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| import torch | |
| import torchvision | |
| from torch import nn | |
| from typing import Tuple | |
| def create_effnetb2_model( | |
| num_classes: int = 3, | |
| seed: int = 42) -> Tuple[torch.nn.Module, torchvision.transforms.Compose]: | |
| """Creates an EfficientNetB2 feature extractor model and transforms | |
| Parameters | |
| ---------- | |
| num_classes : int, optional | |
| Number of classes in the classifier head, by default 3 | |
| seed : int, optional | |
| random seed value, by default 42 | |
| Returns | |
| ------- | |
| Tuple[torch.nn.Module, torchvision.transforms.Compose] | |
| Tuple[EffnetB2 feature extractor model, EffNetb2 image transforms] | |
| """ | |
| # Create EffNetB2 pretrained weights, transforms and model | |
| weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT | |
| transforms = weights.transforms() | |
| model = torchvision.models.efficientnet_b2(weights=weights) | |
| # freeze parameters | |
| for param in model.parameters(): | |
| param.requires_grad = False | |
| # change classifier head | |
| 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 | |