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import torch
import torchvision
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 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
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