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import torch, torchvision | |
from torch import nn | |
def create_effnet_b2_model(num_classes:int=3, # Default to suit our dataset | |
seed:int=42): | |
# Import pretraind model | |
weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT | |
transforms = weights.transforms() | |
model = torchvision.models.efficientnet_b2(weights=weights) | |
# Freeze the base layers | |
for param in model.parameters(): | |
param.requires_grad = False | |
# Change classifier head with random seed for reproducability | |
torch.manual_seed(seed) | |
model.classifier = nn.Sequential( | |
nn.Dropout(p=0.3, inplace = True), | |
nn.Linear(in_features = 1408, out_features = num_classes, bias = True) | |
) | |
return model, transforms | |