1022 lines
39 KiB
Python
1022 lines
39 KiB
Python
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import timm
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import re
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import time
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import math
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import numpy as np
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from functools import partial
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from typing import Optional, Union, Type, List, Tuple, Callable, Dict
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from einops import rearrange, repeat
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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# from mamba_ssm.ops.selective_scan_interface import selective_scan_fn, selective_scan_ref
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DropPath.__repr__ = lambda self: f"timm.DropPath({self.drop_prob})"
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from mmseg.registry import MODELS
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class PatchEmbed2D(nn.Module):
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r""" Image to Patch Embedding
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Args:
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patch_size (int): Patch token size. Default: 4.
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in_chans (int): Number of input image channels. Default: 3.
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embed_dim (int): Number of linear projection output channels. Default: 96.
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norm_layer (nn.Module, optional): Normalization layer. Default: None
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"""
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def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None, **kwargs):
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super().__init__()
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if isinstance(patch_size, int):
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patch_size = (patch_size, patch_size)
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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if norm_layer is not None:
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self.norm = norm_layer(embed_dim)
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else:
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self.norm = None
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def forward(self, x):
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x = self.proj(x).permute(0, 2, 3, 1)
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if self.norm is not None:
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x = self.norm(x)
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return x
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class PatchMerging2D(nn.Module):
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r""" Patch Merging Layer.
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Args:
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input_resolution (tuple[int]): Resolution of input feature.
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dim (int): Number of input channels.
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self, dim, norm_layer=nn.LayerNorm):
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super().__init__()
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self.dim = dim
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self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
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self.norm = norm_layer(4 * dim)
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def forward(self, x):
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B, H, W, C = x.shape
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SHAPE_FIX = [-1, -1]
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if (W % 2 != 0) or (H % 2 != 0):
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print(f"Warning, x.shape {x.shape} is not match even ===========", flush=True)
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SHAPE_FIX[0] = H // 2
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SHAPE_FIX[1] = W // 2
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x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
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x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
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x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
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x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
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if SHAPE_FIX[0] > 0:
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x0 = x0[:, :SHAPE_FIX[0], :SHAPE_FIX[1], :]
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x1 = x1[:, :SHAPE_FIX[0], :SHAPE_FIX[1], :]
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x2 = x2[:, :SHAPE_FIX[0], :SHAPE_FIX[1], :]
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x3 = x3[:, :SHAPE_FIX[0], :SHAPE_FIX[1], :]
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x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
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x = x.view(B, H // 2, W // 2, 4 * C) # B H/2*W/2 4*C
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x = self.norm(x)
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x = self.reduction(x)
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return x
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class SS2D(nn.Module):
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def __init__(
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self,
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d_model,
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d_state=16,
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d_conv=3,
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expand=2,
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dt_rank="auto",
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dt_min=0.001,
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dt_max=0.1,
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dt_init="random",
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dt_scale=1.0,
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dt_init_floor=1e-4,
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dropout=0.,
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conv_bias=True,
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bias=False,
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device=None,
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dtype=None,
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**kwargs,
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):
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.d_model = d_model
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self.d_state = d_state
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self.d_conv = d_conv
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self.expand = expand
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self.d_inner = int(self.expand * self.d_model)
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self.dt_rank = math.ceil(self.d_model / 16) if dt_rank == "auto" else dt_rank
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self.in_proj = nn.Linear(self.d_model, self.d_inner * 2, bias=bias, **factory_kwargs)
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self.conv2d = nn.Conv2d(
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in_channels=self.d_inner,
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out_channels=self.d_inner,
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groups=self.d_inner,
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bias=conv_bias,
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kernel_size=d_conv,
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padding=(d_conv - 1) // 2,
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**factory_kwargs,
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)
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self.act = nn.SiLU()
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self.x_proj = (
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nn.Linear(self.d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs),
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nn.Linear(self.d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs),
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nn.Linear(self.d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs),
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nn.Linear(self.d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs),
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)
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self.x_proj_weight = nn.Parameter(torch.stack([t.weight for t in self.x_proj], dim=0)) # (K=4, N, inner)
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del self.x_proj
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self.dt_projs = (
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self.dt_init(self.dt_rank, self.d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor,
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**factory_kwargs),
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self.dt_init(self.dt_rank, self.d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor,
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**factory_kwargs),
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self.dt_init(self.dt_rank, self.d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor,
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**factory_kwargs),
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self.dt_init(self.dt_rank, self.d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor,
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**factory_kwargs),
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)
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self.dt_projs_weight = nn.Parameter(torch.stack([t.weight for t in self.dt_projs], dim=0)) # (K=4, inner, rank)
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self.dt_projs_bias = nn.Parameter(torch.stack([t.bias for t in self.dt_projs], dim=0)) # (K=4, inner)
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del self.dt_projs
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self.A_logs = self.A_log_init(self.d_state, self.d_inner, copies=4, merge=True) # (K=4, D, N)
