{"id":55793,"date":"2025-08-14T08:34:39","date_gmt":"2025-08-14T00:34:39","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/55793.html"},"modified":"2025-08-14T08:34:39","modified_gmt":"2025-08-14T00:34:39","slug":"%e6%89%8b%e6%8a%8a%e6%89%8b%e6%95%99%e4%bd%a0%e5%a6%82%e4%bd%95%e5%a4%8d%e7%8e%b0%e5%88%ab%e4%ba%ba%e7%9a%84%e8%ae%ba%e6%96%87%ef%bc%88cnn%e7%9b%b8%e5%85%b3%ef%bc%89%ef%bc%88%e4%be%8b%ef%bc%9a","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/55793.html","title":{"rendered":"\u624b\u628a\u624b\u6559\u4f60\u5982\u4f55\u590d\u73b0\u522b\u4eba\u7684\u8bba\u6587\uff08CNN\u76f8\u5173\uff09\uff08\u4f8b\uff1a\u57fa\u4e8e\u96c6\u6210\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u548c Vit \u7684\u773c\u5e95\u56fe\u50cf\u5206\u7c7b\u7814\u7a76\uff09"},"content":{"rendered":"<\/p>\n

\u524d\u8a00<\/h2>\n

\u5b66\u4e60\u4ed6\u4eba，\u63d0\u5347\u81ea\u5df1\u3002\u8d85\u8d8a\u4ed6\u4eba，\u5b9e\u73b0\u521b\u65b0\u3002<\/p>\n

\u672c\u6587\u5c06\u5c55\u73b0 \u5982\u4f55\u4e00\u6b65\u4e00\u6b65\u7684\u590d\u73b0\u4ed6\u4eba\u7684\u6587\u7ae0\u3002\u6ca1\u6709\u5e9f\u8bdd，\u5168\u662f\u5b9e\u64cd\u3002\u8be6\u7ec6\u5c55\u793a\u8bba\u6587\u4ee3\u7801\u590d\u73b0\u7684\u6d41\u7a0b\u3002<\/p>\n<\/p>\n

\u672c\u6587\u590d\u73b0\u7684\u6587\u7ae0\u6765\u81ea：<\/p>\n

\u8881\u5a9b, \u9648\u660e\u60e0, \u67ef\u8212\u5a77, \u7b49. \u57fa\u4e8e\u96c6\u6210\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u548c Vit \u7684\u773c\u5e95\u56fe\u50cf\u5206\u7c7b\u7814\u7a76[J]. Chinese Journal of Lasers, 2022, 49(20): 2007205-2007205-9.<\/p>\n

\u6807\u9898<\/p>\n<\/p>\n

（\u76f8\u5173\u9886\u57df，\u60f3\u590d\u73b0\u7684\u6587\u7ae0，\u8bf7\u53d1\u5728\u8bc4\u8bba\u533a，\u53ef\u5e2e\u590d\u73b0\u3002\u70b9\u4e2a\u5173\u6ce8\u5148）<\/p>\n<\/p>\n

\u89c2\u770b\u672c\u6587，\u8981\u6709\u4e00\u5b9aCNN\u57fa\u7840\u3002\u6ca1\u6709\u53ef\u8df3\u94fe\u63a5：<\/p>\n

\u5c0f\u767d\u5feb\u901f\u4e0a\u624b\u5b9e\u64cdCNN\u56fe\u50cf\u5206\u7c7b（pytorch）\u3002\u770b\u5b8c\u79d2\u53d8\u5927\u795e\u3002_cnn\u56fe\u50cf\u7f16\u7a0b\u600e\u4e48\u5b66-CSDN\u535a\u5ba2<\/p>\n<\/p>\n

