| import torch |
| import torchvision |
|
|
| def create_effnet_b2_model(num_classes:int = 3): |
| """Creates an EfficientNetB2 feature extractor model and transforms |
| |
| Args: |
| num_classes (int, optional): number of classes in the classifier head. Defaults to 3. |
| |
| Returns: |
| model (torch.nn.Module): EfficientNetB2 feature extractor model |
| transforms (torchvision.transforms): EfficientNetB2 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 |
|
|
| |
| model.classifier = torch.nn.Sequential( |
| torch.nn.Dropout(p = 0.3, inplace= True), |
| torch.nn.Linear(in_features=1408, out_features=num_classes) |
| ) |
|
|
| return model, transforms |