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self.Ds = self.D_init(self.d_inner, copies=4, merge=True) # (K=4, D, N)
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# self.selective_scan = selective_scan_fn
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self.out_norm = nn.LayerNorm(self.d_inner)
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self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=bias, **factory_kwargs)
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self.dropout = nn.Dropout(dropout) if dropout > 0. else None
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@staticmethod
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def dt_init(dt_rank, d_inner, dt_scale=1.0, dt_init="random", dt_min=0.001, dt_max=0.1, dt_init_floor=1e-4,
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**factory_kwargs):
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dt_proj = nn.Linear(dt_rank, d_inner, bias=True, **factory_kwargs)
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# Initialize special dt projection to preserve variance at initialization
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dt_init_std = dt_rank ** -0.5 * dt_scale
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if dt_init == "constant":
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nn.init.constant_(dt_proj.weight, dt_init_std)
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elif dt_init == "random":
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nn.init.uniform_(dt_proj.weight, -dt_init_std, dt_init_std)
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else:
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raise NotImplementedError
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# Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max
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dt = torch.exp(
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torch.rand(d_inner, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))
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+ math.log(dt_min)
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).clamp(min=dt_init_floor)
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# Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
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inv_dt = dt + torch.log(-torch.expm1(-dt))
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with torch.no_grad():
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dt_proj.bias.copy_(inv_dt)
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# Our initialization would set all Linear.bias to zero, need to mark this one as _no_reinit
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dt_proj.bias._no_reinit = True
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return dt_proj
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@staticmethod
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def A_log_init(d_state, d_inner, copies=1, device=None, merge=True):
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# S4D real initialization
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A = repeat(
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torch.arange(1, d_state + 1, dtype=torch.float32, device=device),
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"n -> d n",
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d=d_inner,
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).contiguous()
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A_log = torch.log(A) # Keep A_log in fp32
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if copies > 1:
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A_log = repeat(A_log, "d n -> r d n", r=copies)
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if merge:
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A_log = A_log.flatten(0, 1)
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A_log = nn.Parameter(A_log)
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A_log._no_weight_decay = True
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return A_log
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@staticmethod
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def D_init(d_inner, copies=1, device=None, merge=True):
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# D "skip" parameter
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D = torch.ones(d_inner, device=device)
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if copies > 1:
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D = repeat(D, "n1 -> r n1", r=copies)
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if merge:
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D = D.flatten(0, 1)
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D = nn.Parameter(D) # Keep in fp32
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D._no_weight_decay = True
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return D
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def forward_core(self, x: torch.Tensor):
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B, C, H, W = x.shape
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L = H * W
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K = 4
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x_hwwh = torch.stack([x.view(B, -1, L), torch.transpose(x, dim0=2, dim1=3).contiguous().view(B, -1, L)],
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dim=1).view(B, 2, -1, L)
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xs = torch.cat([x_hwwh, torch.flip(x_hwwh, dims=[-1])], dim=1) # (b, k, d, l)
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x_dbl = torch.einsum("b k d l, k c d -> b k c l", xs.view(B, K, -1, L), self.x_proj_weight)
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dts, Bs, Cs = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=2)
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dts = torch.einsum("b k r l, k d r -> b k d l", dts.view(B, K, -1, L), self.dt_projs_weight)
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xs = xs.float().view(B, -1, L) # (b, k * d, l)
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dts = dts.contiguous().float().view(B, -1, L) # (b, k * d, l)
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Bs = Bs.float().view(B, K, -1, L) # (b, k, d_state, l)
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Cs = Cs.float().view(B, K, -1, L) # (b, k, d_state, l)
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Ds = self.Ds.float().view(-1) # (k * d)
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As = -torch.exp(self.A_logs.float()).view(-1, self.d_state) # (k * d, d_state)
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dt_projs_bias = self.dt_projs_bias.float().view(-1) # (k * d)
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out_y = self.selective_scan(
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xs, dts,
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As, Bs, Cs, Ds, z=None,
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delta_bias=dt_projs_bias,
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delta_softplus=True,
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return_last_state=False,
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).view(B, K, -1, L)
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assert out_y.dtype == torch.float
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inv_y = torch.flip(out_y[:, 2:4], dims=[-1]).view(B, 2, -1, L)
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wh_y = torch.transpose(out_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
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invwh_y = torch.transpose(inv_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
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return out_y[:, 0], inv_y[:, 0], wh_y, invwh_y
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def forward(self, x: torch.Tensor, **kwargs):
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B, H, W, C = x.shape
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xz = self.in_proj(x)
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x, z = xz.chunk(2, dim=-1) # (b, h, w, d)
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x = x.permute(0, 3, 1, 2).contiguous()
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x = self.act(self.conv2d(x)) # (b, d, h, w)
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y1, y2, y3, y4 = self.forward_core(x)
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assert y1.dtype == torch.float32
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y = y1 + y2 + y3 + y4
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y = torch.transpose(y, dim0=1, dim1=2).contiguous().view(B, H, W, -1)
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y = self.out_norm(y)
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y = y * F.silu(z)
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out = self.out_proj(y)
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if self.dropout is not None:
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out = self.dropout(out)
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return out
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class VSSBlock(nn.Module):
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def __init__(
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self,
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hidden_dim: int = 0,
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drop_path: float = 0,
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norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6),
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attn_drop_rate: float = 0,
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d_state: int = 16,
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**kwargs,
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):
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super().__init__()
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self.ln_1 = norm_layer(hidden_dim)
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self.self_attention = SS2D(d_model=hidden_dim, dropout=attn_drop_rate, d_state=d_state, **kwargs)
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self.drop_path = DropPath(drop_path)
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def forward(self, input: torch.Tensor):
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x = input + self.drop_path(self.self_attention(self.ln_1(input)))
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return x
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class VSSLayer(nn.Module):
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""" A basic layer for one stage.
