import torch import torchvision from torch import nn from torchvision.models._api import WeightsEnum from torch.hub import load_state_dict_from_url def create_effnetb2_model(num_classes:int=3, seed:int=42): # https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2 def get_state_dict(self, *args, **kwargs): kwargs.pop("check_hash") return load_state_dict_from_url(self.url, *args, **kwargs) WeightsEnum.get_state_dict = get_state_dict # 1. Setup pretrained EffNetB2 weights effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT # DEFAULT = BEST # 2. Get EffNetB2 transforms effnetb2_transforms = effnetb2_weights.transforms() # 3. Setup pretrained model instance effnetb2_model = torchvision.models.efficientnet_b2(weights=effnetb2_weights) # 4. Freeze the base layers in the model for param in effnetb2_model.features.parameters(): param.requires_grad = False # 5. Modify the classifier torch.manual_seed(seed) effnetb2_model.classifier = nn.Sequential( nn.Dropout(p=0.3, inplace=True), nn.Linear(in_features=1408,out_features=num_classes,bias=True) ) return effnetb2_model, effnetb2_transforms