548 lines
21 KiB
Python
548 lines
21 KiB
Python
import time
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import math
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from functools import partial
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from typing import Optional, Callable
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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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try:
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from mamba_ssm.ops.selective_scan_interface import selective_scan_fn, selective_scan_ref
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except:
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pass
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# an alternative for mamba_ssm (in which causal_conv1d is needed)
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try:
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from selective_scan import selective_scan_fn as selective_scan_fn_v1
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from selective_scan import selective_scan_ref as selective_scan_ref_v1
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except:
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pass
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DropPath.__repr__ = lambda self: f"timm.DropPath({self.drop_prob})"
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"""
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CNN在远程建模能力方面的局限性使它们无法有效地提取图像中的特征，而Transformers则受到其二次计算复杂性的阻碍。最近的研究表明，以Mamba为代表的状态空间模型（SSM）可以在保持线性计算复杂度的同时有效地模拟长程相互作用。
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我们介绍了一个新颖的Conv-SSM模块。Conv-SSM将卷积层的局部特征提取能力与SSM捕获长程依赖性的能力相结合。
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"""
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def flops_selective_scan_ref(B=1, L=256, D=768, N=16, with_D=True, with_Z=False, with_Group=True, with_complex=False):
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"""
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u: r(B D L)
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delta: r(B D L)
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A: r(D N)
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B: r(B N L)
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C: r(B N L)
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D: r(D)
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z: r(B D L)
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delta_bias: r(D), fp32
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ignores:
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[.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu]
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"""
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import numpy as np
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# fvcore.nn.jit_handles
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def get_flops_einsum(input_shapes, equation):
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np_arrs = [np.zeros(s) for s in input_shapes]
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optim = np.einsum_path(equation, *np_arrs, optimize="optimal")[1]
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for line in optim.split("\n"):
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if "optimized flop" in line.lower():
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# divided by 2 because we count MAC (multiply-add counted as one flop)
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flop = float(np.floor(float(line.split(":")[-1]) / 2))
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return flop
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assert not with_complex
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flops = 0 # below code flops = 0
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if False:
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...
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"""
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dtype_in = u.dtype
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u = u.float()
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delta = delta.float()
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if delta_bias is not None:
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delta = delta + delta_bias[..., None].float()
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if delta_softplus:
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delta = F.softplus(delta)
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batch, dim, dstate = u.shape[0], A.shape[0], A.shape[1]
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is_variable_B = B.dim() >= 3
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is_variable_C = C.dim() >= 3
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if A.is_complex():
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if is_variable_B:
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B = torch.view_as_complex(rearrange(B.float(), "... (L two) -> ... L two", two=2))
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if is_variable_C:
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C = torch.view_as_complex(rearrange(C.float(), "... (L two) -> ... L two", two=2))
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else:
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B = B.float()
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C = C.float()
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x = A.new_zeros((batch, dim, dstate))
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ys = []
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"""
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flops += get_flops_einsum([[B, D, L], [D, N]], "bdl,dn->bdln")
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if with_Group:
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flops += get_flops_einsum([[B, D, L], [B, N, L], [B, D, L]], "bdl,bnl,bdl->bdln")
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else:
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flops += get_flops_einsum([[B, D, L], [B, D, N, L], [B, D, L]], "bdl,bdnl,bdl->bdln")
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if False:
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...
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"""
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deltaA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A))
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if not is_variable_B:
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deltaB_u = torch.einsum('bdl,dn,bdl->bdln', delta, B, u)
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else:
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if B.dim() == 3:
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deltaB_u = torch.einsum('bdl,bnl,bdl->bdln', delta, B, u)
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else:
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B = repeat(B, "B G N L -> B (G H) N L", H=dim // B.shape[1])
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deltaB_u = torch.einsum('bdl,bdnl,bdl->bdln', delta, B, u)
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if is_variable_C and C.dim() == 4:
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C = repeat(C, "B G N L -> B (G H) N L", H=dim // C.shape[1])
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last_state = None
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"""
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in_for_flops = B * D * N
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if with_Group:
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in_for_flops += get_flops_einsum([[B, D, N], [B, D, N]], "bdn,bdn->bd")
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else:
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in_for_flops += get_flops_einsum([[B, D, N], [B, N]], "bdn,bn->bd")
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flops += L * in_for_flops
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if False:
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...
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"""
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for i in range(u.shape[2]):
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x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
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if not is_variable_C:
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y = torch.einsum('bdn,dn->bd', x, C)
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else:
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if C.dim() == 3:
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y = torch.einsum('bdn,bn->bd', x, C[:, :, i])
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else:
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y = torch.einsum('bdn,bdn->bd', x, C[:, :, :, i])
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if i == u.shape[2] - 1:
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last_state = x
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if y.is_complex():
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y = y.real * 2
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ys.append(y)
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y = torch.stack(ys, dim=2) # (batch dim L)
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"""
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if with_D:
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flops += B * D * L
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if with_Z:
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flops += B * D * L
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if False:
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...
