代码阅读
这一篇我们来分析一下将 multi-scale deformable attention 取代self-attention的transformer的构造。
首先来看一下编码器部分Encoder
class DeformableTransformerEncoderLayer(nn.Module):
def __init__(self,
d_model=256, d_ffn=1024,
dropout=0.1, activation="relu",
n_levels=4, n_heads=8, n_points=4):
super().__init__()
# self attention
self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
self.dropout1 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model)
# ffn
self.linear1 = nn.Linear(d_model, d_ffn)
self.activation = _get_activation_fn(activation)
self.dropout2 = nn.Dropout(dropout)
self.linear2 = nn.Linear(d_ffn, d_model)
self.dropout3 = nn.Dropout(dropout)
self.norm2 = nn.LayerNorm(d_model)
@staticmethod
def with_pos_embed(tensor, pos):
return tensor if pos is None else tensor + pos
def forward_ffn(self, src):
src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
src = src + self.dropout3(src2)
src = self.norm2(src)
return src
def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
# self attention
src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)
src = src + self.dropout1(src2)
src = self.norm1(src)
# ffn
src = self.forward_ffn(src)
return src
实现过程如下图所示。multi-head self-attention使用MSDeformAttn构造，然后两个线性层定义了FFN模块， Norm是nn.LayerNorm， Add表示跨层链接， 中间使用了多层dropout层。
self_attn即MSDeformAttn得输入为：
- query 每个query的特征，在encoder里是每一个level中每个位置点的特征
- reference_points batch_size x query个数 x level个数 x 2 ，每个query每个level的位置点，归一化之后的点，encoder里是每个level的位置点归一化之后的位置
- src backbone的输出，可能是多个stage，是cat之后再flatten的结果
- spatial_shapes 每个level的featmap尺寸
- level_start_index 每个level在flatten的特征向量集上的起始索引
- padding_mask 考虑所有level，每个位置是否mask的标志。
Encoder
上面定义的是每一个encoder layer的实现， transformer中的Encoder是多个相同结构的Encode layer的串联。所以Encoder定义如下：
class DeformableTransformerEncoder(nn.Module):
def __init__(self, encoder_layer, num_layers):
super().__init__()
self.layers = _get_clones(encoder_layer, num_layers)
self.num_layers = num_layers # 堆叠的个数
@staticmethod
def get_reference_points(spatial_shapes, valid_ratios, device):
reference_points_list = []
for lvl, (H_, W_) in enumerate(spatial_shapes):
ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))
ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
ref = torch.stack((ref_x, ref_y), -1)
reference_points_list.append(ref)
reference_points = torch.cat(reference_points_list, 1)
reference_points = reference_points[:, :, None] * valid_ratios[:, None]
return reference_points
def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
output = src
reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
for _, layer in enumerate(self.layers):
output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)
return output
这里的_get_clones()函数时结构的深度复制，即参数是不同的。
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
forward里面即顺序执行的过程，主要要理解的是reference_points的定义。其实就是计算每个level中每个网格点的位置，这里的位置采用的是网格的中心点。这里有一个变量valid_ratios需要解释一下，query的个数是所有的像素位置，包括不同的level， 那么每个query都需要在不同的level上采点，所以需要每个reference_point在每个level上映射后的点，所以这里的valid_ratios在计算时就是公式2里的
函数。于是reference_points的size为
，总共有
个queries。
接下来是编码器部分Decoder
class DeformableTransformerDecoderLayer(nn.Module):
def __init__(self, d_model=256, d_ffn=1024,
dropout=0.1, activation="relu",
n_levels=4, n_heads=8, n_points=4):
super().__init__()
# cross attention
self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
self.dropout1 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model)
# self attention
self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
self.dropout2 = nn.Dropout(dropout)
self.norm2 = nn.LayerNorm(d_model)
# ffn
self.linear1 = nn.Linear(d_model, d_ffn)
self.activation = _get_activation_fn(activation)
self.dropout3 = nn.Dropout(dropout)
self.linear2 = nn.Linear(d_ffn, d_model)
self.dropout4 = nn.Dropout(dropout)
self.norm3 = nn.LayerNorm(d_model)
@staticmethod
def with_pos_embed(tensor, pos):
return tensor if pos is None else tensor + pos
def forward_ffn(self, tgt):
tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout4(tgt2)
tgt = self.norm3(tgt)
return tgt
def forward(self, tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index, src_padding_mask=None):
# self attention
q = k = self.with_pos_embed(tgt, query_pos)
tgt2 = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1))[0].transpose(0, 1)
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
# cross attention
tgt2 = self.cross_attn(self.with_pos_embed(tgt, query_pos),
reference_points,
src, src_spatial_shapes, level_start_index, src_padding_mask)
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
# ffn
tgt = self.forward_ffn(tgt)
return tgt
编码部分如下图所示，每个layer中包含两个部分，即query之间的self-attention，以及query与key之间的cross-attention.
