diff --git a/Untitled1.ipynb b/Untitled1.ipynb new file mode 100644 index 0000000..7ecdd60 --- /dev/null +++ b/Untitled1.ipynb @@ -0,0 +1,325 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn as nn\n", + "from torch import optim as optim\n", + "from torch.utils import data as Data\n", + "from torchvision.datasets import __file__\n", + "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "# ''' \n", + "# 运行环境:PyTorch1.8.0 & Python3.8.1 & Cuda11.1 & Cudnn8.0\n", + "# CPU：i7-10070F RAM:16G 2600 GPU:RTX3070 \n", + "# '''\n", + "class attention1D_layer(nn.Module):\n", + "# ''' \n", + "# 注意力机制模块\n", + "# input x= [batch_size,channels(信号通道数),points(采样点数目)] 多通道一维信号\n", + "# return out = [b*x,b] \n", + "# 其中:b*x 通道权重乘输入信号 ， b为基于神经得到的各采样点权重\n", + " \n", + "# ''' \n", + " def __init__(self,features):\n", + " super(attention1D_layer,self).__init__()\n", + " self.features = features\n", + " # 一维全局平均池化获得全部通道数均值\n", + " self.GobalPooling = nn.AdaptiveAvgPool1d(1)\n", + " \n", + " # 使用一个带Leakly的线性激活函数的BP网络获取采样点权重值 [channels →channels/4 → channels]\n", + " self.Linear1 = nn.Sequential(\n", + " nn.Linear(features,int(features/4)),\n", + " nn.ReLU6())\n", + " self.Linear2 = nn.Sequential(\n", + " nn.Linear(int(features/4),features),\n", + " nn.ReLU6())\n", + " \n", + " #前向传播函数\n", + " def forward(self,x): #传播前，进行维度交换 [batch_size,channels,points]→[batch_size,points,channels]\n", + " b = self.GobalPooling(x.transpose(1,2))\n", + " #全局池化后释放最后一个维度通道数(channels)\n", + " b = b.squeeze(-1)\n", + " #送入BP网络获得b\n", + " b = self.Linear2(self.Linear1(b))\n", + " b = torch.mean(b,0).reshape(1,1,self.features)\n", + " #b = nn.Softmax(b)\n", + " return b*x,b.squeeze(0).squeeze(0)\n", + " \n", + "class Inception_layer(nn.Module):\n", + "# '''\n", + "# 多尺度卷积模块\n", + "# 使用4种模块卷积核处理数据来获取不同大小的感受野 \n", + "# 参考论文:https://arxiv.org/abs/1512.00567 及 InceptionE InceptionV3 InceptionV4\n", + "# '''\n", + " def __init__(self,in_features,out_features):\n", + " super(Inception_layer,self).__init__()\n", + " self.in_features = in_features\n", + " self.Conv1x3 = nn.Sequential(\n", + " nn.Conv1d(in_features,10,kernel_size=1,stride=1,padding=0),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.1),\n", + " nn.Conv1d(10,out_features,kernel_size=3,stride=1,padding=1),\n", + " nn.ReLU() \n", + " )\n", + " self.Conv1x5 = nn.Sequential(\n", + " nn.Conv1d(in_features,10,kernel_size=1,stride=1,padding=0),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.1),\n", + " nn.Conv1d(10,out_features,kernel_size=5,stride=1,padding=2),\n", + " nn.ReLU(),\n", + " )\n", + " self.Conv3x5 = nn.Sequential(\n", + " nn.Conv1d(in_features,10,kernel_size=3,stride=1,padding=1),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.1),\n", + " nn.Conv1d(10,out_features,kernel_size=5,stride=1,padding=2),\n", + " nn.ReLU()\n", + " )\n", + " self.short_cut = nn.Sequential(\n", + " nn.Conv1d(in_features,out_features,kernel_size=1,stride=1,padding=0)\n", + " )\n", + " def forward(self,x):\n", + " part_1 = self.Conv1x3(x)\n", + " part_2 = self.Conv3x5(x)\n", + " part_3 = self.Conv1x5(x)\n", + " x = self.short_cut(x)\n", + " # 返回四种尺度信号词义\n", + " return part_1,part_2,part_3,x" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class ResPart(nn.Module):\n", + "# '''\n", + "# 恒等连接结构 : by conv1→conv3→conv1\n", + "# 考虑输入通道数是否需要调整来满足残差相接: \n", + "# F(x) = adjust(x) + f(x)\n", + "# 参考论文结构:https://arxiv.org/pdf/1512.03385.pdf\n", + "# '''\n", + " def __init__(self,in_features,out_features):\n", + " self.in_features = in_features\n", + " self.out_features = out_features\n", + " super(ResPart,self).