789 lines
28 KiB
Python
789 lines
28 KiB
Python
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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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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(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_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,
|
||
|
|
**factory_kwargs):
|
||
|
|
dt_proj = nn.Linear(dt_rank, d_inner, bias=True, **factory_kwargs)
|
||
|
|
|
||
|
|
# Initialize special dt projection to preserve variance at initialization
|
||
|
|
dt_init_std = dt_rank ** -0.5 * dt_scale
|
||
|
|
if dt_init == "constant":
|
||
|
|
nn.init.constant_(dt_proj.weight, dt_init_std)
|
||
|
|
elif dt_init == "random":
|
||
|
|
nn.init.uniform_(dt_proj.weight, -dt_init_std, dt_init_std)
|
||
|
|
else:
|
||
|
|
raise NotImplementedError
|
||
|
|
|
||
|
|
# Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max
|
||
|
|
dt = torch.exp(
|
||
|
|
torch.rand(d_inner, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))
|
||
|
|
+ math.log(dt_min)
|
||
|
|
).clamp(min=dt_init_floor)
|
||
|
|
# Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
|
||
|
|
inv_dt = dt + torch.log(-torch.expm1(-dt))
|
||
|
|
with torch.no_grad():
|
||
|
|
dt_proj.bias.copy_(inv_dt)
|
||
|
|
# Our initialization would set all Linear.bias to zero, need to mark this one as _no_reinit
|
||
|
|
dt_proj.bias._no_reinit = True
|
||
|
|
|
||
|
|
return dt_proj
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def A_log_init(d_state, d_inner, copies=1, device=None, merge=True):
|
||
|
|
# S4D real initialization
|
||
|
|
A = repeat(
|
||
|
|
torch.arange(1, d_state + 1, dtype=torch.float32, device=device),
|
||
|
|
"n -> d n",
|
||
|
|
d=d_inner,
|
||
|
|
).contiguous()
|
||
|
|
A_log = torch.log(A) # Keep A_log in fp32
|
||
|
|
if copies > 1:
|
||
|
|
A_log = repeat(A_log, "d n -> r d n", r=copies)
|
||
|
|
if merge:
|
||
|
|
A_log = A_log.flatten(0, 1)
|
||
|
|
A_log = nn.Parameter(A_log)
|
||
|
|
A_log._no_weight_decay = True
|
||
|
|
return A_log
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def D_init(d_inner, copies=1, device=None, merge=True):
|
||
|
|
# D "skip" parameter
|
||
|
|
D = torch.ones(d_inner, device=device)
|
||
|
|
if copies > 1:
|
||
|
|
D = repeat(D, "n1 -> r n1", r=copies)
|
||
|
|
if merge:
|
||
|
|
D = D.flatten(0, 1)
|
||
|
|
D = nn.Parameter(D) # Keep in fp32
|
||
|
|
D._no_weight_decay = True
|
||
|
|
return D
|
||
|
|
|
||
|
|
def forward_corev0(self, x: torch.Tensor):
|
||
|
|
self.selective_scan = selective_scan_fn
|
||
|
|
|
||
|
|
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, 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 VSSBlock(nn.Module):
|
||
|
|
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)
|
||
|
|
self.self_attention = SS2D(d_model=hidden_dim, dropout=attn_drop_rate, d_state=d_state, **kwargs)
|
||
|
|
self.drop_path = DropPath(drop_path)
|
||
|
|
|
||
|
|
def forward(self, input: torch.Tensor):
|
||
|
|
x = input + self.drop_path(self.self_attention(self.ln_1(input)))
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class VSSLayer(nn.Module):
|
||
|
|
""" A basic Swin Transformer layer for one stage.
|
||
|
|
Args:
|
||
|
|
dim (int): Number of input channels.
|
||
|
|
depth (int): Number of blocks.
|
||
|
|
drop (float, optional): Dropout rate. Default: 0.0
|
||
|
|
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||
|
|
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||
|
|
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
dim,
|
||
|
|
depth,
|
||
|
|
attn_drop=0.,
|
||
|
|
drop_path=0.,
|
||
|
|
norm_layer=nn.LayerNorm,
|
||
|
|
downsample=None,
|
||
|
|
use_checkpoint=False,
|
||
|
|
d_state=16,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.dim = dim
|
||
|
|
self.use_checkpoint = use_checkpoint
|
||
|
|
|
||
|
|
self.blocks = nn.ModuleList([
|
||
|
|
VSSBlock(
|
||
|
|
hidden_dim=dim,
|
||
|
|
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||
|
|
norm_layer=norm_layer,
|
||
|
|
attn_drop_rate=attn_drop,
|
||
|
|
d_state=d_state,
|
||
|
|
)
|
||
|
|
for i in range(depth)])
|
||
|
|
|
||
|
|
if True: # is this really applied? Yes, but been overriden later in VSSM!
|
||
|
|
def _init_weights(module: nn.Module):
|
||
|
|
for name, p in module.named_parameters():
|
||
|
|
if name in ["out_proj.weight"]:
|
||
|
|
p = p.clone().detach_() # fake init, just to keep the seed ....
