EEBD_AI
/
ai-station-code
11
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
ai-station-code/dimaoshibie/nets/backbone.py

545 lines
23 KiB
Python
Raw Permalink Normal View History

ai集成平台代码初次提交
2025-05-06 11:18:48 +08:00
# ---------------------------------------------------------------
# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# ---------------------------------------------------------------
import math
import warnings
import numpy as np
from functools import partial
import torch
import torch.nn as nn
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2)
with torch.no_grad():
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
r"""
Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
#--------------------------------------#
# Gelu激活函数的实现
# 利用近似的数学公式
#--------------------------------------#
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x,3))))
class OverlapPatchEmbed(nn.Module):
def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
patch_size = (patch_size, patch_size)
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
padding=(patch_size[0] // 2, patch_size[1] // 2))
self.norm = nn.LayerNorm(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
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)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
return x, H, W
#--------------------------------------------------------------------------------------------------------------------#
# Attention机制
# 将输入的特征qkv特征进行划分，首先生成query, key, value。query是查询向量、key是键向量、v是值向量。
# 然后利用 查询向量query 叉乘 转置后的键向量key，这一步可以通俗的理解为，利用查询向量去查询序列的特征，获得序列每个部分的重要程度score。
# 然后利用 score 叉乘 value，这一步可以通俗的理解为，将序列每个部分的重要程度重新施加到序列的值上去。
#
# 在segformer中，为了减少计算量，首先对特征图进行了浓缩，所有特征层都压缩到原图的1/32。
# 当输入图片为512, 512时，Block1的特征图为128, 128，此时就先将特征层压缩为16, 16。
# 在Block1的Attention模块中，相当于将8x8个特征点进行特征浓缩，浓缩为一个特征点。
# 然后利用128x128个查询向量对16x16个键向量与值向量进行查询。尽管键向量与值向量的数量较少，但因为查询向量的不同，依然可以获得不同的输出。
#--------------------------------------------------------------------------------------------------------------------#
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.sr_ratio = sr_ratio
if sr_ratio > 1:
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.apply(self._init_weights)
def _init_weights(self, m):
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)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
B, N, C = x.shape
# bs, 16384, 32 => bs, 16384, 32 => bs, 16384, 8, 4 => bs, 8, 16384, 4
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
if self.sr_ratio > 1:
# bs, 16384, 32 => bs, 32, 128, 128
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
# bs, 32, 128, 128 => bs, 32, 16, 16 => bs, 256, 32
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
# bs, 256, 32 => bs, 256, 64 => bs, 256, 2, 8, 4 => 2, bs, 8, 256, 4
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
else:
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]
# bs, 8, 16384, 4 @ bs, 8, 4, 256 => bs, 8, 16384, 256
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
# bs, 8, 16384, 256 @ bs, 8, 256, 4 => bs, 8, 16384, 4 => bs, 16384, 32
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
# bs, 16384, 32 => bs, 16384, 32
x = self.proj(x)
x = self.proj_drop(x)
return x
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
class DropPath(nn.Module):
def __init__(self, drop_prob=None, scale_by_keep=True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x):
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
class DWConv(nn.Module):
def __init__(self, dim=768):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, H, W)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
return x
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.dwconv = DWConv(hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
self.apply(self._init_weights)
def _init_weights(self, m):
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)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = self.fc1(x)
x = self.dwconv(x, H, W)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio
)
self.norm2 = norm_layer(dim)
self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
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)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
return x
class MixVisionTransformer(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, embed_dims=[32, 64, 160, 256],
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
super().__init__()
self.num_classes = num_classes
self.depths = depths
#----------------------------------#
# Transformer模块，共有四个部分
#----------------------------------#
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
#----------------------------------#
# block1
#----------------------------------#
#-----------------------------------------------#
# 对输入图像进行分区，并下采样
# 512, 512, 3 => 128, 128, 32 => 16384, 32
#-----------------------------------------------#
self.patch_embed1 = OverlapPatchEmbed(patch_size=7, stride=4, in_chans=in_chans, embed_dim=embed_dims[0])
#-----------------------------------------------#
# 利用transformer模块进行特征提取
# 16384, 32 => 16384, 32
#-----------------------------------------------#
cur = 0
self.block1 = nn.ModuleList(
[
Block(
dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[0]