__init__()\n", + " self.in_features = in_features\n", + " self.out_features = out_features\n", + " if in_features != out_features:\n", + " self.conv = nn.Sequential(\n", + " nn.Conv1d(in_features,out_features,kernel_size=1,stride=1,padding=0),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.1),\n", + " nn.Conv1d(out_features,out_features,kernel_size=3,stride=1,padding=1),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.1),\n", + " nn.Conv1d(out_features,out_features,kernel_size=1,stride=1,padding=0),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.1)\n", + " )\n", + " self.adjust_x = nn.Conv1d(in_features,out_features,kernel_size=1,stride=1,padding=0)\n", + " else:\n", + " self.conv = nn.Sequential(\n", + " nn.Conv1d(in_features,out_features,kernel_size=1,stride=1,padding=0),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.1),\n", + " nn.Conv1d(out_features,out_features,kernel_size=1,stride=1,padding=0),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.1),\n", + " nn.Conv1d(out_features,out_features,kernel_size=1,stride=1,padding=0),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.1)\n", + " )\n", + " def forward(self,x):\n", + " if self.in_features != self.out_features:\n", + " short_cut = self.adjust_x(x)\n", + " x = self.conv(x)\n", + " return short_cut.add(x)\n", + " else:\n", + " return self.conv(x).add(x) " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class sbd_mode(nn.Module):\n", + " def __init__(self,in_channels,goal_channel=1):\n", + " super(sbd_mode,self).__init__()\n", + " self.in_channels = in_channels\n", + " self.goal_channel = goal_channel\n", + " self.ResBody_layer1 = ResPart(in_channels,100)\n", + " self.InCeption_layer = Inception_layer(100,100)\n", + " self.ResBody_layer2 = ResPart(400,100)\n", + " self.attention_layer = attention1D_layer(100) \n", + " self.conv = nn.Conv1d(100,self.goal_channel,kernel_size=1)\n", + " self.fully = nn.Sequential(\n", + " nn.Linear(100,200),\n", + " nn.ReLU(),\n", + " nn.Linear(200,100))\n", + " \n", + " \n", + " #\n", + " def forward(self,x):\n", + " x = self.ResBody_layer1(x)\n", + " #print(x.shape)\n", + " p1,p2,p3,x = self.InCeption_layer(x)\n", + " x = torch.cat((p1,p2,p3,x),axis=1)\n", + " #print(x.shape)\n", + " x = self.ResBody_layer2(x)\n", + " x,b = self.attention_layer(x)\n", + " x = self.conv(x)\n", + " x = x.squeeze(1)\n", + " return self.fully(x)\n", + "model = sbd_mode(in_channels=3).to(device) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sbd_mode(\n", + " (ResBody_layer1): ResPart(\n", + " (conv): Sequential(\n", + " (0): Conv1d(3, 100, kernel_size=(1,), stride=(1,))\n", + " (1): ReLU()\n", + " (2): Dropout(p=0.1, inplace=False)\n", + " (3): Conv1d(100, 