mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-11-08 14:39:00 +00:00
273 lines
8.9 KiB
Python
273 lines
8.9 KiB
Python
import argparse
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import logging
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from pathlib import Path
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import wandb
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from torch import optim
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from torch.utils.data import DataLoader, random_split
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from tqdm import tqdm
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from evaluate import evaluate
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from unet import UNet
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from utils.data_loading import BasicDataset, CarvanaDataset
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from utils.dice_score import dice_loss
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dir_img = Path("./data/imgs/")
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dir_mask = Path("./data/masks/")
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dir_checkpoint = Path("./checkpoints/")
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def train_net(
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net,
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device,
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epochs: int = 5,
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batch_size: int = 1,
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learning_rate: float = 1e-5,
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val_percent: float = 0.1,
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save_checkpoint: bool = True,
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img_scale: float = 0.5,
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amp: bool = False,
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):
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# 1. Create dataset
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try:
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dataset = CarvanaDataset(dir_img, dir_mask, img_scale)
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except (AssertionError, RuntimeError):
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dataset = BasicDataset(dir_img, dir_mask, img_scale)
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# 2. Split into train / validation partitions
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n_val = int(len(dataset) * val_percent)
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n_train = len(dataset) - n_val
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train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0))
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# 3. Create data loaders
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loader_args = dict(batch_size=batch_size, num_workers=4, pin_memory=True)
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train_loader = DataLoader(train_set, shuffle=True, **loader_args)
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val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)
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# (Initialize logging)
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experiment = wandb.init(project="U-Net", resume="allow", anonymous="must")
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experiment.config.update(
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dict(
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epochs=epochs,
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batch_size=batch_size,
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learning_rate=learning_rate,
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val_percent=val_percent,
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save_checkpoint=save_checkpoint,
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img_scale=img_scale,
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amp=amp,
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)
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)
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logging.info(
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f"""Starting training:
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Epochs: {epochs}
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Batch size: {batch_size}
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Learning rate: {learning_rate}
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Training size: {n_train}
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Validation size: {n_val}
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Checkpoints: {save_checkpoint}
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Device: {device.type}
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Images scaling: {img_scale}
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Mixed Precision: {amp}
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"""
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)
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# 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for AMP
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optimizer = optim.RMSprop(net.parameters(), lr=learning_rate, weight_decay=1e-8, momentum=0.9)
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scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, "max", patience=2) # goal: maximize Dice score
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grad_scaler = torch.cuda.amp.GradScaler(enabled=amp)
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criterion = nn.CrossEntropyLoss()
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global_step = 0
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# 5. Begin training
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for epoch in range(1, epochs + 1):
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net.train()
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epoch_loss = 0
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with tqdm(total=n_train, desc=f"Epoch {epoch}/{epochs}", unit="img") as pbar:
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for batch in train_loader:
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images = batch["image"]
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true_masks = batch["mask"]
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assert images.shape[1] == net.n_channels, (
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f"Network has been defined with {net.n_channels} input channels, "
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f"but loaded images have {images.shape[1]} channels. Please check that "
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"the images are loaded correctly."
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)
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images = images.to(device=device, dtype=torch.float32)
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true_masks = true_masks.to(device=device, dtype=torch.long)
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with torch.cuda.amp.autocast(enabled=amp):
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masks_pred = net(images)
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loss = criterion(masks_pred, true_masks) + dice_loss(
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F.softmax(masks_pred, dim=1).float(),
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F.one_hot(true_masks, net.n_classes).permute(0, 3, 1, 2).float(),
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multiclass=True,
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)
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optimizer.zero_grad(set_to_none=True)
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grad_scaler.scale(loss).backward()
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grad_scaler.step(optimizer)
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grad_scaler.update()
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pbar.update(images.shape[0])
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global_step += 1
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epoch_loss += loss.item()
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experiment.log({"train loss": loss.item(), "step": global_step, "epoch": epoch})
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pbar.set_postfix(**{"loss (batch)": loss.item()})
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# Evaluation round
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division_step = n_train // (10 * batch_size)
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if division_step > 0:
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if global_step % division_step == 0:
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histograms = {}
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for tag, value in net.named_parameters():
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tag = tag.replace("/", ".")