1.\u901a\u8bfb\u8bba\u6587<\/h2>\n<\/p>\n

\u201c\u672c\u6587\u63d0\u51fa\u4e00\u79cd\u57fa\u4e8eEfficientNet-Vit\u96c6\u6210\u6a21\u578b\u7684\u773c\u5e95\u56fe\u50cf\u5206\u7c7b\u65b9\u6cd5,\u6b64 \u65b9\u6cd5\u5c06\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u6a21\u578bEfficientNetV2-S\u548cVit\u6a21\u578b\u76f8\u7ed3\u5408,\u5206\u522b\u4f7f\u7528\u4e24\u79cd\u5b8c\u5168\u4e0d\u540c\u7684\u65b9\u6cd5\u63d0\u53d6\u773c\u5e95\u56fe\u50cf\u7684\u7279 \u5f81,\u901a\u8fc7\u81ea\u9002\u5e94\u52a0\u6743\u878d\u5408\u7b97\u6cd5\u8ba1\u7b97\u5f97\u5230\u6700\u4f18\u52a0\u6743\u56e0\u5b500.6\u548c0.4,\u5229\u7528\u52a0\u6743\u8f6f\u6295\u7968\u6cd5\u8fdb\u884c\u6a21\u578b\u96c6\u6210,\u4ece\u800c\u83b7\u5f97\u66f4\u597d \u7684\u5206\u7c7b\u7ed3\u679c\u3002\u201d<\/p>\n<\/p>\n

\u4e5f\u5c31\u662f\u8bf4，\u6b64\u6587\u501f\u9274\u4e862\u4e2d\u6a21\u578bEfficientNet\u548cVit，\u7136\u540e\u901a\u8fc7\u4e00\u79cd\u6240\u8c13\u7684\u52a0\u6743\u8f6f\u6295\u7968\u6cd5\u8fdb\u884c\u6a21\u578b\u96c6\u6210，<\/p>\n

\u8fd9\u4e48\u9ad8\u5927\u4e0a\u7684\u8bcd：\u52a0\u6743\u8f6f\u6295\u7968\u6cd5<\/p>\n

\u8bf4\u767d\u4e86\u5c31\u662f2\u4e2a\u6a21\u578b\u8f93\u51fa\u7684\u7c7b\u522b\u6982\u7387 \u52a0\u8d77\u6765\u3002<\/p>\n

\"\"<\/p>\n<\/p>\n

\u4f8b\u5982，<\/p>\n

\u7c7b\u522b A,B,C,D,E\u3002<\/p>\n

\u6837\u672cX，<\/p>\n

\u5728model1 \u4e0a\u7684 5\u4e2a\u7c7b\u522b\u7684\u6982\u7387\u5206\u522b\u662f：0.1 ，0.2， 0.3 ，0.15 ，0.25，\u6240\u4ee5X\u5728model1\u4e0a\u7684\u7c7b\u522b \u4e3aC\u3002<\/p>\n

\u5728model2\u00a0\u4e0a\u7684 5\u4e2a\u7c7b\u522b\u7684\u6982\u7387\u5206\u522b\u662f：0.15 ，0.35， 0.1 ，0.1\u00a0，0.3，\u6240\u4ee5X\u5728model2\u4e0a\u7684\u7c7b\u522b \u4e3aB\u3002<\/p>\n

\u901a\u8fc7\u52a0\u6743\u8f6f\u6295\u7968\u6cd5\u5f97\u5230：\u6837\u672cX，5\u4e2a\u7c7b\u522b\u7684\u6982\u7387\u5206\u522b\u662f：<\/p>\n

0.1×0.6+0.15×0.4\u00a0 \u00a00.2×0.6+0.35×0.4\u00a0 \u00a0 \u00a0 0.3×0.6+0.1×0.4\u00a0 \u00a0 0.15×0.6+0.1×0.4\u00a0 0.25×0.6+0.3×0.4<\/p>\n

\u5373，0.12\u00a0 \u00a00.26\u00a0 0.22\u00a0 0.13\u00a0 0.27<\/p>\n

\u6240\u4ee5\u6700\u7ec8\u8f93\u51fa\u7684\u7c7b\u522b \u4e3aD\u3002<\/p>\n<\/p>\n

\u53e6\u5916，\u6700\u4f18\u52a0\u6743\u56e0\u5b500.6\u548c0.4,\u901a\u8fc7\u81ea\u9002\u5e94\u52a0\u6743\u878d\u5408\u7b97\u6cd5\u8ba1\u7b97\u5f97\u5230\u3002<\/p>\n<\/p>\n

\"\"<\/p>\n

\u5927\u6982\u662f\u901a\u8fc7 \u6982\u7387\u7684\u5206\u5e03 \u8ba1\u7b97\u65b9\u5dee，\u6765\u5f97\u5230\u7684\u3002<\/p>\n<\/p>\n

\u4e3a\u4e86\u589e\u52a0\u521b\u65b0\u70b9，\u6587\u7ae0 \u8fd8\u5bf9EfficientNet，\u505a\u4e86\u6539\u8fdb\u3002<\/p>\n