|
||
|
|
nn.init.kaiming_uniform_(p, a=math.sqrt(5))
|
||
|
|
|
||
|
|
self.apply(_init_weights)
|
||
|
|
|
||
|
|
if downsample is not None:
|
||
|
|
self.downsample = downsample(dim=dim, norm_layer=norm_layer)
|
||
|
|
else:
|
||
|
|
self.downsample = None
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
for blk in self.blocks:
|
||
|
|
if self.use_checkpoint:
|
||
|
|
x = checkpoint.checkpoint(blk, x)
|
||
|
|
else:
|
||
|
|
x = blk(x)
|
||
|
|
|
||
|
|
if self.downsample is not None:
|
||
|
|
x = self.downsample(x)
|
||
|
|
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class VSSLayer_up(nn.Module):
|
||
|
|
""" A basic Swin Transformer layer for one stage.
|
||
|
|
Args:
|
||
|
|
dim (int): Number of input channels.
|
||
|
|
depth (int): Number of blocks.
|
||
|
|
drop (float, optional): Dropout rate. Default: 0.0
|
||
|
|
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||
|
|
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||
|
|
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
dim,
|
||
|
|
depth,
|
||
|
|
attn_drop=0.,
|
||
|
|
drop_path=0.,
|
||
|
|
norm_layer=nn.LayerNorm,
|
||
|
|
upsample=None,
|
||
|
|
use_checkpoint=False,
|
||
|
|
d_state=16,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.dim = dim
|
||
|
|
self.use_checkpoint = use_checkpoint
|
||
|
|
|
||
|
|
self.blocks = nn.ModuleList([
|
||
|
|
VSSBlock(
|
||
|
|
hidden_dim=dim,
|
||
|
|
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||
|
|
norm_layer=norm_layer,
|
||
|
|
attn_drop_rate=attn_drop,
|
||
|
|
d_state=d_state,
|
||
|
|
)
|
||
|
|
for i in range(depth)])
|
||
|
|
|
||
|
|
if True: # is this really applied? Yes, but been overriden later in VSSM!
|
||
|
|
def _init_weights(module: nn.Module):
|
||
|
|
for name, p in module.named_parameters():
|
||
|
|
if name in ["out_proj.weight"]:
|
||
|
|
p = p.clone().detach_() # fake init, just to keep the seed ....
|
||
|
|
nn.init.kaiming_uniform_(p, a=math.sqrt(5))
|
||
|
|
|
||
|
|
self.apply(_init_weights)
|
||
|
|
|
||
|
|
if upsample is not None:
|
||
|
|
self.upsample = upsample(dim=dim, norm_layer=norm_layer)
|
||
|
|
else:
|
||
|
|
self.upsample = None
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
if self.upsample is not None:
|
||
|
|
x = self.upsample(x)
|
||
|
|
for blk in self.blocks:
|
||
|
|
if self.use_checkpoint:
|
||
|
|
x = checkpoint.checkpoint(blk, x)
|
||
|
|
else:
|
||
|
|
x = blk(x)
|
||
|
|
return x
|
||
|
|
from mmseg.registry import MODELS
|
||
|
|
|
||
|
|
@MODELS.register_module()
|
||
|
|
|
||
|
|
class vmamba_model(nn.Module):
|
||
|
|
def __init__(self, patch_size=4, in_chans=3, num_classes=1000, depths=[2, 2, 9, 2], depths_decoder=[2, 9, 2, 2],
|
||
|
|
dims=[96, 192, 384, 768], dims_decoder=[768, 384, 192, 96], d_state=16, drop_rate=0.,
|
||
|
|
attn_drop_rate=0., drop_path_rate=0.1,
|
||
|
|
norm_layer=nn.LayerNorm, patch_norm=True,
|
||
|
|
use_checkpoint=False, **kwargs):
|
||
|
|
super().__init__()
|
||
|
|
self.num_classes = num_classes
|
||
|
|
self.num_layers = len(depths)
|
||
|
|
if isinstance(dims, int):
|
||
|
|
dims = [int(dims * 2 ** i_layer) for i_layer in range(self.num_layers)]
|
||
|
|
self.embed_dim = dims[0]
|
||
|
|
self.num_features = dims[-1]
|
||
|
|
self.dims = dims
|
||
|
|
|
||
|
|
self.patch_embed = PatchEmbed2D(patch_size=patch_size, in_chans=in_chans, embed_dim=self.embed_dim,
|
||
|
|
norm_layer=norm_layer if patch_norm else None)
|
||
|
|
|
||
|
|
# WASTED absolute position embedding ======================
|
||
|
|
self.ape = False
|
||
|
|
# self.ape = False
|
||
|
|
# drop_rate = 0.0
|
||
|
|
if self.ape:
|
||
|
|
self.patches_resolution = self.patch_embed.patches_resolution
|
||
|
|
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, *self.patches_resolution, self.embed_dim))
|
||
|
|
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
