标签：src tgt 模型 mask pos 源码 DETR 256 self
DETR 模型结构源码
目录End-to-End Object Detection with Transformers（DETR）
论文地址：https://arxiv.org/abs/2005.12872
源代码位置： https://github.com/facebookresearch/detr
参考文献： https://www.cnblogs.com/Glucklichste/p/14057005.html
模型整体结构
论文中模型结构
主干网络
- backbone(CNN-Resnet)
- CNN网络
- positional(位置信息)
- transformer
- encoder
- decoder
- predicttion head
模型构建
models/detr.py
# 构建两大模型
# backbone = build_backbone(args)
# transformer = build_transformer(args)
# 模型连接 DETR
#
def build(args):
num_classes = 20 if args.dataset_file != 'coco' else 91
if args.dataset_file == "coco_panoptic":
# for panoptic, we just add a num_classes that is large enough to hold
# max_obj_id + 1, but the exact value doesn't really matter
num_classes = 250
device = torch.device(args.device)
# 包含两大部分， 构建 backbone 和 构建 transformer
backbone = build_backbone(args)
transformer = build_transformer(args)
model = DETR(
backbone,
transformer,
num_classes=num_classes,
num_queries=args.num_queries,
aux_loss=args.aux_loss,
)
if args.masks:
model = DETRsegm(model, freeze_detr=(args.frozen_weights is not None))
backbone
cnn骨架特征提取
backbone的输入和输出
- input shape=(N,3,W,H)
- output shape=(N,2048,W/32,H/32) #针对 Resnet50 C=2048, 针对 Resnet18,Resnet34 C=512
假设输入是(N,C,H,W)，则resnet50输出是(N,2048,H//32,W//32)，1024比较大，
为了节省计算量，先采用1x1卷积降维为256,(hidden_dim=256,在main.py 中设置参数)
最后转化为序列格式输入到transformer中，输入shape=(H*W,N,256)，H=H/32,W=W/32
class Backbone(BackboneBase):
"""ResNet backbone with frozen BatchNorm."""
def __init__(self, name: str,
train_backbone: bool,
return_interm_layers: bool,
dilation: bool):
backbone = getattr(torchvision.models, name)(
replace_stride_with_dilation=[False, False, dilation],
pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d)
# 针对不同的网络，选择了不同的输出大小
num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
super().__init__(backbone, train_backbone, num_channels, return_interm_layers)
···
```python
在 DETR 类中
src 为 backone 的输出 shape=(N,512,W/32,H/32)
# self.input_proj(src) 将 shape=(N,512,W/32,H/32) -> shape=(N,256,W/32,H/32)
hs = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos[-1])[0]
位置信息标注,包含了x,y两个方向的位置信息。编码方式任然采用sincos, 语音序列只是包含了一个方向的位置信息
PositionEmbeddingSine.forward的输入和输出
- input NestedTensor型数据 tensor_list的类型是NestedTensor，内部自动附加了mask，
- x.tensors.shape=((N, 512,W/32, H/32) x.mask.shape=(N,W/32,H/32)
- output: pos.shape=(N, 256, W/32,H/32)
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: NestedTensor):
x = tensor_list.tensors
mask = tensor_list.mask
#x.tensors.shape=((N, 512,W/32, H/32) x.mask.shape=(N,W/32,H/32)
assert mask is not None
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
# 前面输入向量是256，编码是一半sin，一半cos
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
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)
# pos.shape=(N, 256, W/32,H/32) 前128是y方向编码，而128是x方向编码
return pos
transformer
transformer整体构建
model/transformer.py
Transformer 模型构建
包含 encoder decoder
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__()
# 编码
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)
# 解码
decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward,
dropout, activation, normalize_before)
decoder_norm = nn.LayerNorm(d_model)
self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm,
return_intermediate=return_intermediate_dec)
self._reset_parameters()
self.d_model = d_model
self.nhead = nhead
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, src, mask, query_embed, pos_embed):
# flatten NxCxHxW to HWxNxC
# inputs: {src,mask,query_embed,pos} 由 DETR.forward 获取来自 backbone
bs, c, h, w = src.shape
# 先对数据做变换
# 特殊说明 这里是经过backbone 输出的特征 (N,256,W/32,H/32) 之后transformer过程中 输出shape为(H/32xW/32,N,256) 特征的宽和高没有变化，为了书写方法方便，我这里将 W/32,H/32 写成为 W,H
# src=(N,256,W/32,H/32)-> (WH,N,256)
# pos_embed=(N,256,W,H)-> (WH,N,256)
# query_embed=(100,256) -> (100,N,256)
# mask=(N,W,H) -> (N,WH)
src = src.flatten(2).permute(2, 0, 1)
pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
mask = mask.flatten(1)
# 解码 第一层 首次参数设置为0，后续自动更新
tgt = torch.zeros_like(query_embed)
# encoder src=(WH,N,256) mask= (N,WH) pos_embed= (WH,N,256)
# 输出 (WH,N,256)
memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
# decoder tgt=(100,N,256) memory=(WH,N,256),mask=(N,WH)
# pos_embed=(WH,N,256) query_embed=(100,N,256)
# 输出 hs=(decoder_layers, 100, N, 256)
hs = self.decoder(tgt, memory, memory_key_padding_mask=mask,
pos=pos_embed, query_pos=query_embed)
# return (decoder_layers, N, 100, 256) (N, 256, H, W])
return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)
Encoder
编码器结构和输入输出
编码器的输入有三个 src=(WH,N,256) src_mask= (N,WH) pos_embed= (WH,N,256) 注释：W=W/32,H=H/32
- 由图像生成的序列,shape=(WH,N,256)
- 掩码信息,shape= (N,WH)
- 图像序列的空间位置信息,shape=(WH,N,256)
经过6层编码后 输出只有一个 序列,shape和输入的src 序列保持一直,shape=(WH,N,256) 注释：W=W/32,H=H/32
模型细节
- 原始transformer的n个编码器输入中，只有第一个编码器需要输入位置编码向量，但是DETR里面对每个编码器都输入了同一个位置编码向量
- QKV处理逻辑不同，在编码器内部位置编码仅仅和 Q K 相加，V 不做任何处理
TransformerEncoder类
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
class TransformerEncoder(nn.Module):
def __init__(self, encoder_layer, num_layers, norm=None):
super().__init__()
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
# 默认设置了 6个 编码器，循环6遍
# encoder input src=(WH,N,256) src_mask= (N,WH) pos_embed= (WH,N,256)
# output -> output (WH,N,256)
# 包含了多层相同的结构,首尾相连,上一层输出为下一层的输入
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
TransformerEncoderLayer类
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):
# src=(WH,N,256) mask= (N,WH) pos_embed= (WH,N,256)
# with_pos_embed 输入是 src pos {图片序列,位置信息}
# 对 Q K 进行更新
q = k = self.with_pos_embed(src, pos)
# MultiheadAttention 多头注意力机制
# 在编码器内部位置编码仅仅和QK相加，V不做任何处理
src2 = self.self_attn(q, k, value=src, attn_mask=src_mask,
key_padding_mask=src_key_padding_mask)[0]
# 残差
src = src + self.dropout1(src2)
src = self.norm1(src)
# FFN
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):
# encoder src=(WH,N,256) mask= (N,WH) pos_embed= (WH,N,256)
# output=(WH,N,256)
# 默认 normalize_before=False 只对 forward_post 函数注解
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)
Decoder
解码器结构和输入输出
输入参数
解码器的输入 有五个参数 decoder tgt=(100,N,256) memory=(WH,N,256),mask=(N,WH) pos_embed=(WH,N,256) query_pos=(100,N,256)
- tgt 可以理解为上一层解码器的解码输出 shape=(100,N,256) 第一层的tgt=torch.zeros_like(query_embed) 为零矩阵
- memory 最后一个编码器输出 shape=(WH,N,256)
- mask 掩码信息 shape=(N,WH)
- pos 和编码器输入中完全相同位置参数 shape=(WH,N,256)
- query_pos 是可学习输出位置向量， 个人理解 解码器中的这个参数 全局共享 提供全局注意力 query_pos=(100,N,256)
输出参数
- 输出 (decoder_layers, 100, N, 256) decoder_layers 为解码器的数量(层数)，原文默认设置为6层
原始transformer顺序解码操作不同的是，detr一次就把N个无序框并行输出
Obeject Query
针对 query_pos 参数的其他博客解释
论文中指出object queries作用非常类似faster rcnn中的anchor，只不过这里是可学习的，不是提前设置好的。
object queries(shape是(100,256)) 源代码中，这是一个torch.nn.Embedding的对象。
官方介绍：一个保存了固定字典和大小的简单查找表。这个模块常用来保存词嵌入和用下标检索它们。模块的输入是一个下标的列表，输出是对应的词嵌入。
个人理解：query_pos 可以简单认为是输出位置编码,其作用主要是在学习过程中提供目标对象和全局图像之间的关系,相当于全局注意力，必不可少非常关键。代码形式上是可学习位置编码矩阵。和编码器一样，该可学习位置编码向量也会输入到每一个解码器中。我们可以尝试通俗理解：object queries矩阵内部通过学习建模了100个物体之间的全局关系，并且参与到网络的学习当中。
其他细节:
- tgt(第一次输入是query embeding，第二次是上一层的输出out);
- 和编码器一样，只是Q 与 K加上了位置编码信息, V不会加入位置编码
- 引入可学习的Object queries
- 不需要顺序解码，一次即可输出N个无序集合
TransformerDecoder类
class TransformerDecoder(nn.Module):
def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
super().__init__()
self.layers = _get_clones(decoder_layer, num_layers)
self.num_layers = num_layers
self.norm = norm
self.return_intermediate = return_intermediate
def forward(self, tgt, memory,
tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
# decoder tgt=(100,N,256) memory=(WH,N,256),mask=(N,WH) pos_embed=(WH,N,256) query_embed=(100,N,256)
output = tgt
