REVA-QCAV/train.py

import argparse
import logging
from pathlib import Path

import torch
import torch.nn as nn
import torch.nn.functional as F
import wandb
from torch import optim
from torch.utils.data import DataLoader, random_split
from tqdm import tqdm

from evaluate import evaluate
from unet import UNet
from utils.data_loading import BasicDataset, CarvanaDataset
from utils.dice_score import dice_loss

dir_img = Path("./data/imgs/")
dir_mask = Path("./data/masks/")
dir_checkpoint = Path("./checkpoints/")


def train_net(
    net,
    device,
    epochs: int = 5,
    batch_size: int = 1,
    learning_rate: float = 1e-5,
    val_percent: float = 0.1,
    save_checkpoint: bool = True,
    img_scale: float = 0.5,
    amp: bool = False,
):
    # 1. Create dataset
    try:
        dataset = CarvanaDataset(dir_img, dir_mask, img_scale)
    except (AssertionError, RuntimeError):
        dataset = BasicDataset(dir_img, dir_mask, img_scale)

    # 2. Split into train / validation partitions
    n_val = int(len(dataset) * val_percent)
    n_train = len(dataset) - n_val
    train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0))

    # 3. Create data loaders
    loader_args = dict(batch_size=batch_size, num_workers=4, pin_memory=True)
    train_loader = DataLoader(train_set, shuffle=True, **loader_args)
    val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)

    # (Initialize logging)
    experiment = wandb.init(project="U-Net", resume="allow", anonymous="must")
    experiment.config.update(
        dict(
            epochs=epochs,
            batch_size=batch_size,
            learning_rate=learning_rate,
            val_percent=val_percent,
            save_checkpoint=save_checkpoint,
            img_scale=img_scale,
            amp=amp,
        )
    )

    logging.info(
        f"""Starting training:
        Epochs:          {epochs}
        Batch size:      {batch_size}
        Learning rate:   {learning_rate}
        Training size:   {n_train}
        Validation size: {n_val}
        Checkpoints:     {save_checkpoint}
        Device:          {device.type}
        Images scaling:  {img_scale}
        Mixed Precision: {amp}
    """
    )

    # 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for AMP
    optimizer = optim.RMSprop(net.parameters(), lr=learning_rate, weight_decay=1e-8, momentum=0.9)
    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, "max", patience=2)  # goal: maximize Dice score
    grad_scaler = torch.cuda.amp.GradScaler(enabled=amp)
    criterion = nn.CrossEntropyLoss()
    global_step = 0

    # 5. Begin training
    for epoch in range(1, epochs + 1):
        net.train()
        epoch_loss = 0
        with tqdm(total=n_train, desc=f"Epoch {epoch}/{epochs}", unit="img") as pbar:
            for batch in train_loader:
                images = batch["image"]
                true_masks = batch["mask"]

                assert images.shape[1] == net.n_channels, (
                    f"Network has been defined with {net.n_channels} input channels, "
                    f"but loaded images have {images.shape[1]} channels. Please check that "
                    "the images are loaded correctly."
                )

                images = images.to(device=device, dtype=torch.float32)
                true_masks = true_masks.to(device=device, dtype=torch.long)

                with torch.cuda.amp.autocast(enabled=amp):
                    masks_pred = net(images)
                    loss = criterion(masks_pred, true_masks) + dice_loss(
                        F.softmax(masks_pred, dim=1).float(),
                        F.one_hot(true_masks, net.n_classes).permute(0, 3, 1, 2).float(),
                        multiclass=True,
                    )

                optimizer.zero_grad(set_to_none=True)
                grad_scaler.scale(loss).backward()
                grad_scaler.step(optimizer)
                grad_scaler.update()

                pbar.update(images.shape[0])
                global_step += 1
                epoch_loss += loss.item()
                experiment.log({"train loss": loss.item(), "step": global_step, "epoch": epoch})
                pbar.set_postfix(**{"loss (batch)": loss.item()})

