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train_eval_qap.py
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train_eval_qap.py
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import torch.optim as optim
import time
from datetime import datetime
from pathlib import Path
from tensorboardX import SummaryWriter
from src.dataset.data_loader import QAPDataset, get_dataloader
from src.loss_func import *
from src.evaluation_metric import objective_score
from src.parallel import DataParallel
from src.utils.model_sl import load_model, save_model
from eval_qap import eval_model
from src.utils.data_to_cuda import data_to_cuda
from src.utils.config import cfg
def train_eval_model(model,
criterion,
optimizer,
dataloader,
tfboard_writer,
num_epochs=25,
start_epoch=0):
print('Start training...')
since = time.time()
dataset_size = len(dataloader['train'].dataset)
device = next(model.parameters()).device
print('model on device: {}'.format(device))
checkpoint_path = Path(cfg.OUTPUT_PATH) / 'params'
if not checkpoint_path.exists():
checkpoint_path.mkdir(parents=True)
model_path, optim_path = '',''
if start_epoch > 0:
model_path = str(checkpoint_path / 'params_{:04}.pt'.format(start_epoch))
optim_path = str(checkpoint_path / 'optim_{:04}.pt'.format(start_epoch))
if len(cfg.PRETRAINED_PATH) > 0:
model_path = cfg.PRETRAINED_PATH
if len(model_path) > 0:
print('Loading model parameters from {}'.format(model_path))
load_model(model, model_path, strict=False)
if len(optim_path) > 0:
print('Loading optimizer state from {}'.format(optim_path))
optimizer.load_state_dict(torch.load(optim_path))
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=cfg.TRAIN.LR_STEP,
gamma=cfg.TRAIN.LR_DECAY,
last_epoch=cfg.TRAIN.START_EPOCH - 1)
for epoch in range(start_epoch, num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
model.train() # Set model to training mode
print('lr = ' + ', '.join(['{:.2e}'.format(x['lr']) for x in optimizer.param_groups]))
epoch_loss = 0.0
running_loss = 0.0
running_since = time.time()
iter_num = 0
det_anomaly = False
# Iterate over data.
for inputs in dataloader['train']:
if model.module.device != torch.device('cpu'):
inputs = data_to_cuda(inputs)
n1_gt, n2_gt = inputs['ns']
perm_mat = inputs['gt_perm_mat'].cuda()
iter_num = iter_num + 1
# zero the parameter gradients
optimizer.zero_grad()
with torch.set_grad_enabled(True):
with torch.autograd.set_detect_anomaly(det_anomaly):
# forward
pred = model(inputs)
s_pred, affmtx = pred['ds_mat'], pred['aff_mat']
if type(s_pred) is list:
s_pred = s_pred[-1]
multi_loss = []
if cfg.TRAIN.LOSS_FUNC == 'perm' or cfg.TRAIN.LOSS_FUNC == 'hung':
loss = criterion(s_pred, perm_mat, n1_gt, n2_gt)
elif cfg.TRAIN.LOSS_FUNC == 'obj':
loss = criterion(s_pred, affmtx)
elif cfg.TRAIN.LOSS_FUNC == 'custom':
loss = torch.sum(pred['loss'])
else:
raise ValueError('Unknown loss function {}'.format(cfg.TRAIN.LOSS_FUNC))
if cfg.FP16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
det_anomaly = False
for param in model.parameters():
if param.grad is not None and torch.any(torch.isnan(param.grad)):
det_anomaly = True
break
if not det_anomaly:
optimizer.step()
# training accuracy statistic
#acc, _, __ = matching_accuracy(lap_solver(s_pred, n1_gt, n2_gt), perm_mat, n1_gt)
acc = 0
# tfboard writer
loss_dict = {'loss_{}'.format(i): l.item() for i, l in enumerate(multi_loss)}
loss_dict['loss'] = loss.item()
tfboard_writer.add_scalars('loss', loss_dict, epoch * cfg.TRAIN.EPOCH_ITERS + iter_num)
accdict = dict()
accdict['matching accuracy'] = acc
tfboard_writer.add_scalars(
'training accuracy',
accdict,
epoch * cfg.TRAIN.EPOCH_ITERS + iter_num
)
# statistics
running_loss += loss.item() * perm_mat.size(0)
epoch_loss += loss.item() * perm_mat.size(0)