intermediate = []
for layer in self.layers:
output = layer(output, memory, tgt_mask=tgt_mask,
memory_mask=memory_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask,
pos=pos, query_pos=query_pos)
if self.return_intermediate:
intermediate.append(self.norm(output))
if self.norm is not None:
output = self.norm(output)
if self.return_intermediate:
intermediate.pop()
intermediate.append(output)
# intermediate=[outpout...] intermediate[0].shape=(100,N,256)
# return_intermediate = True
if self.return_intermediate:
return torch.stack(intermediate)
return output.unsqueeze(0)
TransformerDecoderLayer类
class TransformerDecoderLayer(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)
self.multihead_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.norm3 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = 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, tgt, memory,
tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
# # decoder tgt=(100,N,256) memory=(WH,N,256),mask=(N,WH) pos_embed=(WH,N,256) query_embed=(100,N,256)
# 解码 第一次注意力机制 tgt=(100,N,256) 是 上一个单元输出 如果是第一次 torch.zeros_like(query_embed)
# query_embed=(100,N,256) query_pos 应该是共享单元，不管多少层都是公用一组数据
q = k = self.with_pos_embed(tgt, query_pos)
tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
# multihead_attn
# query=self.with_pos_embed(tgt, query_pos) 在第二次注意力机制中 对 Q 进行更新
# key=self.with_pos_embed(memory, pos) 在第二次注意力机制中对 K 进行更新
tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
key=self.with_pos_embed(memory, pos),
value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)[0]
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
# FFN
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt
def forward_pre(self, tgt, memory,
tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
# # decoder tgt=(100,N,256) memory=(WH,N,256),mask=(N,WH) pos_embed=(WH,N,256) query_embed=(100,N,256)
tgt2 = self.norm1(tgt)
q = k = self.with_pos_embed(tgt2, query_pos)
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout1(tgt2)
tgt2 = self.norm2(tgt)
tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
key=self.with_pos_embed(memory, pos),
value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)[0]
tgt = tgt + self.dropout2(tgt2)
tgt2 = self.norm3(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout3(tgt2)
return tgt
def forward(self, tgt, memory,
tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
# decoder input tgt=(100,N,256) memory=(WH,N,256),mask=(N,WH) pos_embed=(WH,N,256) query_embed=(100,N,256)
# ISFalse
if self.normalize_before:
return self.forward_pre(tgt, memory, tgt_mask, memory_mask,
tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)
return self.forward_post(tgt, memory, tgt_mask, memory_mask,
tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)
FFN
最后是接了一个FFN,就是两个全连接层，一个用于分类，一个用于回归预测
分类: 一层模型结构
最终预测 MLP模型 是由具有ReLU激活功能且具有隐藏层的3层感知器和线性层计算的。 FFN预测框的标准化中心坐标，高度和宽度， 输入图像，然后线性层使用softmax函数预测类标签
DETR类中
# 输入 hs.shape = (decoder_layers, N, 100, 256)
# 分类 self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
# FFN Linear class input=(decoder_layers, N, 100, 256) output=(decoder_layers, N, 100, num_classes+1)
outputs_class = self.class_embed(hs)
# 预测 self.bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
# MLP Bounding box input=(decoder_layers, N, 100, 256) output=(decoder_layers, N, 100, 4)
outputs_coord = self.bbox_embed(hs).sigmoid()
标签：src,tgt,模型,mask,pos,源码,DETR,256,self 来源： https://www.cnblogs.com/tian777/p/15358183.html
本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享； 2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关； 3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关； 4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除； 5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。