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histograms["Weights/" + tag] = wandb.Histogram(value.data.cpu())
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histograms["Gradients/" + tag] = wandb.Histogram(value.grad.data.cpu())
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val_score = evaluate(net, val_loader, device)
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scheduler.step(val_score)
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logging.info("Validation Dice score: {}".format(val_score))
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experiment.log(
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{
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"learning rate": optimizer.param_groups[0]["lr"],
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"validation Dice": val_score,
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"images": wandb.Image(images[0].cpu()),
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"masks": {
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"true": wandb.Image(true_masks[0].float().cpu()),
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"pred": wandb.Image(
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torch.softmax(masks_pred, dim=1).argmax(dim=1)[0].float().cpu()
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),
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},
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"step": global_step,
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"epoch": epoch,
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**histograms,
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}
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)
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if save_checkpoint:
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Path(dir_checkpoint).mkdir(parents=True, exist_ok=True)
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torch.save(net.state_dict(), str(dir_checkpoint / "checkpoint_epoch{}.pth".format(epoch)))
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logging.info(f"Checkpoint {epoch} saved!")
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def get_args():
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parser = argparse.ArgumentParser(
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description="Train the UNet on images and target masks",
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)
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parser.add_argument(
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"--epochs",
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"-e",
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metavar="E",
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type=int,
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default=5,
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help="Number of epochs",
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)
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parser.add_argument(
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"--batch-size",
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"-b",
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dest="batch_size",
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metavar="B",
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type=int,
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default=1,
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help="Batch size",
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)
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parser.add_argument(
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"--learning-rate",
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"-l",
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metavar="LR",
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type=float,
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default=1e-5,
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help="Learning rate",
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dest="lr",
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)
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parser.add_argument(
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"--load",
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"-f",
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type=str,
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default=False,
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help="Load model from a .pth file",
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)
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parser.add_argument(
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"--scale",
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"-s",
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type=float,
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default=0.5,
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help="Downscaling factor of the images",
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)
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parser.add_argument(
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"--validation",
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"-v",
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dest="val",
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type=float,
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default=10.0,
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help="Percent of the data that is used as validation (0-100)",
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)
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parser.add_argument(
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"--amp",
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action="store_true",
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default=False,
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help="Use mixed precision",
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)
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parser.add_argument(
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"--bilinear",
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action="store_true",
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default=False,
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help="Use bilinear upsampling",
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)
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parser.add_argument(
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"--classes",
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"-c",
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type=int,
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default=2,
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help="Number of classes",
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)
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return parser.parse_args()
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if __name__ == "__main__":
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args = get_args()
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logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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logging.info(f"Using device {device}")
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# Change here to adapt to your data
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# n_channels=3 for RGB images
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# n_classes is the number of probabilities you want to get per pixel
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net = UNet(n_channels=3, n_classes=args.classes, bilinear=args.bilinear)
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logging.info(
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f"Network:\n"
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f"\t{net.n_channels} input channels\n"
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f"\t{net.n_classes} output channels (classes)\n"
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f'\t{"Bilinear" if net.bilinear else "Transposed conv"} upscaling'
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)
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if args.load:
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net.load_state_dict(torch.load(args.load, map_location=device))
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logging.info(f"Model loaded from {args.load}")
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net.to(device=device)
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try:
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train_net(
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net=net,
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epochs=args.epochs,
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batch_size=args.batch_size,
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learning_rate=args.lr,
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device=device,
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img_scale=args.scale,
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val_percent=args.val / 100,
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amp=args.amp,
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)
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except KeyboardInterrupt:
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torch.save(net.state_dict(), "INTERRUPTED.pth")
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logging.info("Saved interrupt")
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raise
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