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#
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmseg.models.builder import BACKBONES
from torch.nn.init import normal_
from ...ops.modules import MSDeformAttn
from .adapter_modules import InteractionBlock, InteractionBlockWithCls, SpatialPriorModule, deform_inputs
from .vit import TIMMVisionTransformer
@BACKBONES.register_module()
class ViTAdapter(TIMMVisionTransformer):
def __init__(
self,
pretrain_size=224,
num_heads=12,
conv_inplane=64,
n_points=4,
deform_num_heads=6,
init_values=0.0,
interaction_indexes=None,
with_cffn=True,
cffn_ratio=0.25,
deform_ratio=1.0,
add_vit_feature=True,
pretrained=None,
use_extra_extractor=True,
freeze_vit=False,
use_cls=True,
with_cp=False,
*args,
**kwargs
):
super().__init__(num_heads=num_heads, pretrained=pretrained, with_cp=with_cp, *args, **kwargs)
if freeze_vit:
for param in self.parameters():
param.requires_grad = False
# self.num_classes = 80
self.use_cls = use_cls
if not self.use_cls:
self.cls_token = None
self.num_block = len(self.blocks)
self.pretrain_size = (pretrain_size, pretrain_size)
self.interaction_indexes = interaction_indexes
self.add_vit_feature = add_vit_feature
embed_dim = self.embed_dim
block_fn = InteractionBlockWithCls if use_cls else InteractionBlock
self.level_embed = nn.Parameter(torch.zeros(3, embed_dim))
self.spm = SpatialPriorModule(inplanes=conv_inplane, embed_dim=embed_dim, with_cp=False)
self.interactions = nn.Sequential(
*[
block_fn(
dim=embed_dim,
num_heads=deform_num_heads,
n_points=n_points,
init_values=init_values,
drop_path=self.drop_path_rate,
norm_layer=self.norm_layer,
with_cffn=with_cffn,
cffn_ratio=cffn_ratio,
deform_ratio=deform_ratio,
extra_extractor=((True if i == len(interaction_indexes) - 1 else False) and use_extra_extractor),
with_cp=with_cp,
)
for i in range(len(interaction_indexes))
]
)
self.up = nn.ConvTranspose2d(embed_dim, embed_dim, 2, 2)
self.norm1 = nn.SyncBatchNorm(embed_dim)
self.norm2 = nn.SyncBatchNorm(embed_dim)
self.norm3 = nn.SyncBatchNorm(embed_dim)
self.norm4 = nn.SyncBatchNorm(embed_dim)
self.up.apply(self._init_weights)
self.spm.apply(self._init_weights)
self.interactions.apply(self._init_weights)
self.apply(self._init_deform_weights)
normal_(self.level_embed)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
torch.nn.init.trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def _get_pos_embed(self, pos_embed, H, W):
pos_embed = pos_embed.reshape(
1, self.pretrain_size[0] // self.patch_size, self.pretrain_size[1] // self.patch_size, -1
).permute(0, 3, 1, 2)
pos_embed = (
F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False)
.reshape(1, -1, H * W)
.permute(0, 2, 1)
)
return pos_embed
def _init_deform_weights(self, m):
if isinstance(m, MSDeformAttn):
m._reset_parameters()
def _add_level_embed(self, c2, c3, c4):
c2 = c2 + self.level_embed[0]
c3 = c3 + self.level_embed[1]
c4 = c4 + self.level_embed[2]
return c2, c3, c4
def forward(self, x):
deform_inputs1, deform_inputs2 = deform_inputs(x, self.patch_size)
# SPM forward
c1, c2, c3, c4 = self.spm(x)
c2, c3, c4 = self._add_level_embed(c2, c3, c4)
c = torch.cat([c2, c3, c4], dim=1)
# Patch Embedding forward
H_c, W_c = x.shape[2] // 16, x.shape[3] // 16
x, H_toks, W_toks = self.patch_embed(x)
# print("H_toks, W_toks =", H_toks, W_toks)
bs, n, dim = x.shape
pos_embed = self._get_pos_embed(self.pos_embed[:, 1:], H_toks, W_toks)
if self.use_cls:
cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_token, x), dim=1)
pos_embed = torch.cat((self.pos_embed[:, :1], pos_embed), dim=1)
x = self.pos_drop(x + pos_embed)
# For CLIP
x = self.norm_pre(x)
# Interaction
if self.use_cls:
cls, x = (
x[
:,
:1,
],
x[
:,
1:,
],
)
outs = list()
for i, layer in enumerate(self.interactions):
indexes = self.interaction_indexes[i]
if self.use_cls:
x, c, cls = layer(
x,
c,
cls,
self.blocks[indexes[0] : indexes[-1] + 1],
deform_inputs1,
deform_inputs2,
H_c,
W_c,
H_toks,
W_toks,
)
else:
x, c = layer(
x,
c,
self.blocks[indexes[0] : indexes[-1] + 1],
deform_inputs1,
deform_inputs2,
H_c,
W_c,
H_toks,
W_toks,
)
outs.append(x.transpose(1, 2).view(bs, dim, H_toks, W_toks).contiguous())
# Split & Reshape
c2 = c[:, 0 : c2.size(1), :]
c3 = c[:, c2.size(1) : c2.size(1) + c3.size(1), :]
c4 = c[:, c2.size(1) + c3.size(1) :, :]
c2 = c2.transpose(1, 2).view(bs, dim, H_c * 2, W_c * 2).contiguous()
c3 = c3.transpose(1, 2).view(bs, dim, H_c, W_c).contiguous()
c4 = c4.transpose(1, 2).view(bs, dim, H_c // 2, W_c // 2).contiguous()
c1 = self.up(c2) + c1
if self.add_vit_feature:
x1, x2, x3, x4 = outs
x1 = F.interpolate(x1, size=(4 * H_c, 4 * W_c), mode="bilinear", align_corners=False)
x2 = F.interpolate(x2, size=(2 * H_c, 2 * W_c), mode="bilinear", align_corners=False)
x3 = F.interpolate(x3, size=(1 * H_c, 1 * W_c), mode="bilinear", align_corners=False)
x4 = F.interpolate(x4, size=(H_c // 2, W_c // 2), mode="bilinear", align_corners=False)
# print(c1.shape, c2.shape, c3.shape, c4.shape, x1.shape, x2.shape, x3.shape, x4.shape, H_c, H_toks)
c1, c2, c3, c4 = c1 + x1, c2 + x2, c3 + x3, c4 + x4
# Final Norm
f1 = self.norm1(c1)
f2 = self.norm2(c2)
f3 = self.norm3(c3)
f4 = self.norm4(c4)
return [f1, f2, f3, f4]
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