model.py

import torchvision
import torch
from torch import nn
def create_effnetb2_model(num_classes:int=3,
                         seed:int=42):
    # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
    
    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
    transforms = weights.transforms()
    model=torchvision.models.efficientnet_b2(weights = weights)
    
    # 4. Freeze all layers in the base model
    for param in model.parameters():
        param.requires_grad=False
        
    # 5. Change classifier head with random seed for reproducibility
    torch.manual_seed(seed)
    model.classifier = nn.Sequential(nn.Dropout(p=0.3, inplace=True),
                                    nn.Linear(in_features=1408, out_features=num_classes))
    
    return model, transforms