Deformable DETR模型学习记录

Deformable-DETR的主要贡献：
1，结合可变形卷积的稀疏空间采用和Transformer的全局关系建模能力，提出可变形注意力机制模型，使其计算量降低，收敛加快。
2，使用多层级特征，但不使用FPN，对小目标有较好效果。

可变形注意力提出的初衷是为了解决Transformer的Q，K的运算数据量巨大问题。作者认为Q没必要与所有的K都计算内积，而是只需要选择几个重要的K即可。
如下图，在该论文中，作者设定找4个K即可，而4个K的位置可以不断进行偏移，偏移过程如下图所示：

因此要解决的问题就是：（1）确定reference point(参考点）。（2）确定每个reference point的偏移量(offset)。（3）确定注意力权重矩阵 A_mqk，其中在Encoder和Decoder中实现方法不太一样。Deformable的计算方式如下：

在Encoder部分，输入的Query Feature ( z_q )为加入了位置编码的特征图(src+pos)， value(x)的计算方法只使用了src而没有位置编码(value_proj函数)。

1. reference point确定方法为用了torch.meshgrid方法，调用函数 get_reference_points，有一个细节就是参考点归一化到0和1之间，因此取值的时候要用到双线性插值的方法。而在Decoder中，参考点的获取方法为object queries通过一个nn.Linear得到每个对应的reference point。

def get_reference_points(spatial_shapes, valid_ratios, device):
 reference_points_list = []
 for lvl, (H_, W_) in enumerate(spatial_shapes):
 # 从0.5到H-0.5采样H个点，W同理 这个操作的目的也就是为了特征图的对齐
 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

（2）计算offset的方法为对 z_q 过一个nn.Linear，得到多组偏移量，每组偏移量的维度为参考点的个数，组数为注意力头的数量。

（3）计算注意力权重矩阵 A_mqk 的方法为 z_q 过一个nn.Linear和一个F.softmax，得到每个头的注意力权重。

如下图所示：

如上图所示：分头计算完的注意力最终会拼接到一起，然后最后过一个nn.Linear得到输入x 的最终输出。

多尺度的Deformable Attention模块也是在多尺度特征图上计算的。多尺度的特征融合方法则是取了骨干网(ResNet)最后三层的特征图C3，C4，C5，并且用了一个Conv3x3 Stride2的卷积得到了一个C6构成了四层特征图。随后会通过卷积操作将通道数量统一为256(也就是token的数量)，然后在这四个特征图上运行Deformable Attention Module并且进行直接相加得到最终输出。其中

Deformable Attention Module算子的pytorch实现如下：
def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):
 # for debug and test only,
 # need to use cuda version instead
 N_, S_, M_, D_ = value.shape # batch size, number token, number head, head dims
 # Lq_: number query, L_: level number, P_: sampling number采样点数
 _, Lq_, M_, L_, P_, _ = sampling_locations.shape
 # 按照level划分value
 value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
 # [0, 1] -> [-1, 1] 因为要满足F.grid_sample的输入要求
 sampling_grids = 2 * sampling_locations - 1
 sampling_value_list = []
 for lid_, (H_, W_) in enumerate(value_spatial_shapes):
 # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
 value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)
 # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
 sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)
 # N_*M_, D_, Lq_, P_
 # 用双线性插值从feature map上获取value，因为mask的原因越界所以要zeros的方法进行填充
 sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,
 mode='bilinear', padding_mode='zeros', align_corners=False)
 sampling_value_list.append(sampling_value_l_)
 # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)
 attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)
 # 不同scale计算出的multi head attention 进行相加，返回output后还需要过一个Linear层
 output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)
 return output.transpose(1, 2).contiguous()

这里的两阶段是受two-stage object detectors的启发，当然这里的改动其实很小：将Encoder输出的memory送入了FFN（前馈神经网络负责类别预测与box预测）将其进行修正后再送入Decoder。

其他方面，Deformable相较于DETR修改了query-num的数量，改为300，但在推理过程中其会仍使用top100的预测框，此外在匈牙利匹配的cost矩阵构建时class的损失由原本的softmax简单运算变为了Focus loss。

