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import numpy as np |
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import os |
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import random |
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import torch |
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def seed_everything(seed=42): |
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random.seed(seed) |
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os.environ['PYTHONHASHSEED'] = str(seed) |
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np.random.seed(seed) |
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if torch is not None: |
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torch.manual_seed(seed) |
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torch.cuda.manual_seed(seed) |
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torch.cuda.manual_seed_all(seed) |
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torch.backends.cudnn.deterministic = True |
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torch.backends.cudnn.benchmark = False |
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def clip_path_map(path): |
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if path=="pathclip-base": |
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return "/bask/homes/a/asiw9691/PathVLM/PathClip/pathclip-base.pt" |
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if path=="conch": |
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return "/bask/homes/a/asiw9691/PathVLM/CONCH/pytorch_model.bin" |
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if path=="uni": |
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return "/bask/homes/a/asiw9691/PathVLM/UNI/pytorch_model.bin" |
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def my_compute_metrics(p): |
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predictions, labels = p |
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predictions = np.argmax(predictions, axis=-1) |
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predictions = predictions[:, 2:] |
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mask = labels != -100 |
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correct = (labels == predictions) & mask |
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accuracy = round(np.mean(correct) *100, 2) |
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return { |
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'accuracy': accuracy, |
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} |
