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model.py
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model.py
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import torch.nn as nn
import torch.nn.functional as F
import torch
from torchvision import models
import numpy as np
import timm
import h5py
from efficientnet_pytorch import EfficientNet
class custom_effnet(nn.Module):
def __init__(self):
super(custom_effnet,self).__init__()
self.model = EfficientNet.from_pretrained('efficientnet-b3')
self.model._conv_stem.in_channels = 1
weight = self.model._conv_stem.weight.mean(1,keepdim=True)
self.model._conv_stem.weight = torch.nn.Parameter(weight)
print(self.model)
num_ftrs = self.model.classifier.in_features
self.model.classifier = nn.Linear(num_ftrs,24)
def forward(self,x):
return self.model(x)
class custom_resnet(nn.Module):
def __init__(self):
super(custom_resnet,self).__init__()
self.model = models.resnet18(pretrained=True)
num_ftrs = self.model.fc.in_features
self.model.fc = nn.Linear(num_ftrs,24)
def forward(self,x):
return self.model(x)
class ConvMod(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,act=True,strides=1):
super(ConvMod,self).__init__()
self.conv = nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=kernel_size,stride=strides)
self.bn = nn.BatchNorm2d(out_channels)
self.act = act
def forward(self,x):
if self.act is True:
x = F.relu(self.bn(self.conv(x)))
else:
x = self.bn(self.conv(x))
return x
class CNN_14(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size):
super(CNN_14,self).__init__()
self.conv1 = nn.Sequential(
ConvMod(in_channels=in_channels,out_channels=out_channels,kernel_size=kernel_size),
ConvMod(64,64,kernel_size=kernel_size)
)
self.conv2 = nn.Sequential(
ConvMod(64,128,kernel_size=kernel_size),
ConvMod(128,128,kernel_size=kernel_size)
)
self.conv3 = nn.Sequential(
ConvMod(128,256,kernel_size=kernel_size),
ConvMod(256,256,kernel_size=kernel_size)
)
self.conv4 = nn.Sequential(
ConvMod(256,512,kernel_size=kernel_size),
ConvMod(512,512,kernel_size=kernel_size)
)
self.pool = nn.AvgPool2d(kernel_size=(2,2))
self.linear = nn.Linear(512,512)
self.classifier = nn.Linear(512,24)
def forward(self,x):
x = self.conv1(x)
x = self.pool(x)
x = self.conv2(x)
x = self.pool(x)
x = self.conv3(x)
x = self.pool(x)
x = self.conv4(x)
x = F.adaptive_avg_pool2d(x,(1,1))
x = x.view(-1,512)
x = self.classifier(x)
return x