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import torch | |
import torchvision | |
from torch import nn | |
from torchvision.models._api import WeightsEnum | |
from torch.hub import load_state_dict_from_url | |
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 | |
def create_effnetb2(num_classes: int=3): | |
effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT | |
effnetb2_transforms = effnetb2_weights.transforms() | |
effnetb2 = torchvision.models.efficientnet_b2(weights="DEFAULT") | |
for param in effnetb2.parameters(): | |
param.requires_grad = False | |
torch.manual_seed(42) | |
torch.cuda.manual_seed(42) | |
effnetb2.classifier = nn.Sequential( | |
nn.Dropout(p=0.3, inplace=True), | |
nn.Linear(in_features=1408, out_features=num_classes) | |
) | |
return effnetb2, effnetb2_transforms | |