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| import torch | |
| import torchvision | |
| from torch import nn | |
| def create_effnetb2_model(num_classes : int = 3, # default output classes = 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 base layers in the model | |
| for param in model.parameters(): | |
| param.requires_grad = False | |
| # 5. Change classifier head with random seet 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, bias = True)) | |
| return model, transforms | |