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