ABLkit/abl/models/lenet5.py

# 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