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Args:
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dim (int): Number of input channels.
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depth (int): Number of blocks.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
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use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
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"""
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def __init__(
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self,
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dim,
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depth,
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attn_drop=0.,
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drop_path=0.,
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norm_layer=nn.LayerNorm,
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downsample=None,
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use_checkpoint=False,
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d_state=16,
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**kwargs,
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):
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super().__init__()
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self.dim = dim
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self.use_checkpoint = use_checkpoint
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self.blocks = nn.ModuleList([
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VSSBlock(
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hidden_dim=dim,
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drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
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norm_layer=norm_layer,
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attn_drop_rate=attn_drop,
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d_state=d_state,
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)
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for i in range(depth)])
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if True: # is this really applied? Yes, but been overriden later in VSSM!
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def _init_weights(module: nn.Module):
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for name, p in module.named_parameters():
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if name in ["out_proj.weight"]:
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p = p.clone().detach_() # fake init, just to keep the seed ....
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nn.init.kaiming_uniform_(p, a=math.sqrt(5))
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self.apply(_init_weights)
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if downsample is not None:
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self.downsample = downsample(dim=dim, norm_layer=norm_layer)
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else:
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self.downsample = None
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def forward(self, x):
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for blk in self.blocks:
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if self.use_checkpoint:
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x = checkpoint.checkpoint(blk, x)
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else:
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x = blk(x)
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if self.downsample is not None:
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x = self.downsample(x)
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return x
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class VSSMEncoder(nn.Module):
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def __init__(self, patch_size=4, in_chans=3, depths=[2, 2, 9, 2],
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dims=[96, 192, 384, 768], d_state=16, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.2,
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norm_layer=nn.LayerNorm, patch_norm=True,
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use_checkpoint=False, **kwargs):
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super().__init__()
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self.num_layers = len(depths)
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if isinstance(dims, int):
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dims = [int(dims * 2 ** i_layer) for i_layer in range(self.num_layers)]
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self.embed_dim = dims[0]
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self.num_features = dims[-1]
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self.dims = dims
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self.patch_embed = PatchEmbed2D(patch_size=patch_size, in_chans=in_chans, embed_dim=self.embed_dim,
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norm_layer=norm_layer if patch_norm else None)
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# WASTED absolute position embedding ======================
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self.ape = False
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if self.ape:
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self.patches_resolution = self.patch_embed.patches_resolution
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self.absolute_pos_embed = nn.Parameter(torch.zeros(1, *self.patches_resolution, self.embed_dim))
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trunc_normal_(self.absolute_pos_embed, std=.02)
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
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self.layers = nn.ModuleList()
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self.downsamples = nn.ModuleList()
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for i_layer in range(self.num_layers):
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layer = VSSLayer(
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dim=dims[i_layer],
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depth=depths[i_layer],
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d_state=math.ceil(dims[0] / 6) if d_state is None else d_state, # 20240109
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drop=drop_rate,
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attn_drop=attn_drop_rate,
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drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
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norm_layer=norm_layer,
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downsample=None,
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use_checkpoint=use_checkpoint,
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)
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self.layers.append(layer)
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if i_layer < self.num_layers - 1:
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self.downsamples.append(PatchMerging2D(dim=dims[i_layer], norm_layer=norm_layer))
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self.apply(self._init_weights)
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def _init_weights(self, m: nn.Module):
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"""
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out_proj.weight which is previously initilized in VSSBlock, would be cleared in nn.Linear
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no fc.weight found in the any of the model parameters
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no nn.Embedding found in the any of the model parameters
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so the thing is, VSSBlock initialization is useless
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Conv2D is not intialized !!!
