ico:增加ico

BFDS_train.py

@@ -1,7 +1,6 @@
 import os
 import logging
 import warnings
-import json
 from datetime import datetime
 import requests
 
@@ -38,7 +37,7 @@ class Argument:
         self.normalize_type = None  # 归一化方式, mean-std/min-max/None
 
         # 模型
-        self.model_name = "ResNet_1d"  # 模型名
+        self.model_name = "ResNet"  # 模型名
         self.bottleneck = True  # 是否使用bottleneck层
         self.bottleneck_num = 256  # bottleneck层的输出维数
 

BFDS_web.py

@@ -403,4 +403,4 @@ with gr.Blocks(title="BFDS WebUI") as app:
     signal_inference_button.click(signal_inference, inputs=[model_file, signal_file_multiple], outputs=signal_inference_output)
 
 app.queue()
-app.launch()
+app.launch(favicon_path="docs/favicon.ico", show_error=True)

models/ResNet12.py

@@ -1,96 +0,0 @@
-import torch
-import torch.nn as nn
-from torchsummary import summary
-
-
-class Block(nn.Module):
-    def __init__(self, in_planes, planes, stride=1):
-        super(Block, self).__init__()
-        # 第一个1x1卷积层，用于通道变换
-        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        # 第二个3x3卷积层，可能进行下采样
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        # 第三个1x1卷积层，恢复通道数
-        self.conv3 = nn.Conv2d(planes, planes, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-
-        # 当输入和输出维度不一致时，使用1x1卷积调整
-        self.shortcut = nn.Sequential()
-        if stride != 1 or in_planes != planes:
-            self.shortcut = nn.Sequential(nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes))
-
-    def forward(self, x):
-        identity = self.shortcut(x)
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        out += identity
-        out = self.relu(out)
-        return out
-
-
-class ResNet12(nn.Module):
-    def __init__(self):
-        super(ResNet12, self).__init__()
-        self.__in_features = 256
-        self.in_planes = 64  # 初始通道数
-
-        # 初始卷积层
-        self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU(inplace=True)
-
-        # 四个残差块层
-        self.layer1 = self._make_layer(64, stride=1)  # 第1层，不降采样
-        self.layer2 = self._make_layer(128, stride=2)  # 第2层，降采样
-        self.layer3 = self._make_layer(256, stride=2)  # 第3层，降采样
-        self.layer4 = self._make_layer(512, stride=2)  # 第4层，降采样
-
-        # 分类层
-        self.avgpool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Linear(512, self.__in_features)  # 输出特征维度为256
-
-    def _make_layer(self, planes, stride):
-        # 每个残差块层包含一个Block
-        layers = []
-        layers.append(Block(self.in_planes, planes, stride))
-        self.in_planes = planes  # 更新输入通道数为当前层的输出通道数
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.layer1(out)
-        out = self.layer2(out)
-        out = self.layer3(out)
-        out = self.layer4(out)
-
-        out = self.avgpool(out)
-        out = out.view(out.size(0), -1)
-        out = self.fc(out)
-        return out
-    
-    
-    def output_num(self):
-        return self.__in_features
-
-
-# 测试代码
-if __name__ == "__main__":
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = ResNet12(num_classes=5).to(device)
-    summary(model, (1, 224, 224))

models/__init__.py

@@ -1,2 +1,2 @@
 from models.CNN import CNN as CNN
-from models.ResNet18_1d import ResNet1D as ResNet_1d
+from models.ResNet18_1d import ResNet1D as ResNet