| import torch | |
| import torchvision | |
| from torch import nn | |
| def create_mobilenetv2_model(num_classes:int=5, | |
| seed:int=42): | |
| model=torch.load('model.pth') | |
| model.to(device) | |
| # 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 # Assuming 'transforms' is defined somewhere | |