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"""
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
nn.init.constant_(m.bias, 0)
|
|
nn.init.constant_(m.weight, 1.0)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
return {'absolute_pos_embed'}
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay_keywords(self):
|
|
return {'relative_position_bias_table'}
|
|
|
|
def forward(self, x):
|
|
x_ret = []
|
|
x_ret.append(x)
|
|
|
|
x = self.patch_embed(x)
|
|
if self.ape:
|
|
x = x + self.absolute_pos_embed
|
|
x = self.pos_drop(x)
|
|
|
|
for s, layer in enumerate(self.layers):
|
|
x = layer(x)
|
|
x_ret.append(x.permute(0, 3, 1, 2))
|
|
if s < len(self.downsamples):
|
|
x = self.downsamples[s](x)
|
|
|
|
return x_ret
|
|
class ConvBNReLU(nn.Sequential):
|
|
def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d,
|
|
bias=False):
|
|
super(ConvBNReLU, self).__init__(
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
|
|
dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
|
|
norm_layer(out_channels),
|
|
nn.ReLU6()
|
|
)
|
|
|
|
|
|
class ConvBN(nn.Sequential):
|
|
def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d,
|
|
bias=False):
|
|
super(ConvBN, self).__init__(
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
|
|
dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
|
|
norm_layer(out_channels)
|
|
)
|
|
|
|
|
|
class Conv(nn.Sequential):
|
|
def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, bias=False):
|
|
super(Conv, self).__init__(
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
|
|
dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2)
|
|
)
|
|
|
|
|
|
class SeparableConvBNReLU(nn.Sequential):
|
|
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1,
|
|
norm_layer=nn.BatchNorm2d):
|
|
super(SeparableConvBNReLU, self).__init__(
|
|
nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation,
|
|
padding=((stride - 1) + dilation * (kernel_size - 1)) // 2,
|
|
groups=in_channels, bias=False),
|
|
norm_layer(out_channels),
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
|
|
nn.ReLU6()
|
|
)
|
|
|
|
|
|
class SeparableConvBN(nn.Sequential):
|
|
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1,
|
|
norm_layer=nn.BatchNorm2d):
|
|
super(SeparableConvBN, self).__init__(
|
|
nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation,
|
|
padding=((stride - 1) + dilation * (kernel_size - 1)) // 2,
|
|
groups=in_channels, bias=False),
|
|
norm_layer(out_channels),
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
|
|
)
|
|
|
|
|
|
class SeparableConv(nn.Sequential):
|
|
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
|
|
super(SeparableConv, self).__init__(
|
|
nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation,
|
|
padding=((stride - 1) + dilation * (kernel_size - 1)) // 2,
|
|
groups=in_channels, bias=False),
|
|
nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
|
|
)
|
|
|
|
|
|
class Mlp(nn.Module):
|
|
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
self.fc1 = nn.Conv2d(in_features, hidden_features, 1, 1, 0, bias=True)
|
|
self.act = act_layer()
|
|
self.fc2 = nn.Conv2d(hidden_features, out_features, 1, 1, 0, bias=True)
|
|
self.drop = nn.Dropout(drop, inplace=True)
|
|
|
|
def forward(self, x):
|
|
x = self.fc1(x)
|
|
x = self.act(x)
|
|
x = self.drop(x)
|
|
x = self.fc2(x)
|
|
x = self.drop(x)
|
|
return x
|
|
|
|
|
|
class GlobalLocalAttention(nn.Module):
|
|
def __init__(self,
|
|
dim=256,
|
|
num_heads=16,
|
|
qkv_bias=False,
|
|
window_size=8,
|
|
relative_pos_embedding=True
|
|
):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
head_dim = dim // self.num_heads
|
|
self.scale = head_dim ** -0.5
|
|
self.ws = window_size
|
|
|
|
self.qkv = Conv(dim, 3 * dim, kernel_size=1, bias=qkv_bias)
|
|
self.local1 = ConvBN(dim, dim, kernel_size=3)
|
|
self.local2 = ConvBN(dim, dim, kernel_size=1)
|
|
self.proj = SeparableConvBN(dim, dim, kernel_size=window_size)
|
|
|
|
self.attn_x = nn.AvgPool2d(kernel_size=(window_size, 1), stride=1, padding=(window_size // 2 - 1, 0))
