PyTorch框架——基于深度学习RepViT神经网络番石榴果实病害识别分类系统
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第一步：准备数据
番石榴果实病害研究的数据集合中包含473张经过标注处理的番石榴果实图片
具体信息如下：
将图片分成三大类：self.class_indict = ['炭疽病', '水果蝇', '健康果实']
第二步：搭建模型
CVPR 2024 | 清华大学提出一种名为RepViT的新架构：轻量级主干设计
CVPR 2024 | 清华大学提出一种名为RepViT的新架构：轻量级主干设计
第三步：训练代码
1）损失函数为：交叉熵损失函数
2）RepViT 代码：
import torch.nn as nn
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
from timm.models.layers import SqueezeExcite
import torch
class Conv2d_BN(torch.nn.Sequential):
def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
groups=1, bn_weight_init=1, resolution=-10000):
super().__init__()
self.add_module('c', torch.nn.Conv2d(
a, b, ks, stride, pad, dilation, groups, bias=False))
self.add_module('bn', torch.nn.BatchNorm2d(b))
torch.nn.init.constant_(self.bn.weight, bn_weight_init)
torch.nn.init.constant_(self.bn.bias, 0)
@torch.no_grad()
def fuse(self):
c, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps)**0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / \
(bn.running_var + bn.eps)**0.5
m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(
0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups,
device=c.weight.device)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class Residual(torch.nn.Module):
def __init__(self, m, drop=0.):
super().__init__()
self.m = m
self.drop = drop
def forward(self, x):
if self.training and self.drop > 0:
return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,
device=x.device).ge_(self.drop).div(1 - self.drop).detach()
else:
return x + self.m(x)
@torch.no_grad()
def fuse(self):
if isinstance(self.m, Conv2d_BN):
m = self.m.fuse()
assert(m.groups == m.in_channels)
identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
identity = torch.nn.functional.pad(identity, [1,1,1,1])
m.weight += identity.to(m.weight.device)
return m
elif isinstance(self.m, torch.nn.Conv2d):
m = self.m
assert(m.groups != m.in_channels)
identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
identity = torch.nn.functional.pad(identity, [1,1,1,1])
m.weight += identity.to(m.weight.device)
return m
else:
return self
class RepVGGDW(torch.nn.Module):
def __init__(self, ed) -> None:
super().__init__()
self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed)
self.conv1 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed)
self.dim = ed
self.bn = torch.nn.BatchNorm2d(ed)
def forward(self, x):
return self.bn((self.conv(x) + self.conv1(x)) + x)
@torch.no_grad()
def fuse(self):
conv = self.conv.fuse()
conv1 = self.conv1
conv_w = conv.weight
conv_b = conv.bias
conv1_w = conv1.weight
conv1_b = conv1.bias
conv1_w = torch.nn.functional.pad(conv1_w, [1,1,1,1])
identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device), [1,1,1,1])
final_conv_w = conv_w + conv1_w + identity
final_conv_b = conv_b + conv1_b
conv.weight.data.copy_(final_conv_w)
conv.bias.data.copy_(final_conv_b)
bn = self.bn
w = bn.weight / (bn.running_var + bn.eps)**0.5
w = conv.weight * w[:, None, None, None]
b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \
(bn.running_var + bn.eps)**0.5
conv.weight.data.copy_(w)
conv.bias.data.copy_(b)
return conv
class RepViTBlock(nn.Module):
def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs):
super(RepViTBlock, self).__init__()
assert stride in [1, 2]
self.identity = stride == 1 and inp == oup
assert(hidden_dim == 2 * inp)
if stride == 2:
self.token_mixer = nn.Sequential(
Conv2d_BN(inp, inp, kernel_size, stride, (kernel_size - 1) // 2, groups=inp),
SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
Conv2d_BN(inp, oup, ks=1, stride=1, pad=0)
)
self.channel_mixer = Residual(nn.Sequential(
# pw
Conv2d_BN(oup, 2 * oup, 1, 1, 0),
nn.GELU() if use_hs else nn.GELU(),
# pw-linear
Conv2d_BN(2 * oup, oup, 1, 1, 0, bn_weight_init=0),
