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run.py
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run.py
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import torch
from utils.generate import generate_output_folder
from models.bulid import build_model
from loss_func.get_loss import get_loss_func
from dataset.get_dataset import getDataset
from trainer.train_riga_padl import train_riga_padl, test_riga_padl
def train(args):
log_folder, checkpoint_folder, visualization_folder, metrics_folder = generate_output_folder(args)
# network
model = build_model(args)
# load pretrained params
if args.pretrained == 1:
params = torch.load(args.pretrained_dir)
model_params = params['model']
model.load_state_dict(model_params)
# dataset
train_set, test_set = getDataset(args)
# optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
# loss_func
loss_func = get_loss_func(args)
if args.net_arch == "PADL" and args.dataset == "RIGA":
train_riga_padl(args, log_folder, checkpoint_folder, visualization_folder, metrics_folder, model, optimizer,
loss_func, train_set, test_set)
def test(args):
log_folder, checkpoint_folder, visualization_folder, metrics_folder = generate_output_folder(args)
# network
model = build_model(args)
# load pretrained params
params = torch.load(checkpoint_folder + "/amp_checkpoint.pt")
model_params = params['model']
model.load_state_dict(model_params)
# dataset
train_set, test_set = getDataset(args)
if args.net_arch == "PADL" and args.dataset == "RIGA":
test_riga_padl(args, visualization_folder, metrics_folder, model, test_set)