\u6539\u8fdb\u5982\u4e0b：<\/p>\n

\u672c\u7814\u7a76\u4f7f\u7528\u4e00\u79cd\u65e0\u53c2\u6570\u6ce8\u610f\u529b\u6a21\u5757SimAM，\u66ff\u6362Fused-MBConv\u7684SE\u6a21\u5757<\/p>\n

\"\"<\/p>\n<\/p>\n

\"\"<\/p>\n<\/p>\n

2.\u5173\u952e\u6280\u672f-\u52a0\u6743\u8f6f\u6295\u7968\u6cd5<\/h2>\n

\u00a0<\/p>\n

EfficientNet\u548cVit\u90fd\u53ef\u4ee5\u5728torchvision.models\u4e2d\u76f4\u63a5\u8c03\u7528\u3002<\/p>\n

model1 = torchvision.models.vit_b_16(pretrained=True)
\nmodel1.heads.head = nn.Linear(model1.heads.head.in_features, 5)<\/p>\n

model2 = torchvision.models.efficientnet_v2_m(pretrained=True)
\nmodel2.classifier[1] = nn.Linear(in_features=1280, out_features=5) <\/p>\n

\u4f46\u9700\u8981\u6ce8\u610f\u4fee\u6539，\u7c7b\u522b\u6570\u91cf\u3002<\/p>\n

\u7528pytorch\u7684torchvision.models\u8c03\u7528\u5404\u79cdcnn\u6a21\u578b，\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6\u65f6，\u600e\u4e48\u624b\u52a8\u6539\u5206\u7c7b\u6570num_classes\u3002_torchvision \u5e38\u89c1\u6a21\u578b\u8c03\u7528-CSDN\u535a\u5ba2<\/p>\n

\u5b8c\u6574\u4ee3\u7801\u5982\u4e0b：<\/p>\n

import torch.nn as nn
\nimport torch
\nfrom torchvision import transforms
\nimport torchvision
\nfrom PIL import Image
\nimport torch.nn.functional as F <\/p>\n

label_dic = {0:"A",1:"B",2:"C",3:"D",4:"E"}<\/p>\n

def usemodel_singleimg(model, test_image):
\n transformer = transforms.Compose([
\n transforms.Resize((224, 224)),
\n transforms.ToTensor(),
\n transforms.Normalize(mean=[0.5, 0.5, 0.5],
\n std=[0.5, 0.5, 0.5])
\n ])<\/p>\n

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
\n model.to(device)<\/p>\n

test_image = test_image.convert("RGB")
\n processed_image = transformer(test_image).unsqueeze(dim=0)
\n processed_image = processed_image.to(device)<\/p>\n

model.eval()<\/p>\n

with torch.no_grad():
\n output = model(processed_image)<\/p>\n

# \u65b0\u589e\u6982\u7387\u8ba1\u7b97\u90e8\u5206
\n probabilities = F.softmax(output, dim=1) # \u6cbf\u7c7b\u522b\u7ef4\u5ea6\u505asoftmax
\n probabilities = probabilities.cpu().numpy()[0] # \u8f6c\u4e3anumpy\u6570\u7ec4\u5e76\u53bb\u6389batch\u7ef4\u5ea6<\/p>\n

_, predicted = torch.max(output.data, 1)
\n class_id = predicted.item()#\u9884\u6d4b\u7c7b\u522bID<\/p>\n

return probabilities.tolist()<\/p>\n

def add(model1,model2,img):
\n probabilities1 = usemodel_singleimg(model1, img)
\n probabilities2 = usemodel_singleimg(model2, img)
\n sum_probabilities = []
\n for i in range(5):
\n sum_probabilities.append(probabilities1[i]*0.6+probabilities2[i]*0.4)<\/p>\n

return sum_probabilities<\/p>\n

def label_sum_probabilities(lst):
\n max_val = max(lst)
\n label = label_dic[lst.index(max_val)]
\n return label<\/p>\n

if __name__ == "__main__":
\n model1 = torchvision.models.vit_b_16(pretrained=True)
\n model1.heads.head = nn.Linear(model1.heads.head.in_features, 5)<\/p>\n

model2 = torchvision.models.efficientnet_v2_m(pretrained=True)
\n model2.classifier[1] = nn.Linear(in_features=1280, out_features=5)<\/p>\n

