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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | 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_nutrition_model import (
TARGET_COLUMNS,
CalorityNutritionCNN,
save_nutrition_checkpoint,
)
from calority_scratch_model import image_to_tensor
def parse_args():
parser = argparse.ArgumentParser(
description="Train Calority's calorie and macro predictor from scratch on mmathys/food-nutrients."
)
parser.add_argument("--dataset", default="mmathys/food-nutrients")
parser.add_argument("--source-split", default="test", help="This dataset currently ships with only a test split.")
parser.add_argument("--image-column", default="image")
parser.add_argument("--output-dir", default="./calority-nutrition-model")
parser.add_argument("--epochs", type=int, default=40)
parser.add_argument("--batch-size", type=int, default=16)
parser.add_argument("--learning-rate", type=float, default=3e-4)
parser.add_argument("--validation-size", type=float, default=0.15)
parser.add_argument("--num-workers", type=int, default=0)
parser.add_argument("--limit", type=int, default=0, help="Optional small limit for quick smoke tests")
return parser.parse_args()
def make_targets(dataset_split) -> torch.Tensor:
rows = [[float(item[column]) for column in TARGET_COLUMNS] for item in dataset_split]
return torch.tensor(rows, dtype=torch.float32)
def make_collate_fn(image_column: str, target_mean: torch.Tensor, target_std: torch.Tensor):
def collate(batch):
images = torch.stack([image_to_tensor(item[image_column]) for item in batch])
targets = torch.tensor(
[[float(item[column]) for column in TARGET_COLUMNS] for item in batch],
dtype=torch.float32,
)
normalized_targets = (targets - target_mean) / target_std
return images, normalized_targets, targets
return collate
def evaluate(model, loader, loss_fn, target_mean, target_std, device):
model.eval()
total_loss = 0.0
total_mae = torch.zeros(len(TARGET_COLUMNS))
total_seen = 0
with torch.no_grad():
for images, normalized_targets, raw_targets in loader:
images = images.to(device)
normalized_targets = normalized_targets.to(device)
predictions = model(images)
loss = loss_fn(predictions, normalized_targets)
raw_predictions = torch.clamp(
(predictions.cpu() * target_std) + target_mean,
min=0,
)
total_loss += loss.item() * images.size(0)
total_mae += torch.abs(raw_predictions - raw_targets).sum(dim=0)
total_seen += images.size(0)
mae = total_mae / max(total_seen, 1)
return total_loss / max(total_seen, 1), mae
def main():
args = parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataset = load_dataset(args.dataset)
source = dataset[args.source_split].shuffle(seed=42)
if args.limit:
source = source.select(range(min(args.limit, len(source))))
split = source.train_test_split(test_size=args.validation_size, seed=42)
train_ds = split["train"]
eval_ds = split["test"]
train_targets = make_targets(train_ds)
target_mean = train_targets.mean(dim=0)
target_std = torch.clamp(train_targets.std(dim=0), min=1.0)
model = CalorityNutritionCNN(output_size=len(TARGET_COLUMNS)).to(device)
collate_fn = make_collate_fn(args.image_column, target_mean, target_std)
train_loader = DataLoader(
train_ds,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn,
)
eval_loader = DataLoader(
eval_ds,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn,
)
loss_fn = nn.SmoothL1Loss()
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)
output_dir = Path(args.output_dir)
best_calorie_mae = float("inf")
for epoch in range(1, args.epochs + 1):
model.train()
running_loss = 0.0
total_seen = 0
progress = tqdm(train_loader, desc=f"epoch {epoch}/{args.epochs}", leave=False)
for images, normalized_targets, _ in progress:
images = images.to(device)
normalized_targets = normalized_targets.to(device)
optimizer.zero_grad(set_to_none=True)
predictions = model(images)
loss = loss_fn(predictions, normalized_targets)
loss.backward()
optimizer.step()
running_loss += loss.item() * images.size(0)
total_seen += images.size(0)
progress.set_postfix(loss=round(running_loss / max(total_seen, 1), 4))
scheduler.step()
eval_loss, mae = evaluate(model, eval_loader, loss_fn, target_mean, target_std, device)
metric_line = ", ".join(
f"{column}_mae={mae[index]:.2f}" for index, column in enumerate(TARGET_COLUMNS)
)
print(f"epoch={epoch} eval_loss={eval_loss:.4f} {metric_line}")
if mae[0].item() <= best_calorie_mae:
best_calorie_mae = mae[0].item()
save_nutrition_checkpoint(model, target_mean, target_std, output_dir)
print(f"saved best nutrition model to {output_dir} with calorie_mae={best_calorie_mae:.2f}")
print(f"done. best_calorie_mae={best_calorie_mae:.2f}")
if __name__ == "__main__":
main()
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