                # Evaluation round
                division_step = n_train // (10 * batch_size)
                if division_step > 0:
                    if global_step % division_step == 0:
                        histograms = {}
                        for tag, value in net.named_parameters():
                            tag = tag.replace("/", ".")
                            histograms["Weights/" + tag] = wandb.Histogram(value.data.cpu())
                            histograms["Gradients/" + tag] = wandb.Histogram(value.grad.data.cpu())

                        val_score = evaluate(net, val_loader, device)
                        scheduler.step(val_score)

                        logging.info("Validation Dice score: {}".format(val_score))
                        experiment.log(
                            {
                                "learning rate": optimizer.param_groups[0]["lr"],
                                "validation Dice": val_score,
                                "images": wandb.Image(images[0].cpu()),
                                "masks": {
                                    "true": wandb.Image(true_masks[0].float().cpu()),
                                    "pred": wandb.Image(
                                        torch.softmax(masks_pred, dim=1).argmax(dim=1)[0].float().cpu()
                                    ),
                                },
                                "step": global_step,
                                "epoch": epoch,
                                **histograms,
                            }
                        )

        if save_checkpoint:
            Path(dir_checkpoint).mkdir(parents=True, exist_ok=True)
            torch.save(net.state_dict(), str(dir_checkpoint / "checkpoint_epoch{}.pth".format(epoch)))
            logging.info(f"Checkpoint {epoch} saved!")


def get_args():
    parser = argparse.ArgumentParser(
        description="Train the UNet on images and target masks",
    )
    parser.add_argument(
        "--epochs",
        "-e",
        metavar="E",
        type=int,
        default=5,
        help="Number of epochs",
    )
    parser.add_argument(
        "--batch-size",
        "-b",
        dest="batch_size",
        metavar="B",
        type=int,
        default=1,
        help="Batch size",
    )
    parser.add_argument(
        "--learning-rate",
        "-l",
        metavar="LR",
        type=float,
        default=1e-5,
        help="Learning rate",
        dest="lr",
    )
    parser.add_argument(
        "--load",
        "-f",
        type=str,
        default=False,
        help="Load model from a .pth file",
    )
    parser.add_argument(
        "--scale",
        "-s",
        type=float,
        default=0.5,
        help="Downscaling factor of the images",
    )
    parser.add_argument(
        "--validation",
        "-v",
        dest="val",
        type=float,
        default=10.0,
        help="Percent of the data that is used as validation (0-100)",
    )
    parser.add_argument(
        "--amp",
        action="store_true",
        default=False,
        help="Use mixed precision",
    )
    parser.add_argument(
        "--bilinear",
        action="store_true",
        default=False,
        help="Use bilinear upsampling",
    )
    parser.add_argument(
        "--classes",
        "-c",
        type=int,
        default=2,
        help="Number of classes",
    )

    return parser.parse_args()


if __name__ == "__main__":
    args = get_args()

    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    logging.info(f"Using device {device}")

    # Change here to adapt to your data
    # n_channels=3 for RGB images
    # n_classes is the number of probabilities you want to get per pixel
    net = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear)

    logging.info(
        f"Network:\n"
        f"\t{net.n_channels} input channels\n"
        f"\t{net.n_classes} output channels (classes)\n"
        f'\t{"Bilinear" if net.bilinear else "Transposed conv"} upscaling'
    )

    if args.load:
        net.load_state_dict(torch.load(args.load, map_location=device))
        logging.info(f"Model loaded from {args.load}")

    net.to(device=device)
    try:
        train_net(
            net=net,
            epochs=args.epochs,
            batch_size=args.batch_size,
            learning_rate=args.lr,
            device=device,
            img_scale=args.scale,
            val_percent=args.val / 100,
            amp=args.amp,
        )
    except KeyboardInterrupt:
        torch.save(net.state_dict(), "INTERRUPTED.pth")
        logging.info("Saved interrupt")
        raise