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train.py
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train.py
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r""" AFN training code """
import argparse
import os
import torch.optim as optim
import torch.nn as nn
import torch
from common.logger import Logger, AverageMeter
from common.evaluation import Evaluator
from common import config
from common import utils
from data.dataset import CSDataset
from models import create_model
import csv
def get_parser():
parser = argparse.ArgumentParser(description='Affinity Feature Strengthening for Vessel Segmentation')
parser.add_argument('--config', type=str, default='config/AFN.yaml', help='Model config file')
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
return cfg
def create_csv(path, csv_head):
# path = "aa.csv"
with open(path, 'w', newline='') as f:
csv_write = csv.writer(f)
# csv_head = ["good","bad"]
csv_write.writerow(csv_head)
def write_csv(path, data_row):
# path = "aa.csv"
with open(path, 'a+', newline='') as f:
csv_write = csv.writer(f)
# data_row = ["1","2"]
csv_write.writerow(data_row)
def main():
global args
args = get_parser()
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(x) for x in args.train_gpu)
Logger.initialize(args, training=True)
# Model initialization
model = create_model(args)
Logger.info("=> creating model ...")
Logger.info("Classes: {}".format(args.classes))
Logger.log_params(model)
# Device setup
Logger.info('# available GPUs: %d' % torch.cuda.device_count())
if torch.cuda.device_count() > 1:
model = model.cuda()
model = nn.DataParallel(model)
Logger.info('Use GPU Parallel.')
elif torch.cuda.is_available():
model = model.cuda()
else:
model = model
# Helper classes (for training) initialization
if args.optimizer.lower() == 'adam':
optimizer = optim.Adam(
model.parameters(),
lr=args.base_lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=args.weight_decay
)
# optim.Adam([{"params": model.parameters(), "lr": args.base_lr, "weight_decay": args.weight_decay}])
print('Optimizer: Adam')
else:
optimizer = optim.SGD(model.parameters(), lr=args.base_lr, momentum=args.momentum,
weight_decay=args.weight_decay)
print('Optimizer: SGD')
if args.lr_update:
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.lr_step, args.gamma)
else:
scheduler = None
if args.weight:
if os.path.isfile(args.weight):
Logger.info("=> loading weight '{}'".format(args.weight))
checkpoint = torch.load(args.weight)
model.load_state_dict(checkpoint['state_dict'])
Logger.info("=> loaded weight '{}'".format(args.weight))
else:
Logger.info("=> no weight found at '{}'".format(args.weight))
if args.resume:
if os.path.isfile(args.resume):
Logger.info("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage.cuda())
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
Logger.info("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
else:
Logger.info("=> no checkpoint found at '{}'".format(args.resume))
Evaluator.initialize()
# Dataset initialization
CSDataset.initialize(datapath=args.datapath)
dataloader_trn = CSDataset.build_dataloader(args.benchmark,
args.batch_size,
args.nworker,
'train',
args.img_mode,
args.img_size)
dataloader_val = CSDataset.build_dataloader(args.benchmark,
args.batch_size_val,
args.nworker,
'val',
'same',
None)
# Train JTFN
best_val_f1 = float('-inf')
best_val_pr = float('-inf')
best_val_r = float('-inf')
best_val_loss = float('inf')
val_score_path = os.path.join('logs', args.logname + '.log') + '/' + 'val_[retrain)f1.csv'
csv_head = ["epoch", "total_loss", "f1", "pr", "recall", "quality", "cor", "com"]
create_csv(val_score_path, csv_head)
for epoch in range(args.start_epoch, args.epochs):
trn_loss_dict, trn_f1, _, _, trn_quality, _, _ = train(epoch, model, dataloader_trn, optimizer, scheduler)
if args.evaluate:
with torch.no_grad():
val_loss_dict, val_f1, val_pr, val_r, val_quality, val_cor, val_com = evaluate(epoch, model,
dataloader_val)