if iter_num % cfg.STATISTIC_STEP == 0:
running_speed = cfg.STATISTIC_STEP * perm_mat.size(0) / (time.time() - running_since)
print('Epoch {:<4} Iteration {:<4} {:>4.2f}sample/s Loss={:<8.4f}'
.format(epoch, iter_num, running_speed, running_loss / cfg.STATISTIC_STEP / perm_mat.size(0)))
tfboard_writer.add_scalars(
'speed',
{'speed': running_speed},
epoch * cfg.TRAIN.EPOCH_ITERS + iter_num
)
running_loss = 0.0
running_since = time.time()
epoch_loss = epoch_loss / dataset_size
#loss_dict = dict()
#loss_dict['loss'] = loss.item()
#tfboard_writer.add_scalars('loss', loss_dict, epoch * dataset_size + iter_num)
save_model(model, str(checkpoint_path / 'params_{:04}.pt'.format(epoch + 1)))
torch.save(optimizer.state_dict(), str(checkpoint_path / 'optim_{:04}.pt'.format(epoch + 1)))
print('Epoch {:<4} Loss: {:.4f}'.format(epoch, epoch_loss))
print()
# Eval in each epoch
accs = eval_model(model, dataloader['test'])
acc_dict = {"{}".format(cls): single_acc for cls, single_acc in zip(dataloader['train'].dataset.classes, accs)}
acc_dict['average'] = torch.mean(accs)
tfboard_writer.add_scalars(
'Eval acc',
acc_dict,
(epoch + 1) * cfg.TRAIN.EPOCH_ITERS
)
scheduler.step()
time_elapsed = time.time() - since
print('Training complete in {:.0f}h {:.0f}m {:.0f}s'
.format(time_elapsed // 3600, (time_elapsed // 60) % 60, time_elapsed % 60))
return model
if __name__ == '__main__':
from src.utils.dup_stdout_manager import DupStdoutFileManager
from src.utils.parse_args import parse_args
from src.utils.print_easydict import print_easydict
args = parse_args('Deep learning of graph matching training & evaluation code.')
import importlib
mod = importlib.import_module(cfg.MODULE)
Net = mod.Net
torch.manual_seed(cfg.RANDOM_SEED)
dataset_len = {'train': cfg.TRAIN.EPOCH_ITERS * cfg.BATCH_SIZE, 'test': cfg.EVAL.SAMPLES}
qap_dataset = {
x: QAPDataset(cfg.DATASET_FULL_NAME,
dataset_len[x],
cfg.TRAIN.CLASS if x == 'train' else None,
sets=x,
fetch_online=False)
for x in ('train', 'test')}
dataloader = {x: get_dataloader(qap_dataset[x], fix_seed=(x == 'test'), shuffle=(x == 'train'))
for x in ('train', 'test')}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = Net()
model = model.to(device)
if cfg.TRAIN.LOSS_FUNC.lower() == 'perm':
criterion = CrossEntropyLoss()
elif cfg.TRAIN.LOSS_FUNC.lower() == 'obj':
criterion = lambda *x: torch.mean(objective_score(*x))
elif cfg.TRAIN.LOSS_FUNC.lower() == 'hung':
criterion = PermutationLossHung()
else:
raise ValueError('Unknown loss function {}'.format(cfg.TRAIN.LOSS_FUNC))
if cfg.TRAIN.OPTIMIZER.lower() == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=cfg.TRAIN.LR, momentum=cfg.TRAIN.MOMENTUM, nesterov=True)
elif cfg.TRAIN.OPTIMIZER.lower() == 'adam':
optimizer = optim.Adam(model.parameters(), lr=cfg.TRAIN.LR)
elif cfg.TRAIN.LOSS_FUNC.lower() == 'custom':
criterion = None
print('NOTE: You are setting the loss function as \'custom\', please ensure that there is a tensor with key '
'\'loss\' in your model\'s returned dictionary.')
else:
raise ValueError('Unknown optimizer {}'.format(cfg.TRAIN.OPTIMIZER))
if cfg.FP16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to enable FP16.")
model, optimizer = amp.initialize(model, optimizer)
model = DataParallel(model, device_ids=cfg.GPUS)
if not Path(cfg.OUTPUT_PATH).exists():
Path(cfg.OUTPUT_PATH).mkdir(parents=True)
now_time = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
tfboardwriter = SummaryWriter(logdir=str(Path(cfg.OUTPUT_PATH) / 'tensorboard' / 'training_{}'.format(now_time)))
with DupStdoutFileManager(str(Path(cfg.OUTPUT_PATH) / ('train_log_' + now_time + '.log'))) as _:
print_easydict(cfg)
model = train_eval_model(model, criterion, optimizer, dataloader, tfboardwriter,
num_epochs=cfg.TRAIN.NUM_EPOCHS,
start_epoch=cfg.TRAIN.START_EPOCH)