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import torch
import torchvision
from torch import nn
def create_model(num_classes = 6, seed = 1):
"""Create an instance of the effnet_b2 model, freezes all layers and changes the classifier head.
Returns: The model and its data transform
"""
#get pretrained model and its transform
weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
model = torchvision.models.efficientnet_b2(weights = weights)
transform = weights.transforms()
#freeze all layers
for param in model.parameters():
param.requires_grad = False
#create a new classifier head with 6 output classes
classifier = nn.Sequential(nn.Dropout(p = 0.2, inplace = True),
nn.Linear(in_features = 1408, out_features = num_classes))
#replace old classifier head with newly created one
model.classifier = classifier
return model, transform
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