))
else:
assert(self.identity)
self.token_mixer = nn.Sequential(
RepVGGDW(inp),
SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),
)
self.channel_mixer = Residual(nn.Sequential(
# pw
Conv2d_BN(inp, hidden_dim, 1, 1, 0),
nn.GELU() if use_hs else nn.GELU(),
# pw-linear
Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0),
))
def forward(self, x):
return self.channel_mixer(self.token_mixer(x))
from timm.models.vision_transformer import trunc_normal_
class BN_Linear(torch.nn.Sequential):
def __init__(self, a, b, bias=True, std=0.02):
super().__init__()
self.add_module('bn', torch.nn.BatchNorm1d(a))
self.add_module('l', torch.nn.Linear(a, b, bias=bias))
trunc_normal_(self.l.weight, std=std)
if bias:
torch.nn.init.constant_(self.l.bias, 0)
@torch.no_grad()
def fuse(self):
bn, l = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps)**0.5
b = bn.bias - self.bn.running_mean * \
self.bn.weight / (bn.running_var + bn.eps)**0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.l.weight.T
else:
b = (l.weight @ b[:, None]).view(-1) + self.l.bias
m = torch.nn.Linear(w.size(1), w.size(0), device=l.weight.device)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class Classfier(nn.Module):
def __init__(self, dim, num_classes, distillation=True):
super().__init__()
self.classifier = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity()
self.distillation = distillation
if distillation:
self.classifier_dist = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity()
def forward(self, x):
if self.distillation:
x = self.classifier(x), self.classifier_dist(x)
if not self.training:
x = (x[0] + x[1]) / 2
else:
x = self.classifier(x)
return x
@torch.no_grad()
def fuse(self):
classifier = self.classifier.fuse()
if self.distillation:
classifier_dist = self.classifier_dist.fuse()
classifier.weight += classifier_dist.weight
classifier.bias += classifier_dist.bias
classifier.weight /= 2
classifier.bias /= 2
return classifier
else:
return classifier
class RepViT(nn.Module):
def __init__(self, cfgs, num_classes=1000, distillation=False):
super(RepViT, self).__init__()
# setting of inverted residual blocks
self.cfgs = cfgs
# building first layer
input_channel = self.cfgs[0][2]
patch_embed = torch.nn.Sequential(Conv2d_BN(3, input_channel // 2, 3, 2, 1), torch.nn.GELU(),
Conv2d_BN(input_channel // 2, input_channel, 3, 2, 1))
layers = [patch_embed]
# building inverted residual blocks
block = RepViTBlock
for k, t, c, use_se, use_hs, s in self.cfgs:
output_channel = _make_divisible(c, 8)
exp_size = _make_divisible(input_channel * t, 8)
layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
input_channel = output_channel
self.features = nn.ModuleList(layers)
self.classifier = Classfier(output_channel, num_classes, distillation)
def forward(self, x):
# x = self.features(x)
for f in self.features:
x = f(x)
x = torch.nn.functional.adaptive_avg_pool2d(x, 1).flatten(1)
x = self.classifier(x)
return x
from timm.models import register_model
@register_model
def repvit_m0_6(pretrained=False, num_classes = 1000, distillation=False):
"""
Constructs a MobileNetV3-Large model
"""
cfgs = [
[3, 2, 40, 1, 0, 1],
[3, 2, 40, 0, 0, 1],
[3, 2, 80, 0, 0, 2],
[3, 2, 80, 1, 0, 1],
[3, 2, 80, 0, 0, 1],
[3, 2, 160, 0, 1, 2],
[3, 2, 160, 1, 1, 1],
[3, 2, 160, 0, 1, 1],
[3, 2, 160, 1, 1, 1],
[3, 2, 160, 0, 1, 1],
[3, 2, 160, 1, 1, 1],
[3, 2, 160, 0, 1, 1],
[3, 2, 160, 1, 1, 1],
[3, 2, 160, 0, 1, 1],
[3, 2, 160, 0, 1, 1],
[3, 2, 320, 0, 1, 2],
[3, 2, 320, 1, 1, 1],
]
return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
@register_model
def repvit_m0_9(pretrained=False, num_classes = 1000, distillation=False):
"""
Constructs a MobileNetV3-Large model
"""
cfgs = [