# \u52a0\u8f7d\u6d4b\u8bd5\u56fe\u7247
\n img = Image.open("cat.png")<\/p>\n

result = add(model1,model2,img)
\n print(result)
\n label = label_sum_probabilities(result)
\n print(label)<\/p>\n

\u8fd0\u884c\u540e，\u8f93\u51fa<\/p>\n

\"\"<\/p>\n<\/p>\n<\/p>\n

3. \u5173\u952e\u6280\u672f-SimAM<\/h2>\n<\/p>\n

\u95ee\u95eeai，\u4ec0\u4e48\u662f simam<\/p>\n<\/p>\n

\"\"<\/p>\n

SimAM：\u8f7b\u91cf\u7ea7\u6ce8\u610f\u529b\u673a\u5236，\u89e3\u9501\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u65b0\u6f5c\u529b\u3010\u539f\u7406\u8bb2\u89e3\u53ca\u4ee3\u7801！！！\u3011_simam\u6ce8\u610f\u529b-CSDN\u535a\u5ba2<\/p>\n<\/p>\n

import torch.nn as nn
\nimport torch<\/p>\n

class SimAM(nn.Module):
\n def __init__(self, lambda_param=1e-5):
\n super().__init__()
\n self.lambda_param = lambda_param
\n self.sigmoid = nn.Sigmoid()<\/p>\n

def forward(self, x):
\n b, c, h, w = x.size()
\n n = h * w – 1
\n mean = torch.mean(x, dim=[2,3], keepdim=True)
\n var = torch.sum((x – mean)**2, dim=[2,3], keepdim=True) \/ n
\n e_t = (x – mean)**2 \/ (4*(var + self.lambda_param)) + 0.5
\n return x * self.sigmoid(e_t)<\/p>\n

if __name__ == "__main__":
\n device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
\n simam = SimAM().to(device)
\n input_images = torch.randn(1, 3, 256, 256).to(device)
\n output = simam(input_images)
\n print(output.shape) <\/p>\n

\u8fd0\u884c，<\/p>\n<\/p>\n

\"\"<\/p>\n<\/p>\n

\u7136\u540e，\u6211\u4eec\u8981\u627e\u5230efficient_v2\u7684\u6e90\u7801，\u7528simam\u4ee3\u66ff，Fused-MBConv\u5c42\u4e2d\u7684SE\u6a21\u5757<\/p>\n

efficient_v2\u7684\u6e90\u7801：<\/p>\n

\u7ecf\u5178CNN\u6a21\u578b（\u5341\u4e09）：EfficientNetV2（PyTorch\u8be6\u7ec6\u6ce8\u91ca\u7248）-CSDN\u535a\u5ba2<\/p>\n

\u53d1\u73b0Fused-MBConv\u5e76\u6ca1\u6709\u6240\u8c13\u7684SE\u6a21\u5757<\/p>\n

\u4f46\u6ca1\u5173\u7cfb，\u76f4\u63a5\u7ed9\u4ed6\u52a0\u4e0aSimAM\u3002<\/p>\n<\/p>\n

\u5b8c\u6574\u4ee3\u7801<\/p>\n

from collections import OrderedDict
\nfrom functools import partial
\nfrom typing import Callable, Optional<\/p>\n

import torch.nn as nn
\nimport torch
\nfrom torch import Tensor<\/p>\n

#===========================SimAM===========================<\/p>\n

class SimAM(nn.Module):
\n def __init__(self, lambda_param=1e-5):
\n super().__init__()
\n self.lambda_param = lambda_param
\n self.sigmoid = nn.Sigmoid()<\/p>\n

def forward(self, x):
\n b, c, h, w = x.size()
\n n = h * w – 1
\n mean = torch.mean(x, dim=[2,3], keepdim=True)
\n var = torch.sum((x – mean)**2, dim=[2,3], keepdim=True) \/ n
\n e_t = (x – mean)**2 \/ (4*(var + self.lambda_param)) + 0.5
\n return x * self.sigmoid(e_t)<\/p>\n

#========================efficient===========================<\/p>\n

def drop_path(x, drop_prob: float = 0., training: bool = False):
\n """
\n Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
\n "Deep Networks with Stochastic Depth", https:\/\/arxiv.org\/pdf\/1603.09382.pdf<\/p>\n