||
|
|
|
||
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||
|
|
dpr_decoder = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths_decoder))][::-1]
|
||
|
|
|
||
|
|
self.layers = nn.ModuleList()
|
||
|
|
for i_layer in range(self.num_layers):
|
||
|
|
layer = VSSLayer(
|
||
|
|
dim=dims[i_layer],
|
||
|
|
depth=depths[i_layer],
|
||
|
|
d_state=math.ceil(dims[0] / 6) if d_state is None else d_state, # 20240109
|
||
|
|
drop=drop_rate,
|
||
|
|
attn_drop=attn_drop_rate,
|
||
|
|
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
||
|
|
norm_layer=norm_layer,
|
||
|
|
downsample=PatchMerging2D if (i_layer < self.num_layers - 1) else None,
|
||
|
|
use_checkpoint=use_checkpoint,
|
||
|
|
)
|
||
|
|
self.layers.append(layer)
|
||
|
|
|
||
|
|
self.layers_up = nn.ModuleList()
|
||
|
|
for i_layer in range(self.num_layers):
|
||
|
|
layer = VSSLayer_up(
|
||
|
|
dim=dims_decoder[i_layer],
|
||
|
|
depth=depths_decoder[i_layer],
|
||
|
|
d_state=math.ceil(dims[0] / 6) if d_state is None else d_state, # 20240109
|
||
|
|
drop=drop_rate,
|
||
|
|
attn_drop=attn_drop_rate,
|
||
|
|
drop_path=dpr_decoder[sum(depths_decoder[:i_layer]):sum(depths_decoder[:i_layer + 1])],
|
||
|
|
norm_layer=norm_layer,
|
||
|
|
upsample=PatchExpand2D if (i_layer != 0) else None,
|
||
|
|
use_checkpoint=use_checkpoint,
|
||
|
|
)
|
||
|
|
self.layers_up.append(layer)
|
||
|
|
|
||
|
|
self.final_up = Final_PatchExpand2D(dim=dims_decoder[-1], dim_scale=4, norm_layer=norm_layer)
|
||
|
|
self.final_conv = nn.Conv2d(dims_decoder[-1] // 4, num_classes, 1)
|
||
|
|
|
||
|
|
# self.norm = norm_layer(self.num_features)
|
||
|
|
# self.avgpool = nn.AdaptiveAvgPool1d(1)
|
||
|
|
# self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
||
|
|
|
||
|
|
self.apply(self._init_weights)
|
||
|
|
|
||
|
|
def _init_weights(self, m: nn.Module):
|
||
|
|
"""
|
||
|
|
out_proj.weight which is previously initilized in VSSBlock, would be cleared in nn.Linear
|
||
|
|
no fc.weight found in the any of the model parameters
|
||
|
|
no nn.Embedding found in the any of the model parameters
|
||
|
|
so the thing is, VSSBlock initialization is useless
|
||
|
|
|
||
|
|
Conv2D is not intialized !!!
|
||
|
|
"""
|
||
|
|
if isinstance(m, nn.Linear):
|
||
|
|
trunc_normal_(m.weight, std=.02)
|
||
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
||
|
|
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_features(self, x):
|
||
|
|
skip_list = []
|
||
|
|
x = self.patch_embed(x)
|
||
|
|
if self.ape:
|
||
|
|
x = x + self.absolute_pos_embed
|
||
|
|
x = self.pos_drop(x)
|
||
|
|
|
||
|
|
for layer in self.layers:
|
||
|
|
skip_list.append(x)
|
||
|
|
x = layer(x)
|
||
|
|
return x, skip_list
|
||
|
|
|
||
|
|
def forward_features_up(self, x, skip_list):
|
||
|
|
for inx, layer_up in enumerate(self.layers_up):
|
||
|
|
if inx == 0:
|
||
|
|
x = layer_up(x)
|
||
|
|
else:
|
||
|
|
x = layer_up(x + skip_list[-inx])
|
||
|
|
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward_final(self, x):
|
||
|
|
x = self.final_up(x)
|
||
|
|
x = x.permute(0, 3, 1, 2)
|
||
|
|
x = self.final_conv(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward_backbone(self, x):
|
||
|
|
x = self.patch_embed(x)
|
||
|
|
if self.ape:
|
||
|
|
x = x + self.absolute_pos_embed
|
||
|
|
x = self.pos_drop(x)
|
||
|
|
|
||
|
|
for layer in self.layers:
|
||
|
|
x = layer(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x, skip_list = self.forward_features(x)
|
||
|
|
x = self.forward_features_up(x, skip_list)
|
||
|
|
x = self.forward_final(x)
|
||
|
|
|
||
|
|
return x
|
||
|
|
|
||
|
|
if __name__=='__main__':
|
||
|
|
net=VSSM().to("cuda:0")
|
||
|
|
img=torch.randn(2,3,512,512).to("cuda:0")
|
||
|
|
out=net(img)
|
||
|
|
print(out.size())
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|