| |
| import numpy as np |
| import torch |
| import torch.nn as nn |
|
|
|
|
| class BasicClassificationLoss(nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.classification_loss = nn.CrossEntropyLoss() |
|
|
| def forward(self, pred_labels, gt_labels): |
| return self.classification_loss(pred_labels, gt_labels) |
|
|
|
|
| def save_model(trained_model, optimiser_used): |
| torch.save(trained_model, 'trainedClassifier.pth') |
| print(",") |
| torch.save(trained_model.state_dict(), 'trainedClassifier_weights.pth') |
| torch.save(optimiser_used, 'optimiserUsed.pth') |
|
|
|
|
| def load_model_for_eval(file_path, model_type): |
| model_template = model_type(416) |
| model_template.load_state_dict(torch.load(file_path, weights_only=True)) |
| model_template.eval() |
| return model_template |
|
|
|
|
| def softmax(unprocessed_logits): |
| logits = np.array(unprocessed_logits) |
| exponentials = np.exp(logits) |
| softmax_arr = exponentials / sum(exponentials) |
| return softmax_arr |
|
|
