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cecd1f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | import argparse
from pathlib import Path
import torch
from datasets import load_dataset
from torch import nn
from torch.utils.data import DataLoader
from tqdm import tqdm
from calority_scratch_model import CalorityFoodCNN, image_to_tensor, save_checkpoint
def parse_args():
parser = argparse.ArgumentParser(description="Train Calority's food model from scratch on a Hugging Face dataset.")
parser.add_argument("--dataset", default="food101", help="Hugging Face dataset name, for example food101")
parser.add_argument("--image-column", default="image")
parser.add_argument("--label-column", default="label")
parser.add_argument("--train-split", default="train")
parser.add_argument("--eval-split", default="validation")
parser.add_argument("--output-dir", default="./calority-scratch-model")
parser.add_argument("--epochs", type=int, default=12)
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--learning-rate", type=float, default=3e-4)
parser.add_argument("--num-workers", type=int, default=0)
parser.add_argument("--limit-train", type=int, default=0, help="Optional small limit for quick smoke tests")
parser.add_argument("--limit-eval", type=int, default=0, help="Optional small limit for quick smoke tests")
return parser.parse_args()
def get_labels(dataset, split: str, label_column: str) -> list[str]:
feature = dataset[split].features[label_column]
if hasattr(feature, "names") and feature.names:
return list(feature.names)
values = sorted(set(dataset[split][label_column]))
return [str(value) for value in values]
def make_collate_fn(image_column: str, label_column: str):
def collate(batch):
images = torch.stack([image_to_tensor(item[image_column]) for item in batch])
labels = torch.tensor([int(item[label_column]) for item in batch], dtype=torch.long)
return images, labels
return collate
def evaluate(model, loader, loss_fn, device):
model.eval()
total_loss = 0.0
total_correct = 0
total_seen = 0
with torch.no_grad():
for images, labels in loader:
images = images.to(device)
labels = labels.to(device)
logits = model(images)
loss = loss_fn(logits, labels)
total_loss += loss.item() * labels.size(0)
total_correct += (logits.argmax(dim=1) == labels).sum().item()
total_seen += labels.size(0)
return total_loss / max(total_seen, 1), total_correct / max(total_seen, 1)
def main():
args = parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataset = load_dataset(args.dataset)
if args.limit_train:
dataset[args.train_split] = dataset[args.train_split].shuffle(seed=42).select(range(args.limit_train))
if args.limit_eval:
dataset[args.eval_split] = dataset[args.eval_split].shuffle(seed=42).select(range(args.limit_eval))
labels = get_labels(dataset, args.train_split, args.label_column)
model = CalorityFoodCNN(num_labels=len(labels)).to(device)
collate_fn = make_collate_fn(args.image_column, args.label_column)
train_loader = DataLoader(
dataset[args.train_split],
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn,
)
eval_loader = DataLoader(
dataset[args.eval_split],
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn,
)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
best_acc = 0.0
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for epoch in range(1, args.epochs + 1):
model.train()
running_loss = 0.0
total_seen = 0
total_correct = 0
progress = tqdm(train_loader, desc=f"epoch {epoch}/{args.epochs}", leave=False)
for images, labels_batch in progress:
images = images.to(device)
labels_batch = labels_batch.to(device)
optimizer.zero_grad(set_to_none=True)
logits = model(images)
loss = loss_fn(logits, labels_batch)
loss.backward()
optimizer.step()
running_loss += loss.item() * labels_batch.size(0)
total_correct += (logits.argmax(dim=1) == labels_batch).sum().item()
total_seen += labels_batch.size(0)
progress.set_postfix(
loss=round(running_loss / max(total_seen, 1), 4),
acc=round(total_correct / max(total_seen, 1), 4),
)
scheduler.step()
eval_loss, eval_acc = evaluate(model, eval_loader, loss_fn, device)
print(f"epoch={epoch} eval_loss={eval_loss:.4f} eval_acc={eval_acc:.4f}")
if eval_acc >= best_acc:
best_acc = eval_acc
save_checkpoint(model, labels, output_dir)
print(f"saved best model to {output_dir} with eval_acc={best_acc:.4f}")
print(f"done. best_eval_acc={best_acc:.4f}")
if __name__ == "__main__":
main()
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