add imbalanced classes video code and kaggle cat vs dog

ML/Pytorch/others/default_setups/CV - Image Classification/utils.py

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