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import torch
import torchvision
from torch import nn
def create_effnet_b2_model(num_classes: int = 3,
seed: int = 42):
"""
Creates an EfficientNetB2 feature extractor model and transforms.
Args:
num_classes (int, optional): number of classes in the classifier head.
Defaults to 3.
seed (int, optional): random seed value. Defaults to 42.
Returns:
model (torch.nn.Module): EffNetB2 feature extractor model.
transforms (torchvision.transforms): EffNetB2 image transforms.
"""
# 1. Setup pretrained weights
weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
# 2.Get transforms
transforms = weights.transforms()
# 3. Cretate the pretrained model
model = torchvision.models.efficientnet_b2(weights=weights)
# 4. Freeze the base layer
for param in model.parameters():
param.requires_grad = False
# 5. Update the classifier head to suit our data with 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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