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import torch
import torchvision
from torch import nn
def create_effnet_b2_model(num_classes : int = 3,
seed : int = 42):
effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
effnetb2_transforms = effnetb2_weights.transforms()
effnetb2 = torchvision.models.efficientnet_b2(weights=effnetb2_weights)
for p in effnetb2.parameters():
p.requires_grad = False
torch.manual_seed(seed)
#torch.cuda.manual_seed(seed)
effnetb2.classifier = nn.Sequential(
torch.nn.Dropout(p=0.3,
inplace=True),
torch.nn.Linear(in_features=1408,
out_features=num_classes,
bias=True)
)
return effnetb2, effnetb2_transforms
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