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"""
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out = y if D is None else y + u * rearrange(D, "d -> d 1")
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if z is not None:
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out = out * F.silu(z)
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out = out.to(dtype=dtype_in)
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"""
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return flops
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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 PatchExpand2D(nn.Module):
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def __init__(self, dim, dim_scale=2, norm_layer=nn.LayerNorm):
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super().__init__()
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self.dim = dim * 2
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self.dim_scale = dim_scale
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self.expand = nn.Linear(self.dim, dim_scale * self.dim, bias=False)
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self.norm = norm_layer(self.dim // dim_scale)
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def forward(self, x):
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B, H, W, C = x.shape
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x = self.expand(x)
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x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale,
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c=C // self.dim_scale)
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x = self.norm(x)
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return x
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class Final_PatchExpand2D(nn.Module):
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def __init__(self, dim, dim_scale=4, norm_layer=nn.LayerNorm):
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super().__init__()
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self.dim = dim
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self.dim_scale = dim_scale
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self.expand = nn.Linear(self.dim, dim_scale * self.dim, bias=False)
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self.norm = norm_layer(self.dim // dim_scale)
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def forward(self, x):
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B, H, W, C = x.shape
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x = self.expand(x)
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x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale,
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c=C // self.dim_scale)
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x = self.norm(x)
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return x
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class SS2D(
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nn.Module): # SS2D（State Space Model 2D）类，它是实现状态空间模型（SSM）在视觉任务中应用的核心部分。这个类将状态空间模型的长程依赖性建模能力与卷积操作结合起来，旨在捕获图像中的长程相互作用，同时保持对局部特征的敏感度。
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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_state="auto", # 20240109
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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_state = math.ceil(self.d_model / 6) if d_state == "auto" else d_model # 20240109
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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.forward_core = self.forward_corev0
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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_corev0(self, x: torch.Tensor):
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self.selective_scan = selective_scan_fn
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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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# x_dbl = x_dbl + self.x_proj_bias.view(1, K, -1, 1)
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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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# dts = dts + self.dt_projs_bias.view(1, K, -1, 1)
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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)
|
||
Cs = Cs.float().view(B, K, -1, L) # (b, k, d_state, l)
|
||
Ds = self.Ds.float().view(-1) # (k * d)
|
||
As = -torch.exp(self.A_logs.float()).view(-1, self.d_state) # (k * d, d_state)
|
||
dt_projs_bias = self.dt_projs_bias.float().view(-1) # (k * d)
|
||
|
||
out_y = self.selective_scan(
|
||
xs, dts,
|
||
As, Bs, Cs, Ds, z=None,
|
||
delta_bias=dt_projs_bias,
|
||
delta_softplus=True,
|
||
return_last_state=False,
|
||
).view(B, K, -1, L)
|
||
assert out_y.dtype == torch.float