This function is taken from the rwightman.
\n It can be seen here:
\n https:\/\/github.com\/rwightman\/pytorch-image-models\/blob\/master\/timm\/models\/layers\/drop.py#L140
\n """
\n if drop_prob == 0. or not training:
\n return x
\n keep_prob = 1 – drop_prob
\n shape = (x.shape[0],) + (1,) * (x.ndim – 1) # work with diff dim tensors, not just 2D ConvNets
\n random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
\n random_tensor.floor_() # binarize
\n output = x.div(keep_prob) * random_tensor
\n return output<\/p>\n

class DropPath(nn.Module):
\n """
\n Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
\n "Deep Networks with Stochastic Depth", https:\/\/arxiv.org\/pdf\/1603.09382.pdf
\n """
\n def __init__(self, drop_prob=None):
\n super(DropPath, self).__init__()
\n self.drop_prob = drop_prob<\/p>\n

def forward(self, x):
\n return drop_path(x, self.drop_prob, self.training)<\/p>\n

class ConvBNAct(nn.Module):
\n def __init__(self,
\n in_planes: int,
\n out_planes: int,
\n kernel_size: int = 3,
\n stride: int = 1,
\n groups: int = 1,
\n norm_layer: Optional[Callable[…, nn.Module]] = None,
\n activation_layer: Optional[Callable[…, nn.Module]] = None):
\n super(ConvBNAct, self).__init__()<\/p>\n

padding = (kernel_size – 1) \/\/ 2
\n if norm_layer is None:
\n norm_layer = nn.BatchNorm2d
\n if activation_layer is None:
\n activation_layer = nn.SiLU # alias Swish (torch>=1.7)<\/p>\n

self.conv = nn.Conv2d(in_channels=in_planes,
\n out_channels=out_planes,
\n kernel_size=kernel_size,
\n stride=stride,
\n padding=padding,
\n groups=groups,
\n bias=False)<\/p>\n

self.bn = norm_layer(out_planes)
\n self.act = activation_layer()<\/p>\n

def forward(self, x):
\n result = self.conv(x)
\n result = self.bn(result)
\n result = self.act(result)<\/p>\n

return result<\/p>\n

class SqueezeExcite(nn.Module):
\n def __init__(self,
\n input_c: int, # block input channel
\n expand_c: int, # block expand channel
\n se_ratio: float = 0.25):
\n super(SqueezeExcite, self).__init__()
\n squeeze_c = int(input_c * se_ratio)
\n self.conv_reduce = nn.Conv2d(expand_c, squeeze_c, 1)
\n self.act1 = nn.SiLU() # alias Swish
\n self.conv_expand = nn.Conv2d(squeeze_c, expand_c, 1)
\n self.act2 = nn.Sigmoid()<\/p>\n

def forward(self, x: Tensor) -> Tensor:
\n scale = x.mean((2, 3), keepdim=True)
\n scale = self.conv_reduce(scale)
\n scale = self.act1(scale)
\n scale = self.conv_expand(scale)
\n scale = self.act2(scale)
\n return scale * x<\/p>\n

class MBConv(nn.Module):
\n def __init__(self,
\n kernel_size: int,
\n input_c: int,
\n out_c: int,
\n expand_ratio: int,
\n stride: int,
\n se_ratio: float,
\n drop_rate: float,
\n norm_layer: Callable[…, nn.Module]):
\n super(MBConv, self).__init__()<\/p>\n

if stride not in [1, 2]:
\n raise ValueError("illegal stride value.")<\/p>\n

self.has_shortcut = (stride == 1 and input_c == out_c)<\/p>\n

activation_layer = nn.SiLU # alias Swish
\n expanded_c = input_c * expand_ratio<\/p>\n

# \u5728EfficientNetV2\u4e2d，MBConv\u4e2d\u4e0d\u5b58\u5728expansion=1\u7684\u60c5\u51b5\u6240\u4ee5conv_pw\u80af\u5b9a\u5b58\u5728
\n assert expand_ratio != 1
\n # Point-wise expansion
\n self.expand_conv = ConvBNAct(input_c,
\n expanded_c,
\n kernel_size=1,
\n norm_layer=norm_layer,
\n activation_layer=activation_layer)<\/p>\n

# Depth-wise convolution
\n self.dwconv = ConvBNAct(expanded_c,
\n expanded_c,
\n kernel_size=kernel_size,
\n stride=stride,
\n groups=expanded_c,
\n norm_layer=norm_layer,
\n activation_layer=activation_layer)<\/p>\n

self.se = SqueezeExcite(input_c, expanded_c, se_ratio) if se_ratio > 0 else nn.Identity()<\/p>\n

