Gao Enhao
3 years ago
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abl/models/lenet5.py
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| # coding: utf-8 | |||
| #================================================================# | |||
| # Copyright (C) 2021 Freecss All rights reserved. | |||
| # | |||
| # File Name ：lenet5.py | |||
| # Author ：freecss | |||
| # Email ：karlfreecss@gmail.com | |||
| # Created Date ：2021/03/03 | |||
| # Description ： | |||
| # | |||
| #================================================================# | |||
| import sys | |||
| sys.path.append("..") | |||
| import torchvision | |||
| import torch | |||
| from torch import nn | |||
| from torch.nn import functional as F | |||
| from torch.autograd import Variable | |||
| import torchvision.transforms as transforms | |||
| import numpy as np | |||
| from models.basic_model import BasicModel | |||
| import utils.plog as plog | |||
| class LeNet5(nn.Module): | |||
| def __init__(self, num_classes=10, image_size=(28, 28)): | |||
| super().__init__() | |||
| self.conv1 = nn.Conv2d(1, 6, 3, padding=1) | |||
| self.conv2 = nn.Conv2d(6, 16, 3) | |||
| self.conv3 = nn.Conv2d(16, 16, 3) | |||
| feature_map_size = ((np.array(image_size) // 2 - 2) // 2 - 2) | |||
| num_features = 16 * feature_map_size[0] * feature_map_size[1] | |||
| self.fc1 = nn.Linear(num_features, 120) | |||
| self.fc2 = nn.Linear(120, 84) | |||
| self.fc3 = nn.Linear(84, num_classes) | |||
| def forward(self, x): | |||
| '''前向传播函数''' | |||
| x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) | |||
| x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2)) | |||
| x = F.relu(self.conv3(x)) | |||
| x = x.view(-1, self.num_flat_features(x)) | |||
| #print(x.size()) | |||
| x = F.relu(self.fc1(x)) | |||
| x = F.relu(self.fc2(x)) | |||
| x = self.fc3(x) | |||
| return x | |||
| def num_flat_features(self, x): | |||
| size = x.size()[1:] | |||
| num_features = 1 | |||
| for s in size: | |||
| num_features *= s | |||
| return num_features | |||
| class SymbolNet(nn.Module): | |||
| def __init__(self, num_classes=14): | |||
| super(SymbolNet, self).__init__() | |||
| self.conv1 = nn.Conv2d(1, 32, 3, stride = 1, padding = 1) | |||
| self.conv2 = nn.Conv2d(32, 64, 3, stride = 1, padding = 1) | |||
| self.dropout1 = nn.Dropout2d(0.25) | |||
| self.dropout2 = nn.Dropout2d(0.5) | |||
| self.fc1 = nn.Linear(30976, 128) | |||
| self.fc2 = nn.Linear(128, num_classes) | |||
| def forward(self, x): | |||
| x = self.conv1(x) | |||
| x = F.relu(x) | |||
| x = self.conv2(x) | |||
| x = F.max_pool2d(x, 2) | |||
| x = self.dropout1(x) | |||
| x = torch.flatten(x, 1) | |||
| x = self.fc1(x) | |||
| x = F.relu(x) | |||
| x = self.dropout2(x) | |||
| x = self.fc2(x) | |||
| return x | |||