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import torch
import torchvision
from torch import nn
def create_vit_model(num_classes:int=3,
seed:int=42):
"""Creates a ViT-B/16 feature extractor model and transforms.
Args:
num_classes (int, optional): number of target classes. Defaults to 3.
seed (int, optional): random seed value for output layer. Defaults to 42.
Returns:
model (torch.nn.Module): ViT-B/16 feature extractor model.
transforms (torchvision.transforms): ViT-B/16 image transforms.
"""
# Create ViT_B_16 pretrained weights, transforms and model
weights = torchvision.models.ViT_B_16_Weights.DEFAULT
transforms = weights.transforms()
model = torchvision.models.vit_b_16(weights=weights)
# Freeze all layers in model
for param in model.parameters():
param.requires_grad = False
# Change classifier head to suit our needs (this will be trainable)
torch.manual_seed(seed)
model.heads = nn.Sequential(nn.Linear(in_features=768, # keep this the same as original model
out_features=num_classes)) # update to reflect target number of classes
return model, transforms
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