Encoder加入了参考点计算，，此外改动了DerormableAttention计算。

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

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

 @staticmethod
 def get_reference_points(spatial_shapes, valid_ratios, device):
 reference_points_list = []
 for lvl, (H_, W_) in enumerate(spatial_shapes):
 # 从0.5到H-0.5采样H个点，W同理 这个操作的目的也就是为了特征图的对齐
 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

详细代码注释如下，这里要控制是否使用iterative bounding box refinement和two stage技巧。iterative bounding box refinement其实就是对参考点的位置进行微调。two stage方法其实就是通过参考点直接生成anchor但是只取最高置信度的前几个，然后再送入decoder进行调整。intermediate数组是一个trick，每层Decoder都是可以输出bbox和分类信息的，如果都利用起来算损失则成为auxiliary loss。

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


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

 # 用来存储中间decoder输出的 可以考虑是否用auxiliary loss
 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
 # iterative refinement是对decoder中的参考点进行微调，类似cascade rcnn思想
 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

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()

 def _reset_parameters(self):
 for p in self.parameters():
 if p.dim() > 1:
 nn.init.xavier_uniform_(p)
 for m in self.modules():
 if isinstance(m, MSDeformAttn):
 m._reset_parameters()
 if not self.two_stage:
 xavier_uniform_(self.reference_points.weight.data, gain=1.0)
 constant_(self.reference_points.bias.data, 0.)
 normal_(self.level_embed)

 def get_proposal_pos_embed(self, proposals):
 num_pos_feats = 128
 temperature = 10000
 scale = 2 * math.pi

 dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)
 dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
 # N, L, 4
 proposals = proposals.sigmoid() * scale
 # N, L, 4, 128
 pos = proposals[:, :, :, None] / dim_t
 # N, L, 4, 64, 2
 pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)
 return pos

 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)

 scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
 grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
 wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl)
 proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
 proposals.append(proposal)
 _cur += (H_ * W_)
 output_proposals = torch.cat(proposals, 1)
 output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
 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

 def get_valid_ratio(self, mask):
 _, H, W = mask.shape
 valid_H = torch.sum(~mask[:, :, 0], 1)
 valid_W = torch.sum(~mask[:, 0, :], 1)
 valid_ratio_h = valid_H.float() / H
 valid_ratio_w = valid_W.float() / W
 valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
 return valid_ratio

 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)):
 # 得到每一层feature map的batch size 通道数量 高宽
 bs, c, h, w = src.shape
 spatial_shape = (h, w)
 spatial_shapes.append(spatial_shape)
 # 将每层的feature map、mask、位置编码拉平，并且加入到相关数组中
 src = src.flatten(2).transpose(1, 2)
 mask = mask.flatten(1)
 pos_embed = pos_embed.flatten(2).transpose(1, 2)
 # 位置编码和可学习的每层编码相加，表征类似 3D position
 lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) 
 lvl_pos_embed_flatten.append(lvl_pos_embed)
 src_flatten.append(src)
 mask_flatten.append(mask)
 # 在hidden_dim维度上进行拼接，也就是number token数量一样的那个维度
 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)
 # 记录每个level开始的索引以及有效的长宽(因为有mask存在，raw image的分辨率可能不统一) 具体查看get_valid_ratio函数
 # prod(1)计算h*w，cumsum(0)计算前缀和
 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)

 # 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
 # 是否使用两阶段模式
 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)
 enc_outputs_coord_unact = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals

 topk = self.two_stage_num_proposals
 topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1]
 topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4))
 topk_coords_unact = topk_coords_unact.detach()
 reference_points = topk_coords_unact.sigmoid()
 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:
 # 这是非双阶段版本的Deformable DETR
 # 将query_embed划分为query_embed和tgt两部分
 query_embed, tgt = torch.split(query_embed, c, dim=1)
 # 复制bs份
 query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1)
 tgt = tgt.unsqueeze(0).expand(bs, -1, -1)
 # nn.Linear得到每个object queries对应的reference point, 这是decoder参考点的方法!!!
 reference_points = self.reference_points(query_embed).sigmoid()
 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

Deformable DETR效率高并且收敛快，核心是Multi-Scale Deformable Attention Module。解决了DETR中收敛慢以及小目标性能低的问题。