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import torch, torchvision
from torch import nn
def create_effnetb2_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.
"""
weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
transforms = weights.transforms()
model = torchvision.models.efficientnet_b2(weights=weights)
for param in model.parameters():
param.requires_grad = False
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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