model.py

import torch
import torch.nn as nn

N_EMOTIONS = 8
N_CELEBRITIES = 17
class CustomModel(nn.Module) :
    def __init__(self,mode = 'emotion') :
        super().__init__()
        self.mode = mode
        
        self.backbone = nn.Sequential(
            #3x224x224
            nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),
            # out: 64 x 222 x 222

            nn.Conv2d(64, 32, kernel_size=3, stride=1, bias=False),
            nn.BatchNorm2d(32),
            nn.LeakyReLU(0.2, inplace=True),
            nn.MaxPool2d(kernel_size=2),
            nn.Dropout(0.2),
            # out: 32 x 110 x 110
            
            nn.Conv2d(32, 32, kernel_size=3, stride=1, bias=False),
            nn.BatchNorm2d(32),
            nn.LeakyReLU(0.2, inplace=True),
            nn.MaxPool2d(kernel_size=2),
            nn.Dropout(0.3),
            # out: 32 x 54 x 54
            
            
            
            
            nn.Flatten(),
        )
        self.in_features = 32*54*54
        self.neck = nn.Sequential(
            nn.Linear(self.in_features,128),
            nn.ReLU(),
            nn.Linear(128,64),
            nn.ReLU()
        )
        self.emotion_classifier = nn.Linear(64,N_EMOTIONS)
        self.celebrity_classifier = nn.Linear(64,N_CELEBRITIES)
    
    def forward(self,image) :
        features = self.backbone(image)
        features = self.neck(features)
        if self.mode=='emotion' : 
            emotion_logits = self.emotion_classifier(features)
            return emotion_logits
        elif self.mode=='celebrity' :
            celebrity_logits = self.celebrity_classifier(features)
            return celebrity_logits
        else : 
            emotion_logits = self.emotion_classifier(features)
            celebrity_logits = self.celebrity_classifier(features)
            return emotion_logits,celebrity_logits

        
        
import torchvision.models as models   
class ResNet50Model(nn.Module) : 
    def __init__(self,mode = 'emotion') :
        super().__init__()
        self.mode = mode
        
        self.backbone = getattr(models, 'resnet50')(False)
        self.in_features = 1000
        self.neck = nn.Sequential(
            nn.Linear(self.in_features,128),
            nn.ReLU(),
            nn.Linear(128,64),
            nn.ReLU()
        )
        self.emotion_classifier = nn.Linear(64,N_EMOTIONS)
        self.celebrity_classifier = nn.Linear(64,N_CELEBRITIES)
    
    def forward(self,image) :
        features = self.backbone(image)
        features = self.neck(features)
        if self.mode=='emotion' : 
            emotion_logits = self.emotion_classifier(features)
            return emotion_logits
        elif self.mode=='celebrity' :
            celebrity_logits = self.celebrity_classifier(features)
            return celebrity_logits
        else : 
            emotion_logits = self.emotion_classifier(features)
            celebrity_logits = self.celebrity_classifier(features)
            return emotion_logits,celebrity_logits