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run_training.py
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run_training.py
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import torch
from models.unet import UNet
from training import training_loop
from data_utils.common import EasyDict
def setup_run_arguments():
args = EasyDict()
args.epochs = 100
args.batch = 4
args.val_percent = 0.2
args.n_classes = 4
args.n_channels = 3
args.num_workers = 8
args.learning_rate = 0.001
args.weight_decay = 1e-8
args.momentum = 0.9
args.save_cp = True
args.loss = "CrossEntropy"
args.checkpoint_path = 'checkpoints/'
args.image_dir = 'data/train/images'
args.mask_dir = 'data/train/masks'
args.from_pretrained = False
return args
def train():
args = setup_run_arguments()
# args = parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"[INFO] Initializing UNet-model using: {device}")
net = UNet(n_channels=args.n_channels, n_classes=args.n_classes, bilinear=True)
if args.from_pretrained:
net.load_state_dict(torch.load(args.from_pretrained, map_location=device))
net.to(device=device)
training_loop.run(network=net,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.learning_rate,
device=device,
n_classes=args.n_classes,
val_percent=args.val_percent,
image_dir=args.image_dir,
mask_dir=args.mask_dir,
checkpoint_path=args.checkpoint_path,
loss=args.loss,
num_workers=args.num_workers
)
if __name__ == "__main__":
train()