# k, t, c, SE, HS, s
[3, 2, 48, 1, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 48, 0, 0, 1],
[3, 2, 96, 0, 0, 2],
[3, 2, 96, 1, 0, 1],
[3, 2, 96, 0, 0, 1],
[3, 2, 96, 0, 0, 1],
[3, 2, 192, 0, 1, 2],
[3, 2, 192, 1, 1, 1],
[3, 2, 192, 0, 1, 1],
[3, 2, 192, 1, 1, 1],
[3, 2, 192, 0, 1, 1],
[3, 2, 192, 1, 1, 1],
[3, 2, 192, 0, 1, 1],
[3, 2, 192, 1, 1, 1],
[3, 2, 192, 0, 1, 1],
[3, 2, 192, 1, 1, 1],
[3, 2, 192, 0, 1, 1],
[3, 2, 192, 1, 1, 1],
[3, 2, 192, 0, 1, 1],
[3, 2, 192, 1, 1, 1],
[3, 2, 192, 0, 1, 1],
[3, 2, 192, 0, 1, 1],
[3, 2, 384, 0, 1, 2],
[3, 2, 384, 1, 1, 1],
[3, 2, 384, 0, 1, 1]
]
return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
@register_model
def repvit_m1_0(pretrained=False, num_classes = 1000, distillation=False):
"""
Constructs a MobileNetV3-Large model
"""
cfgs = [
# k, t, c, SE, HS, s
[3, 2, 56, 1, 0, 1],
[3, 2, 56, 0, 0, 1],
[3, 2, 56, 0, 0, 1],
[3, 2, 112, 0, 0, 2],
[3, 2, 112, 1, 0, 1],
[3, 2, 112, 0, 0, 1],
[3, 2, 112, 0, 0, 1],
[3, 2, 224, 0, 1, 2],
[3, 2, 224, 1, 1, 1],
[3, 2, 224, 0, 1, 1],
[3, 2, 224, 1, 1, 1],
[3, 2, 224, 0, 1, 1],
[3, 2, 224, 1, 1, 1],
[3, 2, 224, 0, 1, 1],
[3, 2, 224, 1, 1, 1],
[3, 2, 224, 0, 1, 1],
[3, 2, 224, 1, 1, 1],
[3, 2, 224, 0, 1, 1],
[3, 2, 224, 1, 1, 1],
[3, 2, 224, 0, 1, 1],
[3, 2, 224, 1, 1, 1],
[3, 2, 224, 0, 1, 1],
[3, 2, 224, 0, 1, 1],
[3, 2, 448, 0, 1, 2],
[3, 2, 448, 1, 1, 1],
[3, 2, 448, 0, 1, 1]
]
return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
@register_model
def repvit_m1_1(pretrained=False, num_classes = 1000, distillation=False):
"""
Constructs a MobileNetV3-Large model
"""
cfgs = [
# k, t, c, SE, HS, s
[3, 2, 64, 1, 0, 1],
[3, 2, 64, 0, 0, 1],
[3, 2, 64, 0, 0, 1],
[3, 2, 128, 0, 0, 2],
[3, 2, 128, 1, 0, 1],
[3, 2, 128, 0, 0, 1],
[3, 2, 128, 0, 0, 1],
[3, 2, 256, 0, 1, 2],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 512, 0, 1, 2],
[3, 2, 512, 1, 1, 1],
[3, 2, 512, 0, 1, 1]
]
return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
@register_model
def repvit_m1_5(pretrained=False, num_classes = 1000, distillation=False):
"""
Constructs a MobileNetV3-Large model
"""
cfgs = [
# k, t, c, SE, HS, s
[3, 2, 64, 1, 0, 1],
[3, 2, 64, 0, 0, 1],
[3, 2, 64, 1, 0, 1],
[3, 2, 64, 0, 0, 1],
[3, 2, 64, 0, 0, 1],
[3, 2, 128, 0, 0, 2],
[3, 2, 128, 1, 0, 1],
[3, 2, 128, 0, 0, 1],
[3, 2, 128, 1, 0, 1],
[3, 2, 128, 0, 0, 1],
[3, 2, 128, 0, 0, 1],
[3, 2, 256, 0, 1, 2],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 1, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 256, 0, 1, 1],
[3, 2, 512, 0, 1, 2],
[3, 2, 512, 1, 1, 1],
[3, 2, 512, 0, 1, 1],
[3, 2, 512, 1, 1, 1],
[3, 2, 512, 0, 1, 1]
]
return RepViT(cfgs, num_classes=num_classes, distillation=distillation)
@register_model
def repvit_m2_3(pretrained=False, num_classes = 1000, distillation=False):
"""
Constructs a MobileNetV3-Large model
"""
cfgs = [
# k, t, c, SE, HS, s
[3, 2, 80, 1, 0, 1],
[3, 2, 80, 0, 0, 1],
[3, 2, 80, 1, 0, 1],
[3, 2, 80, 0, 0, 1],
[3, 2, 80, 1, 0, 1],
[3, 2, 80, 0, 0, 1],
[3, 2, 80, 0, 0, 1],
[3, 2, 160, 0, 0, 2],
[3, 2, 160, 1, 0, 1],
[3, 2, 160, 0, 0, 1],
[3, 2, 160, 1, 0, 1],
[3, 2, 160, 0, 0, 1],
[3, 2, 160, 1, 0, 1],
[3, 2, 160, 0, 0, 1],
[3, 2, 160, 0, 0, 1],
[3, 2, 320, 0, 1, 2],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 320, 1, 1, 1],
[3, 2, 320, 0, 1, 1],
# [3, 2, 320, 1, 1, 1],
# [3, 2, 320, 0, 1, 1],
[3, 2, 320, 0, 1, 1],
[3, 2, 640, 0, 1, 2],
[3, 2, 640, 1, 1, 1],
[3, 2, 640, 0, 1, 1],
# [3, 2, 640, 1, 1, 1],
# [3, 2, 640, 0, 1, 1]
]
return RepViT(cfgs, num_classes=num_classes, distillation=distillation)第四步：统计训练过程中验证集准确率和loss变化
正确率高达99%
第五步：搭建GUI界面
第六步：整个工程的内容
有训练代码和训练好的模型以及训练过程，提供数据，提供GUI界面代码
项目完整文件下载请见演示与介绍视频的简介处给出 ：➷➷➷
该视频展示了一个利用深度学习中的RepViT架构构建的PyTorch平台来实现番石榴果实病害识别与分类系统的B站视频
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