self-attention由nn.MultiheadAttention实现，这里的pos表示的是query之间的位置编码。cross-attention调用的MSDeformAttn， 其输入的query不再是所有的像素位置，而src，src_spatial_shapes依然是所有的level。
decoder
class DeformableTransformerDecoder(nn.Module):
def __init__(self, decoder_layer, num_layers, return_intermediate=False):
super().__init__()
self.layers = _get_clones(decoder_layer, num_layers)
self.num_layers = num_layers
self.return_intermediate = return_intermediate
# hack implementation for iterative bounding box refinement and two-stage Deformable DETR
self.bbox_embed = None
self.class_embed = None
def forward(self, tgt, reference_points, src, src_spatial_shapes, src_level_start_index, src_valid_ratios,
query_pos=None, src_padding_mask=None):
output = tgt
intermediate = []
intermediate_reference_points = []
for lid, layer in enumerate(self.layers):
if reference_points.shape[-1] == 4:
reference_points_input = reference_points[:, :, None] * torch.cat([src_valid_ratios, src_valid_ratios], -1)[:, None]
else:
assert reference_points.shape[-1] == 2
reference_points_input = reference_points[:, :, None] * src_valid_ratios[:, None]
output = layer(output, query_pos, reference_points_input, src, src_spatial_shapes, src_level_start_index, src_padding_mask)
# hack implementation for iterative bounding box refinement
if self.bbox_embed is not None:
tmp = self.bbox_embed[lid](output)
if reference_points.shape[-1] == 4:
new_reference_points = tmp + inverse_sigmoid(reference_points)
new_reference_points = new_reference_points.sigmoid()
else:
assert reference_points.shape[-1] == 2
new_reference_points = tmp
new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points)
new_reference_points = new_reference_points.sigmoid()
reference_points = new_reference_points.detach()
if self.return_intermediate:
intermediate.append(output)
intermediate_reference_points.append(reference_points)
if self.return_intermediate:
return torch.stack(intermediate), torch.stack(intermediate_reference_points)
return output, reference_points
用decoderLayer搭建decoder的过程即顺序执行的过程，每次cross-attention的key和value都来自于相同的量，即encoder的多个level的输出。这里还定义了两个改进的接口，即box迭代细化和两阶段DETR。
Transformer
这部分是最终把Encoder和Decoder组装起来的过程。
class DeformableTransformer(nn.Module):
def __init__(self, d_model=256, nhead=8,
num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=1024, dropout=0.1,
activation="relu", return_intermediate_dec=False,
num_feature_levels=4, dec_n_points=4, enc_n_points=4,
two_stage=False, two_stage_num_proposals=300):
super().__init__()
self.d_model = d_model
self.nhead = nhead
self.two_stage = two_stage
self.two_stage_num_proposals = two_stage_num_proposals
encoder_layer = DeformableTransformerEncoderLayer(d_model, dim_feedforward,
dropout, activation,
num_feature_levels, nhead, enc_n_points)
self.encoder = DeformableTransformerEncoder(encoder_layer, num_encoder_layers)
decoder_layer = DeformableTransformerDecoderLayer(d_model, dim_feedforward,
dropout, activation,
num_feature_levels, nhead, dec_n_points)
self.decoder = DeformableTransformerDecoder(decoder_layer, num_decoder_layers, return_intermediate_dec)
self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
if two_stage:
self.enc_output = nn.Linear(d_model, d_model)
self.enc_output_norm = nn.LayerNorm(d_model)
self.pos_trans = nn.Linear(d_model * 2, d_model * 2)
self.pos_trans_norm = nn.LayerNorm(d_model * 2)
else:
self.reference_points = nn.Linear(d_model, 2)
self._reset_parameters()
这里出现了几个变量two_stage, two_stage_num_proposals, 'level_embed', reference_points以及two_stage的提取proposal过程。
看看其在forward中的作用：
def forward(self, srcs, masks, pos_embeds, query_embed=None):
assert self.two_stage or query_embed is not None
# prepare input for encoder
src_flatten = []
mask_flatten = []
lvl_pos_embed_flatten = []
spatial_shapes = []
for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
bs, c, h, w = src.shape
spatial_shape = (h, w)
spatial_shapes.append(spatial_shape)
src = src.flatten(2).transpose(1, 2) # bs x c x h x w -> bs x c x hw -> bs x hw x c
mask = mask.flatten(1) # bs x hw
pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs x c x h x w -> bs x c x hw -> bs x hw x c
lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) # bs x hw x c + 1 x 1 x c, 每一level提供一个可学习的编码
lvl_pos_embed_flatten.append(lvl_pos_embed) # 分别flatten之后append，方便encoder调用，即所有的keys
src_flatten.append(src)
mask_flatten.append(mask)
src_flatten = torch.cat(src_flatten, 1)
mask_flatten = torch.cat(mask_flatten, 1)
lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) # bs x num_level x 2
# encoder
memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)
# prepare input for decoder
bs, _, c = memory.shape
if self.two_stage:
output_memory, output_proposals = self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes)
# hack implementation for two-stage Deformable DETR
enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory) # 预测输出的score
enc_outputs_coord_unact = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals # 编码后的anchor+相对偏差
topk = self.two_stage_num_proposals
topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1] # 选择最大的topk的proposal
topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) # 选择对应topl score的编码后的框
topk_coords_unact = topk_coords_unact.detach()
reference_points = topk_coords_unact.sigmoid() # 相当于对proposal的微调
init_reference_out = reference_points
pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact)))
query_embed, tgt = torch.split(pos_trans_out, c, dim=2)
else:
query_embed, tgt = torch.split(query_embed, c, dim=1) # Lq x d_model
query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1) # bs x Lq x d_model 每个sample的query相同，参考位置也相同
tgt = tgt.unsqueeze(0).expand(bs, -1, -1) # 初始的query
reference_points = self.reference_points(query_embed).sigmoid() # 每个query是学习到不同的参考位置
init_reference_out = reference_points
# decoder
hs, inter_references = self.decoder(tgt, reference_points, memory,
spatial_shapes, level_start_index, valid_ratios, query_embed, mask_flatten)
inter_references_out = inter_references
if self.two_stage:
return hs, init_reference_out, inter_references_out, enc_outputs_class, enc_outputs_coord_unact
return hs, init_reference_out, inter_references_out, None, None
可以发现 level_embed始终可学习的对于不同level进行额外位置编码的作用。
不考虑two_stage的情况中query_embed是
大小的向量组，2d_model的长度包含query的可学习特征以及初始化的pos编码， reference_points对pos编码特征进行线性变换以得到初始可能的reference点。（这里有点值得思考的问题，相当于query_embed中包含了两类，一类是表观特征，一类是位置编码，那么我们是不是可以理解为表观特征作为模板在编码位置临近进行模板匹配呢？这样我们可以直接提供模板特征和侯选位置。）
考虑two_stage的情况，相当于先利用encoder进行proposals的粗选，即更具score筛选topk个候选位置。那么我看一看怎么由encoder提取proposals：
def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes):
N_, S_, C_ = memory.shape
base_scale = 4.0
proposals = []
_cur = 0
for lvl, (H_, W_) in enumerate(spatial_shapes):
mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1)
valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device))
grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) # H x W x 2
scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2) # 每个sample的有效尺寸
grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale # 归一化
wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl) # 方形的候选框，其实等价于anchor
proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
proposals.append(proposal)
_cur += (H_ * W_) # 每个level的起始索引
output_proposals = torch.cat(proposals, 1) # bs x key_num x 4
output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
# 筛选有效的proposal，将靠近边界的点舍弃
output_proposals = torch.log(output_proposals / (1 - output_proposals))
output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf'))
output_memory = memory
output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
output_memory = self.enc_output_norm(self.enc_output(output_memory))
return output_memory, output_proposals
for循环里是对不同level的所有格点创建不同尺寸的anchor框，scale其实是对有效区域的处理，后续对output_proposals的处理是筛选掉边界附近的候选，输出是对应位置的特征和编码后的proposal， 对应位置的特征用于映射proposal的类别score以及校正偏差。值得注意的是proposal并没有直接使用原始坐标，而是进行了log的编码
， 在forward中的two_stage情况提取reference_points是使用sigmoid函数进行了解码，我们假设偏置量为0，可以发现：
以上就是整个transformer的实现过程，不考虑two-stage的情形就是encoder和decoder的调用，而ecoderlayer和decoderlayer主要是deformAttn的调用。
下一篇我们来看整个deformable DETR的实现，即backbone + transformer以及FFN过程， transformer提供了每个query变换后的embedding和学习到的reference_points, FFN则将其转换为bbox和score。
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