)
for i in range(depths[0])
]
)
self.norm1 = norm_layer(embed_dims[0])
#----------------------------------#
# block2
#----------------------------------#
#-----------------------------------------------#
# 对输入图像进行分区，并下采样
# 128, 128, 32 => 64, 64, 64 => 4096, 64
#-----------------------------------------------#
self.patch_embed2 = OverlapPatchEmbed(patch_size=3, stride=2, in_chans=embed_dims[0], embed_dim=embed_dims[1])
#-----------------------------------------------#
# 利用transformer模块进行特征提取
# 4096, 64 => 4096, 64
#-----------------------------------------------#
cur += depths[0]
self.block2 = nn.ModuleList(
[
Block(
dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[1]
)
for i in range(depths[1])
]
)
self.norm2 = norm_layer(embed_dims[1])
#----------------------------------#
# block3
#----------------------------------#
#-----------------------------------------------#
# 对输入图像进行分区，并下采样
# 64, 64, 64 => 32, 32, 160 => 1024, 160
#-----------------------------------------------#
self.patch_embed3 = OverlapPatchEmbed(patch_size=3, stride=2, in_chans=embed_dims[1], embed_dim=embed_dims[2])
#-----------------------------------------------#
# 利用transformer模块进行特征提取
# 1024, 160 => 1024, 160
#-----------------------------------------------#
cur += depths[1]
self.block3 = nn.ModuleList(
[
Block(
dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[2]
)
for i in range(depths[2])
]
)
self.norm3 = norm_layer(embed_dims[2])
#----------------------------------#
# block4
#----------------------------------#
#-----------------------------------------------#
# 对输入图像进行分区，并下采样
# 32, 32, 160 => 16, 16, 256 => 256, 256
#-----------------------------------------------#
self.patch_embed4 = OverlapPatchEmbed(patch_size=3, stride=2, in_chans=embed_dims[2], embed_dim=embed_dims[3])
#-----------------------------------------------#
# 利用transformer模块进行特征提取
# 256, 256 => 256, 256
#-----------------------------------------------#
cur += depths[2]
self.block4 = nn.ModuleList(
[
Block(
dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[3]
)
for i in range(depths[3])
]
)
self.norm4 = norm_layer(embed_dims[3])
self.apply(self._init_weights)
def _init_weights(self, m):
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)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
B = x.shape[0]
outs = []
#----------------------------------#
# block1
#----------------------------------#
x, H, W = self.patch_embed1.forward(x)
for i, blk in enumerate(self.block1):
x = blk.forward(x, H, W)
x = self.norm1(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
#----------------------------------#
# block2
#----------------------------------#
x, H, W = self.patch_embed2.forward(x)
for i, blk in enumerate(self.block2):
x = blk.forward(x, H, W)
x = self.norm2(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
#----------------------------------#
# block3
#----------------------------------#
x, H, W = self.patch_embed3.forward(x)
for i, blk in enumerate(self.block3):
x = blk.forward(x, H, W)
x = self.norm3(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
#----------------------------------#
# block4
#----------------------------------#
x, H, W = self.patch_embed4.forward(x)
for i, blk in enumerate(self.block4):
x = blk.forward(x, H, W)
x = self.norm4(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
return outs
class mit_b0(MixVisionTransformer):
def __init__(self, pretrained = False):
super(mit_b0, self).__init__(
embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
if pretrained:
print("Load backbone weights")
self.load_state_dict(torch.load("model_data/segformer_b0_backbone_weights.pth"), strict=False)
class mit_b1(MixVisionTransformer):
def __init__(self, pretrained = False):
super(mit_b1, self).__init__(
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
if pretrained:
print("Load backbone weights")
self.load_state_dict(torch.load("model_data/segformer_b1_backbone_weights.pth"), strict=False)
class mit_b2(MixVisionTransformer):
def __init__(self, pretrained = False):
super(mit_b2, self).__init__(
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
if pretrained:
print("Load backbone weights")
self.load_state_dict(torch.load("model_data/segformer_b2_backbone_weights.pth"), strict=False)
class mit_b3(MixVisionTransformer):
def __init__(self, pretrained = False):
super(mit_b3, self).__init__(
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
if pretrained:
print("Load backbone weights")
self.load_state_dict(torch.load("model_data/segformer_b3_backbone_weights.pth"), strict=False)
class mit_b4(MixVisionTransformer):
def __init__(self, pretrained = False):
super(mit_b4, self).__init__(
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
if pretrained:
print("Load backbone weights")
self.load_state_dict(torch.load("model_data/segformer_b4_backbone_weights.pth"), strict=False)
class mit_b5(MixVisionTransformer):
def __init__(self, pretrained = False):
super(mit_b5, self).__init__(
embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
if pretrained:
print("Load backbone weights")
self.load_state_dict(torch.load("model_data/segformer_b5_backbone_weights.pth"), strict=False)