# Point-wise linear projection
\n self.project_conv = ConvBNAct(expanded_c,
\n out_planes=out_c,
\n kernel_size=1,
\n norm_layer=norm_layer,
\n activation_layer=nn.Identity) # \u6ce8\u610f\u8fd9\u91cc\u6ca1\u6709\u6fc0\u6d3b\u51fd\u6570，\u6240\u6709\u4f20\u5165Identity<\/p>\n

self.out_channels = out_c<\/p>\n

# \u53ea\u6709\u5728\u4f7f\u7528shortcut\u8fde\u63a5\u65f6\u624d\u4f7f\u7528dropout\u5c42
\n self.drop_rate = drop_rate
\n if self.has_shortcut and drop_rate > 0:
\n self.dropout = DropPath(drop_rate)<\/p>\n

def forward(self, x: Tensor) -> Tensor:
\n result = self.expand_conv(x)
\n result = self.dwconv(result)
\n result = self.se(result)
\n result = self.project_conv(result)<\/p>\n

if self.has_shortcut:
\n if self.drop_rate > 0:
\n result = self.dropout(result)
\n result += x<\/p>\n

return result<\/p>\n

class FusedMBConv(nn.Module):
\n def __init__(self,
\n kernel_size: int,
\n input_c: int,
\n out_c: int,
\n expand_ratio: int,
\n stride: int,
\n se_ratio: float,
\n drop_rate: float,
\n norm_layer: Callable[…, nn.Module]):
\n super(FusedMBConv, self).__init__()<\/p>\n

assert stride in [1, 2]
\n assert se_ratio == 0<\/p>\n

self.has_shortcut = stride == 1 and input_c == out_c
\n self.drop_rate = drop_rate
\n self.has_expansion = expand_ratio != 1
\n self.simam = SimAM() # \u65b0\u589eSimAM\u6a21\u5757<\/p>\n

activation_layer = nn.SiLU # alias Swish
\n expanded_c = input_c * expand_ratio<\/p>\n

# \u53ea\u6709\u5f53expand ratio\u4e0d\u7b49\u4e8e1\u65f6\u624d\u6709expand conv
\n if self.has_expansion:
\n # Expansion convolution
\n self.expand_conv = ConvBNAct(input_c,
\n expanded_c,
\n kernel_size=kernel_size,
\n stride=stride,
\n norm_layer=norm_layer,
\n activation_layer=activation_layer)<\/p>\n

self.project_conv = ConvBNAct(expanded_c,
\n out_c,
\n kernel_size=1,
\n norm_layer=norm_layer,
\n activation_layer=nn.Identity) # \u6ce8\u610f\u6ca1\u6709\u6fc0\u6d3b\u51fd\u6570
\n else:
\n # \u5f53\u53ea\u6709project_conv\u65f6\u7684\u60c5\u51b5
\n self.project_conv = ConvBNAct(input_c,
\n out_c,
\n kernel_size=kernel_size,
\n stride=stride,
\n norm_layer=norm_layer,
\n activation_layer=activation_layer) # \u6ce8\u610f\u6709\u6fc0\u6d3b\u51fd\u6570<\/p>\n

self.out_channels = out_c<\/p>\n

# \u53ea\u6709\u5728\u4f7f\u7528shortcut\u8fde\u63a5\u65f6\u624d\u4f7f\u7528dropout\u5c42
\n self.drop_rate = drop_rate
\n if self.has_shortcut and drop_rate > 0:
\n self.dropout = DropPath(drop_rate)<\/p>\n

def forward(self, x: Tensor) -> Tensor:
\n if self.has_expansion:
\n result = self.expand_conv(x)
\n result = self.project_conv(result)
\n else:
\n result = self.project_conv(x)
\n result = self.simam(result) # \u65b0\u589e：\u5e94\u7528SimAM\u6ce8\u610f\u529b
\n if self.has_shortcut:
\n if self.drop_rate > 0:
\n result = self.dropout(result)<\/p>\n

result += x<\/p>\n

return result<\/p>\n

class EfficientNetV2(nn.Module):
\n def __init__(self,
\n model_cnf: list,
\n num_classes: int = 1000,
\n num_features: int = 1280,
\n dropout_rate: float = 0.2,
\n drop_connect_rate: float = 0.2):
\n super(EfficientNetV2, self).__init__()<\/p>\n

