = args.hidden_dim
class PositionEmbeddingSine(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one
used by the Attention is all you need paper, generalized to work on images.
"""
def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
super().__init__()
self.num_pos_feats = num_pos_feats
self.temperature = temperature
self.normalize = normalize
if scale is not None and normalize is False:
raise ValueError("normalize should be True if scale is passed")
if scale is None:
scale = 2 * math.pi
self.scale = scale
def forward(self, tensor_list):
x = tensor_list.tensors
mask = tensor_list.mask
not_mask = ~mask
y_embed = not_mask.cumsum(1, dtype=torch.float32)
x_embed = not_mask.cumsum(2, dtype=torch.float32)
if self.normalize:
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
dim_t = self.temperature ** (2 * (dim_t
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
return pos
总结
提示：以上方法可以用来自主构建一个Transformer
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