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train.py
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import argparse
import os, time, yaml
from tqdm import tqdm
from datetime import datetime
from collections import defaultdict
from utils import *
from get_instances import *
def setup(args):
config_path = args.config
with open(config_path, "r") as fr:
configs = yaml.load(fr, Loader=yaml.FullLoader)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#read configs =================================
n_layers = configs['n_layers']
k_iters = configs['k_iters']
epochs = configs['epochs']
dataset_name = configs['dataset_name']
dataset_params = configs['dataset_params']
val_data = configs['val_data']
phases = ['train', 'val'] if val_data else ['train']
batch_size = configs['batch_size']
model_name = configs['model_name']
model_params = configs.get('model_params', {})
model_params['n_layers'] = n_layers
model_params['k_iters'] = k_iters
restore_weights = configs['restore_weights'] #'model', 'all', False
loss_name = configs['loss_name']
score_names = configs['score_names']
optim_name = configs['optim_name']
optim_params = configs.get('optim_params', {})
scheduler_name = configs.get('scheduler_name', None)
scheduler_params = configs.get('scheduler_params', {})
# config_name = configs['config_name'] + '_' + datetime.now().strftime("%d%b%I%M%P") #ex) base_04Jun0243pm
config_name = configs['config_name'] #ex) base
#dirs, logger, writers, saver =========================================
workspace = config_output_dir(args.workspace, configs)
checkpoints_dir, log_dir = get_dirs(workspace, remake=True) #workspace/config_name/checkpoints ; workspace/config_name/log.txt
tensorboard_dir = config_output_dir(args.tensorboard_dir, configs)
logger = Logger(log_dir)
writers = get_writers(tensorboard_dir, phases)
saver = CheckpointSaver(checkpoints_dir)
#dataloaders, model, loss f, score f, optimizer, scheduler================================
dataloaders = get_loaders(dataset_name, dataset_params, batch_size, phases)
model = get_model(model_name, model_params, device)
loss_f = get_loss(loss_name)
score_fs = get_score_fs(score_names)
val_score_name = score_names[0]
optim_params['params'] = model.parameters()
optimizer, scheduler = get_optim_scheduler(optim_name, optim_params, scheduler_name, scheduler_params)
#load weights ==========================================
if restore_weights:
restore_path = configs['restore_path']
start_epoch, model, optimizer, scheduler = saver.load(restore_path, restore_weights, model, optimizer, scheduler)
else:
start_epoch = 0
# if torch.cuda.device_count()>1:
# model = nn.DataParallel(model)
return configs, device, epochs, start_epoch, phases, workspace, logger, writers, saver, dataloaders, model, loss_f, score_fs, val_score_name, optimizer, scheduler
def main(args):
configs, device, epochs, start_epoch, phases, workspace, logger, writers, saver, \
dataloaders, model, loss_f, score_fs, val_score_name, optimizer, scheduler = setup(args)
"""
:start_epoch: The point at which epoch starts from. 0 if restore_weights is False
:phases: list of phases. ['train', 'val'] if val_data is True, else ['train']
:workspace: Where all data are saved.
:checkpoints_dir: intermediate checkpoints and final model path are saved.
:logger: can write log by using logger.write() method
:writers: tensorboard writers
:score_fs: dictionary of scoring functions
"""
logger.write('config path: ' + args.config)
logger.write('workspace: ' + workspace)
logger.write('description: ' + configs['description'])
logger.write('\n')
logger.write('train start: ' + datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
logger.write('-----------------------')
start = time.time()
if args.seed:
set_seeds(args.seed)
for epoch in range(start_epoch, epochs):
for phase in phases: #['train', 'val'] or ['train']
running_score = defaultdict(int)
if phase == 'train': model.train()
else: model.eval()
for i, (x, y, csm, mask) in enumerate(tqdm(dataloaders[phase])):
x, y, csm, mask = x.to(device), y.to(device), csm.to(device), mask.to(device)
with torch.set_grad_enabled(phase=='train'):
y_pred = model(x, csm, mask)
loss = loss_f(y_pred, y)
if phase == 'train':
optimizer.zero_grad()
loss.backward()
if configs['gradient_clip']:
nn.utils.clip_grad_value_(model.parameters(), clip_value=1.0)
optimizer.step()
running_score['loss'] += loss.item() * y_pred.size(0)
y = np.abs(r2c(y.detach().cpu().numpy(), axis=1))
y_pred = np.abs(r2c(y_pred.detach().cpu().numpy(), axis=1))
for score_name, score_f in score_fs.items():
running_score[score_name] += score_f(y, y_pred) * y_pred.shape[0]
#scheduler
if phase == 'train' and scheduler:
scheduler.step()
#write log
epoch_score = {score_name: score / len(dataloaders[phase].dataset) for score_name, score in running_score.items()}
for score_name, score in epoch_score.items():
writers[phase].add_scalar(score_name, score, epoch)
if args.write_lr:
writers[phase].add_scalar('lr', optimizer.param_groups[0]['lr'], epoch)
if args.write_image > 0 and (epoch % args.write_image == 0):
writers[phase].add_figure('img', display_img(np.abs(r2c(x[-1].detach().cpu().numpy())), mask[-1].detach().cpu().numpy(), \
y[-1], y_pred[-1], epoch_score[val_score_name]), epoch)
if args.write_lambda:
print('lam:', model.dc.lam.item())
writers['train'].add_scalar('lambda', model.dc.lam.item(), epoch)
logger.write('epoch {}/{} {} score: {:.4f}\tloss: {:.4f}'.format(epoch, epochs, phase, epoch_score[val_score_name], epoch_score['loss']))
#save model
if phase == 'val':
saver.save_model(model, epoch_score[val_score_name], epoch, final=False)
if epoch % args.save_step == 0:
saver.save_checkpoints(epoch, model, optimizer, scheduler)
if phase == 'train':
saver.save_model(model, epoch_score[val_score_name], epoch, final=True)
for phase in phases:
writers[phase].close()
logger.write('-----------------------')
logger.write('total train time: {:.2f} min'.format((time.time()-start)/60))
logger.write('best score: {:.4f}'.format(saver.best_score))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="")
parser.add_argument("--config", type=str, required=False, default="configs/base_modl,k=10.yaml",
help="config file path")
parser.add_argument("--workspace", type=str, default='./workspace')
parser.add_argument("--tensorboard_dir", type=str, default='./runs')
parser.add_argument("--save_step", type=int, default=10)
parser.add_argument("--write_lr", type=bool, default=False)
parser.add_argument("--write_image", type=int, default=0)
parser.add_argument("--write_lambda", type=bool, default=True)
parser.add_argument("--seed", type=int, default=1)
args = parser.parse_args()
main(args)