for cnf in model_cnf:
\n assert len(cnf) == 8<\/p>\n

norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.1)<\/p>\n

stem_filter_num = model_cnf[0][4]<\/p>\n

self.stem = ConvBNAct(3,
\n stem_filter_num,
\n kernel_size=3,
\n stride=2,
\n norm_layer=norm_layer) # \u6fc0\u6d3b\u51fd\u6570\u9ed8\u8ba4\u662fSiLU<\/p>\n

total_blocks = sum([i[0] for i in model_cnf])
\n block_id = 0
\n blocks = []
\n for cnf in model_cnf:
\n repeats = cnf[0]
\n op = FusedMBConv if cnf[-2] == 0 else MBConv
\n for i in range(repeats):
\n blocks.append(op(kernel_size=cnf[1],
\n input_c=cnf[4] if i == 0 else cnf[5],
\n out_c=cnf[5],
\n expand_ratio=cnf[3],
\n stride=cnf[2] if i == 0 else 1,
\n se_ratio=cnf[-1],
\n drop_rate=drop_connect_rate * block_id \/ total_blocks,
\n norm_layer=norm_layer))
\n block_id += 1
\n self.blocks = nn.Sequential(*blocks)<\/p>\n

head_input_c = model_cnf[-1][-3]
\n head = OrderedDict()<\/p>\n

head.update({"project_conv": ConvBNAct(head_input_c,
\n num_features,
\n kernel_size=1,
\n norm_layer=norm_layer)}) # \u6fc0\u6d3b\u51fd\u6570\u9ed8\u8ba4\u662fSiLU<\/p>\n

head.update({"avgpool": nn.AdaptiveAvgPool2d(1)})
\n head.update({"flatten": nn.Flatten()})<\/p>\n

if dropout_rate > 0:
\n head.update({"dropout": nn.Dropout(p=dropout_rate, inplace=True)})
\n head.update({"classifier": nn.Linear(num_features, num_classes)})<\/p>\n

self.head = nn.Sequential(head)<\/p>\n

# initial weights
\n for m in self.modules():
\n if isinstance(m, nn.Conv2d):
\n nn.init.kaiming_normal_(m.weight, mode="fan_out")
\n if m.bias is not None:
\n nn.init.zeros_(m.bias)
\n elif isinstance(m, nn.BatchNorm2d):
\n nn.init.ones_(m.weight)
\n nn.init.zeros_(m.bias)
\n elif isinstance(m, nn.Linear):
\n nn.init.normal_(m.weight, 0, 0.01)
\n nn.init.zeros_(m.bias)<\/p>\n

def forward(self, x: Tensor) -> Tensor:
\n x = self.stem(x)
\n x = self.blocks(x)
\n x = self.head(x)<\/p>\n

return x<\/p>\n

def efficientnetv2_s(num_classes: int = 1000):
\n """
\n EfficientNetV2
\n https:\/\/arxiv.org\/abs\/2104.00298
\n """
\n # train_size: 300, eval_size: 384<\/p>\n

# repeat, kernel, stride, expansion, in_c, out_c, operator, se_ratio
\n model_config = [[2, 3, 1, 1, 24, 24, 0, 0],
\n [4, 3, 2, 4, 24, 48, 0, 0],
\n [4, 3, 2, 4, 48, 64, 0, 0],
\n [6, 3, 2, 4, 64, 128, 1, 0.25],
\n [9, 3, 1, 6, 128, 160, 1, 0.25],
\n [15, 3, 2, 6, 160, 256, 1, 0.25]]<\/p>\n

model = EfficientNetV2(model_cnf=model_config,
\n num_classes=num_classes,
\n dropout_rate=0.2)
\n return model<\/p>\n

def efficientnetv2_m(num_classes: int = 1000):
\n """
\n EfficientNetV2
\n https:\/\/arxiv.org\/abs\/2104.00298
\n """
\n # train_size: 384, eval_size: 480<\/p>\n