|
|
self.attn_y = nn.AvgPool2d(kernel_size=(1, window_size), stride=1, padding=(0, window_size // 2 - 1))
|
|
|
|
self.relative_pos_embedding = relative_pos_embedding
|
|
|
|
if self.relative_pos_embedding:
|
|
# define a parameter table of relative position bias
|
|
self.relative_position_bias_table = nn.Parameter(
|
|
torch.zeros((2 * window_size - 1) * (2 * window_size - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
|
|
|
# get pair-wise relative position index for each token inside the window
|
|
coords_h = torch.arange(self.ws)
|
|
coords_w = torch.arange(self.ws)
|
|
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
|
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
|
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
|
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
|
relative_coords[:, :, 0] += self.ws - 1 # shift to start from 0
|
|
relative_coords[:, :, 1] += self.ws - 1
|
|
relative_coords[:, :, 0] *= 2 * self.ws - 1
|
|
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
|
self.register_buffer("relative_position_index", relative_position_index)
|
|
|
|
trunc_normal_(self.relative_position_bias_table, std=.02)
|
|
|
|
def pad(self, x, ps):
|
|
_, _, H, W = x.size()
|
|
if W % ps != 0:
|
|
x = F.pad(x, (0, ps - W % ps,0,0), mode='reflect')
|
|
if H % ps != 0:
|
|
x = F.pad(x, (0, 0, 0, ps - H % ps), mode='reflect')
|
|
return x
|
|
|
|
def pad_out(self, x):
|
|
x = F.pad(x, pad=(0, 1, 0, 1), mode='reflect')
|
|
return x
|
|
|
|
def forward(self, x):
|
|
B, C, H, W = x.shape
|
|
|
|
local = self.local2(x) + self.local1(x)
|
|
|
|
x = self.pad(x, self.ws)
|
|
B, C, Hp, Wp = x.shape
|
|
qkv = self.qkv(x)
|
|
|
|
q, k, v = rearrange(qkv, 'b (qkv h d) (hh ws1) (ww ws2) -> qkv (b hh ww) h (ws1 ws2) d', h=self.num_heads,
|
|
d=C // self.num_heads, hh=Hp // self.ws, ww=Wp // self.ws, qkv=3, ws1=self.ws, ws2=self.ws)
|
|
|
|
dots = (q @ k.transpose(-2, -1)) * self.scale
|
|
|
|
if self.relative_pos_embedding:
|
|
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
|
self.ws * self.ws, self.ws * self.ws, -1) # Wh*Ww,Wh*Ww,nH
|
|
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
|
dots += relative_position_bias.unsqueeze(0)
|
|
|
|
attn = dots.softmax(dim=-1)
|
|
attn = attn @ v
|
|
|
|
attn = rearrange(attn, '(b hh ww) h (ws1 ws2) d -> b (h d) (hh ws1) (ww ws2)', h=self.num_heads,
|
|
d=C // self.num_heads, hh=Hp // self.ws, ww=Wp // self.ws, ws1=self.ws, ws2=self.ws)
|
|
|
|
attn = attn[:, :, :H, :W]
|
|
|
|
out = self.attn_x(F.pad(attn, pad=(0, 0, 0, 1), mode='reflect')) + \
|
|
self.attn_y(F.pad(attn, pad=(0, 1, 0, 0), mode='reflect'))
|
|
|
|
out = out + local
|
|
out = self.pad_out(out)
|
|
out = self.proj(out)
|
|
# print(out.size())
|
|
out = out[:, :, :H, :W]
|
|
|
|
return out
|
|
|
|
|
|
class Block(nn.Module):
|
|
def __init__(self, dim=256, num_heads=16, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
|
|
drop_path=0., act_layer=nn.ReLU6, norm_layer=nn.BatchNorm2d, window_size=8):
|
|
super().__init__()
|
|
self.norm1 = norm_layer(dim)
|
|
self.attn = GlobalLocalAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, window_size=window_size)
|
|
|
|
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, act_layer=act_layer,
|
|
drop=drop)
|
|
self.norm2 = norm_layer(dim)
|
|
|
|
def forward(self, x):
|
|
x = x + self.drop_path(self.attn(self.norm1(x)))
|
|
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
|
|
|
return x
|
|
|
|
|
|
class WF(nn.Module):
|
|
def __init__(self, in_channels=128, decode_channels=128, eps=1e-8):
|
|
super(WF, self).__init__()
|
|
self.pre_conv = Conv(in_channels, decode_channels, kernel_size=1)
|
|
|
|
self.weights = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
|
|
self.eps = eps
|
|
self.post_conv = ConvBNReLU(decode_channels, decode_channels, kernel_size=3)
|
|
|
|
def forward(self, x, res):
|
|
x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
|
|
weights = nn.ReLU()(self.weights)
|
|
fuse_weights = weights / (torch.sum(weights, dim=0) + self.eps)