|
||
|
||
inv_y = torch.flip(out_y[:, 2:4], dims=[-1]).view(B, 2, -1, L)
|
||
wh_y = torch.transpose(out_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
|
||
invwh_y = torch.transpose(inv_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
|
||
|
||
return out_y[:, 0], inv_y[:, 0], wh_y, invwh_y
|
||
|
||
# an alternative to forward_corev1
|
||
def forward_corev1(self, x: torch.Tensor):
|
||
self.selective_scan = selective_scan_fn_v1
|
||
|
||
B, C, H, W = x.shape
|
||
L = H * W
|
||
K = 4
|
||
|
||
x_hwwh = torch.stack([x.view(B, -1, L), torch.transpose(x, dim0=2, dim1=3).contiguous().view(B, -1, L)],
|
||
dim=1).view(B, 2, -1, L)
|
||
xs = torch.cat([x_hwwh, torch.flip(x_hwwh, dims=[-1])], dim=1) # (b, k, d, l)
|
||
|
||
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)
|
||
# x_dbl = x_dbl + self.x_proj_bias.view(1, K, -1, 1)
|
||
dts, Bs, Cs = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=2)
|
||
dts = torch.einsum("b k r l, k d r -> b k d l", dts.view(B, K, -1, L), self.dt_projs_weight)
|
||
# dts = dts + self.dt_projs_bias.view(1, K, -1, 1)
|
||
|
||
xs = xs.float().view(B, -1, L) # (b, k * d, l)
|
||
dts = dts.contiguous().float().view(B, -1, L) # (b, k * d, l)
|
||
Bs = Bs.float().view(B, K, -1, L) # (b, k, d_state, l)
|
||
Cs = Cs.float().view(B, K, -1, L) # (b, k, d_state, l)
|
||
Ds = self.Ds.float().view(-1) # (k * d)
|
||
As = -torch.exp(self.A_logs.float()).view(-1, self.d_state) # (k * d, d_state)
|
||
dt_projs_bias = self.dt_projs_bias.float().view(-1) # (k * d)
|
||
|
||
out_y = self.selective_scan(
|
||
xs, dts,
|
||
As, Bs, Cs, Ds,
|
||
delta_bias=dt_projs_bias,
|
||
delta_softplus=True,
|
||
).view(B, K, -1, L)
|
||
assert out_y.dtype == torch.float
|
||
|
||
inv_y = torch.flip(out_y[:, 2:4], dims=[-1]).view(B, 2, -1, L)
|
||
wh_y = torch.transpose(out_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
|
||
invwh_y = torch.transpose(inv_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
|
||
|
||
return out_y[:, 0], inv_y[:, 0], wh_y, invwh_y
|
||
|
||
def forward(self, x: torch.Tensor, **kwargs):
|
||
B, H, W, C = x.shape
|
||
|
||
xz = self.in_proj(x)
|
||
x, z = xz.chunk(2, dim=-1) # (b, h, w, d)
|
||
|
||
x = x.permute(0, 3, 1, 2).contiguous()
|
||
x = self.act(self.conv2d(x)) # (b, d, h, w)
|
||
y1, y2, y3, y4 = self.forward_core(x)
|
||
assert y1.dtype == torch.float32
|
||
y = y1 + y2 + y3 + y4
|
||
y = torch.transpose(y, dim0=1, dim1=2).contiguous().view(B, H, W, -1)
|
||
y = self.out_norm(y)
|
||
y = y * F.silu(z)
|
||
out = self.out_proj(y)
|
||
if self.dropout is not None:
|
||
out = self.dropout(out)
|
||
return out
|
||
|
||
|
||
class ConvSSM(nn.Module):
|
||
"""
|
||
这个类组合了自注意力机制和卷积操作，旨在融合自注意力的全局感知能力和卷积的局部特征提取能力。
|
||
输入特征被分成两部分，一部分通过SS2D自注意力模块处理，另一部分通过一系列卷积层处理。处理后的两部分再次合并，并通过最终的卷积层生成输出特征。
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
hidden_dim: int = 0,
|
||
drop_path: float = 0,
|
||
norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6),
|
||
attn_drop_rate: float = 0,
|
||
d_state: int = 16,
|
||
**kwargs,
|
||
):
|
||
super().__init__()
|
||
self.ln_1 = norm_layer(hidden_dim // 2)
|
||
self.self_attention = SS2D(d_model=hidden_dim // 2, dropout=attn_drop_rate, d_state=d_state, **kwargs)
|
||
self.drop_path = DropPath(drop_path)
|
||
|
||
self.conv33conv33conv11 = nn.Sequential(
|
||
nn.Conv2d(in_channels=hidden_dim // 2, out_channels=hidden_dim // 2, kernel_size=3, stride=1, padding=1),
|
||
nn.BatchNorm2d(hidden_dim // 2),
|
||
nn.ReLU(),
|
||
nn.Conv2d(in_channels=hidden_dim // 2, out_channels=hidden_dim // 2, kernel_size=3, stride=1, padding=1),
|
||
nn.BatchNorm2d(hidden_dim // 2),
|
||
nn.ReLU(),
|
||
nn.Conv2d(in_channels=hidden_dim // 2, out_channels=hidden_dim // 2, kernel_size=1, stride=1)
|
||
)
|
||
self.finalconv11 = nn.Conv2d(in_channels=hidden_dim, out_channels=hidden_dim, kernel_size=1, stride=1)
|
||
|
||
def forward(self, input: torch.Tensor):
|
||
input_left, input_right = input.chunk(2, dim=-1)
|
||
x = input_right + self.drop_path(self.self_attention(self.ln_1(input_right)))
|
||
input_left = input_left.permute(0, 3, 1, 2).contiguous()
|
||
input_left = self.conv33conv33conv11(input_left)
|
||
x = x.permute(0, 3, 1, 2).contiguous()
|
||
output = torch.cat((input_left, x), dim=1)
|
||
output = self.finalconv11(output).permute(0, 2, 3, 1).contiguous()
|
||
return output + input
|
||
|
||
|
||
if __name__ == '__main__':
|
||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||
# 初始化ConvSSM模块，hidden_dim为128
|
||
block = ConvSSM(hidden_dim=128).to(device)
|
||
|
||
# 生成随机输入张量，尺寸为[批次大小, 高度, 宽度, 通道数]
|
||
# 这里批次大小为4，高度和宽度为32，通道数为128（符合hidden_dim的大小）
|
||
input_tensor = torch.rand(4, 32, 32, 128).to(device)
|
||
|
||
# 前向传递输入张量通过ConvSSM模块
|
||
output = block(input_tensor)
|
||
|
||
# 打印输入和输出张量的尺寸
|
||
print("Input tensor size:", input_tensor.size())
|
||
print("Output tensor size:", output.size()) |