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import copy
from typing import List, Tuple, Optional
import torch.nn.functional as F
import einops
import torch
from mmcv.cnn import ConvModule, build_norm_layer
from mmcv.cnn.bricks.transformer import PatchEmbed, FFN, build_transformer_layer
from mmengine.dist import is_main_process
from mmengine.model import BaseModule
from peft import get_peft_config, get_peft_model
from torch import Tensor, nn
# from mmdet.utils import OptConfigType, MultiConfig
from mmpretrain.models import resize_pos_embed
from mmpretrain.models.backbones.vit_sam import Attention, window_partition, window_unpartition
from mmseg.models import BaseSegmentor, EncoderDecoder
from mmseg.models.decode_heads.decode_head import BaseDecodeHead
from mmseg.models.utils import resize
from mmseg.utils import OptConfigType, MultiConfig
from opencd.registry import MODELS
from mmpretrain.models import build_norm_layer as build_norm_layer_mmpretrain
@MODELS.register_module()
class MMPretrainSamVisionEncoder(BaseModule):
def __init__(
self,
encoder_cfg,
peft_cfg=None,
init_cfg=None,
):
super().__init__(init_cfg=init_cfg)
vision_encoder = MODELS.build(encoder_cfg)
vision_encoder.init_weights()
if peft_cfg is not None and isinstance(peft_cfg, dict):
config = {
"peft_type": "LORA",
"r": 16,
'target_modules': ["qkv"],
"lora_alpha": 32,
"lora_dropout": 0.05,
"bias": "none",
"inference_mode": False,
}
config.update(peft_cfg)
peft_config = get_peft_config(config)
self.vision_encoder = get_peft_model(vision_encoder, peft_config)
if is_main_process():
self.vision_encoder.print_trainable_parameters()
else:
self.vision_encoder = vision_encoder
# freeze the vision encoder
for param in self.vision_encoder.parameters():
param.requires_grad = False
for name, param in self.vision_encoder.named_parameters():
if 'down_channel' in name:
param.requires_grad = True
if 'soft_ffn' in name:
param.requires_grad = True
if is_main_process() and peft_cfg is not None:
self.vision_encoder.print_trainable_parameters()
def forward(self, x):
return self.vision_encoder(x)
@MODELS.register_module()
class MLPSegHead(BaseDecodeHead):
def __init__(
self,
out_size,
interpolate_mode='bilinear',
**kwargs
):
super().__init__(input_transform='multiple_select', **kwargs)
self.interpolate_mode = interpolate_mode
num_inputs = len(self.in_channels)
assert num_inputs == len(self.in_index)
self.out_size = out_size
self.convs = nn.ModuleList()
for i in range(num_inputs):
self.convs.append(
ConvModule(
in_channels=self.in_channels[i],
out_channels=self.channels,
kernel_size=1,
stride=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
self.fusion_conv = ConvModule(
in_channels=self.channels * num_inputs,
out_channels=self.channels,
kernel_size=1,
norm_cfg=self.norm_cfg)
def forward(self, inputs):
inputs = self._transform_inputs(inputs)
outs = []
for idx in range(len(inputs)):
x = inputs[idx]
conv = self.convs[idx]
outs.append(
resize(
input=conv(x),
size=self.out_size,
mode=self.interpolate_mode,
align_corners=self.align_corners))
out = self.fusion_conv(torch.cat(outs, dim=1))
out = self.cls_seg(out)
return out
@MODELS.register_module()
class LN2d(nn.Module):
"""A LayerNorm variant, popularized by Transformers, that performs
pointwise mean and variance normalization over the channel dimension for
inputs that have shape (batch_size, channels, height, width)."""
