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import torch
import torchvision
from torchvision.models import efficientnet_b2, EfficientNet_B2_Weights
from torchvision.models._api import WeightsEnum
from torch.hub import load_state_dict_from_url
from torch import nn
def get_state_dict(self, *args, **kwargs):
kwargs.pop("check_hash")
return load_state_dict_from_url(self.url, *args, **kwargs)
def create_effnetb2_model(num_classes:int=3,
seed:int=42):
"""Creates an EfficientNetB2 feature extractor model and transforms.
Args:
num_classes (int, optional): number of classes in the classifier head.
Defaults to 3.
seed (int, optional): random seed value. Defaults to 42.
Returns:
model (torch.nn.Module): EffNetB2 feature extractor model.
transforms (torchvision.transforms): EffNetB2 image transforms.
"""
# Create EffNetB2 pretrained weights, transforms and model
WeightsEnum.get_state_dict = get_state_dict
efficientnet_b2(weights=EfficientNet_B2_Weights.DEFAULT)
weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
transforms = weights.transforms()
model = efficientnet_b2(weights=weights)
# Freeze all layers in base model
for param in model.parameters():
param.requires_grad = False
# Change classifier head with random seed 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),
)
return model, transforms
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