# repeat, kernel, stride, expansion, in_c, out_c, operator, se_ratio
\n model_config = [[3, 3, 1, 1, 24, 24, 0, 0],
\n [5, 3, 2, 4, 24, 48, 0, 0],
\n [5, 3, 2, 4, 48, 80, 0, 0],
\n [7, 3, 2, 4, 80, 160, 1, 0.25],
\n [14, 3, 1, 6, 160, 176, 1, 0.25],
\n [18, 3, 2, 6, 176, 304, 1, 0.25],
\n [5, 3, 1, 6, 304, 512, 1, 0.25]]<\/p>\n

model = EfficientNetV2(model_cnf=model_config,
\n num_classes=num_classes,
\n dropout_rate=0.3)
\n return model<\/p>\n

def efficientnetv2_l(num_classes: int = 1000):
\n """
\n EfficientNetV2
\n https:\/\/arxiv.org\/abs\/2104.00298
\n """
\n # train_size: 384, eval_size: 480<\/p>\n

# repeat, kernel, stride, expansion, in_c, out_c, operator, se_ratio
\n model_config = [[4, 3, 1, 1, 32, 32, 0, 0],
\n [7, 3, 2, 4, 32, 64, 0, 0],
\n [7, 3, 2, 4, 64, 96, 0, 0],
\n [10, 3, 2, 4, 96, 192, 1, 0.25],
\n [19, 3, 1, 6, 192, 224, 1, 0.25],
\n [25, 3, 2, 6, 224, 384, 1, 0.25],
\n [7, 3, 1, 6, 384, 640, 1, 0.25]]<\/p>\n

model = EfficientNetV2(model_cnf=model_config,
\n num_classes=num_classes,
\n dropout_rate=0.4)
\n return model<\/p>\n

if __name__ == "__main__":
\n device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
\n simam = efficientnetv2_m(num_classes = 5).to(device)
\n input_images = torch.randn(1, 3, 256, 256).to(device)
\n output = simam(input_images)
\n print(output.shape) <\/p>\n

\u8fd0\u884c\u540e \"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb690\u6b21\uff0c\u70b9\u8d5e11\u6b21\uff0c\u6536\u85cf21\u6b21\u3002\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u590d\u73b0\u57fa\u4e8eEfficientNet-ViT\u96c6\u6210\u6a21\u578b\u7684\u773c\u5e95\u56fe\u50cf\u5206\u7c7b\u65b9\u6cd5\u3002\u91cd\u70b9\u5185\u5bb9\u5305\u62ec\uff1a1\uff09\u4f7f\u7528\u52a0\u6743\u8f6f\u6295\u7968\u6cd5\uff080.6\u548c0.4\u7684\u6743\u91cd\uff09\u878d\u5408EfficientNetV2\u548cViT\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\uff1b2\uff09\u5728EfficientNetV2\u4e2d\u5f15\u5165SimAM\u65e0\u53c2\u6570\u6ce8\u610f\u529b\u6a21\u5757\u8fdb\u884c\u6539\u8fdb\u3002\u6587\u7ae0\u63d0\u4f9b\u4e86\u5b8c\u6574\u7684PyTorch\u5b9e\u73b0\u4ee3\u7801\uff0c\u5305\u62ec\u6a21\u578b\u96c6\u6210\u3001\u6982\u7387\u52a0\u6743\u8ba1\u7b97\u4ee5\u53caSimAM\u6a21\u5757\u7684\u96c6\u6210\u65b9\u6cd5\uff0c\u5e76\u5c55\u793a\u4e86\u5982\u4f55\u4fee\u6539\u6a21\u578b\u8f93\u51fa\u7c7b\u522b\u6570\u3002\u8be5\u590d\u73b0\u65b9\u6cd5\u9002\u7528\u4e8e\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1\uff0c\u901a\u8fc7\u4e24\u79cd\u4e0d\u540c\u7ed3\u6784\u7684\u6a21\u578b\u96c6\u6210\u63d0\u5347\u5206\u7c7b\u6027\u80fd\u3002<\/p>\n","protected":false},"author":2,"featured_media":55785,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[2394,5758],"topic":[],"class_list":["post-55793","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-cnn","tag-efficient"],"yoast_head":"\n\u624b\u628a\u624b\u6559\u4f60\u5982\u4f55\u590d\u73b0\u522b\u4eba\u7684\u8bba\u6587\uff08CNN\u76f8\u5173\uff09\uff08\u4f8b\uff1a\u57fa\u4e8e\u96c6\u6210\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u548c Vit \u7684\u773c\u5e95\u56fe\u50cf\u5206\u7c7b\u7814\u7a76\uff09 - 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