training/networks/mesonet.py

'''
# author: Zhiyuan Yan
# email: zhiyuanyan@link.cuhk.edu.cn
# date: 2023-0706

The code is mainly modified from the below link:
https://github.com/HongguLiu/MesoNet-Pytorch
'''

import os
import argparse
import logging

import math
import torch
import torch.nn as nn
import torch.nn.functional as F

import torch.utils.model_zoo as model_zoo
from torch.nn import init
from typing import Union
from metrics.registry import BACKBONE

logger = logging.getLogger(__name__)

@BACKBONE.register_module(module_name="meso4")
class Meso4(nn.Module):
	def __init__(self, meso4_config):
		super(Meso4, self).__init__()
		self.num_classes = meso4_config["num_classes"]
		inc = meso4_config["inc"]
		self.conv1 = nn.Conv2d(inc, 8, 3, padding=1, bias=False)
		self.bn1 = nn.BatchNorm2d(8)
		self.relu = nn.ReLU(inplace=True)
		self.leakyrelu = nn.LeakyReLU(0.1)

		self.conv2 = nn.Conv2d(8, 8, 5, padding=2, bias=False)
		self.bn2 = nn.BatchNorm2d(16)
		self.conv3 = nn.Conv2d(8, 16, 5, padding=2, bias=False)
		self.conv4 = nn.Conv2d(16, 16, 5, padding=2, bias=False)
		self.maxpooling1 = nn.MaxPool2d(kernel_size=(2, 2))
		self.maxpooling2 = nn.MaxPool2d(kernel_size=(4, 4))
		#flatten: x = x.view(x.size(0), -1)
		self.dropout = nn.Dropout2d(0.5)
		self.fc1 = nn.Linear(16*8*8, 16)
		self.fc2 = nn.Linear(16, self.num_classes)

     
	def features(self, input):
		x = self.conv1(input) #(8, 256, 256)
		x = self.relu(x)
		x = self.bn1(x)
		x = self.maxpooling1(x) #(8, 128, 128)

		x = self.conv2(x) #(8, 128, 128)
		x = self.relu(x)
		x = self.bn1(x)
		x = self.maxpooling1(x) #(8, 64, 64)

		x = self.conv3(x) #(16, 64, 64)
		x = self.relu(x)
		x = self.bn2(x)
		x = self.maxpooling1(x) #(16, 32, 32)

		x = self.conv4(x) #(16, 32, 32)
		x = self.relu(x)
		x = self.bn2(x)
		x = self.maxpooling2(x) #(16, 8, 8)
		x = x.view(x.size(0), -1) #(Batch, 16*8*8)
        
		return x
	
	def classifier(self, feature):
		out = self.dropout(feature)
		out = self.fc1(out) #(Batch, 16)
		out = self.leakyrelu(out)
		out = self.dropout(out)
		out = self.fc2(out)
		return out
	
	def forward(self, input):
		x = self.features(input)
		out = self.classifier(x)
		return out, x

 
@BACKBONE.register_module(module_name="meso4Inception")
class MesoInception4(nn.Module):
	def __init__(self, mesoInception4_config):
		super(MesoInception4, self).__init__()
		self.num_classes = mesoInception4_config["num_classes"]
		inc = mesoInception4_config["inc"]
		#InceptionLayer1
		self.Incption1_conv1 = nn.Conv2d(3, 1, 1, padding=0, bias=False)
		self.Incption1_conv2_1 = nn.Conv2d(3, 4, 1, padding=0, bias=False)
		self.Incption1_conv2_2 = nn.Conv2d(4, 4, 3, padding=1, bias=False)
		self.Incption1_conv3_1 = nn.Conv2d(3, 4, 1, padding=0, bias=False)
		self.Incption1_conv3_2 = nn.Conv2d(4, 4, 3, padding=2, dilation=2, bias=False)
		self.Incption1_conv4_1 = nn.Conv2d(3, 2, 1, padding=0, bias=False)
		self.Incption1_conv4_2 = nn.Conv2d(2, 2, 3, padding=3, dilation=3, bias=False)
		self.Incption1_bn = nn.BatchNorm2d(11)