100, kernel_size=(3,), stride=(1,), padding=(1,))\n", + " (4): ReLU()\n", + " (5): Dropout(p=0.1, inplace=False)\n", + " (6): Conv1d(100, 100, kernel_size=(1,), stride=(1,))\n", + " (7): ReLU()\n", + " (8): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (adjust_x): Conv1d(3, 100, kernel_size=(1,), stride=(1,))\n", + " )\n", + " (InCeption_layer): Inception_layer(\n", + " (Conv1x3): Sequential(\n", + " (0): Conv1d(100, 10, kernel_size=(1,), stride=(1,))\n", + " (1): ReLU()\n", + " (2): Dropout(p=0.1, inplace=False)\n", + " (3): Conv1d(10, 100, kernel_size=(3,), stride=(1,), padding=(1,))\n", + " (4): ReLU()\n", + " )\n", + " (Conv1x5): Sequential(\n", + " (0): Conv1d(100, 10, kernel_size=(1,), stride=(1,))\n", + " (1): ReLU()\n", + " (2): Dropout(p=0.1, inplace=False)\n", + " (3): Conv1d(10, 100, kernel_size=(5,), stride=(1,), padding=(2,))\n", + " (4): ReLU()\n", + " )\n", + " (Conv3x5): Sequential(\n", + " (0): Conv1d(100, 10, kernel_size=(3,), stride=(1,), padding=(1,))\n", + " (1): ReLU()\n", + " (2): Dropout(p=0.1, inplace=False)\n", + " (3): Conv1d(10, 100, kernel_size=(5,), stride=(1,), padding=(2,))\n", + " (4): ReLU()\n", + " )\n", + " (short_cut): Sequential(\n", + " (0): Conv1d(100, 100, kernel_size=(1,), stride=(1,))\n", + " )\n", + " )\n", + " (ResBody_layer2): ResPart(\n", + " (conv): Sequential(\n", + " (0): Conv1d(400, 100, kernel_size=(1,), stride=(1,))\n", + " (1): ReLU()\n", + " (2): Dropout(p=0.1, inplace=False)\n", + " (3): Conv1d(100, 100, kernel_size=(3,), stride=(1,), padding=(1,))\n", + " (4): ReLU()\n", + " (5): Dropout(p=0.1, inplace=False)\n", + " (6): Conv1d(100, 100, kernel_size=(1,), stride=(1,))\n", + " (7): ReLU()\n", + " (8): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (adjust_x): Conv1d(400, 100, kernel_size=(1,), stride=(1,))\n", + " )\n", + " (attention_layer): attention1D_layer(\n", + " (GobalPooling): AdaptiveAvgPool1d(output_size=1)\n", + " (Linear1): Sequential(\n", + " (0): Linear(in_features=100, out_features=25, bias=True)\n", + " (1): ReLU6()\n", + " )\n", + " (Linear2): Sequential(\n", + " (0): Linear(in_features=25, out_features=100, bias=True)\n", + " (1): ReLU6()\n", + " )\n", + " )\n", + " (conv): Conv1d(100, 1, kernel_size=(1,), stride=(1,))\n", + " (fully): Sequential(\n", + " (0): Linear(in_features=100, out_features=200, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=200, out_features=100, bias=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## train \n", + "import hiddenlayer as h1\n", + "optimizer = optim.Adam(model.parameters())\n", + "loss_func = nn.MSELoss()\n", + "history1 = h1.History()\n", + "canvas1 = h1.Canvas()\n", + "for epoch in range(500):\n", + " all_loss = 0\n", + " for step,(b_x,b_y) in enumerate(data_gen):\n", + " b_x = b_x.to(device)\n", + " b_y = b_y.to(device)\n", + " pre = model(b_x)\n", + " loss = loss_func(pre,b_y)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + " all_loss += loss.item()\n", + " epo_loss = all_loss / 10\n", + " history1.log(epoch,train_loss=epo_loss)\n", + " \n", + " with canvas1:\n", + " canvas1.draw_plot(history1['train_loss']) " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}