|
|
x = fuse_weights[0] * self.pre_conv(res) + fuse_weights[1] * x
|
|
x = self.post_conv(x)
|
|
return x
|
|
|
|
|
|
class FeatureRefinementHead(nn.Module):
|
|
def __init__(self, in_channels=64, decode_channels=64):
|
|
super().__init__()
|
|
self.pre_conv = Conv(in_channels, decode_channels, kernel_size=1)
|
|
|
|
self.weights = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
|
|
self.eps = 1e-8
|
|
self.post_conv = ConvBNReLU(decode_channels, decode_channels, kernel_size=3)
|
|
|
|
self.pa = nn.Sequential(
|
|
nn.Conv2d(decode_channels, decode_channels, kernel_size=3, padding=1, groups=decode_channels),
|
|
nn.Sigmoid())
|
|
self.ca = nn.Sequential(nn.AdaptiveAvgPool2d(1),
|
|
Conv(decode_channels, decode_channels // 16, kernel_size=1),
|
|
nn.ReLU6(),
|
|
Conv(decode_channels // 16, decode_channels, kernel_size=1),
|
|
nn.Sigmoid())
|
|
|
|
self.shortcut = ConvBN(decode_channels, decode_channels, kernel_size=1)
|
|
self.proj = SeparableConvBN(decode_channels, decode_channels, kernel_size=3)
|
|
self.act = nn.ReLU6()
|
|
|
|
def forward(self, x, res):
|
|
x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
|
|
weights = nn.ReLU()(self.weights)
|
|
fuse_weights = weights / (torch.sum(weights, dim=0) + self.eps)
|
|
x = fuse_weights[0] * self.pre_conv(res) + fuse_weights[1] * x
|
|
x = self.post_conv(x)
|
|
shortcut = self.shortcut(x)
|
|
pa = self.pa(x) * x
|
|
ca = self.ca(x) * x
|
|
x = pa + ca
|
|
x = self.proj(x) + shortcut
|
|
x = self.act(x)
|
|
|
|
return x
|
|
|
|
|
|
class AuxHead(nn.Module):
|
|
|
|
def __init__(self, in_channels=64, num_classes=8):
|
|
super().__init__()
|
|
self.conv = ConvBNReLU(in_channels, in_channels)
|
|
self.drop = nn.Dropout(0.1)
|
|
self.conv_out = Conv(in_channels, num_classes, kernel_size=1)
|
|
|
|
def forward(self, x, h, w):
|
|
feat = self.conv(x)
|
|
feat = self.drop(feat)
|
|
feat = self.conv_out(feat)
|
|
feat = F.interpolate(feat, size=(h, w), mode='bilinear', align_corners=False)
|
|
return feat
|
|
|
|
|
|
class Decoder(nn.Module):
|
|
def __init__(self,
|
|
encoder_channels=(64, 128, 256, 512),
|
|
decode_channels=64,
|
|
dropout=0.1,
|
|
window_size=8,
|
|
num_classes=6):
|
|
super(Decoder, self).__init__()
|
|
|
|
self.pre_conv = ConvBN(encoder_channels[-1], decode_channels, kernel_size=1)
|
|
self.b4 = Block(dim=decode_channels, num_heads=8, window_size=window_size)
|
|
|
|
self.b3 = Block(dim=decode_channels, num_heads=8, window_size=window_size)
|
|
self.p3 = WF(encoder_channels[-2], decode_channels)
|
|
|
|
self.b2 = Block(dim=decode_channels, num_heads=8, window_size=window_size)
|
|
self.p2 = WF(encoder_channels[-3], decode_channels)
|
|
|
|
self.p1 = FeatureRefinementHead(encoder_channels[-4], decode_channels)
|
|
|
|
# self.segmentation_head = nn.Sequential(ConvBNReLU(decode_channels, decode_channels),
|
|
# nn.Dropout2d(p=dropout, inplace=True),
|
|
# Conv(decode_channels, num_classes, kernel_size=1))
|
|
self.segmentation_head = nn.Sequential(ConvBNReLU(decode_channels, decode_channels),
|
|
nn.Dropout2d(p=dropout, inplace=True),
|
|
)
|
|
self.init_weight()
|
|
|
|
def forward(self, res1, res2, res3, res4, h, w):
|
|
x = self.b4(self.pre_conv(res4))
|
|
x = self.p3(x, res3)
|
|
x = self.b3(x)
|
|
|
|
x = self.p2(x, res2)
|
|
x = self.b2(x)
|
|
|
|
x = self.p1(x, res1)
|
|
|
|
x = self.segmentation_head(x)
|
|
# x = F.interpolate(x, size=(h, w), mode='bilinear', align_corners=False)
|
|
|
|
return x
|
|
|
|
def init_weight(self):
|
|
for m in self.children():
|
|
if isinstance(m, nn.Conv2d):
|
|
nn.init.kaiming_normal_(m.weight, a=1)
|
|
if m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
|
|
class BasicConv(nn.Module):
|
|
def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True,
|
|
bn=True, bias=False):
|
|
super(BasicConv, self).__init__()
|
|
self.out_channels = out_planes
|
|
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
|
|
dilation=dilation, groups=groups, bias=bias)
|
|
self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None
|
|