data_row_f1score = [str(epoch), str(val_loss_dict['total_loss']), str(val_f1), str(val_pr), str(val_r),
str(val_quality), str(val_cor), str(val_com)]
write_csv(val_score_path, data_row_f1score)
if val_f1 > best_val_f1:
best_val_f1 = val_f1
Logger.save_model_f1(model, epoch, val_f1, optimizer)
if val_f1 >= best_val_f1 and val_pr >= best_val_pr and val_r >= best_val_r:
best_val_f1 = val_f1
best_val_pr = val_pr
best_val_r = val_r
Logger.save_model_all(model, epoch, val_f1, val_pr, val_r, optimizer)
for key in trn_loss_dict.keys():
Logger.tbd_writer.add_scalars('data/loss_train', {'trn_loss_' + str(key): trn_loss_dict[key]}, epoch)
for key in val_loss_dict.keys():
Logger.tbd_writer.add_scalars('data/loss_train_val', {'trn_loss_' + str(key): trn_loss_dict[key],
'val_loss_' + str(key): val_loss_dict[key]}, epoch)
Logger.tbd_writer.add_scalars('data/f1', {'trn_f1': trn_f1, 'val_f1': val_f1}, epoch)
Logger.tbd_writer.flush()
print('Best F1: ', best_val_f1)
Logger.tbd_writer.close()
Logger.info('==================== Finished Training ====================')
def train(epoch, model, dataloader, optimizer, scheduler):
r""" Train AFN """
if torch.cuda.device_count() > 1:
model.module.train_mode()
else:
model.train_mode()
average_meter = AverageMeter(dataloader.dataset)
# max_iter = args.epochs * len(dataloader)
for idx, batch in enumerate(dataloader):
# 1. Forward pass
batch = utils.to_cuda(batch) if torch.cuda.is_available() else batch
output_dict = model(batch)
out = output_dict['output']
pred_mask = torch.where(out >= 0.5, 1, 0)
# 2. Compute loss & update model parameters
loss_dict = model.module.compute_objective(output_dict,
batch) if torch.cuda.device_count() > 1 else model.compute_objective(
output_dict, batch_dict=batch)
loss = loss_dict['total_loss']
optimizer.zero_grad()
loss.backward()
optimizer.step()
if args.lr_update:
scheduler.step()
# 3. Evaluate prediction
f1, pr, r, quality, cor, com = Evaluator.classify_prediction(pred_mask.clone(), batch)
average_meter.update(f1, pr, r, quality, cor, com, loss_dict)
average_meter.write_process(idx, len(dataloader), epoch, write_batch_idx=1)
# Write evaluation results
average_meter.write_result('Training', epoch)
avg_loss_dict = dict()
for key in average_meter.loss_buf.keys():
avg_loss_dict[key] = utils.mean(average_meter.loss_buf[key])
f1 = average_meter.compute_f1()
pr = average_meter.compute_precision()
r = average_meter.compute_recall()
quality = average_meter.compute_quality()
cor = average_meter.compute_correctness()
com = average_meter.compute_completeness()
return avg_loss_dict, f1, pr, r, quality, cor, com
def evaluate(epoch, model, dataloader):
r""" Eval AFN """
# Force randomness during training / freeze randomness during testing
if torch.cuda.device_count() > 1:
model.module.eval()
else:
model.eval()
average_meter = AverageMeter(dataloader.dataset)
for idx, batch in enumerate(dataloader):
# 1. Forward pass
batch = utils.to_cuda(batch) if torch.cuda.is_available() else batch
output_dict = model(batch)
out = output_dict['output']
pred_mask = torch.where(out >= 0.5, 1, 0)
# 2. Compute loss & update model parameters
loss_dict = model.module.compute_objective(output_dict,
batch) if torch.cuda.device_count() > 1 else model.compute_objective(
output_dict, batch_dict=batch)
# 3. Evaluate prediction
f1, pr, r, quality, cor, com = Evaluator.classify_prediction(pred_mask.clone(), batch)
average_meter.update(f1, pr, r, quality, cor, com, loss_dict)
average_meter.write_process(idx, len(dataloader), epoch, write_batch_idx=10)
# Write evaluation results
average_meter.write_result('Validation', epoch)
avg_loss_dict = dict()
for key in average_meter.loss_buf.keys():
avg_loss_dict[key] = utils.mean(average_meter.loss_buf[key])
f1 = average_meter.compute_f1()
pr = average_meter.compute_precision()
r = average_meter.compute_recall()
quality = average_meter.compute_quality()
cor = average_meter.compute_correctness()
com = average_meter.compute_completeness()
return avg_loss_dict, f1, pr, r, quality, cor, com
if __name__ == '__main__':
main()