import argparse
import logging
import os
import random
import sys
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
import torchvision.models as models
from pathlib import Path
from torch import optim
from torch.utils.data import DataLoader, random_split
from tqdm import tqdm
from evaluate import evaluate
from unet.unet_model import UNet
from utils.data_loading import BasicDataset, CarvanaDataset
from utils.dice_score import dice_loss
import segmentation_models_pytorch as smp
device = torch.device('cuda:4' if torch.cuda.is_available() else 'cpu')
dir_img = Path('./data/train/imgs/')
dir_mask = Path('./data/train/masks/')
dir_checkpoint = Path('./checkpoints')
def train_model(
model, device, epochs, batch_size, learning_rate,
val_percent: float = 0.1,
save_checkpoint: bool = True,
img_scale: float = 0.5,
amp: bool = False,
weight_decay: float = 1e-8,
momentum: float = 0.5,
gradient_clipping: float = 1.0
):
best_model_params = copy.deepcopy(model.state_dict())
best_acc = 0.0
best_epoch = 0
# 1. Create dataset
data_transform = transforms.Compose([
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
try:
dataset = CarvanaDataset(dir_img, dir_mask, img_scale)
except (AssertionError, RuntimeError, IndexError):
dataset = BasicDataset(dir_img, dir_mask, img_scale, data_transform)
# 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=os.cpu_count(), pin_memory=True)
train_loader = DataLoader(train_set, shuffle=True, **loader_args)
val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)
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.Adam(model.parameters(),
lr=learning_rate, weight_decay=weight_decay)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=5) # goal: maximize Dice score
grad_scaler = torch.cuda.amp.GradScaler(enabled=amp)
criterion = nn.CrossEntropyLoss() if model.n_classes > 1 else nn.BCEWithLogitsLoss()
global_step = 0
# 5. Begin training
for epoch in range(1, epochs + 1):
model.train()
epoch_loss = 0
with tqdm(total=n_train, desc=f'Epoch {epoch}/{epochs}', unit='img') as pbar:
for batch in train_loader:
images, true_masks = batch['image'], batch['mask']
assert images.shape[1] == model.n_channels, \
f'Network has been defined with {model.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, memory_format=torch.channels_last)
true_masks = true_masks.to(device=device, dtype=torch.long)
with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp):
masks_pred = model(images)
if model.n_classes == 1:
loss = criterion(masks_pred.squeeze(1), true_masks.float())
loss += dice_loss(F.sigmoid(masks_pred.squeeze(1)), true_masks.float(), multiclass=False)
else:
loss = criterion(masks_pred, true_masks)
loss += dice_loss(
F.softmax(masks_pred, dim=1).float(),
F.one_hot(true_masks, model.n_classes).permute(0, 3, 1, 2).float(),
multiclass=True
)
optimizer.zero_grad(set_to_none=True)
grad_scaler.scale(loss).backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), gradient_clipping)
grad_scaler.step(optimizer)
grad_scaler.update()
pbar.update(images.shape[0])
global_step += 1
epoch_loss += loss.item()
pbar.set_postfix(**{'loss (batch)': loss.item()})
# Evaluation round
division_step = (n_train // (5 * batch_size))
if division_step > 0:
if global_step % division_step == 0:
val_score = evaluate(model, val_loader, device, amp)
scheduler.step(val_score)
logging.info('Validation Dice score: {}'.format(val_score))
# Check best accuracy model ( but not the best on test )
if val_score > best_acc:
best_acc = val_score
best_epoch = epoch
best_model_params = copy.deepcopy(model.state_dict())
logging.info("Best model: [" + f'epoch: {best_epoch}, acc: {best_acc:.4f}]')
if save_checkpoint:
Path(dir_checkpoint).mkdir(parents=True, exist_ok=True)
state_dict = model.state_dict()
state_dict['mask_values'] = dataset.mask_values
torch.save(state_dict, str(dir_checkpoint / 'checkpoint_epoch{}.pth'.format(epoch)))
logging.info(f'Checkpoint {epoch} saved!')
# only weight
torch.save(best_model_params, f'epoch_{best_epoch}_acc_{best_acc:.2f}_best_val_acc.pth')
logging.info("Best model name : " + f'epoch_{best_epoch}_acc_{best_acc:.2f}_best_val_acc.pth')
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=50, help='Number of epochs')
parser.add_argument('--batch-size', '-b', dest='batch_size', metavar='B', type=int, default=16, help='Batch size')
parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=0.001,
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:0' 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
"""
model = UNet(n_channels=1, n_classes=5, bilinear=True)
model = model.to(memory_format=torch.channels_last)
logging.info(f'Network:\n'
f'\t{model.n_channels} input channels\n'
f'\t{model.n_classes} output channels (classes)\n'
f'\t{"Bilinear" if model.bilinear else "Transposed conv"} upscaling')
if args.load:
state_dict = torch.load(args.load, map_location=device)
del state_dict['mask_values']
model.load_state_dict(state_dict)
logging.info(f'Model loaded from {args.load}')
model.to(device=device)
train_model(
model=model,
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
)
Datasets
Models