def __init__(self, normalized_shape, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.normalized_shape = (normalized_shape, )
def forward(self, x):
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
@MODELS.register_module()
class SequentialNeck(BaseModule):
def __init__(self, necks):
super().__init__()
self.necks = nn.ModuleList()
for neck in necks:
self.necks.append(MODELS.build(neck))
def forward(self, *args, **kwargs):
for neck in self.necks:
args = neck(*args, **kwargs)
return args
@MODELS.register_module()
class SimpleFPN(BaseModule):
def __init__(self,
backbone_channel: int,
in_channels: List[int],
out_channels: int,
num_outs: int,
conv_cfg: OptConfigType = None,
norm_cfg: OptConfigType = None,
act_cfg: OptConfigType = None,
init_cfg: MultiConfig = None) -> None:
super().__init__(init_cfg=init_cfg)
assert isinstance(in_channels, list)
self.backbone_channel = backbone_channel
self.in_channels = in_channels
self.out_channels = out_channels
self.num_ins = len(in_channels)
self.num_outs = num_outs
self.fpn1 = nn.Sequential(
nn.ConvTranspose2d(self.backbone_channel,
self.backbone_channel // 2, 2, 2),
build_norm_layer(norm_cfg, self.backbone_channel // 2)[1],
nn.GELU(),
nn.ConvTranspose2d(self.backbone_channel // 2,
self.backbone_channel // 4, 2, 2))
self.fpn2 = nn.Sequential(
nn.ConvTranspose2d(self.backbone_channel,
self.backbone_channel // 2, 2, 2))
self.fpn3 = nn.Sequential(nn.Identity())
self.fpn4 = nn.Sequential(nn.MaxPool2d(kernel_size=2, stride=2))
self.lateral_convs = nn.ModuleList()
self.fpn_convs = nn.ModuleList()
for i in range(self.num_ins):
l_conv = ConvModule(
in_channels[i],
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
inplace=False)
fpn_conv = ConvModule(
out_channels,
out_channels,
3,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
inplace=False)
self.lateral_convs.append(l_conv)
self.fpn_convs.append(fpn_conv)
def forward(self, input: Tensor) -> tuple:
# build FPN
inputs = []
inputs.append(self.fpn1(input))
inputs.append(self.fpn2(input))
inputs.append(self.fpn3(input))
inputs.append(self.fpn4(input))
# build laterals
laterals = [
lateral_conv(inputs[i])
for i, lateral_conv in enumerate(self.lateral_convs)
]
# build outputs
# part 1: from original levels
outs = [self.fpn_convs[i](laterals[i]) for i in range(self.num_ins)]
# part 2: add extra levels
if self.num_outs > len(outs):
for i in range(self.num_outs - self.num_ins):
outs.append(F.max_pool2d(outs[-1], 1, stride=2))
return tuple(outs)
@MODELS.register_module()
class TimeFusionTransformerEncoderLayer(BaseModule):
def __init__(self,
embed_dims: int,
num_heads: int,
feedforward_channels: int,
drop_rate: float = 0.,
drop_path_rate: float = 0.,
num_fcs: int = 2,
qkv_bias: bool = True,
act_cfg: dict = dict(type='GELU'),
norm_cfg: dict = dict(type='LN'),
use_rel_pos: bool = False,
window_size: int = 0,
input_size: Optional[Tuple[int, int]] = None,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
self.window_size = window_size
self.ln1 = build_norm_layer_mmpretrain(norm_cfg, self.embed_dims)
self.attn = Attention(
embed_dims=embed_dims,
num_heads=num_heads,
qkv_bias=qkv_bias,
use_rel_pos=use_rel_pos,
input_size=input_size if window_size == 0 else
(window_size, window_size),
)
self.ln2 = build_norm_layer_mmpretrain(norm_cfg, self.embed_dims)
self.ffn = FFN(
embed_dims=embed_dims,
feedforward_channels=feedforward_channels,
num_fcs=num_fcs,
ffn_drop=drop_rate,
dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
act_cfg=act_cfg)
if self.window_size == 0:
in_channels = embed_dims * 2
self.down_channel = nn.Conv2d(in_channels, 1, kernel_size=1, stride=1, bias=False)
self.down_channel.weight.data.fill_(1.0/in_channels)
self.soft_ffn = nn.Sequential(
nn.Conv2d(embed_dims, embed_dims, kernel_size=1, stride=1),
nn.GELU(),
nn.Conv2d(embed_dims, embed_dims, kernel_size=1, stride=1),
)
@property
def norm1(self):
return self.ln1
@property
def norm2(self):
return self.ln2
def forward(self, x):
shortcut = x
x = self.ln1(x)
# Window partition
if self.window_size > 0:
H, W = x.shape[1], x.shape[2]
x, pad_hw = window_partition(x, self.window_size)
x = self.attn(x)
# Reverse window partition
if self.window_size > 0:
x = window_unpartition(x, self.window_size, pad_hw, (H, W))
x = shortcut + x
x = self.ffn(self.ln2(x), identity=x)
# # time phase fusion
if self.window_size == 0:
x = einops.rearrange(x, 'b h w d -> b d h w') # 2B, C, H, W
x0 = x[:x.size(0)//2]
x1 = x[x.size(0)//2:] # B, C, H, W
x0_1 = torch.cat([x0, x1], dim=1)
activate_map = self.down_channel(x0_1)
activate_map = torch.sigmoid(activate_map)
x0 = x0 + self.soft_ffn(x1 * activate_map)
x1 = x1 + self.soft_ffn(x0 * activate_map)
x = torch.cat([x0, x1], dim=0)
x = einops.rearrange(x, 'b d h w -> b h w d')
return x |