self.relu = nn.ReLU() if relu else None
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
if self.bn is not None:
|
|
x = self.bn(x)
|
|
if self.relu is not None:
|
|
x = self.relu(x)
|
|
return x
|
|
|
|
|
|
class SoftPool2d(nn.Module):
|
|
def __init__(self, kernel_size=2, stride=2):
|
|
super(SoftPool2d, self).__init__()
|
|
self.avgpool = nn.AvgPool2d(kernel_size, stride)
|
|
|
|
def forward(self, x):
|
|
x_exp = torch.exp(x)
|
|
x_exp_pool = self.avgpool(x_exp)
|
|
x = self.avgpool(x_exp * x)
|
|
return x / x_exp_pool
|
|
|
|
|
|
class Flatten(nn.Module):
|
|
def forward(self, x):
|
|
return x.view(x.size(0), -1)
|
|
|
|
|
|
class ChannelAtt(nn.Module):
|
|
def __init__(self, gate_channels, reduction_ratio=2, pool_types=['avg', 'max', 'soft']):
|
|
super(ChannelAtt, self).__init__()
|
|
self.gate_channels = gate_channels
|
|
self.mlp = nn.Sequential(
|
|
Flatten(),
|
|
nn.Linear(gate_channels, gate_channels // reduction_ratio),
|
|
nn.ReLU()
|
|
# nn.Linear(gate_channels // reduction_ratio, gate_channels)
|
|
)
|
|
self.pool_types = pool_types
|
|
self.incr = nn.Linear(gate_channels // reduction_ratio, gate_channels)
|
|
|
|
def forward(self, x):
|
|
channel_att_sum = None
|
|
avg_pool = F.avg_pool2d(x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
|
|
max_pool = F.max_pool2d(x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
|
|
avgpoolmlp = self.mlp(avg_pool)
|
|
maxpoolmlp = self.mlp(max_pool)
|
|
pooladd = avgpoolmlp + maxpoolmlp
|
|
|
|
self.pool = SoftPool2d(kernel_size=(x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
|
|
soft_pool = self.mlp(self.pool(x))
|
|
weightPool = soft_pool * pooladd
|
|
# channel_att_sum = self.mlp(weightPool)
|
|
channel_att_sum = self.incr(weightPool)
|
|
Att = torch.sigmoid(channel_att_sum).unsqueeze(2).unsqueeze(3).expand_as(x)
|
|
return Att
|
|
|
|
|
|
class FusionAttention(nn.Module):
|
|
def __init__(self,
|
|
dim=256,
|
|
ssmdims=256,
|
|
num_heads=16,
|
|
qkv_bias=False,
|
|
window_size=8,
|
|
relative_pos_embedding=True
|
|
):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
head_dim = dim // self.num_heads
|
|
self.scale = head_dim ** -0.5
|
|
self.ws = window_size
|
|
|
|
self.qkv = Conv(dim, 3 * dim, kernel_size=1, bias=qkv_bias)
|
|
self.local1 = ConvBN(ssmdims, dim, kernel_size=3)
|
|
self.local2 = ConvBN(ssmdims, dim, kernel_size=1)
|
|
self.proj = SeparableConvBN(dim, dim, kernel_size=window_size)
|
|
|
|
self.attn_x = nn.AvgPool2d(kernel_size=(window_size, 1), stride=1, padding=(window_size // 2 - 1, 0))
|
|
self.attn_y = nn.AvgPool2d(kernel_size=(1, window_size), stride=1, padding=(0, window_size // 2 - 1))
|
|
|
|
self.relative_pos_embedding = relative_pos_embedding
|
|
|
|
if self.relative_pos_embedding:
|
|
# define a parameter table of relative position bias
|
|
self.relative_position_bias_table = nn.Parameter(
|
|
torch.zeros((2 * window_size - 1) * (2 * window_size - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
|
|
|
# get pair-wise relative position index for each token inside the window
|
|
coords_h = torch.arange(self.ws)
|
|
coords_w = torch.arange(self.ws)
|
|
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
|
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
|
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
|
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
|
relative_coords[:, :, 0] += self.ws - 1 # shift to start from 0
|
|
relative_coords[:, :, 1] += self.ws - 1
|
|
relative_coords[:, :, 0] *= 2 * self.ws - 1
|
|
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
|
self.register_buffer("relative_position_index", relative_position_index)
|
|
|
|
trunc_normal_(self.relative_position_bias_table, std=.02)
|
|
|
|
def pad(self, x, ps):
|
|
_, _, H, W = x.size()
|
|
if W % ps != 0:
|
|
x = F.pad(x, (0, ps - W % ps,0,0), mode='reflect')
|
|
if H % ps != 0:
|
|
x = F.pad(x, (0, 0, 0, ps - H % ps), mode='reflect')
|
|
return x
|
|
|
|
def pad_out(self, x):
|
|
x = F.pad(x, pad=(0, 1, 0, 1), mode='reflect')
|
|
return x
|
|
|
|
def forward(self, x, y):