		#InceptionLayer2
		self.Incption2_conv1 = nn.Conv2d(11, 2, 1, padding=0, bias=False)
		self.Incption2_conv2_1 = nn.Conv2d(11, 4, 1, padding=0, bias=False)
		self.Incption2_conv2_2 = nn.Conv2d(4, 4, 3, padding=1, bias=False)
		self.Incption2_conv3_1 = nn.Conv2d(11, 4, 1, padding=0, bias=False)
		self.Incption2_conv3_2 = nn.Conv2d(4, 4, 3, padding=2, dilation=2, bias=False)
		self.Incption2_conv4_1 = nn.Conv2d(11, 2, 1, padding=0, bias=False)
		self.Incption2_conv4_2 = nn.Conv2d(2, 2, 3, padding=3, dilation=3, bias=False)
		self.Incption2_bn = nn.BatchNorm2d(12)

		#Normal Layer
		self.conv1 = nn.Conv2d(12, 16, 5, padding=2, bias=False)
		self.relu = nn.ReLU(inplace=True)
		self.leakyrelu = nn.LeakyReLU(0.1)
		self.bn1 = nn.BatchNorm2d(16)
		self.maxpooling1 = nn.MaxPool2d(kernel_size=(2, 2))

		self.conv2 = nn.Conv2d(16, 16, 5, padding=2, bias=False)
		self.maxpooling2 = nn.MaxPool2d(kernel_size=(4, 4))

		self.dropout = nn.Dropout2d(0.5)
		self.fc1 = nn.Linear(16*8*8, 16)
		self.fc2 = nn.Linear(16, self.num_classes)


	#InceptionLayer
	def InceptionLayer1(self, input):
		x1 = self.Incption1_conv1(input)
		x2 = self.Incption1_conv2_1(input)
		x2 = self.Incption1_conv2_2(x2)
		x3 = self.Incption1_conv3_1(input)
		x3 = self.Incption1_conv3_2(x3)
		x4 = self.Incption1_conv4_1(input)
		x4 = self.Incption1_conv4_2(x4)
		y = torch.cat((x1, x2, x3, x4), 1)
		y = self.Incption1_bn(y)
		y = self.maxpooling1(y)

		return y

	def InceptionLayer2(self, input):
		x1 = self.Incption2_conv1(input)
		x2 = self.Incption2_conv2_1(input)
		x2 = self.Incption2_conv2_2(x2)
		x3 = self.Incption2_conv3_1(input)
		x3 = self.Incption2_conv3_2(x3)
		x4 = self.Incption2_conv4_1(input)
		x4 = self.Incption2_conv4_2(x4)
		y = torch.cat((x1, x2, x3, x4), 1)
		y = self.Incption2_bn(y)
		y = self.maxpooling1(y)

		return y
	

	def features(self, input):
		x = self.InceptionLayer1(input) #(Batch, 11, 128, 128)
		x = self.InceptionLayer2(x) #(Batch, 12, 64, 64)

		x = self.conv1(x) #(Batch, 16, 64 ,64)
		x = self.relu(x)
		x = self.bn1(x)
		x = self.maxpooling1(x) #(Batch, 16, 32, 32)

		x = self.conv2(x) #(Batch, 16, 32, 32)
		x = self.relu(x)
		x = self.bn1(x)
		x = self.maxpooling2(x) #(Batch, 16, 8, 8)

		x = x.view(x.size(0), -1) #(Batch, 16*8*8)
        
		return x
	
	def classifier(self, feature):
		
		out = self.dropout(feature)
		out = self.fc1(out) #(Batch, 16)
		out = self.leakyrelu(out)
		out = self.dropout(out)
		out = self.fc2(out)
		return out

	def forward(self, input):
		x = self.features(input)
		out = self.classifier(x)
		return out, x