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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
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