|
|
## x from res, need global; y from smm, need local
|
|
B, C, H, W = x.shape
|
|
|
|
local = self.local2(y) + self.local1(y)
|
|
|
|
x = self.pad(x, self.ws)
|
|
B, C, Hp, Wp = x.shape
|
|
qkv = self.qkv(x)
|
|
|
|
q, k, v = rearrange(qkv, 'b (qkv h d) (hh ws1) (ww ws2) -> qkv (b hh ww) h (ws1 ws2) d', h=self.num_heads,
|
|
d=C // self.num_heads, hh=Hp // self.ws, ww=Wp // self.ws, qkv=3, ws1=self.ws, ws2=self.ws)
|
|
|
|
dots = (q @ k.transpose(-2, -1)) * self.scale
|
|
|
|
if self.relative_pos_embedding:
|
|
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
|
self.ws * self.ws, self.ws * self.ws, -1) # Wh*Ww,Wh*Ww,nH
|
|
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
|
dots += relative_position_bias.unsqueeze(0)
|
|
|
|
attn = dots.softmax(dim=-1)
|
|
attn = attn @ v
|
|
|
|
attn = rearrange(attn, '(b hh ww) h (ws1 ws2) d -> b (h d) (hh ws1) (ww ws2)', h=self.num_heads,
|
|
d=C // self.num_heads, hh=Hp // self.ws, ww=Wp // self.ws, ws1=self.ws, ws2=self.ws)
|
|
|
|
attn = attn[:, :, :H, :W]
|
|
|
|
out = self.attn_x(F.pad(attn, pad=(0, 0, 0, 1), mode='reflect')) + \
|
|
self.attn_y(F.pad(attn, pad=(0, 1, 0, 0), mode='reflect'))
|
|
|
|
out = out + local
|
|
out = self.pad_out(out)
|
|
out = self.proj(out)
|
|
# print(out.size())
|
|
out = out[:, :, :H, :W]
|
|
|
|
return out
|
|
|
|
|
|
class FusionBlock(nn.Module):
|
|
def __init__(self, dim=256, ssmdims=256, num_heads=16, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
|
|
drop_path=0., act_layer=nn.ReLU6, norm_layer=nn.BatchNorm2d, window_size=8):
|
|
super().__init__()
|
|
self.normx = norm_layer(dim)
|
|
self.normy = norm_layer(ssmdims)
|
|
self.attn = FusionAttention(dim, ssmdims, num_heads=num_heads, qkv_bias=qkv_bias, window_size=window_size)
|
|
|
|
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, act_layer=act_layer,
|
|
drop=drop)
|
|
self.norm2 = norm_layer(dim)
|
|
|
|
def forward(self, x, y):
|
|
x = x + self.drop_path(self.attn(self.normx(x), self.normy(y)))
|
|
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
|
|
|
return x
|
|
|
|
|
|
@MODELS.register_module()
|
|
class RS3Mamba(nn.Module):
|
|
def __init__(self,
|
|
decode_channels=64,
|
|
dropout=0.1,
|
|
backbone_name='swsl_resnet18',
|
|
pretrained=False,
|
|
window_size=8,
|
|
num_classes=7
|
|
):
|
|
super().__init__()
|
|
|
|
self.backbone = timm.create_model(backbone_name, features_only=True, output_stride=32,
|
|
out_indices=(1, 2, 3, 4), pretrained=pretrained,)
|
|
self.conv1 = self.backbone.conv1
|
|
self.bn1 = self.backbone.bn1
|
|
self.act1 = self.backbone.act1
|
|
self.maxpool = self.backbone.maxpool
|
|
self.layers = nn.ModuleList()
|
|
self.layers.append(self.backbone.layer1)
|
|
self.layers.append(self.backbone.layer2)
|
|
self.layers.append(self.backbone.layer3)
|
|
self.layers.append(self.backbone.layer4)
|
|
|
|
self.stem = nn.Sequential(
|
|
nn.Conv2d(3, 48, kernel_size=7, stride=2, padding=3),
|
|
nn.InstanceNorm2d(48, eps=1e-5, affine=True),
|
|
)
|
|
self.vssm_encoder = VSSMEncoder(patch_size=2, in_chans=48)
|
|
encoder_channels = self.backbone.feature_info.channels()
|
|
ssm_dims = [96, 192, 384, 768]
|
|
|
|
self.Fuse = nn.ModuleList()
|
|
self.decoder = Decoder(encoder_channels, decode_channels, dropout, window_size, num_classes)
|
|
for i in range(4):
|
|
fuse = FusionBlock(encoder_channels[i], ssm_dims[i])
|
|
self.Fuse.append(fuse)
|
|
|
|
def forward(self, x):
|
|
h, w = x.size()[-2:]
|
|
ssmx = self.stem(x)
|
|
vss_outs = self.vssm_encoder(ssmx) # 48*128*128, 96*64*64, 192*32*32, 384*16*16, 768*8*8
|
|
|
|
ress = []
|
|
x = self.conv1(x)
|
|
x = self.bn1(x)
|
|
x = self.act1(x)
|
|
x = self.maxpool(x)
|
|
for i in range(len(self.layers)):
|
|
x = self.layers[i](x)
|
|
x = self.Fuse[i](x, vss_outs[i + 1])
|
|
res = x
|
|
ress.append(res)
|
|
|
|
x = self.decoder(ress[0], ress[1], ress[2], ress[3], h, w)
|
|
return x
|
|
|
|
|
|
if __name__=="__main__":
|
|
model=RS3Mamba().to('cuda:3')
|
|
img=torch.randn(1,3,512,512).to('cuda:3')
|
|
out=model(img)
|
|
print(out.size()) |