一、文本介绍
本文修改的模型是Deformable-DETR，修改的位置是在可变形编码器和可变形解码器之间，在可变形编码器和可变形解码器之间加入RT-DERT中的CCFM模块，CCFM模块的输入为可变形编码器的最高层级特征图以及backbone的输出。CCFM模块可以提升模型的特征融合能力。
二、模型图
模型架构
CCFM
三、核心代码
修改deformable_detr.py
主要新增代码
具体deformable_detr.py代码：
# ------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
import copy
from typing import Optional, List
import math
import torch
import torch.nn.functional as F
from torch import nn, Tensor
from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
from util.misc import inverse_sigmoid
from models.ops.modules import MSDeformAttn
class ConvNormLayer(nn.Module):
def __init__(self, ch_in, ch_out, kernel_size, stride, padding=None, bias=False, act=None):
super().__init__()
self.conv = nn.Conv2d(
ch_in,
ch_out,
kernel_size,
stride,
padding=(kernel_size-1)//2 if padding is None else padding,
bias=bias)
self.norm = nn.BatchNorm2d(ch_out)
self.act = nn.Identity() if act is None else get_activation(act)
def forward(self, x):
return self.act(self.norm(self.conv(x)))
class RepVggBlock(nn.Module):
def __init__(self, ch_in, ch_out, act='relu'):
super().__init__()
self.ch_in = ch_in
self.ch_out = ch_out
self.conv1 = ConvNormLayer(ch_in, ch_out, 3, 1, padding=1, act=None)
self.conv2 = ConvNormLayer(ch_in, ch_out, 1, 1, padding=0, act=None)
self.act = nn.Identity() if act is None else get_activation(act)
def forward(self, x):
if hasattr(self, 'conv'):
y = self.conv(x)
else:
y = self.conv1(x) + self.conv2(x)
return self.act(y)
class CSPRepLayer(nn.Module):
def __init__(self,
in_channels,
out_channels,
num_blocks=3,
expansion=1.0,
bias=None,
act="silu"):
super(CSPRepLayer, self).__init__()
hidden_channels = int(out_channels * expansion)
self.conv1 = ConvNormLayer(in_channels, hidden_channels, 1, 1, bias=bias, act=act)
self.conv2 = ConvNormLayer(in_channels, hidden_channels, 1, 1, bias=bias, act=act)
self.bottlenecks = nn.Sequential(*[
RepVggBlock(hidden_channels, hidden_channels, act=act) for _ in range(num_blocks)
])
if hidden_channels != out_channels:
self.conv3 = ConvNormLayer(hidden_channels, out_channels, 1, 1, bias=bias, act=act)
else:
self.conv3 = nn.Identity()
def forward(self, x):
x_1 = self.conv1(x)
x_1 = self.bottlenecks(x_1)
x_2 = self.conv2(x)
return self.conv3(x_1 + x_2)
def get_activation(act: str, inpace: bool = True):
'''get activation
'''
act = act.lower()
if act == 'silu':
m = nn.SiLU()
elif act == 'relu':
m = nn.ReLU()
elif act == 'leaky_relu':
m = nn.LeakyReLU()
elif act == 'silu':
m = nn.SiLU()
elif act == 'gelu':
m = nn.GELU()
elif act is None:
m = nn.Identity()
elif isinstance(act, nn.Module):
m = act
else:
raise RuntimeError('')
if hasattr(m, 'inplace'):
m.inplace = inpace
return m
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)
# top-down fpn
self.lateral_convs = nn.ModuleList()
self.fpn_blocks = nn.ModuleList()
for _ in range(3 - 1, 0, -1):
self.lateral_convs.append(ConvNormLayer(d_model, d_model, 1, 1, act=None))
self.fpn_blocks.append(
CSPRepLayer(d_model * 2, d_model, round(3 * 1.0), act=None, expansion=1.0)
)
# bottom-up pan
self.downsample_convs = nn.ModuleList()
self.pan_blocks = nn.ModuleList()
for _ in range(3 - 1):
self.downsample_convs.append(
ConvNormLayer(d_model, d_model, 3, 2, act=None)
)
self.pan_blocks.append(
CSPRepLayer(d_model * 2, d_model, round(3 * 1.0), act=None, expansion=1.0)
)
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 = []
feats = []
hws = []
for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
if lvl != 0:
feats.append(src)
bs, c, h, w = src.shape
hw = [h, w]
hws.append(hw)
spatial_shape = (h, w)
spatial_shapes.append(spatial_shape)
src = src.flatten(2).transpose(1, 2)
mask = mask.flatten(1)
pos_embed = pos_embed.flatten(2).transpose(1, 2)
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)
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)
# encoder
memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)
fla_src0_1 = memory[:, :hws[0][0] * hws[0][1], :]
tmp_1 = memory[:, hws[0][0] * hws[0][1]:, :]
tmp_2 = tmp_1[:, hws[1][0] * hws[1][1]:, :]
fla_src3_1 = tmp_2[:, hws[2][0] * hws[2][1]:, :]
src3_1 = fla_src3_1.permute(0, 2, 1).view(-1, 256, hws[3][0], hws[3][1])
src0 = fla_src0_1.permute(0, 2, 1).view(-1, 256, hws[0][0], hws[0][1])
proj_feats = feats
proj_feats[2] = src3_1
# broadcasting and fusion
inner_outs = [proj_feats[-1]]
for idx in range(3 - 1, 0, -1):
feat_high = inner_outs[0]
feat_low = proj_feats[idx - 1]
feat_high = self.lateral_convs[3 - 1 - idx](feat_high)
inner_outs[0] = feat_high
upsample_feat = F.interpolate(feat_high, size=(feat_low.shape[2], feat_low.shape[3]), mode='nearest')
inner_out = self.fpn_blocks[3-1-idx](torch.concat([upsample_feat, feat_low], dim=1))
inner_outs.insert(0, inner_out)
outs = [inner_outs[0]]
for idx in range(3 - 1):
feat_low = outs[-1]
feat_high = inner_outs[idx + 1]
downsample_feat = self.downsample_convs[idx](feat_low)
out = self.pan_blocks[idx](torch.concat([downsample_feat, feat_high], dim=1))
outs.append(out)
outs.insert(0, src0)
src_flatten_outs = []
for out in outs:
src_flatten_out = out.flatten(2).transpose(1, 2)
src_flatten_outs.append(src_flatten_out)
src_flatten_outs = torch.cat(src_flatten_outs, 1)
memory = memory + src_flatten_outs
# 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)
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:
query_embed, tgt = torch.split(query_embed, c, dim=1)
query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1)
tgt = tgt.unsqueeze(0).expand(bs, -1, -1)
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
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):
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
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
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
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
def _get_activation_fn(activation):
"""Return an activation function given a string"""
if activation == "relu":
return F.relu
if activation == "gelu":
return F.gelu
if activation == "glu":
return F.glu
raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
def build_deforamble_transformer(args):
return DeformableTransformer(
d_model=args.hidden_dim,
nhead=args.nheads,
num_encoder_layers=args.enc_layers,
num_decoder_layers=args.dec_layers,
dim_feedforward=args.dim_feedforward,
dropout=args.dropout,
activation="relu",
return_intermediate_dec=True,
num_feature_levels=args.num_feature_levels,
dec_n_points=args.dec_n_points,
enc_n_points=args.enc_n_points,
two_stage=args.two_stage,
two_stage_num_proposals=args.num_queries)