mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
synced 2026-02-21 11:18:01 +00:00
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
import torch
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import torch.nn.functional as F
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import os
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from PIL import Image
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import pandas as pd
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import numpy as np
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from tqdm import tqdm
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def check_accuracy(loader, model, device="cuda"):
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num_correct = 0
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num_samples = 0
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model.eval()
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with torch.no_grad():
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for x, y in loader:
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x = x.to(device=device)
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y = y.to(device=device)
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scores = torch.sigmoid(model(x))
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predictions = (scores>0.5).float()
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num_correct += (predictions == y).sum()
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num_samples += predictions.shape[0]
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print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct) / float(num_samples) * 100:.2f}')
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model.train()
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def save_checkpoint(state, filename="my_checkpoint.pth.tar"):
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print("=> Saving checkpoint")
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torch.save(state, filename)
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def load_checkpoint(checkpoint, model, optimizer):
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print("=> Loading checkpoint")
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model.load_state_dict(checkpoint['state_dict'])
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optimizer.load_state_dict(checkpoint['optimizer'])
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def make_prediction(model, transform, rootdir, device):
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files = os.listdir(rootdir)
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preds = []
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model.eval()
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files = sorted(files, key=lambda x: float(x.split(".")[0]))
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for file in tqdm(files):
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img = Image.open(os.path.join(rootdir, file))
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img = transform(img).unsqueeze(0).to(device)
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with torch.no_grad():
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pred = torch.sigmoid(model(img))
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preds.append(pred.item())
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df = pd.DataFrame({'id': np.arange(1, len(preds)+1), 'label': np.array(preds)})
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df.to_csv('submission.csv', index=False)
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model.train()
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print("Done with predictions") |